pmid stringlengths 8 8 | pmcid stringlengths 8 11 ⌀ | source stringclasses 2
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24256735 | PMC4019345 | pmc | 3,697 | {
"abstract": "Genetically identical cells sharing an environment can display markedly different phenotypes. It is often unclear how much of this variation derives from chance, external signals, or attempts by individual cells to exert autonomous phenotypic programs. By observing thousands of cells for hundreds of consecutive generations under constant conditions, we dissect the stochastic decision between a solitary, motile state and a chained, sessile state in Bacillus subtilis. The motile state is memoryless, exhibiting no autonomous control over the time spent in the state, whereas chaining is tightly timed. Timing enforces coordination among related cells in the multicellular state. Further, we show that the three-protein regulatory circuit governing the decision is modular, as initiation and maintenance of chaining are genetically separable functions. As stimulation of the same initiating pathway triggers biofilm formation, we argue that autonomous timing allows a trial commitment to multicellularity that external signals could extend."
} | 261 |
36204280 | PMC9519436 | pmc | 3,698 | {
"abstract": "Biological receptor-ligand adhesion governed by mammalian cells involves a series of mechanochemical processes that can realize reversible, loading rate-dependent specific interfacial bonding, and even exhibit a counterintuitive behavior called catch bonds that tend to have much longer lifetimes when larger pulling forces are applied. Inspired by these catch bonds, we designed a hydrogen bonding-meditated hydrogel made from acrylic acid-N-acryloyl glycinamide (AA-NAGA) copolymers and tannic acids (TA), which formed repeatable specific adhesion to polar surfaces in an ultra-fast and robust way, but hardly adhered to nonpolar materials. It demonstrated up to five-fold increase in shear adhesive strength and interfacial adhesive toughness with external loading rates varying from 5 to 500 mm min −1 . With a mechanochemical coupling model based on Monte Carlo simulations, we quantitatively revealed the nonlinear dependence of rate-sensitive interfacial adhesion on external loading, which was in good agreement with the experimental data. Likewise, the developed hydrogels were biocompatible, possessed antioxidant and antibacterial properties and promoted wound healing. This work not only reports a stimuli-responsive hydrogel adhesive suitable for multiple biomedical applications, but also offers an innovative strategy for bionic designs of smart hydrogels with loading rate-sensitive specific adhesion for various emerging areas including flexible electronics and soft robotics.",
"introduction": "1 Introduction Tissue-adhesive hydrogels have gained increasing popularity as an alternative to sutures, staples and bioglues for efficient wound healing [ [1] , [2] , [3] ]. A number of hydrogel adhesives (HAs) have been used in vivo or in preclinical tests, such as photocrosslinkable gelatin hydrogel-based adhesives [ 4 , 5 ], dry polymer crosslinking for wet adhesion [ 6 , 7 ], and mussel-inspired tissue adhesives [ 8 , 9 ]. Although many HAs have demonstrated potential clinical translation, there are still some major challenges in their clinical applications, including unsatisfying operability and complicated dressing change procedures. So far, most of HAs extensively adhere to various solids ( e.g. , metals, plastics, rubbers, minerals and tissues), whether they are polar or nonpolar [ [10] , [11] , [12] ]. Therefore, surgical instruments, including those made of nonpolar materials, e.g. , polypropylene (PP) and polystyrene (PS), can adhere to HAs, which causes surgeons to encounter great challenges. Recently, a ctenophore-inspired hydrogel that had specific underwater adhesion to biotic surfaces was developed because of its collaborative electrostatic interactions and dynamic catechol chemistry; moreover, it did not adhere to common abiotic surfaces in water [ 13 ]. However, it turns out that this hydrogel had the ability to adhere to both biotic and abiotic surfaces in air. On the other hand, the adhesive strengths of current HAs are usually not adjustable, and the detaching process is not efficient although dressing changes are an essential part of the wound healing process [ 14 ]. To swiftly detach HAs without secondary damage, thermal- and/or photo-responsive hydrogels have been developed with photodegradation- and/or temperature-induced sol-gel transformation properties [ [15] , [16] , [17] ]. Nevertheless, the liquefied dressing can hardly be completely removed from the wound. In the latest work, an injectable mussel-inspired adhesive hydrogel was reported, which could more easily detach from the wound by spraying with a Zn 2+ solution for 10 min [ 18 ]. Similarly, a bioadhesive was developed, which could achieve triggerable benign detachment via treatment with a biocompatible triggering solution (0.5 M sodium bicarbonate and 50 mM glutathione) [ 19 ]. Such detaching methods rely on the diffusion of specific ions and/or other triggering solutions into the hydrogels, which requires a duration of at least 5 min. Although the biocompatibility of these triggering solutions has been demonstrated in vitro or in animal experiments, more clinical tests should be performed to confirm their biosafety. In contrast to synthetic adhesive hydrogels, mammalian cells can only adhere to extracellular substrates/matrices via dynamic receptor-ligand binding mediated by multiple specific adhesion molecules ( e.g. , integrin family and cadherin family). For some bioinert substrates such as polyethylene glycol (PEG) hydrogels [ 20 ] and polydimethylsiloxane (PDMS) elastomers [ 21 ] without further modification, however, it is very difficult for the cells to form adhesion because of the absence of specific ligand molecules like RGD (Arginylglycylaspartic acid) peptides in such extracellular microenvironments ( Fig. 1 A). More strikingly, accumulating evidence has already indicated that the mammalian cell-governed adhesion is usually rate-dependent and even takes on a counterintuitive catch-bond behavior in response to external tensile forces, as quantified by some well-established mechanochemical coupling models ( Fig. 1 B) [ 22 , 23 ]. In essence, the so-called catch bonds, e.g. , integrin-RGD bonds, are a class of protein-ligand bonds that are easily broken under relatively small tensile forces but tend to have much longer lifetimes when larger tensile forces are applied [ [24] , [25] , [26] , [27] ], which is seemingly counterintuitive and hard to reproduce in most man-made systems [ 28 ]. Fig. 1 Design rationale for the loading rate-responsive PNT hydrogel with specific adhesion properties. (A) The specific adhesion and (B) rate-dependent adhesion properties of mammalian cells. (C) Scheme of the application of the PNT hydrogels. (D) Synthesis of the PNT hydrogels. Fig. 1 Very recently, several HAs have demonstrated loading rate-sensitive adhesion behaviors to some extent [ [29] , [30] , [31] ]. However, most of them are essentially non-reversible because the adhesion interfaces are essentially governed by covalent bonds. In such situations, the rate-sensitive adhesion mainly stems from intrinsic viscoelasticity of HAs involved, rather than reversible interfacial physical interactions ( e.g. , hydrogen bonds which also play a pivotal role in regulating cell adhesion complexes [ 22 ]). Here, inspired by the catch bonds, we develop a hydrogen bonding-mediated hydrogel based on acrylic acid (AA), N-acryloyl glycinamide (NAGA) and tannic acid (TA), hereafter referred to as PNT hydrogel, to achieve repeatable, loading rate-sensitive specific interfacial adhesion. This ensures not only the stability of the adhered hydrogel but also the relatively easy detachment when deliberately and slowly peeled off, thus greatly facilitating clinical operation ( Fig. 1 C). In combination with its excellent biocompatibility, antioxidant and antibacterial properties, and wound healing promotion, it is particularly suitable for multiple biomedical applications. Likewise, we present a mechanochemical coupling model on the basis of the classical Bell-Evans theoretical framework [ 32 ] to dissect the nonlinear dynamic response of interfacial adhesion governed by reversible hydrogen bonds to externally applied loads and hence quantitatively reveal the inherent mechanochemical mechanism of loading rate-sensitive interfacial bonding. This provides perspectives and design strategies for the development of biomimetic adhesive hydrogels, which are also indispensable for many emerging fields concerning smart interfacial adhesion.",
"discussion": "3 Discussion Bioinspired hydrogel adhesives (Bio-HAs) have broad applications from drug carriers and wearable devices to tissue repair and wound dressing. Traditionally, these Bio-HAs mimic specific organisms ( e.g. , octopi [ 56 ], mussels [ 57 ], and ctenophores [ 13 ]) with special chemical components and/or micro/nanostructures. As the basic unit that makes up an organism, mammalian cells which exhibit specific, repeatable, and rate-dependent adhesion properties via protein–ligand complexes with catch bonds, have often been inconspicuous. Catch bonds have been found in several proteins including integrin [ 58 ], catenin [ 59 ], cadherin [ 26 ] and actin [ 27 ], and proved to play pivotal roles in regulating cellular behaviors represented by cell adhesion, cell migration, and T cell receptor antigen recognition [ 25 ]. In recent years, attracted by the counterintuitive phenomenon concerning force-enhanced lifetimes of catch bonds, researchers have developed several theoretical models to describe some catch-bond-like behaviors presented by polymer-grafted nanoparticle networks [ 48 ], self-strengthening biphasic nanoparticle [ 47 ], molecular switches [ 28 ], or other active materials [ 49 ]. These models provide meaningful guidelines for the reproduction of catch bonds in man-made systems. Nevertheless, there is still no report on catch bond-inspired synthetic materials with adaptive mechanical responses. As a matter of fact, the biological receptor-ligand interactions generally involve reversable hydrogen bonding, which is somewhat similar in nature to reversible physical adhesion dominated by hydrogen bonds [ 22 ]. Further, the external force-regulated protein conformational changes in the receptor-ligand-modulated bioadhesion are also analogous to the rearrangements of hydrogen-mediated hydrogel networks at adhesive interfaces [ 26 , 58 , 59 ]. These facts inspired us to design the hydrogen bonding meditated PNT-10 hydrogel, which exhibited some unique interfacial bonding properties, as mentioned above. Interestingly, it turned out that the proposed mechanochemical coupling model on the basis of the classical Bell-Evans theoretical framework [ 60 , 61 ] which was originally employed to dissect the physical nature of the biological receptor-ligand interactions, could quantitatively describe the nonlinear dependence of the reversible interfacial adhesion between the developed hydrogel and the underlying substrates ( e.g. , porcine skin in this work) on the rates of applied loads. The mechanochemical model not only deepens our understanding of interfacial adhesion presented by the derived hydrogel, but also provides a new bionic design paradigm for the development of smart adhesion hydrogels. HAs with on-demand removability have attracted considerable attention in advanced dressing designs [ 62 ]. For example, Liang et al. developed a hydrogel consisting of protocatechualdehyde (PA) containing catechol, ferric iron (Fe), and quaternized chitosan (QCS) [ 63 ]. The hydrogels governed by the dual-dynamic-bond cross-linking ( i.e. , catechol-Fe bonds and Schiff base bonds) can be dissolved and removed in an on-demand manner. So far, however, the reports of loading rate-induced removal are still very rare. It is worth pointing out that the intrinsic viscoelasticity of the hydrogel and substrates has already been included in the model because it is very likely to play an import role in regulating the loading-rate sensitive adhesion behaviors as well. For example, a tough alginate-polyacrylamide (Alg-PAAm) hydrogel, which was adhered to porcine skin using bridging polymers and covalent coupling reagents, showed a 2-fold increase in adhesion energy approximately when the applied loading rate varied by two orders of magnitude [ 31 ]. By contrast, the derived PNT-10 hydrogel exhibited an at least 5-fold increase in shear adhesive strength and interfacial adhesive toughness, as confirmed by our experimental data and theoretical model. With the cytocompatibility, antioxidant capability, antibacterial properties, and the unique catch-bond inspired specific, repeatable, and loading rate-sensitive adhesion, the developed PNT-10 hydrogel is expected to serve as a bioadhesive with high operability. In the future, catch bonds can further inspire the design and synthesis of more active materials that can realize programmable mechanical properties and adhesive performance, and bear large mechanical stress. We believe that these predictable materials with catch-bond-like phenomenon will be ideal candidates for multiple applications, such as wound healing, force-responsive drug delivery, flexible electronics, and soft robotics."
} | 3,053 |
32236097 | PMC7112194 | pmc | 3,699 | {
"abstract": "In bacteria functionally related genes comprising metabolic pathways and protein complexes are frequently encoded in operons and are widely conserved across phylogenetically diverse species. The evolution of these operon-encoded processes is affected by diverse mechanisms such as gene duplication, loss, rearrangement, and horizontal transfer. These mechanisms can result in functional diversification, increasing the potential evolution of novel biological pathways, and enabling pre-existing pathways to adapt to the requirements of particular environments. Despite the fundamental importance that these mechanisms play in bacterial environmental adaptation, a systematic approach for studying the evolution of operon organization is lacking. Herein, we present a novel method to study the evolution of operons based on phylogenetic clustering of operon-encoded protein families and genomic-proximity network visualizations of operon architectures. We applied this approach to study the evolution of the synthase dependent exopolysaccharide (EPS) biosynthetic systems: cellulose, acetylated cellulose, poly-β-1,6-N-acetyl-D-glucosamine (PNAG), Pel, and alginate. These polymers have important roles in biofilm formation, antibiotic tolerance, and as virulence factors in opportunistic pathogens. Our approach revealed the complex evolutionary landscape of EPS machineries, and enabled operons to be classified into evolutionarily distinct lineages. Cellulose operons show phyla-specific operon lineages resulting from gene loss, rearrangement, and the acquisition of accessory loci, and the occurrence of whole-operon duplications arising through horizonal gene transfer. Our evolution-based classification also distinguishes between PNAG production from Gram-negative and Gram-positive bacteria on the basis of structural and functional evolution of the acetylation modification domains shared by PgaB and IcaB loci, respectively. We also predict several pel -like operon lineages in Gram-positive bacteria and demonstrate in our companion paper (Whitfield et al PLoS Pathogens, in press) that Bacillus cereus produces a Pel-dependent biofilm that is regulated by cyclic-3’,5’-dimeric guanosine monophosphate (c-di-GMP).",
"introduction": "Introduction The generation of novel genomes through next generation sequencing is creating a wealth of opportunities for understanding the evolution of biological systems. A key challenge is the development of robust and systematic approaches that allow genes to be classified into functional categories and which are also capable of inferring evolutionary relationships. In bacterial genomes, functionally-related genes corresponding to metabolic pathways or protein complexes are often encoded by neighbouring co-regulated and co-transcribed loci, termed an operon. Computational prediction of operons based on the conservation of short inter-genetic distances found between homologous genes across phylogenetically diverse bacteria has been frequently used to predict the biological roles of neighbouring uncharacterized genes [ 1 – 6 ]. Such approaches are valuable for computational inference of gene function in experimentally uncharacterized organisms and facilitate comparative genomics of adaptive traits across phylogenetically diverse bacteria. Analyzing patterns of sequence divergence within each gene yields insights into species-specific functionalities. However, genes in an operon do not function in isolation but typically form parts of higher-order, biological modules ( e . g . protein complexes or metabolic pathways). Consequently, analysing evolutionary events in an operonic context provides additional opportunities to better infer functional relationships. For example, while sequence divergence has the potential to impact the function of a single gene, evolutionary events that alter operon structure (e.g. rearrangements, duplications, gains and losses) have the potential to dramatically alter the overall function of the operon [ 7 , 8 ]. Due to the lack of a systematic framework, very few studies have attempted to examine the influence of evolutionary events on operon structure [ 9 , 10 ]. Phylogenetic-tree based classification of 197 ATP binding motif sequences associated with operon-encoded bacterial ATP-binding cassette (ABC) transporters was successful in resolving two evolutionarily distinct transporter clades associated with import and export functions [ 11 ]. Gene duplications have been shown to play an important role in driving protein superfamily expansion and are positively correlated with bacterial genome size [ 12 ]. Duplications have been found to be associated with biological processes associated with environmental adaptation of species clusters [ 13 ], such as outer membrane polysaccharide export proteins involved in capsule biosynthesis [ 14 , 15 ], and amplification of beta-lactamase enzymes associated with increased antibiotic resistance [ 16 ]. The study of co-localized “gene blocks” across bacteria has also shown that gene duplication, loss, and rearrangement play important roles in shaping the large-scale organization of bacterial genomes [ 10 ]. Key to these analyses is the use of a rigorous and systematic approach for assigning genes into evolutionarily related ‘families’ that are likely to share similar functions. However, the inference of biological function based on sequence similarities of genes or proteins is often complicated by functional divergence arising through recent gene duplication events. A variety of metrics have been employed for determining the relatedness of genes and their protein products from which groups (i.e. clustering) can be defined. These metrics include: evolutionary distances derived through the construction of phylogenetic trees [ 17 – 19 ]; global protein sequence similarities [ 20 – 22 ]; and shared sequence features such as conserved amino acids at specific sites or shared amino acid subsequences, which define motifs or structural domains [ 23 , 24 ]. The aim of these approaches is to automatically resolve large protein families comprising potentially thousands of genes into a smaller number of clusters defining evolutionarily related subfamilies with similar biological roles. An additional challenge faced by clustering methodologies is defining which set of clusters result in an optimal partitioning of the underlying data. To help guide such partitioning, a variety of cluster validation approaches have been devised. These are broadly divided into two categories: external-validation and internal-validation, based on whether previous information is available for the data being clustered [ 25 ]. For example, methods developed for classifying orthologous genes ( i . e . those related by common ancestry) and paralogous genes ( i . e . those emerging from duplication after a speciation event) rely on internal-validation tests. In one such approach, internal branch lengths between one-to-many homologous gene relationships are compared between two species [ 26 ]. In an alternative approach, clustering is employed to define “triangles” of proteins with significant sequence similarity occurring between three distinct species [ 20 , 27 ]. However, to distinguish the finer-scale evolutionary relationships occurring within an orthologous group or gene family, phylogenetic tree construction is required. Such methods have typically focused on well-characterized biological systems, e . g . homologs of the bacterial flagellar and type III secretion system subunits [ 28 ] and diverse systems associated with the type IV filament superfamily [ 29 ], which have utilized an external-validation approach for defining functionally distinct phylogenetic clades. Here we build on these methods and present a framework for the systematic classification and analysis of diverse gene families in the context of operons. Focusing on synthase-dependent exopolysaccharide (EPS) biosynthetic machineries, we use our framework to explore how gene divergence in combination with duplication, loss, and rearrangement events have shaped the evolution of EPS operons, and may have influenced the biofilm producing capabilities of evolutionarily diverse bacteria. EPS are an important component of bacterial biofilms that not only ensure survival in response to limited nutrient availability, but are also involved in antibiotic tolerance, immune evasion and serve as virulence factors in many clinically relevant pathogens [ 30 – 32 ]. Distinct mechanisms have been identified in the production of bacterial EPS, including the well-characterized Wzx/Wzy and ABC transporter-dependent pathways [ 33 ], and synthase-dependent systems [ 34 ]. Typically, Gram-negative synthase-dependent EPS systems are organized as discrete operons comprised of genes encoding: 1) an inner membrane associated polysaccharide synthase; 2) a regulatory domain or co-polymerase subunit responsible for binding the intracellular signaling molecule cyclic-3’,5’-dimeric guanosine monophosphate (c-di-GMP); 3) periplasmic polysaccharide modification enzymes; and 4) a periplasmic tetratricopeptide repeat (TPR) domain coupled with an outer membrane pore [ 34 ]. This operonic organization allows bacteria to acquire complete EPS functionality through discrete lateral gene transfer events and may act as a key driver in niche adaptation [ 35 ]. To date five synthase-dependent EPS have been identified: cellulose, acetylated cellulose, poly-β-1,6-N-acetyl-D-glucosamine (PNAG), alginate and the Pel polysaccharide. While much interest has focused on the molecular basis of biofilm formation, these systems have been characterized for only a relatively limited set of bacterial species. Consequently, little is known concerning how these systems have evolved. Of interest is how mechanisms such as gene divergence, duplication, acquisition, loss, and rearrangement of EPS operons have contributed to bacterial adaptation to diverse environments, and from a human health perspective, contributed to a pathogen’s ability to infect and cause disease. While a previous survey of cellulose EPS machineries has been reported [ 36 ], a comprehensive systematic analysis of all EPS machineries is lacking. In this study, we describe a phylogenetic tree-based clustering method for defining protein sequence subfamilies and apply it to study the evolutionary relationships of operons. This method was employed for the systematic classification of EPS operons predicted from a survey of over a thousand bacterial genomes. Applying a graphical visualization approach, we demonstrate that phylogenetic clustering enables the resolution of discrete EPS operon clades which differ in their organization from experimentally characterized operons, providing valuable insights toward further understanding the roles of gene duplication, rearrangement, and loss/absence in the evolution of biofilm production between phylogenetically diverse species from distinct environmental niches. For example, we demonstrate the biological implications of operon evolution that has been shaped by horizontal gene transfer (HGT) and subsequent divergence, for two cellulose operon clades among Proteobacteria which correspond to the production of cellulose polymers with different structural organizations. Furthermore, we note that most of our operon predictions are novel and demonstrate the value of applying computational predictions to guide the discovery of EPS production in previously uncharacterized species. We highlight an example for Pel production, which was initially identified and characterized in Pseudomonas aeruginosa [ 37 ] and other Gram-negative bacteria. Our approach identified several pel -like operons in some Bacillus spp. and other Gram-positive bacteria, which appeared to be regulated by the intracellular signaling molecule c-di-GMP. In our companion paper (Whitfield et al PLoS Pathogens, in press) we experimentally validate these findings by demonstrating the production of Pel by the Gram-positive Bacillus cereus ATCC 10987 and its regulation by c-di-GMP.",
"discussion": "Discussion In this work we describe a novel and generalizable approach for the systematic classification and presentation of bacterial protein families in the context of their host operon. Protein families are defined as sets of homologs (groups of related sequences having a common evolutionary ancestor) sharing a particular set of sequence motifs or structural domains that can be utilized to determine their biological roles. For example, the PFAM database utilizes curated sets of protein family sequences in the generation of profile HMMs [ 76 ]. A key challenge that complicates the definition of these relationships are evolutionary events such as duplication, gene fusion, and HGT. In attempts to account for such events, a variety of computational approaches have been developed for refining functional assignments. These operate either by graphical clustering of pair-wise protein sequence similarities (e,g, COG [ 27 ], OrthoMCL [ 19 ] and EggNOG [ 20 ]), or through the generation of hierarchical evolutionary relationships and construction of phylogenetic trees (e.g. TreeFAM [ 77 ] and TreeCL [ 78 ]). However, these methods are limited in their ability to provide further resolution of sequence diversity within a family that might otherwise offer additional insights into evolutionary events that allow taxa to adapt to specific environments. Agnostic approaches to define sub-clusters of evolutionarily related protein families have ranged from phylogenetic tree reconstructions [ 79 ] to hierarchical clustering of pairwise global sequence alignments [ 80 ]. Here we present an extension of previous efforts and introduce a novel systematic approach for defining protein sub-family relationships through the clustering of phylogenetic trees. Key to this approach is defining a scoring function that allows a phylogenetic tree to be resolved into optimal clusters that best capture the similarities between cluster members, as well as the dissimilarities between clusters. Combining two clustering quality metrics (Silhouette and Dunn index) and proportion of sequences clustered, we demonstrate that our approach classifies a diverse array of operon-associated protein families into taxonomically consistent and functionally informative sub-clusters. Genomic-proximity networks were also constructed to provide an intuitive means of utilizing phylogenetic clusters to examine diverse mechanisms of operon evolution across taxonomically diverse bacterial genomes. Genomic-proximity networks have previously been utilized for inferring functional relationships [ 81 ], understanding mechanisms underlying bacterial genomic organization into functionally related gene clusters [ 82 ], and transcriptional regulation of bacterial operons [ 83 ]. In this study we extend the application of genomic-proximity networks as a tool for the systematic exploration of operon evolution resulting from locus divergence, loss, duplication, and rearrangement events. To demonstrate the effectiveness of our approach, we applied our methods to classify the synthase-dependent bacterial EPS operon machineries for 5 different polymers: cellulose, acetylated cellulose, alginate, Pel and PNAG. There has been only one previous attempt to classify synthase-dependent EPS operons and this focused specifically on the cellulose system [ 36 ]. In that study, cellulose operons were categorized into four major types, based on the presence or absence of experimentally validated accessory loci involved in cellulose production. Here, we based our analysis on the four core operon loci, bcsABZC , deemed essential for cellulose production. Cellulose operon clades identified in this study showed little consistency with the previously defined four major cellulose operon types [ 36 ], suggesting that the conservation of accessory loci is more variable across bacterial species compared to loci encoding core EPS functionalities. However, one operon type was identified in this analysis, representing the loss of the BcsC outer membrane transporter identified among a subset of alpha-proteobacterial genomes, which include several known cellulose producing species [ 62 , 84 ] suggesting a novel mechanism of cellulose export ( Fig 3(iii) ) [ 36 ]. We also found that the loss of BcsC has resulted in an increased divergence of BcsB loci in these genomes, which highlights the key role of BcsB as an intermediary between cellulose biogenesis and periplasmic transport ( S7 Fig ). In general, inner membrane components involved in EPS polymerization were found to be relatively conserved across all systems examined, while periplasmic and outer membrane components showed a relatively increased degree of evolution, which are likely to have important functional implications. For example, in the cellulose and Pel operon networks ( Figs 3 and 5 and S5 Table ), rearrangement events involving the periplasmic glycosyl hydrolase (BcsZ) and glycosyl hydrolase/deacetylase (PelA) were found to be a defining feature of several operon clades. It is interesting to note that these rearrangements have resulted in a change in the ordering of bcsZ and pelA relative to their respective outer membrane transport pore loci, which highlights the important role of polysaccharide modification in both the biogenesis and regulation of extracelluar EPS transport [ 48 , 85 , 86 ]. Similarly, the rearrangement of alginate modification machinery loci ( algIJF ) was observed as a distinguishing feature of Pseudomonas spp. operon clades. These findings suggest that rearrangement and locus ordering may serve as an important means of regulating EPS production by modifying the timing of translation of modification enzymes, which could affect the assembly of EPS complexes or the structural properties of EPS produced [ 47 , 64 , 87 ]. Furthermore, identifying operon clades through a phylogenetic approach elucidated numerous instances of cellulose whole operon duplications arising from HGT of two evolutionary distinct operon clades ( Fig 4 ). Such large-scale duplications, if they are functional, may either serve as a dosage response to given environmental stressors, as observed in the duplication of bacterial multiple-drug transporter operons [ 88 ], or could be under the regulation of different environmental stimuli. Interestingly, representative species of the two cellulose operon lineages identified in HGT events, e.g. the plant and human pathogens, D . dadantii and S . enterica , respectively, are known to produce structurally distinct forms of cellulose with different properties and roles in pathogenesis [ 89 , 90 ]. In addition, we identified that BcsB divergence was also seen to accompany the rearrangement or horizontal transfer of these operons, which further suggests that it may play a key role in the fine-tuning of cellulose production by coordinating the export of growing cellulose polymers through the periplasm. Furthermore, our analyses of acetylated cellulose, alginate and PNAG operons suggest a dynamic evolutionary scenario for the evolution of EPS biofilm production through the acquisition of novel polysaccharide modification loci. The limited number of acetylated cellulose operons identified, their frequent co-occurrence in alginate possessing species, and significant sequence similarities between acetylation machinery loci, suggests that the cellulose acetylation machinery is likely to have originated from previously existing alginate operons in Pseudomonas spp. The evolutionary trajectories of Gram-positive and Gram-negative PNAG operon lineages appears to have resulted through the fusion of glycosyl hydrolase and deacetylase domains in Gram-negative pgaB loci. A further key finding from this study was the identification of homologous pel operons in the genomes of several Gram-positive bacteria. With the additional identification of homologs of PelD through iterative HMM searches, our analyses have uncovered a novel example of c-di-GMP regulation of biofilm machinery in Gram-positive bacteria. In the accompanying paper we experimentally validate that a predicted pel -like operon in B . cereus ATCC 10987 is responsible for biofilm production and is regulated by the binding of c-di-GMP to PelD (Whitfield et al PLoS Pathogens, in press). Together this work demonstrates a novel integrative approach combining phylogenomics and genomic-context approaches to systematically explore the adaptive implications of sequence divergence of protein families associated with operon associated EPS secretion machineries. Further extension of this work holds great potential as a general approach for elucidating how bacterial operon encoded biological pathways and complexes have contributed to bacterial adaptation to and survival in diverse environmental niches and lifestyles."
} | 5,234 |
34136471 | PMC8201792 | pmc | 3,700 | {
"abstract": "The conversion of Kraft lignin in plant biomass into renewable chemicals, aiming at harvesting aromatic compounds, is a challenge process in biorefinery. Comparing to the traditional chemical methods, enzymatic catalysis provides a gentle way for the degradation of lignin. Alternative to natural enzymes, artificial enzymes have been received much attention for potential applications. We herein achieved the biodegradation of Kraft lignin using an artificial peroxidase rationally designed in myoglobin (Mb), F43Y/T67R Mb, with a covalently linked heme cofactor. The artificial enzyme of F43Y/T67R Mb has improved catalytic efficiencies at mild acidic pH for phenolic and aromatic amine substrates, including Kraft lignin and the model lignin dimer guaiacylglycerol-β-guaiacyl ether (GGE). We proposed a possible catalytic mechanism for the biotransformation of lignin catalyzed by the enzyme, based on the results of kinetic UV-Vis studies and UPLC-ESI-MS analysis, as well as molecular modeling studies. With the advantages of F43Y/T67R Mb, such as the high-yield by overexpression in E. coli cells and the enhanced protein stability, this study suggests that the artificial enzyme has potential applications in the biodegradation of lignin to provide sustainable bioresource.",
"conclusion": "Conclusion In this study, we successfully achieved the depolymerization of the model lignin dimer GGE and Kraft lignin, by using an artificial heme enzyme rationally designed in Mb, F43Y/T67R Mb. Kinetic UV-Vis studies and UPLC-ESI-MS analysis indicated that the artificial enzyme of F43Y/T67R Mb is effective in oxidation of both substrates. Based on the results, we proposed a plausible generation route of the reaction products catalyzed by the enzyme. Molecular modeling structure of the GGE-F43Y/T67R Mb complex provided further information for the binding site of the substrate, as well as bond cleavage in the enzymatic reactions. Moreover, we directly observed the color change of in the process of degradation of Kraft lignin catalyzed by F43Y/T67R Mb, with nine degradation products identified by the ESI-MS analysis. It should be noted that with a covalently linked heme group, the artificial enzyme of F43Y/T67R Mb exhibited considerable protein stability, with the tolerance of organic solvents such as 15% ethanol and 5% DMSO (v/v), and without the inhibition of the oxidant H 2 O 2 even a high concentration of 100 mM. Since the artificial enzyme is readily to obtain by overexpression in E. coli cells, and the oxidant H 2 O 2 is relatively cheap, this study is expected to provide an economic solution for the biodegradation of lignin, which is of great importance to the sustainable utilization of lignin.",
"introduction": "Introduction Lignin accounts for 10∼35% by weight, up to 40% by energy in biomass, and lignin is also by far the most abundant renewable source composed of aromatic units in nature ( Leonowicz et al., 1999 ; Paliwal et al., 2012 ). As shown in Figure 1A , lignin is chemically a cross-linked phenolic polymer ( Yoshikawa et al., 2013 ), and its chemical structure contains three major monomers, p -coumaryl alcohol, coniferyl alcohol, and sinapyl alcohol units ( Figure 1B ), which are linked by carbon-carbon and carbon-oxygen bonds ( Picart et al., 2015 ). The conversion from lignin to chemicals and/or fuels can be traced back to the 1930s, and various lignin depolymerization methods have being explored from academia to industry worldwide ( Zakzeski et al., 2010a ; Li et al., 2015 ; Upton and Kasko, 2016 ; Chan et al., 2020 ; Zhang and Wang, 2020 ). However, although the industrial production of lignin is 5–36 billion tons per year, it is still challenging to efficiently convert into aromatics, and only ∼2% is used in industry, such as for generating binder, surfactant, chelating agent and paper pulp, with the rest burned to produce energy ( Zakzeski et al., 2010b ; Zhang et al., 2011 ; Chan et al., 2020 ; Zhang and Wang, 2020 ). Therefore, it is urgent to develop new technology for utilizing the lignin with high efficiency in industry ( Kamimura et al., 2019 ). FIGURE 1 (A) Representative chemical structure of lignin. (B) Three major monomers of lignin: p -coumaryl alcohol, coniferyl alcohol and sinapyl alcohol unit, respectively. To date, various chemical methods have been developed for degradation and transformation of lignin ( Zakzeski et al., 2010a ; Li et al., 2015 ; Upton and Kasko, 2016 ; Zhang and Wang, 2020 ), such as by the pyrolysis of lignin at high temperature ( Davaritouchaee et al., 2020 ), and by the reaction with ozone ( Stergiou et al., 2008 ). Meanwhile, the harsh conditions such as strong acids/bases (H 2 SO 4 /NaOH) and high temperature (125–320°C)/pressure (0.5–2 MPa) make it difficult to treat the biomass in downstream processes ( Banerjee et al., 2010 ). Comparing to the traditional chemical methods, microbial degradation and enzymatic catalysis provide a gentle way for the degradation of lignin ( Bugg et al., 2020 ; Chan et al., 2020 ; Chauhan, 2020 ). The common enzymes (biological catalysts in living organisms) capable of lignin degradation are laccases and peroxidases (lignin peroxidase, manganese peroxidase, and dye-decolorizing peroxidase, etc.), which are copper-containing and heme-containing enzymes, by using O 2 and H 2 O 2 as an oxidant, respectively ( Singh et al., 2013 ; Chen et al., 2015 ; Lin, 2020 , 2021 ). Note that peroxidases utilize H 2 O 2 or other peroxides in the one-election oxidation of various cosubstrates, whereas peroxygenases insert one of the O atom from the oxidant to the substrates, by using the same catalytic intermediate, Compound I (an oxoferryl heme π-cation radical) ( Hrycay and Bandiera, 2012 ; Wang et al., 2017 ). Cytochromes P450 (CYP450) are a large class of heme-containing monooxygenases catalyzing the incorporation of one atom from O 2 into organic substrates ( Denisov et al., 2005 ). They also exhibit the ability of lignin degradation, however, require the expensive cofactor (such as nicotinamide adenine dinucleotide phosphate, NADPH) ( Mallinson et al., 2018 ). In addition to the pathway of O 2 activation, CYP450 may use the H 2 O 2 shunt, as that of peroxidase/peroxygenase, to generate the catalytic intermediate, Compound I ( Denisov et al., 2005 ). By protein engineering, a series of CYP450 variants were constructed to transform lignin into small aromatic compounds using H 2 O 2 as the oxidant, with the help of decoy molecules ( Mallinson et al., 2018 ; Xu et al., 2019 ; Ariyasu et al., 2020 ; Jiang et al., 2020 ). By rational modification of the heme center of myoglobin ( Figure 2A ), an O 2 carrier, we obtained several artificial heme enzymes, including the artificial dehaloperoxidases, dye-decolorizing peroxidases and DNA nucleases, etc. ( Yin et al., 2018 ; Zhang et al., 2019 , 2020 ; Luo et al., 2020 ). By introducing a Tyr residue (F43Y mutation) in the heme distal pocket, we discovered a new post-translational modification (PTM) of heme protein, i.e., a Tyr-heme cross-link, in the F43Y Mb mutant ( Figure 2B ; Yan et al., 2015 ), which is distinct from those observed for other heme enzymes ( Lin, 2015 , 2018 ). To mimic the heme active of both natural peroxidase and CYP450 those contain a conserve His-Arg pair acting as an acid-base catalyst ( Poulos, 2014 ), we constructed a double mutant of F43Y/T67R Mb by further introducing a distal Arg67 ( Liu et al., 2019 ). Interestingly, we discovered novel Tyr-heme double cross-links in the double mutant ( Figure 2C ), which improve the protein stability. Moreover, the double mutant exhibited considerably enhanced peroxidase activity, by catalyzing the oxidation of various phenolic molecules, which is comparable to those of the natural peroxidases ( Liu et al., 2019 ; Liao et al., 2020 ). Therefore, we envisaged that the double mutant might also have the ability to depolymerize lignin. FIGURE 2 X-ray crystal structures of wild-type (WT) Mb [ A , PDB code 1JP6 ( Urayama et al., 2002 )], F43Y Mb [ B , PDB code 4QAU ( Yan et al., 2015 )], and F43Y/T67R Mb [ C , PDB code 6JP1 ( Liu et al., 2019 )], respectively, showing the heme active site and Tyr-heme cross-links. To test our speculation, we herein investigated the depolymerization of the model lignin dimer GGE and Kraft lignin by the artificial enzyme of F43Y/T67R Mb. The tolerances of organic solvents and H 2 O 2 for the enzyme were studied. Kinetic UV-Vis studies were performed, and the reaction products were identified by UPLC-ESI-MS analysis. Moreover, a molecular modeling study was performed for GGE binding to the enzyme, and a possible catalytic mechanism was also proposed and discussed.",
"discussion": "Results and Discussion Effects of Solvent/H 2 O 2 on the Enzymatic Activity In previous study, we showed that the artificial enzyme F43Y/T67R Mb exhibited peroxidase activity in aqueous buffer solution at ∼pH 5.5 toward oxidation of ABTS ( Liu et al., 2019 ). Meanwhile, the solubility of lignin such as the model compounds GGE and Kraft lignin in aqueous solution is considerably low, which requires the addition of organic solvent such as ethanol and DMSO. However, the presence of organic solvent may cause side effects on the enzyme, such as the denaturation of enzyme and decrease of the enzymatic activity. To test the possibility, we evaluated the peroxidase activity of F43Y/T67R Mb in the oxidation of ABTS at pH 5.5, in the absence and presence of different amounts of ethanol or DMSO. Kinetic studies ( Supplementary Figure 1 ) showed that the presence of 15% (v/v) ethanol has less effect (<5%) on the peroxidase activity ( Figure 3A ), with 20% (v/v) resulted in ∼15% loss of activity. Meanwhile, the presence of DMSO has more profound effect. For example, 5 and 20% (v/v) DMSO resulted in ∼40 and ∼90% loss of the activity, respectively ( Figure 3B ). FIGURE 3 Effects of ethanol (A) and DMSO (B) concentrations on the peroxidase activity of F43Y/T67R Mb, determined by measuring the oxidation of ABTS at pH 5.5 and room temperature. The activity in the absence of the organic solvent was taken as 100%. Values represent the means of three independent experiments (mean ± standard error). (C) Steady-state rates of H 2 O 2 -dependent oxidation of ABTS catalyzed by F43Y/T67R Mb, as a function of H 2 O 2 concentrations. The plot was fitted to the Hill equation. Reaction conditions: 2 μM protein, 0.1 mM ABTS and 50 mM potassium phosphate buffer at pH 5.5. (D) Visual appearance of GGE (2.5 mM) and Kraft lignin (0.25 mM) dissolved in 5% (1–2), 50% ethanol (3), and 5% DMSO (4) for 10 min. We also investigated the H 2 O 2 dependence of the peroxidase activity of F43Y/T67R Mb in oxidation of ABTS ( Figure 3C ). The results showed that the enzyme exhibited a turnover number ( k cat ) of 39.2 ± 2.7 s –1 , corresponding to a specific activity of ∼130.6 U/mg, which is close to that reported for the most efficient native enzyme, horseradish peroxidase (HRP) ( k ca t = 52.5 ± 3.9 s –1 ) ( Rodriguez-Lopez et al., 1996 ). Noted that no obvious inhibition effect was observed, even at a high concentration of 100 mM H 2 O 2 , suggesting the high tolerance of H 2 O 2 for the enzyme. This property is distinct from that of other enzymes, such as an actinobacterial DyP-type peroxidase reported recently, with complete inhibition even at a low concentration of H 2 O 2 (0.05 mM) ( Musengi et al., 2020 ). The lignin model compound GGE, with two monomers of coniferyl alcohol, was found to have a good solubility in aqueous solution containing ∼5% ethanol ( Figures 1 , 3D ). However, Kraft lignin, due to large amounts of the three major monomers with a large average molecular mass of 10,000 Da, could not dissolve by addition of 50% ethanol ( Figure 3 ). Instead, it has a good solubility by an addition of 5% DMSO ( Figures 3D , 4 ), no need of higher concentrations such as 15% ( Singh et al., 2013 ). Therefore, we chose to use the optimal reaction conditions for oxidation of GGE and Kraft lignin by the addition of 5% ethanol and DMSO, respectively, in following sections. FIGURE 4 UPLC-MS traces of the oxidation of guaiacylglycerol-β-guaiacyl ether (GGE, 2.5 mM) catalyzed by F43Y/T67R Mb (5 μM) in the presence of H 2 O 2 (2.0 mM) (A) or in the absence of H 2 O 2 \n (B) , in potassium phosphate buffer (pH 5.5). Detected products are numbered and listed in the table below the chromatograms together with the corresponding m/z values. (C) Analysis of the oxidation products by ESI–MS spectrometry in a positive mode. Oxidation Products of GGE Catalyzed by F43Y/T67R Mb The reaction of GGE by F43Y/T67R Mb allowed a more detailed study of the molecular site of action and catalytic mechanism. At first, we analyzed the titration of F43Y/T67R Mb solution with GGE in the absence or presence of H 2 O 2 by using UV-Vis spectra. The results showed that, compared with the control in the absence of H 2 O 2 ( Supplementary Figure 2 , black spectrum), the Soret peak of the substrate GGE at 276 nm and the Soret peak of the protein at 404 nm were reduced in reaction with H 2 O 2 ( Supplementary Figure 2 , red spectrum). These observations suggest that the oxidation of GGE by F43Y/T67R Mb was happened. Therefore, we further studied the degradation products of GGE catalyzed by F43Y/T67R Mb using UPLC-ESI-MS. As shown in Figure 4A , the UPLC-ESI-MS results revealed a broad product peaks in the range of m/z 400–1,400, which were higher than the molecular weight of the starting GGE [320 Da, observed, 359 Da, [M + K] + ]. Not that the signal of GGE was observed at 0.55 min in the total ion chromatograph (TIC) spectrum ( Figure 4B ). Comparing to the control study in the absence of H 2 O 2 ( Figure 4B ), the TIC spectrum of GGE degradation by F43Y/T67R Mb ( Figure 4A ) showed that seven major products (compounds 1 to 7) were formed, and these new signals emerged at retention time (RT) 0.72, 0.88, 1.19, 2.48, 2.71, 3.64, and 4.36 min, respectively, which were considered to be depolymerization and polymerization products. Moreover, ESI-MS analysis showed that the molecular weights of compounds 1 to 7 correspond to m/z 495, 677, 661, 979, 1,077, 797, and 1,315 Da, respectively ( Figure 4C ). Therefore, these result indicated that recombination of radical products was taken place, generating higher-molecular weight species. At the same time, the obtained UPLC-ESI-MS data could support each expected depolymerization and polymerization product formed by the C-C or C-O bond depolymerization of GGE by F43Y/T67R Mb, as discussed below. The chemical structure of GGE is the major structural unit in Kraft lignin, and there are several possible sites for oxidation or oxidative cleavage ( Yang et al., 2019 ). In this study, the detection of the product with a molecular weight of 495 m/z indicated that the F43Y/T67R Mb can indeed degrade GGE. At the same time, we observed the formation of macromolecular substances, which suggests that the polymer was produced by further coupling of the monomers catalyzed by the enzyme. Based on the UPLC-ESI-MS data, we proposed the generation route of the reaction products. As shown in Figure 5 , the substrate GGE may undergo dimerization and trimerization, producing dimer and trimer, which were detected at the RT 1.190 min and 2.481 min, respectively. Their corresponding molecular weights were matched molecular formula of C 34 H 38 O 12 and C 51 H 56 O 18 , respectively. For oxidative cleavage, the monomers can hardly exist alone, and they will be recombined into new species. For example, by C β -O-C cleavage of GGE, product 1 (erythro-Guaiacylglycerol) will be generated, which further forms a trimer, matching the expected molecular weight of C 30 H 38 O 15 , with a retention time of 0.880 min. FIGURE 5 Depolymerization and polymerization of the guaiacylglycerol-β-guaiacyl ether (GGE) catalyzed by F43Y/T67R Mb. Product 1 is erythro-Guaiacylglycerol, and product 2 is 4-Hydroxy-3-methoxy-a-methylbenzyl alcohol. The corresponding mass of dimer and trimer are shown for clarification. After the first cleavage of GGE, the reaction continued to release the product 2 (4-Hydroxy-3-methoxy-a-methylbenzyl alcohol) by cleavage of C β -C γ and C β -O. Note that the products 1 and 2 may be coupled together and form a heterodimer, with a retention time of 3.638 min and an expected molecular weight of C 38 H 50 O 16 . Moreover, by further oxidation of C α -O of product 2, the product may form a trimer, which has a RT of 0.725 min, with a molecular weight corresponding to the molecular formula of C 27 H 26 O 9 . From the proposed generation route of GGE, we can infer the possibility that the F43Y/T67R Mb could be used to attack Kraft lignin at the corresponding catalytic cracking site. Molecular Docking Structure of GGE-F43Y/T67R Mb Complex In order to provide structural information for GGE binding to F43Y/T67R Mb, the X-ray structure of ferric F43Y/T67R Mb was used as the initial structure for docking with the substrate. In the process of calculating the binding energy, we calculated ten sets of data ( Supplementary Table 1 ), and selected the lowest energy to show the docking complex structure ( Figure 6 ). The result showed that for the most stable structure, GGE binds to the protein surface of F43Y/F46R Mb close to heme active site, and interacts with the surface residues by hydrogen (H)-bonding interactions, including the side chains of Lys63, His64, and Arg67. These observations suggest that the binding model of GGE is favorable for bond cleavage by F43Y/T67R Mb, which also provides information for the binding site of Kraft lignin in enzymatic reactions. FIGURE 6 Molecular docking structure of GGE binding to F43Y/F46R Mb with the lowest binding energy. The structure of GGE is shown in green, and the H-bonding interactions between GGE and protein residues are indicated by dashed lines. Activity Toward the Kraft Lignin Substrate According to the reaction conditions of GGE oxidation catalyzed by F43Y/T67R Mb, we performed the corresponding experiments for Kraft lignin. We first performed UV-Vis kinetic studies for Kraft lignin oxidation by monitoring the changes in the absorbance at 465 nm. As shown in Figure 7A , the oxidation rate catalyzed by F43Y/T67R Mb increased at first ∼60 s and reached a plateau after ∼300 s, with an obvious rate constant ( k obs ) of 0.070 ± 0.003 s –1 . Control study using WT Mb showed that the reaction rate was considerably slow [ k obs = (4.5 ± 0.2) × 10 –3 s –1 , Figure 7B ]. The concentration dependent of Kraft lignin was also investigated for both F43Y/T67R Mb and WT Mb. As shown in Figure 7C , after reaction for 10 min catalyzed by F43Y/T67R Mb, the absorbance at 465 nm increased with the increase of the concentration of Kraft lignin. Note that the activity (∼0.043 a.u./min) with 20 μM Kraft lignin was ∼1.7-fold higher than that reported recently for the wild-type peroxidase Dyp1B (0.0248 a.u./min) ( Rahman Pour et al., 2019 ). Moreover, no inhibition effect was observed for F43Y/T67R Mb at substrate concentrations of Kraft lignin tested (2–20 μM). Meanwhile, in case of WT Mb under the same conditions, the increase of absorbance at 465 nm was much low, with an inhibition effect by Kraft lignin (>10 μM). FIGURE 7 Kinetic studies of Kraft lignin (14 μM) in the presence of H 2 O 2 (2.0 mM) catalyzed by 2 μM F43Y/T67R Mb (A) or WT Mb (B) . Time-dependent changes of the absorbance at 465 nm were shown as insets, and were fitted to the single-exponential decay function. (C) The changes of absorbance at 465 nm after 10 min with different concentrations of Kraft lignin, as catalyzed by F43Y/T67R Mb and WT Mb. The plots were fitted to the linear and Michaelis-Menten equations, respectively. (D) Visual appearance of Kraft lignin (14 μM) oxidation in the absence (1) and presence (2–4) of H 2 O 2 (2.0 mM), WT Mb (3) and F43Y/T67R Mb (4) (2 μM) for 10 min. We also studied the visual appearances upon the oxidation of Kraft lignin ( Figure 7D ). After the assays, the resulting solutions appeared reddish in the treatment of Kraft lignin by WT Mb or F43Y/T67R Mb in presence of H 2 O 2 , with deeper color for the treatment by F43Y/T67R Mb, which was not observed for buffer solution containing Kraft lignin in the absence or present of H 2 O 2 . Therefore, these observations suggest that F43Y/T67R Mb in more effective in oxidation of Kraft lignin compared with the WT Mb. To further investigate the involvement of F43Y/T67R Mb in Kraft lignin degradation, we analyzed the entire reaction products from the incubations of Kraft lignin with the enzyme in presence of H 2 O 2 by mass spectrometry. As shown in Supplementary Figure 3A , the spectrum of the entire reaction revealed a large amount of low molecular weight products compared to the control reaction, i.e., the incubation of Kraft lignin with enzyme in the absence of H 2 O 2 ( Supplementary Figure 3B ). Based on the ESI-MS analysis, we identified several degradation products with molecular weights of 300, 302, 330, 385, 390, and 434 Da, together with 495 Da, as observed for the oxidation of GGE ( Figure 4C ). According to the five subunits of Kraft lignin, the possible chemical structures of these low molecular weight products are shown in Table 1 . Taken together, these results further verified the role of the F43Y/T67R Mb in generating low molecular weight lignin-derived compounds during the decomposition of Kraft lignin. TABLE 1 Estimated degradation products of Kraft lignin catalyzed by F43Y/T67R Mb based on the ESI-MS analysis. The five subunits of Kraft lignin are shown for clarification."
} | 5,425 |
35515284 | PMC9062534 | pmc | 3,702 | {
"abstract": "Mycorrhizal symbioses, which include plant roots and arbuscular mycorrhizal fungi (AMF), can significantly enhance plant resistance and promote the absorption of soil nutrients by plants. A greenhouse experiment was conducted to investigate the effects of three AMF species ( Glomus mosses , Glomus etunicatum and Glomus versiforme ) on the height, biomass, malondialdehyde (MDA) and proline contents and antioxidant enzyme activities of perennial ryegrass ( Lolium perenne ) under different water supply treatments. Potted experimental soil samples were collected from the abandoned rare earth tailings in Ganzhou, Jiangxi. The results showed that all three AMF species infected ryegrass under the different treatments. Under severe drought stress, G. mosses had the most significant effects on the promotion of ryegrass performance. After inoculation, the height and whole-plant biomass of ryegrass increased by 60.44% and 150%, respectively. In addition, inoculation with AMF significantly reduced the content of MDA and proline in the ryegrass leaves in all water supply treatments except the moderate drought stress treatment, in which there was no effect. The leaf antioxidant enzyme activity was also measured. The results showed that under severe drought stress, inoculation with Glomus mosses significantly increased the activities of CAT and SOD in ryegrass and enhanced the resistance of plants. A possible reason that AMF promotes host plant growth and enhances drought resistance is that AMF directly increases the absorption of soil water and minerals by host plant roots and indirectly improves the physiological metabolism of plants.",
"conclusion": "5 Conclusions (1) Ryegrass has unique relationships with the three tested AMF. Under all water supply treatments, G. mosses infected ryegrass at the highest rate. The height and biomass of the inoculated plants significantly increased under drought stress, while inoculation with AMF promoted the growth index of ryegrass under severe drought stress. (2) The MDA and proline contents in the ryegrass leaves gradually increased with greater water stress when the plants were not inoculated. Under severe drought stress, the MDA and proline contents in the leaves of ryegrass treated with G.m were significantly lower than those from plants that were not inoculated. (3) Under moderate and severe drought stress, inoculation with AMF significantly increased the antioxidant enzyme activity of ryegrass leaves and effectively improved the stress resistance of rare earth tailing plants. Inoculation with G.m strains had the most significant effect on drought resistance.",
"introduction": "1 Introduction According to current data, China is the country with the richest reserves of rare earth resources in the world, accounting for 50% of the total reserves worldwide. 1 Twenty-two provinces in China have rare earth deposits, with most deposits being widely distributed and relatively concentrated in remote mountainous areas. The ionic rare earth ore deposits are mostly distributed on the surface of exposed weathered granite with a thickness of approximately 9 m and were mainly mined in southern Jiangxi in the late 1970s. 2 Due to irrational predatory mining, the backward mining technologies of mining enterprises and untimely environmental supervision, environmental protection problems in mining areas have become increasingly prominent during the 30 years of development and utilization of rare earth resources. According to the data, the use of the pool dipping process to produce 1 t rare earth required stripping approximately 300 m 2 of topsoil, which damaged the vegetation by approximately 150 m 2 , resulting in approximately 667 m 2 of desertified land. The vegetation was seriously damaged during the mining process, resulting in almost no vegetation in the mining area. Vegetation damage caused the soil parent material to be directly exposed to the wind and sun for many years, resulting in poor soil and desertification. 3 The reduction in soil microbes and the destruction of the seed bank have caused the entire ecological structure to become unbalanced, making the natural ecological restoration of the abandoned mining areas more difficult. 4 Most of the rare earth minerals are located in remote mountainous areas. The water in the tailing sand is mainly derived from precipitation, and its water retention capacity is poor. Drought is an important factor influencing the difficulty of vegetation restoration on rare earth tails. Therefore, it is essential to improve the ability of plants growing on rare earth tail sand to resist drought stress. The use of biotechnology has been a hot topic of research in terms of improving plant drought resistance. Arbuscular mycorrhizae are reciprocal symbionts formed by Glomus fungi in the soil and more than 80% of higher plants. Studies have shown that mycorrhizal symbionts can promote the absorption and utilization of soil moisture and mineral nutrients by plant roots, protect host plants from drought stress, promote plant growth and increase plant viability in stressed habitats. 5 They play an important role in facilitating ecosystem restoration and reconstruction. Inoculation with arbuscular mycorrhizal fungi (AMF) under water stress increases plant growth and yield. When variables such as habit, life cycle, and water conditions are controlled, the effects of inoculation of AMF on plants can still differ due to interaction effects. 6 In a previous study, three types of AMF were inoculated under different environments experiencing water stress, which were characterized by poor soil nutrients, fragile ecological environments and rare vegetation. 7 Inoculation with AMF in cold and arid areas can increase the survival rate of transplanted seedlings. 8 Furthermore, inoculation with AMF under drought conditions can increase the biological yield of wheat, especially after the four-leaf stage. 9 Under high concentrations of lead and cadmium, inoculation with AMF increases the yield of basil ( Ocimum basilicum ) and ensures its quality under heavy metal stress. 10 Inoculation with AMF improves the rhizosphere soil in mining areas, the number of microorganisms is obviously improved, and a better mycorrhizal ecological effect is obtained. The presence of mycorrhizae promotes the stability of mining ecosystems and maintains their sustainability. 11 Under normal water supply and water control conditions, inoculation with Glomus etunicatum can increase the chlorophyll content of chickpea ( Cicer arietinum ) leaves, 12 and inoculation with AMF enhances the activity of polyphenol oxidase and peroxidase (POD) while also reducing the activity of catalase (CAT). Ryegrass has developed roots and produces clumps and many tillers and easily forms mycorrhizae under natural conditions. Plants inoculated with AMF can better adapt to arid environments. In recent years, AMF have been found to play an important role in the nutrition of plant-soil systems through joint action with plants, and they have great advantages that can be gradually applied to the ecological restoration and reconstruction of mining areas. However, there have been few studies on the use of AMF in rare earth mining restoration. There are also few reports on the phytoremediation of abandoned rare earth mines with microorganisms, and there has been no in-depth study on the effects of phytoremediation. Therefore, the application of AMF to improve the drought resistance of ryegrass has practical significance. 13,14 In this study, three types of AMF were inoculated under different water stress conditions characterized by poor soil nutrients, fragile ecological environments and rare vegetation to investigate the resistance of perennial ryegrass to water stress through the study of the effects on ryegrass growth and nutrient absorption. The objectives were to (1) screen the dominant strains that improve the resistance of ryegrass and (2) provide a scientific and technical basis for the study of AMF for rare earth tailing vegetation restoration.",
"discussion": "4 Discussion Daviess's studies have shown that the infection rates of ryegrass decline with an increase in drought stress. 25 The infection rate results in this study are consistent with this trend: from a normal water supply to severe drought stress, the rates of ryegrass infection by the three AMF were significantly reduced, and the biomass of ryegrass also declined. Inoculation with AMF also significantly promoted the growth of the aerial parts and roots of amorpha ( Amorpha fruticosa Linn). Inoculation with AMF helps to establish a mutually beneficial symbiotic relationship between mycorrhizal fungi and plants. 15 Staddon's research indicates that water stress can affect infection by the three AMF species used in this study by stimulating the germination of AMF and endophytic fungal spores and by affecting the growth and extension of hyphae. 26 There was a significant interaction between the water supply and AMF treatments in this study ( P < 0.05). This interaction was mainly due to the different activities and infestation advantages of the three AMF species under different water conditions. The ryegrass infection rates of the three types of AMF differed under the same water supply conditions, indicating that the symbiotic relationship between AMF and ryegrass is closely related to the species of AMF. Fajardo's research shows that plants and mycorrhizae coexist in a selective relationship, such as that between alfalfa and native strains of AMF. 27 In the current experiment, G.m had the most significant effect on the promotion of ryegrass growth, indicating that G.m has a higher affinity to ryegrass and can play a better role in the stress environment. The symbiotic relationship between AMF and plants will have different effects on the nutrient absorption and the physiological and biochemical reactions of host plants, thereby increasing the tolerance of host plants to various environmental stressors. 28 In addition, the distribution of host plant biomass is affected by water stress and AMF. This study showed that the different water supply treatments significantly affected the root–shoot ratio of ryegrass, while the effects of the AMF treatments were not significant. This result is similar to those of a study by Al-Karaki, who showed that inoculation with AMF had no significant effect on the root–shoot ratio of wheat of different genotypes. 29 When AMF is used for reclaiming in mining areas, it not only improves the soil rhizosphere enzyme activity in the mining area, but also improves the soil rhizosphere micro-ecological environment. 30 The mycorrhizal symbiosis formed by AMF and roots promotes the absorption of nutrients by roots and the growth of parts of the ryegrass plant and also improves the growth of underground parts, explaining why there was no significant effect on the root–shoot ratio. The results showed that G.m formed a good symbiosis with ryegrass roots and had a high mycorrhizal infection rate; therefore, more photosynthetic products are transported to the roots, indicating that there is sufficient carbon available for the formation of the mycorrhizal symbiosis. In contrast, the G.e treatment had a different effect on the root–shoot ratio and growth of the host plant. The results showed that under severe drought stress, inoculation with AMF significantly reduced the content of MDA in the leaves of the host plants, indicating that AMF can reduce damage to the cell membrane system. 31 MDA is a peroxide of cell membrane lipids, and plant tissues are subjected to oxidative stress under adverse conditions. When drought occurs, the plant cell membrane system is damaged, resulting in a significant increase in the amount of MDA in plants and death in plant cells. 32 The results of this study showed that inoculation with the three AMF significantly reduced the MDA content in leaves under severe drought stress. The difference between the G.m treatment and the G.e and G.v treatments was significant, indicating that lipid peroxidation was relatively low and that strong drought resistance occurred. In agreement with Qiangsheng Wu and other results of research on seedlings, inoculation of plants with AMF can effectively alleviate the degree of drought stress and indirectly improve the water metabolism of plants. 33 The accumulation of osmotic adjustment substances, such as proline, is one of the basic adaptations of plants to drought stress. The accumulation of these substances can regulate the intracellular osmotic potential, maintain the water balance, and protect the enzyme activities required for important metabolic activities in the cells, and it is generally considered that drought-tolerant varieties have stronger osmotic adjustment ability. 34 The results of this study showed that the proline content of ryegrass gradually increased in the inoculation treatments as the drought stress increased. Under severe drought stress, inoculation with G.m and G.v reduced the proline content compared to that in the non-inoculation treatment, and this difference was significant for G.m. However, inoculation with G.e increased the proline content of leaves, indicating that the symbiotic mycorrhizal system formed by G.m and ryegrass is more sensitive to drought and that G.m can facilitate a more rapid metabolic response, resulting in stronger drought resistance. Many studies have shown that AMF inoculation can improve the yield of corn, wheat, soybean and other major food crops, improve the nutritional status of crops, and enhance the resistance of crops to drought, salinity and other adverse environmental stresses. 35 At the same time, it also showed that different AMF undergo different osmotic adjustments when associated with ryegrass, which may be related to the influence of the different AMF on host plant biomass allocation. The growth and metabolism of plants are in a stable state under a normal water supply. Plant cells produce a large number of reactive oxygen and superoxide radicals due to metabolic obstruction when suffering from drought stress. These reactive oxygen species and superoxide radicals peroxidize cell membrane lipids with their strong oxidative properties, leading to membrane system and organelle damage and metabolic activity disorders. 36 Plants will actively regulate the rate of reactive oxygen species production in the cells through a protective system, thereby reducing the damage caused by cell membrane lipid peroxidation. Protection systems are divided into enzymatic defence systems and non-enzymatic defence systems. Higher activities of SOD, POD and CAT enzymes in the enzymatic defence system result in a stronger ability to eliminate oxygen free radicals and therefore stronger drought resistance in plants. Mycorrhizal plants are more adaptable under adverse conditions such as drought and saline.The activity of antioxidant enzymes such as superoxide dismutase (SOD), catalase (CAT) and peroxidase (POD) in the body is significantly improved, which can slow down the oxidative damage of plants and enhance drought resistance. 37,38 The results of this study showed that the inoculated AMF mainly increased the enzyme activities (SOD, POD, CAT) to eliminate the accumulation of reactive oxygen species caused by drought stress, thereby reducing the damage caused by cell membrane lipid peroxidation. Studies have shown that inoculation can increase the CAT activity of Caragana korshinskii Kom. Leaves. 39 Amiri found that two AMF ( Glomus intraradices and Glomus mosseae ) significantly increased the CAT activity of Pelargonium graveolens L'Herit. under drought stress. 40 Inoculation with G.m significantly increased the contents of CAT and SOD in ryegrass under severe drought stress, while inoculation with G.e significantly increased the POD content, indicating that the three microbial agents differ in terms of eliminating the regulation of reactive oxygen species in the leaves of ryegrass under drought stress. Research suggests that POD activity is higher in the late stages of stress and that inoculation with AMF can significantly increase POD activity in the early stages of plant growth and enhance the ability of plants to cope with environmental stress in the middle and late growth stages. 41 Water shortage can cause cell membrane damage, and POD activity immediately increases to a critical level to remove peroxide free radicals in plants. Inoculation with AMF can greatly increase this critical level. This is consistent with the report that existing inoculated fungi can significantly increase plant POD activity. 42"
} | 4,170 |
35611949 | PMC9245351 | pmc | 3,703 | {
"abstract": "Despite great scientific\nand industrial interest in waterproof\ncellulosic paper, its real world application is hindered by complicated\nand costly fabrication processes, limitations in scale-up production,\nand use of organic solvents. Furthermore, simultaneously achieving\nnonwetting properties and printability on paper surfaces still remains\na technical and chemical challenge. Herein, we demonstrate a nonsolvent\nstrategy for scalable and fast fabrication of waterproofing paper\nthrough in situ surface engineering with polysilsesquioxane nanorods\n(PSNRs). Excellent superhydrophobicity is attained on the functionalized\npaper surface with a water contact angle greater than 160°. Notably,\nthe engineered paper features outstanding printability and writability,\nas well as greatly enhanced strength and integrity upon prolonged\nexposure to water (tensile strength ≈ 9.0 MPa). Additionally,\nthe PSNRs concurrently armor paper-based printed items and artwork\nwith waterproofing, self-cleaning, and antimicrobial functionalities\nwithout compromising their appearance, readability, and mechanical\nproperties. We also demonstrate that the engineered paper holds the\nadditional advantages of easy processing, low cost, and mechanochemical\nrobustness, which makes it particularly promising for real world applications.",
"conclusion": "Conclusion In summary, we have demonstrated\na one-step strategy to fabricate\nprintable superhydrophobic paper through in situ surface engineering\nwith PSNRs. The PSNR-paper exhibits durable water repellency toward\nharsh external perturbations and shows significantly enhanced strength\nand integrity compared with traditional cellulosic paper after exposure\nto water. Importantly, the PSNR-paper features excellent printability\ntoward widely used inkjet printing techniques and could sustain its\nwater repellency after either printing or writing, due to the delicately\ndesigned oleophilic and hydrophobic PSNRs on its surface. Furthermore,\nthe developed nonsolvent strategy can be directly applied on paper-based\nprints without compromising their readability and functionality, yet\nconventional wet-chemical methods cause irreversible damages to both\nthe printed content and the cellulosic backbone. The PSNR provides\nthe armored paper items with self-cleaning property and antimicrobial\nfunctionality, which could potentially mitigate aging and decomposition\nprocesses and extend the lifespan of paper-based items. This is of\npractical interest for the protection of paper-based items for outdoor\nuse, as well as printed paper objects, such as historic papers, books,\npaintings, etc. Moreover, the PSNR armor strategy takes advantage\nof easy implementation, scalability, and the absence of organic solvents,\nwhich minimizes environmental and safety concerns and, in turn, provides\nopportunities for developing waterproof functional papers from sustainable\nnatural resources.",
"discussion": "Results\nand Discussion The printable superhydrophobic paper was prepared\nby in situ growth\nof PSNRs on cellulosic paper surface through a one-step nonsolvent\nstrategy at room temperature, as shown in Figure 1 a,b. The growth mechanism of the 1D PSNRs\non paper surface is illustrated in Figure 1 c. Under a certain humid atmosphere, nanosized\nwater droplets are formed due to the topographic and chemical heterogeneities\nof the substrate (paper) surface as well as surface tension. 28 , 29 These nanodroplets feature thermodynamic stability owing to the\nreduced chemical potential and thus act as confined reaction volumes\nacross the whole reaction process. 30 The\nreaction was triggered after injection of the precursor (trichloroethylsilane).\nThe volatile precursor reacts with water in the gas phase, yielding\nsoluble monosilanols. Since trichloroethylsilane is more easily hydrolyzed\nthan silanols, further hydrolysis of monosilanols to di- or trisilanols\nin the gas phase is unlikely. Therefore, the water droplets on the\nsubstrate surface are exposed to an atmosphere consisting of chlorosilane,\nwater, and silanol. These silane species react progressively with\nthe water nanodroplets present on the substrate surface via hydrolysis\nand condensation, resulting in the formation and deposition of insoluble\npolysiloxanes, which leads to the growth of one-dimensional nanorods\nsupporting the water droplet (reaction receptacle) at their top end.\nDue to the presence of silanol and siloxanol species, the activity\nof water nanodroplets decreases and more water in the gaseous phase\ntransports from the humid environment to the nanosized water reaction\nvolume to sustain further reaction of hydrolysis and condensation\n( Figure 1 c). The time-dependent\nmorphology of PSNRs ( Figure S1 ) agrees\nwell with the elaborated PSNRs’ growth mechanism. The reaction\nformulas for the hydrolysis and polycondensation of trichloroethylsilane\nare shown in Figure S2 . Owing to the presence\nof hydroxyl groups on the cellulose surface, the formed PSNRs are\nsupposed to be covalently bonded to the cellulosic paper surface through\nthe reactive sites (−Si–Cl or −Si–OH)\nof silane and siloxanol species. 31 Figure 1 Illustration\nof the one-step nonsolvent strategy for designing\nprintable superhydrophobic paper. Schematics showing (a,b) preparation\nof printable superhydrophobic paper via in situ surface engineering\nof PSNRs and (c) growth mechanism of 1D PSNRs. After surface engineering with PSNRs, the functionalized paper\n(PSNR-paper) demonstrates a completely different surface texture at\nthe nanoscale level in contrast to pristine cellulosic paper. To validate\nthis, scanning electron microscopy (SEM) was used to investigate the\nsurface morphology of the paper before and after treatment. Unlike\nthe fibrous surface texture of cellulosic paper ( Figure 2 a), PSNR-paper features micro-nano\nhierarchical structures due to the introduced PSNR layer ( Figure 2 b,c). The uniform\ndecoration of PSNRs is further confirmed by the homogeneous distribution\nof the Si element shown in the energy-dispersive X-ray (EDX) mapping\nimages ( Figure 2 d–f). Figure 2 Structures\nand chemicals evaluation of PSNR-paper and cellulosic\npaper. SEM images of (a) cellulosic paper and (b,c) PSNR-paper at\ndifferent magnifications, as well as (d–f) corresponding EDX\nmapping images. (g) EDX spectra of PSNR-paper and cellulosic paper.\n(h) Single reflection ATR-FTIR absorbance spectra of PSNR-paper, cellulosic\npaper, and pure PSNR. (i) Cross-sectional SEM images of PSNR-paper. Compared with cellulosic paper, a much higher Si\ncontent (∼27\nwt %) and an obvious peak corresponding to Si were observed from the\nEDX spectra analysis for PSNR-paper ( Figure 2 g). In the Fourier transform infrared (FTIR)\nspectra, the bands at 2950 cm –1 and 2900 cm –1 for the PSNR-paper are assigned to the C–H\nvibration of the CH 3 group of the decorated PSNRs, 32 , 33 and the same absorption bands are observed for pure PSNRs (synthesis\ndetails are shown in Materials and Methods section) ( Figure 2 h). These results further prove the successful decoration of PSNRs\non the paper surface. The PSNR layer thickness and the average diameter\nof PSNRs were determined to be 7.0 μm ± 1.3 μm and\n489 nm ± 71 nm, respectively, according to the analysis of cross-sectional\nSEM results, as shown in Figure 2 i. By measuring the weight change of the cellulosic\npaper before and after functionalization, the grafting weight percentage\nof PSNRs was calculated to be 19.8 wt % ± 1.1 wt %. Due\nto its inherent hydrophilicity, cellulosic paper can be easily\nwetted and infiltrated by water ( Figure S3 ). However, the as-prepared PSNR-paper exhibits excellent water repellency;\nfor instance, a water jet can easily bounce off ( Figure 3 a and Movie S1 ) and water droplets show a contact angle of 162° ±\n2° over its surface ( Figure S3 ). The\nintroduced superhydrophobicity was also demonstrated by the mirror-like\nplastron layer when PSNR-paper was immersed in water ( Figure 3 a), and the surface remained\nnonwetting after being taken out. This indicates the existence of\na trapped air cushion between the solid paper surface and water. 24 The excellent water repellency can be ascribed\nto the synergic effect of the low surface energy along with the nanoscale\nsurface roughness ( Figure 2 b) of the decorated PSNR layer. 34 , 35 Figure 3 Ultradurable\nsuperhydrophobicity of the PSNR-paper: (a) Water jet\nbounces off PSNR-paper and the mirror-like plastron layer on the surface\nof PSNR-paper immersed in water. (b) Durability of PSNR-paper under\nexposure to UV illumination, high humidity (90% RH), and extreme temperatures\n(200 °C and −196 °C). The inset images show the static\ncontact angle of the water droplet after each set of tests. (c) Impact\nof corrosion time in HCl and NaOH on water repellency of PSNR-paper.\n(d) Water contact angle (θ CA ) of PSNR-paper after\n24 h corrosion from different tested organic solvents. θ CA as a function of (e) abrasion and (f) bending cycles. Insets\nare the schemes of abrasion tests and images of bending tests. The water-repellent durability of superhydrophobic\nmaterials is\nan important property to be considered in practical applications.\nTherefore, the prepared PSNR-paper was kept under various test conditions\nfor a predetermined time, and its wettability was periodically examined\nthrough static contact angle (θ CA ) measurements ( Figure 3 b). No obvious change\nin θ CA was observed after 12 h exposure to (i) intensive\nUV illumination, (ii) ultrahigh humidity (90% RH), and (iii) extreme\ntemperatures (200 °C and −196 °C), demonstrating\nexcellent durability of the engineered PSNR-paper. Moreover, the PSNR-paper\nshowed outstanding stability when subjected to harsh chemical conditions.\nFor instance, after 90 min exposure to either 0.1 M HCl or 0.1 M NaOH\naqueous solution, the PSNR-paper could maintain its superhydrophobicity\nwith θ CA above 150° ( Figure 3 c), despite slight decreases. Interestingly,\nunlike most superhydrophobic surfaces, 13 , 36 the achieved\nPSNR-paper shows stable water repellency under long-term exposure\nto organic solvents, maintaining a final θ CA of around\n160 °C even after 24 h of immersion ( Figure 3 d). The sustained superhydrophobicity of\nthe solvent-treated PSNR-paper was further revealed by water droplets\nbouncing and rolling off from the slightly titled (5°) surface\n( Figure S4 and Movie S2 ). The ultradurable water repellency of the PSNR-paper can\nbe assigned to the physicochemical stability of the PSNR layer with\nwhich the paper surface is armored. The chemically inert low-energy\nsurface together with the cross-linked structure of polysilsesquioxane\nnanorods provides excellent resistance toward chemical perturbations. 37 , 38 This is further demonstrated by the SEM results of the surface topology\nof PSNR-paper after exposure to HCl, NaOH, DMF, and toluene, as shown\nin Figure S5 . Clearly, the PSNR layer remains\nintact with the paper surface after being exposed to these corrosive\nliquids. The collapse of the PSNRs after organic solvent treatment\nis ascribed to the induced capillary force during the drying process. 39 , 40 The retained PSNRs on the paper surface well explains the durable\nsuperhydrophobicity. Mechanical durability of PSNR-paper was\nexamined by abrasion and\ncyclic bending tests. After 20 abrasion cycles, the PSNR-paper maintained\nits θ CA of above 150° and remained completely\ndry after immersion in water, indicating the sustained water repellency\n( Figure 3 e and Figure S6 ). Additionally, a cyclic bending test\nwas conducted to evaluate the flexibility and mechanical durability. Figure 3 f shows the water\nrepellency of PSNR-paper as a function of bending cycles. No visible\nchange in θ CA was observed despite 500 bending cycles.\nThe preserved water repellency after mechanical damages can be ascribed\nto the maintained PSNRs protected by the microcellulose fibers during\nabrasion, 41 along with the residual polysilsesquioxane\nlayer remaining on the cellulose microfibers, which is evidently revealed\nby the SEM images ( Figure S7 ). These results\ndemonstrate the mechanical durability and flexibility of PSNR-paper. Notably, our strategy can be easily applied for scale-up fabrication\nof superhydrophobic cellulosic paper. We took commercially available\npaper of A4 size (297 mm × 210 mm) as the examined model. As\nshown in Figure S8 and Movie S3 , the A4 paper armored with PSNRs exhibits excellent\nwater repellency, as demonstrated by a water jet bouncing off its\nsurface, whereas unmodified paper can be easily wetted and infiltrated\nby the water jet. Strikingly, no change was observed to the paper\nappearance after being engineered with PSNRs. On the contrary, a significant\nwrinkled surface feature was observed for the paper treated with a\ncommonly adopted wet-chemical method ( Figure S9 ). This is mainly caused by the stretching of cellulose fibers. When\npaper is soaked with the involved liquid (ethanol), the adhesion between\ncellulose fibers would be reduced due to liquid infiltration, causing\nthe paper to swell, which consequently leads to the wrinkled and curled\npaper surface after liquid evaporation. Unlike superhydrophobic\npapers reported elsewhere, the as-prepared\nPSNR-paper can be used directly for printing, owing to its sustained\nappearance and integrity after functionalization. To evaluate the\nprinting performances, both PSNR-paper and cellulosic paper of A4\nsize were printed with the same content. No visible difference between\nthe prints on PSNR-paper and cellulosic paper was found ( Figure S10 ), indicating the outstanding printability\nof PSNR-paper. Importantly, the PSNR-paper even maintains its water\nrepellency after being printed. A muddy water (10 g of soil dispersed\nin 200 mL of water) jet can easily bounce off the PSNR-paper printed\neither with pattern or text content ( Figure 4 a, Figure S11a , and Movie S4 ) and water droplets maintained\nnearly spherical contact on the printed surface ( Figure S12 ), demonstrating the sustained waterproof functionality\nof the PSNR-paper after printing. In a sharp contrast, the water jet\nspread and infiltrated easily while it contacted the printed cellulosic\npaper ( Figure 4 b, Figure S11b , and Movie S4 ). Figure 4 Printable and writable superhydrophobic paper with enhanced integrity.\nComparison of water resistance between printed (a) PSNR-paper and\n(b) cellulosic paper. (c) Photos showing the hand writability of PSNR-paper\nand its preserved superhydrophobicity after handwriting. (d) Ink written\non PSNR-paper remains intact on the surface after long-term exposure\nto water, (e) while it diffuses easily from cellulosic paper to water.\n(f) θ CA of PSNR-paper before and after printing and\nhandwriting. (g) Comparison of the integrity between printed PSNR-paper\nand cellulosic paper after water immersion. (h) Tensile measurements\nand (i) maximum tensile stress and strain for printed PSNR-paper and\ncellulosic paper before and after water exposure. Printed PSNR-paper\nand cellulosic paper after exposure to water are indicated as PSNR-paper-W\nand paper-W, respectively. Moreover, the PSNR-paper enables handwriting as well. Figure 4 c visually shows\nthe writability and preserved water repellency of PSNR-paper. The\nwaterproof property of the handwritten PSNR-paper was further evaluated\nwith ink diffusion tests. Both PSNR-paper and cellulosic paper were\nwritten with water-soluble ink (200 mg of Rhodamine B dissolved with\n10 mL of ethanol) and exposed to water for a same time period (12\nh). The ink on the PSNR-paper stayed intact even after long-term contact\nwith water, whereas it dissolved into water from unmodified paper\nwithin a few seconds, as shown in Figure 4 d,e, respectively. The above results\ndemonstrate that the PSNR-paper simultaneously\npossesses excellent printability, writability, as well as waterproof\nfunctionality either before or after printing and handwriting. These\nfeatures can be ascribed to the introduced oleophilic and hydrophobic\npolysilsesquioxane nanorods on the PSNR-paper surface. The oleophilicity\nof PSNRs (attributed to the surface-exposed ethyl groups) together\nwith their micro-nano rough structure results in the formation of\ncapillary wetting and air cushion toward (oily) ink and water, respectively,\nwhich endows the PSNR-paper with both excellent ink adhesion and outstanding\nwater repellency. The excellent affinity and adhesion of the ink toward\nPSNR-paper is further demonstrated by the rapid absorption and complete\nwetting (contact angle of 0°) of the ink on the paper surface\n( Figure S13 ). Interestingly, after handwriting/printing,\nthe polysilsesquioxane nanorods entangled with each other (due to\nthe capillary action of the ink) and adhered to the paper surface\ninstead of breaking off ( Figure S14 ). The\nnanorods retained on the paper surface, together with the hydrophobicity\nof the loaded oily ink, further confirm the waterproofing properties\nof the handwritten/printed PSNR-paper. The sustained superhydrophobicty\nof the printed and handwritten PSNR-papers was also demonstrated by\nthe measured water contact angles above 150° ( Figure 4 f). Cellulosic paper\ncan easily get wetted by water absorption due\nto its hydrophilic nature and strong capillary action, thereby affecting\nits integrity and functionality. Figure 4 g shows the integrity tests for printed PSNR-paper\nand cellulosic paper after identical immersion time in water. PSNR-paper\nremained totally dry and showed high resistance toward tearing force,\nwhereas cellulosic paper was wetted by water and was easily destroyed.\nTo quantify the mechanical properties, tensile measurements were performed\nwith the papers before and after water treatment ( Figure 4 h,i). PSNR-paper exhibits comparable\ntensile strength (∼9.0 MPa) and stain (∼4.5%) compared\nto pristine cellulosic paper, demonstrating that the surface-engineered\nPSNR layer did not compromise the mechanical properties. After exposure\nto water, cellulosic paper showed a significant reduction in both\ntensile strength and strain, indicating poor integrity. As a sharp\ncontrast, the mechanical strength of PSNR-paper did not change, even\nafter long time exposure to water. These results suggest the excellent\nnonwettability and enhanced integrity of PSNR-paper toward water infiltration\neven after printing. This is of great significance for the use of\nPSNR-paper in practical applications. Endowing paper prints\nwith superhydrophobicity is of great interest\nin real world applications. However, most conventional superhydrophobization\nmethods cannot be used directly on paper-based prints. This is mainly\nbecause of the following: (i) the solvents used in a hydrophobization\nprocess would destroy the printed contents on paper surface due to\ndissolution of ink molecules; (ii) the opaque micro/nanotopographic\nfeatures (i.e., surface roughness required for superhydrophobicity)\nreduce the readability/visibility of the printed content. In\nthis section, we demonstrate the feasibility of our strategy\nfor waterproofing cellulosic papers preprinted with contents. Proof-of-concept\nexperiments are shown in Figure 5 . Interestingly, no observable change was inspected\nfor the visibility and readability of the print ( Figure 5 a,b), which indicates the visible\nlight transparency of the decorated PSNR layer. Further, the functionalized\nprint appearance remains unchanged. However, the print treated with\na conventional wet-chemical method showed unacceptable damage on both\nits exterior (curled surface) and the printed content (ink diffusion),\ncaused by the used solvent during treatment ( Figure S15 ). Figure 5 Transparent superhydrophobic armor for paper-based prints.\nImages\nshowing paper prints (a) before and (b) after armoring with PSNRs\n(print-PSNR). (c) Unmodified paper print contaminated by muddy water.\n(d) A jet of muddy water bounces off the print surface armored with\nPSNRs. (e) θ CA and tensile strength for the prints\n(with and without PSNR-armoring) before and after water exposure.\nPrints armored with and without PSNRs after water exposure are indicated\nas print-PSNR-W and print-W, respectively. (f) Durability of the water\nrepellency for PSNR-armored print under ambient conditions. The inset\nphotograph shows the spherical contact of water droplet after 100\ndays. (g) Time-resolved images showing the self-cleaning property\nof PSNR-armored print after 100 days storage under ambient conditions.\nComparison of antimicrobial property between (h) PSNR-armored paper\nand (i) cellulosic paper. Photographs in panels c and d are used with\npermission from University of Zurich. 42 Copyright 2010 UZH Ursula Meisser. The paper prints armored with PSNRs exhibit excellent waterproofing\nand self-cleaning functionalities; for example, muddy water jets bounce\noff easily from its surface without leaving any trace, whereas unmodified\npaper print was easily wetted and contaminated by the muddy water\n( Figure 5 c,d and Movie S5 ). The static contact angle of a water\ndroplet over PSNR-armored print surface was measured to be around\n160°, and it remained unchanged after long-term exposure to water\n( Figure 5 e). The θ CA of the unmodified print was tested to be ∼120°,\nand it instantly decreased to 0° after water exposure. The excellent\nwaterproofing of the print armored with PSNRs can be attributed to\nthe induced micro-nano surface morphology ( Figure S16 ) as well as the low surface energy of PSNRs. 31 The impact of PSNR armor on the print\nmechanical properties was\ninvestigated, as well, as shown in Figure 5 e. Tensile measurements show that print armored\nwith a PSNR layer features tensile strength comparable to that of\nthe one without any modification, again confirming that the strategy\nemployed does not affect the mechanical properties. The armored print\nexhibits significantly enhanced integrity and strength toward water\nexposure when compared with the untreated one, which is ascribed to\nits waterproof functionalization that prevented cellulose fibers from\ndetaching due to water infiltration. The longevity of water repellency\nfor the PSNR-armored print was evaluated under ambient conditions,\nas well. It was periodically examined through static contact angle\nmeasurements, and the θ CA remains around 160°\nafter 100 days of exposure ( Figure 5 f). Meanwhile, the self-cleaning properties were preserved\nas the dirt and dust contaminations can be easily removed from the\nprint surface by rolling water droplets ( Figure 5 g). Notably, the decorated PSNR armor offers\nthe functionalized paper items with excellent antimicrobial functionality.\nNo microbial growth was observed over the PSNR-armored surface when\nit was exposed to the bacterial species under favorable growing conditions\nfor 24 h ( Figure 5 h).\nOn the contrary, bacterial colonies can be clearly observed on both\nthe perimeter and surface of unmodified paper ( Figure 5 i), highlighting a large amount of microbial\ngrowth. The antimicrobial functionality can be attributed to the intrinsic\nsuperhydrophobicity of the PSNR-decorated surface, which prevents\nthe microorganisms from accessing the moisture and nutrients that\nare required for growth. Moreover, the hierarchical structured surface\nresulting from the decorated PSNRs decreases the contact area between\nmicrobes and the solid substrate, which plays a vital role for reducing\nthe adhesion of bacteria on the surface. 43 In addition, we also showed that the PSNR-armoring protocol can\nbe applied to waterproof other cellulose-based products, such as packaging\nmaterials ( Movie S6 ) and letter envelopes\n( Figure S17 ). The above results have\ndemonstrated the robustness of the engineered\nPSNRs for armoring cellulose-based items, i.e., endowing cellulosic\nobjects with multifaced functionalities but without compromising their\nappearance and properties, which is promising for enhancing the usability\nof cellulosic items and providing great advantages in paper-based\ntechnologies."
} | 5,990 |
33747667 | null | s2 | 3,704 | {
"abstract": "Electrotactile displays can open a new sensory substitution channel to be utilized in a vast array of applications. Our "
} | 30 |
17238290 | PMC1779306 | pmc | 3,705 | {
"abstract": "While there has been much recent focus on the ecological causes of adaptive diversification, we know less about the genetic nature of the trade-offs in resource use that create and maintain stable, diversified ecotypes. Here we show how a regulatory genetic change can contribute to sympatric diversification caused by differential resource use and maintained by negative frequency-dependent selection in Escherichia coli . During adaptation to sequential use of glucose and acetate, these bacteria differentiate into two ecotypes that differ in their growth profiles. The “slow-switcher” exhibits a long lag when switching to growth on acetate after depletion of glucose, whereas the “fast-switcher” exhibits a short switching lag. We show that the short switching time in the fast-switcher is associated with a failure to down-regulate potentially costly acetate metabolism during growth on glucose. While growing on glucose, the fast-switcher expresses malate synthase A ( aceB), a critical gene for acetate metabolism that fails to be properly down-regulated because of a transposon insertion in one of its regulators. Swapping the mutant regulatory allele with the ancestral allele indicated that the transposon is in part responsible for the observed differentiation between ecological types. Our results provide a rare example of a mechanistic integration of diversifying processes at the genetic, physiological, and ecological levels.",
"introduction": "Introduction Adaptive diversification describes the splitting of an ancestral lineage into two derived groups due to frequency-dependent ecological interactions [ 1 ]. During this process, disruptive selection on a common ancestral type drives the creation of diversified ecotypes through a series of adaptive genetic changes [ 1 , 2 ]. Even though this process is of central importance in evolutionary biology [ 3 ], examples are rare where the genetic changes that differentiate adaptively diversified species or ecotypes are known [ 4 – 8 ]. Even when genetic differentiation can be identified, it is often hard to establish a link to phenotypic differentiation, and even harder to show that the associated phenotypes played a causal role in the ecological mechanisms driving diversification [ 4 – 7 ]. Microbial experimental model systems greatly facilitate our ability to connect genotype to phenotype [ 9 ]. For instance, in static microcosms Pseudomonas fluorescens can readily diversify into three morphological types from a common ancestor [ 10 – 13 ]. One of these, the wrinkly spreader, dominates the liquid-air interface by forming a biofilm that it creates by overexpression of a polymer-forming operon [ 7 ]. While static microcosms provide different spatial niches and therefore an obvious mechanism for niche differentiation, it is less clear how diversification can occur in homogeneous liquid culture. We have used the bacterium Escherichia coli to investigate the ecological and genetic mechanisms of adaptive diversification in a homogeneous, well-mixed environment. When E. coli evolve in shaken serial batch culture with daily depletion of a mixture of glucose and acetate, in each batch they first use up all the available glucose and then undergo a diauxic switch to acetate consumption before entering stationary phase, after which they are transferred to a new batch of resources. After 1,000 generations in this homogeneous two-resource environment, E. coli readily diversify into two ecotypes that show different patterns of diauxic resource use. These types were previously dubbed Large and Small to reflect their colony morphology when cultured in a nutrient-rich environment [ 14 – 16 ]. Relative to the Small colonies, Large colonies exhibit high growth rates on glucose, slow growth rates on acetate, and a long lag between growth on glucose and growth on acetate. Thus, the diauxic growth profile of Larges is markedly different from the diauxic growth pattern of Smalls ( Figure 1 A). Figure 1 \n E. coli Growth Profiles of Each Isolated Strain on Glucose/Acetate Mixture and the Change in Glucose and Acetate Concentrations in First Phase of Diauxie (A) Three isolated genotypes (FS, SS, and Anc) consumed glucose (hours 0 to ∼4), then acetate (hours ∼4 until maximum OD), in a standard diauxic (two-stage) growth pattern. The SS (red) closely paralleled the ancestral (black) strain in growth patterns on acetate, while the FS (blue) exhibited a much faster diauxic switch from growth on glucose to growth on acetate and a higher growth rate on acetate (second stage of diauxie). Triangles on the horizontal axis indicate the switching points for each strain. (B) All strains deplete glucose from the medium during the first phase of diauxie (from inoculation into batch culture to the time of diauxic switch). (C) Change of acetate concentration in the medium during the first phase of diauxie. The ancestor and the SS ecotype accumulated acetate as they depleted glucose (Anc: t = 5.418, p = 0.003; SS: t = 11.342, p = 0.0002); however, the FS strain did not accumulate acetate during glucose depletion, indicating that acetate is consumed during growth on glucose ( t = 2.769, p = 0.025 not significant at experimentwise α = 0.017). For panels B and C, data are averages ± 1 mean standard error for three replicates, except for a single preinoculation replicate (open square). The two ecotypes have repeatedly evolved from a single common ancestor, and their coexistence is maintained by negative frequency dependence generated by the daily, sequential depletion of resources [ 14 – 16 ]. This frequency dependence is likely to be generated by a trade-off between the metabolism of glucose and acetate [ 14 ]: a short switching time from glucose to acetate consumption is possible if acetate metabolism is active even during growth on glucose, but this in turn reduces the efficiency of glucose metabolism [ 17 ]. Because growth rate differences better reflect the selection pressures that caused the bacteria to diversify, we will refer to “slow-switchers” (SS), which correspond to Large colonies, and “fast-switchers” (FS), which refers to Small colonies. A fundamental advantage of the E. coli model over evolution in natural systems is the ability of E. coli to survive cryogenic preservation. Ecological and phenotypic change in divergent E. coli strains that evolved from a common clone can be compared to the cryogenically preserved and revived common ancestor, and the ancestral strain can be manipulated genetically to contain alleles from the derived ecotypes and vice versa. These advantages have allowed us to gain an integrative understanding of genetic, phenotypic, and ecological mechanisms underlying sympatric adaptive diversification due to competition for resources in E. coli . Here, we analyse the changes in resource consumption and the concomitant genetic changes of a lab-based, well-documented evolutionary diversification.",
"discussion": "Results/Discussion We isolated FS and SS strains from a diversified bacterial population based on their growth profiles and confirmed that the FS switching lag, i.e., the time elapsed between the end of growth on glucose and the maximum growth rate on acetate, was shorter than that of the ancestral and the SS strains ( F 2,12 = 2841.6, p < 0.0001, Figure 1 A). To test the hypothesis that only the FS type has an active acetate metabolism during growth on glucose, we measured the dynamics of acetate concentrations for the different strains over a full day's growth. In wild-type E. coli , acetate metabolism is repressed during growth on glucose [ 18 ], which incidentally generates acetate as a by-product. In wild-type strains, the net concentration of acetate in the medium should therefore increase during growth on glucose. For randomly selected strains of the FS and SS ecotypes and of their common ancestor, we monitored how the glucose and acetate concentrations changed during log growth on glucose (hours 0 to 4 in Figure 1 A). As was expected if acetate metabolism is inactivated by the presence of glucose, both the ancestor and the SS generated acetate as a by-product during the glucose consumption phase of growth, so that acetate concentration in the medium increased ( Figure 1 B and 1 C). In a striking deviation from this pattern, the FS strain did not accumulate acetate in its medium as it consumed glucose ( Figure 1 B and 1 C). This indicated probable failure of a genetic mechanism to repress acetate usage during growth on glucose. It is likely that failed repression of acetate metabolism in turn allows for a fast diauxic shift to acetate consumption at the end of the glucose phase. In an attempt to test this, we investigated the genetic basis of the de-repression of acetate metabolism in the FS strain. Likely acetate usage candidate genes include acetyl-CoA synthetase (acs), which converts acetate to acetyl-CoA, and the three-locus acetate operon, aceBAK, which converts acetyl-CoA derivatives to malate ( Figure 2 ) [ 20 ]. The operon contains genes encoding malate synthetase A (aceB), isocitrate lyase (aceA), and isocitrate dehydrogenase kinase/phosphatase (aceK), all of which are coexpressed; we selected aceB as a proxy to represent expression of the operon. Figure 2 Schematic of Negative Regulation of the Acetate Operon aceBAK and the iclR Gene by iclR [ 22 , 33 ] Failure to negatively regulate aceBAK would result in constitutive expression of the operon. We measured expression levels using quantitative PCR of aceB for the ancestral and derived strains in medium containing acetate as the sole carbon source, and contrasted this with expression in media containing glucose. When grown on acetate alone, a ceB levels were high and equivalent for the ancestral, SS, and FS strains ( Figure 3 ). This confirms the expectation that the acetate metabolism is active in all three types when growing on acetate. However, when growing on glucose, expression levels of the aceB gene dropped dramatically in the ancestor and the SS strain ( Figure 3 ). This occurred independently of whether acetate was present in the medium or not and confirmed that, in these strains, acetate metabolism is repressed during growth on glucose. In contrast, the FS strain continued to express high levels of aceB, and by inference the entire acetate operon, even when growing on glucose ( Figure 3 ). In the glucose-containing media, aceB expression in the FS was reduced relative to the expression levels in acetate-only medium, but remained high relative to the SS and ancestral expression. The high aceB expression levels of the FS during growth on glucose strongly indicate that the FS ecotype has evolved a genetic mechanism by which the acetate operon remains expressed in the presence of glucose. Figure 3 Relative mRNA Expression from the aceB Gene \n aceB mRNA expression was quantified using quantitative PCR and normalized against 23SrRNA-4 ribosomal mRNA expression. RNA was isolated from cells in early log growth during the first phase of diauxie when glucose was present. In glucose-containing media, the FS has significantly higher aceB expression than the Anc and SS ( F 8,18 = 16.26, p < 0.0001; Tukey HSD FS,SS \n p = 0.032). Symbols: Anc (black), SS (red), and FS (blue). The enhanced aceB expression in the FS ecotype has two potential genetic causes: a change in the regulatory sequence of the acetate operon or a mutation in one of the operon's regulators. Through sequencing we confirmed that the regulatory region of aceB is identical in the FS, ancestor, and the SS. Next, we looked for mutations in the negative regulators of aceBAK because a decrease in negative regulation would cause constitutive expression of the aceBAK operon, congruent with the observed expression level changes. For the FS strain, we discovered that the isocitrate lyase repressor (iclR) gene, a negative regulator of aceBAK [ 21 , 22 ], contains a transposable IS1 genetic element that terminates the iclR transcript when it is two-thirds complete. To determine whether this mutation was prevalent in the FS population in addition to our focal FS strain, we screened nine subsequent isolated FS and SS strains for this iclR IS1 allele: eight of nine FS strains carried the mutant allele, but neither of the SS strains, nor the ancestor, carried this insertion. We additionally PCR screened FS genotypes from two similarly evolved populations and recovered only alleles of ancestral size at this locus. Indeed, we would not expect the exact same mutation (i.e., insertion of a transposon) to occur at the same site in two independently evolved populations, as it is likely that there are many different genetic mechanisms by which regulation of acetate metabolism can be altered. To determine how the IS1 insertion in the iclR gene affected acetate use in the FS, we substituted the ancestral iclR Anc allele into the FS genetic background and then estimated growth profile characteristics of the genetically modified strain. Inserting the ancestral iclR allele resulted in FS strains that had a significantly longer lag when switching from glucose to acetate use ( Figure 4 A and 4 C), however this altered lag was still significantly shorter than that of the ancestral strain ( Figure 4 C). We concluded from this that the mutant iclR IS1 allele in the FS ecotype decreases the amount of time the FS requires to switch from consuming glucose to metabolising acetate, presumably because this allele deregulates the acetate operon and thereby enhances acetate metabolic activity during growth on glucose. Figure 4 Growth Curves and Switching Lags for Genetically Derived Strains The iclR IS1 allele (dark blue) alters switching lag relative to the iclR Anc allele (light blue) in the genetic background of the FS ecotype (A and C) but not the ancestral genetic background with alleles shown in white and black, respectively (B and C; F 3,12 = 32.0, p < 0.0001). Growth rates on glucose and acetate are not measurably affected by alleles at this locus (unpublished data; glucose: F 3,12 = 28.0, p < 0.0001; acetate: F 3,11 = 157.5, p < 0.0001). We also inserted the FS mutant iclR IS1 allele into the ancestral genetic background, although recombinants were much less common in this direction, a fact that provides some evidence for genetic interactions between this locus and genes in the ancestral genome. In these strains with the ancestral genetic background, the mutant iclR IS1 allele did not affect switching times, which remained long ( Figure 4 B and 4 C). We speculate that the rarity of allelic recombinants during the allelic replacement procedure and the lack of change in switching lag indicate the presence of epistatic effects. In particular, the iclR IS1 insertion only seems to be effective in the genetic background of derived strains, but not in the genetic background of ancestral strains. This would not really be surprising, as the ancestor has no known evolutionary history in the glucose-acetate resource environment and does not carry the set of adaptively beneficial mutations that FS and SS must carry at other loci, based on differences in their growth curves from the ancestor ( Figure 1 A). We hypothesize that one or more derived alleles interact with the iclR locus to compound the effect of the iclR IS1 insertion in the derived strains. Although the IS1 element disrupts the down-regulation of the acetate operon sufficiently to alter the switching time between resources, it does not exert sole control over the switching lag in the FS. This is evident because the FS with the iclR Anc allele switched to acetate earlier than either of the genetically modified ancestral strains ( Figure 4 C). Furthermore, in the genetically modified strains, the iclR IS1 (FS) allele did not significantly affect colony morphology or growth rates on glucose or acetate (unpublished data). This clearly indicates that this single mutation is not sufficient to cause all of the resource use changes between FS and the ancestor, or between FS and SS. In particular, iclR does not act alone to cause the critical trade-off in performance that enables coexistence between the FS and SS strains. (Note that the FS strain and the ancestor never coexisted in the same population, and hence we would not expect to see evidence of a trade-off between these strains.) The observed differences in the growth rates and the colony morphology between the various strains must therefore result from additional modifications to metabolism. Nevertheless, our data show that a genetic change in the regulation of genes controlling carbohydrate metabolism has contributed substantially to the differentiation of coexisting ecotypes in E. coli populations. Our results not only confirm that regulatory changes can provide a mechanism for rapid evolutionary change [ 4 , 6 – 8 , 23 ], but they show that such regulatory changes may play a crucial role in processes of sympatric diversification. The results also show that such regulatory changes can act upon phylogenetically ancient central metabolic pathways such as the acetate switch found in microorganisms as diverse as gram negative E. coli , gram-positive Bacillus subtilis , and halophilic archea Haloferax volcanii [ 24 ]. The importance of acetate utilization on niche adaptation in nature is evident within the gammaproteobacteria, the taxonomic class containing E. coli , as evolutionary changes in the acetate utilization are correlated with pathogenicity in both closely related species such as Shigella [ 25 ] and less closely related species such as Yersinia spp [ 26 ]. Our current results demonstrate that similar evolutionary changes can also be observed in the laboratory during experimental evolution, and that such regulatory changes are important in niche specialization and differentiation [ 8 ]. These results establish a link between different levels of biological organization by showing how a genetic modification of gene regulation affects the expression of genes that are important for metabolic pathways, and how this gene expression in turn affects a trade-off in resource use that causes disruptive selection and competitive diversification."
} | 4,582 |
38817924 | PMC11137179 | pmc | 3,706 | {
"abstract": "Rare earth elements (REEs), including those in the lanthanide series, are crucial components essential for clean energy transitions, but they originate from geographically limited regions. Exploiting new and diverse supply sources is vital to facilitating a clean energy future. Hence, we explored the recovery of REEs from coal fly ash (FA), a complex, low-grade industrial feedstock that is currently underutilized (leachate concentrations of REEs in FA are < 0.003 mol%). Herein, we demonstrated the thermo-responsive genetically encoded REE-selective elastin-like polypeptides (RELPs) as a recyclable bioengineered protein adsorbent for the selective retrieval of REEs from coal fly ash over multiple cycles. The results showed that RELPs could be efficiently separated using temperature cycling and reused with high stability, as they retained ∼95% of their initial REE binding capacity even after four cycles. Moreover, RELPs selectively recovered high-purity REEs from the simulated solution containing one representative REE in the range of 0.0001–0.005 mol%, resulting in up to a 100,000-fold increase in REE purity. This study offers a sustainable approach to diversifying REE supplies by recovering REEs from low-grade coal fly ash in industrial wastes and provides a scientific basis for the extraction of high-purity REEs for industrial purposes.",
"conclusion": "4 Conclusion and environmental implications In this study, we demonstrated the RELP as a promising technology for the highly selective extraction of REEs from low-grade REE feedstocks. Current hydrometallurgical and electrochemical technologies are environmentally destructive, have poor selectivity for REEs, and are inefficient for dilute and low-grade feedstocks. Compared to existing LanM-based biosorption approaches, this process requires fewer chemical and energy inputs and generates minimal byproducts or waste solutions. The RELP also demonstrated distinct advantages, including upscale non-chromatographic protein purification and reuse and ease of separation. Notably, the RELP effectively and selectively extracts REEs from the low-grade FA leachate over multiple cycles. The RELP shows potential for REE recovery from other low-grade waste streams (leachate concentrations of REEs <1%) and containing high non-REE levels. In particular, the RELP biosorbent could serve as a platform for extracting high-purity REEs, such as dilute and low-grade REE solutions, and transforming them into more highly pure REE solutions. The recovered REE solution can be precipitated as mineral phases by adding oxalate or carbonate, and subsequently, the precipitates can be roasted to obtain total rare earth oxides. The simplicity, scalability, and economic viability of the method make it promising for industrial adoption. Purification and high reusability are required properties for developing low-cost biosorption technology for REE separation. However, properly designing fermenters with temperature-controlled purification and recovery of REEs will be another scaling-up strategy. This research aligns with UN Sustainable Development Goal 12, promotes a circular economy for a sustainable future, and reflects the role of green chemistry and engineering in sustainable production. By enabling the extraction of critical elements from diverse sources, RELP technology contributes to the sustainable management of REE-containing waste and diversifies the REE supply chain by utilizing fly ash as REE feedstock.",
"introduction": "1 Introduction Rare earth elements (REEs) are vital for modern technology and clean energy. However, their supply is concentrated in a few countries, posing challenges for the transition to clean energy. Diversifying REE sources through sustainable methods and recycling is crucial ( The White House, 2022 ; Carrera et al., 2023 ). Non-traditional REE resources, such as coal byproducts, are abundant and can diversify the REE supply chain ( Park et al., 2017 ). However, conventional REE extraction methods are technically challenging, intricate, and costly due to the low REE content and the high concentrations of competing metals found in these feedstocks. Furthermore, these REE extraction processes, especially hydrometallurgy through solvent extraction, require significant energy input and impose substantial environmental burdens ( Park et al., 2017 ; Mattocks J et al., 2020 ). Hence, developing alternative technologies that enable the selective, efficient, and environmentally friendly recovery of REEs from non-traditional feedstocks is paramount to tackling their supply challenges. Biosorption represents a potentially cost-effective and eco-friendly approach for metal recovery. Biosorption methods using natural and engineered whole cells and natural and engineered proteins have been explored for REE extraction. However, they have limitations, such as poor selectivity and operational issues. Industry and associated research fields generally ignore proteins for the REE life cycle and favor small, artificial chelators instead. However, the recent discovery of lanmodulin (LanM), a natural chelator, offers a sustainable alternative to conventional extraction methods with exceptional selectivity, especially for light REEs ( Cotruvo et al., 2018 ; Deblonde et al., 2020 ; Dong et al., 2021 ; Featherston et al., 2021 ; Hussain et al., 2022 ), and has served as a technological platform for f-element detection, recovery, and separation ( Deblonde et al., 2020 ). However, using only proteins for REE adsorption is not suitable and economical due to the short life, difficult reuse, and expensive single-use of proteins, and the extraction of desorbed REEs from free protein-based biosorbents is not trivial ( Hussain et al., 2022 ). Harnessing LanM for the effective and efficient recovery of REEs requires biosorption material with desirable functionality, stability, and reusability. Thus, to further improve the method efficiency by enabling facile protein reuse, recent studies highlight using a solid–liquid extraction process for the selective recovery of REEs. For example, LanM was immobilized onto agarose microbeads based on thiol–maleimide click chemistry. The resulting biosorbent was used for grouped REE extraction from low-grade feedstock fly ash (FA) leachate in a flow-through format ( Dong et al., 2021 ). In another study inspired by LanM, chimeric protein DLanM containing two copies of LanM was immobilized onto agarose microbeads using a Cnbr-activated amine condensation reaction. The fixed bed column was then packed with DLanM–agarose for the selective recovery of REEs under flow-through conditions ( Cui et al., 2023 ). In a recent study, SpyTag–SpyCatcher (Spy) chemistry was harnessed for bioconjugation to obtain REE-binding biomaterials; SpyCatcher-fused LanM was immobilized on the surface of SpyTag-functionalized magnetic nanoparticles. The engineered biomaterial selectively adsorbed REEs from the geothermal brine and low-grade leachate of coal FA. However, these methods have associated drawbacks of a time-consuming and complex material synthesis and immobilization process, reduced protein functionality, and REE adsorption kinetics upon immobilization ( Dong et al., 2021 ; Xie et al., 2022 ; Ye et al., 2023a ). The reduced protein REE adsorption activity is observed because of the low protein-loading capacity and protein denaturation by chemical reagents and materials upon immobilization. Compared to the free LanM protein, upon immobilization using thiol–maleimide click chemistry, LanM retained ∼67% of the adsorption activity, and one of its REE-binding sites was also destabilized ( Dong et al., 2021 ). In the case of SpyTag–SpyCatcher chemistry, LanM maintained ∼80% of adsorption activity ( Ye et al., 2023a ). The use of toxic chemicals and reagents or stringent conditions reduces the stability/activity of proteins. Moreover, the orientation and conformation of proteins are also hard to control, which may negatively impact protein stability/activity ( Ye et al., 2023b ). It is also important to consider the use of a non-adsorptive support matrix that allows the selectivity of the REE binding protein to determine the purity of the recovered metal solution and avoids potential matrix-mediated sorption of non-REEs and REEs; for example, non-protein-conjugated agarose microbeads showed an adsorption capacity of 4.38 μg mL -1 for La +3 (4,380 ppb or 31 µM) at pH 3.5 ( Brewer et al., 2019a ; Cui et al., 2023 ). We recently introduced a novel method named RExtractor, which employs a protein-based, all-aqueous approach for liquid–liquid phase separation to selectively recover total REEs. This method utilizes a REE-sensitive, genetically encoded elastin-like polypeptide (RELP) that responds to changes in temperature. The technology allows for the repeated use of RELP biosorbents, enduring multiple cycles of adsorption/desorption and phase transitions to recover Tb 3+ . Additionally, the RELP demonstrates resilience to acidic pH levels ranging from 3 to 6. The purification of RELP involves leveraging its unique phase transition behavior using inverse transition cycling (ITC), where the elastin-like polypeptide (ELP) serves as a purification tag ( Hussain et al., 2022 ). This method is advantageous due to its cost-effectiveness and time-saving nature, eliminating the need for chromatography and not being limited by resin capacity ( Meyer and Chilkoti, 1999 ; Hussain et al., 2022 ). Liquid–liquid phase separation (LLPS), also known as coacervation, is a process where macromolecular solutions undergo phase separation, resulting in the formation of a dense, polymer-rich phase and a solvent-rich phase ( Dinic et al., 2021 ). LLPS is commonly observed in proteins containing intrinsically disordered regions, like elastin. Various factors, including temperature, salt concentration, pH, and other biomolecules, such as RNA or ATP, are studied to modulate protein LLPS behavior ( Dignon et al., 2019 ). Elastin LLPS, for example, is driven by temperature and involves entropic interactions between hydrophobic sequences or domains and the disruption of water molecules surrounding the polymer. Inspired by this behavior, ELPs have been designed for numerous biomedical and industrial applications ( Vidal Ceballos et al., 2022 ). ELPs exhibiting lower critical solution temperature (LCST) phase behavior dissolve in aqueous solutions below their transition temperature (Tt) but undergo phase separation into a polymer-rich, insoluble coacervate phase at temperatures above Tt ( Varanko et al., 2020 ). This reversible LCST behavior, known as coacervation, enables the inexpensive purification of ELPs through ITC, pioneered by Meyer and Chilkoti (1999) . In ITC, soluble ELPs are collected below Tt, while insoluble contaminants pellet down during cold spin, and the opposite occurs during hot spin above Tt. By adding salt (not exceeding 3 M) and heat, ELP coacervation above Tt is facilitated, leading to the formation of micron-sized aggregates enriched in ELP fusion proteins. These aggregates can be separated from other soluble cell lysate components via centrifugation (hot spin) ( Hassouneh et al., 2010 ). Phase-transition efficiency of ELPs can be affected by several factors, such as ELP concentration, salt concentration, pH, and temperature. Therefore, there are two convenient ways to modulate the phase transition of ELP-fused proteins: increasing the solution temperature above the inverse T t or increasing the salt concentration to depress the T t below the solution temperature ( Hassouneh et al., 2010 ). For metal desorption from biosorbents, mostly acids, salts, and ligands have been used. However, using salts in the desorption step reduced the REE recovery yield and purity ( Park et al., 2016 ). Most of these studies focus on environmental remediation rather than extracting REEs for industrial applications, for which the purity of the recovered metals is critical. In the current study, we refrain from using salt during the desorption step by inducing the phase separation of RELPs through heating above Tt without the addition of salt ( Figure 1 ) to enhance REE purity through temperature cycling. FIGURE 1 Schematic diagram (Created with BioRender.com) of temperature cycling of RELP for the selective recovery of REEs from FA leachate through the following steps: with the addition of the FA leachate to RELP solution at 4°C for adsorption at pH 4.5; phase separation above Tt and centrifugation at 37°C to remove non-REEs; solubilization and desorption of the bound REEs at 4°C in the desorption buffer; similar to step 2 to collect the coacervate and REEs. For subsequent cycles, RELP coacervates were resolubilized at 4°Q17C, with the addition of fresh FA leachate for its repeated use in the recovery of REEs. The key research needed for this promising new protein-based REE recovery technology is to demonstrate its ability to recover REEs in repeated cycles from low-grade industrial or environmental samples ( Hutchison et al., 2021 ), even at environmentally relevant concentrations. For this purpose, coal combustion residuals, including FA, have been widely investigated as a potential source of REEs ( Stoy et al., 2021 ). Coal power plants worldwide release millions of tons of coal ash annually and are landfilled. The leaching of toxic trace elements and ash spill events make landfills a significant environmental challenge ( Deng et al., 2023 ). The recovery of REEs from FA has several advantages, which include readily available waste products, not requiring extensive excavation, and having a fine powder nature that makes it ideal for chemical processing ( Taggart et al., 2016 ). The present study offers the first direct experimental evidence of a RELP for extracting REEs from unconventional, low-grade REE feedstock in batch operation and its ability to recover low-concentration REEs with high purity. This technology will enable efficient and sustainable REE recovery from FA in order to meet the REE demand while also addressing the increasing environmental concerns with coal combustion residuals. Herein, we report that the use of RELPs for the selective extraction of total REEs from FA using temperature cycling paved the way for a quantitative, sustainable design of protein-based adsorbents for REE recovery.",
"discussion": "3 Results and discussion 3.1 Characterization of the biosorbent The RELP used in this study is the LanM, derived from Methylorubrum extorquens AM1, fused to the C-terminus of the ELP. The generated de novo 3D structural model using AlphaFold2 (DeepMind) ( Jumper et al., 2021 ) is shown in Figure 2A . As expected, the predicted structure showed an unstructured ELP region and the folded LanM protein region, suggesting that ELP fusion does not disrupt the folded structure of LanM. The RELP contains the ELP gene encoded with Val as the guest residue and comprises 150 repeats of the [GVGVP] amino acid motif (ELP[V150]). The RELPs purified through ITC have theoretical molecular weights of 75 kDa and were observed in the protein gel around the 75-kDa protein standard, with a purity above 95% ( Figure 2B ). FIGURE 2 Purification and phase transition behavior of the RELP. (A) Predicted 3D structure of the RELP monomer generated using AlphaFold2. (B) Purified RELP by SDS-PAGE analysis. From left to right, lane 1: molecular weight marker (M); lane 2: before induction (BI); lane 3: after induction (AI); lane 4–6: purified RELPs obtained from three different batches of purification. (C) Turbidity profile for the purified RELP at different concentrations. (D) Reversible size change of the RELP over multiple cycles at below Tt (10°C) and above Tt (37°C). As discussed above, the phase transition of an ELP fusion protein is generally modulated by increasing the solution temperature above the inverse T t , and by increasing the salt concentration, the transition is achieved even at temperatures much lower than T t ( Hassouneh et al., 2010 ). In the current study, we triggered the coacervation of the RELP by increasing the temperature of the purified protein solution above T t , without adding additional salt. For this purpose, following purification, the T t values of the RELP were measured by temperature-programmed turbidimetry. This technique monitors A 350nm of the RELP solution while the temperature increases. As T t is concentration-dependent, T t is characterized by a concentration series. The turbidity profile exhibits a sharp increase corresponding to the RELP phase transition from soluble to insoluble aggregates. The results showed that T t of a RELP decreases as its concentration in solution increases ( Figure 2C ). The reversibility of the RELP phase transition is confirmed by measuring size changes as the ELP transitions from unimer to micron-scale aggregates below T t and above T t , respectively. The micrometer-scale coacervates of the RELP were observed at 37°C via DLS analysis. The DLS analysis also showed that the RELP maintains its reversible phase transition property during three cycles of temperature change. An insoluble coacervate is fully reversible upon cooling of the solution (when the solution temperature was decreased below T t ) and was completely resolubilized ( Figure 2D ). These results demonstrated that the LLPS of the RELP can be triggered by temperature only, and such a reversible transition of the RELP will improve the existing method for the repeated use of the RELP for selective recovery of REEs. 3.2 Reusability of biosorbents for REE recovery from fly ash over multiple cycles We investigated the REE recovery using the RELP from the coal fly ash leachate, an abundant and potentially valuable industrial REE feedstock. The acid leachate of FA (pH 4.5) in this study contained ≈152 mM of total metal ions ( Table 1 ), including ≈5 µM REEs (0.003 mol% REEs, excluding monovalent ions) and other metals (4 mM Mg, 128 mM Na, and 19 mM Ca). Although the original coal fly ash leachate had Al, Si, and Fe, they were removed during pH adjustment. The RELP retained ∼95% of the initial REE binding capacity even after four cycles ( Figure 3 ). The RELP selectively recovered the REEs for repeated cycles and showed minimal recovery for non-REEs, with no substantial decrease in its performance after four cycles of REE recovery from the FA leachate; the results were insignificant for each element between each cycle ( Figure 3 ). The slight, apparent high recovery efficiency for light REEs (Nd and Sm) over heavy REEs ( Figure 3 ) is likely due to the LanM, derived from M. extorquens AM1, favoring the larger and more abundant light REEs than heavy REEs (Gd–Yb) ( Table 1 ), as previously reported ( Deblonde et al., 2020 ; Dong et al., 2021 ). FIGURE 3 Recovery of REEs by RELP (25 µM) from the coal fly ash leachate (pH = 4.5). The recovery percentage was calculated from the amount of metal recovered relative to the amount added. The data are represented as the mean ± SD and were subjected to one-way ANOVA with Tukey’s post hoc test (no significant difference among the four cycles for each REE). In comparison, previous studies showed single-step selective recovery of REEs from complex industrial wastes ( Hussain et al., 2022 ). In a single-step process, the RELP has shown high recovery efficiency for REEs (≈80%) from steel slag leachate containing 41 mM of metal ions ( Hussain et al., 2022 ). Moreover, in another study, the incubation of LanM with leachates of lignite and electronic waste, followed by a single-size exclusion filtration step (using a centrifugal filter with a molecular weight cutoff), showed that all of the non-REE elements remained in the recovered filtrates, and REEs are selectively extracted by the protein fraction left in retentate ( Deblonde et al., 2020 ). The retentates obtained from the filtration assay were digested and used for metal quantification by ICP-MS ( Hemmann et al., 2023 ). Hence, regarding the reusability of biosorbents for the selective and repeated recovery of REEs from complex industrial wastes, this study outperformed comparable REE extraction processes using protein-based technologies ( Deblonde et al., 2020 ; Hussain et al., 2022 ; Hemmann et al., 2023 ). Moreover, the method we used is entirely protein-based, and hence, it does not require any centrifugal filter to separate protein-bound REEs from unbound non-REEs or proteins from recovered REEs after the desorption step. Considering industrial applications, the simplistic non-chromatographic purification and high stability upon reuse of the RELP demonstrate the flexibility and potential for use as a low-cost recovery platform for REEs from complex waste sources. 3.3 Biosorbent for the recovery of high-purity REEs in extremely low-concentration samples REEs have been reported to exist at very low concentrations (picomolar scales) in various low-grade REE sources. We investigated the ability of the RELP to recover REEs from environmentally relevant concentrations (µM to nM range). The RELP was incubated in different solutions containing varying concentrations of REEs (Tb 3+ ) (µM to nM range) and equimolar concentrations of Mg and Zn (∼1 mM each) at pH 5.8. Mg and Zn were selected as representative competing non-REE elements because REEs frequently coexist with these metals at high concentrations (high μM range) in ore and waste streams. The RELP selectively recovers the REE (Tb 3+ ), which has a concentration as low as 1.8 nM ( Table 2 ). Mattocks et al. constructed a LanM-based protein sensor. They confirmed its detection efficiency for REEs using fluorescence resonance energy transfer. The sensor detected REEs within a 10–50 µM range, with a 7-fold ratiometric response but only a weak response to divalent and trivalent metal ions at pH 7.0 ( Mattocks et al., 2019 ). Moreover, in another study, Trp-substituted LanM directly quantified 3 ppb (18 nM) terbium in acid mine drainage using luminescence resonance energy transfer at pH 5.0 ( Featherston et al., 2021 ). These studies anticipate that LanM can be used for detecting and quantifying REEs in extremely low concentrations in environmental and industrial samples. The results suggest the advantage of the RELP system for the selective recovery of high-purity REEs from extremely low-concentration metal solutions. We anticipate further applications of this system by re-engineering the RELP to selectively recover other metal ions (e.g., actinides) present at extremely low levels in seawater ( Mattocks et al., 2022 ). Most previous biosorbent studies are more focused on environmental remediation than the extraction of REEs for industrial purposes, where the purity of the recovered metals is critical. The lanthanide-binding tag-engineered Escherichia coli cells showed REE purity of 0.11% in the extracted solution because of the adsorption of non-REEs to the cell surface, which eluted along with REEs ( Brewer et al., 2019b ). In terms of purity, LanM-immobilized magnetic nanoparticles showed 10.9 mol% REE purity in the recovered solution, which was 1,155-fold higher than in the FA leachate stock solution (0.009 mol%) ( Ye et al., 2023a ). The LanM-immobilized agarose microbead column showed 88.2 mol% REE purity, which was 2,040-fold higher than in the FA leachate stock solution (0.043 mol%) ( Dong et al., 2021 ). The increase in REE purity by the RELP was 10–100 orders of magnitude higher than other benchmark materials, such as LBT-engineered cells and LanM-immobilized magnetic nanoparticles, and comparable to the LanM-based column. Notably, the fold increase in REE purity was also 10–100 order-of-magnitude higher than that of LanM-based biosorbents ( Table 2 ). The results demonstrated that the RELP can be used to obtain high-purity REE mixture solutions by utilizing low-grade REE solutions. However, the RELP-based approach has limitations, such as the inability for intra-REE separation, as was demonstrated by the LanM-immobilized resin. We anticipate achieving even higher yields and product purity for other environmental and industrial waste leachates, which will be the subject of future studies. TABLE 2 REE (Tb 3+ ) purity after recovery by the RELP. The initial molar concentrations of Tb 3+ in each batch of synthetic solutions (ppb) and the amount of Mg 2+ and Zn 2+ added to each solution are approximately 1 mM. The experiments were conducted in triplicates, and values are represented as the mean. Initial Tb 3+ concentration µM (ppb) Tb 3+ purity in initial solution (%) Tb 3+ purity in recovered solution (%) Fold increase in Tb 3+ purity Recovery of Tb 3+ (%) 0.94 ± 1.9*10 −2 (150) 0.044 ± 1.6*10 −3 \n 1.0*10 2 \n 2250 ± 82 78 ± 1.59 9.4*10 −2 ± 1.9*10 −3 (15) 4.0*10 −3 ± 1.6*10 −4 \n 1.0*10 2 \n 22491 ± 828 77 ± 1.56 1.8*10 −2 ± 3.8*10 −4 (3.0) 8.9*10 −4 ± 3.2*10 −5 \n 1.0*10 2 \n 112452 ± 4141 63 ± 1.28 1.8*10 −3 ± 8.6*10 −5 (0.29) 8.8*10 −5 ± 3.9*10 −6 \n 2.1*10 −3 ± 1.5*10 −5 \n 23 ± 0.99 71 ± 3.44 It is noteworthy that the RELP platform can be leveraged to develop a more efficient biosorbent for recovering other critical elements and isotopes, provided it is re-engineered with other lanmodulin variants ( Mattocks et al., 2022 ; Park et al., 2022 )."
} | 6,321 |
23246794 | PMC3595036 | pmc | 3,707 | {
"abstract": "Quorum sensing (QS) regulates the onset of bacterial social responses in function to cell density having an important impact in virulence. Autoinducer-2 (AI-2) is a signal that has the peculiarity of mediating both intra- and interspecies bacterial QS. We analyzed the diversity of all components of AI-2 QS across 44 complete genomes of Escherichia coli and Shigella strains. We used phylogenetic tools to study its evolution and determined the phenotypes of single-deletion mutants to predict phenotypes of natural strains. Our analysis revealed many likely adaptive polymorphisms both in gene content and in nucleotide sequence. We show that all natural strains possess the signal emitter (the luxS gene), but many lack a functional signal receptor (complete lsr operon) and the ability to regulate extracellular signal concentrations. This result is in striking contrast with the canonical species-specific QS systems where one often finds orphan receptors, without a cognate synthase, but not orphan emitters. Our analysis indicates that selection actively maintains a balanced polymorphism for the presence/absence of a functional lsr operon suggesting diversifying selection on the regulation of signal accumulation and recognition. These results can be explained either by niche-specific adaptation or by selection for a coercive behavior where signal-blind emitters benefit from forcing other individuals in the population to haste in cooperative behaviors.",
"introduction": "Introduction There is an increasing awareness of the importance of microbial social interactions ( Crespi 2001 ; West et al. 2006 , 2007 ; Foster et al. 2007 ). Although unicellular organisms, bacteria can express complex coordinated multicellular behaviors, such as biofilm formation, antibiotic production, and secretion of virulence factors. Some of these behaviors require a large quorum of cooperating bacteria to be effective, that is, high cell density. Quorum sensing (QS) is a key communication system that coordinates cooperative behaviors in bacteria in function of cell density ( Crespi 2001 ; Waters and Bassler 2005 ; Keller and Surette 2006 ; West et al. 2006 ). QS involves the production, secretion, and recognition of small signal molecules called autoinducers detected by cognate receptors. Most autoinducers are species specific and thus promote intra-specific communication ( Waters and Bassler 2005 ). An important exception is the AI-2 system that uses as a signal a family of small molecules called autoinducer-2 (AI-2). The enzyme that produces AI-2 (LuxS) is present in both Gram-positive and Gram-negative bacteria. Because of the wide taxonomic distribution of LuxS, and the demonstration of the susceptibility of this system to interspecies interference, AI-2 has been proposed to be a signal produced to mediate both intra- and interspecies communication ( Surette et al. 1999 ; Chen et al. 2002 ; Xavier and Bassler 2005a ; Pereira, Thompson, et al. 2012 ). The substrate for AI-2 synthesis by LuxS is S -ribosylhomocysteine (SRH), which derives from the toxic intermediate S -adenosylhomocysteine (SAH) a product from S -adenosylmethionine (SAM) metabolism, an important and ubiquitous central metabolite of the cell ( fig. 1 ) ( Schauder et al. 2001 ; Winzer et al. 2002 , 2003 ; Xavier and Bassler 2003 ; De Keersmaecker et al. 2006 ). For this reason, AI-2 can be considered a recycling product of SAM, and it has been suggested that it might not be a true signaling molecule in all AI-2-producing bacteria ( Winzer, Hardie, et al. 2002 ; Winzer et al. 2002 ; Vendeville et al. 2005 ; Hardie and Heurlier 2008 ).\n F ig . 1.— AI-2 biosynthetic pathway and Lsr-mediated transport and processing in Escherichia coli . ( A ) The precursor of AI-2 biosynthesis is SAM, an essential compound in central metabolism used as a methyl donor for DNA, RNA, and proteins. Following methyl transfer from SAM to its various substrates, the toxic compound SAH is formed. The Pfs enzyme removes adenine from SAH to form SRH. LuxS acts on SRH to produce homocysteine and AI-2 that released into the extracellular environment. ( B 1) AI-2 is bound by the periplasmic protein LsrB and internalized by the Lsr ATP-binding cassette transporter. Intracellular AI-2 is phosphorylated by LsrK, and the phosphorylated form of the signal (P-AI-2) induces lsr transcription by derepressing the repressor of the lsr operon (LsrR). This results in further assembly of the transporter and rapid AI-2 internalization. LsrF and LsrG proteins are also encoded by the lsr operon and are required for the further processing of intracellular P-AI-2. ( B 2, B 3) Shaded cells represent examples of strains that maintain production of AI-2 although they lack the ability to sequester and process the extracellular AI-2 signal through the Lsr system ( B 2) or lack the Lsr system completely ( B 3). Pentagons represent the AI-2 signal. A major obstacle to understand the role of this molecule as a communication signal has been the lack of information on the molecular mechanisms of AI-2 detection and signal transduction networks in the majority of organisms. Importantly, such mechanisms have now been well characterized in E scherichia coli (reviewed in Pereira, Thompson, et al. 2012 ). In this bacterium, LuxS produces AI-2 during active growth, which is secreted into the extracellular medium where it accumulates in a cell-density manner until it triggers the activation of the Lsr (for LuxS regulated) system in the receptor cells. The genes of the lsr operon encode an ABC transporter responsible for the internalization of AI-2 into the cells and other enzymes that regulate the expression of the operon and further intracellular metabolic degradation of the AI-2 signal ( fig. 1 ). As a result of the activation of this system, AI-2 levels in the extracellular medium peak in midlate exponential phase and rapidly decline at the transition into stationary phase when the signal is removed from the environment ( Wang, Hashimoto, et al. 2005 ; Wang, Li, et al. 2005 ; Xavier and Bassler 2005a , 2005b ). By mediating the removal of AI-2 from the environment, this process can potentially affect any individual cell in the vicinity with AI-2-dependent gene expression, independently of its species identity ( Xavier and Bassler 2005a ; Pereira et al. 2008 ). A recent study showed that the ability to bind and internalize AI-2 signal via Lsr is not ubiquitous among E. coli strains. Two E. coli strains were shown to lack many genes in the operon, and phenotypic assays confirmed lack of function ( Pereira et al. 2009 ). The finding of this unexpected polymorphism leads us to investigate the genetic diversity of the AI-2 system among E. coli natural populations. E scherichia coli is an important component of the mammalian gut microbiome, especially during lactation, and is extremely diverse. It comprises both commensal and pathogenic variants, with different tropisms, and even some environmentally adapted strains ( Kaper et al. 2004 ; Tenaillon et al. 2010 ; Luo et al. 2011 ). The study of genetic variation in this species can thus provide important information on the role of the interspecies signal, AI-2, in an organism that coexists and interacts with many different species in its natural habitat. In E. coli , AI-2 QS regulates many social traits such as virulence ( Zhu et al. 2007 ), biofilm formation ( González-Barrios et al. 2006 ; Herzberg et al. 2006 ; Reisner et al. 2006 ; Lee et al. 2011 ), and chemotaxis and cell motility ( Bansal et al. 2008 ; Hegde et al. 2011 ). If the fine tuning of AI-2 concentration via the LuxS production and Lsr system for AI-2 internalization is necessary to regulate the behavior of E. coli and of other species in the mammalian gut, the invasion of individuals that are impaired in signal production or internalization could affect the microbiota species composition and diversity. Such alterations of gut homeostasis can facilitate infections ( Garrett et al. 2010 ; Clemente et al. 2012 ). In this study, we analyze the genetic diversity of AI-2 production, detection, internalization, and processing at the gene content and nucleotide levels using all complete sequenced genomes of E. coli and Shigella natural strains. We use this information to determine whether selective processes are implicated in the evolution of this system. Many studies have addressed the biochemical mechanisms or the experimental evolution of QS. Oddly, there have been very few studies on the natural genome diversity of QS. Analyses of natural polymorphisms provide an important tool to understand the selective pressures acting on the evolution of social behaviors in microorganisms. The information provided by comparative genomics of natural organisms, which focus on polymorphisms that have passed the filter of natural selection through millions of generations in their natural habitats, are ideal to study the evolutionary relevance of genes and pathways. Here, we took advantage of the large number of genomes available from natural E. coli and Shigella strains to study from a genome-wide perspective the evolution of polymorphism of the different components of the AI-2 system. Our analysis reveals that the AI-2 system follows a unique pattern of genetic diversification that differs significantly from those of species-specific QS systems.",
"discussion": "Discussion We have found that E. coli exhibits a gene repertoire polymorphism in the lsr operon. We have experimentally shown that such polymorphism leads to cells lacking the ability to bind, internalize, and/or process the QS signal. However, all strains maintain a functional LuxS, the synthase of the QS signal, even though the fitness cost of this deletion in monocultures is as low as that of many lsr genes. Overall, the evolution of the genes essential for regulating AI-2 concentration was shown here to be complex and non-neutral. Fifty-eight percent of E. coli strains cannot regulate AI-2 extracellular concentrations, 23 of the 40 strains analyzed lack a functional LsrK, these strains produce AI-2 but do not have the ability to sense or remove AI-2 from itself or others. We did not find any natural strain that lacks the LsrR repressor and still have a potentially functional operon. This suggests counter selection of strains that could be more efficient at removing AI-2. Hence, the overall phenotypic effect of the observed operon pseudogenization is always toward the decrease or total abolishment of AI-2 internalization and removal from the environment. Importantly, the comparative genomic analyses indicate that this functional polymorphism is maintained by natural selection. We find both signatures of selection to lose the operon and selection to maintain it, creating a balanced polymorphism at the level of gene content. This leads to a frequency of the lsr operon intermediate between that of persistent and of volatile genes ( van Passel et al. 2008 ; Kuo and Ochman 2009 ; Touchon et al. 2009 ) The selective pressure to lose the operon is supported by the inference of at least eight independent events of operon inactivation and the observation that pseudogenization, when it occurs, is too fast to be a neutral process. This fast gene extinction dynamics was already observed in the genomes of Salmonella enterica vs. Gallinarum ( Kuo and Ochman 2010 ) and occurs through the same general mechanisms described for bacterial pseudogene formation (e.g., large truncations, small frameshift indels, and stop codons) ( Lerat and Ochman 2005 ; Ochman and Davalos 2006 ). Although our data differ from Kuo and Ochman (2010) in that all genes are inferred to be ancestral and pseudogenization is shared with many strains, some of which very distantly related. In that study, the authors analyze 147 pseudogenes of which only five were shared with the closest related strain and only three are inferred to be ancestral. Our data fit better the models of balancing selection than a random model of gene loss, even though we cannot exclude the possibility that pseudogenization of one gene accelerates the loss of the other genes in the operon. Selection to maintain the ability to respond to extracellular AI-2 is suggested by balancing selection patterns in lsrA gene, as well as the polymorphism observed in all other lsr genes of complete operons that are typical of functional genes. In addition, we inferred through simulation that the majority of the E. coli strains should have already lost the operon unless there is ongoing selective pressure to maintain it. The mechanisms of selection maintaining these polymorphisms are an important line of future research. Theoretical models have shown that balancing selection may occur for diverse reasons and could potentially be quite common ( Gillespie 2004 ). We propose two nonmutually exclusive hypotheses for the maintenance of polymorphisms in this system. We observed that all Shigella and AIEC, which are strains known to replicate within macrophages, lack the lsr operon. The loss of the lsr operon in these strains could thus be a consequence of adaptation to a specific pathovar. This fits the hypothesis that cooperative processes regulated by QS are less important in bacteria with low infectious dose and able to replicate in professional phagocytes ( Gama et al. 2012 ). However, this intracellular niche adaptation hypothesis cannot explain all losses observed in our data because many other strains lack lsr . The other hypothesis relates to the social consequences of mutations in the genes regulating AI-2 QS. We found no measurable growth cost for the loss of AI-2 QS mechanism ( fig. 3 ), but in E. coli , AI-2 regulates costly group behaviors such as virulence and biofilm formation ( González-Barrios et al. 2006 ; Herzberg et al. 2006 ; Reisner et al. 2006 ; Zhu et al. 2007 ; Lee et al. 2011 ). Hence, although QS mutations have little direct metabolic effects, as growth is not affected in monocultures, they are likely to have ecological benefits by providing the cells the ability to exploit social processes in microbiomes. In most QS systems of Gram-negative bacteria, high cell densities are associated with high concentration of the signal. Elements that have lost the ability of producing the signal but still benefit from the information of producers (emitters) are thus noncooperating and have a fitness advantage ( Diggle et al. 2007) , in these systems receptors are much more abundant than emitters ( Patankar and González 2009 ). In contrast, in the E . coli AI-2 QS system, we observed gene repertoire polymorphism at the level of the signal receptor ( lsr operon) and not at the signal emitter ( luxS ). All these gene losses reduce or abolish AI-2 reception and internalization but do not reduce AI-2 production. This can be interpreted as coercive behavior, which is a particular type of social cheating if demonstrated that these cells would benefit from forcing the nearby cells into cooperative behaviors, while themselves refraining from cooperating ( Diggle et al. 2007 ; Foster et al. 2007 ). Consistently, it was shown that E. coli lsr mutants can induce the onset of cooperative behaviors of Vibrio harveyi and V . cholerae even when they are at low quorum ( Xavier and Bassler 2005a ) Because AI-2 has been shown to regulate biofilm formation and virulence traits in E. coli , it is expectable that the cells that do not internalize AI-2 but still contribute to their increased concentration in the extracellular medium will promote the remaining AI-2-sensitive cells in the vicinity to hasten the onset of the behavior they regulate with AI-2. It was recently predicted by Van Dyken and Wade (2012) that when social cheaters are maintained in natural populations as an evolutionary stable strategy, then it should also be expected that cheaters would be characterized by large insertion/deletions, frameshift mutations, or premature STOP codons; these are features that characterize the lsr operons of E. coli natural variants that do not regulate extracellular AI-2. The interpretation that gene repertoire polymorphism in the lsr operon is maintained through a process of social evolution is further strengthened by the observation that luxS , the signal synthase, is present in all strains ( fig. 2 ) even though we detect no fitness effect in the single-gene knockout mutant monocultures ( fig. 3 ). Because of its enzymatic role in recycling products of SAM metabolism, it was suggested that the selective pressure to maintain luxS was primarily to detoxify the cell and recycle the products of SAM metabolism ( Schauder et al. 2001 ; Winzer et al. 2003 ; Vendeville et al. 2005 ; De Keersmaecker et al. 2006 ; Hardie and Heurlier 2008 ). Importantly, we show that a mutant in Pfs, the enzyme immediately upstream of LuxS in the metabolism of SAM, does show a marked growth defect ( fig. 3 ) probably due to the toxic accumulation of SAH ( Schauder et al. 2001 ; Winzer et al. 2002 ). This strongly indicates that Pfs, not LuxS, is the major enzyme responsible for preventing the toxic consequences of SAH accumulation. The similarity in the phenotypic effects and their extreme difference in fitness cost highlight the importance of pfs in central metabolism, and it suggests that the selective pressure to maintain a functional luxS in the cell is not metabolic but social. Naturally, this conclusion has to be contextualized in the whole discussion of the AI-2 as a QS signal; a social cost cannot be attributed to any gene that does not present a strong metabolic cost. We still lack a direct experimental demonstration of such social benefit. This is difficult to show for inter-specific QS because it requires experimentation in complex environments. Nevertheless, it is known that in the vertebrate gut, E. coli experiences a complex multispecies environment where the ability to interact (or interfere) with other cells, of the same or of different species, may influence the evolution of its AI-2 regulation system ( McNab et al. 2003 ). Interestingly, extraintestinal virulence of an E. coli AI-2 QS-negative strain ( E. coli B2S) was shown to be boosted in mix strains infections compared with pure culture infections when mixed with an AI-2 QS positive regarded as commensal ( E. coli MG1655) ( Tourret et al. 2011 ). Hence, QS polymorphisms might lead to exploitation of commensals by pathogens to increase virulence. Overall, our findings suggest that complex adaptations of species with polyclonal interactions, such as E. coli , can be due to genes maintained at intermediary frequencies rather than ubiquitous or pathovar-specific genes."
} | 4,699 |
25104972 | PMC4124493 | pmc | 3,711 | {
"abstract": "Background Metal contamination is widespread and results from natural geogenic and constantly increasing anthropogenic sources (mainly mining and extraction activities, electroplating, battery and steel manufacturing or metal finishing). Consequently, there is a growing need for methods to detoxify polluted ecosystems. Industrial wastewater, surface water and ground water need to be decontaminated to alleviate the contamination of soils and sediments and, ultimately, the human food chain. In nuclear power plants, radioactive metals are produced; these metals need to be removed from effluents before they are released into the environment, not only for pollution prevention but also for waste minimization. Many physicochemical methods have been developed for metal removal from aqueous solutions, including chemical coagulation, adsorption, extraction, ion exchange and membrane separation; however, these methods are generally not metal selective. Bacteria, because they contain metal transporters, provide a potentially competitive alternative to the current use of expensive and high-volume ion-exchange resins. Results The feasibility of using bacterial biofilters as efficient tools for nickel and cobalt ions specific remediation was investigated. Among the factors susceptible to genetic modification in Escherichia coli , specific efflux and sequestration systems were engineered to improve its metal sequestration abilities. Genomic suppression of the RcnA nickel (Ni) and cobalt (Co) efflux system was combined with the plasmid-controlled expression of a genetically improved version of a specific metallic transporter, NiCoT, which originates from Novosphingobium aromaticivorans. The resulting strain exhibited enhanced nickel (II) and cobalt (II) uptake, with a maximum metal ion accumulation of 6 mg/g bacterial dry weight during 10 min of treatment. A synthetic adherence operon was successfully introduced into the plasmid carrying the improved NiCoT transporter, conferring the ability to form thick biofilm structures, especially when exposed to nickel and cobalt metallic compounds. Conclusions This study demonstrates the efficient use of genetic engineering to increase metal sequestration and biofilm formation by E. coli . This method allows Co and Ni contaminants to be sequestered while spatially confining the bacteria to an abiotic support. Biofiltration of nickel (II) and cobalt (II) by immobilized cells is therefore a promising option for treating these contaminants at an industrial scale.",
"conclusion": "Conclusions The two most important issues related to metallic waste processing are 1) reducing the environmental degradation resulting from disposal and 2) recycling the metals of economic interest, such as Co(II) and Ni(II). Due to the high cost of conventional physicochemical methods, microorganisms and plants have been already achieved wide application, for example, in sewage treatment. Successful uses of microbial bioremediation have been reviewed in [ 36 , 37 ]. Our study shows that the development of synthetic biology may play a role in the improvement of bioremediation processes, especially regarding their time requirements. Another important issue is that bacteria can be immobilized on solid supports to allow easy removal of “metal-loaded” bacteria from the bioreactor. This issue was addressed here by enhancing the natural adhesiveness of E. coli in response to the presence of the metal to be refined. If not total, adherence was significantly improved in our synthetic construct, thus paving the way for future developments. Concerning the metal-binding capacities of the strains, we showed here that most of the bound metal was present in the outer envelope. Nevertheless, the presence of the NiCoT transporter enhanced metal internalization by a factor of 1.6 to 1.9. This is of special interest when treating complex contaminated effluents, which often contain large amounts of iron and traces of other divalent cations. We showed here that the tested bacterial cells act as a non-specific metal sponge and that the transporter is able to selectively uptake trace metals. In brief, genetically improved metal accumulation and adherence were successfully implemented, as demonstrated in this work. We suggest that the “Ni/Co Buster” strain could be used to create a new generation of biofilters that are designed for the remediation of Ni(II) and Co(II).",
"discussion": "Results and discussion Conception and design of the engineered strain Our rational design of the Ni/Co Buster strain was aimed at modifying three cell functionalities. First, a constitutive fluorescent version of the MG1655 strain (SCC1, referred to as S29 in this manuscript) was used as a basic chassis to facilitate the monitoring of bacterial dispersion and biomass formation. Then, Ni/Co(II) capture optimized at two levels: preventing metal efflux and increasing metal uptake, as described below. Wild-type E. coli contains a Co and Ni efflux system, called Rcn (Ni and Co Resistance); this system is present inside the MC4100 strain, preventing the accumulation of Co(II) and Ni(II) [ 16 ]. As described in the Methods section, this system was disabled by transferring a rcnA::uidA - kan cassette to the fluorescent MG1655 strain S29 by P1 transduction. As expected, the resulting rcnA strain (S48) showed increased sensitivity to the toxic effects of Ni(II) and Co(II) compared to the parental strain. This result indicates that the P1 transduction procedure successfully inactivated the rcnA gene (data not shown). To further improve the performance of this Co- and Ni-accumulating strain, a genetic device was designed that allowing enhanced uptake of these metals. A synthetic metal uptake gene encoding the Ni(II) and Co(II) transporter (NiCoT) from Novosphingobium aromaticivorans, which is optimized for expression in E. coli, was placed under the control of the strong promoter Ptac , as described in the Methods section. This transporter was chosen due to its outstanding Co(II) accumulation and average Ni(II) accumulation capacities compared to other transporters of the NiCoT family [ 14 ]. Finally, cell adherence was enhanced by implementing synthetic adherent curli machinery (carried by pIG2 [ 18 ]) to prevent cell dispersion in the treated effluent using the P rcn bidirectional promoter. In E. coli , this promotor controls the transcription of rcnR in one direction and controls the transcription of rcnAB in the opposite direction. Between the transcriptional start sites of rcnR and rcnAB , there are two binding sequences for RcnR, a repressor that controls its own expression as well as the expression of rcnAB in response to intracellular concentrations of Ni(II) or Co(II) [ 23 ]. In this construct, the bidirectional promoter Prcn controls the curli operons csgBAEFG in the forward direction and the gene encoding the cognate metallo-regulator RcnR in the opposite direction (Figure 1 ). This design allows reinforced bacterial adherence in the presence of Ni(II) and Co(II) but is not expected to provide an absolute adherence control [ 18 ]. In this work, Ptac - nicoTB was cloned upstream of the Prcn-csgBAEFG construct. Readthrough was prevented by introducing a bidirectional terminator between these regions. The resulting pIG50 plasmid, which is described in Figure 1 , was transformed into the fluorescent rcnA strain S48 to create the Ni/Co Buster strain, which is also referred to as S61 or the “engineered strain” in this paper. Figure 1 Schematic representation of the engineered plasmid conferring constitutive metal uptake and inducible adherence. Two synthetic operons were designed and introduced in the pSB1T3 plasmid to generate pIG50 (Table 1 ). The first operon controls metal uptake by placing the codon-optimized nickel and cobalt transporter gene from Novosphingobium aromaticivorans nicoTB under the control of the strong promoter Ptac . The second operon confers metal-inducible adherence due to genes encoding curli structural (CsgA, CsgB) and assembly (CsgE, CsgF, CsgG) proteins under the control of the nickel and cobalt sensitive promoter from E. coli , Prcn . The bidirectional terminator Bba_B0014 [ 37 ] was added to prevent read-through transcription Ptac , nicoTB -BBa_B0014 and rcn - csgBAEFG were obtained separately by direct synthesis and were assembled in the high-copy vector pSB1T3. The pIG50 plasmid confers enhanced adherence of the MG1655 rcnA mutant to polystyrene Fluorescence and confocal laser scanning microscopy (CSLM) and spectrofluorimetry measurements were used to verify increased adherence of the engineered bacterial strain compared to controls. First, biofilm formation of the GFP-tagged S29 and S61 strains was observed in the presence or absence of 1 μM Ni(II) or Co(II) under a microscope after 5, 15, 24 and 48 h of culture in 96-well polystyrene plates. The behavior of the S29 and S61 strains differed after only 5 hours of incubation at 30°C. Whereas the adherent wild-type cells tended to remain distinct in the control samples without metal, the engineered strain formed clusters (data not shown). The S61 cell clusters evolved a dense biofilm after 24 hours of incubation, whereas the S29 strain failed to develop dense and structured biofilms (Figure 2 A). By comparing strains carrying or not carrying the pIG50 plasmid, we observed that the plasmid impaired bacterial growth. The average doubling time of the transformed cells increased from 256 ± 20 min to 300 ± 17 min (n = 7, t -test, p < 0,001). Although the S29 strain grew better, it did not have the capacity to form a thick biofilm, highlighting the enhanced adherence properties of the pIG50-harboring strain. These results show that the pIG50 plasmid confers increased adherence to polystyrene on the host-cells, even in the absence of metal. In the presence of Ni(II) or Co(II), an additional slight increase in surface occupancy by the engineered strain was observed (Figure 2 A). The increased production of biofilm in response to metals was confirmed using a confocal laser-scanning microscope; thicker biofilms were formed by the engineered strain S61 in the presence of metal (Figure 2 B). Figure 2 The pIG50 plasmid enhances MG1655 adherence to polystyrene. The GFP-tagged MG1655 strain (S29) and its engineered version were transformed with pIG50 (S61) and incubated in 96-well plates for 24 h at 30°C in M63G medium supplemented with various concentrations of metal ranging from 0.05 to 5 μM as indicated in the figure. The efficiency of biofilm formation was estimated based on microscopic observation or the fluorescence of the attached cells as described in the Methods section. A) Fluorescence microscopy. Biofilms formed by the S29 and S61 strains in the presence or absence of 1 μM Ni and 1 μM Co were observed. A sharp increase of adherence resulting from transformation with the pIG50 plasmid was observed. This phenomenon is quantified in Figure 2 C. B) Confocal laser scanning microscopy. The thickness of the biofilm formed by the S61 strain in the presence or absence of cobalt and nickel was estimated using confocal microscopy to illustrate the slight increase in adherence conferred by the presence of metal. Five fields were observed per strain, and three measurements of biofilm thickness were performed per field. The biofilms of the engineered strains were significantly thicker in the presence of metal (p < 0.005, Dunnett’s test). C) Spectrofluorimetry. The effect of metal concentration on S61 biofilm formation was estimated based on the fluorescence intensity of the attached cells. The fluorescence intensity of the S61 biofilms was converted to the corresponding number of cells/mm 2 based on a standard curve as described in the Methods section section and compared to the reference values obtained from biofilms formed under the same conditions by the parental S29 strain. Data represent the mean of 3 replicates (=3 wells), and error bars represent standard deviations. Significant differences are indicated using lowercase letters, and different letters indicate significant differences (Tukey’s test, p < 0.05). To obtain a more accurate view of this phenomenon, the autofluorescent S29 and S61 strains were grown in a 96-well plate in the presence of a wide range of Ni(II) and Co(II) concentrations (7 concentrations ranging from 0.05 to 5 μM of metal ions). After 24 h of culture at 30°C, the supernatant was thoroughly removed, and the fluorescence of the remaining biofilm was measured in each well using a fluorimeter as described in the Methods section. Due to their genomic insertions (Table 1 ), the S29 and S61 strains constitutively produce GFP. The fluorescence of such strains is therefore directly proportional to the number of cells in the two fractions (i.e., in the supernatant and the biofilm); therefore, the fluorescence can be used to estimate the ratio of adherent cells. A standard curve was established to facilitate conversion between units of fluorescence and biomass as described in the Methods section. For all non-toxic metal concentrations ( i.e ., concentrations of less than 2 μM Ni(II) or Co(II)), the biofilms formed by the engineered strain hosted significantly more cells than the parental strain (compare S61 and S29, Figure 2 C). In agreement with the observations performed using fluorescence microscopy (Figure 2 A) and CSLM (Figure 2 B), a slight increase in biofilm formation was detected in the presence of metals by directly measuring cell fluorescence after separating the free and sessile bacteria. No significant change was observed after 48 h of culture (data not shown). Table 1 Bacterial strains and plasmids used in this study Strain Relevant description Reference ARY023 rcnA :: uidA-kan [ 16 ] HYD720 Δ nikA-kan [ 24 ] MG1655 F- λ- S57 MG1655/pIG50 this study S59 MG1655 nikA this study S63 S59/pIG50 this study SCC1 = S29 MG1655 (PA1/04/03 gfpmut3 *Cm) [ 25 ] S48 MG1655 (PA1/04/03 gfpmut3 *Cm) rcnA::uidA-kan , this study S61 “NiCo buster” S48/pIG50 this study 1137 = S71 MG1655 malT :: Tn 10 ompR234 csgA :: uidA-kan [ 26 ] Plasmids Relevant description Reference pSB1C3 pUC19-derived pMB1 (copy number: 100–300) Cm R [ 27 ] pSB1T3 pUC19-derived pMB1 (copy number: 100–300) Tet R [ 28 ] pIG2 rcn-csgBAEFG inserted at sites Eco RI/ Pst I of pUC57 [ 22 ] pIG49 Ptac - nicoTB in pSB1C3 this study pIG50 Ptac - nicoTB - rcn - csgBAEFG in pSB1T3 this study Taken together, these results show that the engineered strain S61 constitutively forms biofilms and that the thickness of these biofilms is moderately but significantly enhanced in the presence of Ni(II) or Co(II). This outcome is consistent with previous experiments showing that the rcn promoter is leaky [ 29 ]. The Prcn promoter was preferred over the curli promoter to reduce the complexity of the genetic regulation of curli-mediated adherence. The curli endogenous promoter is not only regulated by Ni(II) but is one of the most complex promoters in E. coli [ 26 , 30 ] and is affected by a wide range of physiochemical signals, including temperature and osmolarity, and numerous regulators are known to modulate its expression [ 31 , 32 ]. Prcn is more stable with respect to environmental modification [ 23 , 29 ] and increases the ability of cells to bind to their support in the presence of these metals. Both of these properties are expected to make the engineered strain more attractive for industrial application. Cell immobilization is important for bioremediation for three reasons: it increases resistance to pollutants, confines the bacteria, and facilitates removal from the water phase, thereby facilitating the recovery of pollutant metals (reviewed in [ 33 ]). Immobilization can be achieved by adsorption, or by entrapment in a polymer network such as an alginate (methods reviewed in [ 24 ]). Intrinsic entrapment in a biofilm matrix, as designed and realized in this work, is a promising solution that limits the costs associated with immobilization. Efficiency of the engineered metal uptake transporter The specific uptake of Ni(II) or Co(II) was catalyzed by the Ni/Co uptake transporter from Novopshingobium aromaticivorans . This transporter is a single permease that belongs to the class II family of NiCoT transporters [ 10 ] and has been previously been shown to import both Ni(II) and Co(II), although Co(II) is imported with higher efficiency [ 2 , 10 ]. In the engineered strain, the amino acid sequence of the NiCoT transporter was optimized for expression in E. coli (see methods) . The efficiency of the NiCoT transporter was characterized using a quantitative Ni(II) uptake assay. In E. coli , Ni(II) uptake is mediated by the nikABCDE system [ 13 ]; in contrast, no Co-specific uptake system has been described thus far. To measure the specific uptake of Ni(II), experiments were carried out in a nikA mutant strain in the presence of a ten-fold excess of Mg ions to avoid nonspecific Ni(II) uptake via MgtA or CorA Mg transporters [ 13 ]. Moreover, the cells were washed with EDTA before radioactivity counting to prevent nonspecific Ni-binding on the bacterial cell wall. The accumulation of Ni(II) was monitored in a time course assay (30 min) by incubating the cells with 63 Ni. The intracellular concentration of 63 Ni per milligram of bacterial dry weight was then determined as described in the Methods section. Figure 3 A shows that the accumulation of intracellular nickel by the engineered S63 strain begins during the first minutes of contact with the metal. In contrast, the parental nikA strain S59 exhibited little, if any, accumulation of 63 Ni. In the presence of 150 nM of radioactive metal, specific 63 Ni uptake by S59/pIG50 appears to attain equilibrium after 30 minutes, reaching 6 μg/g of BDW (Figure 3 A). This result shows that the pIG50 plasmid carrying the nicoT codon-optimized construct allows rapid and significant Ni(II) accumulation. Therefore, the engineered high-affinity transporter NiCoT from N. aromaticivorans appears to be fully functional in E. coli . Figure 3 Functionality of the NiCoT transporter in the engineered strain. A) Uptake of nickel by the engineered transporter. The strains S59 (MG1655 nikA ) and S63 (S59/pIG50) were cultured in LB medium to an OD 600 of 0.6, at which time 150 nM 63 NiCl 2 was added. The cells were recovered after 0, 2.5, 5, 10, 15, 20 and 30 minutes of contact by filtration using a nitrocellulose filter. The captured nickel was estimated by measuring the radioactivity of the filter. Data represent the mean of 2 replicates, and error bars represent standard deviations. B) Cell viability tests. The sensitivities of wild type (S29), rcnA mutant (S48) and engineered (S61) strains towards nickel and cobalt were compared. Five microliters of cells at the indicated concentrations were spotted onto M63G (supplemented or not supplemented with metal) and incubated at 37°C for 48 h. Metal concentrations ranging from 1 μM to 50 μM were tested. Only one concentration (indicated below the image) is shown, corresponding to the MIC of strain S61. In the presence of 20 μM Ni(II), the engineered strain exhibited no growth; in contrast, the growth of the parental strain was not affected, and the growth of the rcnA chassis was slightly decreased. The physiological effects of metal uptake by the engineered strain were then investigated. Considering that an intracellular accumulation of metal would increase the sensitivity of the strain to metal [ 20 ], we assessed the sensitivity of the engineered strain towards Ni(II) and Co(II). In the absence of metal, the growth of the engineered strain was comparable to that of the S29 parental strain or the S48 rcnA chassis (Figure 3 B). In contrast, distinct phenotypes are observed for the three strains in the presence of 20 μM Ni(II) (a subinhibitory concentration). Whereas the growth of the parental strain was not affected and the growth of the rcnA strain was slightly affected by nickel, a dramatic loss of viability of the engineered strain occurred in the presence of the metal, indicating metal poisoning. Similar results were obtained in the presence of Co(II) at a lower concentration (1 μM). These results are consistent with previous studies that have shown that Co(II) is toxic at lower concentrations than Ni(II) [ 20 , 29 ]. This increase in Co(II) and Ni(II) sensitivity further demonstrates the functionality of the NiCoT transporter. Capture of nickel and cobalt by the engineered “NiCo buster” strain Having verified that the adherence operon and the metal uptake operon were both functional, we assessed the metal-accumulating capacities of the engineered “NiCo buster” (S61) biofilm. Because the S61 strain was engineered by deleting rcnA and adding pIG50, the efficiency of the engineered strain was measured against a strain possessing neither modifications, i.e., the wild-type (S29) strain. The engineered (S61) and parental (S29) strains were grown for 24 h in Petri dishes. After free-floating cells were discarded, the adherent cells were incubated for 10 min in the presence of increasing amounts of metal ions (5, 12, 20 and 50 μM of Ni(II) or Co(II)). The efficiency of metal capture was then quantified using ICP-MS (Inductively Coupled Plasma Mass Spectrometry). For both metals and both strains, cellular sequestration increased with metal concentration. S61 appeared to accumulate slightly more metal than S29 (Figure 4 A) but only within limited range of the tested concentrations; this was especially true for Ni(II). The concentration of captured metals did not reach a plateau in the tested range (0–50 μM). These results, together with the 63 Ni uptake results presented in Figure 3 A suggest that longer contact time might be required to reach the full metal capture potential of the bacterial cells. Moreover, the similar behaviors of the metal sequestration capacities provided by the two strains suggest that the total metal binding capacity of the bacteria arise from both specific and non-specific binding events . Indeed, for both strains, the total metal sequestration increased with external metal concentration. This might be due to nonspecific binding to the bacterial cell surface [ 17 ]. Figure 4 Capture of nickel and cobalt by the “NiCo buster” strain, as measured using ICP-MS. The S61 and S29 strains were cultured for 24 hours in M63G supplemented with the appropriate antibiotic at 30°C in Petri dishes. The medium was then removed and replaced by a metal solution prepared in sterile water. After 10 min (Figure 4 A) or 30 min (Figure 4 B and C) of contact between the bacterial biofilms and the metallic solution, the supernatant was discarded. The biofilm was collected, acidified and mixed with rhodium as an internal standard as described in the Methods section. The metal concentration of each sample was assayed using the ICP-MS technique as described in the Methods section. A) Absolute metal capture yields, expressed as the mass of captured metal divided by the mass of the biofilm. B) Relative contribution of non-specific binding and specific internalization. Absolute metal internalization yields, expressed as the mass of captured metal after washing the cells with EDTA (broken lines) divided by the mass of captured metal without washing the cells with EDTA (plain colors) after incubation in the presence of Ni (grey) or Co (white) for the S61 strain. The values shown are the means of three independent replicates. C) Contribution of the specific internalization provided by the engineered strain. The S29 strain (dashed lines) or S61 strain (plain color) was incubated in the presence of Ni (grey) or Co (white). The values represent the amount of metals assayed inside the cells in three independent samples after treatment with EDTA. To evaluate the fraction of metal ions that bound nonspecifically to extracellular structures and the contribution of the NiCoT transporter, experiments like those presented in Figure 4 A were performed. In this set of experiments, a single metal concentration was used (10 μM), and the contact time between the bacteria and the metal was 30 min to enable equilibrium to be reached. The experiment was repeated using three independent biological replicates. Half of the bacteria were subjected to ICP measurements without any previous treatment, and the remaining half was washed with EDTA before being titrated (see the Methods section). The treatment of intact bacteria with EDTA should remove most of the extracellularly bound metal. Comparing the EDTA-washed and unwashed fractions of the S61 strain showed that 80% of the captured Ni(II) or 90% of the captured Co(II) are bound by the extracellular matrix (Figure 4 B). The comparison between the EDTA-washed WT strain (S29) and the EDTA-washed engineered strain (S61) should demonstrate the contribution of the NiCoT transporter and the absence of the RcnA efflux pump. In the experiment, the S61 strain accumulated 1.6-fold more Ni(II) than did the WT strain, and the S61 strain accumulated 1.9-fold more Co(II) than did the WT strain (Figure 4 C). These results are consistent with those shown in Figure 4 A and clearly show the importance of the NiCoT transporter to the capture of the metals. In conclusion, the E. coli bacteria accumulated 4.8 to 6 mg/g dry weight in only 10 minutes when the cells were exposed to 50 μM Ni(II) or 50 μM Co(II), respectively, and accumulated 1.8 to 3 mg/g of bacterial dry weight when exposed to 20 μM Ni(II) or 20 μM Co(II), respectively. The performance obtained was better than that achieved using transgenic tobacco ( Nicotiana tabacum ) expressing a bacterial NiCoT; in that experiment, the tobacco accumulated 0.6 to 1.5 mg/g of plant dry weight in 30 days when the pre-cultivated, hydroponically grown plants were exposed to 20 μM Ni(II) or 20 μM Co(II), respectively [ 34 ]. Metal hyperaccumulator plants, such as Thlaspi caerulescens , have been reported to accumulate 2.8 mg of Ni/g of whole plant dry weight in hydroponic culture when grown for 3 weeks in presence of 10 μM metal [ 21 , 22 ] (representing slightly better performance, which was achieved at the cost of strongly increased decontamination times). Direct decontamination of wastewater containing low concentrations of non-radioactive Ni(II) and Co(II) might be achieved more rapidly and at lower cost using our engineered bacteria rather than plants that require weeks to grow. Highly contaminated effluents (e.g., from mining and electroplating industries) could first be treated using conventional chemical or physical methods or using new biopolymer biosorbents [ 7 , 12 ]. Bacteria could then be used to polish the wastewater in an additional treatment step to attain very low heavy metal ions concentrations that would comply with increasingly restrictive laws that regulate the maximum acceptable concentrations of metal in water. Our results show that Ni(II) and Co (II) metals are mainly captured by non-specific binding mechanisms. The NiCo Buster design, however, allows to increase the metal internalization by a factor of 1.6 (Ni) to 1.9 (Co), compared to the parental strain. This is of special interest when treating complex effluents, which often contain large amounts of iron and traces of other divalent cations. The engineered E. coli “Co/Ni Buster” strain appears therefore as a promising candidate for the depollution and retrieval of these metals in radioactive effluents due to the rapidity of its action, achieving comparable results to existing systems with at least a 2000-fold reduction of incubation time. In processes using our immobilized strain, the amount (volume) of material necessary to remove radioactive Ni(II) and Co(II) material should be less important, and the removal of these two metals could have a positive impact on the classification of these radioactive wastes. Furthermore, in the future, our genetic construct could be transferred to other organisms, such as Deinococcus radiodurans , a very radioresistant strain in which genes from E. coli can be successfully expressed [ 35 ]. The NiCo Buster system would however require optimization before industrial usage. One limitation is the presence of antibiotic resistant genes in the engineered strain. This raises the possibility for horizontal transfer antibiotic resistance genes from the engineered strain to environmental bacteria. Genome editing would permit to integrate all the exogenous sequences in the chromosome and remove the superfluous sequences."
} | 7,168 |
30634611 | PMC6357076 | pmc | 3,712 | {
"abstract": "Water-repellent surfaces, often referred to as superhydrophobic surfaces, have found numerous potential applications in several industries. However, the synthesis of stable superhydrophobic surfaces through economical and practical processes remains a challenge. In the present work, we report on the development of an organosilicon-based superhydrophobic coating using an atmospheric-pressure plasma jet with an emphasis on precursor fragmentation dynamics as a function of power and precursor flow rate. The plasma jet is initially modified with a quartz tube to limit the diffusion of oxygen from the ambient air into the discharge zone. Then, superhydrophobic coatings are developed on a pre-treated microporous aluminum-6061 substrate through plasma polymerization of HMDSO in the confined atmospheric pressure plasma jet operating in nitrogen plasma. All surfaces presented here are superhydrophobic with a static contact angle higher than 150° and contact angle hysteresis lower than 6°. It is shown that increasing the plasma power leads to a higher oxide content in the coating, which can be correlated to higher precursor fragmentation, thus reducing the hydrophobic behavior of the surface. Furthermore, increasing the precursor flow rate led to higher deposition and lower precursor fragmentation, leading to a more organic coating compared to other cases.",
"conclusion": "4. Conclusions In this study, superhydrophobic coatings are developed through atmospheric pressure plasma polymerization of HMDSO in the jet of nitrogen plasma produced by rotating arc discharges. The details regarding the plasma deposition process and precursor fragmentation dynamics are discussed. The plasma jet is modified by mounting a quartz tube on the jet head, thus confining the plasma jet in a smaller volume. It is shown that this modification leads to structures that are similar to what is observed in atmospheric pressure plasma polymerization of HMDSO in the absence of O 2 . After jet modification, the effects of precursor flow rate and plasma power on surface structure, wetting behavior and surface chemistry is studied. It is shown that increasing the flow rate while keeping the plasma power constant increases precursor fragmentation, leading to higher oxide deposition. On the other hand, increasing the plasma power while keeping the precursor flow rate constant results in faster deposition rates and subsequently thicker coatings, but with a higher oxide content. This demonstrates the significance of “available energy per precursor molecule” parameter, which can significantly affect the precursor fragmentation in the discharge. All conditions studied here lead to superhydrophobic surfaces with static contact angles higher than 150° and contact angle hysteresis lower than 6°.",
"introduction": "1. Introduction A superhydrophobic surface is defined as a surface for which the equilibrium water contact angle (WCA) is higher than 150° [ 1 ] and contact angle hysteresis is lower than 10° [ 2 ]. The concept of superhydrophobicity initially emerged from the investigation of natural surfaces with high contact angle and low contact angle hysteresis, notably the lotus leaf (Nelumbo) surface [ 3 ]. The superhydrophobic characteristics of the micro-nanostructured and wax coated surface of the lotus leaf was first studied by Dettre and Johnson in 1963 [ 4 ]. Since then, several other examples of natural superhydrophobic surfaces have been identified [ 5 ]. During the past few decades, many studies have been done trying to mimic some of the structures observed on natural superhydrophobic leaves to develop artificial superhydrophobic surfaces [ 3 , 6 ]. Such surfaces may find a wide range of applications from textile industry to power network design and maintenance [ 7 ]. Many studies have been done on water-repellent self-cleaning fabrics [ 8 , 9 ]. Superhydrophobic surfaces may be used in biomedical applications, vessel replacements or wound management [ 2 , 10 , 11 ]. Since icephobicity (i.e., low adhesion force between ice and the substrate) shows a correlation with superhydrophobicity [ 12 ], superhydrophobic coatings can be considered as suitable candidates to reduce the ice accumulation on various structures, notably power network equipment [ 13 , 14 , 15 ]. Construction industry can benefit from the development of superhydrophobic surfaces for manufacturing self-cleaning windshields and windows [ 16 , 17 , 18 ]. In marine industry, superhydrophobic coatings can be used to develop anti-fouling surfaces [ 19 ] or to assist in oil-water separation [ 20 ]. Superamphiphobic surfaces in particular (surfaces with both superhydrophobic and superoleophobic characteristics) may be used for such applications [ 21 ]. Furthermore, due to their potential in minimizing the liquid/surface contact area, hydrophobic and superhydrophobic surfaces can be used in anticorrosion application [ 6 , 22 , 23 , 24 ]. One of the most promising approaches to surface modification and coating deposition are plasma-based surface treatment methods, due to their high controllability, relatively low cost, low pollution levels, and short treatment times. Such treatments typically involve removing molecules from the surface (plasma etching/sputtering) and/or depositing a different material on the surface (plasma polymerization) using high-energy plasma-generated-species [ 25 ]. During the past few decades, plasma-based processes have been used for a wide range of applications, such as deposition of various functional coatings [ 10 , 25 , 26 ], modifying surface topography and micro/nano texturing [ 27 ], treatment of tumors and infectious wounds [ 28 , 29 , 30 ], or even treatment of food products [ 31 , 32 ]. Depending on the gas pressure in which the plasma is generated, plasma treatment may be carried out in low-pressure or atmospheric-pressure. Low-pressure plasma treatment typically results in more uniform films and may be used for 2D or 3D treatment due to the spatial homogeneity of the reactive species. Atmospheric-pressure plasmas, on the other hand, are easier to generate and maintain since they do not require cost-intensive vacuum pumps and chambers [ 33 ]. However, due to the open-air configuration in atmospheric-pressure treatment, plasma treatment of oxidation-sensitive materials becomes limited. In this study, the development of an organosilicon-based superhydrophobic surface through atmospheric-pressure plasma deposition of hexamethyldisiloxane (HMDSO) is reported. In this specific paper, emphasis is placed on the precursor fragmentation dynamics and the effects of ‘available energy per precursor molecule’ on coating properties. Molecular fragmentation of HMDSO in plasma leads to the deposition of low-surface-energy methyl groups on the substrate, thus reducing the wettability of the surface. It is often argued that the wetting characteristics is directly linked to the degree of precursor fragmentation since the energy required to break Si-C bond (318 kJ/mol) is less than the energy required to break Si-O bond (452 kJ/mol). Therefore, higher plasma energies typically lead to more polar oxide functions on the surface, which will in turn decrease the surface hydrophobicity [ 25 , 34 , 35 ]. Using a dielectric barrier discharge operating at atmospheric-pressure, Siliprandi et al. have shown that for low HMDSO concentrations (less than 0.3% in their study), the deposition process strongly depends on precursor presence in the plasma. However, beyond this threshold value the deposition process is mostly controlled by the plasma generation power, indicating a power-deficient regime [ 34 ]. In other words, fragmentation can be essentially controlled by adjusting the available energy per precursor molecule; this a well-established concept in low-pressure plasma deposition. Gas phase fragmentation and recombination reactions are also dependent on the residence time of plasma-generated-species. Longer residence times can be linked to higher precursor fragmentation, which correlated to higher oxide content in the case of HMDSO deposition. This has been confirmed by investigating the effects of plasma gas velocity and precursor injection position on surface chemical composition [ 36 ]. For more information on the applications of plasma technology in development of superhydrophobic surfaces, see Reference [ 25 , 37 ]. More specifically, this work reports the development of superhydrophobic coatings on pre-treated alumina-based substrates through atmospheric-pressure plasma deposition of HMDSO in the jet of an open-to-air nitrogen plasma produced by rotating arc discharges. The effects of precursor flow rate and generation power on precursor fragmentation in the discharge and thus surface chemical composition is demonstrated. Furthermore, the wetting behaviors of all coatings are studied through static and dynamic water contact angle measurement, and the results are correlated with the precursor fragmentation dynamics, surface chemical composition, and surface morphology.",
"discussion": "3. Results and Discussion 3.1. Optical Emission Spectroscopy To characterize the discharge zone, optical emission spectra were acquired from all conditions, and the results are presented in Figure 7 . In all cases, spectra is dominated by the nitrogen second positive system located between 325 nm and 425 nm ( Figure 7 b) [ 41 ]. Signal intensity is significantly stronger in PT5P75 in all regions. This is due to the higher plasma power, which leads to a more emitting plasma (note the different scales on Y axis in Figure 7 ). In the near UV region ( Figure 7 a), NOγ system is clearly observed for PT3 and PT5 through four bands located at 234, 245, 257, and 269 nm. As the plasma power increases, the emission from NO disappeared, which may be explained by a higher presence of quenching species in the discharge zone. On the other hand, atomic O lines may be observed between 700 nm and 850 nm in the case of PT5P75 ( Figure 7 c), which is consistent with higher precursor fragmentation in the case of higher plasma power. 3.2. Surface Morphology Surface morphology for different conditions was studied through scanning electron microscopy, and the results are presented in Figure 8 . Comparing different surface structures, it is observed that in PT3 ( Figure 8 a), the precursor flow rate is not high enough for complete coverage of the pre-treated aluminum surface. On the other hand, in PT5 ( Figure 8 b), the substrate is fully covered by the deposition material, while the morphological features originated from the pre-treatment procedure are retained. In the case of PT5P75 ( Figure 8 c), micro-features from the pre-treated substrate are completely buried under the deposited material, resulting in the loss of an important roughness level. This may be due to the higher precursor fragmentation, leading to an increased presence of oxygen in the discharge zone, promoting the formation of silica powders and increasing the size of the deposited particles [ 48 ]. The presence of silica powder in the coating structure (such as the larger deposited agglomerates in Figure 8 c) has an adverse effect of the mechanical stability of the coating, since larger particles become increasingly unstable and easier to remove under external forces. 3.3. Chemical Composition FTIR spectroscopy and XPS were used to study the chemical composition of different samples. Figure 9 shows the full range of FTIR spectra acquired from uncoated pre-treated aluminum, PT3, PT5, and PT5P75. Comparing the spectra related to the pre-treated substrate with those from the coatings, one can readily conclude that most features observed in the spectra are originated from the deposition. In all cases, the fingerprint region of the spectrum is dominated by features common to siloxane-based coatings. The presence of bands related to Si-(CH 3 ) n at around 800 and 1280 cm −1 , along with the bands related to C-H groups at around 3000 cm −1 , confirms the deposition of organic groups through plasma polymerization. Since Si-O-Si band intensity is strongest in the case of PT5P75, the deposition thickness is significantly larger for PT5P75 compared to PT5 and PT3. The presence of carbonyl groups (C=O), evident by the sharp peak at around 1750 cm −1 , is suggestive of higher precursor fragmentation with higher generation power. This increases the presence of oxygen in the discharge zone, which in turn enhances the formation of larger silica-based particles [ 48 ]. This is in agreement with larger deposited features observed in SEM images ( Figure 8 ). In the IR spectra of siloxane-based surfaces, the 500–1700 cm −1 range can provide valuable information regarding the Si-O network [ 43 ]. However, since this region is populated with several organic and organosilicon-based species, the spectra should be deconvoluted into its components to reliably quantify the peak intensity and surface area. In this context, the fingerprint region of the spectra presented in Figure 9 was deconvoluted to distinguish and quantify some of the organosilicon-based species present on the surface. Figure 10 shows the results of this deconvolution and shows the locations of the synthetic curves along with their assigned vibrations. For a brief description of what numbered components represent, see Table 3 . A notable feature of the FTIR spectra presented in Figure 10 is the continuously descending signal from 1300 cm −1 to 1500 cm −1 in the case of PT3 and PT5. Typically, this range is populated by many C-H bending vibrations in methyl (-CH 3 ), methylene (=CH 2 ) and methyne (=CH) [ 51 ]. However, C-H bending vibrations are usually narrower and therefore more distinct than peak indexes 6 and 7. Furthermore, this signal is not observed in the case of PT5P75, where C-H bending vibrations are also expected to occur. Therefore, it is highly unlikely that these peaks are due to C-H bending vibrations. A few other studies have observed several bands in the 1300–1500 cm −1 range in the case of highly porous structures with micrometric pore sizes [ 52 , 53 ]. These bands are not specifically identified in these studies but are only reported for highly porous surfaces. Therefore, an alternative, and more likely, explanation for this descending signal would be through the effects of surface porosity on the spectral background of the FTIR data. In PT3 and PT5, it is likely that the substrate’s pores interfere with the IR absorbance, and therefore the effects of porosity are more pronounced. In PT5P75, deposited material covers a major part of substrate porosity (see Figure 8 ), leading to the absence of any signal in 1300–1500 cm −1 range. In any case, this signal introduces a significant amount of uncertainty to any deconvolution procedure performed on this region. The main characteristic peak of Si-(CH 3 ) n is located at the lower boundary of this range (1275 cm −1 ), and therefore its shape and intensity is heavily affected by peak indexes 6 and 7. In this study, to avoid the uncertainty originated from the overlapping components, a different peak located at around 800 cm −1 (peak index 1) is used as a representative of the organic deposition. It should be noted that Peak 1 may be further deconvoluted into three separate components based on the value of n in Si-(CH 3 ) n [ 36 , 49 , 50 ] . In this study however, the resolution of the FTIR measurement is not small enough to distinguish between these components, and therefore the total surface area under this peak is used as a representative of the organic content. These various states of organic silicon will be investigated later while discussing the results of high resolution Si 2p core peak XPS spectra. Another consideration is the presence of Si-O-Si bending vibration at around 780–800 cm −1 [ 47 , 54 ], which may overlap Peak 1 in the above calculations. However, in organosilicon coatings, the intensity of Si-O-Si bending vibration is typically negligible compared to the Si-C rocking [ 48 , 55 , 56 ]. In order to determine the amount of organic functions in the Si-O-Si network, the ratio between the surface area under the Si-(CH 3 ) n component (peak index 1) and the surface area under quartz-like Si-O-Si component (peak index 3) is calculated. Furthermore, to determine the structural integrity of the Si-O-Si network, the ratio between the surface area under the Si-O-Si TO 2 component (peak index 4) and the surface area under the Si-O-Si TO 1 component (peak index 3) is calculated. Since TO 1 is correlated with a quartz-like structure while TO 2 is correlated with fragments of siloxane chains, a higher TO 2 /TO 1 ratio corresponds to shorter siloxane chains and more disorder in the silica network. These ratios were calculated for PT3, PT5, and PT5P75 and are presented in Table 4 , where A n denotes the surface area under the n th peak index. Since the plasma power is identical for PT3 and PT5, the amount of available energy per HMDSO molecule is higher in the case of PT3. Therefore, higher monomer fragmentation is expected in lower precursor flow rates, which leads to lower Si-(CH 3 ) n /Si-O-Si ratio. This is manifested as a lower A 1 /A 3 ratio for PT3 than PT5. Similarly, since the precursor flow rate is identical for PT5 and PT5P75, higher power leads to higher energy per precursor molecule, which in turn increases the fragmentation and leads to lower Si-(CH 3 ) n /Si-O-Si ratio in the case of PT5P75. To study the structural integrity of the siloxane network, A 4 /A 3 ratio is investigated. For PT3 and PT5, this ratio is almost identical, which is suggestive of similar siloxane structures in both cases. However, as discussed before, the main difference between PT3 and PT5 is in coating thickness and the amount of deposition ( Figure 8 ). In the case of PT5P75, it is shown that increasing the generation power has a significant effect on the Si-O-Si network, leading to shorter siloxane chains (higher TO 2 intensity). This is consistent with higher precursor fragmentation with higher plasma energy. Complementary quantification of the surface chemical composition was acquired through X-ray photoelectron spectroscopy. Figure 11 shows the atomic percentages of Si, C, and O based on the survey spectra. In PT5, lower precursor fragmentation (due to the lower energy per precursor molecule) leads to the highest organic content on the surface, which is manifested as higher C percentage and lower O percentage. PT5P75 has a higher content of silicon and oxygen and a lower content of carbon, which can be linked to a decreased carbon content due to the higher precursor fragmentation. This is also in agreement with the FTIR data, where it was shown that the organic content is reduced with increasing the power. Finally, comparing PT3 with PT5, it is observed that the increased amount of energy available per precursor molecule in the case of PT3 leads to smaller amounts of deposition (lower silicon percentage) with less organic content (lower carbon percentage). High resolution spectra of Si 2p core peak were used to determine the chemical state of silicon atoms in the siloxane structure. Si 2p core peak analysis is done based on the method developed by O’Hare et al. [ 57 , 58 ] for the analysis of siloxane-based coatings using X-ray high resolution spectra of Si 2p core peak. In this method, four different chemical states of silicon atoms are identified based on the number of bonds with oxygen: Q, T, D, and M which are in contact with 4, 3, 2, and 1 oxygen atoms, respectively. These chemical states are shown schematically in Figure 12 and some further details are provided in Table 5 . Based on these chemical states and their respective binding energy in X-ray photoelectron spectra, synthetic curve-fitting models (not presented here) were developed on Si 2p core peak to determine the amount of deposited organosilicon species. These models where developed by (1) restricting the position of (Q) component to 103.69 ± 1 eV based on the calibration experiments on pure quartz samples, (2) restricting the position of (T), (D), and (M) based on their respective shifts from the position of (Q), and (3) forcing equal peak widths for all components. The results from component quantification are presented in Figure 13 . When HMDSO molecules are vaporized and injected into the flowing afterglow region of the atmospheric-pressure plasma jet, they may go through several stages of fragmentation. Si-O bonds, which are weaker than Si-C bonds, break first, generating (M) silicon states which may be deposited on the surface. Further fragmentation results in broken Si-C bonds, removing methyl groups from silicon and replacing them with oxygen atoms in the open-to-air plasma. Therefore, the presence of various silicon chemical states may be interpreted as various degrees of fragmentation. In the case presented here, higher presence of (M) groups and lower presence of (Q) groups in the topmost layer of PT5 is suggestive of lower precursor fragmentation. Even before curve-fitting, higher organic content in PT5 is evident by a 0.5 eV shift observed in Si 2p core peak position towards lower energies. This is consistent with the FTIR data, where it was shown that PT5 exhibits higher Si-(CH 3 ) n /Si-O-Si ratio with a more organized structure. For PT3 and PT5P75, more than 90% of silicon atoms are in contact with 4 or 3 oxygen atoms (Q and T respectively), which is suggestive of high precursor fragmentation. However, in the case of PT3, (Q) and (T) are represented almost equally on the topmost layer of the coating, while in PT5P75 silicon is mostly at (T) state. Based on what has been discussed so far, it is evident that both coatings are the outcome of heavy precursor fragmentation. However, higher presence of precursor molecules in the case of PT5P75 results in the higher implementation of organic functions in the siloxane network, leading to a high concentration of (T) state in PT5P75. Similarly, lower presence of precursor molecules in PT3 reduces the functionalization of siloxane network with organic functions, leading to higher concentration of fully oxidized silicon (Q). At this point, it should be noted that in a typical curve fitting process, the results may be examined by developing a curve model on a different peak related to a different element and confirming the agreement between the results acquired from both models. In the case presented here, this validation can potentially be done by creating synthetic models on C 1s peak based on (T), (D), and (M) functions along with other possible carbon states (such as C-C, C-H, and C=O). However, the binding energies for C 1s (T) , C 1s (D) , and C 1s (M) are in a 0.5 eV range (284.7 eV, 284.5 eV, and 284.2 eV, respectively) [ 57 ]. Furthermore, the binding energy of adventitious carbon (carbon originated from exposure to air or other sources of contamination), which is typically the most prominent source of carbon in XPS data, is also in the same range. Due to the relatively large FWHM of the high-resolution spectra (~2 eV), distinguishing the peaks in such a short range is practically impossible. However, the models presented here were developed with as many physically and chemically relevant restrictions as possible (on peak position, peak width, peak shape, etc.) to ensure mathematical, physical, and chemical accuracy of component quantification. Furthermore, the results from this quantification are in complete agreement with FTIR results and the expectations based on the literature. Therefore, despite some potential limitations, we feel confident that these models can accurately represent the chemical composition of the surface. 3.4. Wetting Behavior The wetting behavior of PT3, PT5, and PT5P75 is studied through static and dynamic water contact angle measurements. Numerous studies and reviews have emphasized the importance of dynamic wetting studies. It is often suggested that if a surface shows hysteresis, i.e., a difference between advancing and receding angles, static contact angle becomes an arbitrary value between advancing and receding angles [ 59 , 60 , 61 ] and tends to miss a significant amount of information regarding the wetting characteristics of a surface. In fact, it has been suggested that since the advancing angle is more sensitive to low-energy components of the surface while the receding angle is more sensitive to high-energy components [ 62 ], one can study them individually to gain a deeper insight on surface functionalities. In this work, dynamic wetting is studied through placing a 4 µL droplet on the surface while keeping the needle in contact with the resting droplet and increasing or decreasing the droplet volume while measuring contact angle at the three-phase interfacial point five times per second ( Figure 14 ). These measurements are then plotted against time and the curve is smoothed using the LOWESS method to account for minor variations (±1°) while retaining the general trend of the curves. More details on this procedure is provided in [ 63 ]. These curves are presented in Figure 15 for PT3, PT5, and PT5P75. As the droplet volume slowly increases, the baseline initially remains the same, resulting in an increase in the measured contact angles. Eventually, the weight of the droplet becomes large enough to expand the baseline, which may be shown in the contact angle vs. time curves as either a drop in the measured values followed by a further gradual increase (sudden baseline expansion), or as a constant measured value with volume after the initial increase (continuous baseline expansion). Advancing contact angle is then determined as the measured value just before baseline expansion [ 39 , 60 ]. Similarly, reducing the droplet volume leads to an initial decrease with a sudden increase (sudden baseline shrinkage) or a constant value (continuous baseline shrinkage). In this case, receding angle is determined as the measured value just before the baseline shrinkage (see Figure 15 ). The fundamental physics behind this procedure have inspired several groups to discuss and compare their knowledge of surface chemistry, surface topography, and interfacial science [ 39 , 59 , 60 , 62 , 63 ]. However, the details of advancing and receding measurements are rarely discussed in detail in the literature, and therefore we are not certain whether other groups have observed the same behavior or their interpretation of how the baseline reacts to increasing/decreasing volume is consistent with ours. Based on the advancing and receding angle values presented in Figure 15 , contact angle hysteresis was calculated for all coatings, and the results are presented in Figure 16 along with the static contact angle values. It should be noted that theoretically, the static contact angle should be between advancing and receding values. However, in the results presented here, some discrepancies may be observed due to different measurement methods: Static contact angle measurements were carried out using the Young–Laplace model, since it approximates the entire shape of the droplet, thus considering the effects of droplet weight or any other distortions in the droplet shape. However, during dynamic measurements, the needle is in contact with the droplet at all times, and therefore Young–Laplace approximation cannot identify the circular shape of the droplet. Therefore, a different approximation method (namely, the tangent method) was used, which only considers the three phase interfacial points and attempts to draw a line at this point tangent to the droplet shape to determine the contact angle. All surfaces are shown to be superhydrophobic (WCA > 150°) with statistically insignificant variations in static contact angle due to the different deposition conditions. However, a significant difference in contact angle hysteresis (CAH) is observed for different samples, which is directly related to surface roughness. Traditionally, it is argued that hysteresis originates from defects, and thus rougher surfaces have higher CAH [ 59 , 64 ]. However, several studies have shown that the effects of roughness on CAH depends on the wetting regime. When water is in contact with the whole surface profile (Wenzel wetting regime), surface features act as obstacles to water motion, reducing the droplet mobility and increasing CAH. On the other hand, when roughness features are small enough so that the capillary effect prevents liquid penetration into the surface asperities (Cassie-Baxter wetting regime), water is only in contact with a small area fraction of surface features, and therefore its motion is not hindered by surface structures [ 62 , 65 ], increasing the droplet mobility and decreasing CAH. In the present work, the lowest contact angle hysteresis is observed in the case of PT5P75. While presenting the chemical characteristics of the developed coatings, it was shown that precursor fragmentation in PT5P75 is higher, leading to more polar oxide content on the surface, which is expected to render the surface less hydrophobic. However, since PT5P75 exhibits higher surface roughness due to larger silica-based particles with surface features as small as only a few nanometers (see Figure 8 ), contact angle hysteresis is very small. In the case of PT3, surface roughness is lower due to the lower amount of deposition and large micrometric surface features originated from the pre-treatment. Finally, PT5 exhibits a CAH higher than PT3 due to the full coverage of the surface with multi-leveled structures, and lower than PT5P75 due to the smaller deposited structures."
} | 7,425 |
32696411 | PMC8144121 | pmc | 3,713 | {
"abstract": "Despite solid wastes’ landfill disposal limitation due to recent European legislation, landfill leachate disposal remains a significant problem and will be for many years in the future, since its production may persist for years after a site’s closure. Among process technologies proposed for its treatment, microbial fuel cells (MFCs) can be effective, achieving both contaminant removal and simultaneous energy recovery. Start-up and operation of two dual-chamber MFCs with different electrodes’ structure, fed with mature municipal solid waste landfill leachate, are reported in this study. Influent (a mix of dairy wastewater and mature landfill leachate at varying proportions) was fed to the anodic chambers of the units, under different conditions. The maximum COD removal efficiency achieved was 84.9% at low leachate/dairy mix, and 66.3% with 7.6% coulombic efficiency (CE) at a leachate/dairy ratio of 20%. Operational issues and effects of cells’ architecture and electrode materials on systems’ performance are analyzed and discussed.",
"conclusion": "Conclusions Two MFCs were operated for treatment of combined poorly biodegradable (BOD/COD = 0.1) landfill leachate and dairy wastewater as co-substrates at various mixing ratios. Both units, with similar architecture but different electrode constituting materials and net cell volumes, were operated under continuous feed. MFC1 was operated for 6 cycle phases, up to 25% leachate percentage in the feed, while MFC2 maintained residual efficiency until reaching a feed composition of 50% leachate, prior to process failure. Both systems achieved their best performance treating a mixture of 20% leachate and 80% dairy wastewater. Premature failure was ascribed to poor electrically performing anodic material in the first cell. As far as the second cell, after a posteriori autoptic examination of the unit, failure was ascribed to accumulated interference of feed-contained solids, which determined clogging of the anode cell free volume in time, favored by suboptimal internal hydrodynamic conditions. Pretreatment of leachate may be the key to operate at higher percentages in the influent solution, lowering the presence of residual non-biodegradable solids or inhibiting waste components. Despite the ultimate process failure, during the first stages of the study, MFC2 performance was quite similar to that reported by other studies. Bioelectrochemical systems have shown consistent sustained, long-term treatment performance of different substrates and good short-term treatment performance of problem substrates such as landfill leachate, especially when fed with fresh leachate. Further attempts in this direction should consider adequate substrate pretreatment or internal hydrodynamic improvements to overcome the drawbacks observed in this study, in particular when aged, poorly biodegradable leachate is fed as substrate.",
"introduction": "Introduction Municipal solid waste (MSW) disposal is a problem with no easy or unique solution. In 2015, 242.3 Mt of MSW was produced in the European Union, 62 Mt of which discarded in landfills. Italy, in this context, produced about 29.5 Mt MSW in 2015, of which 7.8 landfilled (ISPRA 2017 ). Despite the reduction of MSW landfill disposal due to recent European legislation (EU 2018a ; EU 2018b ), leachate generated from decomposition of MSW in landfills is still a significant problem nowadays and will be for many years in the future, since its production may persist for years after a site’s closure. The risk of groundwater pollution by leachate spills from damaged landfill containment is significant, and specific monitoring is normally required in these situations due to the possible spread of harmful pollutants (Capodaglio et al. 2016a ). Leachate characteristics are quite variable, affected by landfill construction and age, local meteorology, waste type, and composition, normally high in COD and ammonia content (Kulikowska and Klimiuk 2008 ; Youcai 2018 ). Typically, a leachate’s BOD/COD ratio decreases from around 0.7 to 0.04 with landfill aging (Sonawane et al. 2017 ), becoming less suitable to biodegradation in time. Leachate contains organic constituents that may be degraded by bacteria already within the landfill, but it also contains ammonia at high concentrations (Kjeldsen et al. 2002 ), heavy metals, and other refractory organic and inorganic compounds that may accumulate in it, inducing bio-toxicity or bio-inhibition (Renou et al. 2008 ; Karrer et al. 1997 ). Collected leachate is typically hauled to off-site treatment facilities, where it may interfere with biological processes due to heavy metal content, high ammonia concentration, or the presence of other xenobiotic pollutants (PAHs, organic halogens, PCBs) that may be refractory, inhibitory, or otherwise affect such processes (Callegari and Capodaglio 2017 ). Leachate may also present unbalanced C/N ratio content (especially in leachates from closed landfills), making it poorly biodegradable, and affect other processes due to its physical-chemical characteristics, e.g., reducing ultraviolet disinfection effectiveness by quenching UV light. All these factors may represent a major ordeal for many conventional treatment facilities, often requiring specific pretreatment. On-site pretreatment units could be specifically designed to address these needs, or even full treatment for subsequent discharge to municipal sewers; however, this may often not turn out as cost-effective. The most common processes for leachate treatment are biological (aerobic or anaerobic) and/or physicochemical, depending on pollutant content. “Emerging” technologies may also be appropriate (Wiszniowski et al. 2006 ). These include chemical oxidation (Kim and Huh 2009 ); adsorption (Foo and Hameed 2009 ); ammonia removal by biodegradation (Capodaglio et al. 2016b ) or stripping (Cheung et al. 1997 ); evaporation, filtration, and reverse osmosis (Di Palma et al. 2002 ); sonication (Nazimudheen et al. 2018 ); Advanced Oxidation Processes (Capodaglio 2018 , 2019 ) and others (Capodaglio 2017 ), depending on leachate composition, and discharge or site-specific constraints. Significant treatment efficiency improvement and decrease of overall treatment costs could be pursued by process combinations, to improve biodegradation of refractory organics (Koh et al. 2004 ; Geenens et al. 2001 ; Cecconet et al. 2017 ). The sustainability of treatment processes in terms of energy input and related environmental emissions is becoming an issue of increasing relevance (Capodaglio and Olsson 2020 ); therefore, related considerations are becoming key discriminants in the choice of technology to be adopted, favoring those that can lead to reduction of either. Microbial fuel cells (MFCs) couple organic matter removal and energy recovery by direct conversion of the chemical energy in the substrate into electrical energy (Li et al. 2011 ; Capodaglio et al. 2013 ; Saba et al. 2017 ). MFCs have been pointed out as a promising bioelectrochemical technology for various types of liquid waste streams, including domestic (Ahn and Logan 2010 ) or industrial (Molognoni et al. 2018 ) wastewaters, and contaminated groundwater (Cecconet et al. 2020 ; Cecconet et al. 2018a ). They were also indicated as an appropriate technology for landfill leachate treatment (Puig et al. 2011 ). The process is carried out by electrochemically active bacteria (EAB) that oxidize organic substrate in an anodic chamber, releasing electrons and protons (Logan et al. 2006 ). Electrons travel through an external electric circuit from the anode to the cathode, while protons pass directly through an ionic selective membrane to reach the cathode. There, both electrons and protons are recombined with the terminal electron acceptor (TEA), such as oxygen or nitrate (Logan and Rabaey 2013 ). MFC performance can be affected by several factors, such as substrate type and concentration, electrode material and surface area, ionic strength, pH, and cell design (Capodaglio et al. 2015 ; Cecconet et al. 2018b ). Selected operating conditions may be exploited to optimize the structure of the cells’ microbiome (Molognoni et al. 2016 ) and improve bioelectrochemical efficiency (Capodaglio et al. 2017 ). MFCs have been used to treat easily biodegradable industrial wastewater (Callegari et al. 2018 ) and difficult-to-treat substrates (Abbasi et al. 2016 ; Srikanth et al. 2016 ). In the latter cases, like in any other biologically mediated processes, biomass acclimation to the specific pollutants is a key element for success (Capodaglio et al. 2010 ). The advantages of this type of technology are low energy inputs and the possibility of direct energy recovery, both strongly dependent on system architecture and operating conditions (Ge et al. 2014 ; Cecconet et al. 2018c ). Landfill leachate as a substrate for MFCs has been investigated under different circumstances (Hu et al. 2017 ; Huang et al. 2018 ; Li and Chen 2018 ; Zhang et al. 2015a ; Zhang et al. 2015b ) either alone or in combination with other processes (Mahmoud et al. 2014 ; Vázquez-Larios et al. 2014 ). Bioelectricity generation by MFCs creates additional opportunities for resource recovery from substrates, including leachate. While organic compounds are directly converted to electrical energy, nutrients (e.g., ammonia) can be recovered via migration and ammonium conversion at high pH resulting from the cathodic reduction (Iskander et al. 2016 ). Metals may also be removed or recovered by bioelectrochemical systems (Cecconet et al. 2018d ). It was also shown that MFCs could produce an effluent water fit for irrigation reuse (Abourached et al. 2016 ). Addition of a readily biodegradable co-substrate is a common strategy to biologically treat substrates normally not suitable to biological processes, and increase overall process efficiency (Luo et al. 2009 ). Simultaneous treatment of landfill leachate and wastewater with MFCs had been explored previously. Hernández-Flores et al. ( 2017 ) reported the combined treatment of leachate and municipal wastewater by adding 30, 50, and 70% of highly biodegradable leachate in the mixture, in this case presence of an increased biodegradable organic matter (leachate) enhanced electricity production. However, few studies dealt with leachates characterized by low biodegradability so far. In this study, mature leachate from a closed landfill, together with agro-industrial (dairy) wastewater as co-substrate, was fed to two differently structured dual-chamber MFCs at varying dilution ratios, to evaluate system performance and overcome process limitations connected to the poor biodegradability of a mature leachate as substrate for bioenergy production. The study also examined the MFC differential behavior in terms of electrodes’ performance, highlighting differences between the two tested materials for their construction. This study brings further insight in the treatment possibility of poorly biodegradable landfill leachate combined with highly degradable organic substrates with the use of bioelectrochemical systems.",
"discussion": "Results and discussion Electric production Microbial fuel cells rely on biological oxidation of wastewater, which effectiveness strongly depends on the nature of the substrate. LL used in the present experimentation is a poorly biodegradable substrate; to enhance its suitability for biological treatment DW, a highly biodegradable substrate was used as co-substrate. Observed energy production did not reflect a specific trend correlated to the varying LL fraction in the feed; however, upon examination of the results, it can be assessed that the most favorable operating condition was observed in phase 4 (15% leachate), where maximum output power peaks were recorded for both MFCs. It must be stressed out that the characteristics of leachate remained constant during the study, while DW parameters changed slightly, as previously shown in Table 1 , although previous studies on substrates from the same source showed consistent excellent degradability and energy production when fed to similar MFCs (Callegari et al. 2018 ). Maximum voltage achieved for MFC1 and MFC2 was 151.1 mV and 509.3 mV, respectively, corresponding to current densities of 4.6 and 15.4 A m −3 . Power density monitored throughout the experimentation is represented in Fig. 2 . MFC1 showed much lower electrical production than MFC2 throughout the whole study, highlighting how important factors such as setup design and adopted materials affect this systems’ performance. MFC1 maintained fair power generation throughout phases 3 and 4, dropping considerably during phase 5 (voltage measured between electrodes stabilized at around 10 mV). MFC2 maintained, instead, higher and stable values of electrical production up to phase 7, after which measured voltage dropped to below 170 mV (corresponding to current density of 5 A m −3 ) under all subsequent operating condition tested. Fig. 2 Power density monitored throughout the experimentation. Error bars report the power range monitored each day In both systems, after the shift from DW-only feed to the 5% LL-DW mix, an instantaneous drop in energy production was observed, which could be attributed to ongoing acclimation of the MFCs’ anodic biomass to the new substrate composition. This acclimation is confirmed by the rapid recovery observed in the following days, with rapid exoelectrogenic biomass activity recovery, which maintained and improved high current production throughout phases 2 and 3 for MFC1, and up to phase 7 for MFC2, even at increasing leachate ratios in the feed. At this point, it seems evident that MFC2 architecture proved to be more efficient for energy recovery than MFC1’s as, both being operated under the same conditions, the latter showed a consistently lower power generation. Electric and organic matter removal efficiency ηCOD throughout the study was measured for each condition tested, and CE was calculated. In the first phases of the study, CE was very low for both systems, probably due to slow adjustment of the exoelectrogenic population to the substrate. Concerning MFC1, CE showed a linear incremental trend (Fig. 3 ), with values ranging from 1 to 6% in the last condition tested, while MFC2 showed more variability, with sudden increase under phases 5 and 6, where the maximum efficiency (26%) was observed, decreased down to around 10% afterwards. Fig. 3 CE and ηCOD in MFC1 and MFC2 COD removal efficiency started at 82.9% for MFC2 and 58.1% for MFC1 in phase 0. It increased in phase 1, achieving the best values for both MFCs, 84.9% and 69.1% for MFC2 and MFC1, respectively, decreasing gradually with the increase of leachate ratio in the feed. During phase 5, COD removal dropped drastically in MFC1, at 7.6%. The unit was then operated until the end of phase 6, with no increase in voltage generation and even lower ηCOD, at 5.7%; therefore, it was decided to stop the operation of this unit. MFC2 maintained high COD removal efficiency (generally at or above 66%, save for a low of about 55% during phase 3) until phase 5. At 25%, LL ratio in the feed conditions became critical: from the previous ηCOD of 66.3%, removal dropped by almost half to 36.5%. This content level of landfill leachate in the influent affected both systems and thus can be considered their operational limit in the studied conditions. MFC2 maintained, however, removal efficiency greater than 30% until phase 10 (LL/DW = 50%), when ηCOD dropped to a low of 8.6%. Polarization curves A final analysis concerned the systems’ polarization and power curves: in addition to representing the electrical behavior of the cells, they allow to establish the real internal resistance value; it was already reported that, to maximize energy production in MFCs, external resistance should be equal to the internal one (Molognoni et al. 2016 ). Polarization and power curves (Fig. 4 ) were determined for each experimental condition: early examination of the observed power curves of the MFCs showed that MFC2’s internal resistance was 21 ± 10 Ω, quite close to the external resistance actually applied (33 Ω), while MFC1’s internal resistance resulted in a staggering 170 ± 18 Ω, five times higher. This difference is largely due to the electrodes constituting materials of the cells and justifies both the initial lower power generation and CE of the first unit. After phase 2, the external resistance of MFC1 was modified to 150 Ω, showing a detectable increase in power density, although no direct benefit was seen in COD removal efficiency during subsequent tests. This modification did not prevent the system to substantially stop being efficient in terms of COD removal and energy recovery between phases 5 and 6. Fig. 4 Polarization and power curves performed during phase 2 The internal resistance detected for MFC2, instead, was similar to the external resistance initially applied; therefore, further analysis of energy losses in the unit was performed. It was found that the largest part ( E t = 55%) of these could be attributed to membrane losses, while the second largest factor affecting energy production was cathode efficiency ( η cat = 32%). Anode efficiency and pH gradient only accounted for 7% and 5% loss respectively, while ionic exchange between anode and cathode could be considered negligible (< 1% loss). Comparative analysis NER throughout the study was evaluated for both units, in volumetric (NER v , net energy recovery per m 3 influent treated) and massive (NER S , net energy recovery per kg COD removed) specific terms. Results are summarized in Fig. 5 : it can be noticed that it was not possible to establish a consistent trend of this parameter in relationship with observed COD removal and CE. MFC1 (Fig. 5 , upper) recovered almost no energy during the first tests, due to suboptimal electric circuit conditions. When sufficient energy production started (phases 3 and 4), values up to 0.022 kWh m −3 treated were observed. As already confirmed by the previously shown data, MFC2 showed better performance, reaching values of NER V of 0.149 kWh m −3 treated during phase 6 (30% leachate). In terms of specific net energy recovery, the best rates were also obtained in phase 6, with NER S of 0.019 kWh kg COD −1 . Fig. 5 NER V and NER S obtained throughout the study: MFC1 (upper) and MFC2 (lower) To compare the results of the present study to others reported in literature, phase 4 was taken as reference for both units tested. Reported studies taken for comparison are summarized in Table 3 . When considering landfill leachate as a substrate, the type of landfill, age, and wastes collected strongly influence performance of a bioelectrochemical system and must be taken into account. Also, pretreatment increase the bioavailability of organic matter in leachate, for example, by performing fermentation, enhancing electricity production and substrate conversion (Mahmoud et al. 2014 ). Along with COD removal, in many studies, nutrients’ removal, such as ammonia and phosphorus, was evaluated. However, not being the main focus in the present work, these were not taken into account for the comparison. Fresh landfill leachate normally has relatively high BOD 5 /COD ratio (0.4–0.6) indicating good biodegradability (Özkaya et al. 2014 ). This ratio generally decreases with the age of the landfill: the present study operated on leachate from a closed landfill, characterized by a low BOD 5 /COD ratio of about 0.1. Table 3 Net energy recovery from landfill leachate bioelectrochemical systems applications (NER V and NER S calculated according to Iskander et al. ( 2016 )) System configuration Leachate COD (mg L −1 ) Operational mode COD removal (%) CE (%) NER V (kWh m −3 tr ) NER S (kWh kg CODrem −1 ) Reference Membrane-less anoxic/oxic 19,200 Continuous 95.1 - - 0.04866 Zhang et al. ( 2015a ) Dual chamber 50,000 Continuous 43 < 1.0 0.05400 0.00251 Özkaya et al. ( 2014 ) Single chamber 12,300 Batch 72 6.7 - 0.01986 Vázquez-Larios et al. ( 2014 ) Single chamber (air cathode) 507 (diluted) Continuous 32 < 2.0 0.0000506 0.00031 Puig et al. ( 2011 ) Membrane-less anoxic/oxic 20,100 Continuous 86 - 0.06648 0.00383 Zhang et al. ( 2015b ) Dual chamber 11,400 Continuous 87 0.6 - 0.00190 Zhang and He ( 2013 ) Dual chamber 300 (diluted 15%) Continuous 26 - - - Nguyen and Min ( 2020 ) Dual chamber 4000 (synthetic) Batch 65.1 - - - Huang et al. ( 2018 ) Dual chamber 2216 Step-feed 53.6 6.9 0.0068 0.0058 Present study (MFC1) Dual chamber 2216 Step-feed 56.2 13.5 0.074 0.00714 Present study (MFC2) Puig et al. ( 2011 ) operated an air cathode MFC with both diluted and raw landfill leachate characterized by low BOD 5 /COD ratios (0.02–0.2) and high salinity, comparable with that used in the present study. During operation with diluted leachate (507 mg COD L −1 , OLR = 1.48 kg COD m −3 ), an air cathode MFC achieved 32% COD removal, and average power density of 6.1 ± 4.2 mW m −3 . With raw leachate fed to the system, OLR increased up to 24.42 kg COD m −3 , achieving up to 37% COD removal and power density of 344 mW m −3 . Observed coulombic efficiency, however, remained below 2%, indicating that substrate degradation was not carried out primarily by exoelectrogenic bacteria, but possibly by methanogens, a commonly found EAB-competing species (Molognoni et al. 2016 ). Most MFC studies in literature concern the use of fresh landfill leachate: this is, in fact, easily biodegradable, leading to an easier and more effective biological treatment, but not necessarily to higher energy recovery efficiency. Özkaya et al. ( 2014 ) operated an MFC with such substrate, characterized by COD up to 50 g L −1 (BOD 5 /COD = 0.65), starting from COD concentration of 1 g L −1 , and reducing gradually the applied OLR up to 50 g L −1 day −1 . Higher OLRs led to lower coulombic efficiency (< 1%, against 35% at lower ORLs). The authors stated that, despite the overall increase in voltage output, decrease in CE may be due to uptake of organics by non-exoelectrogenic processes, such as methanogenesis. Zhang et al. ( 2015a , b ) operated dual-chamber BESs for fresh landfill leachate treatment, characterized by BOD 5 /COD = 0.48, achieving 2.16 W m -3 maximum energy recovery and 95.1% COD removal at OLR of 1.2 kg COD m −3 day −1 . These are the best performance values reported so far in literature. Vázquez-Larios et al. ( 2014 ) operated MFCs with fresh landfill leachate with excellent biodegradability (BOD 5 /COD = 0.86) in a two chambered MFC in batch mode. COD removal of 72% was achieved, with maximum power density of 1.83 W m −3 . The present study shows that both units (MFC1 for part of the tests only), even though fed with diluted old, low biodegradability landfill leachate, achieved satisfactory degradation values and energy recovery parameters in line with those reported in literature for any type of leachate. It should be also noted that not all published studies examined clearly specify the period during which the observed performances were consistently maintained. End of operation analysis To better understand the limitations of landfill leachate treatment, and the causes that led to failure of the process when the ratio LL/DW = 1 (50%) in the feed was reached, an autoptic analysis was performed on the cells at the end of the study. After conclusion of the tests, both MFCs were disassembled to analyze the effects of the continuous operation with landfill leachate mix feed on the constituent materials. Figure 6 shows actual photographs of the anodic chamber of MFC2, indicating solid particles obstructing the spaces between the electrode’s graphite granules, limiting contact possibility between substrate and electrode surface. Notwithstanding a preliminary screening of the leachate performed upon collection, the constant flow of raw landfill leachate, in which colloidal and small solid, non-biodegradable particles may have remained, caused their gradual accumulation in the anodic chamber, reducing its net free volume in time, and consequently its hydraulic retention time, affecting the systems’ overall performance. The effect of internal hydrodynamic conditions and flow distribution on cell performance had already been highlighted in literature (Cecconet et al. 2018b ; Vilà-Rovira et al. 2015 ), and this additional evidence confirms previous findings. In addition, non-pretreated landfill leachate could also have caused partial fouling of the CEM, affecting ion transfer efficiency between chambers, and decreasing overall performance of the unit (Xu et al. 2012 ). Finally, the presence of trace metals and ammonia may also have affected MFC performance with a potential biomass inhibiting effect (Hang et al. 2020 ). Fig. 6 Solid residues observed between the electrode’s graphite granules in the open anodic chamber of MFC2 after the study. The black sheet material indicated by the arrow in the right panel is the cell’s CEM (shown in new original condition in the picture rightmost insert)\n\nDiscussion Results of the study showed that one of the MFCs tested for combined leachate and industrial wastewater treatment obtained initially good results both in terms of COD removal and power generation. The use of DW as co-substrate provided additional nutrients to the EABs and resulted in improved bioelectrochemical degradation of organics, compared with feed with LL only. The unit that achieved the best performance (MFC2) had electrodes built with granular graphite, while the one (MFC1) with GCSS mesh electrodes showed poor performance since start-up. As pointed out by several studies, the performance of MFCs in terms of power output and durability strongly depends on the key components of these systems, the electrodes, which are one of the limiting factors for a generalized applicability of these systems (Gnanakumar et al. 2013 ). Anode and cathode material research is among the most active sector in bioelectrochemical systems, together with unit scalability issues (Abdallah et al. 2019 ). Premature failure of MFC1 could be ascribed to the poor performance of the GCSS mesh electrode material in these conditions. The performance of the granular graphite unit was satisfactory, comparable with that of most similar literature reported studies, until process deterioration, mostly due to physical clogging within the anodic compartment, occurred. Some of the clogging problems detected during this study could be solved by adequate pretreatment of landfill leachate: more particle-selective influent screening should be implemented, possibly in combination with improved cell electrode design allowing efficient free circulation of residual particulate material within the cell. Pretreatment could also be considered in order to enhance leachate biodegradability. Ultrasonication, for example, was shown to increase soluble COD fractions and modify leachate composition in terms of NH 3 -N and acetate concentrations (Nazimudheen et al. 2018 ). High ammonia levels may stripped by air and calcium hydroxide, removing up to 70% of leachate’s ammonia content (Cheung et al. 1997 ). Fermentation processes prior to bioelectrochemical treatment was also reported to enhance MFC power recovery, with organic removal improvement by up to 15 times (Mahmoud et al. 2014 )."
} | 6,857 |
34746577 | PMC8567349 | pmc | 3,714 | {
"abstract": "Bio-based polyurethane\n(PU) has recently drawn our attention due\nto the increasing interest in sustainability and the risks involved\nwith petroleum depletion. Herein, bio-based self-healing PU with a\nnovel polyol, i.e. , eugenol glycol dimer (EGD), was\nsynthesized and characterized for the first time. EGD was designed\nto have pairs of primary, secondary, and aromatic alcohols, which\nall are able to be involved in urethane bond formation and to show\nself-healing and antioxidant effects. EGD was incorporated into a\nmixture of the prepolymer of polyol (tetramethylene ether glycol)\nand 4,4′-methylene diphenyl diisocyanate to synthesize PU.\nEGD-PU showed excellent self-healing properties (99.84%), and it maintained\nits high self-healing property (84.71%) even after three repeated\ntests. This dramatic self-healing was induced through transcarbamoylation\nby the pendant hydroxyl groups of EGD-PU. The excellent antioxidant\neffect of EGD-PU was confirmed by 2,2-diphenyl-1-picrylhydrazyl analysis.\nEugenol-based EGD is a promising polyol chain extender that is required\nin the production of bio-based, self-healing, and recyclable polyurethane;\ntherefore, EGD-PU can be applied to bio-based self-healable films\nor coating materials as a substitute for petroleum-based PU.",
"conclusion": "Conclusions We\ndeveloped a bio-based self-healing PU with antioxidant properties\nby introducing a novel polyol chain extender EGD. To produce a symmetrical\nchain extender from eugenol glycol, we used a protection-coupling-deprotection\nsystem. After protecting the vicinal diol, a yield of 40% was obtained\nin a day without the production of any byproducts. Notably, the chemical\nEGD has never been reported and synthesized from eugenol. The prepared\nEGD-PU showed a high self-healing efficiency and repetitive self-healing\nproperty through transcarbamoylation by pendant hydroxyl groups at\n150 °C. EGD-PU may have the potential to apply for self-healing\ncoating or adhesives. Repetitive self-healing with high efficiency\nis expected to have great applicability compared with urethane bond\nexchange using only a phenolic compound or a Diels–Alder reaction. 53 , 54 In addition, EGD was obtained from eugenol oxide dimer by\nhydroxylation.\nInterestingly, eugenol oxide was successfully dimerized with high\nyield by laccase from Trametes versicolor ( Scheme S2 ). The synthesis of EGD through\nthe enzymatic reaction can be advantageous because of the short chemical\nsteps involved, and this will be further studied by our group. In addition, eugenol, as a lignin decomposition byproduct, was\nupcycled into bio-based self-healing PUs. In particular, the alkene\ngroup in eugenol can be modified into a diol group. This study showed\nthat the pairs of different hydroxyl groups by dimerization are important\nin imparting different effects on PU such as the self-healing and\nantioxidant properties. The effect of hydroxyl group number or position\nvariations on the dynamic properties of PUs needs to be further analyzed\nutilizing other lignin-degraded aromatic monomers.",
"introduction": "Introduction In polyurethane (PU)\nsynthesis, there has been a growing push in\nrecent years to replace petrochemical polyol and employ renewable\nresources by increasing levels of biomass content. In this context,\nlignocellulosic biomass-derived polyols have been developed as ways\nto produce green polyurethane. 1 − 5 PU is a highly versatile polymer that can be used in a wide\nvariety\nof applications including coating, adhesives, elastomers, insulators,\nelastic fibers, foams, and integral skins. 6 Urethane linkage (−NH–(C=O)–O−),\ni.e., the linkage of a carbamate synthesized by the addition reaction\nbetween isocyanate and alcohol groups, is a common feature in all\nPUs. 7 Since the polyols providing the alcohol\ngroups have conventionally been obtained from petrochemical crude\noils and coals, they raise a serious environmental issue regarding\ndepletion of natural resources; 8 , 9 therefore, efforts have\ngone toward developing new technologies for the commercial production\nof bio-based polyols from biomass or its degradation byproducts and\nfor green PU synthesis. 1 , 8 − 12 In addition, the physical and chemical properties\nof PU can be readily adjusted by changing synthetic processes or varying\nthe compositions of the constituting monomers (diisocyanates and polyols). Lignin, the most abundant aromatic polymers in lignocellulose,\nor its destructed oligomer products, when incorporated into PU, can\nincrease the content of green carbon source from biomass 2 and provide enhanced performance scores for PU\nincluding enhanced cross-linking density, 3 antioxidant properties, 4 mechanical strength, 5 and thermal stability. 2 , 9 , 13 However, the utilization of lignin as a\nPU polyol is limited by its high poly-dispersity, low solubility,\nand steric hindrance; 14 therefore, several\nstudies have attempted to develop lignin-derived PUs. 1 , 15 In one example, bio-based di-vanillin PU showed enhanced stiffness\nas the aromatic ring and irreversible covalent bonds provide structural\nstability. 1 However, it lacked a self-healing\nproperty and was vulnerable to microcracks. To achieve self-healing,\ndiverse reversible covalent networks have been introduced into PUs. 16 − 18 The resulting products have an extended life span, improved reliability\nduring applications, and reduced content of polymer wastage. 19 Therefore, dynamic covalent chemistry has been\ninvestigated for its ability to enhance the self-healing of PUs for\napplications in materials for usage in implanted medical devices,\naerospace appliances, and protective coatings. 20 − 22 For example,\nthe reversible disulfide bond and hydrogen bonding in PU contributed\nto the self-healing in flexible electronics. 23 The dynamic disulfide bond-incorporated PU showed an excellent performance\nin 3D printing with its repeatable self-healing and high stretchability. 24 PUs can incorporate dynamic covalent reactions,\nincluding imine formation, 25 boronate ester\nexchange, 26 disulfide exchange, 27 Diels–Alder, 28 and amine/urea exchange reactions. 29 Transcarbamoylation\nrepresents another reversible exchange, as the urethane bond can undergo\nthe exchange reactions for self-healing. 30 , 31 Unfortunately, the reversible exchange reaction typically requires\nhigh temperature (>200 °C) or a catalyst to support self-healing,\nbut rich phenol or hydroxyl groups promote the reaction under mild\nheating in the absence of the catalysts. 31 − 33 As a result,\nwe hypothesized that introducing more hydroxyl groups into the lignin\nderivatives might be an important factor for designing a new polyol\nchain extender of self-healing PU. Eugenol, which is mainly\nobtained in nature from clove oils, 34 can\nbe obtained in large quantities as a byproduct\nof the thermal decomposition of lignin. 35 Eugenol by itself fails to meet the requirements of a PU chain extender,\nas it lacks the difunctional hydroxyl groups that react with the diisocyanate\ngroup. Its alkenyl group can be modified into two or more reactive\nhydroxyl groups through epoxidation–trimerization, 36 or copolymerization with a terminal thiol group-modified\noligomer 37 and soybean oil. 38 Herein, eugenol glycol dimer (EGD), a novel\neugenol-based PU chain extender, was designed to supply symmetrical\nhydroxyl groups ( Scheme 1 ). Since EGD has pairs of primary, secondary hydroxyl, and phenolic\ngroups, such a polyol might provide proper carbamate linkages, self-healing, 31 , 33 and antioxidant properties into the PUs. 39 − 41 Eugenol glycol\nwas obtained via epoxidation and hydroxylation. 42 While eugenol was dimerized with oxidative coupling reagent, 43 eugenol glycol does not directly undergo dimerization.\nIt was believed that diol in eugenol glycol might hinder oxidative\ndimerization by potassium ferricyanide. Herein, by protecting diol\nmoiety, eugenol glycol was dimerized and EGD was successfully synthesized\nvia deprotection. EGD was incorporated into the hard segment of PU\nwith methylene diphenyl diisocyanate (MDI) to replace a traditional\n1,6-hexanediol chain extender. Hence, EGD increased green carbon content\nin PU, and it is also expected to drive self-healing properties via\nreversible transcarbamoylation. In this study, eugenol glycol dimer-incorporated\nPU (EGD-PU) was characterized and examined in terms of its chemical\ncompositions, thermal properties, physical properties, and self-healing\nproperties. Scheme 1 Synthesis of Eugenol Glycol Dimer (EGD) from Eugenol",
"discussion": "Results and Discussions Synthesis of EGD A eugenol glycol dimer compound, EGD,\nwas synthesized according to Scheme 1 . First, eugenol glycol was obtained from eugenol through\nepoxidation by m -chloroperbenzoic acid ( m CPBA) and further hydroxylation by sulfuric acid. 42 To dimerize eugenol glycol, the diol group in eugenol glycol\nwas protected with the cyclic ketal group by the addition of p -toluenesulfonic acid ( p -TSA) in acetone.\nNext, eugenol acetonide was oxidatively dimerized by K 3 Fe 3 (CN) 6 between the C5 and C5′ positions.\nThe eugenol acetonide dimer was then deprotected by ether·HCl,\nand finally, EGD was obtained. The overall synthesis yield from eugenol\nglycol to EGD was 40%. All the intermediate products in EGD synthesis\nwere characterized by HPLC, GC–MS, and 1 H-NMR ( Figures S1–S3 ). In the HPLC spectrum,\nthe intermediates and the final product have retention times of 5.0\n(eugenol glycol), 7.9 (eugenol acetonide), 15.1 (eugenol acetonide\ndimer), and 5.5 min (eugenol glycol dimer). The protection of the\ndiol group and the further dimerization were shown to lead to a longer\nretention time in the C 18 column, which were attributed\nto increases in hydrophobicity and molecular weight, respectively.\nIn GC–MS, the parent peaks of EGD intermediates with trimethylsilyl\nderivatization were observed, and their fragmentation pattern was\nalso verified. The 1 H-NMR spectra of eugenol glycol showed\npeaks between 6.63 and 6.65 ppm; however, these peaks were not observed\nwith EGD, indicating the formation of a covalent linkage between C5\nand C5′ of EGD ( Figure 1 ). Figure 1 1 H-NMR of (a) eugenol glycol and (b) eugenol glycol\ndimer (EGD). Synthesis of EGD-PU Film To synthesize EGD-PU, EGD\nwas added as a chain extender during PU synthesis to initiate further\npolymerization with a prepolymer of poly(tetramethylene ether glycol)\n(PTMEG) and 4,4′-methylene diphenyl diisocyanate (MDI). The\nsynthesis of EGD-PU was designed to form carbamate with two primary\nalcohols among six alcohols in EGD using the same molar ratio of the\nprepolymer ( Scheme 2 ). EGD has two terminal primary alcohols that are much reactive than\nsecondary and aromatic alcohols. Therefore, EGD was expected to offer\npendant hydroxyl groups to EGD-PU, which would then participate in\nthe dynamic exchange reactions during the self-healing process. As\na control, hexanediol polyurethane (HD-PU) was synthesized using 1,6-hexanediol,\na conventional chain extender with a chain length similar to that\nof EGD. Scheme 2 Synthesis of Polyurethane with Control Hexanediol (HD) and\nEugenol\nGlycol Dimer (EGD) The chemical structures\nof the obtained PU films were determined\nby FTIR analysis. The peak at 2270 cm –1 , which corresponds\nto the NCO groups of the PU prepolymers, disappeared in the FTIR spectra\nof EGD-PU and HD-PU, indicating the successful incorporation of EGD\nand HD into PU as chain extenders, respectively. Urethane bonds were\nobserved in all FTIR spectra at 3297 cm –1 (N–H\nstretching), 1733 cm –1 (urethane C=O), 1538\ncm –1 (N–H bending), and 1602 cm –1 (aromatic ring) ( Figure 2 ). 44 The profiles of the hydroxyl\nand carbonyl absorption bands showed remarkable differences between\nEGD-PU and HD-PU. In the FTIR spectrum of EGD-PU, the stretching vibrations\nof free O–H functional groups and C–O were clearly observed\nat 3450 and 1262 cm –1 , respectively, and the additional\npeak of the aromatic ring derived from EGD was observed at 1510 cm –1 . There was also a substantial difference in the aliphatic\nC–O stretching peak ranging from 1090 to 1115 cm –1 . EGD-PU exhibited broad peaks of approximately 1100 cm –1 , which were attributed to the C–O functional groups of the\nfree hydroxyl group (C–O–H) in EGD ( Figure 2 ), while both peaks attributed\nto C–O in PTMEG were observed in all FTIR spectra. These observations\nconfirmed that EGD-PU has pendant hydroxyl groups in its molecular\nstructure. Figure 2 FTIR spectra of HD-PU and EGD-PU. Thermal Properties of PU Films The thermal properties\nof EGD-PU and HD-PU were evaluated using DSC measurement ( Figure 3 ). The first heating\nthermograms of EGD-PU and HD-PU, which were prepared using different\nchain extenders, exhibited the general thermal behavior of a PU elastomer\nprepared using MDI and PTMEG 2000. The enthalpy change at −75\n°C corresponds to the glass transition temperature ( T g ) of the soft segment of PUs, and EGD-PU and HD-PU had\nequal T g values of −73.5 and −74.2\n°C, respectively. Figure 3 (a) First scan and (b) second scan of DSC thermograms\nof HD-PU\nand EGD-PU. T g, ss denotes glass\ntransition temperature of soft segments, and T m, ss denotes melting transition temperature of soft segments. The melting temperatures of PTMEG were observed\nto be 12.1 °C\nfor EGD-PU and 15.0 °C for HD-PU, respectively. The second heating\nthermograms of the both PUs showed a slightly different trend than\nthe first heating thermograms. HD-PU exhibited slightly higher T g and T m at −72.6\nand 19.7 °C, respectively. By contrast, only the strong endothermic\npeak corresponding to the melting of PTMEG was observed in EGD-PU\nat 22.6 °C in the absence of T g .\nThis indicates that the amorphous phase of glass transition was not\nclearly observed as the crystallization of the soft segment of EGD-PU\nincreased. HD-PU did not show a significant change in Δ H m , while EGD-PU showed a significant increase\nin Δ H m from 19.1 to 36.0 J/g. ( Table 1 ) Furthermore, thermogravimetric\nanalysis (TGA) data showed decomposition temperatures of HD-PU and\nEGD-PU above 300 °C. ( Figure S5 and Table S1 ). Table 1 Thermal Properties of HD-PU and EGD-PU HD-PU EGD-PU first heating second heating first heating second heating T g (°C) –74.2 –72.6 –73.5 T m (°C ) 15.0 19.7 12.1 22.6 Δ H m (J/g) 21.0 20.2 19.1 36.0 The stress–strain\ncurves of EGD-PU and HD-PU in tensile\ntests are shown in Figure 4 . EGD-PU (3.41 MPa) exhibited a slightly higher Young’s\nmodulus than HD-PU (2.69 MPa), which is attributed to the introduction\nof the aromatic ring into the hard segment of EGD-PU additionally. 46 The tensile strength of EGD-PU (6.3 MPa) was\nalmost half that of HD-PU (12.1 MPa) because the bulky aromatic structure\nand free hydroxyl groups in EGD weakens the physical cross-linking\nthrough the microphase separation of EGD-PU. 45 , 46 The packing of the hard segments is potentially interfered with\nthe bulky groups, while C–O of PTMEG and the hard segment likely\nformed hydrogen bonds with the pendant hydroxyl groups. The strain\nhardening was hardly observed in the stress–strain curve of\nEGD-PU. Figure 4 Stress–strain curves of HD-PU and EGD-PU. To confirm the presence of the microphase in the bulk morphology,\nSAXS analysis was conducted. In the plot of the scattering vector\n( q ) versus scattering intensity, both HD-PU and EGD-PU\nexhibited a single and broad peak. 46 This\nplotting indicates the presence of a phase-separated morphology in\nboth HD-PU and EGD-PU. According to Bragg’s law, the inter-domain\ndistances of EGD-PU and HD-PU were 17 and 20 nm in SAXS, respectively\n( Figure S4 ). The shorter inter-domain distance\nof EGD-PU indicates less microphase separation between the hard segment\nand the soft segment in EGD-PU. At temperatures under 20 °C,\nEGD-PU exhibited a higher storage\nmodulus than HD-PU, since the introduction of EGD increased the contents\nof rigid aromatic rings. However, when the temperature increased up\nto the rubbery region, EGD-PU showed a lower storage modulus than\nHD-PU as well as a sharp decrease in modulus above 130 °C ( Figure 5 ). This further decrease\nin modulus might be due to the exchanges between the urethane units\nand pendant hydroxyl groups of EGD. 31 , 32 , 47 A peak maximum was observed in the tan δ curves,\nfor both T g of EGD-PU and HD-PU, T g s of −47.6 and – 46.9 °C,\nrespectively; these were comparable to the DSC measurement results.\nHowever, the tan δ curve of EGD-PU was found to be broader than\nthat of HD-PU, indicating less microphase separation of EGD-PU, as\nconfirmed in SAXS ( Figure 5 ). 45 Figure 5 Dynamic mechanical properties\nof the HD-PU and EGD-PU: (a) storage\nmodulus and (b) tan delta. The thermal properties and glass transition temperature of EGD-PU\nwere comparable to those of HD-PU in general. EGD-PU, chain-extended\nwith bulky aromatic rings showed a higher Young’s modulus and\na lower tensile strength than HD-PU. The differences were attributed\nto lower microphase separation between the soft segment and hard segment\nin EGD-PU. Self-Healing Property of EGD-PU To evaluate the self-healing\ncapacity of EGD-PU, the specimens were cut into halves, put together\nwith gentle pressure at 150 °C for one of three time intervals\n(1, 3, and 6 h), and then pulled apart. EGD-PU showed excellent self-healing\nperformance (99.84%) while HD-PU showed minimal self-healing ability\n(25.27%) in 3 h based on the molecular movement and diffusion above T g ( Figure 6 , Table 2 , and Figure S6 ). The self-healing efficiencies\nof the EGD-PU samples were all above 92.99%, and they were reinforced\nwith increasing healing time. Figure 6 Self-healing property in tensile tests after\nheat treatments. (a)\nStress–strain curves of pristine HD-PU and those after self-healing\nat 150 °C for 1, 3, and 6 h as well as (b) those for EGD-PU. Table 2 Mechanical Properties of HD-PU and\nEGD-PU after Heat Treatment for 1, 3, and 6 h sample healing time\n(h) tensile stress\n(MPa) elongation\nat break (%) healing efficiency\n(%) HD-PU 0 12.15 837.43 1 2.53 312.44 20.82 3 3.07 456.19 25.27 6 3.65 544.68 30.04 EGD-PU 0 6.28 998.19 1 5.84 799.26 92.99 3 6.27 797.25 99.84 6 6.32 774.06 100.64 In addition, EGD-PU showed a high\nYoung’s modulus and tensile\nstress after self-healing, indicating that the dynamic carbamate exchange\nreactions enhanced the crack healing. It can be inferred that self-healing\nmainly comes from the exchange reaction between the free hydroxyl\ngroups and the urethane group. To investigate the chemical structural\nchange, the FTIR spectra of EGD-PU and HD-PU before and after self-healing\nat 150 °C are shown in Figure S7 .\nFTIR spectrum of EGD-PU showed the decrease of peak intensity at 1120\ncm –1 corresponding to the secondary hydroxyl group\nafter the self-healing process. 48 It indicates\nthat the secondary alcohol groups in EGD-PU could be used in an exchange\nreaction with the carbamate groups at a specific temperature. On the\nother hand, these peaks of HD-PU were almost overlapped before and\nafter the self-healing process because of the absence of the free\nhydroxyl groups in HD-PU. In addition, significant changes were found\nat 1710 (H-bonded carbonyl group) and 1735 cm –1 (free\ncarbonyl group) in the FTIR spectra ( Figure S7e,f ). 49 In the FTIR spectra of EGD-PU, the\npeak intensity of H-bonded urethane carbonyl group decreased after\nthe self-healing at 150 °C compared with the sample before self-healing.\nWhile the HD-PU did not show the significant changes in the peak intensity\nof both the carbonyl groups before and after self-healing. It indicates\nthat the hydrogen bond between the hard segments in EDG-PU was impeded\nby the structural changes or partial cross-linking due to the exchange\nreaction. That is, designed free hydroxyl in EGD-PU can efficiently\ncontribute to the self-healing process of EGD-PU through the exchange\nreaction with carbamate groups. The exchange reaction with the nucleophilic\naddition of the free hydroxyl groups may cause a configuration change\nby forming cross-linking structures as well as a linear structure\n( Scheme S1 ). To verify whether the\nexchange reaction changes the PU configuration,\nEGD-PU and HD-PU were immersed in DMF for 12 h to evaluate the gel\nfraction; they were then heat-treated. Both pristine PU films completely\ndissolved in DMF after 12 h. Heat-treated EGD-PU film swelled without\nany dissolution and obtained a gel fraction of 54.9%, whereas HD-PU\ndissolved completely after heat treatment for self-healing even though\nthere were no changes in the chemical structure ( Figure 7 ). The carbamate exchange reaction\nin EGD-PU not only rearranges molecular chains to repair damage but\nalso induces the partial gel-like structure, thereby enhancing the\nmechanical properties. The self-healing properties of EGD-PU and HD-PU\nwere evaluated through three repeated cutting and healing tests ( Figure 8 ); the results are\nsummarized in Table 3 . The results showed that the self-healing efficiency of HD-PU decreased\nsignificantly as the number of instance of cutting and healing increased.\nOn the other hand, EGD-PU showed significantly higher self-healing\nperformance in the three repeated tests. Although a slight decrease\nin tensile strength was observed in the third cutting and healing\ntest, EGD-PU still exhibited a higher self-healing efficiency (84.7%)\nthan HD-PU (11.87%). In addition, the crack in the EGD-PU sample healed\nwithout a scar ( Figure S7 ). EGD-PU also\nshowed great recyclability with a heat press at 130 °C for 5\nmin ( Figure 9 ). EGD-PU\nis reprocessable with carbamate exchange after crack formation without\na catalyst, and the cracks are removed under relatively mild conditions\nwith high self-healing efficiency and repeatability. Figure 7 Gel fraction test results\nof the HD-PU and EGD-PU in dimethylformamide(DMF)\nbefore and after heat treatment. BH denotes samples before heat treatment\nand AH denotes those after heat treatment (photograph courtesy of\nSe-Ra Shin. Copyright 2021). Figure 8 Tensile\nproperties of recycled polyurethane elastomer sheets after\nrepeated crushing and hot press remolding: (a) HD-PU; (b) EGD-PU. Table 3 Mechanical Properties of HD-PU and\nEGD-PU after Three Times Repeated Self-Healing Tests sample number of\ncycles tensile stress\n(MPa) elongation\nat break (%) healing efficiency\n(%) HD-PU original 12.15 837.43 first 3.07 456.19 25.27 second 2.47 410.00 20.33 third 1.44 79.26 11.85 EGD-PU original 6.28 998.19 first 6.27 797.25 99.84 second 6.24 775.73 99.36 third 5.32 764.05 84.71 Figure 9 Recyclable capacity of\nEGD-PU with a hot press (photograph courtesy\nof Se-Ra Shin. Copyright 2021). Antioxidant Effect of EGD-PU The antioxidant property\nis one of the most important factors for extending the lifespan of\nPU polymer while maintaining excellent physical properties. 50 , 51 It is expected that the aromatic hydroxyl groups in EGD could exhibit\nantioxidant properties due to their role as radical scavengers because\nthey remained in the molecular chain without participating in the\npolymerization reaction. The antioxidant activity was evaluated using\nthe DPPH method ( Figure 10 ). EGD-PU exhibited 58.1% free radical scavenging activity\nafter 30 min, whereas HD-PU showed little antioxidant activity (10.9%).\nThe DPPH solution containing EGD-PU changed its color from purple\nto dark yellow, indicating that the aromatic hydroxyl groups in EGD-PU\nimpart excellent antioxidant activity to PU by stabilizing the free\nradical. The antioxidant properties of this self-healing PU are expected\nto provide a longer life span and resistance to oxidative degradation\nof PU films. Oxidation of PU makes aging faster and the mechanical\nstrength of PU weaker. Therefore, some researchers added antioxidants\nsuch as vitamin E during PU synthesis. 52 EGD-PU has an antioxidant effect itself derived from EGD containing\nphenol group expecting a longer life span. Figure 10 Antioxidant effect of\nEGD-PU in DPPH (photograph courtesy of Se-Ra\nShin. Copyright 2021)."
} | 6,006 |
28769900 | PMC5513903 | pmc | 3,715 | {
"abstract": "Biofilm represents a way of life that allows greater survival of microorganisms in hostile habitats. Campylobacter jejuni is able to form biofilms in vitro and on surfaces at several points in the poultry production chain. Genetic determinants related to their formation are expressed differently between strains and external conditions are decisive in this respect. Our approach combines phylogenetic analysis and the presence of seven specific genes linked to biofilm formation in association with traditional microbiology techniques, using Mueller Hinton and chicken juice as substrates in order to quantify, classify, determine the composition and morphology of the biomass of simple and mixed biofilms of 30 C. jejuni strains. It also evaluates the inhibition of its formation by biocides commonly used in industry and also by zinc oxide nanoparticles. Genetic analysis showed high heterogeneity with the identification of 23 pulsotypes. Despite the diversity, the presence of flaA, cadF, luxS, dnaJ, htrA, cbrA , and sodB genes in all strains shows the high potential for biofilm formation. This ability was only expressed in chicken juice, where they presented phenotype of a strong biofilm producer, with a mean count of 7.37 log CFU/mL and an ultrastructure characteristic of mature biofilm. The composition of simple and mixed biofilms was predominantly composed by proteins. The exceptions were found in mixed biofilms with Pseudomonas aeruginosa , which includes a carbohydrate-rich matrix, lower ability to sessile form in chicken juice and compact architecture of the biofilm, this aspects are intrinsic to this species. Hypochlorite, chlorhexidine, and peracetic acid were more effective in controlling viable cells of C. jejuni in biofilm, but the existence of tolerant strains indicates exposure to sublethal concentrations and development of adaptation mechanisms. This study shows that in chicken juice C. jejuni presents greater potential in producing mature biofilms.",
"introduction": "Introduction Campylobacter jejuni is one of the pathogens most commonly involved in food-borne gastroenteritis worldwide. It infects about one million people in the United States each year and in Europe this rate reaches more than 200,000 (Scallan et al., 2011 ; European Food Safety Authority, 2015 ). In addition, an estimated number of 1/1,000 clinical cases may result in more severe neurological conditions, including Guillain-Barré Syndrome (Nachamkin et al., 1998 ). The main reservoir of this microorganism is the intestinal tract of birds and other endothermic animals, and is often isolated in chicken meat. Generally, consumption of this undercooked meat is the cause of human host infection (Guyard-Nicodeme et al., 2013 ). The risk is consistent with the high levels of contamination found in studies conducted in Europe, USA and United Kingdom, which shows contamination higher than 70% in chicken carcass flocks (Batz et al., 2012 ; Lawes et al., 2012 ; European Food Safety Authority, 2015 ). Due to the large number of reported cases of campylobacteriosis, it has become necessary to use epidemiological typing, method that allows the characterization and discrimination of bacterial strains. The data obtained in these investigations can be used by public health surveillance in identifying the causes of food outbreaks (Nakari, 2011 ). Among these methods, PFGE, pulsed-field gel electrophoresis, is considered the gold standard in bacterial epidemiological analyzes, since it allows a high discriminatory power compared to other techniques (Goering, 2010 ). The paradox between the rigorous growth conditions of C. jejuni in the laboratory and the ubiquity as an effective and constant pathogen in chicken samples represents one of the most notable characteristics of C. jejuni (Mihaljevic et al., 2007 ). One of the strategies that C. jejuni can use to overcome its fragility in the face of environmental hostility is the ability to form biofilms. These structures represent a mode of growth and survival, in which the bacterial transits from free living to sessile form, attached to a biotic or abiotic surface surrounded by a viscous matrix that protects from stressful environmental conditions (Kostakioti et al., 2013 ). These communities increase the survival of this microorganism under unfavorable conditions, such as the presence of antibiotics and chemical agents (Trachoo and Frank, 2002 ; Joshua et al., 2006 ; Ica et al., 2012 ; Drozd et al., 2014 ). A serious problem in the chicken processing industries is the insufficient removal of organic material composed of a complex mixture of carbohydrates, proteins, lipids, and sugars (Chmielewski and Frank, 2007 ) of the surfaces, which provides an ideal medium for microorganisms to multiply and survive. This environment assists in bacterial fixation to surfaces by altering the physicochemical properties of the surface and by the greater availability of nutrients (Dat et al., 2010 ; Hwang et al., 2012 ). Trying to simulate the nutritional conditions on the abiotic surfaces during processing, a model system with “chicken juice” (Brown et al., 2014 ) is used, based on the supplementation of culture medium with defrosted filter-sterilized poultry exudates (Birk et al., 2006 ). The extracellular matrix is an essential component of bacterial biofilms, and normally, corresponds for more than 90% of the dry mass of a biofilm (Flemming and Wingender, 2010 ). In addition, it allows the cells to remain hydrated and metabolically active, imprisoning nutrients and liquids near the bacterial cells. It also reduces the access of large molecules, such as antimicrobials (Billings et al., 2013 ), allowing bacterial persistence, beyond being structurally important, once it maintains the biofilm shape and ensures its cohesion (Sutherland, 2001 ). Knowing the composition and architecture of the extracellular matrix of biofilms is important, as it helps in the use of tools that improve efficiency and disinfection strategies. The molecular mechanisms that regulate biofilm formation of C. jejuni are still poorly understood. Some of the genes involved in the process include the ones responsible for cell motility ( flaA ) (Reuter et al., 2010 ), cell adhesion ( cadF ), quorum-sensing ( luxS ) (Plummer, 2012 ) and stress response ( dnaJ, cbrA, htrA , and sodB ) (Oh and Jeon, 2014 ). The biofilm formation is flagella-mediated at the first moment of the adhesion, together with the proteins involved in cell adhesion, although its functionality is not crucial (Svensson et al., 2014 ). Detection of quorum-sensing markers indicates ability of binding between cells, development and detachment of biofilm (Plummer, 2012 ). Already the markers involved in the stress response play a decisive role, contributing to a superexpression of the capacity of formation of sessile cells (Oh and Jeon, 2014 ). The aim of this study was to carry out a phylogenetic analysis on C. jejuni strains isolated from chicken carcasses destined for national market and also to exportation, followed by a qualitative and quantitative study on the formation of biofilms, including molecular aspects involving the presence of specific genes, the architecture and composition of these structures and also the interaction of these strains in mixed biofilms under conditions with and without supplementation with chicken juice. Finally, the objective was to evaluate the performance of different chemical agents in the removal of C. jejuni bacterial biomass to establish control strategies at industry.",
"discussion": "Discussion Biofilms of C. jejuni During the last decade, C. jejuni has been regularly presented as the leading cause of bacterial foodborne infections in Europe and the USA. Given the importance to public health of this zoonosis, it is relevant to understand the survival mechanisms adopted by this pathogen. One of the mysteries of the genus Campylobacter is that it is a pathogenic microorganism that survives successfully in the host and industrial environment under stressful conditions, and paradoxically is a mandatory microaerophilic that survives poorly under controlled laboratory conditions. In addition, in comparison to other agents causing foodborne disease, such as E. coli and Salmonella spp., C. jejuni needs a low infective dose (500–800 CFU) to cause disease in the host (Black et al., 1988 ). Although this may contribute to infection, it is still unclear what allows the bacteria to survive during transmission under adverse conditions. Survival in a biofilm would be an explanation to protect bacteria from various environmental stresses, antimicrobial agents and/or disinfectants and the immune response of the host. In this study we found that these structures represent a reservoir of cells and that the level of biofilm formation by C. jejuni is clearly increased under conditions similar to those found in the industry with the presence of chicken juice. The detection of viable cells in significant quantities in biofilms formed in chicken juice corroborates the idea that survival and persistence in the production chain may represent the main problem of contamination in final product. Despite the use of microaerophylia for this study, it is known that the mature biofilm can provide an adequate environment for microaerophilic growth allowing the ideal conditions for maintenance and dissemination of this pathogen (Reuter et al., 2010 ). The biofilm formation involves the succession of several steps, starting with initial adhesion. For this reason, C. jejuni 's ability to adhere to a inert surface was investigated, in order to subsequently assess their ability to initiate and develop the biofilm. The adhesion capacity was variable and lower in the 30 strains tested in MH. The delayed adhesion profile may indicate less ability to acquire the sessile form, but may also be related to the need for a prolonged period of contact with the surface to lead to a stronger future adhesion (Turonova et al., 2015 ). In contrast, in chicken juice the counts showed high adhesion capacity for all strains. The medium supplemented with chicken juice allowed a better condition for adhesion to the inert surface (Li, 2016 ). The results obtained in both colorimetric and quantitative tests revealed the superiority of chicken juice in relation to MH. Chicken carcass exudates contain a complex mixture of carbohydrates, proteins, lipids, and sugars (Chmielewski and Frank, 2007 ), providing an ideal medium for the proliferation and survival of bacteria. The accumulation of these organic materials allows the formation of micro-layers on the surfaces that aid in bacterial fixation, together with greater availability of nutrients (Hwang et al., 2012 ). Thus, in the industrial environment, the presence of meat-based exudates may exacerbate the problem of contamination by C. jejuni . Our results add and are consistent with the findings of Brown et al. ( 2015 ) who also detected the efficiency of chicken juice at different concentrations in the biofilm production for five Campylobacter strains. Genetic apparatus of C. jejuni Once the phenotypic characterization was performed concerning the sessile kind of living, analysis of the specific genes revealed that all strains possess the genes required to develop a biofilm. Thus, gene identification in the strains of C. jejuni did not explain the differences in the classification of the biofilms formed in MH. In contrast, the identification of all the genes surveyed in all strains is consistent with the strong producer character obtained in chicken juice. Therefore, chicken juice is likely to provide all the necessary conditions for expression of the genetic potential recorded by the presence of flaA, cadF, luxS, dnaJ, htrA, cbrA , and sodB genes and this same ability is not detected in MH. The genes linked to quorum-sensing, adhesion, adverse conditions and motility were all previously described as important for the acquisition of the sessile form (Kalmokoff et al., 2006 ; Svensson et al., 2009 ; Howlett et al., 2012 ; Sulaeman et al., 2012 ; Avila-Ramirez et al., 2013 ; van Alphen et al., 2014 ). There are reports that flagellar expression is required for the formation of biofilms by C. jejuni (Lehtola et al., 2006 ; Reeser et al., 2007 ), including flaA and flaB genes (Reuter et al., 2010 ). However, the absence of these characteristics does not completely prevent the acquisition of the sessile form. The advantage in the expression of this characteristic is due to the initial fixation, biofilm structuring, orientation to a pre-existing biofilm in addition to the correlation with other non-flagellar extracellular proteins that contribute indirectly to the sessile lifestyle (Howard et al., 2009 ; Kim et al., 2015 ). Numerous genes in Campylobacter were previously described as mediators of adhesion in vitro . Among them, the cadF gene that encodes the binding proteins CadF fibronectin (Konkel et al., 2010 ). Several enzymes and proteins are already described by the involvement in bacterial protection against oxidative stress, whose action is related to peroxide or superoxide detoxification. Among them, the enzyme superoxide dismutase (SodB) appears as a major regulator in C. jejuni (Flint et al., 2014 ; Kim et al., 2015 ). Some quorum-sensing systems have already been detected in Campylobacter . The production of AI-1 (acyl-homoserine autoinducer) represents one of these mechanisms, which accumulates in the extracellular environment and diffuses freely in the bacterial cytoplasm, which at high levels binds to a cellular transcription enhancer ( luxS ) that encodes the luciferase, a metabolic key enzyme in the SAM recycling pathway (S-adenosylmethionine). This metabolite is essential in the performance of important biosynthetic reactions, such as the methylation of bacterial DNA, the synthesis of polyamines and bacterial vitamins. The most important performance of the luxS gene is associated with the synthesis of a new AI called autoinducer-2 (AI-2). Increased bacterial population growth also promotes elevation of AI-2 concentrations in the environment. The luxS gene acts in the formation of several molecular compounds, which together are called AI-2 variants. These molecules have potential for recognition and inclusion of mixed populations and of the same species in biofilms (Xavier and Bassler, 2005 ). Much of C. jejuni has functional LuxS enzymes and is capable of producing AI-2. However, the presence of nutrients is necessary for the production of AI-2, and these are found in foods, such as milk and chicken juice, even when the microorganisms are kept under adverse conditions, such as in oxidative stress and in low temperatures (Ligowska et al., 2011 ; Parveen and Cornell, 2011 ; Tazumi et al., 2011 ; Plummer, 2012 ). Strategies for the elimination of viable cells of sessile C. jejuni In the poultry industry investigated, the chemical agents: peracetic acid 0.8%, sodium hypochlorite 1% and chlorhexidine 1%; are used by the quality control team. On the other hand, ZnO NPs, represent a potential sanitizing agent for experimental use, with no usual application in hygiene in the food producing industries. The results showed that the three agents used in the industry routine were more effective in elimination, although 9/30 (30.0%) of the strains were identified to be tolerant to at least one of them. In contrast, ZnO NPs showed less efficacy with 13/30 (43.3%) resistant strains and with counts higher than the other agents. The presence of tolerant strains to different sanitizers suggests that the use of these agents in the routine of the industrial environment in an inadequate way can result in the sublethal exposure to these biocides, representing a real risk for the adaptation of these bacteria, besides positively influencing the production of biofilms (Keeratipibul and Techaruwichit, 2012 ; Techaruvichit et al., 2016 ). As for ZnO NPs it is possible that tolerant bacteria have already acquired characteristics that confer this resistance, such as the presence of efflux pumps, ZnO resistance genes and the ability to maintain intact the integrity of membrane. This characteristic has already been identified in Escherichia coli and Enterococcus faecium (Mileyeva-Biebesheimer, 2011 ). Although the use of chemical compounds provides benefits in disinfection, they have the limitation of not destroying the residual structures of the biofilm matrix that may facilitate their resurgence or maintenance (Ohsumi et al., 2015 ). Thus, special efforts are required for the complete removal of highly adherent biofilms adapted to C. jejuni biocides (Techaruvichit et al., 2016 ). Probably, the effectiveness in the control is possible by the association of hygiene plans with different agents, respecting the periods between cleaning, besides strategies, like the periodical rotation of biocides. Architecture and constitution of C. jejuni and mixed biofilms For the three C. jejuni strains under sessile form in the glass beads, with MH substrate plus chicken juice, it was observed in SEM that the structure of the biofilm was quite similar, with a more expanded and stable architecture, besides the presence of irregular coverage along the surface of the sphere, consistent with the presence of several macrocolonies. Differently, in MH, this pattern varied according to the strain, so that the most developed structure observed was the presence of microcolonies that indicate the immature stage of the biofilm. A study by Bronnec et al. ( 2016 ) compared the ultrastructure of two strains of C. jejuni in biofilm under microaerophilic and aerobiose conditions. The authors concluded that the differences revealed the formation of mature and immature biofilm, being a strain-dependent characteristic. The variations in the architecture of the formed biofilms can have relation not only with the nutrient available to the bacterium, but also because it is a strain-dependent character. Turonova et al. ( 2015 ) showed that C. jejuni NCTC 11168 produces biofilm with multilayer type structure, while C. jejuni 81–176 was able to form finger-like biofilm with an open ultrastructure. The capacity to form biofilm with open ultrastructure composed of wells and channels was identified in the three strains of C. jejuni tested in the presence of chicken juice. This type of heterogeneous structure gives the characteristic of a mature biofilm, which allows the formation of interconnected fluxes that aid in the access to nutrients for the cellular aggregates and in the drainage of the metabolic residues (Donlan and Costerton, 2002 ). The composition assays allowed to identify that all strains reduced biomass with treatment with sodium metaperiodate and proteinase K, the last one being more significant. Thus, the treatment of biomass with products of proteolytic action can be considered an effective mechanism for partial degradation, allowing a better penetration of antimicrobial agents into the matrix. Although the use of proteinase K is expensive in the poultry industry, the effectiveness of the tests opens the prospects for the chemical industry to the development of other similar proteolytics and of lower cost, since they will probably not require the necessary purity to be used in molecular techniques. Considering the proteic nature of biofilms, it is possible that the association of potent proteolytics in association with sanitizers is an adequate strategy in the prevention of C. jejuni biofilms. The centesimal composition of MH and chicken juice was compared and it was found that the analysis of 100 mL of chicken juice has 2.79% of protein and 0.06% of carbohydrates. MH contains 1.85% protein and 0.2% of carbohydrate. Even with only 5% of chicken juice in the trials, the presence of a higher protein build-up added to the existence of blood and other unassessed components may have provided C. jejuni not only with the microaerophilic condition required for this microorganism, as well as a greater presence of iron, important conditions for its metabolism and consequent survival and multiplication, which may have had a positive influence on biofilm formation. For the mixed biofilms it was observed that there was an increase in the formed biomass. This increase was significant depending on the microorganism to which the interaction occurred and the medium used. In addition, there was variability in the composition of the formed biofilm. The competitive disadvantage of C. jejuni visualized in the SEM indicates that probably the identified variations in biomass and in the constitution may be more related to the characteristics of the other species than to the interaction itself. SEM images demonstrated that the configuration of mixed biofilms presented the same pattern found in the monospecific biofilm of C. jejuni , in both MH and chicken juice. The exception was restrict to the interaction with P. aeruginosa that presented in addition a more compact and flat conformation with the presence of well delimited pores, and it was also identified a higher biomass in MH in comparison with chicken juice, that presented a significant difference ( p < 0.001) in the colorimetric assay. The predominance of the other species in detriment of C. jejuni , in mixed biofilms, may be related to the biofilm formation time, since C. jejuni is a fastidious and demanding specie. In addition, the prevalence of other species in mixed biofilms has also been described previously and may indicate the existence of selection pressure exerted under C. jejuni in the first days. According to Culotti and Packman ( 2015 ) only after 3 days of formation of the mixed biofilm of C. jejuni and P. aeruginosa was it possible to observe the presence of dispersed and discrete colonies of C. jejuni , which were present only on the surface of the biofilm formed by P. aeruginosa . In addition, the authors also detected that there was a predominance of P. aeruginosa biofilm morphology that remained unchanged in the C. jejuni presence and exhibited the same typical characteristics of the simple P. aeruginosa biofilm. Several authors have already stated that both, co-inoculation and the inclusion of C. jejuni in pre-established biofilms facilitates subsequent growth of the sessile form of this agent (Zhang et al., 2013 ; Culotti and Packman, 2014 , 2015 ). Aswathanarayan and Vittal ( 2013 ) have suggested that different bacterial species secrete enzymes that modify the composition of extracellular polymeric substance (EPS) of biofilms in response to external stresses, resulting in changes in the biofilm architecture in a specific environment. In this way, the inclusion of different species in two substrates (MH and chicken juice) promoted these modifications. The exception found in mixed biofilms with P. aeruginosa in chicken juice may represent a specific characteristic of this specie. Many animal macromolecules have been reported with the ability to form an adherent film, but not always capable of improving biofilm formation. For example, bovine serum albumin reduces formation of biofilms in S. aureus (Xu et al., 2008 ) and Burkholderia cepacia (Hwang et al., 2012 ). On the other hand, it is important for adhesion in Cronobacter (Healy et al., 2010 ). These differences also correlate with changes in the ability to express absorption proteins, leading to a variability in time to biofilm formation (Brown et al., 2015 ). In addition, the composition of the P. aeruginosa biofilm matrix is predominantly of polysaccharides, mainly alginate (Mann and Wozniak, 2012 ), which confers a differentiated structure, which can be detected in SEM and may represent another explanation for difficulty in adherence in the presence of chicken juice. Genotyping The high heterogeneity found in C. jejuni strains is due to the fact that most of them are naturally competent to take the DNA present in the environment and promote recombination in their genome, that is, they execute the transformation mechanism effectively, due to production of extracellular DNAse (Clark et al., 2014 ). The presence of strains with high percentage of phylogenetic similarity in different flocks and in the same one, was also reported by other authors who stated that slaughter conditions may be the main responsible for the presence of strains with a high degree of homology in samples from the same flock, such as the equipment used in animal processing and cross-contamination (Petersen and Wedderkopp, 2001 ; Workman et al., 2008 ). Our approach has shown that the ability of C. jejuni in developing a structured biofilm is highly variable depending on the strain when maintained in MH. However, when there is supplementation with chicken juice, all strains present a strong biofilm producer pattern. The chicken juice allows a greater fixation of C. jejuni as it assigns a surface more conditioned to bacterial adhesion. Genome analysis revealed the high potential of strains in the acquisition of sessile lifestyle, phenotypically proven in chicken juice. Its variable behavior in MH and chicken juice, apparently results from modifications in the expression of genes involved in stress response, adhesion and biofilm formation. The existence of tolerant strains to the tested biocides and most used in the poultry industry suggests the existence of exposure to sublethal concentrations, representing a real risk for the development of adaptation mechanisms. The ultrastructure of simple and mixed biofilms showed the early maturity range when in chicken juice compared to MH. However, in biofilms with P. aeruginosa this pattern is inverted, probably due to the particular characteristics of this species. Identification of the predominantly protein composition of C. jejuni biomass and also in mixed biofilms may aid in the future development of agents of action with proteolytic approach as a prevention and strategy of control. However, it is noteworthy that in mixed culture with P. aeruginosa there is predominance of a polysaccharide matrix. Phylogenetic diversity was evidenced by the presence of 23 pulsotypes, which confirms the intrinsic characteristic of C. jejuni to easily recombine its genome by gene transformation."
} | 6,620 |
20414363 | PMC2857869 | pmc | 3,716 | {
"abstract": "Production of fuels and chemicals through microbial fermentation of plant material is a desirable alternative to petrochemical-based production. Fermentative production of biorenewable fuels and chemicals requires the engineering of biocatalysts that can quickly and efficiently convert sugars to target products at a cost that is competitive with existing petrochemical-based processes. It is also important that biocatalysts be robust to extreme fermentation conditions, biomass-derived inhibitors, and their target products. Traditional metabolic engineering has made great advances in this area, but synthetic biology has contributed and will continue to contribute to this field, particularly with next-generation biofuels. This work reviews the use of metabolic engineering and synthetic biology in biocatalyst engineering for biorenewable fuels and chemicals production, such as ethanol, butanol, acetate, lactate, succinate, alanine, and xylitol. We also examine the existing challenges in this area and discuss strategies for improving biocatalyst tolerance to chemical inhibitors.",
"introduction": "1. Introduction Human society has always depended on biomass-derived carbon and energy for nutrition and survival. In recent history, we have also become dependent on petroleum-derived carbon and energy for commodity chemicals and fuels. However, the nonrenewable nature of petroleum stands in stark contrast to the renewable carbon and energy present in biomass, where biomass is essentially a temporary storage unit for atmospheric carbon and sunlight-derived energy. Thus there is increasing demand to develop and implement strategies for production of commodity chemicals and fuels from biomass instead of petroleum. Specifically, in this work we are interested in the microbial fermentation of biomass-derived sugars to commodity fuels and chemicals. In order for a fermentation process to compete with existing petroleum-based processes, the target chemical must be produced at a high yield, titer and productivity. Sometimes there are additional constraints on the fermentation process, such as the presence of potent inhibitors in biomass hydrolysate or the need to operate at an extreme pH or temperature [ 1 ]. These goals can be difficult to attain with naturally-occurring microbes. Therefore, microorganisms with these desired traits often must be developed, either by modification of existing microbes or by the de novo design of new microbes. While significant progress has been made towards de novo design [ 2 , 3 ], this work focuses on the modification of existing microbes. Humanity has long relied on microbial biocatalysts for production of fermented food and beverages and eukaryotic biocatalysts for food and textiles. We have slowly modified these biocatalysts by selecting for desirable traits without understanding the underlying biological mechanisms. But upon elucidation of the biological code and the development of recombinant DNA technology, we now have the tools to do more than just select for observable traits—we are now able to rationally modify and design metabolic pathways, proteins, and even whole organisms. Much of this rational modification has been in the form of Metabolic Engineering. Metabolic Engineering was defined in 1991 [ 4 , 5 ] and here we use the definition of “ the directed improvement of production, formation, or cellular properties through the modification of specific biochemical reactions or the introduction of new ones with the use of recombinant DNA technology ” [ 6 ]. While Metabolic Engineering has enabled extraordinary advances in the production of commodity chemicals and fuels from biomass, some of which are discussed in this work, we have now reached the point where biological functions that do not exist in nature are desired. Synthetic biology aims to develop and provide these nonnatural biological functions. For many years, the term Synthetic Biology was used to describe concepts that would be classified today as Metabolic Engineering [ 7 ]. However in the last 10 years, terms such as “unnatural organic molecules” [ 7 ], “unnatural chemical systems [ 8 ], “novel behaviors” [ 9 ], “artificial, biology-inspired systems” [ 10 ], and “functions that do not exist in nature” [ 11 ] have been used to describe Synthetic Biology. For the purpose of this review, we will apply the Synthetic Biology definition of “ the design and construction of new biological components, such as enzymes, genetic circuits, and cells, or the redesign of existing biological systems ” [ 12 ]. Synthetic biology has application to many fields, including cell-free synthesis [ 13 ], tissue and plant engineering [ 14 ] and drug discovery [ 15 ], but here we are interested in the modification of microbes for the biorenewable production of commodity chemicals and fuels. Other recent reviews have also dealt with this topic [ 16 – 18 ]. Synthetic biology for the production of a target compound can be expressed as a sequence of the following events, each of which will be discussed in more detail and demonstrated below. (1) Design the metabolic pathways and phenotypic properties of the desired system. What are the desired substrates and products? What are the expected environmental stressors? (2) Choose an appropriate host organism (chassis) based on the following criteria. Which organisms display at least some of the desired properties? How well characterized and annotated are these organisms? Are there molecular biology tools for modification of this chassis? (3) Formulate an implementation approach. What modifications are necessary to achieve the pathways and properties identified in step (1)? Do metabolic pathways need to be added, removed, or tuned? Does the desired pathway or phenotype exist in nature, or does it need to be designed de novo ? (4) Optimize the redesigned system and assess the system properties relative to the ideal. Can the chassis be improved further? Even a simple biocatalyst, such as the laboratory workhorse Escherichia coli , is a complex system of an estimated 4603 genes, 2077 reactions, and 1039 unique metabolites [ 19 , 20 ], and while the steps outlined above are relatively straightforward, it is still difficult to quickly and reliably engineer a biocatalyst to perform desired behaviors [ 21 ]. Systems biology, the standardization of biological systems, and metabolic evolution are all vital to the compensation for this disconnect between the expected and actual biocatalyst behaviors. Through a combination of these powerful techniques, biocatalysts have been redesigned for the production of an astounding array of commodity fuels and chemicals, both natural and unnatural ( Figure 1 and Table 1 ). Here we discuss successful examples involving the production of commodity fuels and chemicals, with a focus on D- and L-lactate, L-alanine, succinate, ethanol, and butanol.\n\n4. Redesign through Introduction of Foreign or Nonnatural Pathways 4.1. Foreign Pathways 4.1.1. Ethanol Ethanol is a renewable transportation fuel. Replacement of gasoline with ethanol would significantly reduce US import oil dependency, increase the national security, and reduce environmental pollution [ 118 ]. However, only 9 billion gallons of ethanol were produced in 2008, and all were from corn-based production. Lignocellulose is generally regarded as an excellent source of sugars for conversion into fuel ethanol. It is, thus, desirable to design or obtain biocatalysts that can utilize all the sugar components in lignocellulose and convert them to ethanol with high yield and productivity in mineral salts medium. Native S. cerevisiae and Z. mobilis strains can efficiently convert glucose to ethanol, but cannot utilize pentose sugars. In contrast, E. coli strains can utilize all the sugar components of lignocelluloses but ethanol is only a minor fermentation product, with mixed acids accumulating as the major fermentation product [ 103 ]. While recent advances have been made engineering the native E. coli metabolic pathways for ethanol production [ 119 ], the most successful example used a foreign metabolic pathway to enable ethanol production from E. coli strain W (ATCC# 9637) [ 1 ]. Redesign for ethanol production was decoupled to three parts: construction of a metabolic pathway for production of ethanol as the major fermentation product, elimination of competitive NADH oxidization pathways, and disruption of side-product formation. The Z. mobilis homoethanol pathway (pyruvate decarboxylase and alcohol dehydrogenase) was introduced as a foreign pathway, enabling redox-balanced production of ethanol at high yield [ 120 ] ( Figure 4(b) ). Then fumarate reductase ( frd ) was disrupted to increase ethanol yield. The resulting strain, KO11, produced ethanol at a yield of 95% in a complex medium [ 121 ]. This strain was developed at the dawn of metabolic engineering and has been used to produce ethanol from a variety of lignocellulosic materials, as reviewed in [ 1 ]. Although the ethanol production rate of KO11 was as high as yeast, the ethanol tolerance and performance in minimal medium did not meet the desired standards. Therefore strain SZ110, a derivative of KO11 modified for lactate production in mineral salts media [ 35 ], was redesigned for ethanol production [ 122 ]. As with the design of KO11, redesign of SZ110 was decoupled to construction of an ethanol synthetic pathway, elimination of competitive NADH oxidization pathways, and blockage of side-product formation. However, this redesign strategy also included the acceleration of mixed sugar co-utilization. The lactate producing pathway was disrupted and the Z. mobilis homoethanol pathway was integrated into the chromosome by random insertion to select for optimal expression. The Pseudomonas putida short-chain esterase ( estZ ) [ 123 ] was introduced to decrease ethyl acetate levels in the fermentation broth and decrease the downstream purification cost. In addition, methylglyoxal synthase ( mgsA ) was inactivated, resulting in co-metabolism of glucose and xylose, and accelerated the metabolism of a 5-sugar mixture (mannose, glucose, arabinose, xylose, and galactose) to ethanol [ 25 ]. After using evolution to increase cell growth and production, the final strain, LY168, could concurrently metabolize a complex combination of the five principal sugars present in lignocellulosic biomass with a high yield and productivity in mineral salts medium [ 25 ]. 4.1.2. L-Lactate As described above, L-lactate is the major component of the biodegradable plastic PLA. Although many lactic acid bacteria produce L-lactate with high yield and productivity [ 124 ], they usually require complex nutrients. E. coli does not have a native pathway for L-lactate production, and therefore introduction of a foreign pathway was necessary. The strategy for redesigning E. coli W3110 for L-lactate production was to eliminate competitive NADH oxidization pathways and then construct the desired L-lactate synthetic pathway ( Figure 4(b) ) [ 125 ]. The L-lactate production pathway, L-lactate dehydrogenase ( ldhL ) from Pediococcus acidilactici, was used and its coding region and terminator were integrated into the E. coli chromosome at the ldhA site, so that ldhL could be expressed under the native ldhA promoter. In addition, since the ldhL gene contains a weak ribosomal-binding region, this region was rationally replaced with ldhA 's RBS [ 125 ]. Following a period of metabolic evolution, the resulting strain, SZ85, synthesized 45 g/L L-lactate in a mineral salts medium with yield near theoretical maximum (94%). However, this strain was a K-12 derivative and displayed the same problems seen with the K12-based D-lactate-producing strain described above, meaning that it was unable to completely ferment high sugar concentrations and had a low productivity (0.65 g/L/h). Therefore, the same design strategy was implemented in an E. coli W (ATCC# 9637) derivative. After further deleting mgsA gene to improve chiral purity and using metabolic evolution to improve cell growth and productivity, the final L-lactate-producing strain, TG108, could convert 12% glucose to 116 g/L L-lactate with an excellent yield (98%) and productivity (2.29 g/L/h) [ 22 ]. 4.1.3. Xylitol The pentahydroxy sugar alcohol xylitol is commonly used to replace sucrose in food and as a natural, non-nutritive sweetener that inhibits dental caries [ 126 ]. Xylitol can also be used as a building block for synthesizing new polymers [ 127 ]. Current xylitol commercial production involves hydrogenation of hemicellulose-derived xylose with an active metal catalyst [ 127 ]. Biological-based processes have also recently been developed, but although high xylitol titer was achieved by some yeast, the process requires complex medium with numerous expensive vitamin supplements [ 128 ]. While E. coli does not have the native capability to synthesize xylitol, a redesign strategy for strain W3110 was proposed involving a foreign metabolic pathway [ 26 ]. In the proposed redesign, glucose would support cell growth and provide reducing equivalents, while xylose would be used as substrate for xylitol synthesis ( Figure 4(d) ). The design strategy consisted of three major components: enabling co-utilization of glucose and xylose, separation of xylose metabolism from central metabolism, and construction of a xylitol production pathway ( Figure 4(d) ). In order to enable co-utilization of glucose and xylose, glucose-mediated repression of xylose metabolism was eliminated by replacing the native crp gene with a cAMP-independent mutant (CRP*). Xylose metabolism was separated from central metabolism by deleting the xylulokinase ( xylB ) gene, preventing the loss of xylose carbon to central metabolism. Finally, xylose reductase and xylitol dehydrogenase from several microorganisms were tested for xylitol synthetic capability, and the NADPH-dependent xylose reductase from C. boidinii (CbXR) was found to support optimal xylitol production. The final strain, PC09 (CbXR), could produce 250 mM (38 g/L) xylitol in mineral salts medium. The yield was 1.7 mol xylitol per mol glucose consumed, which was improved to 4.7 mol/mol by using resting cells. It was proposed that xylitol production could be further improved by increasing supply of reducing equivalents [ 129 ]. 4.1.4. L-Alanine L-alanine can be used with other L-amino acids as a pre- and postoperative nutrition therapy in pharmaceutical and veterinary applications [ 130 ]. It is also used as a food additive because of its sweet taste. The annual worldwide production of L-alanine is around 500 tons [ 131 ], and this market is currently limited by production costs. The current commercial production process converts aspartate to alanine via aspartate decarboxylase, where aspartate is produced from fumarate by aspartate ammonia-lyase catalysis [ 27 ]. An efficient fermentative process with a renewable feedstock such as glucose offers the potential to reduce L-alanine cost and facilitate a broad expansion of the alanine market into other products. SZ194, a derivative of E. coli W (ATCC# 9637) that was previously engineered for D-lactate production, was used as the chassis for L-alanine production [ 27 ] ( Figure 4(b) ). Alanine production in the native strain uses glutamate- and NADPH-dependent glutamate-pyruvate aminotransferase. It is preferable to produce L-alanine directly from pyruvate and ammonia using an NADH-dependent enzyme, and therefore L-alanine dehydrogenase ( alaD ) of Geobacillus stearothermophilus was employed. The native ribosome binding site, coding region, and terminator of alaD gene were integrated into the E. coli chromosome at the ldhA site, so that expression of alaD could be controlled by the native promoter of ldhA , a promoter that has worked well for production of D- and L-lactate, as described above. Further redesign focused on elimination of trace amounts of lactate and increasing the L-alanine chiral purity by deleting mgsA and the major alanine racemase gene ( dadX ). Metabolic evolution increased the final titer and productivity by 15- and 30-fold, respectively ( Figure 3 ). The latest L-alanine producing strain, XZ132, converted 12% glucose to 114 g/L L-alanine with a 95% yield and the excellent volumetric productivity of 2.38 g/L/h [ 27 ]. 4.1.5. Combining Multiple Foreign Pathways in a Single Chassis Although the work described above relied on the introduction of a single foreign pathway, there are other excellent examples that employ pathways from more than one organism in a single host. \n\t\t\t\t\t\t E. coli was redesigned for 1,3-propanediol production using S. cerevisiae pathway to convert glucose to glycerol and a K. pneumonia pathway to convert glycerol to 1,3-propanediol [ 132 ]. E. coli was also redesigned for isopropanol production by combining acetyl CoA acetyltransferase ( thl ) and acetoacetate decarboxylase ( adc ) from C. acetobutylicum with the second alcohol dehydrogenase ( adh ) from C. beijerinckii and E. coli 's own acetoacetyl-CoA transferase ( atoAD ) [ 133 ]. Artemisinic acid, a precursor of antimalarial drug artemisin, was produced by E. coli following the combination of a mevalonate pathway from S. cerevisiae and E. coli , amorphadiene synthase, and a novel cytochrome P450 monooxygenase (CYP71AV1) from Artemisia annua [ 12 , 134 ]. \n\t\t\t\t\t\t S. cerevisiae was redesigned for flavanone production by combining Arabidopsis thaliana cinnamate 4-hydroxylase (C4H), Petroselinum crispum 4-coumaroyl: CoA-ligase (4CL), and Petunia chalcone synthase (CHS), Petunia chalcone isomerase (CHI) [ 135 ]. A similar synthetic system producing hydroxylated flavonols was also constructed in E. coli with additional amplification of C. roseus P450 flavonoid 3′, 5′-hydroxylase (F3′5′H) fused with P450 reductase, Malus domestica flavanone 3 β -hydroxylase (FHT), and Arabidopsis thaliana flavonol synthase (FLS) [ 136 ]. The flavonoid production was significantly increased through further redesigning of the central metabolic system of E. coli to increase precursor ( Malonyl-CoA ) supply [ 137 ]. 4.2. Modification of Natural Pathways for Production of Unnatural Compounds One of the goals of synthetic biology is to design or construct new genetic circuits. In the examples given thus far, existing biological parts have been reassembled to engineer a biocatalyst that efficiently produces a product that already exists in nature. However, metabolic pathways can also be constructed to produce unnatural compounds. As discussed above, directed evolution of proteins can modify their activity such that new substrates are recognized or new products are formed [ 138 ]. For example, novel carotenoid compounds were generated by evolution of two key carotenoid synthetic enzymes, phytoene desaturase, and lycopene cyclase [ 139 ]. Additionally, combinatorial biosynthesis, which combines genes from different organisms into a heterologous host, can also generate new products [ 140 ]. For example, four previously unknown carotenoids were produced by combinatorial biosynthesis in E. coli [ 141 ]. 4.3. De Novo Pathway Design In order to broaden the available biosynthesis space, it is essential to go beyond the natural pathways and design pathways de novo [ 142 ]. Although this exciting design strategy still has many challenges, several successful examples have been reported. For example, a synthetic pathway for 3-hydroxypropionic acid (3-HP) production was designed involving the unnatural isomerization of α -alanine to β -alanine, as mentioned above. In this example the researchers used directed evolution to expand the substrate specificity of lysine 2,3-aminomutase to include α -alanine [ 73 ]. The resulting β -alanine can then be converted to 3-HP through existing metabolic pathways. Unnatural pathways for higher alcohol production in E. coli were designed by combining the native amino acid synthetic pathways with a 2-keto acid decarboxylase from Lactococcus lactis and alcohol dehydrogenase from S. cerevisiae [ 143 ]. The 2-keto acid intermediates in amino acid biosynthesis pathways were redirected from amino acid production to alcohol production, enabling production of 3-methyl-1-pentanol. This pathway was then expanded for production of unnatural alcohols by rational redesign of two enzymes, with the resulting biocatalysts having the ability to synthesize various unnatural alcohols ranging in length from five to eight carbons [ 144 ]. 4.4. Engineering Tolerance to Inhibitory Compounds As our repertoire of biologically-produced compounds increases, tolerance to high product titers becomes more important. Biofuels, such as ethanol and butanol, can inhibit biocatalyst growth, and therefore the tolerance of the biocatalyst needs to be improved [ 145 – 147 ]. As described above, our goal is to use lignocellulosic biomass as a substrate for production of commodity fuels and chemicals. Unfortunately, the processes used to convert biomass to soluble sugars also produce a mixture of minor products, such as furfural and acetic acid, that inhibit biocatalyst metabolism [ 148 ]. Although most of these inhibitors could be removed by detoxification [ 149 ], this additional process would increase operational cost. It is, thus, desirable to obtain microorganisms that are tolerant to these inhibitors and can directly ferment hemicellulose hydrolysate. One approach to increasing tolerance is to understand the mechanism of inhibition. Transcriptome analysis has been used to probe the response to ethanol [ 145 , 150 ], furfural [ 151 ], and butanol [ 147 ]. Another approach is to use directed evolution, as highlighted by the following example. Ethanologenic E. coli strain LY180 (a derivative of LY168 with restored lactose utilization and integration of an endoglucanase, and cellobiose utilization) was used as the chassis to select for furfural resistance through evolution [ 148 ]. The evolved strain, EMFR9, had significantly increased furfural resistance. Reverse engineering efforts, including transcriptome analysis, attributed furfural resistance to the silencing expression of several oxidoreductases. These oxidoreductases use NADPH for furfural reduction, depleting the available pools for biosynthesis. Thus furfural-mediated growth inhibition can be attributed to NADPH depletion [ 148 ], an insight that can be applied to other biocatalyst design projects."
} | 5,651 |
38145361 | PMC10853732 | pmc | 3,717 | {
"abstract": "Abstract To efficiently process the massive amount of sensor data, it is demanding to develop a new paradigm. Inspired by neurobiological systems, an infrared near‐senor reservoir computing (RC) system, consisting of infrared sensors and memristors based on single‐crystalline LiTaO 3 and LiNbO 3 (LN) thin film respectively, is demonstrated. The analog memristor is used as a reservoir in the RC system to process sensor signals with spatiotemporal characteristics. LN crystal structure stacked with oxygen octahedra provides favorable conditions for reliable Mott variable‐range hopping conduction, which provides the memristor with tens of thousands of reservoir states within a large dynamic range. With the characteristics, the analog sensor signals with high data fidelity can be directly fed to the memristive reservoir, and the spatiotemporal features can be separated and mapped. The system demonstrated a dynamic gesture perception task, achieving an accuracy of 99.6%, which highlights the great application potential of the memristor in signal sensor processing and will advance the application of artificial intelligence in sensor systems. Crystal ion slicing techniques are used to fabricate a single‐crystalline thin film for both the memristor and sensor, which opens up the possibility of realizing monolithic integration of a memristor‐based near‐sensor computing system.",
"conclusion": "3 Conclusion In conclusion, a novel infrared near‐senor RC system composed of pyroelectric infrared sensors based on single‐crystalline LT thin film and memristors based on single‐crystalline LN thin film has been proposed and demonstrated in this work. In order to process infrared sensor signals with spatiotemporal characteristics efficiently, the analog LN memristor has been used as a physical reservoir to realize RC. Thanks to the stable oxygen octahedral crystal structure of LN single crystal, reliable Mott‐VRH transport dominates the conduction mechanism, which guarantees excellent properties for high‐dimensional mapping of the infrared sensor signal. The proposed reservoir based on LN memristor exhibits tunably nonlinear STM characteristics, an extremely large dynamic range (with the G max /G min ratio of 1891.78), tens of thousands of finely spaced reservoir states (20 000 states), and good separation properties. As a result, the LN reservoir has sufficient space and margin for mapping and separating the raw analog sensor signals with multiple spatiotemporal features. Finally, a dynamic gesture perception task with spatiotemporal feature fusion was demonstrated based on the robust memristor‐based infrared near‐senor RC system. In this work, the memristor‐based computation of the sensor signal can remain in the analog domain, which can further reduce energy consumption and latency by eliminating the need for conversion to and from digital signal. The proof of concept highlights the great potential of memristor in the highly efficient signal sensor processing sensory system in the future, which will pave the new way for the application of AI in sensor systems with high sensor signal processing ability. The CIS technique for the fabrication of versatile single‐crystalline thin films points out a new direction to explore integrated multifunctional near‐sensor computing systems.",
"introduction": "1 Introduction With the development of smart terminals and the Internet of Things, ubiquitous sensors in our daily life, are dramatically growing in both their number and rate of generating sensor data. [ \n \n 1 \n , \n 2 \n \n ] In conventional sensor signal processing systems, such as data‐intensive image processing tasks ( Figure \n 1 a ), the large amounts of image data detected by optical sensor terminals, including redundant background information or noises, must be filtered and then converted to digital data via analog‐digital‐converters (ADCs) and stored in memory, then transferred to processing units or cloud‐based computing systems for adapting the Von Neumann computing architecture. [ \n \n 3 \n , \n 4 \n \n ] Due to the physical separation of memory and computing unit (i.e., Von Neumann bottleneck), [ \n \n 5 \n \n ] high energy consumption and high latency are inevitable. In addition, sensor signals detected from the real world usually have temporal features. However, it is difficult for some feedforward neural networks, such as convolutional neural networks (CNNs), to handle temporal tasks. [ \n \n 6 \n \n ] These limitations hinder the further development of energy‐efficient and low‐latency sensor signal processing systems. [ \n \n 7 \n , \n 8 \n \n ] \n Figure 1 a) Schematic of traditional image processing architecture, realized by discrete optical sensor terminal with auxiliary modules (such as signal filters and ADCs), memory, and processing unit. Raw analog sensor signals are first filtered and converted to digital signals that are stored in memory. Processing units load data from memory and then transmit outputs back to memory for storage. b) Schematic of the human visual nervous system comprising the retina, optical nerve, and visual cortex in the human brain. It can process image signals with the feature of massively parallel in‐memory computation. However, the human visual system can only see objects in the visible spectrum and cannot see objects in non‐visible spectra, such as in the dark. c) Proposed infrared near‐senor RC system based on LT‐based infrared sensor array and LN‐based memristor array. The raw analog sensor signals of dynamic gestures collected by LT‐based infrared sensors are directly input to the LN reservoirs, and then product abundant reservoir states for gesture recognition realized by a software readout. In human sensory systems, the nervous system composed of trillions of neurons and synapses can process sensory information efficiently. [ \n \n 9 \n \n ] For example, in the human visual system (Figure 1b ), objects are imaged in the retina and send nerve impulses through the optic nerve to the cerebral cortex, where dynamic visual information is processed in a highly parallel and energy‐efficient manner. [ \n \n 10 \n \n ] Inspired by neurobiological systems, neuromorphic computing based on novel devices (including memristors) has emerged to break the Von Neumann bottleneck. [ \n \n 11 \n \n ] Analog‐type memristors with abundant nonlinear dynamics can store information in the form of multilevel conductance states and process sensor signals in an analog fashion. [ \n \n 12 \n , \n 13 \n \n ] Hence, by combining the analog memristor with the sensor, bio‐inspired artificial sensory systems with energy‐efficient and low‐latency sensor signal processing ability can be constructed. [ \n \n 14 \n , \n 15 \n , \n 16 \n \n ] \n As for the neural network, compared with some feedforward neural networks, recursive neural networks (RNNs) with cyclic connections offer an improved ability to process temporal data. More importantly, based on a specialized variant of RNN, memristor‐based reservoir computing (RC) systems recently have been proposed for processing various temporal tasks, such as spoken‐digit recognition [ \n \n 17 \n \n ] and time‐series forecasting. [ \n \n 18 \n , \n 19 \n , \n 20 \n \n ] Compared with the common RNNs, such an RC computational model, which only needs to train the readout layer of the network, can process the temporal data with a low computational cost and latency. [ \n \n 21 \n , \n 22 \n \n ] Therefore, it is promising to use a memristor as a physical reservoir to process sensor signals with low energy consumption and latency and to construct highly efficient artificial sensory systems. [ \n \n 23 \n , \n 24 \n \n ] \n However, as far as we know, the wavelength of reported RC‐based artificial vision systems was mainly concentrated on the visible spectrum [ \n \n 25 \n \n ] and ultraviolet spectrum, [ \n \n 19 \n , \n 24 \n , \n 26 \n \n ] and there are almost no reports on infrared near‐sensor computing systems, although infrared detection has shown irreplaceable advantages in the field of flame detection, [ \n \n 27 \n \n ] thermal imaging [ \n \n 28 \n \n ] and dynamic target recognition. [ \n \n 29 \n \n ] The main reasons that hinder the development of infrared near‐sensor systems are the drawbacks of conventional infrared detectors, such as large volume and low compatibility with the fabrication process of memristor. [ \n \n 30 \n , \n 31 \n \n ] These limitations are adverse to the integration of a compact system and need extra efforts and costs to realize sensor signal tight coupling. [ \n \n 32 \n \n ] \n In addition, for a memristive RC system, the reservoir (i.e., memristor) is not only expected to possess a nonlinear short‐term memory (STM) effect but also desired to have a large dynamic space with rich reservoir states (i.e., intermediate conductance states) to realize high‐dimensional mapping of input signal. [ \n \n 17 \n , \n 33 \n , \n 34 \n \n ] Unfortunately, those existing emerging memristors are generally not satisfactory to meet the requirements of large dynamic space and rich reservoir states simultaneously. [ \n \n 13 \n , \n 35 \n \n ] Though some representative RC systems tried to solve this problem by taking advantage of device‐to‐device variances, [ \n \n 18 \n \n ] additional mask process, [ \n \n 17 \n \n ] or multiple reservoir layers, [ \n \n 26 \n \n ] these approaches not only increase system complexity but also are not easily migrated to other artificial neural networks. [ \n \n 6 \n , \n 36 \n \n ] Moreover, the separation property is also required to separate originally distinct inputs into different classes and be insensitive to inessential signals, such as noises. [ \n \n 34 \n \n ] It should be emphasized that the excellent separation property of the memristive reservoir is indispensable for processing sensor signals, because it ensures that the RC system can update weights according to target signals efficiently, and without interference from inessential signals such as noises (as shown in Figure S1 , Supporting Information). Meanwhile, due to intrinsic disorders and uncontrollable internal dynamics in the memristive layer, traditional memristors based on polycrystalline/amorphous oxide thin film suffered well‐marked cyclic variations, device‐to‐device variations, and fluctuations, which hinder the yield of sufficiently distinguishable intermediate states to achieving high‐efficient network and training separate sensor signal inputs. [ \n \n 12 \n , \n 13 \n \n ] \n In this work, we designed and proposed a novel infrared near‐senor RC system with pyroelectric infrared sensor array based on single‐crystalline LiTaO 3 (LT) thin film and memristors based on single‐crystalline LiNbO 3 (LN) thin film, demonstrating a dynamic gesture perception task (Figure 1c ). The pyroelectric infrared sensors based on single‐crystalline LT thin film and LN‐based memristors based on single‐crystalline LN thin film were prepared by the same manufacturing process, i.e., the crystal ion slicing (CIS) technique, which makes the fabrication process of two devices become compatible. Through defect engineering design, the LN memristor which exhibits reliable analog resistive switching and tunable STM characteristics was used as a dynamic reservoir. Thanks to the stable oxygen octahedral crystal structure of LN single crystal, the electron hopping process was carried out stably under electric field stimulation, yielding 20 000 reservoir states within a large dynamic range of 1891.78. By statistical analysis, the abundant reservoir states with tiny fluctuations allow sufficient margin to separate raw analog sensor signals and can effectively map the spatiotemporal features of original data. As a result, we demonstrated a dynamic gesture perception task with spatiotemporal feature fusion based on the infrared near‐senor RC system.",
"discussion": "2 Results and Discussion 2.1 Analog Resistive Switching Characteristics of Memristor \n Figure \n 2 a illustrates the schematic of the fabrication process of a memristor based on a single‐crystalline LN thin film (LN memristor). At the beginning, a multiple metal layer of Cr (10 nm)/Pt (100 nm)/Cr (30 nm) was sputtering on the He + ‐implanted single‐crystalline LN wafer to act as the bottom electrode. Then the wafer was further bonded with another pristine LN wafer through SiO 2 adhesion layer. After that, the single‐crystalline LN layer split from the single‐crystalline LN wafer, and a single‐crystalline LN thin film was obtained. After annealing and finer chemical mechanical polishing (CMP), the single‐crystalline LN thin film was further irradiated by Ar + beam with an irradiated angle of 0° ( θ = 0° ) to reduce the thickness from 300 to 30 nm. Finally, the fabricated memristive device with a structure of Au/LN/Cr/Pt/Cr (see inset of Figure 2a ) was formed after the Au top electrode with a diameter of 200 µm was fabricated by sputtering. For more details about the fabrication process see Figure S2 (Supporting Information) and Experimental Section. Figure 2 Analog resistive switching characteristics of LN memristor. a) Schematic of the key fabrication process of the memristor based on single‐crystalline LN thin film, including CIS and Ar + beam irradiation. Inset: the cross‐section diagram of the device with a structure of Au/LN/Cr/Pt/Cr. b) Cross‐sectional TEM image of the LN memristor with a vertical Au/LN/Cr/Pt/Cr structure. Scale bar, 50 nm. c) High‐resolution TEM image of the region near the Au/irradiated LN interface (red dotted box in (b)) with the scale bar of 2 nm. d) The corresponding fast Fourier transform patterns of Region 1 and Region 2. e) Typical electroforming process of the memristor. Inset: the statistical analysis of the electroforming voltages of 30 different devices. f) I–V curves of 300 cycles. Statistical analysis on the LRS g) and HRS h) of the memristor over 300 cycles. The variation (∆) is the ratio of the standard deviation to the mean (δ/µ). Values of ln ρ as a function of T \n −1/4 for different applied voltages under LRS i) and HRS j), respectively. k) Schematic diagrams of energy band alignment of LRS and HRS, respectively. The circles and light black balls represent trap sites and electrons, respectively. The arrows represent the electron hopping process. Figure 2b displays the cross‐sectional transmission electron microscopy (TEM) image of the LN memristor. The memristor is configured in a vertical Au/LN/Cr/Pt/Cr structure, in which a LN thin film with a thickness of 30 nm is used as the resistive switching layer. In a high‐resolution TEM image, a uniform amorphous LN layer formed at the top electrode interface (Au/LN interface) by Ar + beam irradiation can be observed, compared with the microstructure of pristine single‐crystalline LN thin film shown in Figure S3 (Supporting Information). The thickness of the amorphous layer is ≈5 nm (Region 1). Single‐crystalline LN was observed in Region 2, which indicates a good single‐crystalline feature of the residual LN layer. The results of the fast Fourier transform shown in Figure 2d further verify that the amorphous LN layer is formed on the single‐crystalline LN thin film layer after Ar + irradiation. Based on systematic chemical composition analysis in our previous work, the previous results have already indicated that a large number of oxygen vacancy ( V \n o ) defects and suboxides of niobium elements were generated in the amorphous layer. [ \n \n 37 \n , \n 38 \n , \n 39 \n \n ] It also has been proven that Ar + irradiation is an effective defect engineering method to regulate the resistive switching behavior of memristors by modulating the V o \n s in the switching layer. [ \n \n 40 \n , \n 41 \n \n ] \n Figure 2e presents the typical electroforming process of the LN memristor. In addition, Figure 2e also displays the distribution of electroforming voltage of 30 different memristor devices. A tight voltage distribution (from 2.6 to 3.3 V) and low device‐to‐device variance (down to 4.06%) further confirm the device uniformity. After electroforming, the device shows extremely uniform analog resistive switching behavior over 300 cycles, which is illustrated in Figure 2f . The voltage sweeping sequence for the I–V measurements is 0 V → maximum of positive voltage (+ V \n max ) → 0 V → maximum of negative voltage (− V \n max ) → 0 V. Figure 2g,h plot the statistical distributions of low resistance state (LRS) and high resistance state (HRS) of the device. The normal Gaussian distributions with ultra‐small standard deviations (δ), i.e., δ = 0.091 µS for LRS and δ = 0.0013 µS for HRS, respectively. The mean values (µ) of LRS and HRS are 4.23 and 0.047 µS, respectively. A quantitative analysis of the cycle‐to‐cycle variances (Δ = δ/µ) shows a value of 2.15% and 2.64% for LRS and HRS, verifying the excellently uniform resistive switching of the LN memristor. In order to reveal the switching mechanism of the LN memristor, temperature‐dependent I–V curves at LRS and HRS were measured within a temperature range of 160–315 K (Figure S4 , Supporting Information). By fitting with possible transport models in oxides, the I–V relationships are in good agreement with the trap‐controlled space‐charge‐limited current (SCLC) mechanism [ \n \n 38 \n , \n 42 \n , \n 43 \n \n ] (Figure S5 , Supporting Information). But in fact, trap‐controlled SCLC is a simplified physical mechanism, it is the contributor of Ohmic, Poole–Frenkel, and hopping conduction when these conductions reach certain magnitudes. [ \n \n 44 \n \n ] Through further evaluation, the I–V relationship under HRS and LRS is well‐fitted with the Mott–VRH model as a linear dependence of ln ρ on T \n −1/4 , where ρ is the resistivity and T is the temperature (Figure 2i,j ). The Mott‐VRH conduction is expressed as below:\n \n (1) \n ρ T = ρ 0 e x p T 0 T 1 / 4 \n where T \n 0 is the characteristic temperature, ρ 0 is a resistivity parameter. Considering an amorphous layer rich in V o \n defects introduced by Ar + beam irradiation, trap levels are distributed in the bandgap of LN crystal (≈4 eV). As shown in the energy band diagram, the trapped electrons undergo a hopping process under an electric field (Figure 1k ). Compared with the hopping process under LRS (160–315 K), the trapped electrons under HRS need to acquire sufficient energy for the hopping transport at a higher temperature (up to 235 K), which might be due to field‐driven differential distributions of V o \n s. [ \n \n 44 \n \n ] This phenomenon can be demonstrated by analyzing the density of states (DOS) at the Fermi level ( N ( E \n F )) and average hopping energy (Δhop). In the Mott–VRH regime, the N ( E \n F ) is related to the characteristic temperature T \n 0 as [ \n \n 45 \n , \n 46 \n \n ] \n \n (2) \n N E F = 24 π k B T 0 ζ 3 \n \n The Δhop is simply as [ \n \n 45 \n , \n 46 \n \n ] \n \n (3) \n Δ hop = 1 4 k B T T 0 T 1 / 4 \n where k \n B is Boltzmann constant. ζ is localization length, which is a multiple of the minimum distance ( a ) between the hopping sites. Here, the minimum hopping sites can be attributed to V \n o s in LN, and a is ≈2.72 Å which corresponds to interatomic distances of O─O. [ \n \n 47 \n , \n 48 \n \n ] For our LN‐based device, the value of Δhop is evaluated at the representative temperature of 300 K. As a result, the DOS at Fermi level ( N (E F ) ≈8.32 × 10 16 eV −1 cm −3 ) under LRS is higher over an order of magnitude than that of HRS ( N (E F ) ≈5.20 × 10 15 eV −1 cm −3 ). Besides, the average hopping energy (Δ hop ) with a value of 0.26 eV at 300 K guarantees electron hopping transport under LRS ( hop (LRS) = 0.26 eV), whereas higher hopping energy is required for HRS (Δ hop (HRS) = 0.53 eV). Overall, the conduction mechanism in our LN memristor is dominated by Mott–VRH conduction, and the V \n o defects in highly‐ordered oxygen octahedral of single‐crystalline LN thin film provide trap sites for electron hopping, which supports the reliable analog resistive switching characteristics of the device. 2.2 Dynamic Response Properties of LN Memristor for Reservoir Computing In an RC system, the reservoir properties of the memristor directly determine the mapping quality of temporal inputs and significantly affect the system performance. Figure \n 3 a plots the transient response of the memristor when stimulated by a voltage pulse with an amplitude of 2.5 V (width is 2 ms). After ≈7 µs, the device conductance reaches a peak and then enters a spontaneous decay process as pulse removal. The conductance changes nonlinearly, and the spontaneous decay shows the STM effect. To further test the nonlinear STM characteristics of the LN reservoir, the amplitude‐dependent dynamic responses of the memristor at the initial state and after being stimulated by a voltage pulse were measured, as shown in Figure 3b . Obviously, the response conductance ( ΔG ) after the removal of the pulse stimulus is defined as the difference between maximum device conductance and initial value, which reveals an enhanced STM effect as the pulse amplitude increases. Applying voltage pulses of different duration times can also achieve tunably nonlinear STM properties (Figure S6 , Supporting Information). Besides, a negative voltage pulse strategy is used for quickly resetting the conductance to the initial state to ensure the device's repeatable operation (Figure S7 , Supporting Information). The nonlinear dynamics of the memristor can be further revealed by consecutive pulse stimuli. Typical short‐term plasticity of biological synapses, namely paired‐pulse facilitation (PPF), demonstrates the ability to process continuous temporal information. Hence, paired pulses were applied to the device and its dynamic response is presented in Figure 3c . During operation, the pulse is set to 2.5 V, and the pulse width is 2 ms. The interval of paired pulses is 15 ms. Taking ΔG \n 1 as the response conductance of the first stimulus and ΔG \n 2 of the second one, the corresponding ΔG \n 2 is obviously higher than ΔG \n 1 , indicating the facilitation effect of synaptic plasticity. In terms of a ratio of ΔG \n 2 to ΔG \n 1 , i.e., the PPF index, it is calculated as 188.6% in this case. This indicates that the LN memristor has the capability to realize nonlinear mapping of input signals. Figure 3 Dynamic responses of LN memristor for reservoir computing. a) Transient response of the LN memristor when stimulated by a voltage pulse (amplitude: 2.5 V, width: 2 ms). A read voltage pulse train (0.5 V, 50 µs) is used to study the device current before applying the stimulation and after removing the stimulation. The inset is a spontaneous decay process, which shows a nonlinear STM effect. b) Amplitude‐dependent dynamic response at the initial state and after being stimulated by a voltage pulse with different amplitudes (2.5/2.7/2.9 V). The response conductance ( ΔG ) after removing the stimulus is defined as the difference between maximum device conductance and initial value. c) PPF effect of the LN memristor. ΔG \n 1 and ΔG \n 2 denote the response conductance of the first stimulus and the second one in paired pulses, respectively. d) Dependence of PPF indexes on pulse interval (from 15 to 1000 ms), fitted by a double‐exponential function. Error bars: the standard deviation calculated from ten measurements. e) Dynamic reservoir states of the LN memristor are stimulated by a continuous voltage pulses sequence consisting of 25 voltage pulses with an amplitude of 2.5 V, width of 2 ms, and interval of 2 ms. The reservoir states are recorded by a read pulse after each write pulse is removed. Defining the conductance value measured after the first stimulus as G \n min and the conductance value measured after the last pulse as G \n max . f) Amplitude‐dependent reservoir states changed with 300 successive pulse stimuli. The ratio of G \n max to G \n min ( G \n max / G \n min ) is adopted to estimate the dynamic range of reservoir. g) Cycling tests of the LN reservoir show excellent cycle‐to‐cycle repeatability during 300 successive pulse stimuli (variation: down to 1.64%). Inset is the variation of 300 reservoir states during ten cycles. The variation is calculated as the standard deviation‐to‐mean. h) The statistical results of the reservoir states in ten cycles for the initial 25 pulse stimuli and the final 25 pulse stimuli are depicted by a box error diagram and normal distribution. i) Tens of thousands of finely spaced reservoir states with a large dynamic range of 1891.78 obtained by a voltage pulse sequence consisting of 20 000 successive pulses (2.5 V, 2 ms). To demonstrate the dependence of PPF on pulse interval, we then tested and plotted the PPF index under various intervals (Figure 3d ). The results show that the PPF index decreases exponentially with increasing pulse intervals. The PPF index versus pulse interval can be fitted by a double‐exponential function as below:\n \n (4) \n PPF index = A 0 + C 1 × e x p − Δ t τ 1 + C 2 × e x p − Δ t τ 2 \n where A \n 0 is a preparameter, C \n 1 and C \n 2 are the facilitation factors and τ \n 1 and τ \n 2 with fitted values of 24.5 and 189.6 ms are the characteristic time constants related to relaxation times, respectively. As a result, a shorter pulse interval induces a higher PPF index or facilitation effect, making the STM generated by different inputs more distinguishable. Therefore, a continuous sequence consisting of 25 pulses with an interval of 2 ms was utilized to evaluate the potentiation process of the reservoir states (Figure 3e ). Each reservoir state is recorded with a read voltage pulse with an amplitude of 0.5 V after each stimulus is removed. Results show that the reservoir state increases almost linearly with the increase in the number of pulse stimuli. Defining the conductance value of the first stimulus as G \n min and the conductance value of the last pulse as G \n max , the ratio of G \n max to G \n min ( G \n max / G \n min ) can be adopted to estimate the dynamic range of the reservoir. Figure 3f presents the amplitude‐dependent reservoir states changed with 300 successive pulse stimuli. The results reveal that the dynamic range exceeds two orders of magnitude and increases further with the pulse amplitude. It is worth noting that the adjustable reservoir states with tiny fluctuations are beneficial to distinguish inputs for efficient mapping. To further assess the impact of fluctuation and the uniformity of repeated operation, a cycling test consisting of 300 consecutive pulses was performed over ten cycles (Figure 3g ; Figure S8 , Supporting Information). After each cycle, the device is reset to its initial state (≈0.43 nA) by using a negative pulse strategy. Results show that the LN reservoir has excellent cycle‐to‐cycle repeatability, which is characterized by consistent linearity, close dynamic range (Figure S8 , Supporting Information), and ultra‐low cyclic variation of reservoir states (down to 1.64%). A more detailed statistical analysis of reservoir states is shown in Figure 3h (1–25th pulses, 276–300th pulses). The small deviation and normal distribution of every state indicate the LN reservoir is capable of separating distinct inputs into different classes. Interestingly, the deviation of state distribution increases gradually with the increase in the number of pulse stimuli, which may be due to the accumulation effect of noise arising from intrinsic dynamics. In order to further demonstrate the capacity of LN reservoir, a voltage pulse sequence containing 20 000 successive pulses (2.5 V, 2 ms) is applied to the LN reservoir (Figure 3i ). The obtained tens of thousands of reservoir states with an extremely large dynamic range of 1891.78 provide rich space to reflect the spatiotemporal feature information on different inputs. Meanwhile, 20 000 finely spaced reservoir states allow sufficient margins to separate each other (Figure S9 , Supporting Information). In terms of energy consumption, it can be estimated by per pulse operation of the LN reservoir and calculated to be 12 picojoules (12 pJ = 2.5 V × 2.4 nA × 2 ms), indicating that the reservoir possesses energy‐efficient characteristics. Overall, the developed LN memristor reservoir exhibits excellent properties, including abundant nonlinear dynamics, STM effect, extremely large dynamic space, and tens of thousands of reservoir states, which guarantees the development of high‐performance RC systems for executing temporal‐dependent recognition tasks. 2.3 An Infrared Near‐Sensor RC System for Dynamic Gesture Perception To validate the ability of nonlinear mapping of real‐world sensor signals on LN reservoir, an infrared near‐sensor RC system based on LT pyroelectric infrared sensors and LN reservoirs was designed and demonstrated for a dynamic gesture perception task. The LT single crystal is a kind of multifunctional material with a similar structure to LN single crystal. [ \n \n 49 \n \n ] It also belongs to a deformed perovskite structure stacked with oxygen octahedra and is one of the most promising materials for infrared detection due to its extremely high pyroelectric coefficient (up to 230 µ C cm −2 K). [ \n \n 50 \n \n ] The pyroelectric output current ( \n i \n \n \n p \n ) can be expressed as i p = p A s Δ T p d t , [ \n \n 51 \n \n ] where \n p \n is the pyroelectric coefficient, \n A \n \n \n s \n is the sensing area, and Δ T p d t represents temperature alteration ratio. It can be seen that the \n i \n \n \n p \n strongly depends on the \n p \n and the Δ T p d t . Thus, developing high‐quality single‐crystalline LT thin film is the guarantee to achieve a high pyroelectric coefficient and improve the sensitivity of temperature change. Here, we prepared a single‐crystalline LT thin film for the infrared sensor by the CIS technique which is the same as that used to fabricate a single‐crystalline LN thin film for the memristor (Figure S10 , Supporting Information, inset of Figure \n 4 d , and Experimental Section). In our previous work, LT‐based pyroelectric infrared sensors have been designed and developed which show excellent pyroelectric properties. [ \n \n 52 \n , \n 53 \n \n ] For this work, the output current signals of the LT‐based infrared sensors are converted into analog voltage signals through a trans‐impedance amplifier (TIA) as the input signals of the memristive reservoir. Figure 4 Memristor‐based infrared near‐sensor RC system for dynamic gesture perception. a) A conceptual schematic of the memristor‐based infrared near‐sensor RC system. The system consists of a 16 × 1 infrared sensor array based on single‐crystalline LT thin film, 16 memristors, and the software readout layer. The gesture moves parallel to the axis of sensing window at the exact speed (0.75 cm −1 s) and varying detection distances ( D ). The moving process is split into 20 time steps. The sensor signals of three dynamic gestures (“Scissor,” “Rock,” and “Paper”) are recorded by an infrared sensor array mounted in a testing box with a sensing window. These raw analog sensor signals are then directly input into the LN memristor reservoir. Finally, the reservoir states collected by a customized FPGA controller are fed to the software readout layer for the recognition task. During performed dynamic gesture perception task, the system is completely in the dark environment to achieve infrared signal detection. b) Schematic of RC systems in which dynamic reservoir maps the inputs only with a single temporal feature (left column) or with spatiotemporal feature fusion (right column). c) Real‐time responses of the infrared sensor measured under a detection distance of 1.5 and 4 cm ( D = 1.5 cm, D = 4 cm). d) Distance‐dependent peak values of the sensor signals under different detection distances ( D = 1.5/2/2.5/3/3.5/4/4.5/6/8 cm). The standard deviation ( δ ) is calculated from 16 LT‐based infrared sensors. Inset is the cross‐section diagram of the infrared sensor with a structure of Au/LT/Cr/Au/Cr. e) A noise test of the infrared near‐sensor RC system in a real working environment. f) Noise spectrum of the raw sensor signals in (e) was analyzed through the Fourier transform. g) Output reservoir states obtained from memristors in Channel five after moving the “Scissor” gesture with a detection distance of 4 cm ( D = 4 cm). The top column is the raw sensor signals collected by an infrared sensor. h) Output reservoir states are stimulated by corresponding digital voltage pulses as a reference. The top column is a digital voltage pulse sequence where voltage pulses are generated by Keithley 4200A‐SCS with a pulse measure unit (PMU). i) The 320 reservoir states (16 channels × 20‐time steps) of three dynamic gestures (“Scissor,” “Rock,” “Paper”). j) Evolution of recognition accuracy of the three dynamic gestures with a detection distance of 4 cm ( D = 4 cm). k) Recognition accuracy of the three dynamic gestures moving at different detection distances ( D = 3.5/4/4.5 cm). The abbreviation symbols (S‐D3.5, R‐D3.5, P‐D3.5, S‐D4.0, R‐D4.0, P‐D4.0, S‐D4.5, R‐D4.5, P‐D4.5) denote the nine possible recognition results, respectively. Taking “S‐D3.5″ as an example, it represents “Scissor” gesture moving at a detection distance of 3.5 cm ( D = 3.5 cm). l) Schematic diagram of the different portions of raw sensor signals in the whole dynamic gesture process (Top column). The gesture moves through 20 time steps which correspond to the whole dynamic gesture process (100%). Confusion matrices of the predicted accuracy using 25% and 75% of the entire dynamic gesture process with different detection distances ( D = 3.5/4/4.5 cm) (Bottom column). As shown in Figure 4a , the infrared near‐sensor RC system consists of a 16 × 1 infrared sensor array based on single‐crystalline LT thin film, 16 memristors based on single‐crystalline LN thin film, and the software readout layer (a 16 × 9 single‐layer perceptron). The sensor array is directly connected to memristive reservoirs to achieve tight coupling of signals through 16 channels. When the hand performs a certain gesture, the raw sensor signals collected from the LT‐based infrared sensor array are fed into the LN reservoirs and mapped to a high‐dimensional system. Since the infrared signals of moving gestures are recorded by the LT‐based infrared sensors in real time the raw sensor signals in different situations are time series data. Hence, the reservoir states output from LN reservoirs possess temporal characteristics. In this demonstration, the sensor signals of three classes of moving gestures (“Scissor,” “Rock,” and “Paper”) are recorded at a sampling frequency of 3.33 Hz. The LT‐based sensors are mounted in a testing box with a sensing window (see Figure S11 , Supporting Information and Experimental Section). Corresponding to the chopper sampling frequency (3.33 Hz), gestures move parallel to the axis of the sensing window at the exact speed (0.75 cm −1 s) and are completed in 20 time steps. The sampling frequency of 3.33 Hz means that the interval of the sensor pulse sequence is 300 ms. Under this sampling strategy, the LN reservoir has a facilitation effect, that is, short‐term memory characteristic (corresponding PPF index as shown in Figure 2d ). After high‐dimensional mapping by the LN reservoir, the dynamic reservoir states are collected by a customized Field‐programmable gate array (FPGA) controller to serve as the input of the readout layer. Figure S12 (Supporting Information) illustrates the test board and operational flow. More importantly, since the detection distance of pyroelectric infrared sensors affects the temperature alteration ratio, sensor signals will vary with the distance between gestures and the sensing window ( D ). Therefore, the analog signals of dynamic gestures in different situations recorded by LT‐based infrared sensors contain temporal and spatial characteristics. In other reported memristive RC systems, the memristive reservoirs mapped the inputs only with temporal features to complete recognition or prediction tasks (Figure 4b (left column)), such as handwriting recognition, [ \n \n 33 \n \n ] speech recognition, [ \n \n 17 \n \n ] and chaotic system prediction. [ \n \n 18 \n \n ] For this task, using an LN reservoir physically implements the fusion processing of temporal features and spatial features of the analog sensor signals. The large dynamic range and rich reservoir states of LN reservoir provide sufficient space for the implementation of spatiotemporal feature fusion (Figure 4b (right column)). First, the properties of LT‐based infrared sensors are evaluated. As plotted in Figure 4c , the real‐time responses of the sensor are measured under a detection distance of 1.5 and 4 cm ( \n D \n = 1.5 cm, \n D \n = 4 cm). The LT‐based infrared sensor can detect targets effectively and output continuous analog signals. It should be noted that the larger peak of the initial sensor signal is because the target induces a higher temperature alteration ratio ( Δ T p d t ) when it just enters the detection field and then recovers after ≈2 pulses. In addition, the sensor exhibits fast response times of ≈15 and 14 ms when the sensing window opens up or turns off, respectively (Figure S13 , Supporting Information). Figure 4d shows the distance‐dependent peaks of sensor signals ( \n D \n = 1.5/2/2.5/3/3.5/4/4.5/6/8 cm). Obviously, a short detection distance (smaller \n D \n ) leads to a higher output signal. In an actual environment, the sensor will be disturbed by various noises, and it will also lead to signal fluctuation. When these raw sensor signals as inputs feed to reservoir, the insensitivity of reservoir to these unessential fluctuations or noises is an important index of separation property for classifying similar inputs into the same class. [ \n \n 34 \n \n ] Hence, a noise test of the near‐sensor RC system in a real‐working environment was carried out. In order to further assess the robustness of the system, we added interference of human activity (including people walking around and slight impact of the test board) during the experiment. The synchronous recording of sensor signals and reservoir outputs is shown in Figure 4e . Despite experiencing significant ambient noises (numerous signal spikes), the LN reservoir consistently produces stable output. Through noise spectrum analysis, in addition to ambient noise, Flicker noise (or 1 / f noise) and white noise can also be detected by the sensitive infrared LT‐based sensor, whereas LN reservoir can remain insensitive to these noises and remain stable (Figure 4f ). The trapped electrons in LN crystals require sufficient energy to jump over the trap barrier into the conduction band or from one trap site to another to achieve Mott–VRH conduction ( ∆ \n hop (LRS) = 0.26 eV, ∆ \n hop (HRS) = 0.53 eV). Therefore, we believe that the noises detected by the infrared sensor are inadequate to excite trapped electrons to complete Mott‐VRH transport so that the LN reservoir is insensitive to noises. Such a robust near‐sensor RC system significantly improves energy efficiency by self‐masking invalid raw data (e.g., various noises) in the noisy analog domain, which is especially important capacity in data‐intensive applications. [ \n \n 8 \n \n ] \n Based on above analysis, the dynamic gesture perception task is implemented on the infrared near‐sensor RC system. Each gesture with a specific detection distance ( \n D \n = 3.5/4/4.5 cm) moves 200 times to generate the dataset, and the dataset is then divided into two groups: 120 randomly selected samples for training and the remaining 80 samples for testing. The total dataset consists of 1800 groups, including nine situations of three gestures with three detection distances. Take the “scissors” gesture with a detection distance of 4 cm ( \n D \n = 4 cm) as an example, the reservoir response to sensor signal trains in Channel 5 and Channel 13 are shown in Figure 4g and Figure S14 (Supporting Information), respectively. Due to nonlinear STM dynamics, the LN reservoir has the ability to distinguish the sensor signal sequences with different spatiotemporal features. During high‐dimensional mapping, the reservoir separates the valuable input signals into different classes, while it is insensitive to inessential noises, demonstrating good separation capacity. More importantly, compared with digital input (Figure 4h ; Figure S14 , Supporting Information), the analog sensor signals with high data fidelity directly feed to the LN reservoir can reflect the spatiotemporal features of raw data at full steam, whereas the large dynamic range with abundant reservoir states allows sufficient margin to distinguish each other. For every gesture movement, the 320 reservoir states (16 channels × 20‐time steps) of each gesture are implemented by LN reservoirs (Figure 4i ). Intriguingly, the spatiotemporal information of sensor signal inputs is synchronously stored in the state matrix at each time step due to the STM property of memristive reservoir. Hence, the entire motion trajectory of dynamic gestures can be captured and recorded by this infrared near‐sensor RC system. [ \n \n 54 \n \n ] \n In order to evaluate the recognition performance of the system, a commonly simple learning algorithm, i.e., single‐layer perceptron (SLP), that can implement the readout functions, is used in our demonstration. Evidently, the recognition accuracy of dynamic gestures ( \n D \n = 4 cm) reaches 100% after a few iterations (Figure 4j , 181 iterations for training and 133 iterations for testing). For a more complex perception task, the dynamic gestures move at different detection distances, characterized by the fusion of spatiotemporal features, which can also be recognized by the system with an average accuracy of 99.6% (Figure 4k ). In addition, as the reservoir possesses the capability of mapping spatiotemporal features of different inputs, the near‐sensor RC system can be used to predict dynamic gestures before they are completed. [ \n \n 18 \n \n ] As illustrated in Figure 4i (top column), the gesture moves through 20 time steps which correspond to the whole sequence (100%). When 25% or 75% portion of the dynamic gesture is recorded by LT‐based infrared sensors, LN reservoirs map the partial inputs and then output corresponding reservoir states to implement forecasting (Figure S15 , Supporting Information). Figure 4i displays the predicted accuracy of 25% and 75% portion for nine situations (three dynamic gestures with different detection distances), and the average recognition rates are both over 99.1%. These results indicate the infrared near‐sensor RC system also has a good recognition capability for the dynamic targets even only with partial spatiotemporal information. Significantly, our infrared near‐sensor RC system directly receives raw analog sensor signals and produces analog reservoir states that are stored in the reservoir without any signal filter, ADC, memory buffering, and other auxiliary modules, which could considerably reduce the power consumption and complexity of the overall system. [ \n \n 34 \n , \n 54 \n \n ] In addition, the system requires only a few training resources, including a few datasets and training times, to achieve dynamic gesture recognition or prediction with spatiotemporal feature fusion. It is further verified that such near‐sensor RC architecture possesses energy‐efficient characteristics and is highly attractive for emerging applications, especially for energy‐efficient edge systems. [ \n \n 55 \n \n ] \n At last, we need to emphasize that CIS technique employed in this work is a potential thin film transfer technique. In this work, both the structure of infrared sensor and the structure of memristor are metal‐insulator‐metal structures, as illustrated in Figure S10 (Supporting Information) and Figure 2b . The fabrication processes of the memristor and infrared sensor are almost the same, as discussed in the Experimental Section and shown in Figure S2 (Supporting Information). Optimistically speaking, by monolithically integrating the infrared sensor with the analog LN memristor through a synchronously uniform CIS process, a compact and reconfigurable sensing‐memorizing‐computing microsystem can be achieved, which can significantly improve the integration density in the future."
} | 11,082 |
36716373 | PMC9963998 | pmc | 3,718 | {
"abstract": "Significance Acetogenic bacteria can fix approximately 20% of the atmosphere’s carbon and reduce C1 feedstocks, such as CO 2 or CO, to multicarbon compounds via the Wood–Ljungdahl pathway, thus playing a prominent role in the global carbon cycle. Using a CRISPR interference (CRISPRi) screen, we measured gene fitness at the genome level under heterotrophic and autotrophic growth conditions and demonstrated a strategy to increase the autotrophic growth rate on the basis of this dataset. Our findings can contribute to advancing the understanding of acetogenesis metabolism and further engineering a cell factory system for the sustainable production of value-added chemicals from industrial waste gases.",
"discussion": "Discussion In this study, we conducted a CRISPRi screen to identify essential genes and potential mutations for improved autotrophic growth in the acetogenic bacterium, E. limosum , that were then validated by testing the phenotypes of the individual candidates. CRISPRi screening has an advantage over gene knockouts in that it can perturb key metabolic pathways and measure gene fitness ( 42 ). In the present study, acetogenesis-related genes in the W–L pathway, hydrogenase, the Rnf complex, and ATP synthase were found to be essential under CO 2 -H 2 and syngas growth conditions at all passages (P1 to P3), except for formate dehydrogenase (ELIM_c2470–ELIM_c2472) ( Fig. 3 A ). Although sgRNAs targeting formate dehydrogenase gradually depleted under autotrophic growth conditions ( SI Appendix , Fig. S8 B ), they were not classified as essential genes ( Fig. 3 A ). Instead, these results indicated that another formate dehydrogenase operon (ELIM_c1991–ELIM_c1993) was induced a twofold increase under autotrophic growth conditions when compared to the glucose condition ( 43 ), which compensated for the enzymatic reaction. Although the required gene set for autotrophic growth has been identified in C. autoethanogenum ( 19 ), significant gaps remain in our understanding of the functions of other genes in heterotrophic and autotrophic acetogenesis. Under autotrophic growth conditions, the electrochemical sodium gradient across the cytoplasmic membrane is essential for cellular bioenergetics because the F 1 F 0 ATP synthase and Rnf complex strictly rely only on the sodium ion potential ( 31 , 44 ). Indeed, our CRISPRi screening revealed that the protein complex for membrane protein insertion/secretion and phosphatidic acid formation process for membrane lipid homeostasis were essential for autotrophic acetogenesis ( Fig. 4 A ). Furthermore, our data suggest that long acyl-ACP concentration during phosphatidic acid biosynthesis was insufficient under autotrophic growth conditions. Based on their negative feedback regulation, we hypothesized that ACC, FabK, and FabH activities were inhibited when long-chain acyl-ACP is accumulated under heterotrophic growth conditions, further suggesting that the fitness of these genes was relatively high compared to that of other genes during phosphatidic acid biosynthesis ( Fig. 4 C ). Conversely, genes encoding ACC, FabK, and FabH were essential for autotrophic growth, implying that they were not regulated by feedback inhibition. Optimal treatment of E. coli with cerulenin, an antibiotic that targets FabF, suppresses acyl-ACP elongation without inhibiting the initial condensing enzyme FabH, resulting in the decreased concentration of long-chain acyl-ACP ( 45 , 46 ). In response to cerulenin treatment, the synthesis of short acyl-ACPs (4 to 10 carbon) by FabH was significantly increased, indicating that the fatty acid biosynthesis process by FabH is promoted when the concentration of long acyl-ACP is low. Interestingly, our CRISPRi data also confirmed not only important membrane proteins such as the Rnf complex and F 1 F 0 ATP synthase but also that those containing a high number of TMs were not required for autotrophic acetogenesis. We hypothesized that inhibition of ion channels/transporters benefited autotrophic growth by stabilizing the TM electrochemical ion gradient for efficient cellular bioenergetics and thus investigated the fitness of membrane proteins according to the number of TMs ( Fig. 4 F ). According to a previous multiomics data analysis ( 9 ), TM proteins were translated with high efficiency under CO 2 -H 2 conditions in A. woodii; however, when the expression of proteins containing a high number of TM domains (9 > TMs) was suppressed in our system, the growth rate significantly increased under both CO 2 -H 2 and syngas growth conditions ( Fig. 4 E ). These results suggested a design principle for constructing a bacterial strain for improving autotrophic growth by maintaining a stable TM electrochemical ion gradient through the elimination of membrane protein expression. Most acetogenic bacteria exhibit high metabolic flexibility to utilize a variety of substrates and are therefore ubiquitously distributed in anoxic ecosystems. Unlike aerobic bacteria, which transfer electrons through NADH and unlimited oxygen, redox balancing is difficult and important in anaerobes because of the low redox potential of the electron acceptors. During substrate oxidation, the W–L pathway contributes to CO 2 fixation and redox balance with energy conservation under heterotrophic growth conditions for more ATP gain (e.g., 4.3 ATP/mol glucose in A. woodii ) than the conventional Embden–Meyerhof–Parnas pathway (2 mol ATP/mol glucose) ( 20 ). Based on transcriptional and translational data, we previously revealed that the W–L pathway contributes to CO 2 utilization and redox balancing with energy conservation via heterotrophic acetogenesis in E. limosum and A. woodii ( 9 ). CRISPRi screening also indicated that genes involved in the W–L pathway played an important role in heterotrophic growth under autotrophic conditions ( Fig. 3 A ). However, TIS results in C. autoethanogenum showed that the W–L pathway was not essential in heterotrophic conditions ( 19 ), suggesting that the role of the W–L pathway among acetogenic strains may be different during heterotrophic growth. CRISPRi data can also be used as a guide to enhance gas fermentation through strain engineering. Increasing gas–liquid mass transfer, optimizing medium and feed gas, and adding renewable electron sources have been attempted to improve productivity during gas fermentation, but meaningful enhancement of autotrophic growth rate has been achieved only through adaptive laboratory evolution (1.44-fold increase compared to the parental strain) under high CO concentration conditions ( 47 ). To identify gene candidates for enhancing autotrophic growth, we validated 12 knockdown strains and identified three repression clones ELIM_c2976 (1.98-fold increase), ELIM_c1795 (1.62-fold increase), and ELIM_c1868 (1.53-fold increase) that had significantly enhanced autotrophic growth rates ( P < 0.05, Student’s t test) when compared to the control strain ( Fig. 6 B ). When they were repressed simultaneously by the dCas12a-based CRISPRi system, it was possible to induce a fourfold increase in the autotrophic growth rate compared to the control ( P < 0.001, Student’s t test). Thus, our CRISPRi screening method was useful for elucidating the principles for engineering gas-fermenting acetogenic bacteria. Unexpectedly, the Rho factor encoded by ELIM_c1795 was a repression target for increasing the autotrophic growth rate. Rho-dependent transcriptional termination acts through the direct contact of RNA polymerase and Rho without a strong structure at the 3′-end. Unlike E. coli , where the rho gene is essential and about 50% of the transcription terminations are Rho dependent ( 48 ), the rho gene is not essential and does not seem to play an important role in E. limosum ( SI Appendix , Fig. S12 A ) similar to observations reported in B. subtilis ( 49 ). Interestingly, the repression of rho affected the expression of methyl-branch, carbonyl-branch, and ATP synthase genes that have an I-shaped transcript 3′-end positions (TEP; U-lacking TEP, presumably Rho dependent) and an L-shaped TEP (U-rich TEP, Rho independent) ( SI Appendix , Fig. S12 B and C ). Although the inhibition of rho gene expression led to a decrease in its RNA levels under CO 2 -H 2 conditions (log 2 fold changes > −0.77 and P adj < 0.001), it did not lead to a decrease in autotrophic growth ( Fig. 6 B ). The results suggest that inhibition of rho expression enhanced autotrophic acetogenesis indirectly through induction of overexpression of fatty acid biosynthesis genes. Moreover, recent discovery of TEPs in E. limosum using Term-seq indicated a decrease in the dissociation of the Rho RNA polymerase under autotrophic conditions, suggesting that RNA polymerase and ribosomal proteins stall near the 3′-end during acetogenesis ( 40 ). Suppression of the rho gene contributed to overall autotrophic growth improvement by facilitating the reuse of effective RNA polymerase and ribosomal proteins by inhibiting the inefficient Rho-dependent RNA termination. Our results also suggested that ATP consumption was reduced by suppressing Rho-dependent transcription termination under autotrophic growth conditions, which increased the autotrophic growth rate. In cluster I, 40.9% of genes were either transcription factors (TFs 18.2%; ELIM_c1740, ELIM_c1868, ELIM_c2379, and ELIM_c3084) or membrane proteins (22.7%; ELIM_c0077, ELIM_c0361, ELIM_c0539, ELIM_c0636, and ELIM_c1128); for example, the autotrophic growth rate was significantly increased (1.53-fold, P < 0.01, Mann–Whitney U test) during the repression of putative TF (ELIM_c1868). Furthermore, our data showed that membrane protein synthesis acted as a bottleneck for autotrophic growth. Therefore, our findings suggested that unknown genes regulated by putative TFs found in cluster I or membrane proteins could be candidates for knockdown/knockout to unlock autotrophic acetogenesis. Collectively, the genome-wide CRISPRi approach was successfully used to screen for genotypes underlying heterotrophic/autotrophic growth–linked phenotypes. In this study, we unraveled gene fitness and identified unknown genes associated with improved autotrophic acetogenesis, the essentiality of heterotrophic acetogenesis, and mutants with an autotrophic growth advantage. These findings assist in understanding acetogenic metabolism and provide a platform for large-scale genome engineering of acetogenic bacteria."
} | 2,622 |
40203116 | PMC11980855 | pmc | 3,719 | {
"abstract": "Soft Li-ion batteries, based on conventional organic electrolytes, face performance degradation challenges due to moisture penetration and safety concerns due to possible leakage of toxic fluorine compounds and flammable solvents under mechanical damage. We design a water-scarce hydrogel electrolyte with fluorine-free lithium salt to achieve wide electrochemical stability window (up to 3.11 volts) in ambient air without hermetic packaging while balancing high stretchability (1348%), ion conductivity (41 millisiemens per centimeter), and self-healing capabilities for mechanically and chemically safe stretchable Li-ion batteries. Molecular synergy between hydrophilicity and lithiophilicity of zwitterionic polymer backbone is revealed by molecular dynamics simulations. The battery exhibits capacity retention under harsh mechanical stresses—enduring stretching, twisting, folding, and multiple through-punctures by a needle—while self-healing from repeated through cuts by a razor blade. Stable ambient operation for 1 month over 500 charge-discharge cycles (average coulomb efficiency, 95%) is achieved. A prototype self-healing electronic system with embedded soft batteries demonstrates practical application as a durable embodied energy source.",
"introduction": "INTRODUCTION Safe and stretchable soft batteries are desirable for applications in wearable electronics, soft robots, and the Internet of Things ( 1 – 4 ). However, most commercial Li-ion batteries are hermetically sealed with non-stretchable packages to prevent (i) the penetration of moisture that degrades performances ( 5 ) and (ii) the leakage of toxic and flammable electrolytes. Recently, deformable/stretchable batteries using conventional organic electrolytes without rigid packages have been reported ( 6 – 8 ) with good stretchability, but they often suffer from performance degradations in the ambient environment due to moisture penetration issues ( 6 , 7 , 9 ), resulting in short operational lifetime. Because the elasticity modulus and moisture permeability of the packaging material are two coupled and opposite factors ( Fig. 1A ) ( 10 , 11 ), there is no good solutions for packaging these batteries. Fig. 1. Mechanically safe and stretchable Li-ion battery with WZH electrolyte. ( A ) Schematic comparison of a typical coin-cell Li-ion battery with the hermetic metal package and a stretchable battery with the non-hermetic elastomer package. Rigid metal has high Young’s modulus with low water permeability, while elastomer has low Young’s modulus and high water permeability, which can degrade the battery performance. ( B ) Illustration of the lithium solvation shell in the zwitterionic hydrogel under swelled condition and water-scarce condition. The water-scarce condition is realized in the hydrogel by controllably loading the fluorine-free lithium salt LiCl to zwitterionic polymer backbone such that a low content of water is adsorbed in equilibrium with the ambient moisture. This allows for the hydrogel electrolyte to stably maintain a wide stability window in the ambient and be functional as a water-scarce electrolyte in a battery without the hermetic rigid package. ( C ) Chemical structure of the water-scarce zwitterionic hydrogel (WZH) polymer backbone synthesized in this work. Hydrogels exhibit attractive properties as a quasi-solid-state electrolyte. Water being the solvent, it is inherently less sensitive to moisture and nonflammable while exhibiting good stretchability and ion conductivity. However, although stretchable aqueous batteries with hydrogel electrolyte have been reported with good stretchability and cycle stability in the ambient ( 12 – 16 ), one key drawback is the narrow electrochemical stability window (ESW) of 1.23 V due to the water electrolysis process. As such, the selection of cathode and anode pairs are limited, and most reported stretchable aqueous batteries are predominantly based on zinc-ion chemistry and show relatively low operation voltage compared to commercial Li-ion batteries ( 1 , 16 – 21 ). The “water-in-salt” (WiS) electrolyte reported in 2015 opens up investigations into highly concentrated aqueous electrolytes containing almost no “free” water molecules ( 22 – 26 ) and gels with incorporated WiS electrolytes ( 27 – 29 ). A key limitation is that highly fluorinated lithium salts such as lithium bis(tri-fluoromethanesulfonyl)imide are heavily loaded in those electrolyte, which is highly toxic to human, expensive, and environmentally persistent ( 30 ). Herein, a water-scarce hydrogel electrolyte with a wide voltage stability window is developed consisting of zwitterionic polymer backbone controllably loaded with fluorine-free lithium salt. A low content of water bound to the hydrogel is maintained via equilibrium with ambient moisture such that the electrolyte can function with a wide ESW without a hermetic rigid package ( Fig. 1B ). For example, experimental results show ~19 wt % water content can be maintained in the hydrogel under an ambient relative humidity (RH) of 50% to enable a high ESW of ~2.97 V, which matches that of those WiS electrolytes. The water-scarce zwitterionic hydrogel (WZH) retains key mechanical properties of conventional hydrogels such as high stretchability (1348% fracture strain) and self-healing capability against mechanical damages. A full Li-ion cell composed of rigid packaging and WZH electrolyte pre-equilibrated in the ambient with a RH of 50% can operate continuously over 2 months for 200 charge-discharge cycles at a charge/discharge rate of 0.2 C to achieve an average coulombic efficiency (CE) of 97%. As a proof of concept, a stretchable Li-ion battery prototype with a non-hermetic elastomer package shows good capacity retentions under harsh mechanical stresses, including folding, twisting, 50% stretching strain, and penetrations by a needle five times at different locations. By using a self-healable elastomer package, the deformable battery can self-heal itself from repeated through cuts by a razor blade and retain 90% of the original capacity. Meanwhile, the stretchable battery without hermetic packaging operates stably for 1 month in the ambient for 500 charge-discharge cycles with an average coulomb efficiency of 95%. To demonstrate its potential applications, a self-healable electronic system is constructed consisting of the soft battery and an electronic circuit that can recover from a through-cut damage and remain operational highlighting the high mechanical safety under extreme mechanical damages.",
"discussion": "DISCUSSION This work presents a WZH electrolyte that can realize a high ESW of up to 3.11 V in ambient air without a rigid hermetic package. By exploiting the strong interactions between lithiophilic zwitterionic groups with lithium salt through the anti-polyelectrolyte effect, the number of water molecules coordinated around the lithium ion is reduced. The free water molecules are further stabilized by functional groups on the zwitterionic polymer backbone through hydration and hydrogen bonding such that the reactivity of water molecules is minimized. The hygroscopic lithium salt is loaded in a controlled fashion with a small amount water content in balance with the ambient humidity. In such water-scarce condition, water molecules compete with zwitterionic groups for lithium ions, freeing them from the polymer backbone for enhanced ion mobility. The high tolerance aqueous system to the moisture as compared to that of organic electrolyte has allowed the fabrication of printed full cell batteries facilely in the ambient without a glovebox. Without the hermetic package, a myriad of stretchable elastomers can be used to enable high stretchability for the full Li-ion cell, while high mechanical and chemical safety is also achieved attributed to the aqueous hydrogel electrolyte. As a proof of concept, a stretchable and self-healing elastomer is chosen as the packaging material to construct soft Li-ion batteries with good capacity retentions under mechanical stresses, including punctured holes by a needle. Furthermore, the prototype cell can recover over 90% of its capacity from a through cut by a razor blade via the self-healing process. One key future direction is to increase the energy density for practical applications by these following steps. First, the mass loading can be further increased by incorporating the 3D microporous architecture as electrodes for high specific surface areas. Second, the selection of cathode and anode can be explored to maximize the utilization of electrolyte ESW. Third, the operation voltage of the battery can be increased by further extending the ambient ESW through optimization in molecular design that reduces hygroscopicity and water reactivity and facilitates stable SEI formation. As such, other battery chemistries with increased energy densities [e.g., LiNi 0.5 Mn 1.5 O 4 / Li 4 Ti 5 O 12 ( 23 ), Lithium metal ( 42 , 56 ), and Li-S ( 57 , 58 )] could potentially be accommodated. Nevertheless, this strategy enables the development of mechanically safe and deformable Li-ion batteries and could potentially be suitable for other energy storage devices such as supercapacitors ( 59 , 60 ), Zn-ion batteries ( 50 ), and metal air batteries ( 61 ). Furthermore, this work also offers the possibility for high-throughput manufacturing under the ambient condition by roll-to-roll printing."
} | 2,349 |
30133138 | PMC6381794 | pmc | 3,721 | {
"abstract": "Summary \n Caffeic acid O‐methyltransferase ( COMT ), the lignin biosynthesis gene modified in many brown‐midrib high‐digestibility mutants of maize and sorghum, was targeted for downregulation in the small grain temperate cereal, barley ( Hordeum vulgare ), to improve straw properties. Phylogenetic and expression analyses identified the barley \n COMT \n orthologue(s) expressed in stems, defining a larger gene family than in brachypodium or rice with three \n COMT \n genes expressed in lignifying tissues. RNA i significantly reduced stem COMT protein and enzyme activity, and modestly reduced stem lignin content while dramatically changing lignin structure. Lignin syringyl‐to‐guaiacyl ratio was reduced by ~50%, the 5‐hydroxyguaiacyl (5‐ OH ‐G) unit incorporated into lignin at 10‐–15‐fold higher levels than normal, and the amount of p ‐coumaric acid ester‐linked to cell walls was reduced by ~50%. No brown‐midrib phenotype was observed in any RNA i line despite significant COMT suppression and altered lignin. The novel \n COMT \n gene family structure in barley highlights the dynamic nature of grass genomes. Redundancy in barley COMT s may explain the absence of brown‐midrib mutants in barley and wheat. The barley COMT RNA i lines nevertheless have the potential to be exploited for bioenergy applications and as animal feed.",
"introduction": "Introduction The properties of plant biomass are largely determined by its composition and in particular by the amount and structure of lignin. These properties influence the digestibility of crop biomass as animal feed (Gressel and Zilberstein, 2003 ) and its potential use as a renewable raw material for an emerging biorefinery industry producing biochemicals and biofuels (Gomez et al ., 2008 ; Halpin et al ., 2010 ; US‐DOE, 2006 ). The lignin content of plant biomass is negatively correlated with saccharification, the enzymatic release of simple sugars (Chen and Dixon, 2007 ; Van Acker et al ., 2013 ), while changing the relative proportions of different lignin units is associated with changes to digestibility (Mechin et al ., 2005 ) and saccharification after acid pretreatment (Studer et al ., 2011 ; Van Acker et al ., 2013 ). The possibility of optimising the content and structure of lignin in biomass to facilitate processes such as biofuel production is a very active area of current research worldwide. In the C4 grasses maize ( Zea mays ) and sorghum ( Sorghum bicolor ), mutations in certain lignin biosynthesis genes, including caffeic acid O‐methyltransferase ( COMT ), give rise to a phenotype of brown midribs that is associated with lower lignin content and higher digestibility (Bout and Vermerris, 2003 ; Vignols et al ., 1995 ). Such bm or bmr mutants are consequently marketed in the USA as superior forage and silage cultivars and some are reported to increase bioethanol yields (Dien et al ., 2009 ). Most research has focussed on the maize bm3 COMT mutant which seems to have the greatest digestibility and feeding value improvement (Barrière et al ., 2004 ). Although the lignin pathway is generally better characterised in dicots than monocots (Anterola and Lewis, 2002 ), COMT's main role in both types of plant appears to be to methylate 5‐hydroxyconiferaldehyde on the route to the synthesis of S units (Osakabe et al ., 1999 ). Nevertheless, COMT is considered a multifunctional enzyme: in Arabidopsis it was shown to be involved in the biosynthesis of sinapate esters (Goujon et al ., 2003 ), it has been annotated as a flavonol OMT (Muzac et al ., 2000 ), and Sorghum bicolor COMT can methylate the flavones luteolin and selgin (Eudes et al ., 2017 ). The brown‐midrib phenotype has not been associated with COMT mutations in C3 grasses such as wheat ( Triticum spp.) and barley ( Hordeum vulgare ), the dominant sources of straw biomass in temperate world regions. Substantial surplus wheat straw is available globally that could be used as a raw material for bioenergy (Copeland and Turley, 2008 ; Kim and Dale, 2004 ) but wheat is not a particularly tractable genetic system for research because of its large polyploid genome. In contrast, barley is an inbreeding true diploid for which substantial genetic and bioinformatic genomic resources are available (Hein et al ., 2009 ; Mascher et al ., 2017 ; Saisho and Takeda, 2011 ), and it is readily and efficiently transformed (Harwood et al ., 2008 ). Barley is a particularly good model for polyploid wheat, diverging from a common ancestor only ~8–9 mya (Middleton et al ., 2014 ). Apart from its use as a research model, barley is the fourth largest global cereal crop by production with ~144 million metric tonnes produced in 2014 (FAOSTAT, 2014 ). It is a staple food in countries such as Ethiopia, but in temperate regions is cultivated primarily for grain use for malting and animal feed (Slafer et al ., 2002 ). The straw can also be used as fodder and forage but has potential for use as a raw material for biorefineries producing chemicals and second generation biofuels. Consequently, we aimed to downregulate COMT in barley to demonstrate the value for agriculture and industrial biotechnology of improving straw digestibility in the small grain temperate cereals.",
"discussion": "Discussion We show here that barley has a larger COMT gene family than brachypodium or rice suggesting COMT duplication in the barley lineage since its evolution from a common ancestor. This is consistent with the extensive gene duplication and expansion of specific gene families revealed in the barley reference sequence (Mascher et al ., 2017 ). All three barley COMTs retain the amino acid residues essential to COMT activity and are preferentially expressed in lignifying tissues strongly suggesting that all three functions in lignin biosynthesis. Nevertheless, duplication seems to have been followed by some divergence in expression pattern, possibly reflecting subfunctionalization in different tissues or cell types (Ober, 2010 ). Several COMT s previously identified in wheat (Jung et al ., 2008 ; Ma and Xu, 2008 ; Wang et al ., 2018 ) are homologues of the barley COMT genes. COMT duplication events have also been noted in ryegrass ( Lolium perenne ) (van Parijs et al ., 2015 ). Given the redundancy in barley COMT genes, RNAi was an appropriate silencing strategy and was effective in suppressing both HvCOMT1 and HvCOMT2 . Reductions in enzyme activity in the primary transformants were relatively moderate compared to reductions in HvCOMT expression and protein levels. This may reflect greater specificity of the antibodies compared to the enzyme assay where other O‐methyltransferases might contribute background activity. Similarly in the maize bm3 mutant, anti‐COMT antibodies could not detect residual COMT protein but enzyme activity was merely reduced (Piquemal et al ., 2002 ). Nevertheless, expression of HvCOMT1 and HvCOMT2 is not abolished in our barley RNAi lines, COMT protein and activity are still present, albeit greatly reduced to levels sufficient to cause significant changes to lignin content and structure. Lignin content was reduced in two barley COMT RNAi lines by 10%–15%. This compares to reductions in Klason lignin content of 25% and 28% when COMT was suppressed in maize (Piquemal et al ., 2002 ) and to reductions of up to 16% of acetyl bromide lignin when COMT was suppressed in perennial ryegrass (Tu et al ., 2010 ). Comparisons are complicated, however, because lignin content was measured at different developmental stages and by different methods in each study. Reduced thioacidolysis yields in the COMT RNAi lines are an indication of changes to lignin structure with a greater proportion of resistant bonds in the lignin. Reductions in the S/G ratio of ~50% in the barley RNAi lines were less than that in knock‐out mutants in maize and Arabidopsis where S units were reduced by ~70% (Barrière et al ., 2004 ) or more (Goujon et al ., 2003 ), respectively. The level of incorporation of the 5‐OH‐G unit was similar to that measured in the maize bm3 mutant (Barrière et al ., 2004 ), maize antisense RNA transgenic lines (Piquemal et al ., 2002 ) and brachypodium mutants (Dalmais et al ., 2013 ; Ho‐Yue‐Kuang et al ., 2016 ) and higher than that measured in the Arabidopsis mutant (Goujon et al ., 2003 ). To our knowledge, this is the first reported quantification of the 5‐OH‐G unit in a temperate cereal. The lack of a consistent reduction in thioacidolysis‐released ferulic acid is similar to what was found in COMT down‐regulated maize antisense RNA lines where there was even a slight increase in ferulic acid released by mild alkaline hydrolysis (Piquemal et al ., 2002 ). Recently, a new lignin sub‐unit, tricin, has been described in grasses (Lan et al ., 2015 ) and COMT has been implicated in its biosynthesis (Eudes et al ., 2017 ; Fornalé et al ., 2017 ). Barley appears to have only low levels of tricin compared to some other Pooideae (e.g. oats, wheat and brachypodium), with just 0.65 mg/g cell wall compared to 7.15 mg/g for oats (Lan et al ., 2016 ). In this study, we detected a reduction to 2% of tricin in barley cell walls after COMT suppression, but levels in control plants were only modestly higher at 3%. In sorghum, similar 2D NMR spectroscopy of bmr12 COMT mutant biomass showed that it also had only 2% of tricin in cell walls, but levels in wild‐type sorghum were higher at 5% (Eudes et al ., 2017 ). Nevertheless our data are consistent with the proposal that COMT is involved in the synthesis of both S lignin units and tricin (Eudes et al ., 2017 ). The maintenance of basal levels of HvCOMT1 and HvCOMT2 expression in the RNAi stems may explain the moderate level of other transcriptional changes. Given this, the number of metabolites that show altered abundance in the RNAi plants is perhaps surprising. Two less abundant metabolites were identified as α‐oxidized β‐O‐4‐ether oligomers of sinapyl alcohol (Sox(8‐O‐4)S, compound 10 ; and S(8‐O‐4)Sox(8‐O‐4)S, compound 11 ) (Figure S9 ). A reduction in the production of sinapyl alcohol in the RNAi plants is consistent with the reduction in S lignin and both result from the deficiency in COMT‐mediated conversion of 5‐hydroxyconiferaldehyde to sinapaldehyde, the precursor of sinapyl alcohol. The structure of Sox(8‐O‐4)S could be proven by an authentic standard (Tsuji et al ., 2015 ), but has not yet been described in plants. The origin of the oxidation of the α‐position of β‐O‐4‐ethers is currently unknown, but has been observed in wild‐type Arabidopsis in 8‐O‐4‐dimers of coniferyl alcohol with either a second coniferyl alcohol (as in Gox(8‐O‐4)G) or ferulic acid (as in Gox(8‐O‐4)ferulic acid; Mnich et al ., 2017 ; Tsuji et al ., 2015 ). The majority of the 108 compounds that were increased in the COMT RNAi lines are of unknown identity. Those containing 5‐hydroxyconiferyl alcohol (compound 1 – 7 ) likely originate from the overproduction of the COMT substrate, 5‐hydroxyconiferaldehyde. This can be converted to coniferyl alcohol, presumably via CAD activity, and incorporated into benzodioxane oligolignols (compound 1 – 4 ) and the benzodioxane structures in the lignin of COMT RNAi plants. Benzodioxane oligolignols have also been found in COMT‐deficient poplar and Arabidopsis (Morreel et al ., 2004 ; Vanholme et al ., 2010 , 2012a , b ). Not all 5‐hydroxyconiferyl alcohol may be used for lignification, however. Hexose and acetylhexose conjugates of 5‐hydroxyconiferyl alcohol (compound 5 – 6 and 7, respectively) also accumulate in COMT RNAi plants and may be destined for vacuolar storage (Dima et al ., 2015 ). A striking observation is the accumulation in COMT RNAi plants of caffeyl alcohol conjugated to hexose (compound 8 ) or acetyl hexose (compound 9 ). This suggests that either caffeyl alcohol or caffealdehyde serve as a substrate for HvCOMT1, HvCOMT2 or both. Caffealdehyde has long been considered as an intermediate of the lignin pathway in several plant species (reviewed in Boerjan et al ., 2003 ). A biosynthetic route to coniferaldehyde of caffeoyl‐CoA → caffealdehyde → coniferaldehyde, catalysed by CCR and COMT, would bypass the more commonly described route caffeoyl‐CoA → feruloyl‐CoA → coniferaldehyde, catalysed by CCoAOMT and CCR. This bypass‐route has been shown to be present in alfalfa (Lee et al ., 2011 ; Parvathi et al ., 2001 ; Zhou et al ., 2010 ). Caffeyl alcohol has also been found as a monomer in lignin of CCoAOMT downregulated Pinus radiata (Wagner et al ., 2011 ), in seeds of vanilla and in several cacti (Chen et al ., 2012 ). However, our data are the first in‐planta evidence that the bypass‐route via caffealdehyde also occurs in grasses. The changes described in lignin content and structure in COMT RNAi plants are likely to be beneficial for saccharification and digestibility, and moderate increases to saccharification were measured in some lines. Reduced lignin content is generally correlated with improvements in saccharification (Chen and Dixon, 2007 ) and downregulation or mutation of COMT has increased saccharification and/or biofuel production in switchgrass and sorghum (Dien et al ., 2009 ; Fu et al ., 2011 ; Saballos et al ., 2008 ; Van Acker et al ., 2013 ). The effect of the proportion of S units in lignin on digestibility is controversial; one study claims that the structure of lignin does not affect fermentation by ruminant microflora (Grabber et al ., 2009 ) while another found an inverse correlation between digestibility and S lignin content (Mechin et al ., 2005 ). Effects on saccharification are likely to depend on the pretreatment used, as reported by Studer et al . ( 2011 ). Incorporation of 5‐OH‐G units into lignin has been hypothesised as beneficial for saccharification; the quinone methide that forms during monomer coupling can be internally trapped by the ‐OH group on a 5‐OH‐G unit in lignin forming benzodioxane units instead of linking to polysaccharides, and that reduction in cross‐linking is likely to improve the access for saccharifying enzymes (Ralph et al ., 2004 ; Vanholme et al ., 2012a , b ). \n COMT duplication events in barley and wheat are sufficient to explain why no brown‐midrib or gold‐hull mutants associated with COMT have been identified in these small grain temperate cereals. We have evidence that orange lemma mutants are the barley equivalent of maize brown‐midrib and rice gold‐hull but none of the orange lemma mutants we have characterised are mutants in COMT (Stephens J, Reetoo N, Daly P, Waugh R, Druka A, Lapierre C and Halpin C, unpublished). Contrary to previous reports (Dalmais et al ., 2013 ; Wu et al ., 2013 ), our phylogenetic analysis identified a single true COMT gene in brachypodium, suggesting that brown‐midrib phenotypes might emerge if COMT was fully knocked out in this species. Various hypotheses were proposed to explain why brown‐midrib phenotypes had not been seen in C3 grasses, but brachypodium plants with brown midribs (or brown‐red lignified tissues) were recently described; all were plants severely suppressed or mutated in CAD (Trabucco et al ., 2013 ; d'Yvoire et al ., 2013 ). The existence of brachypodium plants sufficiently deficient in COMT to be expected to develop brown‐midrib phenotypes has not been definitively evidenced. A mutant in the brachypodium lignin COMT has been identified but displays only moderately altered lignification and the mutant enzyme is still functional (Ho‐Yue‐Kuang et al ., 2016 ). Similarly, transgenic plants overexpressing artificial microRNA designed to silence brachypodium COMT did not have significant changes to S lignin (Trabucco et al ., 2013 ) suggesting that they were not sufficiently COMT‐suppressed. Consequently, it is likely that a full knock‐out of COMT in brachypodium (or other species) will be necessary before brown‐midrib phenotypes are seen or their absence can reasonably prompt other explanations. In this context, it is interesting that COMT is reported to be the third most abundantly expressed gene in poplar stem‐differentiating xylem, accounting for 6% of the proteome (Lin et al ., 2013 ; Shuford et al ., 2012 ) and its near absence is thought necessary before S lignin content is reduced (Wang et al ., 2014 ). In barley and wheat, the difficulties in effectively silencing gene activity to near abolition are likely to be exacerbated when more than one COMT gene needs to be suppressed. For example, our microarray data comparing the COMT RNAi lines with controls showed that, despite efficient gene downregulation, HvCOMT1 and HvCOMT2 expression could still be detected at 4% and 6%–7% of control plant values, respectively. The ability to modify lignin differentially in specific tissues would also have great value in lignin engineering, for example enabling the production of crops that have more digestible stems (less lignin) and roots that sequester more carbon in soil (more lignin). The kind of gene duplication and expansion events described here for barley COMTs could in some cases enable such tissue specific manipulation, if gene sequences and expression patterns have diverged sufficiently to allow individual genes expressed in specific tissues to be targeted for suppression by RNAi. Tissue specific promoters might also place appropriate limitations on RNAi expression, albeit with the complication that small silencing RNAs might move between tissues. The advent of CRISPR‐mediated targeted gene manipulation in plants offers real possibilities for more precise and effective gene manipulations. By careful selection of guide RNA sequences, several homologous genes (multiple gene family members, such as HvCOMT1 and HvCOMT2 , or homeologous genes in polyploid species) can be targeted for mutation while other closely related genes are avoided. Knock out of multiple COMT genes/homeologues in stems of barley and wheat might provide improved cereal straw for use as animal feed or as a feedstock for industrial processing in temperate regions of the world."
} | 4,558 |
37127894 | PMC10369268 | pmc | 3,722 | {
"abstract": "Abstract Although highly desired, it is difficult to develop mechanically robust and room temperature self‐healing ionic liquid‐based gels (ionogels), which are very promising for next‐generation stretchable electronic devices. Herein, it is discovered that the ionic liquid significantly reduces the reversible reaction rate of disulfide bonds without altering its thermodynamic equilibrium constant via small molecule model reaction and activation energy evolution of the dissociation of the dynamic network. This inhibitory effect would reduce the dissociated units in the dynamic polymeric network, beneficial for the strength of the ionogel. Furthermore, aromatic disulfide bonds with high reversibility are embedded in the polyurethane to endow the ionogel with superior room temperature self‐healing performance. Isocyanates with an asymmetric alicyclic structure are chosen to provide optimal exchange efficiencies for the embedded disulfide bonds relative to aromatic and linear aliphatic. Carbonyl‐rich poly(ethylene‐glycol‐adipate) diols are selected as soft segments to provide sufficient interaction sites for ionic liquids to endow the ionogel with high transparency, stretchability, and elasticity. Finally, a self‐healing ionogel with a tensile strength of 1.65 ± 0.08 MPa is successfully developed, which is significantly higher than all the reported transparent room temperature self‐healing ionogel and its application in a 3D printed stretchable numeric keyboard is exemplified.",
"conclusion": "3 Conclusions In summary, we successfully developed a room temperature transparent self‐healing ionogel with a world‐record tensile strength, which shows great potential in emerging stretchable electronic devices. Distinguishing from most existing ionogels, which are noncovalently crosslinked, the dynamic disulfide bonds‐based covalent network provides superior mechanical properties while endowing it with excellent self‐healing ability. Especially, we discovered that IL reduced the exchange reaction rate of reversible disulfide bonds, which may contribute to the high tensile strength of ionogel. Existing studies on dynamic polymers focus on enhancing the reversibility of dynamic bonds, while this newly discovered inhibitory effect will provide a new paradigm to construct materials with unusual properties. Generally, IL has been widely used as a catalyst to enhance the chemical reaction rate. This is the first report that IL reduces the chemical reaction rate. It will initiate a new direction for IL and may lead to a new field of chemistry and materials.",
"introduction": "1 Introduction Ionic liquid (IL)‐based gels (ionogels) refer to IL confined in three‐dimensional (3D) polymeric networks (We defined the IL content of ionogel should be greater than or equal to 40 wt% in this work). [ \n \n 1 \n \n ] Ionogels hold great application prospects for wearable electronics, [ \n \n 2 \n \n ] medical diagnosis, [ \n \n 3 \n \n ] human–machine interfaces, [ \n \n 4 \n \n ] and soft robotics [ \n \n 5 \n \n ] due to their unique characteristics of outstanding stretchability, customizable electrical conductivity, and wide range of operating temperatures, as well as high thermal stabilities. [ \n \n 1 \n , \n 6 \n \n ] However, ionogels are susceptible to damage and lose their functions under complex deformations, leading to safety issues and waste of electronics. [ \n \n 7 \n \n ] \n Room temperature self‐healing performance alludes to the ability of materials to heal themselves upon mechanical damage without the presence of extrinsic stimuli or additional substances for healing, which has drawn increasing attention in recent years. [ \n \n 8 \n \n ] The durability and reliability of ionogels can be greatly improved by inducing the self‐healing property. [ \n \n 9 \n \n ] A series of non‐covalent bonds (i.e., hydrogen bonds, [ \n \n 10 \n \n ] ion bonds, [ \n \n 11 \n \n ] and ion–dipole interactions, [ \n \n 9a,b \n \n ] etc.) have been introduced to construct self‐healing ionogels, but their low mechanical strength (<1 MPa) limits their further applications. Here, we introduced aromatic disulfide bonds with higher reversibility (compared with aliphatic disulfide bonds) into the polyurethane network to endow the ionogel with superior room temperature self‐healing properties. The isophorone diisocyanate (IPDI) was selected as hard segment with a bulky asymmetric structure to prevent the crystallization of the polymer and increase the polymer chain mobility, thus further promoting the exchange of disulfide bonds. The soft segment adopted carbonyl‐rich poly(ethylene‐glycol‐adipate) diols, and the carbonyl groups provided sufficient interaction sites for the IL to endow the ionogel with high transparency, stretchability, and elasticity. Unexpectedly, we discovered that IL significantly reduced the reversible reaction rate of disulfide bonds without altering their thermodynamic equilibrium constant. This feature is critical for enhancing the mechanical strength of the resultant ionogel while keeping its self‐healing property. We also investigated its application using a 3D printed stretchable thin numeric keyboard.",
"discussion": "2 Results and Discussion The design of disulfide bonds‐based crosslinking polyurethane ionogel (I‐SS‐CPU) is schematically illustrated in Figure \n \n 1 a . The I‐SS‐CPU includes extensive non‐covalent interactions (hydrogen bonding between IL 1‐ethyl‐3‐methylimidazolium bis (trifluoromethylsulfonyl) imide ([EMI][TFSI]) and polar group in polyurethane network) along with the inhibitory effect of [EMI][TFSI] on reversible reaction of disulfide bonds. The I‐SS‐CPU without IL is denoted as SS‐CPU. I x ‐SS‐CPU refers to SS‐CPU with x wt% of the IL (concerning the weight of SS‐CPU). The H atoms of –NH in the I‐SS‐CPU and H atoms on imidazolium cation acted as hydrogen‐bond donors, while the strongly electronegative atoms including N, F, and O in the [TFSI] anion and C = O in I‐SS‐CPU functioned as hydrogen‐bond acceptors. Fourier‐transform infrared spectroscopy (FTIR) was utilized to examine the molecular interactions between [EMI][TFSI] and SS‐CPU networks. With the increase in the content of the IL, the C = O, and N–H of CPU chains stretching vibrations moved from 1728 and 3363 cm −1 to 1730 and 3386 cm −1 in the I‐SS‐CPU (Figure 1b ), respectively. In addition, the peaks located at 1346, 1177, 1132, and 1049 cm −1 corresponded to the O = S = O asymmetric, CF 3 , O = S = O symmetric, and S–N–S stretches vibration of the [EMI][TFSI] in I‐SS‐CPUs shifted to 1351, 1183, 1134, and 1054 cm −1 , respectively (Figure S1 , Supporting Information). These results revealed that the hydrogen bonds between the carbonyl and carbamate groups of the CPU chains were replaced by an interaction between the polymeric chains and the IL. Moreover, the strong interaction between the IL and the PU chain likely facilitated the dispersion of [EMI][TFSI] in the PU network and prevented its leaking from ionogels. As shown by dynamic mechanical analysis (DMA) (Figure S2 , Supporting Information), the glass transition temperature ( T \n g ) of the I‐SS‐CPU reduced with increasing [EMI][TFSI] content (Figure 1c ). I 60 ‐SS‐CPU had a much lower T \n g (−74.6 °C) than SS‐CPU (−30.0 °C), indicating the lubricating effect of [EMI][TFSI] on polymer network. As shown in Figure 1d , SS‐CPU and I‐SS‐CPU exhibited excellent thermal stability. The thermal decomposition temperatures of SS‐CPU, I 20 ‐SS‐CPU, I 40 ‐SS‐CPU, and I 60 ‐SS‐CPU were determined to be 275.9, 276.6, 280.3, and 289.1 °C, respectively. In addition, we observed that the mass of the I 40 ‐SS‐CPU remained almost constant over a period of 28 days (Table S1 , Supporting Information). Meanwhile, the FTIR spectra were essential unchanged including the characteristic absorption bands of the PU chain and [EMI][TFSI]. These results indicated the remarkable stability of I 40 ‐SS‐CPU. Furthermore, the high miscibility of the [EMI][TFSI] with polymer networks led to the I 40 ‐SS‐CPU with high transparency. I 40 ‐SS‐CPU had an average transmittance of over 84% for a 1‐mm‐thick film under visible‐light wavelengths of 500–800 nm (Figure 1e ). In addition, I 40 ‐SS‐CPU did not dissolve in tetrahydrofuran, dimethylacetamide, and acetone, indicating its crosslinking structure (Figure S4 , Supporting Information). Figure 1 Design and characterization of the synthesized SS‐CPU and I‐SS‐CPUs. a) Molecular structures of [EMI][TFSI] and the SS‐CPU network and schematic illustration of their interaction. b) FTIR spectra of [EMI][TFSI], SS‐CPU, and the I‐SS‐CPUs (I 20 ‐SS‐CPU, I 40 ‐SS‐CPU, and I 60 ‐SS‐CPU) containing 20, 40, and 60 wt% of IL. c) The determined T \n g of SS‐CPU and I‐SS‐CPUs from DMA tests. d) TGA curves of the SS‐CPU and I‐SS‐CPU. e) Transmittance spectrum of I 40 ‐SS‐CPU film (thickness: 1 mm). The average transmittance of I 40 ‐SS‐CPU was over 84% in the wavenumber range of 500–800 nm. The inset photograph is the I 40 ‐SS‐CPU film (Scale bar: 2 cm). Owing to their wide use and limited‐service life, electronic pollution has become a rapidly growing global problem. Therefore, it is a high desire to develop recyclable electronics to decrease the environmental and economic burdens of such waste. [ \n \n 12 \n \n ] Given the presence of highly dynamic chemical structures and the mobility of the polymer network, the I 40 ‐SS‐CPU exhibited excellent recyclability. This property was evaluated by remolding chopped pieces of I 40 ‐SS‐CPU ( Figure \n \n 2 a and Figure S5 , Supporting Information). In addition, the stress‐strain curves of I 40 ‐SS‐CPU before and after 3 times reprocessing were similar. Furthermore, we investigated the structural and electronic properties of I 40 ‐SS‐CPU before and after reprocessing to verify the reconfiguration property. FTIR spectra showed that the reprocessed I 40 ‐SS‐CPU maintained its original chemical structures (Figure S6 , Supporting Information). Moreover, the ionic conductivity of reprocessed I 40 ‐SS‐CPU (1.19 ± 0.11 × 10 −2 S m −1 ) was negligible change in comparison with the original (1.18 ± 0.16 × 10 −2 S m −1 ), which showed excellent reconfigurability of I 40 ‐SS‐CPU (Figure 2b ). Reconfigurability is closely related to the activation energy of the dissociation of the dynamic covalent network. [ \n \n 13 \n \n ] To further investigate the influence of IL on the reconfiguration characteristics of polymers, the rheological properties of the I 40 ‐SS‐CPU were measured by stress‐relaxation tests at different temperatures (Figure S7 , Supporting Information). For the stress‐relaxation examination, the relaxation modulus was measured as a function of time, while a torsional strain of 5% was applied. With the increase in temperature, the relaxation times of SS‐CPU and I‐SS‐CPU decreased. According to the Maxwell model for viscoelastic fluids, the relaxation times ( τ *) were determined at 37% ( G / G \n 0 = 1/ e ≈ 37%) of the normalized relaxation modulus. The temperature dependence of the relaxation time could be illustrated by the Arrhenius equation: τ ( T ) = τ \n 0 exp( E \n a / RT ), where τ refers to the characteristic relaxation time, τ \n 0 is attributed to the pre‐exponential factor, and E \n a corresponds to the stress relaxation activation energy. The E \n a of SS‐CPU, I 20 ‐SS‐CPU, I 40 ‐SS‐CPU, and I 60 ‐SS‐CPU were determined to be 67.2, 46.9, 36.6, and 45.3 kJ mol −1 , respectively (Figure S8 , Supporting Information and Figure 2c ). The E \n a significantly decreased before the content of the IL in ionogels increased to 40%. These results confirmed that the IL had a significant lubricating effect on polymer networks. Unexpectedly, the E \n a of the I 60 ‐SS‐CPU was higher than that acquired for the I 40 ‐SS‐CPU. It might be attributed to the inhibitory effect of IL on reversible reaction of disulfide bonds. To further reveal the mechanism of disulfide bond exchange reaction with and without IL, electron paramagnetic resonance (EPR) measurements were used to detect the sulfur radical in the SS‐CPU and I‐SS‐CPUs (Figure S9 , Supporting Information). The g ‐values in the ESR derivatives around 2.119 likely corresponded to sulfur radicals. Additionally, the peak intensity was positively correlated with the content of free radicals, which was consistent with the change in activation energy. It may be attributed to the inhibitory effect of IL on the reversible reaction of disulfide bonds. Figure 2 Dynamic properties of I‐SS‐CPU based on IL inhibited reversible reaction of disulfide bonds. a) Stress–strain curves of the original and 3rd reprocessed I 40 ‐SS‐CPU showed full recovery of the mechanical property. b) Electrochemical impedance spectra of original and 3rd reprocessed I 40 ‐SS‐CPU (inset: the conductivity of original and reconfiguration I 40 ‐SS‐CPU). c) The activation energies of the SS‐CPU and I‐SS‐CPU. d) Scheme of dynamic exchange reaction in disulfide bonds bearing small‐molecule model. e) 1 H NMR spectra of mixture compound A and B with and without [EMI][TFSI] for different reaction times at 25 °C. f) Conversion rate of compound C with and without [EMI][TFSI] (conversion ratio = [ C ] t /([ A ] 0 + [ B ] 0 ), [ C ] t = concentration of compound C at time t , [ A ] 0 = original concentration of compound A , [ B ] 0 = original concentration of compound B . A small molecules model was used to demonstrate the inhibitory effect of IL on the reversible reaction of disulfide bonds. As shown in Figure 2d , compound A ‐bis(4‐methoxyphenyl) disulfide and B ‐bis(4‐hydroxyphenyl) disulfide, both containing disulfide bonds, were mixed. The 1 H nuclear magnetic resonance (NMR) spectrum of compound A in the mixture showed proton signals of the hydroxyl at 9.86 ppm (Figure 2e ). With time, a new proton signal at 9.85 ppm was observed, which can be attributed to the product 4‐((4‐methoxyphenyl) disulfanyl) phenol (compound C ), indicating the occurrence of an exchange reaction. The reaction took ≈9 h to reach equilibrium at 25 °C (Figure 2f ). Next, [EMI][TFSI] was added to the aforementioned exchange reaction. It took ≈20 h to reach equilibrium, which is much slower than the non‐incorporated IL sample. Specifically, the dissociation kinetics of the disulfide bond without and with ionic liquid in this small molecular reaction were 0.102 and 0.066 h −1 , respectively (Figure S10 , Supporting Information). It demonstrated the inhibitory effect of IL on the reaction rate of reversible disulfide bonds. However, the reversible reaction endpoints (with and without IL) of disulfide bonds were almost similar. To further verified the effect of ionic liquids on the thermodynamics of disulfide bond exchange reaction, in situ variable‐temperature 1 H NMR spectra of small molecular model reaction were used to calculate equilibrium constants (Figures S11 and S12 , Supporting Information). At 20 °C, the chemical equilibrium constants of the exchange reaction of the small molecules model with and without ionic IL were 0.9981 and 1.0187, respectively. Additionally, the trend of the chemical equilibrium constant of the exchange reaction of small molecules model with IL was similar to without IL with the increasing temperature. These results demonstrated that the IL did not alter either the thermodynamic equilibrium constant of dynamic disulfide bonds. Next, the mechanical properties and self‐healing performance of SS‐CPU and I‐SS‐CPU were investigated. The hydrogen bonds in the original polymer network were replaced by hydrogen bonds between IL and the polymer network. It effectively enhanced the mobility of the PU chains and tuned the mechanical properties of SS‐CPU. Uniaxial and loading‐unloading tensile tests were conducted to evaluate the mechanical properties of the I‐SS‐CPU with different IL contents. SS‐CPU showed a tensile strength and a strain at fracture of 11.46 ± 1.23 MPa and 541 ± 34%, respectively ( Figure \n \n 3 a ). However, a bulky hysteresis loop was detected during the tensile loading‐unloading test, indicating the poor elasticity of the I‐SS‐CPU (Figure 3b ). This large hysteresis can be ascribed to the hydrogen bonds in PU chains that hampered the resilience of the SS‐CPU after stretching. After the introduction of the IL, the hydrogen bonds between the PU chains were largely replaced by the hydrogen bonds between [EMI][TFSI] and the PU chains. Consequently, the hysteresis loop of the I‐SS‐CPU was significantly reduced. At the same time, the I‐SS‐CPU showed higher stretchability and lower Young's modulus than those acquired for the SS‐CPU. For example, I 40 ‐SS‐CPU had a tensile strength, maximum extensibility, and Young's modulus of 1.65 ± 0.08 MPa, 900%, and 283 ± 15 kPa, respectively (Figure 3a ). Furthermore, the elasticities of the I‐SS‐CPU were quantitatively characterized in terms of the residual strain after the tensile loading–unloading at a strain of 200% (Figure 3c ). Compared to the SS‐CPU (with a residual strain of 91.5%), the I‐SS‐CPU exhibited significantly better elasticity with much smaller residual strains. When the amount of loaded IL further increased to 60%, the residual strain of I 60 ‐SS‐CPU (47.7%) was slightly increased than that of I 40 ‐SS‐CPU (45.9%), because the excess IL increased the viscosity, instead of the elasticity, of the material. Figure 3 Mechanical properties and self‐healing performance of SS‐CPU and I‐SS‐CPUs. a) Representative stress–strain curves of the SS‐CPU and I‐SS‐CPUs. b) Tensile loading–unloading curves of SS‐CPU and I‐SS‐CPUs at 200%. c) Residual strains of the SS‐CPU and I‐SS‐CPUs after tensile loading‐unloading at 200%. d) Typical tensile stress–strain plots of original and room temperature healed (after 72 h of the healing process)I 40 ‐SS‐CPU. e) Ashby plot of tensile strength of I 40 ‐SS‐CPU and other room temperature self‐healing ionogels reported in the literature. [ \n \n 4 \n , \n 9 \n , \n 14 \n \n ] f) A photograph of cut and healed I 40 ‐SS‐CPU film with the ability to stretch to twice its original length, after healing for 12 h could be stretched to twice its original length. The self‐healing property of I 40 ‐SS‐CPU was also evaluated in detail. For its bulky self‐healing (Figure 3d ), the rectangle samples of I 40 ‐SS‐CPU were firstly cut into two pieces, then connected at room‐temperature. After 72 h, its tensile strength recovered to 91.6% (1.51 ± 0.14 MPa) of the original stress (1.65 ± 0.08 MPa), which is significantly more than the original tensile strength of all previously reported self‐healing unadulterated ionogels [ \n \n 4 \n , \n 9 \n , \n 14 \n \n ] and the strain recovered to 82.7% (740 ± 10%) of the original (890 ± 20%) (Figure 3e ). Additive materials such as small molecules can also be added to ionogels to enhance tensile strength. This approach mostly led to reducing their transparency and limiting their applications in display devices and electroluminescent devices, and so on. I 40 ‐SS‐CPU with such excellent mechanical and self‐healing properties may attribute to the inhibitory effect of [EMI][TFSI] on the reversible reaction of disulfide bonds. IL significantly reduced the reversible reaction rate of disulfide bonds. Thus, the dissociated units in the dynamic polymeric network were reduced, beneficial for the strength of the ionogel. However, IL unchanged the thermodynamic equilibrium constant of dynamic disulfide bonds. Therefore, the IL did not reduce the healing rate (the percentage of the recovery of mechanical properties, mainly including the tensile strength, elongation, and toughness, after healing) of the I 40 ‐SS‐CPU. The relatively short‐term self‐healing ability of I 40 ‐SS‐CPU was evaluated by healing after 12 h (Figure 3f ). As presented in Figure \n \n 4 a , the rheological results, including storage modulus ( G ′) and loss modulus ( G ″), demonstrated that the transition temperature from an elastomeric network state ( G ′ > G ″) to a viscoelastic liquid state ( G ′ < G ″) was 74 °C. The shear‐thinning property was also observed through the frequency scanning tests. With the increase in frequency at 120 °C, the viscosity of the polymer gradually decreased (Figure 4b ). These demonstrated that the I 40 ‐SS‐CPU has excellent 3D printability at 120 °C. Subsequently, the 3D‐printed letters “D,” “H,” and “U,” and the geometrical shapes of circle, hexagon, and triangle further confirmed the great printability of I 40 ‐SS‐CPU (Figure 4c ). Furthermore, a stretchable thin numeric keyboard was designed and 3D printed (VHB was used as the printing substrate) for wearable user input interfaces, which are highly demanded in next‐generation stretchable electronics (Figure 4d,e ). The function of the keyboard was realized through circuit conduction. Upon the key being pressed, the circuit was connected, and the associated number would then be displayed and recorded. As shown in Figure 4f , the numeric keyboard was easily attached to the hand. Furthermore, to evaluate the self‐healing property of the numeric keyboard, a fracture was created; therefore, the function of the numeric keypad suddenly failed (Figure 4g ). After healing for 1 h, the electrical property of the numeric keypad was restored. Overall, I 40 ‐SS‐CPU holds great promise in customizable self‐healing stretchable electronic devices. Figure 4 The electrical properties and 3D printability of I 40 ‐SS‐CPU. a) Storage modulus, loss modulus, and b) complex viscosity of the I 40 ‐SS‐CPU versus temperature. c) Photographs of 3D printed alphabetic letters, circles, hexagons, and triangles. d) Schematic of the stretchable keyboard. e) Demonstration of the stretchability of the flexible keyboard. f) Photograph of numeric keyboard with a functional display. g) The flexible keyboard was cut off and lose electrical functionality. Then, the stretchable keyboard was healed and restored the electrical function after 1 h (Scale bar: 2 cm)."
} | 5,480 |
38739927 | PMC11733831 | pmc | 3,723 | {
"abstract": "Abstract The quantity of sensor nodes within current computing systems is rapidly increasing in tandem with the sensing data. The presence of a bottleneck in data transmission between the sensors, computing, and memory units obstructs the system's efficiency and speed. To minimize the latency of data transmission between units, novel in‐memory and in‐sensor computing architectures are proposed as alternatives to the conventional von Neumann architecture, aiming for data‐intensive sensing and computing applications. The integration of 2D materials and 2D ferroelectric materials has been expected to build these novel sensing and computing architectures due to the dangling‐bond‐free surface, ultra‐fast polarization flipping, and ultra‐low power consumption of the 2D ferroelectrics. Here, the recent progress of 2D ferroelectric devices for in‐sensing and in‐memory neuromorphic computing is reviewed. Experimental and theoretical progresses on 2D ferroelectric devices, including passive ferroelectrics‐integrated 2D devices and active ferroelectrics‐integrated 2D devices, are reviewed followed by the integration of perception, memory, and computing application. Notably, 2D ferroelectric devices have been used to simulate synaptic weights, neuronal model functions, and neural networks for image processing. As an emerging device configuration, 2D ferroelectric devices have the potential to expand into the sensor‐memory and computing integration application field, leading to new possibilities for modern electronics.",
"introduction": "1 Introduction In recent years, the scaling down based on Moore's Law and versatile function expansion has been the main driving force for the development of current electronic technology. Intelligent electronics are gradually evolving toward high‐density integration, high performance, and low power consumption. However, further scaling of silicon complementary metal‐oxide‐semiconductor (CMOS) at the sub‐5 nm technology node is reaching fundamental physical limits due to short‐channel effects. 2D layered materials with atomically thin properties exhibit innate properties and the potential to break through the bottleneck of traditional silicon‐based complementary architecture, and provide an alternative platform for the development of high‐performance and low‐power consumption integrated circuits. In particular, 2D materials have excellent physical properties in terms of electronic, optoelectronic, thermal, optical, mechanical, and magnetic properties, which has aroused great interest in fundamental research and industrial applications. Since the discovery of graphene in 2004, [ \n \n 1 \n \n ] a variety of 2D materials other than graphene have been developed, including transition metal dichalcogenides (TMDs) and III–VI group compound semiconductor M 2 X 3 (M = Ga, In; X = S, Se, Te), hexagonal boron nitride (h‐BN), black phosphorus (BP), III–VI group layered semiconductors (MX), transition metal carbides and/or nitrides (MXenes). [ \n \n 2 \n , \n 3 \n \n ] There are no dangling bonds on the surface of 2D materials, enabling great reduction of the defect concentration at the interface when combined with other materials. The combination of 2D materials and ferroelectrics has been used to expand the functionality of 2D devices and unlock new possibilities for the in‐sensor/in‐memory computing field since the electrical and optical properties of 2D materials can be effectively modulated with ferroelectrics. [ \n \n 4 \n \n ] Traditional ferroelectric materials are a class of dielectric crystals in which the spontaneous polarization can be switched by an electric field and can be maintained after the external electric field is removed. [ \n \n 5 \n , \n 6 \n , \n 7 \n , \n 8 \n \n ] However, reducing the thickness of a ferroelectric film below a critical value presents a significant challenge for further device integration. This is due to the occurrence of a strong depolarization field, where the positive and negative charges on the film's surface effectively neutralize each other. Consequently, achieving and maintaining ferroelectricity in ultrathin films remains a formidable task. Van der Waal (vdW) ferroelectrics offer a promising avenue for achieving nanoscale thicknesses while preserving the desirable properties of ferroelectricity. So far, various 2D vdW materials with few‐layer or even single‐layer thickness have been reported to be ferroelectric, such as layered perovskites, α‐phase indium selenide (α‐In 2 Se 3 ), etc. [ \n \n 9 \n , \n 10 \n , \n 11 \n , \n 12 \n , \n 13 \n , \n 14 \n , \n 15 \n \n ] 2D ferroelectrics often exhibit semiconducting and even metallic properties, further enhancing the potential of 2D ferroelectrics for electronic and optoelectronic applications. Meanwhile, 2D ferroelectric materials are free of dangling bonds, which will lead to ideal periodic structure and a uniform charge distribution. The weak interlayer interaction in vdW forces‐driven interface contacting will not be affected by surface states, which is beneficial to maintaining the ferroelectric properties. 2D ferroelectric materials are easier to integrate on the substrate and show great potential in realizing low‐dimensional functional devices with novel architectures for neuromorphic computing. [ \n \n 16 \n \n ] Ferroelectric polarization provides high doping density, reversible and non‐volatile channel carrier modulation, and induces permanent interfacial structural changes in heterostructures through electrically controlled lattice adjustments and implements data storage. [ \n \n 17 \n , \n 18 \n \n ] For example, the polarization orientation of the CuInP 2 S 6 (CIPS) has been experimentally verified to be maintained for 2 months. [ \n \n 12 \n \n ] It is critical to achieve long‐term potentiation and suppression of synaptic devices or nonvolatile memories. Besides, ferroelectric channel transistors exhibit excellent performance, including non‐volatile memory (NVM), fast write speed, and neural computation. [ \n \n 19 \n , \n 20 \n , \n 21 \n \n ] 2D ferroelectric devices are advantageous emerging memory devices due to their switchable electric dipoles, fast operation, and non‐destructive readout. They are well‐suited for constructing low‐power, high‐efficiency memory computing integrated systems. In this review, we summarize the latest progress in the integration of 2D materials and ferroelectric materials, from the aspects of material, device structure, working mechanism, and applications to neuromorphic sensing and computing, as shown in Figure \n \n 1 \n . First, we introduce the experiment investigation and original mechanism of ferroelectricity in 2D ferroelectrics according to the direction of ferroelectric polarization. Second, two main types of 2D ferroelectric structures are discussed according to the different roles of ferroelectric components in the devices. Finally, the application of 2D ferroelectric devices for in‐sensing and in‐memory computing (IMC) and neural networks are discussed, and the challenges and prospects in this rapidly developing research field are summarized. Figure 1 2D Ferroelectrics device: materials, structures, and applications. a) Reproduced with permission. [ \n \n 14 \n \n ] Copyright 2017, Springer Nature. b) Reproduced with permission. [ \n \n 12 \n \n ] Copyright 2016, Springer Nature. c) Reproduced with permission. [ \n \n 57 \n \n ] Copyright 2021, Springer Nature. d) Reproduced with permission. [ \n \n 82 \n \n ] Copyright 2023, John Wiley and Sons. e) Reproduced with permission. [ \n \n 54 \n \n ] Copyright 2016, American Chemical Society. f) Reproduced with permission. [ \n \n 84 \n \n ] Copyright 2021, John Wiley and Sons."
} | 1,913 |
23226971 | PMC3512248 | pmc | 3,724 | {
"abstract": "Phylogenomic analyses of archaeal genome sequences are providing windows into the group's evolutionary past, even though most archaeal taxa lack a conventional fossil record. Here, phylogenetic analyses were performed using key metabolic genes that define the metabolic niche of microorganisms. Such genes are generally considered to have undergone high rates of lateral gene transfer. Many gene sequences formed clades that were identical, or similar, to the tree constructed using large numbers of genes from the stable core of the genome. Surprisingly, such lateral transfer events were readily identified and quantifiable, occurring only a relatively small number of times in the archaeal domain of life. By placing gene acquisition events into a temporal framework, the rates by which new metabolic genes were acquired can be quantified. The highest lateral transfer rates were among cytochrome oxidase genes that use oxygen as a terminal electron acceptor (with a total of 12–14 lateral transfer events, or 3.4–4.0 events per billion years, across the entire archaeal domain). Genes involved in sulfur or nitrogen metabolism had much lower rates, on the order of one lateral transfer event per billion years. This suggests that lateral transfer rates of key metabolic proteins are rare and not rampant.",
"conclusion": "4. Conclusions Although metabolic genes are often considered to have been frequently swapped between prokaryotic lineages via the process of lateral gene transfer, a phylogenomic approach using explicit phylogenetic reconstruction and ancestral state reconstruction suggests that lateral transfer events involving metabolic genes are rare, across deep geologic time. These stable transfers likely occurred only a small number of times on the order of <1–3 transfer events per billions of years.",
"introduction": "1. Introduction Although there are still active debates over the extent of lateral gene transfer (LGT) in prokaryotic genomes [ 1 ], phylogenomic analyses using large sets of slowly evolving universally present genes have been producing an increasingly clearer picture of the core evolutionary signal in prokaryotic genomes, particularly for the archaeal domain of life [ 2 , 3 ]. The actual rates of lateral gene transfer (in terms of number of events per billion years) are largely unknown, yet there is a growing recognition that lateral transfer among some sets of genes, particularly metabolic genes, appears to be higher than in other sets of genes [ 4 – 7 ]. Such lateral transfer events have no doubt led to the acquisition of new traits that help to redefine the metabolic niche of microorganisms, and these events have therefore had a profound influence on the evolving biogeochemical cycles on the Earth [ 8 , 9 ]. Previous work on the archaeal domain of life has suggested an emerging evolutionary conservation between the habitat preference and metabolic traits [ 10 ]. These niches have been changing through deep time as biogeochemical cycles gradually became more complex, particularly once oxygen became available in the biosphere. This work is set about explicitly quantifying the rate of lateral gene acquisition events amongst key metabolic genes in the archaeal domain of life, using a phylogenetic approach. Phylogenetic trees of metabolic genes were constructed, and clades that were congruent with the slowly evolving core phylogenomic signal were identified in order to quantify the number of lateral gene transfer events (gains in new metabolic traits) for each gene. Inferred node ages were then used to identify when, in deep time, new metabolic traits could have been acquired via lateral gene transfer. This study shows that lateral transfer rates were highest among the genes using oxygen as a terminal electron acceptor, and lowest amongst genes involved in redox reactions between sulfur and sulfide. Nevertheless, the overall rate of metabolic gene acquisition in the archaeal domain appears to have been low—with cytochrome oxidase having the highest average rate of 3.4–4.0 events per billion years.",
"discussion": "3. Results and Discussion 3.1. Genome Phylogeny, Ancestral State Reconstruction, and Relaxed Clock \n Figure 1 is a phylogenetic tree constructed using a 100-gene concatenated dataset. The branch lengths are scaled to geologic time, using age constraints for the archaeal domain of life developed by Blank [ 11 , 19 ], and the likelihood that the ancestral genomes have cytochrome oxidase is indicated on the nodes of the tree using ancestral state reconstruction. This approach inferred that the ancestor to the Halobacteriales likely contained cytochrome oxidase in its genome. It also infers a high likelihood that the ancestor to Ferroplasma-Picrophilus and the Sulfolobales contained cytochrome oxidase. There is a high likelihood that the ancestor to Thermoproteus-Pyrobaculum contained cytochrome oxidase, yet it is less likely that cytochrome oxidase traces further back in the Thermoproteales. This demonstrates how ancestral state reconstruction can be used to identify potential lateral gene transfer (LGT) events in terms of the acquisition of novel metabolic genes that help define the metabolic niche of an organism (in this case, aerobic respiration). Ultimately, however, to quantify the number of lateral transfer events, one must also closely examine the phylogenetic trees that underlie the trait in question in order to confirm the hypothesis that these ancestors likely did acquire the cytochrome oxidase gene. 3.2. Cytochrome Oxidase In the Euryarchaeota, phylogenetic analysis of cytochrome oxidase subunit I (and the cytochrome oxidase I portion of genes with fused I and III subunits; Figure 2(a) ) shows four well-supported clades. One clade is comprised of CoxAC (where cytochrome oxidase subunits I and III are fused) from Picrophilus and Ferroplasma (labeled as node 1 in the figure). Sequence characteristics and phylogenetic relationships show that these proteins belong to the Heme-copper oxygen reductase family A1 [ 23 ]. This phylogenetic pattern, in addition to ancestral state reconstruction ( Figure 1 ), suggests that CoxAC was likely gained by an LGT event in the ancestor to Picrophilus and Ferroplasma . Three additional clades of cytochrome oxidase were observed in the Halobacteriales: CoxA1 (subunit I, family A1, node 2), CoxA2 (concatenated subunits I and III, family B, node 3), and CoxA3 (subunit I, family B, node 4). Most branching relationships within and between the clades showed moderate to poor support ( Figure 2(a) ). Ancestral state reconstruction suggests the presence of a cytochrome oxidase in the ancestor of the Halobacteriales ( Figure 1 ). Ancestral state reconstruction using each form of the cytochrome oxidase genes predicts CoxA1 and CoxA3 (but not CoxA2, not shown) in the Halobacteriales ancestor. Halobacterium spp. formed the basal branch in the Halobacteriales in the genome tree. However, in the cytochrome oxidase tree, Halobacterium branched higher up in the CoxA1 and CoxA3 clades, suggesting that both sets of genes were acquired by independent lateral transfer events from donors outside the Euryarchaeota. A global phylogenetic tree containing families A and B sequences from Bacteria and Archaea (not shown; [ 23 , 24 ]) shows CoxA1 and CoxA2 falling in a well-supported clade within family A1, and CoxA3 branching within family B. CoxA1 and CoxA2 did not branch as sister taxa with internal relationships consistent with the genome tree, which would be expected if the two were related by ancient gene duplication. CoxA2 homologs had a limited taxonomic distribution—only being observed in Halorubrum, Haloterrigena, Haloarcula, Haloferax, and Halogeometricum. Thus, CoxA2 was likely gained by an LGT event in the ancestor of these species (after the Halobacterium lineage diverged from the main Halobacteriales line of descent). Finally, one sequence from Natronomonas pharaonis (node 5) was rather divergent, branching with Magnetospirillum outside of any of the other archaeal clades. This is consistent with a recent lateral gene transfer event. In sum, five LGT events in the Euryarchaeota can be proposed to explain the phylogenetic pattern for cytochrome oxidase I and III (within the set of euryarchaeal taxa included in this study; Table 1 ). Phylogenetic analysis of cytochrome oxidase subunit II in the Euryarchaeota ( Figure 2(b) ) similarly resulted in four clades. One clade contained Picrophilus and Ferroplasma (node 2). Examination of the physical location of the CoxB with CoxAC genes in the Picrophilus genome showed that they were linked, separated in the genome by a single open reading frame. The same observation was found for Ferroplasma . Thus, the LGT event that led to the acquisition of CoxAC also led to the acquisition of CoxB in the ancestor to Picrophilus and Ferroplasma. Three additional clades (CoxB1, CoxB2, and CoxB3; nodes 2–4) were found containing Halobacteriales taxa. Most branching relationships within and between the clades also showed moderate to poor support. Homologs to CoxB1 and CoxB3 (but not CoxB2) were observed in the two Halobacterium species. Again, Halobacterium CoxB1 and CoxB3 are found high up in the clade. Most genes in the CoxA1 and CoxB1 clades were physically linked, as were the genes for CoxA2 and CoxB2 and CoxA3 and CoxB3. Thus, the three LGT events that led to the acquisition of CoxA1, CoxA2, and CoxA3 also likely led to the acquisition of CoxB1, CoxB2, and CoxB3. The CoxB3 in Natronomonas (node 5) was also physically linked in the genome to CoxA3, that suggesting these two genes were acquired in a single LGT event as well. In sum, the total number of inferred LGT events for cytochrome oxidase subunits in the Euryarchaeota taxa included in this study is five ( Table 1 ). Phylogenetic analysis of cytochrome oxidase in the Crenarchaeota ( Figure 3(a) ) was somewhat more complex. In the Thermoproteales, a fused CoxAC was found in Pyrobaculum calidifontis, P. oguniense , P. sp. 1860, P. aerophilum, and Thermoproteus uzoniensis (node 1). This protein falls under the Heme-copper oxygen reductase family A1 [ 23 ]. Another copy, CoxA, was found in Pyrobaculum calidifontis, P. oguniense, P. sp. 1860, and P. aerophilum (node 2). This second copy, a member of family B, branched separately from the CoxAC clade. Another CoxA in the family B was found in Caldivirga (node 3); however, this sequence did not branch with CoxA sequences from closely related Pyrobaculum spp. A global phylogenetic tree containing families A and B sequences from Bacteria and Archaea (not shown; [ 24 ]) showed Pyrobaculum CoxAC sequences branched with Sulfolobales SoxM sequences and family A1 sequences from Bacteria. Pyrobaculum CoxA sequences branched in a distinct location in the tree, in a well-supported clade with Aeropyrum, Halobacteriales CoxA3, and a wide diversity of bacterial family B sequences. The Caldivirga sequence branched with a phylogenetically distinct group of bacterial family B sequences from the Firmicutes and Proteobacteria. Relationships within the Pyrobaculum species of both clades, nevertheless, are congruent with the genome phylogeny ( Figure 1 ). Thus, the simplest explanation for the observed phylogenetic pattern is three independent LGT events in the Thermoproteales: CoxA in Caldivirga , CoxA in the ancestor to Pyrobaculum spp., and CoxAC in the ancestor to Pyrobaculum-Thermoproteus . Three LGT events in the Thermoproteales were also inferred for Caldivirga and Pyrobaculum spp. using ancestral state reconstruction ( Figure 1 ). Four clades of cytochrome oxidases were observed in the Sulfolobales, corresponding to SoxM (fused subunits I and III, family A1, node 4), SoxB (subunit I, family B, node 5), DoxB (subunit I, family B, node 6), and a distinct clade in Sulfolobus tokodaii and Metallosphaera spp. (FoxA, family B, node 7). SoxM branched sister to Pyrobaculum-Thermoproteus CoxAC (65% and 100% support using MP and MB, resp.). This sister group relationship was also strongly supported in the global phylogeny containing Archaea and Bacteria (not shown), consistent with one LGT gain in the ancestor to the Sulfolobales. SoxB and DoxB formed a poorly to moderately well-supported clade with Pyrobaculum and Aeropyrum CoxA. However, branching relationships between and within the clades were often poorly or moderately supported. It is likely that SoxB and DoxB arose by a single LGT event followed by an ancestral gene duplication event (with subsequent duplications in Metallosphaera and S. tokodaii ), given that branching relationships and the rooting of these two clades are consistent with the genome phylogeny. Nevertheless, phylogenetic trees with crenarchaeal cytochrome oxidases, crenarchaeal family B cytochrome oxidases, and the global family B tree are unresolved, and therefore, it cannot be formally ruled out that SoxB and DoxB were obtained by two independent LGT events. The FoxA clade is a newly identified clade of Heme-copper oxygen reductases found in Sulfolobus tokodaii and Metallosphaera spp. Transcriptional studies show that these genes are expressed under Fe(II)-oxidizing conditions [ 25 ]. Phylogenetic analysis shows that this sequence belongs to the family B of Heme-copper oxygen reductases; however, branching relationships between Sulfolobales SoxB, DoxB, and other archaeal cytochrome oxidases are unresolved ( Figure 3(a) , not shown). Thus, it is possible that FoxB arose by ancient gene duplication from a SoxB or DoxB ancestor, or it could have been gained independently via LGT. Thus, the minimal total number of LGT events involving cytochrome oxidases in the Sulfolobales is 2, while the maximum number of events is 4. In the Desulfurococcales, CoxA and CoxAC (members of Heme copper oxygen reductase families B and A1, resp., nodes 8 and 9) were found in Aeropyrum pernix . Both branched with their respective homologs in distantly related Pyrobaculum, suggestive of two independent LGT events leading to two copies of cytochrome oxidase in Aeropyrum. This scenario is also predicted using ancestral state reconstruction ( Figure 1 ). In summary, a minimum of 7 LGT events, and a maximum of 9, can be proposed to explain the phylogenetic pattern for cytochrome oxidase I and III in the Crenarchaeota ( Table 1 ). The phylogenetic tree for crenarchaeal cytochrome oxidase subunit II was similar to that for subunit I. Two clades containing Pyrobaculum spp. (PoxH and PoxB) were comprised of the same species, with similar branching relationships, to CoxAC and CoxA (nodes 1 and 2). The PoxH and PoxB genes were also physically linked to CoxA and CoxAC genes in the genome; thus, the two LGT events that likely led to the acquisition of CoxA and CoxAC in the ancestor to Pyrobaculum also led to the acquisition of PoxH and PoxB. The Caldivirga homolog branched separately from the Pyrobaculum clades, and this gene was physically linked to CoxA (evidence that the LGT event leading to the acquisition of Caldivirga CoxA also led to the acquisition of CoxB, node 3). In the Sulfolobales, two cytochrome oxidase subunit II clades were found (SoxA and SoxH, nodes 4 and 5). Again, branching relationships within the clades were only moderately or poorly supported; however, most of the genes for SoxA are physically linked to SoxB; thus, the LGT event leading to the acquisition of SoxB in the Sulfolobales also likely led to the acquisition of SoxA. Similarly, homologs in the SoxH clade are physically linked to SoxM; thus, the LGT leading to SoxM acquisition also likely transferred SoxH. Two Aeropyrum lineages are also seen in the cytochrome oxidase II tree, with branching relationships seen in the subunit I+III tree (nodes 8 and 9). One copy branched sister to the PoxH from Pyrobaculum and thus was likely acquired in the same LGT event that led to the acquisition of CoxA in Aeropyrum. The branching position of the second homolog was poorly supported. Nevertheless, this homolog is physically linked to the CoxAC gene in Pyrobaculum , and so it was most likely acquired in the same LGT event that led to the acquisition of CoxAC. In summary, seven LGT events can be proposed to explain the phylogenetic pattern for cytochrome oxidase II in the Crenarchaeota; however, these genes were obtained in the same LGT events that lead to the acquisition of cytochrome oxidase I and III. 3.3. Cytochrome bd-Type Quinol Oxidase In the Euryarchaeota, cytochrome bd quinol oxidase subunit 1 fell into five clades ( Figure 4(a) ). The first clade (node 1) contained Thermococcus gammatolerans and T. sp. AM4, consistent with an LGT event in the ancestor to these closely related species. The second clade contained Halobacteriales taxa (node 2). Halobacterium in this clade branched high up in the clade, suggesting either that the LGT donor came from outside the Euryarchaeota or that euryarchaeal taxa higher up in the tree then became the donor for other euryarchaeal groups. The third clade (node 3) contained Methanosarcina acetivorans and M. barkeri , consistent with another LGT gain in the ancestor to these closely related species. The fourth clade (node 4) was comprised of two adjacent quinol oxidase copies in the Archaeoglobus fulgidus genome, suggestive of a single LGT gain followed by a gene duplication event. The fifth clade was comprised of Thermoplasmatales taxa (node 5). The phylogenetic pattern of duplicate copies in this clade is consistent with a single LGT gain followed by a gene duplication event in the ancestor to Thermoplasma spp. and a second duplication in the ancestor to Ferroplasma and Picrophilus. Ancestral state reconstruction (not shown) was consistent with five gains in the Euryarchaeota. In the Euryarchaeota, cytochrome bd quinol oxidase subunit 2 fell into three clades ( Figure 4(b) ). The first clade (node 1) was comprised of Thermococcus gammatolerans and T. sp. AM4, the second (node 2) contained Halobacteriales taxa with branching relationships that were identical to those seen in the quinol oxidase 1 tree, and the third clade (node 3) contained Methanosarcina acetivorans and M. barkeri . Physical linkage between quinol oxidase 1 and 2 was observed for all euryarchaeal taxa; thus, the LGT events leading to the acquisition of quinol oxidase 1 in the Euryarchaeota also led to the simultaneous acquisition of quinol oxidase 2. In the Crenarchaeota, two copies of cytochrome bd quinol oxidase 1, forming sister clades, were found in Hyperthermus, Acidilobus, and most Thermoproteales taxa ( Figure 5 , nodes 1 and 2). Additional copies also were found in Vulcanisaeta distributa and V. moutnovskia (node 3). Phylogenetic analyses of the Thermoproteales taxa showed that neither sister clade was rooted with Thermofilum (as seen in the genome phylogeny); however, analyses of each sister clade in isolation (not shown) resulted in a tree that was congruent with the genome phylogeny. While this appears to be consistent with two independent LGT events in the ancestor to the Thermoproteales, examination of the genome positioning shows that both copies of quinol oxidase 1 in the Thermoproteales are adjacent in the genome. Thus, the most parsimonious explanation is a single LGT event that transferred two tandem, but distantly related, copies of quinol oxidase into the ancestor of the Thermoproteales. Two tandem copies of quinol oxidase 1 were also seen in Hyperthermus and Acidilobus (nodes 4 and 5), both branching sister to Thermofilum. The most likely explanation is a single LGT event, possibly from Thermofilum, which transferred the two tandem copies into Hyperthermus-Acidilobus (it is unlikely that the ancestor to Hyperthermus-Acidilobus was the donor for the Thermoproteales quinol oxidases, since the Thermoproteales clade is significantly older; Figure 1 ). One additional LGT event likely led to the acquisition of the third quinol oxidase copy in Vulcanisaeta spp. (node 3). Ancestral state reconstruction (not shown) was consistent with three independent LGT events in the Crenarchaeota. 3.4. Dissimilatory Sulfite Reduction DsrA and DsrB are related proteins that derived from an ancient gene duplication event [ 26 ]. The phylogeny of DsrA and DsrB ( Figure 6 ) shows two well-supported clades in the archaeal domain, one containing Archaeoglobus spp. (nodes 1 and 6) and the second containing Caldivirga, Vulcanisaeta, Pyrobaculum, and Thermoproteus species (nodes 2 and 7). Genomic positioning shows that DsrA is linked to DsrB in all species, and one copy of DsrA and DsrB is found in Caldivirga and Vulcanisaeta spp. However, three copies are found in most of the genomes of Pyrobaculum and Thermoproteus spp., all forming a monophyletic group (nodes 3–5 and 8–10). This suggests that multiple gene duplication events occurred involving DsrAB in the ancestor to the Pyrobaculum-Thermoproteus clade, followed by later gene losses in some taxa. Thus, two LGT events are postulated for DsrAB in the archaea: once in the Archaeoglobales and once early in history of the Thermoproteales ( Table 2 ). This is consistent with ancestral state reconstruction of the presence of DsrAB (not shown). 3.5. Thiosulfate Oxidation Thiosulfate sulfurtransferase (SseA) catalyzes the oxidation of thiosulfate to sulfite, while reducing cyanide to thiocyanate [ 27 ]. The phylogeny of the alpha subunit in the Euryarchaeota ( Figure 7(a) ) showed two clades of methanogen sequences (nodes 1–4) and two clades of sequences from the Halobacteriales (nodes 5 and 6). Little is known about thiosulfate metabolism in halophilic archaea; however, growing evidence suggests that many strains are able to oxidize it to sulfite and subsequently reduced to sulfide [ 28 , 29 ]. The four methanogen sequences were found in distantly related euryarchaeal lineages. This provides evidence for four independent LGT events in the methanogens and is consistent to the pattern observed by ancestral state reconstruction (not shown). The Halobacteriales thiosulfate sulfurtransferase fell into two clades (copies 1 and 2), neither of which was rooted with Halobacterium. The sequences from copy 1 were found to be immediately adjacent to sequences from copy 2, suggesting that the two copies have been inherited as a single unit. Ancestral state reconstruction shows a high likelihood that SseA was present in the ancestor of the Halobacteriales (not shown). In the Crenarchaeota ( Figure 7(b) ), thiosulfate sulfurtransferase was found in one member of the Desulfurococcales ( Aeropyrum ), many Thermoproteales, and many of the Sulfolobales. Thiosulfate stimulates growth in Aeropyrum and Pyrobaculum oguniense [ 30 , 31 ], can be oxidized by Pyrobaculum aerophilum [ 32 ], and serves as an electron acceptor in Caldivirga, Thermoproteus uzoniensis, and many Pyrobaculum spp. [ 33 , 34 ]. A single LGT event most likely led to the acquisition of SseA in Aeropyrum (node 1). The Thermoproteales sequences fell into three clades, one containing Caldivirga and P. aerophilum (node 2), another clade containing Caldivirga, Vulcanisaeta distributa, Thermoproteus uzoniensis , and Pyrobaculum spp. (node 3), and a clade containing P. sp. 1860 (node 4). Two LGT events may have led to the acquisition of two distantly related SseA copies in the ancestor to Caldivirga and Pyrobaculum , with a third acquisition in P . sp. 1860. The Sulfolobales sequences fell into two clades (nodes 5 and 6), both of which were rooted with Metallosphaera. Thus, two LGT events likely led to the acquisition of two distantly related SseA copies in the ancestor to the Sulfolobales. The inferred number of LGT events of thiosulfate oxidation in both the Euryarchaeota and Crenarchaeota is 11. 3.6. Sulfur Oxidation and Reduction Sulfur oxygenase reductase (SOR) catalyzes the aerobic disproportionation of sulfur into sulfide and bisulfite. SOR activity in the archaea has been demonstrated for a number of Sulfolobales taxa [ 3 ] and more recently in Acidianus tengchongensis [ 35 ]. Homologs were also found in Sulfolobus tokodaii, S. metallicus, Acidianus, and Desulfurolobus . Phylogenetic analysis ( Figure 8(a) ) shows that these sequences all form a monophyletic group, and thus, SOR was likely gained in the Sulfolobales (node 1). In the Euryarchaeota, putative homologs to SOR were found in the genomes of Ferroplasma and Picrophilus (node 2). Given that these two species are closely related, it is likely that SOR was also acquired via LGT in the ancestor to these two taxa. Sulfur reductase catalyzes the reduction of sulfur or polysulfide to hydrogen sulfide. In the archaeal domain, the SreABCDE gene cluster has been demonstrated in Acidianus to carry out the reduction of sulfur [ 36 ]. Putative homologs have been found in the members of Sulfolobus species (node 1). Their phylogenetic relationships ( Figure 8(b) ) were congruent with the genome phylogeny; thus, sulfur reductase was likely acquired in the ancestor to the Sulfolobales. Flavocytochrome c sulfide dehydrogenase (FCSD) in Bacteria results in the anaerobic oxidation of sulfide to sulfur. Homologs to this FCSD have been identified in a number of Archaea (including Ignicoccus, Caldivirga, Pyrobaculum, Vulcanisaeta, Thermoproteus, Metallosphaera, Acidianus, and Sulfolobus tokodaii ); however, FCSD activity has yet to be demonstrated in the archaeal domain. Phylogenetic analysis shows that archaeal FCSD forms a monophyletic group (node 1, Figure 8(c) ) with a sister group relationship with bacterial proteins with demonstrated FCSD activity. Sequences from the Thermoproteales taxa Caldivirga, Pyrobaculum, Thermoproteus, and Vulcanisaeta formed a monophyletic group (node 2), with branching relationships consistent with the genome phylogeny, and ancestral state reconstruction predicted the presence of this gene in the ancestor of these taxa. An FCSD homolog was also found in Ignicoccus —this was likely gained by an independent LGT event (node 3). Similarly, the FCSD homolog in Acidianus , Metallosphaera , and Sulfolobus tokodaii formed a monophyletic group (node 4), so their common ancestor also likely gained this gene by LGT. Sulfide quinone oxidoreductase (SQO) also catalyzes the anaerobic oxidation of sulfide to sulfur and shows sequence similarity to FCSD as well as to a large number of widely distributed putative homologs annotated as FAD-dependent pyridine-nucleotide disulfide oxidoreductases. In the Euryarchaeota, SQO is found in the Thermoplasmatales (node 1, Figure 9 ). Phylogenetic analyses and ancestral state reconstruction suggested that SQO was acquired by an LGT event in the ancestor of this group. In the Crenarchaeota, SQO (including the SQO from Acidianus with demonstrated activity) was found in the Sulfolobales, and the branching patterns in this group were congruent with the genome phylogeny (node 2). Thus, SQO in the Sulfolobales was likely gained in the ancestor to this group. Archaeal SQO homologs formed a monophyletic group sister to demonstrated SQO homologs in the bacterial domain, distinct from FCSD and the putative pyridine-dinucleotide disulfide oxidoreductases (not shown). 3.7. Nitrate and Nitrite Reduction Nitrate reductase reduces nitrate to nitrite during anaerobic respiration. The catalytic and electron transfer subunits of nitrate reductase (Figures 10(a) , and 10(b) ) in the Euryarchaeota were found in Ferroglobus (node 1) and in a clade that contains several Halobacteriales taxa (node 2). In the Crenarchaeota nitrate, reductase subunits were found in the distantly related taxa Aeropyrum (node 3) , Vulcanisaeta distributa (node 4) , Metallosphaera yellowstonensis (node 5), two strains of Sulfolobus islandicus (node 6), and a clade containing Pyrobaculum spp. (node 7). Because nitrate reductase was not found in Halobacterium spp., it is likely that the latter acquisition occurred after diversification of Halobacterium. The phylogenetic patterning of the nitrate reductase catalytic subunit ( Figure 10(a) ) is consistent with seven LGT events. The phylogenies of NarG and NarH were identical, and the two genes were adjacent in the genomes of all taxa. Thus, the LGT events leading to the acquisition of NarG also likely led to the acquisition of NarH ( Table 3 ). Dissimilatory nitrite reductases (NirK) reduce nitrite to either nitric oxide or nitrous oxide. Copper-type nitrite reductases have been identified in some Halobacteriales taxa ( Figure 11(a) ; [ 37 ]). Phylogenetic analyses show that they fall into a monophyletic group (node 1). The phylogenetic pattern suggests that they could have been acquired once, likely after the diversification of Halobacterium spp. However, ancestral state reconstruction suggests that two independent gains could have also occurred (nodes 1 and 2), once being in the ancestor to Haloferax and Halogeometricum. \n Homologs to bacterial Heme-type nitrite reductases (NirS) have also been identified in the genomes of three Pyrobaculum species. These also formed a well-supported monophyletic group (node 1, Figure 11(b) ). Each of the Pyrobaculum sequences is linked to cytochrome c proteins (not shown) that form part of the nitrite reductase complexes in bacteria. Thus, a single LGT event likely led to the acquisition of Heme-type nitrite reductases in the ancestor to Pyrobaculum spp. Nitric oxide reductase (NorB) reduces nitric oxide to nitrous oxide. Phylogenetic analysis showed four well-supported clades of NorB in the archaeal domain ( Figure 11(c) ). One clade contained the Halobacteriales (but not Halobacterium , node 1), another contained S. solfataricus and S. islandicus species (node 2), a third contained the genus Acidilobus (node 3), and the fourth (ode 4) contained several taxa in the Thermoproteales ( Caldivirga, Vulcanisaeta distributa, Thermoproteus uzoniensis, and several Pyrobaculum spp.).This is consistent with four lateral transfer events. Finally, nitrous oxide reductase (NosZ) reduces nitrous oxide to nitrogen. NosZ homologues ( Figure 11(d) ) were found in only a few archaeal taxa: Haloarcula, Halobacterium, Halobiforma, Haloferax, Halogeometricum, Halopiger, and Halorubrum in the Halobacteriales (node 1), Ferroglobus (node 2), and two species of Pyrobaculum (node 3). The phylogenetic pattern was consistent with three lateral transfer events: once in the Halobacteriales, once in Ferroglobus , and once in the clade containing Pyrobaculum spp. 3.8. Nitrogen Fixation As has been noted by many investigators, the distribution of nifH-like genes (making up “cluster 4”; Figure 12(a) ) is widespread among methanogens, including the basal lineage Methanopyrus . These proteins have recently been shown to form a complex with NifD-like proteins, yet they have no apparent role in nitrogen fixation [ 38 ]. True NifH genes involved in nitrogen fixation, however, show a much smaller taxonomic range. Indeed, the ability to fix nitrogen is found only in a small number of methanogens ( Methanococcus maripaludis, M. thermolithotrophicus, M. aeolicus, M. vannielii, Methanothermobacter thermoautotrophicum, Methanosarcina barkeri, Methanospirillum, and Methanocaldococcus FS406-22 [ 32 , 39 , 40 ]). Many investigators have inferred a significant role for LGT as well as gene duplication events in the evolutionary history of archaeal nitrogen fixation genes, while a cursory look at the NifH phylogenetic tree ( Figure 12(a) ) is consistent with previous proposals that nitrogenase first arose in the methanogens [ 41 ]. A more detailed analysis suggests that this may not be the case. Nitrogenase reductases (NifH) fell into a well-supported clade comprising “clusters 2 and 3” ( Figure 12(a) , clades outlined in green) to the exclusion of proteins related to NifH that constitute “cluster 4” (clades outlined in gray). Within “cluster 2” sequences, an AU test showed that the genome phylogeny and the NifH phylogeny for the Methanococcales were not significantly different ( P = 0.068). Relationships within and between the Methanomicrobiales and Methanosarcina were identical to the genome phylogeny. In contrast, an AU test showed that the “cluster 2” phylogeny (including Methanococcales, Methanobacteriales, Methanomicrobiales, and Methanosarcinales) was significantly different from the genome phylogeny ( P = 2 e \n −5 ). The point of major incongruence was in the positioning of Methanobacteriales with respect to the Methanococcales and the rooting of the Methanococcales. In addition, “cluster 3” sequences showed positioning that was different from the genome tree. This suggests four lateral transfer events of nitrogenase: once in the ancestor to the Methanococcales (node 1), once in the Methanobacteriales (node 2), once in the ancestor to the Methanomicrobiales and Methanosarcinales (node 3), and once again in Methanosarcina spp. (resulting in the acquisition of the alternative nitrogenases of “cluster 3”; node 4). The nitrogenase alpha subunit (NifD) and the FeMo cofactor subunit (NifE) are related to one another, likely as a result of an ancestral gene duplication event [ 42 ]. Observations of relationships in these trees ( Figure 12(b) ) as well as AU tests (not shown) revealed relationships that were not significantly different from the relationships observed in the NifH tree. In terms of genomic positioning, “cluster 2” NifH is linked to “cluster 2” NifD and NifE. This suggests that when NifH was acquired by LGT, so were NifD and NifE ( Table 3 ). 3.9. Degradation of Select Organocompounds Most Thermococcales cultures have never been tested for chitin or chitosan (a form of deacetylated chitin) degradation [ 43 , 44 ]; however, these organisms do inhabit deep sea hydrothermal vent ecosystems which harbor abundant chitin-containing metazoans (such as giant tube worms, crabs, and shrimp). Two genes with sequence similarity to known chitinases are present in the genomes of Pyrococcus furiosus and Thermococcus kodakarensis. Biochemical studies demonstrated that these genes exhibit chitinase activity [ 45 , 46 ]. The chitinase genes are part of a unique chitin degradation pathway that includes at least three additional unique genes (exo- β -D-glucosaminidase, glucosamine-6-phosphate deaminase, and diacetylchitobiose deacetylase) [ 47 , 48 ]. Phylogenetic analyses demonstrated that chitinases in the Thermococcales formed a well-supported monophyletic group (node 1, Figure 13 ), as did the three additional genes involved in chitin degradation (now shown). The chitinase and chitin degradation genes are all closely spaced along the genome. Thus, there was likely a single LGT event that led to the acquisition of chitin degradation genes in the ancestor to the Thermococcales ( Table 4 ). Many Halobacteriales also have chitinase-like genes. The halophilic archaea are not generally considered to be chitin degraders, however a recent study has demonstrated that Halobacterium sp. NRC-1 is capable of degrading chitin [ 49 ] and they live in environments where brine shrimp are often abundant. Thus, it is possible that, as in the Thermococcales, chitin degradation has been underestimated in the Halobacteriales. Several copies of chitinases are seen in the genomes of many Halobacteriales taxa (node 2, Figure 13 ), nevertheless they all appear to form a monophyletic group, and the multiple copies are often adjacent or nearby in the genome. Two subclades were seen to have mirror relationships, suggesting that two copies of chitinases were obtained by LGT in the ancestor of the Halobacteriales. Neither of these subclades, however, is rooted with Halobacterium (as seen in the genome phylogeny), and thus the two subclades likely did not arise as a result of ancestral or recent gene duplication. Rather the two copies were likely acquired by the same LGT event from a donor that came outside of the Thermococcales or Halobacteriales. This was then likely followed by losses of chitinase genes in other taxa in the Halobacteriales, including Natrialba, Halorubrum, Haloquadratum, Natronomonas, Halorhabdus, and Haloarcula. \n Protein sequences related to those that catalyze the degradation of phenolic compounds, such as phenol, catechol, and phenylacetate, were found in the genomes of several archaeal groups (Figures 14 and 15 ). In the bacterial domain, members of class II extradiol dioxygenases have been shown to be capable of adding hydroxyl groups to the phenolic rings of catechol and hydroxyphenylacetate. Sulfolobus solfataricus P2 has been demonstrated to be able to use phenol, catechol, and toluene as sole carbon sources [ 50 ]; however, its close relative S. solfataricus 98/2 cannot be due to an insertion element in the operon containing phenol hydroxylase (which is also transcribed only in the presence of phenol; [ 51 ]). Archaeal relatives of multicomponent monooxygenases that hydroxylate phenol, toluene, and xylene have been observed in Pyrobaculum arsenaticum, Sulfolobus solfataricus strains, and several strains of Sulfolobus islandicus ( Figure 14(a) ). The phylogenetic pattern suggests a single LGT acquisition in Pyrobaculum arsenaticum (node 1), and a second acquisition in the ancestor to S. solfataricus-S. islandicus (node 2). Relatives of monooxygenases that are involved in the hydroxylation of 4-hydroxyphenylacetate were also observed in the Sulfolobales, annotated as HpaH ( Figure 14(b) ). These sequences formed a well-supported clade and were found in Metallosphaera spp ., S. tokodaii, S. acidocaldarius, one strain of S. solfataricus (P2), and all strains of S. islandicus. This suggests two LGT acquisition events: once in Pyrobaculum oguniense (node 1) and once again in the ancestor to the Sulfolobales (node 2). Relatives of class II extradiol dioxygenases that are involved in cleavage of the phenolic rings of catechol and dihydroxyphenol acetate ( Figure 15(a) ) have been found in the genomes of the Sulfolobales. These sequences were observed to fall into two distinct well-supported clades. One clade (node 1) comprised both strains of Sulfolobus solfataricus and four strains of S. islandicus . Biochemical analyses have shown the proteins in both strains of S. solfataricus function as a catechol 2,3 dioxygenase and 4-chlorocatechol dioxygenase [ 50 , 51 ]. The taxonomic distribution in this clade was identical to that found in the clade of phenol hydroxylases in the Sulfolobales (node 2, Figure 14(a) ), their phylogenetic trees were identical, and, indeed, the proteins were found to be located nearby in the genome in all taxa. The second clade (node 2), annotated as HpaD, was more widespread in the Sulfolobales being present in Metallosphaera spp. (two to three copies), S. tokodaii, S. acidocaldarius, S. solfataricus P2,and all strains of S. islandicus . A third ortholog was identified in Pyrobaculum oguniense (node 3). Although the function of the archaeal HpaD ortholog has yet to be demonstrated, phylogenetic analyses suggest its functions as a 3,4-dihydroxyphenyl acetate dioxygenase [ 50 ]. All HpaH sequences in the Sulfolobales genomes were adjacent to the HpaD sequences in class II extradiol dioxygenases ( Figure 14(a) ). Relationships within the catechol dioxygenase and HpaD clades showed only a moderate or poor support; branching relationships however were similar to those observed in the genome phylogeny. Relatives of class III extradiol dioxygenases were also found in the genomes of the Sulfolobales, Thermoplasma acidophilum, Pyrobaculum arsenaticum , and three small groups of distantly related methanogens ( Figure 15(b) ). The biochemical function of these proteins in the archaeal domain is presently not known. A well-supported clade (node 1) contained relatives from Metallosphaera, S. tokodaii, S. acidocaldarius, S. solfataricus 98/2, and all strains of S. islandicus. Again, relationships within the clade were only moderately or poorly supported; however, branching relationships were similar to those in the genome phylogeny suggesting a single lateral transfer event in the ancestor to the Sulfolobales. Interestingly, the genes from all S. islandicus strains were found to be nearby the class II HpaD extradiol sequences, but such juxtaposition was not observed for any other taxa in the Sulfolobales. A homolog is also found in Thermoplasma acidophilum (node 3), consistent with a single gain in this taxon. A lone homolog to class III extradiol dioxygenase was also found in Pyrobaculum arsenaticum (node 3, Figure 15(b) ), located in a similar location in the genome as the protein for phenol hydroxylase (node 1; Figure 14(b) ), and both branch sisters suggesting they arose by a common LGT event followed by gene duplication. The phylogenetic distribution of the methanogen sequences suggests independent LGT events into three distantly related groups of methanogens (nodes 4–6). In sum, the phylogenetic pattern suggests six independent gains of class III extradiol dioxygenases by LGT events. 3.10. Rates of Lateral Transfer \n Table 5 summarizes the number of LGT events per gene and converts this to a rate of number of LGT events over the age span of the archaeal domain (approximately 3.5 billion years). The gene with the highest transfer rate was thiosulfate sulfurtransferase (SseA), calculated to have been transferred 3.1 times per billion years across the entire archaeal domain of life. Cytochrome oxidase, quinol oxidase, and the genes coding for the oxidation of phenolic compounds were also transferred at higher rates (2.6–3.4 events per billion years). Most of other genes showed a small number of lateral transfer events, amounting to less than one transfer event per billion years. This suggests that gene acquisitions, and hence changes in the metabolic niches of archaea, have been slowly changing over geologic time. This observation is consistent with the complexity hypothesis [ 4 – 7 ]."
} | 10,546 |
32038551 | PMC6988714 | pmc | 3,725 | {
"abstract": "Agroforestry, which is the integration of trees into monoculture cropland, can alter soil properties and nutrient cycling. Temperate agroforestry practices have been shown to affect soil microbial communities as indicated by changes in enzyme activities, substrate-induced respiration, and microbial biomass. Research exploring soil microbial communities in temperate agroforestry with the help of molecular tools which allow for the quantification of microbial taxa and selected genes is scarce. Here, we quantified 13 taxonomic groups of microorganisms and nine genes involved in N cycling (N 2 fixation, nitrification, and denitrification) in soils of three paired temperate agroforestry and conventional monoculture croplands using real-time PCR. The agroforestry croplands were poplar-based alley-cropping systems in which samples were collected in the tree rows as well as within the crop rows at three distances from the tree rows. The abundance of Acidobacteria, Actinobacteria, Alpha- and Gammaproteobacteria, Firmicutes, and Verrucomicrobia increased in the vicinity of poplar trees, which may be accounted for by the presence of persistent poplar roots as well as by the input of tree litter. The strongest population increase was observed for Basidiomycota, which was likely related to high soil moisture, the accumulation of tree litter, and the absence of tillage in the tree rows. Soil microorganisms carrying denitrification genes were more abundant in the tree rows than in the crop rows and monoculture systems, suggesting a greater potential for nitrate removal through denitrification, which may reduce nitrate leaching. Since microbial communities are involved in critical soil processes, we expect that the combination of real-time PCR with soil process measurements will greatly enhance insights into the microbial control of important soil functions in agroforestry systems.",
"conclusion": "Conclusion Poplar rows in temperate agroforestry systems increased the abundance of several soil bacterial and fungal groups as compared to the crop rows of agroforestry and monoculture croplands. Tree litter input (leaves, twigs, roots) as well as the abundant and persistent tree roots likely contributed to the stimulation of soil microflora under the trees. In addition, the absence of tillage in the tree rows presumably favored fungal communities, particularly Basidiomycota. The poplar rows further promoted the growth of microorganisms harboring denitrification genes, which was likely due to high soil moisture in the tree rows. The higher abundance of denitrification genes suggests that poplar trees may support removal of nitrate from soil via denitrification, and thus minimize nitrate leaching. We suggest that combining our measurements on microbial abundance and N-cycling genes with measurements of soil processes (such as nutrient leaching and soil greenhouse gas fluxes) will enhance insights into the microbial controls of important soil functions of temperate agroforestry systems such as climate regulation and water purification.",
"introduction": "Introduction Modern agroforestry systems (e.g., alley-cropping of crops and short-rotation trees) have been recognized as multifunctional systems that can reduce nitrate leaching, increase carbon sequestration, and increase pollination services ( Kay et al., 2018 ). Likewise, the practice of agroforestry in Europe can enhance biodiversity and soil fertility relative to monoculture agriculture ( Torralba et al., 2016 ), while also maintaining agricultural productivity ( Pardon et al., 2018 ; Swieter et al., 2018 ) and food safety of small-grain cereals such as wheat ( Triticum aestivum ) and barley ( Hordeum vulgare ) ( Beule et al., 2019b ). In such systems, ecological interactions between crops and trees can yield greater overall resource-use efficiency if the positive interactions outweigh competitive effects ( Cannell et al., 1996 ; van Noordwijk et al., 2015 ). For example, deep-rooting trees are able to take up leached nutrients from soil layers that are not accessible to crops ( Allen et al., 2004 ; Wang et al., 2011 ). Depending on the age of the tree component, temperate agroforestry has shown to increase soil organic carbon (SOC) stocks and soil nutrient availability, especially close to the trees ( Cardinael et al., 2015 , 2019 ; Pardon et al., 2017 ). In order to account for the spatial heterogeneity within agroforestry systems, several studies applied transectal sampling strategies such as sampling in the crop rows at different distances from the tree rows ( Cardinael et al., 2015 ; Pardon et al., 2017 ; Swieter et al., 2018 ). Over the past 20 years, a number of studies investigated soil microorganisms in temperate agroforestry systems by using enzyme assays, substrate-induced respiration or microbial biomass determination (e.g., Seiter et al., 1999 ; Lee and Jose, 2003 ; Mungai et al., 2005 ; Udawatta et al., 2008 , 2009 ; Rivest et al., 2013 ; Weerasekara et al., 2016 ; Sun et al., 2018 ; Beuschel et al., 2019 ). Agroforestry has been shown to increase functional diversity of enzyme activities ( Seiter et al., 1999 ; Mungai et al., 2005 ; Udawatta et al., 2008 , 2009 ; Unger et al., 2013 ; Weerasekara et al., 2016 ). In an alder ( Alnus rubra )-sweet corn ( Zea mays ) alley-cropping system, active bacterial and fungal biomass in the corn row declined with increasing distance from the tree row ( Seiter et al., 1999 ). In contrast to these results, Saggar et al. (2001) reported a strong suppression of soil microbial biomass by pine ( Pinus radiata ) trees planted in grassland. Increased fungi-to-bacteria ratios were reported in the tree row compared to the crop row of agroforestry systems ( Beuschel et al., 2019 ) and the analysis of phospholipid fatty acids showed increased abundance of gram-positive, gram-negative and anaerobic soil bacteria in agroforestry as compared to cropland soil ( Unger et al., 2013 ). Additionally, the integration of trees into agricultural fields decreased the metabolic quotient indicating a greater substrate-use efficiency of soil microorganisms ( Rivest et al., 2013 ; Beuschel et al., 2019 ). Molecular studies investigating microbial communities or functional genes in soils of temperate agroforestry systems are scarce. In an initial study, Udawatta et al. (2008) found that total soil-extractable DNA, used as a proxy for soil microbial biomass, was higher in agroforestry than in cropland and grassland but recommended the use of taxon-specific PCR assays to assess differences in soil microbial communities between the tree and crop rows. Their suggestion has only recently been implemented in a study of temperate agroforestry cropland and grassland which showed increased fungi-to-bacteria ratio under trees, and alterations of ammonium-oxidizing populations ( Beule et al., 2019a ). The investigation of genes involved in soil-N cycling in agricultural systems is important as these genes reveal the genetic potential to control N fluxes such as nitrous oxide (N 2 O) emissions. In a recent large-scale study, Banerjee et al. (2016) investigated soil bacterial communities in Canadian agroforestry systems using amplicon sequencing and concluded that agroforestry affects the abundance of certain bacterial taxa and supported bacterial growth, in general, but did not promote bacterial diversity ( Banerjee et al., 2016 ). The aim of this study was to investigate spatial variation in the abundances of major groups of soil bacteria and fungi and soil-N-cycling genes (N 2 fixation, nitrification, and denitrification) in temperate agroforestry as compared to conventional monoculture cropland. We accounted for the predicted spatial heterogeneity within agroforestry systems and sampled along transects spanning from the center of the tree row to the center of the crop row. We hypothesized that the trees in the agroforestry systems will promote the abundance of soil bacteria, fungi, and N-cycling genes due to improved soil properties from tree litter inputs and absence of tillage in the tree rows.",
"discussion": "Discussion The increased abundance of soil bacteria in the tree rows of the agroforestry systems ( Figures 2A , 3 ) corroborated previous findings of bacterial 16S rRNA abundance in Canadian agroforestry systems ( Banerjee et al., 2016 ). The high WFPS in the tree rows of the agroforestry systems ( Supplementary Table 3 ), a soil property which showed consistent difference from the crop rows and the monoculture croplands across all soil types ( Supplementary Table 3 ), likely contributed to the increased soil bacterial biomass and to the abundance of Acidobacteria, Actinobacteria, and Basidiomycota ( Figure 7 and Supplementary Table 4 ). Furthermore, high WFPS in the tree rows likely increased the abundance of microorganisms harboring narG , nirK , nirS , and nosZ clade I genes ( Figure 7 and Supplementary Table 4 ). Changes in soil moisture have been shown to regulate fungal and bacterial population size under field conditions ( Schnürer et al., 1986 ). Likewise, denitrifier abundance has previously been characterized to respond rapidly to manipulations of WFPS ( Szukics et al., 2010 ). Our results were congruent to the findings of Banerjee et al. (2016) who, among other factors (e.g., SOC), attributed greater bacterial 16S rRNA abundance in Canadian agroforestry systems to greater soil moisture in plots with trees. The trees in our agroforestry systems did not increase SOC ( Supplementary Table 3 ), which was probably due to the relatively young age of our agroforestry systems ( Lee and Jose, 2003 ; Pardon et al., 2017 ). Thus, as opposed to Banerjee et al.’s (2016) findings, the increased soil bacteria at our sites was not attributed to SOC change in the tree rows. Soil bacterial and fungal biomass have repeatedly been shown to increase with plant biomass and above-ground diversity as well as with the amount and diversity of root exudates ( Zak et al., 2003 ; Steinauer et al., 2016 ; Eisenhauer et al., 2017 ; Chen et al., 2019 ). Therefore, we assumed that the tree litter inputs as well as persistent and abundant tree root biomass and the associated root exudates may have contributed to the promotion of soil bacteria and fungi in the tree rows ( Figures 1 – 3 ). Furthermore, we observed the existence of an herbaceous layer under the trees of the agroforestry systems ( Figure 1D ). The biomass of the herbaceous vegetation layer in the tree row was several orders of magnitude smaller than the poplars’ but, in contrast to tree litter, it possesses higher diversity of secondary metabolites ( Theis and Lerdau, 2003 ), which are known to modulate microbial populations ( Chomel et al., 2016 ). The particularly large increase of Basidiomycota in the tree row in all three soil types ( Figure 3B ) clearly demonstrated that the trees strongly promoted this fungal group. Since a large proportion of Basidiomycota are wood-decaying and litter-decomposing fungi ( Lundell et al., 2014 ), tree litters (leaves, twigs, roots) accumulating in the tree row ( Figure 1D ) likely provided Basidiomycota with growth substrate. Similarly, the increased abundance of Ascomycota in the tree row ( Figure 4A ) may be related to an increase of litter-decomposing members of this phylum ( Ma et al., 2013 ). Additionally, the increased soil moisture under the trees ( Supplementary Table 3 ) was likely to have favored fungal growth ( A’Bear et al., 2014 ). In addition to their function as decomposers, Basidiomycota followed by Ascomycota harbor the majority of ectomycorrhizal fungal lineages ( Tedersoo et al., 2010 ). Therefore, colonization of the poplar root system by ectomycorrhizal fungi likely contributed to the increased abundance of Basidiomycota and Ascomycota in the tree row as compared to the crop row of the agroforestry and monoculture croplands. Furthermore, as tillage is expected to damage hyphal networks ( Frey et al., 1999 ), it was plausible that the absence of tillage in the tree row contributed to the increased soil fungi abundance under the trees ( Figures 2B , 3 ). The decrease of AOB amoA gene copies in the tree row ( Figure 5B ) was in line with our previous findings of suppression of AOB amoA gene abundance by poplar trees ( Beule et al., 2019a ). High abundance of AOB in cropland samples can likely be accounted for by fertilization, which is common in conventional agriculture as well as in crop rows of the agroforestry systems. Lower abundance of AOB in soil collected below the trees is in line with the fact that trees are not fertilized, which is also a common practice ( Tsonkova et al., 2012 ). Our results further revealed that the increased genetic potential for denitrification in the tree rows as compared to the crop rows of the agroforestry and the monoculture croplands ( Figures 6A–F ) possibly resulted from the high WFPS in the tree rows ( Supplementary Table 3 ), which enhances denitrification activity ( Wen et al., 2017 ). Similarly, recent studies indicated that soil moisture and denitrifier abundance are positively linked ( Wang et al., 2017 ; Yang et al., 2018 ). In line with these findings, the positive correlations of denitrification genes narG , nirK , nirS , and nosZ clade I to WFPS ( Figure 7 and Supplementary Table 4 ) suggests that high WFPS in the tree rows ( Supplementary Table 3 ) favored denitrifiers. The greater genetic potential for denitrification in the tree rows corroborates the suggestion by Ferrarini et al. (2017) that short-rotation trees enhance the potential for nitrate removal through denitrification. It should be noted that our study relies on a single sampling time and, thus, does not allow temporal extrapolation. On the other hand, detection of this pattern in one-time sampling warrants quantification of denitrification rates in the field (e.g., using 15 N 2 O pool dilution techniques), as was done by Wen et al. (2017) in the forests of Lower Saxony, Germany. Combing such in situ measurements of denitrification with the quantification of denitrification genes, may be a rewarding research direction to assess the climate-regulation functions of temperate agroforestry systems and conventional systems. Most soil properties (except for WFPS) within each soil type were not affected by the management system ( Supplementary Table 3 ), which we assume to be due to the relatively young age of our agroforestry systems ( Lee and Jose, 2003 ; Pardon et al., 2017 ). Therefore, in our correlation analysis, we explored the relationships between microbial groups, N-cycling genes and soil properties across soil types and management systems. The relationship of a high abundance of nifH gene with lower soil fertility (i.e., low SOC, total N, and ECEC) ( Figure 7 and Supplementary Table 4 ) contradicted previous studies ( Morales et al., 2010 ; Huhe et al., 2016 ). These contrasting findings may be explained by the use of different qPCR conditions and primers ( Gaby and Buckley, 2012 , 2017 ) or by the contamination of PCR consumables by nifH -like DNA, which was reported to occur ubiquitously in certain PCR consumables ( Goto et al., 2005 ). The positive correlations of several microbial groups and N-cycling genes with soil fertility indicators (SOC and total N content and ECEC) and finer soil texture ( Figure 7 and Supplementary Table 4 ) manifested the cyclical associations of these biogeochemical parameters – soil fertility fuels microbial groups and nutrient (e.g., N) cycling genes which, in turn, impact soil available nutrients ( Jeffries et al., 2003 ; van der Heijden et al., 2008 ; Schlesinger and Bernhardt, 2013 )."
} | 3,926 |
32410574 | PMC7227074 | pmc | 3,726 | {
"abstract": "Background Bacterial biofilms are surface-adherent microbial communities in which individual cells are surrounded by a self-produced extracellular matrix of polysaccharides, extracellular DNA (eDNA) and proteins. Interactions among matrix components within biofilms are responsible for creating an adaptable structure during biofilm development. However, it is unclear how the interactions among matrix components contribute to the construction of the three-dimensional (3D) biofilm architecture. Results DNase I treatment significantly inhibited Bacillus subtilis biofilm formation in the early phases of biofilm development. Confocal laser scanning microscopy (CLSM) and image analysis revealed that eDNA was cooperative with exopolysaccharide (EPS) in the early stages of B. subtilis biofilm development, while EPS played a major structural role in the later stages. In addition, deletion of the EPS production gene epsG in B. subtilis SBE1 resulted in loss of the interaction between EPS and eDNA and reduced the biofilm biomass in pellicles at the air-liquid interface. The physical interaction between these two essential biofilm matrix components was confirmed by isothermal titration calorimetry (ITC). Conclusions Biofilm 3D structures become interconnected through surrounding eDNA and EPS. eDNA interacts with EPS in the early phases of biofilm development, while EPS mainly participates in the maturation of biofilms. The findings of this study provide a better understanding of the role of the interaction between eDNA and EPS in shaping the biofilm 3D matrix structure and biofilm formation.",
"conclusion": "Conclusions Extracellular DNA (eDNA) and exopolysaccharide (EPS), two essential matrix components of the B. subtilis SBE1 biofilm, cooperate by physically interacting in bacterial biofilms. Over time, the biofilm three-dimensional (3D) structures become interconnected through surrounding eDNA and EPS. eDNA interacts with EPS in the early phases of biofilm development, while EPS mainly participates in the maturation of biofilms. Based on our research, we proposed a model to describe how the eDNA-EPS interaction mediates the construction of the complex 3D biofilm architecture and establishes spatial heterogeneities in B. subtilis SBE1. Complex 3D biofilms form in the following sequence: (1) initial aggregation, bacterial cells are connected and bridged by eDNA and EPS; (2) accumulation, a core of EPS-enmeshed bacterial cells is formed to provide a supporting framework; and (3) maturation, bacterial cells divide and accumulate, and EPS and DNA are evenly distributed in the biofilm (Fig. 7 ). The interaction between eDNA and EPS plays a vital role in the construction of 3D biofilm architecture. In addition, the eDNA-EPS interaction might increase the survival of B. subtilis SBE1 in different environments by allowing eDNA from other microbial species to act as a scaffold on which a community can grow.\n Fig. 7 Proposed model for how extracellular DNA (eDNA)-exopolysaccharide (EPS) interaction modulates 3D architecture of B. subtilis SBE1 biofilm. Complex 3D biofilm forms in the following sequence: (1) initial aggregation, bacterial cells are connected and bridged by eDNA and EPS; (2) accumulation, a core of EPS-enmeshed bacterial cells is formed to provide a supporting framework; and (3) maturation, bacterial cells divide and accumulate, and EPS and DNA are evenly distributed in the biofilm",
"discussion": "Discussion Interactions among matrix components within biofilms are responsible for creating an adaptable structure during biofilm development. However, how interactions between extracellular DNA (eDNA) and exopolysaccharide (EPS) modulate B. subtilis biofilm formation processes and architecture construction is less known. In this study, we focused on elucidating (1) the role of eDNA in the construction of the B. subtilis biofilm three-dimensional (3D) architecture and (2) the interaction between eDNA and EPS during biofilm formation. We found that B. subtilis SBE1 biofilms were dissolved when the DNase I treatments were initiated, whereas the biofilms after 24 h at the time of DNase I exposure were only affected to a minor degree. Young biofilms are easily removed by DNase, but DNase treatment is not effective once the biofilm has aged past a certain point. Such a transition has been documented for, for example, S. epidermidis (at 12 h) [ 10 ], P. aeruginosa (at 80 h) [ 6 ], and Vibrio cholera (at 72 h) [ 12 ]. The use of DNase for biofilm removal is effective but dependent on the age of the biofilm. What causes the resistance of the biofilm to DNase remains to be explored, but this temporary sensitivity suggests either that other extracellular matrix components replace or complement eDNA within the mature biofilm or that eDNA is bound by another component that shields it from enzymatic degradation. Similar results have been observed in Listeria monocytogenes that peptidoglycan, as an additional essential component, is required for DNA-dependent biofilm development [ 33 ]. eDNA has been shown to play an important role in cell-to-cell interconnection during early B. subtilis SBE1 biofilm formation. It has been previously reported that cells can interact during the biofilm accumulation phase of S. aureus through recycled cytoplasmic proteins, which can be linked by eDNA [ 34 ]. Thus, another biofilm component may interact with eDNA to stabilize biofilm structure. Exopolysaccharide is an important extracellular biofilm matrix in B. subtilis . The colocalization of eDNA and EPS observed in native extracellular matrix provided evidence for direct interactions between eDNA and EPS in B. subtilis SBE1 biofilms. Previous studies have been reported that the major EPS component of all B. subtilis biofilms is synthesized by the products of the 15-gene operon eps A-O (referred to as the eps operon) [ 27 , 35 – 38 ]. The molecular structure of EPS has yet to be elucidated. To date, only a subset of EPS genes has been studied individually. EpsA and EpsB act as tyrosine kinase modulators and tyrosine kinases, respectively, and both are required for biofilm formation [ 37 ]. EpsE is a bifunctional protein that coordinates the production of EPS with the cessation of motility [ 38 ]. EpsG is a protein that is presumably involved in EPS polymerization [ 27 ]. Among these genes, the deletion of eps G could prevent surface-adhered biofilm formation even in the ∆ sip W suppressor strain [ 39 ]. The above-described results indicate that eDNA may cooperate with EPS, which promotes cell-cell adhesion during early biofilm development. To confirm this, the ∆epsG mutant was constructed to weaken the function of surface adhesion of EPS during biofilm formation. eDNA colocalized with EPS in B. subtilis SBE1 pellicles but not in ∆epsG strain pellicles. There was also a substantial reduction in the contents of both eDNA and EPS in the biofilms of the ∆ eps G mutant compared to wild-type B. subtilis SBE1. Biofilms formed by the ∆ eps G mutant contained eDNA that did not colocalize with EPS in the biofilms. A similar pattern has been observed in P. aeruginosa PAO1. The Pel and Psl polysaccharides contribute to eDNA release and distribution during PAO1 biofilm development. Biofilms formed by the PAO1∆ pel A mutant contained eDNA in the inner parts of microcolony structures. Biofilms formed by the PAO1∆ psl BCD mutant contained a small amount of eDNA close to the substratum of biofilms [ 40 ]. It is possible that eps G may be involved in eDNA release and distribution during B. subtilis SBE1 biofilm formation. Extracellular DNA interacts with EPS in the early phases of biofilm development, while EPS played a major structural role in the later stages. This transition of the role of eDNA from initial construction of the 3D extracellular matrix to matrix microaggregation is similar to the role of eDNA and lipoteichoic acid (LTA) in biofilms of Streptococcus mutants [ 41 ]. The colocalization of eDNA and EPS in B. subtilis SBE1 pellicles suggested the potential physical interaction between these two components. Previous work has used Isothermal titration calorimetry (ITC) to study the molecular interaction between protein and lipid polysaccharide (LPS) [ 42 ]. The interaction between DNA from S. oneidensis MR and P. putida KT2440 and EPS is more exothermic than DNA from B. subtilis SBE1 and B. subtilis 3610. This selectivity between DNA and EPS may be driven by a small energy difference of the interactions, such as electrostatic and van der Waals forces [ 43 ]. However, EPS of B. subtilis SBE1 can interact with DNA from S. oneidensis and Pseudomonas putida which also inhabit the soil. These species that commonly share soil ecosystems with B. subtilis , maybe associated in multispecies biofilms. In the bulk soil, bacteria are found in patches or microcolonies containing low cell numbers, often composed of different bacterial species [ 44 , 45 ]. When exposed to nutrient sources, these microcommunities have the potential to develop into multispecies biofilms with high bacterial density [ 45 ]. The first step of the succession in an early multispecies biofilm based on the ability of surface/cell-cell attachment of soil bacteria [ 46 ]. Thus, EPS of B. subtilis can interact with DNA from these bacteria, which might enable B. subtilis cells to bind eDNA from these bacteria, initiating the biofilm formation."
} | 2,368 |
34337776 | PMC11468203 | pmc | 3,727 | {
"abstract": "Abstract The extracellular matrix (ECM) forms through hierarchical assembly of small and larger polymeric molecules into a transient, hydrogel‐like fibrous network that provides mechanical support and biochemical cues to cells. Synthetic, fibrous supramolecular networks formed via non‐covalent assembly of various molecules are therefore potential candidates as synthetic mimics of the natural ECM, provided that functionalization with biochemical cues is effective. Here, combinations of slow and fast exchanging molecules that self‐assemble into supramolecular fibers are employed to form transient hydrogel networks with tunable dynamic behavior. Obtained results prove that modulating the ratio between these molecules dictates the extent of dynamic behavior of the hydrogels at both the molecular and the network level, which is proposed to enable effective incorporation of cell‐adhesive functionalities in these materials. Excitingly, the dynamic nature of the supramolecular components in this system can be conveniently employed to formulate multicomponent supramolecular hydrogels for easy culturing and encapsulation of single cells, spheroids, and organoids. Importantly, these findings highlight the significance of molecular design and exchange dynamics for the application of supramolecular hydrogels as synthetic ECM mimics.",
"conclusion": "3 Conclusions In conclusion, supramolecular hydrogels based on combinations of slow and fast exchanging molecules have shown to function as synthetic ECM mimics for diverse cell culture schemes, ranging from single cells to spheroids, and organoids. Effective incorporation and presentation of biochemical cues in these materials is a predominant requirement for their biomedical translation and their use as a synthetic alterative to the ill‐defined and less tunable Matrigel. [ \n \n 54 \n \n ] \n This work demonstrates the importance of molecular exchange dynamics on effective functionalization of supramolecular hydrogels with adhesive ligands. Previous investigations have shown enhanced spreading of cells cultured on or within more quickly relaxing hydrogels that were covalently tethered with RGD ligands. [ \n \n 26 \n , \n 55 \n \n ] Consequently, an intuitive expectation would suggest that for hydrogels containing adhesive ligands, a more dynamic behavior would result in enhanced cell adhesion and spreading. Nevertheless, our results reveal that adhesive ligands are ineffective in supramolecular hydrogels with excessively dynamic behavior, but dampening the exchange dynamics can render these materials cell‐adhesive. The lack of cell adhesion and spreading for the more dynamic hydrogels in this study likely arises from a low binding energy, and hence high binding/unbinding rate, of the UPy‐cRGD additives within the supramolecular fibers, thereby not allowing for the engagement of “molecular clutches” that drive mechanotransduction. [ \n \n 56 \n \n ] The tuning of molecular exchange dynamics can be achieved by altering the molecular design and the ratio between the supramolecular building blocks employed for hydrogel formation. 3D encapsulation of cells in supramolecular hydrogels was previously realized in two‐component systems containing complementary domains. [ \n \n 57 \n , \n 58 \n \n ] The current study presents a strategy to employ non‐gel‐forming regimes of supramolecular building blocks with self‐complementary interactions for 3D encapsulation of cells, spheroids, and organoids at physiological conditions. Previous work has indicated that cell spreading in dynamic hydrogels is not largely determined by the tethering of adhesive ligands, whereas early protein deposition and remodeling were found to play an essential role in this process. [ \n \n 27 \n \n ] However, our results demonstrate that upon optimization of the hydrogels’ dynamic profile, adhesive ligand tethering can drive cellular adhesion and spreading, as well as cell–matrix interactions in multicellular spheroids and organoids. These results, however, do not undermine the critical role that nascent proteins can play at later time points, and the potential interplay between tethered biophysical cues and nascent protein deposition in long term cultures remains to be studied. Moreover, when comparing different systems, it is important to consider that several other factors involved in their design can also impact the effects of their tethered bioactive cues as well as other aspects of their biological performance. These factors include the type of polymer used (natural vs synthetic) as well as the number of possible cross‐links per polymer chain. Finally, the modularity of the supramolecular assembly strategy utilized here offers a substantial advantage for on‐demand variation of other hydrogel properties such as stress relaxation, which are important for directing cellular behavior. The strategy described here also paves the way for effective tethering of additional biochemical cues into supramolecular hydrogels, enabling the modulation of signaling pathways and the regulation of cell proliferation and differentiation.",
"introduction": "1 Introduction Natural extracellular matrix (ECM) in most tissues is a hydrogel‐like fibrous network that hierarchically forms based on directed interactions between small and large molecules, which dictate their assembly into transient supramolecular fibers. [ \n \n 1 \n \n ] This dynamic matrix exhibits various biophysical and biochemical cues that regulate cellular behavior, and therefore plays a central role in vital processes such as tissue growth and regeneration. [ \n \n 2 \n , \n 3 \n \n ] Accordingly, substantial research effort has been devoted toward the development of synthetic hydrogels that can serve as ECM mimics for 3D culture of cells and organoids. [ \n \n 4 \n , \n 5 \n \n ] Such synthetic materials must be biocompatible, and should ideally incorporate bioactive cues that instruct cell behavior. [ \n \n 6 \n , \n 7 \n , \n 8 \n , \n 9 \n , \n 10 \n \n ] \n Recently, dynamic hydrogels based on non‐covalent or dynamic covalent chemistry are increasingly preferred as synthetic ECM mimics. [ \n \n 11 \n , \n 12 \n \n ] Supramolecular hydrogels are dynamic hydrogels that assemble from molecular building blocks through directed, non‐covalent interactions. [ \n \n 13 \n , \n 14 \n \n ] The reversibility of such non‐covalent interactions renders these materials adaptable. [ \n \n 12 \n \n ] This adaptability allows for matrix remodeling and cellular activities such as cell spreading and migration to take place within the hydrogel matrix, without requiring hydrogel degradation or large pore sizes that are otherwise essential in conventional synthetic hydrogels. [ \n \n 12 \n , \n 15 \n , \n 16 \n \n ] Various types of natural and synthetic polymers, such as hyaluronic acid [ \n \n 17 \n \n ] and poly(ethylene glycol) (PEG), [ \n \n 18 \n , \n 19 \n \n ] have been modified with complementary or self‐complementary supramolecular motifs. These motifs form inter‐ and intra‐molecular cross‐links via interactions such as host–guest or hydrogen bonding, which can create 3D interconnected gel networks at sufficiently high cross‐linking densities. [ \n \n 20 \n \n ] \n The assembly processes, the morphologies of assembled structures, and overall properties of supramolecular materials depend not only on the type of supramolecular interactions at play, but also on the molecular design of the covalent framework of the supramolecular building blocks. [ \n \n 21 \n , \n 22 \n , \n 23 \n \n ] For supramolecular hydrogels formed from molecules with bivalent (B) or monovalent (M) fourfold hydrogen bonding designs, we previously observed that viscoelastic properties heavily depend on the ratio between these building blocks. [ \n \n 24 \n \n ] Recently, we found that the exchange dynamics of these M‐ and B‐type molecules within and between assembled supramolecular fibers can be tuned by varying their ratio. [ \n \n 25 \n \n ] \n In dynamic hydrogels formed by ionic or host–guest cross‐links, recent findings highlight the key role of biophysical cues, such as stiffness and stress relaxation, on cellular activity and fate. [ \n \n 26 \n , \n 27 \n , \n 28 \n \n ] However, contradictory outcomes have been observed upon functionalization of dynamic hydrogels with biochemical cues. While the incorporation of integrin‐binding arginine–glycine–aspartate (RGD) ligands is found to enhance cell spreading in some dynamic hydrogel systems, [ \n \n 26 \n \n ] other systems have been shown not to benefit from such functionalization strategies. [ \n \n 27 \n \n ] As of yet, key factors determining the effective functionalization of dynamic hydrogels with biochemical cues are not well understood. Here, we investigate the effect of molecular exchange dynamics on incorporation of bioactive motifs in supramolecular hydrogels. To this end, we use B‐ and M‐type molecules containing ureido‐pyrimidinone (UPy) groups as supramolecular building blocks. UPy groups dimerize via fourfold hydrogen bonding with a binding constant and a life‐time of 6 × 10 7 M −1 and ≈1 s in chloroform, respectively. [ \n \n 29 \n \n ] In water, however, these hydrogen bonds need to be shielded. In our design, this is achieved by incorporation of an alkyl spacer, forming a hydrophobic pocket upon assembly of UPy‐dimers into 1D stacks, which are further stabilized by hydrogen bonding of flanking urea groups ( Figure \n 1 \n ). [ \n \n 30 \n \n ] Further assembly occurs by bundling of the stacks into fibers. [ \n \n 19 \n \n ] By altering the ratio between B‐ and M‐type monomers in this system, we modulate the molecular exchange dynamics in the assembled fibers and therefore the resulting hydrogels. Subsequently, using rheology, fluorescence photo‐bleaching, and cell studies, the exchange dynamics are identified as an important factor determining the effectiveness of functionalization with cell‐binding motifs. Consequently, the non‐gel‐forming regimes of B‐ and M‐type UPy‐based molecules are used to formulate a multicomponent supramolecular hydrogel system as synthetic ECM for 3D encapsulation and culture of cells, spheroids, and organoids. Figure 1 Supramolecular building blocks and their self‐assembly into fibers. a) Molecular structures of bivalent (B)‐type and monovalent (M)‐type molecules as the supramolecular building blocks and additives. For UPy‐PEG‐UPy, n is on average 226 ( M \n n = 10 kDa). b) Representative Cryo‐TEM images showing the morphology of fibers assembled from B‐type and M‐type molecules at different molar ratios. The ureido‐pyrimidinone (UPy)‐based molecules self‐assemble into fibers (of multiple stacks) at physiological pH and temperature, through dimerization via quadruple hydrogen bonds, and lateral stacking induced by flanking urea moieties forming a hydrophobic pocket shielded from the water. Scale bars = 100 nm.",
"discussion": "2 Results and Discussion Two types of supramolecular building blocks were designed for the formulation of our supramolecular hydrogels (Figure 1a ). As the B‐type building block (i.e., UPy‐PEG‐UPy), a PEG chain with a molecular weight of 10 kDa was end‐capped with two UPy moieties. As the M‐type building block (i.e., UPy‐G), an oligo(ethylene glycol) (OEG) chain with a molecular weight of 528 Da was end‐capped with a UPy moiety at one end and a glycine‐amide group at the other end. The glycine‐amide group was included in this design as a biomimetic alternative to the methoxy end groups employed in previously reported M‐type molecules. [ \n \n 24 \n , \n 25 \n \n ] In this case, the peripheral glycine‐amide groups are expected to be presented to cells, while the OEG is shielded from exposure, thereby potentially minimizing the biological consequences of the glycols’ non‐fouling properties. Finally, two variants of M‐type molecules were synthesized containing a sulfonated cyanine dye (UPy‐Cy5) or a cyclic RGD (UPy‐cRGD) end group, as fluorescent or cell‐adhesive supramolecular additives, respectively. The building blocks (with or without additives) were first dissolved at predefined concentrations and ratios in aqueous solutions using an alkaline pH at which the unbound UPy groups were deprotonated. [ \n \n 19 \n \n ] The fiber formation was then induced by neutralizing the solutions. Hereafter, we denote the molar ratios of B and M components for each composition as B \n X \n M \n Y \n , where X and Y indicate the relative molar content of B‐ and M‐type molecules, respectively. Cryogenic transmission electron microscopy (Cryo‐TEM) showed that B‐type molecules assemble into relatively short fibers with a fiber length of ≈160 ± 100 nm, and M‐type molecules into significantly longer fibers of microscale length (Figure 1b and Figure S1 , Supporting Information). The addition of a low amount of B‐type molecules to M‐type molecules (B 1 M 84 ) did not alter the length of the resulting fiber. However, higher B contents (e.g., B 1 M 22 ) impeded the long fiber assembly, most likely due to the high dynamicity of B‐type molecules disrupting the lateral stacking of M‐type dimers. [ \n \n 24 \n , \n 25 \n \n ] We propose that one of the factors determining the higher dynamicity of B‐type molecules in this system originates from the significantly longer and polydisperse nature of the ethylene glycol chain (≈19 times longer), as compared to the shorter, monodisperse chain present in the M‐type molecules. Consequently, these long hydrophilic entities in B‐type molecules destabilize the packing of the assembled cores of the fibers due to entropic and steric mechanisms, [ \n \n 25 \n , \n 31 \n \n ] yielding fibers with relatively higher molecular exchange dynamics. In contrast, the shorter and monodisperse ethylene glycol chain in the M‐type molecules results in less hydrophilic molecules, favoring intermolecular interactions and therefore “tighter” packing into less dynamic structures, which is reflected by the calculated partition coefficients of 0.29 and −4.73 for M‐type (i.e., UPy‐G) and B‐type molecules, respectively. The Cryo‐TEM images also showed an average diameter within a range of 8–10 ± 2 nm for the fibers of the studied compositions. The fiber contrast in these images originates only from the hard block, consisting of UPy and alkyl parts of the molecules, as ethylene glycol chains display no contrast relative to the aqueous background. [ \n \n 32 \n \n ] Accordingly, these results suggest that fiber formation in this system involves the clustering of ≈3–4 discrete stacks per individual fiber. Increasing the concentration of building blocks in solution can result in the formation of a hydrogel network owing to the entanglement and bundling of assembled fibers ( Figure \n 2 a and Figure S2 , Supporting Information). We altered the ratio between the building blocks at a fixed total concentration of 5 wt%, to study the effect of co‐assembly. Hereafter, we denote the samples as B Z M W , where Z and W indicate the wt% of B‐ and M‐type molecules in each composition, respectively (see Table S1 , Supporting Information, for an overview of hydrogel compositions). Figure 2 Hydrogels formulated from different ratios of supramolecular building blocks. a) Schematic illustration of different supramolecular formulations at network and molecular levels. Blue linkages between fibers at network level indicate interfiber cross‐links formed by B‐type molecules, and labels of black arrows at molecular level indicate the rate of molecular exchange dynamics for different formulations. b) Frequency dependence of storage ( G ′) and loss ( G ″) moduli of different compositions of supramolecular hydrogels. c) G ′ and damping factor (tan(delta)) values of hydrogels measured at 1 rad s −1 and 1% strain. d) Stress relaxation behavior of supramolecular hydrogels measured by subjecting the hydrogels to 1% strain. e) Quantification of stress relaxation in hydrogels after 10 min. f) Fluorescence recovery after photo‐bleaching (FRAP) tests performed on hydrogels containing 20 µ m of UPy‐Cy5 supramolecular additives. g) Quantified FRAP results showing the rate of fluorescence recovery during the first 60 s after photo‐bleaching (Initial rate), the timespan during which the Cy5 fluorescence intensity recovers to half its mobile fraction (τ 1/2 ), and the fraction of fluorescence intensity that recovers when fluorescence intensity curves reach plateau values (Mobile fraction). b–g) All hydrogels contained a total polymer content of 5 wt%, and all measurements were performed at 37 °C. All data are shown for n = 3 independent tests per group, and as mean ± s.d. e,g) *, p < 0.05; **, p < 0.01; ***, p ≤ 0.001; one‐way analysis of variance (ANOVA) followed by Bonferroni post hoc. While at 5 wt% the B‐type building blocks (UPy‐PEG‐UPy) formed hydrogels, the M‐type building blocks (UPy‐G) solely were incapable of forming hydrogels, as they failed the inverted‐vial test (Figure S3 , Supporting Information) and exhibited tan(delta) ≥ 1 in rheological measurements (Figure S4 , Supporting Information). Nevertheless, co‐assembly of M‐ and B‐type molecules even with low contents of B‐type molecules (e.g., B0.5M4.5, with a molar ratio of B 1 M 84 ) resulted in the formation of hydrogels, indicating that B‐type molecules can intercalate into M‐type fibers and act as effective interfiber cross‐linkers in this system due to the B nature of these molecules. This co‐assembly approach was highly effective and resulted in solid‐like behavior (i.e., G ′ > G ″) at building block concentrations as low as 0.02 wt% for a fixed molar ratio of B 1 M 84 (Figure S5 , Supporting Information). Nonetheless, our rheological studies showed that hydrogels formed at lower M/B ratios exhibited a more frequency‐dependent viscoelastic response, were less stiff (lower storage modulus [ G ′] values), and showed a less solid‐like behavior(higher tan(delta) values) (Figure 2b,c ). Similar behavior was previously observed for M‐type molecules with a methoxy end‐group, [ \n \n 24 \n \n ] and can be attributed to the higher dynamicity of the B‐type molecules disrupting the vitrification of less dynamic M‐type stacks, as well as the shorter length of such fibers observed in Figure 1b . Next, the effect of M/B ratio on the dynamic behavior of the gel networks was studied with stress relaxation experiments. To this end, a strain of 1% was applied and the decay of the generated stress in the hydrogel networks was monitored over a period of 10 min (Figure 2d ). Hydrogels formed entirely from B‐type molecules (B5) displayed complete stress relaxation during the course of the experiment, while exhibiting a relaxation half‐life (τ 1/2 ) of ≈5 s (Figure 2d) . Notably, increasing the M‐type relative to the B‐type content resulted in hydrogels with slower stress relaxation, which can be attributed to a reduced dynamic behavior of the gel network due to slower molecular rearrangement. At these hybrid compositions (e.g., B0.5M4.5), while the available B‐type molecules establish interfiber cross‐links required for stable network formation, the high content of M‐type molecules renders “tightly packed” fibers that can entangle and exhibit a higher resistance against stress relaxation. Accordingly, these experiments revealed that the network dynamics and stress relaxation in the hydrogels could be varied by altering the M/B ratio. To elucidate whether the dynamic properties of the gel networks were also altered at the molecular level, we performed fluorescence recovery after photo‐bleaching (FRAP) experiments by including traceable M‐type fluorescent additives, UPy‐Cy5, in the hydrogel compositions (Figure 2f ). These UPy‐Cy5 molecules closely resemble the design of UPy‐G molecules and were aimed to co‐assemble with the UPy‐based building blocks, as reported previously. [ \n \n 25 \n , \n 33 \n , \n 34 \n \n ] The FRAP experiments revealed that the dynamic properties of the hydrogels were also dampened at the molecular level at higher M/B ratios, as the M‐type additives showed slower recovery kinetics in B0.5M4.5 (τ 1/2 = 4073 ± 689 s) and B2.5M2.5 (τ 1/2 = 3227 ± 372 s) as compared to the B4.5M0.5 (τ 1/2 = 823 ± 115 s) hydrogels (Figure 2f,g ). Nonetheless, it should be noted that the higher affinity of the UPy‐Cy5 molecules to the compositions with a higher M/B ratio can be also impacted by the higher concentration of UPy moieties present in these samples (Table S1 , Supporting Information). Interestingly, despite the similarities between the recovery kinetics (initial rate and τ 1/2 ) of B0.5M4.5 and B2.5M2.5 groups, the B2.5M2.5 hydrogels exhibited a significantly lower fraction of mobile molecules (50 ± 1%) as compared to the B0.5M4.5 group (67 ± 5%), indicating fundamental differences between the exchange behavior of UPy‐based additives in these groups. This larger fraction of immobile molecules in B2.5M2.5 suggests existence of complex subdiffusion phenomena in this composition involving combinations of fast and slow exchange processes. [ \n \n 35 \n \n ] Elucidating the differences in molecular exchange dynamics of B2.5M2.5 and B0.5M4.5 compositions in the hydrogel state experimentally remains a challenge. However, our Förster resonance energy transfer (FRET) measurements have shown distinct differences in FRET signal when comparing the pristine dispersions of M‐ or B‐type fibers at a low, non‐gel‐forming, building block concentration of 24.75 µ m (Figure S6 , Supporting Information). Such FRET measurements can potentially enable future investigations of differences among the molecular exchange dynamics of co‐assembled samples at shorter time scales. Importantly, our previous FRET experiments have shown that the molecular exchange dynamics of comparable M‐type molecules enormously change upon co‐assembly with comparable B‐type molecules, resulting in increased exchange dynamics owing to disordering of the M‐type molecule packing. [ \n \n 25 \n \n ] \n Notably, the FRAP tests performed on hydrogels containing UPy‐free Cy5 molecules indicated the lack of co‐assembly of these additive molecules with the building blocks, as the Cy5 molecules could freely diffuse within the hydrogel matrix preventing their detectable photo‐bleaching within the timeframe of the experiment (data not shown). We hypothesized that the identified differences in dynamic behavior of the hydrogels formulated at different M/B ratios would determine the exchange dynamics of bioactive additives in the hydrogels and their effects on cells. To evaluate this hypothesis, we studied the adhesion and spreading of cells in contact with different hydrogel compositions, as fundamental read‐outs indicative of cell–matrix interactions. Specifically, cell adhesion and spreading are principal requirements for directing function of a wide range of cells types, and can shed light on the potential applicability of our findings in different biomedical arenas. [ \n \n 36 \n , \n 37 \n \n ] To this end, 3 m m of M‐type adhesive ligands, UPy‐cRGD, were included in hydrogels of different compositions, and the adhesion and morphology of vascular‐derived matrix‐producing myofibroblasts (human vena saphena cells; HVSC) cultured on hydrogel surfaces were studied. Upon UPy‐cRGD inclusion, the viscoelastic properties ( G ′, G ″ and stress relaxation behavior) of the hydrogels remained similar (Figure S7 , Supporting Information). A high cell viability (>90%) was observed for all compositions upon 3 days of culture, with no statistically significant difference among different hydrogels, suggesting cytocompatibility of this supramolecular system (Figure S8 , Supporting Information). Nonetheless, our results revealed that the UPy‐cRGD molecules were ineffective (cell circularity > 0.6 at Day 1; <300 cells at Day 3) in hydrogels with a high content of B‐type molecules, whereas cell adhesion and spreading increased significantly with an increase in the M/B ratio of hydrogels ( Figure \n 3 a–d ). Cell adhesion and spreading were particularly remarkable for cells seeded on hydrogels of B0.5M4.5 composition (cell circularity = 0.3 ± 0.2 at Day 1; 4917 ± 448 cells at Day 3). Furthermore, the intracellular bundles of F‐actin filaments (i.e., stress fibers) were especially prominent in this group, emphasizing superior cell–matrix adhesion through the formation of focal adhesions. [ \n \n 38 \n , \n 39 \n \n ] These results indicate that excessively dynamic hydrogels are not rendered cell‐adhesive upon incorporation of UPy‐cRGD molecules, highlighting the importance of molecular exchange dynamics for the effective incorporation of adhesive ligands in the hydrogels. It is proposed that the lack of cell adhesiveness arises from the low molecular stability of the transient fiber structures, not providing a sufficient retention of incorporated bioactive motifs as required for the formation of focal adhesions by cells via integrin‐cRGD binding. Figure 3 Cell adhesion and spreading on hydrogels with different compositions. a) Representative images of HVSCs after 1 day of culture on different supramolecular hydrogel compositions. b) Number of cells adhered onto hydrogel surfaces after 1 and 3 days of culture. PS indicates polystyrene control. c) Length of longest axis and d) circularity of cells after 1 day of culture on supramolecular hydrogels with different compositions. a–d) Hydrogels contained 3 m m of UPy‐cRGD additives. e) Representative images of HVSCs after 1 day of culture and f) number, g) length of longest axis, h) and circularity of cells adhered after 1 (g,h) or 3 (f) days of culture on B0.5M4.5 supramolecular hydrogels containing different concentrations of UPy‐cRGD or cRGD additives. a,e) Green and blue colors in images indicate actin and nucleus staining, respectively. b–d,f–h) *, p < 0.05; **, p < 0.01; ***, p ≤ 0.001; ****, p ≤ 0.0001; two‐way (b) or one‐way (c,d,f–h) analysis of variance (ANOVA) followed by Bonferroni post hoc. All results were obtained from three to four biologically independent experiments per group, and all values are shown as mean ± s.d. # indicates the groups for which the number of cells present was insufficient for statistically relevant comparison. c,d,g,h) Data points represent features of individual cells, with n comprising the total number of cells per group that were detectable/analyzed among three experiments. To understand the fate of M‐type additives in the hydrogels, we studied the release of UPy‐Cy5 molecules from different hydrogel compositions upon their immersion in PBS solutions. Notably, all the hydrogel compositions displayed a similar UPy‐Cy5 release profile (≈20% cumulative release at Day 3 for all 5 wt% compositions; Figure S9 , Supporting Information), confirming that the difference in cellular response to the hydrogels is due to differences in their dynamic behavior at the molecular level and is not caused by the release of additives into solutions from the non‐cell‐adhesive compositions. Nonetheless, the hydrogels with a lower M/B ratio showed enhanced erosion (e.g., 38.3 ± 0.7% for B4.5M0.5 after 7 days), which can be attributed to the degradation and release of their B‐type building blocks. To confirm that the obtained results are applicable to different cell types, cardiomyocyte progenitor cells (CMPCs) were also cultured on the hydrogels of different compositions, which followed a similar behavior as observed for HVSCs, indicating that these results are not limited to an individual cell type (Figure S10 , Supporting Information). As B0.5M4.5 composition displayed optimal cell attachment and spreading, we chose this M/B ratio for further investigations. Next, we investigated the effect of UPy‐cRGD concentration in the hydrogels on cell adhesion and spreading. Without UPy‐cRGD additives, cell adhesion was minimal and cells exhibited spherical morphology at the hydrogel surface, indicating that cRGD adhesive ligands were responsible for cell adhesion onto the hydrogels (Figure 3e–h ). Upon inclusion of 0.3 m m of UPy‐cRGD in the hydrogels, cell adhesion onto the hydrogels increased significantly and the cells displayed spread‐out morphology, which continued to improve by increasing the UPy‐cRGD concentration. However, non‐modified cRGD additives (i.e., without UPy moiety) were ineffective to promote cell attachment since these molecules do not incorporate in the M‐type fiber and therefore are prone to burst release, as observed for non‐modified Cy5 additives (65.3 ± 1.4% cumulative release at Day 3 from B0.5M4.5 hydrogels; Figure S9 , Supporting Information). It is worth pointing out that the observation of inferior cell adhesion onto hydrogels with excessively dynamic behavior (Figure 3b ) or without UPy‐cRGD additives (Figure 3f ) was followed with additional detachment of weakly adhered cells during the processing (washing) steps necessary for cell staining/imaging, resulting in a low number of cells detectable for microscopic analysis (e.g., one remaining cell detected for 0 m m UPy‐cRGD hydrogels in Figure 3g ). Therefore, we have not included these groups in statistical analyses and have drawn the conclusions above based solely on the quantification of the number of adhered cells (Figure 3b,f ) and the morphological features (Figure 3c,d,g,h ) in the other groups with sufficient numbers of detectable cells. We then investigated the effect of total polymer content on the hydrogels properties by altering the polymer concentration within a range of 2.5–10 wt% at a fixed M/B ratio. Changing the polymer concentration altered the storage modulus of the hydrogels ( Figure \n 4 a ), whereas the network and molecular dynamics of the hydrogels were not largely affected (Figure 4b,c ). All hydrogels displayed a high degree of cell attachment and spreading (Figure 4d–g ). A slight decrease in cell number and spreading was observed upon increasing polymer concentration to 10 wt% (i.e., B1M9), which might be due to the higher PEG content inherent to this concentration. Highly hydrophilic PEG chains are known to exhibit anti‐fouling properties, [ \n \n 40 \n \n ] and have been previously exploited to render supramolecular biomaterials non‐cell‐adhesive. [ \n \n 41 \n , \n 42 \n \n ] Therefore, the long PEG chains might possibly influence cell behavior via potential shielding of the bioactive RGD cell‐adhesive ligands in the supramolecular hydrogels. Consequently, this necessitates clarifying the possible role of PEG content in the differences observed in cell adhesion and spreading on the hydrogels with different compositions (Figure 3 and Figure S11a , Supporting Information). Ideally, designing B‐ and M‐type molecules with PEG spacers of similar length would allow for complete exclusion of possible role of PEG content from the system. We, therefore, synthesized B‐ and M‐type molecules with shorter and longer PEG spacers (5 kDa), respectively, as compared to the current design. Our experimental attempts, however, revealed that these alternatively designed molecules are not suitable for hydrogel formation, as the B‐type molecules with a shorter PEG chain were insoluble in water, while M‐type molecules with a longer PEG spacer produced hydrogels with excessively rapid solubility (complete dissolution at 37 °C in less than 24 h). Nonetheless, more in‐depth analyses of the above‐discussed results can partially rule out the PEG content as the driving factor determining the cellular behavior in this system. First, a direct comparison of the total PEG content of different hydrogel compositions (Figure S11a , Supporting Information) clarifies that B1M9 contained a higher PEG content (5.1 wt%) than all non‐adhesive compositions of B4.5M0.5 (4.3 wt% PEG content), B3.5M1 (3.9 wt% PEG content), and B2.5M2.5 (3.4 wt% PEG content). Despite this higher PEG content, the B1M9 hydrogels successfully facilitated cell adhesion and spreading, while these other compositions (with UPy‐cRGD additives) failed at this role. Second, if the anti‐fouling properties of longer PEG chains present in the B‐types molecules are the driving force behind the lack of cell adhesion in supramolecular hydrogels, a higher molar ratio between the UPy‐cRGD additives and B‐type molecules in hydrogel compositions would result in enhanced cell adhesion and spreading. However, while the cell‐adhesive B0.5M4.5 composition with 0.3 m m UPy‐cRGD content exhibited a UPy‐cRGD/B molar ratio of 0.67, all hydrogel compositions of B2.5M2.5, B3.5M1.5, and B4.5M0.5 with 3 m m UPy‐cRGD content exhibited a higher UPy‐cRGD/B molar ratio (Figure S11b , Supporting Information) yet were not able to support cell adhesion and spreading (Figure 3 ). Nonetheless, these findings cannot fully rule out the potential effect of long PEG chains on cell adhesion at the molecular level. One potential phenomenon could be the formation of a shield‐like layer on the surface of fibrous assemblies for compositions with a high relative content of B‐type molecules. Such an anti‐fouling layer might essentially block the access of cell integrin receptors to the UPy‐cRGD additives embedded within the fibers, thereby shielding these cell binding motifs for a range of hydrogel compositions. Due to the above‐discussed experimental limitations of the current UPy‐based system, this potential phenomenon might be investigated in future studies by systematic comparison with other supramolecular hydrogel systems that are proposed to show higher molecular exchange dynamics, such as those based on benzene‐1,3,5‐tricarboxamide motifs. [ \n \n 14 \n \n ] \n Figure 4 Concentration‐dependent behavior of hydrogels. a) Frequency dependence of viscoelastic behavior, and quantified storage moduli ( G ′) and tan(delta) values (at 1 rad s −1 and 1% strain) of hydrogels with different total polymer concentrations and a fixed M/B ratio. b) Stress relaxation behavior of supramolecular hydrogels measured by subjecting the hydrogels to 1% strain. c) Fluorescence recovery after photo‐bleaching (FRAP) tests performed on hydrogels containing 20 µ m of UPy‐Cy5 additives. Quantified results show the rate of fluorescence recovery during the first 60 s after photo‐bleaching (Initial rate), the timespan during which the fluorescence intensity recovers to half its mobile fraction (τ 1/2 ), and the fraction of fluorescence intensity that recovers when fluorescence intensity curves reach plateau values (Mobile fraction). a–c) All measurements were performed at 37 °C. d) Representative images of HVSCs after 1 day of culture on hydrogels with different polymer concentrations. Green color in images indicates actin staining. e) Number of cells adhered onto hydrogel surfaces after 1 and 3 days of culture. f) Length of longest axis and g) circularity of cells after 1 day of culture on supramolecular hydrogels. h) Representative images of HVSCs upon immunofluorescence staining for nucleus (blue), actin (green), and YAP (red) after 1 day of culture on hydrogels with different polymer concentrations. Arrows indicate the nuclei. i) Quantification of the nuclear/cytoplasmic ratio of the YAP concentration in cells after 1 day of culture. d–i) Hydrogels contained 3 m m of UPy‐cRGD additives. b,c,e,f,g,i) *, p < 0.05; **, p < 0.01; ***, p ≤ 0.001; ****, p ≤ 0.0001; one‐way (b,c,f,g,i) or two‐way (e) analysis of variance (ANOVA) followed by Bonferroni post hoc. All results were obtained from three to four independent experiments per group, and all values are shown as mean ± s.d. f,g,i) Data points represent features of individual cells, with n comprising the total number of cells per group that were detectable/analyzed among three experiments. On a separate note, our results indicate that differences in cell response to different compositions of hydrogels did not originate from altered elasticity, as B0.25M2.25 and B3.5M1.5 exhibited statistically identical storage moduli ( G \n B0.25M2.25 = 7.2±2.0 kPa and G ″ B3.5M1.5 = 8.8±1.6 kPa; p = 0.33), but cell behavior on their surface was significantly different. To analyze the behavior of cells in contact with the supramolecular hydrogels beyond the above‐discussed adhesion and morphological features, we further studied the nuclear localization of Yes‐associated protein (YAP) in cells cultured on different hydrogels. YAP is widely known as a transcriptional regulator that acts as a universal mechanotransducer, mediating the cellular response to the mechanical cues of the ECM. [ \n \n 43 \n , \n 44 \n \n ] Consequently, the YAP nuclear‐cytoplasmic translocation has been correlated with cellular changes in response to materials with different stiffness, degradability, or stress relaxation. [ \n \n 26 \n , \n 43 \n , \n 45 \n \n ] Our results indicated that altering the M/B ratio (B1.5M3.5 vs B0.5.M4.5) and UPy‐cRGD concentration (1 m m vs 3 m m ) among the cell‐adhesive compositions did not affect the YAP translocation in cells cultured onto these hydrogels (Figure S12 , Supporting Information). Nonetheless, when comparing hydrogels with different total polymer concentrations, we observed a significant nuclear localization of YAP for cells cultured on the B1M9 group (Figure 4h,i ). This significant YAP translocation can be attributed to the highly elastic nature of this composition ( G ′ ≈ 90 kPa), as observed previously for 2D culture of cells on such stiff substrates. [ \n \n 43 \n \n ] \n Nonetheles, 3D encapsulation of cells within the hydrogel matrix is indeed a requirement for the successful application of these materials as synthetic ECM. Notably, we observed that M‐type UPy‐G molecules were incapable of forming hydrogels by themselves, and B‐type UPy‐PEG‐UPy molecules did not gelate at low concentrations of <1 wt% ( Figure \n 5 a,b ). This unique combination of features enabled the possibility to exploit the non‐gel‐forming regimes of both B‐ and M‐type molecules in this system to develop a mixing‐induced gelation method for cell encapsulation within the hydrogels at physiological pH and temperature. In this strategy, cells were included in a dispersion of B‐type fibers, and gel formation was initiated upon mixing two separate dispersions, one composed of M‐type, and the other composed of B‐type molecules (Figure 5a,b ). The co‐formulation process resulted in stable hydrogels within 15 min after mixing the two components. Following this approach, HVSCs were encapsulated in 2.5 wt% (B0.25M2.25) and 5 wt% (B0.5M4.5) hydrogels, with or without UPy‐cRGD additives. It should be noted that this mixing‐induced gelation method is mainly applicable to compositions with a high M/B ratio, as higher concentrations of B‐type molecules can self‐gelate at physiological pH, [ \n \n 46 \n \n ] preventing the necessary mixing of B‐ and M‐type components in the dispersion state. Figure 5 Cell encapsulation and spreading in supramolecular hydrogels. a) Schematic illustration of cell encapsulation in hydrogels via mixing of pre‐assembled supramolecular fibers. b) Representative viscoelastic properties of dispersions of 4.5 wt% M or 0.5 wt% B supramolecular fibers and their mixture measured over time. c) Representative images of HVSCs encapsulated within supramolecular hydrogels without or with 3 m m of UPy‐cRGD additives, after live (green color) and dead (red color) staining. d) Quantification of viability of cells encapsulated in the hydrogels without (−) or with (+) 3 m m of UPy‐cRGD additives, as shown in (c). e) Representative images of HVSCs encapsulated in supramolecular hydrogels of B0.25M2.25 composition without or with 3 m m of UPy‐cRGD additives after 3 days of culture. During the culture period, additional EXO‐1 (120 n m ) or TIMP‐3 (5 n m ) treatments are carried out to block exocytosis and protein remodeling, respectively. Green and blue colors in images indicate actin and nucleus staining, respectively. f) Length of longest axis and g) circularity of cells after 3 days of culture in supramolecular hydrogels without (−) or with (+) 3 m m of UPy‐cRGD additives, as shown in (e). d) **, p < 0.01; two‐way analysis of variance (ANOVA) followed by Bonferroni post hoc. f,g) ****, p ≤ 0.0001; one‐way ANOVA followed by Bonferroni post hoc. All biological results were obtained from three independent experiments per group, and their values are shown as mean ± s.d. f,g) Data points represent features of individual cells, with n comprising the total number of cells per group that were detectable/analyzed among three experiments. Nearly all cells remained viable after 1 and 7 days of culture in all hydrogels, except for 2.5 wt% hydrogels without UPy‐cRGD in which cells showed reduced viability (58 ± 9%) upon 7 days of culture (Figure 5c,d ). The reduced viability in this experimental group can be attributed to the absence of sufficient matrix interactions resulting in anoikis, [ \n \n 47 \n \n ] which has been commonly observed for hydrogels free of adhesive ligands. [ \n \n 48 \n , \n 49 \n , \n 50 \n \n ] Cell spreading was evident in both 2.5 and 5 wt% hydrogels that contained UPy‐cRGD (Figure 5c and Figure S13 , Supporting Information). In the 5 wt% hydrogels, however, cell spreading appeared to be delayed as compared to 2.5 wt% samples, possibly as consequence of the higher elasticity of the 5 wt% hydrogels. To determine whether these two hydrogel concentrations exhibit significantly different pore sizes, water‐soluble fluorescent FITC‐Dextran macromolecules with average molecular weights of 20 kDa (Stokes radius ≈ 3 nm), 100 kDa (Stokes radius ≈ 7 nm), or 2000 kDa (Stokes radius ≈ 27 nm) were incorporated within the hydrogels, and their diffusion was studied through FRAP measurements. Smaller macromolecules (≤100 kDa) could freely diffuse within the pores of the hydrogels and could not be photo‐bleached due to their high diffusivity, whereas larger macromolecules (2000 kDa) were photo‐bleached and displayed similar recovery profiles in both hydrogel concentrations (for B0.25M2.25 and B0.5M4.5, τ 1/2 was 6.9 ± 0.1 and 5.8 ± 0.2 s, respectively; Figure S14 , Supporting Information). These results indicate the presence of similar submicron pore sizes within gel networks at both concentrations of 2.5 and 5 wt%. To elucidate the mechanism of cell spreading in the supramolecular hydrogels developed in the current study, we investigated cell spreading in response to the inhibition of nascent protein deposition and remodeling. A recent study highlighted that early protein deposition can significantly impact the behavior of cells encapsulated within dynamic hydrogel matrices. [ \n \n 27 \n \n ] The authors did not observe enhanced spreading for mesenchymal stromal cells upon functionalization of a dynamic hyaluronic acid hydrogel with RGD ligands, and concluded that the mechanism responsible for cell spreading in dynamic hydrogels is not mainly driven by tethered adhesive ligands. To determine whether this previously proposed mechanism is valid in our system, EXO‐1 (2‐(4‐fluorobenzoylamino)‐benzoic acid methyl ester) or TIMP‐3 (tissue inhibitor of metalloproteinase 3) were used to block exocytosis and protein remodeling within hydrogels, respectively. Upon 3 days of culture, HVSCs encapsulated in B0.25M2.25 hydrogels with UPy‐cRGD additives displayed spread‐out morphology (Figure 5e ). Remarkably, in contrast to the results observed previously for dynamic hyaluronic acid hydrogels, [ \n \n 27 \n \n ] EXO‐1 or TIMP‐3 addition did not suppress the spreading of the cells in these hydrogels. These results signify that for supramolecular hydrogels with a tuned dynamic profile, adhesive ligands can overrule the possible effects of deposition and remodeling of nascent proteins on early cell spreading. Spheroids and organoids are multicellular 3D structures that exhibit more biological resemblance to natural tissues compared to single cells, and are therefore increasingly used for regenerative medicine and as tools to study diseases. [ \n \n 51 \n \n ] Although cell–cell interactions play a major role in these 3D cellular constructs, cell–matrix interactions can also highly impact their behavior such as growth and differentiation. [ \n \n 52 \n \n ] Thus, we investigated the potential of our supramolecular hydrogels to direct the behavior of multicellular spheroids. We hypothesized that effective incorporation of adhesive ligands into supramolecular hydrogels can direct spheroids’ behavior via altering cell–matrix interactions at the hydrogel‐spheroid interface. Additionally, these experiments were intended to determine whether the hydrogel compositions with reduced dynamic nature (e.g., B0.25M2.25) still exhibited sufficient matrix adaptability to allow for cellular activities such as cell migration in 3D space. To test this, we formed HVSC and CMPC spheroids and encapsulated them in B0.25M2.25 hydrogels with or without UPy‐cRGD additives. Strikingly, within 1 day after encapsulation, HVSCs started to migrate from the spheroids toward the hydrogel matrix when UPy‐cRGD molecules were incorporated in the gel compositions ( Figure \n 6 a ). After 7 days, cell migration toward the matrix was significant for both spheroid types and was further enhanced at Day 14 for UPy‐cRGD containing groups (Figure 6a–c ). In contrast, no HVSC migration was detected and CMPC spheroids slightly shrank during the culture period when hydrogels did not contain UPy‐cRGD. Importantly, the majority of the cells remained viable for both spheroid types after 14 days of culture in hydrogels with or without UPy‐cRGD additives (Figure 6c ). Figure 6 Multicellular spheroids encapsulated in supramolecular hydrogels. a) Representative images of HVSC and CMPC spheroids encapsulated in supramolecular hydrogels without or with 3 m m of UPy‐cRGD additives. Scale bars = 500 µm (main images of HVSC spheroids), 250 µm (main images of CMPC spheroids), and 50 µm (insets). b) Quantification of migration distance of cells from the initial surface of spheroids into hydrogel matrices. ****, p ≤ 0.0001; one‐way analysis of variance (ANOVA) followed by Bonferroni post hoc. Results were obtained from four biologically independent experiments per group, and the values are shown as mean ± s.d. Data points represent cell migration distance from initial spheroid surface, with n comprising the total number of spheroids per group that were detectable/analyzed among four experiments. c) Representative images of HVSC and CMPC spheroids after 14 days of culture in supramolecular hydrogels without or with 3 m m of UPy‐cRGD additives. d) Representative images of HVSC and CMPC cells after 2 days of culture of spheroids extracted from hydrogels without UPy‐cRGD. c,d) Green and red colors indicate live and dead cells, respectively; Scale bars = 200 µm. a–d) All hydrogels were of B0.25M2.25 composition. Removal of spheroids and organoids from their culture matrix is of remarkable importance for their thorough characterization and therapeutic applications. Therefore, after 14 days, we extracted the spheroids from the hydrogels by disrupting the gel network via gentle mechanical shearing using pipette tips. The extracted spheroids were seeded onto glass slides and were imaged after 2 days of culture. Confocal images showed that cells adhered and spread out on the glass slides, and migrated from the spheroids onto the substrate surfaces (Figure 6d ), revealing that they remained functional within the spheroids during the 14 days culture period. To further evaluate the applicability of this dynamic material system for 3D culture, we next encapsulated human liver hepatocyte organoids in the supramolecular hydrogels and monitored their growth over 7 days of culture in proof‐of‐concept experiments (Figure S15 , Supporting Information). The organoids encapsulated in B0.25M2.25 hydrogels displayed high levels of ATP production and their surface area doubled during the culture period. Moreover, a budding‐like morphology [ \n \n 53 \n \n ] emerged at the organoid periphery when UPy‐cRGD additives were included in the hydrogel composition, indicating the ability of our supramolecular hydrogels to serve as a modular platform for facilitating organoid culture."
} | 12,025 |
37253075 | PMC10281587 | pmc | 3,730 | {
"abstract": "The type VI secretion system (T6SS) is an antibacterial weapon that is used by numerous Gram-negative bacteria to gain competitive advantage by injecting toxins into adjacent prey cells. Predicting the outcome of a T6SS-dependent competition is not only reliant on presence-absence of the system but instead involves a multiplicity of factors. Pseudomonas aeruginosa possesses 3 distinct T6SSs and a set of more than 20 toxic effectors with diverse functions including disruption of cell wall integrity, degradation of nucleic acids or metabolic impairment. We generated a comprehensive collection of mutants with various degrees of T6SS activity and/or sensitivity to each individual T6SS toxin. By imaging whole mixed bacterial macrocolonies, we then investigated how these P . aeruginosa strains gain a competitive edge in multiple attacker/prey combinations. We observed that the potency of single T6SS toxin varies significantly from one another as measured by monitoring the community structure, with some toxins acting better in synergy or requiring a higher payload. Remarkably the degree of intermixing between preys and attackers is also key to the competition outcome and is driven by the frequency of contact as well as the ability of the prey to move away from the attacker using type IV pili-dependent twitching motility. Finally, we implemented a computational model to better understand how changes in T6SS firing behaviours or cell-cell contacts lead to population level competitive advantages, thus providing conceptual insight applicable to all types of contact-based competition.",
"introduction": "Introduction Bacteria thrive by adapting to a wide variety of ecological niches [ 1 ]. This includes commensals that are a part of the host microbiota [ 2 ] or bacterial pathogens colonising a host [ 3 ]. In any environment, resources can be scarce and the competition for survival a serious challenge. The structure of a polymicrobial population steadily establishing in a niche relies on competition [ 4 ] and cooperation [ 5 ]. For example, cooperation arises from the ability of a species to catabolise complex nutrients sources that is then used by other species. In contrast, competition aims to eliminate cheaters and foes and relies on a variety of fighting strategies [ 6 ]. Polymicrobial communities can be highly complex, and for example up to 40,000 species can coexist within the human gut [ 2 ]. These populations can adopt a biofilm lifestyle which contributes stability and resilience [ 7 ]. Due to the complexity of cell-cell interactions, the development of polymicrobial communities is notoriously difficult to predict but has many implications in ecology, industry, and medicine [ 8 ]. The outcome of a competition is dependent on various skills that bacteria have acquired during evolution [ 1 ]. Some species can be very effective at capturing rare elements such as iron, by producing high affinity siderophores [ 9 ] or proteins able to recapture iron from transferrin or lactoferrin [ 10 ]. The depletion of iron is detrimental to species less able to capture it. This strategy to starve others to their death is more reminiscent of a siege in terms of combat, whereas direct competition strategies involve frontal assault. For long it has been known that bacteria such as Escherichia coli release antibacterial toxins named colicins that are able to penetrate and kill related species [ 11 ]. A contact-dependent mechanism was later discovered which allows specific delivery of toxins, such as tRNAse, into related preys [ 12 ]. This system is called CDI for contact-dependent inhibition [ 13 ] and uses specific receptors at the prey-cell surface to make contact [ 14 ]. Interestingly, a contact-dependent system with much broader impact was discovered in Pseudomonas aeruginosa [ 15 ]. The system delivers a cocktail of antibacterial toxins with various biochemical activities and does not seem to display specificity towards a particular kind of bacterial prey [ 16 , 17 ]. It can inject toxic effectors into prokaryotes, but can also target fungi [ 18 ] and other eukaryotic cells such as amoeba or host macrophages [ 19 , 20 ]. It is called the type VI secretion system (T6SS) and is broadly conserved in Gram-negative bacteria [ 21 – 23 ]. The T6SS involves a contractile sheath, TssBC, which is full of Hcp rings loaded with antibacterial toxins. In recent years it has been found that other secretion systems, including the type IV secretion system (T4SS) [ 24 ] or the type VII secretion system (T7SS) [ 25 ] can also inject toxic effectors into prey bacteria. The large variety of T6SS toxins in P . aeruginosa may originate from a complex lifestyle and ability to thrive in a wide range of ecosystems. P . aeruginosa is a Gram-negative pathogen, which is best known for establishing chronic and ineradicable infections in the lungs of cystic fibrosis (CF) patients [ 26 ]. After colonisation, during which P . aeruginosa has to outcompete the resident lung flora [ 27 ], this organism establishes a resistant and resilient biofilm that is no longer susceptible to antibiotic treatment or clearance by immune system [ 3 ]. The biofilm development involves production of an extracellular matrix consisting of exopolysaccharides, including Pel, Psl and alginate [ 28 ], while simultaneously the bacterium refrains from using motility-related devices and notably the flagellum [ 29 ]. This transition or switch in lifestyle from motile to sessile biofilms is tightly regulated by the Gac/Rsm cascade which consists of several two-component regulatory systems, small regulatory RNAs and translational repressors [ 30 – 33 ]. Remarkably the Gac/Rsm-dependent transition from planktonic to biofilm lifestyle [ 34 ] is accompanied by an upregulation of the 3 P . aeruginosa T6SSs [ 35 , 36 ]. This accounts for the necessity to be prepared for a fight when entering the dense polymicrobial niche where contacts with existing microorganisms will be made. The 3 P . aeruginosa T6SSs deliver an array of antibacterial toxins, each being produced in tandem with a cognate immunity to prevent self-intoxication [ 17 , 37 ]. These toxins may have a synergic impact on prey killing rather than providing redundant backup [ 38 ]. Yet the question remains whether injection of a cocktail of toxins is a robust engineering solution that guarantees that at least one toxin would be effective in a specific condition and for a specific target. Here, we systematically assessed the importance and role of individual P . aeruginosa T6SS effectors using a macrocolony assay that allowed to monitor attacker and prey distribution through differential fluorescent tagging as described in previous studies [ 39 , 40 ]. Specifically, our prey cell lacks a single immunity which makes it sensitive to a single T6SS toxin. Our data showed that the level of T6SS activity could be upregulated in an additive manner by manipulating distinct positions in the Gac/Rsm network, and progressive increase in T6SS function is accompanied by a correlative increment in the secretion level and impact on the preys. We observed that individual toxins have variable impact on the ability of the prey to spread within the macrocolony from non-visible restriction to fully preventing any growth. We also observed synergistic effects when the role of two toxins is combined. The effectiveness of toxins also depends on the initial cell density used and the presence/absence of type IV pili as they influence the number of contacts between attacker and prey at the start and later in the competition assay. Our results are supported by biophysical simulations, that not only correctly describe how lineage distribution within community is affected by growth and interactions between individual prey and attacker bacteria, but also allow us to assess how specific parameters such as toxin dose and potency, firing rate or number of interspecies contacts determines lineage distribution within the community.",
"discussion": "Discussion Bacterial competition or warfare is of huge importance for the survival and prevalence of species within complex populations, such as ones found in the plant rhizosphere [ 96 ] or the gut microbiota [ 97 , 98 ]. Understanding and predicting the fate of polymicrobial populations is a key question in microbiology since for example reprogramming microbiota will help fight obesity, colonic diseases or simply prevent pathogen invasion [ 99 ]. Understanding the rumen microbial organisation would have industrial application for the degradation of plant cell walls [ 100 ]. Capturing appropriate soil microbiome composition would support specific agricultural needs [ 101 ] or even aid in targeted forest restoration [ 102 ]. One of the challenges in the field is engineering of relevant experimental model systems that can capture polymicrobial interactions. Studies aiming to understand dynamics of polymicrobial populations substantially benefit from integrating both experimental and theoretical models as those presented here. An equilibrium within a microbial population can be reached when mutual benefits prevail or upon selfish expansion of a single species [ 103 ]. Benefits include ability of some species to catabolise specific nutrient sources yielding products which can be used by others. It also involves the ability of some species to protect from the invasion of foreign organisms [ 104 ], foes or cheaters, which may trigger dysbiosis and collapsing of a well-ordered community. From a holobiont perspective, it is also important to consider that the equilibrium is guided by the host tolerance which can be reciprocated by the beneficial role of microorganisms such as in the digestion, brain-gut axis or plant growth promotion [ 105 ]. There are numerous combinations of strategies for competition between microorganisms, from nutrient scavenging to killing, from a distance or by contact [ 4 ]. Since most organisms will be using one or more of these tools, the competition outcome is dependent on how effective any one weapon is, and which combinations are the most potent. Here we have conducted a comprehensive and systematic evaluation of potency/role of every single P . aeruginosa T6SS toxins. Table 1 lists all currently known toxins, and there is a clear incentive towards the idea that this is yet the tip of the iceberg with many more to be found [ 38 , 53 ]. It is puzzling though why so many toxins would be needed to eliminate a single prey. One may think that avoiding emergence of T6SS resistance would be one reason, but others might be synergy and differential effectiveness [ 38 ]. Here, that is thus ca . 20 strains that have been engineered so that each strain is theoretically susceptible to a single T6SS toxin. These toxins are mainly delivered by the H1- and H2-T6SS since in case of the H3-T6SS, the best characterized substrates are involved in ion uptake, e . g . TseF [ 89 ], but not in direct antagonism. Our data demonstrate that every single toxin counts, and, in most cases, the sensitized prey is limited in its expansion by the attacker within a macrocolony context. In the cases where the impact is not obvious from the macroscopic analysis an adequate toxin combination or a change in conditions could unveil their contribution. A striking example of synergy provided in our study is with the two peptidoglycan hydrolases released by the H1-T6SS, Tse1 and Tse3. Individually sensitized strains in a background deleted for the rsmA gene are not challenged for their expansion when in contact with attacker cells. However, in this same genetic background, the dual sensitization against Tse1 and Tse3 simultaneously, is now showing a very clear prey growth restriction pattern ( Fig 4 ). It suggests that the disruption of both the glycan backbone and the peptidyl bonds in between the glycan chains are needed to collapse the sacculus structure. However, the synergy might be needed only if each enzyme is delivered at very low dose. Such hypothesis could be verified when another genetic background is used, for which we showed that the H1-T6SS production and firing dynamic is higher as compared to the Δ rsmA context, e . g . Δ retS . In this case the strain sensitized to Tse3 is readily challenged by the attacker, although the Tse1-sensitized strain is still resistant ( Fig 4B ). This is an important observation showing that the dose of toxin injected, and thus the firing efficacy is an important parameter in the outcome of competition. High firing rate allows to overcome limitations posed by low toxicity impact of a given toxin, and from this we might conclude that Tse3 is more potent than Tse1. The firing efficacy also helps in resolving the contribution of many toxins delivered by the H2-T6SS and notably the multiple lipases and phospholipases. Whereas Tle3 and PldB sensitization is readily visible using a Δ rsmA background, for other phospholipases, notably PldA and Tle1, slight impact is only visible in a Δ retS background ( S12 Fig ). Furthermore, and in this case that is valid for all H2-T6SS toxins, the sensitivity is clearly observed at 25°C and not at 37°C ( S10 Fig ), which contrast with H1-T6SS toxins. It was previously reported that particular environmental conditions, e . g . osmolarity, pH or temperature, may potentiate the activity of a subset of toxins [ 38 ], here it is more likely that the injection dose is dependent on the temperature as observed by the level of expression of these two systems at these different temperatures. We also used combination of experimental and modelling approaches to confirm that high frequency of toxin injection depends not only on the activity of the system but also on the number of contacts between attackers and preys. The effects of inoculum content on population intermixing have been shown in previous studies both for CDI and T6SS [ 39 , 40 , 92 , 93 ]. These studies mainly establish the role of lytic toxins and advantages between constitutive and retaliation T6SS firing strategies but from a mechanistic point of view T6SS fights might involve a multitude of additional parameters. The contact indeed does not only involve where two cells touch each other, but also whether a T6SS is assembled at this point of contact. We have previously observed that in a Δ rsmA background there are many more H2-T6SS in a cell as compared to H1-T6SS [ 35 ]. We have also proposed that the H1-T6SS is far more rapid at assembling and injecting as compared to the H2-T6SS [ 51 ]. Finally, it was shown that the H1-T6SS operates as a retaliation system, which means responding only upon T6SS attack from an opponent and at the point of contact [ 106 ], which is unlikely the case for H2-T6SS. What would be a better competitive strategy is again a matter of conditions and context. A recent study has proposed that the retaliation, which might intuitively seem not effective in the face of a strong opponent, can be beneficial on the long term [ 107 ]. The model presented in the study suggests that it is in fact very much dependent on how many times the bacterium can fire back once triggered. If that is many times and quick, then it would be cost effective and beneficial. The systematic use of the T6SS might be energetically costly if fired in circumstances where it is not desperately needed. Some bacterial species have evolved compromises here, in which they kept arrays of immunity genes [ 108 , 109 ], while no longer using the T6SS or its toxin, so being defensively equipped to be part of an offensive community. Since the contact is instrumental for T6SS-dependent competition, determinants that are important in promoting contact events might thus influence drastically the outcome. T4P are proteinaceous fibres extending from the bacterial surface, that are used for both cell-cell adhesion and cell-surface adhesion [ 94 ]. Both of these functions are mediated through the same mechanism–dynamic pili polymerisation and de-polymerisation that allows bacteria to exert force against the object that it adheres to [ 110 ]. In case of T4P-mediated cell-cell interactions, adjacent bacteria form aggregates through pili-pili interactions. When T4P adhere to a substrate, this same mechanism is used to propel bacteria across surface through a type of motility termed “twitching” [ 111 ]. Here we showed that lack of T4P-dependent mediated contact between cells allows prey to better resist T6SS attack, likely because of loosen contacts. Strikingly, lack of T4P in the attacker, while the prey is motile, promotes prey escape which results in mixed colonies with a nice crown of prey “escapers” around a uniform centre of T6SS attacker. This is particularly original, since a similar study on impact of T4P on sub-population distribution in whole mixed colonies focusing on Neisseria gonorrhoeae shows the opposite effect. Indeed, in a mix of T4P+ and T4P- strains, N . gonorrhoeae lacking pili are found in the colony outer region [ 112 ]. This has been attributed to a somewhat ubiquitous adhesion-based sorting mechanism resulting in phase separation [ 113 ]. Specific cell-cell adhesion-mediated sorting, where non-adherent sub-population is expelled to the outer population boundary appears to be a biophysical mechanism observed in normal organogenesis, tumorigenesis [ 114 ] and organisation of bacterial populations [ 112 , 115 ]. Furthermore, another study assessing T4P impact on Neisseria cinerea T6SS-mediated killing observed that sub-population of cells that loose T4P escapes otherwise proficient T6SS killing. In this species, it is likely that the presence of T4P does not allow segregation and contact avoidance from the T6SS attacker [ 116 ]. Finally, a previous study looking at T4P contribution to P . aeruginosa swarming has also noted that in a mix of T4P+ and T4P- bacteria, the strains lacking pili are preferentially found at the leading edge of a swarming population [ 117 ]. Altogether it thus appears that presence/absence of T4P, specific growth conditions and T6SS-dependent effective competition, play a role in determining sub-population distribution within a bacterial community and this may drastically vary from one bacterial species to another. Every study model, both experimental and theoretical, is limited by the fact that only specific environmental conditions and a restricted number of parameters are considered. As such making predictions on the outcome of a competition happening in a host like the lungs of cystic fibrosis patients remains a real challenge [ 118 ]. P . aeruginosa in young CF patients is not necessarily the main resident in the lungs but becomes dominant in adults thus the interest in addressing which mechanisms is used to gain advantage in this niche. The full complexity of natural communities is unlikely to be recapitulated in minimal systems such as the one we used here, and competition outcome likely does not depend on one strategy but on the sum of many. Yet we could evaluate the impact of individual T6SS toxins or T4P-dependent contact during competition. We believe that recording the contribution of individual elements, and that is one major advance provided by studies as the one we conducted here, is essential for construction of large and comprehensive datasets. These can be used in conjunction with theoretical tools to gain further understanding on what rules govern outcome of competition. If the T6SS is a key machine in the resolution of microbial competition, genomic approaches showed that despite a broad distribution across phyla and species, there is no rule on who does or does not pose a T6SS [ 119 ]. Trying to figure out the benefit for bacteria of having or not a T6SS would only make sense if we understand which toxins are delivered by these systems, and what are their impact on preys. Further studies should also assess the distribution of cell lineages within the three dimensions of a biofilm [ 120 ] which will further complexify the heterogeneity of the environment and how cells respond and adapt within the different layers of a biofilm."
} | 5,038 |
37938301 | PMC9723799 | pmc | 3,731 | {
"abstract": "A grand challenge in microbial ecology is disentangling the traits of individual populations within complex communities. Various cultivation-independent approaches have been used to infer traits based on the presence of marker genes. However, marker genes are not linked to traits with complete fidelity, nor do they capture important attributes, such as the timing of gene expression or coordination among traits. To address this, we present an approach for assessing the trait landscape of microbial communities by statistically defining a trait attribute as a shared transcriptional pattern across multiple organisms. Leveraging the KEGG pathway database as a trait library and the Enhanced Biological Phosphorus Removal (EBPR) model microbial ecosystem, we demonstrate that a majority (65%) of traits present in 10 or more genomes have niche-differentiating expression attributes. For example, while many genomes containing high-affinity phosphorus transporter pstABCS display a canonical attribute (e.g. up-regulation under phosphorus starvation), we identified another attribute shared by many genomes where transcription was highest under high phosphorus conditions. Taken together, we provide a novel framework for unravelling the functional dynamics of uncultivated microorganisms by assigning trait-attributes through genome-resolved time-series metatranscriptomics.",
"conclusion": "Conclusions and future perspectives In this work, we applied a novel trait-based ‘omics pipeline to a semi-complex, engineered bioreactor microbial community to explore ecosystem-level and niche-differentiating traits. Through recovering 66 MAGs from the EBPR SBR community and using a time-series metatranscriptomics experiment, we were able to extend functional predictions such as identifying multiple attributes of high-affinity phosphate transporters beyond hypotheses made from traits alone. We extended this framework to other significant traits that are distributed among community members such as denitrification and amino acid metabolism. Specifically, we demonstrate that traits with similar expression profiles may be clustered into attributes providing a new layer to trait-based approaches. We believe that identifying expression-based attributes will be a powerful tool to explore microbial traits in natural, engineered, and host-associated microbiomes. Outside of activated sludge systems, trait-based approaches could illuminate how similar secondary metabolite clusters are expressed among different species in a community [ 82 , 83 ], how auxotrophies for amino acid and vitamin cofactors govern interactions [ 84 ], how rhizosphere microorganisms respond to day-night cycles, and identify putative traits that universally exhibit ecosystem-level or niche-differentiating patterns across ecosystems [ 19 , 23 ]. Importantly, our trait-based approach can be used to screen for expected expression patterns of a key trait compared to a model organism, and then prioritize specific microbial lineages for downstream experimental verification with techniques such as Raman-FISH [ 85 , 86 ].",
"introduction": "Introduction A longstanding cornerstone of deterministic ecological theory is that the environment selects for traits. Traits may be defined as any physiological, morphological, or genomic signature that affects the fitness or function of an individual [ 1 ]. Trait-based approaches have become indispensable in macroecological systems to describe fitness trade-offs and the effects of biodiversity on ecosystem functioning [ 2 – 5 ]. Recently, trait-based frameworks have been proposed as an alternative to taxonomy-based methods for describing microbial ecosystem processes [ 6 , 7 ]. Connecting microbial traits and their phylogenetic distributions to ecosystem-level functions can provide powerful insights into the ecological and evolutionary dynamics underpinning community assembly, microbial biogeography, and organismal responses to changes in the environment [ 8 – 10 ]. Additionally, pinpointing the organismal distribution of traits and the ecological selective pressures that enrich them may be leveraged to reproducibly and rationally engineer stable, functionally redundant ecosystems [ 11 – 15 ]. However, applying trait-based approaches to microbial communities is challenging due to the difficulty in identifying and measuring relevant ecological traits for a given ecosystem [ 16 ]. High-throughput sequencing technologies and multi-omics techniques are now routinely used to describe the diversity, activity, and functional potential of uncultivated microbial lineages [ 17 – 21 ]. Improvements in bioinformatics algorithms, and in particular metagenomic binning methods, have allowed for genome-resolved investigations of microbial communities rather than gene-based analyses of assembled contigs [ 22 ]. These (meta) genomes are subsequently leveraged to detect the presence of key genes or pathways and predict specific traits of the whole community [ 19 , 23 ]. Integrating metatranscriptomics data addresses a key limitation, as expression patterns better reflect the actual functional dynamics of a trait compared to gene presence alone. Here, we present TbasCO, a software package and statistical framework for T rait- bas ed C omparative ‘ O mics to identify expression attributes. We adopt the terminology attribute as a hierarchically structured feature of a trait and assert that statistically similar transcriptional patterns of traits across multiple organisms be treated as attributes (Fig. 1 ). This new terminology addresses two key semantic challenges. First, by extending upon the current usage of the term “trait” for the presence and absence of pathways to the corresponding transcriptional patterns. Second, it addresses a limitation of the terminology of “co-expression”, which becomes biologically inaccurate when comparing across independent populations of organisms within a community. In this manner, the identification of expression-based attributes provides a high-throughput and intuitive framework for extending trait-based methods to time-series expression patterns in microbial communities. We implement this trait-based approach to classify transcriptional attributes in a microbial community performing Enhanced Biological Phosphorus Removal (EBPR), a globally important biotechnological process implemented in numerous wastewater treatment plants (WWTPs). Fig. 1 Overview of trait-based comparative transcriptomics approach In genome-resolved metagenomics approaches, representative MAGs are assembled from a microbial community of interest, and the presence and/or absence of key metabolic pathways are used to make inferences of metabolic potential and ecosystem processes. However, metagenomic data alone can only assess the metabolic potential of a given pathway, and do not provide other biologically relevant information such as the timing or induction of these traits. Using time-series metatranscriptomics, we developed a trait-based comparative ‘omics (TbasCO) pipeline that statistically assesses the inter-organismal differences in gene expression pattern of a given trait to cluster into trait attributes. As expression patterns are determined by the time-points assessed in an experiment, it is important to design the sampling regime to capture relevant ecophysiological changes within the ecosystem. The fundamental feature of the engineered EBPR ecosystem is the decoupled and cyclic availability of an external carbon source and terminal electron acceptor. This cycling is often referred to as “feast-famine” conditions and provides a strong selective pressure for traits such as polymer cycling. Accumulation of intracellular polyphosphate through cyclic anaerobic-aerobic conditions ultimately results in net phosphorus removal and accomplishes the EBPR process [ 24 , 25 ]. One of the most well-studied polyphosphate accumulating organisms (PAOs) belongs to the uncultivated bacterial lineage ‘ Candidatus Accumulibacter phosphatis’ (hereby referred to as Accumulibacter) [ 24 , 26 ]. Numerous genome-resolved ‘omics methods have been used to investigate the physiology and regulation of this model PAO enriched in engineered lab-scale enrichment bioreactor systems [ 27 – 35 ]. However, novel and putative PAOs have been discovered that remove phosphorus without exhibiting the hallmark traits of Accumulibacter [ 36 – 41 ]. Additionally, although these lab-scale systems are designed to specifically enrich for Accumulibacter, a diverse bacterial community persists in these environments [ 27 ], and their ecological roles have largely remained unexplored. As a result, the general adaptations of microbial lineages inhabiting the EBPR community are not well understood. Using genome-resolved metagenomics and metatranscriptomics, we assembled 66 species-representative genomes spanning several significant EBPR lineages and identified the distribution of expression-based attributes. We show that while some expression attributes are distributed in few genomes, many are redundant and shared across many lineages. Furthermore, we find that a majority of core traits (as defined by the presence of marker genes) have multiple attributes, suggesting that identifying niche-differentiating expression attributes may be used to reveal a large hidden metabolic versatility when investigating genomic data alone.",
"discussion": "Results and discussion Reconstructing a diverse EBPR SBR community To explore trait-based transcriptional dynamics of a semi-complex microbial community, we applied genome-resolved metagenomics and metatranscriptomics to an EBPR sequencing-batch reactor (SBR) ecosystem (Fig. 2 ). We previously performed a metatranscriptomics time-series experiment over the course of a normally operating EBPR cycle to investigate the regulatory controls of Accumulibacter gene expression [ 42 ]. In this experiment, six samples were collected for RNA sequencing: three from the anaerobic phase and three from the aerobic phase (Fig. 2A ). Additionally, three metagenomes were collected from the same month of the metatranscriptomic experiment, including a sample from the same date of the experiment. We reassembled contemporary Accumulibacter clade IIA and IA genomes that were previously assembled from the same bioreactor system [ 27 , 28 ]. The genomes of Accumulibacter clades IA and IIA are similar by approximately 85% average-nucleotide identity [ 28 , 31 ], which is well below the common species-resolved cutoff of 95%, and these groups have recently been designated as separate species ( Candidatus Accumulibacter regalis and Candidatus Accumulibacter phosphatis, respectively) [ 35 ]. However, we maintain references to the Accumulibacter clade nomenclature based on polyphosphate kinase ( ppk1 ) sequence identity throughout the manuscript (CAPIA and CAPIIA) [ 31 , 50 , 51 ]. During the experiment, the bioreactor was highly enriched in Accumulibacter clade IIA, accounting for approximately 50% of the mapped metagenomic reads and the highest transcriptional counts (Fig. 2B, C ) [ 42 ]. Whereas Accumulibacter clade IA exhibited low abundance patterns but was within the top 10 genomes with the highest total transcriptional counts (Fig. 2C ). Fig. 2 Genome-resolved metatranscriptomics approach of an EBPR system. Application of a genome-resolved metatranscriptomics approach to a lab-scale sequencing batch reactor (SBR) designed to enrich for Accumulibacter. A Schematic of the main cycle parameters and analyte dynamics of an SBR simulating EBPR. Six samples were taken for RNA sequencing within the cycle at time-points denoted by arrows. B Phylogenetic identity and abundance patterns of 66 assembled MAGs from the EBPR system. The phylogenetic tree was constructed from concatenated markers contained in the GTDB-tk with muscle, calculated with RAxML, and visualized in iTOL. A phylogenetic tree of all 66 MAGs with reference genomes and high-quality genomes from Singleton et al. constructed with concatenated markers from GTDB-tk are provided in Supplementary Fig. 1 . Sizes of circles represent relative abundance patterns calculated from metagenomic reads obtained from a sample collected the same day as the metatranscriptomic experiment was performed, and are not to scale. C Transcriptional patterns of each MAG in the anaerobic and aerobic phases of the EBPR cycle. RNA-seq reads from each time-point were competitively mapped to all 66 assembled MAGs and counts normalized by transcripts per million (TPM). Total counts in the anaerobic and aerobic phases for each genome were averaged separately and plotted on a log scale. Order of MAGs from left to right mirrors the order of MAGs in the phylogenetic tree in B from the top of the circle going clockwise. Although this bioreactor system was highly enriched in Accumulibacter, a diverse bacterial community persisted and was active in this ecosystem (Fig. 2B, C ). We reconstructed representative population genomes of the microbial community of the SBR system, resulting in 64 metagenome-assembled genomes (MAGs) of the (non-Accumulibacter) bacterial community. Interestingly, we recovered genomes of experimentally verified and putative PAOs previously not detected in these bioreactors, including two Tetrasphaera spp . (TET1 and TET2) ‘ Candidatus Obscuribacter phosphatis’ (OBS1), and Gemmatimonadetes (GEMMA1). Pure cultures of Tetrasphaera have been experimentally shown to cycle polyphosphate without incorporating PHA [ 37 ], deviating from the hallmark Accumulibacter PAO model. The first cultured representative of the Gemmatimonadetes phylum Gemmatimonas aurantiaca was isolated from an SBR simulating EBPR and was shown to accumulate polyphosphate through Neisser and DAPI staining [ 52 ]. Additionally, Ca. Obscuribacter phosphatis has been hypothesized to cycle phosphorus based on the presence of genes for phosphorus transport, polyphosphate incorporation, and potential for both anaerobic and aerobic respiration [ 38 ], and was enriched in a photobioreactor EBPR system [ 53 ]. Both Tetrasphaera spp . TET1 and TET2, OBS1, and GEMMA1 groups exhibit higher relative abundance patterns than CAPIA but have similar relative transcriptional levels (Fig. 2B, C , Table 1 ). Numerous SBR MAGs among the Actinobacteria and Proteobacteria contain the high-affinity phosphorus transporter pstABCS system, polyphosphate kinase ppk1 , and the low-affinity pit phosphorus transporter (Supplementary Fig. 5 ). Additionally, select MAGs within the Alphaproteobacteria , Betaproteobacteria , and Gammaproteobacteria contain all required subunits for polyhydroxyalkanoate synthesis (Supplementary Fig. 5 ). Other abundant and transcriptionally active groups in the SBR ecosystem that are not predicted to be PAOs are members of the Bacteroidetes such as CHIT1 within the Chitinophagaceae , and Cytophagales members Runella sp. RUN1 and Leadbetterella sp. LEAD1 (Fig. 2B, C , Table 1 ). Interestingly, an uncharacterized group within the Bacteroidetes , represented by BAC1, contributed the third most to the pool of transcripts (Fig. 2C ), and did not show phylogenetic similarity to MAGs assembled from Danish full-scale wastewater treatment systems [ 40 ] (Supplementary Fig. 1 ). Other groups from which we assembled MAGs for that do not exhibit clear roles in EBPR systems were Chloroflexi ANAER1 and HERP1 MAGs, Armatimonadetes FIMBRI1, Firmicutes FUSI1, and Patescibacteria SACCH1. Members of the Chloroflexi are filamentous bacteria that have been associated with bulking and foaming events in full-scale WWTPs [ 54 – 56 ], but also aid in forming the scaffolding around floc aggregates and degrade complex polymers [ 56 – 58 ]. The Patescibacteria (formerly TM7) are widespread but low abundant members of natural and engineered ecosystems, have reduced genome sizes, and may contribute to filamentous bulking in activated sludge [ 22 , 59 ]. To summarize, lab-scale SBRs designed to enrich for Accumulibacter contain diverse bacterial microorganisms [ 27 , 32 ], but their ecological functions and putative interactions remain to be fully understood in the context of the EBPR ecosystem. Identifying expression-based trait attributes among the EBPR SBR community with TbasCO Current metatranscriptomics analyses often employ either a gene-centric [ 31 , 60 – 62 ] or genome-centric approach [ 42 , 63 – 65 ]. In both approaches, highly, differentially, or co-expressed genes are identified and tested for enrichment of specific functions. Enrichment- or annotation-based approaches are employed in numerous metatranscriptomics tools such as MG-RAST, MetaTrans, SAMSA2, COMAN, IMP, and Anvi’o [ 66 – 71 ]. Here, we expand on the use of molecular markers as traits by defining expression attributes by leveraging a priori knowledge from predefined trait libraries, such as the KEGG database [ 72 ], to statistically assess inter-species expression patterns of genes that together form a trait (Fig. 1 ). First, our results showed that there is statistically significant transcriptional conservation of genes at the community level; genes that share an annotation were significantly more similar than expected using two different distance metrics (NRED: p value <2.2e–16, PC: p value <2.2e–16). Extending this statistical analysis to the trait level, we identified 1674 attributes distributed across the 66 genomes. On average, we identified 9.12 genomes per attribute (SD -5.22), with a minimum of 3 genomes and a maximum of 35 (Fig. 3B ). Based on these statistics, we defined redundant attributes as those two standard deviations above the mean (19 genomes). With this cutoff applied, we identified 79 redundant trait attributes mostly belonging to pathways among carbohydrate metabolism, purine metabolism, and fatty acid metabolism categories (Table 2 ). Of 290 traits, we identified 97 traits with two or more attributes identified (33%). Of these, traits in 10 or more genomes were twice as likely to have two or more attributes (65%), suggesting that divergent expression patterns for a trait are common, and may represent a niche-differentiating feature (Fig. 3A ). Henceforth, when multiple attributes are identified for a trait, we refer to these as niche-differentiating attributes. Fig. 3 Clustering and distribution of trait attributes across EBPR SBR community members. Using the TbasCO method, we identified expression-based trait attributes from predefined trait modules in the KEGG library and explored the distribution of these trait attributes across community members. A Distribution of trait-attributes among sets of genomes. Bars represent the number of trait-attributes present in a set number of genomes and colored by KEGG module category. Among a total of 35 genomes, trait attributes present between 3 and 18 genomes are designated as niche differentiating, whereas trait attributes present in 19 or greater genomes are designated as core trait attributes. Inset figure demonstrates the maximum number of attributes for the maximum number of genomes. B Cytoscape network showing the connectedness of genomes to trait attributes. The network was filtered to only include nodes with more than 5 connections, therefore filtering out both genomes with few trait attributes and trait attributes connected to less than 5 genomes. Genomes are represented as squares colored by phylum, and trait attributes are represented as circles colored by KEGG category. The size of both the squares and circles represents the number of connections to that genome or trait attribute, respectively. Table 2 KEGG pathways for core trait-attributes present in greater than 19 genomes. Module description Number of attributes Citrate cycle, second carbon oxidation, 2-oxoglutarate => oxaloacetate [PATH:map00020 map01200 map01100] 13 Citrate cycle (TCA cycle, Krebs cycle) [PATH:map00020 map01200 map01100] 10 Shikimate pathway, phosphoenolpyruvate + erythrose-4P = > chorismate [PATH:map00400 map01230 map01100 map01110] 8 Fatty acid biosynthesis, initiation [PATH:map00061 map01212 map01100] 7 Glycolysis, core module involving three-carbon compounds [PATH:map00010 map01200 map01230 map01100] 7 Adenine ribonucleotide biosynthesis, IMP = > ADP,ATP [PATH:map00230 map01100] 4 Guanine ribonucleotide biosynthesis IMP = > GDP,GTP [PATH:map00230 map01100] 4 Inosine monophosphate biosynthesis, PRPP + glutamine => IMP [PATH:map00230 map01100] 4 Isoleucine biosynthesis, threonine => 2-oxobutanoate => isoleucine [PATH:map00290 map01230 map01100] 3 NADH:quinone oxidoreductase, prokaryotes [PATH:map00190] 3 beta-Oxidation, acyl-CoA synthesis [PATH:map00061 map00071 map01212 map01100] 2 F-type ATPase, prokaryotes and chloroplasts [PATH:map00190 map00195] 2 Valine/isoleucine biosynthesis, pyruvate => valine / 2-oxobutanoate => isoleucine [PATH:map00290 map00770 map01210 map01230 map01100 map01110] 2 CAM (Crassulacean acid metabolism), dark [PATH:map00620 map00710 map01200 map01100 map01120] 1 Cytochrome c oxidase, cbb3-type [PATH:map00190] 1 Cytochrome c oxidase, prokaryotes [PATH:map00190] 1 dTDP-L-rhamnose biosynthesis [PATH:map00521 map00523 map01100 map01130] 1 Leucine biosynthesis, 2-oxoisovalerate => 2-oxoisocaproate [PATH:map00290 map01210 map01230 map01100 map01110] 1 Phosphatidylethanolamine (PE) biosynthesis, PA = > PS = > PE [PATH:map00564 map01100] 1 PRPP biosynthesis, ribose 5 P = > PRPP [PATH:map00030 map00230 map01200 map01230 map01100] 1 Pyruvate oxidation, pyruvate => acetyl-CoA [PATH:map00010 map00020 map00620 map01200 map01100] 1 Semi-phosphorylative Entner-Doudoroff pathway, gluconate => glycerate-3P [PATH:map00030 map01200 map01100 map01120] 1 Threonine biosynthesis, aspartate => homoserine => threonine [PATH:map00260 map01230 map01100 map01110] 1 From the ecosystem perspective, a clear phylogenetic signal is observed in the distribution of attributes, as genomes cluster together by shared trait attributes by phylum with some exceptions, such as genomes belonging to the Bacteroidetes, Actinobacteria , and Proteobacteria clustering together, respectively (Fig. 3C ). For simplicity, we filtered the network to only include nodes with more than 5 connections. Highly redundant trait attributes belonged to modules in the lipid metabolism, energy metabolism, and nucleotide metabolism KEGG functional categories. In contrast, more specialized trait attributes on the periphery of the network or amongst group-specific clusters such as within the Actinobacteria or subsets of the Proteobacteria belonged to amino acid metabolism, biosynthesis of terpenoids and polyketides, metabolism of cofactors and vitamins, and carbohydrate metabolism KEGG modules. Pathways of note that showed a high level of redundancy include the TCA cycle, isoleucine biosynthesis, acyl-CoA synthesis, threonine biosynthesis, and cytochrome c oxidase activity (Table 2 ). Large pathways with hundreds of possible routes such as glycolysis, the TCA cycle, gluconeogenesis, and the pentose phosphate pathway are not included in the main network and are displayed as individual networks (Supplementary Fig. 6 ). We next explored the distribution of non-redundant attributes (e.g. 3–18 genomes) (Fig. 3B ). A total of 796 trait attributes with low redundancy were identified belonging to pathways involved in carbohydrate cofactor and vitamin metabolism including glycolysis, gluconeogenesis, parts of the TCA cycle, tetrahydrofolate biosynthesis, tryptophan biosynthesis, and the pentose phosphate pathway (Table 3 ). Different sets of low redundancy trait attributes were identified within respective phyla (Supplementary Fig. 7 ). Between genomes belonging to the Actinobacteria , Alphaproteobacteria, Bacteroidetes, Betaproteobacteria , and Gammaproteobacteria , low redundancy attributes (belonging to less than half of the total genomes within the phylum) include carbohydrate metabolism, amino acid metabolism and metabolism of cofactors and vitamins (Supplementary Fig. 7 ). Redundant trait attributes within individual phyla belong to core energy metabolism pathways, fatty acid biosynthesis, and carbohydrate metabolism. However, even within individual phyla, non-redundant attributes include different amino acids and cofactors (Extended Table 1 - available on Figshare https://figshare.com/articles/dataset/Lineage-Specific_Core_and_Niche_Differentiating_Traits/15001200 ). Table 3 KEGG Pathways for differentiating trait-attributes present between 3 and 18 genomes. Module_description Number of attributes Glycolysis (Embden-Meyerhof pathway), glucose => pyruvate [PATH:map00010 map01200 map01100] 279 Citrate cycle (TCA cycle, Krebs cycle) [PATH:map00020 map01200 map01100] 208 Gluconeogenesis, oxaloacetate => fructose-6P [PATH:map00010 map00020 map01100] 76 Inosine monophosphate biosynthesis, PRPP + glutamine => IMP [PATH:map00230 map01100] 45 Citrate cycle, second carbon oxidation, 2-oxoglutarate => oxaloacetate [PATH:map00020 map01200 map01100] 31 Heme biosynthesis, plants and bacteria, glutamate => heme [PATH:map00860 map01100 map01110] 27 Tetrahydrofolate biosynthesis, GTP = > THF [PATH:map00790 map00670 map01100] 25 Tryptophan biosynthesis, chorismate => tryptophan [PATH:map00400 map01230 map01100 map01110] 25 Ornithine biosynthesis, glutamate => ornithine [PATH:map00220 map01210 map01230 map01100] 24 Histidine biosynthesis, PRPP = > histidine [PATH:map00340 map01230 map01100 map01110] 17 Pentose phosphate pathway (Pentose phosphate cycle) [PATH:map00030 map01200 map01100 map01120] 16 Lysine biosynthesis, succinyl-DAP pathway, aspartate => lysine [PATH:map00300 map01230 map01100] 12 Uridine monophosphate biosynthesis, glutamine (+ PRPP) = > UMP [PATH:map00240 map01100] 11 As noted previously, one of the most striking findings is that a majority, 65% of traits present in 10 or more genomes have multiple expression attributes. Thus, it seems that while the presence of marker genes suggests many organisms share a particular trait, the presence of niche-differentiating expression profiles suggest an alternative story, that there is a level of hidden metabolic diversity. For example, central carbon metabolism and energy pathways such as the TCA cycle, glycolysis, gluconeogenesis, and the pentose phosphate pathway are oftentimes considered core traits when only analyzing the presence and/or absence of individual markers belonging to these pathways. Among over 1000 high-quality MAGs assembled from full-scale Danish WWTPs, the TCA cycle and pentose phosphate pathway are highly represented among the abundant microorganisms, with glycolysis less so [ 40 ]. Whereas the TCA cycle and pentose phosphate pathway are present among a high number of genomes in the EBPR SBR community, different routes or parts of these pathways have niche-differentiating distributions (Supplementary Fig. 6 , Tables 2 and 3 ). These finer-scale differences in expression of “core” traits may explain the persistence of a diverse community when solely fed acetate, as different lineages could employ similar carbon utilization pathways differently or in more versatile ways. Another salient aspect of this analysis is the astonishingly high number of possible routes within individual pathways here represented by their Disjunctive Normal Forms. For example, accounting for all alternative routes and enzymes, the glycolysis pathway has 100 s of possible routes. Layering upon this many expression attributes reveals a large hidden metabolic versatility. Dimensionality of the high-affinity phosphorus transporter system PstABCS The EBPR ecosystem is characterized by its highly dynamic phosphorus cycles. To explore how different lineages respond to fluctuating phosphorus concentrations, we examined the expression-based attributes for the KEGG module of the high-affinity phosphorus transporter pstABCS (Fig. 4 ). The pstABCS system is an ABC-type transporter that strongly binds phosphate with high affinity under phosphorus-limiting conditions, and therefore we expected that the highest expression levels would be at the end of the aerobic cycle [ 73 ]. In contrast, we found that pstABCS expression was characterized by two different trait attributes. In the first attribute shared by 14 community members, all pstABCS components displayed the highest activity towards the end of the aerobic cycle, when phosphorus concentrations were depleted (Fig. 4 , Attribute 1). Conversely, 11 community members displayed an alternate attribute where the highest activity of pstABCS was at the transition from anaerobic to aerobic phases when phosphorus concentrations are highest (Fig. 4 , Attribute 2). Fig. 4 Trait attributes of the high-affinity phosphorus transporter system pstABCS . Using the TbasCO method, two trait attributes of the high-affinity phosphorus transporter system pstABCS were identified. The pstABCS system consists of a phosphate-binding protein and ABC-type transporter, and the corresponding KEGG orthologs for each subunit are shown. Timepoints 1–3 refer to the three anaerobic phase timepoints, and timepoints 4–6 refer to the three anaerobic phase timepoints (Fig. 1 ). Expression values are log-transformed based on setting the lowest expression value within each genome across the time-series to 0 for each subunit. Specific subunits for some genomes in both attributes are missing to the high cutoff thresholds for annotations. However we kept genomes with 2/4 subunits to show similarities in expression profiles. The first pstABCS trait-attribute includes microbial lineages that exhibited the highest expression of all subunits towards the end of the aerobic cycle, when phosphate concentrations are expected to be lowest. This includes microbial lineages within the Actinobacteria, Proteobacteria, Gemmatimonadetes , and Chloroflexi . The second pstABCS trait-attribute includes lineages that exhibited highest expression of all subunits upon the switch from anaerobic to aerobic phases, or when phosphate concentrations are expected to be the highest. This includes lineages within the Actinobacteria and Proteobacteria . Interestingly, the two Accumulibacter clades IA and IIA are split amongst these separate pstABCS attributes. These results are in agreement with previous results showing that Accumulibacter clade IIC has a canonical pstABCS expression pattern (as in Fig. 4 , Attribute 1), whereas the Accumulibacter clade IA has a non-canonical expression (as in Fig. 4 , Attribute 2) [ 31 ]. By assigning trait attributes, we can extend these findings beyond Accumulibacter to other community members in the SBR ecosystem suggesting that there are conserved ecological pressures driving niche differentiating expression patterns in pstABCS within the EBPR community. Distribution and expression of truncated denitrification steps among EPBR community members Denitrification gene induction is an important ecosystem property linked to the redox status of an environment. In EBPR communities, we find many genomes with diverse and incomplete denitrification pathways, distributed across many lineages denitrification steps expected in denitrifying systems (Fig. 5 ). Among all 66 MAGs, we did not identify any single MAG with a complete denitrification pathway consisting of the genetic repertoire necessary to fully reduce nitrate to nitrogen gas (Supplementary Fig. 5 ). Instead, we identified multiple groups of organisms with truncated denitrification pathways, with steps distributed among cohorts of community members (Fig. 5 ). Fig. 5 Expression dynamics of distributed denitrification routes. Expression of denitrification traits distributed among community members in the EBPR SBR ecosystem. Timepoints 1–3 correspond to the anaerobic phase and timepoints 4–6 correspond to the aerobic phase as referenced in Fig. 1 . A Complete denitrification pathway and associated genetic repertoire with each sequential step. B Trait attributes of expression dynamics for community members with the narGH nitrate reductase system. This trait was the only denitrification trait identified with more than one attribute. C Expression dynamics of the napAB nitrate reductase system. D Expression dynamics of the norBC nitrous oxide reductase system. E Expression of all steps of denitrification starting at nitrite reduction. F Expression of the most complete denitrification route among three community members, with the norC subunit for nitrous oxide reduction missing. Note that OTTO1 only contains nirS but is included in this trait attribute because the expression dynamics are similar to that of the other three genomes for this subunit. For the first steps of reducing nitrate to nitrite, we examined expression attributes of the napAB and narGH pathways (Fig. 5B, C ). For the narGH pathway, two attributes were identified (Fig. 5B ). The first narGH attribute was characterized by high expression in the anaerobic phase, with decreasing transcript levels by the second time point of the anaerobic phase. Genomes containing this attribute included the experimentally verified and putative PAOs Tetrasphaera (TET1 and TET2) and Ca . Obscuribacter (OBS1), respectively. The second attribute was exhibited among members of the Actinobacteria (PROP2, PHYC2, PROP3, and NANO1), Proteobacteria (BEIJ4), and Bacteroidetes (BAC1). The attribute identified for napAB was also more highly expressed anaerobically and included CAPIA, CAPIIA, ALIC1, REYR2, RUBRI1, and BEIJ3. Interestingly, this napAB attribute had expression patterns that quickly decreased in the first aerobic time point, suggesting a tighter regulation than Attribute 1 for narGH . Together, this suggests that the regulation of denitrification within the EBPR ecosystem is a niche-differentiating feature whereby the induction of denitrification pathways occurs either anaerobically or only after anaerobic carbon contact. A smaller cohort contained the genetic repertoire to reduce nitrite to nitrogen gas and exhibited hallmark anaerobic-aerobic expression patterns (Fig. 5E ) These members within the Proteobacteria (OTTO2, BEIJ3, VITREO1, and ZOO1) contained the nirS nitrite reductase, the norBC nitric oxide reductase, and nosZ , and showed highest expression of these subunits towards the beginning of the anaerobic cycle, slowly decreasing over the aerobic period to their lowest in the end of the aerobic cycle. Although BEIJ2 was lacking the norBC system, it contained the nirS nitrite reductase and nosZ subunit, and exhibited similar expression patterns to others in this cohort. Other Proteobacteria lineages only contained the norBC subunits but were expressed in similar fashions (RHODO2, FLAVO1, RHIZO1, and LEAD1) (Fig. 5D ). Accumulibacter clades IA and IIA as well as ALIC1 were the only lineages with near-complete denitrification pathways. These lineages contained the napAB nitrate reductase system as mentioned above, the nirS nitrite reductase, norB (missing a confident hit for the norC subunit), and nosZ . These three lineages also exhibited hallmark upregulation of all steps in the anaerobic phase, with decreased activity after aerobic contact (Fig. 5F ). Interestingly, Accumulibacter clade IA exhibited a higher level of transcripts associated denitrification steps when expression levels were normalized relative to clade IIA, supporting the hypothesis that denitrification is a niche-differentiating feature among clades [ 28 , 31 , 74 ], and possibly a strain-specific trait since denitrification traits cannot be predicted based on ppk1 clade designations [ 32 ]. For example, independent observations in differences among denitrification activities among strains within Accumulibacter clade IC are inconsistent [ 34 , 75 ]. Within the same bioreactor environment, coexisting Accumulibacter clades differ between denitrification abilities and expression profiles [ 31 – 33 ]. Truncated denitrification pathways have also been previously shown to be distributed among community members, with the complete denitrification genetic repertoire only present in few members [ 32 , 33 ], which could be due to extensive horizontal gene transfer of genes comprising denitrification steps [ 32 , 76 ]. Although this experiment was not conducted under denitrifying conditions, our approach could be applied to denitrifying EBPR systems to further understand the distribution of denitrification traits among community members and how to selectively enrich for diverse DPAOs. Biosynthetic potential and expression dynamics of amino acid and vitamin synthesis pathways Although SBRs are designed to enrich for Accumulibacter by providing acetate as the sole carbon source, a diverse bacterial community persists in these setups [ 27 , 32 ]. One hypothesis for the persistence of these bacterial community members may be cooperative interactions due to underlying auxotrophies of amino acid and vitamin biosynthetic pathways in Accumulibacter. Amino acids and vitamin cofactors are metabolically expensive to synthesize, and widespread auxotrophies have been widely documented among microbial communities [ 77 , 78 ]. Specifically, auxotrophies of vitamin cofactors have been shown to fuel bacterial and cross-kingdom interactions with de novo synthesizers [ 79 , 80 ]. To explore this hypothesis in the EPBR SBR community, we analyzed the presence of amino acid and vitamin biosynthetic pathways and their expression patterns among the top 15 genomes based on transcript abundance (Fig. 6 ). Fig. 6 Biosynthetic potential compared to expression of amino acid and vitamin synthesis pathways for top 15 expressed MAGs. Biosynthetic potential and expression patterns of amino acid and vitamin pathways were analyzed for the top 15 genomes with the highest transcriptional counts (Table 1 ). A For a pathway to be considered present for downstream analysis in the TbasCO pipeline, 80% of the pathway had to be present in a genome. Thus, we used this cutoff criterion to discern whether a specific pathway was present or absent in a genome (with the expectation of methionine, as all genomes did not contain at least 80% of the subunits in the KEGG methionine synthase pathway, we inferred the presence of the methionine synthase as presence of this pathway). Orange colored boxes for cofactor biosynthesis pathways represents the presence of that pathway, whereas grey infers absence. For amino acid biosynthetic pathways, amino acids are listed by their side chain groups – charged, polar, hydrophobic, and other. Blue colored boxes for amino acid biosynthesis pathways represents the presence of that pathway, whereas grey infers absence. B Mini-networks of vitamin co-factors. Squares are genomes with the colors matching the color bar in A . Nodes are attributes, where the colored nodes for the tetrahydrofolate attributes represent the different routes. C Mini-networks of amino acid biosynthesis pathways split by type. Colors of nodes for each amino acid represent the different routes for that pathway. Squares represent genomes with colors matching the color bar in A . Within Accumulibacter, there are a few key vitamin cofactor and amino acid auxotrophies that could fuel potential interactions with other community members. Both Accumulibacter clade genomes are missing the riboflavin pathway for FAD cofactor synthesis, as well as known pathways for serine and aspartic acid (Fig. 6A ). The biosynthetic pathway for aspartic acid is distributed among members of the Bacteroidetes and Proteobacteria , whereas only TET2 contains the pathway for serine synthesis (Fig. 5A ). The lack of serine biosynthesis pathways in Accumulibacter and other genomes seems striking given that serine is one of the least metabolically costly amino acids to synthesize [ 81 ]. Interestingly, Accumulibacter clade IIA (strain CAPIIA) does not contain the biosynthetic machinery for thiamine and pantothenate synthesis, whereas clade IA (strain CAPIA) does (Fig. 6A ). Only the CAULO1, HYPHO1, and PSEUDO1 genomes within the Proteobacteria can synthesize thiamine, whereas several other members can synthesize pantothenate (Fig. 6A ). The absence of the pantothenate biosynthetic pathway in Accumulibacter CAP IIA is particularly interesting given that coenzyme A is essential for polyhydroxyalkanote biosynthesis, which fuels the rapid and extensive polymer cycling PAO phenotype of Accumulibacter [ 24 ]. In addition to other community members potentially supporting the growth of Accumulibacter due to underlying auxotrophies, the reciprocal logic may be possible as well. Both Accumulibacter clades contain the pathways for synthesizing tyrosine and phenylalanine, which are missing in a majority of the top 15 active non-Accumulibacter bacterial genomes (Fig. 6A ). Only two other members within the Proteobacteria can synthesize tyrosine and phenylalanine, where RAM1 can synthesize both and PSEUDO1 only phenylalanine. Interestingly, phenylalanine and tyrosine are the second and third most metabolically expensive amino acids to synthesize, respectively, with tryptophan being the most costly [ 81 ]. Additionally, a few highly active non-Accumulibacter bacterial community members lack the biosynthetic machinery for several vitamin cofactors and amino acids, such as FLAVO1 and BAC3 within the Bacteroidetes and the putative PAO Ca . Obscuribacter phosphatis OBS1 (Fig. 6A ). Particularly, RAM1 within the Proteobacteria is missing the biosynthetic machinery for all vitamin cofactors but can synthesize most amino acids including the most metabolically expensive as mentioned above. We next analyzed the distribution of trait-attributes of vitamin and amino acid pathways among these genomes to understand how these biosynthetic pathways are expressed similarly or differently in the EBPR SBR ecosystem (Fig. 6B, C ). Members of the Proteobacteria containing thiamine and cobalamin biosynthetic pathways all express these traits similarly (Fig. 6B ). However, the pantothenate synthesis pathway contains two trait-attributes and is expressed differently among two cohorts. In the first attribute, RUN1, TET1, CAULO1, CAPIA, and PSEUDO1 express the pantothenate pathway similarly. However, OBS1 and TET2 express the pantothenate pathway differently (Fig. 6B ). Because tetrahydrofolate can be synthesized through different metabolic routes, we analyzed the differences in trait attribute expression for all routes in genomes that contained sufficient coverage of this trait. Bacteroidetes and Proteobacteria members mostly cluster together among tetrahydrofolate attributes, whereas the TET1 and TET2 genomes are differentiated (Fig. 6B ). Expression of various groups of amino acids show more differentiated expression patterns for genomes with these pathways. Several amino acids also contain different metabolic routes for biosynthesis, and we analyzed all trait attributes for each amino acid for all routes grouped by type (Fig. 6C ). For the charged amino acids arginine, histidine, and lysine, Proteobacteria and Bacteroidetes members cluster within their phylogenetic groups, respectively, with lysine and histidine expressed differently among these groups (Fig. 6C ). In contrast, arginine is expressed similarly among all Proteobacteria genomes. Among the polar charged amino acids, TET2 is the only genome among the top 15 genomes that contains the pathway to synthesize serine (Fig. 6A ). Several groups contain the pathway for threonine synthesis, and expression of different threonine routes are differentiated among the Proteobacteria, Bacteroidetes , and Tetrasphaera spp ., though they mostly cluster phylogenetically (Fig. 6C ). Notably, the expression patterns for the cysteine and proline biosynthetic pathways do not cluster phylogenetically, such as both Tetrasphaera genomes expressing the proline pathway more similarly to other Proteobacteria and Bacteroidetes (Fig. 6C ). The few lineages that can synthesize tyrosine and phenylalanine (CAPIA, CAPIIA, RAM1, PSEUDO1) show different expression patterns. These results show that beyond the presence or absence of key vitamin cofactor and amino acid biosynthetic pathways, EBPR SBR organisms also display coherent and differentiated expression patterns for these traits, of which the functional consequences remain to be further understood."
} | 11,139 |
29126166 | PMC5716610 | pmc | 3,733 | {
"abstract": "Abstract DNA tetrahedron as the simplest 3D DNA nanostructure has been applied widely in biomedicine and biosensing. Herein, we design and fabricate a series of circular assemblies of DNA tetrahedron with high purity and decent yields. These circular nanostructures are confirmed by endonuclease digestion, gel electrophoresis and atomic force microscopy. Inspired by rotary protein motor, we demonstrate these circular architectures can serve as a stator for a rotary DNA motor to achieve the circular rotation. The DNA motor can rotate on the stators for several cycles, and the locomotion of the motor is monitored by the real-time fluorescent measurements.",
"introduction": "INTRODUCTION In biological system, polypeptide subdomains can self-assemble into complex protein motors to achieve lots of functions such as cargo transport, molecular recognition and cell locomotion ( 1 ). Inspired by the biological machinery, many artificial molecular motors have been designed and fabricated to mimic the motions of protein motors ( 2 ). ATP synthase ( 3 ) and the bacterial flagellar motor ( 4 ) are two rotary protein machines which achieve rotating torque via chemical fuels. It is still challenging to make synthetic nanomachines to mimic these rotary motors ( 5 ). Recently, DNA nanotechnology where DNA is used as material to construct nanostructures, has shown its power in various nanodevice fabrication ( 6 – 12 ). Several DNA nanomachines has been built to achieve the circular rotating motion ( 13 – 22 ). For example, 180 o rotation was achieved either driven by strand displacement reaction ( 13 ) or cation triggering ( 19 ); a clockwise or anticlockwise walking route was accomplished on a bipedal stepper ( 14 ); most recently, a mechanically interlocked nanomotor was constructed to realize random rotation ( 20 ). Even though, there is still large space left to further improve the DNA based rotary motor, because the previous designs are relatively complicate, and the controllability of rotating angle/step is low. A minor change in the rotating step need to redesign the whole DNA sequences. In fact, nature is quite smart and already realize the diversity in rotation by assembling different number of subunits with similar functions. Hence, the artificial rotary motor can be fabricated with the similar manner, that is, assembling the building block/subunit with certain size and similar function together, to get an integral nanomachine. Hence, it is crucial to choose a suitable building block/subunit. DNA tetrahedron, which was first fabricated by Goodman and Turberfield in 2004 ( 23 , 24 ), is one of the simplest 3D nanostructures that is assembled entirely by short oligonucleotides (< 100 nt). DNA tetrahedron has been applied in many fields since it was created due to its unique advantages. For example: (i) The structure is stable enough even inside the cell. Hence, it has been taken as a promising capsule for drug/gene delivery and bioimaging both in vitro and in vivo ( 25 – 31 ). (ii) 3D tetrahedron is structurally rigid. Therefore, it can be used as scaffold to assemble metal nanoparticles or proteins ( 32 – 34 ). Furthermore, DNA tetrahedron has also been used as spacers in biosensors to improve the detection sensitivity ( 35 – 39 ). (iii) DNA tetrahedron is easy to assemble with a high yield and low cost. Four oligonucleotide strands that are achievable on automated DNA synthesis and a simple one-pot annealing step is facile enough to form the structure quantitatively ( 23 , 24 , 40 ). (iv) The tetrahedron can be easily modified both on the vertex and the edge for different functionalities ( 41 , 42 ). The pioneer works show that DNA tetrahedron could be an ideal building block for constructing higher-order structures ( 43 , 44 ). Wang et al. achieved tetrahedron dimer and trimer through the hybridization of single strands extended from the vertexes ( 43 ). Focke's group also assembled a dimer structure by loop hybridization (DNA-DNA kissing strategy) ( 44 ). However, advanced assemblies with DNA tetrahedron as subunit have not been achieved yet. In this work, we explored the possibility of using DNA tetrahedron to construct complex nanostructures. A series of circular assemblies were constructed via a stepwise self-assembly strategy. These circular nanostructures were validated by endonuclease digestion, gel electrophoresis and atomic force microscopy (AFM). Moreover, a rotary DNA motor was achieved by assembling four tetrahedrons with different track strands into a circular stator. The locomotion of the motor was monitored by the real-time fluorescent measurement.",
"discussion": "DISCUSSION The successful assembly of these circular structures lies in two aspects. First, the high purity and yield of the tetrahedron monomers must be guaranteed. The previous work showed that the tetrahedron building blocks need to be purified in advance to achieve a high yield in linear assembly ( 43 ). We found in this work that the tetrahedron can be used directly for further assembly. The key point lies in the concentration of the DNA stands applied for the tetrahedron formation. A relatively high concentration (such as 1 μM) could lead to higher amounts of byproducts than the desired tetrahedron. While in our work, a 100 nM annealing concentration made the tetrahedron pure enough (>95%) for direct higher-order structure assembling. While the stock solution with high concentration can be readily prepared via centrifuge filtration technique. Second, a stepwise assembly strategy was adopted. Hence, the number of overhang sequence was significantly reduced. Only the overhangs used in the same step were required to be different in the sequences, which allowed the recycle of overhang sequences and tetrahedral blocks in other assembly step and simplified the design of a complicate structure. For instance, only two pairs of overhang were used to achieve the nanostructure as large as a circular hexamer. One more pair overhang made the successful construction of the lattice structure possible. The applicable property of the circular architectures was tested on a dynamic rotary motor system. The tetramer was taken as the stator in the present work, and two rotation circles were achieved. Due to the well-defined architectures of these circular assemblies, the relative distance and spatial arrangements between each tetrahedron are fixed. More remarkably, the sizes, steps and walking patterns of the rotary motor can be highly manipulated by the present assembly strategy because the number and position of the tetrahedron are readily controllable. In fact, by sophisticated design of the track sequences, different walking routes can be achieved on the same stator, such as a ‘Z’ path vs the ‘ ⋄ ’ path in the present work. The lattice structures can be also used as stators to achieve multiple walking routes, e.g. ‘S’ or ‘8’ shape. Furthermore, with adoption of functional DNA sequences to the tracks, besides the fuel strands, our stator could also be powered by and/or respond to multiple stimuli module, like those in a reported DNA stepper system with circular DNA as the stator ( 14 ). Whereas, the structural flexibility and the potential uncertainty on the distance and dynamic conformation of the walking route due to the single stranded circular DNA can be overcome in our stators made of DNA tetrahedron here. The circular rotating motion offered the promising potentials to mimic the rotary motions in many essential protein machineries. Though the central shaft was still missing in the present motor, the assembling manner with DNA tetrahedron as the subunit as well as the stepwise circular walking has shown the similarity as the proton channel subunits in ATP synthase and bacterial flagellar motor. In conclusion, tetrahedron as a simple 3D DNA nanostructure built by oligonucleotide strands, was shown to be an ideal building block for constructing self-assembled architectures with well-defined 2D/3D configuration on nanometer-scale. A series of circular tetrahedron assemblies, from dimer to hexamer, and even complicate structures like lattice hexamer and octamer, were fabricated successfully. All of the architectures were well characterized by SEC, gel electrophoresis, enzyme digestion, AFM and DLS experiments. The highlight of using DNA tetrahedron as the building block lies in simple design, easy-preparation, facile assembly and high yields. Higher-order structures can be obtained easily by only reprogramming short overhang sequences, without the needs to redesign the sequences of the entire tetrahedron units. As a proof-of-concept, a rotary DNA motor system was successfully designed by using the circular tetramer as the stator. The DNA rotor could walk stepwise following a circular track. This work offered an indispensable missing piece in the library of DNA nanomotors and enriched the functionality of biomimetic nanomachinery. From this example, it can be envisioned that more complicate motions of biological machinery can be mimicked by DNA nanodevices with complex assembly architecture. This work could inspire a breakthrough in the construction and functions of the rotary DNA motor and extend the biomolecular motors to broad fields, such as templated chemical synthesis ( 46 , 47 ). Furthermore, the facile assembly of DNA tetrahedrons to well-defined architectures would show the promising potentials beyond DNA nanotechnology to drug delivery, bioimaging and other biomedical technologies."
} | 2,375 |
37223464 | PMC10200910 | pmc | 3,734 | {
"abstract": "Metal nanomaterials can facilitate microbial extracellular electron transfer (EET) in the electrochemically active biofilm. However, the role of nanomaterials/bacteria interaction in this process is still unclear. Here, we reported the single-cell voltammetric imaging of Shewanella oneidensis MR-1 at the single-cell level to elucidate the metal-enhanced EET mechanism in vivo by the Fermi level-responsive graphene electrode. Quantified oxidation currents of ~20 fA were observed from single native cells and gold nanoparticle (AuNP)-coated cells in linear sweep voltammetry analysis. On the contrary, the oxidation potential was reduced by up to 100 mV after AuNP modification. It revealed the mechanism of AuNP-catalyzed direct EET decreasing the oxidation barrier between the outer membrane cytochromes and the electrode. Our method offered a promising strategy to understand the nanomaterials/bacteria interaction and guide the rational construction of EET-related microbial fuel cells.",
"introduction": "Introduction Electrochemically active bacteria (EAB) can transfer electrons from the metabolism of organic sources to solid electron receptors [ 1 – 4 ]. Such extracellular electron transfer (EET) process plays an important role in the biogeochemical cycle of elements, and it is widely involved in energy technologies [ 5 – 8 ] such as microbial fuel cells (MFCs), microbial electrosynthesis, and analyte detection [ 9 , 10 ]. Current efforts primarily aimed at the enhancement of the electron transfer kinetics between EAB and electrodes [ 11 – 14 ], and considerable progress has been made in improving the performance of MFCs by optimizing the microorganism selection [ 15 – 17 ] and battery construction [ 18 , 19 ]. Among them, metal/metallic oxide nanomaterials were reported to demonstrate the capability of facilitating either the direct or indirect EET at the microorganism/electrode interface via the interaction with cytochromes [ 4 , 20 – 22 ]. Knowledge of how nanomaterials enhance the electron transfer rate at the microorganism/electrode interface will help the development of high-performance MFCs. However, the detailed mechanism is still unclear. One most popular view is that the good conductivity of nanomaterials could provide a route to connect bacteria with others [ 4 ]. Another hypothesis attributed it to the catalytical properties of metal nanomaterials [ 23 ]. However, it has not been confirmed by experimental results because the complexity of biofilm hinders the understanding of detailed interaction between the electrode and bacteria [ 24 , 25 ]. Single-cell analysis eliminated the limitation of the microorganism and provides a powerful method for promoting the investigation of the EET mechanism [ 18 , 26 – 37 ]. For example, single-cell EET was usually studied by detecting the single-cell current output with micro-/nanoelectrodes [ 26 , 27 , 29 , 31 ]. The Lieber group measured the output current from individual Shewanella oneidensis MR-1 ( S. oneidensis MR-1) [ 26 ] and Geobacter sulfurreducens DL-1 [ 29 ] by combining nanoelectrodes and brightfield microscopy. The Nakanishi group used optical tweezers to capture a single S. oneidensis MR-1 cell on a microelectrode for electrochemical current measurement [ 27 ]. These micro/nano-electrode-based methods provided the highly sensitive and quantitative current measurements of single cells at around 100 fA. However, the rare outer membrane cytochromes (Omc) hindered the voltammetric study of nanomaterial-enhanced EET mechanism at the single-cell level. In our previous work, Fermi level-responsive graphene electrode (FGE) was proposed to offer an extreme detection limit at the attoampere level that enabled the observation of single-molecular level electron transfer in cytochromes [ 38 ]. Thus, it has the potential capabilities to study the EET mechanism at the single-cell level. In this work, the direct EET in single MR-1 cells was studied using FGE. The transparent single-layer graphene (SLG) allows for the transmission of scattering from single cells, which could be collected by the objective. The FGE-based single-cell voltammetric imaging could reduce the possible effects of secreted mediators and allow the electrical characterization of cytochromes or their metal nanoparticle complexes without forming a biofilm. Unlike the metal-enhanced EET mechanism based on the increment in the overall catalytical current, the single and subcellular results revealed a nanomaterial-catalyzed EET mechanism. AuNPs can catalyze the electron transfer of single MR-1 cells by reducing the oxidation potential of Omc, thus improving the EET performance. It provided a potential platform for high-throughput and rapid screening of electricity-producing bacterial cells and constructing MFCs.",
"discussion": "Discussion In summary, we developed a general method to investigate the EET capacity of single electricity-producing bacteria before and after noble metal modification. A better understanding of EET was the key to developing new EAB, determining the fundamental limitations of MFCs, and improving their power extraction. The challenge of studying the direct pathway of EET mainly came from the difficulty to exclude biofilm and secreted mediators in population-level experiments. The FGE-based single-cell analysis could reduce the possible effects of secreted mediators and allow the electrical characterization of cytochromes or their metal nanoparticle complexes without forming a biofilm. Unlike the reported mechanism where metal nanoparticles provided a route to connect bacteria with others in the biofilm, our results revealed a nanomaterial-catalyzed EET mechanism where AuNPs could reduce the oxidation potential of Omc, further improving the EET performance. In addition, FGE's high spatiotemporal resolution capability would enable it to detect the electron transfer in different parts of a single bacterial cell, proving that AuNPs can catalyze the direct EET by reducing the oxidation barrier between Omc and the electrode. The redox reactions at the bacteria/electrode interface were the driving force for output currents. As a result, a lower potential could facilitate the EET process and benefit the development of high-power density MFCs. Our method provided a potential platform for high-throughput and rapid screening of electricity-producing bacterial cells and constructing MFCs."
} | 1,598 |
20598083 | null | s2 | 3,736 | {
"abstract": "In natural systems, bacteria form complex, surface-attached communities known as biofilms. This lifestyle presents numerous advantages compared with unattached or planktonic life, such as exchange of nutrients, protection from environmental stresses and increased tolerance to biocides. Despite such benefits, dispersal also plays an important role in escaping deteriorating environments and in successfully colonizing favourable, unoccupied habitat patches. The α-proteobacterium Caulobacter crescentus produces a motile swarmer cell and a sessile stalked cell at each cell division. We show here that C. crescentus extracellular DNA (eDNA) inhibits the ability of its motile cell type to settle in a biofilm. eDNA binds to the polar holdfast, an adhesive structure required for permanent surface attachment and biofilm formation, thereby inhibiting cell attachment. Because stalked cells associate tightly with the biofilm through their holdfast, we hypothesize that this novel mechanism acts on swarmer cells born in a biofilm, where eDNA can accumulate to a sufficient concentration to inhibit their ability to settle. By targeting a specific cell type in a biofilm, this mechanism modulates biofilm development and promotes dispersal without causing a potentially undesirable dissolution of the existing biofilm."
} | 329 |
26140529 | PMC4681859 | pmc | 3,737 | {
"abstract": "The candidate archaeal phylum ‘Aigarchaeota' contains microorganisms from terrestrial and subsurface geothermal ecosystems. The phylogeny and metabolic potential of Aigarchaeota has been deduced from several recent single-cell amplified genomes; however, a detailed description of their metabolic potential and in situ transcriptional activity is absent. Here, we report a comprehensive metatranscriptome-based reconstruction of the in situ metabolism of Aigarchaeota in an oxic, hot spring filamentous ‘streamer' community. Fluorescence in situ hybridization showed that these newly discovered Aigarchaeota are filamentous, which is consistent with the presence and transcription of an actin-encoding gene. Aigarchaeota filaments are intricately associated with other community members, which include both bacteria (for example, filamentous Thermocrinis spp.) and archaea. Metabolic reconstruction of genomic and metatranscriptomic data suggests that this aigarchaeon is an aerobic, chemoorganoheterotroph with autotrophic potential. A heme copper oxidase complex was identified in the environmental genome assembly and highly transcribed in situ . Potential electron donors include acetate, fatty acids, amino acids, sugars and aromatic compounds, which may originate from extracellular polymeric substances produced by other microorganisms shown to exist in close proximity and/or autochthonous dissolved organic carbon (OC). Transcripts related to genes specific to each of these potential electron donors were identified, indicating that this aigarchaeon likely utilizes several OC substrates. Characterized members of this lineage cannot synthesize heme, and other cofactors and vitamins de novo , which suggests auxotrophy. We propose the name Candidatus ‘Calditenuis aerorheumensis' for this aigarchaeon, which describes its filamentous morphology and its primary electron acceptor, oxygen.",
"introduction": "Introduction Members of the candidate archaeal phylum ‘Aigarchaeota' were first identified in a subsurface hydrothermal ecosystem ( Hirayama et al. , 2005 ; Nunoura et al. , 2005 ; Nunoura et al. , 2011 ) and recently in a terrestrial hot spring as part of the Genome Encyclopedia of Bacteria and Archaea (GEBA) project utilizing single-cell genomics ( Rinke et al. , 2013 ). Aigarchaeota-related 16S rRNA genes have been detected in numerous terrestrial, subsurface, and marine hydrothermal environments ( Hirayama et al. , 2005 ; Nunoura et al. , 2005 ; Costa et al. , 2009 ; Vick et al. , 2010 ; Meyer-Dombard et al. , 2011 ; Cole et al. , 2013 ; De Leon et al. , 2013 ; Takacs-Vesbach et al. , 2013 ). Phylogenetic placement of Aigarchaeota remains ambiguous, but recent phylogenomic studies reveal a close relationship with the Thaumarchaeota and possibly Crenarchaeota ( Brochier-Armanet et al. , 2011 ; Nunoura et al. , 2011 ; Rayman et al. , 2014 ). These reports have provided important insight into the phylogeny and evolution of the Aigarchaeota, yet little is known about the metabolic potential or in situ activity of members of this phylum. Near-complete genome sequences have been generated for several members of the Aigarchaeota using cultivation-independent methods including fosmids ( Nunoura et al. , 2011 ), single-cell amplified genomes ( Rinke et al. , 2013 ; Alba et al. , 2014 ; Hedlund et al. , 2014 ) and metagenomics ( Alba et al. , 2014 ; Hedlund et al. , 2014 ). However, limited genome characterization and metabolic activity analysis has been completed on any members of this group ( Nunoura et al. , 2011 ; Rinke et al. , 2013 ), especially those Aigarchaeota important in geothermal ecosystems of Yellowstone National Park (YNP). Moreover, no attempts have been made to link members of the Aigarchaeota with microbial activity (for example, metatranscriptomics) in relation to other community members and their physicochemical environment. The environmental genome of Candidatus ‘ Caldiarchaeum subterraneum ' was recovered from a mildly acidic (pH=5.1), oxic (~10 μ m dissolved oxygen (DO)) subsurface filamentous ‘streamer' community ( Hirayama et al. , 2005 ; depth ~320 m) at a temperature of 70 °C, and provided the first insight into the evolution and potential metabolism of members of the Aigarchaeota ( Nunoura et al. , 2011 ). Available genomic information from members of this deeply-rooted archaeal lineage suggests a possible evolutionary linkage with Eukarya (for example, an ubiquitin protein modification system; Nunoura et al. , 2011 ). Populations of C. subterraneum might grow via the oxidation of hydrogen or carbon monoxide (CO) coupled to oxygen reduction by a heme copper (terminal) oxidase (HCO) complex. It is possible that these organisms can fix carbon dioxide via the 3-hydroxypropionate/4-hydroxybutyrate or dicarboxylate/4-hydroxybutyrate cycle ( Nunoura et al. , 2011 ). However, C. subterraneum lacked the key enzyme (4-hydroxybutyryl-CoA dehydratase) of these pathways ( Berg et al. , 2010 , Nunoura et al. , 2011 ). Genome sequence of two different Aigarchaeota single-cells were obtained from oxic (DO=42 μ m ), high temperature (81°C), circumneutral (pH=7.1) sediments at Great Boiling Spring ( Rinke et al. , 2013 ). Although the metabolic reconstruction of these Aigarchaeota was not a major focus of that study, the use of oxygen as an electron acceptor appears to be a consistent trait based on the presence of HCOs ( Rinke et al. , 2013 ; Alba et al. , 2014 ; Hedlund et al. , 2014 ). In addition, it has been speculated that some Aigarchaeota may also utilize other electron acceptors, such as oxidized sulfur or nitrogen compounds, but experimental data supporting these traits are unavailable ( Nunoura et al. , 2011 ; Alba et al. , 2014 ; Hedlund et al. , 2014 ). The filamentous ‘pink streamer' communities at Octopus Spring (OS) (Lower Geyser Basin, YNP) have been observed, studied, and intrigued scientists for more than 100 years ( Setchell, 1903 ; Brock, 1967 ; Bauman and Simmonds, 1969 ; Stahl et al. , 1985 ; Reysenbach et al. , 1994 ; Jahnke et al. , 2001 ; Blank et al. , 2002 ). A recent study of the three predominant lineages of Aquificales in YNP ( Takacs-Vesbach et al. , 2013 ) showed that the high temperature (~82 °C) OS streamer community contained several uncharacterized and uncultured taxa, including a novel Aigarchaeota population. The presence of Aigarchaeota in OS presents an excellent opportunity to understand the metabolic capabilities and the activity of this population in situ . Consequently, the objectives of this study were to (i) obtain a detailed genome-based metabolic reconstruction of the predominant Aigarchaeota population present in OS, YNP, (ii) identify the morphology and spatial arrangement of Aigarchaeota and adjacent Thermocrinis spp. using fluorescence in situ hybridization (FISH), and (iii) determine the in situ transcriptional activity of this Aigarchaeota population to elucidate metabolic processes and potential community interactions.",
"discussion": "Results and Discussion De novo Aigarchaeota assembly The sequence assembly of the Aigarchaeota population from OS was compared with all available Aigarchaeota sequence assemblies (>1 Mb total sequence) using tetra-NWF-PCA ( Figure 2a ). The aigarchaeon from OS ( Candidatus ‘ Calditenuis aerorheumensis ', hereafter referred to as Calditenuis aerorheumensis ) exhibits highly similar codon usage and G+C content to aigarchaeon B22 from Great Boiling Spring (GBS) ( Figure 2a ). Further G+C content analysis of assembled sequence confirmed the close relationship of C. aerorheumensis to aigarchaeon B22 with average G+C contents of 60.2 and 61.3%, respectively ( Figure 2b ). These two lineages have an average nucleotide identity of 87% among shared assembled sequence (~0.7 Mb), which suggests that they likely belong to the same novel candidate genus, Calditenuis . Calditenuis aerorheumensis and aigarchaeon B22 share a large amount of deduced protein sequences (~800) >80% amino-acid identity ( Figure 2c ), and also supports their inclusion in the same genus. Aigarchaeon J15 from GBS and Caldiarchaeum subterraneum each form separate clusters in NWF-PCA due to differences in codon usage bias and G+C content ( Figure 2a and b ). The relative abundance of C. aerorheumensis populations has been relatively stable over three sampling years at OS and represent 5.2±2.5% of all the random-sequence reads ( Supplementary Table S1 ). A total of 1422 protein-encoding genes were predicted ( Supplementary Table S3 ), which corresponds to a coding density of ~1.2 protein-encoding genes per kb genome, similar to the protein-encoding density in aigarchaeon B22 of 1.13 protein-encoding genes per kb genome. Genome completeness estimates from conserved archaeal single-copy genes ( Rinke et al. , 2013 ) suggest that the C. aerorheumensis assembly is ~80% complete, which equates to an estimated genome size of ~1.45 Mb. The high protein-coding density taken together with the small-predicted genome size suggests that this organism may have undergone extensive genome streamlining ( Giovannoni et al. , 2005 , 2014 ). FISH identification All known Aigarchaeota encode for actin, which suggests that they are rods or filaments ( Ettema et al. , 2011 ); however, no definitive morphological observations have been made for any member of the Aigarchaeota. FISH with probe Aig800 designed to target terrestrial Group 1 A Aigarchaeota (including aigarchaeon B22) revealed that Calditenuis aerorheumensis populations in OS are filamentous ( Figure 3a and b ) ranging from 0.5 μm (diameter) by up to 20 μm (length), but may be longer due to damage during cryosectioning or sample dehydration. The broad-coverage archaeal FISH probe Arch915 also hybridized to these filaments ( Figure 3a and b ). Calditenuis aerorheumensis filaments were often found in close contact or vicinity of Thermocrinis spp. ( Figure 3a and b ), which were specifically detected using the newly designed probe Aqi338 ( Kubo et al. , 2011 ), and might be related to metabolite sharing and/or auxotrophic requirements of C. aerorheumensis . FISH probes targeting Thermocrinis spp., all bacteria, and all archaea revealed a close association of all microorganisms in the OS streamer community ( Figure 3c and d ). Archaea and Thermocrinis spp. are often found in helical bundled filaments, and Thermocrinis spp. can also be found in contact with other bacteria ( Figure 3c and d ). Scanning electron micrographs of the OS streamer community ( Figure 3e and f ) also confirm the filamentous habit of several population types, which range in diameters of ~0.2–0.5 μm up to ~0.7–1 μm. Small (<20 nm), extracellular polymeric substances (EPS) were evident in SEM images and represent a significant volume of the OS streamer community biomass ( Figure 3e and f ). Phylogeny and distribution Phylogenetic analysis of 16S rRNA genes from the candidate phylum Aigarchaeota across numerous terrestrial, subsurface and freshwater geothermal environments revealed eight genus-level lineages ( Figure 4 ). The C. aerorheumensis population groups with several YNP 16S rRNA gene clones from YNP Bison Pool ‘streamers', as well as single-cell sequence (aigarchaeon B22) from GBS ( Figure 4 ) at temperatures ranging from ~74 °C to 86 °C and pH values of 7.2–7.9. Group 1A Aigarchaeota are found in oxic hot spring ecosystems (DO ~10–53 μ m ), which is consistent across all the Aigarchaeota lineages currently identified by 16S rRNA genes. Aigarchaeota are distributed in geothermal environments at high temperature (68–87 °C) and moderately acidic to alkaline pH values (~5–9). The phylogenetic position of C. aerorheumensis was also confirmed using a conserved set of single-copy ribosomal proteins. The concatenated phylogenetic tree shows that C. aerorheumensis and aigarchaeon B22 are closely related, and form a separate group compared to C. subterraneum and aigarchaeon J15 ( Figure 5 ). In situ metatranscriptome The C. aerorheumensis assembly was utilized to align metatranscriptome reads from OS (~18 million mRNA enriched reads). A total of 544 290 mRNA reads were aligned to C. aerorheumensis , which constitutes ~3% of the total mRNA reads from the streamer community. Analysis of all mRNA reads using Cluster of Orthologous Genes showed that the transcription of a number of cellular processes were higher than their relative abundance in the genome ( Supplementary Figure S1 ). Notably, energy production and conservation, and amino-acid transport and catabolism were over-transcribed compared with the relative abundance in the genome ( Supplementary Figure S1 ). This finding suggests that amino-acid transport and catabolism is an important energy, carbon and nitrogen source for C. aerorheumensis in the streamer community. Respiration A detailed metabolic model was constructed for C. aerorheumensis using curated genome sequence and mRNA transcripts mapped to individual genes in each pathway ( Figure 6 ). Pathways and metabolic functions that exhibited increased transcription levels were emphasized to reflect in situ activity ( Figure 6 ). The streamer community present in OS ( Figure 1 ) oscillates in the high-velocity channel ( Supplementary Movie S1 ), which has aqueous O 2 concentrations ranging from ~20 to 40 μ m ( Supplementary Table S1 ). Gradients in O 2 are likely to occur within the streamer community that could create microaerobic or hypoxic microenvironments (for example, Bernstein et al. , 2013 ). A single HCO complex (_00543–_00544) was identified in C. aerorheumensis that recruited numerous RNA reads from the metatranscriptome (RPKM ~70 000; Figure 6 ), and represents the dominant terminal electron acceptor utilized by C. aerorheumensis in OS. Similar HCO complexes were also identified in 60% of available Aigarchaeota sequence assemblies in IMG/Mer ( Supplementary Table S7 ), which suggests that numerous members of this phylum are aerobic, and that O 2 is an important niche-defining parameter for many members of this phylum. The importance of oxygen to the Aigarchaeota is also exemplified by the presence of genes that code for proteins responsible for degradation of reactive oxygen species in C. aerorheumensis , as well as 65% of Aigarchaeota genomes ( Supplementary Table S7 ). An Fe-Mn superoxide dismutase was identified and transcription of this gene was high (RPKM ~800 000; Figure 6 ). A 2-Cys peroxiredoxin (_00775) and a 1-Cys peroxiredoxin (_00485) were identified and these enzymes are involved in the intra and extracellular degradation of hydrogen peroxide, respectively. Intracellular hydrogen peroxide is formed primarily by superoxide dismutase, and is hazardous to the cell by participating in Fenton reactions with Fe(II) that generate the DNA-damaging hydroxyl radical (•OH; Henle and Linn, 1997 ). Thus, the transcription of the 2-Cys peroxiredoxin is very high (RPKM ~130 000) and is comparable to the transcription levels of superoxide dismutase (RPKM ~800 000). Degradation of extracellular hydrogen peroxide by 1-Cys peroxiredoxin (RPKM ~15 000), which can form by the interaction of UV light and dissolved OC with O 2 ( Draper and Crosby, 1983 ), may be necessary for survival in these environments and/or an important ecosystem service that C. aerorheumensis performs for the OS streamer community, similar to Vibrio pelagius and Synechococcus spp. in the open ocean ( Petasne and Zika, 1997 ). The C. aerorheumensis population in OS also contains an operon encoding a putative tetrathionate reductase (TTR) system (_00420–_00424). To date, archaeal TTRs have only been identified in Pyrobaculum spp. and Archaeoglobus fulgidus ( Cozen et al. , 2009 , Liebensteiner et al. , 2013 ). The TTR operon was transcribed at low abundance (RPKM ~9000; Figure 6 ) and suggests that reduction of other terminal electron acceptors may provide an alternative to oxygen. Tetrathionate (S 4 O 6 2− ) has not been detected in OS; however, Thermocrinis spp. contain genes necessary for the oxidation of reduced sulfur compounds ( Takacs-Vesbach et al. , 2013 ) and may produce S 4 O 6 2− as a byproduct, a feature that has been commonly observed in marine bacteria ( Podgorsek and Imhoff, 1999 ). Reducing equivalents (for example, NADH) generated from the oxidation of OC substrates (see below) are fed into the electron transport chain at the NADH dehydrogenase complex ( Figure 6 ; complex I), which is not encoded in an operon. The membrane-bound succinate dehydrogenase complex ( Figure 6 ; complex II) also feeds the electron transport chain. The C. aerorheumensis assembly does not encode a typical cytochrome C complex III for electron transport to complex IV (HCO); however, several membrane-bound blue copper proteins may serve as electron carries to the HCO or TTR complexes ( Figure 6 ; _00503–_00505). The C. aerorheumensis population from OS has a complete electron transport chain, predominantly coupled to the reduction of oxygen, and adenosine triphosphate (ATP) synthesis driven by an archaeal V-type ATP synthase ( Figure 6 ). Energy metabolism Calditenuis aerorheumensis contains genes necessary for the degradation of different organic carbon (OC) compounds coupled with adenosine triphosphate (organotrophy) and biomass (heterotrophy) synthesis. Major carbon substrates utilized for growth include acetate, fatty acids, amino acids and sugars. These OC constituents may originate from EPS produced by other thermophilic bacteria and archaea in OS and/or from autochthonous dissolved OC ( Supplementary Table S1 ). Acetate is transported by a putative citrate/acetate antiporter (_00932; RPKM ~20 000), and utilized by an acetyl-CoA synthetase (_00897) to produce acetyl-CoA ( Figure 6 ), which can then be fed into the tricarboxylic acid (TCA) cycle or gluconeogenesis. Acetate utilization is further supported by the presence of a functional glyoxylate bypass ( Figure 6 ). The presence of isocitrate lyase (_00945; RPKM ~29 000) and malate synthase (_01089; RPKM ~70 000; Figure 6 ) provides a mechanism to incorporate carbon from acetate into biomass without the loss of CO 2 through the TCA cycle. C. aerorheumensis also has the capacity to utilize fatty acids produced by thermophilic bacteria in OS as carbon and energy sources. This β-oxidation pathway contains an acyl-CoA synthetase (_01439; RPKM ~34 000) that converts fatty acids to acyl-CoA derivatives utilized by three different acyl-CoA dehydrogenases (_01040; _01032; _00259), which are likely specific for fatty acids of certain chain lengths (for example, odd or even). Transcription of one acyl-CoA dehydrogenase was considerably greater than several others (_01032; RPKM ~120 000) and likely corresponds to a preferred fatty acid utilized in situ by C. aerorheumensis . Although the metabolism of fatty acids appears to be distributed across most archaea ( Dibrova et al. , 2014 ), no enzymatic characterization of the different acyl-CoA dehydrogenases has been conducted. Physiological studies have shown that Archaeoglobus fulgidus can grow on either short- and/or long-chain fatty acids ( Khelifi et al. , 2010 ). C. aerorheumensis also exhibits the metabolic capacity to utilize numerous amino acids and oligopeptides as carbon and energy sources. Transporters for amino acids were identified in the de novo assembly ( Figure 6 ), and branched-chain amino acids are transported by a high-affinity ABC transporter that was highly transcribed ( Figure 6 ; RPKM>100 000). A single copy of a dihydroxy acid dehydratase ( ivlD ; _01261) was identified, which may catalyze the conversion of branched-chain amino acids to TCA intermediates. Other amino acids are catabolized by a 2-oxoisovalerate oxidoreductase complex (_00164+_00741), which was also highly transcribed (RPKM>70 000), then incorporated into the TCA cycle ( Figure 6 ). A set of genes containing LigA and LigB homologs were identified in a single operon (_01094–_01099) potentially involved in the degradation of lignin or other aromatic compounds. Transcription levels in this pathway were increased for a hypothetical protein (_01096; RPKM ~200 000) and a ferredoxin ring-hydroxylating dioxygenase (_01095; RPKM ~70 000). The aromatic compound utilized by this pathway could not be identified, but these genes are also found in other Aigarchaeota ( Hedlund et al. , 2014 ). The source pool and outflow channel of OS contain significant concentrations of dissolved OC (~60 μ m ; Supplementary Table S1 ), which would be sufficient to support heterotrophic populations of C. aerorheumensis . It has been suggested that some members of the Aigarchaeota are capable of using CO as an energy source ( Nunoura et al. , 2011 ; Rinke et al. , 2013 ; Hedlund et al. , 2014 ), but physiological evidence is lacking. The C. aerorheumensis assembly contains five copies of putative large-subunit aerobic CO dehydrogenases ( coxL ) (COG1529; _00394; _00730; _01206; _01316; 01360). However, all putative CoxL homologs from Aigarchaeota lack the conserved active site residues (VAYRCSFR) identified in canonical aerobic CO dehydrogenases ( Supplementary Figure S2 ; Dobbek et al. , 2002 ). The gene order of the middle and small subunits (_00406; _00407) is identical to the aerobic CO oxidizer, Oligotropha carboxidovorans , and both contain necessary cofactor binding sites ( Dobbek et al. , 1999 ). The transcription of coxSML genes were elevated ( Figure 6 ; RPKM>150 000), so the function of these enzymes is important to determine and may aid in interpreting in situ physiology. Dissolved CO concentrations measured in OS are below detection (~10 n m ) and CO concentrations in other geothermal waters range from ~30 n m to below detection levels ( Kochetkova et al. , 2011 ). Consequently, C. aerorheumensis populations are not likely utilizing CO as a principal electron donor given the low concentrations of CO relative to other electron donors such as reduced OC compounds (for example, dissolved OC ~60 μ m ; Supplementary Table S1 ). Central carbon metabolism C. aerorheumensis exhibits two routes for the conversion of glucose to pyruvate via either Embden–Meyerhof–Parnas glycolysis or Entner–Doudoroff pathways ( Figure 6 ). A putative glucose ABC transporter operon was identified (_00266–_00269) and transcription was low except for the periplasmic subunit (_00269; Figure 6 ). Gluconeogenesis is also possible and the key enzyme fructose 1,6-bisphosphate aldolase/phosphatase is present, which has the conserved residues (GKDDP) implicated in catalysis ( Say and Fuchs, 2010 ). All genes required for a complete TCA cycle were also identified with the exception of fumarase ( Figure 6 ). Transcription of TCA cycle genes were high (RPKM >10 000), which is consistent with apparent primary sources of OC that C. aerorheumensis populations utilize in situ (that is, acetate, fatty acids and amino acids). A complete oxidative pentose phosphate pathway was identified in C. aerorheumensis with the exception of gluconolactonase, which is also absent in other thermophilic Thaumarchaeota ( Spang et al. , 2012 ; Beam et al. , 2014 ). Autotrophic Crenarchaeota and Thaumarchaeota fix inorganic carbon dioxide via the 3-hydroxypropionate/4-hydroxybutyrate (HP/HB) or dicarboxylate/4-hydroxybuyrate (DC/HB) cycles, which share a key marker enzyme, 4-hydroxybutyryl-CoA dehydratase (4-BUDH; Berg et al. , 2010 ). A gene for a Type-2 4-BUDH (_01438) was identified in the C. aerorheumensis assembly ( Figure 7 ; Supplementary Figure S3 ); however, Type-2 4-BUDHs have yet to be biochemically characterized, and all share a conserved His-292, but lack conserved cysteine residues required for (4Fe-4S) cluster binding ( Berg et al. , 2007 ). The transcription of the Type-2 4-BUDH was very high (RPKM>100 000) in C. aerorheumensis ( Figure 7 ). No other function for this gene can be assigned other than the conversion of 4-hydroxybutyryl-CoA to crotonyl-CoA, which suggests that the Aigarchaeota Type-2 4-BUDHs may be functional in the HP/HB cycle. Moreover, C. aerorheumensis encodes for every gene of the HP/HB pathway, with the exception of NADPH-dependent malonate semialdehyde reductase and methylmalonyl-CoA epimerase ( Figure 7 ; Supplementary Table S8 ). This is the first activity-based measurement that suggests members of the Aigarchaeota may fix carbon dioxide via the HP/HB pathway. Cofactor, vitamin and amino-acid biosynthesis Pathways responsible for the synthesis of all amino acids, F 420 , pantothenate (B 5 ), pyroxidine (B 6 ) and cobalamin salvage were identified in C. aerorheumensis ( Table 1 ). Coenzyme F 420 is utilized in the glucose-6-phosphate dehydrogenase enzyme in the oxidative pentose phosphate pathway in C. aerorheumensis. This cofactor was also identified in the Thaumarchaeota, methanogenic archaea, and ‘Geoarchaeota' ( Kozubal et al. , 2012 ; Spang et al. , 2012 ). On the basis of the available sequence data, C. aerorheumensis cannot synthesize heme, thiamin, riboflavin, niacin and biotin; however, putative transporters were identified for these essential cofactors and vitamins ( Table 1 ). The absence of vitamin and cofactor biosynthesis pathways appears common within this group of archaea ( Supplementary Table S9 ); however, there are only a few complete genomes available for this candidate phylum. The absence of heme biosynthetic genes in C. aerorheumensis and single-cell genomes from the Aigarchaeota ( Supplementary Table S9 ) is surprising because heme is the essential cofactor for oxygen binding in HCOs, and these genes were highly transcribed ( Figure 6 ). The putative heme transporter was found at low transcriptional abundance ( Figure 6 ), which might suggest strict heme recycling in vivo , as high intracellular heme concentrations are often hazardous and/or lethal ( Anzaldi and Skaar, 2010 ). Low transcription levels of transporters for other cofactors and vitamins ( Figure 6 ; except for thiamin and niacin) suggests conservative in vivo control of vitamin and cofactor recycling. Widespread auxotrophy in all known members of Aigarchaeota ( Supplementary Table S9 ) suggest that they rely on other community members to supply them with essential compounds for growth and reproduction. The energetic cost of maintaining complete vitamin and cofactor biosynthesis pathways may have outweighed the cost of acquiring them from external sources ( Giovannoni et al. , 2005 ). Calditenuis aerorheumensis encodes the capacity for natural DNA competence ( dprA ) also present in some Thermoproteales ( Siebers et al. , 2011 ), and may be able to re-acquire complete cofactor and biosynthesis pathways under selection pressure. Motility and attachment Archaeal flagella are more similar to type IV secretion systems found in bacteria and have nothing in common with bacterial flagella other than the use for motion or taxis ( Jarrell and Albers, 2012 ). All genes within an operon for an archaeal flagellum were found in C. aerorheumensis (_00011–_00018), and were transcribed at low levels ( Figure 6 ; RPKM<1500). Flagellar synthesis may be more important during streamer formation and could explain the downregulation of these genes in this ‘mature' community. A pilus system (_01412–_01414) was also identified in C. aerorheumensis . All pilus genes were highly transcribed ( Figure 6 ; RPKM>150 000) and may be involved in cell–cell contact and association of C. aerorheumensis with other community members observed in FISH images ( Figure 3a and b ). The pilus system in C. aerorheumensis may be involved in the formation of EPS, which are abundant and represent a substantial fraction of the community biomass ( Figure 3e and f ). Pili have been identified in other archaea as well, and are involved in cell–cell adherence and/or biofilm formation ( Albers and Meyer, 2011 ). Etymology The provisional taxonomic assignment for this group of Aigarchaeota is ‘ Candidatus : Calditenuis aerorheumensis' alluding to its thermal habitat, filamentous shape and aerobic catabolism. Calditenuis gen. nov. Calditenuis aerorheumensis sp. nov. Genus : Caldi (L. adj.): warm, tenuis (L. adj.): thin or slender. Species: aero (Gr. noun): air or atmosphere, rheumensis (Gr. noun); a stream, current, or that which flows. The Genus name describes the organism's thermophilic nature and thin, filamentous shape. The species name alludes to the organism's principal terminal electron acceptor, oxygen and filamentous ‘streamer' habitat. Locality: oxic (~30 μ m ), high temperature (~75–85 °C) and pH ~7.5–8.5 thermal springs. Diagnosis: a thin, filamentous, aerobic chemoorganohetero(auto)troph from the Aigarchaeota. Comment: Future efforts to isolate members of this genus and all other Aigarchaeota (with the exception of aigarchaeon J15) might consider adding heme to vitamin solutions, which is not common in Wolfe's vitamin solution ( Wolin et al. , 1963 ) typically used in archaeal enrichment cultures. Moreover, C. aerorheumensis is likely oligotrophic and may not respond to high concentrations of C sources and/or vitamins."
} | 7,327 |
31277227 | PMC6669453 | pmc | 3,739 | {
"abstract": "Coral bleaching caused by global warming has resulted in massive damage to coral reefs worldwide. Studies addressing the consequences of elevated temperature have focused on organisms of the class Anthozoa, and up to now, there is little information regarding the mechanisms by which reef forming Hydrozoans face thermal stress. In this study, we carried out a comparative analysis of the soluble proteome and the cytolytic activity of unbleached and bleached Millepora complanata (“fire coral”) that inhabited reef colonies exposed to the 2015–2016 El Niño-Southern Oscillation in the Mexican Caribbean. A differential proteomic response involving proteins implicated in key cellular processes, such as glycolysis, DNA repair, stress response, calcium homeostasis, exocytosis, and cytoskeleton organization was found in bleached hydrocorals. Four of the proteins, whose levels increased in bleached specimens, displayed sequence similarity to a phospholipase A2, an astacin-like metalloprotease, and two pore forming toxins. However, a protein, which displayed sequence similarity to a calcium-independent phospholipase A2, showed lower levels in bleached cnidarians. Accordingly, the hemolytic effect of the soluble proteome of bleached hydrocorals was significantly higher, whereas the phospholipase A2 activity was significantly reduced. Our results suggest that bleached M. complanata is capable of increasing its toxins production in order to balance the lack of nutrients supplied by its symbionts.",
"conclusion": "5. Conclusions This study presented evidence demonstrating that the El Niño–Southern Oscillation 2015–2016 induced a significant decrease in symbiont density in some colonies of M. complanata that inhabit the Mexican Caribbean, indicating that these hydrocorals underwent a severe bleaching. The levels of proteins involved in key cellular processes, such as glycolysis, DNA repair, stress response, calcium homeostasis, exocytosis, and cytoskeleton organization were significantly modified in bleached hydrocorals. Four of the proteins, whose levels were augmented, exhibited amino acid sequence similarity to pore-forming toxins, a phospholipase A2, and a metalloprotease. Accordingly, the hemolytic effect of the soluble proteome of bleached hydrocorals was significantly higher. These results allowed us to infer that bleached M. complanata is capable of increasing its toxins production in order to balance the negative impact of elevated temperature on its autotrophic nutrient input. This may represent a resilience mechanism by which hydrocorals face thermal stress.",
"introduction": "1. Introduction Coral reefs are megadiverse ecosystems that offer a great variety of services to the human population surrounding them [ 1 ]. Calcareous structures formed by corals provide livelihood to a large range of marine species [ 2 ]. The hydrozoan Millepora complanata is an important reef-forming organism that is widely distributed in the Caribbean Sea. This organism belongs to the group commonly known as “fire corals”, which when getting into contact with humans are capable of producing severe burns, blisters, and pain [ 3 ]. Cnidarians are widely recognized as an important source of structurally diverse metabolites, which might represent novel leads for the development of new drugs and biotechnological tools. One of the most remarkable features of cnidarians is their ability to synthesize cnidocystic and non-cnidocystic toxins (neurotoxins, enzymes, and pore-forming toxins) that induce toxic and immunological reactions [ 4 , 5 ]. Most of these toxins are contained within the nematocysts, and are implicated in both cnidarian defense and prey capture [ 6 ]. Previous studies carried out by our research group demonstrated that M. complanata produces hemolysins, phospholipases A2 (PLA2), and proteases [ 7 , 8 ]. Coral reef forming cnidarians live in mutualistic symbiosis with photosynthetic algae of the genus Symbiodinium , commonly named zooxanthellae. In this symbiotic relationship, algae provide approximately 95% of nutrients or metabolic requirements (by photosynthetically fixed carbon) to their cnidarian host [ 9 , 10 , 11 , 12 ]. Environmental stressors, such ocean acidification, elevated salinity, UV radiation, and high temperature can lead to the breakdown of the coral-algae symbiosis. This phenomenon, commonly known as “coral bleaching” [ 13 , 14 , 15 , 16 ], results from the loss of photosynthetic symbionts or algae pigments from cnidarian host cells [ 17 ]. It has been well documented that worldwide coral bleaching events are among the most deleterious effects of global warming, putting the survival of coral reef at serious risk [ 18 , 19 , 20 , 21 ]. In the last 100 years, the average temperature on earth has increased by about 1 °C, and according to records of the US National Oceanic and Atmospheric Administration (NOOA), 2015–2016 were the warmest years recorded in the Earth’s history. Particularly, during the El Niño-Southern Oscillation (ENSO), severe coral bleaching events have occurred due to seawater temperature rise [ 22 ]. Since the first studies about the consequences of thermal stress were carried out, it has been widely demonstrated that after a bleaching event, different cnidarian cellular processes are affected [ 2 , 9 , 13 , 14 , 15 , 16 , 17 , 18 , 19 , 20 , 23 ]. Thermal stress induced upregulated expression levels of antioxidant enzymes (e.g., ascorbate peroxidase, catalase, superoxide dismutase) and heat shock proteins (e.g., HSP70), which are directly correlated with molecular mechanisms responsible for repairing cellular and tissue damage [ 24 ]. Stress induced by high UV radiation and elevated temperatures in Montastraea faveolata caused host DNA damage correlated to p53 gene expression, as well as decreased concentration of D1 protein and photosynthetic pigments in the algal symbionts [ 25 ]. In addition, M. faveolata exposed to high solar radiation showed diminished concentration of mycosporine-like amino acids, whose origin, whether from cnidarians or from their symbionts, was not determined [ 25 ]. It has also been proven that thermal stress and UV-light cause lower enzymatic activity of ribulose-1,5-bisphosphate carboxylase oxygenase (Rubisco) [ 26 ] and injury to the thylakoidal membranes by causing oxidative stress in Symbiodinium cells [ 27 ]. Several genomic and transcriptomic studies conducted in Anthozoa species have shown that thermal stress modifies the expression of genes and transcripts related to growth arrest, chaperone activity, nucleic acid stabilization, removal of damaged macromolecules, metabolism, antioxidant mechanisms, and immune system in both hosts and symbionts [ 28 , 29 , 30 , 31 , 32 , 33 , 34 , 35 , 36 , 37 ]. On the other hand, although there has only been a few proteomic studies on the consequences of elevated temperature on corals, these have provided evidence that important changes occur in the expression of proteins of reef-forming Anthozoans during bleaching [ 38 , 39 , 40 , 41 ]. In Acropora palmata and Acropora microphthalma the proteins, which showed differential expression after bleaching, participate in important cellular processes and components, which include: stress response, UV response, amino acid synthesis, transcription factors, immunity, apoptosis, biomineralization, cytoskeletal, cell cycle, oxidative phosphorylation, anti-oxidant proteins, endo-exo phagocytosis, and calcification [ 39 , 40 ]. Another study indicated that when Pocillopora acuta was subjected to experimental thermal stress, several proteins involved in cytoskeletal structure, immunity, and metabolism were differentially expressed [ 41 ]. It has been observed that heat stress causes damage to the coral host tissue, compromising the physiologic integrity of epithelium in Acropora hyacinthus [ 42 ]. Changes caused by thermal stress have been observed in the proteome of organisms from other phyla, such as the benthic foraminifera Amphistegina gibbosa [ 43 ]. Not only the transcriptome and the proteome, but also the metabolome of both partners of the symbiosis is affected by thermal stress. Significant differences have been observed after bleaching in the lipid (e.g., cell structural lipids) and metabolite profiles (e.g., carbohydrates and signaling compounds) in Pocillopora damicornis [ 44 ] and Acropora aspera , such metabolites are involved in biochemical reactions related to molecular regulation during exposure to environmental stress in cnidarians [ 45 , 46 ]. A metabolomics analysis of the symbiotic anemone Aiptasia sp. confirmed the results obtained from the study of reef forming cnidarians, indicating that thermal stress significantly alters central metabolism, oxidative state, and cell structure [ 47 ]. Studies aimed at evaluating the influence of elevated temperature on the cellular processes of reef forming cnidarians have focused on Anthozoa species, and up to now, very little is known about the cellular response of Hydrozoa species to thermal stress. In a previous study carried out by our research group, we analyzed the impact of thermal stress on the soluble proteomic profile and cytolytic activity of Millepora alcicornis . We found that the levels of 17 key proteins, tentatively identified as related to exocytosis, calcium homeostasis, cytoskeletal organization were modified in bleached M. alcicornis . Moreover, the protein levels of potential toxins, including a metalloprotease, a phospholipase A2 (PLA2), and an actitoxin were also altered [ 48 ]. It is obviously very important to continue studying the consequences of high water temperature on hydrocorals. In this context, the present study was undertaken to investigate the effect of the 2015–2016 El Niño-Southern Oscillation on the soluble proteomic profile and cytolytic activity of Millepora complanata from the Mexican Caribbean through a proteomic approach, in order to contribute to the broader understanding of the molecular processes involved in the response of reef-forming organisms of the class Hydrozoa to global warming.",
"discussion": "3. Discussion During the 2015–2016 El Niño-Southern Oscillation, the highest shallow sea water temperatures were recorded and a severe impact on climate and weather due to this event was documented [ 49 , 50 , 51 ]. The 2015–2016 ENSO brought weather conditions that triggered coral bleaching and mortality worldwide [ 51 ]. In particular, the Caribbean coral reef ecosystems experienced severe bleaching [ 21 , 52 ]. It is well known that reef-forming cnidarians have an ability to counteract the damaging effects of thermal stress varies between organisms from different families or between species. For example, it has been demonstrated that species of the families Acroporidae, Pectiniidae, Alcyonacea, Merulinidae, and Mussidae are particularly vulnerable to the deleterious effects of temperature stress [ 53 ]. While organisms such as the soft coral Sarcophyton ehrenbergi , the massive coral Porites cylindrical , and the blue coral Heliopora coerulea have shown greater resistance to high temperature [ 54 , 55 , 56 ]. Comprehensive reef surveys have revealed that Millepora species (class Hydrozoa) are very susceptible to the bleaching phenomenon [ 57 , 58 , 59 ]. Therefore, considering that thermal stress is often induced during episodic heating events and the great ecological importance of hydrocorals, the aim of the present study was to analyze changes in the soluble proteomic profile and cytolytic activity of Millepora complanata (“fire coral”) that underwent bleaching during the 2015–2016 El Niño-Southern Oscillation in the Mexican Caribbean Sea. Numerous investigations that have addressed the impact of elevated sea water temperature on reef forming cnidarians, from the first reports to the most recent ones, have evaluated the density of symbionts to assess the severity of bleaching [ 60 , 61 , 62 ]. In the case of the present study, we used the aforementioned criterion to determine the degree of bleaching of our collected samples. We found that bleached M. complanata specimens showed a decrease of 80% in the density of symbionts per square centimeter ( Figure 1 ). The decline in symbiont density found in bleached M. complanata turned out to be similar to what has been reported in scleractinian species (symbiont density diminished by up to 50%–80%) [ 63 , 64 , 65 , 66 , 67 , 68 ]. It is worth mentioning that we conducted a parallel study on specimens of M. alcicornis collected in the same location and on the same dates as the M. complanata specimens. Interestingly, our study showed that bleached M. alcicornis specimens showed a decrease of 40% in the density of symbionts per square centimeter [ 48 ]. This suggests preliminarily that M. alcicornis is more thermotolerant than M. complanata . It would be very important to elucidate the mechanism underlying this thermotolerance. It is well demonstrated that either in situ or experimental heat stress significantly affects the synthesis of proteins [ 40 ]. Accordingly, we found that bleached specimens of M. complanata , showed a 20% decrease in the total content of soluble proteins ( Table 2 ). It is very likely that a lesser protein yield in the soluble proteome obtained from bleached specimens is related to the loss of protein components coming from zooxanthellae. The electrophoretic profiles of the soluble proteomes from both unbleached and bleached M. complanata holobiont were similar to those of the soluble proteomes from normal and bleached specimens of M. alcicornis [ 48 ]. Our results also agree with what has been previously observed in the protein profiles of other cnidarians, such as H. magnipapillata (class Hydrozoa), A. elegantissima (class Anthozoa), C. fleckeri (class Cubozoa), and P. noctiluca (class Scyphozoa) [ 69 , 70 , 71 , 72 ]. 3.1. Levels of Proteins Implicated in Key Cellular Processes Were Modified in Bleached M. complanata Seventy one proteins were found in both unbleached and bleached M. complanata holobiont-soluble proteomes, while the levels of 35 proteins were modified by bleaching. Mass spectrometric analysis of differential protein spots indicated that proteins whose levels were altered in bleached hydrocorals were involved in several cellular processes. Sixty one percent of these proteins showed amino acid sequence similarity to proteins that participate in important cell processes, such as: primary metabolism, DNA repair, cytoskeleton formation, signaling, stress response, redox homeostasis, and exocytosis ( Figure 5 ). Bleached M. complanata specimens showed higher protein levels of alfa enolase, UV DNA endonuclease, HSP70, peroxiredoxin-6, and exocyst complex component 4 like protein. Whereas the protein levels of triosephosphate isomerase, DNA endonuclease repair XPF, actin, calmodulin, and hypothetical protein NEMVEDRAFT_v1g45829 were reduced. Worthy of mention is the fact that levels of four proteins, which displayed amino acid sequence similarity to cytolysins were augmented, including a phospholipase A2 (PLA2), an astacin-like metalloprotease toxin 5, and two pore forming toxins, echotoxin-2 and DELTA-actitoxin-Oor1b. In contrast, protein levels of an acidic calcium-independent phospholipase A2-like protein were reduced in bleached M. complanata . Alpha enolase and triosephosphate isomerase were two enzymes, catalysing primary metabolic reactions, whose levels were altered in bleached hydrocorals. Alpha enolase is an enzyme that was previously identified in the transcriptome of Hydra vulgaris [ 73 ], which is phylogenetically close to M. complanata . This enzyme catalyzes the reversible conversion of 2-phosphoglycerate to phosphoenolpyruvate during both glycolysis and gluconeogenesis [ 74 ]. Higher level of alpha enolase in bleached M. complanata suggests increased activity in the glycolytic pathway. This result is in accordance with the response observed in Acropora aspera subjected to experimental bleaching, which showed a significant up-regulation of genes related to carbon metabolism (e.g., glycolysis, tricarboxylic acid cycle, and fatty acids synthesis [ 75 ]. Augmented alpha enolase level in bleached M. complanata might be a response to the diminished supply of energy due to the decrease in symbiont density, since glycolysis constitutes a major source of energy (in the form of ATP) and supplies the precursors for the synthesis of biomolecules such as lipids. On the other hand, the level of triosephosphate isomerase was reduced in bleached M. complanata . This enzyme participates in glycolysis and gluconeogenesis, catalyzing the reversible synthesis of d -glyceraldehyde 3-phosphate from glycerone phosphate [ 76 ]. Our findings differ from the results obtained by Kenkel et al. (2013) who found an increase in the expression of genes encoding enzymes involved in gluconeogenesis in Porites astroides exposed to chronic heat stress [ 32 ]. Those authors proposed that the coral host balances its nutritional deficiency by converting their energetic reserves into carbohydrates. It has been demonstrated that under oxidative stress conditions, the expression of a subset of glycolytic proteins is repressed, while the expression of a few enzymes involved in the pentose phosphate pathway (PPP), which is directly connected to the glycolytic pathway, is induced [ 77 ]. Enzymes of the PPP are critical for preserving cytoplasmic NADPH concentration, which affords the redox power for antioxidant systems [ 78 , 79 ]. The observations above indicate that cells are capable of rerouting the carbohydrate flux from glycolysis to the PPP to counteract oxidative stress. Experiments carried out in Saccharomyces cerevisiae and Caenorhabditis elegans showed that reduction in triosephosphate isomerase expression or activity results in a redirection of the carbohydrate flux, which confers resistance against oxidative stress [ 80 ]. Considering the diminished levels of triosephosphate isomerase in bleached M. complanata , it is possible to hypothesize that thermal stress induces a decrease in triosephosphate isomerase expression in M. complanata as a mechanism to redirect the metabolic flux from glycolysis to the PPP in order to face oxidative stress. However, this hypothesis needs to be proven. The levels of two DNA damage repair proteins were modified in bleached M. complanata . A protein which displays sequence homology with UVSE_BACCR, a component in a DNA repair pathway in Bacillus cereus [ 81 ], was up-regulated. Increased levels of this protein, which removes UV light-damaged nucleotides from DNA, could represent a response from M. complanata to repair the damage caused by high UV radiation and elevated seawater temperatures exposition. In contrast, lower levels of a protein that exhibited sequence homology with a DNA repair endonuclease XPF from the myxosporean Thelohanellus kitauei [ 82 ] were found in bleached M. complanata . Several studies have confirmed DNA damage in temperature-stressed corals, such as Montastraea faveolata , Stylophora pistillata , and Acropora tenuis [ 25 , 55 , 83 ]. Therefore, modified levels of UV DNA endonuclease and DNA endonuclease repair XPF supports that DNA damage occurs in M. complanata specimens that underwent bleaching. Among the proteins whose levels were reduced in bleached hydrocorals was actin. Previous studies carried out on reef forming cnidarians have identified actin as a particularly sensitive protein to temperature stress [ 23 , 32 , 33 , 34 , 39 , 48 ]. In fact, actin genes have been proposed as a gene expression marker of heat stress that could be diagnostic of coral stress in the field [ 35 ]. The results obtained in the present study agree with what was observed in specimens of Porites astreoides [ 35 ] and Stylophora pistillata [ 36 ] subjected to heat stress, which demonstrated significant down-regulation of actin genes. In contrast, in the study we carried out on bleached M. alcicornis specimens, we found higher levels of actin [ 48 ], in a similar way to what was found in the scleractinian coral Acropora palmata [ 39 ]. The actin cytoskeleton is central in various cellular processes including cell motility, mitosis, intracellular transport, endocytosis, secretion, etc. [ 84 , 85 ]. Lower levels of actin in bleached M. complanata specimens may imply modifications in the intracellular transport, plasma membrane interactions, cell shape integrity and in the regulation of gene transcription of proteins that participate in cytoskeletal interactions. Bleached M. complanata specimens also showed lower levels of calmodulin, which is a Ca 2+ sensor protein, whose signaling is important in several cellular processes, such as cell cycle, apoptosis, intracellular transport, and calcium homeostasis [ 86 ]. This result is in agreement with what was found in the reef-building corals Monstastraea faveolata [ 23 ], Acropora palmate [ 28 ], and the symbiotic sea anemone, Anemonia viridis [ 87 ] exposed to experimental heat stress. Again, the results that we obtained in this study differ from what we found in bleached M. alcicornis , which exhibited higher abundance of calmodulin [ 48 ]. In the case of M. complanata , our results suggest that thermal stress provokes a disruption in cell calcium homeostasis. Undoubtedly, discrepancies found in the stress-responses related to the expression levels of actin and calmodulin between different reef forming cnidarians deserve further investigation. Exposure of M. complanata to a thermally-induced bleaching event resulted in increased levels of heat shock protein 70 (HSP70). HSP70s are ubiquitous chaperones that facilitate correct protein folding and bind to partially denatured proteins to inhibit their aggregation. They are also able to renature denatured proteins conferring them repaired active states by an ATP-dependent way [ 88 ]. Since HSPs act as molecular chaperones preventing cellular damage under conditions of environmental stress, regulation of HSPs gene expression has been examined in scleractinian corals and Symbiodinium clades [ 24 , 37 , 88 , 89 , 90 , 91 , 92 , 93 ]. In general, thermal stress induces up-regulation of HSP70 gene expression in both Symbiodinium sp. and corals ( Acropora millepora , A. grandis , A. hyacinthus , Tubastrea cocchinea , Astrangia danae , Montastraea annularis , M. faveolata , Pocillopora damicornis , and Goniastrea aspera ) [ 24 , 37 , 88 , 89 , 90 , 91 , 92 , 93 ]. As expected, our results revealed a greater abundance of cytosolic HSP70 in M complanata exposed to heat stress. Considering that an improved thermotolerance in many marine organisms, including reef building corals, has been related to higher expression of stress-inducible members of the HSP70 family [ 90 , 94 ], it is very likely that up-regulation of HSP70 expression represents a heat-induced stress response of M. complanata to preserve protein structure and functions, and stimulate cellular repair processes to face global warming. Similar to HSP70, peroxiredoxin-6 levels were also elevated in bleached M. complanata . This protein belongs to the family of peroxiredoxins, which neutralize oxidation products generated by reactive oxygen species (ROS) and therefore, protect cells from oxidative stress. Upregulation of this protein has been observed in Acropora microphthalma exposed to solar irradiance and heat stress [ 40 ]. Studies on the effect of heat stress on reef forming cnidarians have highlighted the important role that peroxiredoxins and other antioxidant enzymes, such as ascorbate peroxidase, superoxide dismutase, and catalase play to balance the oxidative damage generated by ROS during coral bleaching [ 95 , 96 , 97 , 98 ]. The observed increase in the levels of peroxiredoxin-6 indicates that M. complanata is dealing with ocean warming by activating its antioxidant mechanisms to prevent or revert damage provoked by ROS. We also found that bleached M. complanata exhibited higher levels of an exocyst complex component 4 like protein in M. complanata . This protein has been proposed as a biomarker of coral heat stress [ 32 , 99 ], therefore our finding was to be expected. The exocytosis multiprotein complex has been related to the process of symbionts expulsion [ 100 ], since it offers spatial targeting of exocytotic vesicles to the membrane [ 101 ]. 3.2. Proteins That Showed Amino Acid Sequence Similarity to Toxins Showed Different Levels in Bleached M. complanata Mass coral bleaching and mortality events that have occurred worldwide over the past three decades have caused great concern about the future of coral reef ecosystems [ 2 , 102 ]. Research on thermal tolerance of reef-forming corals indicates that some reef-forming cnidarians are able to deal with thermal stress, through specific adaptive processes, which include acclimatization, genetic adaptation, and symbiont shuffling, which may ameliorate the adverse consequences and mortality provoked by elevated sea water temperature [ 103 , 104 , 105 , 106 ]. Moreover, the ability to recover from a bleaching episode has been related to the energy reserves and heterotrophic feeding capacity of the cnidarian host [ 107 , 108 , 109 ]. Symbiodinium can provide more than 50% of their photosynthetic products to the cnidarian host [ 10 , 12 , 18 , 110 , 111 , 112 ]. However, after bleaching, recovering corals may heavily rely on alternate sources of fixed carbon, which is acquired via catabolism of energy reserves and/or by increased heterotrophy [ 113 , 114 ]. In fact, some evidence suggest that zooplankton provision may mitigate the negative impact of thermal stress [ 115 ]. Millepora species obtain nutrients from their autotrophic endosymbionts, however, they are also capable of capturing planktonic preys through heterotrophic feeding. Considering that autotropic input is significantly diminished during bleaching episodes [ 116 ], it is possible to hypothesize that under bleaching scenarios, M. complanata may increase the production of their chemical armament with the aim to balance the lack of energy from Symbiodinium algae. As already mentioned above, the method we employed for obtaining the soluble proteomes from unbleached and bleached specimens of M. complanata involved osmotic shock in bidistilled water, which causes the discharge of the nematocysts content [ 48 ]. Interestingly, in the present study we found that bleached hydrocorals had increased levels of two proteins that showed amino acid sequence similarity to the pore forming toxins (PFTs), echotoxin-2 and DELTA-actitoxin-Oor1b, which were previously identified in “giant triton” Monoplex parthenopeus (phylum Mollusca) and the “Sea of Japan anemone” Oulactis orientalis , respectively [ 117 , 118 ]. DELTA-actitoxin-Oor1b belongs to the family of Actinoporins, which are the most abundant and best studied cnidarian PFTs [ 119 , 120 ]. These PFTs have been mainly identified in sea anemone venoms [ 120 , 121 ], although some actinoporin-like toxins have been found in other members of the class Anthozoa and in one species of the class Hydrozoa, Hydra magnipapillata [ 5 , 120 , 121 , 122 ]. Actinoporins are ~20 kDa proteins that spontaneously insert into sphingomyelin containing membranes [ 123 ]. In the case of actinoporin-like toxins from Hydra , they do not target sphingomyelin and display low sequence similarity (~30% identity) to actinoporins [ 124 ]. The actinoporin-like protein from M. complanata , which is predicted to have two α-helices, shares some functional features with three model actinoporins: DELTA-actitoxin-Aeq1a (Equinatoxin II; EqT II) and DELTA-actitoxin-Aeq1b (EqT V) from the “beadlet anemone” Actinia equina [ 125 , 126 ], and DELTA-actitoxin-Ucs1a (UcI) from the “Christmas anemone” Urticina crassicornis [ 127 ] (see Figure S1 of Supplementary material ). The M. complanata actinoporin-like protein bears some conserved actinoporin binding site motifs and an aspartate that is present in the well-recognized actinoporin RDG-motif [ 128 ]. Noteworthy, the actinoporin-like protein we identified in M. complanata has an aromatic cluster motif that is similar to that of EqT II (W147, 151 and 152), which mediates the initial membrane attachment [ 129 , 130 ]. Most anemone actinoporins lack cysteine residues, however, M. complanata actinoporin-like protein owns one cysteine residue, which could include actinoporin-like toxins from Stylophora pistillata [ 122 ] and Hydra magnipappilata [ 131 ]. Augmented levels of the two pore forming like toxins from M. complanata correlated with increased hemolytic activity. Therefore, considering that PFTs are involved in processes such as feeding, digestion, defense, and spatial competition [ 120 , 121 , 128 ], it is very likely that upon loss of autotrophic input, M. complanata improves its heterotrophic capability as a strategy to counteract the loss of symbionts. On the other hand, two proteins that exhibited homology to PLA2 displayed differential abundance in bleached hydrocorals. An acidic PLA2 PA4, previously reported in Nemopilema nomurai [ 132 ], showed elevated levels, whereas levels of an acidic calcium-independent PLA2-like, identified in the transcriptome of Choristoneura fumiferana [ 133 ], were diminished. At present, few cnidarian secreted phospholipases A2 have been isolated and structurally characterized [ 134 , 135 , 136 , 137 , 138 ] and it has been proposed that their functions comprise the capture and digestion of prey [ 139 ]. When assessing the PLA2 activity, we observed that the soluble proteome from bleached hydrocorals elicited a reduced enzymatic activity. This result is consistent with what we obtained in a previous study, in which we found that experimental thermal stress decreased the phospholipase A2 activity of an aqueous extract prepared from M. complanata [ 140 ]. Considering that the net PLA2 activity is the result of the sum of the effects induced by individual enzymes, our results suggest that the PLA2 activity induced by the soluble proteome of bleached M. complanata is mainly produced by enzymes, such as the acidic calcium-independent phospholipase A2 we detected, and other unidentified PLA2s, whose expression is very likely affected by thermal stress. Another protein whose levels were raised in bleached hydrocorals showed homology (more than 30% amino acid sequence similarity) with astacin-like metalloprotease 5. This toxin is a zinc metalloprotease obtained from the spider Loxosceles gaucho , which provokes endothelial cells deadhesion and degradation of fibrinogen, fibronectin and gelatin [ 141 ]. The presence of metalloproteases has been described in several terrestrial animals venoms, such as those of snakes, spiders, centipedes, ticks [ 5 , 141 , 142 , 143 ], and also in soft-body cnidarians such as Podocoryne carnea , Olindias sambaquiensis , Nematostella vectensis , Stomolophus meleagris , Nemopilema nomurai , Rhopilema esculenta , Cyanea nozakii , Aurelia aurita , and Chironex fleckeri [ 5 , 144 , 145 , 146 , 147 , 148 ]. Metalloproteases from venomous animals appear to play an important role in envenomation, allowing the diffusion of toxic venom components by degradation of extracellular matrix. The over expression of this protein may be another indication that M. complanata is increasing the synthesis of toxins to improve its heterotrophic capacity in order to alleviate nutrient limitation derived from the impaired symbiotic relationship between hydrocorals and zooxanthellae. Interestingly, the results obtained in this study agree with what was recently found by Hoepner et al. , [ 149 ], who reported that the venom from the sea anemone Entacmaea quadricolor , exposed to long-term light-induced bleaching, preserve its hemolytic activity and lethality. These findings support the hypothesis that some cnidarians that have suffered bleaching are able to prey heterotrophically, giving them a better chance to resist the effects of thermal stress."
} | 8,047 |
34154664 | PMC8215762 | pmc | 3,740 | {
"abstract": "Background Conventional methods of agricultural pest control and crop fertilisation are unsustainable. To meet growing demand, we must find ecologically responsible means to control disease and promote crop yields. The root-associated microbiome can aid plants with disease suppression, abiotic stress relief, and nutrient bioavailability. The aim of the present work was to profile the community of bacteria, fungi, and archaea associated with the wheat rhizosphere and root endosphere in different conditions. We also aimed to use 13 CO 2 stable isotope probing (SIP) to identify microbes within the root compartments that were capable of utilising host-derived carbon. Results Metabarcoding revealed that community composition shifted significantly for bacteria, fungi, and archaea across compartments. This shift was most pronounced for bacteria and fungi, while we observed weaker selection on the ammonia oxidising archaea-dominated archaeal community. Across multiple soil types we found that soil inoculum was a significant driver of endosphere community composition, however, several bacterial families were identified as core enriched taxa in all soil conditions. The most abundant of these were Streptomycetaceae and Burkholderiaceae . Moreover, as the plants senesce, both families were reduced in abundance, indicating that input from the living plant was required to maintain their abundance in the endosphere. Stable isotope probing showed that bacterial taxa within the Burkholderiaceae family, among other core enriched taxa such as Pseudomonadaceae, were able to use root exudates, but Streptomycetaceae were not. Conclusions The consistent enrichment of Streptomycetaceae and Burkholderiaceae within the endosphere, and their reduced abundance after developmental senescence, indicated a significant role for these families within the wheat root microbiome. While Streptomycetaceae did not utilise root exudates in the rhizosphere, we provide evidence that Pseudomonadaceae and Burkholderiaceae family taxa are recruited to the wheat root community via root exudates. This deeper understanding crop microbiome formation will enable researchers to characterise these interactions further, and possibly contribute to ecologically responsible methods for yield improvement and biocontrol in the future. Supplementary Information The online version contains supplementary material available at 10.1186/s40793-021-00381-2.",
"conclusion": "Conclusions In conclusion: (1) We identified five core microbial taxa associated within the rhizosphere and endosphere of T. aestivum var. Paragon, Streptomycetaceae , Burkholderiaceae , Pseudomonadaceae , Rhizobiaceae and Chitinophageaceae . The consistency of the enrichment of these groups across the soil types and plant growth stages we tested strongly indicates that they are core taxa associated with Paragon var. T. aestivum . (2) At the onset of developmental senescence, significant reductions in the abundance of many taxa were observed, including the whole core endosphere and rhizosphere microbiome, and multiple root-exudate utilising taxa. In particular, Streptomycetaceae abundance was reduced two-fold. This may indicate that active input from the host is required to maintain the abundance of certain families within the endosphere, and strongly indicated that this is the case for exudate utilisers. A significant increase in the total abundance of bacteria and archaea was evident during senescence and potentially increased colonisation of fungal groups associated with necrotrophy and plant tissue degradation. (3) No lineages of archaea were specifically associated with wheat roots. Conflicting data from DGGE and from 16S rRNA gene sequencing indicated that the currently available archaeal 16S rRNA gene databases are not sufficiently complete for this metabarcoding approach. (4) We identified nine taxa within the rhizosphere utilising carbon from wheat root exudates, including aforementioned core taxa of T. aestivum var. Paragon, Pseudomonadaceae and Burkholderiaceae . There was no evidence that the most abundant endosphere bacterial family Streptomycetaceae was using plant exudates within the rhizosphere. The present work has provided novel insights into the composition and variation within the wheat microbiome and how the community changes through developmental senescence. Greater understanding is needed of the role played by the five core taxa associated with T. aestivum var. Paragon, and the mechanisms by which they are able to colonise the root and are supported by the host. This knowledge may inform novel agricultural applications or more ecologically responsible management strategies for wheat.",
"discussion": "Discussion In this work we profiled the microbial communities in the rhizosphere and endosphere of the UK elite Spring bread wheat T. aestivum variety Paragon. We identified the core microbial families associated with the rhizosphere and endosphere of these plants and the subset of microorganisms assimilating plant-derived carbon in the rhizosphere. This study revealed that plant developmental senescence induces shift in the root-associated microbial communities and an increase in microbial abundance in the plant endosphere. Concurrent with established literature [ 6 , 21 , 74 ] we found the soil inoculum to be a major driver of root community composition. Given the contrasting range of soils, wheat varieties, developmental timepoints, and growth management strategies used across studies, drawing direct comparisons is often challenging. For example Schlatter et al . identified Oxolabacteraceae , Comamonadaceae and Chitinophaga as core rhizobacteria for the wheat cultivar Triticum aestivum L. cv. Louise [ 29 ]. Our work corroborates this observation for T. aestivum var . Paragon, all these taxa were identified by SIP as exudate utilising microbes. However, many of the core taxa identified by Schlatter et al . were not identified by the present work. Similarly, for the endosphere community, Kuźniar and colleagues identified Flavobacterium , Janthinobacterium, and Pseudomonas as core microbiota for both cultivars tested, and Paenibacillus as a core taxon for T. aestivum L. cv. Hondia [ 28 ]. We identified Pseudomonadaceae as a core component of the T. aestivum var. Paragon endosphere microbiome and, while Paenibacillaceae were not enriched in the endosphere consistently, we did identify this family as an exudate utiliser within the rhizosphere. Streptomycetaceae were not identified by the study of Kuźniar and colleagues. While these combined results consistently imply a role for common taxa such as Pseudomonadaceae or members of the Burkholderiaceae family, it cannot explain the differences observed in colonisation by other taxa, and in particular Streptomycetaceae . While it is likely this is largely driven by soil type, there is some evidence that for wheat, similarly to barley [ 11 ], plant genotype may be responsible for these differences [ 24 , 28 , 32 , 44 ]. In a study which used the same Church Farm field site as our work, T. aestivum var. Paragon was previously reported to be an outlier compared to other wheat varieties, with a particularly distinct rhizosphere and endosphere community [ 24 ]. Further studies are needed to fully assess how wheat rhizosphere and endosphere communities vary across different wheat cultivars and soil environments, and which of these factors has the greatest influence. While only slight differences were observed between root-growth phase laboratory cultivated plants and stem elongation phase field cultivated plants, significant changes in the abundance of numerous bacterial and fungal taxa occurred at the onset of plant developmental senescence. To our knowledge, the wheat root community has not previously been assessed after senescence, though development has been shown to significantly alter the wheat rhizosphere community [ 22 , 23 ]. One fungal group, Chaetosphaeriaceae , was significantly enriched as the plant senesced. This family represents a relatively diverse group of fungi, although members of this group such as Chaetosphaeria are known to reproduce within decomposing plant tissues, which may explain the four-fold increase in abundance after senescence [ 75 ]. In terms of the overall fungal community composition (Fig. 2 ; B1), the greatest change during senescence was in the Pleosporales group, and this may also contribute to the observed increase in fungal abundance during senescence. This group was excluded from the differential abundance analysis which focused on lower taxonomic ranks. Pleosporales is an order of fungi containing over 28 families [ 76 ], and such a high diversity makes the ecological role of this group difficult to postulate. Some families within the Pleosporales are associated with endophytic plant parasites [ 76 ], including necrotrophic pathogens of wheat Pyrenophora tritici-repentis and Parastagonospora nodorum [ 77 ]. Necrotrophic pathogens specialise in colonising and degrading dead plant cells, and senescent tissues are thought to provide a favourable environment for necrotrophs [ 37 ]. It is interesting to note that this increased fungal colonisation correlated with reduced abundance of fungi-suppressive endophytic bacteria such as Streptomycetaceae [ 78 , 79 ] and Burkholderiaceae [ 80 ] during developmental senescence. The present work, however, cannot provide any direct evidence of a causative relationship driving this correlation. AOA were found to dominate the community in all root compartments. Whilst no selection of specific archaeal lineages within the root could be detected via sequencing, DGGE did indicate a possible shift in community composition across root compartments. The potential for interactions between soil AOA and plant roots remains largely unexplored. There is some limited evidence, however, which may indicate an influence of terrestrial plant root exudates on archaeal communities [ 81 ], and whilst the present work found no clear evidence that the total abundance of archaea changed within the rhizosphere, one study observed a negative correlation between archaeal abundance and plant root exudates [ 82 ]. There is also evidence that AOA can promote plant growth [ 34 ]. The nature of these interactions, however, still remains unclear. There is now mounting evidence that archaeal communities are influenced by plants or plant derived metabolites within the soil, even if they do not utilise host derived carbon. In the future, longer read methods or metagenomics could be applied to better investigate archaeal community dynamics within the root microbiome. Burkholderiaceae -family taxa ( Comamonadaceae and Oxalobacteriaceae ), and Pseudomonadaceae were identified as potential root exudate utilisers within the rhizosphere, in agreement with previous studies [ 42 , 83 ]. These bacterial groups were also consistently enriched in the rhizosphere or endosphere, regardless of soil type. These results imply these families may be selectively recruited to the plants via root exudates, which support Burkholderiaceae and Pseudomonadaceae via photosynthetically fixed carbon. The Pseudomonadaceae family contains a diverse range of plant-beneficial and plant pathogenic strains [ 84 , 85 ] but the literature correlates exudate utilisation with microbial functions which benefit the host plant [ 86 , 87 ], and exudates can have a negative effect on plant pathogens [ 12 ]. While the mechanism of this selectivity remains unknown, it is likely these exudate utilisers are plant beneficial strains. Well studied representatives of this family with plant growth promoting traits include Pseudomonas brassicacearum [ 88 ] and Pseudomonas fluorescens [ 89 ]. Most of the exudate utilising families identified in the present work were fast growing Gram-negative bacteria. As observed by Worsley and colleagues (bioRxiv [ 90 ]), faster growing organisms are labelled more readily within a two-week incubation period. Due to their faster growth rates, these microorganisms can more easily monopolise the plant derived carbon within the rhizosphere and incorporate 13 C into the DNA backbone during DNA replication. Slower growing organisms such as Streptomycetaceae are likely outcompeted for root derived resources in the rhizosphere or the two-week incubation period may be too short to allow the incorporation of the 13 C label into DNA. Streptomycetaceae were the most abundant of the core endosphere enriched families, despite not incorporating root derived carbon in the rhizosphere. This family is dominated by a single genus, Streptomyces . These filamentous Gram-positive bacteria are well known producers of antifungal and antibacterial secondary metabolites, and members of the genus have been shown to promote plant growth [ 79 ], have been correlated with increased drought tolerance [ 91 ], and can protect host plants from disease [ 78 , 79 ]. Streptomyces species make up the active ingredients of horticultural products Actinovate and Mycostop and it has been proposed that plant roots may provide a major niche for these bacteria which are usually described as free-living, soil dwelling saprophytes. In this study Streptomycetaceae accounted for up to 40% of the bacteria present in the endosphere for some plants. Intriguingly, after the plants senesced, there was a two-fold reduction in the abundance of Streptomycetaceae within the endosphere. This a surprising result for a bacterial group typically associated with the breakdown of dead organic matter within soils [ 92 ]. As plants senesce and die, a process of ecological succession occurs, where the tissues are colonised by different microbes (particularly fungi) successively as different resources within the plant tissues are degraded [ 93 , 94 ]. The first microorganisms to colonise will be those rapidly metabolising sugars and lipids, followed later by more specialist organisms which will breakdown complex molecules like lignin and cellulose. While these later stages are typically attributed to fungi, Streptomycetaceae are known to degrade complex plant derived molecules such as hemicellulose and insoluble lignin [ 92 , 95 ]. It could be that our sampling timepoint (late in the developmental senescence process, but prior to most biomass degradation) was too early in this succession process for any biomass fuelled Streptomycetaceae proliferation to be obvious. This, however, cannot explain the reduced abundance of Streptomycetaceae in senesced roots compared to the actively growing plants. This might be explained by a lack of active input from the plant, as the host senesces and resources are diverted to the developing grain [ 35 ] host derived resources may no longer be available to support Streptomycetaceae growth in the endosphere. The DNA-SIP experiment indicated that Streptomycetaceae did not utilise root exudates under the selected experimental conditions, which contradicts the findings of Ai and colleagues [ 43 ]. It must be noted that while Streptomycetaceae were not labelled in the DNA-SIP experiment, this experiment focused on the rhizosphere, and our data demonstrated that Streptomycetaceae primarily colonise the endosphere. Further SIP experiments exploring the endosphere community, with more replicates to account for the high variability, may help to determine whether Streptomycetaceae can utilise plant derived carbon within the endosphere, and if the loss of these resources explains their reduced presence during senescence. Future studies should also investigate how Streptomycetaceae are able to colonise and survive within the endosphere of wheat. During developmental senescence, nitrogen is the main resource diverted to the developing grain [ 35 ]. It is possible that nitrogen, not carbon, is the resource provided by the host plant to support Streptomycetaceae growth. There is precedent for host-derived metabolites such as amino acids or gamma-aminobutyric acid (GABA) acting as a nitrogen source for root associated microbes [ 87 , 96 ]. Additionally, there is evidence that the increased use of nitrogen fertilizer (which correlates with greater total root exudation) was negatively correlated with the abundance of Streptomycetaceae in the rhizosphere [ 23 ]. In the future, 15 N-nitrogen DNA or RNA-SIP could be used to explore whether T. aestivum var. Paragon is able to support Streptomycetaceae within the endosphere via nitrogen containing, host-derived metabolites. Lastly, it must be noted that the identification of core enriched taxa within the roots of T. aestivum var. Paragon cannot be extrapolated to other varieties of wheat; one study even suggests T. aestivum var. Paragon is an outlier amongst UK elite spring bread wheat with a particularly distinct microbiome [ 24 ]. To gain a more detailed understanding of which microbial taxa are associated with the roots of spring bread wheat, more genotypes must be analysed."
} | 4,276 |
31016234 | PMC6474772 | pmc | 3,741 | {
"abstract": "We report a microfluidic system that generates sequential periodic multiflows only with a constant water head pressure.",
"introduction": "INTRODUCTION Control of sequential periodic flows in microfluidic chips has numerous applications, such as layer-by-layer assembly ( 1 , 2 ), sequential-injection analysis ( 3 , 4 ), soft robot actuation ( 5 – 7 ), and spatiotemporal analysis of cells ( 8 – 12 ) and organisms ( 13 – 15 ). These applications, however, have been mainly implemented by dynamic off-chip controllers that require many external connections and instructions for users. However, if microfluidic chips could operate in a predetermined and sophisticated manner without external instructions of the dynamic controllers, then the operation of the chips would be considerably simplified and the cost of using the dynamic controllers would be substantially reduced. Recent microfluidic chips mimicking the operation of electronic circuits aim to implement sequential periodic multiflows without external instructions but partially achieve these functions. Microfluidic logic circuits can control sequential periodic flows, but their operation is still instructed by computer-programmed off-chip controllers ( 16 – 23 ). In our previous studies, we implemented fluidic switching without external controllers, but the switching was limited to only two solution flows ( 24 – 26 ). Recently, a fluidic timer circuit presented a six-step sequential flow, but its initiation was manually triggered without periodic operation ( 27 ). Therefore, a different approach that can truly implement sequential periodic flows is strongly required for the practical applications without any user instructions and external dynamic controllers. In this work, we present a microfluidic device that produces various predetermined forms of sequential periodic flows of multiple solutions without using any dynamic controllers. The device consists of an astable actuator (AA) and a monostable actuator (MA) ( Fig. 1A ). The AA autonomously converts the constant pressure of two input solutions to two pulsatile out-of-phase outflows, and the MA changes the constant pressure of the input solution to a one-shot outflow by a triggered pulse pressure. The connection of the AA and MA produces a system where the MA is periodically triggered by one of the outflows of the AA. As a result, by connecting the AA with multiple MAs in parallel, various flows of multiple solutions can be implemented only with constant input pressures, thereby eliminating dynamic external controllers and user instructions ( Fig. 1 , B and C). These devices have control scalability for complex and sophisticated flows of multiple solutions. We demonstrate the utility of the platform by performing microfluidic operations including dynamic staining of cell nuclei with a four-step fluorescent gradient and playing a touchscreen piano with five metal rods. Fig. 1 Schematic of AA and MA. ( A ) Diagram of AA and MA connected in parallel. AA autonomously converts the constant pressure ( P ) of two input solutions in its two input channels (gray arrows) to two pulsatile out-of-phase outflows in its two output channels (L and R, black arrows). Through a trigger channel (black dashed arrow) that connects R and MA, AA triggers MA only at the moment when the flow in R stops (times t 1 and t 3 ). Simultaneously, MA produces one-shot outflow in its output channel (O, green arrow). A solution applied to an input channel moves to its corresponding output channel without meeting with other solutions. The outflow of L does not trigger MA. ( B ) Flows in each output channel of the system with AA and multiple MAs. The top panel shows that MAs are on the right side (MA R1 to MA R n ) and the left side (MA L1 to MA L m ) of AA. In the bottom panel, square pulses in each time axis denote flow timing of an input solution in the corresponding output channel, and the color of the pulse corresponds to input solution. Flow in R1 (L1) is triggered by the flow termination in R (L). Similarly, flow in R2 (L2) is triggered by the flow termination in R1 (L1). In this way, sequential triggering occurs. This cycle is repeated by AA. ( C ) Generation of various flows at a target channel (T). T is connected to the output channels of MAs and AA. In the cases i to iii , the flows in T are generated by different connection combinations between T and the output channels. The symbol (O) denotes the connection between T and the corresponding output channels, the symbol (X) denotes the disconnection between them, and the symbol (N) denotes no use of the corresponding output channel and MA.",
"discussion": "DISCUSSION On the basis of the analogy between microfluidic and electronic circuits, we have developed microfluidic devices that generate periodic sequential flows of multiple solutions without relying on any dynamic controllers. The devices converted the constant input of the water head pressure to the outputs of orchestrated, sophisticated flow and pressure pulses. As the functions of the devices were preset by the arrangements of AAs and MAs, users do not need to instruct the operation process to the devices through dynamic external controllers. Thus, user instructions and dynamic external controllers, which are the two typical requirements to generate sophisticated pulsed flow and are the bottlenecks for more widespread use of the devices, are truly eliminated. In this study, the number of cases for available outflow in a target channel (T) is substantially larger compared to our previous work ( 24 , 25 ). This enables our current study to implement the sophisticated flow-timing control of multiple solutions without any dynamic controllers. With two input solutions, our previous work using AA has only three cases for flow timing control in T (fig. S8). In contrast, in Fig. 1B , the number is 2 n +1 2 m +1 −1. Herein, the number of input solutions is n + m + 2, where n and m solutions are applied to the MAs on the right side and the left side of the AA, respectively, and two solutions are applied to the AA. For example, only with n = 2 and m = 1, the number of available cases is 31. With different connection combinations between AA and MAs, the system can generate complex periodic multiple parallel flows in a target channel (fig. S9). We note that, because each solution can have different fluidic parameters including chemical concentration, flow rate, and shear stress, the current approach allows us to implement highly sophisticated and various flow of multiple solutions. This is the feat that our previous work cannot achieve with its simple flow-timing control of two solutions. Digital microfluidic circuits using Boolean operation can control the flow timing of multiple solutions ( 16 – 18 ). However, the circuits need encoded serial commands including three pulsatile inputs of clock, control, and trigger pressures. Because of the programmed dynamic inputs, the use of dynamic external controllers is inevitable for the operation of the circuits. In addition, for the circuit to convert the serial inputs to periodic multiple outputs, the circuit requires numerous serially cascaded microfluidic components and suffers significant pressure drop, thus limiting its scalability. In comparison, the operation process of our system is much simpler and has higher scalability. Furthermore, a fluidic timer circuit used a constant pneumatic pressure input and implemented six-step sequential flows ( 27 ), but it did not show sophisticated periodic flows that we presented with the orchestration of AA and MAs. Because of its requirement of comparatively high-pressure input, the timer circuit cannot be periodically triggered by the AA that we developed. The timer circuit operates in the pressure range of 30 to 60 kPa. However, the AA operates in ~10-kPa pressure so that it cannot trigger the timer circuits. This limits the capability for periodic and sophisticated control of flow timing for the timer circuit. As we have shown in Figs. 4 to 6 , the various combinations of the AA and MA enables not only periodic flows but also sophisticated sequential flow patterns. Besides, the pressure level of 30 to 60 kPa is hard to achieve with a water head in a laboratory environment typically having a room height of <3 m. Consequently, the timer circuit used an external controller for its static pressure source. As a proof-of-principle demonstration, we have implemented the dynamic four-step staining of cell nuclei and the actuation of multiple rods to play the touchscreen piano. In the dynamic cell staining, we could not only regulate the sophisticated dynamic switching of fluorescent and blank solutions but also precisely control the interfacial position of the two solutions for the cell nucleus in each state. The interfacial position was maintained with less than 1-μm positional variation by the stable constant pressure of the water head input, thereby enabling dynamic cell staining with high stability. In comparison, even the externally driven microfluidic system with syringe pumps cannot achieve the dynamic cell staining with that high positional stability. For example, the positional variation caused by the pump is calculated to be ~22 μm because of the inherent flow rate fluctuations of the pump produced by the motion of the pump screw ( 30 ). The precise control of fluidic interface and dynamic flow switching in our study can be used in the studies of the spatial and temporal regulations of embryonic development ( 14 ), neuronal nematode behavior ( 15 ), biomolecular diffusion in cells ( 10 , 11 ), and cellular and bacterial chemotaxis ( 12 , 13 ). As another example, the system consisting of an AA and multiple MAs actuated multiple rods to play a touchscreen piano, demonstrating its possible usability in soft robot applications. One of the challenges for untethered operation of soft robots is the development of autonomous actuators that enable the body to deliver preprogrammed behaviors. In a previous study, an AA was used to implement soft analogs of the control and power hardware and implemented 2-bit motion ( 7 , 31 ). For more sophisticated multigait and multijoint locomotion, however, the orchestration of sequential and periodic motion is indispensable. The smart arrangement of AAs and MAs demonstrated in this research enables more sophisticated and complex locomotion. Currently, the size of our device is relatively larger than other microfluidic circuits ( 16 – 19 ). This is because the valves and membrane capacitors in our device are relatively large. However, scaling down the two components would be possible without significant change of device operational parameters including input pressure, flow-switching time, and flow rate. These parameters do not change if (i) mechanical capacitance of a membrane capacitor and (ii) opening threshold pressure of a valve are maintained. The mechanical capacitance of a membrane capacitor is ∝ w 6 / t 3 ( 31 ), where w and t are its width and thickness, respectively. In our device, w and t are 1.5 mm and 30 μm, respectively. If w decreases to 750 μm, then t needs to be decreased to 7.5 μm to maintain capacitance value. This thickness can be easily achieved by spin coating of polydimethylsiloxane (PDMS) in our device, thereby making it possible to maintain the capacitance value. If the width of a valve is decreased, then it increases the opening threshold pressure of the valve. However, the threshold pressure can be tuned by other valve elements including a seat, membrane, and surface of the valve. For example, we showed that the opening threshold pressure of a valve was reduced from 13 to 2 kPa by its valve-seat shape and surface coating ( 32 ). The threshold pressure is further decreased by reducing the thickness of the valve membrane. Hence, scaling down our device is feasible. In terms of operation, our proposed platforms have an inherent limitation because, once the chips are fabricated, their operational ranges and functions are more limited than those that exploit external controllers. For instance, the flow rate and switching period are varied in a limited range unless the chip is redesigned, thus restricting versatility. However, when specific operational ranges and functions are determined in advance and the chips are designed accordingly, they will be useful like application-specific integrated circuit chips in electronics. Hence, we believe that the systems proposed in this study will be useful for numerous applications that need sequential periodic flow control without any user instructions and dynamic external controllers."
} | 3,169 |
35242940 | PMC8873517 | pmc | 3,742 | {
"abstract": "Data in this article provides detailed information on the microbial dynamics and degradation performances in two full-scale anaerobic digesters operated in parallel for 476 days. One of them was kept at 35 °C for the whole experiment, while the other was submitted to sub-mesophilic (25 °C) conditions between days 123 and 373. Sludge samples were collected from both digesters at days 0, 80, 177, 218, 281, 353, and 462. The provided data include the operational conditions of the digesters and the characterization of the sludge samples at the physicochemical level, indicative of the digesters’ degradation performance. It also includes the characterization of the sludge samples at the multiomics level (16S rRNA gene sequencing, metagenomics, and metabolomics profiling), to decipher the changes in the microbial structure and molecular activity. The 16S rDNA gene sequencing, metagenomics, and metabolomics data were generated using an IonTorrent PGM sequencer, an Illumina NextSeq 500 sequencer, and LTQ-Orbitrap XL mass spectrometer respectively. The 16S rDNA gene raw data and the metagenomics data have been deposited in the BioProject PRJEB49115, in the ENA database ( https://www.ebi.ac.uk/ena/browser/view/PRJEB49115 ). The metabolomics data has been deposited at the Metabolomics Workbench, with study id ST002004 (DOI: 10.21228/M8JM6B ). The data can be used as a source for comparisons with other studies working with data from full-scale anaerobic digesters, especially for those investigating the effect of the temperature modification. The data is associated with the research article “ Metataxonomics, metagenomics, and metabolomics analysis of the influence of temperature modification in full-scale anaerobic digesters ” (Puig-Castellví et al [1] )."
} | 443 |
22969755 | PMC3427877 | pmc | 3,743 | {
"abstract": "Light quantity and quality are among the most important factors determining the physiology and stress response of zooxanthellate corals. Yet, almost nothing is known about the light field that Symbiodinium experiences within their coral host, and the basic optical properties of coral tissue are unknown. We used scalar irradiance microprobes to characterize vertical and lateral light gradients within and across tissues of several coral species. Our results revealed the presence of steep light gradients with photosynthetically available radiation decreasing by about one order of magnitude from the tissue surface to the coral skeleton. Surface scalar irradiance was consistently higher over polyp tissue than over coenosarc tissue in faviid corals. Coral bleaching increased surface scalar irradiance by ~150% (between 500 and 700 nm) relative to a healthy coral. Photosynthesis peaked around 300 μm within the tissue, which corresponded to a zone exhibiting strongest depletion of scalar irradiance. Deeper coral tissue layers, e.g., ~1000 μm into aboral polyp tissues, harbor optical microniches, where only ~10% of the incident irradiance remains. We conclude that the optical microenvironment of corals exhibits strong lateral and vertical gradients of scalar irradiance, which are affected by both tissue and skeleton optical properties. Our results imply that zooxanthellae populations inhabit a strongly heterogeneous light environment and highlight the presence of different optical microniches in corals; an important finding for understanding the photobiology, stress response, as well as the phenotypic and genotypic plasticity of coral symbionts.",
"introduction": "INTRODUCTION Coral reefs are among the most productive and diverse ecosystems on Earth and their evolutionary success can be largely attributed to the successful interaction between scleractinian corals and their associated microorganisms, most importantly their microalgal photosymbionts (zooxanthellae) belonging to the dinoflagellate genus Symbiodinium . The quantity of light is a key environmental parameter regulating the nature of this photosymbiosis ( Falkowski et al., 1990 ). Under optimal irradiance regimes, light stimulates symbiont photosynthesis, which provides organic carbon for the coral animal that in turn provides metabolic waste products supporting zooxanthellae photosynthesis ( Muscatine et al., 1981 ). Excess quantities of light, however, readily lead to photoinhibition and can damage the photosynthetic apparatus. Light in combination with elevated temperature can lead to the expulsion of the zooxanthellae (and/or pigment degradation) and the breakdown of the symbiosis ( Lesser, 1996 ; Jones et al., 1998 ; Warner et al., 1999 ). This breakdown, termed coral bleaching, has been intensively studied over the last decades, including a primary focus on the photobiology of zooxanthellae ( Glynn, 1996 ; Brown, 1997 ; Hoegh-Guldberg, 1999 ). Despite such efforts, it is surprising that virtually nothing is known about the actual light regime surrounding the zooxanthellae in hospite , i.e., within the coral tissue, albeit the light microenvironment is a central control factor of the photo- and stress physiology of zooxanthellae and their coral hosts. The optical environment within the host tissue is likely to vary substantially in relation to the ambient macro-environment. First, direct micro-scale measurements of photon scalar irradiance (i.e., the integral quantum flux incident from all directions about a given point) on the coral tissue surface revealed scalar irradiance values reaching up to 200% of the incident downwelling photon irradiance ( Kühl et al., 1995 ). Such enhancement is currently thought to mainly result from multiple scattering of photons in the coral skeleton below the tissue ( Enriquez et al., 2005 ). The aragonite skeleton scatters light isotropically so that photons interacting with the skeleton are diffusely backscattered into the tissue ( Enriquez et al., 2005 ). Diffuse scattering increases the path length of photons per vertical distance traversed, i.e., it enhances the average residence time of photons at a given depth horizon and can thereby lead to local enhancement of scalar irradiance ( Kühl and Jørgensen, 1994 ; Enriquez et al., 2005 ). It is currently assumed that the light field within the coral tissue is diffuse and uniformly enhanced over the incident irradiance ( Enriquez et al., 2005 ; Teran et al., 2010 ). However, the optical environment within the coral may be more complex as tissue–light interactions and the optical properties of coral tissue remain largely unexplored. Photons interacting with tissue can have three different fates: (i) simple unimpeded transmission; (ii) absorption followed by either red-shifted re-emission (as fluorescence or phosphorescence), heat dissipation or dissipation via photochemical reactions such as photosynthesis or radical formation; (iii) scattering and diffraction leading to a redirection of photons out of their original path. The occurrence of these events is determined by a complex interplay between the nature and direction of incident light and the optical properties of the given tissue ( Wilson and Jacques, 1990 ). The optical properties of living tissue are best studied for human skin, but also well-described for terrestrial plants ( Anderson and Parrish, 1981 ; Vogelmann, 1993 ) as well as aquatic sediments and biofilms ( Kühl and Jørgensen, 1994 ). The development and use of fiber-optic microprobes ( Vogelmann et al., 1991 ; Kühl, 2005 ) has facilitated experimental investigation of light microenvironments and optical properties within such systems ( Vogelmann and Björn, 1984 ; Vogelmann, 1993 ; Vogelmann et al., 1996 ). Besides a few preliminary measurements ( Kühl et al., 1995 ; Kaniewska et al., 2011 ), comparable studies on coral tissue are lacking. Kaniewska et al. (2011) mainly focused on comparing larger scale heterogeneity of light fields in different corals and presented only few spot measurements of scalar irradiance at a fixed depth in the coral tissue and no detailed vertical or lateral profiling was done. The presence and nature of micro-scale heterogeneity in coral light fields, both laterally over different coral tissue types and vertically within a given tissue type, have thus not been resolved. Here we used scalar irradiance microprobes ( Vogelmann and Björn, 1984 ; Lassen et al., 1992 ) to characterize the spectral light field and light penetration in coral tissues. The specific aims were (1) to directly measure light penetration in tissue of corals belonging to the family Faviidae, (2) investigate the effect of tissue type (coenosarc and polyp tissue) and loss of pigmentation (bleaching) on light microenvironments for a variety of abundant coral species, and (3) investigate how gradients of light and photosynthesis within coral tissue align with each other. Our results provide the first insight into the basic optical properties of coral tissue and describe the in hospite optical microenvironment of corals from a zooxanthellar perspective.",
"discussion": "DISCUSSION In this study, we used fiber-optic microprobes to obtain the first detailed measurements of vertical and lateral light gradients within and across coral tissues in several species. While the chemical microenvironment of corals has been explored in several studies since microsensors were introduced to coral research ( Kühl et al., 1995 ), only a few examples of scalar irradiance measurements in corals have been published and these have been hampered by difficulties in entering and/or precise positioning in the tissue ( Kühl et al., 1995 ; Kaniewska et al., 2011 ). Hitherto, tissue effects on coral light fields have largely been ignored in coral optics studies that have mostly focused on the role of diffuse backscatter from the coral skeleton and from the coral tissue into the surrounding seawater ( Enriquez et al., 2005 ; Teran et al., 2010 ). Combining micro-incision with scalar irradiance profiling, we have now unequivocally demonstrated the presence of light gradients in corals and present the first evidence that tissue optics is an important factor to consider in coral photobiology. Direct in hospite micro-scale light measurements in corals differ from predictions in previous modeling studies, which have calculated that internal irradiance is homogenously enhanced compared to the external environment based on known downwelling irradiance regimes ( Enriquez et al., 2005 ; Teran et al., 2010 ). We show that a clear spatial stratification exists within coral tissue, where scalar irradiance in the upper coral tissue layers (0–100 μm) can reach up to 200% of incident downwelling irradiance, whilst lower cell layers are subject to more light-limiting conditions ( Figures 6B and 8 ). Our results thus suggest that the light microhabitat of corals is not only determined by the properties of the skeleton, but also largely by the characteristics of the tissue. Until now, coral tissue has simply been treated as a thin layer of light absorbing particles (i.e., zooxanthellae ) on top of the light-diffusing skeleton and overlain by seawater ( Teran et al., 2010 ). We know from other systems, however, such as plant leafs or animal skin, that the properties of the tissue itself can significantly enhance light fields at the tissue interface due to scattering and internal reflection ( Anderson and Parrish, 1981 ; Vogelmann et al., 1996 ; Spilling et al., 2010 ). The peak of scalar irradiance observed here in the upper cell layers suggests that substantial scattering and photon-trapping must occur at the tissue–water interface, potentially resulting from a mismatch in the refractive index of coral tissue and water ( Kühl and Jørgensen, 1994 ). Nevertheless, our results also confirm that the earlier-reported diffuse scattering component of the skeleton is functional in hospite ( Enriquez et al., 2005 ), as seen by a decrease in light attenuation toward the skeleton surface ( Figure 6B ). The occurrence and significance of skeleton backscatter is further exemplified by the continuous enhancement of NIR throughout the coenosarc tissue, where no pigments are present that absorb over these wavelengths ( Figure 7A ). We hypothesized that with increasing incident irradiance, within-tissue PAR would increase exponentially as more light would be transmitted through the tissue and interact with the skeleton, thereby increasing the relative importance of backscattered light from the skeleton at the tissue–skeleton interface. However, we found a constant linear relationship between PAR at the tissue–skeleton interface and incident PAR ( Figure 8 ). Photons are thus efficiently absorbed before they get scattered by the skeleton, indicating that the coral tissue itself also contributes to the high efficiency of light absorption found in corals ( Stambler and Dubinsky, 2005 ). Scalar irradiance at the tissue surface increased by 150% in a bleached coral relative to the surface scalar irradiance in a healthy coral ( Figure 5 ). This was less than expected according to coral skeleton scattering theory ( Teran et al., 2010 ) and again suggests that other light redistributing mechanisms occur within the tissue. However, the nature of light gradients and thus the relative importance of tissue vs skeleton optics will be variable. Coral tissue varies in thickness, metabolite composition, symbiont and host pigment distribution, and abundance, all of which modulate coral tissue optics. Additionally, the role of skeleton optics is variable due to differences in morphology and density. For instance, thick corallite walls guide more light into the coral interior, whilst more dense structures facilitate diffuse backscattering ( Highsmith, 1981 ). Therefore, the optical microenvironment within corals is the result of a complex interplay between skeleton and tissue optical properties, which clearly deserves further investigations. Coral tissue surface scalar irradiance differed on a spatial scale between coral species and tissue types, despite identical incident irradiance regimes ( Figures 3A , B ). Since we excluded the potential for any interference with colony and/or macro-scale light-regulating factors such as colony morphology and orientation ( Anthony et al., 2005 ), we conclude that the observed differences are caused by micro-scale optical properties of coral tissue and skeleton. In faviid corals, host pigments are often locally concentrated toward the polyp mouth (e.g., Salih et al., 2000 ; Oswald et al., 2007 ; but see spectral signatures in Figure 2 ). The enhanced tissue surface scalar irradiance of polyp over coenosarc tissue may be explained partly by the presence of such pigments, which effectively reflect, fluoresce, and scatter light ( Schlichter et al., 1988 ; Salih et al., 2000 ). Previous studies have shown the presence of tissue type-related spatial heterogeneity in photosynthesis ( Ralph et al., 2002 ; Hill et al., 2004 ; Al-Horani et al., 2005 ). For instance, in the coral Galaxea fascicularis O 2 production was shown to be about 10 times higher over polyp than over coenosarc tissue ( Al-Horani et al., 2005 ). Such differences may likely be related to distinct light microenvironments in the coral tissue. For productivity comparisons between species under identical incident irradiance regimes it appears crucial to consider the ability of corals to modulate their own light regime by skeleton structure and tissue organization/movement ( Figures 3A , B ). Tissue and skeleton optical properties have a strong effect on the local light environment that may partly explain observed species- and tissue type-related differences in photosynthesis. Our results show that Symbiodinium populations, inhabiting oral and aboral coral tissue layers of faviid corals, experience steep light gradients with scalar irradiance reaching down to 10% of the surface irradiance in deeper tissue layers ( Figure 6B ); the vertical attenuation of light observed in the coral tissue over a few hundred microns is comparable to the reduction in irradiance that occurs between surface waters and >25 m depth in oceanic waters ( Kirk, 1994 ). Our findings thus call for a revision of the current view on the optical environment surrounding zooxanthellae. On the scale of a single colony, irradiance gradients between light exposed and shaded tissue can lead to both a distinct distribution of Symbiodinium clades and/or differential photoacclimation of the latter ( Rowan et al., 1997 ; Toller et al., 2001 ; Ulstrup et al., 2006 ; Sampayo et al., 2007 ). The potential for such mechanisms occurring within tissue on a vertical micro-scale, for instance between oral and aboral tissue layers, is not known for corals, but is well-studied for terrestrial leaves ( Schreiber et al., 1996 ). It is, e.g., known that shade-adapted chloroplasts exist in the lower tissue layers of sun-adapted leaves and chloroplasts deep within leaves are photoacclimated to local irradiance regimes ( Terashima, 1989 ). We found that maximum rates of photosynthesis occurred in lower parts of coral tissue and not at the surface where scalar irradiance was at its maximum ( Figure 7B ). In fact, the spatial relationship between photosynthesis and light observed here is similar to results obtained from spinach leaves where photosynthetic O 2 production showed a peak deep within the leaf, whilst irradiance maxima were obtained at the top part of the leaf ( Nishio et al., 1993 ). These findings underscore the potential for photoacclimation to different light microclimates within coral tissue. Clades and sub-clades of Symbiodinium exhibit a range of light-harvesting strategies ( Reynolds et al., 2008 ; Ragni et al., 2010 ; Kraemer et al., 2012 ) and it will be interesting in the future to ascertain the location of various clades in coral species that harbor more than one clade, relative to the actual light field characteristics. The optical environment is a primary factor controlling the activity and distribution of phototrophic organisms and the presence of intratissue light gradients must have an effect on the ecophysiology of zooxanthellae in yet unknown ways. Our results also have implications for the understanding of coral bleaching patterns. It has been observed that thick-tissued corals survive stress events better than thin-tissued ones ( Loya et al., 2001 ). It has also been hypothesized that thick coral tissue could provide sheltered light environments for resident zooxanthellae, thereby increasing stress resilience and the survival of thick-tissued corals ( Hoegh-Guldberg, 1999 ). We show here that thick-tissued corals do indeed harbor such sheltered optical microniches ( Figures 6A , B ). This photoprotection is substantial as even under conditions of stressful excess radiation (incident PAR irradiance levels of ~2000 μmol photons m -2 s -1 ) thick coral tissue can harbor low light niches for photosynthesis experiencing about 1/10 of incident irradiance (200 μmol photons m -2 s -1 ; Figure 8 ). Yet another option is that habitat heterogeneity is favored in thick-tissued corals, which in turn leads to a larger symbiont pool with diverse phenotypic or genotypic characteristics and stress resilience ( Rowan et al., 1997 ). We found habitat heterogeneity both in the optical and the chemical environment ( Figures 7A , B ). The coral skeleton represents a diffusion barrier for chemical species, which will lead to a relative build-up of gases in lower tissue layers toward the tissue–skeleton interface as shown here by an increasing O 2 concentration up to ~400% air saturation ( Figure 7B ). Thus, tissue thickness will favor microenvironmental heterogeneity. Whether this then favors a greater pool of symbiont populations (or subpopulations) and if this translates to increased stress resilience remains to be investigated. In conclusion, we show here the first evidence for the presence of strong light gradients within the tissue of symbiotic corals. The optical properties of coral tissue have an important role in controlling microenvironmental light fields within corals. Our results imply that zooxanthellae within one single polyp can be subject to different light microenvironments with irradiance levels spanning over one order of magnitude. These results call for a revision of our current understanding of the interaction between light and corals and provide the very basis for future investigations on microenvironmental optical controls of coral photo- and stress physiology."
} | 4,669 |
26201334 | PMC4511525 | pmc | 3,744 | {
"abstract": "Background Bacteria have developed a repertoire of signalling mechanisms that enable adaptive responses to fluctuating environmental conditions. The formation of biofilm, for example, allows persisting in times of external stresses, e.g. induced by antibiotics or a lack of nutrients. Adhesive curli fibers, the major extracellular matrix components in Escherichia coli biofilms, exhibit heterogeneous expression in isogenic cells exposed to identical external conditions. The dynamical mechanisms underlying this heterogeneity remain poorly understood. In this work, we elucidate the potential role of post-translational bistability as a source for this heterogeneity. Results We introduce a structured modelling workflow combining logical network topology analysis with time-continuous deterministic and stochastic modelling. The aim is to evaluate the topological structure of the underlying signalling network and to identify and analyse model parameterisations that satisfy observations from a set of genetic knockout experiments. Our work supports the hypothesis that the phenotypic heterogeneity of curli expression in biofilm cells is induced by bistable regulation at the post-translational level. Stochastic modelling suggests diverse noise-induced switching behaviours between the stable states, depending on the expression levels of the c-di-GMP-producing (diguanylate cyclases, DGCs) and -degrading (phosphodiesterases, PDEs) enzymes and reveals the quantitative difference in stable c-di-GMP levels between distinct phenotypes. The most dominant type of behaviour is characterised by a fast switching from curli-off to curli-on with a slow switching in the reverse direction and the second most dominant type is a long-term differentiation into curli-on or curli-off cells. This behaviour may implicate an intrinsic feature of the system allowing for a fast adaptive response (curli-on) versus a slow transition to the curli-off state, in line with experimental observations. Conclusion The combination of logical and continuous modelling enables a thorough analysis of different determinants of bistable regulation, i.e. network topology and biochemical kinetics, and allows for an incorporation of experimental data from heterogeneous sources. Our approach yields a mechanistic explanation for the phenotypic heterogeneity of curli fiber expression. Furthermore, the presented work provides a detailed insight into the interactions between the multiple DGC- and PDE-type enzymes and the role of c-di-GMP in dynamical regulation of cellular decisions. Electronic supplementary material The online version of this article (doi:10.1186/s12918-015-0183-x) contains supplementary material, which is available to authorized users.",
"conclusion": "Conclusion In this study we have presented a framework for the analysis of multistable signalling networks with an application to the stationary phase-induced curli regulation system of E. coli . Using a hybrid sequential logical-continuous approach we first verified the potential of the curli regulation system for inducing bistability by incorporating genetic knockout experiments as discrete model constraints. Based on the validated network topology, we derived a reaction-rate model of the system and identified several parameter sets that are capable of inducing bistable dynamics. Notably, most identified parameter sets are located in a biologically meaningful region. In addition, we analysed the dependence of the probability of the stable curli-on and curli-off states on system parameters. We showed that certain parameter variations, e.g. the amount and activities of the DGCs and PDEs, give rise to different switching dynamics between curli-on and curli-off states (i.e. frequent switching or stable differentiation). The present study introduces the first mathematical model of bistable regulation of curli fibers in E. coli based on post-translational interactions of multiple DGC and PDE proteins with the signalling molecule c-di-GMP as the central player. Our results deliver new potential targets for further experimental investigations, which may help to test the modelling hypotheses and to successively deepen the understanding of biofilm control in bacteria.",
"discussion": "Discussion Multistability is a recurring mechanism in biological systems, enabling cellular heterogeneity and differentiation at the phenotypic level [ 6 ]. In order to understand the principles of its regulation, a thorough combination of wet-lab experimentation and mathematical modelling is required. Many computational studies of bistable systems focused either on the network topology [ 17 , 18 ] or on reaction kinetics [ 19 , 21 ]. Here, we combined these two approaches. This enabled us to deal with heterogeneous data sources, such as expression levels in various knockout strains and kinetic rate parameters from in-vitro studies. While multistability in signalling networks may be attributed to transcriptional and translational regulation as well as post-translational dynamics, in this study, we focused on molecular interactions at the post-translational level. In our modelling pipeline we utilised three modelling frameworks in sequence to fully exploit the available data and investigate different aspects of the system on appropriate levels of abstraction. Insights from each modelling- and analysis step were passed on in the pipeline and constituted, together with additional biological information accessible within the next higher detail-resolving formalism, the foundation for more elaborate models. In a first step we used a constraint-based logical modelling approach enabling us to extract essential model characteristics needed to reproduce all available experimental observations. More precisely, we identified a set of logical models consistent with the suggested network topology that generates bistability in the wild type strain and fulfils all constraints given by the genetic data. Furthermore, the logical modelling step revealed that the bistable distribution of the molecular pool of active YdaM molecules (not bound by YciR) is the key determinant of phenotypic heterogeneity of curli expression observed in single-cell experiments. This insight allowed for a model reduction (compare Fig 1 b and d ). The implicit assumption made here was that MlrA and YdaM do not compete for binding to YciR, making MlrA a downstream component of the core regulatory network. Structurally, this is justified by the possibility of independent and simultaneous binding of YdaM and MlrA to YciR due to its large interaction domain (EAL-domain) [ 43 ] and different binding modes of YdaM and MlrA on that domain. In addition, genetic data from Lindenberg et al. [ 7 ] yielded functional arguments against competition between YdaM and MlrA. Within our pipeline, the main result from the logical analysis passed on to the higher resolution modelling is the validation of the underlying network structure. We verified that the reduced topology shown in Fig 1 d can carry a dynamical model in agreement with all available data. Beyond purely topological insights, a closer look at the regulatory mechanisms encoded by the logical functions can also be exploited to derive parameter constraints for the continuous reaction-rate model. Analysing the impact of different regulators on a target in different system states may yield information on relations between production and decay rates. Theoretical results in this direction have been shown for specific classes of ODEs, see e.g. [ 38 ], but they are not yet widely applicable. We suggested how to exploit this idea in a well-supported case for the curli regulation network and identified its potential for the sampling of kinetic parameters. Still, for a systematic application of this strategy the theoretical groundwork needs to be further extended. An even closer intertwining of the formalisms would also have been possible. Automatic conversion procedures could have been used to lift a more abstract model into a more resolved formalism, e.g., to derive a generic ODE system from a given logical model as suggested in [ 44 ]. However, this approach yields models that cannot incorporate additional mechanistic knowledge (e.g. reaction parameters) in contrast to our approach, where we aimed at constructing a biologically realistic model by utilising all available kinetic information. As a consequence, the continuous model in our approach does not necessarily mimic the dynamics of the coarser models in all system states. It rather provides a complementary view based on the much higher detail resolution that allows to re-evaluate and broaden the results of the logical analysis. Thus, a crucial advantage of our approach is the ability to incorporate experimental data from heterogeneous sources, carrying qualitative, semi-quantitative or fully quantitative information. A further possibility would have been to take smaller steps on the modelling scale and add hybrid formalisms to the pipeline, e.g. discrete-continuous systems modelled as hybrid automata or Petri nets based on the boolean models that we derived in the first pipeline step [ 41 , 45 ]. However, we found that in our context this does not provide any clear advantages, since the computational cost of parameter- and dynamic analysis that go beyond qualitative aspects is already close to that of an ODE model while still incorporating many abstractions of the underlying mechanisms [ 46 , 47 ]. The ODE model, derived in the second step of the pipeline, allowed us to identify regions in the multidimensional kinetic parameter space inducing bistable behaviour. Using a Monte Carlo sampling procedure, we identified a sufficiently large amount of parameter sets inducing bistability in the wild type strain and monostable behaviour in particular knockout strains and strains with point mutations (see “ Methods ”). Note that sampling procedures are frequently used for identifying parameter regions inducing bistability [ 23 , 48 ]. We post-hoc compared the identified parameter distributions with experimentally measured parameters in related systems. Thus, we could show that the network topology validated in the logical modelling step is capable of generating bistable dynamics within a biologically meaningful range of kinetic parameters, as shown in Fig. 3 . By conducting a bifurcation of key model parameters we exemplarily analysed the ranges where these parameters induce bistable regimes. This is of particular relevance since it was previously shown that during entry into the stationary phase of the growth cycle the σ S -controlled DGC enzyme YegE is induced, while the expression of the PDE enzyme YhjH (controlled by the flagellar master regulators FlhDC and FliA) is turned down. The remaining levels of the YhjH protein are diluted and degraded within the following cell divisions [ 8 ]. Our results suggest that by fine-tuning the levels of YegE and YhjH the cells maintain parametric configurations, which generate bistable behaviour. Stochastic simulations indicate that a further increase of the protein levels of YegE during the stationary phase might reduce the probability of the curli-off state and increase the probability of the curli-on state (see Fig. 4 a , inlay plots). This is in line with experimental measurements of the distribution of curli cells in bacterial colonies. Serra et al. [ 11 ] showed a spatial variation of this distribution, where the relative amount of curli-on cells increases with an increasing vertical distance to the nutritional source at the bottom of the colony. In addition, Grantcharova et al. [ 10 ] observed a variation of this distribution within different stages of the stationary phase of the bacterial growth cycle. Furthermore, the bifurcation results suggested a narrow parameter region where the system is bistable. Changing these parameters in a way that eliminates the catalytic activity of the c-di-GMP-regulating enzymes YegE and YhjH, or decreases the binding affinity of c-di-GMP to YciR yielded monostable systems which could explain experimental results of corresponding single-gene knockout strains lacking yegE and yhjH or carrying a y c i R AAL point mutation (see Fig. 4 ). Finally, we addressed a possible mechanism inducing spontaneous switching between different stable states. Stochastic simulations of the system indicated that depending on kinetic parameters, qualitatively different types of noise-induced switching dynamics may be observed, where most of the identified parameter sets indicated a higher probability of switching from the curli-off state to the curli-on state than vice versa (72 % ). In line with these results, small chains of curli cells were observed within macrocolonies of E. coli , suggesting that once the cells switch to curli expression, they maintain this state and inherit it to successive cell generations [ 11 ]. We identified a significant subset of parameters (12 % ) exhibiting a low switching probability in either direction (lower left corner in Fig. 5 a ). This suggests that certain parameter configurations might enable a long-term differentiation of the cells into curli producers and non-producers, retaining the status-quo until system parameters change again e.g. due to a variation of environmental conditions. A possible regulatory mechanism for controlling the switching behaviour might be given by the level of separation of steady states within distinct phenotypes. In support of this, we observed that the deviation of stable c-di-GMP levels between curli-off and curli-on phenotypes was much more pronounced in parameter sets that favour a differentiation (rare switchers) than those parameter sets that allow frequent switching between the two phenotypes. A greater separation of these levels may induce a larger (energetic) barrier and robustness against stochastic fluctuations, which could be a potential mechanism controlling the phenotypic plasticity of E. coli . A comparison of the steady state levels in Fig. 7 with experimentally measured numbers of system components, in particular c-di-GMP, in curli-on and curli-off cells may shed further light on this topic. Note that in this study we only focused on the noise resulting from post-translational interactions. The implicit time-scale separation assumption can be justified by the longer time scales of fluctuations in protein copy numbers as compared to the longest simulations in this study (≤100 s). In our simulations, we observed that switching from curli-off to curli-on states was generally fast (≤100 s, see Fig. 5 b ) and may thus not be affected by slower noise processes, that are related to protein copy number variations. However, switching in the opposite direction (curli-on to curli-off) may occur at a slower time-scale (≥100s, see Fig. 5 b ) in some parameter sets and may thus be influenced by the noise resulting from transcriptional and translational events. Further kinetic data may enable a refinement of this model by explicitly including gene expression noise."
} | 3,777 |
34847449 | PMC8637139 | pmc | 3,746 | {
"abstract": "Graphical abstract",
"conclusion": "4 Conclusions In this paper, an efficient method of ultrasonic-assisted chemical etching for preparing superhydrophobic surface on stainless steel was proposed. The mechanism of ultrasound promoting the etching process was studied. It is the first time to demonstrate that the ultrasonic cavitation effect enhanced the etching process from both physical and chemical ways. The local high-temperature and high-pressure produced by cavitation stimulated the etching solution dissociating more H + to positively promote the progress of etching reaction. The generated strong micro-jet and other accompanying phenomena removed the residual reactants on the surface to ensure the etching solution and the sample surface fully contact. The increase of ultrasonic power led to the enhancement of ultrasonic cavitation effect, which not only enhanced the surface superhydrophobicity, but also improved the uniformity of surface wettability. Further research showed that the time of ultrasonic action significantly affected the size and morphology of the micron structures to influence the WCAs. The results indicated that the optimal WCA (163.21°) can be obtained within only etching 7 min. In addition, the as-prepared sample displayed high pressure resistance (up to 2.68 kPa), separation efficiency (all up to 96.8%) and oil flux (up to 6.68 L m −2 s −1 ), indicating excellent oil/water separation performance.",
"introduction": "1 Introduction Superhydrophobic (SH, the static WCA greater than 150° and the sliding angle less than 10°), as one of the main surface properties of solid materials, has attracted extensive attention in the field of biology [1] , chemistry [2] , physics and materials science [3] . Inspired by the excellent hydrophobic properties of natural elements, such as lotus leaves [4] , desert beetles [5] , and the planthopper insect wing [6] etc, artificial SH surfaces have been widely explored in the past couple of decades. At present, there are various methods for preparation of SH surfaces, including chemical etching [7] , [8] , sol–gel [9] , spinning method [10] , [11] , self-assembly method [12] , electrochemical deposition [13] , [14] , template-based method [15] , [16] and so on [17] , [18] , [19] . All the above methods indicate that both abundant surface micro-nano structures and low surface energy are vital for the preparation of SH surface. In recent years, the materials used as SH substrates are becoming more and more diversified, among which metal materials have been particularly concerned on account of their excellent mechanical properties and wild application fields [20] , [21] . Among the common metal materials, stainless steel is extensively employed in the construction industry [22] , aerospace field [23] , medical equipment [24] and offshore operation due to its favorable corrosion resistance and excellent mechanical properties [25] , [26] . Just because of this good corrosion resisting property, it is fairly difficult to form abundant microcosmic morphology on the stainless steel surfaces. Therefore, many different attempts have been made to prepare SH surfaces on stainless steel over the last few decades. Vidal et al. [9] exploited sol–gel method, taking tetraethoxysilane (TEOS) and methyltriethoxysilane (MTES) as precursors to obtain the SH surface with an average WCA of 149° on the stainless steel surface. Wang et al. [27] proposed a scanning electrodeposition method to form SH nickel layer onto stainless steel surface, in which two complex solutions and tedious deposition steps were employed to obtain SH surface with WCA of 152.3°. Wu et al. [28] took advantage of a femtosecond laser for microstructure processing, after which modified the surface with silane reagent (Trichloro (1H, 1H, 2H, 2H-perfluorooctyl) silane to get a WCA of 166.3° on stainless steel surface. Although high WCAs could be obtained by this method, it was actually difficult to achieve large-scale production on account of its expensive equipment and harsh processing conditions. In general, the majority of the above methods to achieve SH stainless steel surfaces either have the disadvantage of expensive equipment or time-consuming, which hinders mass production and industrial application. Although the corrosion resisting property of stainless steel makes it challenging to accomplish hierarchical micro-nano structure, many attempts [29] , [30] still focus on chemical etching method for potential low-cost mass production of SH stainless steel surfaces in recent years, due to its advantages of facile, high-efficiency and low-cost. Zhang et al. [7] etched the stainless steel surface with FeCl 3 ethanol solution as the etchant for 60 min and then modified with nutmeg acid ethanol to prepare the SH stainless steel surface with WCA of 151.6°. Zhang et al. [31] proposed a simple wet chemical etching method to prepare the SH surface, which was chemically etched with HCl solution for 20 min and modified with perfluorinated reagent to obtain the SH surface with WCA of 152°. Liu et al. [32] etched stainless steel surfaces with a mixed solution composed of FeCl 3 , HCl and H 2 O 2 for 20 min and then modified with DTS (CH 3 (CH 2 ) 11 Si(OCH 3 ) 3 ) and Toluene (C 6 H 5 CH 3 ) mixed solution to obtain the micro-nano hierarchical structure with a WCA of 158.3 ± 2.8°. Our group [33] proposed a novel chemical etching method which involving SiO 2 -assisted HF etching processing for 20 min. In this method, intermediate species (H 2 SiF 6 ) were formed to greatly enhance the corrosion process of stainless steel so as to obtain abundant micro-nano hierarchical structures on the surfaces. Even though the surfaces employed by this method were modified with Stearic acid whose price is 2–13 times lower than the fluorine modifier, the SH 304 stainless steel surfaces still achieved a static WCA of 162.45°. Therefore, this method has shown great advantages in not only corrosion efficiency, modifier cost but also hydrophobic properties. Although the etching time of around 20 min is relatively short for the preparation of SH stainless steel surfaces by chemical etching method compared with that in the current literatures, it is still a significant obstacle to the further improvement of production efficiency for mass production. In this paper, we proposed an innovative strategy of ultrasonic-assisted chemical etching to further improve the above etching method. It is found that the WCA of 163.21° can be accomplished with the etching time of only 7 min, which indicates that ultrasonic field significantly promotes the etching process. The relationship between the WCAs and cavitation energy or non-cavitation energy was analyzed, which revealed the promotion mechanism of ultrasonic on the etching process. The results indicated that cavitation energy played a major role in the change of WCA and their variation tendency was approximately uniform. By observing the energy distribution cloud images of the cross sections, it is found that the uniformity of cavitation energy also had an obvious influence on the wettability of the stainless steel surfaces. Moreover, the application of the samples in oil–water separation was also studied, including permeation flux and separation efficiency. The results showed that the proposed simple and time-saving method was promising for the mass production of superhydrophobic stainless steel and its industrial application.",
"discussion": "3 Results and discussions 3.1 Mechanism of ultrasonic field on etching process Fig. 4 shows the relationship between the ultrasonic energy and the WCAs of 304 SS surface under different input powers (120–300 W) at room temperature. The ultrasonic energy in the reactor was separated into cavitation energy and non-cavitation energy as mentioned part 2.2 and attachment A. The results illustrated that the variation tendency of WCAs coincided with that of cavitation energy, which gradually increased with the enhancement of cavitation energy. The increase of cavitation energy meant more cavitation-bubble-collapsions occur resulting in transient high temperature (up to 5000 K) [41] ,ultra-high pressure(about 5.05 × 10 8 Pa) [42] and super strong micro-jet (the rate up to 400 Km/h) [43] , which can significantly promote the etching process in the following ways: (1) Chemical facilitation, the high temperature and pressure produced by the bubble collapsion can generate more radical H + by dissociating the HF solution. The increase of H + not only accelerated the etching reaction of 304SS as shown in Eq (1)-(2), but also promoted the chemical reaction with SiO 2 as shown in Eq (3). The formation of Hexafluorosilicic acid (H 2 SiF 6 ) can further facilitate the corrosion of Fe element [33] . 2Fe + 6H + → 2Fe +3 + 3H 2 (1) 2Cr + 6H + → 2Cr +3 + 3H 2 (2) SiO 2 + 6H + + 6F - → SiF 6 2- +2H 3 O + (3) (2) Physics promotions, smaller cavitation bubbles generated at higher pressure could penetrate into the cavities and the microcracks of the solid phase and destroyed them [44] . Then, strong micro-jet generated by the bubble collapsion can lead to severe local micro turbulence and enhance mass transfer, which resulted in the exfoliation of unstable particles. Fig. 4 The relation between ultrasonic energy and water contact angle at different input power. On the contrary, the tendency of non-cavitation energy was quite different, which rose first and then decreased, with the maximum non-cavitation of 56.24 kPa·kHz reached at input power of 180 W, as shown in Fig. 4 . The decrease of non-cavitation energy during the input power above 180 W mainly resulted from the growing number of cavitation bubbles hindering the propagation of ultrasonic oscillation. More specifically, the WCAs with ultrasonic power of 120 W and 300 W were notably different but the non-cavitation energy of these two cases were almost the same. It was implied that non-cavitation energy has no obvious correlation with the etching process. In summary, the ultrasonic cavitation effect dominated the enhancement of corrosion process, however the oscillation induced by ultrasound not apparently effected the corrosion. To reveal the influence of ultrasonic on the surface morphology, SEM micrograph patterns of the samples with different input power (120–300 W) are presented in Fig. 5 . Compared with the no-ultrasonic-assisted sample as shown in Fig. 5 (a), the ultrasonic-assisted samples showed obviously more abundant lamellar micron-scale structures and “mountain-like” microstructure clusters on both of which the “coral-reef-like” nano-scale structures uniformly covered as shown in Fig. 5 (b)-(e). After surface modification these hierarchical micro-nano structures can easily result in tiny air bags to prevent water penetration, which was governed by Cassie model as shown in Fig. 6 (b). However, despite the surface of samples without ultrasound was slightly etched resulting in some lamellar micron structures, the depth of these structures were obviously shallower and the distribution was more irregular, which caused the contact situation with water accord with Wenzel model as shown in Fig. 6 (a). The results indicated that the ultrasonic field significantly stimulated the development of the microstructure on 304 SS surface during the etching process. Fig. 5 SEM images of SH surfaces prepared at different ultrasonic powers: (a) without ultrasonic, (b) 40%, (c) 60%, (d) 80%, (e) 100%. Fig. 6 Schematic illustrations of the hydrophobicitic state of the samples. (a) after HF etching and modification without ultrasonic, (b) after ultrasonic-assisted HF etching and modification, (c) after 7 min of ultrasonic-assisted HF etching and modification. In addition, the SEM images also illustrated that with the increase of input power, the micron-scale structures deepened obviously, and their distribution was more regular. The lamellar structure interconnected with each other to form “mountain-like” microstructure clusters as shown in Fig. 5 (d)-(e). This was mainly attributed to that the enhancement of the cavitation energy with the increase of input power not only intensified the reaction between the reaction solution and the 304SS substrate surface, but also promoted the strong micro-jet to remove the detached particles from the micro-cracks efficiently. Consequently, all the “mountain-like” microstructure clusters, lamellar microstructures as well as the “coral-reef-like” nano-scale structures resulted in a very high WCA of more than 162°, which was governed by Cassie -Baxter model as shown in Fig. 6 (c). It is surprising to notice that the error magnitude of WCAs decreases obviously with the increase of ultrasonic power as shown in Fig. 4 . This phenomenon indicated that with the increase of input power, the enhancement of ultrasonic cavitation effect significantly improved the uniformity of the formed microstructure. In order to figure out the mechanism of this phenomenon, the uniformity of ultrasonic cavitation energy distribution in the reactor under different input powers were measured as shown in Fig. 7 , in which red represented the high energy area and blue signified the low energy area. In this paper, MATLAB was employed for the signal processing and visualization. It is worth noting that we had carried out logarithmic (lg) operation on the energy values in order to narrow the gap between the energy values which resulted in observing the changes of the energy of the sound field more clearly. All contours showed that the cavitation energy at the center part of the cross section was high, so this part was chosen as the etching area. As shown in Fig. 7 , there was no center of energy distribution for following two reasons. First, the sound field signal measuring device was a self-made equipment. Its planned route is rectangular, while the energy distribution diagram in this paper is circular, so it is difficult for the sensor to reach the central measuring position. Besides, in the process of measuring sound field signals, in order to more clearly reflect the energy distribution of sound field, “interp2” interpolation function was used to interpolate between the energy at the measured positions. The energy near the center could not be interpolated, resulting in the energy at the very center point was hard to be measured according to the planned measurement path (as shown in Fig. 2 (a)). In general, central energy was higher and more uniform, which was treated as high energy area. The results also showed that the distribution of the cavitation energy was greatly un-uniform when the input power was 120 W, as shown in Fig. 7 (a). However, with the increase of input power, not only the proportion of high energy area increased which stimulated the increase of WCAs, but also uniformity of the cavitation energy was obviously improved, which contributed to the improvement of the corrosion uniformity. Fig. 7 Cross section energy distribution of ultrasonic chemical reaction field: (a) 40%, (b) 60%, (c) 80%, (d) 100%. 3.2 Effect of ultrasonic time on wettability In order to reveal the effect of ultrasonic time on the contact angle, the four samples were prepared by setting the ultrasonic input power of 300 W with different etching time ranging from 3 min to 9 min as mentioned in Part 2.1. The results in Fig. 8 illustrated that all the obtained WCAs of the samples were higher than 157°, suggesting that the ultrasonic field successfully enhanced the superhydrophobicity on the surface of stainless steel even the etching time was as short as 3 min. Especially, with the ultrasonic time increasing, the WCAs first increased reaching the maximum value of 163.21° under the ultrasonic time of 7 min and then decreased. The enhancement of contact angles attributed to the increase of ultrasonic-assisted etching time, which stimulated the formation of the “mountain-like” micron-scale structures as shown in Fig. 9 . Obviously, the “mountain-like” microstructures gradually changed from flat-top (as shown in Fig. 9 (a)) to pinnacle (as shown in Fig. 9 (b) and (c)). However, with further increase of etching time, the “mountain-like” microstructures were over-etched and then became flat-top again as shown in Fig. 9 (d). Analogously, Fig. 10 schematically illustrated the detailed changing process of the “mountain-like” microstructures. It was supposed that the corrosion rate for any direction of the microstructures was the same. As the etching time increased from 3 min to 7 min, the top of the “mountain-like” microstructures got sharper and the gully got deeper as shown in Fig. 10 (L 2 -L 4 ), which could prompt to form thicker air cushions between solid and droplets. Nevertheless, when the etching time prolonged from 7 min to 9 min, the etching rate of the very sharp peaks was higher than that of the gully, which was because the previously formed peaks were extremely weak and easily to be destroyed by over-corrosion and cavitation effect, resulting in flat-top “mountain-like” with relatively low height as shown in Fig. 10 L5. The above-mentioned change in the “peak” structure showed the gas storage capacity of the microstructure on the solid surface, which is consistent with the change trend of the surface wettability shown in Fig. 8 . Fig. 8 Relationship between static WCAs and ultrasonic time. Fig. 9 SEM images of SH surfaces prepared under different ultrasound times:3min, (b) 5 min, (c) 7 min, (d) 9 min. Fig. 10 Schematic diagram of original structure (a) and microstructure change with the increase of ultrasonic-assisted etching time: (b) 3 min, (c) 5 min, (d) 7 min, (e) 9 min. It is apparent that there is a specific hierarchical surface structures required for a high contact angle of 163.21° under the ultrasonic time of 7 min. Compared with the process without ultrasonic-assisted corrosion (the optimal etching time was 20 min), not only the preparation time shortened by more than 2 times, but also the contact angles slightly improved (the WCA of obtained surface without ultrasonic-assisted etching process was 162.45°) [33] . This simple and time-saving method is especially vital for the mass production of superhydrophobic stainless steel materials. 3.3 Application of SH stainless steel in separation of oil/water mixtures In order to explore the application of the above superhydrophobic stainless steel in oil/water separation, the performance of meshes was characterized by pressure resistance, separation efficiency and oil flux for various organics (n-hexane, crude oil, petroleum ether, kerosene, dichloromethane). The water pressure resistance presents the maximum height ( h max ) of liquid that the meshes could support [45] .The experimental result showed that the maximum intercepted water height was 27.3 cm (as shown in attachment C), which was higher than that of most of previous reports ( Table 1 ). Superhydrophobic stainless steel mesh with high pressure resistance could withstand greater external pressure and separate oil/water mixtures effectively, which was significant for the practical application. Table 1 Pressure resistance by different methods. Materials Methods Pressure (kPa) References 304 stainless steel Chemical etching method 2.68 This work Zeolite-coated mesh Secondary growth method 0.96 [46] Phenol-formaldehyde resin Spray method 0.951 [47] Stainless steel Electrophoretic deposition method 2.29 [48] Copper mesh Selective electrodeposition method ∼1.3 [49] The separation efficiencies for various organics ranged from 96.8% to 98.4% as shown in Fig. 11 . The high values demonstrated that the as-prepared stainless steel mesh could separate various oil/water mixtures with competent efficiencies. In addition, the durability of the mesh was investigated by taking the kerosene/water mixtures as an example of which the separation efficiency remained more than 97% after 10 cycles of separation (as shown in S1). These results indicated that the as-prepared superhydrophobic stainless steel mesh displayed stable recyclability. Additionally, the flux of oil samples is an important indicator to show the separation rate in the industrial oil/water separation application [50] . Owing to the oleophilicity of the as-prepared mesh, 500 ml oil fully passed through the stainless steel mesh within a few seconds with only gravity-driven force [51] , and all oil flux were larger than 6.68 L m −2 s −1 as shown in Fig. 11 . Due to the excellent corrosion resistance of stainless steel and its good application prospect in the field of oil/water separation, many studies on the preparation of superhydrophobic stainless steel surface and its application on oil/water separation have been reported. Fig. 12 showed the separation efficiencies of superhydrophobic stainless steel surfaces obtained from different methods [48] , [52] , [53] , [54] . It was indicated that the separation efficiency of the as-prepared samples ranked among the highest values reported before. Besides, the WCA (163.21°) of samples could be accomplished with the etching time of only 7 min, and low-cost fluorine-free reagents for modification, which was promising in large-scale industrial production. However, the other methods mentioned in Fig. 12 still existed some inadequacy on preparation time, reagent or equipment cost, which hindered the industrial application of superhydrophobic stainless steel mesh for oil/water separation. Therefore, the high separation efficiency and flux indicated that the as-prepared stainless steel mesh was promising in separation of oil/water owing to the superior surface wettability and the excellent corrosion resistance of the substrate. Fig. 11 Oil/water separation efficiency and oil flux of the SH stainless steel mesh for kinds of sample oils. Fig. 12 Oil/water separation efficiency of the SH stainless steel mesh for kinds of methods. In addition, in the industrial application of oil/water separation, the micro/nano structures and wettability on the surface of superhydrophobic/superoleophilic materials are easily destroyed in harsh environments [55] . Hence, the mechanical stability was investigated by sandpaper abrasion test. The as-prepared meshes were dragged along sandpaper (1500 grits) using 2 N weight for a cycle (20 cm) as shown in Fig. 13 (a). The relationship between WCAs and abrasion cycles suggested that the WCAs gradually decreased and tended to hold steady with the increase of abrasion cycles and even after 10 abrasion cycles the mesh was still superhydrophobic (WCA was 152.25°) as shown in Fig. 13 (b), indicating that the as-prepared stainless steel mesh maintained excellent mechanical durability. Fig. 13 The mechanical stability test: (a)Sandpaper abrasion test. (b)The WCAs after 0–10 abrasion cycle tests."
} | 5,715 |
35055692 | PMC8775938 | pmc | 3,747 | {
"abstract": "Biorefineries are attracting attention as an alternative to the petroleum industry to reduce carbon emissions and achieve sustainable development. In particular, because forests play an important role in potentially reducing greenhouse gas emissions to net zero, alternatives to cellulose produced by plants are required. Bacterial cellulose (BC) can prevent deforestation and has a high potential for use as a biomaterial in various industries such as food, cosmetics, and pharmaceuticals. This study aimed to improve BC production from lignocellulose, a sustainable feedstock, and to optimize the culture conditions for Gluconacetobacter xylinus using Miscanthus hydrolysates as a medium. The productivity of BC was improved using statistical optimization of the major culture parameters which were as follows: temperature, 29 °C; initial pH, 5.1; and sodium alginate concentration, 0.09% ( w / v ). The predicted and actual values of BC production in the optimal conditions were 14.07 g/L and 14.88 g/L, respectively, confirming that our prediction model was statistically significant. Additionally, BC production using Miscanthus hydrolysates was 1.12-fold higher than in the control group (commercial glucose). Our result indicate that lignocellulose can be used in the BC production processes in the near future.",
"conclusion": "4. Conclusions In this study, the optimal culture conditions for G. xylinus ATCC 53524 were investigated using statistical methods to improve BC production using Miscanthus hydrolysates. The derived optimal culture conditions were as follows: temperature, 29 °C; initial pH, 5.1; and NaAlg concentration, 0.09% ( w / v ). Under the derived optimal conditions, the predicted and actual BC yields were 14.07 g/L and 14.88 g/L, respectively. These results demonstrate that our predictive model was statistically significant. In addition, BC production using Miscanthus hydrolysates was 1.12-fold greater relative to the control group wherein commercial glucose was used. Taken together, the results of our study for optimizing the culture conditions using Miscanthus hydrolysates to enhance BC production is expected to provide useful insights into methods for mitigating global warming and improving public health.",
"introduction": "1. Introduction The reckless use of fossil fuels has accelerated greenhouse gas emissions, leading to global climate change [ 1 , 2 ]. Uncontrolled climate change has resulted in catastrophes, such as environmental pollution, reduced food production, and ecological destruction, and these are factors that threaten societal sustainability and public health [ 1 , 2 , 3 ]. The United Nations (UN) has established the United Nations Framework Convention on Climate Change (UNFCCC) and adopted treaties such as the Kyoto Protocol and the Paris Agreement to combat climate change through development goals that require carbon emission reduction [ 1 , 2 , 3 , 4 ]. As a solution, the concept of replacing fossil fuels with biomass which is a sustainable resource has attracted attention [ 1 , 5 , 6 ]. Various studies are being conducted globally to design carbon-neutral platforms that produce value-added materials such as biopolymers, biofuels, and biochemicals using the biorefinery concept [ 1 , 2 , 7 , 8 ]. Bacterial cellulose (BC) is a natural polymer synthesized by bacteria such as Gluconacetobacter , Pseudomonas , Rhizobium , and Sarcina [ 8 , 9 ]. BC can potentially serve as an alternative to plant cellulose (PC) because of its unique properties, such as high water retention capacity, mechanical strength, porosity, elasticity, and biocompatibility [ 8 , 10 , 11 ]. Unlike PC that contains hemicellulose, lignin, pectin, and ash, BC has a high purity and does not require a separate process to remove impurities [ 8 , 10 , 12 ]. In addition, the utilization of BC can help prevent environmental pollution by reducing the cutting of trees, which are the major source of PC [ 8 , 10 , 13 ]. The global BC market was estimated at 250 million US dollar in 2019 and is forecasted to grow to 680 million US dollar by the end of 2025 [ 14 ]. Various companies such as CelluForce (Quebec, QC, Canada), FiberLean ® Technologies (Orono, ME, USA), and Borregaard ChemCell (Sarpsborg, Norway) produce BC which is used in paper, food, pharmaceutical, and cosmetic industries [ 15 ]. However, the high production costs involved in the commercial mass production of BC impose limitations on its usage. The Hestrin–Schramm (HS) medium, which is used mainly for BC production requires large amounts of commercial glucose and glycerol resulting in high production costs [ 8 , 10 , 15 , 16 ]. Therefore, for the economical and sustainable production of BC, it is necessary to replace commercial carbon sources, such as glucose and glycerol, with inexpensive and renewable raw materials. Various studies have investigated the utilization of biomass as a carbon source [ 8 , 10 , 17 ]. The use of conventional biomass from sources such as corn, wheat, and sugar cane for use as a carbon source entails food-related ethical issues [ 8 , 18 ]. In contrast, the use of Miscanthus does not impact food security and has several advantages such as high biomass yields per unit of arable land, the ability to grow easily without requiring pesticides or fertilizers, and a long lifespan [ 8 , 19 ]. The global Miscanthus yield is estimated to be approximately 6.6 Mt/year, making it a potential biomass source that is readily available in large quantities [ 8 , 20 ]. In addition, the utilization of Miscanthus is expected to reduce anthropogenic CO 2 emissions and soil erosion but increase the soil carbon content and biodiversity [ 8 , 21 ]. For these reasons, Miscanthus is considered to be a reasonable source of biomass for biorefineries. In our previous study [ 8 ], lignocellulosic hydrolysates were used for BC production, and the effect of inhibitors on BC production was investigated. The purpose of this study was to maximize BC production by optimizing the culture conditions using Miscanthus hydrolysates as a carbon source. To improve BC production, the culture conditions for Gluconacetobacter xylinus ATCC 53524 with regard to the correlation between temperature, initial pH, and sodium alginate (NaAlg) concentration were optimized using a statistical method.",
"discussion": "3. Results and Discussion CCD was performed to investigate the optimal culture conditions for G. xylinus ATCC 53524 for enhanced BC production. Table 2 shows 20 experiments designed by dividing three independent variables ( X 1 : temperature, X 2 : initial pH, and X 3 : NaAlg concentration) into five different levels (−2, −1, 0, 1, and 2) and the experimental results obtained. The range of response (BC production, g/L) was 0.00–14.63 g/L. The experiments were performed six times (Std nos. 15–20) at the center point to confirm reproducibility. Studies have reported that the addition of NaAlg to the culture medium affects the BC yield, crystallinity index, contact angles, and hydrophilicity [ 25 ]. Zhou et al. [ 26 ] achieved increased BC production (6.0 g/L) using Acetobacter xylinum NUST4.1 by adding 0.04% ( w / v ) NaAlg (BC production of the control group without NaAlg was 3.7 g/L). However, according to Cheng et al. [ 27 ], the addition of NaAlg at concentrations above 0.2% ( w / v ) negatively affected BC production. Therefore, the center point of X 3 was set to 0.08% ( w / v ) to accurately evaluate the effect of the NaAlg concentration on BC production. The model equation for predicting the response was determined using multiple regression analysis of the experimental results as follows: (3) Y = 13.92 − 0.94 X 1 + 0.61 X 2 + 0.33 X 3 − 0.26 X 1 X 2 − 0.34 X 1 X 3 − 0.42 X 2 X 3 − 3.52 X 1 2 − 2.96 X 2 2 − 0.51 X 3 2 \nwhere Y is BC production (g/L), and X 1 , X 2 , and X 3 are the independent variables representing temperature, initial pH, and NaAlg concentration, respectively. Table 3 shows the ANOVA results for the quadratic model of BC production. The F -value, which indicates the accuracy of the model [ 28 ], for the predictive model was 25.28. A value of p < 0.05 was considered statistically significant [ 29 ], and the results of our predictive model were demonstrated to be statistically significant ( p < 0.0001). The model terms affecting BC production were found to be X 1 , X 1 2 , and X 2 2 ; temperature ( X 1 ) was the most significant variable among the three independent variables examined. The statistical acceptability of the predictive model was assessed using the coefficient of determination (R 2 ), and a value close to 1 indicates that the experimental response agrees with the predicted response within the designed experimental range [ 30 ]. The R 2 values higher than 0.8 and a difference between R 2 and adjusted R 2 not exceeding 0.2 indicate the reliability of the model [ 31 ]. The R 2 and adjusted R 2 of our model were 0.9579 and 0.9200, respectively, indicating the statistical acceptability of our model. Figure 1 shows the three-dimensional plots based on Equation (3). Figure 1 a shows the effects of temperature and initial pH on BC production when the NaAlg concentration is the center point ( X 3 = 0). BC production was maximal when both the temperature and initial pH had values approximately equal to the corresponding values at the center points (temperature of 30 °C and initial pH of 5) and showed a marked decrease when the values of both variables deviated from those at the center point. Figure 1 b shows the effects of temperature and NaAlg concentration on BC production when the initial pH is the center point ( X 2 = 0). BC production was maximal when the temperature was approximately equal to that at the center point (temperature of 30 °C and NaAlg concentration of 0.08% [ w / v ]) and decreased sharply as the temperature varied from that at the center point. The effects of initial pH and NaAlg concentration on BC production are shown in Figure 1 c. The results indicate that when the temperature is the center point ( X 1 = 0), BC production was maximal when both the variables had values equal to those at the center point (initial pH of 5 and NaAlg concentration of 0.08% [ w / v ]). Figure 1 b,c show that the NaAlg concentration had no significant effect on BC production compared to the other variables. Numerical optimization was performed using multiple regression model analysis to derive the optimal culture conditions that can maximize BC production. Table 4 shows the culture conditions derived from the numerical optimization for the predicted and actual BC production values. The optimal culture conditions for enhanced BC production derived by the predictive model were as follows: temperature, 29.24 °C; initial pH, 5.09; and NaAlg concentration, 0.09% ( w / v ). The predicted BC production under optimal conditions was 14.07 g/L. To verify the reproducibility of the predictive model, G. xylinus ATCC 53524 was cultured under the derived optimal conditions. The relevance of the model was verified by obtaining BC production approximately equal to 14.88 g/L, indicating that the value for the experimentally obtained yield was approximately 94.2%, consistent with that obtained using the prediction model. In our previous study [ 8 ], we determined the phenolic compound contents in Miscanthus hydrolysates to be as follows: 0.13 g/L acetic acid, 0.16 g/L formic acid, 0.02 g/L furfural, and 0.05 g/L 5-(hydroxymethyl)furfural. These phenolic compounds have been reported to inhibit microbial growth [ 32 ]. However, Miscanthus hydrolysates did not inhibit BC production via G. xylinus ATCC 53524 fermentation, indicating their potential as a renewable raw material [ 8 ]. Therefore, we investigated the optimal culture conditions for improving BC production using Miscanthus hydrolysates. BC production obtained from the control group using commercial glucose was 13.26 g/L, whereas that from the group using Miscanthus hydrolysates under optimal culture conditions was 14.88 g/L. Thus, the Miscanthus hydrolysate group achieved a production equivalent to 112% relative to the production by the control group. A method for calculating the theoretical maximum BC production was shown by Soeiro et al. [ 33 ]. With the glucose concentration of the medium used in this study, the theoretical maximum BC production was determined to be about 36 g/L, and BC conversion using Miscanthus hydrolysates was about 41% of the theoretical maximum for BC production. BC conversion using Miscanthus hydrolysates was greater than that observed in other studies that used hydrolysates of potato peel (32.1%) [ 34 ], orange peel (22.0%) [ 35 ], and sweet sorghum root (34.9%) [ 36 ], indicating that Miscanthus is a promising feedstock for BC production. In a previous study [ 8 ], BC production using Miscanthus hydrolysates was 97.86% relative to that of the control group. As our previous study [ 8 ] focused on the potential utilization of Miscanthus hydrolysates as an inexpensive substrate for BC production, we fermented G. xylinus ATCC 53524 under culture conditions (temperature, 30 °C; initial pH, 6.0; 7 days) that are generally known to promote sufficient growth without considering variables that increase BC production. We aimed to optimize the culture conditions by considering various variables reported to affect BC production, such as temperature, initial pH, and NaAlg concentration, for economical and sustainable BC production by shortening the culture time. We succeeded in shortening the culture time for BC production from 7 days to 4 days due to the effect of these variables which were not considered previously, and the BC production was determined to be 14.88 g/L. These results are presumably due to the effect of the initial pH and NaAlg. Mikkelsen et al. [ 37 ] reported that the optimal pH for BC production by G. xylinus ATCC 53524 was 5.0. In addition, according to Zhou et al. [ 26 ], the addition of NaAlg not only improved BC yield but also enhanced cell growth of A. xylinum . Improvement of BC productivity through optimization of culture conditions will contribute to overcoming the low economic feasibility of biorefinery. Improvement of BC productivity through optimization of culture conditions will contribute to overcoming the low economic feasibility of biorefinery. In particular, our research in which Miscanthus hydrolysate was applied as a useful feedstock provides a direction for sustainable and eco-friendly BC production."
} | 3,640 |
25317564 | PMC4285555 | pmc | 3,748 | {
"abstract": "The mechanisms that underlie the origin of major prokaryotic groups are poorly understood. In principle, the origin of both species and higher taxa among prokaryotes should entail similar mechanisms — ecological interactions with the environment paired with natural genetic variation involving lineage-specific gene innovations and lineage-specific gene acquisitions 1 , 2 , 3 , 4 . To investigate the origin of higher taxa in archaea, we have determined gene distributions and gene phylogenies for the 267,568 protein coding genes of 134 sequenced archaeal genomes in the context of their homologs from 1,847 reference bacterial genomes. Archaea-specific gene families define 13 traditionally recognized archaeal higher taxa in our sample. Here we report that the origins of these 13 groups unexpectedly correspond to 2,264 group-specific gene acquisitions from bacteria. Interdomain gene transfer is highly asymmetric, transfers from bacteria to archaea are more than 5-fold more frequent than vice versa. Gene transfers identified at major evolutionary transitions among prokaryotes specifically implicate gene acquisitions for metabolic functions from bacteria as key innovations in the origin of higher archaeal taxa."
} | 305 |
22363333 | PMC3282944 | pmc | 3,749 | {
"abstract": "Fluxes of greenhouse gases to the atmosphere are heavily influenced by microbiological activity. Microbial enzymes involved in the production and consumption of greenhouse gases often contain metal cofactors. While extensive research has examined the influence of Fe bioavailability on microbial CO 2 cycling, fewer studies have explored metal requirements for microbial production and consumption of the second- and third-most abundant greenhouse gases, methane (CH 4 ), and nitrous oxide (N 2 O). Here we review the current state of biochemical, physiological, and environmental research on transition metal requirements for microbial CH 4 and N 2 O cycling. Methanogenic archaea require large amounts of Fe, Ni, and Co (and some Mo/W and Zn). Low bioavailability of Fe, Ni, and Co limits methanogenesis in pure and mixed cultures and environmental studies. Anaerobic methane oxidation by anaerobic methanotrophic archaea (ANME) likely occurs via reverse methanogenesis since ANME possess most of the enzymes in the methanogenic pathway. Aerobic CH 4 oxidation uses Cu or Fe for the first step depending on Cu availability, and additional Fe, Cu, and Mo for later steps. N 2 O production via classical anaerobic denitrification is primarily Fe-based, whereas aerobic pathways (nitrifier denitrification and archaeal ammonia oxidation) require Cu in addition to, or possibly in place of, Fe. Genes encoding the Cu-containing N 2 O reductase, the only known enzyme capable of microbial N 2 O conversion to N 2 , have only been found in classical denitrifiers. Accumulation of N 2 O due to low Cu has been observed in pure cultures and a lake ecosystem, but not in marine systems. Future research is needed on metalloenzymes involved in the production of N 2 O by enrichment cultures of ammonia oxidizing archaea, biological mechanisms for scavenging scarce metals, and possible links between metal bioavailability and greenhouse gas fluxes in anaerobic environments where metals may be limiting due to sulfide-metal scavenging.",
"conclusion": "Comparisons, Conclusion, and Future Research Trace metals present in microbial enzymes involved in CH 4 and N 2 O cycling include Fe, Co, Ni, Cu, Zn, Mo, and W. While Fe-containing enzymes are present in almost all pathways we reviewed (with the possible exception of archaeal ammonia oxidation), other metals differ by metabolism. For instance, Ni, Co, and W are present in methanogenic enzymes, but not in enzymes involved in aerobic methanotrophy and N 2 O cycling. In contrast, Cu is not used in methanogenesis, but is very important for aerobic methanotrophy and N 2 O cycling. Small amounts of Mo and Zn are common in most pathways. If ongoing biochemical studies confirm that anaerobic methanotrophs use the reverse methanogenesis pathway to oxidize methane, they likely require the same suite of metals as methanogens: Fe, Co, Ni, Zn, and Mo/W. How do metal requirements for CH 4 and N 2 O cycling compare with other ecologically important microbes? The metal stoichiometry of 15 marine eukaryotic phytoplankton showed an average trace metal stoichiometry of Fe 1 Mn 0.53 Zn 0.08 Cu 0.05 Co 0.03 Mo 0.005 (Ho et al., 2003 ; Barton et al., 2007 ). High Mn requirements are likely reflective of the oxygen-generating Mn-containing photosystem II complex. The elemental stoichiometry of CH 4 and N 2 O processing microbes is not nearly as well-constrained as that of eukaryotic phytoplankton, but preliminary studies of methanogens suggest that, after Fe, Ni is an extremely important metal for methanogenesis and anaerobic methanotrophy. Very little is known about the metal stoichiometry of aerobic and intra-aerobic methanotrophs and N 2 O-producing microbes, although it is predicted that their Cu content is much higher than both methanogens and phytoplankton, especially in the case of archaeal ammonia oxidizers. Metallomics – the comprehensive analysis of the entirety of metal-containing species within a cell (Szpunar, 2005 ) – may reveal still more unexpected metals or natural products excreted by microbes to alter metal bioavailability. For example, a recent metallomics study of the hyperthermophilic sulfur-reducing Archeon, Pyrococcus furiosus , revealed 158 unassigned metalloprotein peaks, 75 of which contained metals that the organism was not known to assimilate, such as Pb and U (Cvetkovic et al., 2010 ). Furthermore, it is likely that the metal requirements for each of the pathways reviewed here are amplified by additional metalloenzymes involved in complex metal cofactor biosynthesis, such is the case for hydrogenases and nitrogenases (Rubio and Ludden, 2008 ; Shepard et al., 2011 ). More studies are needed to address gaps in our knowledge of microbial trace metal physiology. Ideally these studies will make use of well-defined media and trace metal-clean conditions, such as Teflon-lined glass bottles for anaerobic studies. Understudied topics include Zn requirements for methanogenesis, trace metal content of ANME enzymes (other than the well-studied Mcr), the influence of Cu on N 2 O production by archaeal enrichment cultures, the biochemistry of Cu-enzymes in archaeal ammonia oxidizers, and strategies for metal-binding ligand production (i.e., methanobactin) in greenhouse gas cycling microbes. The search for alternative N 2 O-consuming enzymes in ammonia oxidizers deserves particular attention, as suggested by the ability of some bacterial nitrifiers lacking the Cu-containing nitrous oxide reductase enzyme to convert N 2 O to N 2 . In order to better connect the findings from biochemical and physiological studies of pure cultures with the larger picture of global greenhouse gas cycling and environmental metal bioavailability, more in situ studies are necessary. Preliminary studies suggest that rates of methanogenesis in peatlands may be controlled by Fe, Ni, and Co availability, since addition of all three metals stimulated methanogenesis in mineral-poor peats. It is currently unknown whether one metal was the primary limiting growth element or whether multiple metals were co-limiting (Saito et al., 2008 ). If co-limitation was occurring, the addition of all three elements would result in higher rates of methanogenesis than any metal added solely. On the other hand, other systems and pathways may be dominantly regulated by the bioavailability of only one scarce metal; this seems to be the case for Co(balt) limitation of methylotrophic methanogenesis in anaerobic digesters, Cu limitation of N 2 O consumption by freshwater denitrifying bacteria and possibly Cu limitation of ammonia oxidizing archaea as suggested by numerous genes encoding Cu-containing proteins in AOA genomes. Lastly, relationships between metal bioavailability and fluxes of greenhouse gases remain largely unexplored, particularly in anaerobic ecosystems. High sulfide environments likely pose particular challenges for microbial metal acquisition due to abiotic sulfide scavenging and precipitation of metal sulfides. Of the bioessential metals involved in greenhouse gas cycling, Mo, Fe, and Cu are most susceptible to sulfide scavenging, followed by Co, Ni, and Zn (Morse and Luther, 1999 ). Microbial metal limitation due to sulfide drawdown may have driven major evolutionary episodes and geochemical transitions in Earth history (Anbar and Knoll, 2002 ; Scott et al., 2008 ; Dupont et al., 2010 ). Several such scenarios have been proposed for metal limitation of microbial greenhouse gas cycling: a decline in seawater Ni due to cooling of the Earth’s mantle in the late Archean may have limited methanogenesis and contributed to the Great Oxidation Event 2.4 billion years ago (Konhauser et al., 2009 ) while low Cu due to sulfide scavenging in the Proterozoic ocean could have led to global warming through the build-up of N 2 O (Buick, 2007 ). The question of how metal bioavailability influences greenhouse gas cycling in ancient and modern ecosystems is just beginning to be investigated.",
"introduction": "Introduction With increasing concern about the future impacts of global climate change, a detailed understanding of the sources and sinks of greenhouse gases is essential to mitigating their environmental impact. Although CO 2 is the most abundant greenhouse gas, many other climatically important gases exist (Montzka et al., 2011 ). The second- and third-most abundant naturally produced greenhouse gases are methane (currently ~1.8 ppm; Heimann, 2011 ) and nitrous oxide (currently ~322 ppb; Montzka et al., 2011 ) and are ~25× and 300× more efficient at absorbing infrared radiation than CO 2 , respectively. Furthermore, CH 4 is oxidized to CO 2 in the atmosphere, contributing to rising CO 2 levels. N 2 O has a very long residence time in the atmosphere (120 years) and reacts with atomic O to form nitric oxide (NO), which is involved in ozone destruction (Montzka et al., 2011 ). Both humans and microbes play important, and often interwoven, roles in the production of CH 4 and N 2 O. Humans have bred and expanded the habitats of ruminants that contain methanogenic archaea in their guts, cultivated rice paddies, and built wastewater and sewage treatment plants where methanogens proliferate. Humans have also extensively applied inorganic N as fertilizer to agricultural soils, leading to increased microbial N 2 O emissions in soils, wetlands, and coastal hypoxic zones (Schlesinger, 2009 ). Understanding the controls and regulation of microbial greenhouse gas emissions is therefore fundamental to quantifying and managing both natural and anthropogenic fluxes of these gases. This review will focus on two potent greenhouse gases, CH 4 and N 2 O, both of which are produced as natural by-products of microbial energy-generating metabolisms. CH 4 is the final product of the anaerobic degradation of organic matter, whereas N 2 O formation results from incomplete conversion of nitrate or nitrite to N 2 . At the heart of the pathways that generate these gases are enzymes that catalyze redox reactions. Many of these enzymes contain transition metals as cofactors for electron transport or as catalytic centers at active sites. Important transition metals in the pathway of CH 4 production (methanogenesis) and anaerobic methane oxidation include Fe, Ni, Co, Mo/W, and Zn, whereas aerobic (and intra-aerobic) methanotrophy and N 2 O production require Fe-, Cu-, and Mo-containing proteins. Only one protein, the Cu-rich nitrous oxidase reductase, is known to reduce N 2 O to N 2 . Physiological studies of pure cultures have shown that optimal metal concentrations for microbial metabolism are orders of magnitude higher than in situ concentrations in most aquatic environments. These findings lead to the question: are some microbes perennially metal-limited in nature? If so, does metal availability exert influence on the flux of greenhouse gases? If not, what mechanisms do microbes use in natural environments to acquire trace metals? Previous environmental studies of metal requirements for microbes have largely focused on those microbes directly involved in CO 2 cycling, principally phytoplankton that consume CO 2 during photosynthesis. Driving these studies was the “Fe hypothesis” by John Martin positing that CO 2 -consuming marine phytoplankton could be fertilized by Fe (Martin and Fitzwater, 1988 ). Far less attention has been aimed at metal requirements for microbes involved in the cycling of non-CO 2 greenhouse gases, although many of these organisms also live in ecosystems with very low metal bioavailability. A notable exception is the enormous wealth of literature generated by the wastewater scientific community about metal (particularly Fe, Ni, and Co) controls on methanogenesis in anaerobic digesters (see Demirel and Scherer, 2011 ). The purpose of this article is to review the current state of literature on the metalloenzymes and trace metal physiology of organisms involved in CH 4 and N 2 O processing. In each section, we discuss environmental studies if they exist and compare metal concentrations for optimal growth of pure cultures to measured values of trace metals in natural environments. In the final sections of the article, we compare the metal requirements of microbes involved in the CH 4 and N 2 O cycles and discuss future research directions. Readers are referred to previous reviews (Rogers and Whitman, 1991 ; Conrad, 1996 ) for other aspects of microbial controls on greenhouse gas cycling."
} | 3,119 |
34787907 | PMC9300135 | pmc | 3,751 | {
"abstract": "Summary \n Biodiversity can reduce or increase disease transmission. These divergent effects suggest that community composition rather than diversity per se determines disease transmission. In natural plant communities, little is known about the functional roles of neighbouring plant species in belowground disease transmission. Here, we experimentally investigated disease transmission of a fungal root pathogen ( Rhizoctonia solani ) in two focal plant species in combinations with four neighbour species of two ages. We developed stochastic models to test the relative importance of two transmission‐modifying mechanisms: (1) infected hosts serve as nutrient supply to increase hyphal growth, so that successful disease transmission is self‐reinforcing; and (2) plant resistance increases during plant development. Neighbouring plants either reduced or increased disease transmission in the focal plants. These effects depended on neighbour age, but could not be explained by a simple dichotomy between hosts and nonhost neighbours. Model selection revealed that both transmission‐modifying mechanisms are relevant and that focal host–neighbour interactions changed which mechanisms steered disease transmission rate. Our work shows that neighbour‐induced shifts in the importance of these mechanisms across root networks either make or break disease transmission chains. Understanding how diversity affects disease transmission thus requires integrating interactions between focal and neighbour species and their pathogens.",
"conclusion": "Conclusions We demonstrate that neighbouring plants can positively and negatively affect disease transmission of the fungal root pathogen R . solani . These divergent neighbour effects were not explained by a simple distinction between hosts and nonhosts, highlighting that functional characterizations within hosts and nonhosts are needed to understand diversity‐disease relationships. Our comprehensive approach, which included 18 root networks differing in plant community composition, shows that differential disease transmission in such root networks is driven by shifts in the relative importance of two different underlying mechanisms: (1) pathogen infection success increasing disease transmission; and (2) the development of plant resistance decreasing disease transmission. These shifts reveal that the importance of these two mechanisms that steer belowground fungal disease transmission is determined by the interaction between the identity and developmental stage of the neighbours and the focal plants. This interaction thus determines whether the roots of the species interfere with or connect the nodes in a plant community's root network. In other words: the root network determines the possible ‘routes’ of pathogen transmission and thereby makes or breaks the transmission chains. Plant community composition may therefore determine which pathogen traits are under selection locally, whilst the presence of a pathogen can shape community composition (Bever et al ., 2015 ), as plants may have higher reproduction success in highly diverse communities with root networks that suppress pathogen transmission. Unravelling these interactive effects of plants and pathogens on the mechanisms underlying belowground pathogen transmission will be crucial to understand the disease‐diversity relationship.",
"introduction": "Introduction The positive relationship between plant biodiversity and productivity (Cardinale et al ., 2012 ; Grace et al ., 2016 ) was initially attributed to belowground resource complementarity among plant species (Tilman, 2001 ; Barry et al ., 2019 ) and the dominance of productive plant species (i.e. the ‘selection effect’; Loreau & Hector, 2001 ). However, an alternative hypothesis related to pathogens has been gaining ground in the last decade. This hypothesis states that root pathogens accumulate in monocultures compared to species‐rich communities, leading to increased productivity in mixtures as pathogens are ‘diluted’ (Maron et al ., 2011 ; Schnitzer et al ., 2011 ; Bever et al ., 2015 ; Cappelli et al ., 2020 ). Although pathogen dilution seems to be a general pattern in diverse communities (Keesing et al ., 2010 ), the opposite effect – pathogen amplification – has also been reported (Power & Mitchell, 2004 ; Halliday et al ., 2017 ). Understanding these divergent effects of diversity on pathogen accumulation and disease pressure, requires a better understanding of pathogen transmission in diverse communities (Keesing et al ., 2006 ; Ampt et al ., 2019 ; Collins et al ., 2020 ). Belowground transmission of plant pathogens is often considered to be a function of the density of host plants (Burdon & Chilvers, 1982 ; Burdon et al ., 2006 ). Belowground, as well as aboveground, the distance between conspecific host plants is in general smaller in monocultures compared to mixtures. However, recent studies on aboveground pathogens indicate that there are additional effects of neighbouring plants in diverse plant communities. For example, the presence or abundance of particular neighbour species either increased or decreased fungal pathogen infestation or damage in mixed forest communities (Hantsch et al ., 2014 ; Setiawan et al ., 2014 ; Field et al ., 2020 ). Yet, for belowground pathogens the effects of neighbouring plants have rarely been addressed (Otten et al ., 2005 ; Cook et al ., 2007 ). Both aboveground and belowground it matters if the neighbour is a host, a nonhost or an asymptomatic host species for a certain pathogen (Roberts & Heesterbeek, 2020 ). Although asymptomatic host species do not display any disease symptoms upon colonization of their tissue by a pathogen (Malcolm et al ., 2013 ), their roots can act as ‘bridges’ for the pathogen to the next susceptible individual plant (Termorshuizen, 2014 ; Palma‐Guerrero et al ., 2021 ). Hence, their presence in the community could potentially increase pathogen transmission. In addition, nonhost species, which are often treated as ‘neutral’ players in plant epidemiology, can also affect pathogen transmission. For example, some nonhosts may actively reduce pathogen transmission by secreting antifungal compounds belowground (Bednarek & Osbourn, 2009 ; Baetz & Martinoia, 2014 ; Yang et al ., 2014 ) or attracting antagonists of the pathogen (Berendsen et al ., 2018 ; Stringlis et al ., 2018 ). A fundamental understanding of the mechanisms by which neighbouring plant species alter belowground pathogen transmission in diverse plant communities is needed to predict the effects of plant diversity on disease dynamics. Belowground transmission of many fungal root pathogens, including economically important species such as Rhizoctonia solani , primarily occurs via hyphal growth from the roots of an infected plant to those of a susceptible plant (Stacey et al ., 2001 ; Raaijmakers et al ., 2009 ). This transmission results in the characteristic disease patches often observed in agricultural monocultures. Infections resulting from hyphal growth between plants are usually referred to as ‘secondary infections’, while ‘primary infections’ are those that arise from individual infectious propagules in the soil, for example at the beginning of a growing season. (Gilligan & Kleczkowski, 1997 ; Gilligan, 2002 ). Infected plants serve as a nutrient source for the pathogen and these nutrients enable further hyphal growth on the root surface or through the soil. Therefore, each successful secondary infection may increase the likelihood of the fungal pathogen finding and infecting its next host sooner (Garrett, 1970 ; Stacey et al ., 2001 ; Simon et al ., 2014 ) (Fig. 1a ). If this mechanism is operating, successful pathogen transmission between plants will be a self‐reinforcing process, with each new secondary transmission event being more likely than the last. Fig. 1 Conceptual overview of hypothesized mechanisms that modify fungal root pathogen transmission. Belowground pathogen transmission via hyphal growth between host plants can depend on (a) the infection successes of the pathogen, i.e. the infection history of the pathogen (here: the number of successful transmission events from a single disease focus), which results in more available nutrients from host tissue for pathogen growth, and/or (b) the ontogenetic development of the host plants, which can increase host resistance (i.e. reduce host susceptibility). Note that the relationships as depicted are not necessarily linear and our models allow for plant development to be linked with either decreased or increased transmission. Another mechanism that affects pathogen transmission is the susceptibility of a host plant, which often depends on the ontogenetic stage of the plant (Develey‐Rivière & Galiana, 2007 ). If resistance of the plant to a pathogen increases with age as plant development progresses, as has been observed for many seedling pathogens including R . solani , this increase in resistance may outpace the growth of the pathogen and thus limit its transmission (Kleczkowski et al ., 1996 ; Otten et al ., 2003 ) (Fig. 1b ). Alternatively, Bailey et al . ( 2000 ) hypothesized that increased root intermingling between host plants with plant age might increase belowground pathogen transmission. The net effect of these mechanisms on pathogen transmission may not only differ between host plant species but may also depend on the presence of neighbouring plants and the host plant's interactions with its neighbours. Here, we test (1) whether different neighbour species can alter the disease transmission of a fungal root pathogen in two host plant species; and (2) whether neighbours affect the role of transmission‐modifying mechanisms (i.e. self‐reinforcing increases in the infection success of the fungal pathogen and increases in plant resistance during development). We performed experiments with focal host plant and neighbour plant combinations across (1) two focal plant species; (2) four neighbour species; and (3) two ages for all neighbour species. For all these combinations, we determined the effects of host identity, neighbour identity and neighbour age on disease transmission of R . solani , which causes damping‐off disease in seedlings (Anderson, 1982 ; González García et al ., 2006 ). In addition, we developed stochastic models to test the relative importance of the two transmission‐modifying mechanisms in mixed plant communities. Together, our experimental and modelling approaches reveal how neighbours affect belowground disease transmission and the underlying mechanisms. This integrated approach is needed to understand whether a neighbour makes or breaks disease transmission chains.",
"discussion": "Results and discussion Neighbour identity and neighbour age alter disease transmission Susceptibility to R. solani damping‐off disease was similar for both focal plant species (i.e. the host species in which transmission was assessed) of our study, the forbs P. lanceolata and L. vulgare ( χ \n 2 (1) = 0.00, P = 1.00). We found that 93% of the inoculated seedlings (i.e. the first focal seedling in each row, Fig. 2a ) of these two species developed disease symptoms in the rows without neighbours present. However, the transmission of R. solani damping‐off disease was significantly higher in P . lanceolata rows than in L . vulgare rows without neighbours present ( F \n 1,27 = 12.80, P < 0.01; Figs 3 , 4 ; Fig. 2 for experimental set up). Fig. 4 Disease transmission of Rhizoctonia solani in host seedlings depends on focal host identity and neighbours. Experimental observations and model fits of disease curves (mean of furthest infected focal seedling over time) for both focal host species: (a) Leucanthemum vulgare and (b) Plantago lanceolata , without and with different neighbour types (horizontal panels). Experimental data in black symbols (squares = young neighbours, triangles = old neighbours; mean ± 95% confidence interval, see Fig. 3 for sample sizes) and solid, thin black lines. Model fits in coloured lines (colour indicates neighbour identity as in Figs 3 , 5 ), based on model parameter estimates from fit of best‐supported model (label, plain = young, bold = old neighbours) to full dataset per treatment (wide, solid line) and on 15 randomly drawn bootstrap samples (thin, dashed lines) to indicate prediction accuracy. See also Fig. 3 (experimental area under the disease progression curve) and Table 1 and Supporting Information Table S1 for experimental stats. The presence of neighbours in the rows of focal host plant species significantly affected disease transmission in the focal host seedlings (i.e. the seedlings of either host species in which transmission was assessed, see Fig. 2b,c ). These effects depended on focal host identity, neighbour identity and neighbour age (Table 2 ). Plantago lanceolata neighbours increased disease transmission in both focal host species, but only at the younger age (i.e. when planted on the same day as the focal host seedlings; young neighbours with L . vulgare : t \n 14 = 9.81, P < 0.001; with P . lanceolata : t \n 14 = 4.06, P < 0.01; Figs 3 , 4 ; Table S4 ). When P . lanceolata neighbours were older than the focal host seedlings (i.e. planted 10 d earlier than the focal host seedlings), disease transmission in both focal host species did not differ from the control without neighbours (old neighbours with L . vulgare : t \n 14 = 1.99, P = 0.13; with P . lanceolata : t \n 14 = −1.27, P = 0.22; Figs 3 , 4 ). The increase in disease transmission with young P . lanceolata neighbours was larger with L . vulgare than with itself as focal host species (significant age × focal interaction; Table S4 ); this may be due to the fact that the disease transmission in P . lanceolata was already higher than in L . vulgare . Table 2 Neighbour effects on disease transmission depend on focal host identity, neighbour identity and neighbour age. Predictor df \n F \n \n P \n Focal host identity 1,192 4.11 <0.05 Neighbour identity 3,192 42.94 <0.001 Neighbour age 1,192 86.49 <0.001 Focal host identity × Neighbour identity 3,192 17.24 <0.001 Neighbour identity × Neighbour age 3,192 4.93 <0.01 ANOVA results for linear mixed effects model of the effects of host identity, neighbour‐identity and ‐age on disease transmission (measured as difference in area under the disease progression curve compared to control without neighbours (ΔAUDPC), see the Materials and Methods section and Supporting Information Fig. S3 ). Nonsignificant interactions were removed from the final model. Type III sum of squares were used. John Wiley & Sons, Ltd The other three neighbour species (the host L . vulgare and two nonhost grasses A. odoratum and F. rubra ) decreased disease transmission in most cases (Figs 3 , 4 ; Table S4 ). Leucanthemum vulgare and A . odoratum neighbours that were older than the focal host seedlings significantly decreased disease transmission in both focal host species (old L . vulgare : t \n 14 = −8.42, P < 0.001; old A . odoratum : t \n 14 = −5,47, P < 0.001; Figs 3 , 4 ; Table S4 ), while young neighbours did not affect disease transmission (young L . vulgare : t \n 14 = 0.43, P = 0.68; young A . odoratum : t \n 14 = 0.46, P = 0.65; Figs 3 , 4 ; Table S4 ). The transmission‐reducing effect of A . odoratum was stronger for focal host L . vulgare than for focal host P . lanceolata (focal host identity: F \n 1,38 = 8.07, P < 0.01; Figs 3 , 4 ; Table S4 ), whereas the neighbour effect of L . vulgare did not differ between focal host species ( F \n 1,36 = 4.03, P = 0.05; Figs 3 , 4 ; Table S4 ). Festuca rubra neighbours decreased disease transmission only for focal host L . vulgare (focal host identity: F \n 1,36 = 6.3, P < 0.05; post hoc : L . vulgare t \n 14 = −3.2, P < 0.05; P . lanceolata : t \n 14 = −0.39, P = 0.70; Figs 3 , 4 ; Table S4 ), both when older and the same age as the focal host seedlings (neighbour age: F \n 1,36 = 1.86, P = 0.18; Figs 3 , 4 ; Table S4 ). In general, disease transmission was consistent with the spread of R . solani through soil, as confirmed through toothpick‐baiting (Fig. S3 ; Table S5 ; Methods S1 ). Together, our experimental data revealed both a difference in disease transmission between the two focal host species and both increasing and reducing effects of neighbouring plants on disease transmission. Our results indicate that although differences in total host density may drive effects of host neighbours, these effects strongly depend on host neighbour identity and age. Transmission‐modifying mechanisms depend on interactions with neighbours To elucidate the interactive effects of focal host identity and neighbours on disease transmission, we developed stochastic models that specify the different mechanisms underlying disease transmission. By comparing support for the models and parameter estimates, we evaluated the evidence for the two transmission‐modifying mechanisms in our experimental data (see the Materials and Methods section; Fig. 1 ; Table 1 ). In our null model the transmission probability ( ρ ) between each pair of focal host plants is a constant (Model 1). In the more complex models (Fig. 1 ), the transmission probability is allowed to change because (1) infection success provides more nutrients to the pathogen for growth (Model 2, with extra scaling parameter ψ ); (2) host plant development over time increases resistance to the pathogen (Model 3, with extra scaling parameter γ ); or (3) both (Model 4). The best‐supported model differed between the two focal host species without neighbours (Tables 3 , S6 , L . vulgare : Model 4, Figs 4(a) , 5(a) ; P. lanceolata : Model 2, Figs 4b , 5b ) revealing two differences in the role of transmission‐modifying mechanisms in the focal host species. Transmission probability increased with infection success in both focal host species but this effect was significantly larger in P . lanceolata than in L . vulgare (Δ ψ 95% CI (0.02,1.4)), suggesting that P . lanceolata seedlings provided more nutrients to the pathogen than those of L . vulgare . In support of this hypothesis, we observed that P . lanceolata had higher root biomass and length than L . vulgare (Fig. S4 ). Across both focal species, our data thus provide proof of principle for the self‐reinforcing transmission mechanism. Most likely, this mechanism is particularly relevant for fungal pathogens that spread through soil via mycelial growth. Moreover, the transmission probability only decreased with plant development in L . vulgare (Fig. 5c ; Table S7 ), which suggests that L . vulgare resistance increased with age. The latter observation was confirmed in an additional experiment where the infection probability of L . vulgare seedlings decreased with their age, while the infection probability did not change for older P . lanceolata seedlings (Fig. S5 ). The development of resistance in L. vulgare outpaced the disease transmission to such an extent that transmission halted before the end of the row (i.e. a plateau in the disease curve: Fig. 4a ). This is consistent with epidemics of R . solani in monoculture crops (Gilligan et al ., 1997 ) and epidemiological models (e.g. Otten et al ., 2003 ; Cook et al ., 2007 ), which often show a characteristic decrease in secondary transmission rate due to development of host resistance. Together, our results show that the lower disease transmission in L . vulgare compared to P . lanceolata is due to differences in the strength of both transmission‐modifying mechanisms between host species. Table 3 Neighbour effects on transmission‐modifying mechanisms depend on focal host identity. Focal host identity Neighbour ΔAIC = AIC null − AIC model i \n Identity Age Type Infection success Plant development \n Leucanthemum vulgare \n None − 28.40 \n − 6.22 \n \n Leucanthemum vulgare \n Young Conspecific host − 17.40 \n − 4.32 \n \n Leucanthemum vulgare \n Old Conspecific host 2.00 −0.70 \n Plantago lanceolata \n Young Heterospecific host − 2.20 \n 1.98 \n Plantago lanceolata \n Old Heterospecific host − 42.64 \n −7.54 \n Anthoxanthum odoratum \n Young Nonhost − 44.24 \n − 4.88 \n \n Anthoxanthum odoratum \n Old Nonhost − 22.44 \n − 0.14 \n \n Festuca rubra \n Young Nonhost − 64.92 \n \n 0.64 \n \n Festuca rubra \n Old Nonhost − 48.04 \n − 5.44 \n \n Plantago lanceolata \n None − 48.74 \n −2.66 \n Plantago lanceolata \n Young Conspecific host −14.82 − 20.40 \n \n Plantago lanceolata \n Old Conspecific host − 36.28 \n − 25.00 \n \n Leucanthemum vulgare \n Young Heterospecific host − 5.68 \n 1.46 \n Leucanthemum vulgare \n Old Heterospecific host − 36.66 \n −3.26 \n Anthoxanthum odoratum \n Young Nonhost −15.16 − 35.06 \n \n Anthoxanthum odoratum \n Old Nonhost −3.78 − 8.82 \n \n Festuca rubra \n Young Nonhost − 31.70 \n −2.34 \n Festuca rubra \n Old Nonhost − 25.82 \n − 29.82 \n Support for transmission‐modifying mechanisms as compared to null model: ΔAIC (AIC null – AIC model i ) < −2 indicates support for a model with a transmission‐modifying mechanism. Best‐supported model indicated in bold text and grey shading (none: null model, infection success only: Model 2, plant development only: Model 3, both: Model 4). In cases where several models provide an equally good fit (ΔAIC (AIC model i – AIC model j ) < 2 ) , the most parsimonious model was considered the best supported. ΔAIC for Model 4 not shown. See Supporting Information Table S6 for complete model selection. John Wiley & Sons, Ltd Fig. 5 Modelling reveals that disease transmission‐modifying mechanisms depend on focal host identity and neighbour effects. (a, b) Disease transmission model predictions of disease transmission probability ( ρ ) over time for both focal host species ((a) Leucanthemum vulgare ; (b) Plantago lanceolata ), without and with different neighbour types (horizontal panels; light colour = young, dark colour = old neighbours). Predictions are based on model parameter estimates from fit of best supported model (label, plain = young, bold = old neighbours) to full dataset per treatment (wide, solid line) and on 15 randomly drawn bootstrap samples (thin, dashed lines) to indicate prediction accuracy. (c) Plant development effect size parameter γ only for treatments (horizontal panels) with significant transmission‐modifying effect of plant development (Model 3 or Model 4 as best supported model). Positive γ values indicate an increase in ρ over time, negative γ values a decrease in ρ over time. Estimate from fit to full dataset per treatment ± 95% bootstrap percentile confidence interval (CI). Colour scale represents area under disease progress curve from experimental data. See Supporting Information Tables S7, S8 for model parameter estimates and pairwise comparisons. In the presence of neighbouring plants, the best‐supported models were often different models from those best‐supported when without neighbours (Tables 3 , S6 ). This already indicates that neighbours change the transmission‐modifying mechanisms of the focal host species. The role of transmission‐modifying mechanisms in disease transmission in L . vulgare was mainly affected by the other host species P . lanceolata . With P . lanceolata neighbours, the transmission solely depended on the infection success of the fungal pathogen (Model 2, infection success of the fungal pathogen; Table 3 ; Figs 4a , 5a ), while the effect of plant development, which reduced disease transmission in L . vulgare without neighbours, disappeared. Thus, P . lanceolata neighbours increased the disease transmission rate in L . vulgare to such an extent that the transmission outpaced the development of disease resistance in the focal hosts. In contrast, L . vulgare neighbours that were older than the focal individuals decreased the disease transmission probability in L . vulgare to almost zero (Fig. 5a ). Therefore, no transmission‐modifying mechanisms could be detected (Model 1, i.e. fixed transmission probability, Table 3 ; Figs 4a , 5a ). Young neighbours of the same species (i.e. L . vulgare ) did not change the role of transmission‐modifying mechanisms in disease transmission in L . vulgare (Model 4; Figs 4(a) , 5(a) ; Tables 3, S7, S8 ) and neither did nonhost neighbours A . odoratum and F . rubra (Model 4; Figs 4(a) , 5(a) ; Tables 3, S7, S8 ), despite the reduction of the transmission by the nonhost neighbours (all but young A . odoratum ). Whether other putative transmission‐reducing mechanisms by neighbouring plants, such as alteration of root growth, root architecture or disease resistance of the focal plants, play a role was not investigated here and will be an intriguing avenue for future studies. While plant development did not play a role in disease transmission in P . lanceolata without neighbours, plant development played an important role when P . lanceolata was with most neighbours (all neighbours except L . vulgare or young F . rubra , Figs 4(b) , 5(b) ; Table 3 ). However, rather than finding that plant development reduced disease transmission probability, as shown for L . vulgare (Fig. 5c ), we found that plant development enhanced disease transmission probability in P . lanceolata with neighbours (Fig. 5c ; Table S7 ). Because it is unlikely that the P. lanceolata host plants become more susceptible to R . solani over time (Gibson et al ., 1999 ; Develey‐Rivière & Galiana, 2007 ), we argue that disease transmission in P . lanceolata is enhanced by a more extensive root network over time, leading to enhanced contact between focal host individuals (Bailey et al ., 2000 ; Leclerc et al ., 2013 ). Conspecific host neighbours (i.e. P . lanceolata ) directly contribute to this root contact network, enhancing the effect of plant development on disease transmission probability in P . lanceolata (Model 3, plant development; Figs 4(b) , 5(b,c) ; Tables 3, S7, S8 ). The development of a root contact network in P . lanceolata enabled disease transmission to overcome the potential transmission‐interfering effects of the nonhosts. When the nonhost neighbour A . odoratum (Model 3, plant development; Figs 4(b) , 5(b) ; Tables 3, S6 ) was older than the focal host individuals, and decreased the transmission in P . lanceolata , the transmission‐modifying effect related to plant development was smaller than with young nonhost A . odoratum neighbours (young vs old A . odoratum neighbours: Δ γ 95% CI (0.42,0.71); Fig. 5(c) ; Table S8 ). Notably, when the nonhost neighbour F . rubra was older than the focal host individuals, it also induced the transmission‐modifying effect related to plant development, although here infection success also affected transmission probability in P . lanceolata (Model 4; infection success of the fungal pathogen and plant development; Figs 4(b) , 5(b,c) ; Tables 3, S7 ). This may indicate a transition towards plant development as the sole transmission‐modifying mechanism, as was found with the other nonhost neighbour A . odoratum . The transmission‐modifying mechanisms indicate that a firm root network of the host species is important for disease transmission. However, the effect of the neighbouring nonhost on disease transmission was also related to their age and thus likely the size of their root system. This combination will affect how intensely nonhost roots are intermingled with host roots (Kesanakurti et al ., 2011 ; Frank et al ., 2015 ), the amount and composition of potential antifungal root exudates (Yang et al ., 2014 ; Li et al ., 2018 ; Schulz‐Bohm et al ., 2018 ), and the antagonistic effects of the nonhost rhizosphere community (Berendsen et al ., 2012 ; Lange et al ., 2015 ; Stringlis et al ., 2018 ). Older host neighbours decrease transmission through delayed onset of transmission Irrespective of changes in the role of transmission‐modifying mechanisms, neighbours also affected the onset of disease transmission, measured as the initial transmission probability ( ρ t \n \n =3 ) in our models. Specifically, we found that in both host species, older host neighbours significantly decreased the initial transmission probability ( ρ t \n \n =3 ) compared to host neighbours of the same age as the focal hosts (Fig. 5a,b ; Table S8 ), which cascaded into significantly decreased disease transmission rates over time, a common consequence of the nonlinear nature of epidemics (Gilligan, 2002 ). In addition, a slower onset of transmission may allow host seedlings to develop resistance, further decreasing disease transmission (Kleczkowski et al ., 1996 ; Otten et al ., 2003 ), as we observed for focal species L . vulgare in our experiment. The intriguing finding that a neighbouring host species (i.e. L . vulgare ) can strongly reduce transmission when it is older may be related to its development of resistance with age. Our additional experiment indicates that, at inoculation, the older L . vulgare neighbours would already have been c . 60% less susceptible than the focal hosts (Fig. S5 ). As the mechanism of developmental resistance to R . solani is not yet fully understood and likely not ubiquitous across plant species (Bateman et al ., 1969 ; Reddy, 1980 ; Yang et al ., 1992 ), we cannot point to a specific transmission‐reducing mechanism in play at this moment. Nonetheless, these findings highlight that variability in plant developmental stages within a host population may contribute to pathogen dilution in diverse communities (Neher et al ., 1987 ; Dwyer et al ., 1997 ; Kauffman & Jules, 2006 ). Conclusions We demonstrate that neighbouring plants can positively and negatively affect disease transmission of the fungal root pathogen R . solani . These divergent neighbour effects were not explained by a simple distinction between hosts and nonhosts, highlighting that functional characterizations within hosts and nonhosts are needed to understand diversity‐disease relationships. Our comprehensive approach, which included 18 root networks differing in plant community composition, shows that differential disease transmission in such root networks is driven by shifts in the relative importance of two different underlying mechanisms: (1) pathogen infection success increasing disease transmission; and (2) the development of plant resistance decreasing disease transmission. These shifts reveal that the importance of these two mechanisms that steer belowground fungal disease transmission is determined by the interaction between the identity and developmental stage of the neighbours and the focal plants. This interaction thus determines whether the roots of the species interfere with or connect the nodes in a plant community's root network. In other words: the root network determines the possible ‘routes’ of pathogen transmission and thereby makes or breaks the transmission chains. Plant community composition may therefore determine which pathogen traits are under selection locally, whilst the presence of a pathogen can shape community composition (Bever et al ., 2015 ), as plants may have higher reproduction success in highly diverse communities with root networks that suppress pathogen transmission. Unravelling these interactive effects of plants and pathogens on the mechanisms underlying belowground pathogen transmission will be crucial to understand the disease‐diversity relationship."
} | 7,932 |
39747058 | PMC11695965 | pmc | 3,753 | {
"abstract": "Microbial utilization of methanol for valorization is an effective way to advance green bio-manufacturing technology. Although synthetic methylotrophs have been developed, strategies to enhance their cell growth rate and internal regulatory mechanism remain underexplored. In this study, we design a synthetic methanol assimilation (SMA) pathway containing only six enzymes linked to central carbon metabolism, which does not require energy and carbon emissions. Through rational design and laboratory evolution, E. coli harboring with the SMA pathway is converted into a synthetic methylotroph. By self-adjusting the expression of TOPAI (topoisomerase I inhibitor) to alleviate transcriptional-replication conflicts (TRCs), the doubling time of methylotrophic E. coli is reduced to 4.5 h, approaching that of natural methylotrophs. This work has the potential to overcome the growth limitation of C1-assimilating microbes and advance the development of a circular carbon economy.",
"introduction": "Introduction The development of green bio-manufacturing technology is one of the important ways to address energy and environmental challenges. Currently, bio-manufacturing primarily uses saccharic raw materials, which compete with human food resources. To address this issue, one-carbon (C1) compounds derived from CO 2 and other renewable sources are selected as alternative feedstocks for chemical production 1 , 2 . The utilization of C1 compounds, such as methanol, for microbial growth and chemical production is expected to significantly advance carbon neutrality and the circular carbon economy. C1 biotransformation offers an ideal approach for the valorization of C1 compounds in the synthesis of biomass, biofuels and chemicals 3 . Natural methylotrophs, such as Bacillus methanolicus , can utilize methanol for cell growth and metabolism, but they present challenges for genetic modification into versatile cell factories for diverse chemicals production 4 , 5 . In contrast, biotechnological work-horses like Escherichia coli and Saccharomyces cerevisiae , possess higher plasticity and a wide range of genetic modification tools, making them promising candidates for C1 utilization and biomanufacturing 6 , 7 . Recent research has focused on developing these model microorganisms into platform for C1 assimilation by introducing homologous or heterologous natural assimilation pathways to enable cell growth and chemical production 8 – 29 . Among these, the ribulose monophosphate (RuMP) cycle is recognized as the most efficient methanol assimilation pathway 8 , 21 , 30 , however, the carbon yield of acetyl-CoA derivatives is limited due to CO 2 emissions. To address these issues, strategies combining of computer-aided design and protein engineering have been explored to create artificial assimilation pathways for the efficient utilization of C1 feedstocks 31 – 34 . While many of these artificial pathways have been validated in vitro, they often lack assimilation capacity in vivo, such as E. coli 35 . Recently, artificial assimilation pathways have been designed not only to overcome compensate for existing limitations, but also to create synthetic methylotrophs or C1-assimilating cell factories through metabolic engineering and adaptive evolution. Despite these advances, synthetic methylotrophs still face challenges such as metabolite toxicity and insufficient cell growth 13 , 25 – 27 . However, only a few mechanisms have been elucidated that enhance cell growth, such as the alleviation of DNA-protein crosslinking (DPC), and modifications to cell membrane components 23 – 27 . Therefore, it is essential to conduct a deeper analysis of the underlying molecular mechanisms in artificial methylotrophs and to develop strategies that can improve cell growth on methanol. In this work, a six-enzyme SMA pathway is designed and constructed to synthesize acetyl-CoA from methanol without carbon emissions. Following the introduction of this pathway into E. coli , a combination of rational design and genome-targeting mutator strategy (GTMS) with adaptive laboratory evolution (ALE) is used to develop synthetic methylotrophs. Omics sequencing reveals that the alleviation of transcriptional-replication conflicts (TRCs) shortens the doubling time of methylotrophic E. coli to 4.5 h. These findings provide valuable insights into the molecular mechanism by which synthetic methylotrophs achieve accelerated cell growth.",
"discussion": "Discussion In this study, an efficient SMA pathway, which required six enzymes to synthesize acetyl-CoA from methanol without the need for ATP or NAD(P)H, was designed and connected to central carbon metabolism. Following the introduction of the SMA pathway into E. coli , a combination of rational design and GTMS-adaptive evolution was used to develop synthetic methylotrophs. Genome and transcriptome sequencing confirmed that, alleviating TRCs improved cell growth and shortened doubling time in methylotrophic E. coli . This study provides some valuable guidance for the future design and application of synthetic methylotrophs. Designing of a short-step, carbon-neutral and energy-saving methanol assimilation pathway can significantly advance the development of a low carbon economy. Firstly, traditional metabolic engineering has primarily focused on introducing natural methanol assimilation pathway, such as the serine cycle, into model microorganisms. However, this pathway involves a 17-step enzymatic reaction, reducing the assimilation efficiency 58 . In contrast, the SMA pathway developed in this study requires only six enzymes to synthesize acetyl-CoA from methanol, making it a more efficient assimilation route. In the SMA pathway, protein-engineered HSA catalyzes an aldehyde condensation reaction between glycolaldehyde and E4P to produce H6P. This enzymatic reaction opens a new channel from methanol intermediates to central carbon metabolites, offering innovative approaches for the artificial design of carbon rearrangement pathway. Secondly, the RuMP pathway, known for its high assimilation efficiency, has been used in constructing synthetic methylotrophs 59 . Similarly, the XuMP pathway was a unique assimilation route in methylotrophic yeast 60 . However, both the RuMP and XuMP pathways all release CO 2 , thereby reducing carbon yield. In this study, a resource-efficient and environmentally friendly SMA pathway was designed and constructed using a modular construction-assembly-optimization strategy. Unlike the RuMP or XuMP pathways, the SMA pathway can achieve carbon neutrality under certain conditions, with no carbon emissions. Thirdly, the Calvin cycle, although it fixes CO 2 and avoids carbon loss, but consumes ATP and NADPH 61 . The SMA pathway, by contrast, is an energy-saving carbon assimilation route that does not require reducing power or energy. The central metabolism of host strains is highly plastic, providing a robust framework for the introduction of metabolic pathway 62 . This efficient SMA pathway is a crucial step toward developing synthetic methylotrophs with accelerated cell growth rate. Synthetic methylotrophs utilizing artificially designed assimilation pathway demonstrate high methanol utilization efficiency and shortened doubling time, overcoming the limitations breaking of natural methylotrophs in chemicals production. Recent research has primarily focused on the relatively simple RuMP cycle 23 , which can be introduced into model microorganisms to create methylotrophs. Although extensive efforts have been made to engineer the RuMP pathway in E coli 21 , the transplanted pathway has struggled with lower methanol utilization rates. To enhance methanol assimilation, different metabolic engineering strategies have been employed, including the co-utilization of carbon source substrates, increasing precursor supply and regulating reducing equivalent 13 , 17 , 63 . Recent breakthrough in the RuMP pathway reduced the doubling time to 4.3 hours 64 and even up to 3.5 h 65 , which is comparable to the doubling time of natural methylotrophs such as Methylobacterium extorquens AM1 with a doubling time of 4 h 66 . However, most currently developed synthetic methylotrophs rely on the RuMP pathway for methanol assimilation. To overcome the growth limitations associated with this pathway, an artificially designed SMA pathway was introduced into host strain E. coli , resulting in methanol-dependent E. coli through metabolic modification. After long-term adaptive evolution, an artificial methylotroph was obtained, achieving an OD of 2.2 and a doubling time of 4.5 h, closely matching the growth performance of natural methylotrophs. The mechanism by which alleviating TRCs shortens the doubling time of synthetic methylotrophs is expected to facilitate the industrial application of C1 compounds as raw material for bio-refinery process. Synthetic methylotrophs inherently disrupt the original metabolic regulation of the chassis, requiring the integration of different reactions with endogenous metabolism to establish a new metabolic balance. Therefore, a deeper understanding of the cell growth mechanism in synthetic methylotrophs could offer valuable insights for their future application. Recent studies have reported several mechanisms involved in synthetic methylotrophs, primarily focusing on reducing formaldehyde toxicity in vivo 24 , regulating cell membrane damage repair 27 , alleviating DNA-protein crosslinking, and balancing metabolic flux 26 . Contrary to these mechanisms, this study identified TRCs as the primary factor contributing to the prolonged doubling time in synthetic methylotrophs. Through combination of omics sequencing analysis and validation through reverse metabolic engineering, it was demonstrated that the overexpression of the TOPAI gene caused the uncoupling of EcTopoI from RNAPs, which in turn caused the accumulation of R-loops and stalling of RNAPs, thereby aggravating TRCs in strain FMX783. Evolutionary processes eventually mitigated this deficiency in TOPAI, alleviating potential TRCs, and restoring the normal growth rate of strain FMX892 on methanol. Future, research will aim to prevent the uncoupling EcTopoI from RNAP to inhibit the excessive accumulation of R-loops, thereby alleviating TRCs and progressively enhancing the growth of synthetic methylotrophs. It was hypothesized that the alteration in TOPAI may be directly related to the assimilation of methanol or formaldehyde in early evolutionary strains, this hypothesis needs to be investigated in future work. Overall, this study provides critical guidance for breaking the cell growth limitations in C1-assimilating microorganisms."
} | 2,669 |
21274572 | PMC3114069 | pmc | 3,754 | {
"abstract": "Species that shelter in a biogenic habitat can influence their refugia and, in turn, play an essential role in shaping local patterns of biodiversity. Here we explore a positive feedback loop between the provisioning rate of habitat-forming branching corals and their associated fishes and show how interactions between two groups of fish—the planktivorous damselfish and predatory hawkfish—altered the feedback. A field experiment confirmed that skeletal growth of branching coral (genus Pocillopora ) increased substantially with increasing numbers (biomass) of resident fishes, likely because they greatly increased the interstitial concentrations of nutrients. Because there is a positive relationship between colony size and number (biomass) of associated fishes (primarily damselfishes in the Family Pomacentridae), a structure–function feedback loop exists in which increasing numbers of damselfish enhance coral growth and larger corals host greater abundances (and species richness) of fish. However, interactions between damselfishes and arc-eye hawkfish, Paracirrhites arcatus , a largely solitary resident, can disrupt this positive feedback loop. Field surveys revealed a marked pattern of fish occupancy related to coral size: Pocillopora colonies of sufficient size to host fish (>40 cm circumference) had either groups of damselfish or an arc-eye hawkfish; only larger colonies (>75 cm) were occupied by both the damselfish and hawkfish. Subsequent short- and long-term experiments revealed that on intermediate-sized Pocillopora colonies, arc-eye hawkfish prevented the establishment of damselfish by suppressing their recruitment. The demographic consequences to the host coral were substantial; in a 1-year-long experiment, intermediate-size Pocillopora occupied by hawkfish grew at half the rate of corals that hosted groups of damselfish. These findings indicate that: (1) species which occupy a biogenic habitat can enhance the provisioning rate of their habitat; (2) such positive feedbacks between community structure and ecosystem function can be disrupted by a strong interactor; (3) even substantial consequences on ecosystem processes that arise can be difficult to discern.",
"introduction": "Introduction There is growing appreciation that structural aspects of a community can simultaneously be a cause and a consequence of an ecological rate process (Cardinale et al. 2006 ). This concept has motivated contemporary interest in understanding reciprocal structure–function feedbacks, together with factors that affect their strength. To date, this perspective has been applied to biodiversity-related questions (Reiss et al. 2009 ), such as the nature of reciprocal relationships and feedbacks between species diversity and resource availability (Cardinale et al. 2006 ), disturbance (Hughes et al. 2007 ), or productivity (Cardinale et al. 2009 ). At the landscape scale, the size and availability of essential habitat patches can help shape local patterns of biodiversity (Holbrook et al. 1990 ; Debinski et al. 2001 ). It is common for habitats important in supporting biodiversity to be biogenic (e.g., trees, giant kelp, coral). Interactions among species associated with a biogenic habitat have the potential to alter such key ecosystem processes as photosynthesis (King and Caylor 2010 ) or habitat provisioning (Holbrook et al. 2008 ), which in turn can have consequences for biodiversity and other important community attributes. For example, anemonefish increase the growth rate of their host anemones (Holbrook and Schmitt 2005 ), which enhances local biodiversity by enabling the coexistence of inferior space competitors (Schmitt and Holbrook 2003 ; Holbrook and Schmitt 2004 ). Positive feedback loops may occur commonly between biogenic habitat and one or more associated species. Such mutualistic relationships range from ants on acacia trees in African savannah (Palmer et al. 2008 ; King and Caylor 2010 ) to fishes sheltering on branching corals on tropical reefs (Holbrook et al. 2008 ). While mutualisms are vital elements of community structure and ecosystem function (Bronstein 1994 ; Wilson and Nisbet 1997 ; Bronstein et al. 2003 ; Bruno et al. 2003 ; Stanton 2003 ; Cahill et al. 2008 ; Callaway et al. 2008 ; Gross 2008 ; Segraves 2008 ), factors that maintain and break down these reciprocally positive interactions are not fully understood (Palmer et al. 2008 ). Several different mechanisms can impact the spatial and/or temporal strength of the positive feedbacks between partners. Some are intrinsic to the participating species. For example, one partner can control the strength of the benefit, such as in plant–mycorrhizal interactions. In these instances, plants can selectively interact with a particular mycorrhizal partner that results in a higher benefit (Heath and Tiffin 2009 ) or differentially reward with nutrients a species that is more beneficial (Bever et al. 2009 ). Mutualisms also can be interrupted by the action of extrinsic factors, such as abiotic disturbances that reduce the availability of one of the partners. When severe drought resulted in a prolonged lack of flowers on figs ( Ficus spp.) in lowland forests of Borneo, there was a corresponding loss of local populations of their species-specific pollinators [fig wasps (Agaonidae); Harrison 2000 ]. Similarly, mismatch in onset of flowering and in first appearance dates of pollinators due to different phenological responses to climate warming may affect persistence of the interacting species (Hegland et al. 2009 ). Biotic interactions, such as predation, herbivory, and interspecific competition, represent another major pathway to the modification of reciprocally positive interactions. One partner can be competitively displaced by a less beneficial one, such as when invasive species of insects pollinate or disperse seeds more poorly than the native species (Dohzono et al. 2008 ; Rowles and O’Dowd 2009 ) or disrupt the pollination behavior of the latter (Dohzono et al. 2008 ; Hansen and Müller 2009 ). Spiders can negatively affect (both as predators and competitors) hemipterans that are digestive mutualists of the carnivorous plant Roridula (Anderson and Midgley 2002 ). In contrast, mutualistic interactions are sometimes promoted by other species in the community. Large herbivores help foster the ant–plant mutualism in acacia tress in the African savannah (Palmer et al. 2008 ), and interactions between symbiotic ants and herbivores can enhance the rate of tree photosynthesis (King and Caylor 2010 ). Thus, such multispecies interactions are increasingly being recognized as having important consequences not only for the strength and persistence of mutualisms, but also for their effect on key ecosystem processes. Factors that greatly modify mutualisms can have especially strong effects on biodiversity and ecosystem function when a participant is a biogenic habitat that is foundational to the community. Stony corals on tropical reefs are a case in point; they provide an essential habitat for a wide range of taxa including fishes, crabs, bivalves, shrimps, and sponges. In turn, these species can confer important benefits to the coral, including provision of nutrients (Meyer and Schultz 1985a , b ; Mokady et al. 1998 ; Holbrook et al. 2008 ), removal of sediments (Stewart et al. 2006 ), protection from predators (Pratchett et al. 2000 ; Pratchett 2001 ), modulation of hydrodynamic conditions (Goldshmid et al. 2004 ), and enhancement of growth (Meyer and Schultz 1985b ; Liberman et al. 1995 ; Holbrook et al. 2008 ). Coral-associated fishes and invertebrates obtain benefits from the host, including food, sites for attachment and, very commonly, refuge from predation. We previously explored the positive effects of damselfish on skeletal growth of their host corals (Holbrook et al. 2008 ). Here we build on those findings by (1) describing the positive structure–function feedback loop between fish abundance and rate of habitat provisioning, and (2) exploring how interactions between groups of sheltering fishes influence the feedback and thus alter the provisioning rate of their habitat. In lagoons of Moorea, French Polynesia, branching corals in the genus Pocillopora are occupied by a number of species of fish, including planktivorous damselfishes in the Family Pomacentridae that can form large groups and predatory arc-eye hawkfish ( Paracirrhites arcatus ) that typically occur singly (Holbrook et al. 2008 ; Kane et al. 2009 ; Schmitt et al. 2009 ). The mortality of juvenile yellowtail dascyllus (Pomacentridae) is much higher when they co-occur with arc-eye hawkfish on a coral. Arc-eye hawkfish directly consume the dascyllus and also compete with them for enemy-free space within the coral shelter (Schmitt et al. 2009 ). Because damselfishes can enhance at least short-term skeletal growth of branching corals (Holbrook et al. 2008 ) and arc-eye hawkfish can reduce survivorship of recently settled damselfish through trophic and competitive interactions (Schmitt et al. 2009 ), this web of interacting species affords an ideal model system to evaluate the indirect effect of biotic factors in modifying habitat provisioning by altering the strength of reciprocally positive feedbacks.",
"discussion": "Discussion In our model system, the local abundance of fish that shelter on a host coral was simultaneously a cause and a consequence of the provisioning rate of its biogenic habitat. The number, biomass, and species richness of fishes that reside among the branches of Pocilloporid corals generally scale positively with colony size because larger colonies provide more habitat space. In a previous long-term colonization experiment in which the amount of initially unoccupied branching coral was manipulated, we found that local populations of damselfishes generally are strongly habitat-limited at our study locality (Holbrook et al. 2000 ; Schmitt and Holbrook 2000 ), with both larval recruitment and per capita survivorship suppressed greatly by increasing densities of resident damselfish on a host coral (Schmitt and Holbrook 1999a , b ; 2000 ; Holbrook and Schmitt 2002 , 2003 ). Hence, factors that enhance the growth of the coral host have cascading effects on the abundance and richness of the associated fishes. The results of field experiments presented here confirm previous observations and short-term manipulations (e.g., Meyer and Schultz 1985a , b ; Holbrook et al. 2008 ) which indicated that the rate of coral skeletal growth scales positively with the abundance (biomass) of sheltering fish. While the precise mechanism for the positive effect of fish on coral growth is not conclusively known, it appears to be related to local nutrient enrichment via excretion of nitrogenous wastes, primarily ammonium, by fishes, which has been hypothesized as a cause for enhanced demographic performance of corals (Meyer and Schultz 1985a , b ; Liberman et al. 1995 ; Holbrook et al. 2008 ) and tropical anemones (Porat and Chadwick-Furman 2004 , 2005 ; Holbrook and Schmitt 2005 ; Roopin et al. 2008 ; Roopin and Chadwick 2009 ). Whatever the mechanism underlying the effect of fish on coral, a reciprocally positive structure–function feedback loop exists between fishes and their host—the coral provides refuge space for fishes, which in turn enhance the production of additional habitat space. The strength of the feedback is influenced by the abundance of resident fishes, with planktivorous damselfishes providing the greatest benefit to their host because they can form large resident social groups on a colony (Figs. 1 , 2 ; Holbrook et al. 2008 ). Arc-eye hawkfish also are frequent inhabitants of Pocillopora colonies, although it is relatively uncommon for more than a single individual arc-eye hawkfish to occur on a host coral due to the social structure of this species, which involves the formation of small (<8 individuals) territorial harems on spatial scales much larger than a colony of branching coral (Kane et al. 2009 ). Because an averaged-sized arc-eye hawkfish and an adult damselfish have about the same biomass (and produce ammonium at roughly the same rate), the difference in social systems between these fish groups determines their relative value as a mutualist partner to the coral. Interactions between hawkfish and damselfish greatly reduce the abundance and biomass of sheltering fish on a colony and, as a consequence, greatly weaken the positive influence of fish on the growth rate of their host. This is particularly the case for small- to medium-sized colonies where the presence of an arc-eye hawkfish can keep a coral host entirely devoid of damselfish by suppressing larval recruitment and subsequent survivorship. This interaction has profound implications for the dynamics of the coral as the mortality rate of a colony generally scales inversely to its size (Hughes and Connell 1987 ). It also has substantial community-level consequences because larger coral colonies support a greater diversity of species (Holbrook et al. 2002 ). Understanding factors that influence the strength of positive feedbacks between partners in a mutualistic interaction is essential for predicting its demographic, population, and community consequences. An increasing number of studies have documented spatial and/or temporal variation in the reward quality of mutualisms, and many of these have identified interactions with other species as an underlying cause (Bronstein et al. 2003 ). Invasive species can disrupt existing mutualisms by reducing the abundance of native partners or by forming new associations with native and/or introduced species (Liu and Pemberton 2009 ; Rowles and O’Dowd 2009 ). However, interactions with co-mutualist and/or antagonist species within intact communities also can alter benefits accrued by the main partners in a mutualism (Bronstein et al. 2003 ). In the system we studied, there were dramatic differences in the benefit conferred on the host coral by the two groups of mutualist fish; after 1 year branching corals that were colonized by damselfish grew twice as much as those that hosted a hawkfish. Despite the differences in reward they offer to their coral hosts, both damselfish and hawkfish gain protection benefits from the association, as they shelter in the coral by night and, when threatened by predators, during the day (Holbrook and Schmitt 2002 ; Schmitt et al. 2009 ). Protection benefits gained by fish from their host corals and other cnidarians (e.g., sea anemones) are well known, and indeed numerous taxa cannot survive apart from their host species (Fautin 1991 ; Fautin and Allen 1997 ; Holbrook and Schmitt 2002 ; Munday 2004 ; Thompson et al. 2006 ; Ollerton et al. 2007 ). The protection mutualism we studied lies at the heart of a network of interacting species with intraguild predation, where both direct and indirect interactions arise from the use of a structural refuge (Schmitt et al. 2009 ). In this situation, mortality of the shared prey (damselfish) is higher in the presence of two intraguild prey [arc-eye hawkfish and red-spotted coral crabs ( Trapezia rufopunctata )], and all three are vulnerable to a suite of mobile intraguild predators that attack from the exterior of the coral (Holbrook and Schmitt 2002 ). Damselfishes suffer high mortality during and just after settlement, mainly arising from competition for refuge space, rather than direct consumption, by the intraguild prey (Schmitt and Holbrook 2007 ; Schmitt et al. 2009 ). In this study, damselfish were not able to become established on medium-sized corals that supported arc-eye hawkfish during a 1-year-long experiment. Only when branching corals reach a sufficiently large size are hawkfish unable to defend them against invading groups of older stage damselfish. Thus, hawkfish are able to disrupt the very strong positive feedback loop between damselfishes and host corals during a substantial portion of the life of the host Pocilloporid coral. A number of mutualisms have been identified in which one partner can control the strength of the benefit afforded to the other, or even exert choice among mutualist partners. For example, host plants can preferentially allocate photosynthate to (Bever et al. 2009 ), or selectively interact with, the most beneficial arbuscular mycorrhizal fungal symbiont (Heath and Tiffin 2009 ). Although it seems obvious that it is more beneficial for branching coral hosts to partner with damselfish, we do not know the degree to which partner choice mechanisms could be operating in the system. It is unlikely that a host coral would be able to actively choose one over another of the fish mutualists. However, corals exhibit considerable morphological variation, often in response to physical factors, such as light and water movement (Kaandorp 1999 ). Arc-eye hawkfish prefer to occupy large colonies with an open branching morphology (Kane et al. 2009 ), and even young hawkfish might respond to aspects of colony architecture during habitat selection. On Moorea, arc-eye hawkfish and damselfish-occupied branching corals are intermingled in the same reef habitats, and we do not exclude the possibility that patterns of occupancy are determined merely by chance encounters between fish and coral hosts as they become available (due to death of previous residents or growth of a small coral to the threshold size for fish occupancy). A number of potential pathways have been identified by which direct and indirect interspecific interactions can affect the existence and strength of mutualisms (Cahill et al. 2008 ; Palmer et al. 2008 ; Rowles and O’Dowd 2009 ). In our system, biotic interactions between mutualists of different value to their shared partner altered the strength of positive feedbacks in a structure–function feedback loop. Our findings illustrate how species interactions can have profound community and ecosystem-level consequences when embedded in a system with reciprocal feedbacks."
} | 4,527 |
38496934 | PMC10938581 | pmc | 3,756 | {
"abstract": "Water-repellent glass\nsurfaces have become increasingly\nimportant\nto ensure clear visibility in outdoor cameras, sensors, and automotive\nwindows. In this study, we investigated a process for the formation\nof nanoscale structures on a glass surface using chemical reactions\nwith hydrogen fluoride gas. Using this approach, nanostructures with\nsuperhydrophobicity, superhydrophilicity, and antireflective properties\nwere formed on glass surfaces with minimal processing time. This mask-free\nmethod, working at atmospheric pressure, can be efficiently integrated\nwithin the float process, a mainstream manufacturing technique for\nflat glass, to introduce nanostructures onto the glass surface. Notably,\nafter treatment with (1- H , 1- H ,\n2- H , 2- H -tridecafluorooctyl)trimethoxysilane\n(FAS-13), a typical hydrophobic agent, the resulting surface exhibited\na maximum water contact angle of 162°. Owing to its low reflectivity\nand superhydrophobicity, this surface is anticipated to find applications\nin not only the design of architectural window glass and vehicle windows\nbut also the development of solar panels and sensor cover glass for\nautonomous vehicles.",
"conclusion": "4 Conclusions In this study, we introduced\na novel approach to control and modify\nglass surfaces at the nanoscale to impart superhydrophilic, superhydrophobic,\nand antireflective properties. The method involved the formation of\na nanostructure on the glass surface through a brief treatment with\nHF gas, followed by cleaning with hydrochloric acid, which produced\na surface with long-lasting superhydrophilicity. This can be converted\ninto a superhydrophobic surface with the addition of a hydrophobic\ntreatment. By carefully adjusting the treatment parameters, superhydrophobicity\ncan be combined with visible-light antireflective properties. This technique offers several advantages over conventional microfabrication\ntechnologies such as RIE. Notably, it can be conducted under standard\natmospheric conditions without the need for a mask. Moreover, it achieves\ncontact angles larger than those via wet etching methods and offers\nhigher visible light transmittance and productivity than those of\nlaser processing. Moreover, it can be seamlessly integrated into the\nwidely used process for manufacturing soda-lime silicate glass, providing\na cost-effective solution. Furthermore, we emphasize the importance\nof controlling the shape\nof the fluoride layer formed in order to tune the surface properties.\nConsidering environmental regulations designed to support the development\nof ecofriendly hydrophobic films, our technology offers a sustainable\nsolution by forming structures directly within the glass, thereby\neliminating the constraint of the requirement for a coating material.\nThe versatility of this technique renders it suitable for a variety\nof applications, not only in traditional contexts such as building\nand automotive windows but also in new fields, including autonomous\nvehicle sensors, solar panels, and medical optical devices.",
"introduction": "1 Introduction Recent technological advancements\nhave led to an increase in cameras\nand sensors being installed outdoors, for example, in drones and autonomous\nvehicles. This amplifies the importance of glass surfaces with water-repellent\ncharacteristics to ensure clear visibility. Furthermore, owing to\nenvironmental concerns, superhydrophobic solar panels are in great\ndemand. As superhydrophobic surfaces can also be expected to be self-cleaning,\nthe demand for superhydrophobic glass surfaces is growing rapidly. 1 Generally, a superhydrophobic surface is\ndefined as a surface with\na static water contact angle greater than 150°. Some definitions\nalso include a contact angle hysteresis of less than 10°. 2 A common method to create such surfaces involves\ncreating micro-to-nano scale structures with low surface energy. 3 − 7 Several approaches to impart water repellency to glass surfaces\nhave been proposed based on the creation of nanostructures on the\nglass surface and the subsequent deposition of a low surface energy\nmaterial. These methods can be broadly categorized into two types:\nnanostructure coating methods 8 − 13 or methods involving glass etching to create micro/nanostructures. 14 − 19 However, existing methods are problematic for a variety of reasons.\nIn nanostructure coating methods, the presence of different materials\non the surface layer causes bond weakening and exfoliation problems\nowing to differences in thermal properties. In methods involving glass\netching, creating micro-to-nanoscale roughness by surface etching\nrequires the induction of localized etching rate differences on the\nsurface. Moreover, to ensure sufficient visible light transmittance\nwithout causing light scattering, the nanostructures should have dimensions\npreferably not greater than 100 nm. 20 Reactive\nion etching (RIE) is a representative method for etching glass. Because\nRIE is an anisotropic etching process, microscale or nanoscale structures\ncan be created by constructing a fine mask or sacrificial layer on\nthe surface to produce localized etching rate differences. Reported\nexamples include methods involving the annealing of nickel particles\nto form fine particles, 14 use of polystyrene\nspheres as masks, 15 and that of SiO 2 films as sacrificial layers. 16 However, all of these methods require complex procedures. Additionally,\nRIE requires a vacuum and, thus, necessitates placing the glass inside\na chamber, which presents disadvantages in terms of productivity,\nsize limitations, and cost. In contrast, attempts to create water-repellent\nnanostructures have been made using wet etching with hydrofluoric\nacid solutions, a method traditionally used for glass processing.\nThis method involves spraying a hydrofluoric acid mist onto the glass\nsurface to perform localized etching and create pillar structures\non the surface. 17 However, although this\nmethod excels in terms of material efficiency, as it does not require\na mask, the resulting contact angle is less than 150°. Laser\nablation is another effective method for creating micro-scale structures\non glass surfaces. 18 , 19 While highly hydrophobic surfaces\nhave been formed using this method, structures formed via laser ablation\nhave typically been on the order of micrometers, leading to the scattering\nof visible light. Moreover, the need to scan the entire glass surface\nwith a laser renders this approach less efficient than chemical treatments. A recent study investigated the use of hydrogen fluoride (HF) gas\nfor etching, and this produced a rougher surface. 21 If superhydrophobic structures can be constructed by using\nHF gas, the high-temperature environment required for the glass manufacturing\nprocess can be utilized, allowing the rapid construction of micro-to-nanoscale\nstructures. However, there are limited studies on glass etching using\nHF gas and no reports on its use for achieving water repellency. In\nthis study, we investigated a method to conveniently fabricate nanostructures\nwith both superhydrophobic and antireflective properties by examining\nthe conditions of the reaction between glass and hydrogen fluoride\ngas. Typically, flat glass is manufactured using the float process, 22 wherein a continuous temperature change from\n1000 °C to room temperature occurs during the formation process.\nTechniques to spray reactive gases during the float process have been\nrealized, 23 and nanostructure construction\non the glass surface using this method would facilitate the integration\nof the glass manufacturing and nanostructure construction processes,\nresulting in an extremely productive superhydrophobic structure construction\nprocess. In this study, we adopted hydrochloric acid cleaning, a frequently\nused method in the glass manufacturing process, as a post-treatment,\nwhich should facilitate future process scale-up. We proposed this\nnovel process and examined the nanostructure formation and control\nrealized using the method.",
"discussion": "3 Results and Discussion 3.1 Nanostructure Construction\nand Surface Characterization Figure 3 shows surface\nand cross-sectional SEM images of the untreated substrate in (a) and\n(b), the HF-treated substrate in (c) and (d), and the substrate after\nHF treatment and then cleaning with 10% hydrochloric acid in (e) and\n(f). The HF treatment was conducted at 500 °C with 5% HF for\n4 s. In the HF-treated glass shown in (c) and (d), a porous film,\nformed at the upper 500 nm of the surface, was apparent. Table 1 presents the surface\ncomposition analysis results obtained using XPS for all of the samples,\nand Figure 4 shows\na plot of these results for the HF-treated substrate. The XPS profile\nof each substrate ( Table 1 ) is shown in S1 . In the HF-treated\nglass, the silicon and oxygen contents were significantly lower than\nthose in the untreated glass, whereas the sodium, magnesium, calcium,\nand fluorine contents were substantially higher. This indicates that\nthe fluorination of the oxide progressed and only silicon fluoride,\nwhich has a low boiling point, evaporated, leaving the other metal\nfluorides on the surface. These results indicate that the cationic\nelements in the glass are fluorinated by HF treatment, leading to\nthe formation of a nanoporous structure. These findings are consistent\nwith previous results. 21 Figure 5 shows the XRD patterns of\nthree types of substrates: an untreated substrate, a substrate cleaned\nwith HCl solution after HF treatment, and an HF-treated substrate.\nThese results confirm the transformation of the cationic elements\nin the glass into fluoride crystals owing to their reaction with HF.\nCleaning with an HCl solution removes these crystals, leading to the\nexposure of an amorphous surface. Figure 3 Scanning electron microscopy (SEM) images\nof the untreated substrate\nin (a,b), the HF-treated substrate in (c,d), and the substrate cleaned\nwith 10% hydrochloric acid after HF treatment in (e,f). Table 1 Surface Composition of Each Substrate\nas Measured by X-ray Photoelectron Spectroscopy (XPS) O (1s) F (1s) Na (1s) Mg (2p) Al (2p) Si (2p) Ca (2p) untreated 61.3 0.1 9.2 2.1 1.0 24.8 1.6 HF 1.8 45.4 31.4 9.2 2.0 2.8 7.4 HCl sol. after HF 62.1 1.6 6.5 2.5 0.2 26.5 0.6 Figure 4 Elemental composition as a function of depth in the substrate treated\nwith 5% HF for 4 s at 500 °C. Figure 5 X-ray\ndiffraction (XRD) patterns of untreated substrate,\nthe substrate\ncleaned with HCl solution after HF treatment, and the substrate treated\nwith 5% HF at 500 °C. After the HF-treated substrate was cleaned with\n10% hydrochloric\nacid, the SEM results revealed the presence of nanostructures on the\nsurface. However, XPS surface analysis results indicated that the\ncomposition did not significantly differ from that of untreated glass.\nThis suggests that the fluoride crystals formed on the surface were\nremoved by a hydrochloric acid cleaning. In this substrate, a trace\namount of fluorine was detected, which can be attributed to a small\namount of fluorine being infused into the glass. Figure 6 shows the Si 2p XPS analysis\nresults. It was confirmed that the Si 2p peak was shifted to the high-energy\nside compared to that of the untreated substrate, which is attributed\nto the formation of Si–F bonds. 25 Figure 6 X-ray\nphotoelectron spectroscopy (XPS) spectra of the untreated\nsubstrate and the substrate treated with HF followed by cleaning with\n10% hydrochloric acid. 3.2 Water\nContact Angle Figure 7 shows the water contact angles\nof each substrate after UV-ozone cleaning and subsequent exposure\nto the atmosphere for 30 d. After UV-ozone cleaning, all the substrates\npossessed hydrophilic surfaces with contact angles below 10°.\nHowever, after 30 d in the atmosphere, the contact angle of the untreated\nsubstrate had increased to approximately 30°. This is attributed\nto the adsorption of organic substances from the atmosphere, which\nreduces surface energy. 26 In contrast,\nboth the HF-treated substrates and those subsequently cleaned with\nhydrochloric acid maintained their hydrophilicity even after 30 d,\nwhich may be attributed to surface roughness. Wenzel introduced “ r” to represent surface roughness, defined as the\nratio of the actual surface area to the projected surface area: 27 1 Figure 7 Water contact angle of\neach substrate immediately\nafter ultraviolet-ozone\ncleaning and after subsequent exposure to atmosphere for 30 d. The relationship between r and\nthe apparent contact\nangle is expressed as 2 where is the apparent contact angle and θ Y denotes the contact angle on a flat surface.\nAs can be inferred from Wenzel’s equation, when the contact\nangle on a flat surface is less than 90°, the surface is considered\nhydrophilic. Figure 8 a,b shows the AFM images of the surface after HF treatment followed\nby cleaning with hydrochloric acid. The r values\ncalculated from the AFM images for the surface treated only with HF\nand for the surface after HCl post-treatment were 1.34 and 2.48, respectively.\nTo achieve an apparent contact angle of 10° or less when the\nflat-surface contact angle is 30°, r must be\n1.14 or higher; thus, these surfaces can retain sufficient hydrophilicity\neven when organic substances from the atmosphere are adsorbed. As\ncondensation occurs when small liquid droplets adhere to the surface,\nthis surface is useful for antifogging applications. 28 , 29 Fogging due to condensation on automotive glass is a safety hazard.\nThis method can be used to address this problem. Figure 8 Atomic force microscopy\n(AFM) images of the substrate after HF\ntreatment (a) and of the substrate after HF treatment and then cleaning\nwith hydrochloric acid (b); height histograms obtained from each AFM\nimage with the average height set to zero (c). Figure 9 shows the\nwater contact angles of each surface after the FAS-13 coating had\nbeen applied. The untreated glass had a contact angle of 103°,\nwhich is the contact angle of FAS-13 on a flat surface and is consistent\nwith previous results. 24 However, the HF-treated\nglass and the glass treated with hydrochloric acid after the HF treatment\nhad water contact angles of 131° and 152°, respectively.\nThese increased contact angles cannot be explained by the Wenzel model\nalone. For a flat-surface contact angle of 103°, r must be 3.85° or higher to achieve a contact angle of over\n150°, owing to the Wenzel effect. However, the presence of an\nair layer at the solid–liquid interface can increase the contact\nangle. 30 The Cassie–Baxter model\nexplains this effect with the following equation: 3 where is the apparent contact angle and f is the solid–liquid\ncontact area fraction. According\nto this equation, to achieve contact angles of 131° and 152°\nwhen the flat-surface contact angle is 103°, the f values were 0.44 and 0.15, respectively. Figure 8 shows the height histograms created by analyzing\nthe AFM images with the average height set to zero. The surface treated\nwith hydrochloric acid after HF treatment yielded a histogram with\nmany low values and a few high values (protrusions from the surface).\nThis is consistent with the SEM image results shown in Figure 3 . On surfaces where a few sparse\nprotrusions sparsely exist, water can be supported near the protrusion,\nthus reducing the solid–liquid contact area fraction f . Because of these shape differences, f decreased after hydrochloric acid cleaning, significantly increasing\nthe surface hydrophobicity. Figure 9 Images of the water droplets placed on (a) the\nuntreated substrate,\n(b) the substrate treated with HF, and (c) the substrate cleaned with\nhydrochloric acid after HF treatment and the subsequent deposition\nof FAS-13. (d) Water contact angles for each substrate. 3.3 Changes Owing to Processing Conditions The upper and lower rows of panels in Figure 10 show surface and cross-sectional SEM images\nof substrates prepared without and with the HCl post-treatment, respectively,\nprocessed by using different reaction temperatures. The processing\nconditions were 5% HF and a reaction time of 4 s. As the reaction\ntemperature increased, the sizes of the grains and pores forming the\nnanoporous structure also increased. The reaction rate between HF\nand glass increases with increasing reaction temperature, leading\nto the formation of a thicker fluoride layer within a set time. Therefore,\nin this study, the fluoride layer thickens with an increase in reaction\ntemperature up to 500 °C. However, when the temperature exceeds\n550 °C, the thickness of the fluoride layer unexpectedly decreases.\nThis suggests that from this temperature, the increase in the rate\nof fluoride sublimation surpasses the increase in the rate of formation. Figure 11 shows the DTA\nresults for the fluoride produced at 500 °C, which features an\nendothermic peak without weight loss at approximately 730 °C,\nlikely corresponding to the melting point of the fluoride layer. Therefore,\nat the experimental temperatures used in this study, the fluoride\nlayer existed in the solid state, suggesting that its size and shape\nchanged as a result of a solid-phase reaction. Figure 12 displays magnified SEM images of the substrates\nprepared by HF treatment at 500 and 600 °C, with and without\nhydrochloric acid post-treatment. The surface morphology after the\nhydrochloric acid treatment resembled that of the interface between\nthe fluoride layer and the glass, and the feature size was similar\nto that of the formed fluoride layer before the hydrochloric acid\ntreatment. These results suggest that the fluoride and glass undergo\na reaction that results in the formation of nanostructures on the\nsurface via HF gas etching. Therefore, to control the shape of the\nnanostructure after hydrochloric acid treatment, the shape of the\nfluoride layer should be properly controlled. Figure 10 Scanning electron microscopy\n(SEM) images of the surface and cross\nsections of the substrates treated at various temperatures. The upper\nrow shows substrates treated with HF, and the lower row shows those\nfurther cleaned with hydrochloric acid. Figure 11 Thermogravimetry–differential\nthermal analysis\n(TG-DTA)\nresults for the fluoride formed at 500 °C. The peak at approximately\n730 °C indicates the melting point. Figure 12 Cross-sectional\nscanning electron microscopy (SEM) images\nof the\nsubstrates treated at 500 and 600 °C: (a,b) substrates after\nHF treatment, while (c,d) substrates after HF treatment followed by\nhydrochloric acid cleaning. Figure 13 shows\nthe water contact angles of the substrates reacted using various temperature\nconditions. Table 2 lists the advancing contact angle (CAA), receding contact angle\n(CAR), and contact angle hysteresis (CAH) values for surfaces that\nshowed a static contact angle greater than 150°. After the HF\ntreatment and subsequent hydrochloric acid cleaning, as the temperature\nincreased, the static contact angle increased, reaching 162°\nfor the substrate treated at 650 °C after hydrochloric acid cleaning.\nFurthermore, for each treatment temperature, the contact angles of\nthe substrates after hydrochloric acid treatment were higher than\nthose of the substrates after HF treatment alone. This demonstrates\nthat the effect of hydrochloric acid cleaning, as discussed in Section 3.2 , holds true\nunder any temperature condition, emphasizing the importance of hydrochloric\nacid cleaning. Increasing the treatment temperature resulted in an\nincrease in the size of the fluoride layer, leading to an increased\nsurface roughness at the interface with the glass. This was maintained\nafter the hydrochloric acid treatment, increasing the spacing between\nsurface protrusions and reducing the solid–liquid contact area. Figure 13 Top:\nwater contact angle vs treatment temperature. The lower images\nshow images of a water contact angle of (a) 143° after HF treatment\nonly at 650 °C and (b) 162° after HF treatment at 650 °C\nfollowed by hydrochloric acid cleaning. Table 2 Advancing Contact Angle (CAA), Receding\nContact Angle (CAR), and Contact Angle Hysteresis (CAH) Values of\nSurfaces Cleaned with Hydrochloric Acid after HF Treatment at Various\nTemperatures CAA CAR CAH untreated 113° 100° 13° 500\n°C 170° 139° 30° 550 °C 169° 138° 31° 600\n°C 169° 147° 22° 650 °C 167° 164° 3° 3.4 Optical Properties Figure 14 presents\nthe transmittance\nresults of the substrates after hydrochloric acid treatment under\nvarious reaction conditions. The “theoretical max” transmittance\nvalue is the theoretical maximum transmittance for single-sided coated\nglass, derived using the following equation: 13 4 where T total is\nthe transmittance of the untreated substrate. These results indicate\nthat the treatment increased the transmittance of the substrate. The\neffective medium theory can be used to calculate the effective refractive\nindex, n eff , as follows: 31 , 32 5 where f represents the filling\nfactor and n g and n air are the refractive indices of glass and air, respectively.\nThis equation suggests that the effective refractive index gradually\ndecreases from the inside of the glass surface owing to the pores\nformed on the glass surface, which indicates an antireflective surface.\nAdditionally, as the treatment temperature was increased to 600 °C,\nthe transmittance improved. This is believed to be because the proportion\nof pores on the surface increased, approaching the ideal value for\nan antireflection film. At a treatment temperature of 650 °C,\nthe transmittance in the low-wavelength range decreased, suggesting\nthat the increased roughness scattered low-wavelength light. Therefore,\nwhen constructing an antireflection layer on glass using this method,\nit is essential to carefully consider the processing conditions to\nappropriately control the shape of the interface between the fluoride\nand glass layers. Figure 14 Transmittance results for substrates cleaned with hydrochloric\nacid after HF treatment at various temperatures. The transmittance\nin the visible light range was improved with respect to the untreated\nsurface for the surfaces treated at each temperature."
} | 5,501 |
39730143 | PMC11730080 | pmc | 3,757 | {
"abstract": "Abstract In this review, we focus on how purple non-sulfur bacteria can be leveraged for sustainable bioproduction to support the circular economy. We discuss the state of the field with respect to the use of purple bacteria for energy production, their role in wastewater treatment, as a fertilizer, and as a chassis for bioplastic production. We explore their ability to serve as single-cell protein and production platforms for fine chemicals from waste materials. We also introduce more Avant-Garde technologies that leverage the unique metabolisms of purple bacteria, including microbial electrosynthesis and co-culture. These technologies will be pivotal in our efforts to mitigate climate change and circularize the economy in the next two decades. One-sentence summary Purple non-sulfur bacteria are utilized for a range of biotechnological applications, including the production of bio-energy, single cell protein, fertilizer, bioplastics, fine chemicals, in wastewater treatment and in novel applications like co-cultures and microbial electrosynthesis.",
"conclusion": "Conclusion In this review, we discussed the metabolic capabilities of PNSB and how they are leveraged for biotechnological applications. Their unique metabolism offers advantages for use in energy production, wastewater treatment, and resource recovery, as well as fertilizer production and SCP. They are also capable of fine chemical production (e.g. carotenoids and CoQ10), and have begun to be explored in depth for their ability to perform electrotrophy and promote valuable compound production in co-culture. The ability of PNSB to use a variety of electron donors and grow under a wide range of environmental conditions make them a suitable candidate for overcoming the high industrial cost points of feedstock and culturing conditions. Many groups have already demonstrated their ability to grow on and remediate wastewater, and optimization of reactor conditions continues to develop beyond lab-scale. In the future, PNSB will undoubtedly play a critical role in developing the CE in the global shift towards a sustainable way of life.",
"introduction": "Introduction In 2022, the ever-increasing global population surpassed eight billion, continuing to exert a heavy burden on the environment and economy (Ritchie et al., 2022 ). Under a business-as-usual situation, waste production is projected to increase by 70% to 3.40 billion tons by 2050. This waste includes non-recyclable products such as synthetic plastics, as well as food and green waste, metals, paper, glass and more [not accounting for greenhouse gas (GHG)-producing energy and commercial processes] (Kaza et al., 2018 ). By 2050, total food demand is expected to increase by more than 30%, putting up to 8% of the global population at risk of hunger (AbdelRahman, 2023 ; Van Dijk et al., 2021 ). To mitigate the negative effects of our current production processes and answer the demand for basic resources, the world must move toward a sustainable circular economy (CE). The CE focuses on reusing, reducing, and recycling with renewable inputs, rather than dead-end resources such as petroleum (Kirchherr et al., 2023 ). The goal is to promote both economic development and environmental sustainability, a feat that many countries display great enthusiasm for but struggle to implement (Vogiantzi & Tserpes, 2023 ). As such, it is critical to develop methods that will promote the effectiveness of the CE, namely approaches that focus on incorporating lost or \"waste\" components, such as end-of-life products, back into value-added products or processes, like energy generation. A major player in building this economy will be microorganisms that fix carbon and remove GHGs, produce valuable compounds for the energy, food, and pharmaceutical industries, and remediate toxic wastes (Jain et al., 2022 ). One group of microorganisms that can perform all these functions, making them an attractive target for contributing to the CE, are the purple non-sulfur bacteria (PNSB). PNSB possess some of the most versatile metabolisms in the microbial world, and as mixotrophs, they can perform the majority of primary metabolisms (described by energy, electron, and carbon source) in addition to specialized processes such as N 2 fixation and H 2 production (Madigan & Jung, 2009 ). They are tolerant to stresses such as high salinity, heavy metals, and toxic contaminants (e.g. H 2 S), and can grow in aerobic and anaerobic conditions on a variety of carbon and electron sources, including raw waste streams (Madigan & Jung, 2009 ). PNSB also naturally produce multiple economically valuable products, including biofuels (Gabrielyan et al., 2015 ; Vasiliadou et al., 2018 ), bioplastics (Higuchi-Takeuchi et al., 2016 ; Monroy & Buitrón, 2020 ; Ranaivoarisoa et al., 2019 ), and fine chemicals (He et al., 2021 ; Sasikala & Ramana, 1995 ; Wang et al., 2012 ). Furthermore, they are proficient at removing organics from wastewater (Dhar et al., 2023 ; Lu et al., 2019 ), and the resulting biomass is valuable as fertilizer (Maeda, 2021 ; Sundar & Chao, 2022 ) and feedstock (Delamare-Deboutteville et al., 2019 ; Hülsen et al., 2018a ; Wada et al., 2022 ). Altogether, these traits make PNSB an extremely attractive target for biotechnological applications compared to current chemical or purely enzymatic methods, which are limited in their lack of self-repair, specific applications, and need for highly specific operating conditions (Asif et al., 2021 ). Here, we discuss how PNSB can be leveraged for sustainable bioproduction, as well as limitations to their widespread application."
} | 1,405 |
39730143 | PMC11730080 | pmc | 3,757 | {
"abstract": "Abstract In this review, we focus on how purple non-sulfur bacteria can be leveraged for sustainable bioproduction to support the circular economy. We discuss the state of the field with respect to the use of purple bacteria for energy production, their role in wastewater treatment, as a fertilizer, and as a chassis for bioplastic production. We explore their ability to serve as single-cell protein and production platforms for fine chemicals from waste materials. We also introduce more Avant-Garde technologies that leverage the unique metabolisms of purple bacteria, including microbial electrosynthesis and co-culture. These technologies will be pivotal in our efforts to mitigate climate change and circularize the economy in the next two decades. One-sentence summary Purple non-sulfur bacteria are utilized for a range of biotechnological applications, including the production of bio-energy, single cell protein, fertilizer, bioplastics, fine chemicals, in wastewater treatment and in novel applications like co-cultures and microbial electrosynthesis.",
"conclusion": "Conclusion In this review, we discussed the metabolic capabilities of PNSB and how they are leveraged for biotechnological applications. Their unique metabolism offers advantages for use in energy production, wastewater treatment, and resource recovery, as well as fertilizer production and SCP. They are also capable of fine chemical production (e.g. carotenoids and CoQ10), and have begun to be explored in depth for their ability to perform electrotrophy and promote valuable compound production in co-culture. The ability of PNSB to use a variety of electron donors and grow under a wide range of environmental conditions make them a suitable candidate for overcoming the high industrial cost points of feedstock and culturing conditions. Many groups have already demonstrated their ability to grow on and remediate wastewater, and optimization of reactor conditions continues to develop beyond lab-scale. In the future, PNSB will undoubtedly play a critical role in developing the CE in the global shift towards a sustainable way of life.",
"introduction": "Introduction In 2022, the ever-increasing global population surpassed eight billion, continuing to exert a heavy burden on the environment and economy (Ritchie et al., 2022 ). Under a business-as-usual situation, waste production is projected to increase by 70% to 3.40 billion tons by 2050. This waste includes non-recyclable products such as synthetic plastics, as well as food and green waste, metals, paper, glass and more [not accounting for greenhouse gas (GHG)-producing energy and commercial processes] (Kaza et al., 2018 ). By 2050, total food demand is expected to increase by more than 30%, putting up to 8% of the global population at risk of hunger (AbdelRahman, 2023 ; Van Dijk et al., 2021 ). To mitigate the negative effects of our current production processes and answer the demand for basic resources, the world must move toward a sustainable circular economy (CE). The CE focuses on reusing, reducing, and recycling with renewable inputs, rather than dead-end resources such as petroleum (Kirchherr et al., 2023 ). The goal is to promote both economic development and environmental sustainability, a feat that many countries display great enthusiasm for but struggle to implement (Vogiantzi & Tserpes, 2023 ). As such, it is critical to develop methods that will promote the effectiveness of the CE, namely approaches that focus on incorporating lost or \"waste\" components, such as end-of-life products, back into value-added products or processes, like energy generation. A major player in building this economy will be microorganisms that fix carbon and remove GHGs, produce valuable compounds for the energy, food, and pharmaceutical industries, and remediate toxic wastes (Jain et al., 2022 ). One group of microorganisms that can perform all these functions, making them an attractive target for contributing to the CE, are the purple non-sulfur bacteria (PNSB). PNSB possess some of the most versatile metabolisms in the microbial world, and as mixotrophs, they can perform the majority of primary metabolisms (described by energy, electron, and carbon source) in addition to specialized processes such as N 2 fixation and H 2 production (Madigan & Jung, 2009 ). They are tolerant to stresses such as high salinity, heavy metals, and toxic contaminants (e.g. H 2 S), and can grow in aerobic and anaerobic conditions on a variety of carbon and electron sources, including raw waste streams (Madigan & Jung, 2009 ). PNSB also naturally produce multiple economically valuable products, including biofuels (Gabrielyan et al., 2015 ; Vasiliadou et al., 2018 ), bioplastics (Higuchi-Takeuchi et al., 2016 ; Monroy & Buitrón, 2020 ; Ranaivoarisoa et al., 2019 ), and fine chemicals (He et al., 2021 ; Sasikala & Ramana, 1995 ; Wang et al., 2012 ). Furthermore, they are proficient at removing organics from wastewater (Dhar et al., 2023 ; Lu et al., 2019 ), and the resulting biomass is valuable as fertilizer (Maeda, 2021 ; Sundar & Chao, 2022 ) and feedstock (Delamare-Deboutteville et al., 2019 ; Hülsen et al., 2018a ; Wada et al., 2022 ). Altogether, these traits make PNSB an extremely attractive target for biotechnological applications compared to current chemical or purely enzymatic methods, which are limited in their lack of self-repair, specific applications, and need for highly specific operating conditions (Asif et al., 2021 ). Here, we discuss how PNSB can be leveraged for sustainable bioproduction, as well as limitations to their widespread application."
} | 1,405 |
39730143 | PMC11730080 | pmc | 3,757 | {
"abstract": "Abstract In this review, we focus on how purple non-sulfur bacteria can be leveraged for sustainable bioproduction to support the circular economy. We discuss the state of the field with respect to the use of purple bacteria for energy production, their role in wastewater treatment, as a fertilizer, and as a chassis for bioplastic production. We explore their ability to serve as single-cell protein and production platforms for fine chemicals from waste materials. We also introduce more Avant-Garde technologies that leverage the unique metabolisms of purple bacteria, including microbial electrosynthesis and co-culture. These technologies will be pivotal in our efforts to mitigate climate change and circularize the economy in the next two decades. One-sentence summary Purple non-sulfur bacteria are utilized for a range of biotechnological applications, including the production of bio-energy, single cell protein, fertilizer, bioplastics, fine chemicals, in wastewater treatment and in novel applications like co-cultures and microbial electrosynthesis.",
"conclusion": "Conclusion In this review, we discussed the metabolic capabilities of PNSB and how they are leveraged for biotechnological applications. Their unique metabolism offers advantages for use in energy production, wastewater treatment, and resource recovery, as well as fertilizer production and SCP. They are also capable of fine chemical production (e.g. carotenoids and CoQ10), and have begun to be explored in depth for their ability to perform electrotrophy and promote valuable compound production in co-culture. The ability of PNSB to use a variety of electron donors and grow under a wide range of environmental conditions make them a suitable candidate for overcoming the high industrial cost points of feedstock and culturing conditions. Many groups have already demonstrated their ability to grow on and remediate wastewater, and optimization of reactor conditions continues to develop beyond lab-scale. In the future, PNSB will undoubtedly play a critical role in developing the CE in the global shift towards a sustainable way of life.",
"introduction": "Introduction In 2022, the ever-increasing global population surpassed eight billion, continuing to exert a heavy burden on the environment and economy (Ritchie et al., 2022 ). Under a business-as-usual situation, waste production is projected to increase by 70% to 3.40 billion tons by 2050. This waste includes non-recyclable products such as synthetic plastics, as well as food and green waste, metals, paper, glass and more [not accounting for greenhouse gas (GHG)-producing energy and commercial processes] (Kaza et al., 2018 ). By 2050, total food demand is expected to increase by more than 30%, putting up to 8% of the global population at risk of hunger (AbdelRahman, 2023 ; Van Dijk et al., 2021 ). To mitigate the negative effects of our current production processes and answer the demand for basic resources, the world must move toward a sustainable circular economy (CE). The CE focuses on reusing, reducing, and recycling with renewable inputs, rather than dead-end resources such as petroleum (Kirchherr et al., 2023 ). The goal is to promote both economic development and environmental sustainability, a feat that many countries display great enthusiasm for but struggle to implement (Vogiantzi & Tserpes, 2023 ). As such, it is critical to develop methods that will promote the effectiveness of the CE, namely approaches that focus on incorporating lost or \"waste\" components, such as end-of-life products, back into value-added products or processes, like energy generation. A major player in building this economy will be microorganisms that fix carbon and remove GHGs, produce valuable compounds for the energy, food, and pharmaceutical industries, and remediate toxic wastes (Jain et al., 2022 ). One group of microorganisms that can perform all these functions, making them an attractive target for contributing to the CE, are the purple non-sulfur bacteria (PNSB). PNSB possess some of the most versatile metabolisms in the microbial world, and as mixotrophs, they can perform the majority of primary metabolisms (described by energy, electron, and carbon source) in addition to specialized processes such as N 2 fixation and H 2 production (Madigan & Jung, 2009 ). They are tolerant to stresses such as high salinity, heavy metals, and toxic contaminants (e.g. H 2 S), and can grow in aerobic and anaerobic conditions on a variety of carbon and electron sources, including raw waste streams (Madigan & Jung, 2009 ). PNSB also naturally produce multiple economically valuable products, including biofuels (Gabrielyan et al., 2015 ; Vasiliadou et al., 2018 ), bioplastics (Higuchi-Takeuchi et al., 2016 ; Monroy & Buitrón, 2020 ; Ranaivoarisoa et al., 2019 ), and fine chemicals (He et al., 2021 ; Sasikala & Ramana, 1995 ; Wang et al., 2012 ). Furthermore, they are proficient at removing organics from wastewater (Dhar et al., 2023 ; Lu et al., 2019 ), and the resulting biomass is valuable as fertilizer (Maeda, 2021 ; Sundar & Chao, 2022 ) and feedstock (Delamare-Deboutteville et al., 2019 ; Hülsen et al., 2018a ; Wada et al., 2022 ). Altogether, these traits make PNSB an extremely attractive target for biotechnological applications compared to current chemical or purely enzymatic methods, which are limited in their lack of self-repair, specific applications, and need for highly specific operating conditions (Asif et al., 2021 ). Here, we discuss how PNSB can be leveraged for sustainable bioproduction, as well as limitations to their widespread application."
} | 1,405 |
39730143 | PMC11730080 | pmc | 3,757 | {
"abstract": "Abstract In this review, we focus on how purple non-sulfur bacteria can be leveraged for sustainable bioproduction to support the circular economy. We discuss the state of the field with respect to the use of purple bacteria for energy production, their role in wastewater treatment, as a fertilizer, and as a chassis for bioplastic production. We explore their ability to serve as single-cell protein and production platforms for fine chemicals from waste materials. We also introduce more Avant-Garde technologies that leverage the unique metabolisms of purple bacteria, including microbial electrosynthesis and co-culture. These technologies will be pivotal in our efforts to mitigate climate change and circularize the economy in the next two decades. One-sentence summary Purple non-sulfur bacteria are utilized for a range of biotechnological applications, including the production of bio-energy, single cell protein, fertilizer, bioplastics, fine chemicals, in wastewater treatment and in novel applications like co-cultures and microbial electrosynthesis.",
"conclusion": "Conclusion In this review, we discussed the metabolic capabilities of PNSB and how they are leveraged for biotechnological applications. Their unique metabolism offers advantages for use in energy production, wastewater treatment, and resource recovery, as well as fertilizer production and SCP. They are also capable of fine chemical production (e.g. carotenoids and CoQ10), and have begun to be explored in depth for their ability to perform electrotrophy and promote valuable compound production in co-culture. The ability of PNSB to use a variety of electron donors and grow under a wide range of environmental conditions make them a suitable candidate for overcoming the high industrial cost points of feedstock and culturing conditions. Many groups have already demonstrated their ability to grow on and remediate wastewater, and optimization of reactor conditions continues to develop beyond lab-scale. In the future, PNSB will undoubtedly play a critical role in developing the CE in the global shift towards a sustainable way of life.",
"introduction": "Introduction In 2022, the ever-increasing global population surpassed eight billion, continuing to exert a heavy burden on the environment and economy (Ritchie et al., 2022 ). Under a business-as-usual situation, waste production is projected to increase by 70% to 3.40 billion tons by 2050. This waste includes non-recyclable products such as synthetic plastics, as well as food and green waste, metals, paper, glass and more [not accounting for greenhouse gas (GHG)-producing energy and commercial processes] (Kaza et al., 2018 ). By 2050, total food demand is expected to increase by more than 30%, putting up to 8% of the global population at risk of hunger (AbdelRahman, 2023 ; Van Dijk et al., 2021 ). To mitigate the negative effects of our current production processes and answer the demand for basic resources, the world must move toward a sustainable circular economy (CE). The CE focuses on reusing, reducing, and recycling with renewable inputs, rather than dead-end resources such as petroleum (Kirchherr et al., 2023 ). The goal is to promote both economic development and environmental sustainability, a feat that many countries display great enthusiasm for but struggle to implement (Vogiantzi & Tserpes, 2023 ). As such, it is critical to develop methods that will promote the effectiveness of the CE, namely approaches that focus on incorporating lost or \"waste\" components, such as end-of-life products, back into value-added products or processes, like energy generation. A major player in building this economy will be microorganisms that fix carbon and remove GHGs, produce valuable compounds for the energy, food, and pharmaceutical industries, and remediate toxic wastes (Jain et al., 2022 ). One group of microorganisms that can perform all these functions, making them an attractive target for contributing to the CE, are the purple non-sulfur bacteria (PNSB). PNSB possess some of the most versatile metabolisms in the microbial world, and as mixotrophs, they can perform the majority of primary metabolisms (described by energy, electron, and carbon source) in addition to specialized processes such as N 2 fixation and H 2 production (Madigan & Jung, 2009 ). They are tolerant to stresses such as high salinity, heavy metals, and toxic contaminants (e.g. H 2 S), and can grow in aerobic and anaerobic conditions on a variety of carbon and electron sources, including raw waste streams (Madigan & Jung, 2009 ). PNSB also naturally produce multiple economically valuable products, including biofuels (Gabrielyan et al., 2015 ; Vasiliadou et al., 2018 ), bioplastics (Higuchi-Takeuchi et al., 2016 ; Monroy & Buitrón, 2020 ; Ranaivoarisoa et al., 2019 ), and fine chemicals (He et al., 2021 ; Sasikala & Ramana, 1995 ; Wang et al., 2012 ). Furthermore, they are proficient at removing organics from wastewater (Dhar et al., 2023 ; Lu et al., 2019 ), and the resulting biomass is valuable as fertilizer (Maeda, 2021 ; Sundar & Chao, 2022 ) and feedstock (Delamare-Deboutteville et al., 2019 ; Hülsen et al., 2018a ; Wada et al., 2022 ). Altogether, these traits make PNSB an extremely attractive target for biotechnological applications compared to current chemical or purely enzymatic methods, which are limited in their lack of self-repair, specific applications, and need for highly specific operating conditions (Asif et al., 2021 ). Here, we discuss how PNSB can be leveraged for sustainable bioproduction, as well as limitations to their widespread application."
} | 1,405 |
39730143 | PMC11730080 | pmc | 3,758 | {
"abstract": "Abstract In this review, we focus on how purple non-sulfur bacteria can be leveraged for sustainable bioproduction to support the circular economy. We discuss the state of the field with respect to the use of purple bacteria for energy production, their role in wastewater treatment, as a fertilizer, and as a chassis for bioplastic production. We explore their ability to serve as single-cell protein and production platforms for fine chemicals from waste materials. We also introduce more Avant-Garde technologies that leverage the unique metabolisms of purple bacteria, including microbial electrosynthesis and co-culture. These technologies will be pivotal in our efforts to mitigate climate change and circularize the economy in the next two decades. One-sentence summary Purple non-sulfur bacteria are utilized for a range of biotechnological applications, including the production of bio-energy, single cell protein, fertilizer, bioplastics, fine chemicals, in wastewater treatment and in novel applications like co-cultures and microbial electrosynthesis.",
"conclusion": "Conclusion In this review, we discussed the metabolic capabilities of PNSB and how they are leveraged for biotechnological applications. Their unique metabolism offers advantages for use in energy production, wastewater treatment, and resource recovery, as well as fertilizer production and SCP. They are also capable of fine chemical production (e.g. carotenoids and CoQ10), and have begun to be explored in depth for their ability to perform electrotrophy and promote valuable compound production in co-culture. The ability of PNSB to use a variety of electron donors and grow under a wide range of environmental conditions make them a suitable candidate for overcoming the high industrial cost points of feedstock and culturing conditions. Many groups have already demonstrated their ability to grow on and remediate wastewater, and optimization of reactor conditions continues to develop beyond lab-scale. In the future, PNSB will undoubtedly play a critical role in developing the CE in the global shift towards a sustainable way of life.",
"introduction": "Introduction In 2022, the ever-increasing global population surpassed eight billion, continuing to exert a heavy burden on the environment and economy (Ritchie et al., 2022 ). Under a business-as-usual situation, waste production is projected to increase by 70% to 3.40 billion tons by 2050. This waste includes non-recyclable products such as synthetic plastics, as well as food and green waste, metals, paper, glass and more [not accounting for greenhouse gas (GHG)-producing energy and commercial processes] (Kaza et al., 2018 ). By 2050, total food demand is expected to increase by more than 30%, putting up to 8% of the global population at risk of hunger (AbdelRahman, 2023 ; Van Dijk et al., 2021 ). To mitigate the negative effects of our current production processes and answer the demand for basic resources, the world must move toward a sustainable circular economy (CE). The CE focuses on reusing, reducing, and recycling with renewable inputs, rather than dead-end resources such as petroleum (Kirchherr et al., 2023 ). The goal is to promote both economic development and environmental sustainability, a feat that many countries display great enthusiasm for but struggle to implement (Vogiantzi & Tserpes, 2023 ). As such, it is critical to develop methods that will promote the effectiveness of the CE, namely approaches that focus on incorporating lost or \"waste\" components, such as end-of-life products, back into value-added products or processes, like energy generation. A major player in building this economy will be microorganisms that fix carbon and remove GHGs, produce valuable compounds for the energy, food, and pharmaceutical industries, and remediate toxic wastes (Jain et al., 2022 ). One group of microorganisms that can perform all these functions, making them an attractive target for contributing to the CE, are the purple non-sulfur bacteria (PNSB). PNSB possess some of the most versatile metabolisms in the microbial world, and as mixotrophs, they can perform the majority of primary metabolisms (described by energy, electron, and carbon source) in addition to specialized processes such as N 2 fixation and H 2 production (Madigan & Jung, 2009 ). They are tolerant to stresses such as high salinity, heavy metals, and toxic contaminants (e.g. H 2 S), and can grow in aerobic and anaerobic conditions on a variety of carbon and electron sources, including raw waste streams (Madigan & Jung, 2009 ). PNSB also naturally produce multiple economically valuable products, including biofuels (Gabrielyan et al., 2015 ; Vasiliadou et al., 2018 ), bioplastics (Higuchi-Takeuchi et al., 2016 ; Monroy & Buitrón, 2020 ; Ranaivoarisoa et al., 2019 ), and fine chemicals (He et al., 2021 ; Sasikala & Ramana, 1995 ; Wang et al., 2012 ). Furthermore, they are proficient at removing organics from wastewater (Dhar et al., 2023 ; Lu et al., 2019 ), and the resulting biomass is valuable as fertilizer (Maeda, 2021 ; Sundar & Chao, 2022 ) and feedstock (Delamare-Deboutteville et al., 2019 ; Hülsen et al., 2018a ; Wada et al., 2022 ). Altogether, these traits make PNSB an extremely attractive target for biotechnological applications compared to current chemical or purely enzymatic methods, which are limited in their lack of self-repair, specific applications, and need for highly specific operating conditions (Asif et al., 2021 ). Here, we discuss how PNSB can be leveraged for sustainable bioproduction, as well as limitations to their widespread application."
} | 1,405 |
39730143 | PMC11730080 | pmc | 3,758 | {
"abstract": "Abstract In this review, we focus on how purple non-sulfur bacteria can be leveraged for sustainable bioproduction to support the circular economy. We discuss the state of the field with respect to the use of purple bacteria for energy production, their role in wastewater treatment, as a fertilizer, and as a chassis for bioplastic production. We explore their ability to serve as single-cell protein and production platforms for fine chemicals from waste materials. We also introduce more Avant-Garde technologies that leverage the unique metabolisms of purple bacteria, including microbial electrosynthesis and co-culture. These technologies will be pivotal in our efforts to mitigate climate change and circularize the economy in the next two decades. One-sentence summary Purple non-sulfur bacteria are utilized for a range of biotechnological applications, including the production of bio-energy, single cell protein, fertilizer, bioplastics, fine chemicals, in wastewater treatment and in novel applications like co-cultures and microbial electrosynthesis.",
"conclusion": "Conclusion In this review, we discussed the metabolic capabilities of PNSB and how they are leveraged for biotechnological applications. Their unique metabolism offers advantages for use in energy production, wastewater treatment, and resource recovery, as well as fertilizer production and SCP. They are also capable of fine chemical production (e.g. carotenoids and CoQ10), and have begun to be explored in depth for their ability to perform electrotrophy and promote valuable compound production in co-culture. The ability of PNSB to use a variety of electron donors and grow under a wide range of environmental conditions make them a suitable candidate for overcoming the high industrial cost points of feedstock and culturing conditions. Many groups have already demonstrated their ability to grow on and remediate wastewater, and optimization of reactor conditions continues to develop beyond lab-scale. In the future, PNSB will undoubtedly play a critical role in developing the CE in the global shift towards a sustainable way of life.",
"introduction": "Introduction In 2022, the ever-increasing global population surpassed eight billion, continuing to exert a heavy burden on the environment and economy (Ritchie et al., 2022 ). Under a business-as-usual situation, waste production is projected to increase by 70% to 3.40 billion tons by 2050. This waste includes non-recyclable products such as synthetic plastics, as well as food and green waste, metals, paper, glass and more [not accounting for greenhouse gas (GHG)-producing energy and commercial processes] (Kaza et al., 2018 ). By 2050, total food demand is expected to increase by more than 30%, putting up to 8% of the global population at risk of hunger (AbdelRahman, 2023 ; Van Dijk et al., 2021 ). To mitigate the negative effects of our current production processes and answer the demand for basic resources, the world must move toward a sustainable circular economy (CE). The CE focuses on reusing, reducing, and recycling with renewable inputs, rather than dead-end resources such as petroleum (Kirchherr et al., 2023 ). The goal is to promote both economic development and environmental sustainability, a feat that many countries display great enthusiasm for but struggle to implement (Vogiantzi & Tserpes, 2023 ). As such, it is critical to develop methods that will promote the effectiveness of the CE, namely approaches that focus on incorporating lost or \"waste\" components, such as end-of-life products, back into value-added products or processes, like energy generation. A major player in building this economy will be microorganisms that fix carbon and remove GHGs, produce valuable compounds for the energy, food, and pharmaceutical industries, and remediate toxic wastes (Jain et al., 2022 ). One group of microorganisms that can perform all these functions, making them an attractive target for contributing to the CE, are the purple non-sulfur bacteria (PNSB). PNSB possess some of the most versatile metabolisms in the microbial world, and as mixotrophs, they can perform the majority of primary metabolisms (described by energy, electron, and carbon source) in addition to specialized processes such as N 2 fixation and H 2 production (Madigan & Jung, 2009 ). They are tolerant to stresses such as high salinity, heavy metals, and toxic contaminants (e.g. H 2 S), and can grow in aerobic and anaerobic conditions on a variety of carbon and electron sources, including raw waste streams (Madigan & Jung, 2009 ). PNSB also naturally produce multiple economically valuable products, including biofuels (Gabrielyan et al., 2015 ; Vasiliadou et al., 2018 ), bioplastics (Higuchi-Takeuchi et al., 2016 ; Monroy & Buitrón, 2020 ; Ranaivoarisoa et al., 2019 ), and fine chemicals (He et al., 2021 ; Sasikala & Ramana, 1995 ; Wang et al., 2012 ). Furthermore, they are proficient at removing organics from wastewater (Dhar et al., 2023 ; Lu et al., 2019 ), and the resulting biomass is valuable as fertilizer (Maeda, 2021 ; Sundar & Chao, 2022 ) and feedstock (Delamare-Deboutteville et al., 2019 ; Hülsen et al., 2018a ; Wada et al., 2022 ). Altogether, these traits make PNSB an extremely attractive target for biotechnological applications compared to current chemical or purely enzymatic methods, which are limited in their lack of self-repair, specific applications, and need for highly specific operating conditions (Asif et al., 2021 ). Here, we discuss how PNSB can be leveraged for sustainable bioproduction, as well as limitations to their widespread application."
} | 1,405 |
39730143 | PMC11730080 | pmc | 3,758 | {
"abstract": "Abstract In this review, we focus on how purple non-sulfur bacteria can be leveraged for sustainable bioproduction to support the circular economy. We discuss the state of the field with respect to the use of purple bacteria for energy production, their role in wastewater treatment, as a fertilizer, and as a chassis for bioplastic production. We explore their ability to serve as single-cell protein and production platforms for fine chemicals from waste materials. We also introduce more Avant-Garde technologies that leverage the unique metabolisms of purple bacteria, including microbial electrosynthesis and co-culture. These technologies will be pivotal in our efforts to mitigate climate change and circularize the economy in the next two decades. One-sentence summary Purple non-sulfur bacteria are utilized for a range of biotechnological applications, including the production of bio-energy, single cell protein, fertilizer, bioplastics, fine chemicals, in wastewater treatment and in novel applications like co-cultures and microbial electrosynthesis.",
"conclusion": "Conclusion In this review, we discussed the metabolic capabilities of PNSB and how they are leveraged for biotechnological applications. Their unique metabolism offers advantages for use in energy production, wastewater treatment, and resource recovery, as well as fertilizer production and SCP. They are also capable of fine chemical production (e.g. carotenoids and CoQ10), and have begun to be explored in depth for their ability to perform electrotrophy and promote valuable compound production in co-culture. The ability of PNSB to use a variety of electron donors and grow under a wide range of environmental conditions make them a suitable candidate for overcoming the high industrial cost points of feedstock and culturing conditions. Many groups have already demonstrated their ability to grow on and remediate wastewater, and optimization of reactor conditions continues to develop beyond lab-scale. In the future, PNSB will undoubtedly play a critical role in developing the CE in the global shift towards a sustainable way of life.",
"introduction": "Introduction In 2022, the ever-increasing global population surpassed eight billion, continuing to exert a heavy burden on the environment and economy (Ritchie et al., 2022 ). Under a business-as-usual situation, waste production is projected to increase by 70% to 3.40 billion tons by 2050. This waste includes non-recyclable products such as synthetic plastics, as well as food and green waste, metals, paper, glass and more [not accounting for greenhouse gas (GHG)-producing energy and commercial processes] (Kaza et al., 2018 ). By 2050, total food demand is expected to increase by more than 30%, putting up to 8% of the global population at risk of hunger (AbdelRahman, 2023 ; Van Dijk et al., 2021 ). To mitigate the negative effects of our current production processes and answer the demand for basic resources, the world must move toward a sustainable circular economy (CE). The CE focuses on reusing, reducing, and recycling with renewable inputs, rather than dead-end resources such as petroleum (Kirchherr et al., 2023 ). The goal is to promote both economic development and environmental sustainability, a feat that many countries display great enthusiasm for but struggle to implement (Vogiantzi & Tserpes, 2023 ). As such, it is critical to develop methods that will promote the effectiveness of the CE, namely approaches that focus on incorporating lost or \"waste\" components, such as end-of-life products, back into value-added products or processes, like energy generation. A major player in building this economy will be microorganisms that fix carbon and remove GHGs, produce valuable compounds for the energy, food, and pharmaceutical industries, and remediate toxic wastes (Jain et al., 2022 ). One group of microorganisms that can perform all these functions, making them an attractive target for contributing to the CE, are the purple non-sulfur bacteria (PNSB). PNSB possess some of the most versatile metabolisms in the microbial world, and as mixotrophs, they can perform the majority of primary metabolisms (described by energy, electron, and carbon source) in addition to specialized processes such as N 2 fixation and H 2 production (Madigan & Jung, 2009 ). They are tolerant to stresses such as high salinity, heavy metals, and toxic contaminants (e.g. H 2 S), and can grow in aerobic and anaerobic conditions on a variety of carbon and electron sources, including raw waste streams (Madigan & Jung, 2009 ). PNSB also naturally produce multiple economically valuable products, including biofuels (Gabrielyan et al., 2015 ; Vasiliadou et al., 2018 ), bioplastics (Higuchi-Takeuchi et al., 2016 ; Monroy & Buitrón, 2020 ; Ranaivoarisoa et al., 2019 ), and fine chemicals (He et al., 2021 ; Sasikala & Ramana, 1995 ; Wang et al., 2012 ). Furthermore, they are proficient at removing organics from wastewater (Dhar et al., 2023 ; Lu et al., 2019 ), and the resulting biomass is valuable as fertilizer (Maeda, 2021 ; Sundar & Chao, 2022 ) and feedstock (Delamare-Deboutteville et al., 2019 ; Hülsen et al., 2018a ; Wada et al., 2022 ). Altogether, these traits make PNSB an extremely attractive target for biotechnological applications compared to current chemical or purely enzymatic methods, which are limited in their lack of self-repair, specific applications, and need for highly specific operating conditions (Asif et al., 2021 ). Here, we discuss how PNSB can be leveraged for sustainable bioproduction, as well as limitations to their widespread application."
} | 1,405 |
39730143 | PMC11730080 | pmc | 3,758 | {
"abstract": "Abstract In this review, we focus on how purple non-sulfur bacteria can be leveraged for sustainable bioproduction to support the circular economy. We discuss the state of the field with respect to the use of purple bacteria for energy production, their role in wastewater treatment, as a fertilizer, and as a chassis for bioplastic production. We explore their ability to serve as single-cell protein and production platforms for fine chemicals from waste materials. We also introduce more Avant-Garde technologies that leverage the unique metabolisms of purple bacteria, including microbial electrosynthesis and co-culture. These technologies will be pivotal in our efforts to mitigate climate change and circularize the economy in the next two decades. One-sentence summary Purple non-sulfur bacteria are utilized for a range of biotechnological applications, including the production of bio-energy, single cell protein, fertilizer, bioplastics, fine chemicals, in wastewater treatment and in novel applications like co-cultures and microbial electrosynthesis.",
"conclusion": "Conclusion In this review, we discussed the metabolic capabilities of PNSB and how they are leveraged for biotechnological applications. Their unique metabolism offers advantages for use in energy production, wastewater treatment, and resource recovery, as well as fertilizer production and SCP. They are also capable of fine chemical production (e.g. carotenoids and CoQ10), and have begun to be explored in depth for their ability to perform electrotrophy and promote valuable compound production in co-culture. The ability of PNSB to use a variety of electron donors and grow under a wide range of environmental conditions make them a suitable candidate for overcoming the high industrial cost points of feedstock and culturing conditions. Many groups have already demonstrated their ability to grow on and remediate wastewater, and optimization of reactor conditions continues to develop beyond lab-scale. In the future, PNSB will undoubtedly play a critical role in developing the CE in the global shift towards a sustainable way of life.",
"introduction": "Introduction In 2022, the ever-increasing global population surpassed eight billion, continuing to exert a heavy burden on the environment and economy (Ritchie et al., 2022 ). Under a business-as-usual situation, waste production is projected to increase by 70% to 3.40 billion tons by 2050. This waste includes non-recyclable products such as synthetic plastics, as well as food and green waste, metals, paper, glass and more [not accounting for greenhouse gas (GHG)-producing energy and commercial processes] (Kaza et al., 2018 ). By 2050, total food demand is expected to increase by more than 30%, putting up to 8% of the global population at risk of hunger (AbdelRahman, 2023 ; Van Dijk et al., 2021 ). To mitigate the negative effects of our current production processes and answer the demand for basic resources, the world must move toward a sustainable circular economy (CE). The CE focuses on reusing, reducing, and recycling with renewable inputs, rather than dead-end resources such as petroleum (Kirchherr et al., 2023 ). The goal is to promote both economic development and environmental sustainability, a feat that many countries display great enthusiasm for but struggle to implement (Vogiantzi & Tserpes, 2023 ). As such, it is critical to develop methods that will promote the effectiveness of the CE, namely approaches that focus on incorporating lost or \"waste\" components, such as end-of-life products, back into value-added products or processes, like energy generation. A major player in building this economy will be microorganisms that fix carbon and remove GHGs, produce valuable compounds for the energy, food, and pharmaceutical industries, and remediate toxic wastes (Jain et al., 2022 ). One group of microorganisms that can perform all these functions, making them an attractive target for contributing to the CE, are the purple non-sulfur bacteria (PNSB). PNSB possess some of the most versatile metabolisms in the microbial world, and as mixotrophs, they can perform the majority of primary metabolisms (described by energy, electron, and carbon source) in addition to specialized processes such as N 2 fixation and H 2 production (Madigan & Jung, 2009 ). They are tolerant to stresses such as high salinity, heavy metals, and toxic contaminants (e.g. H 2 S), and can grow in aerobic and anaerobic conditions on a variety of carbon and electron sources, including raw waste streams (Madigan & Jung, 2009 ). PNSB also naturally produce multiple economically valuable products, including biofuels (Gabrielyan et al., 2015 ; Vasiliadou et al., 2018 ), bioplastics (Higuchi-Takeuchi et al., 2016 ; Monroy & Buitrón, 2020 ; Ranaivoarisoa et al., 2019 ), and fine chemicals (He et al., 2021 ; Sasikala & Ramana, 1995 ; Wang et al., 2012 ). Furthermore, they are proficient at removing organics from wastewater (Dhar et al., 2023 ; Lu et al., 2019 ), and the resulting biomass is valuable as fertilizer (Maeda, 2021 ; Sundar & Chao, 2022 ) and feedstock (Delamare-Deboutteville et al., 2019 ; Hülsen et al., 2018a ; Wada et al., 2022 ). Altogether, these traits make PNSB an extremely attractive target for biotechnological applications compared to current chemical or purely enzymatic methods, which are limited in their lack of self-repair, specific applications, and need for highly specific operating conditions (Asif et al., 2021 ). Here, we discuss how PNSB can be leveraged for sustainable bioproduction, as well as limitations to their widespread application."
} | 1,405 |
39730143 | PMC11730080 | pmc | 3,759 | {
"abstract": "Abstract In this review, we focus on how purple non-sulfur bacteria can be leveraged for sustainable bioproduction to support the circular economy. We discuss the state of the field with respect to the use of purple bacteria for energy production, their role in wastewater treatment, as a fertilizer, and as a chassis for bioplastic production. We explore their ability to serve as single-cell protein and production platforms for fine chemicals from waste materials. We also introduce more Avant-Garde technologies that leverage the unique metabolisms of purple bacteria, including microbial electrosynthesis and co-culture. These technologies will be pivotal in our efforts to mitigate climate change and circularize the economy in the next two decades. One-sentence summary Purple non-sulfur bacteria are utilized for a range of biotechnological applications, including the production of bio-energy, single cell protein, fertilizer, bioplastics, fine chemicals, in wastewater treatment and in novel applications like co-cultures and microbial electrosynthesis.",
"conclusion": "Conclusion In this review, we discussed the metabolic capabilities of PNSB and how they are leveraged for biotechnological applications. Their unique metabolism offers advantages for use in energy production, wastewater treatment, and resource recovery, as well as fertilizer production and SCP. They are also capable of fine chemical production (e.g. carotenoids and CoQ10), and have begun to be explored in depth for their ability to perform electrotrophy and promote valuable compound production in co-culture. The ability of PNSB to use a variety of electron donors and grow under a wide range of environmental conditions make them a suitable candidate for overcoming the high industrial cost points of feedstock and culturing conditions. Many groups have already demonstrated their ability to grow on and remediate wastewater, and optimization of reactor conditions continues to develop beyond lab-scale. In the future, PNSB will undoubtedly play a critical role in developing the CE in the global shift towards a sustainable way of life.",
"introduction": "Introduction In 2022, the ever-increasing global population surpassed eight billion, continuing to exert a heavy burden on the environment and economy (Ritchie et al., 2022 ). Under a business-as-usual situation, waste production is projected to increase by 70% to 3.40 billion tons by 2050. This waste includes non-recyclable products such as synthetic plastics, as well as food and green waste, metals, paper, glass and more [not accounting for greenhouse gas (GHG)-producing energy and commercial processes] (Kaza et al., 2018 ). By 2050, total food demand is expected to increase by more than 30%, putting up to 8% of the global population at risk of hunger (AbdelRahman, 2023 ; Van Dijk et al., 2021 ). To mitigate the negative effects of our current production processes and answer the demand for basic resources, the world must move toward a sustainable circular economy (CE). The CE focuses on reusing, reducing, and recycling with renewable inputs, rather than dead-end resources such as petroleum (Kirchherr et al., 2023 ). The goal is to promote both economic development and environmental sustainability, a feat that many countries display great enthusiasm for but struggle to implement (Vogiantzi & Tserpes, 2023 ). As such, it is critical to develop methods that will promote the effectiveness of the CE, namely approaches that focus on incorporating lost or \"waste\" components, such as end-of-life products, back into value-added products or processes, like energy generation. A major player in building this economy will be microorganisms that fix carbon and remove GHGs, produce valuable compounds for the energy, food, and pharmaceutical industries, and remediate toxic wastes (Jain et al., 2022 ). One group of microorganisms that can perform all these functions, making them an attractive target for contributing to the CE, are the purple non-sulfur bacteria (PNSB). PNSB possess some of the most versatile metabolisms in the microbial world, and as mixotrophs, they can perform the majority of primary metabolisms (described by energy, electron, and carbon source) in addition to specialized processes such as N 2 fixation and H 2 production (Madigan & Jung, 2009 ). They are tolerant to stresses such as high salinity, heavy metals, and toxic contaminants (e.g. H 2 S), and can grow in aerobic and anaerobic conditions on a variety of carbon and electron sources, including raw waste streams (Madigan & Jung, 2009 ). PNSB also naturally produce multiple economically valuable products, including biofuels (Gabrielyan et al., 2015 ; Vasiliadou et al., 2018 ), bioplastics (Higuchi-Takeuchi et al., 2016 ; Monroy & Buitrón, 2020 ; Ranaivoarisoa et al., 2019 ), and fine chemicals (He et al., 2021 ; Sasikala & Ramana, 1995 ; Wang et al., 2012 ). Furthermore, they are proficient at removing organics from wastewater (Dhar et al., 2023 ; Lu et al., 2019 ), and the resulting biomass is valuable as fertilizer (Maeda, 2021 ; Sundar & Chao, 2022 ) and feedstock (Delamare-Deboutteville et al., 2019 ; Hülsen et al., 2018a ; Wada et al., 2022 ). Altogether, these traits make PNSB an extremely attractive target for biotechnological applications compared to current chemical or purely enzymatic methods, which are limited in their lack of self-repair, specific applications, and need for highly specific operating conditions (Asif et al., 2021 ). Here, we discuss how PNSB can be leveraged for sustainable bioproduction, as well as limitations to their widespread application."
} | 1,405 |
39730143 | PMC11730080 | pmc | 3,759 | {
"abstract": "Abstract In this review, we focus on how purple non-sulfur bacteria can be leveraged for sustainable bioproduction to support the circular economy. We discuss the state of the field with respect to the use of purple bacteria for energy production, their role in wastewater treatment, as a fertilizer, and as a chassis for bioplastic production. We explore their ability to serve as single-cell protein and production platforms for fine chemicals from waste materials. We also introduce more Avant-Garde technologies that leverage the unique metabolisms of purple bacteria, including microbial electrosynthesis and co-culture. These technologies will be pivotal in our efforts to mitigate climate change and circularize the economy in the next two decades. One-sentence summary Purple non-sulfur bacteria are utilized for a range of biotechnological applications, including the production of bio-energy, single cell protein, fertilizer, bioplastics, fine chemicals, in wastewater treatment and in novel applications like co-cultures and microbial electrosynthesis.",
"conclusion": "Conclusion In this review, we discussed the metabolic capabilities of PNSB and how they are leveraged for biotechnological applications. Their unique metabolism offers advantages for use in energy production, wastewater treatment, and resource recovery, as well as fertilizer production and SCP. They are also capable of fine chemical production (e.g. carotenoids and CoQ10), and have begun to be explored in depth for their ability to perform electrotrophy and promote valuable compound production in co-culture. The ability of PNSB to use a variety of electron donors and grow under a wide range of environmental conditions make them a suitable candidate for overcoming the high industrial cost points of feedstock and culturing conditions. Many groups have already demonstrated their ability to grow on and remediate wastewater, and optimization of reactor conditions continues to develop beyond lab-scale. In the future, PNSB will undoubtedly play a critical role in developing the CE in the global shift towards a sustainable way of life.",
"introduction": "Introduction In 2022, the ever-increasing global population surpassed eight billion, continuing to exert a heavy burden on the environment and economy (Ritchie et al., 2022 ). Under a business-as-usual situation, waste production is projected to increase by 70% to 3.40 billion tons by 2050. This waste includes non-recyclable products such as synthetic plastics, as well as food and green waste, metals, paper, glass and more [not accounting for greenhouse gas (GHG)-producing energy and commercial processes] (Kaza et al., 2018 ). By 2050, total food demand is expected to increase by more than 30%, putting up to 8% of the global population at risk of hunger (AbdelRahman, 2023 ; Van Dijk et al., 2021 ). To mitigate the negative effects of our current production processes and answer the demand for basic resources, the world must move toward a sustainable circular economy (CE). The CE focuses on reusing, reducing, and recycling with renewable inputs, rather than dead-end resources such as petroleum (Kirchherr et al., 2023 ). The goal is to promote both economic development and environmental sustainability, a feat that many countries display great enthusiasm for but struggle to implement (Vogiantzi & Tserpes, 2023 ). As such, it is critical to develop methods that will promote the effectiveness of the CE, namely approaches that focus on incorporating lost or \"waste\" components, such as end-of-life products, back into value-added products or processes, like energy generation. A major player in building this economy will be microorganisms that fix carbon and remove GHGs, produce valuable compounds for the energy, food, and pharmaceutical industries, and remediate toxic wastes (Jain et al., 2022 ). One group of microorganisms that can perform all these functions, making them an attractive target for contributing to the CE, are the purple non-sulfur bacteria (PNSB). PNSB possess some of the most versatile metabolisms in the microbial world, and as mixotrophs, they can perform the majority of primary metabolisms (described by energy, electron, and carbon source) in addition to specialized processes such as N 2 fixation and H 2 production (Madigan & Jung, 2009 ). They are tolerant to stresses such as high salinity, heavy metals, and toxic contaminants (e.g. H 2 S), and can grow in aerobic and anaerobic conditions on a variety of carbon and electron sources, including raw waste streams (Madigan & Jung, 2009 ). PNSB also naturally produce multiple economically valuable products, including biofuels (Gabrielyan et al., 2015 ; Vasiliadou et al., 2018 ), bioplastics (Higuchi-Takeuchi et al., 2016 ; Monroy & Buitrón, 2020 ; Ranaivoarisoa et al., 2019 ), and fine chemicals (He et al., 2021 ; Sasikala & Ramana, 1995 ; Wang et al., 2012 ). Furthermore, they are proficient at removing organics from wastewater (Dhar et al., 2023 ; Lu et al., 2019 ), and the resulting biomass is valuable as fertilizer (Maeda, 2021 ; Sundar & Chao, 2022 ) and feedstock (Delamare-Deboutteville et al., 2019 ; Hülsen et al., 2018a ; Wada et al., 2022 ). Altogether, these traits make PNSB an extremely attractive target for biotechnological applications compared to current chemical or purely enzymatic methods, which are limited in their lack of self-repair, specific applications, and need for highly specific operating conditions (Asif et al., 2021 ). Here, we discuss how PNSB can be leveraged for sustainable bioproduction, as well as limitations to their widespread application."
} | 1,405 |
39730143 | PMC11730080 | pmc | 3,759 | {
"abstract": "Abstract In this review, we focus on how purple non-sulfur bacteria can be leveraged for sustainable bioproduction to support the circular economy. We discuss the state of the field with respect to the use of purple bacteria for energy production, their role in wastewater treatment, as a fertilizer, and as a chassis for bioplastic production. We explore their ability to serve as single-cell protein and production platforms for fine chemicals from waste materials. We also introduce more Avant-Garde technologies that leverage the unique metabolisms of purple bacteria, including microbial electrosynthesis and co-culture. These technologies will be pivotal in our efforts to mitigate climate change and circularize the economy in the next two decades. One-sentence summary Purple non-sulfur bacteria are utilized for a range of biotechnological applications, including the production of bio-energy, single cell protein, fertilizer, bioplastics, fine chemicals, in wastewater treatment and in novel applications like co-cultures and microbial electrosynthesis.",
"conclusion": "Conclusion In this review, we discussed the metabolic capabilities of PNSB and how they are leveraged for biotechnological applications. Their unique metabolism offers advantages for use in energy production, wastewater treatment, and resource recovery, as well as fertilizer production and SCP. They are also capable of fine chemical production (e.g. carotenoids and CoQ10), and have begun to be explored in depth for their ability to perform electrotrophy and promote valuable compound production in co-culture. The ability of PNSB to use a variety of electron donors and grow under a wide range of environmental conditions make them a suitable candidate for overcoming the high industrial cost points of feedstock and culturing conditions. Many groups have already demonstrated their ability to grow on and remediate wastewater, and optimization of reactor conditions continues to develop beyond lab-scale. In the future, PNSB will undoubtedly play a critical role in developing the CE in the global shift towards a sustainable way of life.",
"introduction": "Introduction In 2022, the ever-increasing global population surpassed eight billion, continuing to exert a heavy burden on the environment and economy (Ritchie et al., 2022 ). Under a business-as-usual situation, waste production is projected to increase by 70% to 3.40 billion tons by 2050. This waste includes non-recyclable products such as synthetic plastics, as well as food and green waste, metals, paper, glass and more [not accounting for greenhouse gas (GHG)-producing energy and commercial processes] (Kaza et al., 2018 ). By 2050, total food demand is expected to increase by more than 30%, putting up to 8% of the global population at risk of hunger (AbdelRahman, 2023 ; Van Dijk et al., 2021 ). To mitigate the negative effects of our current production processes and answer the demand for basic resources, the world must move toward a sustainable circular economy (CE). The CE focuses on reusing, reducing, and recycling with renewable inputs, rather than dead-end resources such as petroleum (Kirchherr et al., 2023 ). The goal is to promote both economic development and environmental sustainability, a feat that many countries display great enthusiasm for but struggle to implement (Vogiantzi & Tserpes, 2023 ). As such, it is critical to develop methods that will promote the effectiveness of the CE, namely approaches that focus on incorporating lost or \"waste\" components, such as end-of-life products, back into value-added products or processes, like energy generation. A major player in building this economy will be microorganisms that fix carbon and remove GHGs, produce valuable compounds for the energy, food, and pharmaceutical industries, and remediate toxic wastes (Jain et al., 2022 ). One group of microorganisms that can perform all these functions, making them an attractive target for contributing to the CE, are the purple non-sulfur bacteria (PNSB). PNSB possess some of the most versatile metabolisms in the microbial world, and as mixotrophs, they can perform the majority of primary metabolisms (described by energy, electron, and carbon source) in addition to specialized processes such as N 2 fixation and H 2 production (Madigan & Jung, 2009 ). They are tolerant to stresses such as high salinity, heavy metals, and toxic contaminants (e.g. H 2 S), and can grow in aerobic and anaerobic conditions on a variety of carbon and electron sources, including raw waste streams (Madigan & Jung, 2009 ). PNSB also naturally produce multiple economically valuable products, including biofuels (Gabrielyan et al., 2015 ; Vasiliadou et al., 2018 ), bioplastics (Higuchi-Takeuchi et al., 2016 ; Monroy & Buitrón, 2020 ; Ranaivoarisoa et al., 2019 ), and fine chemicals (He et al., 2021 ; Sasikala & Ramana, 1995 ; Wang et al., 2012 ). Furthermore, they are proficient at removing organics from wastewater (Dhar et al., 2023 ; Lu et al., 2019 ), and the resulting biomass is valuable as fertilizer (Maeda, 2021 ; Sundar & Chao, 2022 ) and feedstock (Delamare-Deboutteville et al., 2019 ; Hülsen et al., 2018a ; Wada et al., 2022 ). Altogether, these traits make PNSB an extremely attractive target for biotechnological applications compared to current chemical or purely enzymatic methods, which are limited in their lack of self-repair, specific applications, and need for highly specific operating conditions (Asif et al., 2021 ). Here, we discuss how PNSB can be leveraged for sustainable bioproduction, as well as limitations to their widespread application."
} | 1,405 |
39730143 | PMC11730080 | pmc | 3,759 | {
"abstract": "Abstract In this review, we focus on how purple non-sulfur bacteria can be leveraged for sustainable bioproduction to support the circular economy. We discuss the state of the field with respect to the use of purple bacteria for energy production, their role in wastewater treatment, as a fertilizer, and as a chassis for bioplastic production. We explore their ability to serve as single-cell protein and production platforms for fine chemicals from waste materials. We also introduce more Avant-Garde technologies that leverage the unique metabolisms of purple bacteria, including microbial electrosynthesis and co-culture. These technologies will be pivotal in our efforts to mitigate climate change and circularize the economy in the next two decades. One-sentence summary Purple non-sulfur bacteria are utilized for a range of biotechnological applications, including the production of bio-energy, single cell protein, fertilizer, bioplastics, fine chemicals, in wastewater treatment and in novel applications like co-cultures and microbial electrosynthesis.",
"conclusion": "Conclusion In this review, we discussed the metabolic capabilities of PNSB and how they are leveraged for biotechnological applications. Their unique metabolism offers advantages for use in energy production, wastewater treatment, and resource recovery, as well as fertilizer production and SCP. They are also capable of fine chemical production (e.g. carotenoids and CoQ10), and have begun to be explored in depth for their ability to perform electrotrophy and promote valuable compound production in co-culture. The ability of PNSB to use a variety of electron donors and grow under a wide range of environmental conditions make them a suitable candidate for overcoming the high industrial cost points of feedstock and culturing conditions. Many groups have already demonstrated their ability to grow on and remediate wastewater, and optimization of reactor conditions continues to develop beyond lab-scale. In the future, PNSB will undoubtedly play a critical role in developing the CE in the global shift towards a sustainable way of life.",
"introduction": "Introduction In 2022, the ever-increasing global population surpassed eight billion, continuing to exert a heavy burden on the environment and economy (Ritchie et al., 2022 ). Under a business-as-usual situation, waste production is projected to increase by 70% to 3.40 billion tons by 2050. This waste includes non-recyclable products such as synthetic plastics, as well as food and green waste, metals, paper, glass and more [not accounting for greenhouse gas (GHG)-producing energy and commercial processes] (Kaza et al., 2018 ). By 2050, total food demand is expected to increase by more than 30%, putting up to 8% of the global population at risk of hunger (AbdelRahman, 2023 ; Van Dijk et al., 2021 ). To mitigate the negative effects of our current production processes and answer the demand for basic resources, the world must move toward a sustainable circular economy (CE). The CE focuses on reusing, reducing, and recycling with renewable inputs, rather than dead-end resources such as petroleum (Kirchherr et al., 2023 ). The goal is to promote both economic development and environmental sustainability, a feat that many countries display great enthusiasm for but struggle to implement (Vogiantzi & Tserpes, 2023 ). As such, it is critical to develop methods that will promote the effectiveness of the CE, namely approaches that focus on incorporating lost or \"waste\" components, such as end-of-life products, back into value-added products or processes, like energy generation. A major player in building this economy will be microorganisms that fix carbon and remove GHGs, produce valuable compounds for the energy, food, and pharmaceutical industries, and remediate toxic wastes (Jain et al., 2022 ). One group of microorganisms that can perform all these functions, making them an attractive target for contributing to the CE, are the purple non-sulfur bacteria (PNSB). PNSB possess some of the most versatile metabolisms in the microbial world, and as mixotrophs, they can perform the majority of primary metabolisms (described by energy, electron, and carbon source) in addition to specialized processes such as N 2 fixation and H 2 production (Madigan & Jung, 2009 ). They are tolerant to stresses such as high salinity, heavy metals, and toxic contaminants (e.g. H 2 S), and can grow in aerobic and anaerobic conditions on a variety of carbon and electron sources, including raw waste streams (Madigan & Jung, 2009 ). PNSB also naturally produce multiple economically valuable products, including biofuels (Gabrielyan et al., 2015 ; Vasiliadou et al., 2018 ), bioplastics (Higuchi-Takeuchi et al., 2016 ; Monroy & Buitrón, 2020 ; Ranaivoarisoa et al., 2019 ), and fine chemicals (He et al., 2021 ; Sasikala & Ramana, 1995 ; Wang et al., 2012 ). Furthermore, they are proficient at removing organics from wastewater (Dhar et al., 2023 ; Lu et al., 2019 ), and the resulting biomass is valuable as fertilizer (Maeda, 2021 ; Sundar & Chao, 2022 ) and feedstock (Delamare-Deboutteville et al., 2019 ; Hülsen et al., 2018a ; Wada et al., 2022 ). Altogether, these traits make PNSB an extremely attractive target for biotechnological applications compared to current chemical or purely enzymatic methods, which are limited in their lack of self-repair, specific applications, and need for highly specific operating conditions (Asif et al., 2021 ). Here, we discuss how PNSB can be leveraged for sustainable bioproduction, as well as limitations to their widespread application."
} | 1,405 |
39730143 | PMC11730080 | pmc | 3,760 | {
"abstract": "Abstract In this review, we focus on how purple non-sulfur bacteria can be leveraged for sustainable bioproduction to support the circular economy. We discuss the state of the field with respect to the use of purple bacteria for energy production, their role in wastewater treatment, as a fertilizer, and as a chassis for bioplastic production. We explore their ability to serve as single-cell protein and production platforms for fine chemicals from waste materials. We also introduce more Avant-Garde technologies that leverage the unique metabolisms of purple bacteria, including microbial electrosynthesis and co-culture. These technologies will be pivotal in our efforts to mitigate climate change and circularize the economy in the next two decades. One-sentence summary Purple non-sulfur bacteria are utilized for a range of biotechnological applications, including the production of bio-energy, single cell protein, fertilizer, bioplastics, fine chemicals, in wastewater treatment and in novel applications like co-cultures and microbial electrosynthesis.",
"conclusion": "Conclusion In this review, we discussed the metabolic capabilities of PNSB and how they are leveraged for biotechnological applications. Their unique metabolism offers advantages for use in energy production, wastewater treatment, and resource recovery, as well as fertilizer production and SCP. They are also capable of fine chemical production (e.g. carotenoids and CoQ10), and have begun to be explored in depth for their ability to perform electrotrophy and promote valuable compound production in co-culture. The ability of PNSB to use a variety of electron donors and grow under a wide range of environmental conditions make them a suitable candidate for overcoming the high industrial cost points of feedstock and culturing conditions. Many groups have already demonstrated their ability to grow on and remediate wastewater, and optimization of reactor conditions continues to develop beyond lab-scale. In the future, PNSB will undoubtedly play a critical role in developing the CE in the global shift towards a sustainable way of life.",
"introduction": "Introduction In 2022, the ever-increasing global population surpassed eight billion, continuing to exert a heavy burden on the environment and economy (Ritchie et al., 2022 ). Under a business-as-usual situation, waste production is projected to increase by 70% to 3.40 billion tons by 2050. This waste includes non-recyclable products such as synthetic plastics, as well as food and green waste, metals, paper, glass and more [not accounting for greenhouse gas (GHG)-producing energy and commercial processes] (Kaza et al., 2018 ). By 2050, total food demand is expected to increase by more than 30%, putting up to 8% of the global population at risk of hunger (AbdelRahman, 2023 ; Van Dijk et al., 2021 ). To mitigate the negative effects of our current production processes and answer the demand for basic resources, the world must move toward a sustainable circular economy (CE). The CE focuses on reusing, reducing, and recycling with renewable inputs, rather than dead-end resources such as petroleum (Kirchherr et al., 2023 ). The goal is to promote both economic development and environmental sustainability, a feat that many countries display great enthusiasm for but struggle to implement (Vogiantzi & Tserpes, 2023 ). As such, it is critical to develop methods that will promote the effectiveness of the CE, namely approaches that focus on incorporating lost or \"waste\" components, such as end-of-life products, back into value-added products or processes, like energy generation. A major player in building this economy will be microorganisms that fix carbon and remove GHGs, produce valuable compounds for the energy, food, and pharmaceutical industries, and remediate toxic wastes (Jain et al., 2022 ). One group of microorganisms that can perform all these functions, making them an attractive target for contributing to the CE, are the purple non-sulfur bacteria (PNSB). PNSB possess some of the most versatile metabolisms in the microbial world, and as mixotrophs, they can perform the majority of primary metabolisms (described by energy, electron, and carbon source) in addition to specialized processes such as N 2 fixation and H 2 production (Madigan & Jung, 2009 ). They are tolerant to stresses such as high salinity, heavy metals, and toxic contaminants (e.g. H 2 S), and can grow in aerobic and anaerobic conditions on a variety of carbon and electron sources, including raw waste streams (Madigan & Jung, 2009 ). PNSB also naturally produce multiple economically valuable products, including biofuels (Gabrielyan et al., 2015 ; Vasiliadou et al., 2018 ), bioplastics (Higuchi-Takeuchi et al., 2016 ; Monroy & Buitrón, 2020 ; Ranaivoarisoa et al., 2019 ), and fine chemicals (He et al., 2021 ; Sasikala & Ramana, 1995 ; Wang et al., 2012 ). Furthermore, they are proficient at removing organics from wastewater (Dhar et al., 2023 ; Lu et al., 2019 ), and the resulting biomass is valuable as fertilizer (Maeda, 2021 ; Sundar & Chao, 2022 ) and feedstock (Delamare-Deboutteville et al., 2019 ; Hülsen et al., 2018a ; Wada et al., 2022 ). Altogether, these traits make PNSB an extremely attractive target for biotechnological applications compared to current chemical or purely enzymatic methods, which are limited in their lack of self-repair, specific applications, and need for highly specific operating conditions (Asif et al., 2021 ). Here, we discuss how PNSB can be leveraged for sustainable bioproduction, as well as limitations to their widespread application."
} | 1,405 |
39730143 | PMC11730080 | pmc | 3,760 | {
"abstract": "Abstract In this review, we focus on how purple non-sulfur bacteria can be leveraged for sustainable bioproduction to support the circular economy. We discuss the state of the field with respect to the use of purple bacteria for energy production, their role in wastewater treatment, as a fertilizer, and as a chassis for bioplastic production. We explore their ability to serve as single-cell protein and production platforms for fine chemicals from waste materials. We also introduce more Avant-Garde technologies that leverage the unique metabolisms of purple bacteria, including microbial electrosynthesis and co-culture. These technologies will be pivotal in our efforts to mitigate climate change and circularize the economy in the next two decades. One-sentence summary Purple non-sulfur bacteria are utilized for a range of biotechnological applications, including the production of bio-energy, single cell protein, fertilizer, bioplastics, fine chemicals, in wastewater treatment and in novel applications like co-cultures and microbial electrosynthesis.",
"conclusion": "Conclusion In this review, we discussed the metabolic capabilities of PNSB and how they are leveraged for biotechnological applications. Their unique metabolism offers advantages for use in energy production, wastewater treatment, and resource recovery, as well as fertilizer production and SCP. They are also capable of fine chemical production (e.g. carotenoids and CoQ10), and have begun to be explored in depth for their ability to perform electrotrophy and promote valuable compound production in co-culture. The ability of PNSB to use a variety of electron donors and grow under a wide range of environmental conditions make them a suitable candidate for overcoming the high industrial cost points of feedstock and culturing conditions. Many groups have already demonstrated their ability to grow on and remediate wastewater, and optimization of reactor conditions continues to develop beyond lab-scale. In the future, PNSB will undoubtedly play a critical role in developing the CE in the global shift towards a sustainable way of life.",
"introduction": "Introduction In 2022, the ever-increasing global population surpassed eight billion, continuing to exert a heavy burden on the environment and economy (Ritchie et al., 2022 ). Under a business-as-usual situation, waste production is projected to increase by 70% to 3.40 billion tons by 2050. This waste includes non-recyclable products such as synthetic plastics, as well as food and green waste, metals, paper, glass and more [not accounting for greenhouse gas (GHG)-producing energy and commercial processes] (Kaza et al., 2018 ). By 2050, total food demand is expected to increase by more than 30%, putting up to 8% of the global population at risk of hunger (AbdelRahman, 2023 ; Van Dijk et al., 2021 ). To mitigate the negative effects of our current production processes and answer the demand for basic resources, the world must move toward a sustainable circular economy (CE). The CE focuses on reusing, reducing, and recycling with renewable inputs, rather than dead-end resources such as petroleum (Kirchherr et al., 2023 ). The goal is to promote both economic development and environmental sustainability, a feat that many countries display great enthusiasm for but struggle to implement (Vogiantzi & Tserpes, 2023 ). As such, it is critical to develop methods that will promote the effectiveness of the CE, namely approaches that focus on incorporating lost or \"waste\" components, such as end-of-life products, back into value-added products or processes, like energy generation. A major player in building this economy will be microorganisms that fix carbon and remove GHGs, produce valuable compounds for the energy, food, and pharmaceutical industries, and remediate toxic wastes (Jain et al., 2022 ). One group of microorganisms that can perform all these functions, making them an attractive target for contributing to the CE, are the purple non-sulfur bacteria (PNSB). PNSB possess some of the most versatile metabolisms in the microbial world, and as mixotrophs, they can perform the majority of primary metabolisms (described by energy, electron, and carbon source) in addition to specialized processes such as N 2 fixation and H 2 production (Madigan & Jung, 2009 ). They are tolerant to stresses such as high salinity, heavy metals, and toxic contaminants (e.g. H 2 S), and can grow in aerobic and anaerobic conditions on a variety of carbon and electron sources, including raw waste streams (Madigan & Jung, 2009 ). PNSB also naturally produce multiple economically valuable products, including biofuels (Gabrielyan et al., 2015 ; Vasiliadou et al., 2018 ), bioplastics (Higuchi-Takeuchi et al., 2016 ; Monroy & Buitrón, 2020 ; Ranaivoarisoa et al., 2019 ), and fine chemicals (He et al., 2021 ; Sasikala & Ramana, 1995 ; Wang et al., 2012 ). Furthermore, they are proficient at removing organics from wastewater (Dhar et al., 2023 ; Lu et al., 2019 ), and the resulting biomass is valuable as fertilizer (Maeda, 2021 ; Sundar & Chao, 2022 ) and feedstock (Delamare-Deboutteville et al., 2019 ; Hülsen et al., 2018a ; Wada et al., 2022 ). Altogether, these traits make PNSB an extremely attractive target for biotechnological applications compared to current chemical or purely enzymatic methods, which are limited in their lack of self-repair, specific applications, and need for highly specific operating conditions (Asif et al., 2021 ). Here, we discuss how PNSB can be leveraged for sustainable bioproduction, as well as limitations to their widespread application."
} | 1,405 |
39730143 | PMC11730080 | pmc | 3,760 | {
"abstract": "Abstract In this review, we focus on how purple non-sulfur bacteria can be leveraged for sustainable bioproduction to support the circular economy. We discuss the state of the field with respect to the use of purple bacteria for energy production, their role in wastewater treatment, as a fertilizer, and as a chassis for bioplastic production. We explore their ability to serve as single-cell protein and production platforms for fine chemicals from waste materials. We also introduce more Avant-Garde technologies that leverage the unique metabolisms of purple bacteria, including microbial electrosynthesis and co-culture. These technologies will be pivotal in our efforts to mitigate climate change and circularize the economy in the next two decades. One-sentence summary Purple non-sulfur bacteria are utilized for a range of biotechnological applications, including the production of bio-energy, single cell protein, fertilizer, bioplastics, fine chemicals, in wastewater treatment and in novel applications like co-cultures and microbial electrosynthesis.",
"conclusion": "Conclusion In this review, we discussed the metabolic capabilities of PNSB and how they are leveraged for biotechnological applications. Their unique metabolism offers advantages for use in energy production, wastewater treatment, and resource recovery, as well as fertilizer production and SCP. They are also capable of fine chemical production (e.g. carotenoids and CoQ10), and have begun to be explored in depth for their ability to perform electrotrophy and promote valuable compound production in co-culture. The ability of PNSB to use a variety of electron donors and grow under a wide range of environmental conditions make them a suitable candidate for overcoming the high industrial cost points of feedstock and culturing conditions. Many groups have already demonstrated their ability to grow on and remediate wastewater, and optimization of reactor conditions continues to develop beyond lab-scale. In the future, PNSB will undoubtedly play a critical role in developing the CE in the global shift towards a sustainable way of life.",
"introduction": "Introduction In 2022, the ever-increasing global population surpassed eight billion, continuing to exert a heavy burden on the environment and economy (Ritchie et al., 2022 ). Under a business-as-usual situation, waste production is projected to increase by 70% to 3.40 billion tons by 2050. This waste includes non-recyclable products such as synthetic plastics, as well as food and green waste, metals, paper, glass and more [not accounting for greenhouse gas (GHG)-producing energy and commercial processes] (Kaza et al., 2018 ). By 2050, total food demand is expected to increase by more than 30%, putting up to 8% of the global population at risk of hunger (AbdelRahman, 2023 ; Van Dijk et al., 2021 ). To mitigate the negative effects of our current production processes and answer the demand for basic resources, the world must move toward a sustainable circular economy (CE). The CE focuses on reusing, reducing, and recycling with renewable inputs, rather than dead-end resources such as petroleum (Kirchherr et al., 2023 ). The goal is to promote both economic development and environmental sustainability, a feat that many countries display great enthusiasm for but struggle to implement (Vogiantzi & Tserpes, 2023 ). As such, it is critical to develop methods that will promote the effectiveness of the CE, namely approaches that focus on incorporating lost or \"waste\" components, such as end-of-life products, back into value-added products or processes, like energy generation. A major player in building this economy will be microorganisms that fix carbon and remove GHGs, produce valuable compounds for the energy, food, and pharmaceutical industries, and remediate toxic wastes (Jain et al., 2022 ). One group of microorganisms that can perform all these functions, making them an attractive target for contributing to the CE, are the purple non-sulfur bacteria (PNSB). PNSB possess some of the most versatile metabolisms in the microbial world, and as mixotrophs, they can perform the majority of primary metabolisms (described by energy, electron, and carbon source) in addition to specialized processes such as N 2 fixation and H 2 production (Madigan & Jung, 2009 ). They are tolerant to stresses such as high salinity, heavy metals, and toxic contaminants (e.g. H 2 S), and can grow in aerobic and anaerobic conditions on a variety of carbon and electron sources, including raw waste streams (Madigan & Jung, 2009 ). PNSB also naturally produce multiple economically valuable products, including biofuels (Gabrielyan et al., 2015 ; Vasiliadou et al., 2018 ), bioplastics (Higuchi-Takeuchi et al., 2016 ; Monroy & Buitrón, 2020 ; Ranaivoarisoa et al., 2019 ), and fine chemicals (He et al., 2021 ; Sasikala & Ramana, 1995 ; Wang et al., 2012 ). Furthermore, they are proficient at removing organics from wastewater (Dhar et al., 2023 ; Lu et al., 2019 ), and the resulting biomass is valuable as fertilizer (Maeda, 2021 ; Sundar & Chao, 2022 ) and feedstock (Delamare-Deboutteville et al., 2019 ; Hülsen et al., 2018a ; Wada et al., 2022 ). Altogether, these traits make PNSB an extremely attractive target for biotechnological applications compared to current chemical or purely enzymatic methods, which are limited in their lack of self-repair, specific applications, and need for highly specific operating conditions (Asif et al., 2021 ). Here, we discuss how PNSB can be leveraged for sustainable bioproduction, as well as limitations to their widespread application."
} | 1,405 |
39730143 | PMC11730080 | pmc | 3,760 | {
"abstract": "Abstract In this review, we focus on how purple non-sulfur bacteria can be leveraged for sustainable bioproduction to support the circular economy. We discuss the state of the field with respect to the use of purple bacteria for energy production, their role in wastewater treatment, as a fertilizer, and as a chassis for bioplastic production. We explore their ability to serve as single-cell protein and production platforms for fine chemicals from waste materials. We also introduce more Avant-Garde technologies that leverage the unique metabolisms of purple bacteria, including microbial electrosynthesis and co-culture. These technologies will be pivotal in our efforts to mitigate climate change and circularize the economy in the next two decades. One-sentence summary Purple non-sulfur bacteria are utilized for a range of biotechnological applications, including the production of bio-energy, single cell protein, fertilizer, bioplastics, fine chemicals, in wastewater treatment and in novel applications like co-cultures and microbial electrosynthesis.",
"conclusion": "Conclusion In this review, we discussed the metabolic capabilities of PNSB and how they are leveraged for biotechnological applications. Their unique metabolism offers advantages for use in energy production, wastewater treatment, and resource recovery, as well as fertilizer production and SCP. They are also capable of fine chemical production (e.g. carotenoids and CoQ10), and have begun to be explored in depth for their ability to perform electrotrophy and promote valuable compound production in co-culture. The ability of PNSB to use a variety of electron donors and grow under a wide range of environmental conditions make them a suitable candidate for overcoming the high industrial cost points of feedstock and culturing conditions. Many groups have already demonstrated their ability to grow on and remediate wastewater, and optimization of reactor conditions continues to develop beyond lab-scale. In the future, PNSB will undoubtedly play a critical role in developing the CE in the global shift towards a sustainable way of life.",
"introduction": "Introduction In 2022, the ever-increasing global population surpassed eight billion, continuing to exert a heavy burden on the environment and economy (Ritchie et al., 2022 ). Under a business-as-usual situation, waste production is projected to increase by 70% to 3.40 billion tons by 2050. This waste includes non-recyclable products such as synthetic plastics, as well as food and green waste, metals, paper, glass and more [not accounting for greenhouse gas (GHG)-producing energy and commercial processes] (Kaza et al., 2018 ). By 2050, total food demand is expected to increase by more than 30%, putting up to 8% of the global population at risk of hunger (AbdelRahman, 2023 ; Van Dijk et al., 2021 ). To mitigate the negative effects of our current production processes and answer the demand for basic resources, the world must move toward a sustainable circular economy (CE). The CE focuses on reusing, reducing, and recycling with renewable inputs, rather than dead-end resources such as petroleum (Kirchherr et al., 2023 ). The goal is to promote both economic development and environmental sustainability, a feat that many countries display great enthusiasm for but struggle to implement (Vogiantzi & Tserpes, 2023 ). As such, it is critical to develop methods that will promote the effectiveness of the CE, namely approaches that focus on incorporating lost or \"waste\" components, such as end-of-life products, back into value-added products or processes, like energy generation. A major player in building this economy will be microorganisms that fix carbon and remove GHGs, produce valuable compounds for the energy, food, and pharmaceutical industries, and remediate toxic wastes (Jain et al., 2022 ). One group of microorganisms that can perform all these functions, making them an attractive target for contributing to the CE, are the purple non-sulfur bacteria (PNSB). PNSB possess some of the most versatile metabolisms in the microbial world, and as mixotrophs, they can perform the majority of primary metabolisms (described by energy, electron, and carbon source) in addition to specialized processes such as N 2 fixation and H 2 production (Madigan & Jung, 2009 ). They are tolerant to stresses such as high salinity, heavy metals, and toxic contaminants (e.g. H 2 S), and can grow in aerobic and anaerobic conditions on a variety of carbon and electron sources, including raw waste streams (Madigan & Jung, 2009 ). PNSB also naturally produce multiple economically valuable products, including biofuels (Gabrielyan et al., 2015 ; Vasiliadou et al., 2018 ), bioplastics (Higuchi-Takeuchi et al., 2016 ; Monroy & Buitrón, 2020 ; Ranaivoarisoa et al., 2019 ), and fine chemicals (He et al., 2021 ; Sasikala & Ramana, 1995 ; Wang et al., 2012 ). Furthermore, they are proficient at removing organics from wastewater (Dhar et al., 2023 ; Lu et al., 2019 ), and the resulting biomass is valuable as fertilizer (Maeda, 2021 ; Sundar & Chao, 2022 ) and feedstock (Delamare-Deboutteville et al., 2019 ; Hülsen et al., 2018a ; Wada et al., 2022 ). Altogether, these traits make PNSB an extremely attractive target for biotechnological applications compared to current chemical or purely enzymatic methods, which are limited in their lack of self-repair, specific applications, and need for highly specific operating conditions (Asif et al., 2021 ). Here, we discuss how PNSB can be leveraged for sustainable bioproduction, as well as limitations to their widespread application."
} | 1,405 |
36443309 | PMC9705532 | pmc | 3,763 | {
"abstract": "With the advent of the Internet of Things, nanoelectronic devices or memristors have been the subject of significant interest for use as new hardware security primitives. Among the several available memristors, BiFe \\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$$\\mathrm{O}_{3}$$\\end{document} O 3 (BFO)-based electroforming-free memristors have attracted considerable attention due to their excellent properties, such as long retention time, self-rectification, intrinsic stochasticity, and fast switching. They have been actively investigated for use in physical unclonable function (PUF) key storage modules, artificial synapses in neural networks, nonvolatile resistive switches, and reconfigurable logic applications. In this work, we present a physics-inspired 1D compact model of a BFO memristor to understand its implementation for such applications (mainly PUFs) and perform circuit simulations. The resistive switching based on electric field-driven vacancy migration and intrinsic stochastic behaviour of the BFO memristor are modelled using the cloud-in-a-cell scheme. The experimental current–voltage characteristics of the BFO memristor are successfully reproduced. The response of the BFO memristor to changes in electrical properties, environmental properties (such as temperature) and stress are analyzed and consistant with experimental results.",
"conclusion": "Conclusion With billions of electronic devices connected to the Internet worldwide, low-power, nanoscale memristive devices are considered favourable devices for a more secure Internet of Things. Due to their stochastic behaviour, these memristive devices are ideally suited for hardware security applications such as PUFs, TRNGs, and cryptographic algorithms. The \\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$$\\mathrm BiFeO_{3}$$\\end{document} B i F e O 3 -based memristive devices are deemed to be suitable for such applications. In the proposed work, a physics-inspired compact 1D model of an Au/BiFe \\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$$\\mathrm O_{3}$$\\end{document} O 3 (BFO)/Pt/Ti memristor is developed for circuit-level simulations in the field of hardware security applications and neuromorphic circuits. The model successfully simulates resistive switching based on electric field-driven migration of oxygen vacancies and accounts for the intrinsic stochastic nature of the BFO memristor. A cloud-in-a-cell scheme is used in which Newton’s laws are consistently coupled with the Poisson solver. The simulated current–voltage characteristics of the BFO memristor obtained with this scheme agree well with the experimental results. It was found that the set current is mainly determined by the Schottky barrier height and the voltage drop across the BFO/Pt interface, while the reset current is determined by the Schottky barrier height and the voltage drop across the Au/BFO interface. In addition, based on the observations of the simulated and experimental temperature-dependent current–voltage characteristics, we anticipate the presence of a frictional force acting on the oxygen vacancies that increases with temperature. The simulated and experimental results illustrating the effects of temperature, stress, and the retention characteristics of BFO show reasonable agreement. The proposed model is highly efficient and reliable as it consists of various parameters that can be easily tuned to match the experimental results, and the degree of stochasticity can also be adjusted. To further comprehend the switching process in the BFO memristor, the 1D model could be extended to a 2D or 3D model that better represents the real-world BFO memristor.",
"introduction": "Introduction It is highly appreciated how the Internet of Things (IoT) has inevitably integrated into our lives, making it more convenient and efficient. However, with the expansion and vast diffusion of connected devices in the IoT, cybersecurity concerns have also increased. The privacy of individuals, companies and institutions have been highly compromised 1 , 2 . Unfortunately, the classical security solutions (software-level mathematical or algorithmic solutions) are no longer sufficient to secure modern-day applications. The increasing physical and side-channel attacks necessitate the need for alternative solutions 3 . Researchers and engineers have shifted their focus towards finding hardware-level solutions to address security-related challenges in recent times. The hardware-level solutions include the new nano-electronic devices, such as memristive devices or memristors, spintronics, or carbon nanotubes 4 . Explicitly, memristive devices are foreseen as promising candidates for future hardware security applications mainly because of their special properties, such as low power consumption, scalability to the nano grade, fast switching, large off/on ratio, good endurance and reliability 5 – 7 . Also known as the resistive switching random access memory (ReRAMs), these memristive devices are two-terminal devices whose resistance can be changed by applying a suitable electrical input. Apart from the features mentioned above, the switching mechanisms in these devices are intrinsically stochastic, which make these devices highly suitable for hardware security applications like physical unclonable functions (PUFs) 8 , 9 , true random number generators (TRNGs) 10 , and hash functions 11 . So far, many devices have been reported that exhibit resistive switching behaviour; however, in the present work, we focus on the devices where the resistive switching is triggered by ionic motion driven by an electric field 12 . These devices can be either filamentary-type devices involving filaments’ formation or interface-type (also called non-filamentary) devices involving the movement of charged defects. As mentioned by Du et al. 6 , 13 , the high currents induced in filamentary devices during the electroforming process can damage or destroy the device via thermodynamic dielectric breakdown, reducing the reliability of the device. In order to avoid the electroforming process, interface-type devices such as \\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$$\\mathrm{BiFeO}_{3}$$\\end{document} BiFeO 3 (BFO)-based memristors 14 – 16 , double-barrier memristive devices (DBMD) 17 are preferred. The BFO memristive devices have been intensively studied in the memristive community because they exhibit excellent characteristics such as electroforming free switching, long retention time, good endurance, and also offer multistage switching. These features make the BFO device highly recommended for implementing future hardware security applications such as PUFs and TRNGs. Furthermore, the development of existing and new memristive devices for hardware security applications requires a precise understanding of their physical behaviour. It is often challenging to determine the exact switching mechanism using experimental or diagnostic methods. Therefore, simulation models are developed that can contribute significantly to understanding the behaviour of such devices. On the one hand, multi-dimensional computational models (such as 3D kinetic Monte-Carlo) are exploited for an in-depth understanding of resistive switching and moderately include real-world devices’ physical and chemical processes and stochastic behaviour 18 – 20 . However, they are computationally very expensive and, therefore, cannot be used for performing circuit simulations. On the other hand, there are compact or concentrated models based on mathematical formulae. They are fast but do not include the physical and chemical processes in the device, and often, they do not include the intrinsic stochasticity found in these devices 21 – 23 . Figure 1 A flowchart illustrating the goal of the proposed work. The non-grey areas indicate the main steps addressed in the current manuscript. Unlike the state-of-the-art models, we propose a circuit simulator-compatible distributed model for BFO memristor that considers the advantages of both the models mentioned above. It is a one-dimensional (1D) compact model including more or less realistic physics and the experimentally observed stochastic behaviour i.e., the cycle-to-cycle (C2C) variability and device-to-device (D2D) variability observed in BFO memristors. A kinetic Cloud-in-a-cell (CIC) 24 , 25 scheme is used to simulate the resistive switching mechanism based on the ion/vacancy transport. Although it is a distributed model, because we resolve it in a 1D space, it is computationally less demanding and fast. The model is primarily considered to provide an interface between circuit designers and device developers, as shown in Fig. 1 26 . It is used to explore the electrical properties of BFO as entropy sources and the effects of physical variables such as temperature and voltage on the entropy sources. The model can be extended further to investigate the performance of BFO memristive devices for hardware security applications by performing circuit simulations of memristive-PUFs (mem-PUFs) or memristive-TRNGs (mem-TRNGs) with a SPICE-like circuit simulator. It is important to mention that this paper serves as a basis for conducting circuit simulations of mem-PUFs and mem-TRNGs and is mainly intended to demonstrate the capabilities of the proposed model. Therefore, the scope of the paper is initially limited to the compact modelling and parametric investigation steps shown in Fig. 1 . These steps provide circuit designers and device engineers with an insight into device physics. A simulation campaign and experiments of mem-PUFs and mem-TRNGs are planned as the next step. The manuscript is divided into three sections. First, the simulation approach is discussed in detail, explaining the BFO memristive device and its current mechanisms. Then, the simulation results based on the experimentally determined electrical parameters of BFO are discussed and compared with the experimental findings. Finally, an overall summary and significant findings from the current work are provided in the conclusion section.",
"discussion": "Results and discussion \n Figure 4 The simulated IV curves showing ( a ) cycle-to-cycle (C2C) variability obtained for four consecutive voltage sweeps with initial conditions given in the first row of Table 2 . ( b ) The change in internal state of the device ( q ( t )) for C2C. ( c ) Device-to-device (D2D) variability obtained for a single voltage sweep but with different initial conditions given in Table 2 . ( d ) The change in internal state of the device ( q ( t )) for D2D. The maximum applied voltage in both plots is ± 8.5 V. \n First, the 1D compact model of BFO device described above was validated by comparing the calculated current–voltage characteristics ( I – V curve) with the experimentally obtained I – V curve. For this purpose, the simulation model was initialized with the parameters listed in Table 1 . In this state, the device is in its HRS. A voltage sweep with a ramp from 0 through 8.5 V to 0 V was applied to set the device to an LRS, and then from 0 through − 8.5 V to 0 V to reset it back to HRS. For both set and reset process, a sweeping velocity of 0.36 V per 100 ms was applied. The change in applied voltage ( \\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$$V_\\mathrm{device}$$\\end{document} V device ) with time and the resultant I – V curves are shown in Fig. 3 a. From the I – V curves, it can be seen that the model reproduces the analogue behaviour of BFO very well. The calculated I – V curve for the SET process (0 V to 8.5 V to 0 V) agrees with the experimental results both qualitatively and quantitatively. However, although the I – V curve for RESET agrees quite well quantitatively with the experimental I – V curve, the model does not entirely capture the effect of non-zero crossing (at − 3 V) observed in the experimental I – V curve. Sun et al. 36 attributed this type of non-zero crossing behaviour of memristors to three mechanisms. They are the capacitive effect, the ferroelectric polarisation effect, and the presence of an internal electromotive force. Since the ferroelectric polarisation effects are already ruled out for a BFO, the possible reason for non-zero crossing in a BFO can most likely be the capacitive effects. The capacitance-voltage measurements of Shuai et al. 37 , 38 showed the presence of such capacitive effects in BFO. They indicated the presence of simultaneous resistive and capacitive switching in BFO, with HRS corresponding to the low capacitance state and vice versa. Figure 5 ( a ) Voltage drop across different regions of the BFO memristor. ( b ) The change in top and bottom effective Schottky barrier heights ( \\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$$\\Phi _\\mathrm{eff}$$\\end{document} Φ eff ) and effective ideality ( \\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$$n_\\mathrm{eff}$$\\end{document} n eff ) factor as a function of time. ( c ) The simulated I – V curves showing the effect of top and bottom Schottky barrier height on the set and reset process. Curves with \\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$$\\Phi _\\mathrm{0,t}$$\\end{document} Φ 0 , t =0.8 eV overlap in the negative bias region. Table 2 The parameters of the four devices used to determine the I – V curves in Fig. 4 . Device \\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$$\\bar{d_\\mathrm{r}}$$\\end{document} d r ¯ (nm) \\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$$l_\\mathrm{BFO}$$\\end{document} l BFO (nm) \\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$$\\rho$$\\end{document} ρ ( \\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$$\\mathrm cm^{-3}$$\\end{document} c m - 3 ) 1 295.4 600 \\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$$2\\times 10^{16}$$\\end{document} 2 × 10 16 2 306.89 588.2 \\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$$2.6 \\times 10^{16}$$\\end{document} 2.6 × 10 16 3 299.001 601.5 \\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$$2.1 \\times 10^{16}$$\\end{document} 2.1 × 10 16 4 302.23 586.9 \\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$$1.5 \\times 10^{16}$$\\end{document} 1.5 × 10 16 Table 3 The parameters used to simulate the I – V curves in Fig. 6 . T (K) \\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$$\\varepsilon _{r}$$\\end{document} ε r \\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$$\\sigma$$\\end{document} σ ( \\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$$\\Omega \\mathrm{m}$$\\end{document} Ω m ) \\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$$\\lambda _\\mathrm{T}$$\\end{document} λ T 298 52 \\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$$8\\times 10^{-4}$$\\end{document} 8 × 10 - 4 0.0 313 60 \\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$$9\\times 10^{-4}$$\\end{document} 9 × 10 - 4 0.005 323 72 \\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$$1\\times 10^{-3}$$\\end{document} 1 × 10 - 3 0.01 333 100 \\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$$3.5\\times 10^{-3}$$\\end{document} 3.5 × 10 - 3 0.04 343 132 \\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$$5\\times 10^{-3}$$\\end{document} 5 × 10 - 3 0.06 348 145 \\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$$7\\times 10^{-3}$$\\end{document} 7 × 10 - 3 0.062 \n Figure 6 ( a ) The experimentally obtained temperature-dependent current–voltage characteristics ( I – V – T curves). ( b ) Simulated I – V – T curves obtained using the fitting parameter ( \\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$$\\lambda _\\mathrm{T}$$\\end{document} λ T ). The parameters used in the simulation are given in Table 3 . ( c ) I – V – T curves obtained for positive applied voltage and without \\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$$\\lambda _\\mathrm{T}$$\\end{document} λ T . The dashed lines indicate the voltage required to switch the device from HRS to LRS. The legend is same as mentioned in ( b ). ( d ) The calculated average drift velocity ( \\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$$\\nu _\\mathrm{D}$$\\end{document} ν D ) with and without \\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$$\\lambda _\\mathrm{T}$$\\end{document} λ T at different temperatures. \n Figure 3 b shows the change in activation energy ( \\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$${U}_{\\mathrm{A}}$$\\end{document} U A ) across the BFO memristor as a function of time and \\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$$V_\\mathrm{device}$$\\end{document} V device shown in Fig. 3 a. As mentioned earlier, the activation energy across the BFO is not homogeneous but gradually increases from 0.55 to 0.75 eV between 550 and 600 nm.This inhomogeneous \\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$${U}_{\\mathrm{A}}$$\\end{document} U A is due to the presence of the Ti region between 550 and 600 nm and plays an essential role in the vacancy transport responsible for the resistive switching behaviour of BFO. Vacancy transport is illustrated in Fig. 3 c by considering how the charge density in BFO changes during the simulation time of 8 s. Initially, the vacancies are randomly placed across the BFO computational domain, and when a voltage is applied, the vacancies move towards the BFO/Pt interface. Due to the very high activation energy (about 0.75 eV) at the BFO/Pt interface, the change in position of the \\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$$\\mathrm{V}_\\mathrm{O}^{+}$$\\end{document} V O + vacancies near the Au/BFO interface is insignificant. This insignificant change in the position of the \\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$$\\mathrm{V}_\\mathrm{O}^{+}$$\\end{document} V O + vacancies between 2 and 3.2 s sets the device to an LRS with a nearly constant average relative distance ( \\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$${\\bar{d}}(t)\\approx 562$$\\end{document} d ¯ ( t ) ≈ 562 nm). Moreover, the change in the position of the \\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$$\\mathrm{V}_\\mathrm{O}^{+}$$\\end{document} V O + vacancies is so small that they can be assumed to be almost in a trapped state. When a negative write bias is applied after 4 s during the reset process, the activation energy at the BFO/Pt interface drops to about 0.6 eV (as shown in Fig. 3 b), which is sufficient to return the \\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$$\\mathrm{V}_\\mathrm{O}^{+}$$\\end{document} V O + vacancies to their equilibrium position and reset the device to HRS. At the end of the reset process (at 8 s), the average relative distance also drops to an initial value of about 268 nm. The intrinsic stochastic behaviour of BFO is measured in terms of spatial (device to device) and temporal (cycle to cycle) variability. First, the four I – V curves in Fig. 4 a show the temporal variability of the BFO. The curves were obtained for four consecutive voltage sweeps shown in the inset of Fig. 4 a, and the initial conditions used to simulate these curves are given in the first row of Table 2 . Although, the I – V curves calculated for each applied voltage cycle follow a similar trend, they slightly vary from each other. As observed from Fig. 4 b, the slight variation in the I – V curves is likely due to the change in internal state of the device ( q ( t )) from cycle to cycle. This change in q ( t ) is actually due to the random movement of the vacancies as calculated using Eq. ( 12 ). Second, the spatial variability in BFO is illustrated using the four I – V curves in Fig. 4 c for a single voltage sweep shown in the inset. The initial conditions used to simulate the four curves are given in Table 2 . As can be observed, the I – V curves showing spatial variability are more clearly separated from each other than the I – V curves showing temporal variability. In general, based on experimental observations, the temporal variability, is much lower compared to the spatial variability for interface-type memristive devices such as BFO and DBMD 25 . This difference in the temporal and spatial variability of the BFO is mainly essential for implementing mem-PUFs because multiple runs of the same PUF should give almost the same response, but it should be different for different copies of the same circuit. It means that (a) each memristive device in the PUF crossbar should be different from each other (i.e., more spatial variability), and (b) each memristive device should produce nearly the same output every time for the same supply voltage (i.e., comparatively less temporal variability) 6 . Moreover, the difference in the I – V curves showing spatial variability could be mainly due to the different initial conditions used to simulate each curve. The initial conditions more or less directly effect q ( t ), so for this reason we observe a change in q ( t ) as seen in Fig. 4 d. However, apart from the above-mentioned reasons, it is predicted that, several other unknown physical or chemical phenomena may also contribute to this spatial and temporal variability in a true BFO memristor. The voltage drops across different BFO memristor regions are plotted in Fig. 5 a to investigate the rectification process and the physical mechanisms behind the strong voltage dependence. The different regions include the two Schottky contacts at the Au/BFO ( \\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$$\\mathrm{D}_\\mathrm{t}$$\\end{document} D t ) and BFO/Pt ( \\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$$\\mathrm{D}_\\mathrm{b}$$\\end{document} D b ) interfaces, and the \\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$$\\mathrm{BiFeO}_{3}$$\\end{document} BiFeO 3 layer. For a positive bias, a back-to-back rectification is observed with a forward-biased \\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$$\\mathrm{D}_\\mathrm{t}$$\\end{document} D t and reverse-biased \\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$$\\mathrm{D}_\\mathrm{b}$$\\end{document} D b . As can be seen in Fig. 5 a, although there is some voltage drop across the \\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$$\\mathrm{D}_\\mathrm{t}$$\\end{document} D t and \\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$$\\mathrm{BiFeO}_{3}$$\\end{document} BiFeO 3 layer, most of the voltage is blocked by the reverse-biased \\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$$\\mathrm{D}_\\mathrm{b}$$\\end{document} D b . Furthermore, for a negative bias, almost all the applied voltage is blocked by the reverse-biased \\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$$\\mathrm D_{t}$$\\end{document} D t , with a small voltage drop across the \\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$$\\mathrm D_{b}$$\\end{document} D b and a negligible one across the BFO layer. The changes in other properties of the Schottky contacts, such as the barrier height ( \\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$$\\Phi$$\\end{document} Φ ) and the ideality factor ( n ) during the sweep time, are also shown in Fig. 5 b. The change in \\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$$\\Phi$$\\end{document} Φ and n are strongly influenced by the vacancy transport in the BFO. As vacancies move toward the BFO/Pt interface during positive bias, \\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$$\\mathrm D_{b}$$\\end{document} D b becomes non-rectifying, with \\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$$\\Phi _\\mathrm{eff}^\\mathrm{b}$$\\end{document} Φ eff b decreasing from 0.85 to 0.62 eV and \\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$$n_\\mathrm{eff}^\\mathrm{b}$$\\end{document} n eff b from 4.5 to 3.3. This allows electrons to flow easily across the barrier, increasing the current through the BFO memristor. With a negative bias, \\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$$\\Phi _\\mathrm{eff}^\\mathrm{b}$$\\end{document} Φ eff b and \\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$$n_\\mathrm{eff}^\\mathrm{b}$$\\end{document} n eff b increase as the vacancies move away from the BFO/Pt interface and \\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$$\\mathrm D_{b}$$\\end{document} D b becomes rectifying. On the other hand, \\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$$\\Phi _\\mathrm{eff}^\\mathrm{t}$$\\end{document} Φ eff t and \\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$$n_\\mathrm{eff}^\\mathrm{t}$$\\end{document} n eff t increase with a positive bias and decrease with a negative bias. However, their change is almost negligible; therefore, \\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$$\\mathrm D_\\mathrm{t}$$\\end{document} D t can be considered non-flexible and constantly rectifying. Moreover, the initial Schottky barrier heights are considered as one of the entropy sources in hardware security applications. So, it is also important to check the behaviour of a BFO memristor to change in initial Schottky barrier heights. The graph in Fig. 5 c shows the I – V curves with different combinations of \\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$$\\Phi _{0}^\\mathrm{t}$$\\end{document} Φ 0 t and \\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$$\\Phi _{0}^\\mathrm{b}$$\\end{document} Φ 0 b . Ideally, the barrier height can be increased or decreased by reducing or increasing the doping concentration, respectively 39 . Increasing \\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$$\\Phi _{0}^\\mathrm{t/b}$$\\end{document} Φ 0 t / b from 0.75 to 0.9 eV increases the energy required for the electrons to cross the barrier, which reduces the current through the BFO. In contrast, if \\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$$\\Phi _{0}^\\mathrm{t/b}$$\\end{document} Φ 0 t / b is decreased, the electrons can move more easily, which increases the current through the BFO. Also, a change in \\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$$\\Phi _{0}^\\mathrm{b}$$\\end{document} Φ 0 b mainly affects the right loop of the I – V curves, while a similar change in \\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$$\\Phi _{0}^\\mathrm{t}$$\\end{document} Φ 0 t , affects the left loop of the I – V curve. Therefore, as mentioned by Du et al. 15 and observed in Fig. 5 , we can conclude that the set current is mainly determined by the barrier height and the voltage drop across \\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$$\\mathrm D_{b}$$\\end{document} D b , and the reset current by the barrier height and the voltage drop across \\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$$\\mathrm D_{t}$$\\end{document} D t . Figure 7 ( a ) Input voltage source ( \\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$$V_\\mathrm{Device}$$\\end{document} V Device ) with different amplitudes. ( b ) Experimental I – V curves, and ( c ) simulated I – V curves for different maximum \\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$$V_\\mathrm{Device}$$\\end{document} V Device . ( d ) The change in average distance \\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$${\\bar{d}}(t)$$\\end{document} d ¯ ( t ) with time for a positive applied voltage cycle with different amplitudes. ( e ) The change in charge density over time for a positive applied voltage cycle with maximum voltage of 3 V, 5 V and 7 V. The legend for all plots is shown on the top right corner. The response of BFO memristor to changing environmental conditions (e.g., temperature) and stress (e.g., excessive voltage) is illustrated in Figs. 6 and 7 . These factors are important when considering BFO for neuromorphic circuits, hardware security and non-volatile memory applications. Fig. 6 shows the simulated and experimental temperature-dependent \\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$$\\textit{I}$$\\end{document} I – \\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$$\\textit{V}$$\\end{document} V curves. The results are obtained for a writing bias of ± 11 V and different temperatures increasing from 298 to 348 K. According to Eq. ( 11 ), the velocity of the vacancies ( \\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$$\\nu _\\mathrm{D}$$\\end{document} ν D ) increases with increasing temperature. As \\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$$\\nu _\\mathrm{D}$$\\end{document} ν D increases, the maximum voltage required to switch the device to LRS reduces as shown in Fig. 6 b. However, as observed experimentally in Fig. 6 a, the maximum voltage required for switching is the same for all temperatures. No change in the switching voltage suggests that there might be some frictional force acting on the vacancies that decrease their velocity ( \\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$$\\nu _\\mathrm{D}$$\\end{document} ν D ) with increasing temperature. This frictional force could be due to the following reasons: (a) With the increasing temperature, more \\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$$\\mathrm Ti^{4+}$$\\end{document} T i 4 + ions may diffuse into the BiFe \\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$$\\mathrm O_{3}$$\\end{document} O 3 layer due to thermal diffusion. The more Ti diffuses into the BFO layer, the higher the activation energy, resulting in a decrease of \\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$$\\nu _\\mathrm{D}$$\\end{document} ν D , (b) The collision rate between particles increases with increasing temperature, which affects the motion of particles in a device, and (c) the presence of temperature-dependent ferroelectric polarisation switching. To account for the frictional force in BFO, we modify \\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$$\\nu _\\mathrm{D}$$\\end{document} ν D by using a fitting parameter to match the experimental results. So, \\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$$\\nu _\\mathrm{D}$$\\end{document} ν D = \\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$$\\nu _\\mathrm{D}$$\\end{document} ν D (1 − \\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$$\\lambda _\\mathrm{T}$$\\end{document} λ T ) where \\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$$\\lambda _\\mathrm{T}$$\\end{document} λ T could be any number between 0 and 1. In this way, we can reduce \\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$$\\nu _\\mathrm{D}$$\\end{document} ν D of all vacancies for different temperatures. This can be seen in Fig. 6 d, which shows the average \\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$$\\nu _\\mathrm{D}$$\\end{document} ν D with and without including the fitting parameter for different temperatures. The parameters used to simulate I – V curves in Fig. 6 c are given in Table 3 . Therefore, by considering the fitting term, we are able to mimic the temperature-dependent resistive switching in BFO, as shown in Fig. 6 c. The measured and simulated I – V curves at room temperature for different maximum applied voltages are shown in Fig. 7 . The voltage profiles used to obtain the plots are shown in Fig. 7 a. The observations from Fig. 7 b indicate that the shape of the I – V curve in the set and reset direction strongly depend on the maximum applied voltage. At higher applied maximum voltages, the shape of the hysteresis is relatively wider compared to the hysteresis obtained at lower maximum \\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$$V_\\mathrm{device}$$\\end{document} V device . The simulated I – V curves in Fig. 7 c also show a broadening of I – V curves with increasing maximum \\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$$V_\\mathrm{device}$$\\end{document} V device and quantitatively match quite well with the experimental results. However, there is a discrepancy in the simulated I – V curves between voltages − 1.5 and 1.5 V. As mentioned earlier, this discrepancy could be due to capacitive effects or the presence of an internal electromotive force. Furthermore, when the applied voltage is very low, the vacancies do not receive enough energy to drift to the BFO/Pt interface, resulting in switching failure, i.e. the device does not switch to LRS and remains in a HRS. This can be seen in Fig. 7 d, which shows the change in average distance for different maximum applied voltages. The average distance is only tracked for the positive \\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$$V_\\mathrm{-device}$$\\end{document} V - device cycle since we are interested in observing the vacancies drift towards the BFO/Pt interface, which mainly contributes toward switching the device to LRS. For low maximum \\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$$V_\\mathrm{device}$$\\end{document} V device from 3 to 5 V, \\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$${\\bar{d}}(t)$$\\end{document} d ¯ ( t ) is less than 520 nm, which means that certain vacancies do not reach the BFO/Pt interface and are not trapped. This can be better understood from Fig. 7 e that shows the vacancy transport in terms of charge density for a positive applied voltage cycle. The retention time of the device is also severely affected due to the untrapped vacancies in the 3 V and 5 V plots. The retention characteristics of BFO memristor are investigated using the proposed stochastic model. The retention tests were performed by switching the device to LRS or HRS, which are the initial states for this particular study. Then the externally applied voltage was switched off, and the diffusion of the vacancies was recorded. Since the experimental results are obtained for a real BFO device (i.e., 3D), we had to interpolate the 1D model to 3D. In a 3D model, the vacancies can generally move in six directions, while in a 1D space, the vacancies can move either back or forth. So, to fit the simulated results with the experimental results, we use a fitting parameter that restricts the movement of vacancies in BFO by reducing their jump attempts. For this, we randomly pick a number, \\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$$\\beta$$\\end{document} β between 0 and 1, and use the following relation for moving the vacancies: \\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$$\\begin{aligned} \\nu _\\mathrm{D}= \\nu _\\mathrm{D,\\ updated} \\ \\ \\ \\mathrm{for} \\ 0>\\beta<0.33, \\\\ \\nu _\\mathrm{D} = \\nu _\\mathrm{D,\\ present}\\ \\ \\ \\ \\mathrm{for} \\ 0.33>\\beta <1. \\end{aligned}$$\\end{document} ν D = ν D , updated for 0 > β < 0.33 , ν D = ν D , present for 0.33 > β < 1 . where \\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$$\\nu _\\mathrm{D}$$\\end{document} ν D is the drift velocity of the vacancies given by Eq. ( 11 ). The development of the device current was recorded every 10 s with a read voltage of 2 V at room temperature. The simulated and experimental results for a total of 3000 cycles/pulses are shown in Fig. 8 . As observed, BFO shows good retention characteristics. The HRS is stable, and no significant change was observed during the 3000 cycles. For the LRS, the good retention is primarily due to the diffusion of \\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$$\\mathrm Ti^{4+}$$\\end{document} T i 4 + ions that increases the activation energy at the BFO/Pt interface. This high activation energy limits the movement of vacancies i.e., the vacancies get trapped. However, BFO shows a degradation in the LRS current until approximately the \\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$$700\\mathrm{th}$$\\end{document} 700 th cycle, i.e., 2 h before stabilizing. Through simulations, this degradation was found to be due to the diffusion of some vacancies away from the BFO/Pt interface that were not trapped. The relative average distance, \\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$${\\bar{d}}(t)$$\\end{document} d ¯ ( t ) , decreased from 564 to 542 nm, which increased \\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$$\\Phi _\\mathrm{eff}^\\mathrm{b}$$\\end{document} Φ eff b from 0.62 to 0.68 eV, thereby decreasing the current. One possible way to improve retention could be to ensure that all the vacancies are properly trapped by increasing the Ti fluence 31 . The second possibility would be to improve the BFO surface using low-energy \\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$$\\mathrm Ar^{+}$$\\end{document} A r + ion irradiation, as suggested by Shuai et al. 37 . Figure 8 The comparison between simulated and experimental retention characteristics of a BFO memristor. The current evolution is recorded every 10 s for 3000 cycles for both LRS and HRS. Figure 9 Cycle-number dependent plasticity in BFO. The calculated and experimental synaptic change of ( a ) potentiation and ( b ) depression dynamics. \\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$$G_{1}$$\\end{document} G 1 is the conductance measured during the first spike. Finally, a cycle number-dependent plasticity measurement, according to Du et al. 40 . is carried out to assess the model’s performance further. The nominal thickness of the BFO used here is 500 nm, with the top and bottom contacts thickness of 180 nm. For the potentiation spike sequence, an initialization pulse of − 6 V is used for the RESET process. Similarly, for the depression spike sequence, a pulse of 6 V is used for the SET process. A normalized synaptic weight change is calculated in Fig. 9 for 250 similar spikes. The amplitude of the pulse used for potentiation dynamics is 5 V, and for depression dynamics, it is − 5 V. A pulse width of 100 ms and the interval between two pulses of 20 ms is applied. As observed in Fig. 9 the experimental and calculated normalized synaptic weight change follow a similar trend. As mentioned by Du et al. 40 , it was also observed via simulations that with an increase in the potentiation spikes, the oxygen vacancies tend to move towards the Pt/Ti electrode and thus increasing the conductance. For the depression spikes, the change in conductance is not so noticeable, and the oxygen vacancies tend to move back to their inertial position, causing the simultaneous switching of the device into HRS."
} | 14,801 |
38994418 | PMC11234829 | pmc | 3,764 | {
"abstract": "Preparing nanostructured surfaces has been considered an effective method to improve the output of triboelectric nanogenerators (TENGs), but how to quickly prepare materials with a nanostructured surface for TENGs has always been a challenge. Here, polypropylene nanowires and electrospun nylon 11 nanofibers were successfully prepared through a simple and time-saving method with a high success rate. Compared with a flat TENG, the output performance of a dual nanostructured TENG is enhanced by more than 5 times. After 1 H ,1 H ,2 H ,2 H -perfluorooctyl trichlorosilane was assembled on the surface of the polypropylene film, the dual nanostructured TENG achieved the maximum output with the short-circuit current, output voltage, and charge density of 63.3 μA, 1135 V and 161.5 μC m −2 , respectively. Compared with a planar structured TENG, the short-circuit current and output voltage were enhanced by about 18 times, and the charge density was increased by about 36 times. In addition, the TENG showed good working stability with almost no decrease in output after continuous operation for 193 000 cycles. The electricity generated by this TENG can successfully light up 1280 LEDs and continuously power a multi-functional electronic watch. Finally, the triboelectric signal generated by this TENG was used to control an optocoupler switch, indicating good application prospects in a remote control switching circuit.",
"conclusion": "Conclusions PP nanowires and electrospun nylon nanofibers were successfully prepared through a simple and time-saving method with a high success rate. The polypropylene nanowire film and nylon nanofiber film were assembled into a dual nanostructure TENG to enhance its output performance. Furthermore, PFTS was assembled on the surface of the polypropylene film using a vapor deposition method to further enhance the output of the TENG. Among the 12 TENGs in this study, the 200 FPP@Nano NY TENG obtained the maximum I sc , V o , and charge density of 63.3 μA, 1135 V, 161.5 μC m −2 , respectively. Compared with a planar structure TENG, I sc and V o were increased by about 18 times, while the charge density was increased by about 36 times. In addition, the TENG showed no reduction in output after continuous operation for more than 193 000 cycles, demonstrating very good working stability for practical use. The maximum value of output power of the 200 FPP@Nano NY TENG is about 17.0 mW under a loading resistance of 20 MΩ. The electricity generated by this TENG can successfully light up 1280 LEDs and continuously supply power to a multi-functional electronic watch, proving that it has very good application prospects in micro power supply devices. Finally, the 200 FPP TENG was integrated into a PCB circuit to control the ‘on’ and ‘off’ of an optocoupler switch, indicating application prospects in remote control switching circuits.",
"introduction": "Introduction Recently, triboelectric nanogenerators (TENGs) based on the coupling effect of triboelectricity and electrostatic induction have attracted widespread attention due to their advantages such as light weight, diverse materials, easy fabrication, and low cost. 1–3 TENGs have been proven to be an effective low-frequency harvesting technology that can convert various types of mechanical energy, such as human movement, 4,5 wind energy, 6–9 and water energy, 10–12 into electrical energy. The electric energy generated by a triboelectric generator is used for the power supply of electronic equipment, 13,14 as a pressure sensor, 15,16 for motion monitoring, 17–19 health monitoring, 20–24 environmental monitoring, 25–27 air or water purification, 28,29 showing very good application prospects. To improve the applications of the triboelectric nanogenerators, researchers have proposed various methods to improve their output. A common method to improve the output of a triboelectric generator is to micro-nano-process the friction layer material to increase the contact area and thereby increase the amount of generated triboelectric charge. 30,31 In addition, the generation of tribocharges can be improved by chemically modifying the frictional material surface to increase the triboelectric polarity gap between the two friction layer materials. 32–36 Relying solely on nanotechnological treatment of the material surface or chemical modification of the surface of the material has made limited improvements to the performance of TENGs. Therefore, micro-nano processing is combined with chemical surface modification of the materials to effectively improve the output of the triboelectric nanogenerators. 37 At present, the friction layer of a triboelectric generator is composed mainly of two plane films or a plane film and structured film. If both friction layers of the triboelectric generator can be made into nanostructures, the effective contact area may be further increased to improve the output. Current methods for micro-nano treatment of triboelectric material surfaces include inductively coupled plasma etching, template replication, and chemical etching. 38–42 These methods can achieve micro-nano processing of some polymer materials such as PTFE, PI, PDMS and other materials. However, they also face some problems, such as inductively coupled plasma etching, which is less effective at processing thermoplastic materials and whose equipment is more expensive. There have been studies using anodic aluminum oxide (AAO) templates to obtain regular polypropylene nanowires through hot pressing, which can effectively improve the output of the triboelectric nanogenerators. 43 However, the production process of an AAO template is cumbersome, cycle is long, cost is high, and film removal success rate is low, which limits its application. Therefore, it is very necessary to develop a simple and rapid method for preparing polymer nanowires that can be used in conjunction with surface chemical modification methods to improve the output of the triboelectric generators. Herein, polypropylene (PP) nanowires with regular nanostructure were successfully prepared by a hot pressing method. Instead of AAO templates, a polycarbonate (PC) membrane is chosen as the template mainly because it is low in cost, easy to obtain, and has many aperture specifications. The most important thing is that the AAO template takes a long time to demold and generates a lot of heat, which reduces the success rate of demolding. Due to the characteristics of polypropylene being insoluble in chloroform and polycarbonate being soluble in chloroform, putting the hot-pressed mixed film into chloroform for about 30 seconds can completely remove the PC template, which greatly reduces the mold removal time and increases the success rate of mold removal. Assembling a polypropylene nanowire film and nylon (NY) nanofiber film into a dual nanostructure TENG can effectively improve the output of the triboelectric nanogenerator. Compared with flat PP and flat NY, its output is increased by more than 5 times. In addition, 1 H ,1 H ,2 H ,2 H -perfluorooctyltrichlorosilane (PFTS) was successfully assembled on the surface of the polypropylene film using a vapor deposition method to further enhance the output of the triboelectric nanogenerator. Compared with a planar structure TENG, the short-circuit current ( I sc ) and output voltage ( V o ) of the PFTS-enhanced dual nanostructure TENG were increased by about 18 times. The electricity generated by this TENG can successfully light up 1280 LEDs in real time and continuously supply power to a multi-functional electronic watch with a temperature measurement function, proving good application prospects in micro power supply devices. In addition, the nanowire film based TENG was integrated into a printed circuit board (PCB) circuit to control the ‘on’ and ‘off’ of an optocoupler switch, indicating good application prospects in intelligent switch control.",
"discussion": "Results and discussion The PP nanowire array friction layers were prepared by a simplified hot processing technique using a porous polycarbonate membrane as a template, as shown in Fig. 1a . PFTS was successfully assembled on the PP nanowire surface via a vapor deposition method. Nylon nanofibers were fabricated by electrospinning, as shown in Fig. 1b . Details of all experiments can be found in the Experimental section. The PP and NY friction pairs with a size of 4.5 cm × 4.5 cm were assembled into a triboelectric generator with a dual nanowire structure, as shown in Fig. 1c . Fig. 1 Schematic image of the fabrication process of (a) a PFTS-modified PP nanowire array based triboelectric electrode and (b) a nylon nanowire based triboelectric electrode; (c) schematic image of a TENG composed of PP nanowires and nylon nanofibers. Similar to most contact-separation mode triboelectric nanogenerators (CS-TENGs), the working principle of a PP@NY triboelectric nanogenerator is based on the coupling effect of triboelectrification and induction electrification. The working mechanism of a PP-NY based TENG is illustrated in Fig. 2 . As shown in Fig. 2a , before PP contacts NY, no charge transfer occurs. When the PP and NY layers are pressed to contact, positive charges are generated on NY, and negative charges are generated on the PP surface ( Fig. 2b ). Due to the electrostatic induction effect, the Ag conducting layer generates opposite charges to the PP friction layer, resulting in the formation of a current flow from the NY electrode to the PP electrode during the release process ( Fig. 2c ). Once the charges reach the balanced state, no current flows in the circuit ( Fig. 2d ). Similarly, during the pressing process, a reverse current is detected from the PP electrode to the NY electrode ( Fig. 2e ). Thus, by coupling the contact electrification with electrostatic induction effects, alternating electricity is generated while the TENG experiences a contact-separation process. Fig. 2 Scheme of the working mechanism and charge generation process: (a) original separation state, (b) pressed into contact to generate frictional charges, (c) released to generate induced charges, current flows from the NY part to the PP part, (d) reaching the electrical balance state when no current flows in the external circuit, (e) pressed into contact again, when current flows from the PP part to the NY part. \n Fig. 3a–c shows the surface morphology SEM images and cross-sectional SEM image of the PP nanowires fabricated through a PC template with a 400 nm pore size. According to the images, uniform nanowires were successfully fabricated with about 470 ± 45 nm diameter and 8.9 μm length. Fig. 3d–f shows the surface morphology SEM images and cross-sectional SEM image of the PP nanowires fabricated through a PC template with a 200 nm pore size. According to the images, the uniform nanowires were successfully fabricated with about 209 ± 25 nm diameter and 8.3 μm length. To facilitate data comparison, we abbreviate the above two PP nanowires to 400 PP and 200 PP, respectively. Fig. 3g–i shows the surface morphology SEM images and cross-sectional SEM image of the NY nanofibers fabricated through electrospinning. It can be seen from figures that the nylon nanofibers are very uniform with about 426 ± 121 nm diameter. The thickness of the nylon nanofibers film is about 17.5 μm, according to Fig. 3i . The statistical results of the diameters of the polypropylene nanowires and nylon nanofibers can be seen in Fig. S2. † Fig. 3 (a–c) Surface morphology and cross-sectional SEM images of 400 PP nanowires; (d–f) surface morphology and cross-sectional SEM images of 200 PP nanowires; and (g–i) surface morphology and cross-sectional SEM images of NY nanofibers. To study the effect of nanostructure on the output of a triboelectric nanogenerator, friction layer materials with a planar structure and nanowire structure were combined, as shown in Fig. 4a–d . This combination includes main four different groups: flat PP@flat NY, PP nanowire@flat NY, flat PP@NY nanowire and PP nanowire@NY nanowire. Since there are two types of the polypropylene nanowire, 200 PP and 400 PP, a total of six triboelectric nanogenerator outputs with different friction layer materials were tested, and the short-circuit current ( I sc ), output voltage ( V o ) and charge density results are shown in Fig. 4e–g . According to the results, I sc , V o and charge density follow the same trend: that is, the output of the planar structured TENG is smaller than that of the nanostructured TENG. It was demonstrated that nanostructures can increase the contact area and thereby increase the amount of triboelectric charge generated. When the two friction layer materials are flat structures, the output of Flat PP@Flat NY TENG is the smallest, and the corresponding I sc , V o , and charge density are 3.0 μA, 62.5 V and 4.4 μC m −2 , respectively. When both the friction layer materials are nanostructured, the output of the 200 PP@Nano NY TENG is a maximum, and the corresponding I sc , V o , and charge density are 16.0 μA, 391 V and 27.6 μC m −2 , respectively. Even with the same dual nanostructure, the output of the 400 PP@Nano NY TENG is lower than that of the 200 PP@Nano NY TENG. As can be seen from Fig. S1(a–d), † the number of nanopores per unit area of a polycarbonate filter membrane with a pore size of 200 nm is much greater than that of a polycarbonate filter membrane with a pore size of 400 nm. Therefore, the specific surface area of the prepared 200 PP nanofilm is much larger than that of the 400 PP nanofilm, which results in the 200 PP@Nano NY TENG being able to generate more triboelectric charges and obtain a higher output. In summary, the polypropylene nanowires can effectively increase the output of the triboelectric nanogenerators, and higher-density, relatively smaller-diameter nanowire structure can more effectively increase the output of the triboelectric generators. Fig. 4 Schematic images of TENGs composed of (a) flat PP and flat NY, (b) PP nanowires and flat NY, (c) flat PP and NY nanofibers, (d) PP nanowires and NY nanofibers and the output performances of TENGs composed of flat PP, PP nanowires, flat NY and NY nanofibers: (e) I sc , (f) V o and (g) charge density. The composition of the surface of the friction material is another crucial factor that impacts the performance of a TENG. Normally, for a contact-type triboelectric generator, to increase the output of the triboelectric generator, it is necessary to increase the difference in triboelectric polarity of the two friction layer materials as much as possible. It has been demonstrated that fluoropolymers, such as polytetrafluoroethylene (PTFE) and polyvinylidene fluoride (PVDF), are currently among the most appropriate choices as triboelectric negative friction materials for a TENG. This is primarily because fluorine is the most electronegative element among all the elements. To achieve this, we have implemented a surface chemical modification technique to fine-tune the surface composition of the friction material for a TENG. Here, since PFTS has been proven to have very high triboelectric negative polarity, PFTS was chosen as a modifier to chemically modify the polypropylene film surface. Fig. 5a shows the entire process of physical vapor deposition for depositing and assembling PFTS on the surface of the polypropylene film. Specific experimental steps can be found in the Experimental section. Compared with the surface morphology SEM image before surface modification in Fig. 3b and e , the assembly of PFTS has little effect on the surface morphology of planar and nanostructured films, as shown in Fig. 5b–d . To facilitate data comparison, we abbreviate the above three PFTS modified PP films to flat FPP, 400 FPP and 200 FPP, respectively. To prove that PFTS has been successfully assembled on the surface of the flat PP and PP films with nanostructure, XPS was used to analyze the surface composition of PFTS-modified materials. According to Fig. 5e , there is no F1s characteristic peak in the flat PP film or PP film with nanostructure before PFTS modification. After the physical vapor deposition process, obvious F1s characteristic peaks were detected on the surfaces of flat FPP, 400 FPP and 200 FPP, proving that PFTS had been successfully assembled. In addition, the C1s peak transformed into two peaks, –CF 3 and C–C, after chemical modification, which proved that PFTS had been successfully assembled on the surface of the PP film. In addition, EDS was used to conduct surface element analysis on three chemically modified polypropylene films. As shown in Fig. S3, † EDS surface scanning results show that the F element is distributed very evenly on the surface of the polypropylene films, which also proves that PFTS had been successfully assembled on the surface of the PP films. The water contact angle measurement was also used to verify functionalized PP surfaces. As shown in Fig. S4, † when a water droplet dropped on the flat PP surface, a small contact angle of about 87.9° was observed. After fabrication of 400 nm PP nanowire and 200 nm PP nanowire, water contact angles increased to 145.9° and 145.3°, indicating that the nanostructure is an important factor in creating a hydrophobic or super-hydrophobic surface. After assembling PFTS, the water contact angles of flat PP, 400 PP and 200 PP were further increased to 111.7°, 153.2°, and 152.2°, respectively. This is mainly due to the fact that the non-polar (–CF 3 ) end groups in PFTS reduce the surface energy of PP and thereby increase its water contact angle. Fig. 5 (a) Schematic diagram of the process of assembling PFTS on the PP surface; surface morphology SEM images of PFTS-modified: (b) flat PP, (c) 400 PP, and (d) 200 PP; (e) XPS spectra of the PP friction layer before and after PFTS modification; output performances of TENGs composed of flat FPP, 400 FPP, 200 FPP, flat NY and NY nanofibers: (f) I sc , (g) V o and (h) charge density. Like unmodified PP films, a total of six groups of the triboelectric nanogenerators are composed according to different friction layer materials. The short-circuit current, output voltage and charge density results are shown in Fig. 5f–h . Among the six TENGs, the outputs of Flat FPP@Flat NY, 400 FPP@Flat NY, and 200 FPP@Flat NY show a gradually increasing trend, which is the same as that without fluorination modification. Similarly, the outputs of flat FPP@Nano NY, 400 FPP@Nano NY, and 200 FPP@Nano NY show a gradually increasing trend, which is the same as the trend without PFTS modification. However, unlike the output without PFTS modification, the output of the 200 FPP@Flat NY TENG is higher than that of the flat FPP@Nano NY TENG. According to the frictional electrification sequence of the material, the ability of PFTS to be negatively charged during friction is much greater than that of PP, while nylon is very easily positively charged during friction. When the PP film is not modified with PFTS, the ability of polypropylene to generate triboelectric charges cannot be greatly improved simply by nano-treatment of the polypropylene surface. Since NY has a very strong triboelectrically charged ability, the output of a TENG can be improved more by nano-processing NY than by nano-processing polypropylene. Therefore, without fluorination, the output of the 200 PP@Flat NY TENG is smaller than that of the flat PP@Nano NY TENG. When PP is fluorinated, PFTS plays a leading role in improving the output of the TENG, which results in the output of the 200 FPP@Flat NY TENG being higher than that of the Flat FPP@Nano NY TENG. Compared with the output of the planar structure TENG without fluoropolymer modification, after PFTS modification and nano-processing of the nylon material, I sc increased from 3.0 μA to 63.3 μA, V o increased from 62.5 V to 1135 V and charge density increased from 4.4 μC m −2 to 161.5 μC m −2 . I sc and V o increased by more than 18 times, while charge density was increased by about 36 times. It can be seen from the above results that modifying PFTS can greatly increase the amount of tribocharges generated by the triboelectric generator, and PFTS plays a dominant role in improving the output performance of the TENG. To prove that PFTS significantly promotes the output of the TENG, the surface potentials of the PP, PFTS-PP and NY films were tested using KPFM. In Fig. 6a, b, d and e , it can be seen that the surface potentials of PFTS-PP and PP are negative, and the absolute value of the surface potential of PFTS-PP is significantly improved compared to that of PP. The mean potential value decreased from −12.4 V (PP) to −72.9 V (PFTS-PP), proving that the electron-gathering ability of the PFTS-modified PP film is increased nearly 6 times. Fig. 6c and f show the KPFM results of the NY film. The mean surface potential value of nylon 11 is 25.0 V, which proves that the electron-losing ability of nylon is stronger than the electron-gaining ability of PP and weaker than the electron-gaining ability of PFTS-PP. The calculated electrostatic surface potential (ESP) maps in Fig. 6g–i also prove that PFTS has a more negative potential than PP and nylon has a more positive potential. The output performance of a TENG is proportional to the surface charge density, which essentially depends on the charge transfer capability driven by contact electrification. Previous studies have reported that triboelectric positive materials lose electrons from the HOMO, while triboelectric negative materials gain these electrons from the LUMO. 47,48 Therefore, reducing the energy difference between the HOMO and LUMO of the two friction layer materials can effectively enhance the output of the triboelectric generator. According to the calculated HOMO and LUMO energy and diagrams in Fig. 6j and S5, † the energy difference between the LUMO of PP and HOMO of NY (Δ E 1 = 7.33 eV) is larger than the energy difference between the LUMO of PFTS and HOMO of NY (Δ E 2 = 5.42 eV). According to energy potential well models in Fig. 6k and l , when PP and PFTS are in contact with and separated from nylon, respectively, electrons are more easily transferred from the HOMO of nylon to the LUMO of PFTS, resulting in higher output performance. 49–51 Therefore, it has been experimentally and theoretically proven that self-assembled PFTS can effectively improve the output of polypropylene-based triboelectric nanogenerators. Fig. 6 Surface potential map of (a) PP, (b) PFTS-PP and (c) NY; surface potential line curves of (d) PP, (e) PFTS-PP and (f) NY; calculated ESP maps of (g) PP, (h) PFTS and (i) NY; (j) calculated HOMO and LUMO of PP, PFTS and NY; overlapped electron cloud and potential well model of (k) NY vs. PP (l) NY vs. PFTS. From the above study, it can be concluded that the 200 FPP@Nano NY TENG with a dual nanowire structure obtained the highest output, and its corresponding I sc , V o , and charge density are 63.3 μA, 1135 V, 161.5 μC m −2 , respectively. The transferred charge density can more intuitively reflect the ability of the friction layer of the triboelectric generator to generate tribocharges. Compared with relevant research results in recent years that use structural design and surface chemical modification to improve the output of contact-separation mode triboelectric nanogenerators, it was found that by combining the design of double nanostructures and chemical modification methods, the charge density obtained in this work can achieve a relatively higher value, as shown in Fig. 7a and Table S1. † , 43,52–56 As an energy conversion device, stability during operation is crucial for practical application. Herein, a durability test was also conducted under 5 Hz contact frequency for the 200 FPP@Nano NY TENG, and the results are shown in Fig. 7b . It can be seen from the test results that the output did not decrease during the entire 193 000 cycles of 200 FPP@Nano NY TENG operation, proving that the 200 FPP@Nano NY TENG can maintain a stable output for a long time in practical applications. The output performance of the 200 FPP@Nano NY TENG under different humidities was also tested via a sealed acrylic box, as shown in Fig. S6. † As shown in Fig. S7a and S7b, † the output of the TENG shows a decreasing trend as the humidity increases. To more intuitively reflect the changing trend in output with humidity, the relationship between the maximum value of the output and humidity is constructed into a curve, as shown in Fig. S7c and S7d. † As can be seen from Fig. S7c, † as the humidity increases, the maximum value of I sc shows an almost linear decreasing trend with the increase in humidity. The decrease in I sc is relatively slow when the humidity is below 50%, and the rate of decrease increases significantly when the humidity is over 50%. This trend looks more obvious with V o . Overall, the 200 FPP@Nano NY TENG, like most TENGs, does not have anti-humidity characteristics. However, it can maintain a relatively high output with an output of 52.8 μA and 1076 V when used in an environment with humidity below 50%. Fig. 7c shows the I sc values of the 200 FPP@Nano NY TENG under different load resistances from 0.1 MΩ to 1000 MΩ, and its corresponding power. According to the test results, current decreases with the increase in loading resistance, while the calculated output power of the device first rises and later drops with increasing resistance. The maximum value of the output power is about 17.0 mW under a loading resistance of 20 MΩ. As a power supply, the 200 FPP@Nano NY TENG has also been rectified for charging capacitors with different capacities. As shown in Fig. 7d , it took the 200 FPP@Nano NY TENG 33 s to charge 4.7 μF, 10 μF and 47 μF capacitors to 10 V, 4.7 V and 1.0 V, respectively. Furthermore, to verify that the generated electricity can indeed be used as an energy supply, the 200 FPP@Nano NY TENG was rectified to power 1280 LEDs. As shown in Fig. 7e and Video S1, † driven by a linear motor, the 200 FPP@Nano NY TENG can instantly light up 1280 commercial LEDs. Besides, the AC current generated by the 200 FPP@Nano NY TENG can be converted to DC current via an LTC3588-1 module and stored in integrated capacitors as a power supply for a multifunctional electronic watch. Fig. 7f shows the circuit connection diagram of the LTC3588-1 module, TENG and a multi-functional electronic watch. According to Fig. 7g , when the TENG runs for about 150 s, the output voltage of the LTC3588-1 module stabilizes at 1.8 V. To ensure that the electronic watch can run for a long time, it should continue to run for a period of time to charge the capacitor on the module, and the electronic power should not be turned off before 300 s. It can also be seen that the voltage curve can remain stable without a sufficient power supply. As shown in Fig. 7h and Video S2, † after 5 minutes of charging, the LTC3588-1 module can continuously supply power to a multi-functional electronic watch with a temperature measurement function and ensure the normal operation of the electronic watch. This also proves that the 200 FPP@Nano NY TENG can be used for energy storage and to power small electronic devices for a long time. Fig. 7 (a) Comparison of the charge density of CS-TENGs enhanced through structural design and surface modification; (b) long-term stability test of the 200 FPP@Nano NY TENG; (c) I sc and power of the 200 FPP@Nano NY TENG under different load resistances; (d) charging test voltage curves of different capacitors with the 200 FPP@Nano NY TENG as the power supply; (e) photograph of 1280 LEDs connected to the 200 FPP@Nano NY TENG; (f) circuit connection diagram of an LTC3588-1 module as an energy transform device for 200 FPP TENG; (g) the output voltage curve of the LTC3588-1 module before and after turning on the watch; and (h) photograph of the 200 FPP@Nano NY TENG as a power supply for a multifunctional watch. In view of the long-term durability and high frictional electrification performance of the 200 FPP film, it was integrated into an PCB circuit to control the ‘on’ and ‘off’ of an optocoupler switch. Fig. 8a and b show schematic images of the 200 FPP TENG as a trigger signal for controlling an optocoupler switch and PCB schematic diagram of the TENG triggering an optocoupler switch. As shown in Fig. 8c and Video S3 † , eight 200 FPP films were pasted on the PCB using double-sided conductive tape to form a single-electrode TENG, which was used to generate pulse signals that excited the optocoupler switch. Each TENG was used to control a key of an electronic piano from C4 to C5. When a finger touches the FPP film, the triboelectric signal generated can activate the corresponding optocoupler switch and emit the corresponding key sound. By pressing different TENGs, a complete piece of music can be played. It can be seen from Fig. 8d that after processing, the TENG signal changes from an AC signal to a more regular trigger signal. When the optocoupler switch is triggered, the resistance at the load end also changes from 400 kΩ to 0 Ω in Fig. 8e , which proves that the TENG signal can normally turn on the optocoupler switch. Therefore, the 200 FPP@Nano NY TENG with a PFTS-modified double nanowire structure prepared by a simple method has very high output performance and great application prospects in practical use. Fig. 8 (a) Schematic image of the 200 FPP TENG as a trigger signal for controlling an optocoupler switch; (b) PCB schematic diagram of a TENG triggering an optocoupler switch; (c) photograph of the 200 FPP TENG as a trigger signal for controlling an optocoupler switch; (d) comparison of the TENG and trigger signals when the optocoupler switch is triggered; and (e) changes in the resistance of the photocoupler terminal when the optocoupler switch is triggered."
} | 7,480 |
36846306 | PMC9945787 | pmc | 3,767 | {
"abstract": "Cyanobacteria are ideal candidates to use in developing carbon neutral and carbon negative technologies; they are efficient photosynthesizers and amenable to genetic manipulation. Over the past two decades, researchers have demonstrated that cyanobacteria can make sustainable, useful biomaterials, many of which are engineered living materials. However, we are only beginning to see such technologies applied at an industrial scale. In this review, we explore the ways in which synthetic biology tools enable the development of cyanobacteria-based biomaterials. First we give an overview of the ecological and biogeochemical importance of cyanobacteria and the work that has been done using cyanobacteria to create biomaterials so far. This is followed by a discussion of commonly used cyanobacteria strains and synthetic biology tools that exist to engineer cyanobacteria. Then, three case studies—bioconcrete, biocomposites, and biophotovoltaics—are explored as potential applications of synthetic biology in cyanobacteria-based materials. Finally, challenges and future directions of cyanobacterial biomaterials are discussed.",
"conclusion": "5 Conclusion With more CO 2 being emitted into the atmosphere each year, cyanobacteria-based biomaterials have the power to play an important role in mitigating the effects of climate change on our planet through carbon capture. But to fully unlock the potential of these powerful microbes, more work needs to be done in characterizing novel useful cyanobacteria and in developing standardized tools and parts that work for multiple species (especially ecologically relevant species like Prochlorococcus ) [ 75 ]. Innovation in and integration of materials science and synthetic biology will allow for cyanobacteria-based biomaterials to develop from the lab bench to fully scaled industrial applications.",
"introduction": "1 Introduction Cyanobacteria have used photosynthesis to shape the biogeochemical cycles of Earth for billions of years. As the first organisms to evolve oxygenic photosynthesis, this phylum of photoautotrophic bacteria was responsible for the massive oxygenation of the planet two billion years ago which allowed for aerobic multicellular life to develop. Their photosynthetic metabolism significantly reduced the amount of CO 2 in the atmosphere [ 1 ] in multiple, drastic planetary CO 2 reduction events [ 2 ]. Today cyanobacteria are integral to many biogeochemical processes; they are estimated to be responsible for 25% of primary productivity in the ocean [ 3 ] and play an important role in nitrogen fixation and the burial of organic carbon in ocean sediments [ 4 ]. To combat the threat of climate change due to increasing levels of CO 2 in the atmosphere, researchers are exploring alternative production methods, which use CO 2 as an input, rather than an output [ 5 ]. Photosynthetic carbon fixation enables many proposed methods for Carbon Capture, Utilization, and Storage (CCUS) [ 6 ] by using atmospheric or emitted CO 2 as a building block to create products. CO 2 might be stored in products temporarily, as in biofuels, to replace fossil fuel emissions with a carbon neutral product, or could potentially be stored long-term, as in building materials, creating a net-negative drawdown of atmospheric CO 2 [ 7 ]. Cyanobacteria are a prime candidate for biological CCUS technology development for several reasons. These include inexpensive feedstock requirements (they require only sunlight, CO 2 , water and a few nutrients), ease of genetic manipulation, native production of commercially interesting biomolecules [ 8 ], and fast growth rate. In addition to their carbon capture potential, cyanobacteria are more sustainable than traditional microbes used for bioproduction because they do not require sugar as a feedstock (feedstock sugar requires large amounts of arable land to grow). Agriculture is predicted to face more challenges as climate change intensifies, making this feature increasingly important [ 9 , 10 ]. Additionally, many cyanobacteria grow in seawater salinity, so they can be grown at-scale without using limited freshwater resources [ 11 ]. A number of material scientists have made progress developing cyanobacteria-based biomaterials with applications in carbon capture, construction, energy, and food production. Many of these materials could be considered engineered living materials (ELMs) as they utilise living cyanobacteria to perform “smart” functions: assembly, repair, and response to external stimuli [ [12] , [13] , [14] , [148] ]. Researchers have capitalized on the natural biomineralization of cyanobacteria to produce regenerative building material made of a hydrogel-sand scaffold and Synechococcus elongatus PCC 7002 [ 15 ]. A network of Anabaena sp. cells and graphene nanoribbons has been 3D printed onto a fungal platform to produce a photocurrent [ 16 ]. A prototype of a textile-based cyanobacteria biocomposite to capture CO 2 during wastewater treatment has been fabricated [ 17 ]. Some cyanobacteria-based products have already reached commercial-scale: Spira uses genetically engineered cyanobacteria to produce food dye and flavorings [ 18 ], Prometheus Materials makes concrete masonry units (cinder blocks) using biomineralizing cyanobacteria [ 19 ], Lumen Bioscience has developed biologic drugs that use cyanobacteria to deliver therapeutic molecules [ 20 ], and Photanol produces industrial biochemicals extracted from cyanobacteria [ 21 ]. At the same time, synthetic biology researchers have developed a suite of genetic techniques and computational tools to manipulate cyanobacteria, and there is a growing repository of standard biological parts, genome editing tools, and metabolic models for several strains [ 22 ]. So far, there has been little intersection of materials science and synthetic biology in the creation of cyanobacteria-based biomaterials [ 23 , 24 ]. In this review, we will explore the ways in which cyanobacteria-based biomaterials can be improved using synthetic biology, discussing existing genetic, genomic, and computational tools, three case studies of potential applications of synthetic biology in cyanobacteria-based biomaterials development (bioconcrete, biocomposites, and biophotovoltaics), and future directions of the growing field of cyanobacteria-based biomaterials ( Fig. 1 ) . Fig. 1 Schematic illustration of synthetic biology tools that enable development of cyanobacteria-based biomaterials from lab prototypes to industry scale materials. Biomaterials illustrated (from left to right): Biocomposite of cyanobacteria grown on a loofah scaffold for enhanced CO 2 capture [ 25 ] could scale to become part of an industrial carbon capture system for scrubbing CO 2 from flue gas; Bioconcrete block made from biomineralizing cyanobacteria, sand, and gelatin [ 15 ] could scale to become a sustainable structural material that can “regrow”; a 3D printed microarray that captures current from photosynthesis of cyanobacteria colonies [ 26 ] could scale to become an efficient biophotovoltaic energy source. Fig. 1 1.1 Cyanobacteria vs. other photosynthetic organisms Several other photosynthetic organisms have been explored as potential photosynthetic chassis for biocarbon capture, notably plants and eukaryotic microalgae. Plants produce many useful biomolecules (terpenes, fatty acids, phenylpropanoids) [ 27 ], and their macroscopic structure can be utilized to easily form large biomaterials [ 28 , 29 ]. However, a plant-based carbon capture system at scale would require large amounts of arable land and their extremely slow growth makes them difficult to engineer [ 30 ]. The latter is also a limitation for macro-algae (e.g kelp), which have been investigated for their carbon sequestration abilities [ 31 ]. The term ‘microalgae’ often refers to unicellular eukaryotic algae, but may be used more broadly to describe both cyanobacteria and eukaryotic microalgae. This review will use the former definition. Microalgae and cyanobacteria share many of the same characteristics: photosynthesis, adaptability to diverse environmental conditions, and natural production of high-value molecules. They are often used for similar applications [ 32 , 33 ]. Sometimes they are even used together such as in a carbon-capture biocomposite made of a loofah scaffold and cyanobacteria and microalgae consortium [ 25 ]. However, each organism has distinct advantages. Cyanobacteria display the fastest photosynthetic growth rates measured [ 34 ] and their smaller genomes, lack of subcellular organization, and absence of epigenetic gene-silencing allows them to be genetically manipulated more easily [ [35] , [36] , [37] ]. Because they are easier to engineer, more genetic and computational tools have been developed for cyanobacteria than microalgae. Microalgae, on the other hand, may tolerate high light conditions and have more examples of growth at scale [ 38 ]. Other factors to consider include that cyanobacteria are reported to have high rates of UV-induced mutation [ 39 ] and some produce secondary metabolites that are harmful to humans under certain conditions [ 40 ]."
} | 2,286 |
28124823 | PMC5413837 | pmc | 3,768 | {
"abstract": "Abstract 5‐Hydroxymethylfurfural (HMF) is a versatile intermediate in biomass conversion pathways. However, the notoriously unstable nature of HMF imposes challenges to design selective routes to chemicals such as furan‐2,5‐dicarboxylic acid (FDCA). Here, a new strategy for obtaining furans is presented, bypassing the formation of the unstable HMF. Instead of starting with glucose/fructose and thus forming HMF as an intermediate, the new route starts from uronic acids, which are abundantly present in many agro residues such as sugar beet pulp, potato pulp, and citrus peels. Conversion of uronic acids, via ketoaldonic acids, to the intermediate formylfuroic acid (FFA) esters, and subsequently to FDCA esters, proceeds without formation of levulinic acid or insoluble humins. This new route provides an attractive strategy to valorize agricultural waste streams and a route to furanic building blocks without the co‐production of levulinic acid or humins.",
"conclusion": "Conclusions In conclusion, we have shown that abundantly available uronic acids (potentially >1.5 Mt a −1 from 2nd generation nonfood feedstock) can be converted into furan‐2,5‐dicarboxylic acid (FDCA) dimethyl ester through a new 3‐step catalytic route, in an overall unoptimized isolated yield of 45 %, which is competitive with routes starting from glucose. In the first step, uronic acids are isomerized in alkaline water in the presence of Ca 2+ , leading to the selective precipitation of 5‐keto‐aldonic acids (5‐KAs) at RT. In the second step, 5‐KAs were converted into 2‐formyl‐5‐furoic acid (FFA) esters through a mild cyclodehydration in alcoholic solvents in the presence of an acid catalyst. Control experiments showed that uronic acids did not afford FFA esters as products, proving the necessity of the isomerization step. During the conversion of 5‐KA to FFA esters, no levulinic acid and only a limited amount of insoluble humins were formed. In the third and final step, FFA esters were converted to FDCA dimethyl ester through a mild Au‐catalyzed oxidation reaction at RT, using only O 2 as the oxidant. Hereby, we demonstrated that this new route from agro‐residue derived feedstocks such as sugar beet pulp or citrus peels is an attractive strategy for the production of 2nd generation FDCA and its derivatives.",
"introduction": "Introduction One of the most promising examples of biobased building blocks is furan‐2,5‐dicarboxylic acid (FDCA), which has many potential applications in polyesters, polyamides, and plasticizers. 1 The majority of research for the production of FDCA focusses on the use of hexose sugars (i.e., d ‐glucose and d ‐fructose), which are first dehydrated to the intermediate 5‐hydroxymethylfurfural (HMF), 2 followed by oxidation to FDCA (Scheme 1 ). Currently, the main challenge for the efficient synthesis of furans from sugars is the intrinsic instability of HMF. Under the acidic aqueous conditions applied, HMF is easily rehydrated to form levulinic acid and formic acid. 2 , 3 Furthermore, insoluble humins can be formed in high amounts. 4 All these side reactions do not only lead to loss of the desired HMF, but the valorization of levulinic acid and humin side products is required to make the overall route economically viable. 5 Alternative routes have been developed, in which more stable HMF ethers are formed. 6 However, humin formation still occurs, and alkyl levulinates are formed as byproducts. 5 \n Scheme 1 Comparison of the synthesis of FDCA esters using the traditional approach starting from glucose with the alternative route starting from uronic acids. A second disadvantage of using HMF as an intermediate is related to the feedstock used to make the HMF. Currently, the feedstock is made from glucose and/or fructose, which both interfere with food production. For example, commercial d ‐fructose resources are high‐fructose syrups, which are generally produced from starch‐containing food crops such as corn. 5 To eliminate the conflict with food production, a route to FDCA should be developed starting from nonfood lignocellulosic feedstocks such as wood or grasses, or preferably from the residues of agro‐food production. This prompted us to explore the potential of obtaining FDCA esters through an alternative approach, not starting from glucose/fructose and circumventing the intrinsic instability of HMF. We propose that 2‐formyl‐5‐furoic acid esters (FFA esters) can be used as alternatives to HMF (Scheme 1 ). We hypothesize that, owing to the lack of primary alcohol functional groups in FFA esters, they are more stable than HMF because acidic dehydration pathways and subsequent formation of levulinic acid are blocked. 2 , 3 Catalytic oxidation of FFA esters would yield the desired FDCA esters. Scheme 1 shows an overview of the new proposed route to FDCA esters. If FFA esters are the desired intermediates towards FDCA esters, a higher oxidation state of the carbohydrate starting materials is also required. 5‐Keto‐aldonic acids (5‐KAs) are the retrosynthetic precursor for FFA esters (analogous to the dehydration of d ‐fructose to HMF). Uronic acids would then be the retrosynthetic precursors for 5‐KAs (analogous to the isomerization of d ‐glucose to d ‐fructose). Both FDCA and FDCA esters can be used in polyester synthesis (currently the largest potential application area of FDCA), analogous to the use of terephthalic acid and dimethyl‐terephthalate, which are both used in the industrial synthesis of polyethylene terephthalate (PET). 7 \n The proposed route is also implementable on an industrial scale. Uronic acids are present in various large agro‐residue streams. d ‐Galacturonic acid is the most abundant uronic acid and is the main constituent of pectin. Pectin is found in large amounts in sugar beet pulp (contains 900 000 t d ‐galacturonic acid worldwide), 8 chicory pulp, 9 fruit peels such as citrus peels (375 000 t d ‐galacturonic acid worldwide), 10 potato peels/pulp (200 000 t d ‐galacturonic acid in the USA 11 ; 7100 t in the UK), 12 and coffee pulp (ca. 30 000 t d ‐galacturonic acid worldwide). 13 As an example, efforts are being undertaken to scale up the commercial production of d ‐galacturonic acid from sugar beet pulp. 14 \n \n d ‐Glucuronic acid is the second important readily available uronic acid, found in xanthan gum, 15 gum arabic, 16 and wheat bran, 17 and is one of the main pectin constituents in various types of soft‐ and hardwoods. 18 Three other uronic acids are present in nature, for example, d ‐mannuronic acid, l ‐guluronic acid, and l ‐iduronic acid in macroalgae. 15 , 19 However, these have to be obtained by hydrolysis of alginates, which is currently still challenging. Therefore, these uronic acids are not yet readily available. 20 \n Overall, there is already a significant stream of non‐edible uronic acids available (>1.5 Mt d ‐galacturonic acid alone), which can serve as 2nd generation nonfood‐based starting materials for furans such as FDCA. Here, we present a new catalytic 3‐step route to 2nd generation FDCA: 1) Isomerization of uronic acids to their corresponding 5‐KAs. 2) Acid‐catalyzed cyclodehydration to FFA esters. 3) Oxidation to FDCA esters.",
"discussion": "Results and Discussion Isomerization (step 1) As discussed above, the first step in our proposed process is the isomerization of uronic acids to their corresponding 5‐KAs (step 1 in Scheme 1 ). This reaction can be performed by different microorganisms and enzymes, 21 as has been shown for the conversion of d ‐galacturonic acid to 5‐keto‐ l ‐galactonic acid (IUPAC name: d ‐ arabino ‐hex‐5‐ulosonic acid). 22 In addition, chemical isomerization of uronic acids to 5‐KAs in an alkaline medium has also been reported. 23 This is the so‐called Lobry de Bruyn–van Ekenstein transformation. 24 Unfortunately, these isomerization methods result in equilibrium mixtures that contain only a limited fraction of the desired product (e.g., d ‐galacturonic acid/5‐keto‐ l ‐galactonic acid=34:66). 21c To shift the equilibrium to the desired products, we adapted a procedure reported by Ehrlich and Guttmann, 25 who showed that 5‐keto‐ l ‐galactonic acid could be selectively precipitated by Ca 2+ . These authors used poorly soluble Ca(OH) 2 , which has the disadvantage of working in very dilute reaction mixtures. Here, we used a more soluble calcium precursor (CaCl 2 ), which allowed us to use more concentrated solutions. This resulted in an increase of the yield of 5‐keto‐ l ‐galactonic acid from 46 % using Ca(OH) 2 to 82 % using CaCl 2 . The second important uronic acid, d ‐glucuronic acid, was isomerized in the presence of CaCl 2 to afford 5‐keto‐ d ‐mannonic acid (IUPAC name: d ‐ lyxo ‐Hex‐5‐ulosonic acid) in 42 % isolated yield after filtration of the reaction mixture. Thin layer chromatography (TLC) analysis of the filtrate revealed the presence of residual product, apparently caused by the higher aqueous solubility of 5‐keto‐ d ‐mannonic acid. The use of CaCl 2 has the advantage of selectively obtaining one of the isomers. Further optimization is needed to maximize the yield. Out of the five naturally occurring uronic acids, only three different 5‐KAs can be prepared (for a detailed explanation, see Figure S1 in the Supporting Information). Two of those 5‐KAs were prepared as discussed above. The third one, 5‐keto‐ d ‐gluconic acid, (IUPAC name: d ‐ xylo ‐Hex‐5‐ulosonic acid) is commercially available because it is one of the intermediates in the production of vitamin C through fermentation. 26 Therefore, all three possible 5‐KAs are available for the second step, the cyclodehydration to FFA esters. Cyclodehydration (step 2) Keto‐aldonic acid versus uronic acid The second step in our process is the cyclodehydration of 5‐KAs to FFA esters (step 2 in Scheme 1 ). Table 1 displays an overview of the yield of the desired Me‐FFA starting from the three 5‐KAs (entries 1–4) and uronic acids (entries 5–7). 5‐Keto‐ d ‐gluconic acid (K salt) was reacted in methanolic HCl to afford the expected Me‐FFA in 43 % crude yield (Table 1 , entry 1), thereby confirming the previous results reported by Votocek and Malachta (40 % crude yield). 27 The same conditions were applied to 5‐keto‐ l ‐galactonic acid (CaOH salt), which afforded Me‐FFA in 50–65 % crude yield (Table 1 , entries 2 and 3). This yield is significantly higher than previously reported yields (11 % after purification) by Stutz and Deuel, 28 and more in line with the results obtained with 5‐keto‐ d ‐gluconic acid (K salt). Also, 5‐keto‐ d ‐mannonic acid (mixed Ca/Na salt; Table 1 , entry 4) could be converted to the desired product, although in a somewhat lower yield (17 %). We did not study the influence of the cation (K/ Ca). These initial results show that the conversion of all three 5‐KAs to FFA‐esters is possible.\n Table 1 Cyclodehydration of various 5‐keto‐aldonic acids and uronic acid derivatives in MeOH/HCl. [a] \n Entry Substrate Reaction time [h] Crude Me‐FFA yield [b] [mol %] 1 5‐keto‐ d ‐gluconic acid (K salt) 72 43 2 5‐keto‐ l ‐galactonic acid (CaOH salt) 72 50 3 5‐keto‐ l ‐galactonic acid (CaOH salt) 24 65 4 5‐keto‐ d ‐mannonic acid (Ca/Na salt) 24 17 5 \n d ‐galacturonic acid (monohydrate) 24 0 6 \n d ‐galacturonic acid (Na salt) 24 <1 7 \n d ‐glucuronic acid 24 <1 [a] Conditions: substrate (1.0 g) in 3 n MeOH/HCl (10 mL), 65 °C. [b] Isolated yield of the product after extraction without purification (purity≈90–100 % based on GC and NMR analyses). Wiley‐VCH Verlag GmbH & Co. KGaA To verify if the isomerization of uronic acids to 5‐KAs (step 1 in Scheme 1 ) is a necessary step; therefore, the conversion of uronic acids was investigated under the same reaction conditions. No Me‐FFA was formed starting from d ‐galacturonic acid, the parent uronic acid of 5‐keto‐ l ‐galactonic acid [Table 1 , entries 5 (monohydrate form) and 6 (Na salt)]. Instead of Me‐FFA, methyl (methyl galactosid)uronates were the only detectable products (Figure S4). Similar results were obtained when starting from d ‐glucuronic acid, the parent uronic acid of 5‐keto‐ d ‐mannonic acid, that is, no yield of Me‐FFA was observed (Table 1 , entry 7). To summarize, the (unoptimized) conversion of all three possible 5‐KAs afforded Me‐FFA in reasonable yields (17–65 %). However, the parent uronic acids furnished <1 % Me‐FFA under the same reaction conditions, showing that the isomerization of the aldose to the ketose species is required to achieve an effective cyclodehydration. Clearly, the ring conformation of the carbohydrate has a marked influence on the Me‐FFA yield. The uronic acids exist mainly in the six‐membered ring pyranose form in water ( d ‐galacturonic acid: ca. 90 %). 29 This prevents direct cyclodehydration to five‐membered furans, instead leading to undesired degradation. 30 However, the related 5‐keto‐ l ‐galactonic acid adapts a furanose conformation (five‐membered ring) almost exclusively (>99 %). 31 The other two 5‐KAs also prefer a five‐membered ring conformation (>99 % for 5‐keto‐ d ‐mannonic acid 32 and 80–89 % for 5‐keto‐ d ‐gluconic acid). The remainder is in the open chain or lactone form 32 , 33 . The effect of the ring conformation on reactivity has been observed before for the conversion of glucose and fructose to HMF. The highest yields of HMF were achieved starting from d ‐fructose, which has a higher preference for the furanose form (five‐membered ring) in aqueous solution (ca. 25 %), whereas only 1 % of d ‐glucose is in the furanose form. 34 Although still under debate, it is thought that owing to its furanose configuration, d ‐fructose dehydrates more efficiently to HMF, which is also a five‐membered ring. 2 , 35 It is also believed that the fructofuranose form results in a decrease in competitive pathways during cyclodehydration. 36 Here, we found a similar effect for the conversion of uronic acids (six‐membered ring) versus KAs (five‐membered ring). Influence of the nature of the acid The experiments reported in Table 1 were all performed in methanolic HCl. However, for practical reasons, we wanted to switch to acids that are less corrosive, less volatile, and easier to dose. The screening of such acids [H 2 SO 4 , H 3 PO 4 , methane sulfonic acid (MSA), and p ‐toluenesulfonic acid ( p ‐TSA)] showed that the best initial results were obtained using MSA (6–12 equiv.). The next part of this investigation discusses the most important reaction parameters. Table 2 shows an overview of all results of the conversion of 5‐keto‐ l ‐galactonic acid analogues under varying reaction conditions. The following sub‐sections will discuss the most important factors including 5‐KA versus fructose conversion, the stability of the intermediates, mass balance, the influence of water, and the influence of solvent. All products used in subsequent experiments were isolated and purified using silica column chromatography to facilitate fair comparisons.\n Table 2 Cyclodehydration of 5‐keto‐ l ‐galactonic acid (analogues) (5‐KG) and product stability in various alcohols. [a] \n Entry Substrate Alcohol Water removal [b] \n \n t [h] \n T [°C] Intended product Purified yield [c] [mol %] 1 5‐KG.CaOH MeOH no 24 65 Me‐FFA 49 2 5‐KG.CaOH MeOH yes 24 65 Me‐FFA 48 3 5‐KG.CaOH MeOH +6 equiv. H 2 O 24 65 Me‐FFA 38 4 Me‐FFA MeOH no 24 65 Me‐FFA 78 5 HMF MeOH no 24 65 HMF <2 [d] \n 6 \n d ‐Fructose MeOH no 24 65 HMF <1 [d] \n 7 5‐KG.CaOH EtOH no 18 78 Et‐FFA 50 8 5‐KG.CaOH EtOH yes 18 78 Et‐FFA 55 9 5‐KG.CaOH \n n ‐PrOH no 24 97 Pr‐FFA 43 10 5‐KG.CaOH \n n ‐PrOH yes 24 97 Pr‐FFA 45 [a] Conditions: substrate (12 mmol) in MeOH (100 mL), and of MSA (12 equiv.). [b] Active water removal through Soxhlet setup (yes/no) or deliberate addition of water. [c] Isolated yield of product after silica gel chromatography (purity >99 %). [d] Me‐levulinate was isolated as the main product (entry 5: 35 mol %; entry 6: 39 mol %) and significant formation of insoluble humins was observed. Wiley‐VCH Verlag GmbH & Co. KGaA 1 ‐Keto‐aldonic acid versus fructose conversion To determine the advantage of the FFA esters versus the HMF route with respect to the formation of products and side products, we compared the conversion of 5‐keto‐ l ‐galactonic acid (Table 2 , entry 1) with the conversion of d ‐fructose (Table 2 , entry 6). Starting from 5‐keto‐ l ‐galactonic acid, Me‐FFA was isolated in 49 % purified yield after flash column chromatography (Table 2 , entry 1). The crude product already had good purity (>90 %); no levulinic acid (ester) was present, and the amount of insoluble humins in the reaction mixture was very limited (<5 wt %, too little to isolate during workup). This was in contrast to a reaction starting from d ‐fructose that afforded Me‐levulinate and humins as the main products instead of the desired HMF (Table 2 , entry 6). Stability of the intermediates These results prompted us to investigate if the stability of Me‐FFA and HMF under our conditions could explain the differences in product yield. Me‐FFA was stirred for 24 h in MeOH/MSA under reflux to afford a clear, light‐yellow reaction mixture. After workup, Me‐FFA was recovered in 78 % yield (Table 2 , entry 4). For comparison, HMF was exposed to the same conditions (Table 2 , entry 5). The HMF reaction mixture turned black within 30 min, and after workup (after 24 h) no HMF was recovered. The main products were Me‐levulinate (35 mol %) and insoluble humins. Figure 1 shows a picture of both reaction mixtures to illustrate the difference in stability further. Hence, the presence of substituents with a high oxidation state (aldehyde and carboxylic acid) on the FFA furan ring significantly reduced the propensity for acid‐catalyzed hydration and subsequent formation of levulinic acid and humins compared to HMF. 2 \n Figure 1 Appearance of reaction mixtures during stability tests. Left: Me‐FFA (after 24 h reaction time). Right: HMF (after 30 min reaction time). Reaction conditions: Substrate (12 mmol), methanol (100 mL), MSA (12 equiv.), reflux. Mass balance Although we did not observe any levulinic acid formation from the 5‐KAs (by NMR, GC–MS, or HPLC analysis), the isolated yields were less than 100 % at full conversion. This indicates that side products were formed. To identify these products and obtain insight into the competitive reaction pathways, alternative (non‐aqueous) work‐up procedures of the crude reaction mixtures were applied (Figures S5–S7). 37 , 38 NMR and HPLC–MS analyses indicated that, apart from the desired FFA esters, the reaction mixture consisted of a complex mixture of glycosides. These glycosides can be further valorized, for example, by transacetalization with higher alcohols to produce surfactants. A full characterization of this complex mixture falls outside of the scope of the present study but will be part of future work. Influence of water Because water is inevitably formed as a co‐product during the cyclodehydration, we investigated the influence of water on the conversion, Me‐FFA selectivity, and humin formation. From 1 mol 5‐KA (CaOH salt) a maximum of 6 mol water is formed (step 2 in Scheme 1 ). We investigated the influence of water on the cyclodehydration in two ways: First, by deliberately adding water to the reaction mixture, and second, by actively removing water during the reaction (Table 2 , entries 1–3). The addition of 6 equiv. of water to the substrate resulted in a decrease in the Me‐FFA yield from 49 to 38 % (Table 2 , entries 1 and 3). Moreover, visual inspection of the reaction mixture indicated that the amount of insoluble humins also increased (visual observation only because accurate gravimetric analysis was hampered by the presence of salts). Although water and methanol do not form an azeotrope, we tried to actively remove water by applying a Soxhlet setup filled with 3 Å molecular sieves. However, the purified yield of Me‐FFA did not change (48–49 %, Table 2 , entries 1 and 2) in methanol. Influence of solvent We investigated the influence of removing water by using two higher alcohols, ethanol and propanol. Because these alcohols do form azeotropes with water, the water removal was expected to be more efficient in the Soxhlet setup. Another effect of changing to other solvents under reflux was the increased reaction temperature. 5‐Keto‐ l ‐galactonic acid (CaOH salt) was reacted in ethanol with 12 equiv. of MSA for 18 h, with and without active water removal (Table 2 , entries 7 and 8). The yields of purified Et‐FFA (50–55 %) of both reactions were slightly higher compared to the reactions in methanol (48–49 % Me‐FFA, Table 2 , entries 1 and 2), indicating an advantageous effect of temperature. The increased yield during active water removal in ethanol indicates the detrimental effect of water on the selectivity of the dehydration reaction. To further investigate the effect of solvent on the conversion of 5‐keto‐ l ‐galactonic acid, 1‐propanol was used. Without active water removal, Pr‐FFA was isolated in 43 % purified yield (Table 2 , entry 9). Removal of the water slightly increased the purified yield of Pr‐FFA to 45 % (Table 2 , entry 10). The cyclodehydration of all 5‐KAs afforded the desired FFA esters in moderate‐to‐good isolated yields (17–55 %). The best yields were obtained in ethanol with continuous removal of water. Although further optimization is required, the isolated yield after column chromatography (55 %) was already quite high and competitive with HMF yields starting from glucose (realistic process yields are approximately 55 %). 39 \n Oxidation (step 3) The final step in the new route to FDCA was the catalytic oxidation of FFA esters to FDCA esters (step 3 in Scheme 1 ). We investigated the effectiveness of oxidation using Au/C, a base, and O 2 at RT. The Au/C catalyst contained 1.3 wt % Au with an average Au particle size of 2.2 nm, and was prepared through cationic adsorption (Figure S3). The FFA esters were oxidized according to a previously reported procedure. 40 Me‐FFA and Et‐FFA were oxidized in methanol, using 10 mol % NaOMe as a base. The reactions were performed at RT using a 1:344 Au/substrate ratio, and 5 bar O 2 overpressure. Both Me‐FFA and Et‐FFA gave full conversion to FDCA dimethyl ester in >99% selectivity within 22 h. The main advantage of FFA ester oxidation compared to the oxidation of HMF is that the oxidation of the primary alcohol of HMF is often the most challenging. 41 This primary alcohol is not present in FFA esters, and only the more reactive aldehyde functionality needs to be oxidized, which can be performed under very mild conditions."
} | 5,656 |
29188186 | PMC5699529 | pmc | 3,769 | {
"abstract": "Effective metabolic engineering of microorganisms relies on balanced expression of both heterologous and endogenous genes to channel metabolic flux towards products of interest while achieving reasonable biomass buildup. To facilitate combinatorial pathway engineering and facile genetic operation, we engineered a set of modular cloning vectors compatible with BioBrick standards, called YaliBricks, to allow for rapid assembly of multigene pathways with customized genetic control elements (promoters, intronic sequences and terminators) in the oleaginous yeast Yarrowia lipolytica . We established a sensitive luciferase reporter and characterized a set of 12 native promoters to expand the oleaginous yeast genetic toolbox for transcriptional fine-tuning. We harnessed the intron alternative splicing mechanism and explored three unique gene configurations that allow us to encode genetic structural variations into metabolic function. We elucidated the role of how these genetic structural variations affect gene expression. To demonstrate the simplicity and effectiveness of streamlined genetic operations, we assembled the 12 kb five-gene violacein biosynthetic pathway in one week. We also expanded this set of vectors to accommodate self-cleavage ribozymes and efficiently deliver guide RNA (gRNA) for targeted genome-editing with a codon-optimized CRISPR-Cas9 nuclease. Taken together, the tools built in this study provide a standard procedure to streamline and accelerate metabolic pathway engineering and genetic circuits construction in Yarrowia lipolytica .",
"conclusion": "4 Conclusion The NanoLuc luciferase assay is a simple and efficient reporting system for gene expression analysis. It also has the potential of being integrated into an automated workflow to vastly improve its throughput, and with its rapid optical detection method, this assay makes an indispensable tool for characterizing genetic parts in large-scale studies. In the context of genetic toolbox development in model organisms, we have established a reliable reporter system and opened the doors for a biosensor-based, top-down approach to pathway engineering and genetic circuits evaluation in Y. lipolytica . Given the complexity of eukaryotic gene expression, this reporter system will facilitate the characterization of novel genetic elements, as well as the elucidation of unique regulatory mechanisms. In addition, the 12 endogenous promoters characterized here have expanded our library of genetic parts for fine-tuning gene expression. Positional effects and gene configurations were also examined in the context of the prokaryotic operon, pseudo-operon, and monocistronic gene arrangements to elucidate how genetic structural variation could lead to distinct gene expression patterns. Using these three genetic configurations, we proposed a novel strategy to generate functional operon-type gene expression through intron alternative-splicing mechanism. We studied the positional effects and discovered the mutual transcriptional inhibition among the adjacent gene cassette. This invention facilitates the control and optimization of gene expression at the proper level while avoiding the need to modify the complex transcriptional machinery. The five-gene pathway for the pigmented compound violacein was rapidly assembled in Y. lipolytica using the self-replicating YaliBrick vectors. Applying the gene expression-tuning strategies outlined in this paper, a high-producing violacein-producing strain was quickly identified via visual screening, and titers were subsequently quantified with HPLC. Incorporating the highly efficient self-cleavage ribozyme allows us to generate functional single guide RNA (sgRNA) and introduce site-specific indel mutations and frame-shift mutations, paving the way for further genome-editing optimization in Y. lipolytica . Taken together, the tools and strategies developed in this work can streamline the genetic manipulation of Y. lipolytica and accelerate the construction of multiple gene pathways for diverse industrial applications. Our work provides an efficient and versatile toolkit to upgrade Y. lipolytica as a powerful workhorse for natural product and oleochemical production.",
"introduction": "1 Introduction The oleaginous yeast Yarrowia lipolytica has become the organism of choice for the production of oleochemicals ( Abghari and Chen, 2014 , Xu et al., 2016 ), biofuels ( Ledesma-Amaro and Nicaud, 2016 , Xu et al., 2017 ) and acetyl CoA-derived metabolites ( Zhu and Jackson, 2015 , Ledesma-Amaro et al., 2016 ). It has been extensively studied as a model organism for lipid accumulation ( Beopoulos et al., 2009 ) and degradation ( Fickers et al., 2005 ), dimorphism ( Ruiz-Herrera and Sentandreu, 2002 , Morales-Vargas et al., 2012 ) and protein secretory pathway ( Beckerich et al., 1998 ). Y. lipolytica is also recognized as a “generally regarded as safe” (GRAS) organism ( Groenewald et al., 2014 ) for the production of large quantities of citric acid ( Papanikolaou et al., 2002 , Papanikolaou et al., 2008 ), α-ketoglutarate ( Zhou et al., 2010 ) and succinic acid ( Cui et al., 2017 ) in the food industry. Coupled with its ability to natively utilize a wide range of substrates, such as glucose, fructose, glycerol and hydrocarbons, its low pH tolerance and strictly aerobic nature ( Abghari and Chen, 2014 , Ledesma-Amaro et al., 2016 ), these properties make this yeast a very attractive candidate for industrial biotechnology applications. Notable examples of its industrial applications include the production of eicosapentaenoic acid (EPA)-rich products ( Xue et al., 2013 , Xie et al., 2015 ), conjugated linoleic acid ( Zhang et al., 2013 ), and cost-efficient production of lipid biofuels ( Blazeck et al., 2014 , Qiao et al., 2017 , Xu et al., 2017 ). Recent advances in understanding of Y. lipolytica's metabolic pathways, the sequencing of its genome ( Liu and Alper, 2014 ) and further expansion of its genetic toolbox have resulted in several successful endeavors to metabolically engineer this unconventional yeast to produce high-value compounds, e.g. limonene ( Cao et al., 2016 ) and γ-decalactone ( Gomes et al., 2012 , Braga and Belo, 2016 ). These achievements demonstrate the potential of Y. lipolytica as an industrial microbe to produce novel compounds and expand beyond its regular portfolio of fatty acids, fatty alcohols, biofuels, and protein production. Although a number of gene over-expression and deletion tools have been established in Y. lipolytica ( Hong et al., 2012 ), the library of available tools is not as developed as that of other yeasts such as Saccharomyces cerevisiae . This is due in part to Y. lipolytica having a high frequency of non-homologous end-joining (NHEJ), while S. cerevisiae mainly utilizes the highly efficient homologous recombination repair machinery to repair DNA double-strand breaks ( Kretzschmar et al., 2013 , Madzak, 2015 ). Fickers et al. developed a gene disruption method by combining the sticky-end polymerase chain reaction (SEP) method and Cre-lox recombination system ( Fickers et al., 2003 ). However, acceptable rates of recombination only occur when long homology regions (0.5–1 kb) are used, making this method less efficient in the absence of the KU70 gene deletion ( Madzak, 2015 ). Another hurdle in efficient genome-editing in Y. lipolytica lies in the fact that it has a less-defined non-coding RNA processing system. To solve this challenge, Schwartz et al. (2016) has used a hybrid tRNA promoter to express gRNA, allowing for efficient CRISPR-Cas9 targeting in Yarrowia . Alternatively, Gao et al. (2016 ) demonstrated that the Type II RNA polymerase, in combination with the highly efficient hammerhead ribozyme and HDV ribozyme, could efficiently deliver gRNA and achieved multiplex genome-editing in Y. lipolytica . In addition to genome-editing tools, advances have been made to develop a multi-copy, plasmid-based gene expression system, which is suitable for gene-expression fine-tuning due to the simplicity of plasmid construction and high transformation efficiency compared to the laborious and time-consuming genome manipulation process ( Liu et al., 2014 , Madzak, 2015 ). For example, Liu et al . engineered the low-copy CEN plasmid by fusing different promoters upstream of the centromeric region and improved both the copy number and gene expression level ( Liu et al., 2014 ). In light of these advances, there is a pressing need for development of facile genetic tools that are tailored for modular and combinatorial pathway engineering in Y. lipolytica ( Xu et al., 2013 , Xu and Koffas, 2013 ). Indeed, metabolic engineering community has embraced the concepts of standardization and modularization to enable rapid construction of large pathway libraries covering a broad range of gene expression space ( Xu et al., 2013 , Xu et al., 2014 , Xu et al., 2014 ). This is often achieved by creating interoperable genetic parts with well-defined gene expression metrics. Here we report the development of a set of BioBrick vectors and its application in metabolic engineering. The engineered 12 YaliBricks vectors comprise four compatible restriction enzyme sites to enable modular pathway engineering and facile genetic operation. We established a sensitive and reliable luciferase reporter platform and characterized 12 endogenous promoters that have broad range of transcriptional dynamics. We demonstrate the versatility of the engineered YaliBrick vectors to construct gene clusters with unique transcriptional configurations: operon, pseudo-operon, monocistronic. In addition, we constructed a 12 kb, 5-gene biosynthetic pathway for the antibiotic pigment violacein using YaliBrick as a proof of concept for quick pathway assembly and screening. We incorporated our YaliBrick vector with genome-editing feature and successfully introduced indel mutation and frame-deletion deletion of the CAN1 (arginine permease) genomic loci of Y. lipolytica . We envision that the YaliBrick vectors will provide a powerful platform to facilitate modular manipulation of multigene pathways and the construction of complex genetic circuits in oleaginous yeast. These pYaliBrick vectors, along with several other relevant plasmids used in this study, will be deposited to Addgene soon for public access.",
"discussion": "3 Results and discussions 3.1 Developing a bioluminescence reporter system Effective metabolic engineering of microorganisms relies on assembling and rewiring biosynthetic pathways to channel carbon flux towards producing compounds of interest. One of the most important aspects is to modulate gene expression and ideally achieve a balanced pathway expression with no metabolic bottlenecks. As such, it is crucial for metabolic engineers to be able to monitor gene expression levels via a stable, consistent, and reliable reporter system. In the case of genetic parts characterization, there are several reporter systems developed for this application; most assays utilize fluorescence or luminescence to detect and quantify gene expression. Fluorescence reporter activity can be measured either by the fluorescence intensity of the protein itself (as in the case of hrGFP and mCherry) or the specific enzymatic activity of the reporter using a fluorescent substrate (e.g. 4-methylumbelliferyl-β-D-galactopyranoside, MUG assay) ( Haugwitz et al., 2008 ). On the other hand, bioluminescent assays utilize luciferase and, depending on the intrinsic nature of the enzyme, they require either luciferin and ATP or only coelenterazine as the substrate ( Haugwitz et al., 2008 ). To determine the best reporter system, four reporter genes hrGFP, mCherry, E. coli β-glucuronidase (EcUidA), and luciferase gene NanoLuc were constructed under the control of a constitutive TEF promoter carried by a self-replicating plasmid. We observed that the fluorescence readout was choppy and there was no consistent fluorescence expression pattern in both hrGFP or mCherry (Fig. 1A and B). Humanized hrGFP exhibited increased mean fluorescence intensity (MFI) with time across the four parallel biological replicates ( Fig. 1 A), albeit the sample-to-sample variation was large. Interestingly, mean fluorescence intensity (MFI) of mCherry was observed leveling off with time, possibly due to yeast cell autofluorescence (wavelength of mCherry emission is 610 nm). This indicates that either the expression of hrGFP and mCherry under the constitutive TEF promoter is very weak, or at least the yeast autofluorescence interferes with the detection. Humanized GFP (hrGFP) probably demonstrated better stability and functionality than mCherry under the tested condition; this is further corroborated by the fact that hrGFP reporter could be detected in Y. lipolytica using flow cytometry ( Blazeck et al., 2011 , Hussain et al., 2016 ), albeit with low sensitivity and detection limit. Fig. 1 Evaluating the reliability and consistency of different reporter systems in Y. lipolytica . (A) hrGFP fluorescence protein as the reporter. MFI, mean fluorescence intensity. Four biological replicates were tested. (B) mCherry fluorescence protein as the reporter. Four biological replicates were tested. (C) β-glucuronidase (GUS) reporter with X-gluc (5-Bromo-4-chloro-3-indolyl-β-D-glucuronide) as substrate. Blue product could be quantified based on a spectrophotometric absorbance assay. (D) β-glucuronidase (GUS) reporter with MUG (4-methylumbelliferyl-beta-D-glucuronide) as substrate. Fluorescence product could be quantified with a fluorometric assay. Four biological replicates were tested. (E) Luciferase Nanoluc as the reporter. MLI, mean luminescence intensity. Three biological replicates were tested. Fig. 1 Next, we tested whether we could use the E. coli β-glucuronidase (encoded by uidA ) as a reporter. β-glucuronidase (GUS) enzymatic activity in Y. lipolytica was measured using both fluorescent substrate (MUG, 4-methylumbelliferyl-β-D-galactopyranoside) and histochemical staining substrate (X-gluc, 5-bromo-4-chloro-3-indolyl-β-D-glucuronic acid). The colorimetric GUS staining assay took about 24 h to develop the color and did not demonstrate satisfactory detection of GUS activity ( Fig. 1 C). On the other hand, the fluorescent MUG assay was able to detect varying levels of enzymatic activity across all biological replicates of EcUidA-expressing strains with minimal background noise ( Fig. 1 D). Also, intron characterization was performed with the MUG assay by comparing enzymatic activity in stains with and without the intronic sequence fused downstream of the TEF promoter, revealing a 238-fold difference in fluorescence intensity in the strain with the intron-fused EcUidA over the intron-less EcUidA. This demonstrates the MUG assay's broad dynamic range spanning across about 2 orders of magnitude. The main disadvantage of the MUG assay is the requirement to individually quench each sample in stop buffer before measuring fluorescence intensity, making it a rather inefficient and laborious process for multiplex assays. Another disadvantage is that the MUG assay is not amenable to time-course analysis of gene expression studies due to the fast kinetics of the MUG fluorophore emission. To address these concerns, we turned to test the NanoLuc luciferase reporter by measuring the rate of luminescence emission from lysates of NLuc-expressing Y. lipolytica cells. Integration of the rate of luminescence emission exhibited a linear response curve with relatively small deviation from the mean luminescence intensity (mL) ( Fig. 1 E). Against the negative control BirA, the luminescence reporter in the NLuc-expressing strain yielded 104-fold higher signal than the baseline (BirA strain). The sensitivity of both the luciferase assay and MUG assay were similar when the strong, constitutive TEF promoter was used. Bioluminescent luciferase assays have become the preferred reporter system for gene expression studies due to their high sensitivity and broad dynamic range ( Masser et al., 2016 ). The biggest advantage of using NanoLuc luciferase is the minimal background noise due to its independence of ATP. In contrast to the larger GUS reporter (EcUidA is encoded by 603 amino acid residues) and hrGFP reporter (240 amino acid residues), the NanoLuc luciferase only contains 171 amino acid residues, giving NanoLuc the advantage of quicker translation and folding and better expressibility. This makes NanoLuc luciferase an ideal reporter for monitoring rapid gene expression changes with minimal protein expression burden. Its smaller size is also beneficial in the construction of fusion proteins or split-proteins due to less probability of interfering with the functionality of the tagged protein. 3.2 Characterizing transcriptional activity of 12 promoters The highly complex transcriptional machinery in eukaryotes has resulted in many engineering efforts to identify and characterize core promoters and their proximal sequences. Given that endogenous gene expression elements are often used in pathway engineering in eukaryotes, and their transcriptional machinery is poorly understood and hard to predict, Hussain et al. systematically examined several common promoter components in yeast and engineered hybrid promoters by using in-tandem repeats of upstream activating sequences in conjunction with downstream truncated promoters to allow for a degree of predictability in gene expression ( Hussain et al., 2016 ). For example, they have identified several promoters that are sensitive to oleic acid, and created an oleic acid-responsive hybrid acyl-CoA oxidase (POX2) promoter with modified core promoter and proximal sequences ( Hussain et al., 2016 ). Blanchin-Roland et al. (1994) reported that Y. lipolytica extracellular protease (XPR2) promoter was regulated by a multitude of factors including pH, carbon, nitrogen and peptones. In addition, upstream activating sequence (UAS) regions have been suggested as the major player in mediating the response to those factors ( Wagner and Alper, 2016 ). Dulermo et al . engineered variants of the TEF and LEU promoters by coupling different UAS1 elements in tandem and found that there is a positive correlation between the number of tandem UAS elements and expression levels ( Dulermo et al., 2017 ). In all, promoter engineering efforts in Y. lipolytica are highly restricted to a number of well-characterized promoters. Expanding the number of available endogenous promoters will provide alternative targets for further transcriptional engineering. It will also lead to the discovery of orthogonal promoters with minimal interdependence on native cell transcriptional machinery when there are considerations to decouple heterologous gene expression from the biomass precursor pathway. This allows for a more customizable approach in combinatorial gene expression optimization to selectively target different metabolic pathways. We selected 11 endogenous promoters in addition to the well-characterized TEF promoter for genetic parts characterization ( Table 1 ). Given Y. lipolytica's ability to accumulate up to 50% of its dry cell weight as lipids ( Shi et al., 2016 ), the promoters chosen are primarily centering around the lipogenic pathways: the supply of acetyl-CoA and malonyl-CoA, the TCA metabolic pathway, glycolysis, the supply of NADPH from pentose phosphate pathway, and the lipid oxidation pathway ( Fig. 2 A). Unsurprisingly, the TEF promoter exhibited strongest expression levels, since the native TEF gene codes for the abundant translational elongation factor 1-α, which is essential for gene translation in all cells. Furthermore, in the case of Y. lipolytica , the TEF gene only exists as a single copy in the chromosome, implying that its promoter is very strong. The remaining 11 promoters derived from TCA cycle, glycolysis, pentose phosphate pathway or lipid oxidation pathway, demonstrated relative transcriptional activity ranging from 0.7% to 29.7% of TEF promoter activity ( Fig. 2 B). The top three candidates in this group are promoters found in glycolysis (pGAP promoter driving the expression of glyceraldehyde-3-phosphate dehydrogenase), glyoxylate cycle (pICL1 driving the expression of the isocitrate lyase), and a key precursor step of fatty acid biosynthesis (pACL2 driving the expression of the ATP: citrate lyase). The bottom two candidates in this promoter panel are involved in the supply of malonyl-CoA (pACC1driving the expression of acetyl-CoA carboxylase) and the supply of NADPH (pZWF1 driving the expression of the PP pathway glucose-6-phosphate dehydrogenase), which have been identified as the rate-limiting steps for efficient lipid biosynthesis ( Tai and Stephanopoulos, 2013 , Wasylenko et al., 2015 ). Fig. 2 Determining the transcriptional activity of 12 native promoters in Y. lipolytica . (A) Metabolic map of the 12 chosen structural genes that encode critical enzymes catalyze lipid biosynthesis in Y. lipolytica . (B) Characterizing the transcriptional activity of the 12 chosen promoters with luminescence as the readout. The details of the 12 chosen promoters can be found in Table 1 . Fig. 2 Table 1 List of promoters and their corresponding vector names. Table 1 Transcription unit Annotation Promoter name Vector name Translational elongation factor EF−1 alpha YALI0C09141g ylTEF promoter pYaliA1 Acyl-CoA: diacylglycerol acyltransferase YALI0E32769g ylDGA1 promoter pYaliB1 Acetyl-CoA-carboxylase 1 YALI0C11407g ylACC promoter pYaliC1 ATP citrate lyase 2 YALI0D24431g ylACL2 promoter pYaliD1 Isocitrate dehydrogenase NAD+ subunit 2 mitochondrial YALI0D06303g ylIDH2 promoter pYaliE1 Fatty acid synthase subunit beta YALI0B15059g ylFAS2 promoter pYaliF1 Fatty acid synthase subunit alpha YALI0B19382g ylFAS1 promoter pYaliG1 Isocitrate lyase 1 YALI0C16885g ylICL1 promoter pYaliH1 POX4 Fatty-acyl coenzyme A oxidase YALI0E27654g ylPOX4 promoter pYlaliJ1 ZWF1 Glucose-6-phosphate dehydrogenase YALI0E22649g YlZWF1 promoter pYaliK1 Cytosolic NADP-specific isocitrate dehydrogenase YALI0F04095g ylIDP2 promoter pYaliL1 Glyceraldehyde 3-phosphate dehydrogenase YALI0C06369g ylGAPHD promoter pYaliM1 Christen et al . performed metabolic flux analysis on several aerobic species of yeast including Y. lipolytica and found that the flux through the TCA cycle and pentose phosphate pathway equals the glycolytic flux ( Christen and Sauer, 2011 ). In addition, Liu et al . demonstrated that the glyoxylate shunt is always active throughout different stages of growth, thus ensuring that the metabolite pools for the TCA cycle are replenished ( Liu et al., 2016 ). Taken together, the flux through the glyoxylate shunt and the reactions leading to acetyl-CoA formation would be high in correlation to measured metabolite pools. However, there is no clear correlation between high flux and promoter strength. Additional studies would be needed to determine optimal activation conditions for the promoters. It is generally accepted that promoter architecture in eukaryotic organisms is extremely sophisticated in their regulatory mechanisms and the transcriptional responses are not as predictable compared to prokaryotic systems ( Hussain et al., 2016 , Portela et al., 2017 ). A notable example is the Snf1-mediated acetyl-CoA carboxylase regulation ( Seip et al., 2013 , Shi et al., 2014 ). The 11 promoters identified in this study have not been fully characterized under different carbon, nitrogen and oxygen conditions. It is also noted that the sampling was performed at the late-exponential phase; it is known that gene activation and repression are correlated to the stages of growth. As such, the expression levels determined using the reporter assay may not reflect their optimal potential. Promoter activation and regulation can be mediated by different factors such as carbon source, nitrogen availability, pH, and metabolite levels ( Madzak, 2015 , Hussain et al., 2016 ). Further investigation is needed to fully elucidate these new promoters to better understand each promoter element and determine its function in regulating gene expression and lipogenesis. In addition, characterizing promoters responsible for gene expression in central carbon metabolism and lipogenesis would better inform future metabolic engineering efforts in fine-tuning native pathways without the need for replacement of the native promoter, which avoids potential disruption of regulatory mechanisms. 3.3 Gene configuration engineering Comparative genomic studies revealed that Y. lipolytica is phylogenetically distant from other hemiascomycetous yeasts. It is considered a non-conventional, intron-poor species; however, among other hemiascomycetes, it has the highest number of introns in its genome to date. Yarrowia's relatively high intron density of 0.17 indicates that alternative splicing (AS) may play a major role in increasing the variance and complexity of its transcriptome ( Mekouar et al., 2010 ). The outcome of AS of mRNA generally falls into two categories: mRNA that can be translated into functional proteins or nonsense-containing mRNA that may generate truncated, potentially non-functional proteins. For example, alternative splicing has led to generation of two unique MDH transcripts encoding malate dehydrogenases with different N-terminal signaling peptides; upon translation, the expressed malate dehydrogenase is either localized in the mitochondria, cytosol or peroxisome ( Kabran et al., 2012 ). Eukaryotes have a quality control mechanism, nonsense-mediated mRNA decay (NMD), that targets mRNA with premature termination codons for degradation, thus preventing their translation. Mekouar et al . investigated several instances of AS in Y. lipolytica gene models and found that it exhibits all modes of alternative splicing, predominantly generated by intron retention ( Mekouar et al., 2010 ). However, the resulting transcripts usually incorporated an early termination codon and led to nonsense-containing mRNAs. Regardless, this concept of transcriptional interference provides a potential strategy to regulate gene expression levels without the need for in-depth promoter engineering and understanding of the complicated regulatory mechanisms underlying each promoter. In our pYaliBrick vectors, four compatible restriction enzyme sites are strategically placed in between the gene regulatory elements to allow for easy genetic operation when performing DNA assembly: operon, pseudo-operon, and monocistronic gene configurations ( Fig. 3 A). By taking advantage of isocaudomers with compatible ends, we can reuse the restriction sites infinitely in subsequent cloning steps, enabling rapid assembly of gene fragments without the need to design unique primers to incorporate new sites. This method is particularly useful for assembling pathways with multiple similar homologous regions or repeated parts, which is technically challenging for homologous recombination-based methods such as Gibson assembly or yeast transformation-based gene assembly ( Xu et al., 2013a ). Fig. 3 Intron alternative splicing allows the construction of three gene configurations in Y. lipolytica . (A) Modular cloning procedure to generate three gene configurations in Y. lipolytica . (B) A detailed layout of a two-gene pathway varying in gene configuration and positional order. (C) Gel digestion pattern of the six gene configurations: lane 1, B-o-N; lane 2, B-p-N, lane 3, B-m-N; lane 4, ladder; lane 5, N-o-B; lane 6, N-p-B and lane 7, N-m-B. (D) Yeast transformation assay to evaluate the efficiency of the Ura3 marker restoring Ura3 auxotrophic phenotype. Plate 1, Ura3 marker positive control; plate 2, U-o-M; plate 3, U-p-M; plate 4, U-m-M; plate 5, M-o-U; plate 6, M-p-U; plate 7, M-m-U. U is an abbreviation for Ura3 and M is an abbreviation for mCherry. (E) Positional effects of gene configuration on gene expression efficiency as evaluated by luminescence readout in the six genetic variations. RLU: relative luminescence unit. Error bars are the standard deviation calculated from triplicate readouts. Fig. 3 The three gene configuration mimics the prokaryotic gene expression system. An operon configuration encodes two structural gene sharing the same promoter and terminator, with each of the gene containing an intron derived from the TEF gene at their 5′ end. The intron is built upstream of the structural gene but downstream of the ATG exon to allow spliceosome processing of multiple intron sequences ( Blumenthal, 1998 ) and potentially generate functional mRNA transcripts encoding different enzyme functionality ( Fig. 3 B). Similarly, a pseudo-operon configuration encodes two structural gene sharing the same terminator but driven by separate promoters. And a monocistronic configuration encodes two structural gene driven by separate promoters and terminators ( Fig. 3 B). Our standard cloning procedure allows us to streamline the genetic operation and rapidly create the three unique gene configurations ( Fig. 3 A). For a 2-gene pathway, six unique genetic organization could be easily obtained ( Fig. 3 B) and later screened by double digestion analysis ( Fig. 3 C). A ladder pattern of gel electrophoresis image indicates the three unique gene configurations, as demonstrated in Fig. 3 C. To test how these three gene configurations affect expression levels, we assembled six distinct genetic constructs varying in gene order and configuration. The two genes encode the yeast auxotrophic marker Ura3 and a fluorescence reporter ( Supplementary Fig. S4 ). Upon transformation into the Ura3-deficient (Ura3 - ) strain, we evaluated which gene configuration could better complement the Ura3-deficient phenotype. Colony based screening on CSM-Ura media allowed us to compare the relative expression level of the Ura3 gene and inform us how genetic structural variation correlates with mRNA transcription ( Fig. 3 D). This complementation experiment demonstrates that six out of the seven constructs could functionally express the Ura3 gene, with the exception of plate No. 3 which corresponds to Ura3 and mCherry arranged in pseudo-operon form ( Fig. 3 D). To further validate this result, we then constructed six additional plasmids bearing two genes: BirA and NLuc. BirA is used as a control to investigate positional effects. All constructs displayed weaker expression of luminescence relative to the single NLuc control ( Fig. 3 E), which is not unusual as gene expression becomes weaker in tandem genetic constructs driven by similar promoters due to transcriptional inhibition ( Shearwin et al. 2005 ). Constructs harboring NLuc as the first gene exhibited strongest light emission in the monocistronic format (N-m-B, Fig. 3 E) followed by operon (N-o-B, Fig. 3 E). However, the pseudo-operon configuration (N-p-B, Fig. 3 E) showed baseline signal, indicating the possibility of non-functional NLuc. The pseudo-operon configuration N-p-B contains an additional promoter within the transcript ( Fig. 3 B). Given that NLuc is functionally expressed when it is configured as an operon ( Fig. 3 D and E), we can infer that the non-functionality observed in the pseudo-operon configuration may be due to the lack of the transcriptional termination signal, thus preventing the functional maturation of the first messenger RNA which encodes the NLuc luciferase. Thus, the transcript generated cannot be translated into functional NLuc, although BirA is functionally expressed. When NLuc is located downstream of BirA, the intron in front of NLuc moves further down along the genetic construct. The weak luminescence signal detected in the operon construct (B-o-N, Fig. 3 E) indicates that the spliceosome prefers to splice the intron which is in close proximity to the exon. That is, splicing the intron on the first gene BirA may inhibit splicing of the intron on the second gene (NLuc). This alternative splicing inhibition provides a novel approach to adjust the transcripts ratio of two proteins. On the other hand, BirA-NLuc in both pseudo-operon (B-p-N) and monocistronic (B-m-N) configurations retained NLuc functionality due to the production of functional NLuc mRNA and thus functional proteins, as indicated by the relatively strong luminescence signal ( Fig. 3 E). In addition, positional effects within the monocistronic configuration exhibited a 4.4-fold decrease in NLuc gene expression when NLuc is placed downstream of BirA compared to the N-m-B construct where NLuc is placed upstream of BirA ( Fig. 3 E). Taking this into consideration, designing metabolic pathways in a tandem configuration will require stronger promoters to drive the transcription of the downstream genes due to this positional effect. These results establish a foundation to study how introns affect the processing of mRNA. It also provides an alternative approach to encode complex metabolic activity into a well-defined genetic configuration. 3.4 Functional expression of multigene pathway Violacein is a pigmented, indolocarbazole compound with interesting bioactivities such as antibacterial, anticancer, antiviral, trypanocidal, and antioxidant properties ( Leal et al., 2015 , Durán et al., 2016 ). These properties make violacein an attractive antibiotic target. The gene cluster vioABCDE isolated from Chromobacterium violaceum is responsible for converting the precursor tryptophan to violacein via a combination of enzymatic and non-enzymatic steps. VioA, VioC, and VioD are flavin-dependent oxygenases, with VioA catalyzing the first key step in converting L -tryptophan to 2-iminoindole pyruvate ( Fig. 4 A). VioB is a heme protein that yields the dimeric structure due to the C-C bond formation between the β-carbons in the benzylic rings of the two tryptophan molecules. The unstable intermediate formed by VioAB then goes through an indole shift catalyzed by VioE. At this point, the VioABE intermediate protodeoxyviolaceinic acid ( Hirano et al., 2008 ) can directly be catalyzed either by VioD followed by VioC, or directly by VioC, to form violaceinic acid and deoxyviolaceinic acid ( Fig. 4 A), respectively ( Hoshino, 2011 ). Fig. 4 Modular assembly of the 12 kb five-gene violacein biosynthetic pathway with pYaliBricks in Y. lipolytica . (A) Five biosynthetic steps lead to the synthesis of violacein from tryptophan. A detailed description of the biosynthetic reactions could be found in the main text. (B) In-parallel cloning procedure to assemble the five-gene pathway. (C) Plasmid map of the final five-gene construct. CEN1-1, yeast centromere containing the autonomous replication sequence; ORI1001, yeast replication origin; Leu2, leucine auxotrophic marker; TEF, promoter; XPR2, terminator; AmpR, ampicillin resistance maker. (D) Digestion pattern of the recombinant plasmid containing the five-gene violacein biosynthetic pathway. (E) Representative picture of violacein-producing Y. lipolytica grown on CSM-Leu plate. Fig. 4 Previous studies have taken advantage of its strong violet color to develop quick visual screening tools. The purple pigment has allowed for evaluating DNA assembly strategies and balancing expression for various biotechnological applications ( Lee et al., 2013 , Mitchell et al., 2015 ). To test our design strategies in a quick and effective manner, we constructed the 12 kb five-gene pathway for violacein production in our standardized pYaliBrick vectors. Our engineered pYaliBricks support the modular and in-parallel assembly of multiple gene constructs via streamlined genetic manipulation ( Fig. 4 B). To construct the five-gene pathway along with the regulatory control elements (promoters, introns and terminators), we performed three rounds of cloning in one week and assembled the five-gene pathway in a monocistronic configuration as illustrated in Fig. 4 C. Sequence verification was quickly confirmed by the unique fragmentation pattern via restriction digestion ( Fig. 4 D). Upon transformation into the Po1g Y. lipolytica host, colonies exhibited various color ranging from white to deep purple ( Supplementary Fig. S5 ). The phenotypic variance seen indicates the possibility of genetic instability of our Y. lipolytica host in retaining all functional genes in the violacein biosynthetic pathway. A dark purple colony was subsequently chosen ( Fig. 4 E) and cultivated in CSM-Leu minimal media, HPLC quantification of the chosen strain yielded about 31.5 mg/L violacein. This is the first demonstration of the functional expression of the five-gene violacein pathway in oleaginous yeast, albeit further strain optimization is required to improve the cost-competitiveness. The pYaliBrick assembly vectors provide a rapid method of assembling large pathways containing multiple reused parts (promoter, intron, terminator), which otherwise would not be attainable through Gibson assembly cloning. Given that the process involves no PCR amplification and only relies on subcloning using highly specific restriction enzymes, it is not necessary to sequence the construct for each assembly. The high fidelity and specificity of restriction enzyme ensures the accuracy of the pYaliBrick assembly platform. The simplicity and ease of manipulation to combine and configure multiple genes in one vector makes the pYaliBrick DNA assembly method highly predictable and efficient. In addition to the 12 promoters that are built into our pYaliBrick cloning system, the constructed vectors provide a unique approach to balancing gene expression. Collectively, we were able to construct a 12 kb natural product biosynthetic pathway containing vioABCDE and all necessary gene regulatory elements into the pYaliBrick vector and demonstrated violacein production in Y. lipolytica . 3.5 Accommodating genome-editing feature into pYaliBricks CRISPR-Cas9 mediated genome editing technique has revolutionized the field of metabolic engineering and synthetic biology ( Qi et al., 2013 ; Wang et al., 2016 ). The RNA-guided DNA nuclease (Cas9) specifically targets the genomic loci that are complementary to the designed spacer (or guide) sequence adjacent to a PAM motif (protospacer adjacent motif). A crRNA (CRISPR RNA) containing the 20 bp designed protospacer sequence joins with the tracrRNA (trans-activating CRISPR RNA) to form an active scaffold and recruit Cas9 endonuclease to target the genome and induce a site-specific double-strand break ( Jiang et al., 2013 , Wagner and Alper, 2016 ). The double-strand break can be repaired either through non-homologous end joining (NHEJ) or homology-directed repair (HDR). A synthetic guide RNA (sgRNA) can be designed to contain both the crRNA and the tracrRNA for the facile delivery of guide RNA. The processing of tracrRNA and crRNA largely relies on the type III RNA polymerase and the activity of the ribonuclease P to deliver non-coding RNA. The less-characterized non-coding RNA processing system poses a potential challenge to efficiently deliver functional gRNA in Y. lipolytica ( Dujon et al., 2004 , Acker et al., 2008 ). To encode gRNA into pYaliBrick vectors, we adopted the highly efficient self-cleavage ribozyme strategy ( Bayer and Smolke, 2005 , Gao and Zhao, 2014 , Gao et al., 2016 ) and modified the type II RNA promoter to drive the expression of the single guide RNA (sgRNA). We chose to edit the CAN1 (arginine permease) genomic locus, as this genetic marker allows us to easily evaluate the mutation efficiency based on canavanine resistance. The sgRNA along with the tracrRNA are flanked by the hammer-head ribozyme at the 5′ end and the HDV ribozyme at the 3′ end ( Fig. 5 A). Functional expression of the designed gene cassette leads to the generation of hybrid ribozyme and gRNA transcripts. The two ribozymes catalyze a selective hydrolysis reaction that cleaves the linking phosphodiester to give rise to a functional sgRNA transcript ( Fig. 5 B). Fig. 5 CRISPR-Cas9 mediated genome-editing of CAN1 gene in Y. lipolytica . (A) Design of single guide RNA (sgRNA) with type II promoter (TEF) driving the expression of CAN1 single guide RNA. CAN1: gRNA that target the arginine permease; HHR: hammer-head ribozyme; tracrRNA: trans-activating CRISPR RNA. (B) Secondary structure of the transcribed sgRNA flanked by upstream hammer-head ribozyme and downstream HDV ribozyme. Red arrow indicates the self-cleavage site. (C) PCR product fragments of the mutant CAN1 analyzed by gel electrophoresis. (D) CAN1 mutation efficiency in both transient sgRNA delivery and plasmid-based sgRNA delivery. (For interpretation of the references to color in this figure legend, the reader is referred to the web version of this article.) Fig. 5 Selective screening of yeast colonies grown on CSM-Arg plate supplemented with canavanine allowed us to easily identify canavanine resistance. PCR amplification of the mutant CAN1 led to the production of distinct gene fragment sizes, with some of the colonies completely lacking the CAN1, while others possessed a truncated form of CAN1 ( Fig. 5 C). Transient expression of sgRNA in linear cassette form (the exact PCR product is shown in Fig. 5 A) along with a NLS-tagged Cas9 results in about 92% of colonies that were resistant to canavanine. Plasmid-based delivery of sgRNA was also tested and revealed no significant difference in the mutation rate compared to the transient expression of sgRNA ( Fig. 5 D). PCR product of the mutant CAN1 was later sequenced to verify the exact mutation across the CAN1 genomic locus. Our sequencing results indicate plasmid-based gRNA delivery yields about 12.5% on-target indel mutation, compared to the 7.2% on-target indel mutation in the transient expression ( Fig. 5 D). We believe that the low on-target indel mutation arose from the NHEJ (non-homologous end joining) repair mechanism, which is dominant in Y. lipolytica . Interestingly, we also observed complete deletion and partial deletion of CAN1 in the mutants ( Fig. 5 C), indicating the complexity of the DSB repair mechanism in this yeast."
} | 10,527 |
23402562 | PMC3604861 | pmc | 3,770 | {
"abstract": "This study introduces a newly isolated,\ngenetically tractable bacterium\n( Pseudogulbenkiania sp. strain MAI-1)\nand explores the extent to which its nitrate-dependent iron-oxidation\nactivity is directly biologically catalyzed. Specifically, we focused\non the role of iron chelating ligands in promoting chemical oxidation\nof Fe(II) by nitrite under anoxic conditions. Strong organic ligands\nsuch as nitrilotriacetate and citrate can substantially enhance chemical\noxidation of Fe(II) by nitrite at circumneutral pH. We show that strain\nMAI-1 exhibits unambiguous biological Fe(II) oxidation despite a significant\ncontribution (∼30–35%) from ligand-enhanced chemical\noxidation. Our work with the model denitrifying strain Paracoccus denitrificans further shows that ligand-enhanced\nchemical oxidation of Fe(II) by microbially produced nitrite can be\nan important general side effect of biological denitrification. Our\nassessment of reaction rates derived from literature reports of anaerobic\nFe(II) oxidation, both chemical and biological, highlights the potential\ncompetition and likely co-occurrence of chemical Fe(II) oxidation\n(mediated by microbial production of nitrite) and truly biological\nFe(II) oxidation.",
"introduction": "Introduction Fe(II)/Fe(III) is an important redox couple\nin natural environments. 1 In anoxic systems,\niron oxidation can be mediated\nby several biological agents, such as anoxygenic phototrophs 2 , 3 and nitrate-dependent chemotrophs. 4 , 5 While the enzymatic\nmachinery for Fe(II) oxidation has been identified and characterized\nfor two anoxygenic phototrophs, 3 , 6 , 7 comparable catalysts have not yet been identified for nitrate-dependent\nchemotrophs. Toward this end, we isolated a fast growing Fe(II) oxidizing,\nnitrate-dependent chemotroph from the iron-rich tropical Lake Matano, 8 with the intention of developing it into a model\ngenetic system. However, work with the isolate highlighted a second,\noften overlooked aspect of Fe(II) oxidation in anoxic environments:\ndirect chemical interaction with nitrite (a form of chemodenitrification 9 ). Being able to distinguish the mechanisms and\nturnover rates of direct biological versus abiotic components of anaerobic\nFe(II) oxidation is necessary to gain a complete understanding of\nthe biogeochemical coupling of the N and Fe redox cycles. Here, we\nexpand our understanding of chemodenitrification by experimental elucidation\nof how organic ligands promote abiotic Fe(II) oxidation by nitrite,\nand discuss its relevance to assessing the potential co-occurrence\nof chemical and biological Fe(II) oxidation. The isolation and\ncharacterization of an increasing number of microorganisms\ncapable of nitrate-dependent anaerobic Fe(II) oxidation in recent\nyears 4 , 5 , 10 − 16 has revealed the potential for chemotrophic recycling of Fe(II)\nin anoxic systems. However, deconvolving the chemical and biological\naspects of this process remains challenging in many environmental\nsettings 17 , 18 and even laboratory studies. 19 , 20 The complication arises whenever denitrifying organisms reduce nitrate\nin iron-rich anoxic systems, where the metabolic intermediate nitrite\ncan oxidize Fe(II). 21 − 26 This was recently highlighted in a review by Picardal, 27 which underscored that while biologically induced\n(through the production of nitrite during biological denitrification),\nFe(II) oxidation can be abiotically catalyzed and proceed by chemodenitrification.\nBecause Fe(II) oxidation may also be directly catalyzed by (potentially\nthe same) denitrifying organisms, two competing pathways exist whose\nprecise mechanisms and relative importance in nature are poorly understood.\nWhile the physiology of nitrate-dependent Fe(II)-oxidizing bacteria\nhas been the subject of a growing number of studies, 16 , 24 , 28 − 30 the chemical\naspect of anaerobic Fe(II) oxidation by nitrite has received less\nattention, 27 , 31 despite its relevance to constraining\nthe extent of its microbial counterpart. Rapid oxidation of\nFe(II) by nitrite in strongly acidic conditions\nwas described as early as 1936, 32 with\nhigh reaction rates linked to the generation and subsequent degradation\nof nitrous acid (p K a = 3.4). At circumneutral\npH, nitrite is stable and anaerobic Fe(II) oxidation requires a catalyst\nor suitable Fe(II)-containing mineral to proceed at appreciable rates.\nAcceleration of this process has been reported with a number of specific\nFe(II) mineral phases and catalysts, such as Cu 2+ , 33 iron oxides and hydroxides, 31 , 34 − 37 green rust, 25 , 38 as well as siderite 39 and vivianite, 23 and\neven microbial surfaces, 22 providing possible\nreaction mechanisms for Fe(II)-oxidizing chemodenitrification. The\nsame is true for nitrate, which is generally less reactive toward\nFe(II) than nitrite at circumneutral pH, 40 but can similarly benefit from metal and mineral catalysis. 41 , 42 However, metals and surfaces are not the only agents for chemical\ncatalysis. While the kinetic effects of ligands (including EDTA, NTA,\nand citrate) on iron redox processes in oxic environments have been\nexplored before 43 − 46 and often lead to acceleration of Fe(II) oxidation, much less is\nknown about their effects in the absence of molecular oxygen. Several\nstudies have investigated the effect of ligands on iron redox processes\nin acidic conditions and solvents, 47 , 48 but with the\nnotable exception of studies on microbial Fe(II) oxidation in the\npresence of EDTA, 13 , 49 little is known about the impact\nof ligands at circumneutral pH. Here, we investigate the effect\nof several Fe(II)-chelating ligands\non iron-oxidizing chemodenitrification to (1) assess true biological\nFe(II) oxidation in the newly isolated β-proteobacterium Pseudogulbenkiania sp. strain MAI-1 and (2) elucidate\nthe role ligands could play more generally in abiotic Fe(II) oxidation\nin laboratory and environmental settings. We use Paracoccus\ndenitrificans as a model strain to show how Fe(II)\noxidation can appear to be directly biologically catalyzed when, in\nfact, much of this activity may only be indirectly biologically mediated.\nWe describe the kinetics and potential reaction mechanism of the chemical\noxidation of Fe(II) by nitrite observed in these experiments and discuss\ntheir relevance for the interpretation of laboratory and environmental\nstudies. We place our findings in the context of chemical and biological\noxidation rates reported in the literature to evaluate their relative\nimportance in anaerobic Fe(II) oxidation.",
"discussion": "Discussion Reaction Mechanism and\nKinetics Understanding the kinetics\nof Fe(II) oxidation in the presence of ligands provides the tools\nfor predicting the potential effects of ligand-enhanced Fe(II) oxidation\nin microbial systems. The total consumption of Fe(II) and nitrite\n(Table 1 ) suggests that Fe(II) oxidation by\nnitrite proceeds with 2:1 Fe(II)/NO 2 – stoichiometry regardless of complexation (no ligand, PPHA, citrate),\nwith the notable exception of NTA, which appears to deplete Fe(II)\nand NO 2 – in a 1:1 ratio. The 2:1 stoichiometry\nis in agreement with literature reports that the predominant product\nof nitrite reduction at pH regimes between 6 and 8 is N 2 O, 22 , 33 , 36 , 37 , 62 according to the following\nrepresentative net reaction: 1 where Fe 2+ can be unbound Fe 2+ or a ligand-bound Fe(II)-L species, and Fe 3+ can\nbe ligand-bound Fe(III)-L or contained within an (oxy)hydroxide mineral\n(e.g., FeOOH). This net reaction likely comprises a number of elementary\nreaction steps; we consider the following three to contextualize our\nobservations: 2 3 4 Equations 3 ( 63 ) and 4 ( 64 ) proceed rapidly at circumneutral pH, with eq 2 being the rate limiting step ( k 1 ≈ k 2 ). Accordingly, the reaction\nconsumes 2 Fe(II) for every NO 2 – , except\nin the case of NTA. Both citrate and NTA complexes with ferrous iron\ncan bind nitric oxide such that the following reactions can occur\nin competition with eq 3 : 5 [6] However,\nFe(II)-NTA forms a considerably stronger\ncomplex with NO ( k 6 ≈ 2.1 × 10 7 M –1 s –1 , K eq = 10 6.26 ) 54 , 65 , 66 than Fe(II)-citrate ( k 5 ≈ 4.4 × 10 5 M –1 s –1 , K eq = 10 2.83 ) 66 or Fe 2+ alone\n( k 3 ≈ 6.2 ×\n10 5 M –1 s –1 , K eq = 10 2.65 ), 63 potentially preventing eq 4 from\nproceeding. For example, if 100 μM Fe(II) reacted with 100 μM\nNO 2 – to form NO in the presence of 2\nmM NTA, more than 99.98% of the produced NO would form the highly\nstable Fe(II)-NTA-NO complex. The 1:1 stoichiometry of Fe(II) oxidation\nby nitrite observed in the presence of NTA is likely a consequence\nof this stable Fe(II)–NTA–NO complex formation. As expected,\nwe confirmed evolution of N 2 O during Fe(II) oxidation by\nnitrite by gas chromatography in the presence of citrate, but no N 2 O formed in the presence of NTA ( Supporting\nInformation Figure S8); the formation of the Fe(II)–NTA–NO\ncomplex could be observed instead ( Supporting\nInformation Figure S9). Based on the rate-limiting, Fe(II)\nand NO 2 – dependent first reaction step\n(eq 2 ), a plausible\nscheme for the overall reaction kinetics is a second-order rate expression\nwith overall rate constant k app in analogy\nwith oxidation of Fe(II) and Mn(II) by O 2 57 , 67 7 8 where Fe(II) comprises the total\npool of ferrous\niron (free Fe 2+ as well as all complexed Fe(II)). Given\nthe equimolarity of initial total Fe(II) and NO 2 – in our experimental setup, we integrate eqs 7 and 8 to yield the following decay equations\n(see the Supporting Information for details): 9 10 Least-squares\nfits of eqs 9 and 10 to\nour experimental results for Fe(II) and NO 2 – depletion provide two separate estimates\nof the overall rate constant k app for\neach condition (Tables 1 and 2 ). Reactions without a ligand and with low citrate or PPHA\nare better described by a linear least-squares fit (apparent zero-order\nkinetics) and are therefore considered kinetically unresolved (no k app determined). Elementary reaction steps and\nkinetic constraints for these conditions cannot be deduced from our\nobservations, and it remains unclear why the reactions appear to be\nzero-order. Oxidation in these conditions likely proceeds as a consequence\nof ferric (oxy)hydroxide precipitation (observed visually) and subsequent\nheterogeneous autocatalysis as reported by Tai and Dempsey (2009). 37 Apparent zero-order kinetics could reflect the\ncomplex balance between the generation of catalytic mineral surfaces\nand depletion of dissolved Fe(II) and nitrite. At higher concentrations\nof citrate and NTA, the reactions remained\nhomogeneous and are in agreement with a second-order kinetic interpretation\nof our data (Tables 1 and 2 and Supporting Information Figure\nS7). Rate constants derived from Fe(II) oxidation and nitrite reduction\nagree well within their 95% confidence intervals, lending further\ncredence to the model. The pH remained close to 7.0 in all conditions,\nwith an average change of 0.1 by the end of the experiment ( Supporting Information Table S1), suggesting\nthat the presence of the ligands, rather than fluctuations in pH are\nresponsible for the observed differences in reaction kinetics. The\nreaction progression observed in the presence of PPHA suggests that\nchelation of Fe(II) by the humic acid moieties (10% of the initial\nFe(II) pool is organically complexed) has little to no effect on the\nkinetics of iron oxidation (see Figure 1 , PPHA\nand CIT + PPHA). Rather than accelerating Fe(II) oxidation, PPHA appears\nto have a slight retarding effect. In contrast to experiments without\na ligand, PPHA is likely to impede iron oxide formation and autocatalysis\nas a result of its high affinity for Fe(III). In combination with\ncitrate, PPHA leads to diminished formation of the Fe(II)-citrate\ncomplex ( Supporting Information Table S2),\nwhich appears to reduce the overall reaction rate (Table 1 ). Additional information for predicting the\ncontribution of chemical\nFe(II) oxidation, especially in well-defined laboratory systems, can\nbe gained from identifying the reactive species. In analogy to Fe(II)\nand Mn(II) oxidation by O 2 , the overall rate constant k app observed in our experiments can likely be\nexplained in terms of the weighted sum of the oxidation rates of individual\nFe(II) species 57 , 67 k app = ∑ k i α i where α i is the fraction of each Fe(II) species in solution and k i the species-specific second-order rate\nconstant for oxidation by nitrite. A comparison of k app with the extent of Fe(II) complexation for each experimental\ncondition (Figure 4 ; Supporting\nInformation Table S2) suggests that the Fe(II)-L complex is\ninvolved in accelerating Fe(II) oxidation, although the effect is\nligand-specific (no effect for PPHA, variable magnitude for citrate\nand NTA). The observed reaction rates at low species fractions of\nFe(II)-L (<20%) suggest the existence of other Fe(II) species with\nappreciable nitrite-dependent oxidation rates. We speculate that the\ncarbonate species Fe(II)–CO 3 –OH – and Fe(II)–(CO 3 ) 2 2– ( Supporting Information Table S2) could\nprovide such reactive species in analogy to their role in Fe(II) oxidation\nby molecular oxygen. 57 However, the precise\nmechanism and species-specific reaction rates k i for the observed oxidation of Fe(II) by\nnitrite are beyond the scope of this report and await further study.\nDue to the uncertainty surrounding the reactive species involved,\nwe recommend caution in applying the rate constants derived in Tables 1 and 2 to aqueous environments\nwith widely differing Fe(II) complexation, pH, or ionic strength. Figure 4 Rate constants\nincrease with increasing degree of Fe(II) complexation.\nSecond-order rate constants for oxidation experiments in the presence\nof citrate (black symbols) and NTA (gray symbols) are plotted against\nthe degree of Fe(II) complexation by citrate/NTA. Rate constants derived\nfrom [Fe(II)] depicted as circles (○), constants derived from\n[NO 2 – ] as squares (□). Error bars\nindicate 95% confidence intervals (Tables 1 and 2 ). Details on speciation can be found\nin Supporting Information Table S1. Larger\nconfidence intervals for data reported in Table 2 are a consequence of reduced temporal resolution and greater deviation\nfrom the assumption that initial Fe(II) and NO 2 – concentrations are equimolar. Biological Fe(II) Oxidation by Pseudobulkeniania sp. Strain MAI-1 Using the kinetic rate constants derived\nfor the oxidation of Fe(II) by nitrite in the presence of NTA with\nthe nitrite accumulation measured in culture of MAI-1 (Figure 2 ), we modeled the purely abiotic Fe(II) oxidation\nthat would result from the interaction of Fe(II) with the accumulated\nnitrite (Figure 2 , bottom), assuming the presence\nof cell surfaces 22 to have negligible effects\non purely chemical oxidation. Even if we conservatively assume the\nupper 95% confidence interval for the rate constant (8.13 M –1 s –1 ; see Table 1 ) and that\nproduced NO is biologically consumed (thus leaving more Fe(II) free\nto react by preventing formation of the highly stable Fe(II)-NTA-NO\ncomplex), abiotic oxidation would maximally account for ∼30%/35%\n(solid vs dashed curve) of the observed Fe(II) oxidation after 28\nh (time point indicated by vertical line in Figure 2 ). In fact, a 6× higher rate constant (combined with\nbiological consumption of any produced NO) would be required to attribute\nobserved Fe(II) oxidation to purely chemical processes (Figure 2 , dotted model). Based on the kinetic quantification\nof chemical oxidation of Fe(II), it thus becomes evident that Pseudogulbenkiania sp. MAI-1 can directly oxidize\nFe(II), establishing the organism as a novel neutrophilic nitrate-dependent\nchemotroph with unambiguous biological Fe(II)-oxidizing activity.\nThe potential to easily genetically manipulate this strain makes it\na good candidate for elucidating the machinery involved in biological\nFe(II) oxidation. Whether the biological component of Fe(II) oxidation\nin MAI-1 occurs via a dedicated enzyme system or via nonspecific reactions\nwith redox active components of the cell, such as periplasmic thiols\nor components of the electron transport chain, 25 , 26 is a question that could be addressed in the future. Chemical vs\nBiological Fe(II) Oxidation in Laboratory and Environmental\nStudies Given the aforementioned difficulty in discriminating\nbetween chemical and biological contributions to anaerobic Fe(II)\noxidation in many systems, it can be informative to compare Fe(II)\noxidation rates observed in a variety of environmental and laboratory\nsettings. Table 3 provides an overview of the\nmaximal Fe(II) oxidation rates reported in a number of publications\non chemical and biological Fe(II) oxidation in nitrite/nitrate rich\nanoxic environments at circumneutral pH. Several observations are\nparticularly noteworthy: (i) The majority of observed maximal rates\nof chemical and biological Fe(II) oxidation fall within a similar\nrange of values (∼10–100 μM/h), highlighting the\nlikely competition and co-occurrence of chemical and biological processes\ninvolved in the coupled biogeochemical cycling of iron and nitrogen.\nMoreover, because nitrite is produced and often accumulates during\nthe microbial denitrification process, they are intrinsically coupled.\nThis biologically induced chemical oxidation of iron (via the microbial\nproduction of nitrite) in organic rich environments such as soils\nand wetlands is likely to contribute significantly to the cycling\nof iron and immobilization of metal contaminants and organic pollutants\non iron (oxy)hydroxides. High oxidation rates reported for environmental\nsamples with mixed contributions from biological and chemical catalysis 20 illustrate the interplay of these processes\nand call for caution in interpreting an observed effect to stem from\nsolely one or the other mechanism. (ii) In the case of mineral accelerated\nFe(II) oxidation, the presence of amorphous hydrous ferric oxide (HFO/ferrihydrite) 9 , 31 , 37 and green rust 42 appears to cause the most significant acceleration of Fe(II)\noxidation (see Table S3 for additional detail on rate constants derived\nfor mineral catalysis). This effect is likely to be highly relevant\nin natural settings where poorly crystalline iron oxides are ubiquitous.\nHowever, it is also important to consider this effect in laboratory\nstudies where iron oxides precipitate over the course of an experiment\nand can provide catalytic surfaces for chemodenitrification as suggested\npreviously. 23 − 25 (iii) In the case of ligand-enhanced Fe(II)\noxidation by nitrite, the absence of a major effect of the humic acid\nrepresentative PPHA and low environmental abundance of the anthropogenic\nligand NTA (maximal levels of 10–100 nM in aqueous systems), 1 suggests that citrate (detected in soil solutions\nin appreciable quantities, ∼100 μM range) 68 is likely to be the only ligand investigated\nin this study that could be relevant in natural systems. In laboratory\nstudies of iron oxidizing microorganisms in the presence of citrate\nor NTA, the ligands’ effect on oxidation kinetics is a crucial\naspect of Fe(II) depletion that cannot be disregarded. This is particularly\nclear from the experiment reported in Figure 3 that confirms ligand-enhanced chemical oxidation of Fe(II) by nitrite\ncan be an important side effect of microbial denitrification. Here,\nchemical Fe(II) oxidation could be mistaken for direct biological\ncatalysis by P. denitrificans ; while\ndirect catalysis may indeed be at play, it would simply be challenging\nto unambiguously identify without appropriate controls. In conclusion,\nthis study serves as a reminder of the complex interplay between direct\nand indirect biological effects involving metal transformations. In\nthe case of denitrifying microorganisms, the extent to which these\ndifferent processes catalyze Fe(II) oxidation likely depends on the\nprecise culturing conditions and must be evaluated on a case-by-case\nbasis. Table 3 Maximal Rates of\nFe(II) Oxidation\nReported for Various Anaerobic Processes at Circumneutral pH (25–30\n°C, Except Where Otherwise Indicated) experimental\nconditions max. rates pH buffer Fe(II) nitrite nitrate ΔFe(II) [μM/h] reference Chemical (Abiotic) +30 mg/L lepidocrocite (γ-FeOOH) 7.5 autotitration 0.2 mM 0.2 mM –7 ( 36 ), Figure 5 +30 mg/L lepidocrocite (γ-FeOOH) 8.5 autotitration 0.2 mM 0.2 mM –40 ( 36 ), Figure 5 Fe(II) as siderite (10 g/L ∼ 80 mM) 6 MES/PIPES/HEPES 10 g/L 4.6 mM –265 ( 39 ), Figure 5 Fe(II) as siderite (10 g/L ∼ 80 mM) 6.5 MES/PIPES/HEPES 10 g/L 4.6 mM –169 ( 39 ), Figure 5 Fe(II) as siderite (10 g/L ∼ 80 mM) 7.9 MES/PIPES/HEPES 10\ng/L 4.6 mM –140 ( 39 ), Figure 5 +2.5 mM Fe(II) as HFO, 64 μM\naverage solid-bound Fe(II) 6.8 PIPES 0.38 mM 0.38 mM –158 ( 37 ), Table 1, #6 +17.5 mM Fe(III)\nas HFO, 188 μM average solid-bound Fe(II) 6.8 PIPES 0.34 mM 0.32 mM –301 ( 37 ), Table 1, #11 F(II) as green rust 8.25 autotitration 10.81 mM 14.2 mM –139 ( 42 ), Table 1 +2 mM NTA 7 bicarbonate 2 mM 2 mM –192 this study, Table 1 +2 mM CIT 7 bicarbonate 2 mM 2 mM –134 this study, Table 1 +10 mM CIT, P. denitrificans spent medium 7 bicarbonate 5 mM 5 mM –1695 this study, Table 2 +10 mM\nCIT, P. denitrificans culture 7 bicarbonate 5 mM 5 mM –1910 this\nstudy, Table 2 Mixed (Chemical\n+ Biological) D. frappieri strain G, Fe(II)\ncomplexed by 10 mM NTA ∼7 bicarbonate 4.8 mM 1.4 mM 2.5 mM –294 ( 20 ), Figure 5 D. frappieri strain G, Fe(II)\nas smectite ∼7 bicarbonate 3 mM 1.4 mM 5 mM –175 ( 20 ), Figure 6 Pseudogulbenkiania sp. MAI-1, Fe(II)-NTA 7 bicarbonate 4 mM 5 mM 10 mM –360 this study, Figure 2 Chemotrophic enrichment culture, +1 mM acetate 7 bicarbonate 10 mM ? 3 mM –106 ( 4 ), Figure 1 enrichment culture containing Sideroxydans species 6.8 bicarbonate 10 mM ? 4 mM –156 ( 29 ), Figure 1a Pseudogulbenkiania strain 2002 6.8 bicarbonate 10 mM ? 2.2 mM –74 ( 16 ), Figure 4 strain HidR2, +1 mM acetate 6.7 bicarbonate 6 mM <30 μM 5 mM –66 ( 14 ), Figure 2 Ferroglobus placidus , 85C 7 bicarbonate 2 mM up to 550 μM 0.64 mM –173 ( 5 ), Figure 4 cell suspension\nof D. suillum , grown on acetate + nitrate 6.8 bicarbonate 10 mM ? 10 mM –4700 ( 12 ), Figure 3a Paracoccus\nferrooxidans , +25\nmM EDTA, +1 mM ethanol 7 bicarbonate 25 mM ? 5 mM –1600 ( 13 ), Figure 3a Acidovorax sp. strain\nBoFeN1, +2 mM acetate 6.8 bicarbonate 2.5 mM <1 mM 5 mM –48 ( 15 ), Figure 2 Acidovorax sp. strain BoFeN1, +5 mM acetate 7 bicarbonate 10 mM 0 mM 10 mM –240 ( 30 ), Figure 1a Acidovorax sp. strain 2AN, +1.6 mM acetate 6.85 bicarbonate 8.3 mM up to 1 mM 5 mM –158 ( 24 ), Figure 2a Acidovorax sp. strain\n2AN, +\n4 mM EDTA, +1.2 mM ethanol 7 PIPES 4 mM ? 5 mM –970 ( 49 ), Figure 3c Dechloromonas sp. UWNR4, + 4\nmM EDTA, +1.2 mM ethanol 7 PIPES 4 mM ? 5 mM –950 ( 49 ), Figure 3d lake sediment slurry ∼7 bicarbonate 1.4 mM 0.01 mM 1 mM –6 ( 69 ), Figure 3 Phototrophic Rhodopseudomonas palustris strain\nTIE-1, + 0.2 mM citrate 7 bicarbonate 4.5 mM –21 ( 3 ), Figure 2 Rhodobacter capsulatus strain\nSB1003, +0.2 mM citrate 7 bicarbonate 0.1 mM –34 ( 3 ), Figure 4 Rhodobacter\ncapsulatus strain\nSB1003, +1 mg/L HA 7 bicarbonate 0.1 mM –50 ( 70 ), Figure 4 Rhodobacter capsulatus strain\nSB1003, +0.2 mM NTA 7 bicarbonate 0.1 mM –112 ( 70 ), Figure 4"
} | 5,829 |
34644114 | PMC8514099 | pmc | 3,772 | {
"abstract": "Tough elastomers that resist fracture at high loads are not optimum for sustaining many cycles at low loads.",
"conclusion": "Concluding remarks Soft and tough DN elastomers tagged with mechanofluorescent probes provide novel insights on fatigue crack propagation. Upon polymer chain elongation until failure, these probes turn into fluorescent moieties of high quantum yield, stability to photobleaching, and ideal for quantifying cumulative damage by sacrificial bond scission in specimens that suffer negligible damage per unit time (i.e., cycle). Hence, this combination of network design and damage quantification has enormous potential to understand fracture of soft materials under a range of complex loading configurations like multiaxial fatigue and cavitation ( 39 ). DN elastomers exhibit a trade-off between fatigue resistance and fracture toughness. While dissipating energy by sacrificial bond scission over large damage zones remains essential for resisting crack propagation at high loads, stabilizing the damage zone by mitigating the accumulation and localization of sacrificial bond scission events is critical for sustaining numerous cycles of low load. Such stable damage zone is attained in DN elastomers by stiffening the filler network (i.e., strain hardening the region ahead of the crack tip) to reduce the probability of sacrificial bond scission and promote bifurcation of the crack front. Given the similarity in composition, modulus, and bulk hysteresis of EA 0.2 EA and EA 0.5 EA, this inversion in reinforcement when transitioning from monotonic to cyclic loading is remarkable. DN elastomers exhibit outstanding fatigue thresholds. Cycling the fatigue-resistant elastomer, EA 0.5 EA, for 400,000 cycles at low applied energy release rates G ≈ G 0 ≈ 550 J m −2 leads to negligible crack growth. This cyclic fatigue threshold is substantially higher than that of conventional elastomers and intrinsic to the multiple-network architecture. However, DN elastomers suffer from strain-dependent damage under cyclic loading, developing a zone of accumulated and delocalized damage at high applied energy release rates G that is prone to sudden localization and fast crack growth. This dependence of damage on the applied load serves as experimental evidence to refine current molecular models of fracture, like that of Lake and Thomas ( 6 ), Olsen and co-workers ( 40 ), and Zhao and co-workers ( 41 ), where the contribution of damage to the fracture energy results only from scission of stretched polymer chains in a damage zone of mesh size. Last, DN elastomers are nearly elastic (i.e., negligible mechanical hysteresis) at low strains but dissipate energy by sacrificial bond scission above a critical strain. This combination of mechanical properties is characteristic of elastic dissipaters: soft materials composed of a stiff, brittle, and elastic phase embedded in a soft, stretchable, and elastic matrix ( 42 ). Thus, DN elastomers behave as molecular composites, as seminally reported by Millereau et al. ( 14 ). Additional examples of elastic dissipaters include composites of poly(dimethyl siloxane) elastomers ( 8 , 42 ) and polyacrylamide hydrogels ( 43 ). These materials dissipate energy only at high strains to prevent catastrophic failure and delocalize the stress concentration to delay crack growth over numerous cycles of low strain. Other materials, instead, dissipate energy at every strain for toughness and delocalize the strain concentration for fatigue resistance. Examples include semicrystalline poly(vinyl alcohol) hydrogels ( 44 , 45 ) and strain-crystallizable natural rubber ( 4 ). Such fundamental understanding of the mechanical properties serves to develop novel soft, tough, and durable materials for engineering applications (e.g., tyres and dampers), energy conversion and storage devices (e.g., wearable electronics, ion gels), and medicine (e.g., prosthetics). However, we advise caution when drawing analogies between DN elastomers and other soft materials because subtle differences in molecular structure and stress transfer between soft and stiff domains could change the underlying mechanisms governing mechanical fatigue and fracture toughness.",
"introduction": "INTRODUCTION Elastomers are ubiquitous in engineering applications that require large reversible deformations such as tyres, belts, dampers, and seals ( 1 ). Although toughness remains an important design consideration to prevent catastrophic failure at high loads, mechanical lifetime is often controlled by the progressive growth of an inherent flaw over numerous cycles of low load. This mechanical degradation is evaluated with durability tests based on fatigue crack propagation, where the crack growth rate of a precracked specimen is monitored over a range of cyclic deformations (or energy release rates) ( 2 , 3 ). Typically, elastomers suffer from fatigue crack propagation at low cyclic loads and exhibit cyclic fatigue thresholds G 0 \n ~ 0.05 to 0.1 kJ m −2 (at which the crack is effectively stopped) substantially below the fracture toughness G c ~ 1 to 100 kJ m −2 (as measured by crack propagation under monotonic loading) ( 4 , 5 ). A fundamental understanding of this fracture behavior would serve to mitigate overengineering, extend lifetime, and facilitate the transition toward a more sustainable elastomer economy. Fatigue crack propagation has been previously investigated in elastomers ( 3 , 6 – 8 ) and, more recently, in hydrogels ( 9 – 12 ). A comparison of the fracture behavior of these materials under monotonic and cyclic loading illustrates some vexing features of fatigue crack propagation. Under monotonic loading, precracked specimens of hydrogels based on ionically cross-linked alginate interpenetrated with covalently cross-linked polyacrylamide exhibit a fracture toughness G c ~ 10 kJ m −2 similar to that of filled elastomers ( 13 ). However, under cyclic loading, these hydrogels have a crack growth rate dc / dN ~ 100 nm cycle −1 significantly higher than that of filled elastomers dc / dN ~ 1 nm cycle −1 at the same applied energy release rate G ~ 0.1 kJ m −2 ( 7 , 10 ). These observations indicate not only that filled elastomers have a longer mechanical lifetime than hydrogels but also that the mechanisms responsible for reinforcement change when transitioning from monotonic to cyclic loading. This is the reason why energy dissipation by irreversible damage during the first cycle of deformation (i.e., so-called Mullins effect) is not necessarily correlated with fatigue resistance ( 11 , 12 ). As the subsequent cycles are usually at high frequency and low load, it is the evolution of the crack tip with sustained opening and closing that is presumably key for fatigue crack propagation. Here, we use soft and tough double-network (DN) elastomers as model materials to investigate fatigue crack propagation. These are composed of a prestretched, stiff, and continuous filler network embedded in a highly extensible, soft, and incompressible matrix network. Although their elastic properties are mainly controlled by the filler network, as in conventional filled elastomers, their fracture toughness primarily results from energy dissipation by sacrificial bond scission during stress transfer to the matrix network ( 14 , 15 ). This toughening mechanism was recently demonstrated by tagging the filler network with mechanoluminescent damage-activated probes (i.e., mechanophores), monotonically loading a precracked specimen, and visualizing sacrificial bond scission ahead of the crack tip in a time-resolved manner ( 16 ). However, under cyclic loading a similar precracked specimen suffers negligible scission of sacrificial bonds per cycle, making visualization of cumulative damage by sacrificial bond scission more valuable to understand fatigue crack propagation. Thus, we tagged the filler network of DN elastomers with mechanofluorescent probes based on π-extended anthracene-maleimide adducts. Upon polymer chain elongation until failure, these probes undergo a force-induced cycloreversion reaction that results in π-extended anthracene moieties (i.e., fluorophores) of high quantum yield, stability to photobleaching ( 17 , 18 ), and ideal for visualizing and quantifying damage by sacrificial bond scission near the crack surface of fractured specimens ( 19 ). This combination of network design and damage quantification is what ultimately provides unprecedented insights on the difference between mechanical durability and fracture toughness in elastomers.",
"discussion": "RESULTS AND DISCUSSION We synthesized two DN elastomers through sequential free radical polymerization of ethyl acrylate (EA). Detailed synthetic conditions, compositions, and mechanical properties are provided in Materials and Methods and the Supplementary Materials (fig. S1 and tables S1 to S3), but their primary difference lies in the cross-linking density of the filler network, which is 0.2 mole percent (mol %) for EA 0.2 EA and 0.5 mol % for EA 0.5 EA ( Fig. 1A ). Thus, EA 0.2 EA is tougher and more extensible than EA 0.5 EA (see onset of strain hardening and strain at break in Fig. 1B ) even if both elastomers have the same monomer composition, prestretch of the filler network, Young’s modulus ( Table 1 and Fig. 1 , inset), and negligible mechanical hysteresis under step cyclic loading until fracture (fig. S3). Note that as previously reported by Ducrot et al. ( 16 ), these DN elastomers do not dissipate energy during the first cycle of deformation because of their moderate prestretch λ 0 ≈ 1.7, relative to that of other Mullins-like soft materials like triple-network elastomers (λ 0 ≈ 2.5) ( 14 ) and osmotically swollen DN hydrogels (λ 0 ≈ 4.6) ( 11 , 20 , 21 ). However, this elastic behavior (i.e., lack of mechanical hysteresis) under uniaxial deformation is misleading because energy dissipation by sacrificial bond scission is actually important for toughening and localized in a large strain region ahead of the crack tip referred to as damage zone ( 16 ). Fig. 1. Uniaxial deformation of DN elastomers. ( A ) Schematic of DN elastomers indicating differences in cross-linking density of the filler network. ( B ) EA 0.2 EA and EA 0.5 EA have a similar modulus (inset) but a different onset of strain hardening and strain at break. Table 1. Composition and mechanical properties of DN elastomers. The volume fraction ϕ FN , prestretch λ 0 , and maximum extensibility λ m FN of the filler network, as well as the Young’s modulus E , strain at break λ break , toughness G c , and areal density of sacrificial polymer chains Σ 0 of the double-networks, are presented. \n ϕ FN \n \n λ 0 \n \n λ m FN \n \n E (MPa) \n \n λ break \n \n G c (kJ m −2 ) \n \n Σ 0 (chains m −2 ) \n \n EA 0.2 EA \n 0.19 1.74 6.70 0.98 3.51 2.70 4.8 × 10 16 \n EA 0.5 EA \n 0.22 1.67 4.89 1.05 2.74 1.97 7.2 × 10 16 Differences in mechanical properties between the two DN elastomers are subtle but important for rationally engineering fatigue-resistant elastomers. As outlined by Suo and co-workers in their seminal investigations on hydrogels, energy dissipation by sacrificial bond scission leads to toughening in monotonic loading, but accumulation of these scission events ahead of the crack tip could favor crack growth and decrease fatigue resistance ( 11 , 12 ). Given that EA 0.2 EA and EA 0.5 EA exhibit near-perfect elasticity until failure, we deemed them good model materials to (i) investigate the role of cross-linking density of the filler network on fatigue crack propagation and (ii) build a multiscale picture of fracture under cyclic and monotonic loading using mechanofluorescent probes to reconstruct three-dimensional (3D) maps of cumulative damage by sacrificial bond scission. Trade-off between durability and toughness in DN elastomers We evaluated the fatigue crack propagation of the two DN elastomers with durability tests similar to those pioneered for vulcanized elastomers ( 22 ) and tough hydrogels ( 12 ). Precracked pure-shear specimens were subjected to cyclic loading at a frequency of 10 Hz and an applied energy release rate G, and the evolution of the crack length c monitored with the applied number of cycles N ( Fig. 2A ). These durability tests on specimens of pure-shear geometry lead to an applied energy release rate G independent of crack length c ( 22 , 23 ), enabling optimum use of mechanofluorescent probes by applying a range of energy release rates G to the same tagged specimen in a successive and stepwise fashion (see detailed estimates of G from λ in the Supplementary Materials, pure-shear loading curves in fig. S4, and loading conditions in table S5). Results are presented in Fig. 2B (and fig. S5A) and indicate that the crack length c grows discontinuously (i.e., nonsteadily) with the applied number of cycles N , meaning that there is a distribution of crack growth rates dc / dN for each applied G (see example for λ = 1.47 in Fig. 2B , inset). Average crack growth rates dc / dN and 95% confidence intervals are summarized in Fig. 2C and reveal a power-law (i.e., Paris’ law) behavior dc / dN ~ G 3.4 for DN elastomers like that of natural rubber ( 4 ), SBR ( 5 ), and DN hydrogels ( 11 ). Figure 2C also illustrates that two crack growth regimes, referred to as fast and slow due to their distinct dc / dN , coexist near the onset of catastrophic failure (see filled and empty squares in Fig. 2C and videos of fatigue fracture tests in movies S1 and S2). These are observed at G ≈ G c for EA 0.5 EA but at G < G c for EA 0.2 EA (compare dashed lines at G c with empty squares at G at break in Fig. 2C ), indicating that subtle changes in the cross-linking density of the filler network have major consequences in the fatigue crack propagation of DN elastomers. EA 0.5 EA is more fatigue resistant than EA 0.2 EA because it exhibits a lower crack growth rate dc / dN at the same applied energy release rate G, but it is also more brittle because it undergoes catastrophic crack propagation under monotonic loading at a lower critical energy release rate G c . This trade-off between durability and toughness when stiffening the filler network is remarkable when compared to previous reports by Suo and co-workers on DN hydrogels with bulk energy dissipation, where stiffening the matrix network results in both a lower fatigue threshold and fracture toughness ( 11 , 12 ). The difference might result from entanglements and friction between the polymer chains upon deformation, which are more important in DN elastomers than in DN hydrogels even if both materials are composed of a filler network that acts as a topological constraint to the matrix network ( 24 ). Thus, both the filler and matrix networks play a role on fracture under monotonic and cyclic loading, but this is challenging to understand solely by bulk mechanical testing, particularly in DN elastomers without hysteresis under step cyclic loading until failure. Fig. 2. Fatigue crack propagation of DN elastomers. ( A ) Schematic of a pure shear fatigue fracture test. The pure shear geometry is ideal for studying fracture because the applied energy release rate G(λ) is independent of crack length c ( 22 , 23 ). ( B ) Crack growth of EA 0.5 EA is discontinuous and dependent on the applied stretch λ and energy release rate G(λ). Inset: Representative distribution of crack growth rates dc / dN for λ = 1.47 or G(λ) = 1642 J m −2 . A similar fracture behavior is observed for EA 0.2 EA (fig. S5). ( C ) Fatigue crack propagation of EA 0.2 EA and EA 0.5 EA. EA 0.5 EA is more fatigue resistant than EA 0.2 EA despite its lower toughness G c (dashed vertical lines). Open (☐) and closed (■) symbols, respectively, correspond to fast and slow crack growth regimes at the same applied energy release rate G(λ). Error bars represent 95% confidence intervals. Figure 2C can also be used to estimate the cyclic fatigue threshold G 0 from the energy release rate G at which the crack does not grow. Although the cyclic fatigue threshold G 0 is theoretically well defined, experimentally, there is always a concern about the number of cycles used to detect crack growth. Here, we had a spatial resolution of 250 μm pixel −1 while monitoring the crack length c , meaning that the minimum crack growth rate dc / dN that we could measure over a basis of 400,000 cycles is of order 1 nm cycle −1 . This threshold condition is more rigorous (and time consuming) than the 100 nm cycle −1 used for tough hydrogels ( 10 , 11 ), but still one order of magnitude below the 0.1 nm cycle −1 used for commercial metallic alloys ( 25 ). As EA 0.2 EA exhibits crack growth rates dc / dN ~ 10 nm cycle −1 even at low G ~ 441 J m −2 , we only determined the cyclic fatigue threshold G 0 \n ~ 550 J m −2 of EA 0.5 EA. This G 0 is similar to that of composite elastomers based on a hard elastomeric lattice embedded in a soft matrix ( 8 ) and is remarkable when compared to that of conventional elastomers G 0 \n ~ 50 J m −2 ( 26 – 28 ) and single-network EA 0.5 (we did not measure G 0 for single-network EA 0.5 , but we expect it to be below its fracture toughness G c \n ~ 150 J m −2 ). Thus, sacrificial bonds provide elastomers with both improved cyclic fatigue thresholds and fracture toughness. This reinforcement under both cyclic and monotonic loading is analogous to that reported by Suo and co-workers on tough hydrogels ( 10 , 11 , 13 , 20 ) and intrinsic to the DN architecture. However, the reinforcement mechanism is different. While energy dissipation by sacrificial bond scission is essential for toughening, its role on fatigue resistance remains unclear. A previous model on fatigue crack propagation in glassy polymers, where toughening results from damage by plastic flow (i.e., crazing), suggests that formation of a crazed region ahead of the crack tip favors crack growth under cyclic loading ( 29 ). However, it is unclear whether this model also describes rubbery DN elastomers ( T g = −15°C) that develop a damage zone before crack growth. To gain insights into the role of molecular damage on fatigue crack propagation, we reconstructed 3D maps of damage by sacrificial bond scission in the fractured DN elastomers. Visualization and quantification of damage by sacrificial bond scission Postmortem fluorescent maps of the fracture surface enable visualization and quantification of damage accumulated ahead of the crack tip both before and during crack growth (see example of fluorescent image in Fig. 3B ). Details of the methodology are provided in Materials and Methods, in the Supplementary Materials, and in a recent contribution from our group ( 19 ). The general idea is based on using the fluorescence of a 3D slab to quantify the per-voxel fraction ϕ xyz of π-extended anthracene moieties resulting from a force-induced cycloreversion reaction in the filler network ( Fig. 3A ). Given that the probability to break a polymer chain diverges near its limiting extensibility, we considered ϕ xyz as representative of the fraction of bond scission events in the filler network [this hypothesis has been verified in previous work ( 19 ) and theoretically described by Dubach et al. ( 30 )]. Averaging along the crack length x and specimen thickness z (see Fig. 3B ) results in profiles of sacrificial bond scission as a function of the distance y away from the crack surface φ y = ( 1 wt ) ∫ 0 t ∫ 0 w φ xyz dxdz (1) where w is the width (1000 μm) and t is the thickness (160 μm) of the 3D fluorescent slab. Alternatively, integrating ϕ xyz along y yields spatial maps of local damage per unit area of crack propagation Σ xz ¯ , a dimensionless number representing the excess number of broken polymer layers in the filler network per unit area of crack growth Σ xz ¯ = 2 ( ν x Σ 0 ) ∫ 0 L φ xyz dy (2) Fig. 3. Postmortem mapping of damage by sacrificial bond scission. ( A ) Mechanophores based on π-extended anthracene-maleimide adducts yield π-extended anthracene moieties upon force ( f )-induced cycloreversion. ( B ) Fluorescent microscopy in a fractured specimen reveals high internal damage near the crack surface and around crack bifurcations. Damage by polymer chain scission is quantified over a distance L = 300 μm from the crack surface. Energy release rates G i (λ i ) were applied in a subsequent and stepwise fashion to optimize the use of tagged and probe-expensive pure-shear specimens. Photo credit: Gabriel E. Sanoja, ESPCI Paris. Here, ν x and Σ 0 are the volumetric and areal density of elastic polymer chains, so that the ratio Σ 0 /ν x represents the mesh size of the filler network. These properties can be determined from the modulus using a procedure detailed in the Supplementary Materials (tables S2 and S4) and in the work of Millereau et al. ( 14 ). We estimate the damage Σ xz ¯ by integrating over a distance y from the crack surface L ≈ 300 μm at which ϕ xyz ≈ 0, so that we quantify most of the fluorescence and mitigate artefacts from optical vignetting. This distance L is significantly larger than the mesh size of the filler network, Σ 0 /ν x ≈ 10 nm, indicating that damage by sacrificial bond scission extends rather far from the crack surface and into the bulk. Equation 1 serves to define a dimensionless number representing the average damage per unit area of crack propagation Σ ¯ Σ ¯ = Σ Σ 0 = 2 ( ν x Σ 0 ) ∫ 0 L φ y dy (3) where Σ is the average number of broken polymer chains in the filler network per unit area of crack growth. Note that Σ ¯ is simply the average of Σ xz ¯ along the crack length x and specimen thickness z and is physically based on the Lake and Thomas molecular theory of fracture of unfilled single-network elastomers ( 26 ), where the minimum energy dissipated upon creation of an interface Γ 0 is that required to break a monolayer of stretched elastic polymer chains Γ 0 = N x U b Σ 0 (4) Here, N x is the number of monomers per elastic polymer chain, and U b is the energy stored per C─C bond along the stretched polymer backbone [traditionally, the energy of a C─C bond ≈ 350 kJ mol −1 but recently revised by Craig and co-workers to ≈60 kJ mol −1 based on a probabilistic view of bond scission ( 31 )]. Hence, Σ ¯ represents the excess number of broken layers in the filler network per unit area of crack growth and is related to the energy dissipated by covalent bond scission upon fracture Γ d Γ d = Γ 0 Σ ¯ = N x U b Σ 0 Σ ¯ (5) Effect of cross-linking density of the filler network on the accumulation and localization of damage during fatigue crack propagation of DN elastomers The visualization and quantification of damage by sacrificial bond scission in fractured specimens is a novel tool to understand the role of the cross-linking density of the filler network on the crack growth rate of DN elastomers subject to cyclic loading. However, interpreting spatially resolved information on damage is challenging because of the complex relationship between the applied energy release rate G (or strain λ) and the resulting crack growth rate dc / dN and damage per unit area of crack propagation Σ ¯ . As illustrated in Fig. 4 , the tougher and more extensible EA 0.2 EA suffers more damage than EA 0.5 EA when fractured under cyclic loading at the same applied energy release rate G ≈ 1.0 kJ m −2 . The damage maps in Fig. 4A are rather homogeneous along the crack length x and specimen thickness z , with some inhomogeneous artefacts attributed to image stitching and optical vignetting (see details in the Supplementary Materials). This observation is consistent with the featureless side view of the fracture surface (see images for the plane z = 100 μm in Fig. 4B ) and the monotonically decreasing y profiles of damage from the crack surface ϕ y ( Fig. 4C ). EA 0.2 EA exhibits a considerably higher dc / dN than EA 0.5 EA at the same G ≈ 1.0 kJ m −2 (compare fatigue curves in Fig. 2C ), meaning that less cycles N result in more damage Σ ¯ for the same unit area of crack growth. Fig. 4. Damage of DN elastomers fractured under cyclic loading at G ≈ 1.0 kJ m −2 . ( A ) Spatial maps of local damage per unit area of crack propagation, Σ xz ¯ , reveal that EA 0.2 EA is more damaged than EA 0.5 EA. ( B ) Representative images at z = 100 μm [white dashed line in (A)] illustrate that featureless fracture surfaces are associated with homogeneous damage maps along the crack length. Photo credit: Gabriel E. Sanoja, ESPCI Paris. ( C ) Profiles of sacrificial bond scission ϕ y reveal that EA 0.2 EA has a more concentrated and localized damage zone than EA 0.5 EA. Inset: Enlarged profile of sacrificial bond scission ϕ y of EA 0.5 EA ( D ) Schemes of damage zones of DN elastomers undergoing fatigue crack propagation. EA 0.5 EA has reduced (low ϕ y and Σ ¯ ) and delocalized (high L 1/2 ) damage ahead of the crack tip and, hence, can sustain many cycles of low deformation before crack growth. Figure 4 also illustrates changes in damage localization with the cross-linking density of the filler network. Although it is difficult to unambiguously define damage localization, we estimated the thickness of the surface layer over which the DN elastomers are damaged from the distance over which ϕ y decreases to half its maximum value, L 1/2 . EA 0.5 EA has a larger L 1/2 than EA 0.2 EA (compare y profiles of ϕ y in Fig. 4C ), indicating that stiffening the filler network results in a more delocalized and gradual decrease of damage ahead of the crack tip during fatigue crack propagation (see schemes of damage zones in Fig. 4D ). Thus, the cross-linking density of the filler network affects both the accumulation Σ ¯ and localization L 1/2 of damage ahead of the crack tip. We attribute this effect to differences in the large strain behavior between the two DN elastomers. Although EA 0.2 EA and EA 0.5 EA require somewhat similar bulk strains λ to attain the same applied energy release rate G (see Table 2 ), the local strain of EA 0.2 EA ahead of the crack tip (i.e., inside the damage zone) must be significantly larger than that of EA 0.5 EA because of its higher extensibility (we did not measure the strain at the crack tip, but can infer the qualitative strain fields of Fig. 4D from images of crack blunting in fig. S10). Large strains ahead of the crack tip, as well as those actually experienced by the filler network relative to its limiting extensibility, increase the probability of damage by sacrificial bond scission, promote stress transfer to the matrix network, and favor crack growth. Hence, EA 0.2 EA is more damaged than EA 0.5 EA per loading cycle, meaning that more Lake and Thomas monolayers of polymer chains break in the filler network of the more extensible DN elastomer upon crack growth. Table 2. Loading conditions and damage properties of DN elastomers fractured under cyclic loading at G ≈ 1.0 kJm −2 . The applied stretch λ, energy release rate G, crack growth rate dc / dN , average damage per unit area of crack propagation Σ ¯ , and the damage penetration length L 1/2 are presented. \n λ \n \n G(J m −2 ) \n \n dc / dN × 10 5 \n \n (mm cycle −1 ) \n \n \n \n Σ ¯ \n \n \n \n L 1/2 (μm) \n \n EA 0.2 EA \n 1.45 942 11.4 68 75 \n EA 0.5 EA \n 1.33 932 1.27 15 127 As insightfully noted by a reviewer, this assumption of strain-mediated bond scission and activation of the π-extended anthracene-maleimide adduct is different from that made by Chen et al. ( 32 ), where the force-mediated ring-opening and activation of spyropiran was used to quantify and map the stress of analogous multiple-network elastomers with λ 0 ≈ 2.5 and demonstrate that stiffening the filler network increases the stress ahead of the crack tip upon quasi-static loading at the same applied energy release rate G. In reality, neither assumption is strictly valid because bond scission is strongly influenced by thermal fluctuations, meaning that it is more accurately described as a barrier crossing (i.e., activated) process with an energy landscape biased by an external force ( 33 , 34 ). Polymer chains experience a range of forces and conformations upon bulk deformation so that their scission is mediated not only by strain and stress but also by temperature and bond dissociation energy. Hence, it is possible that bond scission is primarily mediated by stress when polymer chains are stretched in response to large quasi-static loads, in agreement with Chen et al. ( 32 ), but by strain amplitude when polymer chains are cycled in response to low cyclic loads and friction may be more important for energy dissipation. This change in molecular mechanism when transitioning from quasi-static to cyclic loading explains why the strain-hardened elastomer, EA 0.5 EA, is less damaged and more fatigue resistant than its tougher counterpart, EA 0.2 EA, even if it is cycled at a higher stress (and lower strain) amplitude. In addition, it highlights that the average damage by sacrificial bond scission Σ ¯ does not directly reflect the total amount of energy dissipated upon crack growth or, namely, that the applied energy release rate G governs the nonlinear stress and stretch fields ahead of the crack tip (i.e., like the stress intensity factor in linear elastic fracture mechanics or the J integral in nonlinear elastic crack tip solutions) but not the energy dissipated upon crack growth. A more detailed discussion on how to interpret the applied energy release rate G in the context of fracture of soft materials is provided by Long et al. ( 35 ). To better understand the role of bond scission on fatigue crack propagation, we systematically quantified the damage of the two DN elastomers across a range of applied energy release rates G. Stable damage zone for durability Beyond the structure-property relationships of DN elastomers, the visualization and quantification of damage by sacrificial bond scission also serves to build a multiscale picture of fatigue fracture. As illustrated in Fig. 5 , the damage per unit area of crack growth Σ ¯ increases with the applied energy release rate G with a power law Σ ¯ \n ~ G 2.1 . This scaling exponent is lower than that observed for the crack growth rate, dc / dN ~ G 3.4 ( Fig. 2C ), and indicates that damaging DN elastomers results in a more significant increase in dc / dN . Fracture under cyclic loading at high applied G requires less cycles N and results in more damage per unit area of crack growth Σ ¯ . Thus, numerous cycles at low deformation are less damaging than few cycles at large deformation or, namely, more energy is dissipated by sacrificial bond scission when transitioning from cyclic to monotonic loading. Fig. 5. Damage of DN elastomers fractured under cyclic loading over a range of G. EA 0.2 EA is more damaged than EA 0.5 EA over the entire range of applied energy release rates G(λ). In addition, both EA 0.2 EA and EA 0.5 EA are more damaged at high energy release rates G, although less cycles N are required to attain the same unit area of crack growth. Open (☐) and closed (■) symbols, respectively, correspond to fast and slow crack growth regimes. Dashed vertical lines indicate the fracture toughness G c . Error bars represent 95% confidence intervals. Details of the catastrophic failure under cyclic loading are also revealed by inspecting the postmortem damage maps of the two coexisting regimes of crack growth at high applied G (see filled and empty squares in Figs. 2C and 5 ). DN elastomers are more damaged in the slow than in the fast crack growth regime (compare fast and slow damage maps of either EA 0.5 EA cycled at G ≈ 2.1 kJ m −2 in Fig. 6A or of EA 0.2 EA cycled at G ≈ 1.2 kJ m −2 in fig. S6), having maps with heterogeneities (i.e., hotspots) that indicate damage accumulation at or near crack bifurcations (see representative images for the plane z = 100 μm in Fig. 6B ). These damage heterogeneities are sometimes reflected in the y profiles of ϕ y but, sometimes, lost in the xz average ( Eq. 1 ) because of insufficient damage accumulation in the crack bifurcations (compare y profiles in Fig. 6C ). Nonetheless, the y profiles of ϕ y can be used to estimate an average Σ ¯ and L 1/2 and better understand fatigue fracture ( Table 3 ). Accumulation of damage over a large region ahead of many bifurcations (high Σ ¯ and L 1/2 ) is associated with a low dc / dN , whereas localization of damage over a small region ahead of few bifurcations (low Σ ¯ and L 1/2 ) is associated with a high dc / dN (see schemes of damage zones in Fig. 6D ). This interplay between accumulation and localization of damage is also evidenced in the fracture surfaces [see scanning electron microscopy (SEM) images in fig. S11], which exhibit many bifurcation events in both regimes but are rougher when the crack grows slowly. Hence, DN elastomers experience a fluctuating local energy release rate G local ahead of the crack tip that depends not only on the bulk strain but also on the mesoscopic bifurcation of the crack front and on the probability of sacrificial bond scission inside the damage zone. A fluctuating G local is consistent with the discontinuous crack growth (i.e., slowdown at λ = 1.41 in Fig. 2B and speedup at λ = 1.31 in fig. S5) and the catastrophic dc / dN observed at G < G c for EA 0.2 EA ( Fig. 2C ) during fatigue crack propagation. Fig. 6. Damage of EA 0.5 EA fractured under cyclic loading at G ≈ 2.1 kJm −2 . ( A ) Spatial maps of local damage per unit area of crack propagation Σ xz ¯ reveal that EA 0.5 EA is more damaged in the slow than in the fast crack growth regime. ( B ) Representative images at z = 100 μm [white dashed line in (A)] illustrate that crack bifurcations are associated with hotspots in damage maps. Photo credit: Gabriel E. Sanoja, ESPCI Paris. ( C ) Profiles of sacrificial bond scission ϕ y reveal that EA 0.5 EA undergoes catastrophic crack propagation because of sudden localization of damage ahead of the crack tip. ( D ) Schematics of the damage zone of EA 0.5 EA in the slow and fast crack growth regimes. Fast crack growth is associated with a highly localized (low L 1/2 ) and reduced damage (low ϕ y and Σ ¯ ) ahead of the crack tip. Table 3. Loading conditions and damage properties of DN elastomers fractured under cyclic loading at high applied energy release rates G. The applied stretch λ, energy release rate G, crack growth rate dc / dN , average damage per unit area of crack propagation Σ ¯ , and the damage penetration length L 1/2 are presented. \n λ \n \n G (J m −2 ) \n \n Regime \n \n dc / dN × 10 5 \n \n (mm cycle −1 ) \n \n \n \n Σ ¯ \n \n \n \n L 1/2 (μm) \n \n EA 0.2 EA \n 1.49 1205 Fast 144 52 52 Slow 6.40 81 69 \n EA 0.5 EA \n 1.62 2069 Fast 161 5 58 Slow 20.0 77 100 A multiscale picture of fatigue crack propagation results from damage quantification over a range of applied G and is based on two stages: formation of a damage zone and crack growth. This mechanism is similar to that postulated by Williams ( 29 ) for glassy polymers like poly(methyl methacrylate), where cyclic loading leads to a plastic zone before fracture. At low applied G, the stress is primarily sustained by the filler network, there is random scission of sacrificial bonds ahead of the crack tip, and crack growth is controlled by the maximum extensibility of the matrix network. This matrix network is the same for both DN elastomers, and the more strain hardened elastomer, EA 0.5 EA, has the lower probability of crack growth. At high applied G, an important fraction of the stress is transferred from the filler to the matrix network ( 32 ), and sacrificial bond scission can suddenly localize in a zone of reduced Σ ¯ and localized L 1/2 damage that leads to fast crack growth. This transition is similar to that observed by Millereau et al. ( 14 ) at the yield point of multiple-network elastomers of lower volume fraction of filler network ϕ FN ≈ 0.03, where damage by sacrificial bond scission localizes at the fronts of a necked region and is more likely to occur in the elastomer with the lower yield stress, EA 0.2 EA [we could not measure the yield stress of DN elastomers because it is lower than the stress at break, but we know it is lower in EA 0.2 EA from Millereau et al. ( 14 ) and Chen et al. ( 36 )]. Hence, EA 0.5 EA has a more stable damage zone, a lower probability of crack growth, and a better fatigue resistance than EA 0.2 EA. A more stable damage zone also increases the probability of crack growth through a bifurcated pathway. Upon bifurcation of the crack front, the applied G decreases to G local (i.e., the strain field becomes more homogeneous), and damage delocalizes ahead of coexisting crack fronts. This delocalization enables significant damage accumulation before crack growth and leads to more marked differences in Σ ¯ and L 1/2 between the slow and fast crack growth regimes in the more fatigue-resistant elastomer, EA 0.5 EA ( Table 3 ). As a result, damage by sacrificial bond scission and bifurcations of the crack front reinforce each other (i.e., integrate a positive feedback loop) to control the macroscopic crack growth rate dc / dN of DN elastomers. These mechanisms operate at distinct length scales and depend on the areal density of sacrificial bonds, as well as on the applied load. Stiffening the filler network strain hardens the region ahead of the crack tip, decreases the probability of sacrificial bond scission, and delocalizes damage over a large region of coexisting bifurcations. This damage zone is more stable to cyclic loading but dissipates less energy by sacrificial bond scission. Hence, fatigue-resistant elastomers capable of sustaining many cycles at low loads are not necessarily optimum in terms of energy dissipation and toughness. We explored this issue by quantifying damage in DN elastomers fractured under monotonic loading. Energy dissipation for toughness To assess the ability of DN elastomers to dissipate energy, we monitored the crack length c of a precracked pure-shear specimen subjected to monotonic loading at a fixed stretch rate until fracture (see crack growth curves in fig. 7B and videos of pure-shear fracture tests in movies S3 and S4). As illustrated in Fig. 7B , catastrophic propagation is preceded by a regime of slow crack growth that we refer to as initiation and restrict to Δ c ≈ 1 mm. Under this constraint, the onset of crack initiation is at a stretch λ i ≈ 1.4 for EA 0.5 EA and λ i ≈ 1.7 for EA 0.2 EA. Fig. 7. Fracture of DN elastomers under monotonic loading. ( A ) Schematics of a pure-shear fracture test. ( B ) Crack growth and loading curve of EA 0.5 EA. The crack growth transitions from the initiation to the propagation regime at the critical stretch λ c (dashed vertical line). Like fracture under cyclic loading, the cross-linking density of the filler network has a significant effect on the accumulation and localization of damage during monotonic fracture. EA 0.5 EA exhibits more heterogeneous damage maps than EA 0.2 EA irrespective of whether the crack grows in the initiation or propagation regime (compare damage maps in Fig. 8A , roughness of fracture surfaces in fig. S12, and fluorescent images for z = 100 μm in fig. S13), indicating that it is more prone to damage delocalization through bifurcation of the crack front. The y profiles of ϕ y also reveal that EA 0.5 EA accumulates Σ ¯ and delocalizes L 1/2 more damage than EA 0.2 EA during crack initiation but not during crack propagation (see Fig. 8, B and C , and Table 4 ). This change in the damage zone is consistent with the trade-off between fatigue resistance and toughness and in agreement with molecular models that describe the toughness of DN hydrogels and glassy polymers ( 37 , 38 ). DN elastomers capable of accumulating and delocalizing damage at G < G c should be more fatigue resistant, whereas those that dissipate more energy at G ≈ G c should be tougher. The energy dissipated by sacrificial bond scission during monotonic crack propagation, Γ d , can be estimated from Eq. 5 and is roughly ≈5% of the fracture energy G c , suggesting that other molecular processes such as polymer chain friction and stress transfer to the matrix network are important for resisting fracture (i.e., toughening) at high applied energy release rates G when nonlinear deformations and the damage zone ahead of the crack tip are most prominent. However, these processes likely favor fatigue crack propagation at low applied energy release rates G when energy dissipation by polymer chain friction might become more significant and the damage zone ahead of the crack tip is controlled by the local strain amplitude. Fig. 8. Damage of DN elastomers fractured under monotonic loading. ( A ) Spatial maps of Σ xz ¯ reveal that EA 0.5 EA exhibits a more heterogeneous damage than EA 0.2 EA in the initiation and propagation regimes. Profiles of ϕ y during crack ( B ) initiation and ( C ) propagation illustrate that the length scale over which bonds are broken depends on whether the energy supplied during loading is above or below G c . Table 4. Damage properties of DN elastomers fractured under monotonic loading. The average damage per unit area of crack propagation Σ ¯ and the penetration length L 1/2 are presented. \n Regime \n \n \n \n Σ ¯ \n \n \n \n L 1/2 (μm) \n \n EA 0.2 EA \n Initiation 72 57 Propagation 141 129 \n EA 0.5 EA \n Initiation 153 223 Propagation 95 32 Quantification of damage by sacrificial bond scission serves to rationally engineer tough and fatigue-resistant elastomers. Under monotonic loading, softening the filler network toughens DN elastomers because more energy is dissipated at the critical energy release rate G c , whereas stiffening the filler network improves the fatigue resistance of DN elastomers because the damage zone is more stable to cyclic loading at subcritical energy release rates G. This is a clear demonstration of the role of network architecture on the damage zone, as well as on the trade-off between mechanical durability and fracture toughness. Concluding remarks Soft and tough DN elastomers tagged with mechanofluorescent probes provide novel insights on fatigue crack propagation. Upon polymer chain elongation until failure, these probes turn into fluorescent moieties of high quantum yield, stability to photobleaching, and ideal for quantifying cumulative damage by sacrificial bond scission in specimens that suffer negligible damage per unit time (i.e., cycle). Hence, this combination of network design and damage quantification has enormous potential to understand fracture of soft materials under a range of complex loading configurations like multiaxial fatigue and cavitation ( 39 ). DN elastomers exhibit a trade-off between fatigue resistance and fracture toughness. While dissipating energy by sacrificial bond scission over large damage zones remains essential for resisting crack propagation at high loads, stabilizing the damage zone by mitigating the accumulation and localization of sacrificial bond scission events is critical for sustaining numerous cycles of low load. Such stable damage zone is attained in DN elastomers by stiffening the filler network (i.e., strain hardening the region ahead of the crack tip) to reduce the probability of sacrificial bond scission and promote bifurcation of the crack front. Given the similarity in composition, modulus, and bulk hysteresis of EA 0.2 EA and EA 0.5 EA, this inversion in reinforcement when transitioning from monotonic to cyclic loading is remarkable. DN elastomers exhibit outstanding fatigue thresholds. Cycling the fatigue-resistant elastomer, EA 0.5 EA, for 400,000 cycles at low applied energy release rates G ≈ G 0 ≈ 550 J m −2 leads to negligible crack growth. This cyclic fatigue threshold is substantially higher than that of conventional elastomers and intrinsic to the multiple-network architecture. However, DN elastomers suffer from strain-dependent damage under cyclic loading, developing a zone of accumulated and delocalized damage at high applied energy release rates G that is prone to sudden localization and fast crack growth. This dependence of damage on the applied load serves as experimental evidence to refine current molecular models of fracture, like that of Lake and Thomas ( 6 ), Olsen and co-workers ( 40 ), and Zhao and co-workers ( 41 ), where the contribution of damage to the fracture energy results only from scission of stretched polymer chains in a damage zone of mesh size. Last, DN elastomers are nearly elastic (i.e., negligible mechanical hysteresis) at low strains but dissipate energy by sacrificial bond scission above a critical strain. This combination of mechanical properties is characteristic of elastic dissipaters: soft materials composed of a stiff, brittle, and elastic phase embedded in a soft, stretchable, and elastic matrix ( 42 ). Thus, DN elastomers behave as molecular composites, as seminally reported by Millereau et al. ( 14 ). Additional examples of elastic dissipaters include composites of poly(dimethyl siloxane) elastomers ( 8 , 42 ) and polyacrylamide hydrogels ( 43 ). These materials dissipate energy only at high strains to prevent catastrophic failure and delocalize the stress concentration to delay crack growth over numerous cycles of low strain. Other materials, instead, dissipate energy at every strain for toughness and delocalize the strain concentration for fatigue resistance. Examples include semicrystalline poly(vinyl alcohol) hydrogels ( 44 , 45 ) and strain-crystallizable natural rubber ( 4 ). Such fundamental understanding of the mechanical properties serves to develop novel soft, tough, and durable materials for engineering applications (e.g., tyres and dampers), energy conversion and storage devices (e.g., wearable electronics, ion gels), and medicine (e.g., prosthetics). However, we advise caution when drawing analogies between DN elastomers and other soft materials because subtle differences in molecular structure and stress transfer between soft and stiff domains could change the underlying mechanisms governing mechanical fatigue and fracture toughness."
} | 11,577 |
36574684 | PMC9910456 | pmc | 3,773 | {
"abstract": "Significance Unlike dendrites on traditional textured wetting (hydrophilic) and nonwetting (hydrophobic) surfaces, which are sharp, pointy, and branching, dendrites on state-of-the-art micro/nanostructured oil-impregnated surfaces are thick and lumpy without pattern. The unique dendrite morphology is attributed to oil wicking due to dendrite growth from the vapor phase. The presence of dendrites on the outer shell of frozen droplets causes the oil in the wetting ridge to migrate into the porous dendritic network by creating a capillary pressure imbalance between the surface texture and the dendrites. By capturing the effects of oil chemistry, oil viscosity, and wetting ridge volume on dendrite morphology and oil depletion rate using a regime map, this work informs the rational design of depletion-resistant oil-impregnated ice-repellent surfaces.",
"discussion": "Results and Discussion In this study, we used hydrophilic, superhydrophobic, and Krytox oil-impregnated aluminum surfaces (SLIPS). Sandblasting and boehmitization ( 29 , 30 ) were used to create micro- and nano-scale roughness on the aluminum surfaces (scanning electron microscopy images, SI Appendix , Fig. S1 A – D ). The fabrication protocol is discussed in Materials and Methods . Furthermore, the design considerations that are undertaken in fabricating the test samples are discussed in SI Appendix , Text S1 ( 20 , 31 – 35 ). The surface wettability is characterized by measuring the contact angle (equilibrium, advancing, and receding) and contact angle hysteresis. These measurements are provided in SI Appendix Fig. S1 E and Text S2 ( 18 , 20 , 36 – 39 ) and summarized in SI Appendix , Table S1 . In a typical experiment, a millimeter-sized water droplet was deposited on a substrate ( SI Appendix , Fig. S1 F ). The substrate temperature was lowered below the freezing point by turning on a thermoelectric cold stage to initiate ice crystal nucleation and growth (icing/frosting). Irrespective of the surface wettability, droplets freeze in four distinct stages: subcooling, recalescence, main-stage freezing, and dendrite growth ( Fig. 1 and SI Appendix , Figs. S2 and S3 ) ( 40 , 41 ). Fig. 1. Dendrite morphology. Freezing of a water droplet on ( A ) hydrophilic, ( B ) superhydrophobic, and ( C-E ) oil-impregnated surfaces. The oils used for impregnation are ( C ) Krytox oil, ( D ) silicone oil, and ( E ) hydroxy terminated Poly(dimethylsiloxane) (hydroxy-PDMS) oil. Irrespective of surface wettability, a water droplet freezes in four stages: subcooling, recalescence, main-stage freezing, and dendrite growth. Unlike the sharp, pointy, and branching dendrites on hydrophilic and superhydrophobic surfaces, the dendrites on oil-impregnated surfaces are thick and lumpy without pattern. The first step in the freezing process involved subcooling wherein the droplet temperature was lowered below the freezing point (first column, Fig. 1 ) ( 42 ). Subcooling was followed by the rapid freezing of the outer shell of the droplet (~few milliseconds) while the inside of the droplet was still liquid water in a process that is referred to as recalescence (second column, Fig. 1 ) ( 42 – 44 ). Main-stage freezing (third column, Fig. 1 ) followed recalescence where an ice-water front that formed in the basal plane propagated vertically upward ( 42 ). The volumetric expansion of the droplet while undergoing phase transition (liquid-to-solid phase change) results in a pointy tip at the apex of the fully frozen droplet ( 40 , 43 , 45 – 50 ). Detailed discussion on the stages of freezing is provided in SI Appendix , Text S3 . Continued cooling after main-stage freezing caused ice dendrites to grow directly from the vapor phase (fourth column, Fig. 1 ) ( 51 ). Prior investigations have shown that the radially growing dendrites are sharp, pointy, and branching like a Christmas tree on traditional hydrophilic ( Fig. 1 A ) and superhydrophobic ( Fig. 1 B ) surfaces ( 25 , 43 ). The dendrite morphology, however, changed drastically and became thick and lumpy without pattern when the micro/nanostructured surface was impregnated with perfluorinated Krytox oil ( Fig. 1 C ), silicone oil ( Fig. 1 D ), or hydroxy terminated Poly(dimethylsiloxane) (hydroxy-PDMS) oil ( Fig. 1 E ) ( 52 ). Movie S1 shows the four stages of freezing on surfaces with different wettability. We hypothesize that the unique dendrite morphology shown in Fig. 1 C – E is due to the presence of oil within the porous structures of the textured surface. The additional diffusion barrier (mass transfer resistance) created by the wrapping layer coupled with the temporal and spatial nonuniformity of the encapsulating oil layer favors localized dendrite growth that prohibits the dendrites from forming branches, resulting in thick and lumpy ice dendrites without pattern ( SI Appendix , Fig. S4 and Text S4 ) ( 26 , 37 – 39 , 53 , 54 ). We attribute the unique dendrite morphology on oil-impregnated surfaces to oil wicking that is initiated by dendrite growth ( 55 – 57 ). The dendrites, which have a smaller characteristic length than the micro/nanostructures on the surface, generally generate larger capillary pressure than the surface texture. This pressure imbalance causes oil to wick from the surface into the porous dendritic network, resulting in wetting ridge migration and oil loss ( SI Appendix , Fig. S5 and Text S5 ). The oil wicked into the dendrites (arrows, Fig. 2 A – C ) encapsulates the outer shell of the frozen droplet ( 25 , 58 ). Our measurements show that Krytox oil, silicone oil, and hydroxy-PDMS favor oil wicking by presenting an energetically favorable condition for the initial spreading of oil on water ( SI Appendix , Table S2 ). This spreading of oil on water results in a ≈20 to 50 nm thick nonuniform wrapping oil layer ( 26 , 39 ). The time-lapse images in Fig. 2 A – C and Movies S2 and S3 show that wetting ridge migrates and oil propagates through the dendrites when dendrites start to grow from the vapor phase on the outer shell of frozen droplets. The oil wicking dynamics on dendrites is an interesting topic that should be pursued in future studies. Fig. 2. Oil wicking and fractal analysis. ( A – C ) Time-lapse images showing oil wicking from the textured surface into the dendritic porous network (wetting ridge migration). ( D ) Fractal dimension as a function of time for a superhydrophobic surface. The dendrites reach a steady-state growth pattern when further dendrite growth is inhibited by the warm surrounding air. ( E and F ) Representative images taken during transient (t = 60 s) and steady-state (t = 600 s) growth in D. After allowing the wetting ridge to fully migrate ( 58 ) and the water droplet to freeze, the droplet was melted by setting the temperature on the thermoelectric stage to room temperature. Time-lapse images of the melting process show that the oil trapped within the dendritic pores was released to reconstruct the wetting ridge to its near original size ( SI Appendix , Fig. S6 and Text S5 , and Movie S4 ), which shows that the oil was not consumed during freezing. Its presence within the dendritic pores, however, affects dendrite growth, resulting in a unique dendrite morphology. We used fractal analysis ( 59 , 60 ) to quantify the shape complexity of dendrites (fractal dimension, F D ) ( SI Appendix , Fig. S7 and Text S6 ). Our results show that hydrophilic and superhydrophobic surfaces have steady-state growth patterns that are characterized by a nearly constant fractal dimension ( F D ≈ 1.20, Fig. 2 D ). Representative images captured during transient and steady-state growth stages are shown in Fig. 2 E and F . We attribute the steady-state growth pattern to the melting of dendrites at their tips due to the warm surrounding air (≈20 °C). Movie S5 shows the in-situ computation of the fractal dimension as a function of time. Our experiments show that the dendrite morphology is a strong function of oil chemistry. The various lubricants used in this study have different interfacial tensions and chemical properties, leading to a wide range of wetting behavior including spreading coefficients ( SI Appendix , Text S8 ) ( 61 – 63 ). When the micro/nanostructured surface was impregnated with Krytox or silicone oil, the dendrites were short, thick, and lumpy ( Fig. 1 C and D ). However, when mineral oil was used for impregnation ( Movie S6 ), the dendrites became sharp and pointy ( Fig. 3 A – C ), similar to those on hydrophilic and superhydrophobic surfaces. We attribute this similarity in dendrite morphology to the negative spreading coefficient of mineral oil on water, which is measured using the pendant drop method in our experiments ( 64 , 65 ) ( SI Appendix , Text S8 ). The spreading coefficient measures the likelihood of oil spreading on water to form a wrapping layer ( 31 , 49 , 66 ). In our experiments, all interfacial tensions and the associated spreading coefficients are measured at room temperature (≈20 °C). Due to the limitation on our equipment (DSA100E, KRÜSS GmbH), we were not able to measure interfacial tension below 0 °C. Using an upright laser scanning confocal microscope (LSM 710, Carl Zeiss), we visualized the top of the sessile water droplet (red box, Fig. 3 D ) to investigate the presence or absence of a wrapping oil layer. In agreement with our spreading coefficient measurements ( SI Appendix , Table S2 ), our confocal images show that mineral oil does not form a wrapping layer as evidenced by the absence of oil in Fig. 3 E . Krytox and silicone oils, however, spread on water and form a wrapping layer ( Fig. 3 F ) due to positive spreading coefficient ( SI Appendix , Table S2 ). Fig. 3 G – I shows increased dendrite shape complexity as the oil used for impregnation changes from silicone to mineral oil. This result agrees with our hypothesis, which attributes the unique dendrite morphology reported in this study to the presence of oil on the outer shell of frozen droplets. These results also indicate the potential of reducing oil depletion by using mineral oil for lubrication instead of silicone or Krytox oil. Fig. 3. Dendrite shape complexity. ( A–C ) Freezing of a water droplet on a mineral oil-infused surface with sharp and pointy dendrites. ( D ) A droplet residing on an oil-impregnated surface. ( E ) Absence of a wrapping layer (negative spreading coefficient) when the textured surface is impregnated with mineral oil. ( F ) Presence of a wrapping layer (positive spreading coefficient) when the surface is impregnated with Krytox oil. ( G–I ) Impact of oil chemistry on dendrite growth where dendrites become sharp and pointy as the oil chemistry changes from silicone to mineral oil. ( J ) Unlike superhydrophobic surfaces, which exhibit only one plateau, Krytox oil-infused surfaces show two plateaus. ( K ) Representative images taken from high-speed images at different stages of the dendrite growth. The four images correspond to the four shaded regions in J . Analysis of the evolution of the fractal dimension for the different surfaces shows that hydrophilic and superhydrophobic surfaces exhibit one plateau ( Fig. 2 D ), while oil-infused surfaces (Krytox and silicone) can exhibit two or three plateaus depending on the oil viscosity and initial wetting ridge volume. As an example, the evolution of the fractal dimension for Krytox General-Purpose Lubricants (GPL) 100 (K100) with two plateaus is shown in Fig. 3 J . We attribute the first and second plateaus to steady-state growth patterns in the presence (abundance) and absence (scarcity) of lubrication oil, respectively. In the initial stages of dendrite growth, there is plenty of oil available to wick into the additional surface area created by the dendrites. This abundant oil supply from the wetting ridge gives rise to thick and lumpy dendrites with a low fractal dimension (first plateau, F D ≈ 1.10). As the dendrites continue to grow, oil becomes scarce, resulting in a second plateau with F D ≈ 1.20, a similar fractal dimension to that of superhydrophobic surfaces. This experimental result is consistent with our hypothesis that attributes the low dendrite complexity and low fractal dimensions to the presence of oil. Representative time-lapse images of the different stages of dendrite growth on the Krytox oil infused surface including the transient states are shown in Fig. 3 K . In addition to the oil chemistry, we observed that oil viscosity and wetting ridge volume play a crucial role in dendrite morphology. We used oil viscosity at room temperature (≈20 °C) for this discussion since lubricants on the market are identified by their room temperature viscosity. The viscosities at −20°C of all the oils used in this study, which were measured using a rheometer ( SI Appendix , Fig. S8 and Text S7 ), are provided in SI Appendix , Table S3 . In our experiments, we varied the oil viscosity while maintaining the initial wetting ridge volume nearly constant ( Fig. 4 A and B ). When the viscosity is below 150 cP (K100, 23 cP) ( SI Appendix , Table S3 ), the dendrites swelled and grew in bundles (≈40 to 80 μm diameter, Fig. 4 A ). When the oil viscosity exceeded 150 cP (K105, 1013 cP), the dendrites became thin, sharp, and pointy ( Fig. 4 B ). In our experiments, the respective fractal dimensions for K100 and K105 were 1.12 and 1.21 as shown in Fig. 4 C . Additionally, we varied the wetting ridge volume while maintaining the oil viscosity constant as shown in Fig. 4 D and E . The dendrite morphology was sharp and pointy ( Fig. 4 D ) when the wetting ridge volume ( SI Appendix , Fig. S9 and Text S9 ) was less than 10% of the droplet volume (dimensionless volume V* < 0.1). When the wetting ridge volume exceeds 10%, the dendrites became thick and lumpy ( Fig. 4 E ). The results of the wetting ridge variation at constant viscosity are shown in Fig. 4 F . These results support our hypothesis that the presence (abundance) or absence (scarcity) of lubricant oil affects dendrite morphology. Fig. 4. Effect of oil viscosity and wetting ridge volume on dendrite morphology. ( A ) Dendrites are thick and lumpy when the oil viscosity is <150 cP. ( B ) When the oil viscosity exceeds 150 cP, the dendrites become thin and sharp. ( C ) Fractal dimensions for low viscosity (K100, 23 cP) and high viscosity (K105, 1013 cP) oils. ( D ) Dendrites are sharp and pointy when the wetting ridge volume is <10% of the droplet volume. ( E ) Dendrites swell and grow in bundles when the wetting ridge volume is ≥10% of the droplet volume. ( F ) Fractal dimensions for small wetting ridge (3% of droplet volume) and large wetting ridge (19%). Interestingly, we observed three distinct steady-state growth patterns ( Fig. 5 A ) when the oil viscosity exceeds the critical viscosity (≈150 cP). The first plateau has the same fractal dimension as traditional superhydrophobic surfaces ( F D ≈ 1.20) because oil has not wicked far enough to reach the dendrites due to large oil viscosity (K103, 157 cP). We call this a viscous-limited growth regime (310 s < t < 585 s, Fig. 5 A ). When the oil wicking reaches the dendrites, a second plateau with a lower fractal dimension emerges (620 s < t < 980 s). Finally, a third plateau emerges when the oil in the wetting ridge runs out (t > 1,080 s). Representative images of the dendrite morphology for the different growth stages are shown in Fig. 5 B . Fig. 5. Steady-state growth pattern and regime map. ( A ) Three steady-state growth patterns. Because of slow oil wicking, the first plateau has the same fractal dimension as traditional superhydrophobic surfaces. When the oil reaches the dendrites, the fractal dimension decreases and reaches a second plateau with a lower fractal dimension. Oil scarcity leads to a third plateau that is similar to superhydrophobic surfaces. ( B ) Representative images of dendrites for each growth stage. ( C ) Regime map for Krytox oil showing the impact of oil viscosity and wetting ridge volume on dendrite morphology. Dendrites are sharp and pointy (yellow section, lower right corner) when the oil viscosity is ≥150 cP and/or the wetting ridge volume is <10% of the droplet volume. Dendrites become thick and lumpy (blue section, top left corner) when the oil is less viscous (<150 cP) and/or the wetting ridge volume is ≥10% of the droplet volume. By nondimensionalizing wetting ridge volume and oil viscosity, we developed a regime map that shows the different dendrite morphologies that can emerge for Krytox oil-impregnated surfaces ( Fig. 5 C and SI Appendix , Fig. S10 ). The dimensionless viscosity (μ*) describes how viscous the oil is with respect to water (1 cP). The regime map in Fig. 5 C shows that dendrites are sharp, pointy, branching, and Christmas tree-like (large F D , yellow, lower right corner) when a) the oil viscosity is large (μ* ≥ 150) and/or b) the wetting ridge is small (V* < 0.1). Dendrites become thick and lumpy (small F D , blue, top left corner) when a) the oil viscosity is low (μ* < 150) and/or b) the wetting ridge is large (V* ≥ 0.1). These experiments show that oil viscosity and wetting ridge volume play a pivotal role in dendrite growth and morphology. In summary, we show a unique dendrite morphology on micro/nanotextured oil-impregnated surfaces. Unlike the hairy, sharp, and pointy dendrites on traditional hydrophilic and superhydrophobic surfaces, the dendrites on oil-impregnated surfaces are short, thick, and lumpy without pattern. Experimental results show that the distinct dendrite morphology on oil-infused surfaces is due to oil wicking and the associated wetting ridge migration, which is attributed to capillary pressure imbalance between the dendrites and the surface texture. The difference in characteristic length scale between the dendrites and the surface texture initiates and sustains oil wicking on the outer shell of frozen droplets. Our experiments show multiple steady-state growth patterns depending on the presence/abundance and absence/scarcity of oil within the porous dendritic network. Moreover, we captured the effect of oil chemistry, oil viscosity, and wetting ridge volume on dendrite morphology by developing a regime map. The regime map shows that dendrites are thin, sharp, and pointy with large fractal dimensions when the oil viscosity is ≥150 cP and/or the wetting ridge volume is <10% of the droplet volume, while the dendrites become short, thick, and lumpy with low fractal dimensions when the oil viscosity is <150 cP and/or the wetting ridge volume is ≥10% of droplet volume. Our results indicate the potential to reduce oil depletion rate by using mineral oil-infused surfaces. The insights gained from this work present strategies to reduce oil loss that can aid the adoption of lubricant-infused surfaces for practical applications."
} | 4,727 |
37054512 | null | s2 | 3,775 | {
"abstract": "Bacteria are single-celled organisms, but the survival of microbial communities relies on complex dynamics at the molecular, cellular, and ecosystem scales. Antibiotic resistance, in particular, is not just a property of individual bacteria or even single-strain populations, but depends heavily on the community context. Collective community dynamics can lead to counterintuitive eco-evolutionary effects like survival of less resistant bacterial populations, slowing of resistance evolution, or population collapse, yet these surprising behaviors are often captured by simple mathematical models. In this review, we highlight recent progress - in many cases, advances driven by elegant combinations of quantitative experiments and theoretical models - in understanding how interactions between bacteria and with the environment affect antibiotic resistance, from single-species populations to multispecies communities embedded in an ecosystem."
} | 236 |
23478651 | null | s2 | 3,776 | {
"abstract": "Since its inception, the discipline of microfluidics has been harnessed for innovations in the biomedicine/chemistry fields-and to great effect. This success has had the natural side-effect of stereotyping microfluidics as a platform for medical diagnostics and miniaturized lab processes. But microfluidics has more to offer. And very recently, some researchers have successfully applied microfluidics to fields outside its traditional domains. In this Focus article, we highlight notable examples of such \"unconventional\" microfluidics applications (e.g., robotics, electronics). It is our hope that these early successes in unconventional microfluidics prompt further creativity, and inspire readers to expand the microfluidics discipline."
} | 185 |
39277633 | PMC11401882 | pmc | 3,777 | {
"abstract": "Expanding and intensifying agriculture has led to a loss of soil carbon. As agroecosystems cover over 40% of Earth’s land surface, they must be part of the solution put in action to mitigate climate change. Development of efficient management practices to maximize soil carbon retention is currently limited, in part, by a poor understanding of how plants, which input carbon to soil, and microbes, which determine its fate there, interact. Here we implement a diversity gradient by intercropping undersown species with barley in a large field trial, ranging from one to eight undersown species. We find that increasing plant diversity strengthens positive associations within the rhizosphere soil microbial community in relation to negative associations. These associations, in turn, enhance community carbon use efficiency. Jointly, our results highlight how increasing plant diversity in agriculture can be used as a management strategy to enhance carbon retention potential in agricultural soils.",
"introduction": "Introduction Biologists have empirically tested how diversity loss can impact ecosystem processes due to shifts in energy fluxes and matter that are underlying ecosystem functioning 1 , 2 . Long-term ecological experiments have been crucial for increasing our understanding on how biodiversity enhances the provision of ecosystem productivity 2 , 3 , stability 4 , 5 and resilience to climate extremes 6 . While the relationship between plant diversity and above ground plant productivity is to date the best studied ecosystem function 7 , 8 . More recently, it has been recognized that many of the mechanisms that promote positive biodiversity-ecosystem functioning relationships take place belowground 9 – 11 . The potential of plant diversity to influence soil carbon (C) cycling has been recognized 12 , 13 , as soils are the biggest reservoir of terrestrial carbon and soil-atmosphere C feedbacks plays an important role in defining the world’s climate evolution in the next decades 14 . While plant biomass and root exudates are the primary source of C into soils, ultimately it is the microbial activity influenced by the biodiversity of microorganisms living in the soil that will decompose (i.e. mineralize) plant compounds into more recalcitrant, less available soil C pools 15 – 19 . While the abiotic controls of C retention in soil are better understood 20 , 21 , the importance of fungi and bacteria-derived C for soil carbon formation has been recently recognized 16 , 18 , 22 . It has been recently hypothesized that higher complexity of microbial-derived soil organic matter (SOM) compounds might translate into higher metabolic costs for decomposition and consequentially longer residence times in soil 23 . Recent findings support this hypothesis as microbial community composition explained the SOM chemical signature in a study that manipulated microbial diversity 16 . This same study showed that community composition influenced the thermal-stability of SOM and more thermal-stable SOM is less available to decomposition and might persist longer in soils 16 . When microbes metabolize plant C, a fraction of this C is allocated to growth and the resulting microbial biomass can ultimately contribute to soil C pools through exudation and cell death 16 – 19 . Growth efficiency or carbon use efficiency (CUE) represents the fraction of C taken up by microbial cells and retained in biomass as opposed to being respired. Predictions of soil carbon stocks are sensitive to the assumptions made about microbial CUE 24 . Diverse microbial communities allocate more C to growth in relation to respiration than species-poor communities 25 , which could be explained in part by higher levels of complementarity between community members under high diversity 26 . Complementarity effects characterize processes such as niche differentiation and facilitation that arise from species interactions, and enhance resource use efficiency and productivity in more diverse communities 27 . If plant diversity or specific plant root traits fosters complementarity effects within the belowground microbial community 27 , it can enhance microbial abundance 12 , growth 13 and consequently microbial turnover would increase 15 , promoting C retention in soils via greater microbial community CUE 16 – 18 . These processes have been previously integrated in the conceptual framework of the soil “microbial carbon pump” (MCP). The MCP framework captures the long-term cumulative effect of microbial catabolism and anabolism on SOM formation 17 , 18 . Two decades ago, a seminal plant diversity experiment was established, the Jena experiment, which has provided evidence that increasing plant diversity is followed by an increase in soil carbon content 12 , 13 , 28 . The results of this experiment also helped to further elucidate the importance of the rhizosphere microbial community in understanding the role of plant-microbe interactions for soil functioning 29 – 31 . For example, more diverse plant communities increase accessibility of root exudates for the rhizosphere microbial community 30 , which may have consequences for community CUE and C cycling dynamics. As agricultural land represents almost half of Earth’s land surface today 32 , it becomes crucial to elucidate if findings observed within biodiversity experiments can be reproduced within an agricultural context. Reproducing the diversity effects observed within biodiversity experiments within an agricultural context faces various challenges 11 , 33 . For example, most diversity experiment results were obtained starting with even abundances of different plant species. However, in an agricultural context, one or a few crops purposely dominate and the influence of intercropping with other species (i.e. diversity effect) might be different and/or reduced due to their limited abundances. Another challenge of diversification in an agricultural context is the implementation of high diversity treatments which is accompanied by increasing complexity for the farmers regarding sowing, harvesting and other management practices during plants’ growing season. For example, the highest diversity treatment in Cedar Creek experiment was 16 species 4 , while in the Jena experiment, it consisted of 60 different grassland species 13 , which would represent a substantial effort considering agricultural management practices. Thus, we must investigate if we can yield positive results at lower levels of plant diversity in settings where the main crop is the dominant species within an agricultural context to facilitate their implementation by farmers. To bridge the gap between biodiversity research and agricultural sciences, the TwinWin plant diversity intercropping farming experiment was established in 2019 (see Supplementary Information; https://carbonaction.org/en/projects/ ). The TwinWin experiment has been designed to evaluate how a main agricultural crop grown with a gradient of undersown plant diversity (planted in intercropping) influences the provisioning of ecosystem functions compared to the monoculture of the main crop. Toward this end, barley is planted as a monoculture, as well as under increasing levels of undersown plant diversity (i.e.: barley plus 1 undersown species, barley plus 2 undersown species; barley plus 4 undersown species and barley plus 8 undersown species). The undersown species were chosen based on two root functional traits: nitrogen fixation capacity and rooting depth 34 . The species with no nitrogen fixation and shallow roots are Lolium perenne and Phleum pratense , N-fixers with shallow roots are Trifolium hybridum and Trifolium repens , N-fixers with deep roots are Medicago sativa and Trifolium pratense and deep rooters with no nitrogen fixation capacity are Festuca arundinacea and Cichorium intybus . The overall aim of this study is to provide empirical evidence for the response of microbial community CUE in the soil rhizosphere to a plant diversity gradient in agricultural soils. A previous study showed that plant diversity enhanced C uptake within the rhizosphere microbial community 30 . Our overarching hypothesis is that undersown plant diversity influences the microbial associations in the rhizosphere of the main crop, mediating the associations within the belowground microbial community with consequences for soil C cycling dynamics (Fig. 1 ). Our specific hypotheses are: (1) plant diversity has a positive influence on microbial CUE in the rhizosphere; (2) an increase in plant diversity will increase soil organic carbon; and (3) plant diversity has a positive influence on microbial associations in the rhizosphere, which should influence community CUE. To test these hypotheses, we sample soil from the TwinWin experiment during the growing season and estimate CUE using the 18 O–H 2 O substrate-independent method, sequence the bacterial and fungal communities and evaluate their association networks and determine the soil C quantity and quality. Using structural equation modeling to distinguish between direct and indirect drivers of CUE, our results suggest that plant diversity influences the positive associations within the microbial community, which contributes to increasing community CUE. Fig. 1 TwinWin field experiment and sampling design. Graphical visualization of our hypotheses that (1) a plant diversity gradient influences soil biotic associations in the rhizosphere and that (2) such changes influence the “balance” between growth and respiration increasing microbial community CUE. Positive associations within the soil microbial community are shown in blue while negative associations are shown in red along the plant diversity gradient ( a ). Sampling design of Barley rhizosphere within the TwinWin field experiment. Number of field plot replicates sampled for each treatment and within plot replication. The number of rhizosphere samples collected within the same plot was adjusted to 6 or 8 (pseudo-replication) to yield twenty-four replicates for each specific treatment to allow the construction of association networks within each plant diversity treatment. For “Barley + 1” treatment, the sampling was focused on plots from four undersown species: M. sativa (AA) , T. hybridum (AC), L. perenne (IR), and F. arundinacea (FA). The total number of rhizosphere soil samples collected was 168 ( b ). Figure 1a was created with BioRender.com released under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International license.",
"discussion": "Results and discussion Plant diversity drives soil carbon cycling in an agricultural soil We observed higher soil organic carbon content in rhizospheric soils with higher diversity of undersown species accompanying barley (Fig. 2a ). This is consistent with previous findings 12 , 13 , 28 . Plant diversity can increase plant biomass inputs into soil due to increased productivity 8 , 35 , which might explain why higher total soil organic C content is observed in more diverse plant communities 12 , 28 . Moreover, within an agricultural context having other plants growing with barley also means that once the barley is harvested, the other plants continue to grow in the field gaining the function of”cover crops” while under barley monoculture there is no further plant growth after barley’s harvest (i.e. plots are bare after harvest except for barley residues). The now “cover crops” continue to grow during autumn and spring resulting in additional plant biomass that will be incorporated within the upper soil layer before sowing the seeds in the following up season with a shallow tillage ( ± 5 cm). We observed a positive relationship between plant biomass and plant diversity in spring (Fig. 2d ) but not during the summer barley growing season (Fig. 2e, f ). The spring biomass measurement captures only the undersown species and weeds biomass as the barley plants do not resprout after harvest. These results are in agreement with the growing literature showing the positive impact of cover-crops on soil C content 36 , 37 . This additional plant biomass observed at the higher levels of plant richness in spring likely contributes to the observed increase of SOC along this diversity gradient. Further, it has also been suggested that plant diversity per se is important for soil carbon build up, indicating that other diversity-related mechanisms are important in addition to the diversity-induced increase in biomass 35 . It is also important to consider that while the effects of plant diversity on belowground processes become progressively stronger over time 28 , 37 , here we are studying the early responses of soil C cycling to increasing plant diversity within two years into the establishment of the TwinWin plant diversity farming experiment. The results of the Jena experiment suggests that more years into the experiment are needed to allow us to better understand SOC dynamics at different soil depths and the role of distinct plant functional groups 28 . Fig. 2 Plant diversity effects on soil carbon quantity, quality, plant biomass, and soil carbon cycling processes in the rhizosphere. Total organic carbon (%) ( a ), Thermal stability index (R index) ( b ), thermal lability index (I index) ( c ), spring above ground plant biomass ( d ), summer above ground plant biomass ( e ), total yearly above ground plant biomass ( f ) measured at the plot level within the TwinWin during two consecutive years. Growth ( g ), respiration ( h ) and CUE ( i ) observed in the rhizosphere of barley along the undersown plant diversity gradient (log). Significant relationships were evaluated with linear mixed models with location in the field (block or plot) as random effect and are presented by solid lines when significant ( p < 0.05); exact p values are given next to the R 2 values ( n = 167, df = 163). The shaded area denotes 95% confidence intervals around the mean values. Source data are provided as a Source Data file. The first results from this agricultural experiment show that manipulating undersown plant diversity in an agricultural context may have multiple positive effects as SOC increase provides various co-benefits to farmers beyond the major agricultural crop growing season 11 , 33 . Nevertheless, it is important to highlight the potential tradeoffs between intercropping with other plants and the main crop yield 38 . In this experiment, we observed no significant reduction in barley biomass and yield with increasing plant diversity (Supplementary Figs. 2 – 4 ). A previous study in this site observed a small negative and only marginally significant effect ( P = 0.073) of plant diversity on yield, and this influence was driven mostly by herbicide application in barley monoculture treatment and not due to plant richness per se 39 . Moreover, we observed that the largest differences in yield were observed within the D1 treatments (Supplementary Figs. 2 – 4 ), depending on species identity rather than undersown diversification. A meta-analysis of 226 field experiments showed that while intercropping leads on average to small yield penalty for grains, a decrease in yield is not always the case specially combined with moderate N fertilizer applications 38 . However, management changes should be carefully evaluated so as not to compromise main crop yield which could lead to further conversion of non-arable land into agricultural thus exacerbating the climate crisis 14 , 32 . It should also be noted that the benefits of diversification schemes may vary among years, and prove particularly beneficial during drought years 40 . In addition to the soil C content, we evaluated the quality of the rhizospheric soil organic carbon (SOC) with the rock-eval ramped thermal analysis which allows the SOC to be divided into thermally labile and thermally stable fractions (I index and R index, respectively) 41 . Ramped thermal analyses of soil samples have been shown to be a promising technique to disentangle distinct organic matter (OM) compounds differing in the energy needed for thermal decomposition 42 – 44 and has been shown to be informative of microbial activity and functioning in soils 45 . Moreover, it was also shown that the thermally stable soil C fraction is less prone to further decomposition 16 suggesting that the thermal-stable signal captures soil C that will persist longer in soil. We observed no significant differences of thermally stable C fraction (R index) under the different plant diversity treatments (Fig. 2b ). The same was observed for the thermal labile soil C fraction (Fig. 2c ). While various studies have investigated the impact of plant diversity on soil C content 28 , 35 , 36 , 46 , 47 , few have evaluated the plant diversity effect on C quality and its consequences for C persistence in soils 19 . More efforts are needed in this direction to evaluate changes in C quality through time since the beginning of diversification and if the higher C pools observed under higher plant diversity in other experimental sites are more resistant to decomposition. Future studies should assess whether agricultural practices can increase the residence time of soil C despite rising soil temperatures, as this may determine the fate of additional soil C on an increasingly warmer planet 45 , 48 . Empirical link between plant diversity and microbial CUE Microbes are key regulators of Earth’s massive soil carbon stocks, determining the partitioning of plant inputs into microbial biomass which might become part of soil organic matter 16 – 18 versus respiratory carbon dioxide release to the atmosphere. For this reason, understanding the influence of plant productivity, composition and diversity on microorganism physiology is of ultimate importance if we want to disentangle microbial-mediated processes relevant for C cycling 17 – 19 . As microbial necromass makes up to 50% of SOM 18 , it is crucial to foster our understanding on the drivers of microbial growth and microbial biomass formation in soils. Carbon use efficiency (CUE) describes the proportion of a cell’s resources converted into microbial biomass relative to the total resources consumed. It is thought to be an important microbial physiology parameter influencing the amount of necromass produced per unit of substrate consumed 49 , and therefore, connecting microbial biomass to potential SOM formation 50 . Here we used a substrate independent method, the 18 O-water method 47 , to evaluate microbial CUE in the rhizosphere of barley under increasing plant diversity. Interestingly, respiration and growth, the two components of CUE, responded differently to plant diversity. While growth was significantly enhanced with increasing levels of plant diversity (Fig. 2d ), respiration was not (Fig. 2e ). As CUE is the compilation of these two factors, CUE of the microbial community within the rhizosphere of barley increased along the undersown diversity gradient (Fig. 2f ). These results can help to understand the mechanisms leading to higher SOC content in soils of more diverse plant communities, as observed here (Fig. 2a ) and in previous studies 12 , 46 . A community that grows more efficiently should result in an increased abundance of microorganisms per gram of soil 13 and consequently, in more iterative cycles of microbial growth and death (i.e. turnover). A higher microbial turnover 15 – 18 under high plant diversity should accrue SOM content over time 28 . Plant diversity modulates the associations within the microbial community in the rhizosphere There is a growing consensus that the key to understand soil functioning lies in the rhizosphere, where plants and soil meet. This is the area surrounding plant roots and is a major hotspot of soil functioning 51 . A seminal review suggested that to foster sustainable agriculture it is crucial to capitalize on the multitrophic rhizosphere-mediated interactions 52 . Here, we used network analyses to capture how a plant diversity gradient impacts the potential associations within the bacterial and fungal microbial communities in the rhizosphere of barley (Fig. 3a, b ; methods; Supplementary Figs. 5 – 8 , 11 – 15 ). To do this we built co-occurrence networks evaluating the potentially positive and negative associations within the microbial community 53 (Fig. 3a ; see methods). We are using the term “potential associations” to take into consideration that networks derived from co-occurrence data are limited in their capacity to infer direct interactions 54 . Positive associations can arise both through shared responses to the environment or through co-operative interactions but it is not possible to distinguish between the two. The nodes in the networks are representing the bacterial and fungal species and the links between the nodes their potential associations. Interestingly, we observed an increase in connectivity (number of links normalized by the number of nodes present in each network) within the positive networks along the diversity gradient, while the connectivity of negative networks shows a tendency to decrease with increasing plant diversity (Fig. 3b, c , Supplementary Figs. 3 – 5 ). Co-occurrence networks have inherent method-driven biases and limitations 54 – 57 . Our experimental design was conceived to replicate these inherent limitations of network analysis along the diversity gradient (e.g. we used the same number of samples within each network; see methods). Therefore, captured differences in the structure of the network, are likely due to the distinct plant diversity treatments. Overall, our results suggest that plant diversity enhanced positive associations within the bacterial community compared to the negative associations (Fig. 3c , Supplementary Figs. 7 – 8 ). Fig. 3 Network analysis design and findings. Network analysis approach to evaluate the impact of undersown diversity on microbial associations within the rhizosphere of barley across the diversity gradient ( a ). Bacterial networks showing edges in blue if representing positive associations or in red if representing negative associations and the colors of the nodes represent the different bacterial phylum within each plant diversity treatment from top to down: barley monoculture, barley + 1, barley + 4 and barley + 8, respectively ( b ). The Ratio of positive to negative network parameter of degree centrality which captures network connectivity at each undersown diversity treatment ( c ). Significant differences between treatments are indicated by different letters (linear mixed effects models with block as the random effect using the nlme package, and ANOVA type III to correct for unbalanced design, P = 5 × 10 −7 , df = 163, n = 167). In the boxplots, whiskers denote the minimum value or 1.5× interquartile range (whichever is more extreme), and box denotes interquartile range. The horizontal line denotes the median. Biological replicates: n = 24, 95, 24 and 24 for barley monoculture, barley +1, barley + 4 and barley + 8, respectively. Source data are provided as a Source Data file for Fig. 3c. To evaluate how connectivity changes in positive networks in relation to negative networks, in addition to their graphical visualization (Fig. 3b ), we extracted network parameters related to connectivity as the degree centrality, which captures highly connected taxa or hubs (Fig. 3c , Supplementary Figs. 7 – 8 ). When computing the ratio between the degree centrality of positive networks to the degree centrality of negative networks it corroborates our observation of enhancing connectivity in positive compared to negative networks along the diversity gradient (Fig. 3c ). This relative increase in positive associations could be due in part to the enhancement of cross-feeding within the microbial community under high plant diversity compared to low plant diversity. Cross-feeding occurs when excreted by-products of metabolism of an organism or population benefits others 58 – 60 , and is considered a key mediator of positive interactions within microbial communities 61 . Here we are hypothesizing that a more complex compilation of plant exudates and the combination of distinct litter chemistries under higher plant diversity may induce positive interactions due to cross-feeding and facilitation mechanisms (e.g. the degradation of plant by-products and its derivatives by one organism can facilitate growth of some of its neighbors who have a higher affinity to the by-product compared to the primary plant compound) 58 , 61 . Cross-feeding has been suggested to act as a niche construction for microorganisms 58 . Thus, if high plant diversity creates a higher niche space for the soil microbial community, this could explain why we observe higher microbial growth and growth efficiency (CUE) per gram in these soils (Fig. 2d–f ). The observed increase in connectivity in the positive network could be an indicative of more cooperative associations 61 within the microbial community with increasing plant diversity. Simultaneously, the connectivity of negative networks tends to decrease with increasing plant diversity (Supplementary Fig. 8 ) which could mean that competition between community members decreases with plant diversity 62 . Previous studies showed that agricultural management can influence microbial association networks 63 and that network properties might be related to microbial-controlled ecosystem functions 64 . Here we show how a plant diversity gradient influences the soil bacterial network connectivity with potential consequences for soil-C cycling. We also built the association networks using arithmetic subtraction to isolate the effect of the diversity gradient within the networks (methods; Supplementary Figs. 6 and 13 ) 53 . Using such an approach to compute the networks confirms our findings discussed previously of high positive network connectivity under higher plant diversity compared to low plant diversity in comparison to negative networks (Fig. 3c , Supplementary Figs. 6 – 8 ). The shift in network structure observed from barley monoculture to barley plus 1 and subsequent higher plant diversity treatments suggest changes in network organization at the community level 65 . The changes in network structure resulting in the”tightening” of the network increases the probability of species being connected to each other through direct or indirect pathways 66 . While this can be a result of enhanced cross-feeding as previously discussed, indirect effects within networks have also been acknowledged to explain the persistence of mutualism among species across time 60 . It has been shown that chemical succession in the rhizosphere of a grass during plant growth controls the microbial community assembly via microbial metabolite substrate preferences 65 . Thus, if different plant species produce a distinct assemblage of root exudates during plant growth, it should result in a differential recruitment of microbial species. The introduction of additional species can change how pre-existing species are indirectly linked to each other 66 . Our study is limited to answer the question of how distinct plant species control community assemblage as our experimental design was conceived to evaluate how undersown diversity modifies the microbial associations within the barley rhizosphere (methods). However, we observed that at the first level of diversity (barley plus 1) the undersown species identity influenced the barley rhizosphere microbial community associations (Supplementary Fig. 5 ). While the network structure of barley rhizosphere under Lolium perenne , Trifolium hybridum and Festuca arundinacea are relatively similar to each other, the network structure in plots with Medicago sativa shows to be very distinct from the other three species in its connectivity. Interestingly, Medicago sativa achieved higher plant cover in this diversity farming experiment compared to other undersown species at the same diversity level (Supplementary Fig. 16 ). This suggests that undersown species abundance also plays a role on influencing the major crop rhizosphere’s processes. Alternatively, Medicago sativa is a deep rooting and nitrogen fixing specie. These changes could be in part related to the deep rooting and N fixation strategies and the recruitment of distinct microorganisms at lower depth. Previous findings suggested that changes in species associations resulted in greater changes in N- compared to C-cycling in soils 67 . We observed that changes in different bacterial phyla are responsible for the shifts in positive and negative network parameters (Fig. 4 ). While Acidobacteriota, Gemmatimonadota, and Pseudomonadota are more strongly related to positive changes in positive networks, Actinomycetota, Chloroflexota and Verrucomicrobiota are negatively related to changes in positive networks. This analysis also shows how the changes in the relative abundance of bacterial phylum are distinctively related to respiration, growth and CUE. In line with recent findings, we show that increases in relative abundance of Bacillota is negatively related to CUE 19 . However, it is important to consider that recent investigations have not identified any robust functional gene markers of CUE 68 . Moreover, it has been shown that inefficient taxa can increase their CUE with changes in abiotic conditions, while more efficient taxa showed a decrease in CUE with the same changes in abiotic conditions 68 . Our results suggests that CUE reflect community-scale dynamics 69 and it is a flexible parameter changing in response to how abiotic and biotic conditions influence the microbial communities (Figs. 4 – 5 ). Future research should foster our understanding on how distinct plant functional groups (e.g. root depth and N-fixation) impact microbial community assembly and interactions to identify plant traits able to steer soil microbial communities to promote specific soil functions. Fig. 4 Relationship between network parameters and the response of microbial physiology to the relative abundance of different bacterial phylum across the plant diversity treatments. Heat map of positive and negative network parameters and microbial physiology measurements (respiration, growth, and CUE) in response to changes in bacterial phylum relative abundances. Weights: represent the strength of the relationship between two vertices; eigen centrality: captures the relevance of the different OTUs for the network and degree centrality is a parameter computing the connectivity among OTUs within the networks. Acidobac.: Acidobacteriota ; Actinom.: Actinomycetota ; Bacillo.: Bacillota ; Chlorofl.: Chloroflexota ; Gemmat.: Gemmatimonadota ; Pseudo.: Pseudomonadota and Verrucom.: Verrucomicrobiota . Spearman correlation (two-tailed test) coefficients in as white where p -value exceeded 0.05, or in blue or red if the p -value is lower than 0.05 and the correlation coefficient positive or negative, respectively. Source data are provided as a Source Data file. Fig. 5 Structural equation model showing the relative importance of plant diversity, soil properties and plant biomass on CUE. Significant paths are shown in blue if positive or in red if negative. Path width corresponds to degree of significance as shown in the lower left and standard coefficient for each path is shown on a circle within each path. The amount of variance explained by the model (R 2 ) is shown for each response variable. Soil properties: composite variable of soil properties (i.e. pH, Calcium (g/kg soil), C/N ratio and Cation exchange capacity (cmol/kg soil)), Plant diversity: composite variable of Simpson’s plant diversity index calculated based on species present in a plot and the plant cover measurements; Stand plant biomass: cumulative plant biomass measured in spring and summer from 2019 to 2021 at the plot level; Bacterial community composition: NMDS axis 1 of bacterial community structure; Positive network connectivity: positive eigen centrality from bacterial positive networks; Respiration/MBC: mass specific respiration; Growth/MBC: mass specific growth; CUE: carbon use efficiency. Global goodness-of-fit: Fisher’s C. Measures of overall model fit are shown in the lower left. Source data are provided as a Source Data file. Bacterial networks responded to plant diversity while fungal networks did not respond to undersown plant diversity (Fig. 3 , Supplementary Figs. 2 – 3 and 8 – 12 ). This could be due to the distinct response of fungi and bacteria to C inputs from plants 70 , 71 . While bacteria should respond faster to root exudate chemistry 71 , fungi are known to dominate the first stages of litter decomposition 70 and fungal assembly processes can be influenced by tillage which is applied in this agricultural experiment 72 . Moreover, previous findings show that, fungal networks are more resistant to environmental stimuli 73 , and that changes in fungal networks led to fewer changes in soil functioning than changes in bacterial networks 73 , 74 . These results could be influenced by the different ages of these experiments. The TwinWin agricultural experiment has been recently established and it is likely that changes in fungal parameters are observed once the plant treatment effects accumulate over time 75 , 76 . CUE as a function of interactions between biotic and abiotic drivers We used structural equation modeling (SEM) to determine the degree to which the different components (plant diversity, plant biomass, soil properties, carbon quantity, bacterial community composition, network structure, mass specific respiration and mass specific growth) influence directly or indirectly CUE (Fig. 5 and Supplementary Figs. 17 – 19 ). The model path structure was based on the assumption that plant biomass and plant diversity drive CUE directly, but also indirectly by impacting the association between microorganisms and the soil C pool (Supplementary Fig. 17 ). We used the SEM to test the following hypotheses: (1) we expect plant diversity to strengthen positive associations between microbes due to cross-feeding and mutualistic interactions; (2) we expect mass specific respiration and mass specific growth to increase with increasing positive association in the microbial community, because co-existence mechanisms underlies complementary interactions that increase community efficiency; (3) we expect that increasing plant biomass will lead to higher levels of total carbon because of increased carbon inputs through dead roots and root exudation in the rhizosphere which in turn will influence mass specific respiration and mass specific growth of soil microbes due to increased substrate availability; and (4) we expect that if cross-feeding and facilitation mechanisms are captured within the positive associations these should more strongly influence growth than respiration resulting in a more efficient (less expensive) community growth, and therefore increasing CUE (Fig. 5 and Supplementary Figs. 17 – 19 ). Although soil properties are considered a controlling variable for CUE 45 , 77 , our structural equation model indicates that they influenced CUE only indirectly via changes in the biotic components influencing the composition of the microbial community, the mass specific growth and stand plant biomass (Fig. 5 ). In our SEM model soil property is a composite variable containing soil pH, C/N ratio, calcium content and the cation exchange capacity. Plant diversity positively influenced the connectivity of positive microbial association networks and the bacterial community. We cannot make conclusions from the signal of the path coefficient between bacterial community composition and network connectivity because community composition is represented by the first axis of the non-metric multidimensional scaling (NMDS) of the bacterial community, which has an arbitrary direction. Carbon quantity had a negative effect on the positive network connectivity. Interestingly, mass specific respiration was only influenced by soil carbon quantity while mass specific growth was also impacted by soil properties. Previous studies have shown that bacterial composition act as a direct driver of CUE in forest soils 19 and agricultural soils 78 , however in our model the community composition only indirectly impacted CUE by driving associations between microorganisms. This shows that the impact of a microbial community on CUE can play out through a variety of mechanisms including associations among community members. A recent study hypothesized that biotic interactions are underestimated drivers of microbial CUE 79 , we empirically showed that the degree of connectivity within positive network associations has a positive effect on CUE (Fig. 5 , Supplementary Fig. 19 ). Because CUE is a composite variable of respiration and growth, to capture the mechanisms controlling microbial physiology we incorporated the mass specific rates of these processes in the model as previously 45 . It is important to highlight that a substantial fraction of CUE variation remains unexplained in the model, meaning that other important factors are not captured here. For example, previous studies have shown that the costs of extracellular enzyme production 80 and the availability of dissolved soil organic C 77 are factors influencing the community CUE. Moreover, while it has been shown that the presence of fungi increases community CUE 25 , our results suggested that associations among fungi could have a negative influence on community CUE (Supplementary Fig. 19 ). It has also been shown that root biomass controls the accessibility of plant derived C to the bacterial community in the rhizosphere 30 , 31 . In the TwinWin experiment, it is not possible to fully disentangle plant diversity from the plant and/or root biomass and root exudation. We show that plant diversity has an impact on plant biomass enhancing plant input into the soil in spring (Fig. 2d–f ) and that plant biomass has a positive influence on soil C content measured from barley’s rhizospheric soil (Fig. 5 ). Plant biomass and the positive associations within the microbial community positively influenced CUE in our model. Altogether our results highlight how changes in plant community diversity may influence microbial communities with consequences for soil C-cycling. Current agriculture intensification practices lead to a decrease in associated biodiversity 81 . Thus, managing agroecosystems with multiple goals and functions becomes a crucial goal for agriculture in the next decades 11 , 33 . The barley used in this experiment (variety Harbinger) is the most popular malt barley planted in Finland accounting for 35% of malt barley area (16000 hectares in 2017). Similarly to other major agricultural crops, this barley cultivar has been bred for maximum performance in monoculture. While barley yield has not been significantly decreased with increasing undersown diversity (Supplementary Fig. 2a ) we observed potential competition with Medicago sativa which decreased barley yield (Supplementary Fig. 2b ). Thus, it is urging to advocate breeding programmes for crop varieties to be performed using mixtures to exploit complementarity among crop species to further enhance intercropping benefits 82 , 83 . While in this study we showed how plant diversity drives belowground ecosystem processes relevant for soil C-cycling in agriculture, a voluminous number of studies already highlighted that plant diversity is important for other ecosystem functions 3 , 5 , stressing the need for an “ecological intensification” within agroecosystems 11 , 33 . Further progress requires integrating our ecological knowledge regarding agroecosystems to the social and economic constrains of modifying management practices. Previous results suggests that small farmers play a strategic role for biodiversity conservation 84 , 85 and promotion of sustainability in agroecosystems 85 – 87 . The implementation of diversity within agroecosystems across space and time is labor intensive and is more likely to be implemented by small-scale farmers 86 – 89 . Policy mechanisms to promote “carbon-farming” must take into consideration the threat that small farmers will face in the next decades 90 . Comprehending the importance of public policies supporting agroecological production systems by linking the right to produce healthy food without compromising the provision of ecosystem functions and biodiversity protection is of ultimate importance for the responsible management of our agroecosystems and fostering sustainability in agriculture."
} | 10,192 |
33671610 | PMC7926402 | pmc | 3,779 | {
"abstract": "Nowadays, the self-healing approach in materials science mainly relies on functionalized polymers used as matrices in nanocomposites. Through different physicochemical pathways and stimuli, these materials can undergo self-repairing mechanisms that represent a great advantage to prolonging materials service-life, thus avoiding early disposal. Particularly, the use of the Joule effect as an external stimulus for self-healing in conductive nanocomposites is under-reported in the literature. However, it is of particular importance because it incorporates nanofillers with tunable features thus producing multifunctional materials. The aim of this review is the comprehensive analysis of conductive polymer nanocomposites presenting reversible dynamic bonds and their energetical activation to perform self-healing through the Joule effect.",
"conclusion": "7. Conclusions Self-healing materials activated by the heat produced by the Joule effect are a family of materials that have the characteristics of both electrical/thermal conduction and self-recovery by different chemical mechanisms. External and internal Joule heating composites systems are increasingly being reported in the scientific literature. Works based on intrinsic self-healing materials led by Diels-Alder cycloaddition chemistry and supramolecular interactions are the most frequently reported ones. A few examples of extrinsic self-healing nanocomposite materials have also been reported, opening great opportunities for new investigations. The main characteristics of the reported systems is that the composite materials must be conductive and possess a type of self-healing mechanism activated by temperature. The first approaches for achieving this kind of technology were composite materials based on polymers and conductive fibers with optimal results regarding self-healing ability. However, problems such as fiber breaks hinder the conduction of the material. As a solution, nanofillers give a great leap forward for this technology, since the conduction of the material becomes an intrinsic part of it. As damage occurs, the healing system behavior is intrinsic in nature, making it possible to achieve around 100% functionality after damage/healing procedures. Although the mechanical stability of intrinsic self-healing composites is compromised when acting at the softening temperatures of the material, nevertheless, fillers help to support the dimensional stability of the materials by interfacial interactions (chemical/physical) that reinforce the whole system, resulting in high-temperature melting points. With the advances at the nanoscopic levels, microcracks can be repaired very effectively. However, larger cracks need much research to generate highly efficient nanocomposites. For instance, shape memory assisted self-healing polymer composites are a great approach that works by reducing macro-sized cracks to microcracks as a result of the shape memory effect provided by entropic energy stored and junction points (physical and chemical reversible crosslinking) [ 118 ]. These types of materials display low softening temperature, making them flexible for application as smart materials. As a future perspective, it has been well observed that self-healing composites have the advantage of being produced by a great quantity of bulk materials at industrial scale. Among them, epoxy monomers, EVA and PCL, and conductive nano/fillers such as carbon fibers, graphitic particles, and metal wires are currently commercially available. Multifunctional polymers with reactive chemical groups that can act as good stabilizers of nanofillers, while at the same time showing self-healing ability, are also a reality nowadays. However, much more effort must be devoted towards their production at industrial scale for real-world applications. Specifically, it is necessary to consider the effect of the positive and negative temperature coefficients that can occur in the material during healing procedures by the Joule effect. The latter is related to increasing/decreasing the temperature and raising/lowering the conductivity of the nanocomposite. It is indeed crucial, since heating and cooling cycles might break the percolative filler network rendering internal stresses to the material which has a direct correlation with the amorphous and crystalline degree of the matrix. Additionally, the thermal conductivity of the polymeric matrix is an important factor to consider, because the heat distribution must be uniform throughout the material, so that the energy used for healing will be optimal. Finally, the Joule effect has attractive characteristics for developing self-healing smart materials, since the increase of temperature at localized regions in material failures is useful for early detection, thus avoiding critical damage. At the same time, the electrical stimulus as trigger for self-healing effects is a simple and efficient economical way to repair materials in service, thus avoiding material replacement, maintenance and early disposal.",
"introduction": "1. Introduction Self-healing is the natural ability of living organisms to repair tissue damage and to endure harsh environments through dynamic mechanisms [ 1 , 2 ]. Inspired by nature, self-healing materials are typically designed with synthetic polymeric components that undergo self-repairing mechanisms under different stimuli conditions [ 2 ]. Polymeric components can go through the self-healing process aided by grafted functional chemical groups on the backbone of the polymer [ 3 , 4 , 5 ]. Such functionalized polymers bearing chemical groups that display reversible bonds represent a great advantage in terms of physical and chemical responses to different stimuli for self-healing. Among several factors, the tunable melting point and melt flow in functional polymers are useful parameters to design materials able to undergo crack healing processes. The latter has been demonstrated to be a key factor for repairing structural damage [ 3 ], shape recovery [ 6 ], and dimension stability of materials [ 7 , 8 , 9 ]. The search for highly efficient self-healing polymers and nanocomposites has been addressed through different approaches [ 10 ]. Particularly, polymer matrices used in nanocomposites ranged from rubbers to thermoplastics/thermoset polymers [ 11 , 12 , 13 ]. Regarding thermoset and crosslinked rubber matrices, there are many issues to overcome, mainly due to their lack of re-processability after service, as compared thermoplastics. However, by combining specific functionalities in the nanocomposite, such as reversible polymer networks and active nanofillers, many possibilities for producing self-healing nanocomposite materials finely tuned at the nano scale have been opened [ 4 ]. The key characteristic of these chemically functionalized nanocomposite materials is the production of interactions that respond to different stimuli, such as heat, light, or electricity, to perform self-healing [ 14 ]. In addition, self-healing polymers and nanocomposites appear to be profitable and promising alternatives for producing long-lasting materials [ 15 , 16 ]. This stems from the fact that nano/composites are widely used in applications such as the automotive industry [ 17 ], textile industry [ 18 ], electronics [ 19 ], to name a few examples. Therefore, self-healing composites represent a great alternative to overcome environmental issues generated by thermoplastics and thermoset land-fields, so having more durable and eco-friendly materials is a current challenge that the academy and R&D industrial departments have decided to tackle [ 20 , 21 ]. Functional polymers and fillers represent a great advantage for producing self-healing nanocomposites. This comes from their high amount of production as commodities and the endless possibilities of combination between them to generate composites that show different characteristics and applications. Self-healing polymer nanocomposite systems have gained a lot of attention due to the combination of functional polymers with different types of nanofillers such as silica, clay, metal, and carbonaceous nanoparticles. These nanofillers substantially improve the strength, modulus, and toughness of polymeric matrices, as well as the formation of the percolative network to transport external stimuli inside the polymer matrix for repairing [ 22 ]. For instance, electrically self-healing nanocomposites work through nanoscopic heat generation when an electric current passing through a conductive nanostructured network (e.g., well-connected CNTs, metallic nanoparticles, graphite/graphene networks). The so-called Joule-effect (or resistive heating) activates the thermal self-healing ability of self-mendable matrices to heal damage on local areas [ 23 , 24 ]. To fulfill the condition of self-healing, two main approaches have been extensively reported in the literature: the so-called extrinsic and intrinsic self-healing mechanisms. The extrinsic one is based on micro-capsular and micro-vascular systems that contain repairing agents [ 25 , 26 ]. These agents generally polymerize, repairing the damage [ 27 , 28 ]. The problem lies in the limited amount of repair agent [ 9 ], where upon its depletion, the material loses the ability to self-repair [ 14 ]. Intrinsic self-repairing systems have reactive groups bearing polymer backbones [ 3 ] that undergo reversible bond interactions, both covalent and non-covalent, upon external stimuli. These intrinsic systems can theoretically be repaired many times due to their intrinsic character [ 14 ]. Self-healing materials can present many bond interactions, enclosed in two large groups: the so-called non-covalent and covalent interactions ( Figure 1 ). The former includes lower energy dynamic non-covalent bonds such as van der Waals interactions, π–π stacking, dipole–dipole interactions, hydrogen bonding, ionic interactions, metal–ligand coordination, and host–guest interactions [ 28 ]. The latter includes the highest energetic group, dynamic covalent bonds. Although in this group there are many mechanisms, the most commons are Diels-Alder chemical interactions, transesterification reaction, disulfide bonds, imine bonds, boron-based bonds, and alkoxyamine [ 28 ]. From this group, the Diels-Alder (DA) reaction is highlighted for its thermally self-healing behavior [ 29 ]. To induce self-healing processes in polymer nanocomposites, polymer chains may diffuse into the damaged zone. For polymers, mobility of macromolecular chains occurs at temperatures above their glass transition [ 28 ], so that temperature plays an important role in polymer self-repairing [ 30 ]. It can be provided by thermal energy such as conventional heating in ovens [ 29 ], or by heating by microwaves and infrared irradiation [ 31 , 32 , 33 , 34 ]. Additionally, inductive heating can be applied by using current coils located in the damage region to be repaired [ 35 , 36 , 37 ]. Finally, Joule heating occurs in conductive composite materials, mainly aided by the conductive filler network when a current circulates through it, as previously mentioned [ 37 , 38 ]. Currently, the production of high-tech manufactured materials requires both damage detection and self-repair to avoid waste [ 39 , 40 ], so the concept of in-service repair is gaining strength, especially in materials that are difficult to access [ 36 ]. Therefore, in this review we explore the Joule effect as an ideal candidate as a self-healing stimulus for in-service self-healing. This review briefly covers the general topics of self-healing nanocomposites [ 41 ] and further focuses on healing by Joule effect found in literature ( Figure 2 ). The approach of using Joule heating in conductive smart materials covers functional polymer matrices in combination with conductive nanofillers. We identify the chemical pathways that are thermally stimulated in which dynamic covalent bonds, such as the Diels-Alder interactions, alkoxyamine bond and Au-S bonds, are present. We also identify dynamic ionic bonds, in which butyl bromide-based molecules are found, and we also find that supramolecular interactions with the so-called thermoplastic/thermoset blend self-healing system are activated by Joule effect.",
"discussion": "6. Discussion Self-repair in polymer nanocomposites can be achieved by different chemical and physical approaches as discussed in the previous sections. To trigger the healing properties by the Joule effect, a nanocomposite must be both thermally and electrically conductive and must exhibit thermally self-healing properties. To achieve this combination of properties, polymer matrices are combined with conductive fillers, providing synergistic effects of both components such as percolative pathways that help to carry electrical and thermal energy for self-healing processes generated from the Joule effect. Intrinsic systems rely mainly on the matrix ability for self-healing, which in turn depends on the molecular weight of the polymer, its viscoelastic behavior, and the activation/reactivity of functional groups at crack interfaces. Particularly, the interfacial interaction between matrix and filler appears to be a crucial factor determining the effectiveness of healing activation of intrinsic self-healing systems. Anchoring the polymer to the filler leads to better mechanical performance of the system. However, it might influence the free volume and flow of polymer chains for healing mechanisms. The entanglement, wetting and physical/chemical interaction of polymer chains plays crucial roles in crack healing, which will in turn provide optimal recovery of mechanical performance and healing efficiency. The main task is to find the ideal electrical conditions for a correct and efficient self-healing, considering the optimal ratio between the components and reactivity without sacrificing mechanical performance. Among many fillers, carbonaceous and metallic fibers and nanostructures are the most used ones in combination with polymer matrices [ 38 , 87 ]. These fillers provide thermal and electrical conductivity to the composite. Through these, electricity can be supplied to the systems as external energy source to generate heat by Joule effect. This energy can activate properties such as shape memory [ 88 , 116 ] and self-healing in functionalized composites [ 53 , 94 ]. To generate internal Joule heating through nanofillers such as graphene, carbon nanotubes, gold nanoparticles, carbon black, silver and copper nanowires, the fillers must be properly dispersed and stabilized to fulfill percolative pathways. The latter comprises an infinite network that presents random paths throughout the material generated by a tunneled electron transport networks [ 79 ]. Effective dispersion and stabilization prevent the aggregation of the filler for optimal energy transport. This is fulfilled by the functionalization of the filler surface (oxidation/reduction), so that it displays better interfacial interaction with polymer matrices. The problem of excessive functionalization, particularly for graphitic carbonaceous materials, is that the hybridization of carbons changes from planar (sp 2 ) to tetrahedral (sp 3 ), which causes the decrease in electrical conduction and mechanical performance of the entire system. Additionally, excessive coating on the fillers by the matrix may hinder filler contacts, thus decreasing the percolative filler network. As a result, the conduction is not achieved prompting to increase energy input and filler concentration for sufficient percolative pathways. The minimum filler concentration needed to form a percolative network must be pinpointed to avoid excessive filler concentration before reaching the composite failure due to the lack of effective interfacial interaction between matrix and filler [ 79 ]. For the design of these materials, it is also necessary to consider the effect of the positive and negative temperature coefficient of expansion that can occur in the material. The latter might considerably affect the conductivity of the systems during heating procedures. In particular, the cooling cycles might break the percolative network of nanofillers hindering the healing ability by Joule effect. Therefore, the amorphous and crystalline degrees of a polymer/blend matrix are particularly important to control the effective interfacial interaction between matrix and filler. Nanofillers showing electrical/thermal conductivity in the polymeric matrix must provide heat distribution uniformly throughout the material, so that the energy is used optimally. Finally, it is necessary for the filler network to be correctly formed so that during heating-cooling cycles, the percolating network will not be lost, and thus the self-healing ability, recyclable and reprocessability of the polymer matrix will not be hampered [ 118 , 119 ]."
} | 4,217 |
37205168 | null | s2 | 3,780 | {
"abstract": "Deep learning has redefined AI thanks to the rise of artificial neural networks, which are inspired by neuronal networks in the brain. Through the years, these interactions between AI and neuroscience have brought immense benefits to both fields, allowing neural networks to be used in a plethora of applications. Neural networks use an efficient implementation of reverse differentiation, called backpropagation (BP). This algorithm, however, is often criticized for its biological implausibility (e.g., lack of local update rules for the parameters). Therefore, biologically plausible learning methods that rely on predictive coding (PC), a framework for describing information processing in the brain, are increasingly studied. Recent works prove that these methods can approximate BP up to a certain margin on multilayer perceptrons (MLPs), and asymptotically on any other complex model, and that zerodivergence inference learning (Z-IL), a variant of PC, is able to exactly implement BP on MLPs. However, the recent literature shows also that there is no biologically plausible method yet that can exactly replicate the weight update of BP on complex models. To fill this gap, in this paper, we generalize (PC and) Z-IL by directly defining it on computational graphs, and show that it can perform exact reverse differentiation. What results is the first PC (and so biologically plausible) algorithm that is equivalent to BP in the way of updating parameters on any neural network, providing a bridge between the interdisciplinary research of neuroscience and deep learning. Furthermore, the above results in particular also immediately provide a novel local and parallel implementation of BP."
} | 424 |
32257632 | PMC7103203 | pmc | 3,781 | {
"abstract": "Background Angelica sinensis seedlings are grown in alpine uncultivated meadow soil with rainfed agroecosystems to ensure the quality of A. sinensis after seedling transplantation. The aim was to investigate the rhizosphere bacterial and fungal communities during the growth stages of A. sinensis seedlings. Methods The bacterial and fungal communities were investigated by HiSeq sequencing of 16S and 18S rDNA, respectively. Results Proteobacteria and Bacteroidetes were bacterial dominant phyla throughout growth stages. Fungal dominant phyla varied with growth stages, dominant phyla Ascomycota and Chytridiomycota in AM5, dominant phyla Basidiomycota, Ascomycota and Zygomycota in BM5, and dominant phyla Basidiomycota and Ascomycota in CM5. There was no significant variation in the alpha-diversity of the bacterial and fungal communities, but significant variation was in the beta-diversity. We found that the variation of microbial community composition was accompanied by the changes in community function. The relative abundance of fungal pathogens increased with plant growth. We also identified the core microbes, significant-changing microbes, stage-specific microbes, and host-specific microbes. Plant weight, root length, root diameter, soil pH, rainfall, and climate temperature were the key divers to microbial community composition. Conclusions Our findings reported the variation and environmental drivers of rhizosphere bacterial and fungal communities during the growth of A. sinensis seedlings, which enhance the understanding of the rhizosphere microbial community in this habitat.",
"conclusion": "Conclusions The study for the first time reported the variation and environmental drivers of rhizosphere bacterial and fungal communities during the growth of A. sinensis seedlings. Bacterial dominant phyla were Proteobacteria and Bacteroidetes, and fungal dominant phyla were Ascomycota, Basidiomycota, Chytridiomycota and Zygomycota. The variation in microbial community composition was accompanied by community function changes. We identified the core microbes, significant-changing microbes, stage-specific microbes, and host-specific microbes. Fungal pathogen relative abundance increased with plant growth. R. solani was an opportunistic pathogen that involved in A. sinensis root rot. Therefore, the study increased the understanding of the rhizosphere bacterial and fungal communities of A. sinensis seedlings. In further studies, the relationship between root exudates and stage-specific microbes should be investigated. In addition, a method with the combination of quantitative and relative abundance of microbial communities could contribute to a better understanding for population variation.",
"introduction": "Introduction Angelica sinensis (Oliv.), Diels (Umbelliferae), is an herbaceous perennial plant, widely used in natural medicines in China. In cultivation, it has a three-year growth cycle, fostering the seedlings in the first year, transplanting the seedlings and harvesting the fleshy roots in the second year, and collecting the seeds in the third year. Dingxi is the major producing area for A. sinensis in China, accounting for 70% of the country’s production each year. Rhizosphere microbes are closely related to plant growth. They are considered the second genome of the plant and have pivotal functions in plant health and productivity, such as in nutrient cycling, pathogen suppression, growth promotion, and abiotic stress tolerance ( Berendsen, Pieterse & Bakker, 2012 ; Strecker et al., 2016 ). Yet, the formation of rhizosphere microbe communities is affected by environmental factors such as soil pH ( Hardoim et al., 2011 ), soil temperature ( Lareen, Burton & Schafer, 2016 ) and plant development ( Chaparro, Badri & Vivanco, 2014 ). Much research has focused on rhizosphere microbial communities during the plant development, including microbial composition, community diversity, and core microbes ( Tkacz et al., 2015 ; Vimal et al., 2017 ). Generally, the microbial communities around the roots of different plants are dominated by different microbial phyla, for example, the bacterial phyla Acidobacteria and Proteobacteria for black peppers ( Xiong et al., 2015 ), and the bacterial phyla Proteobacteria, Actinobacteria and Acidobacteria as well as the fungal phyla Ascomycota, Zygomycota and Basidiomycota for apples ( Franke-Whittle et al., 2015 ). Additionally, the alpha- and beta-diversities of the rhizosphere bacteria of potato plants are influenced differently by plant growth stage ( Pfeiffer et al., 2017 ). In the rhizosphere fungal community of potato plants, alpha-diversity is stable but beta-diversity differs with growth stage ( Zimudzi et al., 2018 ). Moreover, the core rhizosphere bacteria have been identified in potatoes ( Pfeiffer et al., 2017 ), blueberries ( Jiang et al., 2017 ), and Arabidopsis ( Schlaeppi et al., 2014 ). Rhizosphere communities are functionally diverse, which may be closely related to community composition ( Zimudzi et al., 2018 ). Many microbes usually inhabit the rhizospheres of different plants and play a role in ecological functions, such as C, N and S cycling ( Li et al., 2014 ; Fierer et al., 2012 ), indole acetic acid production ( Kuffner et al., 2010 ), and biocontrol against plant-pathogenic fungi ( Adrangi et al., 2010 ). However, many other microbes comprising bacteria and fungi are plant pathogens and they are not conducive to plant health ( Peix, Ramirez-Bahena & Velazquez, 2018 ; McGovern et al., 2006 ). Traditionally, A. sinensis seedlings are grown in alpine uncultivated meadow soil with rainfed agroecosystems to ensure seedling quality. Many studies have focused on the rhizosphere microbial communities of different plants, but currently, little is known about the rhizosphere microbial communities of A.sinensis seedling cultivated in this habitat. Thus, this study focused on bacterial and fungal communities during the growth stages and had three objectives: (1) to investigate global microbial diversity and potential microbial functions, (2) to find core microbes, significant-changing microbes, and specific microbes, and (3) to identify environmental factors driving the microbial community variation.",
"discussion": "Discussion In our study, Proteobacteria and Bacteroidetes were the dominant phyla. They also were the dominant phyla in other plant rhizosphere. Compared to the previous works, the Proteobacteria relative abundance in this study was higher than that in these plants, including ramie ( Zhu et al., 2018 ), tomatoes ( Shao et al., 2018 ), potatoes ( Weinert et al., 2011 ), and Arabidopsis ( Bulgarelli et al., 2012 ). This implies that Proteobacteria is generally adapted to the rhizosphere environment across diverse plant species. In terms of fungi, there were the distinct dominant phyla between the growth stages, but these dominant phyla, Basidiomycota, Ascomycota, Chytridiomycota and Zygomycota, have been identified as dominant phyla in previous studies on Panax notoginseng ( Tan et al., 2017 ), ramie ( Zhu et al., 2018 ), and wheat and canola ( Schlatter et al., 2019 ). However, unlike plant and animal ecology, there is not a clear definition for the dominant phylum in microbial ecology until now. Previous studies have shown that the alpha-diversity of the rhizosphere bacterial and fungal communities does not significantly change with plant development, but the beta-diversity significantly changed ( Chaparro, Badri & Vivanco, 2014 ; De Souza et al., 2016 ; Zimudzi et al., 2018 ). The similar results were also found in this study. Thus we speculate that the abundance of some microbes could significantly change between growth stages, or the microbes that dwelt on the certain stage could be present in microbial community succession. Notably, the beta-diversity and function of the bacterial and fungal communities significantly changed with the growth stages, suggesting that microbial community composition variation was accompanied by the changes in the community function ( Philippot, Raaijmakers & Van Der Putten, 2013 ). Some plant pathogens were present during the growth stages, such as Pseudomonas viridiflava ( Albu et al., 2018 ), Rhodococcus fascians ( Putnam & Miller, 2007 ), Rhizobium larrymoorei ( Bouzar & Jones, 2001 ), and Rhizoctonia solani ( Fang, 1983 ). R. solani that involved in A. sinensis root rot as one of the pathogens was present in this study, indicating that R. solani was an opportunistic pathogen. Normally, R. solani may have a neutral relationship with the host plant, but if the plant is stressed, then this relationship can change to cause the plant disease. The core microbes were a subset (less than 7.0%) of global microbiome, which could facilitate the design of plant growth promoting rhizobacteria for A. sinensis seedlings. However, we find that the percentage of core microbes in different studies varies widely ( De Souza et al., 2016 ; Pfeiffer et al., 2017 ), and the fact that the core microbial community composition significantly changed during the growth stage in our study is contrary to the previous findings ( De Souza et al., 2016 ; Pfeiffer et al., 2017 ). These may be mainly caused by the distinct definition of core microbes in different studies ( Lundberg et al., 2012 ; Saunders et al., 2016 ; Meier, Avis & Phillips, 2013 ). Among the significant-changing bacterial genera, 43% of them belonged to Proteobacteria phylum, for example, the Sphingomonas , Rickettsia and Reyranella of Alphaproteobacteria, the Massilia of Betaproteobacteria, and the Arenimonas and Panacagrimonas of Gammaproteobacteria. The stage-specific microbes could be used as the indicators of a growth stage. Those on the rhizosphere are in low abundance and could be more susceptible to ecological drift ( Lankau, Hong & Mackie, 2012 ). It has been shown that low abundance microbes can play an important ecological function, for example, in host health ( Nuccio et al., 2016 ) and microbial community stability and diversity ( Shade et al., 2014 ). Root exudates as a nutrient play an important role for rhizosphere microbial recruitment ( Reinhold-Hurek et al., 2015 ). So we surmise that the stage-specific microbes could be related to the special root exudates inducing the fast response of microbes ( Zhang, Vivanco & Shen, 2017 ). Five bacterial and two fungal genera were considered as the host-specific microbes. For bacteria, Massilia is a major group of bacteria associated with many plants. Members of Massilia were reported to show plant growth promotion traits, including indole acetic acid production ( Kuffner et al., 2010 ), siderophore production ( Chimwamurombe et al., 2016 ), and biocontrol against plant-pathogenic fungi ( Adrangi et al., 2010 ). Other bacteria are also reported with plant growth promotion, such as Methylotenera related to C cycling ( Kalyuzhnaya et al., 2010 ), Ramlibacter to N and P cycling ( Props et al., 2019 ), Lysobacter to the suppression of soil phytopathogens ( Lazazzara et al., 2017 ), Anaeromyxobacter to Fe reduction ( Suriyavirun et al., 2019 ), and Devosia to root nodules and nitrogen fixation ( Rivas et al., 2002 ). For fungi, Itersonilia is known as a fungal pathogen. For example, Itersonilia perplexans appearing in this study causes petal, foliar, and seedling blight and root cankers on host plants ( McGovern et al., 2006 ). Overall, the beneficial microbes and plant pathogens dwelt on a balanced microbial ecosystem of A. sinensis seedlings ( Vimal et al., 2017 ). In our results, pH, T, RF, RD, PW and RL were the important drivers for the bacterial and fungal community composition, which were consistent with previous studies ( Chaparro, Badri & Vivanco, 2014 ; Liang et al., 2015 ; Aslam et al., 2016 ; Nuccio et al., 2016 ). Previous reports have described the similar results that Alpha-, Beta-, and Gammaproteobacteria were the predominant classes in the plant rhizosphere ( Cardinale et al., 2015 ; Gottel et al., 2011 ). According to the sensitivity of the alpha-, beta-, delta-, and gammaproteobacteria to environmental factors, the plant growth promoting rhizobacteria for A. sinensis seedlings should be from the members of Beta- and Deltaproteobacteria. In short, Proteobacteria variation in our study confirmed that Proteobacteria are r-strategists, able to quickly adapt to a changing environment ( Brzeszcz et al., 2016 ). Finally, all of results in our study are based on the taxonomic relative abundance, but the reported studies have demonstrated that the observed differences with relative abundances can cover those with the actual taxonomic abundances ( Zhang et al., 2017 ; Tkacz, Hortala & Poole, 2018 ). The method of combining the quantification and relative abundance of the microbial communities should be used in the further studies ( Lou et al., 2018 ; Guo et al., 2019 )."
} | 3,233 |
36179068 | PMC10098509 | pmc | 3,782 | {
"abstract": "Abstract The chemical industry is transitioning to more sustainable and biobased processes. One key element of this transition is coupling energy fluxes and feedstock utilization for optimizing processes, routes and efficiencies. Here, we show for the first time the coupling of the Kolbe electrolysis at the anode with a subsequent microbial conversion of the cathodically produced co‐product hydrogen. Kolbe electrolysis of valeric acid yields the liquid drop‐in fuel additive n ‐octane. Subsequently, the solvent isopropanol is produced by resting Cupriavidus necator cells using gaseous electrolysis products (esp. CO 2 and H 2 ). The resting microbial cells show carbon efficiencies of up to 41 % and Coulombic/Faradaic efficiencies of 60 % and 80 % for anodic and cathodic reactions, respectively. The implementation of a paired electrolyser resulted in superior process performances with overall efficiencies of up to 64.4 %."
} | 234 |
33551759 | PMC7854533 | pmc | 3,785 | {
"abstract": "Collective phenomena fascinate by the emergence of order in systems composed of a myriad of small entities. They are ubiquitous in nature and can be found over a vast range of scales in physical and biological systems. Their key feature is the seemingly effortless emergence of adaptive collective behavior that cannot be trivially explained by the properties of the system's individual components. This perspective focuses on recent insights into the similarities of correlations for two apparently disparate phenomena: flocking in animal groups and neuronal ensemble activity in the brain. We first will summarize findings on the spontaneous organization in bird flocks and macro-scale human brain activity utilizing correlation functions and insights from critical dynamics. We then will discuss recent experimental findings that apply these approaches to the collective response of neurons to visual and motor processing, i.e., to local perturbations of neuronal networks at the meso- and microscale. We show how scale-free correlation functions capture the collective organization of neuronal avalanches in evoked neuronal populations in nonhuman primates and between neurons during visual processing in rodents. These experimental findings suggest that the coherent collective neural activity observed at scales much larger than the length of the direct neuronal interactions is demonstrative of a phase transition and we discuss the experimental support for either discontinuous or continuous phase transitions. We conclude that at or near a phase-transition neuronal information can propagate in the brain with similar efficiency as proposed to occur in the collective adaptive response observed in some animal groups.",
"conclusion": "Conclusions The emergence of order in systems composed of a myriad of small entities exhibits many parallels between animal groups and neuronal populations in the brain. We summarized new experimental findings for the brain on the emergence of scale-invariant correlations and scale-invariant population sizes and discussed their similarities and differences compared to collective behavior in animals. We show that for both fields of research there are fascinating arguments for systems to be positioned near a phase transition to support propagation of local information throughout the entire system. Future experimental work on the role of cell types and microcircuit mechanisms in maintaining these scale-free dynamical features are crucial for understanding how the brain processes information.",
"introduction": "Introduction The collective movement of animal groups has been the subject of great interest for many decades, with the early work focusing on model simulations (Aoki, 1982 ; Reynolds, 1987 ). It is now well-accepted that collective properties in animal groups are closely related to the general study of collective phenomena in physics, which initially was focused on phase transitions in equilibrium systems composed of many, locally interacting particles (Stanley, 1971 ; Ma, 1976 , 1985 ), but eventually was expanded to include far-from-equilibrium systems (Meakin, 1987 ; Kertesz and Wolf, 1989 ; Martys et al., 1991 ). Many biological systems were found to fit into this latter category specifically when considering systems of self-driven particles to model movements of ants (Millonas, 1992 ; Rauch et al., 1995 ), fish schools (Huth and Wissel, 1992 ) and bird flocks resulting in the seminal model by Vicsek et al. ( 1995 ) for flocking in biological systems based on local interactions impacted by noise. Since then, variations of the Vicsek model (Grégoire and Chaté, 2004 ; Chate et al., 2008 ) as well as other models that utilize attraction and distance rules (Couzin et al., 2002 ; Romanczuk et al., 2009 ) have been combined with experimental observations to capture population dynamics of many species such as locust swarms (Huepe et al., 2011 ), ants (Gelblum et al., 2016 ), fish schools (Tunstrøm et al., 2013 ), migrating white storks (Nagy et al., 2018 ), and cycling pelotons (Belden et al., 2019 ) with a major goal to understand the emergence of collective behavior from the mechanistic interactions between individuals [for a review, see e.g., Wang and Lu ( 2019 )]. These observations support the idea that biological systems seem to be naturally poised near a phase transition (Bak, 1996 ), where they might benefit from order yet maintain adaptability to changing environmental conditions, an idea that is increasingly gaining attraction including the brain (Chialvo, 2010 ; Mora and Bialek, 2011 ; Plenz, 2012 ; Hesse and Gross, 2014 ; Plenz and Niebur, 2014 ). The initial theoretical debate has been enriched recently by an ever-improving ability to simultaneously track many biological elements (neurons, birds, midgets, etc.) over time, such that now the ideas are being challenged and contrasted by the experimental findings in the usual manner of statistical mechanics. In this note, we focus on the behavior of the system correlation properties, the central tenet of statistical mechanics. For the sake of discussion, our starting point will be the work by Cavagna et al. in 2010, who demonstrated that starlings in a flock exhibit spatial correlations much longer than the length of direct interactions between neighboring birds (Cavagna et al., 2010 , 2018 ). Specifically, they showed that the correlation length, i.e., the distance at which correlations drop below zero, grows monotonically with flock size ( Figure 1A ) and is, therefore, scale-free. The absence of any characteristic scale in the correlations is known to be a hallmark of critical systems (Wilson, 1979 ). For the human brain, early evidence of scale-free correlation functions was found for ongoing neuronal activity assessed indirectly using the blood oxygen level dependent signal (BOLD) (Expert et al., 2011 ) followed by the demonstration of correlation length to grow with the size of the observed brain region ( Figure 1B ) (Fraiman and Chialvo, 2012 ), exactly as was described for starling flocks. These remarkable population-spanning correlations were replicated for a network model of the brain with experimentally based interareal connectivity when the network dynamics was tuned to criticality (Haimovici et al., 2013 ). Since then, they have also been observed for bacterial colonies (Chen et al., 2012 ), insect swarms (Attanasi et al., 2014b ), and globular proteins (Tang et al., 2017 , 2020 ). Here, we explore specifically the analogy in scale-free correlations between animal groups and brain dynamics at the scale of local population activity during motor outputs in nonhuman primates and down to the cellular scale of single neuron interactions during sensory processing in mice. We will demonstrate that this analogy goes beyond phenomenology and shares the same formal scaling relations which suggest common underlying principles. Figure 1 Scale-free growths in correlations length is observed in bird flocks and the mammalian brain at different scales and using different recording techniques. (A) Correlations in the velocity fluctuations of pairs of starlings in flocks of different sizes. Fluctuations are obtained by subtracting from each bird's velocity vector ( left ) the center-of-mass velocity of the flock ( middle ). Correlation length, defined as the distance at which correlations of the fluctuations reaches zero, scales linearly with flock size ( right ) in line with expectations from critical dynamics. Adapted from (Cavagna et al., 2010 ). (B) Correlations obtained from blood oxygenated level dependent (BOLD) signals using fMRI to measure ongoing neuronal activity of the human brain. Left : Average correlation between voxel pairs drops with distance between voxels as a power law ( solid line ), while phantom data drops exponentially ( dashed line ) and spatially shuffled data is constant ( dotted line ). Adapted from (Expert et al., 2011 ) Middle : Correlation length, ξ, from fluctuations in BOLD data scales linearly with the size of the brain area observed ( black circles ) or when pooling areas together ( red diamonds ). Adapted from Fraiman and Chialvo ( 2012 ). Right : Mutual information between voxel pairs decays with pair distance, allowing for the definition of “mutual information length,” ξ I , in analogy to correlation length. ξ I scales linearly with the size of the brain area observed ( black circles ). Adapted from Fraiman and Chialvo ( 2012 ). (C) Correlations in the fluctuations of LFP amplitudes from prefrontal cortex in nonhuman primates during a working-memory task using high-density microelectrode arrays. Left/middle : LFP vectors depicting phase and amplitude on the array without/with subtraction of the population average ( blue arrow, left ) in analogy to velocity distributions in flock data. Right : Correlation length scales linearly with (sub)array size for both ongoing ( blue ) and evoked ( red ) data. Adapted from Ribeiro et al. ( 2020 ). Inset : Mutual information length scales linearly with (sub)array size for both ongoing ( blue ) and evoked ( red ) data. (D) Correlations in the fluctuations of neuronal activity from primary visual cortex in mice during visual stimulation using 2-photon imaging. Left : Example field-of-view showing cells used for the analysis. Middle : Average correlation of activity fluctuations between pairs of neurons decays with distance as well as with the size of the observed window ( colors ). Right : Correlation length scales linearly with observed window size for both gray screen ( gray ) or drifting gradings ( red ). Adapted from Ribeiro et al. ( 2020 )."
} | 2,419 |
26350967 | PMC4600107 | pmc | 3,788 | {
"abstract": "ABSTRACT Clostridium aceticum was the first isolated autotrophic acetogen, converting CO 2 plus H 2 or syngas to acetate. Its genome has now been completely sequenced and consists of a 4.2-Mbp chromosome and a small circular plasmid of 5.7 kbp. Sequence analysis revealed major differences from other autotrophic acetogens. C. aceticum contains an Rnf complex for energy conservation (via pumping protons or sodium ions). Such systems have also been found in C. ljungdahlii and Acetobacterium woodii . However, C. aceticum also contains a cytochrome, as does Moorella thermoacetica , which has been proposed to be involved in the generation of a proton gradient. Thus, C. aceticum seems to represent a link between Rnf- and cytochrome-containing autotrophic acetogens. In C. aceticum , however, the cytochrome is probably not involved in an electron transport chain that leads to proton translocation, as no genes for quinone biosynthesis are present in the genome.",
"introduction": "INTRODUCTION The anaerobic autotrophic formation of acetate from a CO 2 -H 2 gas mixture was first described in some detail in 1932 ( 1 ). Anaerobic sludge was used for those experiments, so that no single organism could be identified that was responsible for this metabolic activity. The first pure culture of such a bacterium was isolated by Wieringa ( 2 ), who later described it as Clostridium aceticum ( 3 , 4 ). This is an anaerobic, endospore-forming bacterium, able to grow autotrophically on a CO 2 plus H 2 gas mixture as well as heterotrophically on sugars, organic acids, and alcohols ( 3 , 5 ). Unfortunately, the strain was thought to be lost in the late 1940s. Many attempts of reisolation failed until 1980 ( 6 ), and so the pathway of autotrophic acetogenesis (now called the Wood-Ljungdahl pathway) was elucidated by using Moorella thermoacetica (formerly Clostridium thermoaceticum ) (for a review, see reference 7 ). Except for Wieringa, only the group of Barker and colleagues worked with C. aceticum in the 1940s, publishing a paper on the nutritional requirements of this organism ( 8 ). And it was in the culture collection of Horace A. Barker (University of California, Berkeley) that one of us (G. Gottschalk), during a sabbatical in 1979, rediscovered a spore preparation of the original C. aceticum ( 5 ), which thus became available for research again, after being apparently lost for 4 decades. Shortly before, in 1977, another autotrophic acetogen was isolated, Acetobacterium woodii ( 9 ). Detailed investigations on its bioenergetics revealed a different mode of energy conservation. A. woodii is sodium dependent, uses a so-called Rnf system for generation of a sodium ion gradient, and possesses an Na + -dependent ATPase for ATP generation ( 10 – 12 ). M. thermoacetica instead contains cytochromes and menaquinone, and it obviously generates a proton gradient, which is used by a H + -dependent ATPase for energy conservation. A respective scheme has been proposed by Das and Ljungdahl ( 13 ). However, this organism grows poorly under autotrophic conditions ( 14 ). In his analysis of the rediscovered C. aceticum , Manfred Braun provided biochemical evidence for the presence of cytochromes in this bacterium, which was only published in his Ph.D. thesis ( 15 ). As C. aceticum shows good growth with a CO 2 +H 2 mixture, we performed genome sequencing in order to elucidate possible differences with respect to energy conservation in M. thermoacetica .",
"discussion": "DISCUSSION The most surprising feature arising from the C. aceticum genome sequence is the (so far) unique position with respect to energy conservation in autotrophic acetogens ( Table 2 ). The closest autotrophic acetogenic relatives are Clostridium difficile AA1, Clostridium glycolicum DSM 1288, and Clostridium mayombei DSM 6539 ( 29 ). However, BLAST analysis of C. aceticum 16S rRNA gene sequences and a subsequent reconstruction of a phylogenetic tree indicated Natronincola ferrireducens was the closest relative ( Fig. 3 ). This organism is described as an alkaliphilic, anaerobic, peptolytic, and iron-reducing bacterium ( 30 ). Inspection of the genome sequence revealed that N. ferrireducens also contains the Wood-Ljungdahl gene cluster, in an identical arrangement as found in C. aceticum . N. ferrireducens also contains gene clusters encoding sodium/proton antiporters highly similar to the ones from C. aceticum . It is noteworthy that such gene clusters can also be found in phylogenetically related bacteria whose genome sequences are publicly available. These include more distantly related bacteria, such as N. peptidivorans , Alkaliphilus transvaalensis , A. peptidifermentans , A. metalliredigens , C. halophilum , and C. litorale ( Fig. 3 ). All of these bacteria are described as being alkaliphilic and may therefore be dependent on a sodium/proton antiporter. TABLE 2 Occurrence in acetogens of systems involved in energy conservation Species and strain/culture collection ID Presence (+) or absence (−) of system Rnf complex Ech hydrogenase Cytochromes Quinones a NfnAB A. woodii WB1/DSM 1030 + − − − − Ah. arabaticum Z-7288/DSM 5501 + − − + + Ca. hydrogenoformans Z-2901/DSM 6008 − + + + + C. aceticum /DSM 1496 + − + − − C. autoethanogenum /DSM 10061 + − − − + C. difficile 630/DSM 27543 + − − − + C. ljungdahlii PETC/DSM 13528 + − − − + E. limosum KIST612/ATCC 8486 + − − − − M. thermoacetica /ATCC 39073 − + + + + T. kivui /DSM 2030 − + − − + Ta. phaeum PB/DSM 12270 − + − + − Tr. primitia ZAS-2/DSM 12427 + − − − + a Based on presence of key enzymes (UbiA, UbiD, UbiX, and UbiE). Ah. , Acetohalobium ; Ca ., Carboxydothermus ; Ta ., Thermacetogenium ; Tr ., Treponema . FIG 3 Phylogenetic tree based on bacterial 16S rRNA sequences from closely related bacteria of C. aceticum and selected autotrophic acetogenic bacteria. Accession numbers shown in bold indicate the availability of the genome sequence. Two asterisks after a strain name indicate the presence of a gene cluster coding for a multisubunit sodium/proton antiporter and formate:hydrogen lyase. An uppercase letter A following a name indicates that the strain has been described to be capable of autotrophic growth. In C. aceticum , the net ATP generation is obviously performed by an Rnf complex, which is probably proton dependent. However, as in M. thermoacetica one (or more) cytochrome(s) is present. In contrast to M. thermoacetica , the cytochrome(s) in C. aceticum cannot be used for generation of a proton gradient, as C. aceticum lacks the ability to synthesize quinones. This leaves the question as to the C. aceticum cytochrome function. In the archaeon Methanosarcina acetivorans , a cytochrome c is involved in the electron transfer from Rnf to the heterodisulfide reductase ( 31 ). Rnf might also be the electron donor for cytochrome c in C. aceticum , but the acceptor obviously might be different. Four nitrite reductase genes are present, but there is no gene encoding a nitrate reductase. Cytochrome-independent enzymes are encoded by nirB (CACET_c02780) and nasD (CACET_c16220). The other two nitrite reductase genes (CACET_c22020 and CACET_c22150) encode enzymes which are known to recruit cytochrome c for catalysis. So, this might be the physiological function of the cytochrome in C. aceticum . In M. thermoacetica , nitrate is a preferred electron sink over CO 2 when cells are grown on vanillin and vanillate, preventing acetate and favoring CO 2 formation ( 32 ). In this organism, the cytochrome was essentially absent in cells grown in the presence of nitrate ( 33 ). Also, the nitrite reductases of M. thermoacetica are, according to sequence homology, not cytochrome dependent; these findings therefore further strengthen the physiological function differences for cytochromes in C. aceticum and M. thermoacetica . Another striking difference was the absence of a cluster of phosphotransacetylase and acetate kinase genes. In fact, a phosphotransacetylase could not be annotated. However, one or both of the annotated phosphotransbutyrylase genes or one of the pduL genes might take over this function. Phosphotransacetylase activity could clearly be determined in crude extracts, and acetate is the dominant fermentation product. As already mentioned, pdu genes in S. enterica are responsible for coenzyme B 12 -dependent 1,2-propanediol (1,2-PD) degradation ( 27 ). PduCDE catalyze propionaldehyde formation (from which PduQ can generate propanol), and PduP catalyzes propionyl-CoA synthesis. PduL converts propionyl-CoA into propionyl phosphate, from which propionate is generated by PduW. PduL also exerts phosphotransacetylase activity (0.4 U/mg of protein), as shown by analysis of Pta − and PduL − mutants ( 27 ). pdu genes have also been found in A. woodii and other acetogens ( 34 ). Another important feature in some acetogens, including M. thermoacetica , C. autoethanogenum , and C. ljungdahlii , is the presence of an electron-bifurcating NAD + -dependent reduced ferredoxin:NADP + oxidoreductase complex (NfnAB), which produces 2 molecules of NADPH from NADH and reduced ferredoxin ( 35 , 36 ). In the two clostridia, nfnA and nfnB are fused into a single gene ( 35 ). No such gene(s) could be detected in C. aceticum . Also, A. woodii does not contain nfnAB genes ( 21 ). For C. ljungdahlii ( 25 ) and M. thermoacetica ( 37 ), the possibility has been discussed that the reduction of methylene-THF to methyl-THF could be a site of electron bifurcation. For A. woodii , such a possibility has been excluded ( 38 ). However, in M. thermoacetica the reaction is coupled via flavin-based electron bifurcation with the endergonic reduction of a still-unknown electron acceptor ( 37 ). Whether such a reaction also adds to energy conservation in C. aceticum cannot be answered yet. An alignment of methylene-THF reductases from M. thermoacetica , A. woodii , C. aceticum , C. ljungdahlii , and C. autoethanogenum shows almost complete identity of all important amino acid residues (based on data provided by Mock et al. [37], which show some inconsistencies in some amino acid positions). Thus, biochemical data are required to answer this question. Finally, sodium/proton antiporters as found in C. aceticum are not present in M. thermoacetica , C. ljungdahlii , or A. woodii . Although we have not yet detected a sodium dependency of C. aceticum , there might be metabolic reactions which involve sodium ions or their transport across the cytoplasmic membrane. Thus, C. aceticum might not only represent the link between the cytochrome-containing, Rnf-lacking and the cytochrome-lacking, Rnf-containing autotrophic acetogens, but also it might close the gap between proton- and sodium-dependent species of this metabolic group. This might be further supported by the close relationship to N. ferrioxidans , which was isolated from a soda lake and is considered alkaliphilic. A recent hypothesis was proposed, however, to bioenergetically classify acetogens into those containing either an Rnf complex or an Ech complex ( 38 ). This is based on the fact that the generation of a proton gradient by cytochromes and quinones in M. thermoacetica has not been biochemically verified yet and that M. thermoacetica carries genes with homology to those of an Ech complex. However, the functions of these respective gene products have not been verified either. The genome sequence of T. kivui was recently published, and this organism carries neither rnf nor cytochrome/quinone genes, but instead ech genes ( 39 ). So, in this case the proposed classification might be suitable, but for general acceptance the biochemical functions of cytochromes and quinones in M. thermoacetica need to be verified. Two recent publications reported a very incomplete and a 53-contig C. aceticum genome sequence ( 40 , 41 ). Probably due to this incomplete sequence, those authors missed all the essential features described above. The fermentation of l -malate and fumarate by C. aceticum very much resembles the reactions described for Clostridium formicoaceticum ( 42 ). Fumarate is dismutated to succinate, acetate, and CO 2 . Succinate stems from direct reduction of fumarate, while acetate and CO 2 are produced from pyruvate, which is derived via malate and oxaloacetate. Fumarate clearly represented the better substrate, yielding succinate as the dominant product. With l -malate, mostly acetate was formed. A lactate dehydrogenase gene (CACET_c16820) was found in the genome sequence of C. aceticum . However, under all conditions tested, neither lactate formation nor lactate utilization could be detected. The genome organization does not show a combination with etf genes, which encode an electron transfer flavoprotein. Such a pattern has been found in A. woodii , where a lactate dehydrogenase/Etf complex uses flavin-based electron confurcation to drive endergonic lactate oxidation with NAD + ( 43 ). Thus, the function of the putative lactate dehydrogenase in C. aceticum still remains to be elucidated. The lack of a citrate synthase gene appears puzzling at first glance. However, the Re -citrate synthase from C. kluyveri was found to be phylogenetically related to homocitrate synthase and isopropylmalate synthase ( 44 ). A subunit of isopropylmalate synthase in fact exhibits Re -citrate synthase activity. In C. aceticum , CACET_c09870 and CACET_c09880 are annotated as isopropylmalate synthase subunit genes, but the gene products do not show high homology in a BLAST search to the C. kluyveri enzyme. However, the proteins are in the same COG and KOG categories (COG0119, KOG2367) and contain the same Pfam domains (00682 and 08502) as their C. kluyveri counterparts. Most likely, such an enzyme replaces the missing Si -citrate synthase in C. aceticum . In general, acetogens with a low pH optimum (e.g., C. ljungdahlii and C. autoethanogenum ) form acetate and ethanol, whereas those with a more neutral pH optimum (e.g., A. woodii ) produce only acetate under standard growth conditions. Having an alkaliphilic pH optimum of 8.3 ( 5 ), C. aceticum belongs rather to the latter group and, in fact, produces only acetate under standard growth conditions. However, the situation is certainly not only pH dependent, but also more complex. In the genome of C. aceticum , genes encoding seven alcohol dehydrogenases and two acetaldehyde dehydrogenases were found. Also, A. woodii has been reported to produce ethanol at high (40 mM), but not low (10 mM), sugar concentrations ( 45 ). A significant effect of phosphate concentration on ethanol production by A. woodii could be demonstrated. Maximal ethanol production occurred at 3.2 mM phosphate; starting at a concentration of 8.4 mM phosphate, ethanol was no longer produced. A striking feature is the often highly conserved clustering of Wood-Ljungdahl pathway genes in acetogens ( Fig. 1 ). Surprisingly, if all genes involved in this pathway are organized in one or at most two gene clusters, two genes of the glycine decarboxylase or glycine cleavage complex are found in these clusters as well, namely, the genes for protein 3 ( lpdA , dihydrolipoamide dehydrogenase) and protein 2 ( gcvH , lipoate-containing protein H of the glycine cleavage system). On the other hand, genes for the remaining proteins of the complex (P1 and P4) are always missing. A recent hypothesis proposed formatotrophic growth via a reductive glycine pathway that contains elements of the Wood-Ljungdahl pathway as well as the glycine cleavage or synthesis system ( 46 ). In this pathway, formate is converted to methylene-THF, which is converted to glycine and further to serine and pyruvate. However, for this pathway to function in acetogens, all four proteins would be required (P1 to P4). So, why then would only two genes be located in the Wood-Ljungdahl gene cluster? However, there might be another possibility. About 30 years ago, Pezacka and Wood described a CO dehydrogenase-disulfide reductase in M. thermoacetica ( 47 ). The enzyme seems to be involved in acetyl-CoA formation. An amino acid composition was reported ( 47 ); however, it was impossible to identify the respective gene product from the genome sequence of M. thermoacetica based on these data. So, lpdA and gcvH gene products might catalyze such a reaction. Biochemical experiments are required to prove or disprove this hypothesis."
} | 4,138 |
38248622 | PMC10813684 | pmc | 3,790 | {
"abstract": "In the field of three-dimensional object design and fabrication, this paper explores the transformative potential at the intersection of biomaterials, biopolymers, and additive manufacturing. Drawing inspiration from the intricate designs found in the natural world, this study contributes to the evolving landscape of manufacturing and design paradigms. Biomimicry, rooted in emulating nature’s sophisticated solutions, serves as the foundational framework for developing materials endowed with remarkable characteristics, including adaptability, responsiveness, and self-transformation. These advanced engineered biomimetic materials, featuring attributes such as shape memory and self-healing properties, undergo rigorous synthesis and characterization procedures, with the overarching goal of seamless integration into the field of additive manufacturing. The resulting synergy between advanced manufacturing techniques and nature-inspired materials promises to revolutionize the production of objects capable of dynamic responses to environmental stimuli. Extending beyond the confines of laboratory experimentation, these self-transforming objects hold significant potential across diverse industries, showcasing innovative applications with profound implications for object design and fabrication. Through the reduction of waste generation, minimization of energy consumption, and the reduction of environmental footprint, the integration of biomaterials, biopolymers, and additive manufacturing signifies a pivotal step towards fostering ecologically conscious design and manufacturing practices. Within this context, inanimate three-dimensional objects will possess the ability to transcend their static nature and emerge as dynamic entities capable of evolution, self-repair, and adaptive responses in harmony with their surroundings. The confluence of biomimicry and additive manufacturing techniques establishes a seminal precedent for a profound reconfiguration of contemporary approaches to design, manufacturing, and ecological stewardship, thereby decisively shaping a more resilient and innovative global milieu.",
"conclusion": "5. Conclusions In conclusion, the incorporation of biomimetic smart materials in the greater technological field of 4D printing holds significant promise in influencing the trajectory of self-transforming entities. The capacity of these materials to demonstrate dynamic responses, adaptability, and self-transformation presents novel opportunities across diverse industries. The incorporation of biomimetic smart materials exhibits significant potential for pioneering applications across a range of fields, including healthcare, robotics, architecture, and aerospace applications. The fusion of biomimetic smart materials with 4D printing technology offers a transformative path towards environmental sustainability. Drawing inspiration from nature’s intricate designs, this integration empowers materials with adaptability and self-transformation capabilities, revolutionizing manufacturing paradigms. These biomimetic materials not only reduce waste generation but also minimize energy consumption and the overall environmental footprint. By enabling inanimate objects to evolve, self-repair, and adapt to their surroundings, this synergy pioneers a sustainable future. Despite the presence of challenges such as material control, scalability, and durability, ongoing research and technological advancements are being undertaken to tackle these concerns. Researchers are actively focusing on enhancing material properties, improving scalability, and ensuring long-term durability to facilitate broader industrial applications. As the investigation and enhancement of biomimetic smart materials in 4D printing continue, we are facilitating the progression of groundbreaking developments in personalized medicine, adaptable robotics, sustainable architecture, and other related fields. These materials possess promising potential to significantly transform industries and influence the advancement of self-transforming objects, thereby offering various enhancements in a number of fields, ultimately contributing to a more sustainable and technologically advanced future.",
"introduction": "1. Introduction The technology of 3D (three-dimensional) printing has revolutionized manufacturing and design, enabling the creation of complex and customized objects with ease. Building upon this transformative technology, the emergence of 4D (four-dimensional) printing has taken additive manufacturing to the next level [ 1 ]. Unlike traditional static 3D printing, 4D printing introduces the dimension of time, allowing objects to transform their shape, properties, or functionality over time in response to external stimuli. This groundbreaking concept has opened up new frontiers in engineering, materials science, and robotics, offering unprecedented opportunities for innovation and application [ 2 ]. At its core, 4D printing encompasses the integration of smart materials that can undergo controlled and programmed shape changes or property alterations when triggered by specific stimuli. These stimuli can range from temperature variations, humidity, light, or even mechanical forces. The materials used in 4D printing possess the remarkable ability to respond to these external cues, initiating a transformation process that leads to dynamic and adaptive behavior [ 3 ]. Temporal integration in 4D printing introduces the dimension of time, enabling dynamic transformations and shape changes in printed objects over time in response to various stimuli like temperature, light, or moisture. This evolution significantly expands production possibilities compared to static 4D printing. Benefits of temporal integration in 4D printing include increased complexity and functionality, adaptive and responsive properties, and enhanced customization as well as functional evolution [ 3 ]. Static 4D printing involves objects that have predefined, fixed transformations or shape changes. Temporal integration surpasses this by introducing a time-based element, allowing for continuous or triggered changes. While static 4D printing is remarkable, temporal integration elevates its capabilities by offering more dynamic and adaptive structures [ 3 ]. The fundamental principle behind 4D printing lies in the precise design and fabrication of smart materials with the desired shape-changing properties. Biomimetic smart materials utilized in 4D printing showcase dynamic adaptability, responding to diverse external cues in a way akin to natural responsiveness. These materials react to stimuli like temperature shifts, humidity fluctuations, pH changes, light exposure, or specific chemicals [ 4 ]. Triggered by these factors, they undergo alterations, resulting in shape modifications, expansions, contractions, or property changes. By emulating biological responsiveness to environmental cues, these biomimetic materials enable adaptable and dynamic behaviors in 4D printing, fostering applications across fields such as medicine, robotics, and architecture. Smart material selection stands as a critical determinant for achieving desired shape-changing characteristics in 4D printing. The choice of smart materials significantly influences the object’s responsiveness to stimuli and its ability to undergo precise transformations over time. Factors such as the material’s inherent properties, including shape memory, responsiveness to specific triggers (like temperature, light, or moisture), durability, and compatibility with the printing process, profoundly impact the efficacy of shape changes [ 4 ]. Moreover, considerations regarding the intended application and environmental conditions further shape the selection process, emphasizing the pivotal role of smart material choice in determining the success and functionality of 4D-printed objects. Notably, the development of shape-shifting materials has revolutionized the landscape of 4D printing. One prominent example is the utilization of smart materials, such as hydrogels and shape memory polymers, which can transform their shapes in response to external stimuli like temperature, light, or moisture [ 4 ]. Researchers have successfully demonstrated the creation of intricate structures that can self-assemble or morph into predetermined shapes, showcasing the potential for applications in various fields, including biomedical devices, aerospace components, and flexible electronics. Additionally, the integration of multi-material and multi-scale 3D printing techniques has led to the production of complex, functional, and customizable products, from light-weight, high-performance automotive parts to intricate, patient-specific medical implants, underscoring the versatility and potential of additive manufacturing in modern technology [ 5 ]. These materials can be classified into two main categories: shape memory materials and stimuli-responsive materials [ 6 , 7 ]. Shape memory materials have the capacity to “remember” a specific shape and return to it when activated by an external stimulus. Stimuli-responsive materials, on the other hand, can undergo reversible changes in their properties or shape when exposed to specific triggers [ 8 , 9 ]. To realize the full potential of 4D printing, a multidisciplinary approach is essential. Researchers and engineers draw from fields such as materials science, mechanical engineering, computer science, and design to develop innovative strategies for material selection, design optimization, and fabrication techniques [ 10 , 11 ]. Computer modeling plays a pivotal role in forecasting and optimizing 4D-printed structures by simulating and predicting their dynamic behaviors and shape changes over time [ 12 ]. Through advanced computational algorithms, these models can simulate the response of materials to different stimuli, allowing for the prediction of structural transformations. By analyzing various parameters such as material properties, environmental conditions, and design intricacies, computer models can optimize the printing process, predict how structures will morph or adapt, and fine-tune designs to regulate and control specific shape changes. This enables a more precise and efficient creation of 4D-printed objects with tailored and regulated dynamic behaviors [ 13 ]. Biomimetic smart materials epitomize a scientific endeavor rooted in nature’s teachings. These materials, shaped by a profound understanding and emulation of the intricate architectures, adaptive behaviors, and responsive mechanisms inherent in diverse biological systems, span the vast spectrum from macroscopic entities like plants and animals to the microscopic domains of microbes and cellular structures [ 14 ]. This scientific pursuit involves an exhaustive exploration of nature’s evolutionarily refined strategies developed over billions of years. Scientists aim to decipher and replicate these exceptional material properties and functionalities previously beyond the scope of conventional methodologies. By meticulously studying nature’s design principles, biomimetic smart materials pave the way for innovations that not only imitate but also surpass the efficiency and adaptability ingrained in natural systems [ 15 ]. One of the fundamental objectives driving the development of biomimetic smart materials lies in emulating nature’s remarkable adaptability and responsiveness to environmental stimuli [ 16 ]. Across various biological systems, there exists a pervasive capacity to sense and dynamically react to alterations in temperature, humidity, light exposure, pressure, and chemical cues [ 17 ]. Integrating these inherent responsive capabilities into synthetic materials represents a scientific pursuit aimed at creating innovative materials capable of real-time adaptation, morphing, or self-healing, mirroring the intricate mechanisms observed in living organisms [ 18 ]. This scientific pursuit involves a multidisciplinary approach, encompassing materials science, bioengineering, and nanotechnology, among other fields, to replicate and harness the dynamic responsiveness witnessed in biological systems. Researchers meticulously study and replicate these natural mechanisms, seeking to imbue synthetic materials with similar adaptive properties for applications from biomedical devices to advanced engineering and beyond. The potential applications of biomimetic smart materials are vast and diverse. From engineering to medicine, robotics to architecture, these materials hold promise for enhancing the performance, sustainability, and functionality of various systems [ 19 , 20 , 21 , 22 ]. They offer new opportunities for designing advanced prosthetics, developing self-repairing infrastructure, creating adaptive textiles, fabricating responsive sensors, and revolutionizing energy harvesting and storage [ 23 , 24 , 25 , 26 , 27 ]. In example, the pioneering work of Voronkina et.al. [ 28 ] discusses the intricate structure of the Aphrocallistes beatrix spong while looking into the fundamental principles of bioarchitecture, aligning with the biomimetic framework advocated in the broader discourse. The study’s findings, showcasing the positioning of actin filaments within a biosilica-based honeycomb structure, underscore the potential for biomimetic material design [ 28 ]. This empirical evidence not only supports the synthesis of biomimetic models through advanced manufacturing techniques like 3D printing but also hints at specialized genetic mechanisms guiding silicate biosynthesis in unique marine environments. Incorporating these insights into the discourse surrounding biomaterials and contemporary manufacturing enhances the paradigm shift towards ecologically conscious design and production practices, ultimately fostering innovative applications that echo nature’s solutions and drive sustainability in object design and fabrication. Several biomimetic smart materials have emerged as crucial building blocks in the fields of biomimicry and 4D printing [ 29 ]. Shape memory alloys (SMAs) are among the most important materials, known for their ability to “remember” and recover their original shape when subjected to certain stimuli [ 30 , 31 ]. They find applications in various fields, including aerospace, robotics, and medicine [ 32 , 33 ]. Another vital material is shape memory polymers (SMPs), which can undergo significant deformation and recover their original shape when triggered by specific stimuli. SMPs are particularly valuable in biomedical applications, such as tissue engineering and drug delivery systems. Additionally, hydrogels, inspired by the water-absorbing properties of biological tissues, have garnered significant attention [ 34 ]. These soft, water-swollen materials exhibit remarkable responsiveness to external stimuli, making them ideal candidates for applications in soft robotics, biomedical devices, and sensors [ 35 ]. Moreover, electroactive polymers (EAPs) imitate the electrical signaling properties of biological systems, enabling them to undergo shape changes or actuation when an electric field is applied. EAPs have tremendous potential in fields such as artificial muscles, haptic interfaces, and biomimetic sensors. These biomimetic smart materials stand at the forefront of research and development, driving innovations and shaping the future of adaptive and responsive technologies [ 36 , 37 , 38 ]. Biomimetic smart materials represent a pivotal advancement in materials science, with sustainability at their core [ 39 ]. By drawing inspiration from nature’s adaptive and self-regulating mechanisms, these materials offer a promising avenue for sustainable innovation. One of their most compelling sustainability aspects lies in their ability to reduce waste and energy consumption. Through self-transformation and adaptability, these materials can optimize their performance in response to changing conditions, thus minimizing the need for constant replacements or interventions [ 40 ]. This inherent durability and efficiency align with the principles of a circular economy, where resources are conserved, and environmental impacts are reduced. Additionally, the development of biomimetic smart materials promotes responsible sourcing and production practices, fostering a more environmentally conscious approach to material design. As such, these materials not only offer exciting possibilities for novel applications but also play a significant role in shaping a more sustainable and eco-friendly future [ 41 ]. Biomimetic smart materials stand at the forefront of technological advancement, embodying a multifaceted approach that harmonizes seamlessly with several of the key Sustainable Development Goals (SDGs) set forth by the United Nations [ 42 ]. By harnessing nature-inspired design principles and cutting-edge technology, these materials play a pivotal role in driving innovation and sustainability across various domains [ 43 , 44 ]. More specifically, as far as “SDG 9—Industry, Innovation, and Infrastructure” is concerned, these materials epitomize innovation, offering groundbreaking solutions that revolutionize industry practices and infrastructure development. Their resource-efficient, self-regulating attributes not only enhance productivity but also contribute significantly to the overarching goal of fostering sustainable industry and infrastructure [ 45 , 46 ]. Also, as far as “SDG 11—Sustainable Cities and Communities” is concerned, in the field of urbanization, biomimetic materials have emerged as transformative agents. They empower the creation of adaptive, resilient urban structures that can thrive amidst evolving environmental challenges. These materials contribute substantially to the sustainable development of cities and the promotion of resilient communities [ 47 , 48 ]. In addition, in compliance with “SDG 12—Responsible Consumption and Production”, biomimetic materials are a characteristic example of responsible consumption and production. By extending the lifespan of products and minimizing waste, they embody sustainable manufacturing and consumption practices. Their innate resource optimization capabilities align closely with SDG 12’s objectives [ 49 ]. Regarding “SDG 13—Climate Action”, these materials play a pivotal role in the global endeavor to combat climate change. Through their ability to adapt to changing environmental conditions and reduce energy consumption, they contribute significantly to mitigating climate-related challenges and advancing climate action initiatives [ 50 ]. Regarding “SDG 14—Life Below Water” and “SDG 15—Life on Land”, biomimetic materials draw inspiration from the intricacies of ecosystems, thus aligning seamlessly with the preservation and sustainable management of terrestrial and aquatic environments. Their design principles are inherently linked to the goals of conserving biodiversity and maintaining ecological balance [ 51 , 52 , 53 ]. Lastly, bearing in mind “SDG 17—Partnerships for the Goals”, the development and implementation of biomimetic materials necessitate interdisciplinary collaboration and cooperation. Researchers, industries, governments, and stakeholders unite to pioneer these transformative technologies, thereby nurturing global partnerships and facilitating collective efforts aimed at realizing a sustainable future [ 54 ]. In summary, biomimetic smart materials exemplify a holistic and dynamic approach to addressing several pivotal SDGs. Their innovative character, sustainability-driven ethos, and capacity for responsible resource utilization make them instrumental in advancing the global mission for a more sustainable and equitable future, aligning impeccably with the comprehensive development agenda outlined by the United Nations. Figure 1 depicts the aforementioned relevant Sustainable Development Goals (SDGs) proposed by the United Nations. Challenges associated with biomimetic smart materials for 4D printing arise from the complexity of replicating the intricate functionalities found in living organisms. One major challenge lies in achieving precise control over material properties and responses, ensuring that the desired shape changes or property alterations occur predictably and reliably. Developing materials with the necessary mechanical, thermal, and chemical properties while maintaining responsiveness to stimuli requires a deep understanding of material science and biology. Additionally, scalability and compatibility with existing 4D printing techniques pose challenges, as manufacturing processes need to be optimized for large-scale production without compromising the materials’ functionality. Moreover, long-term durability and stability of biomimetic smart materials remain critical concerns in ensuring their practical viability and longevity in real-world applications [ 55 ]. However, the challenges of biomimetic smart materials also present significant opportunities for innovation and advancement. The ability to replicate and harness the adaptability and self-regulating mechanisms of living organisms opens up new possi-bilities for engineering design. By leveraging biomimicry principles, researchers can develop materials that exhibit enhanced functionality, responsiveness, and resilience. These materials have the potential to revolutionize fields such as healthcare, robotics, architecture, and more. Biomimetic smart materials enable the creation of self-transforming objects with unprecedented capabilities, from shape-changing structures to programmable soft robots. Furthermore, they offer the prospect of sustainable and eco-friendly solutions by drawing inspiration from nature’s efficient and resource-conserving systems. The advancement of biomimetic intelligent materials within the realm of 4D printing not only affords avenues for scientific and technological progress but also aligns with the overarching objective of fostering enhanced adaptability and intelligence in our environment.",
"discussion": "4. Discussion According to the aforementioned shape memory materials, including shape memory alloys (SMAs), shape memory polymers (SMPs), and electroactive polymers (EAPs), represent a diverse class of materials with unique properties that have garnered significant interest in the fields of engineering, materials science, and biomedical technology. Each of these materials demonstrates distinctive characteristics in terms of their responsiveness to external stimuli, actuation capabilities, and applications. In this context, a comprehensive comparison table, outlining the key features of these materials, their response mechanisms, actuation speeds, and varied applications, can be found in Table 8 , highlighting their contributions to the advancement of modern technologies. A complementary factor that might have notable impact in several aspects of shape memory biomimetic materials is texture. Texture, in the context of biomimetic shape memory materials, refers to the surface characteristics and structural features of the mate-rial. The effect of texture on the shape memory of biomimetic shape memory materials can be significant and is often a subject of research and development in materials science [ 126 ]. Firstly, the texture of biomimetic shape memory materials can affect their mechanical properties [ 127 ]. For example, surface roughness or patterns inspired by natural structures can influence the material’s stiffness, flexibility, and overall mechanical performance. These properties, in turn, can impact the material’s ability to undergo shape memory transformations. Secondly, the texture of biomimetic materials can be designed to enhance shape fixity and recovery [ 128 ]. Mimicking the microstructures found in certain natural materials, such as muscle fibers or collagen, can contribute to improved shape memory performance. These textures may guide and control the deformation and recovery processes in the material. Also, texture can affect surface interactions, such as adhesion and friction, which play a role in the shape memory behavior of biomimetic materials [ 129 ]. Optimizing the surface texture can reduce friction during shape recovery, allowing for smoother and more efficient transitions between different shapes. Some biomimetic shape memory materials are responsive to temperature changes. Texture can influence how these materials respond to temperature variations [ 130 ]. For instance, certain surface textures may enhance heat transfer, affecting the speed and efficiency of the shape memory transition at different temperatures. In addition, the texture of biomimetic materials is crucial in applications where biocompatibility is essential, such as in medical implants [ 131 ]. Mimicking the texture of natural tissues can improve integration with biological systems, and this, in turn, can impact the overall performance and stability of shape memory materials in biological environments. Lastly, texture can be strategically engineered on the surface of biomimetic shape memory materials to create “smart surfaces” that respond to external stimuli [ 132 ]. This can include designing textures that respond to specific biochemical signals or environmental conditions, expanding the range of applications for these materials. While biomimetic smart materials offer exciting opportunities for 4D printing and the creation of self-transforming objects, several challenges need to be addressed to fully harness their potential. One significant challenge lies in the precise control of material properties and responses. Biomimetic smart materials often exhibit complex behaviors that need to be accurately replicated and controlled in the manufacturing process. Achieving consistent and predictable shape changes, as well as appropriate responses to stimuli, requires a deep understanding of material science, biology, and engineering. Researchers ensure trigger reliability and specificity for external stimuli-induced changes in biomimetic smart materials through meticulous material design and testing methodologies. They focus on engineering materials with precise molecular or structural responsiveness to intended stimuli, conducting rigorous testing to validate trigger reliability and specificity. This process involves thorough experimentation to verify that the material responds accurately and consistently to the designated stimuli while remaining inert to other environmental factors. By fine-tuning material composition, molecular structures, and fabrication techniques, researchers strive to achieve a high degree of reliability and specificity in triggering desired changes within biomimetic smart materials, ensuring their efficacy in real-world applications. The integration of biomimetic smart materials into widespread industrial use faces challenges concerning scalability and compatibility with existing 4D printing techniques. While initial successes in laboratory settings showcase the potential, transitioning these materials from small-scale prototypes to large-scale production presents significant hurdles. To accommodate biomimetic smart materials effectively, adaptations in manufacturing processes are crucial. This involves optimizing existing techniques or developing new methodologies tailored for industrial-scale applications while preserving the material’s functionality and performance. This necessitates a delicate balance between scalability, cost-effectiveness, and maintaining high-quality production standards, posing an ongoing area of research and innovation within the field. Changes needed to accept biomimetic smart materials on a larger scale encompass advancements in printing technologies, such as improved precision and speed, while ensuring materials’ compatibility with mass production methods. Additionally, standardization of material properties, better characterization methods, and advancements in post-processing techniques are imperative to streamline integration into industrial workflows. Moreover, research efforts focus on developing novel material formulations that offer enhanced manufacturability, durability, and responsiveness to stimuli, facilitating their seamless adoption within existing 4D printing frameworks. Another challenge lies in the long-term durability and stability of biomimetic smart materials. Ensuring that these materials retain their functionality over extended periods is crucial for their practical viability in real-world applications. Factors such as degradation, fatigue, and aging can impact their performance and longevity. Exploring ways to enhance the durability and stability of these materials, as well as understanding their long-term behavior under different environmental conditions, is essential for their successful implementation. Additionally, the development of biomimetic smart materials for 4D printing requires multidisciplinary collaboration among experts from various fields, including materials science, biology, engineering, and design. Bridging the knowledge gap and fostering effective communication among these disciplines is vital to fully capitalize on the potential of biomimetic smart materials. Collaborative efforts can accelerate advancements and address the challenges associated with these materials, leading to transformative applications in various industries. The incorporation of biomimetic smart materials as raw materials in 4D printing is poised to play a pivotal and transformative role in the pursuit of environmental sustaina-bility and the mitigation of our ecological footprint. This innovative fusion of biomimic-ry-inspired materials and cutting-edge 4D printing technology presents an unprecedented opportunity to address pressing environmental challenges and usher in a sustainable and adaptive future. Biomimicry, as a well-established discipline grounded in emulating nature’s intri-cate solutions, serves as the foundational framework for the development of materials imbued with remarkable attributes, including adaptability, responsiveness, and self-transformation. The resulting symbiosis between advanced manufacturing techniques and nature-inspired materials holds immense potential to yield objects capable of dynamic responses to environmental stimuli. By harnessing the power of biomimetic smart materials in 4D printing, we are pre-sented with a unique opportunity to address several critical environmental goals. For example, regarding the mitigation of waste generation, the utilization of biomimetic mate-rials allows for the creation of objects that can adapt and evolve, reducing the need for disposable products. This, in turn, minimizes waste generation and contributes to a circular economy. Thus, energy consumption can be minimized while the self-transformative and adaptive capabilities of these materials can lead to the development of energy-efficient products. This translates to reduced energy consumption throughout the lifecycle of these objects. Also, the environmental footprint can be reduced due to the biomimetic materials’ innate ability to self-repair and adapt promotes the longevity of products, reducing the need for constant replacements. This leads to a decreased environmental footprint associ-ated with manufacturing, transportation, and disposal. In this context, a sustainable future emerges wherein inanimate objects transcend their static nature and evolve into dynamic entities capable of self-repair, adaptation, and responsive behaviors in harmony with their surroundings. The consolidation of biomim-icry and 4D printing techniques establishes a seminal precedent for a profound reconfig-uration of contemporary approaches to design, manufacturing, and ecological steward-ship. Addressing these challenges will require continued research, innovation, and technological advancements. By overcoming these hurdles, biomimetic smart materials can truly shape the future of self-transforming objects and unlock new possibilities in fields such as healthcare, robotics, architecture, and aerospace, transforming the way we design, manufacture, and interact with adaptive materials and structures."
} | 8,038 |
29907312 | null | s2 | 3,791 | {
"abstract": "Humans have domesticated many plant species as indispensable sources of food, materials, and medicines. The dawning era of synthetic biology represents a means to further refine, redesign, and engineer crops to meet various societal and industrial needs. Current and future endeavors will utilize plants as the foundation of a bio-based economy through the photosynthetic production of carbohydrate feedstocks for the microbial fermentation of biofuels and bioproducts, with the end goal of decreasing our dependence on petrochemicals. As our technological capabilities improve, metabolic engineering efforts may expand the utility of plants beyond sugar feedstocks through the direct production of target compounds, including pharmaceuticals, renewable fuels, and commodity chemicals. However, relatively little work has been done to fully realize the potential in redirecting central carbon metabolism in plants for the engineering of novel bioproducts. Although our ability to rationally engineer and manipulate plant metabolism is in its infancy, I highlight some of the opportunities and challenges in applying synthetic biology towards engineering plant primary metabolism."
} | 294 |
23481603 | PMC3604775 | pmc | 3,798 | {
"abstract": "ABSTRACT Microorganisms grow under a remarkable range of extreme conditions. Environmental transcriptomic and proteomic studies have highlighted metabolic pathways active in extremophilic communities. However, metabolites directly linked to their physiology are less well defined because metabolomics methods lag behind other omics technologies due to a wide range of experimental complexities often associated with the environmental matrix. We identified key metabolites associated with acidophilic and metal-tolerant microorganisms using stable isotope labeling coupled with untargeted, high-resolution mass spectrometry. We observed >3,500 metabolic features in biofilms growing in pH ~0.9 acid mine drainage solutions containing millimolar concentrations of iron, sulfate, zinc, copper, and arsenic. Stable isotope labeling improved chemical formula prediction by >50% for larger metabolites (>250 atomic mass units), many of which were unrepresented in metabolic databases and may represent novel compounds. Taurine and hydroxyectoine were identified and likely provide protection from osmotic stress in the biofilms. Community genomic, transcriptomic, and proteomic data implicate fungi in taurine metabolism. Leptospirillum group II bacteria decrease production of ectoine and hydroxyectoine as biofilms mature, suggesting that biofilm structure provides some resistance to high metal and proton concentrations. The combination of taurine, ectoine, and hydroxyectoine may also constitute a sulfur, nitrogen, and carbon currency in the communities.",
"conclusion": "Conclusion. We used an untargeted approach based on stable isotope labeling coupled with high-resolution mass spectrometry to uncover metabolites within natural and cultivated AMD microbial communities. We confirmed the identification of taurine and hydroxyectoine and used community proteogenomic data to determine which organisms are capable of producing or consuming these osmolytes. From this, we suggest that to mitigate less protection from EPS in early biofilm development, Leptospirillum group II bacteria may produce more ectoine and hydroxyectoine in order to cope with greater exposure to the high-ionic-strength AMD solution. We determined the specific chemical formulae of many other metabolites that were not identified because they are not present in current metabolic databases and suitable pure compound standards for a defined set of candidate molecules were not available. The set of abundant but not identified compounds could include novel metabolites, with potentially high biological and chemical relevance.",
"introduction": "Introduction Over the past decade, metagenomic approaches have illuminated the metabolic potential of communities without the need for cultivation and isolation ( 1 , 2 ). Leveraging the genomic context uncovered by this method, community proteomics and transcriptomics can provide insight into the potential function of coexisting microorganisms in situ . However, these analyses are blind to the flux of small-molecule metabolites that are foundational to the physiological or phenotypic state of an organism. Metabolomic measurements can bring to light the key intra- and extracellular metabolites involved in cellular processes such as ion homeostasis, redox status, nutrient cycling, energetics, and cell-cell signaling (e.g., see references 3 and 4 ). By capturing relative sizes of the metabolite pools, metabolomics is a reflection of the net expression of many genes, pathways, and processes. Metabolomics studies may prove particularly useful in studying adaptation to extreme environments, since metabolites essential to survival in these environments may be relatively abundant (e.g., for ion homeostasis). Yet, studies of metabolites have typically targeted specific molecules of interest from isolated organisms. Few studies have leveraged untargeted, high-throughput metabolomics techniques to study microbial adaptation (e.g., adaptation of Pyrococcus furiosus to temperature stress [ 5 ] and Streptomyces coelicolor to salt stress [ 6 ]). Traditional physiological studies of microbial isolates have defined many adaptation mechanisms to extreme conditions, including improved membrane selectivity and stability, detoxification, enhancement of cellular repair capabilities, and alteration of macromolecular structures. However, adaptation of organisms to their environments in part relates to behavior within a community context—a facet that is not captured in typical laboratory-based studies of isolates but is captured by community metabolomics studies. The abundance of some compounds (e.g., sugars and amino acids) results from uptake, consumption, and excretion by many different organisms; thus, the overall concentration reflects the net metabolic state of the community. Untargeted metabolomics has tremendous potential for hypothesis generation in microbial communities since it provides a direct biochemical observation of the community metabolism. However, only a few studies (e.g., see reference 7 ) have used untargeted metabolomics to study adaptation to environmental challenges in a community context. This is in part because metabolomics methods are still challenging (relative to the more standardized omics approaches such as transcriptomics) due to a wide range of experimental complexities often associated with the environmental matrix (e.g., abundant salt can result in extensive experimental artifacts). To investigate community-level adaptations to the simultaneous challenges of high proton and metal concentrations, we examined the metabolome of microbial biofilm communities in an acid mine drainage (AMD) environment (Richmond Mine, Iron Mountain, CA). Previous research in the system demonstrated that proteins and metabolite features exhibited correlative patterns reflective of functional differentiation of bacterial species ( 8 ), suggesting that combining omics approaches may prove useful in defining adaptation strategies specific to particular groups of organisms. Microbial biofilms found at Richmond Mine grow at low pH (typically 0.5 to 1.2) and elevated temperature (30 to 56°C) in solutions containing millimolar concentrations of sulfate, iron, zinc, copper, and arsenic ( 9 )—conditions that together make untargeted metabolomics extremely challenging. Here, we used a combination of stable isotope probing and untargeted metabolomics to facilitate the identification of metabolites from in situ and laboratory-cultivated AMD microbial biofilms. We integrated metabolite identifications with previously acquired genomic and proteomic data to elucidate adaptation to acidophilic and metal-rich conditions on the metabolic level.",
"discussion": "RESULTS AND DISCUSSION Community compositions of AMD biofilms with different growth strategies. For metabolomic analyses presented here, biofilm samples were collected from the air-AMD solution interface of the AB-muck site within the Richmond Mine on 15 July 2011 (here referred to as the mine biofilm sample). The mine biofilm was categorized as late developmental stage, based primarily on biofilm thickness. AMD biofilms were also grown in laboratory bioreactors using the mine biofilm as inoculum for three different lengths of cultivation time: 26, 35, and 51 days (here identified as BR-26days, BR-35days, and BR-51days, indicating bioreactor growth and length of cultivation). In all cases, the cultivated biofilms were thick and well developed at the time of sampling. BR-26days and BR-35days were grown with 15 N-labeled ammonium sulfate (resulting in 15 N-labeled nitrogen atoms in the biofilm metabolites) in order to identify the number of nitrogen atoms in each metabolite and also to confirm biological origin. The community composition of these biofilm samples was determined using fluorescence in situ hybridization (FISH) targeting identification of broad phylogenetic groups as well as individual species and strains (see Fig. S1 in the supplemental material). Insufficient biomass precluded FISH analyses on BR-35days. Prior studies showed that biofilms in the Richmond Mine are dominated by Leptospirillum group II, a chemoautotrophic iron-oxidizing bacterium ( 1 , 10 ). Genomic reconstructions revealed two distinct strains of Leptospirillum group II, referred to as the 5-way and UBA genotypes ( 11 , 12 ). In this study, the Leptospirillum group II 5-way genotype was more abundant than the UBA genotype in both bioreactors. Conversely, nearly all of the bacteria in the mine biofilm belonged to the Leptospirillum group II UBA genotype, which is consistent with prior studies showing that the UBA genotype typically predominates over the 5-way genotype in the mine bacteria ( 13 ). Previous studies have shown that as the biofilms mature and thicken, they also diversify, with increasing proportions of Leptospirillum group III bacteria and Archaea , as well as other low-abundance taxa from the Eukarya , Firmicutes , and Actinobacteria lineages ( 1 , 14 ). The bioreactor biofilms had a much higher percentage of Leptospirillum group III bacteria than that seen in the mine biofilm (22 to 32% compared to only 2% in the mine). Geochemical and FISH data collected previously in the mine suggest a positive correlation between the abundance of Leptospirillum group III and ammonium concentrations ( r = 0.96, n = 6), which may suggest that ammonium concentrations in the bioreactors (2 mM compared to an average concentration of 177 µM in the mine) favor Leptospirillum group III. Recent studies also indicate that high ferric iron concentrations in the bioreactors relative to mine solutions may also select for Leptospirillum group III (S. Ma, S.E. Spaulding, B.C. Thomas, J.F. Banfield, submitted for publication). The abundance of Archaea varied between the mine and bioreactor biofilms. Archaea made up 29 to 35% of the communities in the BR-26days and mine biofilms. Conversely, BR-51days had much higher percentages of Archaea (61%), which may be the result of longer cultivation time than that of BR-26days. The low-abundance community member Sulfobacillus was found in all samples (0.2 to 1.2%). Eukaryotes were not identified by FISH but are not necessarily absent from the biofilms; the inherent heterogeneity of the biofilms and patchy eukaryal distribution may prohibit visualization in microscopy with a random sampling design. Detection and chemical formula prediction of metabolites found in AMD enabled by stable isotope labeling. Metabolites were extracted from natural and cultivated AMD biofilms (mine, BR-26days, BR-35days, and BR-51days) and analyzed using liquid chromatography coupled to high-resolution mass spectrometry (LC-MS). Both hydrophilic interaction liquid chromatography (HILIC) and C 18 reverse-phase (RP) columns were used to separate polar and nonpolar organics, respectively. LC-ESI-MS (LC-MS with electrospray ionization) is one of the most widely used metabolomic platforms given its versatility in separation techniques coupled with the wide range of compounds that can be desorbed/ionized ( 15 ). Generally, LC-MS approaches fall into one of two categories: (i) targeted analyses, which aim to quantify changes within a defined set of known metabolites diagnostic of a given phenotype of interest, and (ii) untargeted analyses, which seek to discern both novel and previously characterized metabolites ( 16 , 17 ). An untargeted LC-MS approach was used in the current study, rather than the commonly used gas chromatography-mass spectrometry (GC-MS) approach, to maximize the diversity of metabolites detected. More than 3,500 raw features (ions with unique retention time and mass-to-charge ratio combinations) were identified in each of the RP and HILIC data sets. These uncurated data, however, included features associated with background chromatography noise and features also found in extraction blanks, as well as multiple adducts and fragment ions of the same compounds. In order to obtain the highest-quality data possible, we used a strict manual curation strategy to identify compounds of interest in our samples and prevent errors associated with automated peak detection, deconvolution, and alignment. Three-way visualization plots ( 18 ) of the 14 N-biofilm, 15 N-biofilm, and extraction blank data sets (available at http://geomicrobiology.berkeley.edu/pages/metabolites.html ) were used to narrow down the large list of raw features to “pure spectra,” that is, the spectra that would likely result from a pure compound within the biological matrix. From this visualization, as well as manual observation of very abundant peaks, we generated a list of 241 likely parent ion features (the highest-intensity feature from a pure spectrum) from the RP and HILIC analyses ( Table 1 ). TABLE 1 Comparison of the number of features and metabolites resulting from various levels of curation Analytical column No. of features after three-way visualization analysis No. of metabolites After manual curation Containing nitrogen With chemical formula Agilent Zorbax SB-C 18 column 191 56 27 38 Acquity UPLC BEH HILIC column 50 24 19 18 Eighty parent compounds (56 in RP and 24 in HILIC) ( Table 1 ) were confirmed after grouping coeluting features and identifying common adducts, fragment ions, and neutral losses, including those associated with esterification of carboxylic acids (presumably as a result of extraction buffer and residual acidic AMD). The number of metabolites found here is consistent with those in other untargeted LC-MS metabolomic studies from complex, natural biological matrices ( 19 – 22 ). Forty-eight percent of the reverse-phase (RP) metabolites and 79% of the HILIC metabolites contained at least one nitrogen atom (determined by stable isotope labeling), providing a strong level of certainty that the compounds are of biological origin. Mass spectra of all compounds were manually visualized to ensure absence in extraction blanks. The use of stable isotopes greatly aids in determining the chemical formulae of unknown metabolites by constraining the possible elemental composition ( 19 , 23 , 24 ). Here, using the number of nitrogen atoms informed by stable isotope labeling, chemical formulae were determined for 38 of the RP features and 18 of the HILIC features (see Table S1 in the supplemental material). The nitrogen labeling method also informs the formulae of compounds without nitrogen, as their chemical formulae are constrained by the “zero” nitrogen count. Generally, high-mass-accuracy (<5-ppm) mass spectrometers can confidently assign unique chemical formulae for features under ~200 to 250 atomic mass units (amu) ( 23 , 25 ). Indeed, we were able to confidently assign chemical formulae for most features of <250 amu without the assistance of nitrogen labeling. For those 37 features above 250 amu in Table S1 , nitrogen labeling allowed us to confidently assign chemical formulae to 20 of them, twice as many formulae as were possible with spectral information alone. Features for which chemical formulae could not be determined either were of high m/z or low spectral quality or had an unidentified adduct. Metabolite annotation and identification. We obtained MS/MS spectra at collision energies of 10, 20, and 40 eV on all features with sufficient peak heights (available at http://geomicrobiology.berkeley.edu/pages/metabolites.html ). The chemical formulae and MS/MS data were matched with metabolites in online databases (MetaCyc, KEGG, MassBank, and METLIN) (see Table S1 in the supplemental material). MS/MS data from more than 90% of the features had no match to metabolites in MS/MS databases (MassBank and METLIN), and as per protocols established by the Chemical Analysis Working Group of the Metabolomics Standards Initiative, these metabolites are classified as “unknown” ( 26 , 27 ). While these data may speak to the novelty of some AMD metabolites, it must also be noted that these databases rely on commercially available standards, which are estimated to represent only half of all biological metabolites ( 15 ). From this analysis, we found three metabolites that were particularly interesting and warranted further investigation within the context of the biofilm community: (i) phosphatidylethanolamine (PE) lipids, (ii) taurine, and (iii) hydroxyectoine. Unusual lyso phosphatidylethanolamine lipids and methylated derivatives previously identified in the Richmond Mine ( 28 ) were also found in the mine and bioreactor biofilms presented here. Fischer et al. ( 28 ) suggested a link between these lipids and the Leptospirillum group II UBA genotype based on correlations of lipid and proteome abundance patterns. Interestingly, we found features consistent with some of these same lipids (same m/z and retention times for 454.294 and 480.309) in pure cultures of Acidomyces richmondensis , a fungus known to be abundant in the mine and often the dominant eukaryal species ( 29 , 30 ). The A. richmondensis genome contained 8 genes predicted to be involved in the synthesis, methylation, and binding of PEs, some of which were expressed in community transcriptomic and proteomic data (C.S. Miller, A.C. Mosier, S.W. Singer, C. Pan, B.C. Thomas, J.F. Banfield, unpublished data). Together, these results show that fungi and bacteria may both be involved in the metabolism of these lipids. Fischer et al. ( 28 ) suggested that these lipids may prevent uptake of toxic levels of iron cations in AMD biofilms. Taurine: metabolic interactions and abundance within the AMD community. Among the metabolites present in the AMD biofilms, we identified taurine (2-aminoethanesulfonic acid) by comparing MS/MS spectra with a taurine chemical standard (see Fig. S2 in the supplemental material). Taurine is a phylogenetically ancient compound ( 31 ) and is involved in numerous physiological functions across disparate forms of life, including membrane stabilization, stimulation of glycolysis and glycogenesis, regulation of phosphorylation, and antioxidation (references 31 and 32 and references therein). Some microbes can use taurine as an exclusive source of carbon, nitrogen and sulfur (references 33 and 34 and references therein). Taurine is a particularly effective osmoregulator and is used as a compatible solute by a variety of microorganisms (e.g., see references 31 and 35 and references therein). Compatible solutes (generally very soluble, low-molecular-weight organic molecules) can be accumulated in the cytoplasm as a mechanism for coping with hyperosmotic stress. Compatible solutes can also protect proteins, nucleic acids, and membranes from the harmful effects of heat, freezing, drying, and oxygen radicals (references 36 to 38 and references therein). Although many of the potential roles for taurine are relevant, its properties as a compatible solute may be particularly useful in microbial adaptation to the high-ionic-strength waters within the Richmond Mine ( 9 ). We explored genomic sequences of ~20 AMD biofilm community members (including bacteria, archaea, and fungi) to determine which organisms may produce taurine ( Fig. 1 ). This data set contains nearly 80,000 gene sequences and captures essentially all organisms representing more than a few percent of the community. Reciprocal BLAST searches against the KEGG database indicated that archaea and bacteria in the AMD communities are unable to generate taurine (no archaea or bacteria are known to synthesize taurine). The only archaeal or bacterial enzyme potentially involved in taurine biosynthesis was a glutamate decarboxylase (EC 4.1.1.15), which has broad functionality in several different metabolic pathways. FIG 1 Taurine metabolism by prokaryotes and eukaryotes in the AMD biofilms. We evaluated the likelihood for eukaryotic taurine biosynthesis using the genome of the dominant fungus, A. richmondensis ( Fig. 1 ). The A. richmondensis genome contains cysteine dioxygenase (EC 1.13.11.20) and glutamate decarboxylase (EC 4.1.1.15) genes involved in two routes of taurine metabolism. Enzymes mediating the oxidation of hypotaurine to taurine and 3-sulfino- l -alanine to l -cysteate were not evident; however, it has been shown that these reactions may occur nonenzymatically (references 39 and 40 and references therein). Ferric iron, found in high concentrations in the mine, may act as a chemical oxidant of these compounds, thereby completing the pathways of taurine production in A . richmondensis . Efforts to identify taurine in a pure culture of A . richmondensis failed, although culture conditions may not have favored taurine production. We also assessed the potential for taurine degradation using community genomic sequence data ( Fig. 1 ). While bacteria and archaea in the mine carry genes for some enzymes involved in taurine degradation pathways, they do not appear to have a definitive or complete mechanism for the breakdown of taurine. The most likely candidate route of degradation is via gammaglutamyltranspeptidase (EC 2.3.2.2) found in Sulfobacillus and three archaeal species ( Ferroplasma , Cplasma, and Gplasma); however, this enzyme has broad activity and is also involved in cyanoamino acid, glutathione, and arachidonic acid metabolism. Sulfobacillus also has genes encoding two taurine transport proteins (TauA and TauC), suggesting that taurine may indeed have a biological role in this organism. Taurine diffuses slowly through cell membranes, and taurine biotransformation enzymes are usually soluble and intracellular, so transport of taurine into the bacterial cell is required for utilization of the compound ( 31 , 41 ). Cells responding to hyperosmolar conditions can increase intracellular taurine content via active transport of taurine into the cell. Genomic evidence suggests that the fungal species A. richmondensis is capable of degrading taurine via taurine catabolism dioxygenase TauD/TfdA enzymes. TauD is a dioxygenase that converts taurine to sulfite and aminoacetaldehyde, with reaction requirements of oxygen, Fe 2+ , and α-ketoglutarate ( 42 ). In Escherichia coli , the tauD gene is expressed only under conditions of sulfate starvation ( 42 , 43 ). There are 12 copies of the taurine catabolism dioxygenase tauD/tfdA genes in the A. richmondensis genome. Interestingly, transcripts of all 12 tauD/tfdA genes were detected in a fungal streamer biofilm community from the mine (Miller et al., unpublished). Two of these transcripts were relatively abundant in the transcriptome (ranked in the top 1,500 transcripts out of 10,305 total genes), and their proteins were also detected in a community metaproteome (Miller et al., unpublished). Other Dothidiomycetes (the fungal class including A. richmondensis ) genomes contain between 1 and 10 copies of taurine catabolism dioxygenase genes per genome (based on BLAST searches). Given the genomic potential for taurine biosynthesis and likelihood for degradation in the AMD biofilms, we evaluated the abundance of taurine across the different growth conditions (natural mine biofilms and biofilms grown in bioreactors for 26, 35, and 51 days) based on peak heights in the MS spectra (see Fig. S3 in the supplemental material). Taurine concentrations were an order of magnitude higher in all three bioreactor biofilms than in the mine biofilm. This discrepancy is likely explained by different biogeochemical conditions in the mine and bioreactor biofilms. It is possible that the bioreactor communities generate more taurine relative to the mine communities or, equally possible, that more taurine is consumed in the mine. Metagenomic evidence implicates A. richmondensis as the dominant organism involved in taurine biosynthesis and degradation. The low-abundance community member Sulfobacillus may also have the potential for taurine consumption; while FISH shows higher numbers of Sulfobacillus in the mine biofilms, the percentage of the total community is only on the order of 1%. Hydroxyectoine: metabolic interactions within the AMD community. We identified hydroxyectoine (confirmed with chemical standards and MS/MS spectra; see Fig. S4 in the supplemental material) and possibly ectoine (correct mass and chemical formula but incomplete MS/MS data) in natural and cultivated AMD biofilms and were interested in their role as compatible solutes used in adaptation to hyperosmotic stress. Ectoine biosynthesis occurs in three enzymatic steps: (i) l -diaminobutyric acid transaminase (EctB or ThpB) converts l -aspartate-beta-semialdehyde into l -diaminobutyric acid, (ii) acetylation to N -γ-acetyldiaminobutyric acid occurs via l -diaminobutyric acid acetyltransferase (EctA or ThpA), and (iii) cyclic condensation then leads to the formation of ectoine through ectoine synthase (EctC or ThpC) ( 44 – 46 ). Hydroxyectoine is primarily generated through the hydroxylation of ectoine by ectoine hydroxylase (EctD or ThpD) ( 47 , 48 ). Compared to ectoine, hydroxyectoine can confer additional protective properties against heat stress ( 47 ) and freeze-drying ( 49 ). Complete ectoine and hydroxyectoine biosynthesis pathways have been identified previously in one archaeal genome, Nitrosopumilus maritimus ( 50 ), and in over 50 bacterial genomes with particular representation among the alpha- and gammaproteobacteria and Actinobacteria (reference 38 and references therein). In the AMD biofilms, both the 5-way and UBA genotypes of Leptospirillum group II have all of the genes necessary for ectoine and hydroxyectoine biosynthesis ( ectABCD ), and their protein products have been identified by community proteomics ( 51 ). Previous studies showed high numbers of ectoine synthase proteins in Leptospirillum group II grown under nonoptimized culture conditions; however, ectoine synthases were found in levels similar to those in the natural biofilm upon optimization of the culturing media ( 52 ). Leptospirillum group II EctB proteins were significantly more abundant in low-pH cultures (pH 0.85 versus pH 1.45), suggesting greater osmotic stress during growth in more acidic solutions ( 53 ). Interestingly, Leptospirillum group II was the first acidophilic bacterium described with a complete pathway for biosynthesis of ectoine and hydroxyectoine ( 51 ). Other AMD community members also have genes in the ectoine biosynthesis pathway. ectB genes were found in some archaea (Cplasma, Eplasma, and Ferroplasma ), and Sulfobacillus has both ectA and ectB genes; however, ectoine biosynthesis by these organisms cannot be confirmed since the complete Ect operon was not found. We evaluated the abundance of ectoine and hydroxyectoine biosynthesis proteins in Leptospirillum group II bacteria across different growth stages of AMD biofilms (early, mid-, and late growth stages) ( Fig. 2 ), using previously acquired quantitative proteomics data ( 54 ). EctA proteins were not found in any of the samples, which may suggest low abundance. Protein abundance of EctB, EctC, and EctD generally decreased with biofilm development, suggesting that more ectoine and hydroxyectoine are produced in the early growth stages. In early biofilm development, the organisms may have greater exposure to the AMD solution because the biofilm is still thin and friable and contains less extracellular polymeric substance (EPS) ( 55 ). EPS has been reported to provide protection from a variety of environmental stresses, including osmotic shock (e.g., see reference 56 and references therein). Thus, with less protection in early biofilm development, Leptospirillum group II bacteria may produce more ectoine and hydroxyectoine in order to cope with greater exposure to the high-ionic-strength AMD solution. FIG 2 Abundance of ectoine and hydroxyectoine biosynthesis proteins across different growth stages of AMD biofilms. Data are based on previously acquired quantitative proteomics data (54). Some organisms are capable of concurrently using ectoine as an osmoprotectant and as an energy and carbon substrate ( 57 – 59 ). Genes involved in ectoine metabolism have been identified in Sinorhizobium meliloti ( eutABCD ) ( 57 ) and Halomonas elongata ( doeABCD ) ( 58 ). In the AMD biofilms, some of these genes involved in ectoine utilization have been identified in Sulfobacillus and Firmicutes genomes, but not the complete operons. Conclusion. We used an untargeted approach based on stable isotope labeling coupled with high-resolution mass spectrometry to uncover metabolites within natural and cultivated AMD microbial communities. We confirmed the identification of taurine and hydroxyectoine and used community proteogenomic data to determine which organisms are capable of producing or consuming these osmolytes. From this, we suggest that to mitigate less protection from EPS in early biofilm development, Leptospirillum group II bacteria may produce more ectoine and hydroxyectoine in order to cope with greater exposure to the high-ionic-strength AMD solution. We determined the specific chemical formulae of many other metabolites that were not identified because they are not present in current metabolic databases and suitable pure compound standards for a defined set of candidate molecules were not available. The set of abundant but not identified compounds could include novel metabolites, with potentially high biological and chemical relevance."
} | 7,367 |
29780372 | PMC5945886 | pmc | 3,799 | {
"abstract": "The bovine rumen hosts a diverse microbiota, which is highly specialized in the degradation of lignocellulose. Ruminal bacteria, in particular, are well equipped to deconstruct plant cell wall polysaccharides. Nevertheless, their potential role in the breakdown of the lignin network has never been investigated. In this study, we used functional metagenomics to identify bacterial redox enzymes acting on polyaromatic compounds. A new methodology was developed to explore the potential of uncultured microbes to degrade lignin derivatives, namely kraft lignin and lignosulfonate. From a fosmid library covering 0.7 Gb of metagenomic DNA, three hit clones were identified, producing enzymes able to oxidize a wide variety of polyaromatic compounds without the need for the addition of copper, manganese, or mediators. These promiscuous redox enzymes could thus be of potential interest both in plant biomass refining and dye remediation. The enzymes were derived from uncultured Clostridia, and belong to complex gene clusters involving proteins of different functional types, including hemicellulases, which likely work in synergy to produce substrate degradation.",
"conclusion": "Conclusion In this study, functional metagenomics was used to discover new redox enzymes and metabolic pathways from the bovine rumen microbiome, active on various aromatic substrates derived from textile dyeing and the chemical treatment of lignocelluloses in the pulp and paper industries. None of these enzymes require the addition of metals or mediators to the reaction media, giving them a particular advantage over the laccases and peroxidases that are the main enzymes currently used in biorefining and dye bioremediation. Sequence analysis revealed that each of the contigs contained several redox enzymes of different functional and structural families, which probably work in synergy to degrade and metabolize the targeted substrates. In Contigs 1 and 2, the target enzymes were related to amino acid metabolism. Sequence analysis also suggested that the redox enzymes produced by the clones and identified in this study would not require the supply of oxygen. Their electron acceptors/donors would instead be, for instance, ions, cobalamin, or NAD(P), which are synthetized or absorbed in the host strain cytoplasm. Microbial processes using recombinant bacteria able to produce the entire pathways discovered here would thus be more appropriate than enzyme-based processes, which would require the addition of cofactors. In addition, two of the genomic loci discovered in this study harbor genes that encode both redox enzymes capable of acting on lignin derivatives and hemicellulases, suggesting that these bacterial enzymes could act synergistically to break down the plant cell wall network. Nevertheless, in order to accurately identify the functions of the different enzymes encoded on these loci, transcriptomic analysis and rational truncation of the fosmid inserts will be required, as well as an in-depth structural characterization of the products released from a simple model substrate. Finally, their suitability for use in biorefineries will have to be established by testing these clones on native lignin matrixes, and the way they function on this kind of substrate will also need to be studied further. The datasets generated in the course of this study are available in the repository of the DDBJ/EMBL/GenBank Nucleotide Sequence Database under accession numbers LT674548 , LT674549 , LT674550 4 .",
"introduction": "Introduction Oxidoreductases are a large family of enzymes – including laccases, alcohol oxidases, monooxygenases, mono- and oligosaccharide oxidases, lignin peroxidases, cytochrome C oxidases, NADPH oxidases, and monoamine oxidases – that catalyze oxidation–reduction (redox) reactions, and which have a broad variety of substrate specificities and reaction mechanisms. Several oxidoreductase enzymes are already being used in industrial applications, such as dye decolorization, soil and water bioremediation, and biorefining. For decades, most of our understanding of aromatic compound-degrading microorganisms has come from functional genomics or studying model microbial communities ( Pieper et al., 2004 ). However, most of the microorganisms capable of breaking down aromatic compounds remain uncharacterized as a result of our inability to isolate and culture them. This is why the search for novelty remains a challenge. Functional metagenomics is a highly efficient tool in the search for novel biocatalysts among the huge diversity of uncultured microbes. Several metagenomic studies performed on microbial communities derived from polluted environments have led to the identification of new oxygenases involved in the degradation of aromatic compounds, which could have potential applications for bioremediation (for a review, see Ufarté et al., 2015 ). In 2005, Ferrer et al. (2005) discovered a polyphenol oxidase with laccase activity derived from a bovine rumen metagenome, which was the first functionally characterized member of this new enzyme family ( Beloqui et al., 2006 ). While this study proved that redox enzymes acting on polyaromatic compounds can be retrieved from uncultured ruminal bacteria, this particular enzyme and its homologs have never been tested on lignin or its derivatives. In fact, examples of ecosystems that have been screened in order to identify enzymes involved in the degradation of lignin or lignin-derived products are scarce. A bacterial laccase acting on guaiacol, a product of lignin combustion, was first discovered while conducting activity-based screening of a metagenome sampled from mangrove soil ( Ye et al., 2010 ). In addition, a pseudo-laccase requiring the use of exogenous Cu(II) for oxidase activity was retrieved from a coal bed metagenome being screened for lignin catabolic activity ( Strachan et al., 2014 ). Recently, a laccase isolated from acidic bog peat using a PCR-based method was characterized and showed specificity for phenolic substrates that could be linked to lignin degradation ( Ausec et al., 2017 ). These pioneering studies highlight the promiscuity of many oxidoreductases toward this kind of substrate, and the metagenome flexibility in loci encoding polyaromatic degrading pathways. However, the main obstacle to their discovery and characterization is the lack of experimental screens of redox enzymes, which would allow the exploration of a sufficiently large sequence space to permit such rare enzymes to be identified. This is especially true for those enzymes acting in anaerobic or microaerobic conditions, whose potential for discovery using activity-based approaches is limited ( Brown and Chang, 2014 ). It is worth noting that such enzymes are probably very rare in certain ecosystems. For example, no laccase sequence could be found in the cow rumen metagenome studied by Ausec et al. (2011) . In rumens, lignin is present in the form of dietary plant cell wall constituents and is known to be partly degraded by anaerobic fungi and bacteria ( Ruiz-Dueñas and Martínez, 2009 ; Abrão et al., 2017 ; Baba et al., 2017 ). One of the reasons for this could be that lignin degradation is an oxidative process, which has been described mostly with reference to aerobic ecosystems where di-oxygen can act as an electron acceptor. Due to their relative low abundance, lignin-degrading bacteria remain hard to detect and further research is thus needed to deepen our understanding of the different lignin degradation mechanisms that occur in the bovine gastrointestinal tract. As part of this research, we used functional metagenomics to identify novel redox enzymes from uncultured ruminal bacteria. We describe a new strategy comprising three main steps that combines standard redox reactions with two innovative methods to extend the characterization of hit clones. Primary screening was performed on model substrates for redox reactions, allowing the retrieval of three isolated hit clones. Thereafter, the hit clones were used in a newly developed screening method based on colored semi-reflective films to determine their ability to depolymerize lignin derivatives. Their potential to eliminate industrial dyes was then investigated by testing their ability to degrade a panel of aromatic dyes on solid or liquid media. Finally, the sequences of the hit clones were annotated, permitting the genes involved in the catabolism of aromatic compounds, including lignin derivatives, to be identified.",
"discussion": "Results and Discussion Metagenomic Library Screening The library consisted of 19,968 E. coli fosmid clones, covering in total 0.7 Gbp of the metagenomic DNA from the rumen bacteria, with each clone containing a 30–40 kb DNA insert. The library was first screened for the ability to metabolize a depolymerized product of native lignin, namely lignin alkali, used as the sole carbon source for metagenomic clone growth. Lignin alkali, or kraft lignin, is the main by-product produced during the alkaline sulfide treatment of lignocelluloses in the pulp and paper industry. A minimal medium with kraft lignin such as this has already been used to isolate strains able to degrade this substrate ( Raj et al., 2007 ). However, this screen has never been used to identify enzymes from metagenomic libraries, in which each clone only contains a small fraction of the genome from the native bacterium, limiting its substrate harvesting and metabolic potential. The functional assay carried out as part of this study allowed three clones to be identified that were able to grow in a mineral medium with kraft lignin as a the sole carbon source. The hit frequency was 0.015%. This is a value comparable to the hit yield found for oxidase screening in the rumen ecosystem (0.007% in Beloqui et al., 2006 ), although the latter was not specific to the degradation of lignin-related products. In contrast, the hit frequency was lower than that obtained from environments contaminated with polyaromatic compounds, such as activated sludge (0.09% in Suenaga et al., 2007 ) and oil-contaminated waters (0.2% in de Vasconcellos et al., 2010 and 3% in Silva et al., 2013 ). Characterization of Redox Activity Determination of Optimal Reaction Conditions The enzymatic characterization of the metagenomic clones allowed enzyme stability to be assessed, as well as the enzymes’ versatility and efficiency toward structurally different substrates, i.e., mediators and dyes. All the assays were performed using cell extracts, which contained both the recombinant enzymes, and molecules from the cellular metabolism of E. coli such as ions, cofactors, and even enzymes. In order to characterize the substrate specificity of the three clones, optimal conditions of activity were determined by monitoring the oxidation of ABTS at various pHs and temperatures ( Table 2 ). All clones displayed optimal activity at acidic pH values, optimal pH being 4.5 for Clones 1 and 3, and 5.0 for Clone 2. Oxidative activity was totally lost when the pH value was higher than 6.0 (Clones 1 and 3), and with Clone 2 it was lost after the pH reached 5.5 ( Figure 1A ). The optimal temperature was 60°C for Clones 1 and 3, and 50°C for Clone 2 ( Figure 1B ). However, after 30 min at their optimal temperatures, only 13, 11, and 31% of activity remained for Clones 1, 2, and 3, respectively, indicating a moderate thermal stability for the enzymatic extracts. The optimal reaction temperature was thus determined according to the stability of the extracts over a 17 h incubation period. Optimal reaction temperatures were found to be 50°C for Clones 1 and 3, and 30°C for Clone 2 ( Figure 1C ). Table 2 Optimal conditions for ABTS oxidation for the hit metagenomic clones. Optimal conditions Clone 1 Clone 2 Clone 3 pH 4.5 5.0 4.5 Temperature (°C) 60 50 60 Temperature for long-term reactions (°C) 50 30 50 FIGURE 1 Optimal reaction conditions for ABTS oxidation. (A) Determination of optimal pH at 30°C. (B) Determination of optimal temperature at optimal pH. (C) Determination of optimal temperature at optimal pH, after 17 h of incubation at each temperature. Data are expressed as a percentage of the highest value for each clone. Blue, Clone 1; red, Clone 2; green, Clone 3. The oxidative enzymes of Clones 1, 2, and 3 were not laccases or peroxidases: the addition of a copper metal or hydrogen peroxide ion to the reaction media had no significant effect on the reaction rate of the clones. Enzymatic Activity on Model Substrates Using optimal reaction conditions ( Table 2 ), substrate specificity was characterized by comparing the activity of the cell extracts on five mediators whose structure and redox potential were known: ABTS, TEMPO, HBT, acetosyringone, and syringaldehyde. Syringaldehyde and acetosyringone are phenolic compounds, and are two of the main products of the degradation of syringyl-rich lignins. They are characterized by the presence of two methoxy substituents in ortho positions of the phenol, which lowers their redox potential. They have stable radicals, since the substituents have a steric hindrance effect on polymerization due to radical fusion. The TEMPO molecule is a stable radical characterized by a nitroxyl group that benefits from the steric protection provided by the four methyl groups adjacent to the nitroxyl group N–O 2 . The methyl groups prevent a double bond occurring between carbons adjacent to nitrogen. ABTS contains two sulfonate groups that can be deprotonated. The HBT substrate is characterized by an N–OH group for which enzymatic oxidation is mediated by the formation of the highly active nitroxyl radical >N–O ∙ , caused by the removal of an electron followed by the release of a proton ( Camarero et al., 2005 ; Morozova et al., 2007 ; Tavares et al., 2008 ; Torres-Duarte et al., 2009 ; Pardo et al., 2013 ). Clone 1 was active on all substrates and displayed the highest activity compared to the other two clones ( Figure 2 ). Clone 2 displayed low levels of activity on all substrates, although activity was nevertheless detectable on all substrates, especially TEMPO. Clone 3 was the most significantly active on acetosyringone and syringaldehyde. Overall, the three clones exhibited a considerable degree of flexibility toward structurally different substrates, with Clones 1 and 3 demonstrating the highest efficiency. Figure 2 shows the background noise for E. coli , confirming that it has the enzymatic machinery for oxidation. This background noise varied depending on the mediator, and also on whether Clone 2 or Clones 1 and 3 were being tested, due to differences in reaction conditions ( Table 2 ). FIGURE 2 Effect of mediator type on the metagenomic clones’ oxidative activity after 17 h of incubation, at optimal pH and temperature. Escherichia coli activity is represented by hatched lines. Blue, Clone 1; red, Clone 2; green, Clone 3. Lignin-Derivative Depolymerization The results of the primary screening indicated the presence of ruminal bacterial oxidoreductases, which may be involved in lignin degradation. Nevertheless, at this stage, we had not found evidence of their ability to break down polymeric lignin. We thus developed a depolymerization screening strategy, using colored semi-reflective films of polymeric sulfonated lignin ( Cerclier et al., 2011 ), which is a by-product of the chemical pulping process ( Lebo et al., 2001 ). As is the case with butterfly wings, the principle of this method is based on structural colors; that is, colors arising from light interference and not the presence of dyes. Modulation of the color of the semi-reflective nano-layers of polymer depends on film thickness and the refractive index of the final film. Incident light hits the air–film interface, where part of it is reflected back while the rest is transmitted into the film. The second reflection occurs at the film–substrate interface. Net reflected light intensity depends on the combination of reflected light waves from both interfaces. This principle can be exploited in order to detect the degradation of enzymatic activities by polysaccharide ( Cerclier et al., 2011 ). However, the construction of such semi-reflective nano-layers of soluble lignin derivatives has not until now been attempted. In this study, cell extracts from the three hit clones were tested for their ability to degrade a film composed of a mix of resin and sulfonated lignin. Since the resin used to polymerize the substrate onto the layers contained aromatic molecules that could be attacked by oxidative enzymes, a film composed of a mix of resin and xyloglycan served as a control to indicate the level of resin degradation. The results are presented in Figure 3 . The abiotic control represented by the reaction buffer at pH 4.5 did not react with the polymer layers. But the biotic negative control ( E. coli with an empty vector) slightly affected the color of both the sulfonated lignin/resin and the xyloglucan/resin films, with the color change suggesting a decrease in layer thickness. This may be due to a slight breaking down of the structure of the biopolymer/resin layers caused by the oxidative activity of E. coli previously observed on ABTS and other mediators. FIGURE 3 Degradation of semi-reflective layers of xyloglucan and sulfonated lignin by the hit clones. The three metagenomic clones affected the color of the sulfonated lignin/resin layer to a greater extent than did the biotic negative control, with Clone 2 being the most effective. We also observed a slight alteration in the xyloglucan/resin layer caused by Clones 2 and 3 which was comparable to that observed for the control clone, suggesting that the E. coli enzymes had a slight degrading effect on the resin. In contrast, the xyloglucan/resin layer was considerably degraded by Clone 1, suggesting that it produces activity that is able to degrade xyloglucan. Moreover, we found that no mono- or oligosaccharide was produced by Clone 1 after incubation with xyloglucan (HPAEC-PAD data analysis not presented here). We therefore put forward that Clone 1 may be affecting the xyloglucan network by altering its cohesion, as was recently shown for lytic polysaccharide monooxygenases ( Villares et al., 2017 ). This new screening approach has thus allowed us to demonstrate that the enzymes produced by Clone 2 were able to depolymerize solid layers of sulfonated lignin. Once this method is optimized and automated for production in micro-plate format with stable sulfonated lignin layers of homogeneous thickness, it will allow large libraries to be screened for lignin-depolymerization activities, at a throughput of hundreds of thousands of assays per week. This should make it possible to massively increase the rate of discovery and engineering of microbial ligninases derived from cultivated and non-cultivated bacteria and fungi. Applications in Dye Elimination Since the metagenomic clones displayed highly flexible specificity toward aromatic substrates, they were tested for their ability to degrade a panel of eight polycyclic aromatic dyes of diverse structures ( Table 1 ), in order to evaluate the potential of the produced enzymes for industrial dye elimination. Five AZO dyes, one anthraquinonic, and two triphenylmethane were tested. They all contained aromatic rings with the potential for fusion, as well as various types of functional groups linked to these aromatic rings (-OH, -CH 3 , -NCH 3 , -SO 3 H, -SO 3 Na, and -NH 2 ), which have been found to promote dye mineralization ( Spadaro et al., 1992 ). Two types of assays were performed, these being an assessment of the ability of the metagenomic clones to metabolize the targeted dyes (by monitoring their growth on a minimal medium with the dye as the sole carbon source), and dye discoloration assays in a liquid medium. Only Clone 3 was able to grow on the selective medium with the least structurally complex dye molecule on our dye list – TO – as the sole carbon source. Cytoplasmic extracts of the three clones were then incubated in liquid media containing the dyes. After reaction, the formation of solid precipitates at the bottom of wells, associated with a discoloration of the supernatant, was observed, and this was even true to a certain extent for wells containing the control clone ( Figures 4 , 5 ). Clone 1 was able to discolor all the dye solutions, while Clones 2 and 3 had a more specific effect on MG/CBR3BA/RBBR and MG/RB5/CBR3BA/RBBR, respectively. Quantification of discoloration is frequently performed in order to assess biocatalysts for dye bioremediation. Several studies have shown that aromatic compounds were either degraded or precipitated by peroxidases and polyphenol oxidases. Precipitation was attributed to the formation of phenoxy radicals followed by their spontaneous polymerization ( Cooper and Nicell, 1996 ; Durán and Esposito, 2000 ; Mielgo et al., 2001 ; Mohan et al., 2005 ). Khan and Husain (2007) , meanwhile, examined discoloration produced by potato and brinjal polyphenol oxidases. Dye treatment resulted in the formation of insoluble precipitates that the authors attributed to the formation of quinone-derivatives, which mediate the aggregation of aromatic pollutants. These precipitates can be easily removed from the reaction mixture by simple centrifugation, sedimentation, or filtration. Laccases also decolorize AZO dyes due to a nonspecific free radical mechanism which causes phenolic compounds to form. Their relatively low substrate specificity is associated with the use of intermediate substrates (i.e., chemical mediators) which assist in the oxidation of different substrates by facilitating electron transfer from O 2 to the laccase substrate ( Forootanfar et al., 2012 ; Si et al., 2013 ). They have the advantage of not requiring H 2 O 2 for an oxidation reaction to be produced, as this is an expensive co-substrate (and a potential pollutant) which is considered to be responsible for dye precipitation, possibly due to free-radical formation followed by polymerization of various aromatic compounds ( Bhunia et al., 2001 ). Few studies involving laccases have mentioned such by-products, although a study by Zille et al. (2005) did report the production due to polymerization of large numbers of coupled products, leading to a darkening of the solution. Despite the potential benefits they could bring to such depollution processes, laccases present obstacles to the biorefining of plant lignocelluloses, since they are inhibited by copper chelation caused by lignin, and also due to their double ability to depolymerize and repolymerize lignin, blocking access by cellulose- and hemicellulose-degrading enzymes to their substrates ( Ruiz-Dueñas and Martínez, 2009 ). FIGURE 4 Dye degradation by the hit clones, in solid and liquid media. Histogram: quantification of the extent of liquid medium discoloration after reaction. The proportion of discoloration due to E. coli enzymes is represented by hatched lines. Blue, Clone 1; red, Clone 2; green, Clone 3. FIGURE 5 Dye discoloration caused by the hit metagenomic clones. The picture shows the supernatants of the reaction media after reaction with the cellular extracts. 1, malachite green; 2, reactive black 5; 3, tropaeolin O; 4, amaranth; 5, acid fuchsin; 6, cibracon brillant red 3BA; 7, remazol brilliant blue R; and 8, reactive orange 16. Under our assay conditions, the enzymes identified as part of this study needed neither a mediator, nor the addition of copper or H 2 O 2 , in order to be active in the culture medium. This is thus of particular interest in terms of their potential industrial applications, both in biorefining and bioremediation. Sequence Analysis In order to identify the genes encoding the proteins responsible for oxidative activity, the three metagenomic DNA inserts were sequenced. The reads from Clones 1, 2, and 3 were assembled into single contigs of 39.3, 22.5, and 33.7 kb, respectively. The high sequencing depth (100×) allowed accurate gene prediction. The number of predicted genes was 28, 18, and 29 for Clones 1, 2, and 3, respectively. Functional Annotation The results of the BLASTP comparison with the NCBI_NR and Swissprot protein databases, as well as protein signature detection using InterProscan, are provided in Supplementary Table S1 . Mining our metagenomic sequences for laccase encoding genes by comparing these with the LccED database did not produce significant results. This would be consistent with the fact that copper is not essential to the activity we detected for these three clones. Functional annotation allowed the identification of at least one gene for each clone that might be responsible for the activity detected (Supplementary Table S1 ). In Contig 1, ORF 15, annotated as D -3-phosphoglycerate dehydrogenase (PGDH), was the most probable target. The best BLASTP hit with proven activity was a distant D -lactate dehydrogenase from Lactobacillus pentosus (from the phylum Firmicutes; Taguchi and Ohta, 1991 ), which shares 29% identity across 65% of the sequence length. The results from InterProScan confirmed this annotation, highlighting a D -isomer-specific 2-hydroxyacid dehydrogenase catalytic domain, a NAD-binding domain, and an ACT C-terminal domain that is known to be related to a wide range of metabolic enzymes regulated by amino acid concentration. Other studies have already reported that dehydrogenases are involved in dye discoloration ( Tilli et al., 2011 ). Additionally, some of them (cellobiose dehydrogenases) are redox enzymes that have an effect on the degradation of plant cell walls ( Levasseur et al., 2013 ). In the metagenomic locus under consideration here, the dehydrogenase encoding gene is preceded by genes likely to constitute a functional cluster (ORFs 2–12) and which are involved in the transport, binding, and degradation of plant cell wall derived oligo- and polysaccharides. The dehydrogenase encoding gene is separated from the last CAZy encoding gene by two other ORFs coding for enzymes that could also participate in the redox process observed in this study, and which present similarities with enzymes from the L -serine biosynthesis pathway previously described in Dey et al. (2005) . ORF 14, annotated as phosphoserine aminotransferase, and ORF 13, which possesses a methyltransferase tsaA-like domain, might indeed be involved in oxidative processes brought about by the removal of methyl groups. Indeed, like dehydrogenases, it has been shown that methyltransferases are involved in some of the degradation of lignin derivatives by fungi and bacteria ( Jeffers et al., 1997 ; Masai et al., 2007 ). In Contig 2, a cluster of three genes (ORFs 15, 16, and 17 annotated to code for a methionine synthase, a methyltransferase, and a metallo-dependent hydrolase) was proposed to code for the proteins responsible for the redox activity. ORFs 15 and 17 did not present any significant similarity with biochemically characterized proteins. The BLAST hit for the ORF 16 product which was the best characterized was the cobalamin-dependent methionine synthase (MetH) from Thermotoga maritima , with 66% query coverage, 36% identity, and an e -value of 2 e- 92. B12-dependent MetH is a large modular enzyme that uses the cobalamin cofactor as a methyl donor or acceptor in methyl transfer reactions ( Evans et al., 2004 ). This enzyme was proposed to catalyze the conversion of 5-methyltetrahydrofolate and L -homocysteine to tetrahydrofolate and L -methionine in the final step of de novo methionine biosynthesis. It requires methylcobalamin as a cofactor. In addition, ORF 17 presents an amidohydrolase domain. Amidohydrolases are known to be involved in a variety of trans -methylations and rearrangement reactions for the transport and metabolism of amino acids, particularly methionine, where cobalamin and methyl-cobalamin are used as cofactors ( Rodionov, 2003 ). It thus seems that the gene cluster evidenced in Clone 2 could be a new pathway of L -methionine biosynthesis. The redox activities we observed could be due to the fact that L -methionine is an essential amino acid required for a large number of important cellular functions, including the methylation of aromatic compounds by methyltransferases ( Jeffers et al., 1997 ; Rodionov, 2004 ; Masai et al., 2007 ). The Contig 3 sequence is particularly rich in gene coding for oxidizing enzymes. The contig contains a gene cluster organized as previously described in a study on the catabolism of phenolic compounds by facultative anaerobe bacteria ( Carmona and Díaz, 2005 ). In these bacteria, the degradation of the phenolic substrate is caused by its reduction to a non-aromatic compound by a heterotetrameric reductase. In Contig 3, ORFs 6, 7, 8, and 9 were annotated as the δαβγ subunits of a 2-oxoglutarate ferredoxin oxidoreductase. The BLASTP hits which were best characterized were the subunits of a distant 2-oxoisovalerate oxidoreductase functionally associated with amino acid catabolism ( Heider et al., 1996 ; Tersteegen et al., 1997 ). The domains predicted using InterProScan assigned the δ- and β-subunits to 4Fe-4S ferredoxin iron–sulfur and thiamine-diphosphate-binding domains, respectively. The α- and γ-subunits carried signatures of the catalytic domains of a pyruvate flavodoxin/ferredoxin oxidoreductase and a pyruvate/ketoisovalerate oxidoreductase, respectively. This complex acts on the aldehyde or oxo group of donors with ferredoxin as an acceptor and coenzyme A. It has been previously proposed that the reaction that produces reduced ferredoxin also produces a reduction in aromatic rings ( Boll et al., 2002 ; Dorner and Boll, 2002 ), which would explain the phenotypes observed in this study. In addition, in Contig 3, the oxidoreductase-encoding genes were close to a gene encoding a putative transporter (ORF 2) specific to a redox cofactor, i.e., riboflavin. Other genes encoding redox proteins were found on the opposite strand of the contig. They belong to a cluster of nine genes (ORFs 11–19) of which six (ORFs 14–19) are annotated as the RnfBAEDC complex. Biegel et al. (2011) have published an extensive review of structurally equivalent complexes. These are membrane-bound electron/ion transport systems occasionally associated with cytochrome C, as would also seem to be the case with this cluster (ORFs 11 and 12), and have been found to be involved in the respiratory chain. The Rnf complex/cytochrome C association was previously shown to be co-expressed in Methanosarcina acetivorans ( Li et al., 2006 ). These complexes are present in a wide variety of prokaryotes and are functionally assigned as NADH-oxidoreductase. The Rfn couples the flow of electrons from the reduced ferredoxin to NAD+ thereby generating a sodium ion gradient across the cytoplasmic membrane. In the Acetobacterium woodii strain, the Rnf complex is involved in the reduction of caffeate, p -coumarate, and ferulate, three widespread components of soil deriving from the degradation of lignin ( Müller et al., 2008 ). Consequently, the phenotypes found in this study are the product of the involvement of multiprotein complexes that could not have been retrieved from short metagenomic DNA fragments. In addition, these multi-enzymatic systems ensure cascades of redox reactions, which most likely also involve E. coli cellular metabolites. Taxonomic Assignment and Sequence Prevalence in the Bovine Rumen Microbiome It was impossible to accurately assign the three metagenomic inserts from a taxonomical point of view, as their sequences were too distant from any available sequenced genome. A MEGAN analysis using low stringent criteria revealed that the sequences were probably derived from Firmicute bacteria. Indeed, 6/28, 9/18, and 5/29 ORF sequences from Clones 1, 2, and 3, respectively, were assigned to the phylum Firmicutes. It should be noted that the vast majority of ORFs from Contigs 1 and 3 (22 and 23 ORFs, respectively) were assigned to bacteria from environmental samples without any further taxonomic information other than the fact that they showed high identity to segments of two fosmid sequences from the rumen metagenome of two Jersey cows in a study by Wang et al. (2013) which involved activity-based screening of polysaccharide degradation. Contig 1 had 59% sequence coverage and 90% identity with Contig 33 from the study by Wang et al. (2013) , while Contig 3 from our study showed 60% sequence coverage and 87% identity with Contig 1549a. Contigs 33 and 1549a were assigned to the phylum Firmicutes. They were retrieved from metagenomic clones active on plant cell wall polysaccharides They contained CAZy-encoding genes similar to the GH10- and GH94-encoding genes found in our Contigs 1 (ORFs 11 and 12) and 3 (ORF 28). It is interesting to note that such gene clusters were retrieved both in the course of the screening of polysaccharide degrading enzymes ( Wang et al., 2013 ) and, in the present study, redox enzymes acting on lignin derivatives. This constitutes the first piece of evidence that bacterial putative ligninases and CAZymes can be encoded by the same genomic loci, dedicated to the degradation of walls of plant cells."
} | 8,318 |
33285963 | PMC7516608 | pmc | 3,800 | {
"abstract": "The dynamics of cellular aggregates is driven by the interplay of mechanochemical processes and cellular activity. Although deterministic models may capture mechanical features, local chemical fluctuations trigger random cell responses, which determine the overall evolution. Incorporating stochastic cellular behavior in macroscopic models of biological media is a challenging task. Herein, we propose hybrid models for bacterial biofilm growth, which couple a two phase solid/fluid mixture description of mechanical and chemical fields with a dynamic energy budget-based cellular automata treatment of bacterial activity. Thin film and plate approximations for the relevant interfaces allow us to obtain numerical solutions exhibiting behaviors observed in experiments, such as accelerated spread due to water intake from the environment, wrinkle formation, undulated contour development, and the appearance of inhomogeneous distributions of differentiated bacteria performing varied tasks.",
"introduction": "1. Introduction Bacterial biofilms provide basic model environments for analyzing the interaction between mechanical and cellular aspects of three-dimensional self-organization during development. Biofilms are formed when bacteria encase themselves in a hydrated layer of self-produced extracellular matrix (ECM) made of exopolymeric substances (EPS) [ 1 ]. This habitat confers them enhanced resistance to disinfectants, antibiotics, flows, and other mechanical or chemical agents [ 2 ]. Research on modeling biofilms has increased steadily during the past few decades resulting in the understanding of a number of features. Continuous models for uniform cell distributions are useful in basic culture systems [ 3 ]. Individual based models [ 4 , 5 ] and cellular automata [ 6 ] may capture variable thickness, density, and structure. However, current models focus more on deterministic mass transfer and extracellular structure, than in random cell processes. Interest on fluctuations in intracellular concentrations, for instance, has arisen due to their significance in phenotypic variability as well as in gene regulation and stochasticity of gene expression [ 7 , 8 ], with consequences for development and drug resistance [ 9 ]. Recent experiments with Bacillus subtilus biofilms on agar provide a case study in which we can test models incorporating new aspects. Once bacteria adhere to a surface, they differentiate in response to local fluctuations created by growth, death, and division processes, to variations in the concentrations of nutrients, waste, and autoinducers, to cell–cell communication [ 10 ]. Some of them become producers of exopolymeric substances (EPS) and form the extracellular matrix (ECM). EPS production increases the osmotic pressure in the biofilm, driving water from the agar substrate and accelerating spread [ 11 ]. In addition, the matrix confers the biofilm elastic properties. Wrinkles develop as the result of localized death in regions of high cell density and compression caused by division and growth [ 12 ]. As the biofilm expands, complex wrinkled patterns develop, see Figure 1 . This phenomenon is linked to gradients created by heterogeneous cellular activity and water migration [ 13 ]. Eventually, the wrinkles form a network of channels transporting water, nutrients, and waste to sustain it [ 14 , 15 ]. Biofilm spread due to osmosis can be accounted for by two-phase flow models and thin film approximations [ 11 ]. Instead, wrinkle formation has been reproduced by means of Von Kármán-type theories [ 13 , 16 ]. Delamination and folding processes are further analyzed in [ 17 ] by means of neo-Hookean models. In [ 18 ], a poroelastic approach provides a unified description of liquid transport and elastic deformations in the biofilm. To incorporate fluctuations in a more natural way, here we propose a mixture model allowing to distinguish the different phenotypes forming the film. Biofilm structure is greatly influenced by environmental conditions. When they grow in flows, we find bacteria immersed in large lumps of polymer, typically forming fingers and streamers [ 19 ] in the surrounding current. In contrast, biofilms spreading on air–agar interfaces contain small volume fractions of extracellular matrix [ 11 ], producing wrinkled shapes with internal water flow. This motivates different treatments of the extracellular matrix, see [ 20 , 21 ] for biofilms in flows and [ 4 , 5 , 11 , 22 ] for biofilms on interfaces with air or tissues, for instance. In the latter case, when internal fluid flow is taken into account, the small fraction of matrix is usually merged in one biomass phase with the cells [ 11 , 22 ]. Some experimental studies suggest a viscoelastic rheology for biofilms [ 23 , 24 ]. The analysis of the mixture and poroelastic models we consider shows that, depending on the volume fractions of solid biomass and fluid, the viscosity of the fluid, the Lamé constants of the solid, the densities, and hydraulic permeability of the fluid/solid system, the characteristic time for variations in the displacement of the solid, and the characteristic length of the network in the macroscopic scale, the resulting mixture can be considered as monophasic elastic, monophasic viscoelastic, or truly biphasic mixture/poroelastic [ 25 , 26 ]. The paper is organized as follows. Section 2 introduces the solid–fluid mixture model. Section 3 discusses ways to incorporate details of cell behavior. We present a cellular automata approach based on dynamic energy budget descriptions of bacterial metabolism. With the aid of asymptotic analysis [ 11 , 27 ], we construct numerical solutions displaying behaviors consistent with experimental observations. Finally, Section 4 discusses our results, the advantages, and limitations of our approach, as well as future perspectives and possible improvements.",
"discussion": "4. Discussion Growth of cellular aggregates involves mechanical, chemical, and cellular processes acting in different time scales. Bacterial biofilms provide basic environments to test hypotheses and mathematical models against experimental observations. Recent experimental work with Bacillus subtilis reveals a host of phenomena during biofilm formation and spread. Different approaches have been exploited to account for different aspects: thin film equations and two-phase flow models for accelerated spread caused by osmosis [ 11 ], elasticity theory for the onset of wrinkle formation [ 12 , 35 ], Von Karman-type approximations for wrinkle branching [ 13 , 16 ], and Neo Hookean models for contour undulations and fold formation [ 17 ]. In principle, poroelastic models allow to consider liquid transport and elastic deformation in a unified way [ 18 ], though detachment and blister formation require further developments [ 15 ]. Current models take mainly a deterministic point of view, thus, random cell behavior linked to fluctuations is poorly accounted for. However, cell differentiation [ 10 ] to incorporate new phenotypes performing new tasks, such as autoinducer and EPS matrix production, plays a key role in biofilm development. Elementary cellular automata approaches were implemented in [ 13 , 18 ] and used to generate nonuniform residual stresses partially defining the biofilm shape. Here, we develop a hybrid computational model, combining a solid—fluid mixture description of mechanical and chemical processes with a dynamic energy budget based cellular automata approach to cell metabolism. Cellular automata representations are convenient from a computational point of view, as they allow for simple rules to transfer information between individual cells and the film. However, they provide too crude a representation of bacterial geometry. In our framework, this representation could be improved by resorting to different agent based models. Individual-based models, originally developed to study biofilms in flows [ 20 , 21 ], have recently been adapted to describe biofilms spreading over air–agar interfaces and solid–semisolid interfaces [ 4 , 5 ]. Similarly, immersed boundary methods introduced to study bodies immersed in fluids are being extended to study biofilm spread in flows [ 38 ] and at interfaces [ 39 ]. We could resort to Individual based or Immersed boundary approaches for a better description of bacterial geometry and their spatial arrangements. Working with biofilms spreading on an air–agar interface, we have chosen to represent the presence of small fractions of polymeric matrix in an effective way, as done in previous related work [ 11 , 22 ]. The biomass formed by bacteria and polymeric threads is considered one phase [ 11 ], with elastic properties as in [ 22 ]. The liquid transporting dissolved chemicals is considered a fluid phase. Production of EPS also affects internal liquid flow by osmosis, mechanism we include in our equations for the fluid phase. Depending on the relative fractions and the properties of each phase as well as the characteristic times and lengths, the whole system may display an elastic, fluid, viscoelastic, or truly poroelastic behavior [ 25 , 26 ]. This formulation allows to derive effective equations for the dynamics of the interfaces including the effect of biomass growth, fluid, and osmotic pressures through residual strains and stresses. Resorting to individual-based or immersed boundary representations of cells, we might describe the polymeric matrix as a network of threads instead [ 4 , 38 ], but we should define heuristic rules for their behavior. Constructing numerical solutions of the full model is a computational challenge, out of the scope of the present work. Instead, we construct numerical solutions, in particular, geometries, guided often by asymptotic simplifications. In this way, we show that the model is able to reproduce behaviors experimentally observed: accelerated spread due to water intake [ 11 , 15 ], wrinkle formation and branching [ 12 , 14 , 15 ], layered distributions of differentiated cells [ 10 ], development of undulations in the contour [ 15 , 17 ], and appearance of regions containing a high volume fraction of water [ 14 , 15 ]. Existing models are devised to explain specific behaviors in relation with particular experiments. An advantage of our approach is that a single model can be used to display all those behaviors and to simulate or even analyze under which conditions they are observed, as the model allows for asymptotic analysis in specific situations. The partial study of different phenomena also suggests empirical expressions for magnitudes representing cellular activities required by the mixture model, such as source terms or residual stresses, which can be inserted in it to reduce computational costs. Our simulations of biofilm spread and wrinkle formation use parameter values experimentally measured for Bacilus subtilis biofilms in [ 11 , 12 ], producing reasonable qualitative and quantitative results. However, the parameters for the dynamic energy budget systems for cell metabolism, as well as those appearing in the concentration equations are taken from Pseudomonas aeruginosa studies [ 9 ]. The probability laws for the cellular automata model and the balance equations for differentiated cell populations involve additional unknown parameters. Thus, our model involves a collection of parameters that should be fitted to experimental data, specially as far as cell metabolism is concerned. Experimental measurements of bacterial dynamics allowing to fit such parameters are yet missing."
} | 2,887 |
26104709 | null | s2 | 3,804 | {
"abstract": "Proteinaceous components of the biofilm matrix include secreted extracellular proteins, cell surface adhesins, and protein subunits of cell appendages such as flagella and pili. Biofilm matrix proteins play diverse roles in biofilm formation and dissolution. They are involved in attaching cells to surfaces, stabilizing the biofilm matrix via interactions with exopolysaccharide and nucleic acid components, developing three-dimensional biofilm architectures, and dissolving biofilm matrix via enzymatic degradation of polysaccharides, proteins, and nucleic acids. In this article, we will review functions of matrix proteins in a selected set of microorganisms, studies of the matrix proteomes of Vibrio cholerae and Pseudomonas aeruginosa, and roles of outer membrane vesicles and of nucleoid-binding proteins in biofilm formation."
} | 208 |
33353122 | PMC7766424 | pmc | 3,805 | {
"abstract": "Aluminium being one of the most abundant elements is very toxic for plants causing inhibition of nutrient uptake and productivity. The aim of this study was to evaluate the potential of microbial consortium consisting of arbuscular mycorrhizal fungus (AMF), rhizobia and PGPR for counteracting negative effects of Al toxicity on four pea genotypes differing in Al tolerance. Pea plants were grown in acid soil supplemented with AlCl 3 (pH KCl = 4.5) or neutralized with CaCO 3 (pH KCl = 6.2). Inoculation increased shoot and/or seed biomass of plants grown in Al-supplemented soil. Nodule number and biomass were about twice on roots of Al-treated genotypes after inoculation. Inoculation decreased concentrations of water-soluble Al in the rhizosphere of all genotypes grown in Al-supplemented soil by about 30%, improved N 2 fixation and uptake of fertilizer 15 N and nutrients from soil, and increased concentrations of water-soluble nutrients in the rhizosphere. The structure of rhizospheric microbial communities varied to a greater extent depending on the plant genotype, as compared to soil conditions and inoculation. Thus, this study highlights the important role of symbiotic microorganisms and the plant genotype in complex interactions between the components of the soil-microorganism-plant continuum subjected to Al toxicity.",
"conclusion": "5. Conclusions Thus, inoculation of pea with the studied microbial consortium consisting of AMF, rhizobia and PGPR, increased biomass production and improved nutrition of plants grown in acid soil having elevated Al concentration, resulting in partial amelioration of Al toxicity. Pea genotypes significantly differed in Al tolerance and in response to the introduced microbes; however, no clear relationship between these traits was found. Positive effects of inoculation on Al-treated plants were accompanied by the increase in root mycorrhization and formation of symbiotic nodules, suggesting active interactions of plants with the introduced AMF and rhizobia. Our important and original observation was the decrease in concentration of mobile (water soluble) Al accompanied by the increase in macro- and micronutrients in the rhizosphere of inoculated plants. Thanks to this, we connected Al immobilization by the introduced symbiotic microorganisms in the rhizosphere with the uptake of this toxicant by plant shoots. We propose that the introduced microorganisms, particularly AMF Glomus sp. 1Fo, immobilized Al through the solubilization of soil phosphates and whereby mitigating Al toxicity. The increase in nutrient concentrations in the rhizosphere, particularly Mo, P and S, concentrated negative effect of Al on their mobility and uptake by Al-treated plants. Previously we showed significant inhibition of nutrient uptake caused by Al toxicity in the studied pea genotypes grown in hydroponics [ 16 ]. Although previously we postulated that maintenance of nutrient homeostasis is a crucial Al tolerance mechanism for pea in hydroponics, the importance of this mechanism could be limited in acid soil, where availability of many nutrients was relatively higher as compared to neutralized soil. The most prominent factor shaping the rhizosphere microbial community was the plant genotype highlighting it important role in complex interactions between the components of the soil-microorganism-plant continuum subjected to Al toxicity. However, the role of the observed phenomenon in plant tolerance to Al toxicity needs more detailed investigation. Thus, inoculation of pea with symbiotic microbial consortium increased Al tolerance of plants most probably due to immobilization of Al in the rhizosphere, decrease in Al concentrations in plants and improvement of N-fixation and nutrient uptake from soil.",
"introduction": "1. Introduction Elevated concentration of mobile aluminium ions is the main reason for phytotoxicity of acid soils resulting in the inhibition of plant growth and limitation of crop productivity [ 1 , 2 , 3 ]. The mechanisms of plant tolerance to Al toxicity have been intensively studied and involve exudation of organic acids and H + ions from roots and secretion of mucilage to immobilize Al in the rhizosphere, internal detoxification within plant tissues, sequestration of Al in the vacuole, induction of antioxidative activity and efflux of Al from the root tissues [ 4 , 5 , 6 , 7 , 8 , 9 ]. These studies were performed mostly with wheat, barley, maize, soybean and Arabidopsis thaliana and demonstrated the prevalence of a particular mechanism for different plant species and cultivars. As for pea ( Pisum sativum L.), differences in growth response to toxic Al between cultivars [ 10 , 11 ], the importance to counteract Al-induced oxidative stress [ 7 , 12 , 13 ], immobilization Al in roots by pectin [ 14 ] and the protective effect of micronutrient boron [ 15 ] were described. Our previous report demonstrated valuable intraspecific variability of pea in Al tolerance and showed that the increase in the rhizosphere pH, Al precipitation in root zone and maintenance of the plant nutrient homeostasis are principal tolerance mechanisms of this species [ 16 ]. Pea ( Pisum sativum L.), being a legume species, may be considered as a relatively Al-sensitive crop as compared to cereals [ 11 , 17 , 18 , 19 ]. A vulnerability of leguminous plants in terms of Al toxicity is a high sensitivity in the formation of symbiosis with microorganisms, particularly with nitrogen-fixing nodule bacteria [ 20 , 21 ]. Negative effects of Al on nodule initiation, induction of oxidative stress in nodules and inhibition of nitrogen fixation were reported for pea [ 22 , 23 , 24 ]. At the same time Al-tolerant and efficient rhizobia nodulating various legume crops were characterized [ 25 , 26 , 27 , 28 , 29 ]. Moreover, Rhizobium sp. isolated from nodule of chick pea was able to bind Al 3+ due to production of siderophores, suggesting capability of this bacterium to protect the plant against Al toxicity [ 30 ]. However, little is known about rhizobia forming efficient symbiosis with pea grown in acid soils and the role of such bacteria in combating Al stress in legume plants. On the other hand, Al tolerant symbiotic arbuscular mycorrhizal fungi (AMF) are often present in acid soils and can alleviate toxicity of this element for plants [ 31 , 32 ]. The main mechanism of beneficial effect of AMF is related to mobilization of soil P, resulting in formation of insoluble phosphates with Al in the rhizosphere and inside plant roots. Another mechanism is due to the improved uptake of other nutrients (Ca, Mg, K and Fe) by plants, which is often inhibited by Al [ 33 , 34 , 35 , 36 ]. Such effects were described for several plant species but not for pea. It was also shown that many soil bacteria are tolerant to toxic Al concentrations due to efflux of Al from cells and exudation of Al-binding ligands [ 37 , 38 ]. However, interactions of such bacteria, including plant growth-promoting rhizobacteria (PGPR), with plants were scarcely studied. The Al tolerant PGPR strain Viridibacillus arenosi IHBB7171 produced auxins, possessed 1-aminocyclopropane-1-carboxylate (ACC) deaminase and stimulated growth of pea, but its effect on the plants under Al stress was not studied [ 39 ]. Inoculation of maize plants grown in acid soil with P-solubilizing Burkholderia sp. decreased Al accumulation in roots, promoted root elongation and thereby combated Al toxicity [ 40 ]. These findings suggest that symbiotic microorganisms may play important role in counteracting negative effects of Al on plants. Increased root exudation of organic compounds in response to Al toxicity was repeatedly described for various plant species [ 2 , 3 , 5 ], including pea [ 16 ]. This can exert a significant effect on the composition and activity of rhizosphere microorganisms [ 41 ], since they use root exudates as a nutrient source and thereby interact with plants [ 42 ]. Genotype specific changes in the rhizosphere microbial community were also observed in soybean cultivars differing in Al tolerance [ 43 , 44 ]. However, up to now the role of rhizosphere microbiome in plant response to elevated Al concentrations in acid soils is little studied. The aim of our study was to evaluate the potential of symbiotic microorganisms for improving growth and nutrient uptake of plants grown in acid soil and to estimate their role in adaptation of plants to Al toxicity. For this purpose, four pea genotypes differing in Al tolerance and a microbial consortium consisting of AMF, rhizobia and PGPR were used. An attempt was also made to relate the response of plants to Al and inoculation with changes in the composition of the rhizosphere microbial communities.",
"discussion": "3. Discussion 3.1. Plant Biomass Our previous study with hydroponics showed that treatment with 80 µM AlCl 3 for 10 days decreased root and shoot biomass of VIR1903 and VIR8473 about two times, whereas growth of VIR7307 and VIR8353 was not significantly affected [ 16 ]. It was the reason for taking these pea genotypes in the present study as Al-sensitive and Al-tolerant, respectively. However, the expected genotypic differences in growth response to Al toxicity were not found when the plants were cultivated in soil. Under soil conditions VIR1903 can be considered as more tolerant to Al, since only shoot biomass of uninoculated plants decreased in Al-supplemented soil ( Figure 1 a). The more Al-sensitive pea genotype was VIR8473, since Al treatment decreased shoot and seed biomass and seed number of both inoculated and uninoculated plants ( Figure 1 ). In this respect, genotypes VIR7307 and VIR8353 occupy an intermediate position. The observed inconsistency in genotypic response to Al-toxicity might be due to different growth conditions in hydroponics and soil. Interestingly, VIR1903 exuded higher amounts of organic acids, particularly succinate, as compared to other studied genotypes cultivated in hydroponics [ 16 ]. The chelation of Al with organic acids exuded by roots is considered as one of the most important mechanisms of plant tolerance to this toxicant [ 2 , 3 , 5 , 8 , 9 ]. However, the amount of exuded organic acids did not correlate with growth inhibition by Al treatment of hydroponically cultivated pea genotypes [ 16 ]. Nevertheless, complexing of Al by these compounds might be important in soil system leading to the increase in Al tolerance of VIR1903. The plants grown in Al-supplemented soil as a rule had less biomass and seed number, suggesting presence of stress caused by Al ( Figure 1 ). Inoculation with microbial consortium increased shoot and/or seed biomass with variation depending on pea genotype. Namely, the inoculated VIR1903 had increased shoot biomass, genotype VIR8473 had increased seed biomass, whereas no effect was observed on VIR8353. A positive effect of inoculation was evident for both plants grown in neutralized and Al-supplemented soils. The results showed genotype dependent response of pea to the introduced microbes. It is in line with the report demonstrating very high polymorphism of pea in interactions with AMF and rhizobia [ 45 , 46 ]. Our results confirmed that inoculation with symbiotic microbes, such as AMF [ 31 , 32 , 33 ], rhizobia [ 30 ] or PGPR [ 40 ], improve plant growth in the presence of toxic Al in soil; however, this is the first time we have described such an effect on peas and applied a consortium of all three microsymbionts for this purpose. 3.2. Symbiotic Structures The observed negative effect of Al on the number and/or biomass of pea nodules confirmed previous reports showing that nodulation process is sensitive to Al toxicity in various legumes [ 20 , 21 ], including pea [ 22 , 23 , 24 ]. Here, indigenous rhizobia presented and nodulated uninoculated pea plants grown in Al-supplemented soil and inoculation with R. leguminosarum bv. viciae RCAM1079 significantly increased nodule number and biomass. This suggests that the nodules were formed with Al-tolerant rhizobia. Previous reports described Al-tolerant R. leguminosarum bv. trifolii nodulating clover [ 25 ], R. miluonense [ 29 ] and R. leguminosarum bv. phaseoli nodulating common bean [ 27 ], Bradyrhizobium sp. nodulating mung bean [ 26 ], Sinorhizobium meliloti nodulating alfalfa [ 28 ] and Rhizobium sp. nodulating chick pea [ 30 ]. In our experiment, the nodule number positively correlated with nodule biomass (r = +0.76; p < 0.001; n = 64) and the latter positively correlated with shoot (r = +0.29; p = 0.019; n = 64) and seed (r = +0.50; p < 0.001; n = 64) biomass. This indicates an important role of nitrogen fixing symbiosis for pea growth and adaptation to elevated Al concentration on soil. The uninoculated pea plants were actively colonized by indigenous AMF in both neutralized and Al-supplemented soils ( Figure 2 ). Most probably this was the reason for minor effect of the introduced Glomus sp. 1Fo on quantitative parameters of mycorrhizal infection. It was difficult to establish what proportion strain Glomus sp. 1Fo occupied. Nevertheless, positive effects of inoculation with Glomus sp. 1Fo on AMF colonization were observed in roots of VIR7307 and VIR8353. It is known that Al tolerant AMF present in acid soils and help plants to alleviate toxicity of this element [ 31 , 32 ]. Here, there was a positive correlation between relative vesicular richness and shoot biomass of VIR1903 (r = +0.54; p = 0.030; n = 16). Mycorrhizal colonization intensity in roots of VIR8353 positively correlated with shoot (r = +0.57; p = 0.025; n = 16) and seed (r = +0.64; p = 0.008; n = 16) biomass, and with seed number (r = +0.62; p = 0.010; n = 16). Similar positive correlations were found for the relative arbuscular richness in roots of this pea genotype (r varied from +0.60 to +0.64; p < 0.015; n = 16). In contrast, the relative arbuscular richness in roots of VIR7307 negatively correlated with seed biomass (r = −0.60; p = 0.015; n = 16). Moreover, mycorrhizal colonization intensity in roots of VIR8473 negatively correlated with seed biomass (r = −0.62; p = 0.010; n = 16) and seed number (r = −0.57; p = 0.022; n = 16), as well as the relative arbuscular richness of this genotype negatively correlated with seed biomass (r = −0.59; p = 0.016; n = 16). This observation showed that the intensity of the formation of mycorrhizal structures associated with opposite effects on plant growth and depended on the plant genotype. Interactions between AMF and plants are very complicated and varied from mutualism to parasitism [ 47 , 48 , 49 , 50 ]. For example, inoculation with a high inoculum density of Glomus spp. increased mycorrhization of roots but decreased root and shoot biomass of pea [ 51 ]. Xavier and Germida [ 52 ] did not find correlation between colonization intensity of roots by AMF and growth parameters of pea, and the effects of AMF on plant growth varied from positive to negative depending on fungal species. It was also shown that AMF induced retardation of pea development, which, however, did not lead to a decrease in plant productivity [ 53 ]. However, little is known about the specificity of the plant growth depression by AMF, which is due to the plant genotype within one species. Behaviour of the introduced PGPR populations in the rhizosphere of plants subjected to Al stress was scarcely studied. We found no significant differences in the number of Ps. fluorescens SPB2137 in the rhizosphere of pea genotypes grown in both soils. This may suggest that Ps. fluorescens SPB2137 has high Al tolerance and shows a nonspecific interaction with the studied genotypes. Interestingly, inoculation with PGPR Herbaspirillum seropedicae increased nitrogen fixation and shoot N concentration only in Al-tolerant rice cultivars, which exuded bigger amounts of carbon into the rhizosphere, as compared to Al-sensitive cultivars [ 54 ]. However, the survival rate of H. seropedicae on roots was similar for all rice cultivars. 3.3. Rhizosphere pH and Al Concentrations Rhizosphere pH was not affected by inoculation of pea plants grown in neutralized and Al-supplemented soils. This means that the observed decrease in mobile forms of Al in the rhizosphere of inoculated plants ( Figure 3 b) was not due to changes in soil pH. To explain the observed effect, it can be assumed that the introduced microorganisms released into the rhizosphere various substances that bound aluminium into insoluble forms. Probably, this was a defence response of microorganisms, since the effect manifested itself only at an increased concentration of Al in the rhizosphere when adding this toxicant. All components of the microbial consortium could take a part in this phenomenon, since the ability to bind Al and alleviate Al toxicity was previously described for AMF [ 31 , 32 ], nodule bacteria [ 30 ] and PGPR [ 40 ]. Another possibility is that the introduced microorganisms activated exudation of Al-binding compounds by pea roots. It was shown earlier that inoculation with Ps. putida increased exudation of organic compounds of wheat and maize by about two times [ 55 ]. An increase in root exudation can occur due to an additional concentration gradient of organic compounds directed from the root and caused by the trophic activity of microorganisms [ 56 ], as well as due to the influence of microbial metabolites [ 57 , 58 ]. The ability of PGPR to increase the intensity of photosynthesis [ 59 , 60 ] also can increase the influx of photosyntates into the roots and thereby activate exudation. Aluminium concentration in the rhizosphere positively correlated with Al concentration in pea shoots (r = +0.42; p < 0.001; n = 64) but negatively correlated with seed number (r = −0.33; p = 0.007; n = 64). This indicated the interrelation of rhizosphere processes with Al uptake by shoot and plant productivity. In turn, shoot Al concentration negatively correlated with shoot (r = −0.76; p < 0.0001; n = 64) and seed (r = −0.54; p < 0.0001; n = 64) biomass, seed number (r = −0.72; p < 0.0001; n = 64), nodule number (r = −0.27; p = 0.029; n = 64) and nodule biomass (r = −0.50; p < 0.0001; n = 64). These correlations point to the toxic effect of Al absorbed by plants on pea growth and symbiosis with nodule bacteria and complement previous reports about Al toxicity for pea [ 7 , 10 , 11 , 12 , 13 , 16 ] and its nodulation efficiency [ 20 , 21 , 22 , 23 , 24 ]. Our study for the first time connected Al availability in the rhizosphere and Al uptake by plant shoots with immobilization of this toxicant by the introduced symbiotic microorganisms. 3.4. Nitrogen Uptake The pea genotypes grown in Al-supplemented soil had similar (VIR8353), decreased (VIR8473) or even increased (VIR1903 and VIR7307) shoot N concentrations, and VIR1903 had increased seed N concentration, as compared with those grown in neutralized soil ( Table 1 ). This suggests that Al did not induce N deficiency and the observed decrease in shoot and seed N contents ( Table 1 , Figure 4 a) in such plants was due to inhibition of plant growth in Al-supplemented soil. Opposite effects of these soils on the uptake of 15 N also evident and depended on pea genotype. Such a complex situation may be a consequence of the simultaneous participation of genotypic differences in Al tolerance, efficiency of interaction with rhizobia and assimilation of mineral N. High intraspecies variability of pea in these traits was described previously [ 16 , 45 ]. In addition, shoot Al concentration positively correlated with shoot (r = +0.78; p < 0.0001; n = 64) and seed (r = +0.46; p < 0.001; n = 64) N concentration, but negatively correlated with shoot (r = −0.32; p = 0.010; n = 64) and seed (r = −0.42; p = 0.001) N content. The rhizosphere Al concentration negatively correlated with shoot N content (r = −0.25; p = 0.043; n = 64) and 15 N content (r = −0.38; p = 0.002; n = 64). It was shown previously that Al decreased shoot biomass and N concentration of pea grown in Al-supplemented hydroponics as a result of inhibition of N-fixing symbiosis with rhizobia [ 22 ]. Here for the first time we showed that the uptake of N and fertilizer 15 N by pea plants grown in Al-supplemented soil was generally less as compared with those grown in neutralized soil ( Figure 4 ). At the same time, inoculation with the microbial consortium alleviated negative effects of Al on nitrogen nutrition. The observed effects might be due to both the improved N 2 fixation by symbiosis with R. leguminosarum bv. viciae RCAM1079 and increased fertilizer or soil N uptake in symbiosis with Glomus sp. 1Fo and Ps. fluorescens SPB2137. 3.5. Phosphorus Uptake In line with the results obtained for N (see above), the Al-treated pea plants had increased shoot P concentrations and decreased shoot P contents ( Figure 5 ). Inoculation positively affected both P concentration and P content in shoots and seeds, confirming that the improvement of P uptake and subsequent immobilization of Al with phosphates in mycelium and plant tissues are important mechanisms for counteraction of Al toxicity by AMF [ 31 , 32 , 61 ]. Increased shoot P concentrations in Al-treated plants due to inoculation with AMF were shown for barley [ 35 ], sorghum [ 34 ], broomsedge [ 62 ] and tulip-poplar [ 36 ]. Here we expanded it for pea plants and also showed genotype dependent effect. In our experiment, shoot Al and P concentration did not correlate (data not shown). However, a negative correlation was found between Al and P concentrations in the rhizosphere of Al-treated plants (r = −0.56; p = 0.001; n = 32). The ability of AMF to decrease Al concentration in the sand, where tulip-poplar plants were cultivated, was previously shown [ 36 ]. Our results suggest that immobilization of Al in the rhizosphere with phosphates might occur and contribute to alleviation of Al toxicity for pea roots. On the other hand, the observed effect could be due to P mobilization activity not only by AMF Glomus sp. 1Fo but also by Ps. fluorescens SPB2137, since this strain (unpublished data) and this PGPR species actively solubilize both inorganic and organic phosphates [ 63 , 64 ]. 3.6. Uptake of Other Nutrients by Plants A lower concentration of several nutrients (particularly Fe, Mg and Mn) in the rhizosphere of pea plants grown neutralized soil as compared with Al-supplemented soil was probably due to differences in soil pH values. It is known that mobility of these nutrients is closely depended on soil pH and usually higher in acid soils; however, mobility of Mo, P and S in acid soils is low [ 65 , 66 ]. These assumptions are in line with the obtained results for nutrient concentrations in the rhizosphere of the studied peas (with variations depending on genotype) when comparing plants grown in two soils ( Figure 5 and Figure 6 ; Table S1 ). Our original observation is that inoculation increased nutrient concentrations in the rhizosphere, particularly counteracting the decrease in Mo, P and S concentrations in the rhizosphere of Al-treated plants. Molybdenum is an important element for nitrogen fixation being a cofactor of nitrogenase [ 67 ], whereas P [ 68 , 69 ] and S [ 70 ] are crucial elements for Al tolerance in plants. On the other hand, relatively high availability of several nutrients, such as Fe, K, Mg, Mn and Ni ( Figure 6 ; Table S1 ), in Al-supplemented soil might neutralize negative effect of Al on plant mineral nutrition. Previously we showed significant inhibition of nutrient uptake caused by Al toxicity in hydroponically grown peas, including the studied genotypes [ 16 ]. It was concluded that maintenance of nutrient homeostasis is a crucial Al tolerance mechanism for pea. However, here we speculate that the importance of this mechanism could be limited in acid soil, where availability of many nutrients was relatively higher as compared to neutralized soil. Taking this into account, the reason for the discrepancy between the Al tolerance of the studied pea genotypes in hydroponics [ 16 ] and soil (this study) might be the different availability of nutrients. In addition, the physicochemical and environmental differences between hydroponics and soil, as well as resident soil microorganisms, could influence interactions of pea plants with Al. Positive effects of AMF on shoot concentrations of nutrients, such as Ca, Fe, Mg and Mn, in plants subjected to Al stress ware reported for switchgrass [ 33 ], sorghum [ 34 ], barley [ 35 , 71 ] and tulip-poplar [ 36 ]. Our study showed significant increase in shoot and seed concentrations of these and other nutrients in pea grown in Al-supplemented soil and inoculated with microbial consortium containing AMF Glomus sp. 1Fo ( Figure 7 , Tables S2 and S3 ). It can be assumed that this was mainly due to the interaction of peas with Glomus sp. 1Fo. This hypothesis is supported by the increase in nutrient uptake by pea grown in Cd-supplemented soil and inoculated with a consortium, in which this AMF strain was used with other rhizobia and PGPR strains [ 72 ]. Influence of inoculation with rhizobia and/or PGPR on the concentration of these nutrients in plants in the presence of toxic Al received little attention in the literature. 3.7. Rhizosphere Bacterial Communities The observed taxonomic structures have common traits with the earlier described wheat and pea rhizospheric microbiomes but differ from those by shifts in abundance of few taxonomic groups only [ 73 , 74 , 75 , 76 ], e.g., representatives of Proteobacteria , Firmicutes and Thaumarchaeota . Analysis of the alpha-diversity showed no significant differences in diversity indices between treatments with different soil conditions, plant genotype and microbial inoculation. This might be explained by the fact that the studied factors affected only small fraction of the total microbiome. Beta diversity patterns suggest that the main factor shaping the taxonomic structure of the rhizosphere microbiome in this experiment is the plant genotype as compared to soil conditions and inoculation. The mechanism of this separation can be explained by variation of taxa abundances in rhizosphere of different plant genotypes rather than in taxa presence/absence patterns, that was evident from the difference between separations in weighted in unweighted modes. This assumption was analysed in detail with statistics of differential abundances of a particular phylotype. The most notable differences observed in rhizosphere microbiomes caused by plant genotype were related to the differential abundance of Alpharoteobacteria ( Stenotrophomonas ), Gammaproteobacteria ( Burkholderia-Caballeronia-Paraburkholderia, Bradyrhizobium, Yersinia , Unclassified Burkholderiaceae ) and Actinobacteria ( Gaiella ). It is known that many representatives of Burkholderia , Enterobacter and Bradyrhizobium are PGPR and have the capacity to solubilize phosphates [ 77 , 78 ]. However, in our experiment there were no clear interrelations between the abundance of that bacteria and concentration of mobile phosphorus in the rhizosphere. To compare effects of the studied factors (plant genotype, soil conditions and inoculation) on the rhizosphere community structure we calculated the “active fraction” of the microbiome characterizing a sum of all phylotypes with statistically significant changes in their abundances in response to the particular factor. The size of this fraction indicated how big the part of the total microbiome was affected by each factor and their interactions. Accordingly to the size of the “active fraction”, we ranged factors in their significance: plant genotype > aluminium > aluminium + inoculation > inoculation. It is in line with observations where plant genotype was an important factor for shaping rhizosphere microbiome in the presence of toxic Al [ 43 , 44 ], but AMF inoculation had no effect on the alpha diversity [ 79 ] and PGPR had minor effect on the rhizosphere microbiome [ 80 ]"
} | 7,028 |
34434200 | PMC8380989 | pmc | 3,806 | {
"abstract": "Efficient conversion of lignocellulosic biomass into biofuels is influenced by biomass composition and structure. Lignin and other cell wall phenylpropanoids, such as para -coumaric acid ( p CA) and ferulic acid (FA), reduce cell wall sugar accessibility and hamper biochemical fuel production. Toward identifying the timing and key parameters of cell wall recalcitrance across different switchgrass genotypes, this study measured cell wall composition and lignin biosynthesis gene expression in three switchgrass genotypes, A4 and AP13, representing the lowland ecotype, and VS16, representing the upland ecotype, at three developmental stages [Vegetative 3 (V3), Elongation 4 (E4), and Reproductive 3 (R3)] and three segments (S1–S3) of the E4 stage under greenhouse conditions. A decrease in cell wall digestibility and an increase in phenylpropanoids occur across development. Compared with AP13 and A4, VS16 has significantly less lignin and greater cell wall digestibility at the V3 and E4 stages; however, differences among genotypes diminish by the R3 stage. Gini correlation analysis across all genotypes revealed that lignin and p CA, but also pectin monosaccharide components, show the greatest negative correlations with digestibility. Lignin and p CA accumulation is delayed compared with expression of phenylpropanoid biosynthesis genes, while FA accumulation coincides with expression of these genes. The different cell wall component accumulation profiles and gene expression correlations may have implications for system biology approaches to identify additional gene products with cell wall component synthesis and regulation functions.",
"conclusion": "Conclusion In conclusion, this study advances our knowledge of the relationships between switchgrass developmental stage and genetic background with GE, cell wall composition, and digestibility. Under greenhouse growth conditions, lignin, HCAs, and ED differ significantly, and in a developmentally dependent manner, among genotypes. Though developmental cell wall profiles differed among genotypes, general relationships emerged. Correlations between cell wall phenolics and phenylpropanoid biosynthesis GE revealed that FA accumulation may precede, or be less stable than, p CA and lignin accumulation. Expression of phenylpropanoid biosynthesis genes in mid-development seems likely to be an indicator of cell wall properties, especially lignin, p CA, and ED, at harvest. Systems analysis with more genotypes and global expression analysis will be needed to confirm these conclusions. The recommendation from the analysis presented here would be that such studies should probe GE in whole or partial tillers at mid-development as an indicator of biomass properties at harvest.",
"introduction": "Introduction Due to increased transportation energy usage and urgency to reduce fossil fuel use, demand for advanced fuels is predicted to increase to 79.5 billion liters by 2022 ( US CRS Report, 2009 ), about 7% of annual petroleum utilization ( EIA, 2019 ). Biofuels from lignocellulosic biomass, such as the leaves and stems of perennial grasses, hold promise to sustainably fulfill a significant fraction of the alternative fuel requirement with low greenhouse gas emissions ( Schmer et al., 2008 ; Gelfand et al., 2013 ). Biochemical processes are now being deployed that convert polysaccharides, typically cellulose, from plant cell walls into alcohol fuels ( Youngs and Somerville, 2012 ; Torres et al., 2016 ). However, lignin and hydroxycinnamic acids (HCAs) covalently crosslink cell walls and reduce saccharification efficiency during biomass enzymatic digestibility (ED) ( Sattler and Funnell-Harris, 2013 ). Altering expression of single genes, especially those from the phenylpropanoid biosynthesis pathway, which synthesizes lignin and HCAs, improves biomass processing efficiency ( Baxter et al., 2014 ; Li et al., 2018 ). Still, questions remain as to which manipulations are optimal and how genetic diversity can be harnessed to achieve simultaneous biomass composition and yield improvements. One approach to address this is to associate transcriptomes with biomass properties at harvest, but designing such studies requires an initial understanding of the relationships between gene expression (GE) and composition across development and genotypes. This study aims to reduce this knowledge gap for switchgrass. Among potential dedicated bioenergy grasses, switchgrass ( Panicum virgatum L.) is a front-runner species for lignocellulosic feedstock production in the United States ( Bouton, 2007 ; Casler et al., 2011 ; Bartley et al., 2013b ). Switchgrass is a C4, warm-season perennial that produces high annual biomass yield (typically ≥12 Mg/ha) and exhibits broad environmental adaptation ( Lowry et al., 2019 ). This primarily outcrossing species consists of upland and lowland ecotypes and possesses high genetic diversity ( Zalapa et al., 2011 ; Lu et al., 2013 ). Lowland genotypes are typically tetraploid, whereas uplands are octaploid or tetraploid ( Lu et al., 2013 ). Most switchgrass cultivars have only undergone a few rounds of selection, and more genetic diversity exists within a cultivar than among cultivars ( Cortese et al., 2010 ). That said, there are established characteristics that typify the ecotypes. Relative to upland cultivars under the same conditions, lowlands tend to cease growth later and have longer, thicker stems, contributing to lowlands typically accumulating greater biomass than uplands ( Lowry et al., 2014 ). On the other hand, uplands exhibit greater drought and cold tolerance than lowlands ( Stroup et al., 2003 ; Ayyappan et al., 2017 ). The relationships between GE and cell wall properties across development and among ecotypes and genotypes remain under-explored. Generally, as plants mature, secondary cell wall formation and lignification occur; as a result, mature tissue contains a higher proportion of lignin and is less digestible ( Boerjan et al., 2003 ). Previous cell wall and GE analyses revealed variations among developmental stages and internodes from lowland switchgrass of the Alamo cultivar ( Mann et al., 2009 ; Shen et al., 2009 ; Escamilla-Treviño et al., 2010 ; Shen et al., 2013 ). Cell wall digestibility, a key output of biomass composition, varies across switchgrass development due to changes in cell wall components, with strong negative correlations between digestibility and total lignin, lignin monomers, and HCAs in Alamo switchgrass ( Shen et al., 2009 ; Hu et al., 2010 ). However, whether developmentally associated cell wall changes are consistent among genotypes has not to our knowledge been examined. When genotypic variation effects on switchgrass cell wall composition has been examined among cultivars, including across ecotypes, these studies have focused on a single stage ( Lemus et al., 2002 ; Hu et al., 2010 ). This work reports the cell wall composition, ED, and lignin biosynthesis GE of a series of developmentally matched samples from A4, AP13, and VS16 switchgrass genotypes. We find that the measured cell wall parameters, especially phenylpropanoid content, vary across development and in many cases among genotypes. For example, VS16 is more digestible than A4 and AP13 at earlier developmental stages but not at reproduction. Generally, expression of phenylpropanoid biosynthesis genes precedes lignin accumulation, suggesting that early GE may be an indicator of cell wall properties later in development.",
"discussion": "Discussion This study examined the generality of switchgrass cell wall synthesis GE and cell wall composition among genotypes toward establishing methods for analyzing the determinants of cell wall composition and biorefining suitability across genotypes. Among the examined switchgrass genotypes at the V3 and E4 stages, lignin content, HCAs, and cell wall digestibility varied significantly, but the differences diminished at the R3 stage. The low abundance of lignin and HCAs in upland VS16 samples relative to lowland A4 and AP13 samples was associated with high ED. Due to superior digestibility, VS16 biomass at V3 and E4 stages has the potential to yield more biofuel than A4 and AP13, but VS16 loses this advantage by the R3 stage. A caveat of this work is that the greenhouse conditions might have had a greater influence on upland, VS16 ( Casler et al., 2004 ). Still, the combined effect of developmental stage and genetic background should be considered when selecting cultivars with promising cell wall traits or when genetically modifying plant biomass for higher saccharification efficiency ( Ashworth et al., 2017 ). For example, a plant with delayed lignin accumulation, such as that exhibited by VS16 under the tested conditions, may have a greater time to accumulate biomass before lignin deposition decreases saccharification efficiency. In contrast to phenylpropanoids, we observed fewer and less pronounced differences in sugar composition among genotypes and stages. While lignification and hydroxycinnamates are determining factors for reduced saccharification and cell wall digestibility ( Dien et al., 2006 ; McCann and Carpita, 2008 ; Buanafina, 2009 ), non-cellulosic polysaccharide components from xylan, pectins, arabinogalactan proteins, and xyloglucan also reduce digestibility ( DeMartini et al., 2013 ; Chung et al., 2014 ; Li et al., 2019 ). Indeed, the negative correlations that we observe between ED and the pectic sugars, rhamnose and galactose, are consistent with these observations. Still, in our results, polysaccharide component abundance changed little across developmental stages and tiller segments. Thus, lignin remains a key genetic engineering target since it varies across development and among genotypes, while polysaccharide content appears relatively static and perhaps less tolerant of manipulation. The relative timing of phenylpropanoid biosynthesis GE and cell wall properties provides targets for further study of cell wall-related GE strategies to control harvested biomass digestibility. Phenylpropanoid biosynthesis GE is the highest at the E4 stage, apparently leading to the approximately twofold increase in lignin from the E4 to R3 stage. This result was consistent with our overall analysis indicating that GE in prior stages (N GE vs. Δ[(N + 1) - N CW ] and N GE vs. Δ[(N + 2) - N CW ] models) rather than concurrent expression (N GE vs. N CW model) captures expected positive correlations between phenylpropanoid biosynthesis transcript and a change in lignin abundance ( Figure 8 ). Indeed, lignin deposition may be a slow process, in that it includes precursor synthesis, dehydrogenation, and polymerization ( Raes et al., 2003 ). An additional explanation consistent with the data is that lignin undergoes turnover, i.e., breakdown and recycling, that is more rapid than accumulation during earlier developmental stages. Besides evidence for changes to the lignin-containing Casparian strip of roots ( Vermeer et al., 2014 ), we are unable to find clear evidence of this in the literature, suggesting that additional research is required. Consistent with our results, transcriptomics of maize internode subsegments also showed that phenylpropanoid biosynthesis peaks prior to maximal lignin accumulation ( Zhang et al., 2014 ). In our analysis, which includes different genotypes, the observation that correlations between GE and a decrease in ED are the most negative in the N GE vs. Δ[(N + 2) - N CW model hints that high phenylpropanoid biosynthesis GE earlier in development may establish later digestibility recalcitrance. Furthermore, the delay models suggest that expression of all the examined phenylpropanoid biosynthesis genes correlates with lignin accumulation, albeit with different relationships with different genes. Potentially indicating an absence of functional conservation across species, this includes COMT3 , for with the tobacco ortholog did not vary with development ( Pellegrini et al., 1993 ). The HCAs show different accumulation kinetics and GE correlations compared with lignin. Though FA is also synthesized by phenylpropanoid biosynthesis genes, it is mainly esterified to arabinoxylan ( Bartley et al., 2013a ). This different polymer destination may explain why FA positively correlates with phenylpropanoid biosynthesis genes in the concurrent (N GE vs. N CW ) model but not in the delay models. It may also indicate active turnover of FA and/or xylan in the wall ( Franková and Fry, 2011 ). These patterns—early expression controlling lignin accumulation, but sustained expression controlling FA accumulation—may be used to insinuate regulators and other enzymes involved in these alternative processes. Likewise, the difference in correlations between phenylpropanoid biosynthesis GE with p CA and lignin abundance ( Figure 8 ) hints at extra control of p CA synthesis, which is consistent with new evidence that hydroxycinnamoylated monolignols are synthesized by different enzymes than un-acylated monolignols ( Takeda et al., 2018 ). This notion is supported by the absence of a correlation between C3’H and p CA abundance, since reduction of C3’H in rice does not alter abundance of cell wall monolignols esterified with p CA ( Takeda et al., 2018 )."
} | 3,315 |
36711825 | PMC9882294 | pmc | 3,807 | {
"abstract": "Microbial communities such as swarms or biofilms often form at the interfaces of solid substrates and open fluid flows. At the same time, in laboratory environments these communities are commonly studied using microfluidic devices with media flows and open boundaries. Extracellular signaling within these communities is therefore subject to different constraints than signaling within classic, closed-boundary systems such as developing embryos or tissues, yet is understudied by comparison. Here, we use mathematical modeling to show how advective-diffusive boundary flows and population geometry impact cell-cell signaling in monolayer microbial communities. We reveal conditions where the intercellular signaling lengthscale depends solely on the population geometry and not on diffusion or degradation, as commonly expected. We further demonstrate that diffusive coupling with the boundary flow can produce signal gradients within an isogenic population, even when there is no flow within the population. We use our theory to provide new insights into the signaling mechanisms of published experimental results, and we make several experimentally verifiable predictions. Our research highlights the importance of carefully evaluating boundary dynamics and environmental geometry when modeling microbial cell-cell signaling and informs the study of cell behaviors in both natural and synthetic systems.",
"introduction": "INTRODUCTION Diffusive signaling coordinates multicellular processes from embryogenesis and tissue development ( 1 ) to microbial quorum sensing ( 2 – 6 ). In the classical picture of diffusive signaling, the diffusible components are confined to the vicinity of the cells, either by external barriers such as an embryonic envelope, or by the cell membranes themselves in the case of direct cell-to-cell molecular exchange. In these cases, global signaling properties are determined by basic transport parameters such as the diffusivity of the signaling molecule or the speed of advective flow within the cell population ( 7 ). However, in other cases, the diffusible components are free to escape at the population boundaries, or are otherwise affected by properties of the surrounding medium such as fluid flow. These cases include microbial communities such as biofilms or swarms, whose boundaries are usually open and dynamic, and which often form at the interfaces of solid substrates and fluid flows ( 8 ). In such environments, responses at the macroscopic (population) level depend on both the features of the domain in which the cells grow and the dynamics of the constituent cells. In particular, open boundaries can significantly impact signaling behavior within the community. Such impacts include modulation of signaling depth and spatial signaling profiles ( 9 ), as well as the challenges that signaling systems face when trying to respond to time-varying flows in a spatiotemporally robust manner ( 8 , 10 ). Despite the importance of these impacts, signaling in open geometries has been understudied relative to signaling in closed geometries. As we will show, open geometries can induce counterintuitive signaling characteristics. Laboratory experiments aimed at characterizing signaling in microbial communities often rely on microfluidic devices. Such experiments allow researchers to characterize the behavior of spatially extended systems, thereby facilitating the design of microbial consortia that maintain desired population fractions ( 11 ) or produce emergent spatiotemporal patterns ( 12 ). Here signaling provides the necessary intercellular communication pathway to coordinate responses and achieve population-level phenotypes. In typical microfluidic experiments, cells are forced to grow in a monolayer, thereby allowing for imaging of large populations at high resolution. Such imaging capability facilitates the investigation of consortia-scale spatiotemporal dynamics, emergent collective behavior, and nematic effects ( 13 , 14 ). Importantly, many microfluidic devices employ open boundaries between the cell population and the surrounding fluid in order to supply media to cells and remove waste products and excess cells. Open boundary geometries can strongly impact the dynamics of growing microbial collectives and therefore place such microfluidic devices into the same understudied paradigm as the aforementioned biofilms and swarms. Here, we use mathematical modeling to investigate the effects of open, advective boundaries on cell-cell signaling within a bacterial monolayer. Surprisingly, in contrast to the closed-boundary case, we find that the spatial extent of signaling from a source cell does not depend on the diffusion coefficient, but rather depends entirely on the population geometry. When the signal can degrade, we find that the signaling extent is determined by the minimum of the geometric lengthscale and the classical lengthscale set by the ratio of diffusion to degradation. Further, we find that flow at the boundary can introduce signal gradients within the population—even if flow is absent within the population itself—due to the diffusive exchange of signaling molecules with the boundary region. We compare our results to published data on bacterial monolayers in a microfluidic device that signal via a quorum-sensing factor.\n\nFlow outside the population introduces signal gradients within the population Thus far we have considered the effects of open boundaries, but not fluid flow in the boundary regions. Surrounding flows are common in natural settings ( 8 , 10 ), and flow is often desired or operationally necessary in channels bounding the trapping region in a microfluidic device ( 25 , 26 ). To investigate the effects of boundary flow on signaling in a bacterial population, we return to the simplest case of a homogeneous population (all cells secrete the signal) with no signal degradation ( Fig. 3A ). We introduce flow at a constant velocity ν in the x -direction within the flow channels ℱ ± that lie outside the upper and lower boundaries of the trapping region ( y = ± L /2). Because flow breaks the translational symmetry of the signal profile in the x -direction, we assume the width of the trapping region, w , is finite. Specifically, we allow the trapping region to extend from x = 0 to x = w and impose reflective conditions at these boundaries:\n \n (12) \n ∂ x c ( 0 , y ) = ∂ x c ( w , y ) = 0. \n \nWe will see later that the signal profile in the flow channels (and thus the trapping region) is largely insensitive to the boundary conditions at these ends. Since we model the trapping region 𝒯 as a two-dimensional domain, it is convenient to average over the z -direction in the flow channels. Indeed, this type of dimension reduction is often performed when studying pollutant transport in rivers ( 27 ) or shallow-water flows ( 28 ). Let b ± ( x , y ) denote the concentration of signaling molecule in ℱ ± , averaged over the z -direction. The dynamics in the trapping region and flow channels obey\n \n (13) \n c ˙ = D ∇ 2 c + α , \n \n \n (14) \n b ˙ ± = D ∇ 2 b ± − v ∂ x b ± , \n \nwhere α is the signal production rate and ν is the flow velocity. Although cells can exit the trapping region and enter the flow channels, we assume that signal production in the channels is negligible. We also assume that there is no flow in the trapping region. However, the trapping region and the flow channels are coupled by diffusion of molecules across the boundaries. Correspondingly, we impose continuity of the profiles at the boundaries,\n \n (15) \n c ( x , ± L / 2 ) = b ± ( x , ± L / 2 ) , \n \nas well as their derivatives, ∂ y c | y =± L /2 = ∂ y b ± | y =± L /2 . To solve Eqs. (13) and (14) , we assume that whereas the concentration in the flow channels is heterogeneous in the x -direction due to the flow, it is homogeneous in the y -direction, so that b + ( x , y ) = b − ( x , y ) = b ( x ). Such an approximation is valid when the length of the flow channels is an order of magnitude larger than their width ( 27 ). Because b no longer depends on y , the net flux of signal into the flow channels can no longer be accounted for by enforcing continuity of the y -derivative at the boundaries. Instead, this flux appears as an effective source term in Eq. (14) whose magnitude is determined by flux balance (the validity of this argument will be addressed post hoc at the end of this section). Specifically, in a slice of width Δ x , the flux of signaling molecules out of the trapping region, αA Δ x , must equal the flux into the two flow channels, α ^ ( 2 A f ) Δ x , where A and A f are the cross-sectional areas of the trapping region and each flow channel, respectively ( Fig. 1B ). Thus, the effective source term is α ^ = α / 2 Θ for the area ratio Θ= A f / A , which we refer to as the flow-channel capacity. Correspondingly, Eq. (14) becomes\n \n (16) \n b ˙ = D ∂ x 2 b − v ∂ x b + α / 2 Θ . \n \nWe impose absorbing boundary conditions on the flow channels at either end,\n \n (17) \n b ( 0 ) = b ( w ) = 0 , \n \nwhich corresponds to rapid removal of signaling molecules there. In Appendix B , we show that for long flow channels with sufficiently fast flow, the profile b ( x ) in the bulk is insensitive to the boundary conditions. We solve Eqs. (13) and (16) , with the boundary conditions in Eqs. (12) , (15) , and (17) , using separation of variables ( Appendix C ). The result is\n \n (18) \n b ( x ) = α L 2 D ϕ 2 2 Θ ξ ( x w − 1 − e ξ x / w 1 − e ξ ) , \n \n \n (19) \n c ( x , y ) = α L 2 D [ f ( y ) + ∑ n = 0 ∞ J n cos ( n π x w ) cosh ( n π y ϕ L ) ] , \n \nwhere\n \n (20) \n J 0 = ϕ 2 2 Θ ξ ( 1 2 − 1 ξ − 1 1 − e ξ ) , \n \n \n (21) \n J n > 0 = − ϕ 2 Θ ξ sech ( n π 2 ϕ ) { 1 − ( − 1 ) n n 2 π 2 + ξ [ 1 − ( − 1 ) n e ξ ] ( n 2 π 2 + ξ 2 ) ( 1 − e ξ ) } . \n \nHere, f ( y ) is as in Eq. (4) , ξ = νw / D is the Péclet number of the flow channel (a dimensionless determinant of the flow strength relative to diffusion), and ϕ = w / L is the aspect ratio of the trapping region. Note that for either Θ → ∞ or ξ → ∞, Eq. (18) reduces to b ( x ) = 0, and Eq. (19) reduces to Eq. (4) because J n → 0. The reduction occurs because in either of these limits—very large flow channels or very fast flow, respectively—molecules leaving the trapping region never return, and the flow channel becomes an absorbing boundary. Eqs. (18) and (19) are shown, for representative values of ξ , Θ, and ϕ , in Fig. 3B : c ( x , y ) is the surface and b ( x ) is the long edge. We see that the concentration increases in the flow channels along the flow direction. For ξ ≫ 1, the increase in b ( x ) is linear in x , sufficiently far from the boundaries at x = 0 and x = w ( Appendix B ). We see that the concentration increases not only in the flow channels (the edge), but also within the cell population (the surface). Thus, diffusive coupling between the flow channels and the trapping region induces a signal gradient in cell population, even though the population itself is not subjected to the flow. To get a sense of the magnitude of the gradient within the cell population, we plot in Fig. 3C the derivative ∂ x c ( x , y ), scaled by the characteristic lengthscale w and concentration value αL 2 / D , and evaluated at the midpoint of the trapping region, x = w /2 and y = 0, as a function of the flow strength ξ and the flow-channel capacity Θ. We see that the gradient vanishes in the two absorbing-boundary limits mentioned above (Θ → ∞ and ξ → ∞). On the other hand, the gradient can be large for flow of intermediate strength and channels of limited capacity. For example, the case plotted in Fig. 3B , corresponding to the blue circle in Fig. 3C , has parameters estimated from recent microfluidic experiments with E. coli ( 9 ), and we see that the gradient is substantial. We comment further on this point in the Discussion. Our solution Eq. (19) relies on the validity of the effective source term α /2Θ in Eq. (16) . This term is a local approximation in x for the rate of increase of flow channel concentration due to diffusive coupling with the trapping region under a flux balance argument. Our use of the effective source term is therefore an approximation, which we validate by computing the transverse flux, − D × ∂ y c × δ Δ x , evaluated at the boundary y = ± L /2, where the trap depth is δ = A / L . Indeed, considering only the term f ( y ) in Eq. (19) , we have\n \n (22) \n − D ∂ y α L 2 D f ( y ) ( A / L ) Δ x = ( α A Δ x ) / 2 \n \nat this boundary location, which after volume scaling is equivalent to the effective source term α /2Θ. Thus, the additional flux due to the series solution in Eq. (19) is a residual flux that violates our original assumption regarding the validity of the effective source term. To simplify our model, enforce the flux boundary coupling, and obtain a prediction that the concentration gradient within the cell population is linear in x in the bulk (away from the left and right boundaries), we replace the series solution in Eq. (19) with the linear flow channel approximation\n \n (23) \n b ( x ) = α L 2 D ϕ 2 2 Θ ξ x w = 1 2 Θ α x v . \n \n(This is Eq. (43) from Appendix B. ) This enforces the flux balance approximation since the diffusion operator in Eq. (13) satisfies ∇ 2 ( c ( x , y ) + b ( x )) = ∇ 2 c ( x , y ) when b is linear. The resulting c ( x , y ) is quadratic in y and linear in x :\n \n (24) \n c ( x , y ) = α L 2 D [ f ( y ) + ϕ 2 2 Θ ξ x w ] = α L 2 D f ( y ) + 1 2 Θ α x v . \n \nHere f ( y ) is given by Eq. (4) . Note that this simplification is self-consistent in that it satisfies our flux boundary coupling assumption. Eq. (24) suggests that in the bulk (away from the left and right boundaries), boundary flow induces a linear (in x ) gradient within the cell population. Fig. 3B is consistent with this prediction.",
"discussion": "DISCUSSION We described and analyzed a tractable model to explain how advective-diffusive boundary conditions shape signaling response in spatially-extended microbial communities. We assumed bacteria are trapped in a monolayer within a region bounded by two adjacent channels through which fluid flows. In the limit of zero flow speed with large, absorbing channels, we found that the signaling lengthscale is determined (or, with degradation, bounded) by the monolayer geometry, not the diffusion coefficient, because diffusion disperses molecules but also hastens their loss at the boundaries. We also found that flow at the boundaries can induce significant signal gradients in the population and that this effect is most pronounced with small flow channels at intermediate flow speeds. Although we based the model on a microfluidic trap setting, a similar approach can be used to describe more general situations. For instance, a thin bacterial film growing in a pipe could be modeled by assuming that an adjacent channel lies above a layer of cells. Our results could have significant impact on quorum sensing in microbial populations. A principal function of quorum-sensing (QS) circuits in natural systems is the detection of a quorum of cells that triggers induction of a gene network. For example, a QS signal can trigger the production of proteins that release the extra-cellular matrix so that cells move to a mobile state under starvation ( 2 ). Pai and You have described this as the QS circuit’s sensing potential, which depends on the local environment and a threshold level of signaling molecule sensed by the cell ( 3 ). Our results can be used to generalize this sensing potential framework to include environmental influence on QS activation. We did not model cellular responses to the QS signal, but assumed that cells that express the signal do so uniformly. Bacteria can respond to QS signals in complex ways, however. Dalwadi and Pearce have used a model similar to the one we analyzed to show that positive feedback can act as a low-pass filter and ensure a robust collective response to oscillatory flow ( 8 ). In their model the flow passes over the surface of a cell population trapped in a pocket. Their analytical results are based on the assumption that diffusion across this surface dominates the diffusion in the direction of the flow, allowing them to derive a tractable one-dimensional PDE for the signal concentration in the direction perpendicular to the flow. Our model provides several experimentally testable predictions. First, for bacterial collectives growing in geometries with open boundaries, chemical signaling depth can be independent of the diffusion rate of the signaling molecule. Second, when a flow channel borders a bacterial collective, signaling molecule flux into the flow channel can induce a graded signal concentration profile there. This graded profile in the flow channel can induce signaling molecule concentration gradients within the bacterial collective , even when the bacterial collective is isogenic. In this way, flow may play a role in differentiation. These predictions are testable, as bacteria such as E. coli can be engineered to respond to the presence of a quorum-sensing signal by producing a fluorescent protein in a graded manner, or when signal concentration reaches a threshold. As a first step in comparing our results to experiments, we can consider a previous study in which a sender-receiver system of the type in Fig. 2A was constructed in a microfluidic device ( 9 ). The height of the trapping region was L = 100 μ m, from which Eq. (6) predicts that the signal should extend for a lengthscale of Λ ≈ L / π ≈ 32 μ m. The measured lengthscale was Λ = 20 μ m, which agrees within a factor of two. The prediction could be refined by considering the effects of boundary flow ( Fig. 3 ) on the sender-receiver geometry ( Fig. 2 ), which could conceivably increase the predicted lengthscale (the experimental parameters w = 2000 μ m, D = 500 μ m 2 /s, ν ~ 25 μ m/s, A = 100 μ m × 1 μ m, and A f = 10 μ m × 10 μ m give ξ = νw / D = 100, Θ = A f / A = 1, and ϕ = w / L = 20, as in Fig. 3B ). On the other hand, the fact that cells in the experiment are nematically ordered with their long axis pointing toward the open boundaries, as in Fig. 2A ( 13 , 14 ), could conceivably decrease the predicted lengthscale because diffusing molecules are subject to steric barriers more often in the x -direction than in the y -direction. Even without these refinements, it is encouraging that our prediction is close to the experimental observation. Our modeling could be extended, for example to include diffusion of signaling molecules across the cell membranes. Currently we assumed that cell-internal and cell-external signaling molecule concentrations are equal at steady state. This is tantamount to assuming that the diffusion rate of signaling molecules through the cell membrane, d , is infinite. When d is low, however, cross-membrane timescale, which scales as d −1 , can become important. First, when 0 < d < ∞, in steady state cell-internal and cell-external signaling molecule concentrations will differ in the trapping domain. This difference will increase as d decreases. Second, when the cell membrane is impermeable ( d = 0), cells will sequester all of the signaling molecules they produce before said cells exit the trapping region, resulting in no signaling through the extracellular space. This sequestration effect will continue to limit cell-cell signaling efficacy when d > 0, provided the d −1 timescale is long relative to other system timescales. We anticipate that this and other modeling advances can be included in future work."
} | 4,935 |
20189949 | PMC2885270 | pmc | 3,809 | {
"abstract": "The complete genome sequence of the thermophilic sulphur-reducing bacterium, Deferribacter desulfuricans SMM1, isolated from a hydrothermal vent chimney has been determined. The genome comprises a single circular chromosome of 2 234 389 bp and a megaplasmid of 308 544 bp. Many genes encoded in the genome are most similar to the genes of sulphur- or sulphate-reducing bacterial species within Deltaproteobacteria . The reconstructed central metabolisms showed a heterotrophic lifestyle primarily driven by C1 to C3 organics, e.g. formate, acetate, and pyruvate, and also suggested that the inability of autotrophy via a reductive tricarboxylic acid cycle may be due to the lack of ATP-dependent citrate lyase. In addition, the genome encodes numerous genes for chemoreceptors, chemotaxis-like systems, and signal transduction machineries. These signalling networks may be linked to this bacterium's versatile energy metabolisms and may provide ecophysiological advantages for D. desulfuricans SSM1 thriving in the physically and chemically fluctuating environments near hydrothermal vents. This is the first genome sequence from the phylum Deferribacteres .",
"conclusion": "3.8. Conclusions Genome analysis of D. desulfuricans SSM1 revealed its versatile energy and carbon metabolisms and its machineries for sensing and responding to the environmental changes in hydrothermal vent habitats. We showed that the molecular systems such as the multihaem c -type cytochrome clusters, two-component signal transducers, and abundant chemotaxis components could be tightly linked to the adaptation mechanisms required to adapt in physically and chemically variable environments. The multiple signal transduction systems for sensing dynamic changes in carbon source and temperature, and the type IV pili, which are likely to be useful for clinging to the chimney, allow D. desulfuricans to survive in such a harsh environment. The genome sequence of D. desulfuricans SSM1 should provide many clues for the better understanding of bacterial life in environments around hydrothermal vents from the ecological and evolutionary points of view. The sequences, as well as the gene information shown in this paper, are available in the web databases, ExtremoBase ( http://www.jamstec.go.jp/gbrowser/cgi-bin/top.cgi ) and DOGAN ( http://www.bio.nite.g.o.jp/dogan/Top ).",
"introduction": "1. Introduction Generally, the greatest challenge to thermophilic microorganisms living on hydrothermal vents may be posed by the risk of being swept out of the range of the vent and thereby losing the temperature range and necessary chemical supplies. They solve this problem by clinging to rocks in communal mats or swimming with a whip-like flagellum as sensing temperature or chemical stimuli to guide their directional movements. 1 Deferribacter desulfuricans SMM1 T (DSM 14783 T ) has been isolated from a deep-sea hydrothermal vent chimney at the Suiyo Seamount in the Izu-Bonin Arc, Japan. 2 The strain SMM1 is thermophilic (optimal temperature, 60–65°C) and a strictly anaerobic heterotroph capable of using complex organic compounds such as yeast extract and tryptone, ethanol, and various organic acids as sources of energy and carbon. There are three other species in the genus Deferribacter — D. abyssi and D. autotrophicus , which have been isolated from deep-sea hydrothermal vent environments, and D. thermophilus , which has been isolated from a subseafloor petroleum reservoir. 3 – 5 These bacteria are strictly anaerobic chemolithotrophs utilizing various organic compounds and H 2 as electron donors and nitrate, S 0 ( D. desulfuricans , D. abyssi , and D. autotrophicus ), and Fe (III) and Mn (IV) ( D. thermophilus , D. abyssi , and D. autotrophicus ) as electron acceptors. Such versatility for energy generation may provide an ecological advantage for deep-sea vent-dominating chemolithotrophs as has been proposed for members of Aquificales and Epsilonproteobacteria . 6 It has been shown, depending on the hydrothermal vent chimney environment, that Deferribacter -related species are the dominant species in these locations. 7 , 8 Thus, it is intriguing to compare the genomes of Deferribacter -related species with other deep-sea vent chemolithotrophic species such as Thiomicrospira crunogena , 9 Nautilia profundicola , 10 Sulfurovum sp. NBC37-1, 11 Nitratiruptor sp. SB155-2, 11 and Persephonella marina 12 in order to highlight the genomic features that reflect their lifestyle in the environment of deep-sea hydrothermal vent chimneys. Here, we report the complete genome sequence of thermophilic D. desulfuricans SMM1 determined as the first published bacterial genome sequence from the phylum Deferribacteres . We provide a comparative analysis of the genome of D. desulfuricans SMM1 with those of five other chemolithotrophs isolated from deep-sea hydrothermal vent chimneys.",
"discussion": "3. Results and discussion 3.1. General genome features The genome of Deferribacter desulfuricans SSM1 contains a single circular chromosome of 2 234 389 bp and a megaplasmid (pDF308) of 308 544 bp (Table 1 ). Their average G + C contents are 31.1% and 24.5%, respectively. The chromosome displays two clear GC skew transitions that likely correspond to the DNA replication origin and terminus (Fig. 1 ). Annotation of the chromosomal sequence reveals 2117 CDSs, of which 1404 (66%) can be functionally assigned. The megaplasmid encodes 257 CDSs, whereas more than two-thirds are unique and exhibit no apparent similarity with any of the CDSs present in the database. Interestingly, 17 copies of gene cluster encoding two transposases belonging to the IS 200 and IS 605 family present in the megaplasmid but none were found in the chromosome. The chromosome contains two ribosomal RNA operons with a 16S–23S–5S rRNA gene alignment. In all, 43 tRNA genes were identified (Table 1 ). In addition to the regular tRNA genes, the D. desulfuricans genome also contains selC for the selenocysteine tRNA. The other components necessary for the selenocysteine system, e.g. selenocysteine synthetase (SelA), the specific elongation factor (SelB), selenophosphate synthetase (SelD), and seryl-tRNA synthetase (SerS), are also present in the genome. As for small RNA-encoding genes, potential genes for RNase P RNA (RnpB) and 6S RNA (SsrS) were assigned by using the Rfam database.\n Table 1 Summary of genome features of D. desulfuricans SSM1 Characteristics Chromosome Plasmid Size (bp) 2 234 389 308 544 G + C content (%) 31.1 24.5 No. of identified protein-coding genes Total 2117 257 Functionally assigned 1404 63 Conserved hypothetical 494 20 Hypothetical 219 174 Pseudogenes 17 9 Average gene length (bp) 976 993 Coding density (%) 93.7 88.2 No. of identified RNA genes rRNA operons 2 — tRNA 43 — Small RNA a 2 — Size (bp) of genomic islands (% G + C content) DDGI-1 35 072 (31.4%) — DDGI-2 20 995 (28.2%) DDGI-3 36 900 (31.0%) CRISPR elements 2 2 a Includes genes for RNase P RNA and 6S RNA. Figure 1 Circular representation of the D. desulfuricans SSM1 genome. (A) Chromosome. (B) Megaplasmid pDF308. From the inside, the first and second circles show the GC skew (values greater than or less than zero are indicated in green and pink, respectively) and the G + C percent content (values greater or smaller than the average percentage in the overall chromosome or plasmid are shown in blue and sky blue, respectively) in a 10-kb window with 100-bp step, respectively. The third and fourth circles show the presence of RNAs (rRNA, tRNA, and small RNA genes); CDSs aligned in the clockwise and counterclockwise directions are indicated in the upper and lower sides of the circle, respectively. Different colours indicate different functional categories: red for information storage and processing; green for metabolism; blue for cellular processes and signalling; grey for poorly characterized function; and purple for RNA genes. The outermost circle shows the location of genomic islands (red) and CRISPR/Cas systems (blue). The ‘0’ marked on the outmost circles corresponds to the putative replication origin, and the putative replication termination site of the chromosome is at 1.23 Mb. The D. desulfuricans chromosome contains three genomic islands, termed DDGI-1 (coordinates 278 054–313 125), DDGI-2 (coordinates 687 244–708 238), and DDGI-3 (coordinates 864 851–901 750), possibly acquired via horizontal gene transfer (Fig. 1 and Table 1 ). They have many specific features of genomic island, such as the tRNA gene locus at junctions and the presence of direct repeats and phage integrases (DEFDS_0277, DEFDS_0708, and DEFDS_0871), but no anomalous GC content. 31 Most of the genes in the genomic islands encode hypothetical proteins, but some of those appeared to encode the functional proteins related to the adaptation mechanisms for the hydrothermal vent environments. For example, the DDGI-1 includes two heavy-metal transporting P-type ATPases (DEFDS_0300 and DEFDS_0301). The heavy-metal transporters could be responsible for heavy metal tolerance in the hydrothermal vent environment. The DDGI-2 has a toxin–antitoxin system (DEFDS_0716 and DEFDS_0717) involved in phage defence and the stress response. 32 Similar to most other thermophiles, D. desulfuricans has the CRISPR elements together with their associated genes ( cas ), which would serve as immunity against phages, possibly by an RNA-interference-like mechanism. 33 The CRISPR/Cas systems were identified in the chromosome (coordinates 77 878– 86 387) and the plasmid (coordinates 267 868–276 377). Both systems have an identical repeat, and the cas genes also show a high relevance with each other. Regarding the repeat sequence and structure of the cas genes, the systems of D. desulfuricans were closely related with those observed in other thermophiles, such as Sulfurihydrogenibium azorense , Thermoanaerobacter pseudethanolicus , and the homo-acetogen Clostridium thermocellum . Therefore, the D. desulfuricans CRISPR/Cas systems may have been acquired horizontally via the megaplasmid. 3.2. Orthologous relationships among the bacterial species Among the proteins identified in the D. desulfuricans genome, ∼30% of them showed the highest similarity to those species from Deltaproteobacteria , especially Geobacter , Pelobacter , Desulfovibrio , and Syntrophobacter spp., and ∼14% were found to be most similar to those from clostridial species. The remaining proteins showed the highest similarities to several species from Aquificae (5.3%), Gammaproteobacteria (4.8%), and Epsilonproteobacteria (3.7%). Approximately half of the proteins were shared between D. desulfuricans and deltaproteobacterial species, such as Geobacter sulfurreducens PCA (1106 orthologs), Pelobacter carbinolicus (1046 orthologs), Syntrophobacter fumaroxidans (985 orthologs), and Desulfovibrio vulgaris subsp. vulgaris Hildenborough (936 orthologs), although D. desulfuricans is phylogenetically distant from Deltaproteobacteria . For the next step of orthologous analysis, we performed multivariate analyses on the basis of the gene repertories from 51 bacterial species in order to schematically express the orthologous relationships of D. desulfuricans among other bacterial species (Fig. 2 A). As expected, the correlation map indicated that D. desulfuricans and the deltaproteobacterial species, which have strong orthologous relationships, gathered around the centre of map. The same analysis to focus on six species of the class Deltaproteobacteria with D. desulfuricans showed that D. desulfuricans is especially related to three species, G. sulfurreducens (gsu), P. carbinolicus (pca), and D. vulgaris (dvu), possessing sulphur- or sulphate-reducing properties (Fig. 2 B). This result seems to attribute that these four species share many genes involved in physiological and metabolic properties such as anaerobiosis and assimilations system for small organic molecules (e.g. acetate, pyruvate, and lactate) as carbon and energy source. Figure 2 Ordination plot of bacterial genomes using NMDS. (A) Analysis with 50 species belonging to 12 phyla and 5 classes, and D. desulfuricans SSM1. (B) Analysis with six species within Deltaproteobacteria and D. desulfuricans SSM1. Distances were calculated from gene profiles based on COG families. The abbreviation corresponding to the KEGG organism code is used as the label for the species name (detailed explanations are described in Supplementary Table S1 ). The labels are colour-coded according to their taxonomic groups (phylum/class): red, D. desulfuricans SSM1 (def); orange, Deltaproteobacteria ; yellow, Epsilonproteobacteria ; blue, other Proteobacteria ; purple, Firmicutes ; green, Chlorobi ; black, Aquificae , Thermotogae , and Deinococcus-Thermus ; and grey, other bacteria. 3.3. Central metabolism Deferribacter desulfuricans grows heterotrophically using a variety of organic acids (formate, acetate, propionate, pyruvate, and lactate) with nitrate or S 0 as a primary electron acceptor. 2 As shown in Fig. 3 , the reconstructed central metabolic pathways from the D. desulfuricans genome certainly showed that these organic acids could be utilized as energy and carbon sources via the oxidative tricarboxylic acid (TCA) cycle and various anaplerotic pathways. Acetate used as a D. desulfuricans SSM1 growth substrate can be activated to acetyl-CoA either via a single-step reaction by an acetyl-CoA synthetase (Acs: DEFDS_0854) or a two-step reaction by acetate kinase (AckA: DEFDS_1816) and phosphate acetyltransferase (Pta: DEFDS_1815). The AckA–Pta reaction can also operate in acetate production from acetyl-CoA in a fermentative metabolism of pyruvate, and the enzymatic action of AckA results in ATP production by substrate-level phosphorylation. Lactate may be oxidized to pyruvate by a malate dehydrogenase with a broad substrate specificity. In contrast to acetate and lactate, propionate is fed directly into the TCA cycle via succinyl-CoA. Propionyl-CoA, produced by the activation of propionate by acetyl-CoA synthetase, is converted to succinyl-CoA through a methylmalonyl pathway, including a propionyl-CoA carboxylase (DEFDS_1227-8), a methylmalonyl-CoA epimerase (DEFDS_1935), and a methylmalonyl-CoA mutase (MutA: DEFDS_2077 and MutB: DEFDS_1225). The catabolism of pyruvate reflects the anaerobic nature of D. desulfuricans . Conversion of pyruvate to acetyl-CoA is performed by either pyruvate ferredoxin oxidoreductase (POR: DEFDS_0568) or pyruvate-formate lyase (PFL: DEFDS_2103). Figure 3 Central metabolism based on potential growth substrates and metabolic capacities reconstructed from the D. desulfuricans genome. This figure displays the flow of carbon in the metabolism of various organic acids (acetate, propionate, butyrate, lactate, and glycerol) predicted from the genome information of D. desulfuricans SSM1. The reversible and irreversible reactions catalysed by enzymes are indicated with both and single arrowhead, respectively. POR, pyruvate ferredoxin oxidoreductase; PFL, pyruvate formate-lyase; Pyc, pyruvate carboxylase; Pck, phosphoenolpyruvate carboxykinase; PykA, pyruvate kinase; Ppd, pyruvate phosphate dikinase; MaeB, malate dehydrogenase (oxaloacetate-decarboxylating); Mdh, malate dehydrogenase; SucCD, succinyl-CoA synthase; Sdh, succinate dehydrogenase; OOR, 2-oxogultarate ferredoxin oxidoreductase; Fba, fructose-bisphosphatase; Pfk, 6-phosphofructokinase; Acs, acetyl-CoA synthetase; Ack, acetate kinase; Pta, phosphate acetyltransferase; AOR, aldehyde ferredoxin oxidoreductase; GlpK, glycerol kinase; GlpD, glycerol-3-phosphate dehydrogenase; Thl, acetoacetyl-CoA thiolase; Hbd, 3-hydroxybutyryl-CoA dehydrogenase; Crt, 3-hydroxybutyryl-CoA dehydratase (crotonase); Bcd, butyryl-CoA dehydrogenase; Ptb, phosphate butyryltransferase; Buk, butyrate kinase; Rnf, Rnf-type ion-translocating electron transport complex; Etf, electron transfer flavoprotein complex; PccAB, propionyl-CoA carboxylase; MutAB, methylmalonyl-CoA mutase; 2Pi, diphosphate; and Fdox/Fdred, ferredoxin, oxidized and reduced forms respectively. Deferribacter desulfuricans is capable of growing using formate as its sole source of carbon and energy. According to the reconstructed metabolic pathways from the D. desulfuricans genome, formate is oxidized to CO 2 by membrane-bound formate dehydrogenase (Fdh: DEFDS_1329-31), which is energetically coupled with the respiratory nitrate or S 0 reduction. Methanogens and homo-acetogens are known to assimilate formate to acetyl-CoA via the Wood–Ljungdahl pathway (reductive acetyl-CoA pathway). 34 Since the reductive acetyl-CoA pathway is absent in the D. desulfuricans genome, formate must be assimilated by another pathway. A potential reaction for formate assimilation is the direct or indirect conversion of formate and acetyl-CoA to pyruvate, the reverse reaction of pyruvate-formate lyase or the POR-catalysing carboxylation of CO 2 coupled with the formate oxidation by the Fdh protein. The pyruvate can then be converted to various biosynthetic intermediates by the TCA cycle and anaplerotic pathways (Fig. 3 ). The D. desulfuricans genome has a variety of anaplerotic pathways, which include the three enzymes, malic enzyme (MaeB: DEFDS_1074), pyruvate carboxylase (Pyc: DEFDS_1275), and phosphoenolpyruvate carboxykinase (PckA: DEFDS_1461). These pathways replenish the intermediates of the TCA cycle for gluconeogenesis and amino acid biosynthesis, and their anaplerotic CO 2 fixation may be an important function for D. desulfuricans living heterotrophically in a limited organic carbon such as hydrothermal vent environment. In the gluconeogenesis pathway, pyruvate may be dominantly produced by POR from acetyl-CoA and further converted to phosphoenolpyruvate by pyruvate phosphate dikinase (PpdK: DEFDS_0235). The genome possesses genes for the Embden–Meyerhof–Parnas (EMP) pathway and the non-oxidative branch of the pentose phosphate pathway (Fig. 3 ). The EMP pathway includes fructose-1,6-bisphosphatase (DEFDS_1408), which is a key enzyme in gluconeogenesis. The TCA cycle of D. desulfuricans includes 2-oxoglutarate ferredoxin oxidoreductases (OOR: DEFDS_0922-25 and DEFDS_0804-5) instead of 2-oxoglutarate dehydrogenases that are typically found in aerobic bacteria. These enzymes catalyse the reversible oxidative decarboxylation of 2-oxoglutarate to form succinyl-CoA, while they are also key enzymes of the reductive TCA (rTCA) cycle. Therefore, the TCA cycle has the potential to proceed in the reverse direction. Although no ATP-dependent citrate lyase (AclAB) is present, the presence of other key enzymes of the rTCA cycle suggests that the rTCA cycle could be partially operative in D. desulfuricans . Actually, oxaloacetate is produced from pyruvate and phosphoenolpyruvate by the anaplerotic enzymes, Pyc and PckA, respectively, and then oxaloacetate is converted to 2-oxoglutarate by operating the TCA cycle in the reductive direction. Among the previously characterized members of the genus Deferribacter , D. abyssi and D. autotrophicus grow autotrophically. 3 , 5 We have also isolated several strictly autotrophic strains of D. desulfuricans from different deep-sea hydrothermal environments (data not shown). Thus, the autotrophy may not be an unusual feature within the genus Deferribacter . The lack of an autotrophic phenotype in the strain SMM1 is presumably caused by the lack of the aclAB genes. Genome analysis suggested the capability of butyrate-fermentation in D. desulfuricans , although it has not been confirmed experimentally. In this pathway, as shown in Fig. 3 , butyryl-CoA is converted from acetyl-CoA by the four enzymes—acetoacetyl-CoA thiolase (Thl; DEFDS_1837), 3-hydroxybutyryl-CoA dehydrogenase (Hbd; DEFDS_1836), 3-hydroxybutyryl-CoA dehydratase (Crt; DEFDS_1835), and butyryl-CoA dehydrogenase (Bcd; DEFDS_1834)—and the electron transfer flavoprotein complex (Etf; DEFDS_1831-1832). The order of this gene cluster is conserved in the genomes of Geobacter metallireducens , Geobacter uraniireducens , and a butyrate-producing clostridial species, Butyrivibrio fibrisolvens . 35 Butyryl-CoA is converted to butyrate by phosphate butyryltransferase (DEFDS_1240) and butyrate kinase (DEFDS_1241) resulting in the generation of ATP by the substrate-level phosphorylation. This process can be reversible under a certain condition 36 and may thus serve for the butyrate utilization. As shown in Fig. 3 , the D. desulfuricans genome also contains a set of six genes ( rnfCDGEAB , DEFDS_0487-92) related to the potential membrane-bound electron transport complex (Rnf) recently found in various bacteria and archaea. 37 – 39 In nitrogen-fixing bacteria, the Rnf complex transports electrons from NADH to ferredoxin, which donates electrons to nitrogenase. 40 It is further proposed that the same enzyme apparently runs in the reverse direction: electron transfer from reduced ferredoxin to NAD + driving the electrogenic pumping of Na + out of the cell. 39 Regional synteny for the rnf genes in the D. desulfuricans genome was found in several clostridial genomes, such as Clostridium tetani , Clostridium kluyveri , and Halothermothrix orenii . It is supposed that the Rnf complex is involved in the regeneration of NADH for the fermentation of butyrate. 41 Since the D. desulfuricans genome indicated the possible fermentation of butyrate and the requirement of reduced ferredoxin for the anabolic reactions, the Rnf complex may be operative in either the forward or reverse flow of electrons, or both. In addition, it is suggested that glycerol can be utilized by glycerol kinase (GlpK, DEFDS_1230), sn -glycerol-3-phosphate dehydrogenase (GlpD, DEFDS_1577), and triosephosphate isomerase (DEFDS_0129). However, no growth was observed when glycerol was used as the sole source of energy and carbon, 2 and this phenomenon is presumably explained by the lack of a glycerol-uptake-facilitator protein. Supporting the heterotrophic ability of D. desulfuricans , a variety of transporters for mono-, di-, and tricarboxylates are encoded within the genome. Actually, the genes encoding two sodium:solute symporter (SSS) family proteins (DEFDS_1547 and DEFDS_1574) and a YaaH family protein, which are presumably involved in acetate uptake, were identified in the genome. In addition, the genes encoding five transporter systems of tripartite ATP-independent periplasmic (TRAP) family (DEFDS_0682-4, DEFDS_0770-2, DEFDS_1399-1400, DEFDS_1846-7, and DEFDS_2070-1) may be involved in uptake of various dicarboxylates such as fumarate, malate, and succinate. In terms of tricarboxylate transporters, the genes for two citrate transporters (DEFDS_1180 and DEFDS_2162) were identified in the genome. On the other hand, the genome encodes two phosphotransferase system sugar transporters for fructose and mannose, although D. desulfuricans could not grow by using sugar. 3.4. Respiration Since D. desulfuricans SSM1 is capable of using molecular hydrogen (H 2 ) as an energy source, 2 a membrane-bound NiFe-hydrogenase (Hyd) (DEFDS_0075-77) would function as an H 2 -uptake hydrogenase. As shown in Fig. 4 , the oxidation of molecular hydrogen is coupled to the reduction of menaquinone. The genome has two distinctive nitrate-reducing enzymes for respiration, which are coupled to the electron transfer chains (Fig. 4 ). One is a membrane-bound (Nar: DEFDS_2086-89) and the other is a periplasmic (Nap: DEFDS_1819-23) nitrate reductase, which does not directly contribute to the generation of a proton motive force but contributes to redox balancing. 42 It is generally known that bacteria have Nar-type nitrate reductases, but some bacterial species have a Nap-type or both types of nitrate reductase. Nap-type nitrate reductase usually functions as a two-subunit enzyme comprising a catalytic subunit (NapA) that binds a bis-MGD cofactor and a [4Fe-4S] cluster, and an electron transfer subunit (NapB) that binds two c -type haems. However, since D. desulfuricans does not possess the napB gene, the nitrate reductase of D. desulfuricans may be monomeric, which is differentiated from the heterodimeric NapAB structure typically found in bacteria. Actually, NapB-independent periplasmic nitrate reductases have been reported for some species in Deltaproteobacteria and Clostridia . 43 The organization of Nap genes in the D. desulfuricans genome is similar to that of Desulfovibrio desulfuricans . Although nitrate is sequentially reduced to N 2 (denitrification) or NH 4 (ammonification) in many cases, no enzyme for nitrite reduction was identified in the D. desulfuricans genome. This is consistent with experimental results, because the accumulation of nitrite is observed during culture of this organism. 2 Figure 4 Genome-based models for the energy-conserving electron-transport pathways of D. desulfuricans SSM1. Reducing power acquired by catabolic metabolism (NADH, succinate, and sn -glycerol-3-phosphate) or by oxidation of hydrogen and/or formate is used to reduce sulphur compounds and nitrate, and potentially iron ions via the quinol pool for energy conservation or dissipation. Membrane-binding components are indicated with cylinders or cones, where the upper and the lower reactions are catalysed on the periplasmic side and the cytoplasmic side, respectively. Periplasmic components are indicated with ellipsoids or spheres. Hyd, membrane-binding NiFe-hydrogenase; Nar, respiratory membrane-bound nitrate reductase; Nap, periplasmic nitrate reductase; Psr, polysulphide reductase; Phs, thiosulphate reductase; Ttr, tetrathionate reductase; Fdh, formate dehydrogenase; Nuo, proton-pumping NADH dehydrogenase; Nqr, sodium-translocating NADH:quinone oxidoreductase; Sdh, succinate dehydrogenase; Cyt bd, cytochrome bd quinol oxidase; Cyt bc, cytochrome bc complex; Glp, sn -glycerol-3-phosphate dehydrogenase; G3P, sn -glycerol-3-phosphate; and DHAP, dihydroxyacetone phosphate. It has been reported that the reduction of energy-yielding sulphur compounds (sulphur-respiration) is catalysed by some key enzymes: a polysulphide reductase (Psr) in Wolinella succinogenes ( Epsilonproteobacteria ), 44 a thiosulphate reductase (Phs) in Salmonella enterica ( Gammaproteobacteria ), 45 and a hydrogenase-sulphur reductase multienzyme (Sre) in Acidianus ambivalens ( Crenarchaeota ) or Aquifex aerolicus ( Aquificae ). 46 , 47 Psr and Phs reduce soluble polysulphide derived from S 0 to sulphide, whereas Sre is considered to reduce S 0 directly to sulphide. Genome analysis revealed that D. desulfuricans has three gene clusters encoding Psr/Phs enzymes, DEFDS_0670-72, DEFDS_1691-93, and DEFDS_1697-99. The genes in each cluster encode three subunits that construct the following membrane-bound complex enzyme: a catalytic subunit with molybdopterin (PsrA/PhsA), an electron transfer subunit with the [Fe–S] cluster (PsrB/PhsB), and a membrane anchor subunit (PsrC/PhsC). The amino acid sequences of the PsrA/PhsA subunits contain the motif necessary for translocation of the protein towards the periplasmic space via the twin arginine-translocation systems, suggesting that the polysulphide reduction should occur in the periplasmic space. Moreover, an alternative molybdopterin-containing enzyme, tetrathionate reductase (Ttr), is encoded by DEFDS_1446-47, but it is unclear whether this enzyme is involved in energy conservation because of the absence of a membrane anchor subunit. Deferribacter desulfuricans SSM1 presumably uses the polysulphides chemically formed from the reaction of S 0 and sulphide, which readily occurs in hydrothermal vents, 48 and therefore it needs to transfer polysulphide to the periplasmic space across the outer membrane ( Supplementary Fig. S1A ). The periplasmic Sud protein in W. succinogenes has been proposed to serve as a polysulphide-binding protein and to transfer polysulphide-sulphur to the active site of polysulphide reductase. 49 A Psr/Phs gene cluster (DEFDS_0670-72) in the D. desulfuricans genome is associated with the genes coding an outer membrane porin and lipoprotein (DEFDS_0667-68). The amino acid sequence of DEFDS_0667 represents the functional domain of porin_O_P (phosphate-selective porins O and P) that are conserved in anion-specific porins. 50 The lipoprotein DEFDS_0668 contains a rhodanese (thiosulphate sulphurtransferase) domain as well as the Sud protein, although there is no homology between the two proteins. Thus, these two proteins may facilitate sulphur respiration in D. desulfuricans as a polysulphide-specific porin in the outer membrane and a polysulphide-binding protein in the periplasmic space, respectively. This locus is adjacent to other genes related to respiration, two multihaem c -type cytochromes (DEFDS_0665-66) and a cytochrome bc complex (DEFDS_0674-77). Interestingly, a similar locus without the Psr/Phs genes was found in the genomes of the iron- and sulphur-reducing bacteria, Geobacter spp. and Desulfuromonas acetoxidans DSM 684 ( Supplementary Fig. S1B ). The D. desulfuricans genome has a gene cluster (DEFDS_0741-60) that includes many genes encoding membrane-bound or periplasmic multihaem c -type cytochromes, which are potentially distributed in the inner or outer membranes or in the periplasmic space ( Supplementary Fig. S2A ). As demonstrated in G. sulfurreducens and Shewanella oneidensis , multihaem c -type cytochromes may be involved in the potential dissimilatory reduction of metal ions. 51 Although D. desulfuricans SSM1 cannot grow by iron reduction, the capability of iron reduction is a symbolic physiological trait of the genus Deferribacter . 3 – 5 On the basis of cultivation-dependent estimations, the members of the genus Deferribacter have been predicted to be the most numerically abundant iron-reducing prokaryotes in deep-sea vents. 7 , 8 , 52 Therefore, the multihaem c -type cytochromes in D. desulfuricans may be a remnant of the metal reduction system. As shown in Fig. 4 , reductants produced from the oxidation of organic compounds would be oxidized by a proton-pumping NADH dehydrogenase (Nuo: DEFDS_1972-85) and succinate dehydrogenase (Sdh: DEFDS_0685-88) to provide reducing equivalents for the nitrate- and polysulphide reductions. The D. desulfuricans genome has an alternative complex for NADH oxidation, sodium-translocating NADH:quinone oxidoreductase (Nqr: DEFDS_1913-18). This complex possibly allows the generation of a sodium gradient, which could energize an Na + -dependent symporter of the organic substrates. This organism found to possess many sodium gradient-dependent symporters such as two solute:sodium symporters (SSS family), a nucleobase:cation symporter-2 (NCS2) family protein (DEFDS_1105), an alanine or glycine:cation symporter (AGCS) family protein (DEFDS_1601), and phosphate:sodium symporter (PNaS) family protein (DEFDS_0081). 3.5. Signal transduction, motility, and chemotaxis Deferribacter desulfuricans SSM1 and its relatives inhabit the ever-changing steep physical and chemical gradients of hydrothermal vent chimney. The thermophilic microorganisms living on hydrothermal vents are generally sensing temperature or chemical stimuli to guide their directional movements by swimming with a whip-like flagellum. Thus, the D. desulfuricans genome should have the molecular machinery for sensing and responding to environmental changes. Indeed, D. desulfuricans has genes for various signalling systems, as other vent chemolithotrophs do 9 – 11 (Table 2 ). More than 70 genes for two-component signal transduction (TCS) systems, including 23 histidine kinases, 41 response regulator genes, and 13 hybrid histidine kinases with regulator domains, were found in the D. desulfuricans genome. The frequency of TCS systems found in the D. desulfuricans genome is the highest (34 genes per megabase) among vent bacteria (ranging from 11 to 22 genes per megabase). Although most of them could not be functionally assigned due to very low homologies with well-defined systems, some functions were assumed to be phosphate regulation (PhoBR) and ammonium assimilation (NtrBC).\n Table 2 Abundance of genetic components for signal transduction systems in the genomes of representative deep-sea vent chemolithotrophs Signal transduction systems Domains (Pfam) Deferribacter desulfuricans Thiomicrospira crunogena Nautilia profundicola Nitratiruptor sp. SB155-2 Sulfurovum sp. NBC37-1 Persephonella marina Genome size (bp) 2 234 389 2 427 734 1 676 444 1 877 931 2 562 277 1 930 284 Two-component signal transduction systems Sensor histidine kinase PF00512/PF02518 23 13 11 15 20 8 Response regulator PF00072 41 24 16 19 28 14 Hybrid 13 7 1 1 9 0 Total 77 44 28 35 57 22 Cyclic diGMP signalling systems GGDEF PF00990 12 20 17 11 5 12 EAL PF00563 1 5 3 2 1 4 HD-GYP PF01966 3 3 2 0 0 2 GGDEF-EAL 3 16 11 19 12 12 Total 19 44 33 32 18 30 Chemotaxis Methyl-accepting chemotaxis proteins PF00015 14 14 11 8 0 5 Chemotaxis proteins 32 13 3 13 0 6 Total 46 27 14 21 0 11 Signal transduction proteins were identified by querying the predicted gene products to the Pfam and COG databases. Genes with significant matches ( E -value of less than 1E−5) were assigned a product description and classified using a set of rules based on the domain architecture of the protein. The final results were manually verified. As an alternative signalling system, the D. desulfuricans genome has many genes relevant to a cyclic diguanylate (c-diGMP) signalling system, 53 although these were less abundant than in other deep-sea vent bacteria (Table 2 ). This system is characterized by either diguanylate cyclase (GGDEF domain) or phosphodiesterase (EAL and HD-GYP domains) activity and is relevant to biofilm formation, motility, and virulence. 53 In addition, at least one PAS domain was found in seven histidine kinase proteins and two c-diGMP signalling proteins as additional input modules. PAS domains are known to sense changes in the redox conditions inside or outside cells. 54 It is worthy to note that the D. desulfuricans genome has a higher proportion of intracellular signalling systems than those of other deep-sea vent chemolithotrophs ( Supplementary Fig. S3 ). These sensors may be essential for the versatile energy acquisition, carbon metabolisms, and the chemotaxis and redoxtaxis of D. desulfuricans SSM1 that are necessary under highly variable environmental conditions. This genome encodes a large number of methyl-accepting chemotaxis proteins (14 MCPs) as observed in other deep-sea vent bacteria (5–14 MCPs), except for the immotile Sulfurovum sp. NBC37-1 (Table 2 ). These chemotaxis-specific receptors would, in part, contribute to the versatile sensing capabilities of D. desulfuricans . The MCP encoded by DEFDS_1855 possesses a Cache (Ca 2+ channels and chemotaxis receptors) domain that is involved in the binding of amino acids and carbohydrates in the MCP of Bacillus subtilis . 55 It is also notable that multiple che clusters (six clusters of che-1 to che-6 ) were identified in the genome of D. desulfuricans , whereas most deep-sea vent microorganisms usually have one or two che gene clusters (Table 2 and Fig. 5 ). The che-2 cluster is associated with genes for flagellar biosynthesis, suggesting that flagellar formation and motility may be linked with the chemotaxis pathway. Deferribacter desulfuricans SSM1 has almost all of the genes necessary for the flagellar apparatus although motility has not yet been observed in this strain. 2 On the other hand, since motility has been confirmed in several strains of D. desulfuricans recently isolated by us, flagellar-based motility could be an intrinsic genetic potential in D. desulfuricans . Moreover, all che clusters, except for che-4 , contain genes that encode a fusion protein (CheA/Y) consisting of histidine kinase (CheA) and response regulator (CheY) domains. In other bacteria, the gene clusters containing cheA/Y are involved in specific functions other than chemotaxis. For instances, the Pseudomonas aeruginosa Wsp cluster is involved in biofilm formation, 56 and the Myxococcus xanthus Frz cluster functionally controls type IV pili-based motility for cellular aggregation. 57 Also, the gene cluster 3 of Rhodospirillum centenum is involved in cyst cell development. 58 As shown in Fig. 5 , the che-3 and che-5 gene clusters of D. desulfuricans , in particular, have a similar gene organization to each other. There are significant homologies (more than 22% identity at the level of amino acid sequences) between CheA/Y proteins in D. desulfuricans and other organisms. Thus, various D. desulfuricans chemotaxis-like systems may be responsible not only for chemotaxis but also for other cellular functions involved in cell–cell interactions. In fact, this genome contains several gene clusters potentially involved in the formation of a type IV pilus that plays a role in the adhesion of bacteria to host cells and solid surfaces, and mediates bacterial twitching motility. Some genes encoding pilus-related proteins such as PilA of the major prepilin (DEFDS_1243), PilB of the assembly ATPase (DEFDS_1109 and DEFDS_1256), PilQ of secretin (DEFDS_1255), and PilD of prepilin peptidase (DEFDS_0112) were detected. Considering the presence of these genes, it is possible to propose a lifestyle of clinging to the chimney surface for survival under a constantly variable environment. 59 Figure 5 Gene arrangement of the representative chemotaxis-like gene clusters in the genomes of D. desulfuricans and related bacteria. Six chemotaxis-like gene clusters ( che-1 to che-6 ) identified in the D. desulfuricans genome are compared with those CheA/Y-containing clusters that have been experimentally verified: M. xanthus Frz, 57 P. aeruginosa Wsp, 56 R. centenum Cluster 3, 58 and Synechocystis sp. PCC 6803 Tax2. 68 3.6. Adaptation to thermal environment As in other thermophilic microbes, the proteome of D. desulfuricans , which can grow up to 70°C should be stable at least at the maximum growth temperature. Generally, it is known that a protein's amino acid composition has a great influence on its thermostability, and the proteins of thermophiles show a tendency to possess fewer non-charged amino acids and more charged amino acids than those of mesophiles. 60 Actually, all of the proteins encoded in the D. desulfuricans genome were found to have fewer histidine, glutamine, and threonine residues, but more glutamic acid and lysine residues than mesophiles ( Supplementary Fig. S4 ). Both charged amino acids presumably contribute to increase the thermostability of the protein by enhancing the occurrence of salt bridges and ion pairs. 61 Also, the stabilization of DNA and RNA at a high temperature is indispensable for survival in the hydrothermal environment. It has been suggested that polyamines, RNA methyltransferases, and protamine P1 could contribute to the thermoadaptation of Geobacillus kaustophilus , which has a similar maximum temperature for growth (74°C) as D. desulfuricans . 62 In Thermus thermophilus , which can grow up to 85°C, the inactivation of genes for polyamine biosynthesis or tRNA (adenine-N1)-methyltransferase (TrmI) results in a thermolabile phenotype. 63 The D. desulfuricans genome encodes genes necessary for putrescine and spermidine synthesis, speA (DEFDS_1288) and speBDE (DEFDS_0967-9). In addition, we identified two genes for norspermidine synthesis—carboxynorspermidine dehydrogenase (DEFDS_1287) and carboxynorspermidine decarboxylase (DEFDS_1286). Norspermidine and norspermine are commonly found in hyperthermophilic bacteria. 64 These polyamines stabilize DNA by binding to nucleic acids and can induce aggregation or conformational changes of DNA. 65 On the other hand, a homolog (DEFDS_0605) of TrmI found in T. thermophilus , and four tRNA and three tRNA/rRNA methyltransferase genes in total were identified in D. desulfuricans . The genes described above appear to contribute to the thermoadaptation of D. desulfuricans as well as other thermophilic bacteria. Deferribacter desulfuricans possesses many molecular chaperons used for protein folding and unfolding such as DnaJ-DnaK-GrpE (DEFDS_2113-4) and the HrcA repressor (DEFDS_2113-6), GroEL-GroES (DEFDS_0240-39), and small heat shock proteins categorized into Hsp20 family (DEFDS_1576 and DEFDS_1707). This organism also possesses ATP-dependent heat shock-responsive proteases, which is thought to be concerned in providing thermotolerance to cell on exposure to heat stress, such as HslVU (DEFDS_2155-6), ClpPX (DEFDS_0193-4), and Lon (DEFDS_0195 and DEFDS_1706). 3.7. Antioxidant system Deferribacter desulfuricans SSM1 is a strict anaerobe: its genome possesses many genes potentially associated with resistance to oxidative stress. DEFDS_1019 and DEFDS_0573 encode a superoxide reductase (SOR) and a rubredoxin (Rd), respectively. The SOR–Rd proteins can catalyse the reduction of superoxide to hydrogen peroxide. Putative rubrerythrins (Rbr) encoded by DEFDS_0019, DEFDS_1568, and DEFDS_1971 further reduce hydrogen peroxide to water. 66 DEFDS_1568 is located adjacent to the gene encoding the peroxide repressor ( perR ) (DEFDS_1567). A series of these genes presumably construct the antioxidant system of D. desulfuricans SSM1 inhabiting the oxic–anoxic transition zone in the deep-sea hydrothermal environment. Additionally, genes for peroxiredoxins (DEFDS_0018 and DEFDS_1350), thioredoxin (DEFDS_1167), and thioredoxin reductase (DEFDS_0503) may be involved in the antioxidant system. On the other hand, D. desulfuricans possesses genes encoding two potential dioxygen scavenging systems. The first one is a cytochrome bd quinol oxidase (DEFDS_1619-1620), which is a respiratory terminal oxidase, and the second one is a rubredoxin-oxygen oxidoreductase (Roo), whose gene is located next to the rubredoxin gene (DEFDS_0573). In the sulphate-reducing Desulfovibrio -related species, the latter enzyme is proposed to be the cytoplasmic terminal oxidase for the non-energy-conserving respiratory reduction of O 2 or nitric oxide. 67 Thus, the bd -type oxidase and Roo of D. desulfuricans may also serve to relieve the environmental and intracellular oxidative stresses. 3.8. Conclusions Genome analysis of D. desulfuricans SSM1 revealed its versatile energy and carbon metabolisms and its machineries for sensing and responding to the environmental changes in hydrothermal vent habitats. We showed that the molecular systems such as the multihaem c -type cytochrome clusters, two-component signal transducers, and abundant chemotaxis components could be tightly linked to the adaptation mechanisms required to adapt in physically and chemically variable environments. The multiple signal transduction systems for sensing dynamic changes in carbon source and temperature, and the type IV pili, which are likely to be useful for clinging to the chimney, allow D. desulfuricans to survive in such a harsh environment. The genome sequence of D. desulfuricans SSM1 should provide many clues for the better understanding of bacterial life in environments around hydrothermal vents from the ecological and evolutionary points of view. The sequences, as well as the gene information shown in this paper, are available in the web databases, ExtremoBase ( http://www.jamstec.go.jp/gbrowser/cgi-bin/top.cgi ) and DOGAN ( http://www.bio.nite.g.o.jp/dogan/Top )."
} | 10,946 |
36781665 | PMC10104932 | pmc | 3,810 | {
"abstract": "The contamination of soil and water by metals such as mercury (Hg) and cadmium (Cd) has been increasing in recent years, because of anthropogenic activities such as mining and agriculture, respectively. In this work, the changes in the rhizosphere microbiome of Lolium perenne L. during the phytoremediation of soils contaminated with Hg and Cd were evaluated. For this, two soil types were sampled, one inoculated with mycorrhizae and one without. The soils were contaminated with Hg and Cd, and L. perenne seeds were sown and harvested after 30 days. To assess changes in the microbiome, DNA isolation tests were performed, for which samples were subjected to two-step PCR amplification with specific 16S rDNA V3-V4 primers (337F and 805R). With mycorrhizae, changes had been found in the absorption processes of metals and a new distribution. While with respect to microorganisms, families such as the Enterobacteriaceae have been shown to have biosorption and efflux effects on metals such as Hg and Cd. Mycorrhizae then improve the efficiency of removal and allow the plant to better distribute the absorbed concentrations. Overall, L. perenne is a species with a high potential for phytoremediation of Cd- and Hg-contaminated soils in the tropics. Inoculation with mycorrhizae modifies the phytoremediation mechanisms of the plant and the composition of microorganisms in the rhizosphere. Mycorrhizal inoculation and changes in the microbiome were associated with increased plant tolerance to Cd and Hg. Microorganism-assisted phytoremediation is an appropriate alternative for L. perenne .",
"conclusion": "Conclusions The results show that adding mycorrhizae to the phytoremediation process with L. perenne considerably favors the absorption and distribution of both metals in the structural parts of the plant (shoot and root). However, it has been observed that mycorrhizae stimulate a better microbiome-plant interaction, improving the concentration of metal removed in the soil and increasing the diversity of families of microorganisms in the soil. It has been shown that up to 75% of the families of microorganisms detected have been reported in different studies involving some mechanism of contaminant removal. Examples of this are stabilization and biosorption, which help increase metal tolerance in plants and in many cases increase metal accumulation. In this study, it was found that the addition of mycorrhizae favors this increase in the accumulation of both metals. Some other microorganisms have not been reported to have a mechanism involving metal removal processes, but in this study, they have been identified, as is the case of Aquaspirillaceae , Bacterium Ellin7509 , Bacteroidetes vadinHA17 , BIrii41 , Microscillaceae , and Holophaga sp. that it is recommended for further studies to carry out tests of mechanisms involved in the removal of metals, especially Hg and Cd. The results have shown that the root microbiome, both mycorrhizal and mycorrhizal, plays an important role in plant growth, improving yield and regulating soil fertility.",
"introduction": "Introduction Heavy metal contamination has caused serious environmental problems, generating degradation in ecosystems, as well as direct damage to human health. Of the 118 elements known to man, 98 are metals, which have played a fundamental role in the development of civilizations. The problem has focused on the rapid demographic growth and industrialization that have led to serious problems of pollution and deterioration of the environment, especially in developing countries (Wu et al. 2022 ). Among the metals of greatest environmental concern are lead (Pb) and mercury (Hg), followed by beryllium (Be), barium (Ba), cadmium (Cd), copper (Cu), manganese (Mn), nickel (Ni), tin, (Sn), vanadium (V), and zinc (Zn) (Gan et al. 2017 ; C. Li et al. 2022a , b , c ; Singh et al. 2020 ). Industrial and mining activity releases metals such as mercury and cadmium into the environment, which are very harmful to human health and most living forms. Anthropogenic activities pollute the soil, and these metals bioaccumulate in plants, increasing their danger; later their concentration in living beings biomagnifies, so the ingestion of contaminated plants or animals can cause intoxication symptoms (Budianta 2021 ; Yang et al. 2022 ). Despite abundant evidence of these harmful effects on health, exposure to heavy metals continues and can increase due to the lack of a consensual and concrete policy in different countries, especially in developing countries. Mercury is still used extensively in the gold mines of Latin America, while cadmium is found in many fertilizers and pesticides that are applied daily in agricultural areas. Cadmium (Cd) and mercury (Hg) accumulation in soils has been rapidly increased due to natural (e.g., sediments) and anthropogenic (e.g., mining and agriculture) process (Y. Li et al. 2022a , b , c ; Liu et al. 2020 ; Yuan et al. 2022 ). These heavy metals are non-biodegradable. Countries located in the tropical zone contribute approximately 30% of Hg pollution worldwide. Especially, South America contributes 2% to this phenomenon (Singh and Kumar 2020 ). Colombia is the third country in the world with the highest mercury pollution, after China and Indonesia, emitting approximately 75 tons per year into the environment (MADS 2014 ). In Colombia, there are areas with a high concentration of mercury reported up to 340 μg/m 3 in the air (300 times higher than the World Health Organization guideline for maximum public exposure to mercury vapor) (MADS 2014 ). Even in some urban areas of Colombian mining municipalities such as Segovia, mercury concentrations vary between 40,000 and 80,000 ng/m 3 , far exceeding the permissible value of 10 ng/m 3 (UNIDO 2017 ). The average level of cadmium in soils has been located between 0.07 and 1.1 mg/kg, with a natural base level that would not exceed 0.5 mg/kg. Some soils can have higher levels of cadmium because the rocks that formed them had cadmium in their composition. For example, phosphate rocks, which are the raw material for all phosphate fertilizers, contain levels of heavy metals that vary according to their geographical origin but are generally higher than the first in the earth’s crust (Wiggenhauser et al. 2019 ). Cadmium remains in a significant proportion in industrial fertilizers and is subsequently applied to the soil together with phosphorus (Jensen and Mosbæk, 1990 ). The increase in these pollutants in the soil has played an important role in recent years since agricultural ecosystems and mining areas have been affected mainly by the increase in population (Eliana Andrea et al. 2019 ). Consequently, the health of the soil determines the stability and balance of ecological systems. The increase of metals such as Hg and Cd in soils can exceed their buffering capacity, leading to possible spread throughout the environment and being able to enter the food chain (Biswas et al. 2020 ; W. Li et al. 2022a , b , c ). Therefore, a soil remediation approach that involves living organisms (plants and microorganisms) with high adsorption capacity and high availability is necessary. Among these processes is phytoextraction, which is considered a commercial phytoremediation method with greater projection in the coming years (Zhao et al. 2019 ). Studies on the remediation of metals such as Hg and Cd have focused on removal processes of these from plants (Cruz et al. 2021 , 2019 ; Leudo et al. 2020 ). L. perenne (ryegrass) is a grass native to Europe, Asia, and North Africa and is now widely distributed throughout the world, including the Americas and Australia. This plant has been extensively studied due to its great response to abiotic stress exposure of metals such as Hg and Cd (Cruz et al. 2021 ). Some authors have verified its phytoremediation capacity of heavy metals individually of Cd and Hg (Cruz et al. 2021 , 2019 ). On the other hand, studies have also been carried out in which this plant is placed in symbiosis with mycorrhizae, finding promising results in the elimination of Hg in soils (Leudo et al. 2020 ). Regarding the molecular response, alterations of the GST gene have been observed, which is important because this gene encodes proteins that can help eliminate toxins from the plant (Cruz et al. 2021 ). However, it has been argued that of all the technologies used for metal removal, bioremediation using microorganisms has gained the most attention, due to its better ability to resist rapid mutation and environmental evolution, compared to plants and animals (Zhao et al. 2019 ). By understanding the process and content of genetic information within a contaminated sample, 16S amplicon sequencing allows us to understand the broad changes in community diversity over time, which combined with metagenomics increases the resolution and sensitivity of understanding microbial communities (Poretsky et al. 2014 ). For this reason, different amplicon sequencing and metagenomic analyses have been developed with the evaluation of the collective genomes and the biosynthetic machinery of the soil microflora (Handelsman et al. 1998 ). Gene-directed metagenomics has also been developed to investigate metal-contaminated soils using polymerase chain reaction–based targeting in conjunction with pyrosequencing. Also, deep sequencing of rRNA genes and functional regions has been shown to help in the development of new bioremediation strategies (Bell et al. 2011 ; Brennerova et al. 2009 ; Huang et al. 2022 ; Iwai et al. 2010 ; Malla et al. 2018 ). It has been proven that from metagenomic analysis, it is possible to identify indicator species that are specific for certain contaminants that may be targeted, and with this, effective ways to modify or resist adverse environmental conditions could be identified (Huang et al. 2022 ). For this reason, the main objective of this work had been the metagenomic evaluation of a phytoremediation process of soils contaminated with Hg and Cd by means of L. perenne -mycorrhizae and the changes in the soil microbiome.",
"discussion": "Results and discussion Phytoremediation of Cd- and Hg-contaminated soil A total of 460 ASVs were obtained, and data with relative abundance > 2% were retained for further ecological parameter analysis. The results show a higher decrease in Hg concentration compared to Cd concentration in soils, whether mycorrhizal or not (Fig. 1a ). Cd concentration decreased on average 5.3-fold, while Hg concentration decreased on average 11.5-fold compared to the initial concentration in soil (Table 1 ). The presence of mycorrhizae generated significant changes in the bioaccumulation of Cd and Hg (Fig. 1b and 1c ). On average, root Cd and Hg concentrations were fourfold higher in the mycorrhizal treatment compared to the non-mycorrhizal treatment. Cd and Hg concentration in shoots was on average twofold lower in the treatment with mycorrhizae than in the treatment without mycorrhizae. A higher removal of Cd and Hg in the soil for treatment with mycorrhiza showed that the presence of this fungus mitigates the toxicity of the pollutants and increases the tolerance of the plant to this metals (Lounès-Hadj Sahraoui et al. 2022 ). Therefore, its bioaccumulation capability is improved. Thus, mycorrhiza-assisted phytoremediation is shown to be a promising alternative for L. perenne as eco-sustainable technique to control and mange soil pollution. Fig. 1 Cadmium (Cd) and mercury (Hg) concentration in (a) roots and (b) shoot of L. perenne . ( A ) Soil concentration, ( B ) Root concentration, and ( C ) Shoot concentration. Data (means ± SE, n = 3) followed by different subscripts denotes significant differences between treatments at p < 0.05 according to Tukey’s HSD. Treatments: L. perenne and L. perenne -mycorrhizae The presence of mycorrhiza had a significant effect on BCF Plant/Soil and TF Shoot/Root factors (Fig. 2a and 2b ). In the L. perenne-mycorrhizae treatment, the BCF Plant/Soil increased 1.1-fold for Cd and 1.6-fold for Hg, compared to the non-mycorrhizal plant. Mycorrhization generated an 8- and fivefold decrease in TF for Cd and Hg, respectively. In line with the results of this study, mycorrhizae had been reported to influence the fate of metals in the rhizosphere through various phytotechnologies (Lounès-Hadj Sahraoui et al. 2022 ), in this case, phytostabilization and phytoextraction. According to the results, the presence of mycorrhizae modified the phytoremediation mechanism of L. perenne from a phytoextraction process to a phytostabilization process. The results without mycorrhiza are like those found in other studies with L. perenne (Huang et al. 2018 ). In the removal of Pb, it has been observed that the bioaccumulation of the metal occurs in greater quantity in the shoot, while in the root this concentration is lower (Huang et al. 2018 ). Fig. 2 Cadmium (Cd) and mercury (Hg) bioconcentration factor ( BCF Plant/Soil ) ( A ) and translocation factor ( TF Shoot/Root ) ( B ) of L. perenne . Data (means ± SE, n = 3) followed by different subscripts denotes significant differences between treatments at p < 0.05 according to Tukey’s HSD. Treatments: L. perenne and L. perenne -mycorrhizae The phytoremediation capability of L. perenne is limited for metals such as single Hg and Cd or a mixture of metals (Gavrilescu 2022 ; Li et al. 2020 ; Zhang et al. 2019 ). Therefore, as mentioned above and as shown in Fig. 1 , the application of mycorrhizae stimulates the bioaccumulation of metals in the plants, changing the distribution of metals, helping their uptake and the increase in metal removal. Difference in the microbial community Samples were successfully sequenced. Reads per sample, phred quality score (Qscore), and GC obtained are listed in Table 3 . Qscore 20% was higher than 91 and GC% was close to 50 across the samples, indicating the good quality of the sequencing process. In addition, rarefaction curves achieved the plateau for all samples suggesting that the sequencing depth was high enough to analyze the diversity. Table 3 Number of reads per sample and the phred quality score (Qscore) and GC contained Sample Treatment Number of read R1 Q20% R2 Q20% R1 Q30% R2 Q30% R1 GC% R2 GC% S1 Hg + M 55,965 99 98 74 89 53 53 S2 Cd + M 197,104 98 95 69 59 51 51 S3 B 95,013 99 97 89 77 57 56 S4 Hg 184,749 98 91 50 37 49 50 S5 M 183,458 99 95 75 63 51 51 S6 M 191,550 99 96 75 64 51 51 S7 Cd 121,782 99 97 90 72 57 56 S8 Hg 167,614 99 97 87 78 57 56 S9 Hg + M 66,639 99 98 80 85 56 56 S10 B 222,212 99 97 90 74 58 57 S11 Cd + M 144,498 99 97 86 73 56 55 S12 Cd2 107,460 99 96 90 64 54 54 Based on the taxonomic affiliation of ASVs, bacterial relative abundance between the treatments is illustrated in the stacked bar chart (Fig. 3 ). It is noteworthy, that for most sequences were possible to assign in family level, being the total abundance of bacterial families lower in the soil treated with mycorrhiza. This is probably due to an effect of mycorrhizal addition on the composition of the microbial community, in a similar way as has been suggested by other authors (Fan et al. 2020 ; Zhou et al. 2022 ). However, the variability between biological replicas and the limited number of samples make it difficult to observe statistical differences among the groups and find any conclusion about this phenomenon. For all treatments, except one replicate with mercury without mycorrhiza (Hg), the most abundant bacterial family was Enterobacteriaceae , indicating the organic composition of the soil (Cernava et al. 2019 ). Fig. 3 Relative abundance of bacteria families for the different treatments and replicas. The 16 s amplicon sequences variant were plotted with a cutoff of 2%. In the X axis numbers indicated the replica (1, first and 2, second) and the treatments (B, control without contamination; CD, cadmium contamination; H, mercury; B + M, control-mycorrhiza without contamination; CD + M, cadmium contamination with mycorrhiza; HG + M, mercury contamination with mycorrhiza) The estimation of the alpha diversity was performed and is presented in Fig. 4 . High values of Shannon index on the control sample, indicated higher riches in the microbial community in comparison with the contaminated soil. This observation could indicate an effect of these contaminants on soil microbial communities, where some populations may be susceptible to the presence of these contaminants, despite the known bioaccumulative effect of L. perenne and its ability to remove contaminants. Fig. 4 Comparison of alpha diversity among the treatments: a the ASV observed among the treatments, b the Shannon index among the treatments Statistical comparison between the number of observed ASVs and the Shannon index was performed using the DESseq2 statistical package and Kruskal–Wallis test. No statistical differences ( p -value > 0.05) were found between treatments in observed ASVs and Shannon index (Fig. 4 ). These results indicated that there are not significant differences in the diversity of the whole bacterial community. However, the DESeq2 analysis found that there are some taxa in the mycorrhizal-only soil with a significantly different ( p -value < 0.05) frequency than the control, suggesting the potential for mycorrhizal treatment to modify soil microbial composition (Fan et al. 2020 ). In other studies with L. perenne , it has been found that its roots form arbuscular-type mycorrhizae (Leudo et al. 2020 ), which, according to some authors, makes them unique model systems for the study of interactions between plants and microorganisms (Gómez-Sagasti et al. 2021 ). In this study, it has been proven that mycorrhizae favor the presence of other microorganisms that help in the performance of metal uptake by the roots. In contrast, the rhizobacteria found in this study, which are resistant to metals, have in some cases improved plant growth (Fig. 1 and Fig. 2 ) despite the presence of metals. (Breton-Deval et al. 2022 ). According to other authors, it can be asserted that the availability of nutrients is being increased, through the biotransformation or sequestration of metals, across the modification of the metal-plant interaction (Breton-Deval et al. 2022 ; Gupta et al. 2017 ; Novo et al. 2018 ). Therefore, the roots that are interacting within the niche with innumerable microbial communities influence the growth of the plant. The presence of mycorrhizae allows the increase of communities of microorganisms and significantly improves resistance to stress. An improvement has been observed in the absorption of metal concentrations (Fig. 2 ) and their distribution between the root and the shoot (Kumawat et al. 2022 ; Panke-Buisse et al. 2015 ). Similarly, it allows the joint formation of the plant root microbiome (Kumawat et al. 2022 ; Philippot et al. 2013 ; Rich et al. 2017 ). The results presented in this study give an overview of the enormous diversity of species, the amazing interactions, and the complex structure within the rhizosphere. This permits an approximation to the understanding of the biological character of the root system and its microbiota in the process of phytoremediation of Hg and Cd from L. perenne (Hacquard 2016 ; Kumawat et al. 2022 ). The design of this study made it difficult to observe other differences with the DESeq 2 analysis, probably due to the few replicates taken per treatment, because of the high costs of the sequencing process. Nonetheless, taxa with a frequency 100 sequences higher than the control soil were calculated (taxon count). Shigella sonnei / Escherichia fergusonii were found overexpressed in all contaminated soil with and without mycorrhizae, Streptococcus mitis in the Cd and mycorrhizae treatment (Cd + M), Delftia tsuruhatensis / Delftia lacustris in the (Cd) treatment, and Neisseria cinerea / Neisseria perflava in (Hg). In addition, Prevotella melaninogenica and Vulgatibacter incomptus were enriched in soil with mycorrhizae (M) (Table 4 ). Table 4 The taxon with frequency 100-fold higher in comparison with the control Taxonomy SILVA Taxonomy blast Percentage identity p -value Taxa count Condition Streptococcus S. mitis 99,78 > 0,05 208 Cd + M Escherichia-Shigella S. sonnei/E. fergusonii 99,78 > 0,05 490 Cd + M Escherichia-Shigella S. sonnei/E. fergusonii 99,78 > 0,05 2653 Cd + M Escherichia-Shigella S. sonnei/E. fergusonii 99,78 > 0,05 138 Cd + M Escherichia-Shigella S. sonnei/E. fergusonii 99,78 > 0,05 182 Cd + M Escherichia-Shigella S. sonnei/E. fergusonii 100 > 0,05 153 Cd + M Escherichia-Shigella S. sonnei/E. fergusonii 99,78 > 0,05 507 Hg + M Escherichia-Shigella S. sonnei/E. fergusonii 99,78 > 0,05 131 Hg + M Neisseria N. cinerea/N. perflava 99,11 > 0,05 126 Hg Escherichia-Shigella S. sonnei/E. fergusonii 99,78 > 0,05 503 Hg Escherichia-Shigella S. sonnei/E. fergusonii 99,78 > 0,05 109 Hg Escherichia-Shigella S. sonnei/E. fergusonii 99,78 > 0,05 2214 Hg Escherichia-Shigella S. sonnei/E. fergusonii 99,78 > 0,05 154 Hg Escherichia-Shigella S. sonnei/E. fergusonii 99,78 > 0,05 139 Hg Delftia D. tsuruhatensis/D. lacustris 99,78 > 0,05 270 Cd Escherichia-Shigella S. sonnei/E. fergusonii 99,78 > 0,05 1239 Cd Escherichia-Shigella S. sonnei/E. fergusonii 99,78 > 0,05 241 Cd Escherichia-Shigella S. sonnei/E. fergusonii 99,78 > 0,05 107 Cd Escherichia-Shigella S. sonnei/E. fergusonii 99,78 - - M Escherichia-Shigella S. sonnei/E. fergusonii 99,78 - - M Prevotella 7 P. melaninogenica 99,78 - - M Escherichia-Shigella S. sonnei/E. fergusonii 99,78 - - M Escherichia-Shigella S. sonnei/E. fergusonii 99,78 - - M Neisseria N. cinerea/N. perflava 99,11 - - M Uncultured actinobacterium V. incomptus 83,37 - - M Escherichia-Shigella S. sonnei/E. fergusonii 99,78 - - M To assess the change in bacterial community structure and composition by beta diversity, a principal component analysis (PCoA) was performed. It has been found that controls without contaminant and mycorrhizae clustered differentially with the rest of the treatments, supporting the idea that microbial diversity is affected by metal contamination despite the bioaccumulative effect of the plant (Fig. 5a ). Fig. 5 Principal component analysis (PCoA). a all treatments and b controls In the same way, to verify that the addition of mycorrhiza in the soil effectively generates alterations in the microbial community, the analysis was carried out between the control treatment and the control with mycorrhiza, observing considerable changes in beta diversity, confirming the observation in the ASV distribution (Figs. 3 and 5b ). Relationship between microbiome changes and phytoremediation The microbial community analyses in the phytoremediation process show that the plant is not carrying out the phytostabilization process alone but is possibly working in consortium with the microbial community present in the soil and that it may be tolerable to metals such as Cd and Hg. A clear example of this is the presence of families such as the Enterobacteriaceae , which has been reported by other authors in the biosorption and efflux of metals such as Hg and Cd (Dashti et al. 2019 ; Hassen et al. 1998 ; Priyadarshanee et al. 2022 ). Table 5 shows the remediation mechanisms for metals associated with the microorganisms that have been found in this study and have been reported in the literature. There are some of these microorganisms that have not been reported and were found in almost all the treatments applied, such as Aguaspirillaceae , bacterium ellin , Bacteroidetes vadin , BIrii41 , Microscillaceae , and Holophaga , which may possibly be associated with some remediation mechanism. This suggests that they may be involved in the removal process or may only be able to survive in this type of environment. It is evident that except for three families of microorganisms (Table 5 ), all the other families present a remediation mechanism for the case of Cd, of which the reduction, efflux, secretion, increase the tolerance of the plants can be highlighted, biosorption among others. In the case of Hg, it is evident that only four families have been reported with some mechanism involved in its removal, such as Streptococcaceae , Rhodanobacteraceae , Moraxellaceae , and Bacillaceae , with mechanisms such as reduction, efflux, secretion, bioremediation, detoxification, and biosorption (Bae et al. 2002 ; Baldiris et al. 2018 ; Chang et al. 2012 ; Dashti et al. 2019 ; De et al. 2008 ; De and Ramaiah 2007 ; Kinoshita et al. 2013 ; Pushkar et al. 2021 ; Seong et al. 2021 ; Simon et al. 2017 ; Tuzen et al. 2009 ; Verma and Kuila 2019 ). Table 5 Remediation mechanisms for metals associated with microorganisms found in metagenomic analyses Family name Metals report Mechanism involved Reference Aquaspirillaceae No reports - - Bacillaceae Cr + 6, Pb, Cd, Hg Reduction, efflux, secretion (Baldiris et al. 2018 ; Banerjee et al. 2019 ; Dashti et al. 2019 ; De et al. 2008 ; De and Ramaiah 2007 ; Hassen et al. 1998 ; Priyadarshanee et al. 2022 ; Pushkar et al. 2021 ; Shaw and Dussan 2018 ; Zhu et al. 2019 ) Bacterium Ellin7509 No reports - - Bacteroidetes vadinHA17 No reports - - BIrii41 No reports - - Burkholderiaceae Cd, Pb Increases tolerance in plants ( Brassica campestris and ryegrass) to pollutants such as Cd; increases Pb accumulation in sorghum (Ni et al. 2021 ; Wu et al. 2020 , 2019 ) Chitinophagaceae Fe, Pb, Cd, Cu Tolerant (Giongo et al. 2020 ; Karray et al. 2020 ) Enterobacteriaceae Hg, Cd Biosorption, efflux (Dashti et al. 2019 ; Hassen et al. 1998 ; Priyadarshanee et al. 2022 ) Gemmatimonadaceae Pb, Cd Stabilization, heavy metals were associated with distinct microbial communities, and these microbes may contribute to the bioremediation of heavy metals (Chun et al. 2021 ) Holophagaceae As, Fe, Sb, Cu, Cd Might be able to tolerate or metabolize, increases under elevated copper concentrations, tolerant (Giongo et al. 2020 ; Sutcliffe et al. 2019 ; Xu et al. 2020 ) Methylophilaceae Cr, Cu, Zn, Cd High tolerance to metals, microorganism with high potential for soil remediation; presence of this family in areas contaminated by Cd (Gong et al. 2021 ; Wang et al. 2016 ) Microscillaceae No reports - - Moraxellaceae Cd, Hg Moraxella sp., a bacterium known to survive in contaminated environments; Cd and Hg bioremediation (Bae et al. 2002 ; Verma and Kuila 2019 ) Neisseriaceae Pb, Cu, Zn, Cd, Fe Bioadsorption, tolerant (Chaturvedi and Archana 2014 ; Ghimire and McCarthy 2018 ; Giongo et al. 2020 ) Nitrosomonadaceae Cr, Pb, Cd Metal reduction (Cr), abundant family in environments contaminated by As, Cd, Cr, Ni, Hg (Caliz et al. 2012 ; Chen et al. 2018 ; Chun et al. 2021 ; Drewniak et al. 2016 ) Pirellulaceae Cd Reduction (Dai et al. 2020 ) Prevotellaceae Cd Cd resistance (Ramírez-Acosta et al., 2021) Rhodanobacteraceae Hg Bacteria with mechanisms for the detoxification of Hg, grow in environments with Hg. Hg resistance genes (Seong et al. 2021 ; Simon et al. 2017 ) Rhodothermaceae Co, Ni, As Metabolism of metals (Cerqueda-García et al. 2020 ; Gu et al. 2017 ) Sphingobacteriaceae Cr + 6, Pb, Cd Biosorption, reduction (Chun et al. 2021 ; Prabhakaran et al. 2019 ; Pushkar et al. 2021 ) Streptococcaceae Cd, Cr, As, Hg, CH 3 Hg Biosorption (Chang et al. 2012 ; Kinoshita et al. 2013 ; Tuzen et al. 2009 ) Uncultured actinobacterium Heavy metals in general Bioadsorption, plant growth, helps plants withstand higher metal stress; Pb, Zn, Cr, Cd, Cu, As, and Ni (Bankar and Nagaraja 2018 ; El Baz et al. 2015 ) Uncultured Holophaga sp. No reports - - Xanthomonadaceae Cr + 6, Cu, Fe, Pb Reduction, tolerant (Baldiris et al. 2018 ; Giongo et al. 2020 ; Pushkar et al. 2021 ) The results demonstrate that the root microbiome, both without and with mycorrhiza, plays an important role in promoting plant growth to improve yield and may also regulate soil fertility, as other authors have argued (Sharaff et al. 2020 ; Yadav et al. 2020 ). It is important then to understand the microbiome of L. perenne in the remediation processes of soils contaminated with Cd and Hg, to increase removal efficiencies. Therefore, the application of mycorrhizae considerably improves the removal efficiency of these metals and redistributes them in the different parts of the plot, increasing the percentages of soil removal. These microbiomes demonstrated that they have the capacity to promote plant growth and rise Cd and Hg removal directly or indirectly through the release of hormones or the release of organic or enzymatic nutrients and the supply of nutrients (Kumar et al. 2019 ; Yadav et al. 2021 )."
} | 7,208 |
39668197 | PMC11638258 | pmc | 3,811 | {
"abstract": "Nowadays, traffic congestion is a significant issue globally. The vehicle quantity has grown dramatically, while road and transportation infrastructure capacities have yet to expand proportionally to handle the additional traffic effectively. Road congestion and traffic-related pollution have increased, which is detrimental to society and public health. This paper proposes a novel reinforcement learning (RL)-based method to reduce traffic congestion. We have developed a sophisticated Deep Q-Network (DQN) and integrated it smoothly into our system. In this study, Our implemented DQL model reduced queue lengths by 49% and increased incentives for each lane by 9%. The results emphasize the effectiveness of our method in setting strong traffic reduction standards. This study shows that RL has excellent potential to improve both transport efficiency and sustainability in metropolitan areas. Moreover, utilizing RL can significantly improve the standards for reducing traffic and easing urban traffic congestion.",
"conclusion": "Conclusion and future work RL gives substantial benefits in the application of transportation systems, where real-time adaptive control is critical to increasing efficacy and efficiency. Traffic Control approaches that rely on prespecified models of these processes are perceived to have a substantial disadvantage compared to the ability to learn through dynamic interaction with the environment. This paper introduces an innovative RL technique utilizing the DQL algorithm to minimize traffic congestion effectively. The system is structured based on an intersection-centered traffic model, emphasizing its ability to optimize waiting times and improve reward systems. This study’s findings represent a significant advancement in traffic management, creating an effective method for decreasing traffic congestion. Our current method effectively manages road intersections and makes optimal decisions to reduce traffic congestion. This advancement shows significant potential and is a crucial addition to traffic control. In the future, our model will be enhanced to work with real-time traffic data and optimization. This development will enable our system to connect to the internet, allowing the model to receive real-time data. In this capability, the agent can make informed decisions and adopt the optimal lane for vehicle movement. To address the high computational complexity in DQL for large-scale traffic networks, first, feature extraction, and dimensionality reduction techniques will reduce the state and action space. Secondly, more efficient neural network architectures will be used to improve processing efficiency. Additionally, techniques such as experience replay and target networks will stabilize learning and reduce redundant computations. Parallel computing and distributed learning will also be utilized to manage large-scale data by distributing the computational load across multiple processors, thereby cutting computational costs.",
"introduction": "Introduction Urban areas 1 , 2 are more and more facing the problem of traffic congestion. In the transportation sector, this issue dramatically impacts travel time, fuel consumption, an operating costs. Moreover, congestion significantly contributes to pollution, resulting in a severe environmental impact 3 , 4 . Several studies have been conducted to develop efficient traffic management systems to address this pressing issue. Recent initiatives have concentrated explicitly on Intelligent Transportation Systems (ITS). These efforts aim to improve the safety, effectiveness, and environmental sustainability of traffic control systems. Researchers aim to develop creative solutions within the ITS field to reduce congestion and enhance urban sustainability 5 . It is essential, to address critical aspects 6 such as lowering delay and queue length to optimize traffic flow at an intersection. Traffic congestion can be reduced by improving traffic management that is closely linked to the intersection routes’ layout and structure. Implementing strategic initiatives to optimize and improve traffic flow on these routes is crucial for attaining a smoother and more efficient traffic experience 7 , 8 . By efficiently reducing congestion in several routes at an intersection, They automatically decrease the overall traffic flow. By strategically managing and reducing congestion on particular routes 9 , we have created a smoother and more synchronized traffic movement. This method improves the intersection’s efficiency and helps creating a smoother, less crowded transit network. Motivation and contribution of this research The motivation behind this research stems from the pressing global issue of traffic congestion, which has become a significant challenge due to the rapid increase in vehicle numbers without corresponding expansion in transportation infrastructure. This imbalance has led to numerous adverse consequences, including increased road congestion and pollution, which have far-reaching impacts on society and public health. To address this critical problem, we aim to leverage advanced technologies, specifically RL, to devise innovative solutions for reducing traffic congestion. RL, particularly DQN, presents a promising approach that harnesses computational intelligence to optimize traffic flow and alleviate congestion effectively. By developing and implementing a sophisticated DQL model within a transportation system, the researchers seek to demonstrate the transformative potential of RL in enhancing transport efficiency and sustainability in urban areas. The study’s motivation is grounded in the urgent need to adopt intelligent, data-driven approaches to tackle traffic congestion, promoting safer, more efficient, and environmentally sustainable urban mobility. The expected outcomes of this research include setting new traffic reduction standards and advancing urban transportation systems by applying cutting-edge RL methodologies. Ultimately, this work aims to contribute to developing more intelligent, more adaptive urban transportation networks capable of addressing the challenges posed by growing vehicle populations and limited infrastructure capacities. This study presents the development of an advanced traffic reduction system that utilizes intelligent technologies to minimize delays. We proposed a RL framework for the system, a type of machine learning, to efficiently optimize traffic flow and reduce congestion. Our intelligent traffic management technology combines sophisticated algorithms with up-to-date data to minimize delays and improve overall efficiency. This novel approach signifies substantial progress in traffic control systems, possibly resolving current urban mobility difficulties. The contribution of this research is as follows: Focusing on specific benchmark methods to ensure successful traffic reduction implementation for enhancing a traffic-free smart city. Applying advanced, customized layer based method for making efficient traffic reduction. Developing an advanced DQN to maintain the traffic reduction system in an intersection. Performing RL technique of state, action, and rewards successfully in the traffic reduction domain. Focusing on minimizing queue length and increasing rewards at each length. Organization of the paper The research is divided into multiple areas, each with a specific function. Section “ Related work ” summarizes relevant literature on the topic and serves as the basis for the investigation. Section “ Research methodology ” details the research design and methodology employed in the study. The study’s findings are detailed in Section “ Result analysis ”, while. Section “ Discussion ” contains a thorough topic analysis and a critical assessment of the results and their consequences. Section “ Conclusion and future work ” concludes our work and provides suggestions for future research directions.",
"discussion": "Dataset analysis and discussion Our approach used two XML datasets 66 , both containing time series data. The first dataset contains environmental data, including vehicle ID, route, depart Lane, and depart Speed. This dataset is crucial for creating an intersecting environment consisting of 205 edges representing unique environment points used to train our agent. The second dataset focuses on route information and includes attributes such as edge ID, lane ID, index, length, shape, and function. These criteria are crucial for determining the paths within a junction. By effectively joining these two datasets, we could train our agent successfully. The model benefits from integrating environmental and route data, enhancing knowledge of system dynamics. After the training, our model produced two extra datasets named “plot_queue_data” and “plot_reward_data”. The datasets were crucial for the following testing step. We have assessed the wait length and rewards by analyzing the data produced by our trained agent during testing. The comprehensive testing procedure enabled us to evaluate the model’s performance and effectiveness in real-world situations, offering vital insights about queue dynamics and rewards obtained by the agent. We were evaluated 30 episodes during our testing phase using specific parameters. The maximum number of steps permitted was established at 240, while the quantity of cars produced for testing remained at 1000. The testing setup included a neural network structure of four layers with a learning rate 0.001. Our testing primarily focused on four specific actions: NSA (North-South Arms), NSLA (North-South Left Arms), EWA (East-West Arms), and EWLA (East-West Left Arms). The actions were intentionally selected to reduce queue length and maximize incentives in the system. This method enabled us to thoroughly assess the model’s performance in many scenarios, giving us a detailed insight into it’s skills in improving queue dynamics and reward results.\n\nDiscussion We have created a novel system designed to decrease significantly traffic congestion at intersections by utilizing a complex method based on Deep DQL in an RL. Implementing DQN in a traffic reduction system offers the unique advantage of continuously improving traffic flow efficiency through real-time adaptive learning. Unlike traditional static algorithms, DQL can dynamically adjust traffic signals based on current conditions, learning from past traffic patterns and behaviors to optimize future decisions. This leads to reduced congestion, shorter travel times, and lower emissions as the system becomes increasingly adept at managing varying traffic volumes and unexpected disruptions, ultimately enhancing urban mobility and environmental sustainability. The technology begins by training an agent in a accurately designed environment replicating real-world intersection situations. The agent in this setting learns to evaluate the current condition of the intersection by analyzing whether vehicles are present or not in particular lanes. The agent analyses the states to find the best vehicle route, aiming to reduce queue duration and improve traffic flow. Our solution is centered upon a neural schema consisting of many nodes, each indicating the occupancy status of a lane (0 for empty, 1 for occupied). The agent uses the DQL model to assess the values linked to the nodes, producing Q-values that direct decision-making toward optimal traffic management solutions. Upon obtaining the current state (st), the agent strategically plans the vehicle trajectory by determining the next state (St+1) that maximizes traffic flow and minimizes congestion. This technique entails carefully calculating node values to evaluate possible actions and identify the most effective action. Additionally, our system views queue durations as a vital parameter for reducing traffic. For training our model, we focus on several specific parameters: total_episodes, max_steps, num_layers, width_layers, batch_size, learning_rate, training_epochs, num_states, num_actions, and gamma. We set each parameter to its optimal value to ensure the most effective training of our agent. To test our model, we focus on several parameters: max_steps, episode_seed, num_states, and num_actions. We set each parameter’s optimal value to create the most effective traffic reduction system. These testing parameters are executed to compute the queue length and rewards. Based on the testing results, the agent can make decisions regarding the environment. The parameters chosen for this model reflect several advancements over previous works. Increasing the num_layers and width_layers enhances the network’s depth and capacity, allowing it to better capture and represent complex patterns compared to shallower or narrower architectures used in earlier studies. Adjustments in batch_size and learning_rate improve the stability and efficiency of training; a larger batch size and a more adaptive learning rate schedule can lead to smoother convergence and better generalization, addressing the training issue found in prior methods. Additionally, extending total_episodes and training_epochs offers the model more exposure to diverse scenarios, mitigating underfitting problems encountered before. Changes in ‘gamma‘ refine how future rewards are valued, potentially improving the model’s alignment with the problem’s dynamics and enhancing decision-making over the long term. Overall, these configurations aim to build on past limitations by offering a more robust and efficient learning process, leading to improved model performance and reliability in both training and testing phases. When a route has a low queue length, showing smoother traffic flow, the agent gives it priority over congested alternatives. Updating Q-values requires forecasting the future condition of cars, allowing the agent to enhance its comprehension of the best traffic control tactics. The system evolves continuously through an iterative process, dynamically adjusting to changing traffic conditions to maximize efficiency at intersections. During the last stage of our system, the agent distributes rewards according to its selected actions, a crucial step in encouraging efficient traffic control. The agent receives the most rewards by choosing the most efficient lane and demonstrating successful intersection navigation. If the selected lane is not as successful, the benefits decrease over time, prompting the agent to seek out better ways. Our solution continuously demonstrated superior queue lengths and rewards through thorough testing, highlighting its effectiveness in reducing traffic congestion. Figure 11 displays the comprehensive traffic reduction system, with each state readily visible graphically. The model was validated after an extensive investigation. An Intel(R) Core(TM) i7 CPU, 16GB RAM, and 12 GB GPU was used for the entire training procedure on a Windows 10 computer. TensorFlow 2.2.1 and Python 3.12.3 implemented all offensive automatic traffic reduction models. Python libraries such as TensorFlow, frequently used to create image classification models, may be managed more easily with the help of Spider. The findings confirm the system’s effectiveness and create a significant historical record of traffic patterns, providing essential insights for improving urban mobility. Our technology shows the potential to revolutionize urban traffic management by effectively combining reward mechanisms with intelligent decision-making. Our technology can adjust to changing traffic conditions and choose the best routes, leading to a more efficient and sustainable urban environment."
} | 3,896 |
30746446 | PMC6357760 | pmc | 3,812 | {
"abstract": "Artificial microswimmers display adaptive locomotion by autonomously morphing in response to physical changes in the environment.",
"conclusion": "CONCLUSION In summary, we use magnetic hydrogel nanocomposites as a programmable matter to engineer microswimmers inspired by the form, locomotion, and plasticity of model microorganisms. We present methods for dynamic modulation of shapes, magnetization profiles, and locomotion gaits on the same device. A careful analysis of swimming performance at different viscosities provided a guideline to build a single machine that manifests multiple stable configurations, each optimized for a different locomotion gait. We perform shape adaptation in response to mechanical constraints and variation in osmotic pressure via the coordination between the elastic and viscous stresses. Our approach for solving the navigation problem reduces the number of elements to be controlled and therefore can have advantages in terms of speed, versatility, and cost. The manufacturing process is high throughput and scalable, which together open up doors for the development of a variety of adaptive soft microrobots.",
"introduction": "INTRODUCTION Microorganisms manifest a diverse set of molecular motility machinery to effectively navigate complex environments and occupy a variety of ecological niches ( 1 ). Swimming in bacteria arises from the mechanical interactions between the actuated flagella, cell body, and the drag generated by the flow ( 2 , 3 ). Hydrodynamic drag is dominated by viscous forces at low Reynolds number, which, in turn, depend on the shape of the moving object. Bacteria can adopt alternate shapes and sizes over the course of their life cycles to optimize their motility ( 4 – 6 ). In addition to modulating cell body shape, bacteria can also use the form and structure of the propulsive system for advanced maneuverability in complex environments. Bending of the hook enhances motility in Caulobacter crescentus ( 7 ), while monotrichous Vibrio alginolyticus outperforms multiflagellated Escherichia coli in climbing nutrient gradients with the aid of a flagellar buckling instability ( 8 ). A polymorphic transition in the flagellar filament enables Shewanella putrefaciens to escape from physical traps ( 9 ). The development of microscopic artificial swimmers that can cross biological barriers, move through bodily fluids, and access remote pathological sites can revolutionize targeted therapies ( 10 – 13 ). Seminal work demonstrated the feasibility of following the example of prokaryotic ( 14 , 15 ) or eukaryotic ( 16 ) flagellum for building magnetically controlled microswimmers that have the ability to exhibit nonreciprocal motion. However, unlike living cells, these mechanical devices neither sense their local environment nor react to changes in physical conditions. Addressing these issues with traditional robotic solutions based on electronic circuitry would require highly sophisticated manufacturing processes and result in orders of magnitude increase in the size of the machines. Utilization of biological actuators and sensors for engineering autonomous biohybrid robotic devices is an intriguing alternative ( 17 ). Although the field is in its infancy, proof-of-concept examples have already demonstrated the potential ( 18 – 20 ). Here, we focus on artificial materials to pave the way for building robust, tunable, and durable engineering solutions. Fluid-structure coupling in hydrogel-based compliant machinery may present a possible mechanism for autonomous regulation of morphology and function. Using origami design principles as a framework, a variety of folding techniques have been introduced for the development of three-dimensional (3D) flexible microstructures ( 21 ). Production of programmable self-folding films at microscale can be achieved via patterning of multiple layers with different swelling properties ( 22 – 24 ) or creation of spatial concentration gradients ( 25 , 26 ). However, these methods provide limited control over the mechanical and magnetic properties of the machine. In our previous work, we have shown that the form and magnetization profile of self-folded micromachines can be independently programmed by incorporating magnetic nanoparticles (MNPs) into sequentially patterned hydrogel layers ( 27 ). In this work, we introduce a simple and versatile method for engineering magnetically controlled soft micromachines as 3D reconfigurable multibody systems from a nanocomposite hydrogel monolayer. We present a set of design strategies for self-regulation of motility and maneuverability by using the interplay among viscous, elastic, magnetic, and osmotic forces. We show that reconfigurable body can continuously morph in accordance with the properties of the surrounding fluid, a feature that leads to passing through constrictions and enhancement of locomotion performance. Using elastohydrodynamic coupling in shape-shifting and gait adaptation enables enticing opportunities for microrobots navigating inside obstructed, heterogeneous, and dynamically changing environments.",
"discussion": "RESULTS AND DISCUSSION Building soft microswimmers with bioinspired locomotion We followed a variant of origami, called kirigami, to design and fold compliant 3D microstructures from a thermoresponsive gel composite reinforced with MNPs. The fabrication process involves cutting initiated by photolithography and folding upon hydration of the polymerized layer. We generated nonuniform distribution of MNPs along the thickness direction to form two distinct layers of hydrogels with significantly different swelling ratios through sedimentation or application of magnetic forces. Differential swelling upon hydration along the film thickness resulted in self-folding of monolayer structures (see section S1). The curvature of the folded sheet was proportional to the MNP concentration (fig. S1). The folding axis of each compartment was parallel to the alignment of encapsulated MNPs due to constrained swelling in the direction of reinforcement. Particle alignment was performed by the application of uniform magnetic fields during sample preparation (fig. S2). The folding axis and the magnetic anisotropy of each compartment were coupled to each other and both defined by the orientation of the reinforcing MNPs. We fabricated hundreds of micromachines with complex 3D architectures from the same film using a single-step photolithography process ( Fig. 1A ). Fig. 1 Design, development, and actuation of bioinspired microswimmers. ( A ) A kirigami approach to building mass customized soft microswimmers through a single-step photolithography. UV, ultraviolet. ( B ) Schematic illustration of the bacteria taken as inspiration for this study and the optical images of the engineered artificial microswimmers. ( C ) Out-of-plane alignment (δ ≠ 0) of MNPs lead to nonzero misalignment angle ϕ. The optical images showing two swimmers with identical shapes and varying ϕ are shown. ( D ) A comparison of the motility of microswimmers swimming in fluids with different viscosities. ( E ) Motility of the flagellated tubular microswimmers and helical microswimmers encoded with two different magnetic anisotropies rotating in a solution with a viscosity of 3 mPa·s. ( F ) Effect of body size on the motility of the tubular microswimmers. The swimmers were driven at 2 Hz with a field strength of 20 mT in all experiments, unless stated otherwise. All bar graphs represent average ± SEM ( n = 6 measurements for each microswimmer and three different swimmers tested per condition). We focused on three different configurations inspired by widely studied microorganisms, C. crescentus , Helicobacter pylori , and Borrelia burgdorferi ( Fig. 1B ). Bacteria swim by rotating propeller-like organelles, called flagellar filaments, which extend from the cell body ( 28 ). Artificial microswimmers can mimic this motion if the magnetic moment of the machine is perpendicular to its long axis ( 29 ). We explored the effect of shape anisotropy on the magnetization profile of the microswimmers. In-plane ( x - y plane) alignment of MNPs in the unfolded monolayer resulted in a magnetization parallel to the folding axis and a misalignment angle (ϕ), which is the angle between the folding axis and magnetic fields, of zero. In other words, the structures resemble compass needles that align their long axis to the direction of the external magnetic field ( 30 ). To address this limitation, we fabricated microswimmers with varying out-of-plane particle alignment while keeping the in-plane particle alignment constant. Out-of-plane alignment of MNPs has no effect on the final 3D shape due to the relatively small thickness (~30 μm) of the monolayer compared to its overall size. With the magnetization component in z axis, folded structures acquired a magnetic moment in the radial direction, where φ was equal to the angle of the out-of-plane alignment of MNPs with respect to the x - y plane ( Fig. 1C ). Optimal motility at different viscosities requires different gaits Previous work on C. crescentus has shown that the flexibility of the hook generates cell body precession that leads to a 3D helical motion ( 7 ). The slantwise motion of the cell body during precession develops thrust, adding to that developed by the flagellar filament. On the other hand, the helical shape of Vibrio cholerae enhances motility within a polymer network, a feature suggested to be important for its pathogenicity ( 31 ). We systematically explored the potential advantage of this morphological diversity by building microswimmers with different body plans and actuating them in fluids with varying viscosity. Although we did not design a separate flexible hook that connects the tail and the body, we can still engineer microswimmers that follow 3D helical trajectories by coordinating their morphology with magnetization profile ( 32 ). The Reynolds number is ranging from 10 −2 to 10 −4 in all the experiments presented in this work; thus, swimming is performed under laminar flow. The normalized velocity of microswimmers is reported to provide a more accurate comparison of performance ( 16 ). For this reason, we express motility as U / fL × 1000, where U is the forward velocity, f is the rotating frequency, and L is the body length of the micromachines ( 33 ). In a sucrose solution with a similar viscosity to blood (3 mPa·s), the flagellated microswimmers with a tubular body and a flexible planar tail moved much faster compared to other prototypes ( Fig. 1D ). The superior performance of this configuration compared to tubular body–helical tail and helical body–helical tail combinations can be explained by the enhanced body precession induced by the oar-like propulsion of the tail. Misalignment of the body with respect to the external magnetic field, together with the flexibility of the tail, leads to helical motion. The forward motility of the flagellated microswimmers was significantly higher when φ = 30° ( Fig. 1E ). While flagellated microswimmers benefited from both helical motion and corkscrew motion in this configuration, helical microswimmers suffered from extra drag generated by wobbling. Tuning φ to 90° resulted in a completely different picture. The motility of the flagellated swimmers markedly dropped because of the absence of helical motion, while helical microswimmers performed corkscrew motion without wobbling. These results show that the presence of a nonhelical body is advantageous if the body can generate large-amplitude helical motion. The increase of viscosity monotonically decreased the motility of all microswimmers, but the drop was drastic for flagellated microswimmers with a planar tail. With increasing viscosity, viscous forces start to attenuate helical motion of the cell body, and the body becomes a source of passive drag that primarily impedes the motility. Furthermore, the lack of wobbling on the body eliminates bending of the tail, and in the absence of chirality, the tail cannot break the time-reversal symmetry to generate propulsion. Helical microswimmers were the fastest at high viscosity because the only relevant motion became the corkscrew motion. The body of microswimmers had a higher magnetic torque compared to their tail, and thrust was mainly generated by the body, which made the tail obsolete at high viscosity. A larger body provided higher motility at low viscosity because those machines traced a helical path of higher amplitude (see section S2 and fig. S3). However, at high viscosity, body precession was attenuated, and therefore, smaller body provided higher motility ( Fig. 1F ). Our results conflict with the argument that helical body does not provide a significant enhancement of motility for H. pylori inside viscous solutions ( 34 ). The reason for this discrepancy lies in the body rotation; only the flagellum is actively rotated in bacteria, and their bodies show a very slow counterrotation to balance torque while we are simultaneously rotating the body and the tail with the same angular speed. Morphology and magnetization profile together determine performance during navigation Along with motility, maneuverability plays a key role for bacteria in rapidly detecting and tracking nutrient gradients. By adjusting the relative frequency and the length of reorientation phases, cells are able to adapt to the changing local chemical environment. Experimental evolutionary analysis of motile behavior of E. coli has shown that evolved strains had increased swimming velocity and frequency of reorientation, which together led to enhanced chemotaxis in porous media ( 35 ). Experiments with E. coli have also shown that body size and shape control the average reorientation angle and time for a motile cell to change its direction of motion ( 36 ). We investigated the maneuverability of artificial microswimmers by inducing deflections in the yaw angle during swimming ( Fig. 2 ). A highly maneuverable microswimmer is expected to quickly change its movement direction with a small change in control signal. For gentle disturbances where the orientation of the rotating magnetic field is instantaneously changed for less than 10° around the yaw axis, all swimmers corrected their heading almost immediately. For stronger perturbations with 45° yaw rotation, both the body and tail geometry played an important role in the dynamic response of the compliant swimmers. Fig. 2 Elastic instabilities and optimization of maneuverability. The yaw angle Ψ was instantly changed 45°, while the swimmers were driven at 2 Hz with a field strength of 20 mT in a solution with a viscosity of 3 mPa·s. ( A ) Time-lapse images of a microswimmer with a tubular body and helical tail during the reorientation of its swimming direction. ( B ) Changing the tail to a planar geometry and ϕ to 30° led to a complete loss of motility. ( C ) Changing the body geometry to a helix significantly reduces the reorientation time by providing instant recovery. ( D ) Quantitative comparison of reorientation time for different prototypes. ( E ) Effect of body size on the maneuverability of microswimmers swimming at varying viscosities. ( F ) Role of magnetic anisotropy on the maneuverability of the microswimmers with tubular and helical bodies. Scale bars, 1 mm. All bar graphs represent average ± SEM ( n = 6 measurements for each microswimmer and three different swimmers tested per condition). During a successful maneuver, the body responds to the control signal before the tail, as the magnetization of the body is significantly higher. The speed of body rotation in response to the applied torque depends on the magnetization of the body and the hydrodynamic drag, which, in turn, are determined by the body geometry and magnetic volume. The rapid change in body orientation generated a buckling instability on the flexible tail for swimmers with a tubular body. This instability led to a transient wobbling motion on the machine body until the tail reoriented with the main axis of the body, and this delay is quantified by the recovery time Δ t rec ( Fig. 2A ). The overall delay between the change in the control signal and the completion of reorientation of the swimming direction is denoted by Δ t orient . Swimmers with a planar tail are more susceptible to instabilities. The contribution of the helical tail to stabilization can be explained by the effectively higher stiffness of helical geometry compared to a planar structure and the attenuated precession on the body. At extreme cases (φ close to 0), change in direction resulted in a tumbling motion, which manifests itself as loss of motility ( Fig. 2B ). Although inertial forces play no role at a small scale, elastic instabilities occur due to the extreme compliance of the propulsion apparatus. One of the best demonstrations in nature is the flicks in V. alginolyticus , which arise from an off-axis deformation of the flagellum caused by the buckling of the hook ( 37 ). Switching the body morphology from a tube to a helix resulted in superior performance. The helical body generates propulsion together with the helical tail while applying pulling force on the filament, thus preventing buckling instability. As a result, the body and the tail simultaneously reorient along the direction of magnetic field during maneuvers ( Fig. 2C ). While Δ t rec is more than 4 s for swimmers with a tubular body, it is less than 0.5 s for swimmers with a helical body moving at the same velocity. Helical microswimmers showed the best performance as expected because they do not deal with body and tail coordination ( Fig. 2D and movie S1). We then explored the combined effect of body size and viscosity on maneuverability using the swimmers shown in Fig. 1F . We prepared tubular machines with smaller folding diameter by increasing the nanoparticle concentration in the film formulation. Regardless of the value of the viscosity, smaller body provided a comparative advantage by lowering rotational drag and increasing magnetic torque ( Fig. 2E ; see section S2 for additional information). We next studied how magnetization profile may affect maneuverability of microswimmers. So far, they were encoded with φ = 90° to prevent wobbling motion during forward swimming. Experiments on helical swimmers encoded with φ = 30° showed that wobbling motion can significantly affect Δ t orient during maneuvers ( Fig. 2F ). Likewise, Δ t orient of flagellated swimmers with φ = 30° was significantly higher than that of swimmers with φ = 90° because the rapid change in the yaw angle destabilized the machines and transformed the wobbling motion into a tumbling motion. To our surprise, adding a tail to the wobbling helical swimmer significantly enhanced its performance by completely preventing the occurrence of tumbling motion (movie S2). These results pose a trade-off between motility and maneuverability at low viscosity, as the reorientation time increases with decreasing φ and the motility increases with increasing φ. The capability of dynamically remagnetizing the body would provide a method for adjusting the motility and maneuverability on demand. Magnetically reinforced nanocomposites were remagnetized in a direction other than the direction of MNP alignment when the applied magnetic field was significantly higher than the magnetic field applied for the alignment of particles during fabrication (see section S3, fig. S4, and movie S3). Exploiting elastohydrodynamic coupling for gait adaptation Unlike swimmers with rigid propulsion mechanisms, the coupling between magnetic forces, filament flexibility, and viscous drag determines propulsion efficiency of compliant swimmers ( 16 ). This nonlinear relationship is described by dimensionless sperm number defined as Sp = L f / ( A ξ ⊥ ω ) 1 / 4 (1) where L f is the length of the flagellum and A is the bending stiffness. For a slender filament ( L f ≫ a ), the perpendicular viscous coefficient is given by ξ ⊥ = 4 π μ d log ( L f a ) + 1 / 2 . The radius a is approximated by the geometric mean ( a = t × w ) of the thickness t and width w of the filament. For Sp ≪ 1, bending forces dominate, and the filament is effectively straight. Artificial microswimmers must operate away from this regime, and optimal motility is predicted for Sp of the order of unity. We asked whether the elastohydrodynamic properties can be exploited to trigger a gait transition in response to changes in viscosity ( Fig. 3A and movie S4). Analytical solution of the equations of motion for an actuated flexible tail predicted that the number of helical turns would increase with increasing Sp (see section S4 and fig. S5). The bending stiffness was obtained from the measured values of the elastic modulus of the hydrogel and the filament geometry (fig. S7). Figure 3B shows time-lapse optical images of a tubular microswimmer with a planar tail, encoded with φ = 30° swimming at different viscosities and rotating frequencies. As described before, this configuration generated a strong helical motion at low viscosity (3 mPa·s) that completely ceased at high viscosity (15 mPa·s). At relatively low viscosity and rotation frequency (Sp = 4), the internal and external stresses were mostly dissipated at the joint, which led to body precession. Constraining the body precession by setting φ to 90° reduced motility and enabled coiling in the tail. Coiling of the tail was observable at higher viscosity and frequency (Sp = 7.2) both in the analytical solution (fig. S5) and experimental data ( Fig. 3B ). This morphological transformation led to the emergence of corkscrew motion and enhanced motility ( Fig. 3C and fig. S5D). Fig. 3 The efficiency and mode of motility are controlled by the body plan. ( A ) A schematic illustration of microswimmers swimming with an oar-like propulsion strategy at low viscosity and performing corkscrew motion at high viscosity due to coiling of the flexible tail. CCW, counterclockwise; CW, clockwise. ( B ) The optical images and ( C ) motility along with schematic representations of microswimmers with shape-shifting tails moving at varying viscosities and rotating frequencies. Scale bar, 500 μm. ( D ) Optical images of a flexible helix passing through curved conduit morphologies with the flow rate of 2 ml/min. ( E ) Shape change driven by velocity gradients in a conduit with a constriction and the flow rate of 5 ml/min. ( F ) Computational model exploring shear-induced elongation in a conduit with a slowly varying constriction. Shape adaptation in complex channels under viscous flow Gradients in ambient fluid velocity are pervasive in microbial habitats, and bacteria exhibit directed movement responses due to shear by using their body shape ( 38 , 39 ). The extraordinary flexibility of red blood cells enables them to change shape under shear forces as they pass through vessels significantly smaller than their diameter ( 40 ). Inspired by these elastohydrodynamic features, we exposed helical microswimmers to controlled shear flows in glass capillaries. Bending facilitated passage through highly curved microchannels. The deformation was elastic, and swimmers completely recovered their shape after passing through the corner under the externally applied flow with a rate of 2 ml/min ( Fig. 3D ). Increasing the stiffness of the filaments reduced deformation and led to obstruction of the channel (movie S5). We conducted experiments in which the filaments were transported by a constant volume flux through a cylindrical channel with a constriction. Snapshots of experimental results are given in Fig. 3E . The passage through the constriction can be accommodated by a number of forces. Streamlines of the flow follow the conduit geometry; thus, the hydrodynamic drag forces exerted on a flexible filament produce a deformation that facilitates passage. These forces have components along the conduit axis and along a normal axis pointing inward, while both components promote passage. On the one hand, shear-induced elongation due to the difference in flow rates experienced by different parts of the body as it passes through the constriction decreases the radius of the helix, thus facilitating passage. On the other hand, the component of the hydrodynamic force pointing toward the conduit axis tends to compress the filament. This effect plays a more dominant role for helices going through sharp constrictions. The presence of the wall also modulates hydrodynamic forces acting on the filament. Lubrication stresses in the direction normal to the confining wall must be considered in the case of very narrow constrictions. To systematically explore the effect of shear-induced elongation, we built a numerical model by approximating the filament as a series of elastic segments of uniform helical shape (shown in the top panel of Fig. 3F ). See section S5 for the details of the formulation. Snapshots of relevant experimental results and numerical simulations are given in Fig. 3 (E and F, respectively) (see movie S6). The simulations with slowly varying constriction were based on the experimental conditions and measured value of the Young’s modulus ( E = 9 kPa). The helix has three turns, a radius of 1.25 mm, a contour length of 33.3 mm, and a helix angle of 0.25π in its reference configuration. As the helical filament entered the constriction, its axial length increased because of the higher flow rates experienced by parts of the helix closer to the constriction. This observation was faithfully captured in the simulation results. The plots of the speed of the two ends of the helix and the change in axial length are shown in fig. S6A. The front end speeds up as the filament enters the constriction, elongating the helix, and slows down as the filament exits the constriction, uniaxially compressing the helix. This leads to a deformed spiral shape of reduced local radius toward the side that was further in the constriction. The decrease of radius accompanying the elongation enabled the helix to pass through the channel. Reduction in the flow rate generated a mirrored shape at the exit of the constriction, and the helix eventually regained its original shape ( Fig. 3E ). All these shape transformations were qualitatively captured by the simulations ( Fig. 3F ), which opens up the possibility to program the deformation of microswimmers for a given flow profile. Machines with a tubular body are less able to perform this accordion move, as the tubular body cannot be stretched by the shear stress. At flow rates higher than 5 ml/min, all tested machines passed through constrictions smaller than their diameter simply by getting compressed between the walls of the channel. Navigation based on squeezing comes with the risk of obstructing the channel, depending on the surface roughness and chemistry of the machines as well as the channel. Autonomous shape-shifting driven by osmolarity An appealing strategy to use different body plans for navigating in heterogeneous fluids engineers a shape transformation triggered by the microrheology of the fluid. Incorporation of stimulus-responsive materials opens the door to fabrication of microdevices that can react to changes in ambient temperature, pH, or osmolarity. Bacterial movement in search of environments with optimal water content is termed osmotaxis. Experiments performed with E. coli in polymer solutions revealed a long-term increase in swimming speed ( 41 ), which scale with the osmotic shock magnitude ( 42 ). Inspired by this mechanism, we tuned the mechanical and swelling properties of the nanocomposite by adding a hydrophilic comonomer and reducing the cross-linking degree (section S6 and movie S7). The increase in osmolarity dehydrates the swollen hydrogel and thereby reconfigures the body shape (fig. S7). Our data suggest that a tubular body with a planar tail is preferable for swimming at low viscosity, while a helical morphology would perform better at high viscosity. We built a reconfigurable microswimmer programmed to undergo a shape transformation between these two configurations in response to an increase in sucrose concentration ( Fig. 4A ). While the motility gradually decreased with increasing viscosity, the step-out frequency increased because of reduction of body size. Step-out frequency denotes the maximum rotating speed at which the swimmers can still synchronize with the external rotating magnetic field. Reduction of body size provided two enhancements that led to higher step-out frequency, higher magnetization, and lower drag force. With the ability to increase the rotating frequency, the machines could be operated at a higher velocity. Therefore, the microswimmer with the programmed shape change exhibited a sustained velocity and enhanced maneuverability, despite the increase in viscous forces ( Fig. 4B ). To our knowledge, this is the first time that an artificial microswimmer increases its maximum rotating speed and maintains forward velocity with increasing viscosity. On the other hand, nonreconfigurable swimmers with the same initial configuration suffered from a significant drop in motility ( Fig. 1F ) and longer reorientation time ( Fig. 2E ) at higher viscosities. Different polymorphic forms are observed in nature under changing solvent conditions such as pH value, salinity, and temperature. To investigate the propulsion provided by reconfigurable helices, we developed two types of configurations that respond to changes in osmolarity by continuously coiling (type I) or uncoiling (type II), respectively ( Fig. 4C and fig. S8). Type II helices sustained their motility, despite the changes in viscosity, while type I helices were slowed down by increasing drag ( Fig. 4D ). On the other hand, type I helices performed better when it came to navigation as expected. Fig. 4 Optimization of motility through shape-shifting driven by osmotic or shear stress. ( A ) Motility and step-out frequency of microswimmers in response to changes in sucrose concentration. The optical images show the effect of osmotic stress on the body and tail shapes. ( B ) Sustained velocity and enhanced maneuverability of microswimmers in response to changes in sucrose concentration. ( C ) Polymorphic transitions driven by osmolarity. Type I, continuously coiling with increasing osmolarity; type II, continuously uncoiling with increasing osmolarity. ( D ) The motility of the microswimmers can be kept constant by using polymorphic transitions to counteract viscous drag. All bar graphs represent average ± SEM ( n = 6 measurements for each microswimmer and three different swimmers tested per condition)."
} | 7,682 |
38212999 | PMC10799744 | pmc | 3,815 | {
"abstract": "Abstract Motivation Microbes are essential part of all ecosystems, influencing material flow and shaping their surroundings. Metabolic modeling has been a useful tool and provided tremendous insights into microbial community metabolism. However, current methods based on flux balance analysis (FBA) usually fail to predict metabolic and regulatory strategies that lead to long-term survival and stability especially in heterogenous communities. Results Here, we introduce a novel reinforcement learning algorithm, Self-Playing Microbes in Dynamic FBA, which treats microbial metabolism as a decision-making process, allowing individual microbial agents to evolve by learning and adapting metabolic strategies for enhanced long-term fitness. This algorithm predicts what microbial flux regulation policies will stabilize in the dynamic ecosystem of interest in the presence of other microbes with minimal reliance on predefined strategies. Throughout this article, we present several scenarios wherein our algorithm outperforms existing methods in reproducing outcomes, and we explore the biological significance of these predictions. Availability and implementation The source code for this article is available at: https://github.com/chan-csu/SPAM-DFBA .",
"introduction": "1 Introduction Microbes are present in almost all known biotic environments and their metabolism affects the flow of materials in their ecosystems. Microbes form intricate networks of interacting cells from various taxonomic branches with distinct functional traits which makes predicting their behavior challenging. However, determining the role of microbial life in their ecosystems can be a key to solving numerous challenges that we face today. Imbalance in human gut microbiome is consistently linked with diseases such as inflammatory bowel disease ( Segata et al. 2012 ). At a larger scale, microbial metabolism is a major player in geochemical cycles on earth ( Rousk and Bengtson 2014 ). Metagenomics studies provide detailed information about the membership and biochemical functions of microbiomes. However, predicting the phenotype of microbial communities from their genotype is by nature a complex problem and has been an ongoing effort for the past few decades ( Song et al. 2014 , Haruta and Yamamoto 2018 , Kumar et al. 2019 , Oriano et al. 2020 ). Trophic interactions between microbes are an important factor that significantly contributes to the evolution of microbiome composition and function in various ecosystems ( Phelan et al. 2012 , Amundson et al. 2022 ) and it further complicates the prediction of emergent properties of microbial communities. Understanding and predicting the dynamics of microbial systems has remained largely unknown despite the enormous growth in multiomics techniques and it requires a wholistic modeling approach ( Schmidt et al. 2021 ). Mathematical models at different abstraction levels have been developed with the goal of making predictions that can explain the experimentally observed phenotypes ( Mahadevan et al. 2002 , Zomorrodi and Maranas 2012 , Khandelwal et al. 2013 , Song et al. 2014 , Zomorrodi et al. 2014 , Khodayari and Maranas 2016 , Bauer et al. 2017 , Chan et al. 2017 , Kumar et al. 2019 , Cai et al. 2020 , Dukovski et al. 2021 ). GEnome-scale metabolic Models (GEMs) provide a detailed view of the biochemical networks of cells that are inferred from the genome of the organism of interest. GEMs generally contain up to thousands of biochemical reactions. Predicting the emergent properties of microbial communities by merely determining flux through such biochemical reactions is one of the main challenges in systems biology that yet remains to be addressed ( Song et al. 2014 , Oriano et al. 2020 ). Flux balance analysis (FBA) is a bottom-up approach that provides a scalable method for simulating cellular metabolism in the absence of reaction kinetic parameters ( Orth et al. 2010 ). FBA converts the system of differential equations resulting from mass balance across a cell to a linear programming (LP) problem by assuming steady state condition across the cells and defining a biologically relevant objective function ( Orth et al. 2010 ). Despite the defined objective function and the constraints on the flux values, FBA solutions are rarely unique, and feasible solutions form a large space where distinct phenotypes can coexist. Dynamic flux balance analysis (DFBA) applies FBA in each timepoint, and using the calculated extracellular fluxes, changes in extracellular metabolites with time are calculated. These rates of changes are used in turn to form a system of differential equations that describe the concentration profiles of different species in the system over time ( Mahadevan et al. 2002 , Uygun et al. 2006 , Höffner et al. 2013 , Gomez et al. 2014 , Henson and Hanly 2014 , Willemsen et al. 2015 , Zhao et al. 2017 , Scott et al. 2018 , Schroeder and Saha 2020 , de Oliveira et al. 2023 ). However, the problem of lack of a unique solution in FBA propagates through time. Consequently, in the cases where the attempt is to model the dynamics of a heterogenous microbial community, one is faced with an extremely open solution space where different solutions can represent significantly different phenotypes while all phenotypes can satisfy FBA requirements. More importantly, DFBA relies on instantaneous biomass maximization assumption. Although in simple cases this assumption might result in realistic simulations ( Mahadevan et al. 2002 ), in many other cases, it fails to predict the observed behavior of microbial systems ( Cai et al. 2020 ) because depending on the environment, maximizing instantaneous growth rate can result in low fitness in future or even extinction. For example, cells that excrete extracellular amylase to breakdown starch are spending energy to do so and lower their instantaneous fitness in turn. However, secreting amylase is required for degrading starch to smaller molecules such as glucose for the cells’ future use. Instantaneous biomass maximization will not allow any extracellular amylase secretion, unless previously set as a constraint on the model, while amylase secretion has been frequently observed in nature ( Zhang et al. 2016 , Song et al. 2019 , Far et al. 2020 ). Therefore, it is important to put the concept of Nash equilibria and evolutionary stability in this context of metabolic interactions ( Cai et al. 2020 , Schmidt et al. 2021 ). A remedy proposed by Zomorrodi and Segrè (2017) was to determine Nash equilibria of the systems with several metabolic strategies of interest pre-defined. It beautifully captures the experimental results of some well-known microbial games of metabolic interactions. However, in general, it is virtually impossible to enumerate all possible metabolic strategies because of the high dimensional and continuous nature of the solution space defined by the mass balance constraints, directionality constraints, and nutrient availability. Therefore, an algorithm that can cover the entire possible solution space to a satisfactory extent when determining these stable interactions, and meanwhile does not rely on the instantaneous biomass maximization assumption, will greatly improve capability of predicting stable microbial interactions. In this article, we aim to address these challenges by introducing a new modeling approach that integrates reinforcement learning (RL) into DFBA to model microbial metabolism in a microbiome as a decision-making process. From this perspective, microbial cells evolve by trying different metabolic strategies and learning how to improve their long-term fitness by tuning their behavior using a reinforcement learning algorithm. In this framework, each GEM is modeled as an agent capable of making decisions. The decisions in this context are flux regulations in the metabolic network and the agents make these decisions using the observable environment states. Assuming that “bad decisions” are filtered through the natural selection process, we use reinforcement learning algorithms to find the strategies that lead to the long-term optimal behavior of microbes in the system that they are interacting with. In other words, microbial models learn how to interact by trial and error in their environment through self-play mechanism ( Laterre et al. 2018 ), without the need to pre-define metabolic and regulatory strategies. Reinforcement learning has shown great promise in solving very complex problems in the past decade ( Mnih et al. 2013 , Silver et al. 2018 , Brown et al. 2020 ) and have been used with success in different fields of science and engineering ( Mousavi et al. 2017 , Treloar et al. 2020 , Jebellat et al. 2021 , Kargar et al. 2022 , Kiran et al. 2022 , Lotfi et al. 2022 ). Although still relying on FBA, this approach is fundamentally different from biomass maximizing agents assumed commonly in traditional FBA and DFBA as the long-term consequences of actions are also considered in a dynamic context to find strategies that are also performing well in future rather than only an instance of time. In several cases, discussed shortly, greedily optimizing for biomass production will lead to early community extinction. Rationally, such strategies should be eliminated by the natural selection process. The strategies taken by RL agents after training can be useful to understand why certain types of behaviors are observed in real microbial systems.",
"discussion": "4 Discussion and conclusion In this article, we presented a novel algorithm that provides insights into microbial interactions by allowing the microbial agents to freely explore flux regulation strategies and select metabolism regulation strategies that lead to their higher long-term fitness of the agents. This way we can explain the observed phenotypes in multiple communities that current algorithms fail to explain. Defining this problem in a FBA framework forces the strategies to be inside a space where mass balance and flux constraints are still satisfied. Another advantage of using FBA is that the underlying LP problems can be solved efficiently. We examined this algorithm on multiple test scenarios that emulate biologically relevant scenarios. In scenarios where agents should coexist with other agents, the agents learned aspects of interacting with others while still tried to maximize their own return. The outcome of starch-amylase system has an interesting interpretation. Cheating in microbial communities can significantly affect the amount which large molecules such as starch are degraded in hydrolysis ( Velicer et al. 2000 , Rainey and Rainey 2003 , Greig and Travisano 2004 , Yurtsev et al. 2013 , Harrington and Sanchez 2014 , Popat et al. 2015 , Szilágyi et al. 2017 , Abisado et al. 2018 , Heyer et al. 2019 , Morales et al. 2021 , Han and Liang 2022 ). Taking spatial heterogeneity into consideration revealed that in communities with higher mass transfer limits, the agents secrete more amylase and starch utilization becomes higher ( Supplementary Fig. S3 ). The reason behind this observation is that low mass transfer implies that the glucose that is produced by an agent will stay away from the other agent that could possibly cheat, and in turn, the agents will see more positive signal by secreting amylase. With this algorithm, we were able to explore other types of microbial interactions in a dynamic context. One problem that we were interested in was that whether we can explain metabolite exchange between auxotrophic strains through this framework ( Mee et al. 2014 , Zengler and Zaramela 2018 ). We hypothesized that without any predetermined exchange strategies or community level objectives, the optimal agents can find metabolite exchange with other agents strategy to maximize their own long-term fitness. Optimal agents in Toy-NECOM-Auxotrophs learned that exchanging A and B will increase their long-term fitness. To see if this algorithm can be used for genome-scale model in real environments, we created an environment of two E.coli auxotrophs, tyrosine and phenylalanine, inspired by the experiments in Mee et al. (2014) . Although we did not use any sort of experimentally observed phenotypic data, the agents learned to exchange the amino acid that they can produce, and the other agent cannot. Interestingly, our simulations indicate that the phenylalanine mutant achieves superior growth compared to the tyrosine mutant, Fig. 6 , which follows the same trend as is experimentally observed and reported in Mee et al. (2014) . Being able to predict such emergent behaviors of microbiomes by purely relying on metabolic capability of the cells and ecological first principles is what distinguishes SPAM-DFBA from the other existing algorithms. An interesting study ( Hoek et al. 2016 ) reported the behavior change of auxotrophs when inserted in an environment that supplies all the components that they need for growth. In this scenario, they shift their exchange strategy to uptake all the compounds from the environment and stop secreting the metabolites further. Our simulations showed similar shift for auxotrophic agents which reflects that the agents adapt their strategies according to the changes in the environment, Fig. 5 , and shows assuming that cells are maximizing their own long-term fitness can reproduce several real scenarios is missed by simple DFBA. Previous cases revealed that agents that depend on each other for survival will evolve to exchange metabolites with each other and when this strict dependence does not exist anymore selfish behaviors emerge. Toy-NECOM-Facultative-Exchange provides more evidence for this trend. In this case, if a community level objective such as, total community biomass maximization, is defined then there will be A and B exchange between Agent 1 and Agent 2 ( Khandelwal et al. 2013 , Cai et al. 2020 ). This is the result predicted by the direct extension of FBA where a microbial community is optimized as one compartmentalized model. However, this is not what SPAM-DFBA predicts. In this case, A and B exchange strategy is exploitable by the agents. Since the agents do not rely on each other for survival, any exchange of A and B is exploitable by either of the agents in the case of resource limitation. Consequently, the agents finally adhere to taking up any A and B, limited by the kinetic rules provided for the model, which exist in the environment which is shown in Supplementary Fig. S4 . This is consistent with the previous NECom prediction and game-theoretical analysis ( Cai et al. 2020 ), and exactly matches simple DFBA prediction. SPAM-DFBA is well suited for answering important questions in the field of microbiology by predicting the emergent behavior of microbiomes using metabolic capability of the cells in contrast with commonly used ecological models such as Generalized Lotka-Volterra ( Bomze 1983 , Hofbauer and Sigmund 1998 , Venturelli et al. 2018 ), which do not base their predictions on the metabolic network of the microbes. SPAM-DFBA is a dynamic framework, and the environment changes such as resources limitations can be simulated, while methods based on FBA, and not DFBA, cannot make such considerations which is critical and can significantly shape microbial interactions ( Hoek et al. 2016 ). Another advantage of this approach is that optimization is done at individual model level instead of community level objectives. This means that unrealistic interactions discussed in detail in ( Cai et al. 2020 ) are avoided. If a particular random action is advantageous to the long-term fitness of an agent, this behavior gets reinforced in the policy of the agent using the PPO algorithm. We believe that this has a lot of similarities to the process of natural selection. We would like to emphasize that our method does not imply that the microbial cells are intelligently seeking the optimal behavior in their environment. However, it is the resemblance of this algorithm to the process of natural selection that results in more realistic predictions for a given environment. There are multiple future directions to further improve SPAM-DFBA. While the current implementation can scale to multiple GEMs in a manageable amount of time, more efficient implementations will help simulate more complex microbiomes with hundreds of taxa. Another particularly interesting venue is to completely remove the need for LP solvers by letting the agents sample the feasible action space. This not only could improve the speed dramatically, but it also relaxes any assumption imposed on the metabolism of the agents under optimization formulation. Improvements in sample efficiency of RL algorithms can also improve the efficiency of this algorithm in future. Hyperparameters such as clipping threshold or learning rates also affect the efficiency and stability of the learning process for the agents. Although we used same hyperparameters for all the case studies, optimal combination of the hyperparameters can be explored either by exhaustive search or using appropriate optimization techniques ( Boroujeni and Pashaei 2021 , Kiran and Ozyildirim 2022 ). As an example, we have examined the effect of important hyperparameters on the learning process for “Toy-Exoenzyme-Single-Agent” ( Supplementary Fig. S5 ). In this article, we just showed the potentials of formulating DFBA as a RL problem and discussed how this approach can predict microbial interactions in simple communities. Applying this approach to more complex ecosystems and validation with experimentally observed phenotypes is a natural next step for future studies."
} | 4,454 |
28782029 | PMC5533541 | pmc | 3,817 | {
"abstract": "A triboelectric eye blinking sensor with robustly high sensitivity achieves prominence as a human-machine interface.",
"introduction": "INTRODUCTION Human beings have never stopped their pursuit of making their life more convenient and fascinating. A human-machine interface (HMI)—a novel communication channel between a human and an external device—is one way to turn a virtual thought into realistic action. Unlike traditional HMIs as hand operation, speech input, etc., HMIs based on bioelectrical signals ( 1 – 4 ) have advantages of “hands-free” or “aphasia,” especially for patients suffering from amyotrophic lateral sclerosis. To date, the bioelectrical signals applied to HMIs include neuron signals ( 1 , 2 , 5 ), as well as electrocorticogram ( 6 ), electroencephalogram (EEG) ( 3 ), electromyogram (EMG) ( 7 ), electrooculogram (EOG) signals ( 8 – 10 ), etc. Among these techniques, EEG, EMG, and EOG are noninvasive. EEG-based HMI is the most commonly used method and has been proven useful for paralyzed patients to communicate with the external world to relatively low cost ( 3 , 11 ). However, the low signal-to-noise ratio (SNR) of the scalp-recorded EEG signals (in microvolts), the lack of efficient resolution in modeling, and consequently higher requirements for the classification algorithm ( 12 , 13 ), as well as a long period of training, limit the wide use of EEG-based HMIs ( 14 ). Furthermore, a multielectrode with electrolyte gel [usually named “wet electrode” ( 15 )] takes much time to prepare, and the gel can only keep excellent electrical conductivity for 2 hours. These drawbacks make EEG-based HMIs stable only under favorable laboratory conditions, and unstable in daily life because of various disturbances, including mechanical artifacts such as EMG and EOG signals ( 4 ). Nevertheless, EMG and EOG can be used as good control signals for healthy people and even “lock-in” patients who could still blink their eyes ( 16 ). As to these people, EMG and EOG techniques are more practical for everyday situations than EEG-based HMIs ( 4 , 16 ). In particular for EOG, it is a technique serving both healthy and disabled persons. EOG is based on signal collection from the corneal-retinal potential difference in the process of eye movements. The fundus is usually defined as the negative pole and the cornea as the positive pole ( 17 ). The potential difference is determined in principle at least on two exposed electrodes (usually Ag/AgCl electrodes as wet electrodes) pasted around the sensitive eyes, which bring discomfort and poor aesthetics. In addition, the amplitude of EOG is very weak, ranging between 50 μV and 3.5 mV ( 17 ), which is often masked by noise and difficult to detect without sophisticated and expensive electronics. Also, as for these weak signals, facial muscle movement can easily produce artifacts ( 17 ). Therefore, EOG would be inappropriate for some applications, such as driving a car, piloting a plane, or operating a motorized wheelchair. A noninvasive and sensitive aesthetic sensor that is usable, stable, and comfortable is desired for serving the particular groups of people discussed above to solve these problems in bioelectrical-based HMI systems. In recent years, the fast development of nanotechnology has provided possible strategies for problems in the field of bioelectric signal collection and HMIs ( 18 – 20 ). Among these technologies, a new system—the triboelectric nanogenerator (TENG) ( 21 – 24 )—was invented and quickly developed on the basis of contact electrification and electrostatic induction ( 25 , 26 ), with unique advantages of high output, low cost, light weight, applicability of structure design, prominent stability, robustness, etc. ( 27 – 30 ). Because TENGs can generate electricity from almost all types of mechanical motions, including touching ( 31 ), sliding ( 32 , 33 ), rotation ( 34 , 35 ), vibration ( 36 ), etc., they can serve as self-powered sensors for a similarly wide range of motions, such as touch/pressure sensors ( 37 ), vibration sensors ( 38 ), biomechanical sensors ( 39 ), electronic skin sensors ( 40 ), acoustic sensors ( 41 ), pulse wave sensors ( 42 ), synthesized multifunctional sensors ( 43 ), and more. For the pulse wave sensor, Yang et al . ( 42 ) have reported a bionic membrane sensor that noninvasively monitors the extremely weak arterial pulse from the subject’s carotid artery, chest, and wrist. This inspired us to consider whether a TENG-based micromotion sensor could be used as a novel sensing device as an alternative to traditional EOG technique and could make a significant breakthrough on the mechnosensational HMI. Here, a noninvasive, highly sensitive (~750 mV), easy-to-fabricate, stable, small, light, transparent, flexible, skin-friendly, low-cost, durable, and reusable TENG-based sensor for translating the real-time micromotion of eye blink into control command is presented. This mechnosensational TENG (msTENG) [as a sensor, it can be regarded as a type of the present ongoing “dry electrode” ( 15 , 19 )] with a multifilm structure is designed on the basis of a single-electrode mode and thus could be flexibly mounted and hidden behind an eyeglass arm to form a wearable sensor ( 44 ). The voltage curves of the device with different parameter structures are tested systematically, and synchronous measurement illustrates that voltage amplitude from the msTENG is significantly larger (hundred times) than that from an EOG. On the basis of this high sensitivity, the as-fabricated msTENG smart sensor glasses are used to control household appliances with a simple signal processing circuit. Furthermore, a wireless module is introduced to develop a hands-free virtual keyboard typing system. This work for the first time brings a TENG-based sensor to the field of mechnosensational HMIs, and it promises to make a significant breakthrough on mechnosensational HMIs in conditions of daily life.",
"discussion": "DISCUSSION Nowadays, keeping pace with the rapid development of artificial intelligence is an important and urgent task for the sensor technology. Here, we have developed a TENG-based, highly sensitive, noninvasive micromotion sensor that is skin-friendly, reusable, small, and light to translate eye blink to control command for HMIs and have mounted it on the arms of glasses to construct two practical HMI systems—the smart home control system and the hands-free typing system. Both systems performed extremely well on the basis of simple hardware circuit and software program. These systems are merely ordinary demonstrations that bring us inspiration to apply new technology to a traditional research field. To benefit from TENG, the msTENG sensor is distinct and unique in its fundamental mechanism, which can effectively avoid problems, such as poor SNR and inconvenient operation in mechnosensational HMIs. Moreover, considering the easy fabrication process and the common materials used, the msTENG sensor is cost-effective and suitable for mass production to serve people dependent on ambient intelligence. With the innovative assembly of msTENGs in different body places, people can foresee great potential of TENG-based sensors in intelligent robotics."
} | 1,803 |
25271119 | null | s2 | 3,818 | {
"abstract": "In collective resistance, microbial communities are able to survive antibiotic exposures that would be lethal to individual cells. In this review, we explore recent advances in understanding collective resistance in bacteria. The population dynamics of 'cheating' in a system with cooperative antibiotic inactivation have been described, providing insight into the demographic factors that determine resistance allele frequency in bacteria. Extensive work has elucidated mechanisms underlying collective resistance in biofilms and addressed questions about the role of cooperation in these structures. Additionally, recent investigations of 'bet-hedging' strategies in bacteria have explored the contributions of stochasticity and regulation to bacterial phenotypic heterogeneity and examined the effects of these strategies on community survival."
} | 211 |
34512082 | PMC8409933 | pmc | 3,819 | {
"abstract": "Abstract Fungal endophytes have been recorded in various plant species with a richness of diversity, and their presence plays an essential role in host plant protection against biotic and abiotic stresses. This study applied the Illumina MiSeq sequencing platform based on the amplification of fungal ribosomal ITS2 region to analyze fungal endophytic communities of two oak species ( Quercus mongolica and Q. serrata ) with different oak wilt disease susceptibilities in Korea. The results showed a total of 230,768 sequencing reads were obtained and clustered at a 97% similarity threshold into 709 operational taxonomic units (OTUs). The OTUs of Q. serrata were higher than that of Q. mongolica with the number of 617 OTUs and 512 OTUs, respectively. Shannon index also showed that Q. serrata had a significantly higher level of fungal diversity than Q. mongolica . Total of OTUs were assigned into 5 fungal phyla, 17 classes, 60 orders, 133 families, 195 genera, and 280 species. Ascomycota was the dominant phylum with 75.11% relative abundance, followed by Basidiomycota with 5.28%. Leptosillia, Aureobasidium and Acanthostigma were the most abundant genera detected in Q. serrata with the average relative abundance of 2.85, 2.76, and 2.19%, respectively. On the other hand, Peltaster , Cladosporium and Monochaetia were the most common genera detected in Q. mongolica with the average relative abundance of 4.83, 3.03, and 2.87%, respectively. Our results indicated that fungal endophytic communities were significantly different between two oak species and these differences could influence responses of host trees to oak wilt disease caused by Raffaelea quercus-mongolicae .",
"conclusion": "5. Conclusion Our results indicate that the diversity of endophytic fungi was significantly different between two oak species, and the biggest difference of fungal endophytic community occurred in stem tissues. A total of 709 OTUs were obtained in both two oak species. In which, the OTUs of Q. serrata were higher than that of Q. mongolica with the number of 617 OTUs and 512 OTUs, respectively. Total of OTUs were assigned into 5 fungal phyla, 17 classes, 60 orders, 133 families, 195 genera, and 280 species. Ascomycota was the dominant phylum with 75.11% relative abundance in all samples, followed by Basidiomycota with 5.28%, while Kickxellomycota, Mortierellomycota, Mucoromycota, and other fungi had very low relative abundance of 0.01, 0.02, 0.07, and 0.01% respectively. In the total, 19.50% of fungal OTUs remained unidentified. Leptosillia, Aureobasidium and Acanthostigma were the most abundant genera detected in Q. serrata with the average relative abundance of 2.8, 2.76, and 2.19%, respectively. On the other hand, Peltaster , Cladosporium and Monochaetia were the most common genera detected in Q. mongolica with the average relative abundance of 4.83, 3.03, and 2.87%, respectively.",
"introduction": "1. Introduction Endophytes are microorganisms that colonize internal living tissues of plant, but without causing any immediate over adverse effects or during a part of their life cycle reside inside plant tissue of host trees without doing substantive harm [ 1 , 2 ]. Endophytes may include bacterial and fungal species or others belong to actinomycetes and mycoplasma [ 1 ]. In addition, bioactive compounds produced by endophytes play an important role in fitness enhancements for host plants [ 1 , 3 ]. On the other hand, endophytes can improve the ability of plants' nutrient uptake and protect host plants against biotic and abiotic stresses [ 3 , 4 ]. Fungal endophytes have been used to extract antimicrobial compounds and produce antibiotic drugs [ 1 ]. Endophytic fungi have been recorded in various plant species with a richness of diversity [ 1 , 2 ]. The most common endophytic fungi that have been identified belong to Ascomycota, followed by Basidiomycota and other fungi [ 1 , 2 ]. It is well known that metagenomics based on next-generation sequencing (NGS) technologies helps scientists to discover and understand the diversity, function, and evolution of the uncultivated microbiology of diverse environments or habitats [ 5 ]. Metagenomics is considered as an optimal tool to discover the nucleic acids from uncultivated microbes in different environments [ 5 , 6 ]. Metagenomics is defined as a technical chain rooted in genomics, microbial genetics, microbial ecology, and bioinformatics to analyze directly genomes of microbial community sampled from their natural habitats [ 5 ]. It is also applied to recover target genes through sequencing to determine not only the microbial diversity but also their roles in environmental samples [ 5–7 ]. Among the NGS technologies, Pyrosequencing (Roche 454) and Solexa (Illumina) sequencing systems have been extensively used to explore microbial communities in different environments [ 5 ]. For instance, results of 454 sequencing indicated that fungal communities in the phyllosphere of Quercus macrocarpa were significantly different either between urban and nonurban environments or between different seasons [ 8 , 9 ]. Differences in bacterial and fungal endophytic communities between Acer campester and A. platanoides leaves were deciphered by using Illumina MiSeq sequencing [ 10 ]. Fungal endophytic communities in needles of Pinus sylvestris were also analyzed by using Illumina MiSeq sequencing, and the study showed the most abundant fungi was assigned to phylum Ascomycota with 91.2% of the samples [ 11 ]. In addition to the two above-mentioned, the Ion Torrent Personal Genome Machine (PGM) sequencing was also applied to analyze endophytic fungal communities in Eucalyptus grandis [ 12 ] or investigate microbial community dynamic in liquid waste [ 13 ]. Quite a few studies about endophytic fungi in Korea have been studied in recent years [ 14–19 ]. For instance, Alternaria spp., Cladosporium spp., and Penecillium spp. were the dominant endophytic fungi in several medicinal plants [ 14 ]. Species richness and diversity indices of endophytic fungi were different among three Halophytes, namely Sedum oryzifolium , Lysimachia mauritiana , and Aster spathulifolius [ 17 ]. The most common genera of endophytic fungi isolated from Pinus thunbergii were identified as Fusarium, Penicillium, and Trichoderma [ 16 ], while Lophodermium conigenum and Annulohypoxylon turcatum were dominant in Pinus densiflora and Juniperus rigida , respectively [ 18 ]. However, endophytic fungi obtained from P. densiflora and J. rigida were influenced by host plants’ distribution and had a lower diversity index than that of Larix kaempferi [ 15 ]. Studies on endophytic fungi of other coniferous trees, namely Cryptomeria japonica , Pinus koraiensis , Pinus rigida , etc. have been also reported, with a total of about 80 taxa belonged to 52 genera [ 19 ]. Applying metagenomics to analyze microbial community from various environments was also conducted in Korea [ 20–26 ]. Of which, NGS platform of Roche 454 was applied to identify airborne fungal community in Seoul [ 21 ], while Illumina MiSeq platform was performed to compare soil higher fungal communities associated with dead and living Korean fir ( Abies koreana ) in Jeju island [ 26 ]. However, using metagenomics to analyze endophytic fungal communities in forest trees is still limited. Quercus serrata was known as the most susceptible oak species to the oak wilt fungus Raffaelea quercivora in Japan [ 27 ], but Quercus mongolica is highly susceptible to the oak wilt fungus Raffaelea quercus-mongolicae , and Q. serrata is relatively resistant to this pathogen in Korea [ 28 ]. The difference in susceptibility of these two oak species to R. quercus-mongolicae could be partially due to differences in their endophytic fungal communities. Hence, our objectives in the present study were to (i) compare species richness and diversity of endophytic fungi between Q. mongolica and Q. serrata and to (ii) characterize taxonomic structures of endophytic fungi that differ between two oak species through Illumina MiSeq sequencing platform.",
"discussion": "4. Discussion A total of 709 distinct OTUs were generated from two oak species ( Figure 1 ). Among these, there were 59.2% OTUs were distributed in both Q. serrata and Q. mongolica , while 27.8 and 13.0% OTUs were only detected in Q. serrata and Q. mongolica , respectively ( Figure 1 ). It was indicated that Q. serrata is highly resistant to oak wilt fungus ( R. quercus-mongolicae ), while Q. mongolica is highly susceptible to this pathogen [ 28 ]. Our results showed that the Shanon index of endophytic fungi in Q. serrata was significantly higher than that of Q. mongolica ( Figure 3 ). This is consistent with previous findings that either endophytic fungi of disease-resistant species had a higher diversity index compared with susceptible species to disease [ 36 ] or fungal endophytic community had a significant difference between pathogen-infected trees and healthy trees [ 10 , 37 , 38 ]. For instance, endophytic fungi colonized in Rosa multiflora had higher diversity than Rose multiflora var. carnea in several specific developmental stages of plants where R. multiflora showed high resistance to powdery mildew disease and R. multiflora var. carnea was highly susceptible to this pathogen [ 36 ]. Two maple trees, Acer campestre and Acer platanoides had a significantly higher fungal richness and diversity in control leaves compared to leaves infected with pathogens [ 10 ], while European beech ( Fagus sylvatica ) showed a significant difference in the fungal species composition between living and decaying leaves [ 38 ]. Fungal diversity indices were different among plant tissues of two oak species ( Table 2 ). Principal Coordinate Analysis (PCoA) also showed that the fungal endophytic community had a difference in various plant tissues (leaf, petiole, twig, branch, stem, and root), and the biggest difference between two oak species was indicated in stem samples ( Figure 6 ). Fungal endophytic communities were affected not only by plant varieties, plant tissues but also by management practices, plant's development stages, and hosts' ecological conditions [ 8 , 9 , 36 , 39 ]. The diversity of endophytic fungi isolated from plants was also different among parts [ 40–42 ] with a higher abundance in leaves compared to other tissues [ 40 , 41 ]. Our study obtained that the highest diversity index of endophytic fungi was indicated in the leaf sample of Q. mongolica , while endophytic fungi of Q. serrata showed the highest diversity index in the petiole sample ( Table 2 ). Figure 6. Principal Coordinate Analysis (PCoA) based on the weighted UniFrac distances for fungal communities from plant tissues of two oak species ( Quercus mongolica and Quercus serrata ) indicating two distinct groups in the stem tissues between two oak species using two principal coordinates (PC1 and PC2). Ascomycota was dominant in both Q. mongolica and Q. serrata , with the average relative abundances of 67.34 and 82.88%, respectively ( Figure 4(A) and Supplementary Table S1 ). Ascomycota is the most dominated endophytic fungi colonized in many plants that have been indicated in previous studies [ 11 , 36 , 39 , 43–45 ]. The most abundant fungal genus in Q. mongolica was Peltaster with average relative abundances of 4.83% and this genus was mainly colonized in petiole tissue with relative abundances of 28.50%, while Leptosillia was the most common endophytic fungi in Q. serrata with average relative abundances of 2.85% and it was mainly colonized in stem tissue with relative abundances of 17.04% ( Figure 5 and Supplementary Table S1 ). To date, the information about endophytic fungi identified as Peltaster spp. and Leptosillia spp. is still limited. Cladosporium spp. and Aureobasidium spp. were dominant endophytic fungi in both Q. mongolica and Q. serrata ( Supplementary Table S1 ). Cladosporium spp. accounted for 3.03 and 1.99% of endophytic fungi in Q. mongolica and Q. serrata respectively, while Aureobasidium spp. had 1.80% in Q. mongolica and 2.76% in Q. serrata ( Supplementary Table S1 ). Cladosporium spp. have been reported as dominant fungal endophyte from various either host trees such as oak, ash, and cinnamon species [ 46–48 ] or other hots plants, such as sweet citrus [ 49 ], rice [ 50 ], and wheat [ 51 ]. Aureobasidium spp. was one of the most common endophytic fungi colonized in several tree species [ 52 ], of which, endophytic fungus Aureobasidium pullulans accounted for 24.2% of detected fungi in European ash ( Fraxinus excelsior ) [ 53 ] and was the most dominant taxon in European aspen ( Populus tremula ) [ 54 ]. Moreover, Aureobasidium pullulans was also one of major endophytic fungi in maize and common bean [ 55 , 56 ]. Studies on bioactive compounds obtained from Cladosporium spp. and Aureobasidium spp. were also conducted in biological controls of plant pathogens [ 57–59 ]. For instance, Cladosporium spp. isolated from stem of Sesbania grandiflora can inhibit development of bacterial and fungal pathogens in Thailand such as Staphylococcus aureus, Escherichia coli, and Cryptococcus neoformans [ 57 ]; Cladosporium sp., an endophytic fungus isolated from a medicinal plant ( Cyclosorus parasiticus ), had antibacterial activity against Staphylococcus aureus and Salmonella enterica damaging on C. parasiticus [ 60 ]. In addition, extracellular enzymatic activities of Cladosporium spp. isolated from various medicinal plants were also reported [ 61 , 62 ]. On the other hand, Aureobasidium spp. isolated from Posidonia oceanica can produce antimicrobial compounds, namely hydroxylated decanoic acids against Candida albicans and Staphylococcus aureus , and aureobasidin which exhibited insecticidal activity against the larval settlement of Balanus amphitrite larvae [ 59 ]. Among Aureobasidium spp ., A. pullulans has been identified as one of the biocontrol agents to control various fruit postharvest pathogens such as Monilinia laxa on plums, peaches, sweet cherries, apricots, and table grapes [ 63–66 ]; Botrytis cinerea on apples, sweet cherries, tomatoes. and table grapes [ 63 , 66–68 ]; Penicillium expansum on apples, lemons [ 63 , 67 , 69 ]; Monilinia fructicola, Monilinia polystroma and Monilinia fructigena on sweet cherries, peaches, and apricots [ 64 , 65 ]; Colletotrichum acutatum on apples [ 67 ]; Penicillium italicum and Penicillium digiatum on citruses, apples, and lemons [ 67 , 69 ]. Aureobasidium pullulans was also used to control plant pathogens, namely Phytophthora infestans causing tomato late blight [ 70 ], Rhizoctonia solani causing damping-off in tomato, bean, and soybean seedlings [ 71 , 72 ], Fusarium culmorum causing Fusarium head blight of common wheat ( Triticum aestivum ) [ 73 ], and Neofusicoccum parvum causing stem canker disease in apple trees [ 74 ]. Other phytopathogenic fungi such as Fusarium oxysporum and Alternaria alternata were also inhibited by A. pullulans isolated from healthy grapevines [ 75 ]. Moreover, A. pullulans was also identified as plant growth promoters for bean and soybean plants [ 72 ], while Aureobasidium sp. isolated from Boswellia sacra not only displayed extracellular enzymatic activities but also produced indole acetic acid (IAA) for promoting growth of B. sacra [ 76 ]. In brief, Aureobasidium spp. had a higher abundance in Q. serrata than Q. mongolica , and these fungi showed a wide range of bioactive activities, especially antifungal and insecticidal activities. Thus, they could play important roles in pathogen tolerance and repellent of insect vector in oak wilt pathosystem. Oak wilt disease caused by R. quercus-mongolicae has emerged rapidly in Korea since 2004 [28], and Ceratocystis quercicola , a novel Ceratocystis species can cause a very low level of damage on Quercus variabilis [ 77 ]. However, these two fungal species were not found in the present study. To the best of our knowledge, this is the first study that was conducted to analyze endophytic fungal community from Q. mongolica and Q. serrata based on the ITS2 region through Illumina MiSeq. Our results provided a better understanding of differences in diversity of fungal endophyte colonized in these two oak species. These differences could affect the interactions between endophytic fungi and host tree species in producing specific enzymes or volumes of bioactive compounds, therefore, the responses of Q. mongolica and Q. serrata to oak wilt pathogen are different."
} | 4,182 |
40305515 | PMC12043173 | pmc | 3,821 | {
"abstract": "Our society relies heavily on plastic, but most plastic is petroleum-based and non-biodegradable resulting in major negative environmental impacts. Polyhydroxyalkanoates (PHAs) are a type of biopolymer that can be produced from microorganisms cultured on renewable feedstocks, such as glycerol. PHAs are biodegradable and have properties similar to petroleum-based plastics. Several Priestia megaterium isolates have been demonstrated in previous studies to produce PHA when cultured on glycerol, but there has been no comparison of strains available in public repositories. Such comparison would be useful to identify the most promising strains for further development. In this study, we screened a number of P. megaterium strains from readily accessible repositories for their ability to produce PHA from glycerol. The strains had a wide range of growth and PHA production characteristics on glycerol: cell dry weight (0.5–5.7 g/L), percent PHA (4–42%), PHA titer (0.1–1.9 g/L), and yield (26–303 mg/g). The time course of PHA production varied widely among the different strains. There was also a dramatic difference in molecular weights which ranged from 119 kD to 402 kD. This information will be valuable to groups in selecting a PHA strain to develop based on their specific requirements.",
"conclusion": "Conclusions This study provides comparison of microbial PHB production using glycerol feedstock from different P. megaterium strains that are available from readily accessible repositories. Based on this information, strains can be selected for further production optimizations depending on user priorities. For instance, if a longer window of stable PHA titer were essential, one could select a strain such as NRRL B-350 which maintains a constant titer from 24–96 hours. Alternatively, the common industrial strain DSM 319 could be selected if production speed were most critical since PHB rapidly accumulated to the maximum titer at only 8 h. Although the PHB titer does rapidly decrease after 12 hours, the DSM 319 strain could serve as a target for genetic modification to maintain higher levels of polymer over time. Besides the different PHB production profiles, the P. megaterium strains were also demonstrated to produce biopolymers in a range of molecular weights. While high molecular weight polymers are valued for their higher tensile strength, lower molecular weight PHB could also have utility if greater flexibility is required. Thus, different P. megaterium strains could be selected for PHB production depending on the end applications. Regardless of the specific strain that is chosen for PHB production from glycerol feedstock, optimization of growth conditions would also be expected to increase PHB levels. Simply growing the bacteria in bioreactors should yield large improvements compared to the flask conditions used in this study. Furthermore, modifications of the culture media (temperature, pH, ammonium level, glycerol source and concentration, etc.) can all impact PHB production in P. megaterium .",
"introduction": "Introduction Modern society is dependent on the benefits of petroleum-based plastics in all aspects of our economy; however, there are significant negative environmental consequences of this lifestyle. Traditional plastic production results in the release of harmful greenhouse gases into the atmosphere, and the plastic itself is generally not biodegradable thus polluting the land and water for many years. Therefore, there are intense efforts to develop sustainable alternatives. One promising approach is the use of polyhydroxyalkanoates (PHAs) which are biodegradable polyesters produced by certain microorganisms [ 1 , 2 ]. The characteristics of PHAs vary widely based on their specific subunit composition, and these polymers can emulate the properties of many different traditional plastics [ 3 , 4 ]. The most commonly produced PHA is poly-3-hydroxybutyrate (PHB) which is composed of 3-hydroxybutyrate subunits. PHB has properties (tensile strength [~30 MPa] and modulus [~3.25 GPa]) that are similar to polypropylene [ 5 ]. A major barrier to broad adoption of PHA polymer is the higher cost compared to traditional plastics. One of the major expenses of PHA production is the feedstock used to culture the microorganisms. To alleviate this problem, waste streams from different processes have been explored as feedstocks for PHA production [ 6 – 10 ]. Recently, glycerol has emerged as a promising substrate because it is a major byproduct of biodiesel manufacturing [ 11 – 13 ]. The production of biodiesel has been increasingly yearly and is predicted to reach 50 billion liters by 2030 [ 14 , 15 ]. Approximately, 1 g of glycerol is generated for every 10 g of biodiesel. Although there are currently applications for glycerol that do not involve PHA production, the amount of glycerol made available through the biodiesel industry far exceeds the current market. The bacterium Priestia megaterium (formerly known as Bacillus megaterium ) is an excellent candidate to produce economical levels of PHA. P. megaterium was the first microorganism discovered to produce PHA, specifically PHB [ 16 ]. The bacterium has been widely used in biotechnological applications to produce industrial biochemicals [ 17 ]. The fact that P. megaterium is a Gram-positive bacterium is an advantage over Gram-negative bacteria containing contaminating lipopolysaccharide endotoxins. There are many reagents available to genetically engineer P. megaterium [ 18 , 19 ]. P. megaterium is capable of growing on a wide variety of feedstocks, and economic modeling has demonstrated that it is feasible to commercially manufacture PHB from glycerol [ 20 ]. To produce PHB, P. megaterium uses a heteromeric PHB synthase enzyme composed of PhaC and PhaR subunits [ 21 ]. Multiple research groups have demonstrated PHA production from P. megaterium using glycerol as a feedstock; however, the majority of these studies utilize isolates not available in public strain repositories [ 20 , 22 – 30 ]. Additionally, experimental culturing conditions vary greatly between these studies, making direct comparisons impossible. In order to facilitate further development of P. megaterium for PHA production and select appropriate strains for functional modification by genetic engineering [ 31 ], we have conducted a side-by-side comparison of 12 publicly-available strains in this study. P. megaterium strains were compared for their ability to produce PHB from glycerol feedstock under identical experimental conditions. Cell biomass, polymer percent, titer, and yield were monitored over the course of time. Polymers were analyzed for monomer composition, molecular weight, and thermal properties. Knowledge of the PHB production characteristics will allow groups to make informed decisions about which strain to utilize and further develop based on their industrial priorities. All the strains in this study are easily obtainable from cell repositories available to the public.",
"discussion": "Results and discussion Twelve Priestia megaterium strains in our collection that were obtained from publicly accessible repositories, including two common industrial strains [ 37 ], QM B1551 and DSM319, were studied ( Table 1 ). The majority of the strains were acquired from the ARS Culture Collection (IL, USA) and were all deposited by different researchers. The strains were assayed for growth and PHB production on defined media containing glycerol as carbon substrate, and timepoints were collected for up to 96 hours ( S1 Fig ). Interestingly, although QM B1551 grew on solid media with glycerol substrate, this strain did not have significant growth in liquid culture. PV 586 is a plasmidless derivative of QM B1551 and likewise did not grow in liquid culture. 10.1371/journal.pone.0322838.t001 Table 1 P . megaterium strains examined in this study. Strain Source 16S rRNA NCBI accession Alternate nomenclature NRS 269 ATCC OR561997* QM B1551 BGSC NC_014019 PV 586 BGSC YYBm1 BocaScientific DSM319 DSMZ CP001982 NRRL B-349 NRRL OR561998* ATCC 8245; NRS 245 NRRL B-350 NRRL OR561999* ATCC 7056; NRS 239 NRRL B-352 NRRL OR562000* ATCC 7703; NRS 615 NRRL B-1367 NRRL OR562001* APF 12; NRRL B-940 NRRL B-1851 NRRL OR562002* ATCC 11478 NRRL B-3254 NRRL OR562003* ATCC 11561 NRRL B-14308 NRRL NZ_CP009920 ATCC 14581 *This study. 16S rRNA were sequenced as described in Materials and methods. The ten P. megaterium strains that did grow in liquid culture demonstrated a wide variety of growth and PHB production profiles ( Table 2 and S1 Fig ). To illustrate these differences, three strains with distinct PHB production profiles were compared ( Fig 1 ). YYBm1 has the fastest time of maximum PHB titer at 8 h whereas NRRL B-3254 and NRRL B-350 do not accumulate significant levels of polymer until 24 h ( Fig 1A ). At its peak PHB titer at 48 h, NRRL B-350 produced more PHB that YYBm1 (1.9 g/L vs 1.2 g/L). NRRL B-3254 had the lowest PHB titer of the three strains at 0.8 g/L. 10.1371/journal.pone.0322838.t002 Table 2 Growth and PHB production. Strain Time (max) PHB (g/L) CDW (g/L) PHB (%) Yield (mg/g) Productivity (g/L/h) NRS 269 24 1.8 ± 0.4 5.3 ± 1.3 34.7 ± 2.7 116 ± 10 0.08 ± 0.02 YYBm1 8 1.2 ± 0.1 2.9 ± 0.2 42.0 ± 0.5 303 ± 15 0.15 ± 0.01 DSM 319 8 0.7 ± 0.0 2.7 ± 0.2 27.5 ± 1.4 184 ± 15 0.09 ± 0.00 NRRL B-349 12 0.5 ± 0.0 2.9 ± 0.1 18.9 ± 0.8 96 ± 5 0.05 ± 0.00 NRRL B-350 48 1.9 ± 0.1 5.1 ± 0.2 37.7 ± 3.0 98 ± 6 0.04 ± 0.00 NRRL B-352 48 1.6 ± 0.2 4.4 ± 0.1 37.4 ± 4.5 83 ± 9 0.03 ± 0.00 NRRL B-1367 48 1.8 ± 0.5 4.6 ± 1.6 40.1 ± 3.1 99 ± 21 0.04 ± 0.01 NRRL B-1851 24 1.9 ± 0.1 5.7 ± 0.1 32.5 ± 2.3 114 ± 8 0.08 ± 0.00 NRRL B-3254 24 0.8 ± 0.0 3.5 ± 0.1 21.8 ± 1.0 106 ± 6 0.03 ± 0.00 NRRL B-14308 12 0.1 ± 0.0 0.5 ± 0.0 22.9 ± 1.4 64 ± 4 0.01 ± 0.0 10.1371/journal.pone.0322838.g001 Fig 1 Time course of growth and PHB production of three P. megaterium strains. (A) PHB titer, (B) cell dry weight, (C) residual glycerol, and (D) residual ammonium. All data is the average of three replicates. NRRL B-3254 had the longest period of stable PHB titer with little change from 24 h to 96 h ( Fig 1A ). On the other hand, after the NRRL B-350 PHB titer peaks at 48 h, the amount drops steadily from 1.9 g/L to 0.8 g/L at 96 h. YYBm1 showed the greatest change in PHB titer where the titer rapidly dropped to very low levels from 8 h to 12 h. The biomass levels of NRRL B-3254 and NRRL B-350 trend proportionally with their PHB titers with NRRL B-350 biomass decreasing after 48 h and the NRRL B-3254 biomass not showing a dramatic decrease from 24 h to 96 h ( Fig 1B ). On the other hand, the biomass of YYBm1 does not parallel the PHB titer. Although the PHB titer drops almost completely after 8 h, the YYBm1 biomass increases up to 24 h before gradually decreasing up to 96 h. The consumption rates of glycerol and ammonium were initially much greater for YYBm1 compared to NRRL B-3254 and NRRL B-350 which is consistent with the more rapid initial growth rate of YYBm1 ( Fig 1C and 1D ). Only NRRL B-350 consumed all the available glycerol which is reflected in the highest PHB titer among the three strains at 48 h. YYBm1 is a mutant of DSM 319 in which the nprM and xylA genes are deleted. Similar to its mutant derivative, DSM 319 also had a maximum PHB titer at 8 h ( Table 2 and S1 Fig ). Although the accumulated biomass (cell dry weight) of both DSM319 and YYBm1 were similar, the YYBm1 strain had a greater %PHB (42% vs 28%) and thus a higher PHB titer (1.2 g/L vs 0.7 g/L). It is unclear why the YYBm1 strain had a higher PHB titer relative to the DSM319 parent strain. It is possible that the deletion of the nprM and xylA genes allows more resources to be directed towards PHB synthesis. In addition, compared to the other P. megaterium strains, both the DSM 319 and YYBm1 strains had relatively narrow windows of time in which PHB was rapidly produced and then broken down after 12 h. Thus, it is possible that both strains had similar maximum PHB titers which occurred at slightly different times that were not captured in our sample collection regimen. The sizes of the polymers were analyzed by GPC ( Table 3 ). Among the strains that were grown on glycerol, DSM 319 had the highest molecular weight (402 kD). The other strains all had molecular weights that ranged from 119 kD to 319 kD. It is known that polymers from bacteria cultured on glycerol are typically smaller than those cultured on glucose [ 12 ]. Glycerol is known to serve as a chain terminator in PHB polymerization and can attach to the end of the polymer via the primary or secondary hydroxyl of the molecule. Previous studies have demonstrated that using glycerol as feedstock decreased the molecular weight of the polymer, and this effect was correlated with the concentration of glycerol [ 38 , 39 ]. 10.1371/journal.pone.0322838.t003 Table 3 Gel permeation chromatography of PHB from different P. megaterium strains. Strain Mw Mn Mp Mv Mz Mz + 1 PD NRS 269 194,004 58,917 126,776 164,702 519,133 970,682 3.29 YYBm1 307,124 103,686 201,719 267,073 680,527 1,113,765 2.96 DSM 319 402,080 128,726 233,228 344,661 965,724 1,633,550 3.12 NRRL B-349 225,822 73,445 145,250 194,160 581,779 1,175,519 3.08 NRRL B-349 (glucose) 1,055,093 125,250 156,139 817,187 3,289,967 5,008,052 8.42 NRRL B-350 122,965 40,088 85,552 106,286 310,448 629,947 3.07 NRRL B-352 159,570 67,585 116,933 142,216 316,430 509,398 2.36 NRRL B-1367 118,774 56,722 98,298 107,886 206,790 299,807 2.09 NRRL B-1851 151,765 55,479 104,888 132,079 356,781 643,445 2.74 NRRL B-3254 318,926 74,376 128,447 257,364 1,020,734 1,756,349 4.29 NRRL B-14308 205,697 59,473 124,135 173,019 579,159 1,108,353 3.46 Although MK386891 had a higher PHB titer than NRRL B-1851, the growth time for the MK386891 was much longer (64 h vs 24 h) which translated into a higher productivity for the NRRL B-1851 (0.08 g/L/h vs 0.04 g/L/h). All polymers were isolated from cultures grown with glycerol substrate unless otherwise specified. Mw (weight averaged MW), Mn (number averaged MW), Mp (peak MW), Mv (viscosity average MW), Mz (third moment), and PD (polydispersity). All MW are expressed in daltons. There is likely an additional mechanism responsible for the lower molecular weights. P. megaterium PHB polymerase is a Class IV synthase composed of two subunits: the main PhaC synthase and a smaller PhaR accessory [ 21 ]. The PhaRC synthase complex has been demonstrated to also have an alcoholysis scission activity [ 40 ]. The enzyme can use an alcohol to cleave the polymer internally thereby capping the smaller polymer with a new molecule [ 41 ]. When recombinant E. coli strains transformed with Class IV synthases were cultured on Luria Bertani (LB) media, the initial molecular weight of the polymer decreased from 10 5 to 10 4 Da over the course of 12 h to 60 h [ 42 ]. The GPC chromatogram of the intermediate timepoints showed the initial polymer gradually reducing in size to develop a bimodal distribution that eventually resolved to a single peak of lower molecular weight polymer. This reduction in molecular weight is believed to be caused by the alcoholysis scission activity using endogenously produced ethanol [ 40 ]. When the NRRL B-349 strain was grown in media containing glucose instead of glycerol, the intermediate time point that was analyzed showed the polymer had a higher molecular weight than that of the polymer from the culture grown in glycerol ( Table 3 ). The GPC chromatogram of the two polymer samples showed that the polymer from the glucose culture had a bimodal distribution similar to that seen in the previous study [ 42 ] ( Fig 2 ). It is likely that the polymer from the glucose culture would have eventually resolved into a single peak of lower molecular weight at a later time point. 10.1371/journal.pone.0322838.g002 Fig 2 Molecular weight distributions of polymer from NRRL B-349. Polymers were extracted from cells grown in cultures supplemented with glycerol (solid) or glucose (dashed). The polymers produced by the NRRL B-349 cultured on either glycerol or glucose were also compared by FT-IR ( Fig 3 ). Both polymers had very similar spectra with multiple bands (1720, 1452, 1379, and 1277 cm -1 ) that are characteristic of PHB [ 25 , 31 , 43 ]. The strongest band at 1720 cm -1 corresponds to the ester carbonyl group. The bands at 1452 cm -1 and 1379 cm -1 are attributed to -CH 2 and -CH 3 groups, respectively. The thermal properties of both polymers were analyzed by differential scanning calorimetry (T m , T c , ΔH m , and X c ) and thermogravimetric analysis (T 10% and T max ) ( Table 4 ). These values were similar between both polymers indicating that the thermal properties were not dramatically impacted by the difference in polymer molecular weight distribution. 10.1371/journal.pone.0322838.t004 Table 4 Thermal properties of polymers extracted from strain NRRL B-349 grown in cultures supplemented with glycerol or glucose. DSC. TGA. \n Polymer \n T m \n (°C) T c \n (°C) ΔH m \n (J/g) X c \n (%) T 10% \n (°C) T max \n (°C) Glycerol-based PHB 178.4 74.2 59.5 40.8 284.2 296.0 Glucose-based PHB 180.3 73.3 64.4 44.4 281.2 293.7 T m , melting temperature; T c , crystallization temperature; ΔH m , enthalpy of fusion; X c , percent crystallinity; T 10% (temperature at 10% decomposition); and T max (temperature at maximum decomposition rate). 10.1371/journal.pone.0322838.g003 Fig 3 FT-IR of polymers from NRRL B-349. Polymers were extracted from cells grown in cultures supplemented with glycerol (solid) or glucose (dashed). There is a great deal of variation in the PHA production profile and molecular weights among the multiple strains analyzed ( Tables 2 and 3 ) although the reason for these differences is currently unknown. The strains may have different tolerances to glycerol or metabolize the feedstock with varying efficiencies. Another possibility is that the PHB polymerases might have different activities. Thus, a more efficient PHB polymerase could lead to more and/or higher molecular weight biopolymer. The PHB polymerases might also have different alcoholysis scission activities which would also modulate the final molecular weight of the biopolymer. Table 5 summarizes results from previous work producing polyhydroxyalkanoates from P. megaterium strains cultured on glycerol feedstock including strain NRRL B-1851 from this study. Many of the strains from the past studies have not been deposited in publicly accessible repositories. It is difficult to compare the different strains due to the variety of growth conditions. For instance, the culture temperatures ranged from 25°C-37°C while the timepoints analyzed varied from 12 h to 84 h. Even the glycerol in the media varied with some groups using pure lab grade glycerol while others used crude preparations of glycerol from biodiesel waste streams. In general, the highest PHB titers were derived from bioreactors employing batch and fed-batch feed strategies compared to flasks. A direct comparison was made when Moreno et al. grew P. megaterium strain B2 in both flask and bioreactor and obtained approximately three times more PHB in the latter [ 27 ]. 10.1371/journal.pone.0322838.t005 Table 5 PHB production from P. megaterium cultured on glycerol feedstock. Strain Culture time (h) PHB titer (g/L) CDW (g/L) PHB (%) yield (mg/g) productivity (g/L/h) Reference NRRL B-1851 flask (batch) 24 1.9 5.7 33 114 0.08 This study ASL11 flask (batch) 120 0.92 1.87 50 58 0.008 [ 44 ] B2 flask (batch) 14 0.43 1.26* 34 NR 0.03* [ 27 ] BBST4 flask (batch) 48 1.14 3.8 30 38 0.02 [ 28 ] DSMZ32 flask(batch) 72 0.66 2.68 25 20 0.009 [ 45 ] LVN01 (BmGD) flask (batch) 30 0.25 3.17 8 NR 0.01* [ 24 ] MK386891 flask (batch) 64 2.73 6.82 40 NR 0.04* [ 26 ] MTCC10086 flask (batch) 48 0.65 1.54 39 NR 0.01* [ 23 ] OU303A flask (batch) 48 NR NR 62 NR NR [ 30 ] DSM32 flask (fed batch) 48 0.054 NR NR NR 0.001* [ 22 ] B2 bioreactor (batch) 11 1.2 3.87 31 NR 0.11 [ 27 ] BBST4 bioreactor (batch) 42 3.4 5.7 60 NR 0.08 [ 25 ] DSM 509 bioreactor (batch) 58 3.4 10.4 32 367 0.057 [ 46 ] environmental isolate bioreactor (batch) 42 4.8 7.7 62 300 0.11* [ 20 ] DSM 509 bioreactor (fed batch) 48 2.83 4.73 60 NR 0.06* [ 29 ] DSM 509 bioreactor (fed batch) 52 4.94 8.3 61 549 0.095 [ 46 ] LVN01 bioreactor (fed batch) 36 0.72 1.91 38 NR 0.02 [ 24 ] *Personal communication When compared to other strains that were previously grown in flasks, several of the strains in this study had higher PHB titers (1.8–1.9 g/L) than all of the previously studied strains except for P. megaterium strain MK386891 (2.73 g/L)."
} | 5,158 |
19705820 | null | s2 | 3,822 | {
"abstract": "Ribulose-1,5-bisphosphate (RuBP) carboxylase/oxygenase (Rubisco) is a globally significant biocatalyst that facilitates the removal and sequestration of CO2 from the biosphere. Rubisco-catalyzed CO2 reduction thus provides virtually all of the organic carbon utilized by living organisms. Despite catalyzing the rate-limiting step of photosynthetic and chemoautotrophic CO2 assimilation, Rubisco is markedly inefficient as the competition between O2 and CO2 for the same substrate limits the ability of aerobic organisms to obtain maximum amounts of organic carbon for CO2-dependent growth. Random and site-directed mutagenesis procedures were coupled with genetic selection to identify an \"oxygen-insensitive\" mutant cyanobacterial (Synechococcus sp. strain PCC 6301) Rubisco that allowed for CO2-dependent growth of a host bacterium at an oxygen concentration that inhibited growth of the host containing wild-type Synechococcus Rubisco. The mutant substitution, A375V, was identified as an intragenic suppressor of D103V, a negative mutant enzyme incapable of supporting autotrophic growth. Ala-375 (Ala-378 of spinach Rubisco) is a conserved residue in all form I (plant-like) Rubiscos. Structure-function analyses indicate that the A375V substitution decreased the enzyme's oxygen sensitivity (and not CO2/O2 specificity), possibly by rearranging a network of interactions in a fairly conserved hydrophobic pocket near the active site. These studies point to the potential of engineering plants and other significant aerobic organisms to fix CO2 unfettered by the presence of O2."
} | 396 |
37024470 | PMC10079924 | pmc | 3,823 | {
"abstract": "The emergence of spatial organisation in biofilm growth is one of the most fundamental topics in biofilm biophysics and microbiology. It has long been known that growing biofilms can adopt smooth or rough interface morphologies, depending on the balance between nutrient supply and microbial growth; this ‘fingering’ transition has been linked with the average width of the ‘active layer’ of growing cells at the biofilm interface. Here we use long-time individual-based simulations of growing biofilms to investigate in detail the driving factors behind the biofilm-fingering transition. We show that the transition is associated with dynamical changes in the active layer. Fingering happens when gaps form in the active layer, which can cause local parts of the biofilm interface to pin, or become stationary relative to the moving front. Pinning can be transient or permanent, leading to different biofilm morphologies. By constructing a phase diagram for the transition, we show that the controlling factor is the magnitude of the relative fluctuations in the active layer thickness, rather than the active layer thickness per se. Taken together, our work suggests a central role for active layer dynamics in controlling the pinning of the biofilm interface and hence biofilm morphology.",
"introduction": "Introduction Biofilms are diverse in their morphology. Biofilms grown under flow can be smooth or rough, or even ‘mushroom-shaped’ 1 – 3 , while biofilms on liquid interfaces can show intricate wrinkly patterns 4 . Characterising distinct types of biofilm spatial structure, and the mechanisms by which they emerge, can lead to a better understanding of the underlying principles of this multicellular assembly process. It is also a prerequisite for understanding phenomena including genetic mixing and hence potential for cooperation, the extent of pathogen adhesion, as well as antibiotic penetration and the chances of fixation of antibiotic-resistant mutants 5 – 9 . Biofilm spatial structure is often characterised in terms of the interface roughness, i.e. the standard deviation of the biofilm height. From a mechanistic point of view, it is well established that the interplay between local growth and the nutrient concentration field is important in controlling interface roughness 10 – 13 . Dockery and Klapper 11 showed the existence of a fingering instability in which a local ‘bump’ on the growing interface tends to grow larger, since microbes in the ‘bump’ have better access to nutrients (diffusing from above) than those in adjacent areas; the growing bump then depletes nutrients from adjacent regions of the interface, further enhancing its growth. However, growth-generated pressure within the biofilm tends to fill in troughs in the biofilm interface, counteracting the tendency towards fingering 11 , 14 . The balance between nutrient supply and microbial growth clearly lies at the heart of the biofilm-fingering transition. Analysis of the reaction-diffusion equation for nutrient close to the growing biofilm led Dockery and Klapper to identify a dimensionless controlling parameter \\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$${({D}_{B}Y({k}_{S}+{S}_{{\\mathrm{bulk}}})/({L}_{y}^{2}\\rho {\\mu }_{{\\mathrm{max}}}))}^{\\frac{1}{2}}$$\\end{document} ( D B Y ( k S + S bulk ) / ( L y 2 ρ μ max ) ) 1 2 , which describes the balance between nutrient transport and microbial growth, and is associated with the distance that nutrient penetrates into the biofilm 11 . Here, L y is the horizontal system size, Y is the yield (units of biomass produced per unit of nutrient consumed), k S the nutrient concentration for half-maximal growth, S bulk is the nutrient concentration far from the biofilm, D B is the diffusion constant of nutrient within the biofilm and ρ is the biomass density within the biofilm. In earlier work using a cellular automaton model, Picioreanu et al. 13 had identified a similar parameter, but with S bulk in place of the factor ( k S + S bulk ) (and the system height in place of the lateral width). Nadell et al. 15 also proposed a related parameter combination with units of distance, the ‘active layer depth’, to describe the thickness of the layer of growing cells at the top of the biofilm. While the combined parameters identified in these works are different, they all express the idea that the extent of nutrient penetration into the biofilm, which depends on the balance between nutrient supply and growth, is central in controlling spatial structure. Growth occurs only in this ‘active layer’ close to the biofilm interface that has access to nutrients, while cells deeper within the biofilm are not able to grow 15 – 19 (Fig. 1 ). This phenomenon is observed in simulations 15 , 19 and experimental flow cells 18 , 20 as well as in in vivo samples 18 . In this study, we use individual-based simulations of growing biofilms to investigate in detail the connection between the active layer and the biofilm-fingering transition. We develop a computational method that allows us to simulate biofilm growth over long times, to obtain a clear picture of the steady-state spatial structure. We observe three qualitatively different types of biofilm growth, each with a distinct active layer behaviour and interface roughness trajectory. These growth types are distinguished qualitatively by their active layer dynamics. We show that the formation of gaps in the active layer can lead to local parts of the interface ‘pinning’, or becoming stationary and falling behind the advancing biofilm interface. These pinning sites, which can be transient or permanent, ultimately lead to the fingering of the interface. Therefore we argue that, while the average active layer thickness is important, active layer dynamics also play a key role in the fingering transition. Framing our results in the form of a phase diagram, we find that the biofilm pinning transition is controlled by the relative fluctuations in the active layer thickness, i.e. by the ratio between the standard deviation of the active layer thickness and its average. Interestingly, this corresponds closely with the combined parameter proposed by ref. 11 . Since the standard deviation reflects fluctuations in the active layer thickness, this supports the hypothesis that active layer dynamics play a key role in driving the spatial structure of the growing biofilm interface. Fig. 1 The concept of the active layer. A biofilm configuration generated in our simulations is shown, with the cells in the biofilm colour-coded according to their specific growth rate. The nutrient concentration field is shown on the blue scale. The nutrient is consumed by cells at the top of the biofilm, so that cells deeper in the biofilm are deprived of nutrients and do not proliferate. The active layer is defined as the layer of growing cells at the top of the biofilm (see Methods for further details).",
"discussion": "Discussion In this work, we used individual-based computer simulations to investigate the spatial structure of growing bacterial biofilms. Our simulations include the effects of local nutrient limitation (modelled via a reaction-diffusion equation) and mechanical pushing between the cells (modelled via a ‘shoving’ algorithm; see Methods). Varying the nutrient concentration and the maximal specific growth rate of the bacteria, we observed a diversity of biofilm morphologies, ranging from smooth to highly-fingered interfaces. The active layer of growing cells at the biofilm interface plays a central role in biofilm morphology; previous work has suggested that the balance between nutrient transport and consumption controls active layer thickness; this balance can be expressed by a dimensionless combined parameter 11 , 13 , 15 . Interestingly, in our simulations, the dimensionless parameter correlated better with the relative fluctuations of the active layer thickness than with the mean active layer thickness. This suggests that active layer dynamics play an important role in driving biofilm structure; a conclusion that is supported by a detailed analysis of our simulations. Collisions between local gaps in the active layer lead to interface pinning, in which a part of the interface stops growing relative to the rest of the biofilm. Interface pinning then leads to fingering. Our simulations could be classified into three ‘phases’ of biofilm growth. In the ‘unpinned’ phase, the interface is smooth and does not pin, and the active layer is thick and unbroken. The ‘transiently pinned’ phase is characterised by the appearance of transient local pinning sites along the interface and large temporal fluctuations in the interface roughness. In the ‘pinned’ phase, the interface develops fingers, which arise from pinning sites that appear but do not disappear; correspondingly, the interface roughness increases throughout the simulation. Using the coefficient of variation of the active layer thickness as a control parameter, we were able to plot a phase diagram for biofilm pinning. The finding that the coefficient of variation of the active layer thickness is a better control parameter than the mean strengthens our view that fluctuations in the active layer are important in controlling biofilm spatial structure. The form of the phase diagram can also tell us about the underlying nature of a phase transition. Statistical physics distinguishes ‘discontinuous’ transitions, in which the order parameter jumps discontinuously from zero to a finite value at a critical value of the control parameter, from ‘continuous’ transitions, in which the order parameter changes continuously from zero to a finite value as the control parameter varies (Supplementary Fig. 11) 25 . This distinction has relevance for the kinetics of the phase transition since, for equilibrium systems, a discontinuous transition implies that stochastic fluctuation is required to overcome an activation barrier (i.e. it is a nucleated process), while for a continuous transition, the transition happens spontaneously 25 (Supplementary Fig. 12) . A similar picture can hold for non-equilibrium phase transitions; see, e.g. ref. 31 . In practice, the distinction between discontinuous and continuous transitions can become blurred by finite size effects (one only observes a true discontinuity in the phase diagram for systems of infinite size) 32 . In our phase diagram, there is a large jump in the value of the order parameter between the transiently pinned and pinned phases (Fig. 7 c). Therefore, we tentatively suggest that this may be a discontinuous transition, with the apparent smoothing arising from finite size effects. In this discontinuous transition scenario, the transiently pinned phase would arise only in systems of finite size (including real biofilms), while a hypothetical biofilm of infinite lateral size would transition directly from the unpinned to the pinned state upon varying the control parameter. This might also suggest that the transition to a pinned state is a nucleation phenomenon (Supplementary Fig. 12) , such that a critical fluctuation, e.g. the appearance of a gap in the active layer that is wide enough that it does not close up again, may be needed to initiate biofilm fingering (Supplementary Fig. 12) . This point could be clarified in future simulations by systematically varying the system size. Our study is limited to parameter sets for which the active layer is thickness is at least several cell diameters. For extreme parameter sets (very small S bulk or very large μ max ), a different type of biofilm morphology can emerge, in which the fingers split into multiple branches. This phenomenon shows apparent similarity with diffusion-limited aggregation in statistical physics 33 , and may be worthy of investigation in future work. In this work, we have taken care to study biofilm growth over long times, once a steady state has been reached; this required the development of a computationally efficient clipping algorithm (see Methods and Supplementary Material) . We note that at earlier times in our simulations, the interface roughness can appear to reach a plateau (even in the pinned phase), before later increasing (Supplementary Fig. 13) . Therefore, in shorter simulations, it may be hard to know whether the true steady state has been reached. Our long-time simulations suggest that, in the pinned phase, the interface roughness does not, in fact, reach a steady state but rather continues to increase because the tips of the fingers continue to grow while the interface remains pinned at the troughs. In contrast, a finite steady-state roughness would correspond to an interface that has stalled in its net growth. From a practical point of view, the fact that the biofilm fingers continue to grow in our simulations presents computational issues since the use of our clipping algorithm is constrained in the case of fingered biofilms (see Methods). This means that while the steady state of the active layer dynamics and interface roughness behaviour can be reached, it remains challenging to reach the full steady state of the pinned interface fraction in the case of the pinned biofilms (see, e.g. Supplementary Fig. 8) . Our simulations are performed in two dimensions, for reasons of computational feasibility. The dimensionality of a system can have profound effects on phase transitions 24 : therefore, it will be important to determine in future work whether the same phenomena occur in 3D models. We also note that the representation of mechanical interactions in our simulations is rather crude (the iDynoMiCS algorithm simply resolves overlaps due to growth by a random ‘shoving’ algorithm; see Methods and ref. 21 ). Other studies have represented mechanical interactions in more detail 14 , 34 – 36 ; use of such algorithms may lead to deeper insight into the role of mechanical interactions in the pinning transition. Our work has an interesting analogy with pattern formation in crystal growth, where complex crystal morphologies arise from instabilities in the advancing solidification front 37 . The local rate of crystal growth can be limited by the rate of diffusion of heat away from the crystallisation front (e.g. crystallisation of small molecules or metals), or by the rate of diffusion of molecules to the growth front (e.g. in polymer crystallisation). This leads to fingering instabilities similar to the nutrient-driven fingering instability in biofilm growth 11 . The emergence of different crystal forms might also have parallels with the emergence of mutant clones in a biofilm. However, we note that in crystal growth, the morphological instability is ultimately limited by surface tension, which tends to smooth the interface 37 , while the limiting factor for biofilm growth is less clear (although it probably involves mechanical interactions). We also note that in a crystal, growth occurs only right at the interface, while for a biofilm, there is a growing region of finite thickness at the interface (the active layer). Our work also has a clear connection with the statistical physics of pinning-depinning transitions in interface growth. Here, diverse interface growth phenomena are grouped into a small number of ‘universality classes’, based on their scaling behaviour (the values of the exponents in plots of, e.g. roughness vs time). Phenomenological stochastic differential equations are then used to describe interface growth within a particular universality class; for example, the classical Kardar–Parisi–Zhang (KPZ) equation describes fluctuations of an unpinned growing interface, while the addition of a quenched noise term to the KPZ equation leads to a model for interface pinning. This ‘quenched KPZ (qKPZ)’ model shows a flat phase with no pinning sites, a pinned phase and an intermediate phase in which pinning sites are overcome 24 , 38 (although it is unclear whether this model predicts monotonically increasing roughness in the pinned phase). The qKPZ equation has been applied to biofilm growth 39 , 40 , but the source of the quenched noise is usually ascribed to external inhomogeneities in the environment. In our simulations, there are no such inhomogeneities; rather, pinning arises from the spontaneous emergence of gaps in the active layer. In the interface growth theory literature, KPZ-type models also exist where interface pinning arises from internal fluctuations in the growth process 38 , 41 , 42 , or where the growing interface is coupled to a non-equilibrium field (such as a nutrient field) 43 . However, the relevance of such models for bacterial biofilms and colonies has not been investigated. It is also possible that biofilm growth might be described by an alternative type of interface growth model, such as diffusion-limited aggregation 33 . While our focus here was on a more mechanistic analysis of the role of the active layer, it would certainly be interesting in future work to clarify the connection with interface growth theory by measuring the scaling exponents for biofilm growth in individual-based simulations. From a biological point of view, our simulations are, of course, highly simplified. Perhaps most importantly, our model does not include the extracellular matrix (EPS), which means that our simulated biofilms have a much higher cell density than flow cell biofilms formed by, e.g. Pseudomonas aeruginosa . We would also expect the mechanical properties of the biofilm to be strongly influenced by EPS 44 . This could affect the predictions of the model, since, for example, EPS-mediated interactions might act non-locally (between cells relatively far apart in the biofilm), which could alter the phase behaviour. Attachment and detachment of planktonic bacteria from the biofilm 6 , and possible external forces acting on the biofilm (e.g. from host tissue or mucus as in infected cystic fibrosis lung tissue 45 ) are also expected to strongly affect the spatial structure. We also neglect many other features of real biofilms, such as fluid flow, chemical signalling between cells and phenotypic changes associated with biofilm growth. The size of our simulated biofilms is also unrealistic. In order to reach the steady state, which is necessary for a rigorous analysis of the underlying physics, our simulations generate extremely thick biofilms, much thicker than those seen in experimental flow cell experiments. Routine characterisation of surface roughness in confocal laser-scanning microscopy images of flow-cell biofilms is now possible 46 , 47 . Previous experimental studies of biofilm development have mainly focused on early-stage biofilms, where mechanisms such as collective surface motion 48 , transitions between 1, 2 and 3-dimensional forms 22 , 49 , 50 and biofilm seeding from preformed aggregates 2 , 51 have been discussed. Motility can also play a role in later-stage biofilms, where mushroom-shaped structures can form in which non-motile bacteria form ‘stalks’ while motile bacteria form ‘caps’ 3 . Up to now, few studies of dynamical tracking of changes in biofilm structure, such as the formation and annihilation of bulges in the growing interface that we see in the ‘transiently pinned phase’, have been performed for mature biofilms. Our study suggests that such analysis, while technically challenging, could lead to interesting insights—although it is clear that biological mechanisms, including cell motility, that are not considered in our study, may prove to be important. Despite the simplicity of the model that has been studied in this work, our simulations reveal fundamental insights into the spatial structure of growing biofilms. Specifically, our work points to pinning of the growing interface, as a driver for spatial structure. Furthermore, our simulations reveal a key role for the dynamics of the active layer in driving the creation and annihilation of pinning sites at the biofilm interface, resulting in transitions in spatial structure, with drastic effects on the interface roughness."
} | 5,065 |
30890173 | PMC6423740 | pmc | 3,824 | {
"abstract": "Background Acetate is one of promising feedstocks owing to its cheap price and great abundance. Considering that tyrosine production is gradually shifting to microbial production method, its production from acetate can be attempted to further improve the economic feasibility of its production. Results Here, we engineered a previously reported strain, SCK1, for efficient production of tyrosine from acetate. Initially, the acetate uptake and gluconeogenic pathway were amplified to maximize the flux toward tyrosine. As flux distribution between glyoxylate and TCA cycles is critical for efficient precursor supplementation, the activity of the glyoxylate cycle was precisely controlled by expression of isocitrate lyase gene under different-strength promoters. Consequently, the engineered strain with optimal flux distribution produced 0.70 g/L tyrosine with 20% of the theoretical maximum yield which are 1.6-fold and 1.9-fold increased values of the parental strain. Conclusions Tyrosine production from acetate requires precise tuning of the glyoxylate cycle and we obtained substantial improvements in production titer and yield by synthetic promoters and 5′ untranslated regions (UTRs). This is the first demonstration of tyrosine production from acetate. Our strategies would be widely applicable to the production of various chemicals from acetate in future. Electronic supplementary material The online version of this article (10.1186/s12934-019-1106-0) contains supplementary material, which is available to authorized users.",
"conclusion": "Conclusions In this study, we demonstrated a method for efficient production of tyrosine from acetate. Initially, the SCK1 strain with amplification of the PEP to tyrosine synthetic pathway was utilized. To maximize carbon flux toward tyrosine, the acetate uptake and gluconeogenic pathways were additionally amplified. Although the overexpression of the linear pathway itself could not improve tyrosine production due to metabolic imbalance, rational pathway optimization could be achieved by precise regulation of the glyoxylate cycle. Finally, the engineered strain, SCKAPG4, produced 0.70 g/L tyrosine (a 1.6-fold increase compared to parental strain) with 20% of the theoretical maximum yield. Although the achieved titer is lower than previous studies (Additional file 1 : Table S1) and the engineered strains still produced more tyrosine using glucose (1.23 g/L, Additional file 1 : Fig. S3), the results are still promising to show the potential of acetate as an alternative feedstock. Future studies including acetate toxicity, related stress response, process optimization, and balancing between PEP and E-4-P would enhance the production further. In addition, these strategies will be utilized for producing other gluconeogenesis- and TCA-derived chemicals.",
"discussion": "Results and discussion Evaluation of SCK1 for tyrosine production from acetate Previously, we had reconstructed the tyrosine production pathway in SCK1, an E. coli K-12 W3110 strain using the synthetic promoters and UTRs (Table 1 ) [ 17 ]. Although we further overexpressed ppsA due to its crucial gluconeogenic activity during glucose utilization [ 17 ], the SCK1 strain without additional engineering was chosen as this gene is natively up-regulated during acetate assimilation [ 32 , 35 ]. Table 1 Bacterial strains and plasmids used in this study Name Description Source Strains E. coli Mach1-T1 R Cloning host Invitrogen SCK1 W3110 Δ tyrR aroG :: P BBa_J23100 -synUTR aroG - aroG fbr \n tyrA :: P BBa_J23100 -synUTR tyrA - tyrA fbr P aroABCDELtyrB -UTR aroABCDELtyrB :: P BBa_J23100 -synUTR aroABCDELtyrB [ 17 ] SCKE SCK1/pACYCduet-1 This study SCKA SCK1/pACA This study SCKP SCK1/pACP This study SCKAP SCK1/pACAP This study SCKDIAP SCK1 Δ iclR:: FRT-Km R -FRT/pACAP SCKAPG1 SCK1/pACAPG1 This study SCKAPG2 SCK1/pACAPG2 This study SCKAPG3 SCK1/pACAPG3 This study SCKAPG4 SCK1/pACAPG4 This study SCKAPG5 SCK1/pACAPG5 This study Plasmids pKD46 Red recombinase expression vector, Amp R [ 40 ] pM_FKF PCR template for FRT-Kan R -FRT, pMB1 ori, Amp R , Km R [ 12 ] pACYCduet-1 p15A ori, Cm R , E. coli expression vector Novagen pACA p15A ori, Cm R , P BBa_J23100 -synUTR acs - acs -Ter BBa_B1006 This study pACP p15A ori, Cm R , P BBa_J23100 -synUTR pck - pck -Ter BBa_B1006 This study pACAP p15A ori, Cm R , P BBa_J23100 -synUTR acs - acs -Ter BBa_B1006 -P BBa_J23100 -synUTR pck - pck -Ter BBa_B1006 This study pACAPG1 p15A ori, Cm R , P BBa_J23100 -synUTR acs - acs -Ter BBa_B1006 -P BBa_J23100 -synUTR pck - pck -Ter BBa_B1006 -P BBa_J23104 -synUTR aceA - aceA -Ter BBa_B1006 This study pACAPG2 p15A ori, Cm R , P BBa_J23100 -synUTR acs - acs -Ter BBa_B1006 -P BBa_J23100 -synUTR pck - pck -Ter BBa_B1006 -P BBa_J23118 -synUTR aceA - aceA - Ter BBa_B1006 This study pACAPG3 p15A ori, Cm R , P BBa_J23100 -synUTR acs - acs -Ter BBa_B1006 -P BBa_J23100 -synUTR pck - pck -Ter BBa_B1006 -P BBa_J23116 -synUTR aceA - aceA -Ter BBa_B1006 This study pACAPG4 p15A ori, Cm R , P BBa_J23100 -synUTR acs - acs -Ter BBa_B1006 -P BBa_J23100 -synUTR pck - pck -Ter BBa_B1006 -P BBa_J23109 -synUTR aceA - aceA - Ter BBa_B1006 This study pACAPG5 p15A ori, Cm R , P BBa_J23100 -synUTR acs - acs -Ter BBa_B1006 -P BBa_J23100 -synUTR pck - pck -Ter BBa_B1006 -P BBa_J23100 -synUTR aceA - aceA -Ter BBa_B1006 This study Amp ampicillin, Cm chloramphenicol, Km kanamycin; R resistance \n Initially, its ability for tyrosine production was investigated in modified minimal medium with 10 g/L acetate as the sole carbon source (Fig. 2 ). Although acetate is known to inhibit cellular growth [ 12 ], the SCK1 strain showed moderate cell growth (0.47/h) compared to the growth rate of the wild-type W3110 strain (0.45/h), even with overexpression of genes related to tyrosine production. After 30 h of fermentation, the strain successfully produced 0.43 g/L tyrosine by consuming all provided acetate. The yield was 11% of the theoretical maximum yield (0.375 g tyrosine/g acetate; see “ Methods ” section and Additional file 1 for the calculation). Fig. 2 Fermentation profile of the SCK1 strain. The left y -axis shows OD 600 and the right y -axis indicates concentration of the accumulated tyrosine; the y -offset indicates the concentration of the remaining acetate. The x -axis denotes time. Symbols: black circle, cell biomass (OD 600 ); red squares, tyrosine; blue diamonds, acetate. Error bars indicate the standard deviation from three independent cultures \n Effect of acs and pck overexpression on tyrosine production The precursors, PEP and E-4-P, should be readily available for enhancing tyrosine production [ 17 ]. However, the acetate utilization rate is known to be slower than the rate of glucose utilization [ 32 ] and it potentially decreases tyrosine production. To accelerate acetate assimilation, the acetate uptake pathway was amplified by overexpression of acs [ 11 , 12 , 36 ]. The synthetic expression cassette for acs was constructed with the strong constitutive promoter (P J23100 ) and synthetic 5′ UTR (Additional file 1 : Table S3) in a low copy plasmid (pACYCduet-1), and the final plasmid was named pACA. Then, the SCKA strain harboring the resulting pACA was cultivated to evaluate the effect of acs overexpression (Fig. 3 a, b). The introduction of pACYC yielded 1.4-fold enhanced acs expression indicating the successful overexpression (Additional file 1 : Figure S1a). Unlike our previous observations [ 12 , 13 ], overexpression of acs slightly reduced specific acetate consumption rate (a 1.2-fold, 0.18 g/g DCW/h) (Fig. 3 a, b). Nevertheless, tyrosine production was increased by a 1.2-fold to 0.53 g/L. Furthermore, the yield was notably increased (a 1.6-fold), suggesting efficient utilization of acetate. Fig. 3 Effect of the expression of acs and pck . Cell biomass ( a ) and tyrosine production ( b ) after 30 h cultivation. Error bars indicate the standard deviation from three independent cultures \n Next, we investigated the effect of overexpressing pck ; this gene was selected as it is known as a major reaction for PEP supplementation [ 32 ]. We constructed the SCKP strain (harboring a synthetic pck overexpression cassette in the pACP plasmid) in a similar manner that used for generating the SCKA strain. 1.4-fold increased level of pck expression was observed in SCKP strain (Additional file 1 : Figure S1b). In this case, the SCKP strain showed a marginal reduction in cell biomass (a 1.1-fold, 1.9 g DCW/L, Fig. 3 a) in addition to more reduced specific acetate consumption rate (a 1.7-fold, 0.13 g/g DCW/h, Table 2 ); these negative results were probably due to the reduced TCA cycle activity with a loss of oxaloacetate (Fig. 1 a, b). Even with the lowered acetate consumption, tyrosine production was maintained in a similar level (0.40 g/L, Fig. 3 b) with slightly higher yield (a 1.2-fold increase). This result indicates that carbon flux was directed to tyrosine synthesis by overexpression of pck . Table 2 Fermentation profile of engineered E. coli Strain Dry cell weight (g/L) Specific acetate consumption rate (g/g DCW/h) Acetate consumption (g/L) Tyrosine (g/L) Percentage yield (%) a SCK1 2.0 ± 0.1 0.22 ± 0.01 9.93 0.43 ± 0.03 11 ± 1 SCKA 2.0 ± 0.2 0.18 ± 0.00 8.33 0.53 ± 0.00 17 ± 0 SCKP 1.9 ± 0.2 0.13 ± 0.03 8.38 0.40 ± 0.02 13 ± 1 SCKAP 1.5 ± 0.4 0.14 ± 0.04 5.44 0.35 ± 0.05 17 ± 2 SCKAPG1 2.8 ± 0.1 0.21 ± 0.03 10.00 0.48 ± 0.02 9 ± 1 SCKAPG2 2.8 ± 0.2 0.21 ± 0.02 10.00 0.42 ± 0.10 12 ± 3 SCKAPG3 2.9 ± 0.1 0.19 ± 0.02 10.00 0.47 ± 0.01 12 ± 0 SCKAPG4 2.8 ± 0.2 0.21 ± 0.02 10.00 0.70 ± 0.11 20 ± 3 SCKAPG5 2.6 ± 0.1 0.20 ± 0.01 10.00 0.49 ± 0.04 12 ± 1.0 SCKDIAP 1.0 ± 0.2 0.05 ± 0.00 4.79 0.33 ± 0.01 18 ± 1 a Percentage yield indicates the ratio of actual yield to theoretical maximum yield expressed in percentage (%) \n The effect of combined strategies was also investigated. Both synthetic expression cassettes were integrated into a single plasmid to obtain pACAP. Higher gene expression of each gene was maintained in the SCKAP strain (SCK1 harboring the pACAP plasmid, Additional file 1 : Figure S1a, b). Similar to the observations above, the SCKAP strain showed low cell biomass (a 1.3-fold decrease, 1.5 g DCW/L) and specific acetate consumption (a 1.6-fold decrease, 0.14 g/g DCW/L). Consequently, 1.2-fold decreased tyrosine production was also observed (0.35 g/L). However, the yield and intracellular PEP level were improved (a 1.7-fold increase and 2.3-fold, respectively) compared to SCK1 strain (Additional file 1 : Figure S2) which implies that tyrosine production pathway was amplified. To enhance tyrosine production, we believed that reduced biomass formation and acetate consumption should be recovered by pathway optimization. Tuning the glyoxylate cycle by varying the expression of aceA for improved tyrosine production We attempted to additionally activate glyoxylate cycle to enhance the PEP availability. However, it should be elaborately controlled to maintain sufficient generation of ATP and NADH via the TCA cycle (Fig. 1 b) [ 34 ]. The precise flux control could be implemented by varying the transcriptional efficiency of aceA encoding the first enzyme of glyoxylate cycle [ 12 , 34 ]. The aceA expression cassettes, with a series of constitutive promoters (J231 series), were integrated into the pACAP plasmid, resulting in the pACAPG1-5 plasmids (Table 1 ). The SCKAPG1-5 strains (SCK1 harboring pACAPG1-5, respectively, Table 1 ) showed successfully diversified AceA activity of up to 8.3-fold (Fig. 4 a). As shown in Fig. 4 b, the activation of the glyoxylate cycle may lead to an increase in cell biomass, as the activated anaplerosis recovered cell biomass formation. In particular, the slight difference in AceA activity between strains SCKAP and SCKAPG1 was sufficient for stimulating cell growth (a 1.8-fold increase, 2.8 g DCW/L), as no notable increase in cell biomass was observed with higher AceA activity. In contrast, tyrosine production was critically affected by AceA activity, and maximum tyrosine production was observed in the SCKAPG4 strain with the second highest AceA activity (Fig. 4 c, d). In addition, the SCKAPG5 strain with maximum AceA activity showed lower tyrosine production than the SCKAPG4 strain (Fig. 4 c). These results suggest that the optimal flux redistribution around the glyoxylate cycle and TCA cycle achieved by regulating aceA expression is critical for tyrosine production. The SCKAPG4 strain showed 0.70 g/L tyrosine production, which is 2.0-fold higher than that of the SCKAP strain and 1.6-fold higher than that of the SCK1 strain. The acetate consumption rate was substantially recovered (Table 2 ) and the yield was also significantly increased to 20% of the theoretical maximum (Table 2 ) [ 17 ]. Furthermore, a 5.4-fold increased intracellular PEP level was observed compared to SCK1 strain as we expected (Additional file 1 : Figure S2). Collectively, these results indicate that carbon flux distribution can be successfully used for efficient tyrosine production from acetate. Fig. 4 Effect of glyoxylate cycle activation. Comparison of normalized specific isocitrate lyase activity measured at 12 h ( a ), and cell biomass ( b ) and tyrosine production ( c ) after 30 h cultivation, and the fermentation profile of the SCKAPG4 strain ( d ). The left y -axis shows OD 600 and the right y-axis indicates the concentration of accumulated tyrosine; the y -offset indicates the concentration of the remaining acetate. The x -axis denotes time. Symbols: black circle, cell biomass (OD 600 ); red squares, tyrosine; blue diamonds, acetate. Error bars indicate the standard deviation from three independent cultures \n Comparison with iclR deletion approach Finally, we compared our strategy with the conventional strategy for activation of the glyoxylate cycle, the deletion of iclR [ 37 ]. The SCKDIAP strain (SCKAP with iclR deletion) showed significantly increased AceA activity (Fig. 4 a); however, severely reduced cell biomass (a 1.4-fold decrease, 1.5 g DCW/L, Fig. 4 b) and tyrosine production (a 1.1-fold decrease, 0.33 g/L, Fig. 4 c) were observed compared to the SCKAP strain. These parameters are lower even compared to the SCKAPG3 strain with similar AceA activity. This can be explained by the regulation of iclR ; activation of aceK, encoding isocitrate dehydrogenase kinase/phosphatase, blocks the oxidative flux in TCA cycle [ 37 ], resulting in severe imbalance in tyrosine production. Overall, these results indicate that our precise tuning strategy is more efficient and has considerable potential for chemical production [ 12 , 34 ]."
} | 3,680 |
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