pmid stringlengths 8 8 | pmcid stringlengths 8 11 ⌀ | source stringclasses 2
values | rank int64 1 9.78k | sections unknown | tokens int64 3 46.7k |
|---|---|---|---|---|---|
31119081 | PMC6510217 | pmc | 4,589 | {
"abstract": "The Legume-Rhizobium symbiosis has been proposed as a promising technique for the phytoremediation of contaminated soils due to its beneficial activity in symbiotic nitrogen fixation. However, numerous studies have shown that excessive heavy metals reduce the efficiency of symbiotic nodulation with Rhizobium and inhibit plant growth. In this study, we aimed to evaluate the synergistic effects of IAA-producing bacteria and Rhizobium on Medicago lupulina growth under Cu and Zn stress. Pot experiments showed that 400 mg kg −1 Cu 2 + and Zn 2 + greatly inhibited plant growth, but dual inoculation of Medicago lupulina with Sinorhizobium meliloti CCNWSX0020 and Agrobacterium tumefaciens CCNWGS0286 significantly increased the number of nodules and plant biomass by enhancing antioxidant activities. Under double stress of 400 mg kg −1 Cu 2 + and Zn 2 + , the nodule number and nitrogenase activities of dual-inoculated plants were 48.5% and 154.4% higher, respectively, than those of plants inoculated with Sinorhizobium meliloti . The root and above-ground portion lengths of the dual-inoculated plants were 32.6% and 14.1% greater, respectively, than those of the control, while the root and above-ground portion dry weights were 34.3% and 32.2% greater, respectively, than those of the control. Compared with S. meliloti and A. tumefaciens single inoculation, coinoculation increased total Cu uptake by 39.1% and 47.5% and increased total Zn uptake by 35.4% and 44.2%, respectively, under double metal stress conditions. Therefore, coinoculation with Sinorhizobium meliloti and Agrobacterium tumefaciens enhances metal phytoextraction by increasing plant growth and antioxidant activities under Cu/Zn stress, which provides a new approach for bioremediation in heavy metal-contaminated soil.",
"conclusion": "Conclusion Excessive heavy metal decreases nodulation and reduces nitrogenase activity of M. lupulina . The coinoculation of host plants with S. meliloti and PGPB A. tumefaciens alleviated heavy metal toxicity and promoted plant growth. These two strains have one or more properties of nitrogen fixation, copper and zinc resistance and IAA production. It is likely that coinoculation improved plant growth via these properties. When grown in medium containing high concentrations of Cu and Zn, we found that the roots of the coinoculated plants accumulated more Cu and Zn. The results confirm the importance of coinoculation to improve the heavy metal tolerance of plants and promote plant growth and phytostabilization efficiency.",
"introduction": "Introduction Pollution of the biosphere by heavy metals, such as copper and zinc, has increased dramatically since industrial production and extensive use of chemical fertilizers and pesticides, as well as the use of industrial waste waters for irrigation ( Ampofo & Awortwe, 2017 ; Malinowska & Jankowski, 2017 ). Although copper and zinc are essential trace elements for most living organisms, as they participate in electron transport, redox, and other metabolic reactions, excess copper or zinc could induce various morphological, physiological, and biochemical dysfunctions directly or indirectly in organisms. The most frequent and common consequence of copper or zinc toxicity to cells is to produce excessive reactive oxygen species (ROS). ROS can disrupt the redox status of cells and cause oxidative stress, leading to lipid peroxidation, membrane dismantling and damage to DNA, proteins and carbohydrates ( Ajina et al., 2017 ; Zhang et al., 2016 ). It is necessary to remove excessive copper or zinc from contaminated soil. Many methods such as immobilization through pH alterations, removal, sequestration, and phytoextraction have been tested to extract heavy metal pollutants from contaminated soil ( Saavedra et al., 2018 ; Bano et al., 2018 ; Huang et al., 2016 ). In these methods, the unique ability of plants to remediate heavy metals has been widely investigated. Phytoremediation is using plants to take up heavy metals from the soil and accumulate metals in their tissue ( Mleczek et al., 2017 ; Rezania et al., 2015 ). However, plants are not necessarily tolerant and may be subject to metal toxicity at high metal concentrations, leading to low biomass and poor remediation efficiency in high-concentration heavy metal-contaminated soil ( Ebbs & Kochian, 1997 ). Therefore, it is a great challenge to search for a way to promote plant growth in heavy metal-contaminated soil. Rhizospheric microorganisms play an important role in plant nutrition, mineral dissolution and the production of plant growth-promoting substances ( Paungfoolonhienne et al., 2014 ; Tashi-Oshnoei, Harighi & Abdollahzadeh, 2017 ). If plant biomass is increased, their net capacity to extract metals from soil is also improved; hence, better growth of plants greatly improves phytoremediation efficiency. Fatima and Ahmed found that Bacillus cereus significantly reduced the deleterious effects of Cr and promoted the growth of Lens culinaris growing in a chromium-contaminated environment ( Fatima & Ahmed, 2018 ). In addition, several attempts have been made to illustrate the importance of endophytic bacteria on plant growth promotion and phytoremediation ( Kong et al., 2017 ; Kuramshina, Smirnova & Khairullin, 2018 ; Ali et al., 2017 ). Among these plant growth-promoting rhizobacteria (PGPR), N 2 -fixing soil bacteria, namely, Rhizobia, are well known for their ability to establish symbiotic associations with legumes and develop into the structures called root nodules ( Barauna et al., 2016 ). Thus, the nitrogenase complex catalyzes the ATP-dependent reduction of N 2 to ammonium in root nodules ( Khadka et al., 2017 ). Legume plants-Rhizobium symbiotic systems play a key role in enhancing the nitrogen pool of soil, leading to an increase in biomass and accumulation of heavy metals in contaminated soil ( Hao et al., 2014 ; Pajuelo et al., 2011 ). The dry weight and nitrogen content of peas inoculated with Rhizobium sp . Rp15, isolated from heavy metal-polluted soil, were more than those of the control group when they grew in the Ni 2+ - and Zn 2+ -contaminated soil ( Wani, Khan & Zaidi, 2008 ). Many environmental conditions, such as drought stress ( Staudingera et al., 2016 ), extreme temperature ( Ryalls et al., 2013 ; Peltzer, Abbott & Atkins, 2002 ), salinity ( Latrach et al., 2014 ) and the presence of heavy metals ( Klimek-kopyra et al., 2015 ), singly or in combination, could affect nodule development, legume plants growth and finally plant biomass. Thus, the effects of Rhizobium on legume plant growth promotion under high soil metal contamination are not always satisfactory, and remediation efficiency is still relatively low ( Klimek-kopyra et al., 2015 ; Sanchezpardo, Fernandezpascual & Zornoza, 2012 ). An alternative to single inoculation of Rhizobium for enhancing plant growth has been to use mixed inoculation or coinoculation to improve plant growth ( Kumar et al., 2017 ; Haro et al., 2018 ). A large number of such studies have reported that coinoculation could promote plant growth and increase metal accumulation capacity in plant tissue. Coinoculation of Acinetobacter sp. RG30 and Pseudomonas putida GN04 with phosphorus-soluble, IAA-producing and siderophore-producing bacteria significantly increased Cu extraction by maize ( Rojas-Tapias, Bonilla & Dussán, 2014 ). Most of these studies were conducted for single heavy metals and low concentrations of heavy metals in soil. In fact, metal-contaminated soils are mostly caused by more than two kinds of heavy metals, and some heavy metal concentrations are very high in the soil. The ability of Rhizobium to convert nitrogen into ammonia is relevant for plant nutrition since nitrogen is an essential and sometimes limiting nutrient for plant growth in heavy metal contaminated soil. We therefore selected two bacterial strains with complementary functions (N 2 fixing and Cu-resistant Sinorhizobium meliloti CCNWSX0020 and indole-producing and Zn-resistant Agrobacterium tumefaciens CCNWGS0286) as experimental subjects. The aims of this study were to (1) study the effects of coinoculated bacteria on the metal tolerance of Medicago lupulina under high concentrations of Cu or Zn stress; (2) study the effects of coinoculation on Medicago lupulina growth and Cu/Zn uptake under dual stress of high concentrations of Cu and Zn; and (3) by measuring the activity of antioxidant enzymes, identify the possible mechanism of coinoculation of S. meliloti and A. tumefaciens in alleviating Cu and Zn stress in plants. The results provide some insight into how coinoculation affected the antioxidant activity of host plants and enhanced legume defense systems to excess Cu/Zn, thus providing an efficient strategy to facilitate the ability of host plants to remediate heavy metal-contaminated soil.",
"discussion": "Discussion Many kinds of transition metals, such as copper and zinc, are essential elements for plant growth. These transition metals are involved in a wide variety of metabolic pathways at low concentrations. However, they are toxic to plants if their concentrations exceed normal levels ( Seregin & Kozhevnikova, 2006 ). In this study, seedling growth was significantly inhibited when 400 mg kg −1 Cu 2+ and Zn 2+ were applied ( Figs. 1 and 2 ). The results were consistent with those for lead and cadmium, which decreased root length, shoot length and percent germination ( Hassan et al., 2016 ). The decline in M. lupulina biomass was presumably due to root damage or oxidative stress caused by excess Cu and Zn ( Liu et al., 2018 ). The adverse effects reduced the uptake of essential mineral nutrients, altered water balance, and decreased the activity of various enzymes and chlorophyll ( Muradoglu et al., 2015 ; Alyemeni et al., 2018 ; Li et al., 2015 ). Plants maintain many complex relationships with diverse soil organisms, such as bacteria, protozoa, fungi, nematodes and annelids, living around them ( Gange, Eschen & Schroeder, 2012 ). When seeds germinate and plants grow, they typically acquire specific bacteria and fungi that exist in the native soil. The influence of soil microorganisms on soil quality and plant health has recently received more emphasis. These microorganisms can promote plant acquisition of nutrients ( Vimal et al., 2017 ), mineralization of organic phosphorus ( Meyer et al., 2017 ), and production of phytohormones ( Kurepin et al., 2015 ), alleviating the negative effects of environmental stress. Some metal-resistant microorganisms could also promote plant growth under heavy metal stress conditions; thus, more plant biomass increases the efficiency of phytoremediation. Dabrowska et al. (2017) observed that bacterial strains could promote plant growth by Brassica napus L. under Cd, Cu, Pb and Zn stresses. Therefore, plant growth-promoting bacteria (PGPB) have been widely used to increase the capacity of host plants to tolerate and absorb heavy metals from soil ( Kamran et al., 2017 ). In the current study, all treatments of PGPB either alone or in combination showed significant positive influences on plant growth under Cu and Zn stress ( Fig. 4 ). Although either single or dual inoculations of M. lupulina with the bacteria also increased the dry weight and length of plant roots and aboveground portions compared with those of uninoculated controls, dual inoculation of S. meliloti and A. tumefaciens produced significantly more dry weight than single inoculation of any bacteria ( Figs. 3 and 4 ). PGPB are able to promote plant growth through one or several mechanisms, such as the production of phytohormones such as indole-3-acetic acid (IAA), siderophores, ACC-deaminase and phosphate solubilization ( Yu et al., 2017 ; Chandra et al., 2018 ). PGPB also enhance symbiotic nitrogen fixation through promoting root development in general and root hair formation in particular, resulting in more potential colonization sites for rhizobia ( Ahemad & Kibret, 2014 ). Based on the analysis of the features of A. tumefaciens CCNWGS0286 in previous work, this bacterium could produce high levels of IAA even under 1.0 mM Zn stress ( Hao et al., 2012 ). Although other plant growth-promoting traits, such as acetoin production, ACC deaminase activity, and organic P solubilization abilities were not detected, A. tumefaciens CCNWGS0286 could still promote the growth of Robinia pseudoacacia under heavy metal stress ( Hao et al., 2012 ). Additionally, S. meliloti CCNWSX0020 can establish a normal symbiotic relationship with the host plant under Cu stress since it is tolerant to excess Cu, and nitrogenase activity could be detected in this study, indicating that effective nodules were formed under Cu and Zn stress ( Fig. 5 ). However, the N content in M. lupulina showed no significant increase compared with that in control plants under Cu and Zn stress ( Fig. S1 ). This might be because the negative effects of heavy metals reduced the number of nodules and decreased nitrogenase activity. The positive effects of coinoculation of M. lupulina with S. meliloti and A. tumefaciens were also observed. A significant increase in N content in plants was detected after coinoculation of S. meliloti and A. tumefaciens, which significantly increased the N content of the above-ground portion by 19.1% compared to that of control in the presence of 400 mg kg −1 Cu 2+ and Zn 2+ . Similar results were observed by Sepulvedacaamano et al. (2018) , who found that coinoculation of lentil with Rhizobium leguminosarum bv. viciae AG-84 and Pseudomonas umsongensis LY50a improved the progression of nodulation by 85.0% and increased nitrogen fixation in comparison to those of rhizobia inoculation alone. Coinoculation leading to high biomass might be attributed to IAA produced by plant growth-promoting bacteria. IAA increases plant cell division, enlarges the root system and the number of Rhizobium-infection sites ( Tanimoto, 2005 ), and subsequently promotes nitrogen uptake and plant growth. To analyze effects on heavy metal accumulations in M. lupulina of single or synergistic inoculation with S. meliloti and A. tumefaciens under Cu/Zn stress, the total metal uptake in plant tissues was measured. The results showed that inoculation with either A. tumefaciens or S. meliloti significantly increased the total uptake of Cu or Zn in plants under single metal stress ( Table 2 ). This result might be due to the fact that the bacteria we used could promote plant growth by producing plant growth-regulating substances, N fixation or effects on metal solubility and bioavailability, all of which affect metal uptake ( Pajuelo et al., 2011 ). However, under double metal stresse conditions, the total metal absorbed by the plants under different inoculation treatments was less than that under single metal stress, except for Zn uptake by plants inoculated with A. tumefaciens and S. meliloti . Our results also indicated that there was no significant difference in the amount of heavy metals absorbed by noninoculated and single-inoculated plants under 400 mg kg −1 Cu and Zn stress. Compared with S. meliloti and A. tumefaciens single inoculation, coinoculation increased total Cu uptake by 39.1% and 47.5% and increased total Zn uptake by 35.4% and 44.2%, respectively ( Table 2 ). We speculated that the growth of single bacterium was inhibited under the combined stress of two high concentrations of heavy metals, thereby reducing beneficial effects on plants. Metal-resistant complementary bacteria ( S. meliloti tolerant to Cu and A. tumefaciens tolerant to Zn) could survive and increase nutrition, plant biomass and tolerance of plants to Cu/Zn stress ( Fatnassi et al., 2015 ). Excessive heavy metals not only hamper metabolic processes in plant cells, but also increase the generation of reactive oxygen species (ROS) such as superoxide free radicals (O 2 − ), hydroxyl free radicals (OH − ), and hydrogen peroxide (H 2 O 2 ) through Fenton-like reactions ( Tsang et al., 1996 ). The elevated levels of ROS can cause oxidative stress via disturbing the redox status equilibrium in plant cells. Usually, superoxide dismutase (SOD), catalase (CAT), ascorbate peroxidase (APX), and glutathione reductase (GR) are protective enzymes of the glutathione (GSH) peroxidase system to handle external oxidative stress, which can directly or indirectly clear intracellular excessive ROS. Becana et al. (2000) suggested that the promotion of plant antioxidant defenses by rhizobia may improve symbiotic performance, especially under nonoptimal conditions. Kong et al. (2015) showed that the expression level of antioxidant genes, i.e., CuZnSODc, CuZnSODp, CAT, and APXc, in plants increased in the presence of excess Cu(II) when the plants were inoculated with Rhizobium. We detected the activity of CAT, APX and SOD in different plant tissues and found that the enzyme activities of the coinoculated plants were enhanced in comparison with those of noninoculated or single-inoculated plants at different growth stages under Cu/Zn stress ( Fig. 6 ). In addition, S. meliloti could produce acidic exopolysaccharides, which act as a diffusion barrier against H 2 O 2 ( Davies & Walker, 2007 ). Thus, it is speculated that coinoculation leads to increased nitrogen nutrition and antioxidant enzyme activities, thereby alleviating heavy metal toxicity and enhancing metal ion accumulation in plant tissue."
} | 4,379 |
32381722 | null | s2 | 4,590 | {
"abstract": "Nature integrates complex biosynthetic and energy-converting tasks within compartments such as chloroplasts and mitochondria. Chloroplasts convert light into chemical energy, driving carbon dioxide fixation. We used microfluidics to develop a chloroplast mimic by encapsulating and operating photosynthetic membranes in cell-sized droplets. These droplets can be energized by light to power enzymes or enzyme cascades and analyzed for their catalytic properties in multiplex and real time. We demonstrate how these microdroplets can be programmed and controlled by adjusting internal compositions and by using light as an external trigger. We showcase the capability of our platform by integrating the crotonyl-coenzyme A (CoA)/ethylmalonyl-CoA/hydroxybutyryl-CoA (CETCH) cycle, a synthetic network for carbon dioxide conversion, to create an artificial photosynthetic system that interfaces the natural and the synthetic biological worlds."
} | 235 |
23278742 | null | s2 | 4,591 | {
"abstract": "The immune system is made up of a diverse collection of cells, each of which has distinct sets of triggers that elicit unique and overlapping responses. It is correctly described as a 'system' because its overall properties (e.g. 'tolerance', 'allergy') emerge from multiple interactions of its components cells. To mobilize a response where needed, the majority of the cells of the system are obligatorily highly motile and so must communicate with one another over both time and space. Here, we discuss the flexibility of the primary immunological synapse (IS) with respect to motility. We then consider the primary IS as an initiating module that licenses 'immunological circuits': the latter consisting of two or more cell-cell synaptic interactions. We discuss how two or three component immunological circuits interact might with one another in sequence and how the timing, stoichiometry, milieu, and duration of assembly of immunological circuits are likely to be key determinants in the emergent outcome and thus the system-wide immune response. An evolving consideration of immunological circuits, with an emphasis on the cell-cell modules that complement T-antigen-presenting cell interaction, provides a fundamental starting point for systems analysis of the immune response."
} | 321 |
39129761 | PMC11310149 | pmc | 4,592 | {
"abstract": "Forage maize is a versatile crop extensively utilized for animal nutrition in agriculture and holds promise as a valuable resource for the production of fermentable sugars in the biorefinery sector. Within this context, the carbohydrate fraction of the lignocellulosic biomass undergoes deconstruction during ruminal digestion and the saccharification process. However, the cell wall’s natural resistance towards enzymatic degradation poses a significant challenge during both processes. This so-called biomass recalcitrance is primarily attributed to the presence of lignin and ferulates in the cell walls. Consequently, maize varieties with a reduced lignin or ferulate content or an altered lignin composition can have important beneficial effects on cell wall digestibility. Considerable efforts in genetic improvement have been dedicated towards enhancing cell wall digestibility, benefiting agriculture, the biorefinery sector and the environment. In part I of this paper, we review conventional and advanced breeding methods used in the genetic improvement of maize germplasm. In part II, we zoom in on maize mutants with altered lignin for improved digestibility and biomass processing.",
"conclusion": "Conclusion and future perspectives Lignin plays a fundamental role in various agronomically relevant traits. Lignin offers tolerance against biotic stresses, provides structural support to stems to prevent lodging, yet it also acts as a biomass recalcitrance factor limiting cell wall degradability. Current evidence suggests that reducing lignin content in maize can be achieved without compromising plant growth and biomass yield by choosing the right gene or by introgression of the corresponding mutation into the most appropriate genetic background. There is still considerable potential for new lignin-engineering strategies in maize, as many genes involved in lignin biosynthesis and its regulation have yet to be investigated ( Barrière et al., 2015 ; Penning et al., 2019 ). Genomic studies have shown that lignin biosynthesis genes are often part of multigene families, of which in most cases only a single member has been analyzed. Knocking-out multiple members of the same gene family, which has only now become possible by CRISPR-based gene editing, is a promising strategy to further improve the processability of lignocellulosic biomass. In addition, CRISPR-based gene editing allows the generation of a range of alleles, not only knock-out alleles, but also weak alleles that should allow the tuning of lignin content and composition without affecting yield, as illustrated by editing CCR2 in poplar ( De Meester et al., 2020 ). Furthermore, by analyzing lignin mutants and transgenics, it has become clear that lignin is tolerant towards large compositional shifts. This opens perspectives to redirect the lignin pathway towards the overproduction of rare natural monomers, or even to let the plant synthesize alternative monomers by expressing exotic genes. For example, ectopic overexpression of the two penultimate genes of the scopoletin biosynthesis pathway in lignifying cells in Arabidopsis has resulted in the incorporation of scopoletin into the lignin polymer, making it more susceptible to alkaline pretreatments ( Hoengenaert et al., 2022 ). Several other examples and strategies exist and have been reviewed ( Mottiar et al., 2016 ; Vanholme et al., 2019 ; de Vries et al., 2021 ). For future research and applications, a few critical factors need to be considered with regard to the genotype to work with, and the environment where the experiments are carried out. First, it needs to be recognized that most studies on the effects of lignin engineering on digestibility have been conducted in greenhouses. The main reason for this is that the effect of a genetic modification is easier to determine in a stable greenhouse environment. Experiments with transgenic plants under field conditions require a regulatory permit, which discourages most researchers from conducting field trials. Nevertheless, field trials are essential to validate the effect of the mutation. Field trials represent a realistic environment with fluctuating weather conditions (wind, UV radiation, rain, drought), exposure to abiotic stressors and variations in soil type and soil microorganisms. Furthermore, the planting density in the field differs from that of plants grown in pots under the controlled greenhouse settings. Thus, results obtained in the greenhouse may not always align with observations made under field conditions ( Tuberosa, 2012 ; Nelissen et al., 2014 ). When improving a commercially relevant trait, it is therefore advisable to conduct the experiments directly in the field to select the most promising mutants, and only then study their more subtle effects in a greenhouse setting for purely scientific purposes. Second, it is important to recognize that genotypes typically used in experiments and transformation (laboratory strains) are not optimized for cell wall degradability. As a result, genomic modifications can lead to substantial improvements in the digestibility in these genotypes, whereas the improvement does not necessarily translate into elite inbreds or their hybrids. It will be essential to either edit elite inbred lines if transformation protocols exist for them, or to use other methods such as transgenerational editing. Importantly, when the mutations are recessive, both elite inbred lines will need to be edited in order to create a mutant hybrid. Only when the mutant hybrid outperforms, and preferentially in different environments, the strategy can be considered successful. Navigating the future of maize breeding requires embracing cutting-edge gene editing technology. By addressing the challenges associated with genotype selection and field validation, we can adopt a sharper approach to develop modern maize varieties that contribute to a sustainable agriculture and bio-based economy.",
"introduction": "Introduction Maize, also known as corn, plays a multifaceted role in agriculture, serving as a crucial resource for food, feed and the production of basic chemicals such as fuels, plastics, etc. Maize silage represents the areal part of maize – including leaves, stems, cobs and seeds – stored under anaerobic conditions for conservation, to be used as ruminant feed ( Barrière, 2017 ). The primary energy source in maize silage comes from starch in the kernels and from the cell wall carbohydrates. The cell wall, in the animal feed field known as neutral detergent fiber (NDF), is composed mainly of two types of carbohydrates, cellulose and hemicellulose, and the aromatic heteropolymer, lignin. NDF digestibility is influenced by the genetic background, the environmental conditions, field management and timing of harvest, and typically varies between 40 to 50% ( Allen et al., 2003 ; Barrière, 2017 ). At the cell wall level, NDF digestibility is primary influenced by the lignin content, lignin composition and the ferulate cross-linkages between lignin and hemicelluloses ( Wolf et al., 1993 ; Fontaine et al., 2003 ; Grabber et al., 2009 ; Courtial et al., 2013 ; Barrière, 2017 ). Consequently, breeding efforts to improve the NDF digestibility often target the lignin characteristics \n 1 \n . Besides its use as ruminant feed, maize lignocellulosic biomass (i.e., maize stover consisting of the leaves, stems and cobs without seeds) has been identified as a promising resource for the biorefinery ( Torney et al., 2007 ; Vermerris et al., 2007 ; Lorenzana et al., 2010 ; van der Weijde et al., 2013 ; Torres et al., 2015b ; Saratale et al., 2019 ). During biorefining, the carbohydrate fraction of the lignocellulosic biomass is enzymatically deconstructed into primary sugars, a process called saccharification. These primary sugars can then be used in fermentation reactions to produce renewable materials and biofuels ( Lips, 2022 ). Similar to ruminal digestibility, the intrinsic resistance of the maize stover to enzymatic degradation, also known as biomass recalcitrance, is mainly caused by the presence of lignin ( Torres et al., 2015a ). Therefore, breeding towards a reduced lignin amount, an altered lignin composition, or an altered interaction between lignin and hemicelluloses can be advantageous to improve both feed digestibility and the industrial saccharification process ( Torres et al., 2016 ). In this review, we start by providing a brief history of conventional maize breeding methods, followed by an overview of more advanced breeding strategies. Next, we focus on maize mutants and transgenic lines with a modified lignin content and composition, and their effect on digestibility and biomass processing efficiency."
} | 2,178 |
38984837 | PMC11632578 | pmc | 4,593 | {
"abstract": "Abstract Renewable chemicals, which are made from renewable resources such as biomass, have attracted significant interest as substitutes for natural gas‐ or petroleum‐derived chemicals to enhance the sustainability of the chemical and petrochemical industries. Polybutylene adipate terephthalate (PBAT), which is a copolyester of 1,4‐butanediol (1,4‐BDO), adipic acid (AA), and dimethyl terephthalate (DMT) or terephthalic acid (TPA), has garnered significant interest as a biodegradable polymer. This study assesses the non‐biological production of PBAT monomers from biomass feedstocks via heterogeneous catalytic reactions. The biomass‐based catalytic routes to each monomer are analyzed and compared to conventional routes. Although no fully commercialized catalytic processes for direct conversion of biomass into 1,4‐BDO, AA, DMT, and TPA are available, emerging and promising catalytic routes have been proposed. The proposed biomass‐based catalytic pathways toward 1,4‐BDO, AA, DMT, and TPA are not yet fully competitive with conventional fossil fuel‐based pathways mainly due to high feedstock prices and the existence of other alternatives. However, given continuous technological advances in the renewable production of PBAT monomers, bio‐based PBAT should be economically viable in the near future.",
"introduction": "1 Introduction Green chemistry research has focused on the green synthesis of chemicals and polymers. \n [1] \n This trend underscores a commitment to sustainable methodologies with a specific focus on mitigating environmental impact and fostering resource efficiency. In the context of the green synthesis of chemicals and polymers, methodologies involving benign reagents, renewable feedstocks, and energy‐efficient processes are gaining prominence. The primary focus has been to minimize or eliminate the utilization and generation of hazardous substances, coupled with a strategic effort to avoid inefficiencies and the production of unwanted materials, which embodies a proactive approach towards waste reduction. In particular, special attention needs to be given to polymers in this context. Although synthetic polymers are remarkably involved in the comfort and facilitation of human life and have indispensable roles in industrial activities, \n [2] \n they have a detrimental impact on ecosystems and are considered as “a rapidly increasing, long‐term threat”. \n [3] \n \n Bioplastics currently represent approximately 0.5 % of the over 400 million tons of annual plastic production. \n [4] \n Polybutylene adipate terephthalate (PBAT) has emerged as a pivotal player in the quest for sustainable and eco‐friendly materials, as it is a promising alternative to traditional plastics, helping to address the global challenge of plastic pollution with low recycling rate of <10 %. \n [5] \n Moreover, PBAT can readily be produced at large scale, having the physical properties required for making flexible films that rival those from conventional plastics. \n [5] \n In 2023, the global production capacity of PBAT accounted for 8.9 % of global biodegradable bioplastic production capacity (Figure 1 ). \n [6] \n PBAT is commonly used in various industries including food packaging, agricultural, and textile. \n [7] \n PBAT often blends with other biodegradable polymers (e. g., polylactic acid (PLA)) to enhance mechanical and thermal properties the final product (e. g., biodegradable films). \n [8] \n For example, the mulch films made by blending PBAT and PLA (blending ratio may vary according to manufacturers) are widely utilized to improve soil conditions and enhance crop productivity in agriculture. \n [9] \n The conventional production of PBAT from fossil fuel‐derived sources raises environmental concerns, necessitating a paradigm shift towards renewable resources for monomer production. In response to this imperative, biomass has gained prominence as a sustainable feedstock, aligning with global efforts to promote environmentally responsible practices in polymer synthesis.[ \n 10 \n , \n 11 \n ]\n Figure 1 Global production capacities of biodegradable bioplastics (abbreviations in legend: PBAT: polybutylene adipate terephthalate; PBS: polybutylene succinate; PLA: polylactic acid; PHA: polyhydroxyalkanoates; SCPC: starch‐containing polymer compounds; CR: regenerated cellulose films). The data was taken from a report prepared by European Bioplastics. \n [6] \n \n PBAT is industrially synthesized from different monomers including 1,4‐butanediol (1,4‐BDO), adipic acid (AA), and dimethyl terephthalate (DMT) or terephthalic acid (TPA). These monomers are typically made from nonrenewable resources such as crude oil. Nevertheless, the synthesis of such petrochemicals is energy‐ and emissions‐intensive,[ \n 12 \n , \n 13 \n ] and the materials involved in the petrochemical synthesis are often toxic and explosive. Thus, the current monomer production is based on “brown chemistry” as opposed to “green chemistry”. To enhance the sustainability of PBAT monomer production, several approaches have been developed to obtain the PBAT monomers from plastic waste via enzymatic hydrolysis, \n [14] \n solvent recovery,[ \n 15 \n , \n 16 \n ] and catalytic degradation.[ \n 17 \n , \n 18 \n ] Producing PBAT using monomers derived from other renewable resources, such as biomass, can contribute to solving problems such as resource depletion, greenhouse gas emissions, and environmental threats. In the context of a global movement towards sustainability, the investigation into biomass‐derived PBAT is both timely and consequential, as biomass has taken center stage as a sustainable feedstock, aligning seamlessly with global initiatives promoting environmentally responsible practices in polymer synthesis.[ \n 19 \n , \n 20 \n ] Within the broader context of an international movement towards sustainability, the investigation into biomass‐derived PBAT is even more important. This review serves not only as a scholarly contribution to the ongoing scientific discourse but can also function as a guiding compass for industries committed to sustainable materials. To this end, this review aims to provide an up‐to‐date summary of the research on catalytic processes to convert biomass into the monomers of PBAT (i. e., 1,4‐BDO, AA, DMT, and TPA) and provides a comprehensive overview of the current landscape of how to produce PBAT monomers from biomass‐derived feedstocks. Technical analyses of renewable catalytic routes from biomass‐derived feedstocks to those chemicals are conducted to determine which can produce the highest yield of each chemical, based on state‐of‐the‐art results in existing literature. Moreover, this study emphasizes heterogeneous catalytic processes because with homogeneous catalytic processes it is usually difficult to isolate target products from the final product mixtures containing the homogeneous catalyst."
} | 1,713 |
35245282 | PMC8926250 | pmc | 4,594 | {
"abstract": "Most microbes live in spatially structured communities (e.g., biofilms) in which they interact with their neighbors through the local exchange of diffusible molecules. To understand the functioning of these communities, it is essential to uncover how these local interactions shape community-level properties, such as the community composition, spatial arrangement, and growth rate. Here, we present a mathematical framework to derive community-level properties from the molecular mechanisms underlying the cell-cell interactions for systems consisting of two cell types. Our framework consists of two parts: a biophysical model to derive the local interaction rules (i.e. interaction range and strength) from the molecular parameters underlying the cell-cell interactions and a graph based model to derive the equilibrium properties of the community (i.e. composition, spatial arrangement, and growth rate) from these local interaction rules. Our framework shows that key molecular parameters underlying the cell-cell interactions (e.g., the uptake and leakage rates of molecules) determine community-level properties. We apply our model to mutualistic cross-feeding communities and show that spatial structure can be detrimental for these communities. Moreover, our model can qualitatively recapitulate the properties of an experimental microbial community. Our framework can be extended to a variety of systems of two interacting cell types, within and beyond the microbial world, and contributes to our understanding of how community-level properties emerge from microscopic interactions between cells.",
"introduction": "Introduction Biological interactions pervade all of life. Interactions at lower levels of organization can confer new functionality at higher levels. For example, interactions between different cell types determine the functioning of organs and tissues in multicellular organisms and interactions between different species determine the processes an ecosystem performs [ 1 , 2 ]. In natural systems, interactions often arise in spatially structured settings, where individual entities interact mostly with others that are close by in space [ 3 ]. When interactions are local, the spatial organization of the different entities defines their network of interaction. A central question is how the properties of biological systems emerge from this network of interactions. This question has primarily been studied in the context of multicellular organisms, however it is also particularly relevant in the context of microbial communities [ 4 , 5 ]. Microbial communities perform essential processes on our planet, and these processes often arise from interactions between species [ 6 , 7 ]. Most microbial communities form spatially structured biofilms, where cells are embedded in an extracellular polymeric matrix that limits their movement [ 8 ]. In these communities, cells modify their local environment by secreting and taking up chemical compounds, and cells thus influence their neighbors’ growth, survival, and metabolic activity [ 9 – 16 ]. Most interactions are mediated by diffusible molecules and their strength decays with the distance between two interacting cells [ 17 – 21 ]. As a result, cells only interact within a limited distance, and the spatial organization of cells within the community determines which interactions are realized. To predict and control the functioning of microbial systems, we need to uncover how cells interact in space and how these interactions determine community-level properties, such as the community composition, spatial arrangement, and growth rate [ 22 ]. Recent studies have made progress in this direction by characterizing the spatial arrangement of cells [ 10 , 23 , 24 ], the range over which cells interact [ 10 , 11 , 21 , 25 ], how the sign of the interaction affects the spatial arrangement of the community [ 26 , 27 ], and how the spatial arrangement of cells affects community properties, such as their growth and species composition [ 5 , 19 , 23 , 27 – 29 ]. Despite these recent advances, we do not understand well how local interaction rules scale up to determine community-level properties. In previous work, we demonstrated that local interaction rules can be measured in synthetic microbial communities [ 11 ]. There, we focused on two-dimensional cross-feeding communities, consisting of two cell types, where each type could not produce an amino acid and could only grow in mixed communities by receiving this amino acid from the other type. We showed that the two cell types interact within a small interaction range and that the growth rate of a cell increases with the fraction of the partner type within the cell’s interaction range. The interaction range and the maximum growth rate can be different for the two cell types, and describe the local interaction rules between cells. Moreover, we previously developed a biophysical model to derive how these local interaction rules depend on the biophysical parameters of the underlying molecular exchange [ 11 ]. We found that the interaction range is mostly set by the uptake rate of the exchanged molecules, while the maximum growth rate is set by the leakage rate of these molecules. Thus, the local interaction rules between cells arise from molecular-level parameters. Our previous biophysical model gives important insight into how local interactions rules depend on the biophysical properties of cell-cell interactions, however it has two main limitations: i) it models two-dimensional communities, while in nature most microbial communities grow in three-dimensional biofilms; ii) it does not model how the local-interaction rules affect community level properties, such as the equilibrium frequency of different cell types, their degree of spatial clustering, and the community growth rate. In this work we address these limitations in two steps. We first extend our previous model to three-dimensional communities, and we derive the local interaction rules from the biophysical parameters of the underlying cell-cell interactions. Second, we present a new graph-based model to derive the community-level properties from these local interaction rules. By combining these two models, we can directly estimate key steady-state properties of the community, such as its composition, degree of spatial clustering, and its productivity, from the biophysical parameters of the cell-cell interactions. We apply our model to cross-feeding communities and show that community-level properties are strongly influenced by a small number of key molecular parameters underlying the cell-cell interactions (e.g., the uptake and leakage rates of molecules). Moreover, we extend the model to other types of communities and compare its predictions to data we previously obtained from an experimental cross-feeding community. Taken together, our results suggests that, at least for simple biological systems, it is possible to scale up from molecular-level properties, to individual-level properties, to community-level properties. Properties at each level can be predicted from a few key quantities of the level below ( Fig 1 ). These findings thus further our understanding of how scales are connected in biological systems. 10.1371/journal.pcbi.1009877.g001 Fig 1 A mathematical framework to scale up from molecular-level properties, to individual-level properties, to community-level properties. We previously measured the local interaction rules for a cross-feeding community (B) and showed that these can be derived (arrow 1) from the molecular mechanisms of the interaction (A). Here we developed a mathematical model that derives community-level properties (C) either from measured local rules (arrow 2) or directly from the underlying molecular mechanisms (arrow 3). (A) The community consists of two types of Escherichia coli : Δ P is unable to produce the amino acid proline and Δ T is unable to produce the amino acid tryptophan. Cells exchange amino acids with the environment through active uptake (with rate r u ) and passive leakage (with rate r l ). Amino acids are exchanged between cells through diffusion in the environment (with rate D ). All rates differ between the two amino acids. (B) Local interaction rules can be fully described by two fundamental quantities: the size of the interaction neighborhood ( r Δ T , r Δ P , left); and the growth function of a cell (characterized by μ ^ , right). Each dot corresponds to the measured growth rate of a single cell, n = 2610 for ΔP and n = 2162 for ΔT, the line shows the result of a linear regression, data reproduced from [ 11 ]. (C) We derive analytical expressions for steady state community-level properties, such as the equilibrium frequency of the two types, their spatial arrangement, and growth rate.",
"discussion": "Discussion We developed a mathematical framework to describe the community level properties of spatially structured communities of two species from information about the cell-cell interactions that take place ( Fig 1 ). We primarily focused on microbial cross-feeding systems and used pair approximation to derive analytical expressions for the community-level properties from knowledge of the local interaction rules ( Fig 2 ). These rules are defined by two fundamental quantities: the size of the interaction neighborhood and the maximum growth rate that cells achieve when they are completely surrounded by the partner type; both quantities typically differ for the two cell types in the community. We showed that these local interaction rules can directly be derived from key biophysical parameters of the underlying molecular mechanisms, such as uptake and leakage of the exchanged molecules ( Fig 1 ). We worked out expression for the local interaction rules from the biophysical parameters for several scenarios demonstrating how our framework can help elucidate how local interaction rules arise from the molecular exchanges, and how the local interaction rules scale up to determine community properties. For spatial cross-feeding communities, we found that the local and global properties can change independently. The global composition of the community (i.e. the equilibrium frequency of the two types) is set by the ratio of the maximum growth rates, which mostly depends on the leakage rates of the exchanged metabolites. In contrast, the composition of the local neighborhood, the one that matters the most for the cells, is set by the neighborhood size, which mostly depends on the ratio of the uptake and diffusion rates. Different biophysical parameters thus control the global and the local properties of the community. Generally, the framework we developed allows to scale up from molecular-level properties, to individual-level properties, to community-level properties. Small neighborhood sizes reduce the frequency of the partner type around each cell. This happens because cells place their offspring close by in space. In our model we assumed that cells cannot actively move. However, cells could overcome the negative effect of having a small neighborhood size if they could actively move to locations with a higher frequency of the partner type. Without such active movement, cross-feeding cells in a spatial system have a lower birth rate than in equivalent well-mixed system ( Fig 4 ), reducing the overall productivity of the community. This can even lead to the collapse of the community: cells in asymmetric communities (i.e. communities where the two type have different maximum growth rates) can stably coexist in well-mixed system, yet they might not coexist in a spatial system, if they interact at small ranges ( Fig 4 ). Moreover, dimensionality matters: given a fixed interaction range ( R , determined by a set of molecular parameters) cells growing in two dimensional colonies typically have fewer neighbors ( r ∝ R 2 ) compared to cells growing in three dimensional structures ( r ∝ R 3 ). Communities will thus have a lower productivity in two-dimensional colonies than in three-dimensional clusters, provided that the interaction range is largely independent from the dimensionality of the system. Our biophysical model suggests that the interaction range is indeed comparable between two- and three-dimensional systems (Fig A in S1 Text ). However, a recent study came to the opposite conclusion: van Tatenhove-Pel et al . (2020) [ 21 ] found that the interaction range in three dimensions is shorter than in two dimensions. This difference can likely be explained by the different spatial arrangement that were considered: we calculated the interaction range around larger patches of producer cells, while van Tatenhove-Pel et al . (2020) [ 21 ] calculated it for a single, isolated, producer cell. When there is only a single producer cells, only few molecules are produced and increasing dimensionality decreases the interaction range as they are spread over a larger volume. However, when the number of producer cells is larger (as will be the case in most communities that are not too asymmetric) this effect becomes less important and the interaction range becomes largely independent of dimensionality, as we found. Despite these differences, both our work and that of van Tatenhove-Pel et al . (2020) [ 21 ] show clearly that spatial structure and dimensionality have important effects on the dynamics of microbial communities. We tested our framework using an experimental community of two cross-feeding bacteria. We found that we can quantitatively predict the equilibrium composition of our experimental community, however a well-mixed model could do so as well ( Fig 6D ). For the particular community we studied, spatial structure thus appears to have a negligible effect on the global composition of the community. Despite this, spatial structure has important consequences for this community: it causes cells to become surrounded by their own type, which leads to a reduction in community productivity ( Fig 6E and 6F ). These effects cannot be predicted by a well-mixed model, but they are explained, at least qualitatively, by our pair-approximation model. Quantitatively our model performed less well in predicting these properties, however it can still give a order-of-magnitude estimate of the extend to which spatial structure matters for a given community. To properly validate our model, it would be essential to perform additional experiments with different communities. This could be done in several ways: biophysical parameters (and thus local rules) vary between molecules, species, and transport pathways; changing any of these would place a community in a different part of the model parameter space. For example, one could repeat our experiments using communities that exchange different building blocks or that are composed of different species. Moreover, one could directly change relevant biophysical parameters using genetic engineering, for example by tuning the uptake rate of molecules by manipulating the corresponding uptake pathways. Finally, thanks to recent advances in image analysis techniques [ 37 ], it has now become feasible to perform similar experiments in three-dimensional biofilms, making it possible to experimentally test the effects of dimensionality on community-level properties. Previous work has shown that short-range interactions provide an advantage to cooperative interactions, because they separate cooperative types from non-cooperators [ 28 , 38 – 40 ]. In general, when it is good to be surrounded by your own type, we expect short-range interactions to be beneficial, as for example when two cell types inhibit each other. When it is good to be surrounded by the other type, we expect short-range interactions to be detrimental, as for example when two cell types exchange beneficial resources. When a community consists of more than two cell types, the situation can be more complex. Previous studies have shown that short-range interaction can be beneficial for cross-feeding communities that contain non-producing cells: the short-range interactions harm the producing cells, but they harm the non-producers even more, and thus prevent them from taking over [ 23 , 41 , 42 ]. Our framework can also model systems beyond the bacterial world, as long as they are composed of two interacting types. For example, a central question in tissues homeostasis is how two (or more) different cells types can control each other’s growth by exchanging diffusible growth factors to maintain proper tissue functioning [ 43 , 44 ]. These cellular systems are typically spatial, in the sense that interactions act on a finite distance. From the point of view of modeling, spatial structure introduces complexity in the mathematical representations, as well as increasing the parameter space of models. Our framework provides a simple approach to study the equilibrium properties and can assist in the design of synthetic systems and tissue engineering, as it allows for the prediction of system-level properties from molecular scale parameters [ 45 ]. Overall, our model offers a versatile representation of spatial system of interacting cells that can be adapted to various types of interactions. Many biological systems are spatially structured and multi-scale: they consist of individual entities (e.g. cell types or species) that interact with each other in space. Interactions at different levels determine the global properties of the system (e.g. multicellular organism or microbial community). To understand such biological systems, it is important to scale between levels of organization. Our work provides a contribution to this effort by creating a mathematical framework that can scale from molecular mechanisms, to local interaction rules, to global system properties."
} | 4,453 |
39776657 | PMC11701728 | pmc | 4,595 | {
"abstract": "Elastomers are of great significance in developing smart materials for information encryption, and their unique self-healing and highly flexible properties provide innovative solutions to enhance security and anti-counterfeiting effectiveness. However, challenges remain in the multifunctional combination of mechanical properties, self-healing, degradability, and luminescence of these materials. Herein, a chemodynamic covalent adaptable network (CCAN)-induced robust, self-healing, and degradable fluorescent elastomer is proposed. Thanks to the CCANs, the resulting elastomer exhibits a tensile strength of 33.44 MPa (300 times higher than that of a linear elastomer) and an elongation at break of 1265%, and its mechanical properties can be restored to about 20 MPa after 72 h of healing at room temperature, and a self-healing efficiency of 94.67% can be realized for 24 h at 70 °C. Simultaneously, the dynamic chemical balance of keto and enol structural transitions of curcumin chain segments can be driven by CCANs, realizing multi-color (from yellow to violet) display and broad wavelength (300–500 nm) excitation, which in turn enables surface read-write and color rosette and QR code pattern printing. In addition, it can also achieve adaptive degradation under biological, alkaline, and hot water conditions. This work has guiding significance for developing the next generation of high-performance multifunctional elastomer materials, which have potential applications in the field of smart anti-counterfeiting materials and smart flexible optoelectronics.",
"conclusion": "3 Conclusions In summary, a high-strength, self-healing, degradable, and reprocessable multicolor fluorescent elastomer is successfully developed based on CCANs. The resulting elastomer shows a tensile strength 300 times higher than that of an ordinary linear elastomer and exhibits good toughness, room temperature self-healing, and reprocessability. Meanwhile, the self-healing efficiency of the obtained elastomers is as high as 94.67% at 70 °C for 24 h with the assistance of phenol–carbamate bonds. Based on the dynamic chemical balance of keto and enol structural transitions triggered by CCANs, a wide color band display can be achieved by simple ink pH adjustment, surface reading and writing, and pattern printing can be easily realized. Upon the hydrolyzability of polyester, chemical degradability of curcumin, thermal dissociation of phenol–carbamate bonds, and dynamic reversible dissociation of CCANs in the elastomers, different degradation environments (enzyme degradation, alkaline degradation, hot water degradation) and degradation rates can be realized with good environmental selectivity and adaptability. This work provides valuable guidance for the development of self-healing, degradable, and high-strength multifunctional materials for the field of smart anti-counterfeiting materials and smart flexible optoelectronics.",
"introduction": "1 Introduction In the 21st century, with the rapid development of human society, the speed and convenience of information transmission have been greatly improved, but this is also accompanied by the great risk of information leakage. 1–4 Therefore, information encryption has become particularly important. So far, various functional materials, such as fluorescent/phosphorescent/quantum dot materials, 5–8 structural color materials, 9,10 liquid crystal elastomers, 11,12 wrinkled photonic elastomers, 13 etc. , have been developed as information encryption materials. Among them, elastomers have attracted much attention due to their excellent deformation and load-bearing capacity. To date, a variety of fluorescent materials based on elastomers have been developed, such as stretchable and highly mechanically robust intrinsic fluorescent elastomers, 14 damage-detectable fluorescent elastomers, 15,16 and high-strength repairable fluorescent elastomers. 3,6 Furthermore, a series of colored encrypted materials have been developed by relying on doping luminescent materials, 5,17 changing excitation light, 7,18 and applying external stimuli ( e.g. , mechanical, 19,20 thermal, 21 and electrical 22 ). Multi-color encryption significantly increases the technical difficulty of counterfeiting through color combinations and variations. Compared to single-color anti-counterfeiting technologies, multi-color schemes require more complex chemical and optical matching, posing higher technical barriers to counterfeiters. Meanwhile, elastomer-based multi-color encryption materials can combine self-healing and multi-color luminescent properties, so that even if the surface of the material is damaged, the original anti-counterfeiting function can still be maintained after repair and the service life can be extended. Although attention has been focused on the mechanical properties or self-healing properties of fluorescent elastomers to improve their stability and durability, these devices tend to have relatively short service intervals, and their post-processing has become a non-negligible problem. If non-degradable, a large amount of polymer waste will accumulate, which will cause huge pollution pressure on the ecological environment and seriously affect the health of human beings and other organisms. Therefore, it is of great significance to develop multifunctional elastomers that are degradable, self-healing, high-strength, and luminescent. Covalent adaptive networks (CANs) are polymer networks with dynamically reversible covalent bonds. 23–25 Such materials are characterized by a molecular structure that contains reversible bond exchanges under certain conditions ( e.g. , temperature, light, and pH) to drive the rearrangement of network backbone segments, endowing the material with robustness, self-healing, recyclability, shape memory, and other dynamic properties. Recently, many materials with high strength, self-healing, and recyclability have been developed based on covalent adaptive networks. Zhang et al. 26 introduced dynamic thiocarbamate bonds into light-curable methacrylates to prepare reprocessable and self-healing 4D-printed polyurethanes with Young's modulus of 1.2 GPa and tensile strength of 61.9 MPa. Zhai et al. 27 proposed a dynamic covalent and supramolecular design of a dynamic covalent ionomer based on lipoic acid with hierarchical dynamic bonding, in which lithium bonding contributes to ion dissociation and dynamic disulfide bond recombination, and the integration of lithium bonding and binary hydrogen bonding enhances its mechanical properties, self-healing ability, reprocessing, and recyclability. Zhang et al. 28 also demonstrated a fast reprocessable and closed-loop recyclable covalent adaptive network of spiroborate-connected ions. Also, Wei et al. 29 combined low molecular weight polylactic acid and lipoic acid to obtain a covalent adaptive network with strong mechanical properties and controlled degradability. Zhao et al. 30 also introduced dynamic aggregation-induced luminescent molecules as both dynamic cross-linking points and fluorescent probes into the covalent adaptive liquid crystal network, which achieved simultaneous, fast, and non-destructive visualization of the cross-linking structure and driving properties of the materials, and endowed them with early warning of the driving limit. Despite these advances, thresholds remain. These methods are less applicable for fluorescent elastomers, and there still exists a contradiction between the design concepts of mechanical and self-healing properties, which is difficult to realize to achieve a balance between the two. 31,32 You et al. 33 attempted to use highly dynamic four-armed crosslinking units with internal catalytic oxime-carbamate groups to obtain ionogels based on covalently adapted networks as a way to balance their self-healing ability and mechanical properties but were still limited to a small strength (tensile strength 4.55 MPa). If degradation as well as highly tunable fluorescence properties are considered simultaneously, the design of such multifunctional elastomers is a great challenge, which has been seldom reported till now. 34 Based on the above-mentioned concerns, we designed a unique chemodynamic covalent adaptative network (CCAN)-induced robust, self-healing, degradable, and luminescent elastomer. Curcumin, a naturally degradable material, acts as a chain extender. And dynamically reversible B–O bonds act as a cross-linking agent. The curcumin segments in the elastomer possess special keto- and enol-type reciprocal isomers, which can be used to trigger a dynamic chemical balance for structural transitions in CCANs. The fabricated PICB 1.0 PU elastomer shows a tensile strength of 33.44 MPa with elongation at break of 1265.34%, its tensile strength can be restored to about 20 MPa with the assistance of an aqueous ethanol solution for 72 h at room temperature, and its repair efficiency is as high as 94.67% for 24 h at 70 °C. Furthermore, it shows reprocessability and multi-environment adaptive degradation performance. Remarkably, the dynamic chemical balance of the keto group and enol structure of curcumin at the surface interface can be triggered by CCANs, that is, adjusted by a simple ink pH, to achieve multicolor (from yellow to violet) display and broad wavelength (300–500 nm) excitation on the same carrier. Based on this, functions such as multicolor display and information encryption are easily realized on the same carrier and at 395 nm excitation wavelength. Moreover, ethanol can regulate the microenvironment of the surface interface and promote the isomerization and rearrangement of curcumin to achieve erasure. This work not only provides new ideas for the development of high-performance multifunctional elastomeric materials but also sheds light on the design and application of colorful information encryption elastomeric materials.",
"discussion": "2 Results and discussion 2.1 Synthesis and characterization of elastomers Adaptive chemistry combines both molecular and supramolecular chemical properties with reversible dynamic structures dominated by both non-covalent interactions and dynamic covalent bonds, which plays a unique role in molecular chain dissociation and association, favoring the functional combination of elastomers in terms of self-healing properties and degradability. The synthesis process of the CCAN-induced elastomer is illustrated in Fig. 1a . Bis-isocyanate terminal precursors are synthesized by polycondensation between polycaprolactone (PCL) polyols and isophorone diisocyanate (IPDI). Also, curcumin, a natural biodegradable biomass, is introduced into the polymer network as a chain extender to obtain linear polyurethane (PU) chains, denoted as PICPU elastomer. Subsequently, 1,4-benzenediboronic acid (BDA) is introduced into the polyurethane network to achieve dynamic reversible crosslinking of the linear PU backbone (Table S1 † ), denoted as PICBPU elastomer. Finally, PICBPU elastomer films are obtained by casting and drying. The molecular weight results of the different elastomers are presented in Fig. S1 and Table S2. † Fig. 1 (a) Schematic illustration of the structures of the chemodynamic covalent adaptation network-induced elastomer. (b and c) FT-IR spectra. (d) Stress–strain curves of the different elastomers. Additionally, the surface elemental compositions of the PICBPU elastomer films are analyzed by energy dispersive X-ray spectroscopy (EDS), including C, N, O, B, etc. (Fig. S2 † ). In addition, the content of elemental B in the elastomer structure is determined by inductively coupled plasma optical emission spectrometer (ICP-OES) testing to be 0.35 wt%, which is close to the theoretical content (0.37 wt%). The structural compositions of PICBPU elastomers are determined by Fourier transform infrared spectroscopy (FT-IR), as shown in Fig. 1b and c . In Fig. 1b , the typical –NCO peak at 2243 cm −1 and its disappearance in the elastomer network 35,36 indicate the complete consumption of the added monomers. The peaks at 1726 and 1233 cm −1 are attributed to the C \n \n\n<svg xmlns=\"http://www.w3.org/2000/svg\" version=\"1.0\" width=\"13.200000pt\" height=\"16.000000pt\" viewBox=\"0 0 13.200000 16.000000\" preserveAspectRatio=\"xMidYMid meet\"><metadata>\nCreated by potrace 1.16, written by Peter Selinger 2001-2019\n</metadata><g transform=\"translate(1.000000,15.000000) scale(0.017500,-0.017500)\" fill=\"currentColor\" stroke=\"none\"><path d=\"M0 440 l0 -40 320 0 320 0 0 40 0 40 -320 0 -320 0 0 -40z M0 280 l0 -40 320 0 320 0 0 40 0 40 -320 0 -320 0 0 -40z\"/></g></svg>\n\n O and C–O stretching vibrations in the carbamate bonds, 37,38 while the bending vibration at 3367 cm −1 is attributed to the –NH groups in the carbamate bonds, proving the sequential incorporation of PCL polyols and curcumin into the main chain of the PICPU elastomer. Furthermore, the peak at 1628 cm −1 corresponds to the C O peak in the curcumin structures, 39,40 whereas the attenuation and shift of the C O vibrational peak at 1628 cm −1 is found after the addition of BDA, which is attributed to the formation of B–O bonds and B–O coordination bonds between the β-diketone structure in curcumin and BDA. The FT-IR results confirm the successful synthesis of the designed PICBPU elastomers. Moreover, the thermal properties of PICBPU elastomers are further tested by thermogravimetric analysis (TGA) and differential scanning calorimetry (DSC). As shown in Fig. S3, † the initial decomposition temperature of the elastomers is around 283 °C, indicating its good thermal stability. PCL is a crystallizable chain segment that exhibits a distinct melting peak at 40 °C in PICPU elastomers (Fig. S4 † ). With the addition of BDA, dynamic reversible B–O bonds are formed between the segments, breaking the orderly arrangement of the PCL segments and inhibiting their crystallization, as evidenced by the absence of a distinct melting peak at 40 °C in the DSC curves. With the increase of dynamic bonds, the glass transition temperature ( T g ) gradually decreases, with T g of PICB 1.0 PU around −40 °C, which favors the migration of chain segments and the reorganization of dynamic bonding at room temperature, which in turn affects its self-healing properties. Generally, the T g of polymers increases with the increase of cross-linking density because the increase of cross-linking points restricts polymer molecular chain movement. However, elastomers based on B–O bonds as a reversible CCAN greatly reduce the limiting effect of the cross-linking point on the ability of molecular chain movement. In addition, materials with higher crystallinity usually have higher T g because the crystalline structure provides stronger intermolecular interactions and rigidity, limiting the chain segment motion. Whereas the introduction of dynamic B–O bonds effectively reduces the formation of crystalline regions, as shown in the DSC curves (Fig. S4 † ), the degree of crystallinity of PCL is effectively reduced with the increase of B–O bonds. This means that the motility of the chain segments increases and thus the migration of chain segments can occur at lower temperatures, leading to a decrease in T g . Overall, the B–O bonds were found to be effective in reducing the crystallinity of the elastomers due to their role as dynamic reversible cross-linking points, leading to a decrease in T g . The dynamic mechanical properties of the different elastomers are also analyzed using a dynamic thermomechanical analyzer (DMA). The curves of storage modulus ( E ′), loss modulus ( E ′′), and loss factor (tan δ ) at different temperatures are shown in Fig. S5. † According to the curves of storage modulus (Fig. S5a † ) and DSC (Fig. S4 † ), the T g of the four elastomers is between −40 and −30 °C, which is lower than room temperature, indicating that the molecular chains are in a relatively active state at room temperature with the ability to adjust the conformation and movement. This is conducive to activating the dynamic bonds in the hard chain phase, allowing the material to balance its self-healing ability and mechanical strength. Additionally, the results of the loss factor curve (Fig. S5c † ) show that the loss factor of the elastomer presents a high level and a wide effective temperature range. Considering the introduction of CCANs in elastomers and the formation of dynamic crosslinking points between elastomer frameworks, these elastomers may exhibit excellent mechanical properties. Accordingly, to assess the mechanical strength and toughness of the elastomers, the stress–strain curves of the elastomer are measured. The results are shown in Fig. 1d and Table S3. † The tensile strength and fracture elongation of the curcumin-extended PICPU elastomer are 0.10 MPa and 2161.42 ± 3.76%, respectively. In contrast, the mechanical properties of the PICPU elastomer are substantially improved after BDA cross-linking. The tensile strengths of PICB 0.25 PU, PICB 0.5 PU, and PICB 1.0 PU elastomers are 9.35 ± 0.96, 16.07 ± 0.95, and 33.44 ± 1.49 MPa, respectively, while the fracture elongations are 1983.62 ± 95.69%, 1795.29 ± 142.21%, and 1265.34 ± 43.75%, respectively. Furthermore, with the continuous increase of BDA content, there is little change in the tensile strength and fracture elongation of the elastomer, as shown in Fig. S6. † Overall, the tensile strength of PICBPU elastomers is significantly improved, with the tensile strength of PICB 1.0 PU elastomer being over 300 times that of the ordinary curcumin-extended PU, which is attributed to the formation of CCANs. Also, we prepared PCL-IPDI and PCL-IPDI-BDA polymer networks to serve as a control. From Fig. S7, † it can be found that the tensile strength of the PCL-IPDI sample was around 0.2 MPa, whereas it increased to around 2 MPa with the addition of BDA, which suggests that the B–OH groups in BDA may form hydrogen bonds with the carbamate groups, which in turn improves the mechanical properties of the polymers. To this end, we investigated the hydrogen bonding interactions of BDA by FT-IR spectroscopy. The C O stretching region was deconvoluted into two subpeaks belonging to free C O and hydrogen-bonded C O, respectively. The percentage of hydrogen-bonded C O in PCL-IPDI was calculated to be 26.19%, whereas the percentage of hydrogen-bonded C O in PCL-IPDI-BDA was 28.06%, which is a slight increase in the percentage of hydrogen bonding, suggesting that BDA forms hydrogen bonds in the PCL-IPDI network and improves its mechanical properties. However, this small difference (∼1.9%) is in contrast to the significant improvement in mechanical properties observed with the addition of BDA, mainly because there are other interactions such as B–N coordination bonding to provide additional cross-linking sites, 41,42 as shown in Fig. S7. † Although the formation of partial hydrogen bonds and B–N coordination bonds by BDA in PCL-IPDI-BDA results in cross-linking points and improves its mechanical properties, it is still much lower than the properties achieved by the system containing curcumin units (tensile strength of 33.44 MPa and elongation at break of 1265.34%). This is due to the fact that curcumin not only acts as a chain extender in the main chain, but can also provide reactive groups for BDA, forming dynamic cross-linking points with B–O bonds by esterification of the enolized structure of curcumin and the B–OH groups in BDA, resulting in the formation of CCANs, which are much stronger than the network formed by physical bonds alone. In addition, curcumin is an acid/alkali-sensitive and photosensitive biomaterial, and the B–O bond is a dynamic chemical bond sensitive to water. Therefore, the photostability and humidity stability of the elastomers are also investigated as shown in Fig. S8, † as well as the effect of pH adjustment on the mechanical properties during the synthesis process as shown in Fig. S9. † Overall, the prepared elastomers show weaker humidity stability and good photostability, and the pH modulation of the system has less effect on the formation of the CCAN in the structures, which may be due to the volatilization of the acid and the reaction of the alkali with CO 2 during the later curing process. The reasons why the mechanical properties of this elastomer can be significantly improved are as follows. Curcumin molecular chain segments are constantly in dynamic chemical balance in terms of enol–keto conformation. When they are in the enol conformation, the generated hydroxyl groups are supposed to undergo a dynamic borate esterification reaction with the boronic acid groups in BDA, forming a rigid chemically cross-linked hard chain segment. These CCANs can retain a considerable number of rigid chemical cross-links when the elastomer is subjected to external forces, providing high strength. They can also be dissociated in reaction to large amounts of stress. These significantly avoid stress concentration and enhance the toughness of the material. To elucidate the underlying mechanisms of the mechanical behavior of PICB 1.0 PU elastomers, 2D-SAXS is performed to analyze the structural changes during the dynamic phase of the tensile process ( Fig. 2b ). In the static state, scattering halos are found in the samples, indicating a clear microphase separation in the PICB 1.0 PU elastomer. When the elastomer is stretched up to 200%, the 2D-SAXS scattering pattern turns into a butterfly shape, indicating deformation of the hard phases along the stretching direction. As the PICB 1.0 PU elastomer is continuously stretched to 400% strain, the long axis of the elliptical 2D-SAXS scattering pattern gradually becomes longer, indicating that the hard phases gradually decompose with the stretching of the PICB 1.0 PU elastomer. To facilitate a more comprehensive assessment of the dynamic microstructural changes, the 1D integration curves of the SAXS patterns are compared ( Fig. 2a ). As the strain increases from 0% to 400%, the average distance between the hard phases perpendicular to the tensile direction decreases while the strength decreases, indicating a high orientation of the hard phases along the stretching direction. This indicates that CCANs can achieve effective dissociation under external forces to dynamically adapt to external mechanical loads. We also collect temperature-dependent FT-IR spectra (20–90 °C) to further understand the dynamic interaction mechanism of PICB 1.0 PU elastomers. These spectra can track the motion of different functional groups, thus providing insights into molecular interactions ( Fig. 2c ). The spectrum bands located at 1650–1600 and 1400–1300 cm −1 belong to the C O groups in curcumin and the B–O bonds, respectively, thus allowing the tracking of dynamic bonding changes. According to the variable temperature FT-IR spectra of PICB 1.0 PU elastomers, the C O stretching bands are red-shifted, and B–O bonds are blue-shifted on heating, indicating dissociation of C O groups and B–O bonds, respectively. To provide higher resolution for fine molecular motions, two-dimensional correlation spectroscopy (2D COS) analysis is carried out, which is sensitive to a variety of fine dynamic interactions and helps to provide comprehensive information at the molecular level. 2D COS is generated from all variable-temperature IR spectra, both synchronous and asynchronous ( Fig. 2d ). According to Noda's judging rule, 43,44 the order of precedence in the heating process can be determined as (→ indicates prior or earlier; see Table S4 † for operational details to support the information):1340 cm −1 → 1361 cm −1 → 1612 cm −1 → 1314 cm −1 → 1635 cm −1 , i.e. , ν (B–O) (ligand bonding) → ν (B–O) (chemical bonding) → ν (C O) (bonded) → ν (B–O) (free) → ν (C O) (free). Note that the two new peaks at 1635 and 1612 cm −1 identified by the 2D COS asynchronous spectra are split from the original peak at 1628 cm −1 in the 1D spectra and are attributed to free C O and bonded C O bonds, respectively. The results indicate that the B–O bonds first respond to temperature perturbation, which in turn leads to the dissociation of CCANs. Overall, the CCANs in this work are driven by the intrinsic dynamic chemical balance of curcumin, which can dynamically adapt to the external mechanical loads. Resultantly, it is of great significance for the improvement of the mechanical properties of elastomers. PICB 1.0 PU elastomer is chosen as the subject for further study because of its superior properties. Fig. 2 (a) 1D SAXS profiles at different strains of the PICB 1.0 PU elastomer. (b) 2D SAXS patterns of the PICB 1.0 PU elastomer during stretching. (c) Temperature-variable transmission IR spectra of PICB 1.0 PU elastomer upon heating from 20 to 90 °C (interval: 5 °C). (d) 2D COS synchronous and asynchronous spectra generated from (c). In 2D COS spectra, red color represents positive intensities, while blue color represents negative intensities. 2.2 Self-healing and reprocessing properties With the assistance of CCANs, the fabricated elastomers are inherently reversible, with the ability to dissociate, reorganize, and rearrange between different structures to achieve damage recovery, improve their damage tolerance, extend the service life of the material, and reduce the safety hazards associated with damaged materials. Therefore, the effect of self-healing temperature and conditions on the self-healing ability of PICB 1.0 PU elastomer is studied by testing cut dumbbell-shaped samples, as illustrated in Fig. 3a . Firstly, the self-healing performance of PICB 1.0 PU elastomer at different healing times at room temperature is evaluated. As shown in Fig. 3b and c , the tensile strength of the fractured PICB 1.0 PU elastomer increases with increasing healing time. When healed for 72 h at room temperature, the tensile strength can recover to 15.33 MPa, with a self-healing efficiency of 45.83%. Additionally, B–O bonds are sensitive to water, and under the action of water, the exchange efficiency of B–O bonds can be greatly improved, 36,45 which is expected to further enhance the self-healing efficiency of PICB 1.0 PU elastomer at room temperature. However, PICB 1.0 PU elastomer is composed of a large amount of hydrophobic PCL and curcumin, and its surface exhibits certain hydrophobicity (Fig. S10a † ), 46–48 which is unfavorable for water wetting the surface, making it difficult to achieve the water-assisted effect. Therefore, it is necessary to increase the wettability of water with the surface to enhance the activity and exchange efficiency of B–O bonds at the fracture interface. Therefore, a mixed solution of ethanol and water is chosen to effectively wet the interface (Fig. S10b † ). As shown in Fig. 3d and e , when the fractured PICB 1.0 PU elastomer interface is treated with a mixed solution of ethanol and water for 5 min and then healed for 72 h at room temperature, the tensile strength can recover to 20.08 MPa, with a self-healing efficiency of 60.04%. It can be seen that after solvent treatment, the tensile strength and self-healing efficiency of the fractured PICB 1.0 PU elastomer are greatly improved, proving that the activity and exchange efficiency of B–O bonds at the fracture interface are effectively enhanced with the assistance of solvent. However, due to the limited number of B–O bonds between molecular chains, there is little change in self-healing efficiency when the healing time is increased to 96 h (Fig. S11 † ). Additionally, macroscopic photos of the healed PICB 1.0 PU elastomer are shown in Fig. 3g , which show that it does not fracture after bending, twisting, and stretching, further showing that the fractured specimen is effectively repaired. Fig. 3h illustrates the self-healing mechanisms of PICB 1.0 PU elastomers at room temperature, mainly including the recombination and exchange of hydrogen bonds and B–O bonds. Current self-healing elastomers require not only the introduction of dynamic bonding but also a certain degree of flexibility of the polymer chains. Otherwise, the lack of polymer chain interfacial mobility at the contact interface often causes few interfacial dynamic bonds, which makes it difficult to realize self-healing behavior under mild conditions. In contrast, as for the hard segments in PICB 1.0 PU, both curcumin and BDA have rigid conjugated benzene ring structures, which can be chemically crosslinked by esterification. As for the soft chain segments, the polyester molecules with ester bonds are not as flexible as polyether. Accordingly, the overall design of PICB 1.0 PU seems to favor high strength and toughness instead of self-healing capability. However, the CCANs allow the fracture interface of the elastomeric material to realize spontaneous dynamic bonding/debonding in the absence of external stimuli such as force, light, heat, etc. This creates the conditions for this structurally rigid elastomer to exhibit self-healing ability under mild conditions. Fig. 3 (a) Schematic diagram of the self-healing process of PICB 1.0 PU elastomers. (b) Stress–strain curves and (c) mechanical properties after healing of damaged PICB 1.0 PU elastomers for 24 h, 48 h, and 72 h without treatment at room temperature. (d) Stress–strain curves and (e) mechanical properties after healing of damaged PICB 1.0 PU elastomers for 24 h, 48 h, and 72 h at room temperature with the assistance of an aqueous ethanol solution. (f) Self-healing efficiency of damaged PICB 1.0 PU elastomers after healing for different times at room temperature. (g) Macrophotographs of different deformation actions of damaged PICB 1.0 PU elastomers after healing. (h) Schematic illustration of the self-healing mechanism of damaged PICB 1.0 PU elastomers at room temperature. The CCANs in this work can trigger additional dynamic chemical mechanisms at higher temperatures, as well as significantly enhancing polymer chain mobility, leading to a significant increase in self-healing efficiency. Compared to conventional urethane bonds, phenol–carbamate bonds have a lower dissociation temperature, 49,50 which is expected to further enhance the elastomer's self-healing performance. To explore the dissociation temperature of phenol–carbamate bonds in PICB 1.0 PU elastomers, in situ infrared spectroscopy is conducted at different temperatures, as shown in Fig. 4a . It is observed that when the temperature reached 70 °C, the –NCO groups were detected at 2272 cm −1 , indicating the dissociation of phenol–carbamate bonds. Therefore, the self-healing efficiency of fractured PICB 1.0 PU samples healed at 70 °C at different times is investigated. From Fig. 4b and c , it can be seen that the tensile strength of fractured PICB 1.0 PU elastomers increases with increasing healing time, with a self-healing efficiency reaching as high as 94.67% after only 24 h of healing. The FTIR spectrum after healing is the same as that of the original elastomer (Fig. S12 † ), which also proves the recovery of its broken bonds. When the temperature is below 70 °C, the self-healing efficiency of samples healed for 24 h is only around 60% (Fig. S11 † ), further demonstrating the significant role of dissociation and recombination of phenol–carbamate bonds in enhancing self-healing performance. In summary, the fabricated PICB 1.0 PU elastomers exhibit outstanding self-healing performance, which can meet the requirements for damage repair in different usage environments. Fig. 4 (a) In situ FT-IR spectra of PICB 1.0 PU elastomers. (b) Stress–strain curves, (c) mechanical properties, and (d) self-healing efficiency of damaged PICB 1.0 PU elastomers after healing for 6 h, 12 h, and 24 h at 70 °C. (e) Schematic illustration of solvent recovery and reprocessing of PICB 1.0 PU elastomers and (f) stress–strain curves for multiple cycles. Based on the CCANs, compared to traditional irreversible crosslinking points, the elastomer chains can dissociate and restructure between segments. Thus, the recyclability of the elastomer is investigated, with the schematic process shown in Fig. 4d . The results indicate that the prepared elastomer, assisted by DMF solvent at 70 °C, can dissolve and form films again, and after undergoing three cycles of dissolution and film formation, its tensile strength and elongation at break are similar to those of the original samples, showing that CCANs can realize multistage dynamic chemical dissociation for important implications in the field of recycling and reprocessing. 2.3 Fluorescence properties It has been reported that curcumin molecules possess high electron delocalization and π–π conjugated structures, which can be used as fluorescent dyes based on aggregation-induced mechanisms. 51,52 In addition, curcumin exhibits pH sensitivity, which can be modulated by regulating the environmental pH to trigger ketone and enol transitions in the curcumin structures (Fig. S13 † ), 53–55 as a way to achieve different fluorescent colors (Fig. S14 † ). Here, curcumin is introduced into the PU backbone as a chain-expanding component, which is located in the hard phase concentrated area and can further increase its entanglement density through B–O bond cross-linking (Fig. S15 † ). This satisfies its aggregation-induced luminescence conditions, which can potentially lead to the generation of fluorescent properties and acid/alkali-induced color changes on the elastomer surface. To investigate this, the UV-Vis absorption spectra of curcumin ethanol solution and curcumin- and BDA-liganded ethanol solutions are first compared ( Fig. 5a ). From Fig. S14, † it can be observed that the curcumin ethanol solution exhibits strong absorption peaks in the range of 300–500 nm, indicating that it can produce fluorescence under UV irradiation. Moreover, under different pH conditions, there is a noticeable shift in the excitation wavelength, with alkaline conditions showing a significant red shift compared to acidic conditions, attributed to the sensitivity of curcumin to acid/alkaline conditions resulting in structural isomerization. After curcumin coordinates with BDA, there is a significant red shift in both the maximum excitation wavelength and the excitation wavelength under acidic conditions ( Fig. 5a ), as well as a weak blue shift under alkaline conditions, without losing its fluorescence properties, showing a wide range of excitation wavelengths. Moreover, different color changes can be observed under different pH conditions, as shown in Fig. S16. † On this basis, the fluorescence properties of acid-treated PICB 1.0 PU, PICB 1.0 PU, and alkali-treated PICB 1.0 PU elastomers are investigated, and their emission spectra, excitation spectra, and quantum yields are recorded. As shown in Fig. 5b , it can be found that the maximum emission wavelengths of acid-treated PICB 1.0 PU, original PICB 1.0 PU, and alkali-treated PICB 1.0 PU are 580 nm, 575 nm, and 640 nm, respectively, and the maximum emission intensities gradually decrease. In the excitation spectra, it can be found that the maximum excitation intensity of the three PU samples decreases gradually at the excitation wavelength of 468 nm ( Fig. 5c ). In addition, the quantum yields of acid-treated PICB 1.0 PU, original PICB 1.0 PU, and alkali-treated PICB 1.0 PU are 0.43%, 0.13%, and 0.02%, respectively, and their quantum yields gradually decrease ( Fig. 5d ). Compared with the original PICB 1.0 PU, the enol structures of curcumin in acid-treated PICB 1.0 PU samples are converted to ketone structures, while the enolized structures in alkali-treated PICB 1.0 PU samples are further increased. Analyzing the above results suggests that the ketone structures coordinated with BDA are more conducive to electron transfer and produce stronger fluorescence properties compared to the enol structures. Furthermore, the macroscopic views of PICB 1.0 PU under visible and UV irradiation are observed, showing reddish-black color under visible light ( Fig. 5f 1 ) and yellow color under UV irradiation ( Fig. 5f 2 ), which suggests that fluorescent properties are produced. Moreover, changes in the fluorescence properties of the PICB 1.0 PU samples after immersion in ethanol solutions of different pH values are observed ( Fig. 5e ). It can be found that under UV irradiation, the PICB 1.0 PU samples show different colors, which transition from bright yellow to dark blue and then to purple as the pH increases. Also, through the masking method and modularity, based on the different pH-based induction, a colorful rose pattern demonstration is achieved in the same carrier at 395 nm excitation wavelength in the right image of Fig. 5d . Analysis of the FT-IR spectra of the original and acid/alkaline treated elastomer samples (Fig. S17 † ) reveals changes in the intensity and peak shift of the C O absorption peak at 1628 cm −1 after acid or alkaline treatment. The surface C O absorption peak area increases significantly under acid induction, indicating that the proportion of its ketone structure increases, while the C O absorption peak area significantly decreases under alkali induction, indicating that the proportion of its enol structure increases. This indicates that the curcumin structure on the elastomer surface undergoes structural isomerization induced by different pH, with the ratio of its ketone and enol structures reaching a dynamic chemical balance. Overall, the CCAN network can be triggered by acid/alkali-induced structural changes to achieve a multicolor fluorescence display. In addition, in order to further understand the effect of this structural change of acid and alkaline surface treatment on the mechanical properties of elastomers, we test the mechanical properties of the elastomers after immersion in different pH solutions, as shown in Fig. S18. † The results also show that the surface treatment has little effect on the elastomer, which may be due to the fact that this pH-adjusted structural change in curcumin is limited to the surface only, as illustrated in the inset in Fig. S18. † Fig. 5 (a) UV absorption spectra of different pH ethanol solutions of curcumin coordinated without and with BDA. The emission spectra ( λ ex = 468 nm) (b), excitation spectra (c), and quantum yields ( λ ex = 468 nm) (d) of acid-treated PICB 1.0 PU, PICB 1.0 PU, and alkali-treated PICB 1.0 PU elastomers. (e) Photographs of PICB 1.0 PU elastomers in the original and after writing under visible light and UV irradiation. (f) Schematic illustration of the mask method pattern etching process as well as (g) application demonstration diagrams. Furthermore, we conduct reading and writing tests on the surface of the PICB 1.0 PU elastomer using ethanol solutions with pH of 3 and 11 as acidic ink and alkaline ink, respectively. As shown in Fig. 5e , when writing the English letters “BIT” on the surface with acidic ink and alkaline ink, it can be observed that under visible light, they appear red-black ( Fig. 5f 3 ), while under UV irradiation, they appear with a yellow background, displaying the letters “BIT” in bright yellow and navy blue ( Fig. 5f 4 ), respectively, thus achieving their readability and information encryption functions. Notably, the surface-drawn patterns can be removed with anhydrous ethanol, as shown in Fig. 5f 5 and f 6 , achieving erasable and rewritable functions on the elastomer surface. Specific ketone- and enol-type structural balances in curcumin on the elastomer surface can be exquisitely regulated by acidic and alkaline inks. The obtained dynamic chemical balance can be maintained for a long time even after the inks were evaporated. Interestingly, when the ethanol solution is used to treat the surface, it drastically destroys the obtained dynamic chemical balance states, and thus the corresponding chemical history is eliminated. After the evaporation of ethanol, the keto- and enol-chemical balance in the curcumin structure can be restored to the original state, realizing facile information erasure. Additionally, as a proof-of-concept for anti-counterfeiting labels, QR code anti-counterfeiting labels are created using the masking method by spraying acidic ink, as shown in Fig. 5g . Under UV irradiation, the surface presents a visible QR code pattern that is scannable. These observations demonstrate the enormous potential of PICBPU elastomers in data storage and encryption applications. 2.4 Degradation performances Elastomers have abundant applications in the field of smart materials due to their excellent strength and ductility, and the consequent accumulation of waste and environmental pollution, thus making the issue of their degradability particularly important. The prepared CCAN-induced elastomers are rich in reversible dynamic structures that facilitate the dissociation of molecular chains and are expected to achieve the collapse of the polymer network and thus polymer fragmentation with the help of hydrolyzable polyester and degradable curcumin. Here, based on the structural characteristics of PICB 1.0 PU elastomers, their degradability properties are tested and evaluated. As is well known, polyesters can undergo enzymatic chain cleavage under natural and biological conditions, ultimately completing degradation and metabolism. 56,57 Therefore, we evaluate the degradability of PICB 1.0 PU elastomers in phosphate-buffered saline (PBS) with or without lipase. As shown in Fig. 6a , the change in mass loss of the PICB 1.0 PU elastomer over time is displayed. It can be observed that with increasing immersion time, the mass loss gradually increases, reaching approximately 30 wt% after 30 days in PBS solution with lipase. This indicates that under the action of lipase, the PCL polyol soft chain segments of the PICB 1.0 PU elastomer undergo decomposition. Furthermore, by analyzing the FT-IR spectra of the PICB 1.0 PU elastomer before and after degradation (Fig. S19 † ), it is observed that the absorption peaks of the C–O and C O of the PCL segments at approximately 1233 cm −1 and 1726 cm −1 , respectively, decrease or disappear, and the characteristic peaks around 3200 cm −1 become broader. 58,59 This indicates that the ester bonds in the PU backbone were cleaved, suggesting that its degradation is mainly due to the cleavage of molecular chain segments. Fig. 6 (a) Mass loss curve of PICB 1.0 PU elastomer in the PBS solution without and with lipase. (b) Mass loss curves and (c) degradation rates of PICB 1.0 PU elastomers in the presence of alkali solutions. (d) Mass loss curve, (e) FT-IR spectra, and (f) schematic illustration of the degradation mechanism of PICB 1.0 PU elastomers in the hot water environment. Next, curcumin is a naturally biodegradable biomaterial that can undergo chemical degradation under different conditions, such as light exposure, alkaline environments, and auto-oxidation. 55,60 Therefore, we also tested the degradation of curcumin in 0.1 M sodium hydroxide by UV-Vis spectrophotometric analysis as shown in Fig. S20. † It can be noticed that the intensity of the absorption spectrum of curcumin under alkaline conditions decreased dramatically compared to the initial one, which indicates that curcumin decomposed under alkaline conditions. Based on this, when curcumin is used as a chain extender for PU elastomers, PICB 1.0 PU elastomers are expected to chemically degrade under alkaline conditions. To investigate this, the degradation of PICB 1.0 PU elastomers in 0.1 M NaOH solution (organic solvent/H 2 O = 1 : 1, v/v) at different times is explored, as shown in Fig. 6b . It can be observed that the PICB 1.0 PU elastomer can be completely degraded within 7 h in THF solution, followed by ethanol and methanol, and lastly in pure NaOH solution with a mass loss of up to 30 wt% within 35 h. By calculation, the degradation rates of the elastomers in THF, ethanol, methanol, and pure NaOH solution are determined to be 21.01, 5.76, 4.07, and 0.75 mg cm −1 h −1 , respectively ( Fig. 6c ). Through analysis of the FT-IR spectra of the elastomers before and after degradation (Fig. S21 † ), it is observed that the characteristic peak of the C O at 1628 cm −1 in the curcumin component of the backbone disappears. This indicates that the curcumin component undergoes decomposition under alkaline conditions, leading to the breakage and cleavage of the PU chain segments. Finally, it was mentioned earlier that the phenol-carbamate bonds can undergo dissociation at 70 °C, curcumin is insoluble in water, and the isocyanate groups are sensitive to water. Based on this, the chain extender curcumin is expected to dissociate when placed in an aqueous solution above 70 °C. Therefore, the degradation of PICB 1.0 PU elastomers in hot water at 90 °C is investigated, as shown in Fig. 6d . It can be observed that the PICB 1.0 PU elastomer dissociates in hot water at 90 °C for 200 h, with a mass loss of up to 60 wt%. From the analysis of the FT-IR spectra before and after the degradation of the elastomers ( Fig. 6e ), it can be found that the C O intensity of curcumin in the elastomer is reduced at 1628 cm −1 , implying that the curcumin component is dissociated from the backbone. Additionally, analysis of the gel permeation chromatography (GPC) curve of the elastomer after degradation (Fig. S22 † ) reveals a molecular weight reduction to 8457 g mol −1 , nearly 8 times lower than the original, confirming the successful detachment of curcumin from the backbone. Furthermore, the yellow color of the hot water after elastomer dissociation in Fig. 6d also confirms this. Moreover, when the 90 °C hot water containing degradation products is cooled to room temperature, the solution color becomes lighter, and a red precipitate forms at the bottom of the liquid. Analysis of the degradation products in aqueous solution by FTIR and NMR characterization confirmed that the dissociated products are curcumin components ( Fig. 6e and S23 † ). This implies that there is a dynamic chemical-structural balance in the curcumin structure in the CCANs, which can undergo dynamic dissociation, offering the possibility of recycling and reusing small molecules such as curcumin and BDA. The degradation mechanism of PICB 1.0 PU elastomers in hot water is illustrated in Fig. 6f , primarily stemming from the dissociation and association of phenol–carbamate bonds, the hydrophobicity of curcumin, the water solubility of BDA, and the sensitivity of isocyanate groups to water. Overall, elastomers based on CCANs are capable of degradation in different environments and rates due to the hydrolyzability of polyester, chemical degradability of curcumin, and thermal dissociation of phenol–carbamate bonds with excellent environmental selectivity and adaptability, and are expected to have potential applications in multiple fields."
} | 11,713 |
34885525 | PMC8658260 | pmc | 4,597 | {
"abstract": "Mechanical energy is the most ubiquitous form of energy that can be harvested and converted into useful electrical power. For this reason, the piezoelectric energy harvesters (PEHs), with their inherent electromechanical coupling and high-power density, have been widely incorporated in many applications to generate power from ambient mechanical vibrations. However, one of the main challenges to the wider adoption of PEHs is how to optimize their design for maximum energy harvesting. In this paper, an investigation was conducted on the energy harvesting from seven piezoelectric patch shapes (differing in the number of edges) when attached to a non-deterministic laminated composite (single/double lamina) plate subjected to change in fiber orientation. The performance of the PEHs was examined through a coupled-field finite element (FE) model. The plate was simply supported, and its dynamics were randomized by attaching randomly distributed point masses on the plate surface in addition to applying randomly located time-harmonic point forces. The randomization of point masses and point force location on a thin plate produce non-deterministic response. The design optimization was performed by employing the ensemble-responses of the electrical potential developed across the electrodes of the piezoelectric patches. The results present the optimal fiber orientation and patch shape for maximum energy harvesting in the case of single and double lamina composite plates. The results show that the performance is optimal at 0° or 90° fiber orientation for single-lamina, and at 0°/0° and 0°/90° fiber orientations for double-lamina composites. For frequencies below 25 Hz, patches with a low number of edges exhibited a higher harvesting performance (triangular for single-lamina/quadrilateral for double-lamina). As for the broadband frequencies (above 25 Hz), the performance was optimal for the patches with a higher number of edges (dodecagonal for single-lamina/octagonal for double-lamina).",
"conclusion": "4. Conclusions In this study, an investigation of the energy harvesting from seven piezoelectric patch shapes (differing in the number of edges) was conducted. PEH models were attached to a non-DS laminated composite plate (single-lamina and double-lamina), and the performance was examined through a coupled-field FE model. The structures’ dynamics were randomized by subjecting the host plate to randomly distributed point masses and randomly located harmonic point forces. The randomized condition generated structural uncertainties required to achieve the effect of an infinite plate structure with the response of a non-DS system. The analysis was performed by employing the ensemble-average responses of the developed electrical voltage across the piezoelectric models. In the case of the single-lamina composite, it was observed that the performance was optimal when the fibers were oriented at either 0 ° or 90 ° . At frequencies below 25 Hz , the quadrilateral and triangular patches possessed higher harvesting performance. The latter had a higher power generation capacity; however, the patches had a higher number of edges (i.e., dodecagonal, octagonal and hexagonal) exhibited better performance at higher frequency ranges. The dodecagonal patch contributed to the highest overall CFAV in the case of single-lamina composite. As for the double-lamina composite, the performance was found optimal at 0 ° / 0 ° and 0 ° / 90 ° fiber orientations. The performance of the quadrilateral patch was highest within the low-frequency range but decreased drastically at higher frequency ranges. It was observed that the octagonal patch contributed to the highest overall CFAV for the double-lamina composite. Figure 13 represents the normalized maximum cumulative voltages for single and double-lamina composites.",
"introduction": "1. Introduction Ambient mechanical vibration is ubiquitous; harvesting this energy to generate electricity has been considered a pivotal point for researchers in fulfilling the desire for renewable green energy [ 1 , 2 , 3 ]. Various studies have proposed vibration energy harvesting technologies that can efficiently harness ambient vibration energy and provide sustainable power sources. These technologies demonstrate potential use in powering electronics in a variety of applications, such as micro-powered electronic devices, embedded sensors in structures, medical devices, and wireless sensor networks [ 4 , 5 , 6 , 7 , 8 ]. Moreover, the piezoelectric materials have been used as sensors and/or actuators in the form of layers or patches embedded and/or surface bonded on structural plates for vibration control Bodaghi et al. [ 9 ] The energy harvesting mechanism based on piezoelectric energy harvesting has received the utmost interest from many researchers. Piezoelectric energy harvesters (PEHs) are the prominent harvesting systems owing to their high energy conversion capabilities and simple structure [ 10 ]. The versatility of PEHs has facilitated their incorporation in various energy harvesting techniques such as fluid-based energy harvesting (Truitt and Mahmoodi, 2013) [ 11 ]. Due to the growing interest in PEHs, many investigations have been conducted on the performance optimization of PEHs for optimum power generation. These studies were focused on optimizing the performance of various PEH types, such as the cantilever-type PEH, MEMS-based PEH, and sandwich-type PEH. Park et al. [ 12 ] performed an optimization study on a cantilever-type PEH in which the free tip is excited by the rotary motion of a mechanical device and maximized the power output. Wein et al. [ 13 ] performed topology optimization of a cantilever-type PEH using stress norm constraints to optimize the electrical power for a sinusoidal vibrational excitation. Zhu et al. [ 14 ] conducted a theoretical and experimental analysis to study the effect of geometrical dimension on the energy harvesting performance of a unimorph cantilever-type PEH with fixed resonance frequency. Kim and Le [ 15 ] used a finite element analysis-based topology optimization to calculate the topologies of a substrate plate and a piezoelectric layer in a vibrating cantilever plate application. The designs with optimal topologies were proposed for three different piezoelectric materials, and their voltage output was compared to investigate the most suitable material for the maximum harvesting capacity. Sordo et al. [ 16 ] proposed optimizing a MEMS-based PEH by maximizing the number of resonant modes within the narrow operating bandwidth. Song et al. [ 17 ] proposed an optimization strategy to maximize the power output density of a PVDF-based cantilever-type energy harvester. This strategy showed that the power output mainly depends on the maximum allowable stress within the beam structure and the device’s working frequency, which can be obtained by adjusting the geometry of the piezoelectric layers. Izadgoshasb et al. [ 18 ] explored the effect of the orientation of the cantilever beam structure on the power generation efficiency of the PEH. This type of PEH was proved to have higher voltage output than the conventional PEHs due to the selection of materials and geometry of the core and the metal layers. Kim et al. [ 19 ] investigated the performance optimization of cantilever-based PEH by adjusting the position of the piezoelectric generators based on the changes in the phase angle of the substrate. Ichige et al. [ 20 ] proposed a PEH with mechanical metamaterials for the elastic layer optimized for high power output and low resonance frequency. Peralta et al. [ 21 ] investigated the performance of a bimorph cantilever-type PEH subjected to base excitation to optimize its performance. They performed a parametric study to investigate the effect of shape perturbation on the fundamental frequency, amplitude, and frequency response function. Most of these studies have investigated the performance optimization of the cantilever-type PEHs at the dominant resonant modes, which are predominantly evident at the low-frequency range. However, investigations on the performance of PEHs at the high-frequency ranges are seldom found in research. Modelling the vibration response of structures at high-frequency ranges is more challenging than the low-frequency range [ 22 ]. The vibration response can be divided into three main frequency ranges: low, mid, and high-frequency. The response exhibits dominant resonant peaks at the low-frequency range, and the structure response tends to have a high variance. In contrast, the high-frequency peaks are no longer distinct, and the response variance becomes smaller. The mid-frequency range holds the characteristics of both the low and the high-frequency ranges [ 23 ]. Structures vibrating at the low-frequency range are subjected to long-wavelength deformations and are referred to as deterministic subsystems (DSs); these structures are insensitive to structural uncertainties and can be analyzed using deterministic modelling techniques such as the finite element analysis (FEA). The structures vibrating at high-frequency ranges are subjected to short-wavelength deformations and are sensitive to uncertainties. Such structures are referred to as non-deterministic subsystems (Non-Ds) and analyzed using statistical modelling techniques such as statistical energy analysis (SEA) [ 24 ]. The mid-frequency is the range where some components in the system have distinct modal responses and others that behave statistically all in the same frequency range. Therefore, a single modelling technique may not be sufficient to predict the response of such structures [ 25 ]. Although the computational resources for FEA can handle smaller elements to capture short wavelengths, it would not be efficient due to computational expenses. Therefore, at high-frequency ranges, SEA is performed to get the ensemble average and predict the response of such vibrations. Hence, SEA is more favourable to use to analyze the dynamics of high-frequency vibrations. In SEA, the modal overlapping factor ( MOF ) is used to quantify the degree of overlap in the modal response. MOF is defined as the ratio between the modal bandwidth at the half-amplitude and the average modal spacing, and it can be used to establish the three main frequency ranges: low, mid, and high-frequency. For orthotropic plates, the average modal frequency spacing is one of the parameters used in SEA analysis; which can be analytically defined in terms of mechanical properties of the plate using geometric mean of the wave velocities in two principal directions x and y [ 26 ]: (1) δ f ¯ o r t h o = h 3 A c L x ’ c L y ’ \n (2) c L x ’ = E 1 / ρ ( 1 − υ 12 2 ) , c L y ’ = E 2 / ρ ( 1 − υ 21 2 ) \nwhere h is the thickness, A is the surface area, while E 1 and E 2 are modulus of elasticity, υ 12 and υ 21 are Poisson’s ratios in two principal directions, and ρ is the mass density. ( c L x ’ and c L y ’ ) are the longitudinal wave speed in the x and y directions, respectively (as shown in Figure 1 ). Equation (1) is only valid for wavelength much greater than the thickness of the plate. The MOF of flexural modes in thin orthotropic plates can be analytically written as [ 27 ]: (3) MOF = π 2 f η δ f o r t h o \nwhere η is the damping loss factor and f is the frequency. MOF can be used to indicate the high-frequency threshold value, i.e., MOF = 2.5 for plates. The effect of uncertainties due to mass variations, stiffness variations due to boundary conditions, and discontinues has a minimal influence on the low-frequency modes. However, at high-frequency, the structural response is sensitive to such uncertainties. Due to this, the response will be non-deterministic, and the ensemble average is taken. Literature involving energy harvesting have addressed such case; however, the change of the structure anisotropy has not been investigated in the literature. In this paper, an investigation on the performance of the ceramic-type PEH attached to a non-DS composite plate was conducted using FEA. The investigation focuses on changing the piezoelectric shape and the plate’s anisotropy on the developed voltage. The study proposed seven different piezoelectric patch shapes holding the material properties of the PZT-5A attached to a simply supported laminated composite. Randomly located point masses and time-harmonic point forces were distributed on the host plate to generate uncertainties in the structure, producing the effect of an infinite plate that generates the effect of a non-DS. The investigation was performed by employing the ensemble average response. The electric potential across the piezoelectric patch was obtained, and the ensemble responses were analyzed. This paper is organized as follows: Section 2 presents the methodology where the mathematical modelling and the FEA model construction are discussed including: composite laminate construction, geometry, material properties, meshing and boundary conditions. Section 3 complies with the ensemble response results obtained from the performed simulations and discusses the response behavior. The paper ends with a summary of the completed work and presents the finding of the research."
} | 3,315 |
39276241 | PMC11401804 | pmc | 4,598 | {
"abstract": "A key aspect of sustainable bioeconomy is the recirculation of renewable, agricultural waste streams as substrates for microbial production of high-value compounds. One approach is the bioconversion of corn stover, an abundant maize crop byproduct, using the fungal maize pathogen Ustilago maydis. U. maydis is already used as a unicellular biocatalyst in the production of several industrially-relevant compounds using plant biomass hydrolysates. In this study, we demonstrate that U. maydis can grow using untreated corn stover as its sole carbon source. We developed a small-scale bioreactor platform to investigate U. maydis processing of corn stover, combining online monitoring of fungal growth and metabolic activity profiles with biochemical analyses of the pre- and post-fermentation residues. Our results reveal that U. maydis primarily utilizes soluble sugars i.e., glucose, sucrose and fructose present in corn stover, with only limited exploitation of the abundant lignocellulosic carbohydrates. Thus, we further explored the biotechnological potential of enhancing U. maydis´ lignocellulosic utilization. Additive performance improvements of up to 120 % were achieved when using a maize mutant with increased biomass digestibility, co-fermentation with a commercial cellulolytic enzyme cocktail, and exploiting engineered fungal strains expressing diverse lignocellulose-degrading enzymes. This work represents a key step towards scaling up the production of sustainable compounds from corn stover using U. maydis and provides a tool for the detailed monitoring of the fungal processing of plant biomass substrates. Graphical abstract \n Supplementary Information The online version contains supplementary material available at 10.1186/s40643-024-00802-3.",
"conclusion": "Conclusions Our study describes the implementation of a microtiter plate screening platform for the analysis of U. maydis growing on corn stover. The method combines the online monitoring of fungal growth and metabolic parameters with compositional analysis of the pre- and post-fermentation residue, allowing for a detailed characterization of the microbe performance and the utilization of the diverse carbohydrate sources present. Our results demonstrate that U. maydis is able to utilize corn stover as the sole nutrient source. The quasi-continuous monitoring of scattered light, Gfp fluorescence, pH and OTR, along with the microscopic and biochemical estimation of the amount of fungal material in the post-fermentation residue allowed us to reach an unprecedented level of detail to profile fungal growth. Additionally, the method allowed us to dissect the specific utilization of each of the diverse carbohydrate sources present in corn stover. Our data reveal that U. maydis mostly utilizes soluble sugars i.e., glucose, sucrose and fructose when growing on corn stover, while only a small fraction of the lignocellulosic carbohydrates are hydrolyzed. This result might be unexpected, given the well documented repertoire of potential lignocellulose-degrading enzymes encoded in U. maydis genome (Mueller et al. 2008 ; Doehlemann et al. 2008 ; Couturier et al. 2012 ; Reyre et al. 2022 ). The low expression level of these lignocellulose-degrading enzymes under the fermentation conditions used might explain this observation. Although further research is necessary to explore this and other possibilities, these results highlight the biotechnological potential of enhancing degradation of the abundant lignocellulose materials contained in corn stover to improve fungal performance. Even though the low overall substrate utilization ratio of corn stover by U. maydis in our system limits direct industrial applications, it also unveils prospective applications to improve the process by multiple angles. All approaches considered here were successful in boosting fungal performance but with varying degrees of effectiveness, with Celluclast® supplementation showing the best increase in fungal performance. In future applications, the method could also be used to study the effect of enzymes added exogenously to the fermentation reaction or secreted by U. maydis as the pipeline enables the investigation of enzyme properties including catalytic activity, substrate specificity or synergistic effects on complex natural lignocellulosic substrates. The enhanced performance observed when using bm3 -derived corn stover highlights that lignocellulose traits of the plant biomass affect the plant biomass bioconversion process. The semi-high throughput nature of the online monitoring method is compatible with large-scale screenings to evaluate the suitability of different plant biomass sources. The extensive diversity of genetic resources available for maize could be screened to identify favorable plant biomass bioconversion traits, such as a higher abundance of specific substrates, lower presence of inhibiting compounds, or modified wall architecture facilitating a better enzyme accessibility. Similarly, it could be applied to the assessment of mechanical, thermal, or chemical pretreatments aiming to change the structure, accessibility or degradability of the diverse lignocellulose components. In sum, the outlined screening method serves as the foundational framework for prospective applications of corn stover as a renewable substrate for consolidated bioprocessing involving U. maydis as a valuable tool to scale-up the fermentation from laboratory scale to production scales.",
"discussion": "Results and Discussion Ustilago maydis can utilize corn stover The ability of the fungus U. maydis to grow on increasing amounts of dried, milled corn stalks was investigated (Fig. 1 A). The performance of U. maydis was assessed online using a BioLector® system, where scattered light was quasi-continuously monitored to measure the media's turbidity. This measurement served as a proxy for U. maydis growth as scattered light correlates with cell density (Samorski et al. 2005 ). The previously characterized U. maydis P oma bgl1 strain (Geiser et al. 2016 ) was modified to express the green fluorescent protein (Gfp) as a second method to monitor and quantify fungal growth. In the control with fungal inoculum but without corn stover, only a slight uptick in the scattered light was observed, reaching a maximum of 13.9 ± 0.1 a.u. after 22 h. This increase is likely due to residual nutrients still present in the inoculum media. In contrast, when the media was supplemented with corn stover, there was a notable increase in the scattered light, indicative of fungal biomass production. This surge began around 6 h after inoculation in all corn stover concentrations tested. While the maximum scattered light values were observed after 23 h using 3 g/L or 10 g/L corn stover, increasing the corn stover material to 20 g/L resulted in a maximum signal after 14 h (Fig. 1 A). Microscopic inspection of the cultures after 16 h confirmed the presence of dividing fungal cells in the yeast form, mirroring cells grown in glucose (Glc) as sole carbon source (Fig. 2 ). Fig. 1 Online performance monitoring of Ustilago maydis on corn stover. ( A ) Scattered light reading of fungal growth in medium supplemented with 3 g/L (light blue), 10 g/L (purple) or 20 g/L (black) ground B73 corn stem material from 4 individual plants in comparison to medium without addition of B73 stem material (red) (n = 2). ( B ) Scattered light (black) and Gfp fluorescence (green) monitoring of fungal growth in medium supplemented with 20 g/L of B73 (filled icons) or bm3 (white icons) plant material as carbon source. The data are shown as the results of two independent fermentation experiments with independent fungal inoculums and 4 or 6 plants of B73 or bm3 , respectively. The AVG ± SD is calculated from the resulting n = 8 and n = 12 for B73 and bm3 , respectively. ( C ) pH (blue) and oxygen transfer rate (OTR; brown) monitoring of fungal growth in medium supplemented with 20 g/L of B73 (filled icons) or bm3 (white icons) plant material as carbon source. The pH data are shown as the results of the same two independent fermentation experiments as scattered light and Gfp fluorescence, resulting in n = 8 and n = 12 for B73 and bm3 , respectively. The OTR was monitored during one fermentation and the data are the AVG ± SD of n = 4 and n = 6 replicates for B73 and bm3, respectively Fig. 2 Growth of U. maydis \n Gfp P oma bgl1 on corn stover. The used strain produced cytoplasmic Gfp accumulating in the cytosol and nucleus. ( A ) Cultivation of U. maydis with glucose as single carbon source visualized 16 h post inoculation (dividing cell). Scale bar, 10 µm. ( B ) Cultivation of U. maydis with corn stover as single carbon source visualized 16 h post inoculation (dividing cell). Scale bar, 10 µm. ( C ) Cultivation of U. maydis with corn stover as single carbon source visualized 16 h post inoculation (culture overview). White arrowheads indicate autofluorescent biomass particles. Scale bar, 20 µm U. maydis growth performance on 20 g/L corn stover was further characterized by measuring metabolic activity parameters such as pH and oxygen transfer rate (OTR) in parallel to scattered light (Fig. 1 B and C) combining BioLector® with µRAMOS technologies (Ladner et al. 2016 ). Measurements of the scattered light in cultures can be influenced by cell shape, cell size, and the corn stover particles in the system (Kunze et al. 2014 ). However, in the conditions tested, online monitoring of the Gfp fluorescence emitted by the employed U. maydis strain mirrored the scattered light curve. These results indicate that both methods can be used to estimate fungal growth. Together with the initiation of the exponential growth phase after 6 h, the pH of the media decreased from its initial pH of 5.8 to a minimum of 5.4 after 12 h. This initial drop in pH may be indicative of increased metabolic activity involving for example the production of organic acids, or the release of acetic acid resulting from the breakdown of plant wall material. Aligning with the onset of the stationary growth phase, the pH of the media began to rise, and reached a 6.3 value by the end of the cultivation period (Fig. 1 C). A similar shift in pH in U. maydis fermentations have been interpreted as a fungal response to nutrient limitations characterized by a transition to less acidic metabolic pathways (Geiser et al. 2014 ; Terfrüchte et al. 2018 ). The OTR rapidly increased during the exponential growth phase consistent with an increase in metabolic activity of the fungus, as U. maydis metabolizes substrates and actively replicates, consuming oxygen. The OTR reached its maximum after 14 h, coinciding with the maximum fungal cell density determined by Gfp fluorescence and scattered light measurements. After a short plateau, the OTR decreased rapidly suggesting a decline in the metabolic activity (Flitsch et al. 2016 ; Ladner et al. 2016 ). OTR values reached a basal level after 20 h, which remained constant until the end of cultivation (Fig. 1 C). The Gfp fluorescence signal increased correlating with the increment in scattered light signal, further confirming fungal proliferation and its quantitative traceability in the developed system (Fig. 1 B). Collectively, these results show that U. maydis can utilize corn stover as a carbon source. Moreover, the developed screening method allows a detailed characterization of the fungal performance on corn stover in a microtiter scale by simultaneously recording diverse growth (scattered light and Gfp) and metabolic activity (OTR and pH) parameters online."
} | 2,914 |
31700999 | PMC6824861 | pmc | 4,599 | {
"abstract": "Electrical signals in networks of nanoparticles emulate correlated avalanches of signals and criticality in the brain.",
"introduction": "INTRODUCTION There is currently huge interest in building neuromorphic computational systems that operate on brain-like principles to perform certain tasks (e.g., classification and pattern recognition) much more efficiently than on traditional computers. Neuromorphic processing has been implemented in a number of hardware platforms, using components ranging from traditional silicon-based technology ( 1 , 2 ), crossbar memristor arrays ( 3 – 6 ), and novel nanoscale device elements that use atomic-scale dynamics to mimic the behavior of synapses and neurons ( 7 – 9 ). All of these approaches rely on highly ordered architectures, but neuromorphic computational paradigms such as reservoir computing (RC) ( 10 – 12 ) require complex networks with strong inherent spatiotemporal correlations ( 13 , 14 ). Maximal computational performance ( 15 – 17 ) in these systems requires network architectures that are not only complex but also critical ( 18 ). At a critical point ( 17 – 20 ), the underlying network is scale free ( 21 , 22 ), and the dynamics are characterized by scale-invariant avalanches, as observed in many systems ranging from earthquakes to biological extinctions and magnetization dynamics ( 23 ). Put simply, a critical system is poised such that any event is likely to trigger subsequent events, leading to a cascade ( 15 ). Avalanches of events are an important feature of both in vivo and in vitro recordings of neuronal signals ( 24 – 27 ), and substantial evidence has accumulated that the brain itself operates at a self-organized critical point ( 20 ), which optimizes information processing, memory, and information transfer ( 15 – 19 ). Therefore, in designing brain-inspired computational systems, it is natural to look for systems that might exhibit similar critical behavior and, especially, systems that would allow electronic signal processing. The percolation transition Percolating systems ( 28 ) are obvious candidates for investigation since the concept of percolation ( Fig. 1 ) is central to discussions of criticality and avalanches ( 17 ). The two-dimensional percolating system of interest here comprises conducting nanoparticles deposited on an insulating substrate ( 29 ) ( Fig. 1A ). During deposition (see Materials and Methods), particles come into contact and form groups that are separated from each other by tunnel gaps. The groups increase in size and complexity as more particles are deposited until the onset of conduction across the system ( Fig. 1, B and D ) when the fraction of the surface that is covered with conducting elements is p c ~68% ( 28 – 31 ). This onset marks the percolation threshold, which is a critical point between insulating and conducting phases ( 28 ) at which avalanches might be expected to propagate ( 17 ) (see next paragraph). We hypothesized that when poised at this phase transition, the percolating assembly of nanoparticles might be promising for neuromorphic computing since (i) the correlation length diverges ( 28 ) and so long-range correlations are expected; (ii) it has previously been established that synapse-like atomic-scale switching processes occur in the tunnel gaps ( 29 ); (iii) devices are stable over periods of months ( 32 ) as required for real-world applications; and (iv) the complex networks are self-organized by simple, cost-effective processes ( 32 ). Fig. 1 Device schematic, atomic switches, and percolating networks. ( A ) Top: Schematic diagram illustrating our simple two-terminal contact geometry and the percolating network of nanoparticles. The different colors represent groups of particles that are in contact with one another. The zoomed region shows a schematic of the growth of an atomic filament within a tunnel gap (switching event) when a voltage is applied. Bottom: The same network schematic presented so as to show the conducting pathways (black) that result from atomic filament formation within the gaps between groups. ( B ) Schematic showing the conductance of a device during deposition of conducting nanoparticles follows ( 28 ) a power law ~( p − p c ) 1.3 ( 30 ) (the critical surface coverage is p c ~68%). arb, arbitrary units. ( C ) A scanning electron microscope image of a percolating device. Scale bar, 200 nm. ( D ) Schematic diagrams showing the following: left—in the subcritical, insulating phase at low coverage, groups of particles are small and well separated, so that if an atomic switch connects two groups, then there are few possibilities that this will trigger another switching event; right—in the supercritical, conducting phase at higher coverages, highly connected pathways across the network mean that when an atomic filament bridges a tunnel gap, an avalanche can propagate only to a few nearby tunnel gaps; center—in the critical phase, avalanches propagate on multiple length and time scales. See also fig. S2 for further details. Near the percolation threshold, tunneling across gaps between particles contributes to the conductivity of the network (see Fig. 1 and fig. S1), and it is necessary to go beyond standard approaches to continuum percolation that consider only ohmic connections between well-connected particles ( 28 ). Percolating-tunneling systems have, however, received relatively little theoretical attention [see ( 31 , 33 ) and references therein), and quantities such as the relevant percolation critical exponents are yet to be calculated. Experimentally, it is observed that external electrical stimuli cause conductive filaments to be continually formed and broken within the tunnel gaps ( Fig. 1A , insets) ( 29 ), leading to a complex and dynamical network comprising conducting pathways, switches, and tunnel junctions (further illustrated in fig. S2). Behavior of critical networks The importance of criticality for neuromorphic computing was discussed in previous work on silver nanowire networks ( 13 ), but criticality in self-organized nanodevices has never been investigated systematically. Here, we investigate criticality in percolating nanoparticle networks using analysis techniques similar to those previously used on recordings of neuronal signals ( 24 – 27 ). Figure 1 and fig. S2 show how avalanches are generated in the dynamical percolating network. When an atomic switch is activated in one tunnel gap, consequent changes in the electric field (and current) in other tunnel gaps cause further events at other locations in the network ( 34 ). That is, each successive switching event leads to others, creating an avalanche. As illustrated in Fig. 1D and fig. S2, the surface coverage is important in controlling the network connectivity: If too many (or too few) events are triggered, then the avalanche will accelerate uncontrollably (or die quickly)—critical dynamics are characterized by power law distributions in which there is no single characteristic length or time scale ( 17 ) and in which there is a small but important chance that very large avalanches are observed [see Fig. 1 , fig. S2, and ( 15 )]. While the propagation of avalanches is commonly described as a percolation phenomenon ( 17 ), avalanches have not previously been reported in percolating networks.",
"discussion": "DISCUSSION The avalanches of switching events observed here exhibit statistics that are qualitatively and quantitatively similar to those of the avalanches observed in cortical tissue ( 25 , 41 ) and in very recent work on whole organisms ( 26 ). Within the brain, criticality is hypothesized to play a role in cognition, adaptive behavior, and disease diagnosis and underpins several therapeutic and diagnostic opportunities ( 19 ). Criticality in our system originates from long-range spatiotemporal correlations in the network of atomic switches, as required ( 18 ) for neuromorphic computing strategies such as RC ( 10 ). In RC, spatiotemporal correlations are required to allow projection of input signals into higher-dimensional spaces; corresponding output signals can then be combined using linear regression to perform computational tasks such as classification, pattern recognition, and time series prediction ( 10 – 12 ). The need to build critical systems to provide a platform for RC has previously been highlighted in ( 17 , 18 ). Our percolating networks meet that need. Previous demonstrations of RC using nanoscale devices have relied on small numbers of device elements ( 11 , 12 ) because integration into appropriate physical networks is challenging and/or expensive. Furthermore, it is not clear that complex/critical networks could be achieved using lithographic processing. In contrast, the straightforward deposition methods used here immediately provide the required complex network of switches, are inexpensive, and allow electronic readout of correlated signals from the network as required for integration with other electronic components. We believe our processes are scalable because they are compatible with standard complementary metal-oxide semiconductor (CMOS) processing. Our percolating networks are naturally suited to RC since deposition onto multielectrode arrays will straightforwardly provide the required multiple inputs and outputs. Ultimately, we envisage architectures in which the percolating network is deposited onto chips with predefined CMOS circuitry that processes input and output signals. The question of how best to code input/output signals for particular computational tasks has so far been given little consideration in the RC literature and will require further investigation since combinations of temporal and spatial input/output coding are possible ( 12 ). We believe that percolating devices could provide many further opportunities for brain-like computing; for example, three-dimensional percolating systems could provide additional network complexity. While our attempts to tune the operating point of the system by varying the surface coverage were unsuccessful (see Materials and Methods), it may yet be possible to tune the system through the critical point using other external control parameters. In addition, strategies can be envisaged that would incorporate a wide range of alternative switching elements within the networks, such as phase-change materials, oxide-based memristors, and electrochemical switches ( 6 – 9 ). Last, we highlight the need for theoretical work/simulations to support device development. Percolating-tunneling networks have so far been relatively little studied ( 31 , 33 ), and important quantities such as the degree distribution for network connections ( 21 ), percolation critical exponents ( 28 ), and precise universality class ( 23 ) are yet to be elucidated."
} | 2,697 |
30066464 | PMC6116750 | pmc | 4,602 | {
"abstract": "Summary Phytoremediation is a green and sustainable alternative to physico‐chemical methods for contaminated soil remediation. One of the flavours of phytoremediation is rhizoremediation, where plant roots stimulate soil microbes to degrade organic contaminants. This approach is particularly interesting as it takes advantage of naturally evolved interaction mechanisms between plant and microorganisms and often results in a complete mineralization of the contaminants (i.e. transformation to water and CO \n 2 ). However, many biotic and abiotic factors influence the outcome of this interaction, resulting in variable efficiency of the remediation process. The difficulty to predict precisely the timeframe associated with rhizoremediation leads to low adoption rates of this green technology. Here, we review recent literature related to rhizoremediation, with a particular focus on soil organisms. We then expand on the potential of rhizoremediation to be a model plant‐microbe interaction system for microbiome manipulation studies.",
"introduction": "Introduction Phytoremediation is the use of plants to remediate contaminated environments (usually soils, but also water). Many processes can be involved in the removal of the pollutants such as phytovolatilization (the removal of volatile compounds through plant tissues), phytotransformation (the transformation of contaminants from one state to another), phytostabilization (the stabilization of mobile contaminants in the soil), phytoextraction (the removal of trace elements from the soil and its fixation in plant tissues). Although all these processes involve both the plant and its microbiota, rhizoremediation clearly stands out as an integrated plant‐microbes endeavour. Rhizoremediation is the degradation of organic pollutants in the soil zone surrounding the plant roots (the rhizosphere), usually as a result of the stimulation of the catalytic activities of microorganisms by the plant roots (Pilon‐Smits, 2005 ). For many organic contaminants, such as most petroleum hydrocarbons, rhizoremediation results in the complete mineralization of the contaminants, effectively removing it from the environment. The principle behind rhizoremediation is simple: as the plant roots colonize the contaminated soil, as for any soil, they associate with a subset of the microorganisms present in the soil and stimulate them through the exudation of a variety of organic compounds (Kuiper et al ., 2004 ) (Fig. 1 A). Some of the microbes stimulated by the root exudates are also able to degrade petroleum hydrocarbons. Many facets of the rhizosphere environment make this soil zone particularly appropriate for the degradation of organic contaminants. First, the plant secondary metabolites that are part of the exudates are often structurally very similar to organic contaminants (Singer et al ., 2003 ). This results in a heightened presence and activity of microbes being able to degrade organic contaminants in the rhizosphere, even in the absence of contaminants (Yergeau et al ., 2014 ). Second, because of the presence of the root exudates, the rhizosphere microbial communities are generally more active and more abundant than microbial communities in the bulk soil (i.e. not under the influence of the roots) (Smalla et al ., 2001 ; Kowalchuk et al ., 2002 ). Third, the rhizosphere is generally recognized as a hotspot for horizontal gene transfer (Van Elsas and Bailey, 2002 ), and plasmids were shown to help microorganisms adapt to contamination stress and degrade organic compounds (Top and Springael, 2003 ; Sentchilo et al ., 2013 ). Additionally, some root exudates help detach organic contaminants from the organic matter present in soil, making them more available to microbes (Gao et al ., 2010 ). Altogether, this again highlights the distinct roles of plants and microorganisms during rhizoremediation: the plant act as a promoter for microbial degraders, by providing them with a suitable environment and stimulating them through root exudates. The suitability of the rhizosphere environment for microbial processes related to the degradation of hydrocarbons also exposes one of the major pitfalls of rhizoremediation: it only works where plant roots are. Therefore, in compacted or very clayey soils, or in cases where contamination is deeper than the root zone, or at too high concentration for roots to survive, rhizoremediation is not effective. As root growth patterns and exudates amount and quality differ between different plants, even between closely related genotypes (O'Toole and Bland, 1987 ; Jones et al ., 2004 ; Manschadi et al ., 2006 ), the choice of an appropriate plant genotype is crucial in rhizoremediation. Figure 1 Major plant–microbe interactions occurring during rhizoremediation. In (A), plant root exudates (1) stimulate hydrocarbon‐degrading bacteria and (2) help to desorb contaminants attached to soil particles, making them more available to rhizobacteria. In (B), rhizosphere microorganisms promote plant growth through, among many other mechanisms, (3) the production of plant hormones and (4) the degradation of 1‐aminocyclopropane‐1‐carboxylic acid ( ACC ), the precursor of the stress hormone ethylene. PHC , petroleum hydrocarbons; alkB , alkane mono‐oxygenase, ndoB , naphthalene dioxygenase, xylE , catechol‐2,3‐dioxygenase; OA , oxalic acid; CA , citric acid; PAH , polycyclic aromatic hydrocarbon; PGPR , plant growth promoting rhizobacteria; IAA , indolacetic acid; CK , cytokinin; GA , gibberellic acid; ACC , 1‐aminocyclopropane‐1‐carboxylic acid; ACC d, ACC deaminase; ACO , ACC oxidase. The rhizosphere microbes, especially bacteria, are thought to be the major players in organic contaminant degradation during rhizoremediation (Bell et al ., 2014a , b ; El Amrani et al ., 2015 ), and recent plant‐microbe metatranscriptomic studies confirmed that the hydrocarbon degradation genes expressed in the root‐rhizosphere environment were mostly linked to bacteria (Gonzalez et al ., 2018 ; Yergeau et al ., 2018 ) (Box 1 ). Petroleum hydrocarbon contamination is often composed of a mixture of saturated aliphatic (alkanes) and aromatic hydrocarbons (including polycyclic aromatic hydrocarbons, PAHs). Microorganisms can degrade virtually all the hydrocarbons present in petroleum through various pathways, although with different efficiencies. In addition to this central role, microbes also have another major role in rhizoremediation (Fig. 1 B). Indeed, microbes known as plant growth promoting rhizobacteria (PGPR) are recognized to have the capacity to increase plant growth (Kloepper and Schroth, 1978 ), and the ones that can increase root growth are particularly interesting in the context of rhizoremediation. On top of their ability to promote the growth of plants through the production of plant hormones or the mobilization of nutrients, PGPR also have the capacity to reduce plant stress through various mechanisms (Rajkumar et al ., 2012 ; De Zelicourt et al ., 2013 ), including through the reduction of ethylene concentrations in the roots (Glick et al ., 1998 ; Glick, 2003 ) which would allow a plant to grow in highly contaminated environments without the adverse effects of stress (Burd et al ., 2000 ). Box 1 The holobiont and the hologenome. All multicellular eukaryotes are associated with a wide diversity of microorganisms, forming an inseparable entity known as a holobiont (Rosenberg et al ., 2009 ; Bordenstein et al ., 2015 ; Van Opstal and Bordenstein, 2015 ; Theis et al ., 2016 ). This observation has led Ilana Zilber‐Rosenberg and Eugene Rosenberg to enounce the hologenome theory of evolution that states that the hologenome (the combined genomes of the host and its microbiota) forms one of the units of evolution (Zilber‐Rosenberg and Rosenberg, 2008 ). Consequently, it is predicted that holobionts can rapidly evolve/adapt through their microbiota by: (i) horizontal gene transfer among their existing microbiota, (ii) recruitment of new microbes from the environment, (iii) shifts in the relative abundance/gene expression of various members of the microbiota. These mechanisms are thought to enable holobionts to adapt within a single or a few generations (Voss et al ., 2015 ; Rosenberg and Zilber‐Rosenberg, 2016 ). It has recently been shown that the response of willows to stressful conditions (soil contamination) results in large shifts in the metatranscriptome of root and rhizosphere bacterial and fungal communities, but not in the plant root transcriptome (Gonzalez et al ., 2018 ; Yergeau et al ., 2018 ). Taken together, these results emphasize the importance of the plant microbiota in the response to environmental stresses and confirms that microbiota manipulation is a viable alternative to optimize phytoremediation (El Amrani et al ., 2015 ; Quiza et al ., 2015 ). Rhizoremediation offers a unique system to study plant‐microbe interactions and experiment with microbiome manipulation approaches. First, the response variable of interest is easily measurable: a lowered soil contamination. Second, the hydrocarbon degradation pathways are well known, and the genes are well represented and annotated in databases (e.g. the biocatalysis/biodegradation database, http://eawag-bbd.ethz.ch/ ). Third, the capacity to degrade hydrocarbons is widespread among bacteria, and major players, such as Pseudomonas and Rhodococcus can be easily cultured. It is thus relatively easy to create consortia of hydrocarbon‐degrading bacteria, follow their fate in the environment using molecular tools and measure their effect on rhizoremediation efficiency. It is also possible to measure the effects of various manipulations on the hydrocarbon‐degrading microbiota abundance and activities in the rhizosphere using relatively inexpensive molecular tools such as qPCR."
} | 2,474 |
38374280 | PMC10876929 | pmc | 4,605 | {
"abstract": "Biomolecular systems are dependent on a complex interplay of forces. Modern force spectroscopy techniques provide means of interrogating these forces, but they are not optimized for studies in constrained environments as they require attachment to micron-scale probes such as beads or cantilevers. Nanomechanical devices are a promising alternative, but this requires versatile designs that can be tuned to respond to a wide range of forces. We investigate the properties of a nanoscale force sensitive DNA origami device which is highly customizable in geometry, functionalization, and mechanical properties. The device, referred to as the NanoDyn, has a binary (open or closed) response to an applied force by undergoing a reversible structural transition. The transition force is tuned with minor alterations of 1 to 3 DNA oligonucleotides and spans tens of picoNewtons (pN). The DNA oligonucleotide design parameters also strongly influence the efficiency of resetting the initial state, with higher stability devices (≳10 pN) resetting more reliably during repeated force-loading cycles. Finally, we show the opening force is tunable in real time by adding a single DNA oligonucleotide. These results establish the potential of the NanoDyn as a versatile force sensor and provide fundamental insights into how design parameters modulate mechanical and dynamic properties.",
"introduction": "Introduction Biomolecular functions are often driven by inter- and intramolecular forces. Thus, elucidating the forces within and between biomolecular systems provides critical insight into the mechanisms of their functions 1 – 3 . Molecular force spectroscopy has been a powerful approach for probing the interactions that are responsible for these forces and providing mechanistic insight into function 4 – 7 . However, current force spectroscopy techniques have limitations such as challenges with force measurements in constrained environments. For instance, both magnetic and optical tweezers necessitate the use of large (> 1 µM) beads, which act as handles for applying forces on nanoscale samples 6 – 9 . Atomic force microscopy requires the sample be attached to a cantilever tip 4 , 6 , 10 – 13 . These methodologies are limited to systems where space is available for the handles, which makes it challenging to implement these approaches within cells 14 , 15 and nanofluidic devices 16 , 17 . Nanomechanical devices are a promising alternative approach to probe molecular forces, but this requires versatile device designs that can easily be tuned to respond to a wide range of forces. Here we present the development of a DNA Origami (DO) nanodevice that has the potential to address these challenges, with a focus on establishing simple changes in design parameters that allow versatile tuning of the force response. DO nanotechnology has significant promise in developing nanodevices for complex functions including drug delivery 18 – 20 , molecular sensing 21 , 22 , and probing single molecule dynamics and interactions 23 – 28 . More specifically, DO has been established as a useful approach for single molecule force sensing, with demonstration of DO devices applying and responding to both tensile and compressive forces 29 – 32 . Complex and dynamic 3-dimensional DO nanodevices can perform prescribed functions through controlled actuation, making their use precise and reproducible 30 , 33 – 35 . DO devices are biocompatible, functionalizable, and on the nanometer (nm) size scale, which are key characteristics that position them to function within complex nanoscale environments. For example, DNA duplexes 36 , 37 and DO platforms 29 have been successfully implemented to investigate cellular forces by connecting these constructs between a cell and a surface. In the case of DNA duplexes, distinct constructs allow the measurement of different forces ranging from ~ 10 pN (constructs that rupture through unzipping) to ~ 50 pN (constructs that rupture in shear). However, duplex constructs are irreversible. Recent efforts 38 have developed reversible construct designs by adding a loop that keeps two strands that form a single interaction pair tethered together in the open state, but these still rely on different devices for different forces, and devices are not easily exchanged since the constructs are generally directly attached to the glass surface. Furthermore, the limited stability of duplex DNA could limit their use in other biological environments. DO devices are more stable than duplexes 39 providing an advantage for some applications, and the ability to integrate multiple force-sensitive interactions in DO devices provides modularity to tune force response without the need for redesign of the primary structure. Prior work 29 has demonstrated the inclusion of multiple hairpins in a DO device allows for tuning the rupture response over the range of ~ 8–19 pN, but these prior DO designs are not adjustable in real-time. DO devices provide the potential to be modified and tuned without the need for redesign of the primary structure and to be modified in realtime 40 . Here we take a distinct design approach where a base device is folded and then one or more ssDNA molecules are added to introduce one or more force-sensitive interactions after folding, or even after initial testing, to control the force-response of the device. We focus on a DO nanodevice, the NanoDyn (ND), which has been previously shown to be sensitive to compressive depletion forces 30 . Hudoba et al. introduced the ND as a sensitive reporter of compressive depletion forces due to local molecular crowding on the order of 100 femtoNewtons (fN) and with a lower limit of force detection of 40 fN. Here, we build on that research and demonstrate the utility of the ND not only as a highly sensitive reporter of compressive depletion forces, but also as a robust, dynamic device capable of responding to tensile forces ranging from a few picoNewtons up to tens of picoNewtons (pN) where device design parameters allow tunable control of the force response. Taking advantage of the modular nature of the ND, we show that an individual single stranded DNA molecule, which we refer to as a zipper strand, can be modified to set the force response and be incorporated after folding and purifying the ND. This allows for rapid and efficient tuning of the device and eschews the need to fold and purify a separate structure for different force applications. We investigated its response to tensile forces and determined that it can be tuned to be sensitive to a range of forces through the adjustment of 1 to 3 zipper strands. We show that the ND detection force can be adjusted between 5 and 13 pN by changing a single zipper strand within the device. We then demonstrate that by incorporating multiple zippers in parallel, the ND responds at forces of about 30 pN with the potential of even higher force induced opening. We find that more stable interactions (opening forces ≳ 10 pN) lead to a higher reclosure probability. Finally, we show that the force response range of the ND can be adjusted in real time by iteratively incorporating DNA zippers in situ. This study lays the groundwork for a modular and versatile force responding probe that has the potential to be used in complex biological systems where traditional force spectroscopy techniques are challenging or impractical to implement.",
"discussion": "Discussion In this work, we demonstrated that the DNA origami ND can function as a modular nanodevice that can be tuned to detect a range of tensile forces. The modular design allowed the base-structure to be folded, purified, and stored without force-responding loops. Then, immediately before use, the force detection range was customized by incorporating one or more zipper stands. We demonstrated that varying the zipper region length within a single force-responding loop modulated the opening force over a range of threefold up to a maximum unzipping force of about 15 pN 43 , 44 . We then showed that including multiple force-responding loops in parallel enabled a wider range of opening forces up to 26 pN with only 3 relatively weak zippers, which exceeds the inherent unzipping force of 15 pN. We found that the interaction stability affected the reversibility of ND, with median opening forces below 10 pN not reclosing reliably, indicating that they will not function well for the sensing of repeated force cycling. However, devices with a median opening force above 10 pN repeatedly reclosed, which confirms their utility in detecting repeated force applications. Finally, we showed that single DNA zipper strands can be iteratively incorporated into the ND during a force measurement. This opens the possibility for tuning the force-responding range of single NDs in real time during a measurement. This work expands the utility of using nanoscale force sensors as a complementary approach to existing force spectroscopy techniques. The ND has the potential to probe a wide range of forces in constrained environments where it can be difficult to implement other force spectroscopy techniques 4 , 6 – 12 . We previously showed that the ND can operate in crowded environments and detect compressive depletion force in the range of 0.05 to 1 pN 30 . Here in this work, we demonstrated the same ND base structure can also be used to detect tensile forces from 6 to at least 26 pN. The ability for the ND to operate in different modes for detecting both compressive and tensile forces with order of magnitude different force ranges indicates its high versatility for a DNA origami device 23 , 25 , 26 , 29 – 31 , 50 , 51 . In comparison to the previous study presented in Dutta et al . 29 , our results indicate the ND provides a wider dynamic range of force sensing. This is consistent with the idea that integrating the force-responding loops between the two-barrel structures allows for a more balanced distribution of the force on these force-responding loops. There is the potential for further versatility of the ND since up to six force-responding loops could be included in the ND, each with independent nucleotide sequences to which DNA zippers can be incorporated independently and reproducibly. It will be important to ensure efficient incorporation of all zipper stands. While we achieved full incorporation of 3 zippers within about 80% of NDs, further optimization will be important as additional force-responding loops are used. However, the number of zippers could be directly detected with single molecule fluorescence and photobleaching, which could alleviate the need for further optimization. Assuming an opening force of 10 pN of force per force-responding loop, using six force-responding loops should result in an opening force of more than 60pN of force. These large forces do occur in biological systems including the forces on phage genomes during viral packaging 52 , 53 and the forces on mitotic chromosomes during mitosis 54 . However, measurements of these high forces will require covalent attachments or multiple non-covalent attachments to prevent failure of the attachment before device opening and force detection 13 , 55 . The overall length of the ND at 100 nm in length is advantageous for constrained environments. However, for experiments requiring smaller devices, the overall length of the ND could be reduced by designing shorter barrels, while retaining the loop regions. The shortened length could be accomplished with the same DNA scaffold by increasing the width, or with a shorter DNA scaffold. In addition to our current method of monitoring relative length change with magnetic tweezers, a fluorophore pair that undergoes Förster Resonance Energy Transfer (FRET) can be incorporated into the ND with 2 fluorophore labeled oligos, as reported in Hudoba et al. 30 , where high FRET reports a closed state and low FRET reports the open state. This will allow detection of a force range in environments where attaching a force handle is not possible. In the broader context of applications for the ND, there is significant potential for investigating cell–cell and cell-surface interactions based on previous studies. ssDNA hairpins 36 , 37 and DNA origami platforms 29 have been successfully implemented to investigate intercellular forces. The ND could be used similarly where the modularity of the ND could complement these previously published elegant studies by enabling a wider range of force-sensing. Furthermore, iterative zipper incorporation would allow the force sensor to be tuned in conjunction with changes in the extracellular environment that cause the cells to adapt by changing their cell-surface interactions. Future studies will be needed to investigate these potential applications of this versatile nanoscale device."
} | 3,199 |
28245058 | null | s2 | 4,609 | {
"abstract": "Anoxygenic photosynthetic prokaryotes arose in ancient oceans ~3.5 billion years ago. The evolution of oxygenic photosynthesis by cyanobacteria followed soon after, enabling eukaryogenesis and the evolution of complex life. The Archaeplastida lineage dates back ~1.5 billion years to the domestication of a cyanobacterium. Eukaryotic algae have subsequently radiated throughout oceanic/freshwater/terrestrial environments, adopting distinctive morphological and developmental strategies for adaptation to diverse light environments. Descendants of the ancestral photosynthetic alga remain challenged by a typical diurnally fluctuating light supply ranging from ~0 to ~2000 μE m"
} | 169 |
34912849 | PMC8667554 | pmc | 4,610 | {
"abstract": "An artificial cell is a simplified model of a living system, bringing breakthroughs into both basic life science and applied research. The bottom-up strategy instructs the construction of an artificial cell from nonliving materials, which could be complicated and interdisciplinary considering the inherent complexity of living cells. Although significant progress has been achieved in the past 2 decades, the area is still facing some problems, such as poor compatibility with complex bio-systems, instability, and low standardization of the construction method. In this review, we propose creating artificial cells through the integration of different functional modules. Furthermore, we divide the function requirements of an artificial cell into four essential parts (metabolism, energy supplement, proliferation, and communication) and discuss the present researches. Then we propose that the compartment and the reestablishment of the communication system would be essential for the reasonable integration of functional modules. Although enormous challenges remain, the modular construction would facilitate the simplification and standardization of an artificial cell toward a natural living system. This function-based strategy would also broaden the application of artificial cells and represent the steps of imitating and surpassing nature.",
"introduction": "1 Introduction With the million years of development, biological cells have established a high structural hierarchy and complex metabolic networks. The complexity has greatly hindered our understandings of the basic principles or mechanisms of life activities, probably limiting the future applications of life science. Moreover, scientists have always wondered whether a living cell can be artificially achieved or even synthesized de novo . Some synthetic compartments with cell sizes have attracted considerable attention as cell mimics, for their potential to uncover the inherent complexity of life. These are so-called artificial cells, which can help us to further explore the origin of life ( Szostak et al., 2001 ; Rasmussen et al., 2004 ; Yang et al., 2013 ) and provide breakthroughs into application fields, such as drug delivery ( Zhang et al., 2008 ; Liu et al., 2009 ; Ashley et al., 2011 ), gene therapy ( Chang, 2005 ; Prakash and Jones, 2005 ), biosensing, and diagnostic ( Pardee et al., 2016 ; Jayaraman et al., 2018 ). Generally, there are two strategies to construct an artificial cell, top-down and bottom-up. The top-down strategy usually starts with a living cell. By eliminating or replacing the genome of a living cell, it can reach a “minimal cell” with minimal gene information for survival ( Gibson et al., 2010 ). However, this strategy might be limited to the inherent complexity of living cells themselves. The other strategy, bottom-up, aims to construct a cell-mimic by assembling from nonliving materials. As Richard Feynman declared, “What I cannot create, I do not understand.” The bottom-up strategy can leave design space to customize artificial cells with unique properties, which indicates that it cannot only reconstruct a living cell, but also create an unnatural system to discover new possible life forms and develop new exciting applications. The fundamental thinking to construct a bottom-up artificial cell can be summarized as the compartment of biochemistry networks, including gene replication, transcription, translation, metabolism, and signal transmission. The complicated reaction networks should be kept in a strictly organized and efficient state in a tiny space. Therefore, the compartment is a key process, as a cell mimic should not only be physically separated from the environment to form an independent individual but still keep in touch with the outside. Up to now, several materials have been applied to encapsulate the cell-free system. Lipids are most similar to the natural membrane, thus widely utilized to construct artificial cells ( Noireaux and Libchaber, 2004 ; van Nies et al., 2018 ). Furthermore, other functional materials, such as polymers ( Huang et al., 2013 ; Ugrinic et al., 2018 ), hydrogels ( Park et al., 2009 ; Lai et al., 2019 ), and coacervates ( Sokolova et al., 2013 ; Dora Tang et al., 2014 ), are also developed to meet new requirements. These compartments have provided a relatively stable interior and also enabled the communication between inside and outside ( Shen et al., 2018 ; Lai et al., 2019 ). As there is no clear and pervasive criterion for living, an artificial cell is usually synthesized to resemble one or more functions of a living cell and may differ from each other. Nonetheless, the ultimate goal is to reproduce cellular function and customize artificial cells based on personal application. The construction of bottom-up artificial cells has achieved significant progress since the 21st century. Simple biological functions can be realized inside a confined environment, including but not limited to the protein synthesis ( Noireaux et al., 2005 ; Park et al., 2009 ), photosynthesis ( Lee et al., 2018 ; Berhanu et al., 2019 ), membrane proliferating and division ( Matsuo et al., 2019 ), and signal transmission ( Niederholtmeyer et al., 2018 ; Joesaar et al., 2019 ). However, as natural cells are quite different from each other and capable of diverse life activities, it would be impossible to mimic every specific function. An effective strategy is to rebuild the functional networks by combining several basic blocks, just like Lego. In this case, people could create what they need through a standardized method. From the common ground of natural cells, the basic functions could be summarized into four aspects: substance metabolism, energy supplement, proliferation, and communication. Generally, the substance metabolism promotes dynamic communication among substrates, thus providing the material base for life. As most life activities are energy dissipative, it is crucial for the artificial cell to gain sufficient energy to support its functions. Another specific characteristic of a living system is the ability of proliferation, that is, to grow, copy themselves, and split. There seem to be three distinctive processes, but they should be considered as a whole for their equally critical efforts toward the subsequent generations. Moreover, an artificial cell should also have the ability to exchange information with and respond to the environment or other living systems. Based on the communication, it may adjust the internal activities, which paves the way for the adaptive evolution toward a real living system ( Szostak et al., 2001 ). In the following context, we mainly focus on the latest development of these function modules, the basic principles of construction, and their development in the future. Then we discuss their combination toward a complex artificial cell. Here we demonstrate that the unity should observe several disciplines to realize efficient synergistic behaviors. Through the integration of these four modules, one can realize the full complexity of a living cell in a methodical way or even customize the artificial cell with unnatural functions ( Figure 1 ). FIGURE 1 Bottom-up construction of artificial cells based on the functions. Considering the inherent complexity of living cells, constructing artificial cells through various functional modules seem feasible. The effective assembly of these modules can finally perform specific functions, such as chemical production, division and differentiation, or communication. Conversely, the desired functions can instruct the creation of various new modules. In this case, the customization of artificial cells based on the desired functions might eventually be realized."
} | 1,937 |
23139700 | null | s2 | 4,613 | {
"abstract": "Recent advances in microfluidics have enabled the molecular-level study of polymer dynamics using single DNA chains. Single polymer studies based on fluorescence microscopy allow for the direct observation of non-equilibrium polymer conformations and dynamical phenomena such as diffusion, relaxation, and molecular stretching pathways in flow. Microfluidic devices have enabled the precise control of model flow fields to study the non-equilibrium dynamics of soft materials, with device geometries including curved channels, cross-slots, and microfabricated obstacles and structures. This review explores recent microfluidic systems that have advanced the study of single polymer dynamics, while identifying new directions in the field that will further elucidate the relationship between polymer microstructure and bulk rheological properties."
} | 211 |
33587796 | PMC8048578 | pmc | 4,615 | {
"abstract": "Summary Methanol is an ubiquitous compound that plays a role in microbial processes as a carbon and energy source, intermediate in metabolic processes or as end product in fermentation. In anoxic environments, methanol can act as the sole carbon and energy source for several guilds of microorganisms: sulfate‐reducing microorganisms, nitrate‐reducing microorganisms, acetogens and methanogens. In marine sediments, these guilds compete for methanol as their common substrate, employing different biochemical pathways. In this review, we will give an overview of current knowledge of the various ways in which methanol reaches marine sediments, the ecology of microorganisms capable of utilizing methanol and their metabolism. Furthermore, through a metagenomic analysis, we shed light on the unknown diversity of methanol utilizers in marine sediments which is yet to be explored.",
"conclusion": "Concluding remarks Methanol is an important compound in the global biogeochemical cycles, which has received little attention in marine sediments. There are still many gaps in our knowledge on the prevalence of methanol in these environments, how it becomes available and which microorganisms are involved in its cycling. The utilization of non‐canonical metals in methanol dehydrogenases requires a rethinking of bio‐active metals involved in this process. Cultivation approaches fail to recover the vast majority of microorganisms from marine sediments. The use of metagenomic data provides a reliable indication of the diversity and potential functions of specific microorganisms. In this regard, our approach of data mining marine sediment metagenomes for key methanol metabolism genes indicated this compound is released and utilized in anoxic marine sediments worldwide in a variety of ways. Thus, it can be concluded that methanol utilization is an active force in anoxic marine sediments, based on their genomic presence in these environments. This methodology can aid in the further understanding of the ecology of marine anoxic systems.",
"introduction": "Introduction Marine sediments are rich in biomass and a source of unknown microbial diversity, with microbial cell densities as high as 10 9 cells per cubic centimetre (up to five orders of magnitude higher than the water column) (Jørgensen and Boetius, 2007 ). Marine sediments consist of deposits of clay, decaying organic matter, calciferous remains and other solids. While oxygen can diffuse in these sediments, it is rapidly consumed by aerobic organisms, in an oxic layer ranging from a few millimetres to several meters in depth, depending on a variety of factors such as organic matter input to sediment surface, sediment permeability, turbation by water currents or macrofauna, water column height, microbial activity or proximity to continental shelves (Glud, 2008 ; D'hondt et al ., 2015 ). The underlying sediment remains anoxic, where microbial fermentation and anaerobic respiration are the main metabolic processes. Molecules containing no carbon–carbon bonds such as trimethylamine, dimethylsulfide, methane and methanol are suggested to be important energy sources for microorganisms in these environments (Yanagawa et al ., 2016 ; Chistoserdova and Kalyuzhnaya, 2018 ; Sun et al ., 2019 ). As the fermentation products of common osmolytes or carbohydrates, these compounds are widely present in marine systems. Besides converted by microbial activity, they can influence the climate as atmospheric aerosols and as such, their role in marine environments is in general well‐reviewed (Reisch et al ., 2011 ; Lidbury et al ., 2017 ; Timmers et al ., 2017 ; Sun et al ., 2019 ). However, there is a lack of information on the microbial utilization of methanol in anoxic marine sediments. In this review, we aim to present what is currently known about the presence and fate of methanol in anoxic marine sediments. To provide insight into anaerobic microbial methanol utilization in diverse marine sediments, we performed a metagenome mining of 246 published metagenomes of anoxic marine sediments for key methanol utilization genes. This effort reveals the ubiquitousness of several genes involved in anaerobic methanol conversion in marine sediments, further supporting the importance of methanol‐utilizing microorganisms in these environments."
} | 1,075 |
31164613 | PMC6632161 | pmc | 4,616 | {
"abstract": "Due to the ongoing crises of fossil fuel depletion, climate change, and environmental pollution, microbial processes are increasingly considered as a potential alternative for cleaner and more efficient production of the diverse chemicals required for modern civilization. However, many issues, including low efficiency of raw material conversion and unintended release of genetically modified microorganisms into the environment, have limited the use of bioprocesses that rely on recombinant microorganisms. Cell-free metabolic engineering is emerging as a new approach that overcomes the limitations of existing cell-based systems. Instead of relying on metabolic processes carried out by living cells, cell-free metabolic engineering harnesses the metabolic activities of cell lysates in vitro. Such approaches offer several potential benefits, including operational simplicity, high conversion yield and productivity, and prevention of environmental release of microorganisms. In this article, we review the recent progress in this field and discuss the prospects of this technique as a next-generation bioconversion platform for the chemical industry.",
"conclusion": "4. Conclusions Cell-free metabolic engineering is expected to provide an alternative route for biological production of chemical compounds. As systems developed through cell-free metabolic engineering are independent of cell viability and growth and insulated from toxicity of the synthesized chemicals, they can offer increased flexibility and higher conversion efficiency. While most of the studies on this topic, including those discussed in this review, have been conducted using purified or extracted enzymes, an interesting approach would be to integrate cell-free metabolic engineering with PCR and cell-free protein synthesis to establish a directly programmable metabolic engineering platform [ 56 ]. For this concept to be developed into a practical method, additional techniques will be needed, including methods for expressing functional proteins and for regulating the expression levels of exogenous enzymes. Considering the marked progress in the development of such techniques [ 57 , 58 , 59 ], it is likely that genetically programmed and controlled cell-free metabolic engineering platforms will soon emerge.",
"introduction": "1. Introduction Owing to recent advances in genetic and genomic engineering techniques, microbial cells are increasingly being used as self-replicating microreactors that can produce diverse materials from exogenously introduced genes [ 1 , 2 ]. However, the use of living cells often prevents us from harnessing their full synthetic power. Living systems operate only within narrow condition ranges, including temperature, salt concentration and solvent properties. Toxicity or metabolic burden also limit high-volume production of recombinant products. In addition, the interconnectedness of cellular metabolic pathways often reduces substrate flux into synthetic pathways, thus lowering product yield and conversion efficiencies. Most of these problems stem from the requirement of living cells to maintain balanced homeostasis [ 3 ]. Liebig’s law of the minimum teaches us that deterioration of any essential cellular component can result in failure of the entire system, thus preventing the operation of the desired pathways. In theory, many of these problems can be avoided by using the individual biological components specifically required to produce the target products. In fact, the use of purified biosynthetic machinery in cell-free systems has a long history that spans several decades. A prominent example is the use of purified recombinant DNA polymerase. Purified DNA polymerase can be used for many more tasks than it performs in living cells. In addition to its common use for rapid DNA amplification in thermal cyclers, the DNA synthesis activity of DNA polymerases has been widely used for many applications in combination with various reagents and conditions, including diagnostic techniques and genetic mutagenesis [ 4 ]. Cell-free use of biosynthetic machinery has also been expanded to protein production, which is more complicated and requires many enzymes and translational factors. These components were purified or extracted from cells and successfully reconstituted to produce recombinant proteins directed by genetic programming contained in the reaction mixtures [ 5 , 6 , 7 , 8 , 9 ]. Cell-free metabolic engineering is the latest addition to these recent efforts to harness cellular functions outside of cells and it involves the use of purified or crude enzymes to produce chemical compounds [ 10 , 11 ]. Liberated from the requirement of maintaining cellular viability and growth, cell-free metabolic engineering provides far greater design flexibility and wider operational conditions for synthetic metabolic pathways. Cell-free metabolic engineering systems also offer important benefits that cannot be attained using living cells, including quantitative and precise assessment of performance by direct sampling, rapid cycles of design-build-test iterations and the capability to use non-natural or non-biological components. While the concept of cell-free metabolism was introduced as early as 100 years ago with the demonstration of ethanol production in crude yeast lysate [ 12 ], the use of enzymes has long been relegated to an auxiliary role in the production of structurally complex intermediates via organic synthesis approaches. However, growing demand for cleaner and more efficient chemical processes along with notable advances in genetic engineering and enzyme technology have led to recognition of cell-free synthetic approaches as a promising method for synthesizing the diverse range of chemical compounds used in industrial implications. This review summarizes recent efforts to harness the principle of cell-free synthesis to reproduce intracellular reaction pathways outside of a model system and to reach yields and productivity that are not achievable with current cell-based methods. In particular, our discussion focuses on two closely related topics: synthesis of enzymes that catalyze chemical conversion pathways with industrial implications and production of important chemicals via cell-free use of the necessary enzymes. We also discuss the potential to integrate cell-free enzyme synthesis and metabolic engineering to build DNA-programmed, cell-free metabolic engineering systems."
} | 1,607 |
40072809 | PMC11904071 | pmc | 4,617 | {
"abstract": "Highlights \n This review provides an overview of recent developments in soft ionic sensors inspired by biological sensory systems, focusing on their material properties and working principles. The features and working principles of natural and artificial sensing systems are investigated in terms of six categories: vision, tactile, auditory, gustatory, olfactory, and proximity sensing. The challenges encountered in developing soft ionic sensors and the future research directions to overcome these issues are discussed.",
"conclusion": "Conclusions and Future Directions In this review, we explore nature-inspired soft ionic sensors, focusing on the unique features of natural sensory systems, working mechanisms, and potential applications. Natural sensory systems exhibit remarkable sensitivity to environmental changes. These sensory systems provide valuable insights for the development of artificial sensors. These nature-inspired sensors have been advanced through integration with research fields of soft materials and iontronics. For example, soft ionic materials with adaptive properties (e.g., volume, resistance, color, and transparency) in response to external stimuli broaden their range of applications in nature-inspired sensors. We summarized soft ionic sensors into six types based on their sensing targets, performance, materials, and characteristics (Table 1 ). To compare the performance of soft ionic sensors, key metrics such as sensing range, sensitivity, accuracy, response time, and recovery time were investigated. However, due to the lack of standardized measurement protocols, sensors performance may vary under diverse environmental conditions. For instance, the ionic conductivity of these ionic sensors is affected by environmental temperature, which can be described with the Arrhenius equation [ 155 ]. The performance of soft ionic sensors is also affected by relative humidity. Under dry conditions, evaporation can potentially reduce sensor performance due to the loss of solvent molecules within ionic materials. For these reasons, the standardized measurement protocols are required for the quantitative evaluation of soft ionic sensor performance. For instance, sensor performance measurements could be conducted under standard room conditions (26 °C, 50% relative humidity), similar to the one-sun conditions in photovoltaics [ 156 ], or the ZT factors utilized in thermoelectric materials [ 157 ]. Such conditions provide a guideline for standardized measurements protocols, facilitating reproducibility and ensuring consistent fabrication of soft ionic sensors. Table 1 A summary of the soft material-based sensors Mimicked organ Sensing type Target Performances Material Refs Sensing range Sensitivity/ Accuracy Response/ Recovery time Characteristics Eyes Vision Light (photons) Focal length: 56 − 61 mm N/A N/A Field of view (FOV): 40˚ Tunable microlens Liquid crystal elastomer, PDMS [ 169 ] N/A N/A 30 s upon 230 mW cm −2 / N/A Lignt transmission: 10% − 70% Liquid crystal elastomer [ 170 ] Focal length: 1.37 mm N/A N/A Field of view (FOV): 160˚ Radius of curvature (R): 6.96 mm PDMS [ 92 ] Focal length: 16 mm N/A 19.2 ms / 23.9 ms Field of view (FOV): 100.1˚ Resolution: 4.6 \\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}$$\\times$$\\end{document} × 10 8 cm −2 PDMS [ 25 ] Skin Tactile Deformation (pressure) 0.7 − 20 N N/A / 98.7% with a machine learning model N/A Receptor density: 0.0625 U cm −2 PAAm hydrogel, silicone elastomer [ 110 ] 1.8 Pa − 50 kPa 25.8 nF kPa −1 over 10 kPa / N/A 47 / 63 ms Emulate the Piezo2 nanochannel TPU ionogel, PDMS [ 27 ] Strain Deformation (strain) > 600% N/A 140 / 230 ms Gauge factor: 3.94 PNIPAM hydrogel [ 1 ] > 1860% N/A 165 / 155 ms Gauge factor: 1.74 − 3.17 PAAm hydrogel [ 171 ] Ears Auditory Vibration (frequency) 1 − 150 Hz 4121 kPa −1 in stress / > 99% N/A Gauge factor: 12,787 at strain Anisotropic conductive gel [ 172 ] 20 Hz − 2 kHz 153—217 nF kPa −1 or 24 uC N −1 at a bias of 1.0 V / N/A N/A Detecting underwater sound PAAm hydrogel [ 173 ] 20 Hz − 3 kHz 900 nF kPa −1 / N/A N/A Gate-free hydrogel-graphene transistor PAAm hydrogel-graphene [ 174 ] 60 Hz − 20 kHz 0.173 mV kHz −1 / N/A N/A Linearity: maintain sensor linearity up to 1800 mV PVA hydrogel-graphene [ 26 ] 10 Hz − 100 kHz 24 mV Pa −1 / N/A N/A Detect sound underwater from different directions (0 − 90°) PAA-co-PAAm ionogel [ 175 ] Tongue Gustatory Ammonia 0.2454 − 1.25 ppm N/A N/A Colorimetric hydrogel with adhesive and self-healing properties PVA hydrogel [ 176 ] D(-)fructose (sweetness), NaCl (saltiness), acetic acid (sourness) 0.086 − 0.51 M N/A / 83.4% with a machine learning model N/A / < 40 min Durability: maintain sensitivity after 10 days Poly(DMAPS-co-HEMA) hydrogel [ 177 ] NaCl (saltiness) 0.02 − 6 wt% N/A < 1 s / N/A Durability: retains 10% of taste memory after 1000 s Chitosan ionogel [ 178 ] Monosodium glutamate (MSG), disodium inosinate (IMP) 10 –15 − 10 –2 M N/A N/A Detection of umami substances in fermented fish PAAm hydrogel-carbon nanotube [ 179 ] Potential of hydrogen (pH) 4.0 − 7.5 pH N/A N/A Durability: no obvious color changes in 5 weeks HEAA ionogel [ 180 ] Tannin acid (astringency), polyphenol (bitterness) 0.0005 − 1 wt% 0.292 wt% −1 / N/A < 10 s / N/A Durability: 10 days under 25 °C, 60% relative humidity PAAm hydrogel [ 28 ] Nose Olfactory Short-chain fatty acids (SCFAs) 0.07 − 1.30 ppm N/A / < 91.6% with a machine learning model N/A Durability: retained 50% functionality after 16 weeks P(VDF-HFP) ionogel [ 181 ] Mixed gas (H 2 , NH 3 , and C 2 H 5 OH) 0 − 1 ppm of NH 3 & 0 − 50 ppm of H 2 & C 2 H 5 OH N/A N/A Simultaneous detection of mixed-gas components P(VDF-HFP) ionogel [ 182 ] Viral proteins (H5N1, H1N1, and COVID-19) 0.1 fg mL −1 − 10 ng mL −1 N/A < 10 min / N/A Multi − channel ion − gated transistor PVA ionogel, PDMS [ 183 ] Nitrogen dioxide (NO 2 ) 2.66 − 600 ppm N/A / 81.2% with a machine learning model N/A Long-term retention time (19,000 s) PEGDA Ionogel [ 36 ] Electro- receptor Proximity Electrical field Distance: > 240 cm N/A N/A Sensing induced voltage: 720 mVpp PAAm hydrogel [ 40 ] Distance: 5 − 90 cm N/A / > 97% with a machine learning model N/A Ionic conductivity: 0.845 S m −1 PAAm-co-EG hydrogel, PDMS [ 41 ] Nature-inspired soft ionic sensors are key components in a wide variety of applications, particularly in human–machine interfaces and the field of soft robotics [ 158 ]. The high stretchability and softness of these sensors enable humans to more closely interact with soft robotics, resulting in seamless integration across various wearable device applications [ 159 , 160 ]. The stretchable sensors also have the potential in biosignal monitoring devices, providing user comfort during daily life [ 161 ]. Furthermore, recent advancements have enhanced their versatility, with specific features incorporated into each environment [ 162 ]. Biomimetic actuators, as well as sensors, demonstrate immense potential with their material (e.g., thermo-responsiveness, phase change, adhesion, self-healing) [ 163 – 167 ]. As an industrial application, they can be integrated with soft sensors and electronics [ 168 ]. For instance, such integration replicates the function of a human hand, allowing the adaptive grasping of various shapes and sizes. However, even though previous studies have proven the potential of promising soft ionic sensors, they are still in the early stages and face several challenges before they can be used in daily life. Here, we suggest future research directions in terms of four key perspectives: sensing in aquatic environments, achieving biodegradability for implantation, ensuring mechanical sustainability, and improving electrochemical stability (Fig. 12 ). Fig. 12 Schematic of future research directions for soft ionic sensors. a Sensing in aquatic environments, b achieving biodegradability for implantation, c ensuring mechanical sustainability, and d improving electrochemical stability are several remaining challenges to improve soft ionic sensing capability Sensing in Aquatic Environments Sensing capability of passive electroreception in aquatic environments has inspired the development of artificial proximity sensors. For instance, some species including rays and sharks locate their prey using electroreceptors densely distributed on their skin [ 184 , 185 ]. Inspired by these sensing capabilities, artificial proximity sensors can detect the relative distance of an object by measuring changes in electric fields. These proximity sensors are applicable as wearable devices, allowing humans to interact with their surroundings without physical contact. Some of proximity sensors that operate based on detecting changes in an electric field might suffer from reduced sensing capabilities in underwater, as moisture in the body can shield interactions with electric field (Fig. 12 a). To address this issue, replicating the dense electroreceptor networks observed in certain species offers an effective approach to improving the sensitivity of proximity sensors. This approach allows proximity sensors to achieve higher spatial resolution, enabling sensitive perception of target positions through the comparison of electric field intensities at each electroreceptor. As another example, the signal processing methods with machine learning present an effective approach in underwater sensing [ 186 ]. The feature extraction based on machine learning offers accurate pattern recognition and noise filtering from complex signal data. Therefore, utilizing machine learning techniques could improve the detection of low-level sensory signals and extraction of high-level features in aquatic environments. Achieving Biodegradability for Implantation Conventional wearable devices are commonly employed for monitoring vital signs in humans, yet their low deformability causes discomfort when attached to the skin surface. To address this inconvenience, implantable devices have been proposed. Skin-like soft materials with biocompatibility and a low Young's modulus have shown numerous applications in implantable devices [ 187 – 190 ]. However, several issues are still raised to utilize soft materials in implantable devices. For example, the implantation of devices necessitates surgical insertion and removal, which can cause inevitable pain and potentially leave scar on the skin (Fig. 12 b). To meet this issue, biodegradability is a key property that enables the design of implantable devices within the human body. Biodegradable devices are designed to be naturally eliminated from the body without additional surgical removal after task completion. One of the major challenges in implantable devices is their limited capability to control degradation time in the body. The ability to control the degradation time of implanted devices is necessary to extend their operational period or accelerate their removal in the body. To manage degradation period, endowing stimuli-responsive capability to the biodegradable materials could be one of attractive approaches including external stimuli (e.g., electromagnetic fields, thermal stimuli, or vibrations) [ 191 – 193 ]. Further advancements in functional soft materials with controllable biodegradation will lead to enable significant developments in implantable devices. Ensuring Mechanical Sustainability The low modulus of elasticity of ionic materials allows ionic material-based sensors to easily adapt and conform to external deformations, thus making them suitable for application to human skin. Further, the low level of modulus has contributed to enhancing their pressure and strain sensing capabilities [ 194 ]. For instance, soft ionic materials with a low Young’s modulus exhibit high compliance. The compliance of these materials ensures the stability of the interface for attachable devices, even under dynamic deformation of human skin [ 195 ]. However, the soft ionic materials must be resilient to maintain consistent sensing performance (Fig. 12 b). Repeated and excessive compressive and tensile deformation can cause unendurable volumetric changes in the polymer chain, which can in tern lead to unwanted permanent damage. These deformations can reconfigure polymer networks, potentially deteriorating their intrinsic resilience. Intense efforts to improve the mechanical properties of soft materials have been reported. The dehydration of soft ionic materials is a significant issue that can occur in ambient conditions. To address this issue, incorporating hygroscopic materials (e.g., LiCl, CaCl 2 , ethylene glycol) could be an effective approach in preventing evaporation [ 196 , 197 ]. However, these materials exhibit high sensitivity to humidity, which could result in unexpected swelling. The elastomeric encapsulation with chemical adhesion can protect sensor components and prevent evaporation, similar to the natural barrier of fruit peels. For instance, an ionic wire, fabricated by encapsulating conductive gel with a silicone tube, prevents evaporation of soft ionic materials [ 47 ]. The synthesis of polymers with double-network structures is an effective strategy to enhance mechanical properties of materials. For example, double-network hydrogel is a soft material that is stretchable and improves its toughness through covalent and ionic cross-linking of the polymer network [ 60 , 198 ]. The enhanced toughness is achieved by the unzipping of ionic cross-links in the double-network structure, thereby effectively dissipating energy. These double-network hydrogels demonstrate robust recovery capability after deformation caused by mechanical impact. However, excessive external force applied to the hydrogel causes the plastic deformation of polymer networks and inevitable failure. To recover their initial state and thus enhance durability, the self-healing capability has been introduced. The self-healing capability of materials enhances the durability of soft sensors, enabling them to recover their initial functionality for repeated use. The self-healing properties of soft materials, similar to skin, allow them to recover from mechanical damage and maintain consistent sensing performance [ 53 , 199 ]. Despite their self-healing properties, soft materials may exhibit limitations, such as misalignment or decreased transparency at the healed regions. In addition, a highly entangled polymer network was also introduced with greatly outnumbered cross-links by entanglements [ 200 ]. For example, a hydrogel with this highly entangled network demonstrates enhanced mechanical properties, such as high toughness, resistance to fatigue, stretchability, and compliance, due to the transmission of tension within the polymer network. The entangled networks provide the hydrogel with high elasticity and fatigue threshold (~ 240 J m −2 ), along with a lower friction coefficient due to their longer polymer chains. The hydrogels with entangled networks demonstrate high wear resistance and mechanical stability, maintaining their polymer network in aquatic environments. However, the high Young’s modulus resulting from the dense entanglements inevitably causes lower compliance compared to conventional hydrogels. To address this issue, the synthesis methods for materials to balance the density of cross-links and entanglements could be further explored. Improving Electrochemical Stability Soft ionic materials have replaced conductive components in electronic devices based on their ionic conductivity, where ions are used as charge carriers. Because electron-based conductors are still dominant worldwide, the application of ionic conductors to electron-based devices requires that they be compatible with electronic conductors. A capacitive component called the EDL is formed at the interface between the ionic and electronic conductors, and this component is not present at the interface between electron-based conductors. At the interface, inevitable electrochemical reactions can occur upon the application of voltages exceeding the electrochemical window (Fig. 12 c) [ 73 ]. An electrochemical reaction, such as charge transfer cross the interface, occurs when the voltage across the EDL exceeds its electrochemical window (~ 1 V) [ 59 ]. To ensure electrochemical stability, a common approach to avoid electrochemical reactions has involved designing an electrical circuit to dissipate the voltage applied to the EDL. The relatively high capacitance of the EDL (0.1 F m −2 ) suppresses the electrochemical reaction by maintaining a low voltage drop below 1 V across the EDL. It prevents charge transfer at the interface, thus enabling ionic materials to be utilized even in high-voltage applications. However, this approach is limited by the fact that it is only applicable to certain applications containing a capacitive part connected in series to the EDL. To reduce electrochemical reaction risks, various studies have been conducted to expand the electrochemical window using organogels or ionogels. These studies demonstrate higher electrochemical stability compared to hydrogels, leading to enhanced system reliability. By using ionic liquids (ILs), for instance, the electrochemical window was increased up to 6 V, ensuring enhanced electrochemical stability [ 201 ]. In addition, the systemization of these ionic devices has the potential to reduce electrochemical reactions at the interfaces of electron-based devices. In the future, ionic components such as ionic diodes, actuators, power sources, communicators, computational circuits could be integrated into fully ionic systems [ 167 , 202 – 207 ]. Future research focusing on the systemization of ionic devices will provide innovative solutions to address electrochemical reactions at ionic-electronic interfaces.",
"introduction": "Introduction As a result of millions of years of evolution based on natural selection, many natural creatures have optimized and refined various abilities to detect changes in diverse environments. For example, humans have evolved biological sensory systems comprising the five primary senses of sight, touch, hearing, taste, and smell, which all enable us to interact with the world in certain ways. These senses are, respectively, facilitated by specialized sensory organs: the eye, ear, skin, nose, and tongue. These organs use specialized receptors to perceive and convert external stimuli from the environment. The diverse types of external stimuli are converted by biological receptors into visual, mechanical, or electrochemical signals. The biological sensory systems conducting these signal conversions are known for their remarkable adaptability and high sensitivity to external environmental factors [ 1 ]. Moreover, the sensory systems are capable of conducting various functions within a single organ [ 2 ]. With remarkable and optimized characteristics, the biological sensory systems have inspired humans to replicate their own sensory systems [ 3 – 6 ]. Consequently, recent developments in electronics have led to the creation of artificial sensors mimicking those sophisticated human sensory systems [ 7 – 11 ]. However, replicating the intricate sensory systems remains a significant challenge. The rigid and electron-based materials, which are employed for conventional sensors, are unsuitable for bioinspired sensors. The conventional rigid sensors are incapable of deformation, thus restricting their use on irregularly deformable surfaces. To meet the issue, soft ionic materials have been proposed, endowing both flexibility and stretchability [ 12 , 13 ]. In addition, their inherent ionic conductivity enables them to replicate the working principle of signal transmission within biological sensory systems. With these strategies, artificial bioinspired sensors have been introduced, transmitting external stimuli into electrical signals with high sensitivity, efficient power consumption, and fast response times [ 14 – 18 ]. Numerous advancements in biomimetics have been achieved that show impressive potential utility in wearable device [ 19 , 20 ], human–machine interfaces [ 21 – 23 ], artificial organs [ 24 ], and so on. Numerous soft ionic sensors have been designed to imitate the unique human sensory system [ 26 – 30 ], particularly by receiving stimuli and converting them into chemoelectrical signals, thus allowing for interactions with the environment (Fig. 1 ). Sensors designed to emulate human sensory systems can be categorized into three functional types based on photo [ 25 ]/mechano [ 31 – 33 ]/thermo [ 34 , 35 ]/chemoreceptor [ 36 ]. First, an artificial eye, mimicking photoreceptors that detect external light and convert it into visual images, is presented (Fig. 1 a) [ 30 ]. Next, mechanoreceptors react to external mechanical stimuli, including pressure, vibration, and acoustic waves [ 37 ]. Inspired by the mechanoreceptors of the human sensory system, artificial hair cell ear (Fig. 1 b) [ 26 , 37 ] and artificial skin (Fig. 1 c) [ 27 ] are designed to detect sound and pressure, respectively. Not only the mechanoreceptors, but the thermoreceptors have also provided the inspiration for artificial skin with a wider temperature detection range than that of human skin [ 38 , 39 ]. Lastly, the chemoreceptors of the human gustatory and olfactory system have inspired the creation of an artificial tongue (Fig. 1 d) [ 28 ] and a colorimetric nose sensor (Fig. 1 e) [ 29 ] to detect target chemical substances. Fig. 1 Soft ionic sensors inspired by human. The various applications of such sensors have been demonstrated by emulating the sensing principles and structural characteristics of the human sensory organs, including a eyes, b ears, c skin, d tongue, and e nose. a is reprinted with permission [ 25 ]. Copyright 2020, Springer Nature. b is reprinted with permission [ 26 ]. Copyright 2021, American Chemical Society. c is reprinted with permission [ 27 ]. Copyright 2017, Wiley–VCH. d is reprinted with permission [ 28 ]. Copyright 2020, American Association for the Advancement of Science. e is reprinted with permission [ 29 ]. Copyright 2020, Wiley–VCH The evolutionary traces of natural organisms provide researchers with innovative insights into the development of soft ionic sensors and the enhancement of sensing performance (Fig. 2 ) [ 40 – 44 ]. For example, the proximity sensing capability to detect prey and localize position is a significant capability that facilitates the exploration of natural environments [ 47 ]. The proximity sensing capability found in rays (Fig. 2 a) [ 40 ] and sharks (Fig. 2 b) [ 41 ] provides creative inspiration for designing proximity sensors. The antennae sensory system of ants (Fig. 2 c), which detects pressure, vibration, and magnetic and chemical stimuli, shows potential for multifunctional sensing applications [ 42 ]. The remarkable structural features observed in nature have also inspired the novel design of sensors with unique characteristics [ 48 – 52 ]. The structural features of the camel’s cavity (Fig. 2 d) have been incorporated into sensors to achieve enhanced humidity sensitivity [ 43 ]. The hydrophobic characteristics of lotus leaves have provided the surfaces of such leaves with a self-cleaning capability (Fig. 2 e) [ 44 – 46 ]. These structural features provide sights that can help improve sensitivity, broaden the range of sensing targets, and provide multifunctionality. These characteristics of the natural organisms have been integrated with soft ionic materials in various applications, where they have been shown to boost the performance and utility of human–machine interfaces. Fig. 2 Overview of nature-inspired soft ionic devices. Various sensing applications are inspired by natural organisms, including a ray, b shark, c ant, d camel, and e lotus. The unique characteristics of soft ionic materials in sensors are capable of realizing the particular working principle of each organism, such as rays’ proximity sensing, sharks’ proximity detection, ants’ tactile and magnetic sensing, camels’ humidity detection sensing, and the structural characteristics of the lotus's superhydrophobic surface. a is reprinted with permission [ 40 ]. Copyright 2021, American Association for the Advancement of Science. b is reprinted with permission [ 41 ] Copyright 2022, American Association for the Advancement of Science. c is reprinted with permission [ 42 ]. Copyright 2024, Springer Nature. d is reprinted with permission [ 43 ]. Copyright 2022, American Chemical Society. e is reprinted with permission [ 44 – 46 ]. Copyright 2009, Elsevier, Copyright 2005, Springer Nature, Copyright 2018, Springer Nature Soft ionic materials offer numerous advantages for biomimetic device design [ 53 , 54 ]. For example, conventional sensors, which generally consist of rigid materials, have relatively poor adaptability to substrate deformation, resulting in deteriorating seamless human–machine interaction. The high Young’s modulus of rigid materials presents challenges in mimicking materials, particularly in replicating the substances found in natural organisms. Meanwhile, soft ionic materials exhibit characteristics originating from their low modulus, such as stretchability (Fig. 3 a) [ 55 ], flexibility (Fig. 3 b) [ 56 , 57 ], and softness (Fig. 3 c) [ 58 ]. These properties of soft materials enable them to mimic the capability of natural organisms adapt to ambient environments. Their high transmittance facilitates the transmission of optical information through the materials, paving the way for advancements in transparent electronics. (Fig. 3 d) [ 59 , 60 ]. By attaching to the skin, transparent electronics can conduct medical tasks, providing visual information about skin conditions [ 61 – 63 ]. Polar liquids, such as water and organic liquids, filled into the polymer network of the material readily dissolve ions, thus endowing the ionic materials with ion conductivity (Fig. 3 e) [ 64 ]. The relatively low cost of water and organic solvents makes it possible to fabricate ionic materials both in large volume and in three dimensions (Fig. 3 f) [ 65 ]. These fascinating characteristics of soft ionic materials make them particularly suitable for biomimetic applications and human–machine interfaces [ 66 – 68 ]. Fig. 3 Outstanding properties of soft ionic materials. a Stretchability, b flexibility, c softness, d transparency, and e ionic conductivity of soft ionic materials facilitate a wide range of applications, including soft ionic sensors. f Cost-effectiveness and tunable curing properties of soft ionic materials allow them to be easily fabricated in a three-dimensional structure Iontronics, which has advanced as a sophisticated technology, bridges rigid electronic devices and soft biological systems by controlling ions as charge carriers [ 69 , 70 ]. As a representative material in iontronics, hydrogel has been demonstrated as transparent and soft electronics, including sensors, due to its high stretchability and ionic conductivity [ 44 , 71 ]. Furthermore, compared to silver nanowires and carbon nanotubes, soft ionic materials such as hydrogel exhibit more stability of resistance under equal stretchability. With these notable advantages, ionic materials-based device can be utilized as ionic conductor (Fig. 4 a) [ 59 ]. Soft ionic materials can serve as electrolytes, forming the electrical double layer (EDL) at the interface between the electrode and the electrolyte (Fig. 4 b) [ 72 ]. In ionic materials, both cations and anions are transmitted toward each electrode by voltage difference (Fig. 4 c). The formation of EDL prevents electrochemical reactions at the interface, while it also allows electric potential to be transmitted to an external circuit. Devices using ionic materials can operate with electrochemical stability within the applied voltage range of about 1 V across the EDL (Fig. 4 d) [ 59 , 73 ]. The milestone research on soft ionic materials has contributed to the development of soft ionic material-based devices, including bioinspired soft ionic sensors. Fig. 4 Working principle of soft ionic material as an ionic conductor. a Ionic conductor and a dielectric layer are connected in series [ 59 ]. b Electrical double layer (EDL) is formed at the interface of anions (or cations) in electrolyte and holes (or electrons) in electrode. c Both cations and anions migrate through the electrolyte, thus generating ionic currents under input voltage. d EDL prevents the migration of ions and electrons, thus inhibiting electrochemical reactions within the electrochemical window In this review, we discuss nature-inspired soft ionic sensors, with a focus on their features of evolved natural sensory systems, biological sensing mechanisms, and various applications. The biological sensing principles are analyzed while focusing on four key receptors: mechanoreceptors, thermoreceptors, chemoreceptors, and photoreceptors. Cross-disciplinary advancements in soft materials and iontronics have provided new insights into nature-inspired soft ionic sensors (Fig. 5 ). From the perspective of human sensory systems, we present an overview of nature-inspired soft ionic sensors, including vision, tactile, auditory, gustatory, olfactory. We extend diverse range of artificial sensors to include proximity sensors inspired by the electroreception capabilities of natural organisms, which are distinct from the five basic human senses [ 30 , 31 , 59 ]. We will explain the biological sensing mechanisms of these sensors, along with an understanding of sensory systems. Finally, we discuss several challenges and propose strategies for real-world applications. Fig. 5 Features of nature-inspired soft ionic sensors. The three promising fields of soft materials, nature inspiration, and iontronics have led to various advancements in the field of sensors. The ionic conductivity and flexibility of soft materials lead to the observed improvements in nature-inspired soft ionic sensors"
} | 7,575 |
30464767 | PMC6234839 | pmc | 4,618 | {
"abstract": "Droughts strongly affect carbon and nitrogen cycling in grasslands, with consequences for ecosystem productivity. Therefore, we investigated how experimental grassland communities interact with groups of soil microorganisms. In particular, we explored the mechanisms of the drought-induced decoupling of plant photosynthesis and microbial carbon cycling and its recovery after rewetting. Our aim was to better understand how root exudation during drought is linked to pulses of soil microbial activity and changes in plant nitrogen uptake after rewetting. We set up a mesocosm experiment on a meadow site and used shelters to simulate drought. We performed two 13 C-CO 2 pulse labelings, the first at peak drought and the second in the recovery phase, and traced the flow of assimilates into the carbohydrates of plants and the water extractable organic carbon and microorganisms from the soil. Total microbial tracer uptake in the main metabolism was estimated by chloroform fumigation extraction, whereas the lipid biomarkers were used to assess differences between the microbial groups. Drought led to a reduction of aboveground versus belowground plant growth and to an increase of 13 C tracer contents in the carbohydrates, particularly in the roots. Newly assimilated 13 C tracer unexpectedly accumulated in the water-extractable soil organic carbon, indicating that root exudation continued during the drought. In contrast, drought strongly reduced the amount of 13 C tracer assimilated into the soil microorganisms. This reduction was more severe in the growth-related lipid biomarkers than in the metabolic compounds, suggesting a slowdown of microbial processes at peak drought. Shortly after rewetting, the tracer accumulation in the belowground plant carbohydrates and in the water-extractable soil organic carbon disappeared. Interestingly, this disappearance was paralleled by a quick recovery of the carbon uptake into metabolic and growth-related compounds from the rhizospheric microorganisms, which was probably related to the higher nitrogen supply to the plant shoots. We conclude that the decoupling of plant photosynthesis and soil microbial carbon cycling during drought is due to reduced carbon uptake and metabolic turnover of rhizospheric soil microorganisms. Moreover, our study suggests that the maintenance of root exudation during drought is connected to a fast reinitiation of soil microbial activity after rewetting, supporting plant recovery through increased nitrogen availability.",
"conclusion": "Conclusion The results from this study confirm our first hypothesis that the frequently observed weakening of the link between plant photosynthesis and soil microbial carbon cycling during drought is due to reduced microbial uptake rather than to reduced root exudation. Our data from the 13 C pulse labeling experiments clearly show that recently assimilated plant carbon accumulates in the rhizosphere in the form of EOC during drought and that this accumulation is linked to reduced microbial uptake of plant-derived carbon. When the soil dries out, the limited diffusion leads to lower accessibility of root exudates for non-mycorrhizal fungi and bacteria. In addition, higher reductions of 13 C tracer allocation to growth-related fatty acid markers in comparison to the water-soluble MBC fraction, also in AM fungi, indicate adjustments in microbial metabolic activity; that is, the formation of osmolytes to prevent cell desiccation is favored over growth. Our second hypothesis that drought leads to the accumulation of root sugars and EOC and that these easy degradable carbon sources are available for priming plant and soil microbial activity after rewetting, is only partially supported by the data. Indeed, we found that carbohydrates accumulated in roots and that the decreased microbial uptake was linked to increased EOC concentrations during drought. However, what causes the depletion of drought-accumulated carbon after rewetting remains unclear. Root sugars could either be used to support the regrowth of shoots or may be invested in plant-microbial interactions to gain more nutrients from soil organic matter decomposition. Drought-accumulated EOC that is not flushed away during the rewetting potentially further fuels the Birch effect, i.e., high microbial carbon and nitrogen mineralization shortly after rewetting. To determine how the preservation of belowground carbon pools during drought is related to microbial activity in the early phase of ecosystem recovery, future studies are needed to trace the flux of 13 C label applied at drought in soil after rewetting. Ultimately, our results indicate that the link between plants and soil microorganisms plays a crucial role in the short-term response of carbon and nitrogen cycling to drought-rewetting events.",
"introduction": "Introduction Climate change threatens the functioning of terrestrial ecosystems, which will very likely suffer from more frequent extreme events induced by the ongoing global warming ( IPCC, 2012 ). A large part of the terrestrial biosphere consists of grassland ecosystems that cover approximately 40% of the vegetated land surface and strongly contribute to soil carbon storage ( White et al., 2000 ). The functioning of grasslands and their role in the global carbon cycle are particularly placed at risk by periods of severe drought ( Reichstein et al., 2013 ; Frank et al., 2015 ). Grasslands in some areas may experience more severe drought effects, such as, for example, in the European Alps, which are affected by faster temperature increases compared to the global average ( Beniston, 2005 ; Auer et al., 2007 ). Extreme droughts typically lead to reduced carbon assimilation in plants ( Huang and Fu, 2000 ; Naudts et al., 2011 ; Roy et al., 2016 ; Ingrisch et al., 2018 ) and reduced carbon transfer to the roots and the rhizosphere ( Fuchslueger et al., 2014a , 2016 ; Hasibeder et al., 2015 ; Karlowsky et al., 2018 ), resulting in a lower soil CO 2 efflux ( Ruehr et al., 2009 ; Barthel et al., 2011 ; Burri et al., 2014 ). Consequently, the reduced belowground carbon allocation (BCA) weakens plant–microbial interactions ( Brüggemann et al., 2011 ). Because soil microorganisms strongly depend on plant-derived carbon inputs ( Wardle et al., 2004 ; Bardgett et al., 2005 ), important soil functions, such as the microbial mineralization of nitrogen and phosphorous, are limited during drought ( Stark and Firestone, 1995 ; Borken and Matzner, 2009 ; Delgado-Baquerizo et al., 2013 ; Fuchslueger et al., 2014b ; Canarini and Dijkstra, 2015 ; Dijkstra et al., 2015 ). In addition, symbiotic interactions with arbuscular mycorrhizal (AM) fungi, which strongly increase the drought resistance of plants ( Allen, 2007 ), are affected by severe drought ( Karlowsky et al., 2018 ). So far, whether the weakening of the link between plants and soil microorganisms during drought (i.e., the reduced soil microbial usage of recently assimilated plant-derived carbon) is due to (1) the altered carbon allocation of plants leading to reduced root exudation, (2) the limited substrate mobility in the rhizosphere, or (3) a slowdown of soil microbial metabolism is unknown. Possibly, these three mechanisms appear at the same time and interact with each other. Drought has been shown to induce a shift of carbon allocation from the aboveground to the belowground plant organs ( Palta and Gregory, 1997 ; Huang and Fu, 2000 ; Burri et al., 2014 ) and to increase the amounts of soluble sugars in the roots ( Hasibeder et al., 2015 ; Karlowsky et al., 2018 ). The latter two studies also showed that drought-induced reductions of storage sugar concentrations are more pronounced in shoots than roots. The increase of soluble root sugars has been attributed either to osmotic regulation to support the survival of root biomass ( Sicher et al., 2012 ; Hasibeder et al., 2015 ) while maintaining the carbon demand for respiration ( Barthel et al., 2011 ) or to increased fine root growth to enhance plant access to deeper soil water resources ( Huang and Fu, 2000 ; Burri et al., 2014 ). Until now, whether these drought-reduced changes in plant carbon allocation to stored reserve sugars versus soluble root sugars that are linked to exudation are affecting the carbon released into the rhizosphere has been unknown. In a recent meta-analysis of the scarce existing literature, Preece and Peñuelas (2016) found that drought can have variable effects on the rhizospheric carbon release. Strikingly, the authors of this study reported a trend toward increased root exudation per gram of plant biomass (including either root and shoot biomass or shoot biomass only) under moderate drought. However, the root biomass response to drought strongly varies among the different studies ( Kreyling et al., 2008 and references therein), potentially affecting the total amount of carbon released to the rhizosphere. For example, Fuchslueger et al. (2014a) found that a slightly increased root to shoot ratio during drought was mirrored by higher amounts of plant-derived carbon in the extractable organic carbon (EOC) of soil. The drying of soil itself has major impacts on the exudate transfer from the release site to rhizospheric microorganisms, which might increase the competition for substrates between functionally different microbial groups. In contrast to AM fungi, which are directly connected to the root carbohydrate pool, saprotrophic fungi (SF) and bacteria depend on the diffusion of substrates for their nutrition ( Manzoni et al., 2012 ). As the lower water content during drought conditions limits the diffusion of substrates ( Skopp et al., 1990 ), the uptake of nutrients by SF and bacteria is limited. Moreover, experimental results suggest that the microbial activity in the soil depends on the environmental conditions that affect diffusion pathways between substrate sources and microorganisms ( Nunan et al., 2017 ). Consequently, if root exudation is increased along with root growth during drought, plant-derived solutes likely will accumulate in the rhizosphere due to reduced microbial carbon mineralization. Indeed, increased amounts of dissolved organic carbon immediately after the rewetting of dried soils ( Canarini et al., 2017 ) suggest the existence of such accumulations. These additional carbon sources could further contribute to the pulse of soil respiration, which appears after rewetting and is associated with higher soil microbial activity and nitrogen mineralization ( Birch, 1958 ). The so-called ‘Birch effect’ is present in planted and unplanted soils ( Canarini et al., 2017 ) and has been suggested to primarily originate from osmolytes, which accumulate in microbial cells during drought conditions ( Fierer and Schimel, 2003 ). As a stress response to desiccation, the synthesis of microbial osmolytes is increased at the expense of membranes for cell growth ( Schimel et al., 2007 ). To prevent the bursting of cells due to excessive water uptake, accumulated osmolytes need to be rapidly metabolized after rewetting ( Warren, 2014 ). The metabolically active microorganisms are probably also able to use excess plant-derived carbon, which could support plant recovery by further increasing the nitrogen mineralization rate in the soil. Plant carbon allocation is best analyzed by pulse-labeling of the plant canopy with 13 C-enriched CO 2 and tracing of the assimilated 13 C by compound specific carbon isotope ( 13 C/ 12 C) ratios of plant non-structural carbohydrates (NSCs) ( Bahn et al., 2013 ; Karlowsky et al., 2018 ). Similarly, root exudation and the subsequent microbial carbon uptake can be determined by combining the K 2 SO 4 extraction and chloroform fumigation method ( Vance et al., 1987 ) with 13 C analysis ( Malik et al., 2013 ). This allows the flow of plant-derived carbon in EOC and microbial biomass carbon (MBC) from soil to be traced. The water-soluble EOC is mainly a proxy for the exuded plant carbon (Supplementary Figure S1 ), with minor contributions of AM fungi exudation ( Drigo et al., 2010 ; Balasooriya et al., 2012 ; Kaiser et al., 2015 ), which is also directly linked to the plant-derived carbon (Supplementary Figure S1 ). To determine the uptake of plant-derived carbon by the different soil microbial groups, compound-specific 13 C isotope analysis on phospholipid fatty acid (PLFA) markers from soil can be used ( Kramer and Gleixner, 2006 ). A comparison of the 13 C incorporation into MBC and into PLFA markers allows distinctions to be made between the growth and maintenance of soil microorganisms ( Malik et al., 2015 ). To study the rhizospheric processes, we used a common garden experiment on a mountain meadow using species representing the local meadow community. Our main objective was to assess the effects of drought and rewetting on the response of plant–microbial carbon transfer as a fundamental part of ecosystem functioning ( Wardle et al., 2004 ; Bardgett et al., 2005 ; Schimel et al., 2007 ; Brüggemann et al., 2011 ). We performed two 13 C pulse chase campaigns, a first at peak drought and second shortly after rewetting, and studied the response of carbon assimilation, allocation and transfer to soil microbial markers. Specifically, we hypothesized that the weakening of the link between plant and soil processes during drought is mainly due to decreased transfer of microbial carbon substrates in the rhizosphere and osmotic effects and is not due to decreased carbon release from roots increasing the competition for carbon between microorganisms. Furthermore, we expected that drought would lead to an accumulation of root sugars and easily degradable EOC in soil, which are available for priming plant and soil microbial activity after rewetting.",
"discussion": "Discussion In a previous experiment on intact vegetation-soil monoliths from a managed meadow and an abandoned grassland, we found that drought-induced reductions of plant photosynthetic activity ( Ingrisch et al., 2018 ) were coupled to strong reductions in plant storage NSCs, especially above ground, whereas BCA was maintained at a constant level (abandoned grassland) or even increased (managed meadow) relative to the total carbon uptake ( Karlowsky et al., 2018 ). The carbon allocated to roots was largely recovered in drought-accumulated soluble sugars, whereas the uptake of plant-derived carbon in fatty acid biomarkers of root-associated microorganisms (AM fungi, SF and bacteria) was strongly reduced. Overall, these responses were greater in the managed meadow compared to the abandoned grassland, which likely also profited from enhanced AM fungal growth during drought. Furthermore, we found that after rewetting, the carbon uptake of the SF and bacteria was enhanced in the managed meadow ( Karlowsky et al., 2018 ), which was reflected by higher plant nitrogen uptake and a faster recovery of aboveground biomass compared to the abandoned grassland ( Ingrisch et al., 2018 ). However, we were not able to assess whether the accumulation of root sugars during drought affected the release of carbon to the rhizosphere, nor were we able to determine how the drought-induced shift toward belowground allocation in the meadow might be related to its quick recovery after rewetting. Therefore, the aim of this study was to further elucidate the mechanisms underlying the link between plant photosynthesis and soil microbial carbon cycling during drought and after rewetting. The Link Between Plant and Soil Microbial Processes at Peak Drought Surprisingly, drought had no significant effect on the total plant biomass. However, the decrease in shoot biomass and the concurrent increase in fine root biomass indicate that drought led to a shift in plant carbon allocation toward the belowground organs. Similar results have been found before in drought experiments on managed grasslands ( Kahmen et al., 2005 ; Burri et al., 2014 ) and were attributed by the authors to an adaptation of plants in order to forage the limited water in dry soil. However, the root biomass response to drought can vary ( Kahmen et al., 2005 ) and depends on the severity of the drought ( Kreyling et al., 2008 ). Another root response occurring together with increased BCA is the accumulation of root sugars, especially sucrose ( Hasibeder et al., 2015 ; Karlowsky et al., 2018 ). Such accumulations of root sugars can indicate an adjustment to dry conditions ( Hasibeder et al., 2015 ) by increasing the concentration of osmolytes that prevent cells from desiccation ( Chaves et al., 2003 ; Chen and Jiang, 2010 ). In our study, simultaneously increased concentrations of free glucose and fructose in roots (data not shown) further point to osmotic adjustment ( Chen and Jiang, 2010 ). Independently of its usage, the carbon needed to maintain BCA originates either from recent assimilates or from remobilized aboveground storage compounds. In previous studies, drought increased the proportion of recently assimilated carbon allocated belowground ( Palta and Gregory, 1997 ; Huang and Fu, 2000 ; Burri et al., 2014 ; Hasibeder et al., 2015 ; Karlowsky et al., 2018 ). Here, we could not identify this effect (Figure 3A ), suggesting a higher contribution of shoot storage is needed to maintain BCA during drought, as indicated by the depletion of shoot fructan and starch. This might be due to stronger negative effects of drought on carbon assimilation than in the previous studies. Diverging results for the belowground allocation of freshly assimilated carbon have been reported before by Sanaullah et al. (2012) in a lab-based mesocosm experiment with monocultures and different mixtures of two grasses and one legume, whereas Ruehr et al. (2009) even found that drought increased the residence time of new carbon in leaves from beech trees. Of course, as woody species, trees have additional aboveground storage organs, which likely modify their drought response compared to herbaceous species. As a consequence, the source of the typically observed increase of BCA during drought might vary between fresh assimilates and older reserve carbohydrates, depending on the severity of drought, the timing in the year, as well as the functional composition or type of plants. In general, as previously concluded by Bahn et al. (2013) , under reduced carbon supply, BCA in grassland seems to be maintained at the expense of aboveground storage (Figure 3A ). Furthermore, the increase of nitrogen content in the roots (g N m -2 ) of drought-treated plants (Table 1 ) suggests that the disturbance-adapted meadow plants actively preserve their resources belowground during extreme drought, likely to facilitate quick recovery after rewetting ( Karlowsky et al., 2018 ). FIGURE 3 Effects of drought (A) and rewetting (B) on carbon fluxes and pools in grassland ecosystem. (A) During drought, assimilation (A) is reduced (reductions shown as dashed arrows). This leads to reduced carbon allocation to aboveground storage decreasing its pool size (effects on pool sizes shown as “+” or “–” signs). Presumably, carbon allocation to shoot growth, maintenance and respiration (R) is also reduced during drought (fluxes that were not determined in this study are represented by gray arrows). Belowground carbon allocation (BCA) is maintained during drought and leads to the accumulation of root sugars because carbon allocation to storage and mycorrhizal interactions are reduced. The size of the root storage pool is unaffected, as its activity is reduced during drought. Root sugars are partially used for root growth and maintenance. Furthermore, there is ongoing exudation (Ex) of new assimilates by roots but not by AM fungi (AMF), leading to an increase of the extractable organic carbon (EOC) in the soil, as the carbon uptake and metabolic activity of saprotrophic fungi (Sapro) and bacteria (Bact) is strongly reduced during drought. Shortly after rewetting (B) carbon assimilation and allocation mostly recovers. Because reductions still occur in the shoot storage pool, it is likely that priority is given to shoot re-growth. Accumulations of root sugars and EOC observed during drought rapidly vanish after rewetting and are likely used for priming soil microbial activity. In addition, the root sugar pool is reduced due to a faster carbon turnover, which is associated with increased transfer of newly assimilated carbon to saprotrophic fungi and (by tendency) bacteria in the rhizosphere, indirectly suggesting increased root/mycorrhizal exudation. Most interestingly, the altered plant resource allocation patterns did not disrupt the release of recently assimilated carbon to the rhizosphere during drought (Figure 3A ), as visible by the high amount of 13 C tracer in the soil EOC fraction, which exceeded control levels shortly after labeling. A similar enrichment of plant-derived carbon in the EOC pool was found by Fuchslueger et al. (2014a) and was attributed by the authors to the role of root exudates in reducing friction resistance in soil and maintaining root-soil connectivity. However, the strong reduction in 13 C recovered in the microbial biomass of drought mesocosms points to decreased microbial uptake of recent plant-derived carbon, which probably led to the strong accumulation of carbon in the EOC pool. Nonetheless, increased root exudation during drought, as evidenced by a recent mesocosm study on tree saplings ( Preece et al., 2018 ), could have further contributed to the greater EOC pools in the soil. Notably, the relative 13 C allocation to MBC was much less reduced by drought compared to microbial marker fatty acids (Figure 1A ). This finding may imply that drought-reduced microbial growth, which can be estimated by the production of new fatty acids, and led to the accumulation of osmotically active compounds in MBC ( Schimel et al., 2007 ). Osmolytes, e.g., amino acids in bacteria and polyols in fungi, are essentially highly water soluble and are more easily recovered than hydrophobic fatty acid-containing lipids in the MBC, which is extracted using aqueous K 2 SO 4 solution. Moreover, reduced substrate diffusion, assumed to be the main limiting factor for bacterial activity in dry soil ( Skopp et al., 1990 ; Stark and Firestone, 1995 ; Nunan et al., 2017 ), cannot explain the reduced 13 C tracer uptake by AM fungi during drought, since mycorrhizal interactions do not depend on substrate diffusion in the soil. Unexpectedly, bacterial biomass was generally higher in drought-treated mesocosms (Table 3 ). A high resistance to drought was expected for the slow-growing, Gram-positive (actino)bacteria but not for the Gram-negative bacteria with their thin cell wall ( Schimel et al., 2007 ; Lennon et al., 2012 ). Possibly, Gram-negative bacteria profited from the increased root growth and exudate availability during drought, as the increased amounts of EOC in drought mesocosms at peak drought labeling suggested. If this scenario occurred at earlier stages of drought, when soil moisture conditions were not limiting the bacterial activity, then Gram-negative bacteria could have used the easily consumable carbon from the EOC pool for their growth. Similarly, we did not expect the concentration of AM fungi marker in drought mesocosms to be reduced compared to the controls (Table 3 ). This contrasts previous findings from grassland monoliths ( Karlowsky et al., 2018 ), showing an increase of the (AM + saprotrophic) fungi:bacteria ratio at peak drought. This difference could be due to the use of sieved soil in mesocosms, because the mycorrhizal network strongly interacts with soil structure ( Rillig and Mummey, 2006 ). Other explanations include increased competition for plant carbon between fine roots and AM fungi, or a lower plant dependence on AM fungi due to (a) lower nutrient demand of senescing shoots or (b) higher nutrient availability resulting from decreased competition with soil microorganisms. Additionally, the selected plant species might have interacted differently with AM fungal populations ( Legay et al., 2016 ; Mariotte et al., 2017 ). Additionally, bacterial foraging of senescing AM fungi structures cannot be excluded and might have contributed to the increase in the Gram-negative bacteria during drought, too. Carbon Allocation and Plant–Microbial Interactions During Recovery After rewetting, the mesocosm communities quickly recovered from drought, and both the shoot biomass and the root:shoot ratio were restored to control levels (Table 1 ). The higher fine root growth observed during drought was ceased at recovery labeling, possibly to support the re-growth of shoot biomass. However, the mechanisms behind the change in fine root biomass remain unclear, and thus, we cannot exclude the possibility that this observation was due to initial differences between the mesocosms used for the peak drought labeling and the mesocosms used for the recovery labeling. In general, the root response to drought-rewetting seems to be highly variable because previous studies either found an increase ( Fuchslueger et al., 2016 ; Karlowsky et al., 2018 , abandoned grassland) or no change ( Karlowsky et al., 2018 , managed meadow) in the fine root biomass after rewetting. In the latter study, the root response depended on the land use and was attributed to variable needs of water and nutrient uptake by fine roots, resulting from differences in the recovery of the dominant plant-microbial interactions. On the other side, in this study, the plant 13 C tracer uptake and allocation supports the hypothesis that carbon resources are preferentially invested into the regrowth of shoot biomass after rewetting (Figure 3B ). Despite recovered 13 C tracer dynamics, the reduced shoot fructan pool indicates that, during the recovery phase, plants invested more carbon into structural carbohydrates or into respiration (e.g., for repair processes) than in storage. This investment was underpinned by the higher turnover of 13 C tracer in shoot starch, which suggests a faster utilization of recent assimilates from transitory storage ( Bahn et al., 2013 ) in plants recovering from drought. The reduced concentrations of root sucrose after rewetting could also be a result of the preferential use of newly assimilated carbon for shoot regrowth, decreasing the BCA during recovery ( Zang et al., 2014 ). However, since only a marginal effect was observed on the average 13 C tracer incorporation in root sucrose and apparently a faster utilization of recent assimilates occurred in roots (Supplementary Figures S6M–O ), most likely, the reduced sucrose concentrations were a result of increased root-rhizosphere carbon transfer ( Hagedorn et al., 2016 ). According to a shift in root functioning from resource preservation to nutrient acquisition, the uptake of fresh plant-derived carbon completely recovered for all microbial groups, and the carbon transfer to saprotrophic fungi even increased in the drought mesocosms (Figure 3B ). These microorganisms were also found to rapidly take up recent plant-derived carbon in grasslands ( de Deyn et al., 2011 ). In contrast to a previous study on the meadow ( Karlowsky et al., 2018 ), we could not find significant excess uptake of 13 C tracer in bacteria. However, we cannot exclude that the use of sieved subsoil in this study led to altered microbial responses compared to the undisturbed topsoil in the previous study, as the initial microbial community and its functioning might have differed. Moreover, the rapid uptake of plant-derived carbon by saprotrophic fungi agrees with a recently introduced framework for carbon flow in the rhizosphere by Ballhausen and de Boer (2016) , who proposed that a large fraction of the labile carbon from root exudation is primarily taken up by saprotrophic fungi prior to its consumption by fungus-feeding bacteria. As expected, AM fungi generally took up the largest fraction of plant-derived carbon in the soil microbial community ( Drigo et al., 2010 ; Mellado-Vázquez et al., 2016 ; Karlowsky et al., 2018 ) but recovered slowly after rewetting the dried soil ( de Vries et al., 2012 ; Meisner et al., 2013 ; Karlowsky et al., 2018 ). Interestingly, despite their lower abundance, AM fungi completely recovered their 13 C tracer uptake in drought treatments at the recovery labeling, suggesting that the efficiency of plant-mycorrhizal carbon flow increased at this time to support the recovery of the hyphal network. The recovery of soil microbial growth after drought is typically preceded by a pulse of soil respiration directly after rewetting ( Birch, 1958 ). However, those sources other than the released microbial osmolytes that contribute to the Birch effect are not well known, especially in planted soils ( Canarini et al., 2017 ). Here, we found accumulations of carbon in the root sugar and soil EOC pools during drought, which quickly disappeared after rewetting. This strongly suggests that the release of these easy degradable carbon sources after the end of drought contributes to the acceleration of the soil microbial activity. Data not yet published on soil respiration from the 13 C pulse labeling experiment described by Karlowsky et al. (2018) indicate that carbon assimilated during drought contributes to the Birch effect, as 13 C applied to the monoliths during peak drought could be recovered in the soil respiration pulse after rewetting. Consequently, this means that the plant-derived carbon, which cannot be used by soil microorganisms during drought, is available for priming the microbial organic matter cycle in soil after rewetting. Such priming effects, e.g., observed after amending soil samples with fresh plant litter ( Thiessen et al., 2013 ), are well-known to support plant growth by increasing nutrient mineralization from soil organic matter. An increase in nitrogen mineralization especially has been reported after rewetting dried soils ( Borken and Matzner, 2009 ; Canarini and Dijkstra, 2015 ), and this increase probably contributed to the increased root and shoot nitrogen concentrations found at the recovery in this study. Additionally, the transport of preserved nitrogen from roots to shoots could have led to the significantly increased shoot nitrogen concentrations in drought treatments. As the leaf nitrogen concentration typically correlates with the photosynthetic activity ( Wright et al., 2001 ; Milcu et al., 2014 ), the increased nitrogen uptake likely facilitated the higher assimilation rates needed for recovery ( Ingrisch et al., 2018 )."
} | 7,664 |
36880312 | PMC10053025 | pmc | 4,619 | {
"abstract": "A myriad of natural surfaces such as plant leaves and insect wings can repel water and remain unwetted inspiring scientists and engineers to develop water-repellent surfaces for various applications. Those natural and artificial water-repellent surfaces are typically opaque, containing micro- and nano-roughness, and their wetting properties are determined by the details at the actual liquid–solid interface. However, a generally applicable way to directly observe moving contact lines on opaque water-repellent surfaces is missing. Here, we show that the advancing and receding contact lines and corresponding contact area on micro- and nano-rough water-repellent surfaces can be readily and reproducibly quantified using a transparent droplet probe. Combined with a conventional optical microscope, we quantify the progression of the apparent contact area and apparent contact line irregularity in different types of superhydrophobic silicon nanograss surfaces. Contact angles near 180° can be determined with an uncertainty as low as 0.2°, that a conventional contact angle goniometer cannot distinguish. We also identify the pinning/depinning sequences of a pillared model surface with excellent repeatability and quantify the progression of the apparent contact interface and contact angle of natural plant leaves with irregular surface topography.",
"conclusion": "4. Conclusions By imaging through the droplet, we developed a method that allows facile direct observation of the progression of the wetting interface with excellent detail and repeatability. The method is flexible and can be applied to a variety of micro- and nano-rough water repellent samples, both artificial and natural, without requiring the sample to be transparent. Moreover, we show the method can be applied on two different types of microscopes, a regular optical microscope as well as DHM. The through-drop imaging method can provide rich information on the progression of the interface, including time series data on the apparent contact area A app and contact line irregularity ε CL . The time resolution is only limited by the acquisition speed of the camera, in our case 100 Hz. Considering many wetting events happen in milli-seconds, a high acquisition rate is important. The repeatability and precision of through-drop imaging allow the measurement of the apparent mean contact angle θ YL with a significantly smaller error than contact angle goniometry. This allows distinguishing samples with similarly high contact angles that would otherwise be reported as the same. The great sensitivity and repeatability of our method may open new doors for the scientific study of water-repellent surfaces. Considering its capabilities and relatively easy implementation, our method may be easily adopted. We expect our method to benefit scientists and engineers studying repellent surfaces in multiple disciplines, including biology, material science, interfacial science, pharmaceuticals, civil engineering, and manufacturing.",
"introduction": "1. Introduction Water-repellent, or hydrophobic, surfaces have attracted phenomenal attention due to the large number of examples found in nature, such as plant leaves 1 and petals 2 and insect legs and wings, 3 and to their diverse applications in, for example, self-cleaning, 4 oil–water separation, 5 nano-assembly, 6 anti-icing, 7 and anti-foaming. 8 Their water-repellent behaviour is often dictated by the chemical and micro- and nano-rough features of the surface at the wetting interface with the liquid. 9 Observing the details of the wetting interface of droplets on surfaces, including the contact line (CL) 10,11 and contact area morphologies, 12–15 is key to understanding the wetting behaviour of water-repellent surfaces. 16 Such understanding is crucial for developing advanced functional materials and devices, such as robust superhydrophobic surfaces 17,18 and superhydrophobic 3D printed nanoporous objects. 19 Most water-repellent surfaces are opaque and contain micro- and nano-rough features. Currently there is no effective method that can image and quantitatively analyse the advancing and receding wetting interface on those surfaces. Many methods for imaging the wetting interface can only partially image the CL and contact area or are limited to stabilized interfaces but not their progression. For example, an environmental scanning electron microscope (ESEM) provides high contrast imaging of the outer CL of droplets, provided the contact angle is low enough to avoid obscuring the CL, 10,20 but provides no information of the internal contact area. Alternatively, the droplet and sample can be frozen under cryogenic scanning electron microscope (cryo-SEM) 14 and destructively inspected using for instance a focused ion beam, 21 to provide a static picture of the wetting state prior to freezing. In turn, X-ray tomography can provide detailed three-dimensional observation of the interface that reveals trapped air bubbles and heterogeneities at the droplet-sample interface, though imaging may take minutes. 15,22 Force-based microscopy techniques, such as atomic force microscopy 23 and scanning droplet adhesion microscopy, 24 provide great probe controllability and can construct images of the wetting inhomogeneities of hydrophobic surfaces or the interfacial topography. 25 However, force-based microscopy techniques do not reveal the progression of the interface. Optical imaging techniques are promising for measuring advancing and receding wetting interfaces. For example, confocal microscopy can observe changes to the CL and contact area on superhydrophobic samples 26,27 and reflection interference microscopy can measure the thickness of the air layer trapped at the interface. 27,28 These methods require the sample to be imaged through. However, most repellent samples are opaque and even when the materials themselves are transparent, the light scattering from the micro roughness makes imaging difficult. Immersion objectives 13 and imaging through a sessile droplet 12 can observe the interface regardless of sample transparency. Those methods, however, do not provide quantitative analysis of the CL progression because they either have poor image stability due to low droplet controllability or have a limited field-of-view. Contact angle goniometry (CAG) on the other hand, is the gold standard of surface wetting characterization, but cannot measure the CL and contact area. Moreover, despite its wide applications CAG suffers from high uncertainty due to optical and baseline positioning errors. 29 The errors are most significant in the superhydrophobic regime, with uncertainties of multiple degrees for contact angles above 150°, 30 limiting its applicability in this type of samples. Here we report the direct imaging and quantitative analysis of the advancing and receding apparent CL on opaque micro- and nanorough surfaces using a transparent droplet probe. Using a normal optical microscope, our method reveals the details of the progression of the apparent contact area and apparent contact line irregularity at a video speed of 100 Hz. Advancing and receding contact angles between 160° and 180° are accurately calculated on superhydrophobic nanostructured surfaces, which CAG cannot distinguish due to an experimental standard deviation over 1°. By precisely controlling the volume and location of the droplet probe, experimental uncertainties as low as 0.2° are achieved. The contact angles were also measured using the transparent probe on a Digital Holographic Microscope (DHM), achieving similar uncertainty. Furthermore, the method was used to detect the pinning sequences on pillared surfaces with excellent repeatability and observe wetting interfaces on samples with a large variety of surface topographies, including natural samples with sophisticated wetting properties and artificial samples with defects.",
"discussion": "3. Results and discussion 3.1. Advancing and receding contact area, contact line irregularity and contact angle We measured the advancing and receding wetting interface on four types of superhydrophobic silicon nanograss, labelled #A, #B, #C and #D. The four types of silicon nanograss differed in height from approximately 5.0 μm (nanograss #A) to 1.1 μm (nanograss #D), as shown in Fig. 2(a) and Fig. S3, S4 (ESI † ). They also differed from each other in the density of spikes (Table S1, ESI † ). However, the advancing and receding contact angles for all four types were approximately 170°, when measured with a commercial contact angle goniometer (Table S2, ESI † ), where the difference between types was smaller than the uncertainties of the measurements. 30,31 The measurements were performed at a single location repeated 10 times ( Fig. 2 , Fig. S5 and Movie S1, ESI † ) and also at 10 different locations (Fig. S6, ESI † ). Fig. 2 Advancing and receding contact lines and contact areas on four different types of silicon nanograss surfaces, labelled #A–#D. Measurements were obtained at the same location on the respective samples and repeated 10 times. (a) SEM images of different types of silicon nanograss. (Scale bars: 5 μm) (b) Apparent contact area A app as a function of sample stage displacement for each type of silicon nanograss; the solid lines represent the mean, and the shaded area is bounded by the minimum and maximum values of observations. (c) Apparent contact line irregularity ε CL and (d) contact angle θ YL obtained from Young–Laplace calculation, as a function of interface diameter. (e) Illustration of ε CL calculation based on the top-view image. A circle (blue line) was fitted to the CL (green line); the inner and outer differences in area A* were calculated, shown as the dashed area in the inset, and then divided by the perimeter of the fitted circle, l C . (f) Mean and standard deviation of ε CL during the advancing and receding phases. (g) Mean and standard deviation of advancing and receding θ YL , for each type of nanograss. (h) Advancing and (i) receding CL progression for one measurement. The color indicates how long the CL was present at each pixel. (Scale bar: 200 μm). To quantitatively analyse the interface progression, we developed a machine vision algorithm to detect the contact area and perimeter (see Materials and methods and algorithm S1, ESI † ). The apparent contact area, A app , was measured as a function of sample stage displacement, shown in Fig. 2(b) . We split the measurement results into advancing, transition, and receding phases. During the advancing phase, A app grew smoothly and highly repeatably for all samples in both the single location and different locations measurements. After the sample stage reverses direction, A app did not necessarily shrink proportionally to displacement. We define the transition phase during which the CL resisted movement (see Fig. S9, ESI † for details), present in all nanograss types (black section of the curves in Fig. 2(b)–(d) ). In particular, nanograss #D showed little change during this phase, suggesting CL pinning. The receding phase follows the transition phase. For all samples, A app exhibited greater variations during the receding phase compared to the advancing phase ( Fig. 2(b) and Fig. S5a, ESI † ). The greater shaded area in nanograss in #C and #D, in Fig. 2(b) , is not the result of random variation but of a trend in the displacement area relation (Fig. S5a, ESI † ). We attribute this to the degradation of the sample surface after being repeatedly touched by the droplet, since the variations are related to the number of measurements. Nevertheless, the curves are similar, demonstrating that the droplet was affected by the same chemical and topographical inhomogeneities. We also determined the apparent contact line irregularity, ε CL , which describes the arithmetic average deviation of the CL from a circle. The calculation of ε CL is illustrated in Fig. 2(e) and discussed in detail in Section 2 (ESI † ). We measured ε CL during advancing and receding of the CL ( Fig. 2(c) ). The measurement shows that nanograss #A experienced more chaotic CL progression, attributed to the greater gaps between spikes. Despite the chaotic CL evolution, it is noticeable that A app is rather smooth and repeatable, and the transition phase is small (see nanograss #A in Fig. 2(b) ). We attribute this to the lower density of interface features touching the droplet, leading to sparser depinning events ( Fig. 2(a) and Table S1, ESI † ). Nanograss #D showed an approximately constant ε CL during the transition phase, suggesting that the CL was fully pinned (see also nanograss #D in Fig. S5c, ESI † ). The mean and standard deviation of ε CL during advancing and receding is shown in Fig. 2(f) for each type of nanograss. Except for nanograss #A, we found the advancing ε CL to be significantly lower than the receding ε CL . The higher ε CL values result from the chaotic movement of the CL as it depins from each surface feature. The repeatability and precision of the method allows us to numerically calculate the apparent mean contact angle, θ YL , during the whole measurement, shown in Fig. 2(d) . The simulations use axisymmetric Young–Laplace equation to solve the shape of the droplet from which the contact angle is derived (see Section 1 and Fig. S1 for details, ESI † ). We calculated the mean and standard deviation of the advancing and receding phases of θ YL , shown in Fig. 2(g) . The advancing θ YL is 178.9 ± 0.2°, 179.0 ± 0.2°, 178.8 ± 0.2° and 178.0 ± 0.3°, for nanograsses #A, #B, #C and #D respectively, (Table S2, ESI † ). We attribute this to a similar mechanism to that studied by Schellenberger et al. on pillared surfaces, 26 where they demonstrate that the CL advances as the liquid–air surface gradually bends down, leading to a macroscopic contact angle near 180°. On the other hand, the receding θ YL varies across the different types of nanograss, with progressively smaller mean value: 177.7 ± 0.5°, 177.3 ± 0.4°, 171.9 ± 0.7° and 164.3 ± 0.4° for nanograss #A, #B, #C and #D respectively. We note that the main source of uncertainty in the calculation of θ YL for each frame comes from the error in the measurement of the interface radius in our top-view machine vision analysis. In our method the sensitivity of θ YL to an error of one pixel in the estimation of the interface radius is ∼0.1° per px near θ YL = 180° (see Fig. S15, ESI † ). In comparison, commercial CAG can have an error of multiple degrees per each pixel of error in the positioning of the baseline, which defines de plane where the droplet touches the sample. 29 Complementary to the contact angle values produced by the Young–Laplace model using a conventional optical microscope, we also used the transparent probe on a DHM to directly measure the contact angle from the 3D profile of the droplet and wetting interface, available in Table S2 (ESI † ) (see Materials and methods and Fig. S7, ESI † for Experimental details). The results show that the advancing contact angle on all samples is indeed >178° and the receding contact angles were 178.8 ± 0.5° and 178.6 ± 0.4° for nanograss #A and #B respectively, confirming their superhydrophobicity and low contact angle hysteresis. The receding contact angles on nanograss #C and #D were not possible to measure due to technical limitations of the DHM related to the maximum measurable sample slope angle. We also produced CL progression maps of the movement of the CL during advancing and receding, shown in Fig. 2(h) and (i) respectively. These maps reveal how the CLs move differently on each sample despite having similar topographical features. The advancing CL in nanograss #A shows many interaction sites where we interpret the CL wraps around the highest spikes of the sample (see also Video S1 and Fig. S8, ESI † ). Surprisingly, nanograss #B shows the most continuous advancing CL, which agrees with the lowest mean advancing ε CL in Fig. 2(f) . Nanograss #B has very similar wetting properties to #A, with similarly high contact angle and low contact angle hysteresis. However, its features are just below the camera's pixel size, of approximately 0.75 μm (Table S1, ESI † ) and individual interaction sites cannot be resolved. The CL in nanograss #C advances similarly to nanograss #B but more irregularly, while in #D it advances in concentric steps. During receding the CL moves in irregular stepwise manner for all nanograss types, due to pinning on surface features ( Fig. 2(i) ). A trend is observed where the time spent by the CL at the maximum perimeter versus the interior of the interface increases from nanograss #A to #D. We also note that the CL progressions in Fig. 2(i) include both the transition and receding phases. For this reason, nanograss #C and #D show signs of the CL pinning at the perimeter of the interface during the transition phase. At the same time, the CL in nanograsses #C and #D spends less time in the center regions, with #D detaching from the droplet in only a few frames. Additionally, we imaged the evolution of the wetting interface when the droplet probe was sliding on a silicon nanograss surface containing a scratch (Fig. S13, ESI † ). We were able to observe how the scratch affected wetting when it entered and exited the interfacial region, which provides useful information for studying the influence of defects on wetting. A video recording of the experiments can be found in Movie S4 (ESI † ). 3.2. Pinning and depinning sequence on pillared surfaces To evaluate the sensitivity and repeatability of our method, we observed the pinning and depinning events on a water-repellent micropillar model surface with a pillar diameter of 20 μm, period of 80 μm and height of 44 μm ( Fig. 3 , Fig. S10 and Table S4, ESI † ). We varied the initial alignment between the droplet and the pillars in three cases, centered on one pillar, Fig. 3(a) , the middle of four pillars, Fig. 3(b) , and the middle of two pillars, Fig. 3(c) (see also Movie S2, ESI † ). Fig. 3 Pinning and depinning order on silicon micropillars. Measurements were performed for three droplet-pillar alignment cases and repeated 10 times. (a)–(c) Top-view of the pinning experiments; where the contacting pillars are bright, the red cross denotes the initial position of the center of the droplet. (Scale bar 200 μm) (d)–(f) Maps of the pinning order of each pillar. The numbers in the maps indicate the pinning order, and the colors represent the pinning time. (g)–(i) Mean pinning times and standard deviation relative to the first pinning pillar. (j)–(l) Maps of depinning order. The numbers in the maps indicate the depinning order, and the colors represent the depinning time. (m)–(o) Mean depinning times and standard deviation relative to the first depinning pillar; the initial alignment of the probe was centered on one pillar (a), (d), (g), (j), (m), the middle of four pillars (b), (e), (h), (k), (n), and the middle of two pillars (c), (f), (i), (l), (o). The experimental results demonstrate excellent time synchronization of pinning events in many cases. For example, in the case where the droplet was centered on one pillar, after first touching the center pillar, the four neighbouring pillars (order number 2–5) always pinned within 10 ms on all 10 measurements, Fig. 3(d) and (g) . The difference in droplet-pillars alignment leads to dramatic differences in the sequence and number of pinning events; for example, in the case where the droplet was aligned between four pillars, Fig. 3(b) , only 16 pillars were contacted for the same sample stage displacement, as opposed to 21 pillars in the centered case, Fig. 3(a) . In the mid-four case, synchronization in groups was also similar, except for the last group, which took approximately 1.82 s for all four pillars to pin (order number 13–16 in Fig. 3(h) ). For the mid two case of Fig. 3(c) , there was little synchronization between pinning events due to initial misalignment of the droplet from the mid-point between two pillars. In this case, there was no simultaneous group of pillars at the same distance to the geometric center of the droplet. However, the individual pinning events of each pillar remain highly repeatable, demonstrating that our method can detect the influence of minor pillar misalignment. Depinning events are mostly an abrupt process compared to pinning, with all pillars depinning almost simultaneously ( Fig. 3(m)–(o) ). The initial alignment of the droplet with the sample also dramatically affects the depinning sequence and duration. In the centered and mid two cases, depinning was almost simultaneous, with all depinning occurring in about 50 ms and 200 ms, respectively. We note that in mid two case, Fig. 3(o) , the top error bars represent the standard deviation of the measurements, while the bottom error bars are limited at zero. In the mid-four case, it took over 12 s for all pillars to depin following the depinning of the first outer pillar. Nevertheless, depinning also exhibits extremely good repeatability, with all events occurring within 370 ms. 3.3. Imaging of wetting interface on biological leaves and scratched surface We also explored the applicability of the transparent droplet probe to biological samples by measuring the progression of the wetting interface on two types of plant leaves, Maranta and Musa (see Fig. 4 and Fig. S11, ESI † ). The Maranta's surface is flatter overall, with smaller scale features, while the Musa exhibits greater surface roughness and topographical variations; see Fig. S12 and Table S5 (ESI † ). Fig. 4 Imaging of wetting interface on plant leaves. All measurements were obtained at the same location of the respective samples and repeated 10 times. (Scale bars: 200 μm) (a) Image of a Maranta leaf. (b) Optical micrograph of the wetting interface between the droplet probe and the Maranta leaf; the green line encloses the detected apparent contact area. (c) Evolution of apparent contact area A app as a function of sample stage displacement for the Maranta leaf; the solid line represents the mean, and the shaded area is bounded by the minimum and maximum values of observations. (d) Progression of apparent CL irregularity ε CL and (e) Young–Laplace contact angle, θ YL , as a function of interface diameter. (f) Image of a Musa (Oriental Dwarf) leaf, and the respective (g) wetting interface, (h) apparent contact area, (i) apparent contact line irregularity and (j) Young–Laplace contact angle. Example snapshots of the evolution of the wetting interfaces are shown in Fig. 4(b) and (g) , where the apparent contact area algorithmically identified is enclosed by a green line (see Movie S3, ESI † for more details). As Fig. 4(b) and (g) show, the apparent wetting interfaces of both leaves are rather inhomogeneous. The interface of the Maranta consists of many subtle variations, while the interface of the Musa is dominated by fewer but more significant variations, which we attribute to surface topography features. The progression of A app as a function of sample stage displacement is shown in Fig. 4(c) for the Maranta leave and Fig. 4(h) for the Musa leave (see also Fig. S11, ESI † ). Despite the surface inhomogeneities of the leaves, it is surprising to see that A app is rather reproducible in both cases. Both leaves demonstrate high but reproducible ε CL value, Fig. 4(d) and (i) for the Maranta and Musa respectively. Maranta leaf has a mean ε CL of 3 μm/6 μm (advancing/receding) and for Musa a ε CL of 5 μm/5 μm (advancing/receding), which we attribute to their microscale topographical features (see Table S6, ESI † ). We also calculated the θ YL for both plant leaves, shown in Fig. 4(e) and (j) . The mean advancing θ YL were 178.6 ± 0.3° and 179.7 ± 0.4° and the mean receding θ YL were 159 ± 4° and 168 ± 5°, for Maranta and Musa leaves respectively. The resulting contact angle hysteresis were 20 ± 5° and 12 ± 5° respectively, indicating that the Musa leaf is more hydrophobic than the Maranta leaf. The advancing contact angle values measured with our method are more than 10° greater than those obtained using CAG: advancing 166.9 ± 3.7° and 166.5 ± 4.0° and receding 166.4 ± 4.2° and 167.7 ± 1.7° for Maranta and Musa leaves respectively. The lower advancing CAG contact angle values can be attributed to the non-flatness of the leaves (see Fig. S12, ESI † ), which obscures the actual CL. The non-flatness can also contribute to the greater CAG uncertainty due to the dependence of the baseline on the CL position. On the other hand, the non-flatness and inhomogeneous wetting properties of the plant leaves lead to greater uncertainty also in our θ YL model, which reflects mostly in the receding contact angle values."
} | 6,212 |
23475644 | null | s2 | 4,623 | {
"abstract": "Bacillus subtilis is a soil-dwelling Gram-positive bacterial species that has been extensively studied as a model of biofilm formation and stress-induced cellular differentiation. The tetrameric protein, SinR, has been identified as a master regulator for biofilm formation and linked to the regulation of the early transition states during cellular stress response, such as motility and biofilm-linked biosynthetic genes. SinR is a 111-residue protein that is active as a dimer of dimers, composed of two distinct domains, a DNA-binding helix-turn-helix N-terminus domain and a C-terminal multimerization domain. In order for biofilm formation to proceed, the antagonist, SinI, must inactivate SinR. This interaction results in a dramatic structural rearrangement of both proteins. Here we report the full-length backbone and side chain chemical shift values in addition to the experimentally derived secondary structure predictions as the first step towards directly studying the complex interaction dynamics between SinR and SinI."
} | 258 |
22958130 | null | s2 | 4,624 | {
"abstract": "In many streptococci, quorum sensing utilizes secreted, linear peptides that engage cognate receptors to coordinate gene expression among members of a local population. Streptococcus mutans employs the secreted peptides CSP and XIP to stimulate production of antimicrobial bacteriocins and to induce development of competence for genetic transformation. Recent progress in the field reveals that these pathways not only monitor the presence of signal emitters but also sense environmental factors. Both kinds of information are integrated by regulatory networks that then generate multiple outcomes, even among parallel cells growing in identical conditions. In this issue of Molecular Microbiology, Son and co-workers investigate how two medium types shape cellular responses to CSP and XIP pheromones in individuals across a population. Their findings characterize restrictive properties of media differing in peptidic fragment content and reveal unusual signalling properties that contribute to bimodal responses of gene expression."
} | 258 |
39455737 | PMC11511842 | pmc | 4,625 | {
"abstract": "Anaerobic digestion is one of the most promising options for the disposal of biodegradable food waste. However, the relatively high content of oil in food waste inhibits the conversion efficiency of anaerobic digestion because of the accumulation of long-chain fatty acids (LCFAs). In this study, activated anaerobic sludge was acclimated to accommodate high-oil conditions. The methane yield of high-oil food waste digested by the acclimated sludge increased by 24.9% compared to that digested by the raw sludge. To determine the internal changes in the acclimated sludge, the shifts in the microbial communities during the acclimation period were investigated via high-throughput sequencing (HTS) based on the 16 S rRNA gene. The results indicated that Bacteroidetes, Firmicutes, Chloroflexi and Proteobacteria were the dominant bacteria at the phylum level. The acclimation time allows some functional bacterial taxa to proliferate, such as Clostridium and Longilinea, which are able to degrade LCFAs and turn them into small organic molecules that also have nutrient value for other bacteria. Among the archaeal communities, the hydrogenotrophic methanogen Methanobacterium nearly supplanted the acetotrophic methanogen Methanosaeta. The time profiles of volatile fatty acids (VFAs) and pH during this period provided additional evidence for the success of the acclimation. This study demonstrated the effectiveness of acclimation and the dynamic of microbial communities, which further contributed to the management and resource utilization of high-oil food waste.",
"conclusion": "Conclusions The activated anaerobic sludge was acclimated to high-oil conditions. Compared with that of the raw sludge, the methane yield of the high-oil food waste anaerobically digested by the acclimated sludge improved by 24.9%. During acclimation, some functional bacterial taxa, such as Clostridium and Longilinea, which are able to degrade LCFAs and turn them into small organic molecules that have nutrient value for other bacteria, are allowed to proliferate. For the archaeal communities, the hydrogenotrophic methanogen Methanobacterium nearly supplanted the acetotrophic methanogen Methanosaeta. The time profiles of pH and VFA validated the success of acclimation. The findings of this study serve as a basis for emphasizing the effectiveness of acclimation and the dynamic of microbial communities and are able to provide an alternative solution to further high-oil waste management and resource utilization because the efficiency of anaerobic digestion for high-oil waste is obviously improved by acclimation.",
"introduction": "Introduction Rapid growth of the world population has led to a fast acceleration of food waste production 1 . The amount of food waste produced globally is reportedly 1.3 ~ 1.4 billion tons per year 2 . In China, 125 million tons of food waste were produced in 2020 3 . Food waste contains an abundance of organic compounds, and the resource utilization of food waste has gradually become a global issue that needs to be urgently addressed 4 . Considering the negative impacts of incineration and landfill dumping on the disposal of food waste, anaerobic digestion has become one of the most promising options for recovering energy and materials 5 . During the anaerobic digestion process, the organic matter of food waste is converted to CH 4 and CO 2 by fermentative bacteria through the hydrolysis, acidogenesis, acetogenesis, and methanogenesis stages 6 . Compared with those in other regions, the unique Chinese dietary habits result in a relatively higher oil content in Chinese food waste, ranging from 3 to 17% (wet basis) 7 . The collection of trapped oil and its illegal reutilization as cooking oil have serious health impacts 8 . Although the theoretical biogas conversion rate of oil (94.8%) is greater than that of protein (71%) and carbohydrates (50.4%) 9 , a high-oil content inhibits anaerobic digestion because of the accumulation of long-chain fatty acids (LCFAs) 10 . The oil is first hydrolyzed to glycerol and LCFAs in the absence of oxygen. LCFAs are the major intermediate products of oil biodegradation and are then further converted to acetate and hydrogen through the β-oxidation process by acetogenic bacteria and finally to CH 4 by methanogenic archaea 11 . In an anaerobic environment, the adsorption of LCFAs onto the microbial surface forms blocking layers, affecting the transportation of nutrients to the cell 12 , 13 . Since a high-oil content has adverse effects on food waste anaerobic digestion, it is meaningful to explore the effective solution of high-oil food waste anaerobic digestion. Some research demonstrated that the mixing intensity control can disturb the solubility characteristics of oil, further improve the methane yield of high-oil food waste 14 . Recent studies indicated that the methane yield was enhanced from 438 mL/g-VS to 587 mL/g-VS as the oil contents increased, however when the oil content exceeded the limit, the system collapsed 15 . It has been also reported that the methane yield is improved through the co-digestion of oil and sludge 16 , 17 . Therefore, after being exposed for an adequate amount of time, anaerobic microbial communities may adapt to relatively high oil contents. Previous studies mainly focused on the performance of methane yield, the dynamic of microbial communities is seldom investigated. Determination of the characteristics of microbial communities is important for improving the anaerobic digestion of high-oil food waste. Since many microbes that participate in anaerobic digestion construct a complex microbial community, it is difficult to analyze the detailed mechanism involved 18 . The different stages of anaerobic digestion are coordinated by abundant microbial communities that work in a symbiotic relationship. The microbial communities significantly fluctuate with changes in parameters such as pH, volatile fatty acids (VFAs), and substrate 19 . Fortunately, several molecular techniques are available for microbial community detection, and high-throughput sequencing (HTS) based on the 16 S rRNA gene is the most compelling method due to its high precision 20 . Studies have analyzed the shifts in microorganisms during the co-digestion of oil and sludge 21 , 22 . The roles of typical functional microbes in the anaerobic digestion process have been revealed 23 , 24 . However, shifts in microbial communities during the acclimation process have seldom been reported, and the mechanism by which microbes contribute to the system’s adaptation to high-oil conditions is not distinctly understood. In this study, the aim was to investigate the detailed microbial community structure and diversity in the process of acclimatizing to high-oil food waste. The time profiles of VFAs and pH were analyzed to provide a biochemical context for the dynamic of microbial communities during the acclimation process. In addition, the differences in the anaerobic digestion of high-oil food waste by raw and acclimated sludge were also determined to demonstrate the effectiveness of acclimation.",
"discussion": "Results and discussion Influence of oil contents on food waste anaerobic digestion The fundamental experiments were performed to determine the influence of oil content on food waste anaerobic digestion. Figure 1 (a) shows the cumulative methane yield of food waste with various oil contents. As the oil content increased from 2.5 to 5%, the cumulative methane production increased by 10.7% and 27.8% respectively compared to the blank group. The increase indicated that moderate oil addition contributed to methane production because of the hydrolysis of LCFAs 29 . However, further increases in oil content (7.5% and 10%) inhibited the methane yield compared to that in the 5% oil content group, indicating that excess LFCA hindered cellular permeability and mass transport. Specifically, compared with those of the blank group, the 7.5% and 10% oil contents increased the cumulative methane yield by 21.8% and 16.8%, respectively. The results revealed that oil addition was beneficial for biogas production, but the increase in methane yield decreased when the oil content exceeded 5%. \n Fig. 1 ( a ) The cumulative methane yield of food waste with different oil contents; ( b ) The daily methane yield of food waste with different oil contents. \n Figure 1 (b) shows the daily methane yield of food waste with various oil contents. The peak of the daily methane yield was delayed as the oil content increased. The peaks of the 2.5%, 5%, 7.5%, and 10% oil content groups were delayed by 1, 2, 4, and 5 days, respectively, compared to those of the blank group. Additionally, the peak values of daily methane yield and convertibility reached a maximum at a 5% oil content. The results indicated that oil addition improved the biogas yield due to its high methane production potential. The hydrolysis of oil generates LCFAs, which further produce methane 30 . However, oil addition also delayed the appearance of a daily methane yield peak, which was likely related to the transport limitation caused by bacteria being coated in a layer of LCFAs 31 . Methane yield of the acclimated sludge Figure 2 shows the effects of high-oil food waste anaerobically digested by acclimated and raw sludges. After 20 days, the cumulative methane yield of the acclimated sludge increased by 24.9% compared to that of the raw sludge. The peak of daily methane yield occurred 4 days earlier after acclimation, and the highest daily methane yield increased by 18.2%. The results indicated that the acclimated sludge adapted to the high-oil conditions and was beneficial for the resource utilization of high-oil food waste. Acclimation is a process that involves gradual adjustments within the microbial community modulated by gradual changes in the environment, improving microbial performance or survival 32 . As a result of acclimation, microorganisms were able to tolerate higher oil levels than unacclimated microorganisms. Thus, the efficiency of anaerobic digestion of high-oil food waste was obviously improved. However, the internal changes that occur during the acclimation process remain unclear. Hence, the dynamic of the microbial community was studied in the following text. \n Fig. 2 ( a ) Accumulative methane yield for the raw sludge and acclimated sludge; ( b ) daily methane yield for the raw sludge and acclimated sludge. \n Time profiles of VFA and pH during the acclimation process The time profiles of VFA and pH were determined to validate the effectiveness of acclimation. Initially, the decomposition of oil produced LCFAs and VFAs, and LCFAs adhered to the surface of the methanogenic bacteria, inhibiting the activity of the methanogenic bacteria by attaching to cells and reducing mass transfer, ultimately leading to the accumulation of VFAs 33 . As microbial communities gradually adapt to high-oil conditions, LCFAs are converted to VFAs, and VFAs are rapidly consumed with the generation of acetic acid 34 . As shown in Fig. 3 , the concentration of acetic acid significantly increased in the initial 20 days. On subsequent days, the concentrations of various VFAs remained stable. The time profile of pH is shown in Fig. 4 . In the initial days, the pH decreased sharply due to the accumulation of VFAs. When the VFA concentration was maintained within a stable range in the later days, the pH also tended to stabilize. The time profiles of VFA and pH indicated that the acclimation was successful. \n Fig. 3 Time profile of VFAs during the acclimation process. \n \n Fig. 4 Time profile of pH during the acclimation process. \n Dynamic of microbial community structure and diversity during the acclimation period To determine the effect of acclimation, HTS based on the 16 S rRNA gene analysis was performed to investigate the dynamic of the microbial community. Figure 5 shows the shifts in the relative abundances of bacteria at the phylum level. The main phyla in the bacterial communities were Bacteroidetes, Firmicutes, Chloroflexi and Proteobacteria. During the acclimation process, Bacteroidetes and Firmicutes accounted for the largest proportion, and their relative abundance increased progressively. The most important contribution of Bacteroidetes to waste processing is carbohydrate hydrolysis into acetate, butyrate and propionic acid 35 . Whereas Firmicutes produce extracellular enzymes that primarily contribute to the biodegradation of macromolecular organics 36 . According to previous research, the Clostridiaceae, Syntrophomonadaceae, Syntrophaceae and Bacteroidaceae families are responsible for the biodegradation of LCFAs 37 , 38 . Among these bacteria, Clostridiaceae and Syntrophomonadaceae belong to the phylum Firmicutes, and Bacteroidaceae belongs to the phylum Bacteroidetes. This observation revealed that the abundances of nutrient-supplying and LCFA-degrading bacteria increased significantly with the progressive injection of oil, indicating that the anaerobic functional bacteria gradually adapted to the high-oil conditions and proliferated during the acclimation process. \n Fig. 5 Dynamic of bacterial communities at the phylum level. \n \n Fig. 6 Dynamic of bacterial communities at the genus level. \n The 20 most prevalent genera identified in this study were chosen for heatmapping to analyze the potential roles of the bacterial taxa and the dynamic of the bacterial communities in detail, as presented in Fig. 6 . The heatmap shows that whereas the relative abundance of Desulfovibrio decreased during acclimation, those of Anaerolinea, Clostridium and Longilinea increased. The shifts in the relative abundance of typical functional bacteria during acclimation are shown in Table 2 . Anaerolinea is a genus in the phylum Chloroflexi, and when it is cultured with hydrogen-trophic methanogens, its growth rate increases significantly 39 . The phylum Firmicutes member Clostridium has the ability to produce organic acids 40 . Longilinea is a genus of the phylum Chloroflexi, and its most important role in waste processing is to metabolize different types of carbohydrates to generate organic acids 41 . Desulfovibrio is a genus of the phylum Thermodesulfobacteria, and it consists of sulfate-reducing bacteria 42 , 43 . During the acclimation period, the abundance of bacteria that biodegrade macromolecular organic matter increased, indicating that the microbial community had an enhanced ability to decompose macromolecular oil and LCFAs. \n Table 2 Shifts in the relative abundance of typical functional bacteria during acclimation. Phylum Relative abundance (%) Genus Relative abundance (%) Day 0 Day 10 Day 20 Day 30 Day 0 Day 10 Day 20 Day 30 Chloroflexi 22.0 21.6 12.4 13.2 Anaerolinea 0.99 1.58 1.81 2.42 Longilinea 0.25 0.47 0.63 0.75 Firmicutes 10.4 16.2 25.1 27.6 Clostridium 0.63 1.25 1.39 1.50 \n The dynamic of archaeal communities at the genus level is illustrated in Fig. 7 . The genus Methanobacterium accounted for the majority of the relative abundance, reaching approximately 80%. Methanobacterium belongs to the phylum Euryarchaeota and typically exists in anaerobic digestion reactors, the rumen, rice soil, rotten wood, etc. The major metabolic substrates of Methanobacterium are hydrogen, carbon dioxide, methanol, and formate 44 . The relative abundance of Methanobacterium declined during the initial 10 days of acclimation, probably due to the lack of required metabolic substrates. During the acclimation process, oil was gradually hydrolyzed into glycerol and fatty acids, while glycerol was further decomposed into hydrogen, carbon dioxide, and small organic acids. Therefore, the relative abundance of Methanobacterium gradually increased after 10 days. Methanosaeta is a genus of the phylum Euryarchaeota. It is an acetotrophic methanogenic archaea that typically exists in high-salt seafloor sediments, animal gastrointestinal tracts and anaerobic digestion reactors. The major metabolic substrate of Methanosaeta is acetic acid 45 . The hydrolysis of oil produces LCFAs, and the accumulation of LCFAs contributes to acidification to a certain extent 46 . Anaerobic bacteria tend to generate hydrogen under environmental acidification. Furthermore, the generated hydrogen was conducive to the growth of hydrogenotrophic methanogens and adverse to the growth of acetotrophic methanogens 47 , 48 . Thus, as acclimation progressed, the abundance of Methanosaeta gradually decreased. \n Fig. 7 Dynamic of archaeal communities at the genus level. \n The dynamic of the microbial community indicated that the acclimated sludge adapted to high-oil conditions, which is favorable for biogas production. RDA (redundancy analysis) was used to visualize the correlations between the operational conditions (e.g. pH and oil content) and variations in microbial community (Fig. 8 ). The angle between the vector of pH and the vector of oil content is greater than 90◦, indicating that the effect of pH was negatively correlated with the effect of oil content. It also proved that the increase in oil content led to the accumulation of LCFA and a decrease in pH. At the same time, it can be seen that part of the microbial communities adapted to the high-oil environments and proliferated with acclimation; while another part of the microbial communities cannot adapt to the high-oil environments and gradually disappeared. \n Fig. 8 RDA analysis of microbial communities."
} | 4,356 |
25342973 | PMC4205766 | pmc | 4,626 | {
"abstract": "Background Obtaining a better understanding of the complex mechanisms occurring\nduring lignocellulosic deconstruction is critical to the continued growth of\nrenewable biofuel production. A key step in bioethanol production is\nthermochemical pretreatment to reduce plant cell wall recalcitrance for downstream\nprocesses. Previous studies of dilute acid pretreatment (DAP) have shown\nsignificant changes in cellulose ultrastructure that occur during pretreatment,\nbut there is still a substantial knowledge gap with respect to the influence of\nlignin on these cellulose ultrastructural changes. This study was designed to\nassess how the presence of lignin influences DAP-induced changes in cellulose\nultrastructure, which might ultimately have large implications with respect to\nenzymatic deconstruction efforts. Results Native, untreated hybrid poplar ( Populus\ntrichocarpa x Populus deltoids )\nsamples and a partially delignified poplar sample (facilitated by acidic sodium\nchlorite pulping) were separately pretreated with dilute sulfuric acid (0.10 M) at\n160°C for 15 minutes and 35 minutes, respectively . Following extensive\ncharacterization, the partially delignified biomass displayed more significant\nchanges in cellulose ultrastructure following DAP than the native untreated\nbiomass. With respect to the native untreated poplar, delignified poplar after DAP\n(in which approximately 40% lignin removal occurred) experienced: increased\ncellulose accessibility indicated by increased Simons’ stain (orange dye)\nadsorption from 21.8 to 72.5 mg/g, decreased cellulose weight-average degree of\npolymerization (DP w ) from 3087 to 294 units, and increased\ncellulose crystallite size from 2.9 to 4.2 nm. These changes following DAP\nultimately increased enzymatic sugar yield from 10 to 80%. Conclusions Overall, the results indicate a strong influence of lignin content\non cellulose ultrastructural changes occurring during DAP. With the reduction of\nlignin content during DAP, the enlargement of cellulose microfibril dimensions and\ncrystallite size becomes more apparent. Further, this enlargement of cellulose\nmicrofibril dimensions is attributed to specific processes, including the\nco-crystallization of crystalline cellulose driven by irreversible inter-chain\nhydrogen bonding (similar to hornification) and/or cellulose annealing that\nconverts amorphous cellulose to paracrystalline and crystalline cellulose.\nEssentially, lignin acts as a barrier to prevent cellulose crystallinity increase\nand cellulose fibril coalescence during DAP.",
"conclusion": "Conclusions This study is another important step in providing the required data\nfor a comprehensive analysis of biomass in an effort to optimize the integrated\noperations of pretreatment and enzymatic hydrolysis. In particular, key molecular\nfeatures related to biomass recalcitrance, specifically cellulose ultrastructure and\naccessibility were, studied. In the absence of lignin spacer along with\nhemicellulose removal after DAP, changes occurred to cellulose ultrastructure\ninclude increases in cellulose% crystallinity, cellulose crystallite size, cellulose\ncrystalline transformation, and cellulose accessibility accompanied by a decrease of\ncellulose DP. NMR and WAXD results indicated that lignin presence played a key role\nin preventing cellulose crystallite co-crystallization and coalescence during DAP.\nThis indicates lignin acts as a barrier which restricts cellulose crystallinity\nincrease and cellulose crystallite growth, and that partial delignification instead\nof complete lignin removal is better for enhanced sugar yield.",
"discussion": "Results and discussion In an effort to assess potential cell wall compositional and\nchemistry changes occurring during delignification and DAP, especially those\nassociated with biomass degradation and consequently to changes in enzymatic\nhydrolysis, cell wall compositional analysis was conducted. Cell wall compositional analysis Carbohydrate and Klason lignin (K-lignin) content for the\nuntreated, delignified, and dilute acid pretreated poplar solids are reported in\nFigure 1 . DAP as a cost-effective\npretreatment method that significantly reduces lignocellulosic recalcitrance by\nremoving hemicellulose, disrupting lignin-hemicellulose matrix, and redistributing\nlignin [ 17 ]. Delignification\n(holocellulose pulping) of the native poplar with starting K-lignin of about\n23 wt% (Table 1 PL23-t0; t indicates DAP\ntime in minutes) for 15 minutes resulted in a K-lignin content of about 19 wt%\n(PL19-t0 sample) and increased the relative glucan and xylan contents in the\nresidual solid from 49 to 56% and 22 to 23%, respectively. Further,\ndelignification for an additional 15 minutes dropped lignin content to about\n14 wt% (to produce the PL14-t0 sample), however, there was little change in the\nrelative glucan and xylan contents. Based solely on this data, it seems reasonable\nto suggest that limited delignification had little effect on the cell wall\ncarbohydrate components. Figure 1 \n Klason lignin, glucan, and xylan contents from\ndilute acid pretreated poplar with reduced lignin contents. \nSample code with definition is in Table 1 . Table 1 \n Pretreatment methods and conditions of\npoplar \n \n Sample code \n \n Poplar sample \n \n Starting % Klason lignin \n \n Pretreatment conditions \n PL23-t0 Native 23.2 – PL23-t15 Dilute acid pretreated 23.2 0.1 M\nH 2 SO 4 ,160°C,\n15 minutes PL23-t35 Dilute acid pretreated 23.2 0.1 M\nH 2 SO 4 ,160°C,\n35 minutes PL19-t0 Delignified 19.2 – PL19-t15 Delignified then dilute acid pretreated 19.2 0.1 M\nH 2 SO 4 ,160°C,\n15 minutes PL19-t35 Delignified then dilute acid pretreated 19.2 0.1 M\nH 2 SO 4 ,160°C,\n35 minutes PL14-t0 Delignified 14.3 – PL14-t15 Delignified then dilute acid pretreated 14.3 0.1 M\nH 2 SO 4 ,160°C,\n15 minutes PL14-t35 Delignified then dilute acid pretreated 14.3 0.1 M\nH 2 SO 4 ,160°C,\n35 minutes When native poplar sample (PL23-t0) was subjected to DAP for\n15 minutes (to produce sample PL23-t15), there was a significant reduction of\nxylan from 21 to 1% (PL23-t15), with a corresponding increase of the relative\nglucan and Klason lignin contents. When the residence time of DAP was extended to\n35 minutes, the residual solids had a slightly lower relative glucan and higher\nrelative lignin content than the solids collected after 15-minutes pretreatment.\nThis could be a result of hydrolytic degradation of cellulose but also, in part,\nresult from the re-polymerization of polysaccharide degradation products forming\npseudo-lignin [ 31 ]. Delignification\nof poplar followed by DAP resulted in a similar initial increase in relative\nglucan and Klason lignin contents, but a slight decrease in relative glucan\ncontent with increasing DAP residence time. Though delignification to a greater\nextent, followed by DAP, seems to correspond with greater magnitudes of\nchange. Fourier transform infrared (FTIR) spectroscopy analysis Relative changes in cell wall chemistry can be extracted from\nvarious absorption bands and presented in Table 2 . The normalized Fourier transform infrared (FTIR) absorption\nspectra of lignocellulose at a band position of\n1,424 cm −1 is primary due to the presence of\ncellulose, specifically the CH 2 scissor motion of cellulose\n[ 32 - 34 ]. A decrease in the spectral band at\n1,424 cm −1 , as well as other cellulose specific\nspectral bands, can be used to determine possible degradation isolated to\ncellulose during sodium chlorite delignification followed by pretreatment. An\nincrease in spectral intensity at 3,340 cm −1 \nrepresented an increase in cellulose-hydrogen bonding and indicated possible\nco-crystallization. Spectral intensity at 2,900 and\n1,367 cm −1 was attributed to C-Hbond stretching and\nrelative decreases in those bands suggested general degradation to the biomass was\noccurring based on the removal of methyl and methylene groups. After DAP the\nreduction in the band intensity from around 1,740 cm −1 \nrepresenting carbonyl groups associated with lignin [ 35 ], typically indicated possible cleavage of\nassociation of polysaccharide with lignin and the removal of acetylated\nhemicellulose. The presence of acetyl groups have long be thought to inhibit\nenzymatic hydrolysis, and the de-acetylation that occurred during DAP suggested\nhemicelluloses hydrolysis that would facilitate the cellulose hydrolysis to sugar\nconversion [ 36 , 37 ]. In all substrates after DAP, spectral\nintensity increased at 1,595 and 1,510 cm −1 \nrepresenting aromatic rings [ 34 , 38 ], indicated\nthe increase in Klason lignin content after DAP. The reduction of spectral\nintensity at 1,240 cm −1 in all pretreated samples was\ntentatively attributed to the cleavage of acetyl groups. In addition, FTIR\nsemi-quantitative analysis can examine the relative structural change in cellulose\ncrystalline and amorphous components. The reduction of ratio\nI α /I β suggested the reduction of\nI α and/or the increase of I β, \nand/or cellulose crystalline allomorph transformation from\nI α to I β . The increase of ratio\n1,100/900 cm −1 plus a reduction in\n900 cm −1 suggested that the cellulose amorphous\ncomponents were degraded to some extent and that amorphous cellulose could be\ntransformed into crystalline cellulose. Table 2 \n Relative changes in poplar samples after dilute acid\npretreatment by Fourier transform infrared spectroscopy \n \n Band position \n \n Assignment \n [ \n 32 \n - \n 38 \n ] \n \n Pretreatment conditions \n \n PL23-t0 \n \n PL23-t15 \n \n PL23-t35 \n \n PL19-t0 \n \n PL19-t15 \n \n PL19-t35 \n \n PL14-t0 \n \n PL14-t15 \n \n PL14-t35 \n 3340 O-H stretching, related to cellulose-hydrogen\nbonds 1.7 1.6 1.5 2.1 1.9 2.2 2.1 1.8 2.3 2900 C-H stretching, related to biomass methyl/methylene\ngroup 0.8 0.8 0.9 0.9 0.8 1.0 0.9 0.8 1.1 1740 Carbonyl bonds ascribed to hemicelluloses 1.3 -- -- 1.6 -- -- 1.5 -- -- 1595 Lignin aromatic ring stretch 0.7 0.8 0.8 0.6 0.7 0.7 0.5 0.5 0.7 1510 Lignin aromatic ring stretch 0.6 0.9 1.0 0.5 0.8 0.8 0.4 0.7 0.7 1424 CH 2 scissor motion in\ncellulose 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1367 Aliphatic C-H stretch in CH 3 \n 1.1 0.9 0.8 1.3 1.1 1.1 1.4 1.2 1.1 1265 Ester absorption associated with uronic acid -- 1.1 1.2 -- 1.2 1.1 -- 1.3 1.2 1240 C-O absorption from acetyl group cleavage 1.6 1.3 1.2 2.2 1.1 1.0 2.0 1.2 1.1 1059 C-O stretch in secondary alcohol 5.0 3.6 3.4 -- 4.0 4.3 -- 5.3 4.9 1100/900 Crystalline to amorphous cellulose ration 3.0 4.4 4.5 2.9 3.7 4.5 2.7 4.1 4.4 750/710 I α /I β \n 0.9 0.6 0.4 0.3 0.3 0.2 0.3 0.3 0.2 900 Amorphous cellulose 1.1 0.6 0.6 1.2 0.8 0.8 1.2 0.9 0.7 Sample code with definition is in Table 1 . Cellulose degree of polymerization The cellulose average DP was determined following virtually\ncomplete lignin and hemicellulose removal and then using a published gel\npermeation chromatography (GPC) procedure [ 17 ]. The cellulose DP results were used to determine the ratio of\nterminal to interior β-glucosidic bonds that can be effectively used to analyze\nthe relative change in cellulose chain length. Figure 2 shows the effect delignification and DAP had on poplar\ncellulose number-average DP (DP n ) and weight-average DP\n(DP w ). Delignification alone had a very limited effect on\neither DP n or DP w , displaying at\nmost a 5% reduction. This result is in good agreement with a previous study\nanalyzing the effect of holocellulose pulping on cellulose molecular weight.\n[ 39 ] However, DAP caused dramatic\nreductions in cellulose DP with increased residence time. The fact that a large\nportion of the cellulosic component had been hydrolytically degraded during DAP\ncould have had large implication on concurrent changes in cellulose\nultrastructure, specifically the change of cellulose crystallinity. However,\nsignificant differences between cellulose DP for the sample that was subjected to\nDAP only and samples that was delignified with sodium chlorite followed by DAP\nwere not detected. This is most likely a result of the cellulose reaching its\nleveling-off DP [ 17 ]. Figure 2 \n Average number and weight degree of polymerization\nof cellulose. DP n : number-average\ndegree of polymerization DP w : weight-average degree\nof polymerization. Sample code with definition is in Table 1 . Cellulose ultrastructure and crystallinity by nuclear magnetic resonance\nspectroscopy In an effort to better understand the detailed ultrastructural\nchanges occurring within cellulose during partial delignification followed by DAP,\n 13 C cross-polarization (CP) magic-angle spinning\n(MAS) NMR spectroscopy experiments were applied to isolated cellulose to determine\nthe relative intensity of crystalline and amorphous ultrastructural components of\ncellulose, following published procedures [ 40 , 41 ]. These\nresults were then used to support observations made via FTIR analysis and to\nunderstand how crystalline and amorphous ultrastructural components of cellulose\nvary as a result of DAP and delignification followed by DAP. Cellulose% crystallinity or CrI was obtained via two-peak\nintegration of the 13 C CP/MAS spectrum of isolated\ncellulose. Cellulose CrI was calculated by taking the ratio of the integral of the\ncellulose C 4 -crystalline carbon region (δ approximate to 85\nto 92 ppm) over the integral of whole cellulose C 4 -carbon\nregion (δ approximate to 80 to 92 ppm), and the results are shown in\nFigure 3 . Delignification alone had\nlittle effect on cellulose CrI, in contrast, DAP generated an increase from 57 to\n65%, approximately. Extended DAP residence time caused a further increase in the\ncellulose crystallinity, and this trend continued for samples that were subjected\nto delignification followed by DAP. This increase in cellulose CrI with\npretreatment, in part, resulted from the preferential hydrolysis and removal of\namorphous cellulose, an inference supported by the FTIR results and cellulose DP\ndata. Figure 3 \n Percent crystallinity of cellulose from dilute acid\npretreated poplar with reduced lignin contents. Sample code\nwith definition is in Table 1 . The relative proportion of cellulose crystalline allomorphs,\nincluding I α , I β , and\nparacrystalline and amorphous cellulose at accessible and inaccessible fibril\nsurfaces can also be extracted from the same C 4 -carbon\nregion in the 13 C CP/MAS spectrum of isolated cellulose\nusing a more complex seven-peak model and a least-squared non-linear fit. The\nresults of this analysis are shown in Figure 4 . Delignification seems not to have a significant effect on\ncellulose ultrastructure for both crystalline and amorphous components, which is\nin good agreement with a previous study analyzing the effect of acidified sodium\nchlorite treatment on pure cellulose [ 30 ]. However, DAP caused an observed increase in relative\ncellulose I β content, which was accompanied by a reduction\nin resonances representing cellulose I α+β and\nI α content, suggesting cellulose\nI α was subject to preferential degradation and/or\ntransformation into cellulose I β during hydrothermal\nconditions. The latter would occur via H-bonding disruption and rearrangement\nunder pretreatment conditions. This could be regarded as a cellulose annealing\n[ 42 ]. Paracrystalline cellulose is\na form of cellulose that is less ordered than crystalline cellulose but more\nordered than amorphous cellulose, and has been proposed to exist on the\nsub-surface of crystallites as a thin molecular layer [ 43 ]. Further ordering of amorphous cellulose\ninto these paracrystalline layers could contribute to the observed increase in CrI\nand expansion of the crystalline lattice. All pretreated samples had a higher\nrelative intensity for paracrystalline than the native or solely delignified\nsamples. Figure 4 \n The relative% cellulose crystalline allomorphs,\nparacrystalline cellulose and cellulose fibril surface. Para:\nparacrystalline cellulose; Inacc: Inaccessible fibril surface; Acc-1,\nAcc-2: Accessible fibril surface 1 and 2. Sample code with definition is\nin Table 1 . Two forms of non-crystalline cellulose have been identified within\nthe C 4 -carbon region in a 13 C\nCP/MAS spectrum of isolated cellulose, amorphous cellulose at accessible and\ninaccessible (fibril-to-fibril contact) fibril surfaces. The relative proportion\nof cellulose at accessible and inaccessible fibril surfaces is also shown in\nFigure 4 . Delignification did not alter\nthe inaccessible fibril surfaces but slightly decreased accessible surfaces,\nhowever, DAP generated obvious reduction in inaccessible and accessible surfaces.\nIn conjunction with GPC and crystallinity results, it could suggest hydrolysis and\ndegradation of amorphous cellulose is kinetically favored over that of crystalline\ncellulose during DAP, longer residence time could cause cellulose\nrecrystallization into crystalline I β , which proceeds with\ninduced hydrogen bonding process in solvent of high polarity acidic system. In addition to the relative proportion of cellulose crystalline\nallomorphs and amorphous cellulose at accessible and inaccessible fibril surfaces,\nthe C 4 -carbon region in a 13 C\nCP/MAS spectrum of isolated cellulose along with a simple geometric cellulose\nfibril model [ 44 , 45 ] can estimate the average lateral fibril\ndimension (LFD) and lateral fibril aggregate dimension (LFAD) of cellulose. The\nresults of this analysis are shown in Figure 5 . Delignification had little effect on LFD but generated an\nincrease in LFAD from approximately 35 to 39 nm, which could be attributed to\nlignin removal [ 46 , 47 ]. DAP seems to cause an increase in LFD and\nLFAD, where the extent of increase was directly correlated to the increase in\npretreatment residence time. The effect of DAP on the increase of LFD and LFAD was\nfurther enhanced by greater delignification. Figure 5 \n Lateral fibril dimension (LFD) and lateral fibril\naggregate dimension (LFAD) of treated poplar cellulose. \nSample code with definition is in Table 1 . Cellulose crystallite size analysis Wide-angle X-ray diffraction (WAXD), a more traditional but also\ncomplementary technique to NMR spectroscopy to extract information detailing\ncellulose ultrastructural features, is particularly sensitive to crystalline\nregion due to their regular or repetitive arrangement of atoms. Two important\nmeasureable parameters are d hkl , distance between atomic planes perpendicular to ( hkl ) direction and L hkl , the distance or size of crystalline order in the ( hkl ) direction. As shown in Figure 6 , delignification, DAP and delignification followed\nby DAP increased cellulose crystallite size to different extents. The increasing\ntrend of cellulose crystallite size was in good qualitative agreement with the\ncellulose fibril dimensions extracted from NMR spectroscopy in Figure 5 . DAP increased cellulose crystallite size of\ndelignified substrates from 3.0 to 3.1 nm to 4.0 to 4.2 nm, which is interesting\nbecause scattering studies of sliced intact poplar samples indicate the cellulose\nfibril-fibril distance is approximately 4.0 nm [ 27 ], and any increase in the crystallite size beyond 4.0 nm\nimplies neighboring microfibrils coalesce by expelling any interstitial biopolymer\nor solvent. Cellulose microfibril coalescence would be mainly reflected in a\ndecrease of accessible cellulose surfaces, enlargement of LFADs, and the increase\nof cellulose% crystallinity. Moreover, the increase in the FTIR absorption bands\nester linkages of covalent lactone bridges through esterification process could\nrelate to occurred hornification [ 27 ], and the change of hydrogen-bonded hydroxyl group in poplar\nsamples subjected to severely delignification followed by DAP also supports the\ncellulose crystallites growth via co-crystallize and coalesce. Figure 6 \n Crystallite size (L200) for treated poplar\nsamples. Sample code with definition is in Table 1 . Simons’ stain The changes of cellulose accessibility to cellulase caused by DAP\nand delignification followed by DAP were also evaluated to further study lignin\nimpact on the accessible surface area of cellulose for downstream enzymatic\nhydrolysis. Simons’ stain testing has been used to evaluate the accessibility of a\nlignocellulosic substrate by applying two dyes: Direct Blue (DB) 1 and Direct\nOrange (DO) 15 [ 48 ]. DB 1 has a\nwell-defined chemical formula with a molecular diameter of approximately 1 nm,\nwhereas DO 15 is a poly-condensation product of 5-nito- o -toluenesulfonic acid with a molecular diameter in the range of\napproximately 5 to 36 nm. These two dyes absorb different wavelengths of light,\nhave different molecular sizes, and most importantly, have different binding\naffinities for cellulosic surfaces. Therefore, the ratio of DO 15 and DB 1 dye\n(O/B) adsorbed into the biomass can be used to indicate the relative accessibility\nof cellulose in a lignocellulosic substrate. Arantes and Saddler [ 49 ] found that the higher the O/B ratio, the\nlower the protein loading required for efficient hydrolysis. However, it is also\nnecessary to analyze the maximum amount of DO 15adsorbed especially when large\namounts of the smaller DB 1 dye are adsorbed by a substrate and cause a decrease\nin the overall O/B ratio. In this case, there may be a significant amount of large\npore and cellulose accessibility, but analysis based solely on the low O/B ratio\nmay skew the data interpretation [ 19 ]. As shown in Figure 7 , the\nDO 15 adsorptions for samples which had not been subjected to DAP (PL23-t0,\nPL19-t0, and PL14-t0) were 21.8, 23.1, and 29.7 mg/g. This increase in DO 15\nadsorption suggests delignification increases cellulose accessibility to some\nextent. For all samples under DAP for 35-minutes residence time, significant\nincreases in the amount of DO 15 adsorbed were observed. The PL23-t0, PL19-t0, and\nPL14-t0 samples displayed an increase from 21.8 to 68.5 mg/g, 23.1 to 70.4 mg/g,\nand 28.7 to 72.5 mg/g, respectively after DAP for 35 minutes. This result\nindicated that DAP significantly increased cellulose accessibility such that\nappreciable amounts of enzymes could have access to cellulose in spite of the fact\nthat DAP actually increases the Klason lignin content. This suggested the\nincreased cellulose accessibility was mainly due to the hemicellulose removal\n[ 20 ], lignin-hemicellulose phase\nseparation [ 27 ], and/or lignin\nredistribution caused by DAP. Figure 7 \n The maximum amount of direct orange 15 dye and\ndirect blue 1 dye adsorbed by untreated and pretreated\npoplar. Sample code with definition is in Table 1 . Enzymatic sugar release Cellulose ultrastructural changes, measured as a function of\npretreatment severity, were evaluated using enzymatic sugar release assays. Sugar\nyields were calculated through dividing the glucose contents in enzymatic\nhydrolysis liquid from native, delignified, and dilute acid pretreated poplar\nsamples by the glucan contents from carbohydrate analysis on those native and\ntreated starting materials. Figure 8 \nsummarizes the glucose yield after enzymatic hydrolysis for the delignified\nsubstrate after DAP with respect to the unpretreated sample. DAP on undelignified\nsubstrate produced a 60% sugar yield (PL23-t35), and delignification without DAP\nalso produced a 57.5% yield (PL14-t0). However, initial delignification followed\nby a second DAP step dramatically enhanced downstream enzymatic hydrolysis to\nfacilitate sugar yields of approximately 80% for PL14-t35. In addition, P14-t0\nwith a lower cellulose accessibility (Figure 7 ) and higher sugar yield than P23-t15 suggests delignification\ncould contribute more to the extent of enzymatic hydrolysis than DAP, as\ndelignification enhances both enzymes macro-accessibility to cellulose and\nhemicelluloses, as well as enzymes effectiveness [ 50 ]. Figure 8 \n Glucose yields as a result of downscaled enzymatic\nhydrolysis. Sample code with definition is in\nTable 1 . Enzymatic hydrolysis of cellulosic biomass is restricted by\nsubstrate recalcitrant factors and influenced by treatment methods. In combination\nwith the results above, reduction in poplar cellulose DP caused by DAP could\nincrease cellulose chain-reducing ends [ 51 ], and short chains with a weak hydrogen-bonding network could\nmake cellulose more amenable to enzymatic deconstruction [ 52 ]. The increase of cellulose accessibility to\ncellulases caused by DAP is mainly due to the expansion of the pore size and\nvolume, and the increase of a specific surface area, which thereby improve\ncellulases adsorption on cellulose surface [ 53 , 54 ]. The\ninevitable increase of cellulose crystallinity and crystallite size with\nmicrofibril coalescence after DAP seems to have a negative effect on enzymatic\nhydrolysis since crystalline regions reduce the resulting enzymatic degradation of\ncellulose [ 16 ]. However, some changes\non cellulose crystalline allomorphic states may be beneficial to enhance the sugar\nyield in enzymatic hydrolysis, such as the increased proportion of paracrystalline\ncellulose. It is believed to be located on the surface of crystallites as thin\nmonocellular layers which weaken the crystallites, increase cellulose dissolution\nand accessibility to reagents, and cause intra-lattice swelling [ 43 ]. Furthermore, studies of acidified sodium\nchlorite treatment on Avicel cellulose with different crystallinities have proved\nthat those minimal changes on crystalline and amorphous cellulose by sodium\nchlorite had no detectable effect on cellulose digestibility [ 30 ], which suggests the major role of sodium\nchlorite treatment on biomass is removing lignin with intact cellulose left, and\nthereby enhancing the cellulose digestibility. Lignin content and distribution had a more pronounced effect on\nbiomass recalcitrance and enzymatic digestibility, especially for poplar\n[ 29 ]. However, complete lignin\nremoval on corn stover by sodium chlorite treatment following DAP has been found\nto reduce cellulose conversion [ 30 ],\nwhich was proposed to be attributed to cellulose microfibril aggregation in the\nabsence of lignin and hemicellulose. This was confirmed by our NMR and WAXD\nanalysis that indicated lignin presence played a key role in preventing cellulose\ncrystallites increased propensity to co-crystallize and coalesce during DAP, which\ntherefore suggested partial delignification instead of complete lignin removal\ncould better benefit the sugar yield. Furthermore, partial delignification with\nhemicelluloses removal during DAP retained cell wall spatial structure without\nelimination of all lignin spacer, increased specific surface area, reduced its\nirreversible adsorption to the enzyme [ 55 , 56 ], and, to\nlimited extent, caused the cellulose fibril coalescence to provide an optimal\npretreated biomass for subsequent enzymatic deconstruction."
} | 6,655 |
32115818 | null | s2 | 4,627 | {
"abstract": "The interiors of plants are colonized by diverse microorganisms that are referred to as endophytes. Endophytes have received much attention over the past few decades, yet many questions remain unanswered regarding patterns in their biodiversity at local to global scales. To characterize research effort to date, we synthesized results from ~600 published studies. Our survey revealed a global research interest and highlighted several gaps in knowledge. For instance, of the 17 biomes encompassed by our survey, 7 were understudied and together composed only 7% of the studies that we considered. We found that fungal endophyte diversity has been characterized in at least one host from 30% of embryophyte families, while bacterial endophytes have been surveyed in hosts from only 10.5% of families. We complimented our survey with a vote counting procedure to determine endophyte richness patterns among plant tissue types. We found that variation in endophyte assemblages in above-ground tissues varied with host growth habit. Stems were the richest tissue in woody plants, whereas roots were the richest tissue in graminoids. For forbs, we found no consistent differences in relative tissue richness among studies. We propose future directions to fill the gaps in knowledge we uncovered and inspire further research."
} | 330 |
36055202 | null | s2 | 4,628 | {
"abstract": "Great progress has been made in understanding gut microbiomes' products and their effects on health and disease. Less attention, however, has been given to the inputs that gut bacteria consume. Here, we quantitatively examine inputs and outputs of the mouse gut microbiome, using isotope tracing. The main input to microbial carbohydrate fermentation is dietary fiber and to branched-chain fatty acids and aromatic metabolites is dietary protein. In addition, circulating host lactate, 3-hydroxybutyrate, and urea (but not glucose or amino acids) feed the gut microbiome. To determine the nutrient preferences across bacteria, we traced into genus-specific bacterial protein sequences. We found systematic differences in nutrient use: most genera in the phylum Firmicutes prefer dietary protein, Bacteroides dietary fiber, and Akkermansia circulating host lactate. Such preferences correlate with microbiome composition changes in response to dietary modifications. Thus, diet shapes the microbiome by promoting the growth of bacteria that preferentially use the ingested nutrients."
} | 270 |
36893280 | PMC10089216 | pmc | 4,629 | {
"abstract": "Significance Spontaneous building of bio-inspired organization with both accurate morphologies and well-defined functions is still highly challenging. We illustrate a versatile approach to control assemblies of complementary “staple” and “brick” proteins into supramolecular accurate architectures by characterizing de novo superhelixcrystals. For this purpose, we exploit the highly selective binding surfaces of repeat proteins to generate robust close contacts. We design the brick protein with a semi-lock washer shape by splitting and appending the sequence of the partner protein to its terminal modules. Equimolar mixture results in sequential growth generating long tubular superhelices. This strategy paves the way to chimeric proteins able to organize functions on designed structures by origami processes.",
"discussion": "Results and Discussion Protein Design. The αRep family has been designed as a repeated architecture of structurally similar modules ( 32 ). Each repeated motif of 31 amino acids forms two α-helices. When stacked on the previous module, the successive repeats are bound together with an azimuthal angle of 22° that naturally leads to a curved toroid. Yet, an out-of-plane twist angle between two consecutive motifs distorts the overall donut shape in such a way that a 16-repeat αRep will adopt a lock-washer shape equivalent to one turn of a potential right-handed superhelix. In principle, an αRep fold could be extended by concatenating more repeats to form a right-handed superhelical macromolecule. However, synthesizing and purifying very long proteins, which tend to be poorly expressed and insoluble, is highly impractical. We demonstrate a general alternative strategy, which consists in connecting small and stable “brick” αRep proteins comprising a few motifs with a “staple” protein that forces their head-to-tail assembly into a predefined geometry that mimics the covalent αRep superhelix. Our general concept is to create a heterotrimeric αRep junction in which a staple protein ( Fig. 1 A and B , orange) mediates the assembly of two consecutive bricks ( Fig. 1 A and B , blue and cyan) with the same geometry as an αRep with twice as many repeats. The propagation of this trimeric junction from an equimolar brick:staple mixture leads to infinite helical supramolecular architectures. In order to generate such junctions, we took advantage of the known crystal structure of a face-to-back complex between two αReps (PDB code 8AW4 ( 41 ) and SI Appendix , section 1 ). This complex involves two proteins herein referred to as the “bait” and “bBE3” (as “back-binder” E3). The bait αRep comprises a 3-repeat core (I 1 -I 2 -I 3 , Fig. 1 C ) and two terminal repeats named N- and C-caps. The back binder bBE3 was selected from the αRep library ( 37 ) and is a specific binder of the bait protein ( Fig. 1 C – E and SI Appendix , section 1 ). The crystal structure shows that bBE3 ( Fig. 1 D and E ) binds to the convex back surface made by the (I 1 -I 2 -I 3 ) modules of the bait protein using its own hypervariable concave surface as is typical for all selected αRep binders. The side chains located on the convex - or “back” - surface of the I 1 -I 2 -I 3 internal repeats of the bait protein were modified to differ from the repeated sequence found in these positions in other αReps. Therefore, the back-binder of the bait protein is not able to bind its own back surface, which would presumably have prevented its selection from the library. In the bBE3/bait complex, the variable side chains of the first two turns of the back-binder helices fit in the grooves located between the helices on the convex side of repeats I 1 -I 2 -I 3 . A closer view of the interaction surface is shown in Fig. 1 E ( SI Appendix , Fig. S1 ). The interface between the two proteins, as assessed by PDBePISA, involves 24 residues from the bait protein and 29 residues from the back-binder. An extensive hydrogen bond network, salt bridges, and hydrophobic interactions are established between the variable side chains (yellow) of the back-binder protein (orange) and the side chains (purple) of residues located on the convex surface of the bait protein repeats I 1 -I 2 (blue) and I 3 (red). Isothermal titration calorimetry shows that the bBE3/bait complex forms in a 1:1 stoichiometry and its dissociation constant, K D = 68 nM, indicates a strong affinity in the range observed for other αRep-based protein pairs ( Fig. 2 A ) ( 37 ). The crystal structure of a bBE3/bait complex was obtained with a variant of the bait protein possessing different side chains on its concave surface. This further demonstrates that the recognition of the “back” convex surface of the bait protein is independent of the specific sequence on the concave surface of the bait protein. Fig. 1. Design principle of self-assembling helical protein origami. ( A ) Side and ( B ) axial views of a ribbon model of the supramolecular αRep helix based on the crystallographic structure reported in (PDB Code 8AW4) ( 41 ). The elementary assembly motif comprises two brick proteins (blue and cyan) linked by a staple protein (orange). ( C ) Schematics and ( D ) crystallographic structure of the interaction pair formed by the “bait” αRep protein (N- and C-capped I 1 I 2 I 3 , blue-red) and the selected bBE3 back-binder (ABCDE, orange) used as origami staple. ( E ) Magnified view of the bBE3-bait interacting surface in ( D ) highlighting the variable side chains (yellow) of bBE3 that directly contact the side chains (purple) located on the convex surface of the bait. ( F and G ) Schematics and ( H ) computational ribbon model of the brick protein ( F ) before and ( G and H ) after cleaving the His-tagged N-cap (HT N N C ) and C-cap (C C HT C ) with TEV protease. X indicates internal repeats with arbitrary hypervariable sequences. ( I ) Schematic and ( J and K ) computational ribbon model of the heterotrimeric junction driving the origami assembly that is composed of two concatenated bricks stapled together by a bBE3 back-binder. The bait-like pairing partner of bBE3 is reconstituted from the C-terminal repeats (I 1 I 2 , blue) of one brick and the N-terminal repeat (I 3 , red) of the next brick. ( J ) and ( K ) show the lateral and axial views of the assembled heterotrimeric motif that is propagated in the superhelical origami [see colored sections in ( A and B )]. Fig. 2. Fast and robust supramolecular assembly of brick/bBE3. ( A ) Calorimetric titrations of bait (25 μM) with the staple bBE3 (375 μM). The dissociation constant (K D = 68 ± 23 nM) and stoichiometry (n = 1.05) are extracted from the saturation curve. ( B ) Photograph of the 20 μM bBE3 staple solution before mixing ( Left ) and of the white and turbid suspension rapidly obtained when mixing identical volumes of 20 μM of each brick and bBE3 ( Right ). ( C ) Turbidity measurement by light scattering at 350 nm as a function of time after mixing brick and staple bBE3 in equimolar ratio (total protein concentration 20 μM) at 4 °C (black) and 25 °C (red). ( D – F ) SDS-PAGE analysis of monomers and assembly in ( D ) a staple bBE3 fraction, ( E ) a brick fraction, ( F ) mixed staple and brick fractions before centrifugation (M), supernatants (S) and pellets (P) obtained after 30 min 14,000 g centrifugation and resuspension in the same volume buffer ( Material and Methods ). 10 μL of each sample was run after incubation of the mixtures at 1, 2, 4, and 8 μM concentrations. Next, we hypothesized that such an avid back-binder protein would template the recombination of its full cognate partner when the matching surface is split into two distinct molecules. Thereby, two proteins, each terminated by one half of the bait repeats ( Fig. 1 I ) would be forced into a trimeric junction, with the back-binder acting as a staple to recover its full recognition partner. By applying this principle to the two termini of a same αRep protein, with a circular permutation of the bait repeats, I 3 is appended to the N-terminal when the bait repeats I 1 -I 2 are appended to the C-terminal of the cap-free αRep resulting in a generic “brick” protein shown in Fig. 1 F – H . The structural regularity of repeat proteins ensures that the appended I 1 -I 2 and I 3 exhibit the same geometry as in the bait. The central modules of the brick do not contribute to the self-assembly and can be chosen with arbitrary external surfaces. When the staple protein is exposed to the brick protein, it associates with the C-terminal repeat of one brick and the N-terminal repeat of another identical brick ( Fig. 1 I – K ), resulting in a potentially infinite head-to-tail polymerization of adjacent bricks held together laterally by the staples. A model of staples binding adjacent bricks was built based on the experimental structure of the bBE3/bait complex and is shown in Fig. 1 A and B . Two bricks (in blue) joined by one staple (in orange) form the elementary motif that is repeated along a regular superhelix. Protein Synthesis. In this work, we demonstrate the self-assembly principle of bricks and staples by constructing an eight-repeat brick protein comprised of five central repeats plus the internal bait modules I 1 -I 2 and I 3 fused at the C-end and N-end, respectively, according to the circular permutation design schematized in Fig. 1 G . However, internal αRep repeats exhibit hydrophobic faces that tend to aggregate, making them difficult to mass produce. To prevent this, αReps are terminated by first and last motifs with polar exofaces, named N- and C-cap. Yet, these caps would also block the desired reconstitution of the bait motif with the staple protein and therefore the self-assembly. To circumvent this, a TEV protease cleavage site is inserted between each cap and the brick sequence ( Fig. 1 F ). The final whole sequence of the brick protein is presented in the SI Appendix , section 1 . Prior to mixing with the staple protein, the caps are cleaved by TEV protease. The cap-free brick is separated from the two cap peptides as well as from the TEV protease bearing poly-histidine tags (HT) using a Ni-NTA purification column. Although repeat proteins are commonly produced with the N and C cap repeats, it appears here that the folded brick protein remains stable and soluble in a cap-free form, once cleaved. The cleaved brick ( Fig. 1 G and H ) is composed of eight repeated motifs equivalent to one half-turn of the self-assembled superhelix ( Fig. 1 I – K ) that will bear two staples per turn on diametrically opposed sites as shown in Fig. 1 A and B . Characterization of the Brick and Staple Assembly. When a 10-μM stoichiometric solution of brick and staple bBE3 is mixed, a white turbid precipitate rapidly forms ( Fig. 2 B ). Time-resolved light scattering at 350 nm detects the onset of assembly within minutes after mixing and reaches a plateau after 15 to 20 min at 25 °C, suggesting a fast kinetics of the association ( Fig. 2 C ). Lowering the temperature to 4 °C virtually suppresses macroscopic assembly as no turbidity at 350 nm is observed, even after several hours, indicating a significant contribution of the hydrophobic interaction to the assembly. The terminal surface between two adjacent bricks is identical to the one between two covalently bound repeats inside an αRep ( SI Appendix , section 2 ). This repeat–repeat interaction comprises a large hydrophobic contribution from the side chain packing ( SI Appendix , Fig. S2 A ). The cap-free bricks expose hydrophobic clusters at both termini, resulting in significant hydrophobic interactions that contribute to the head-to-tail alignment of two consecutive bricks ( SI Appendix , Fig. S2 B ), locked by the staple binding. Each isolated brick or staple protein and equimolar mixtures is monitored by SDS-PAGE at 20 °C as initial mixtures (M) and as supernatant (S) and pellet (P) after centrifugation, for final protein concentrations comprised between 1 and 8 μM ( Fig. 2 D – F ). At 1 to 2 μM, both proteins remain soluble in pure or mixed solution and no pellet is formed. At 4 μM, the pure proteins remain soluble, but an insoluble pellet is observed for the mixture that contains both proteins in comparable amounts. This is even more pronounced at 8 μM ( Fig. 2 F ). Unlike the pure solutions, the proteins appear in the precipitate above 4 μM and their concentration in the supernatant decreases. The brick concentration even vanishes at 8 μM, suggesting a slight excess of staples in this particular experiment ( Fig. 2 F ). These results indicate that macroscopic self-assembly is triggered by protein concentrations of 2 to 4 μM beyond which the amount of precipitate increases with the protein concentration. The onset of assembly occurs at a micromolar critical concentration that is significantly larger than the bBE3/bait dissociation constant K D (68 nM). This can be attributed to the entropic cost of the discontinued bound surface of the bait protein when it is split into two distinct subparts as terminal sections of adjacent bricks. The macroscopic precipitate was characterized by fluorescence microscopy ( SI Appendix , S3 ), negative-stain transmission electron microscopy (TEM), and small-angle X-ray scattering as shown in Figs. 3 and 4 . Fig. 3. Electron microscopy of individualized protein origami superhelix. ( A ) Transmission electron micrograph of scattered self-assembled protein superhelices. TEM contrast is enhanced by negative staining with 150 mM ammonium molybdate. ( B ) Normal distribution of the diameter (7.6 ± 0.8 nm) and ( C ) lognormal distribution of the length (260 ± 150 nm) of the tubular superhelices. Normal distributions of ( D ) the pitch (55.6 ± 2.2 Å) and ( E ) relative directional angle (115° ± 2°) of the periodical white ellipsoids observed along the superhelices. ( F ) Magnified TEM image of one fibrillary structure showing periodical braid-like patterns. ( G ) Lateral projection of the structural model of the superhelix highlighting the dense areas (staple, tangential section of the bricks) from where the molybdate stain is excluded. They form pairs of ellipsoids with main directions at 115° from each other and with a pitch of ca. 60 Å. ( H ) Overlaid representation of ( F ) and ( G ). Fig. 4. Massive ordering of superhelical protein origami into quasi-crystalline bundles. ( A ) SAXS diffraction pattern of a 10-μM supramolecular assembly suspended in water. The peaks are indexed to the P2 1 22 orthorhombic symmetry group, with elementary cell parameters a = 477 Å, b = 95 Å, and c = 58.2 Å. This is a (x6, x3, x1) superlattice of a subcell (a = 80 Å, b = 65 Å, and c = 58 Å). ( B and C ) Wide field and zoomed cryo-TEM images of brick-bBE3 superhelix aligned into large-scale compact bundles. ( D ) Magnified view of a bundle tip showing highly ordered parallel tubules. ( E ) Multiple fragments of superhelix bundles with different orientations aligned along the electron beam. [Scale bars are ( B ) 2 μm, ( C , D , and E ) 100 nm.] TEM and SAXS Structural Analysis. Negative-stain transmission electron microscopy (TEM) has been used to identify the structural features composing the molecular architecture. In order to obtain individualized supramolecular objects, we have used 3 wt% (150 mM) ammonium molybdate (NH 4 ) 2 MoO 4 , a TEM contrast agent compatible with the near-neutral pH conditions initially used for the assembly. The proteins, which appear bright over a dark stained background ( Fig. 3 A ), form filaments with a highly uniform apparent width of 7.6 ± 0.8 nm ( Fig. 3 B ) in good agreement with the nominal 8.4 ± 0.8 nm outer diameter of the model of Fig. 1 . The filament length follows a standard lognormal distribution with a mean value of about 260 nm and the longest filaments reaching near 1,000 nm ( Fig. 3 C ). Increasing the concentration of the equimolar mixtures from 2.5 to 10 μM results in a higher concentration of filaments and also augments both the mean and spreading of their length distribution ( SI Appendix , Fig. S4 ). This behavior suggests a growth mechanism where the number of nuclei is set by the protein concentration and the superhelix elongation proceeds by incremental addition of bricks and staples at the filament termini, as long as monomers are available ( 42 , 43 ). The low dissociation constant of the brick/staple complex ensures that unbinding events do not limit the lengthening nor induce the fragmentation of the supramolecular assembly. Magnified views of a filament as displayed in Fig. 3 F reveal a braid-like contrast with a pitch along the main axis of 55.6 ± 2.2 Å ( Fig. 3 D ). As a negative stain, the molybdate is excluded from the core of the proteins and creates a white contrast where the staple and bricks are seen tangentially as depicted in Fig. 3 G . The observed periodicity is consistent with the staple pitch of 60 Å in the molecular model. The braid-like pattern is also characterized by a uniform angle of 115° ± 2° between two opposite white ellipsoids ( Fig. 3 E ), which matches the 115° angle measured on the lateral projection of the superhelix as shown in Fig. 3 G and H . These negative-stained TEM observations strongly suggest that the dried protein superstructures consist of the programmed self-assembled superhelices. The massive supramolecular assembly obtained in the absence of added salt was monitored by small-angle X-ray scattering (SAXS). The well-defined set of diffraction peaks shown in Fig. 4 A demonstrates a highly ordered crystalline structure rather than isolated monodisperse objects ( 44 ). Different batches of molecular assemblies result in similar patterns with very minor variations of the peak positions or intensities. Interestingly, the intense peak at q = 0.0108 Å −1 corresponds to a distance of 58 Å that matches the pitch between two staples along the superhelical model presented in Fig. 1 A and B . The global analysis of all the peaks found in this highly reproducible SAXS pattern could be matched to the orthorhombic space group with cell parameters a = 477.3 Å, b = 194.8 Å, and c = 58.2 Å ( SI Appendix , Table S1 ). We observe the presence of the (2 1 1) and (3 3 1) peaks that indicate that these data correspond to a (6×3×1) superlattice of a generic subcell (a = 80 Å, b = 65 Å, and c = 58 Å) that comprises two pairs of bricks and staples. The rectangular 80 × 65 Å cell originates from the diametrically opposed bBE3 decoration along the superhelix with a very strong staple interdigitation between adjacent superhelices. The 58 Å longitudinal periodicity matches the distance between staples positioned on the same side of the helix. The orthorhombic space group stems from the crystal symmetries, which is, in turn, imposed by the protein chirality, leaving the twofold or screw axes as the only compatible symmetry elements. The missing ( h00 ) peaks with odd h ( SI Appendix , Table S1 ) are strongly in favor of a screw axis along a and an antipolar packing of the helical superstructures aligned head to tail along the c axis. The head-to-tail packing along a gives rise to a first x2 supercell. We interpret the additional x3 superlattice along a and b as a low amplitude distortion mode between neighboring helices along b , that is identically propagated along a as suggested by the observation of the (3 3 1) peaks. The most likely distortion type is a tilt modulation that occurs in chiral fibers when the helical pitch does not exactly match the c parameter ( 45 ). Such superlattice induction has been observed in the rare crystals of chiral fibers where the helices have to solve the frustration between their intrinsic symmetry and the crystal packing symmetry ( 46 , 47 ). Note that positional distortions, like the ones observed with DNA ( 48 ), are improbable due to the tight packing. The occasional variations in the positions and intensities of some diffraction peaks are attributed to slight differences in the crystal packing from one assembly to another ( SI Appendix , Fig. S5 ). The thermal stability of the self-assembled superstructures was monitored up to 75 °C ( SI Appendix , Fig. S6 ). No change in the SAXS pattern was observed up to 50 °C, indicating a robust crystalline organization over a wide range of temperatures. Around 55 °C, a 2 to 4 Å reduction of the (0 0 1) and (6 0 0) peak positions accompanied by an increase of the (12 0 0) intensity and a vanishing (3 3 1) intensity reveals the superlattice melting into the lowest symmetry crystal made of anti-parallel superhelices. This transition is fully reversible upon cooling albeit with a small (5 °C) hysteresis. The higher temperature structure remains stable up to 75 °C. Notwithstanding the minor supercoiling variations, the pairing of brick and staple bBE3 therefore appears to result in strongly interacting antiparallel superhelices. We note a tighter packing of unstained helices in the absence of added salt since the (a,b) SAXS parameters are significantly smaller than the apparent outer diameter measured in negative-stained TEM ( Fig. 3 ). Cryo-EM Imaging. The highly ordered unstained supramolecular assembly was investigated in a quasi-native and hydrated state by cryo-electron microscopy. Fig. 4 B – D show that the self-assembly results in extremely large bundles of parallel tubules reaching at least tens of micrometers in length and several micrometers in diameter. In the terminal region of the bundles ( Fig. 4 C ), the tubules split apart from each other, which strongly suggests that the bundles are made of individualized tubules rather than being a three-dimensional cocrystal of both proteins. The extent of the bundled tubules confirms the massive self-assembly of the brick and staple proteins, which is a direct consequence of their high mutual affinity driven by the programmed trimeric junction recognition further stabilized by strong inter-superhelix interactions. Closer examination of cryo-fractured bundle segments aligned along different orientations with respect to the electron beam ( Fig. 4 E ) reveals a few typical EM patterns that yield more precise information on the close packing of the superhelix inside the bundles. Four different patterns are analyzed in Fig. 5 . In Fig. 5 A , straight and parallel thick lines are clearly observed that show a highly regular, periodical, and staggered decoration of darker dots separated by a brighter uniform space. A statistical analysis of the positions of the black dots relative to their three nearest neighbors is detailed in SI Appendix , section S8 . It reveals that the distance between successive dark dots on the same side of the superhelix is 6.4 ± 1.2 nm, which is consistent with the superhelical pitch measured in stained TEM (5.56 ± 0.22 nm), SAXS (c = 5.8 nm), and the model shown in Fig. 1 A (6.0 ± 0.5 nm). Further structural confirmation is obtained by the systematic analysis of fast Fourier transforms (FFT) of selected areas, as shown in Fig. 5 B . The large set of diffraction spots is fully indexed by taking into account the group symmetry and lattice parameters identified in SAXS and simulating a diffraction pattern using the Carine software. Without any other adjustment parameter, all spots in Fig. 5 B were indexed to specific ( hkl ) consistent with the zone axis {100}. Similarly, Fig. 5 D , G , and J shows cryo-EM images along the zone axes {101}, {111}, and {011}, respectively. Their corresponding FFT patterns in Fig. 5 E , H , and K were fully indexed by the same method using the exact same SAXS parameters. In particular, one can notice the clear extinctions of the spots ( h00 ) in Fig. 5 K and ( 0k0 ) in Fig. 5 B and E with h and k odd in full agreement with the SAXS patterns. The packing model derived from SAXS and consistent with these four orientations is shown, in Fig. 5 C , F , I , and L , for 9 superhelices similar to Fig. 1 A . The distances d hkl between Miller planes in each zone axis are measured directly in Fig. 5 B , E , H , and K and compared with a good agreement to the SAXS-derived values in Table 1 . A systematic increase of 6 ± 1% or 17 ± 2% is observed for the cryo-EM d hkl compared to the SAXS d hkl that is attributed to the crystal isotropic expansion during the cryogenic vitrification of the cryo-EM samples. This model establishes that the gray straight region with the black dots on either side in Fig. 5 A is the inner space of the superhelix with an apparent thickness of 2.8 ± 1.0 nm ( SI Appendix , Fig. S7 ). The black dots are the tangential segments of the brick proteins within the helix. The staples from neighboring tubules are aligned and strongly interdigitated along the a axis, therefore accounting for the larger parameter a = 80 Å compared to b = 65 Å. Finally, several characteristic Moiré patterns were also observed that further confirm the crystalline organization of the antiparallel superhelices ( SI Appendix , S9 ). Fig. 5. 2D crystals of superhelical protein origami. Zoomed areas of cryo-EM images similar to Fig. 4 D and E with different orientations with respect to the e-beam axis in ( A , D , G , and J ) are fast Fourier transformed in ( B , E , H , and K ) to emulate selected area electron diffraction patterns. ( C , F , I , and L ) display a 3D model consisting of 3×3 superhelices shown in Fig. 1 A , arranged in a P2 1 22 orthorhombic crystal with lattice parameters as determined by SAXS and oriented like the TEM images. ( A – C ) [100] zone axis viewed in ( A ) direct space cryo-EM image, ( B ) fully indexed FFT image of ( A ) and ( C ) 3D model of 3×3 superhelices in the same orientation. Similar data are shown along the ( D – F ) [101], ( G – I ) [111], and ( J – L ) [011] zone axes. Note the extinctions of the ( h00 ) spots in panel ( K ) and ( 0k0 ) spots in panels ( B ) and ( E ) with h and k odd. [Scale bars are ( A , D , and G ) 50 nm, ( J ) 20 nm, ( B , E , H , and K ) 0.1 nm −1 , ( C , F , I , and L ) 10 nm.] Table 1. Miller interplanar distances d hkl along zone axes {100}, {101}, {111}, and {011} derived from FFT cryo-EM images shown in Fig. 5 and SAXS data Zone axis (hkl) cryoEM d hkl (nm) SAXS d hkl (nm) Relative variation {100} (001) 6.23 5.82 +7% (011) 4.61 4.33 +7% (020) 3.39 3.24 +5% {101} (101) 4.93 4.70 +5% (111) 4.07 3.80 +7% (020) 3.46 3.24 +7% {111} (011) 5.12 4.33 +18% (101) 5.61 4.70 +19% (110) 5.97 5.03 +18% {011} (200) 4.59 3.98 +16% (011) 4.95 4.33 +14% (111) 4.41 3.81 +16% The relative variation with respect to the SAXS-derived values is shown in the rightmost column.\n\nDiscussion Taken together, the SAXS, electron microscopy, and molecular model of the self-assembly yield a precise description of the supramolecular organization induced from the onset of the brick and staple recognition events. Fig. 6 A – C gives a molecular model of the 2D crystalline ordering of antiparallel αRep superhelices viewed along the three main crystal axes. The superhelices formed by the bricks are aligned parallel to each other. The staples sit at the junction between two successive bricks that are brought together by hydrophobic interactions of their cap-free ends ( SI Appendix , Fig. S2 ), therefore reforming the cognate interface of the staple/bait complex ( Fig. 1 E ). One striking aspect visible in Fig. 6 A and B is the ridges-into-grooves packing of bBE3 staples from neighboring helices. This fortuitous assembly is made possible by the periodicity (2 staples per turn) and the steric compatibility of the protruding staple of one superhelix in register with the interstaple space of the neighboring antiparallel superhelix ( Fig. 6 D ). Due to high regularity and the length of the assembly, even a relatively weak local interaction between the staples of adjacent superhelix could become predominant in the highly regular interhelix final assembly. This prominent interdigitation is first favored by interstaple hydrophobic interactions as detailed in SI Appendix , section 10 . Yet, the ionic strength dependence of the assembly suggests that the main contribution originates from the complementary surface charges between the outer surface of the staple and the inner groove of the helix made from the side surfaces of the bricks. This is clearly illustrated in the models of surface charges shown in Fig. 6 E – G . At neutral pH and ionic strength of 0.2, the convex surface of the staple is positively charged and aligned in-register with negative charges lining the inner surface of the bricks, thus accounting for an electrostatic zipping of one superhelix alongside its neighbor ( Fig. 6 F ). Fig. 6 G presents the facing electrostatic charges at an interface normal to the a axis where the charge intercalation occurs. The overlapped representation shows in black regions where positive charges of one planar set of superhelices overlap negative charges of the next planar set of superhelices. These regions create the attractive force responsible for the interhelix cohesion that shows a characteristic +10° and −10° tilt resulting from the fact that the staples are immobilized on the helix at an 80° angle with respect to the main axis. The resulting electrostatic torques acting on the two sets of staples coming from two adjacent superhelices are counterrotating ( Fig. 6 G , black arrows). This strain, which is sensitive to variations in ionic strength, local pH, and temperature, could be responsible for the variable supercoiling and changes in packing detected in SAXS patterns from one batch to another. The temperature-dependent structural transition monitored by SAXS ( SI Appendix , Fig. S6 ) suggests that a moderate input of energy is sufficient to modify the staple–staple repulsive steric interaction, allowing for further interpenetration. The (600) peak shifts from 82.5 Å to 78.5 Å, which, in turn, decreases the helical pitch period from 58.8 Å to 56.8 Å. Incidentally, in negative-stain TEM, the positively charged patches on the back surfaces of the staples and exposed bricks ( Fig. 6 D ) are bound by the highly negative molybdate ions accounting for the separation of entirely negative individualized superhelices. The precise and programmable three-dimensional spatial ordering of the staple and brick proteins is directly visualized by cryoelectron tomography as shown in Fig. 6 H – J and in the Movie S1 ( SI Appendix , S11 ). Cryo-EM 120°-tilt series of superhelix crystals were recorded from which 3D tomograms were reconstructed as detailed in the Materials and Methods section. Fig. 6 H – J shows the 3D model resulting from the segmentation of 25 consecutive sections from a 430 × 175 × 160 nm 3 sub-tomogram (pixel size: 1.12 nm). The on-axis ( Fig. 6 H ) and basal ( Fig. 6 I ) views of the model confirm the layered close-packing of parallel superhelices with a hollow inner cavity and match the (001) and (100) zone axis as depicted in Fig. 6 A and B , respectively. Fig. 6. 3D structural and electrostatic modeling and cryotomography of the protein origami crystal. ( A – D ) Structural model of the superhelix 2D crystal derived from the SAXS and EM data and viewed along the ( A ) {001}, ( B ) {010}, and ( C ) {100} zone axis. The strong interdigitation of the staples along the a axis is visible in ( A ) and ( B ) while the alternating black dots observed experimentally are clearly visible in ( C ) as tangential segments of the superhelix. In ( D ), the brick proteins are hidden to show, along the a axis view, how the staples of the green superhelix interdigitate with the staples of the red superhelix in the front and the ones of the blue superhelix in the back. ( E – G ) Electrostatic surface of the superhelical crystal viewed along the ( E ) {001], ( F ) {010}, and ( G ) {100} zone axis ( 49 , 50 ). ( F ) The antiparallel superhelices are overlapped with a schematic of the positions and signs of the opposed charged surfaces: Protruding positive surface from the back of the staple (blue segments) face the deep negative superhelix grooves (red segments). The shaded ribbon structure represents the position of the left superhelix 4 inside the crystal with respect to the right superhelix 2 as labeled in ( E ). ( G ) Top ( Left ) and bottom ( Right ) surfaces of the superhelices 1, 2 and 3, 4, respectively, viewed along the a axis showing the positively charged back side of the staple and negatively charged inner groove of the superhelix. The central overlayed representation indicates, in black, regions where opposite charges coincide while the color is preserved when similar charges are aligned in this view along the a axis. The black regions point where positively charged staples of one layer fit into the negatively charged grooves of the other layer. The relative angle between the two sets of staples is highlighted with the black lines. The resulting electrostatic torques acting on the upper and lower layers are counterrotating (black arrows), accounting for the distortion detected in SAXS patterns. ( H – J ) Model views of a cryoelectron tomogram (cryo-ET) of a small crystal of superhelices. Out of a 430×175×160 nm 3 reconstructed tomogram (cubic pixel size 1.12 nm) delineated in yellow in ( H ), three layers of parallel superhelices could be modeled and are shown in ( H ) along the c axis, ( I ) along the b axis, and ( I ) in 3D with three particular cryo-ET cross-sections. ( SI Appendix , S11 and Movie S1 ) Importantly, the massive hierarchical assembly shown here demonstrates that the design of the brick structure and the mutual recognition surfaces of the two αRep proteins based on molecular models does lead to the expected superstructure. When combined with the evolutionary optimization of the brick/back-binder interaction by phage display, this modular approach appears capable of producing not only supramolecular assemblies, e.g., superhelices, with programmed geometry, but also higher-order architectures. The protein origami forms within minutes after mixing at room temperature. This is fast compared to DNA origami which requires elevated temperatures and extended time to reach the targeted folded structure. Despite the very mild formation conditions, the supra-structure is extremely robust even at 75 °C, due to a combination of hydrophobic and strong electrostatic network plus an extended hydrogen bond network at the staple/brick cognate surface. Future designs of the surface charges of targeted residues on the convex surface of the back-binder and the concave surface of the brick, as well as shifting the relative position of the staples along the superhelix, will allow tuning of the intersuperhelix interactions. We will then be able either to limit the self-assembly at the individual superhelix stage, even at low ionic strength, or promote the organization of a 2D helix crystal sheet. Known affinity-dependent protein assembly is limited to a few available high-affinity binding pairs where a specific protein is associated with its small-molecule cognate partner [e.g., streptavidin/biotin, DHFR/methotrexate, ( 16 )]. Yet, the recent concomitant development of highly diverse repeat protein libraries and selection methods allows the identification of specific binders with submicromolar K D for most folded protein targets, thus expanding the pool of affinity protein pairs ( 33 , 35 , 37 ). The main challenge resides in selecting binders for a specific area of the targeted bait structure. The careful design of the binder selection strategy, which could include a counter-selection step for unwanted binders, makes it possible to screen for such adequate binders ( 51 ). With the principles demonstrated here, other types of affinity protein pairs can serve as a staple/split bait pair to be appended to a brick scaffold, thereby opening numerous possible routes to generalizing the concept of designable protein origami. The choice of the bait protein to be split and appended to any arbitrary “spacer” unit could be derived from natural ( 52 ) or computationally designed ( 53 ) repeat proteins as their structural regularity would facilitate the brick and staple assembly mechanism. Further increase of structural and functional complexity of the brick itself, within the supramolecular complexes, could be designed with the recent advent of AlphaFold2 ( 54 ), RoseTTAFold ( 55 ), and Protein MPNN ( 56 ) computational platforms. Imagination is the sole limit of future expansion of our proof of principle that will certainly include controlling of the origami size, embedding polydentate branching structures toward on-demand geometry of the supramolecular assembly, and eventually enzymatic or nanomaterial functionalization strategies."
} | 9,215 |
28211844 | PMC5464397 | pmc | 4,631 | {
"abstract": "Advances in our ability to systematically introduce and track controlled genetic variance in microbes have fueled high-throughput reverse genetics approaches in the past decade. When coupled to quantitative readouts, such approaches are extremely powerful at elucidating gene function and providing insights into the underlying pathways and the overall cellular network organization. Yet, until now all efforts for quantifying microbial macroscopic phenotypes have been restricted to monitoring growth in a small number of model microbes. We developed an image analysis software named Iris, which allows for systematic exploration of a number of orthogonal-to-growth processes, including biofilm formation, colony morphogenesis, envelope biogenesis, sporulation and reporter activity. In addition, Iris provides more sensitive growth measurements than current available software, and is compatible with a variety of different microbes, as well as with endpoint or kinetic data. We used Iris to reanalyze existing chemical genomics data in Escherichia coli and to perform proof-of-principle screens on colony biofilm formation and morphogenesis of different bacterial species and the pathogenic fungus, Candida albicans . Thereby we recapitulated existing knowledge but also identified a plethora of additional genes and pathways involved in both processes.",
"introduction": "Introduction High-throughput reverse genetics can provide unprecedentedly rich information on gene function and cellular network organization 1 . Both gene-gene and gene-drug interactions rely on accurately measuring the phenotypic change between perturbed and unperturbed states. Up until now, fitness-related measures have been exclusively used for quantifying such changes. For pooled barcoded libraries, this is a necessity, as sequencing reports on the relative abundance of each mutant in the experiment 2 . While options increase when using ordered libraries 1 , growth has dominated as the simplest phenotypic readout, reporting on effects at multiple levels and scales 3 . Concordantly, currently available software tools 4 – 9 are tailored to measure colony size in high-density arrayed plates as a proxy of growth. Yet many bacterial core programs, such as biofilm, motility, competence and sporulation occur at stages when cells slow down or stop growing completely. Other processes impact cellular physiology but do not have a measureable effect on growth. Unfortunately, current tools cannot quantify such orthogonal-to-growth readouts, and even large-scale efforts to map their underlying genetic determinants have relied on qualitative or semi-quantitative metrics 10 – 12 . We developed the versatile and extensible image-analysis platform Iris, which can quantify multiple features of high-density arrayed microbial colonies. Iris is open-source, works with a variety of different microbes, and has add-on features that make it compatible with low-throughput or kinetic data. Iris has been successfully used by a number of labs 13 – 16 . Here we illustrate its ability to report both on a series of colorimetric and colony-morphology based assays. By significantly expanding the palette of macroscopic features quantified, Iris sets the foundation for a new era of high-throughput phenotyping in microbes.",
"discussion": "Discussion We have developed Iris, a freely available software for quantifying macroscopic colony phenotypes. In addition to more sensitive measures of microbial growth on plates, Iris features quantification modules tailored for colorimetric and colony-morphology-based assays, reporting on biofilm formation, sporulation, envelope integrity and reporter activity. These new features, which include an algorithm for quantifying colony structure, as well as the modularity and open-source nature of the software, make Iris unique in the field. Our effort concurs well with the advance of high-throughput reverse genetics approaches in microbiology, with currently >20 microbes having arrayed deletion libraries. Iris is also compatible with low-throughput measurements and kinetic data, offering a solution to labs that want to quantify such phenotypes for their targeted studies. Similarly, Iris can deal with diverse microbes, enabling studies of natural isolate collections or interspecies interactions. We benchmarked Iris by quantifying the different phenotypes our assays captured. In this process, we revealed phenotypes for many genes that remained previously unresponsive, even when probing >300 conditions with growth as a readout 19 . These tens of examples of new gene-condition associations can serve as the stepping stone for targeted mechanistic dissection of gene function. In addition to gaining insights into previously uncharacterized genes, our screens revealed previously unappreciated connections of known pathways, such as the potential role of the overall GTP pool in biofilm formation, a different role of phenazines in P. aeruginosa early biofilm development, new c-di-GMP synthases involved in P. aeruginosa biofilm formation, and transcriptional regulators involved in C. albicans filamentation. In addition to the growth-unrelated readouts, we explored how colony growth is captured more accurately, and its relations with time. Our new readout, colony integral opacity is more sensitive than size for quantifying growth defects. Therefore, we used it to reanalyze the existing E. coli chemical genomics data 19 , providing novel insights into the functions and associations of many genes. Combining the info from colony size and integral opacity, we measured density-related characteristics of colonies, which linked to the inability to form proper colony structures or to extracellular material secretion ( Supplementary Discussion ). In summary, we created a long awaited tool for the microbiology community, which will facilitate quantitative reverse genetics approaches – both at a targeted and a large-scale level. We will maintain and update the software to meet future needs of users. As colony detection and quantification is microbe- and media- dependent, slight deviations of the current available modules can be developed when necessary."
} | 1,544 |
29118851 | PMC5667448 | pmc | 4,632 | {
"abstract": "Background Using globally abundant crop residues as a carbon source for energy generation and renewable chemicals production stand out as a promising solution to reduce current dependency on fossil fuels. In nature, such as in compost habitats, microbial communities efficiently degrade the available plant biomass using a diverse set of synergistic enzymes. However, deconstruction of lignocellulose remains a challenge for industry due to recalcitrant nature of the substrate and the inefficiency of the enzyme systems available, making the economic production of lignocellulosic biofuels difficult. Metatranscriptomic studies of microbial communities can unveil the metabolic functions employed by lignocellulolytic consortia and identify novel biocatalysts that could improve industrial lignocellulose conversion. Results In this study, a microbial community from compost was grown in minimal medium with sugarcane bagasse sugarcane bagasse as the sole carbon source. Solid-state nuclear magnetic resonance was used to monitor lignocellulose degradation; analysis of metatranscriptomic data led to the selection and functional characterization of several target genes, revealing the first glycoside hydrolase from Carbohydrate Active Enzyme family 11 with exo-1,4-β-xylanase activity. The xylanase crystal structure was resolved at 1.76 Å revealing the structural basis of exo-xylanase activity. Supplementation of a commercial cellulolytic enzyme cocktail with the xylanase showed improvement in Avicel hydrolysis in the presence of inhibitory xylooligomers. Conclusions This study demonstrated that composting microbiomes continue to be an excellent source of biotechnologically important enzymes by unveiling the diversity of enzymes involved in in situ lignocellulose degradation. Electronic supplementary material The online version of this article (10.1186/s13068-017-0944-4) contains supplementary material, which is available to authorized users.",
"conclusion": "Conclusions In summary, our results indicate the ability of sugarcane bagasse adapted microbial community in deconstructing lignocellulosic biomass by removing the cellulose and hemicellulose fractions. The taxonomic binning and expression profile of GHs illustrate the degradation of lignocellulosic biomass complexity. Phylogenetic analysis also suggested a growing participation of eukaryotic microorganisms in this process, indicating that the organisms studied up to now may not represent the major organisms that degrade plant biomass in nature. Expression of genes selected from the metatranscriptome library revealed challenging. However, considering the industrially appealing features of proteins described here, we proved the importance of this line of study. The isolated enzymes warrant further study to characterize their structure and verify their ability to enhance commercially available cocktails, as have been proposed.",
"discussion": "Discussion Plant cell walls are effectively degraded in various natural ecosystems by the action of microorganisms that act cooperatively by secreting an array of lignocellulolytic enzymes. In recent years, metatranscriptomic analysis applied to these ecosystems has begun to provide an insight into how lignocellulose breakdown is accomplished in situ [ 2 , 38 , 39 ]. Here, we investigated the time course degradation of sugarcane bagasse by a microbial community derived from compost. Based on sugarcane bagasse biomass analysis, we showed that the lignin component remained mostly unchanged and was not significantly modified by microbial activities. Our analysis was in agreement to a previous study [ 1 ] showing that biomass loss is mostly attributed to cellulose and hemicellulose degradation. Despite this limitation, composting community remained metabolically active during the experiment as surveyed by RNA sequencing. Next, we explored the metatranscriptome-assembled library, by focusing on screening the resulting database for CAZymes. Although the predicted GHs accounted for a small fraction (0.87%) in our composting community metatranscriptome, this was similarly observed by others who investigated various lignocellulytic communities such as rice-straw enriched compost (0.97%) [ 1 ], soil-contacting sugarcane bagasse (0.97%) [ 3 ], termite lumen (0.78%) [ 11 ], bovine rumen (0.78%) [ 10 ], and macropod foregut (0.71%) [ 14 ]. Amongst GHs, oligosaccharide-degrading enzymes from GH3 family were highly expressed in our study. These enzymes are fundamental in lignocellulolytic processes [ 5 ] and were abundant in other lignocellulolytic environments [ 40 ]. Mhuantong et al. explored the metagenome of a microbial community extracted from soil-contacting sugarcane bagasse [ 3 ]. Six out of the 10 most abundant GH families in the reported metagenome are amongst the most expressed GHs in each week of our metatranscriptome. Therefore, despite the different environments and techniques used, these communities have a reasonable level of similarity. Enzymes from auxiliary activity families, attributed to lignin oxidative modification and lytic polysaccharides degradation, accounted for a very small fraction (3.3%) of all predicted CAZymes in our metatranscriptome, supporting the lack of sugarcane bagasse lignin removal or structural changes in this polymer. This could be associated with a low relative abundance of fungi in our composting cultures, especially in early stages of the time course. Experimental design that uses liquid culturing of compost inoculum could have an effect on fungal growth and hence ligninolytic enzymes expression [ 41 ]. Recent studies showed that the composting conditions without liquid phase were preferable for CAZymes enrichment [ 42 ]. Other factors such as medium composition [ 43 ], temperature, agitation, and inoculum source could also play critical role for suppressing fungal growth. Majority of CAZymes predicted in our studies had bacterial origin, similar in composition and structure to other studies [ 2 , 3 , 17 , 40 , 44 ]. Our community was dominated by a metabolically diverse Proteobacteria and Bacteroidetes. As observed previously, Proteobacteria dominates oxygenated habitats [ 3 ] and Bacteroidetes are known for their contribution to the largest reservoir of CAZymes in various environments [ 3 , 40 ]. Interestingly, in the later stages of composting process, CAZymes expression shifted towards Eukaryotes and Animal kingdom. Representatives of nematodes, protists and other groups will be present in a composting spot, but it is unlikely that they would survive weeks in the submerged cultures. One explanation can be that the algorithm LCA did not assign phylogeny correctly. Also de novo assembly of metatranscriptomics reads and their mapping without reference genome can produce errors. However, in recent years, an increasing evidence of Eukaryotic invertebrates showed that their critical role in the hydrolysis of plant cell wall [ 45 – 48 ] and this aspect of our work should be further investigated. Our comprehensive analysis led to identification of potentially, novel CAZy proteins. The recombinant expression efficiency in this work demonstrates the challenge that remains in characterizing novel genes derived from culture-independent approaches using heterologous systems. The solubility was confirmed for three target proteins (26%) but was lower than the 53% rate usually obtained in our laboratory using the same expression system [ 49 ]. A β-1,3-(4)-glucanase with specificity towards substrates with higher β-1,3 to β-1,4 ratio [ 50 , 51 ], and no activity for CMC was found in our study. Compost7_GH6 was highly tolerant to an alkaline environment, an essential characteristic for application in detergent industry [ 52 ]. Compost13_GH10 presented substrate specificity and hydrolysis profile of a typical endoxylanase [ 53 – 55 ]. In contrast, the enzyme compost21_GH11 presented a hydrolysis profile of a typical exo-enzyme, releasing xylobiose from xylan and xylooligosaccharides. The structure of compost21_GH11 (Fig. 7 b–d) shows a typical GH11 fold of a β-jelly-roll [ 56 – 60 ]. The architecture of other GH11 members shows the same pattern with little variation in the secondary structures lengths [ 56 ]. Despite 32 three-dimentional structures of GH11 members that have already been solved ( http://www.cazy.org/GH11_structure.html ), the compost21_GH11 structure reveals two extra loops previously unseen in the other family members. However, multiple alignment analysis revealed that there are many other proteins that might have these extra loops. Here, we show that loop EL2 blocks one side of compost21_GH11 active site, transforming this enzyme into an exo-1,4-β-xylanase that acts from the non-reducing end. To our knowledge, the present study describes the first example of an exo-xylanase from the GH11 family. compost21_GH11 has high activity on insoluble polymeric xylan, in contrast to GH8 exo-oligoxylanases that show preference for soluble xylooligosaccharides [ 61 , 62 ]. It has been reported that xylooligosaccharides are strong cellulase inhibitors, whereas xylose and xylobiose have a smaller inhibitory effect [ 63 ]. As commercial enzymatic cocktails might have insufficient xylanase activity, a significant amount of xylooligomers accumulates in the reaction [ 20 ]. Hence, supplementation of enzyme cocktails with compost21_GH11 proved to increase their performance when there are xylooligomers in the reaction mixture. Therefore, in biomass treatment processes where xylooligomers accumulate [ 20 ], supplementing cocktails with compost21_GH11 will improve enzyme performance."
} | 2,405 |
37803170 | PMC10558546 | pmc | 4,636 | {
"abstract": "Ensuring plant resilience to drought and phosphorus (P) stresses is crucial to support global food security. The phytobiome, shaped by selective pressures, harbors stress-adapted microorganisms that confer host benefits like enhanced growth and stress tolerance. Intercropping systems also offer benefits through facilitative interactions, improving plant growth in water- and P-deficient soils. Application of microbial consortia can boost the benefits of intercropping, although questions remain about the establishment, persistence, and legacy effects within resident soil microbiomes. Understanding microbe- and plant-microbe dynamics in drought-prone soils is key. This review highlights the beneficial effects of rhizobacterial consortia-based inoculants in legume-cereal intercropping systems, discusses challenges, proposes a roadmap for development of P-solubilizing drought-adapted consortia, and identifies research gaps in crop-microbe interactions.",
"conclusion": "Conclusions and perspectives Single or composite effects of environmental factors impact the fitness and performance of both, the plants, and the soil-dwelling microorganisms. Thus, emphasizing the need of analyzing interactions within the plant-rhizosphere soil continuum. Microbiome-based soil management notably legume-cereal intercropping systems, holds great promise in sustaining plants’ growth, stress tolerance, and the promotion of soil ecosystem functioning and health. Most of the studies discussed in this review highlight the advantage of using multi-strain microbial consortia owing to the variety of outcomes they can provide. Indeed, in advanced agricultural research, multi-strains microbial inoculants are gaining much attention, as shifting from single specific microbes to more diverse microbial inoculants are likely more efficient in stimulating crops agricultural outputs as well as soil ecosystem functioning. On the other hand, beneficial legume-cereal intercropping systems, are nowadays well known for their high productivity relative to sole-cropping. This is likely attributed to the shifts in rhizospheric microbial communities driven by intercropped species. However, most of the studies addressing the impact of intercropping system in concert with microbial inoculants on plant growth and soil microbial communities have been done under controlled conditions. This may overestimate the beneficial effects of these practices while neglecting the effects of environmental factors on the reciprocal interaction between plants and their associated microorganisms as well as on the durability of their effects. Moreover, the residual effect of microbial inoculants on multiple cropping cycles is still up to debate as many studies focused on evaluating the microbially-mediated growth promoting phenotypes for a short period, yet it remains uncertain whether subsequent crops benefited from these effects. Therefore, we urge future research to fill these gaps as it is crucial to predict the ecological significance of microbial inoculant, plant diversity and their subsequent trajectory in the plant-soil system. With this in mind, we advocate for further studies to address the research gaps proposed below: New approaches to consortia construction starting from isolation and screening to the final formulation step should be determined, a priori, while considering the diversity and compatibility amongst consortia individuals. Applied research efforts in this direction will help us to design and produce efficient multi-strain inoculants that could reflect reproducible results on both above- or below-ground crop performance under controlled and field conditions. This should also consider the capacity of microbial inoculants to establish and persist within indigenous soil microbial communities and throughout multiple cropping cycles. To maximize profit of microbial biofertilizers, further attention should be oriented to study the legacy effect of microbial inoculants under different cropping systems and in the presence of different environmental conditions that represents an ultimate threat to crops growth and production, for instance drought and nutrients deficiency to build a sustainable next generation agriculture. As microbial legacies can be under the control of various factors, we recommend further studies that attempt to unravel the interrelationship between all these factors – environmental stresses, cropping system, and microbial consortia - and their possible outputs in a specific system (e.g., intercropping system), as this would help developing promising inoculants with the possibility to resist to stresses as to induce lasting effects. Studying the combined effect of intercropping systems along with efficient microbial consortia will provide a necessary background for the successful application of biofertilizers and to determine the degree to which the later would contribute to enhancing the advantage of intercropping systems.",
"introduction": "Introduction Nowadays, feeding the growing population is becoming one of the world’s major concerns due to the ever-increasing need for agricultural products 1 , 2 . Water scarcity represents one of the serious threats that has emerged over the past few decades, deteriorating the whole plant-soil system 3 , 4 . In addition to drought, phosphorus (P) deficiency is viewed as one of the main nutrient limitations restraining worldwide crops growth and production owing to its irreplaceable role and its limited availability 5 – 8 . Drought and P-deficiency, either as individual or combined constraints, cause severe disruptions of the plants’ morphological, physiological, biochemical, and molecular processes 9 – 11 . In many regions of the world, the incidence and the extent of drought and P-deficiency, together, are expected to increase which will put more pressure on the agricultural sector as there is a direct relationship between soil water status, P movement, and plant growth and yield 12 . The addition of P fertilizers is considered as an efficient strategy to compensate for P-deficiency and to stimulate plants’ growth under water-deficit conditions 6 , 13 . Although, being unquestionably needed for crop production, large amount of these water-soluble P fertilizers may be rendered unavailable for plants due to fixation by cations such as Al 3+ , Fe 3+ , Ca 2+ , clay particles, or transformed into organic forms, hence their efficacy is reduced 14 , 15 . Chemical P-fertilizers are manufactured based on rock phosphate (RP), a non-renewable P source that has been recently exploited for its direct use in high P-retention soils 16 . However, thorough investigations are still needed to increase the RP agronomic efficiency. In this context, growing interest has been given to exploiting biological resources, notably, rhizosphere soil microorganisms as they represent a low-input, environmentally friendly biotechnology to improve P nutrition and enhance plant growth 17 , 18 . Among these, P-solubilizing bacteria (PSB) are soil microorganisms that can transform insoluble P into soluble P forms, thus making it available for plants 19 – 21 . More interestingly, many of the PSB were found able to enhance the plants’ tolerance to several abiotic stresses, encompassing drought 22 . Rhizosphere dwellers are also known as the second plant genome as their contribution to plant growth promotion is likely synchronized by the host-plant itself 23 . Indeed, pioneering studies on the root-rhizobacteria interaction have focused on the effect of rhizo-deposits on plant-specific rhizo-microbiome synchronization 24 – 26 . The interrelationship between plant roots and beneficial rhizobacteria has been studied since the early 1980’s 27 . Ever since, greatest importance was given to decipher the interactional mechanisms that leads to successful and robust plants’ rhizosphere colonization by beneficial plant growth promoting rhizobacteria (PGPR). Rhizospheric microorganisms are generally endowed with a wide spectrum of plant growth promoting traits. Therefore, it is generally believed that inoculating drought and/ or P-stressed plants with microorganisms unifying various growth promoting traits can cooperate among themselves to give the highest positive results (Fig. 1 ). A good example of this, is the cooperative association between nodule endophytic bacteria, the so-called rhizobia, and other rhizosphere bacteria which have been found to allow better response to disturbances and adaptation to harsh environmental conditions 27 . Fig. 1 Drought-tolerant consortia and intercropping for resilient agriculture. Drought-tolerant P-solubilizing consortia, when combined with intercropping systems, have the potential to significantly improve plants’ resilience to abiotic stresses such as water scarcity. This synergistic approach may not only benefit the current crop cycle but also leave a lasting positive impact on subsequent cropping cycles. By enhancing nutrient availability and stress tolerance, these consortia offer a promising strategy for sustainable and resilient agricultural practices. Furthermore, it was recently reported that among the different agricultural management practices, cropping systems, notably intercropping, represent a promising strategy to increase crops productivity, minimize fertilizers inputs, and ameliorate fertility in N and P-deficient soils 28 – 31 . For instance, legume-cereal intercropping systems particularly debated in this review, has been reported to support better growth of both intercrops under stressful environmental conditions 32 , 33 (Table 1 ) (Fig. 1 ). Owing to their lower competition with cereals, legumes were reported to increase the N as well as P nutrition of cereals in low input soils, improving the availability of water resources, and increasing the land productivity 32 – 35 . The dynamic interaction within the soil microbial communities of intercropped plant species was also reported to be a major driver of interspecific facilitation in intercropping systems 28 , 32 , 36 . In fact, intercropped species may have some control over the shifts in the structural and functional composition of soil rhizobacteria 29 , 37 , 38 . As an example, bacterial and fungal diversity varied in intercropped, as compared to sole cropped, wheat and soybean species 32 . Table 1 A non-exhaustive list of studies highlighting the positive interactions between microbial inoculants and intercropping systems on plant growth and stress tolerance. Intercropped plants Stressors PGPR strains Strains origin Stress avoidance mechanisms Growth promotion traits Production system Reference Pigeon pea – maize – Enterobacter sp. C1D Pseudomonas sp. G22 Rhizobium sp. IC3109 Sediment sample at an industrial waste effluent Groundnut rhizosphere Pigeon pea nodules – P-solubilization, N-fixation Cd and Cr-tolerance, ACC-deaminase, IAA, and siderophore, DAPG production Greenhouse experiment 27 Faba bean – maize P-deficiency Desert soil Rhizobium leguminosarum Faba bean nodules Interspecific facilitation in Rhizobium-faba bean-maize intercropping system conferred advantage in nodulation, facilitated P nutrition in desert soil Symbiotic N-fixation Field experiment 148 Faba bean – durum wheat P-deficiency Rahnella aquatilis \n Pseudomonas sp Rhizosphere soil of wheat and faba bean Co-inoculation with PGPR along with intercropping enhanced the bioavailability of nutrients notably N and P, eventually improving plant growth P and K solubilization, N-fixation, exopolysaccharides, IAA and siderophores production Greenhouse experiment 39 Fenugreek – barley Low-rainfall soils Sinorhizobium meliloti F42 Variovorax paradoxus F310 Fenugreek nodules Inoculation improved fenugreek nodulation parameters, increased P and N bioavailability which all enhanced growth parameters P solubilization, symbiotic N-fixation, IAA, ACC, HCN and siderophore production Greenhouse and field experiments 58 Finger millet – pigeon pea Drought stress Rhizophagus irregularis \n Pseudomonas fluorescens \n Bradyrhizobium sp. Wheat roots Pigeon pea nodules The shallow-rooted finger millet profited from intercropped deep- rooted pigeon pea as it has access to a bottom wet soil layer,allowing better water and mineral nutrients uptake. AMF and PGPR also increased N and P uptake by both plants under drought-conditions P-solubilization, symbiotic N-fixation, and production of IAA, siderophores, ACC-deaminase, and diacetyl-phloroglucinol Greenhouse experiment 59 Soybean – maize – Rhizophagus irregularis \n Streptomyces sp. Bacillus megaterium AMF spores Synergistic use of AMF, PGPR, and intercropping induced greatest plant growth reponses and highest P-uptake, at 50% of the regular recommended P rate for maize-soybean intercropping. P-solubilization/mineralization (phytate), siderophore and IAA production Greenhouse experiment 31 Red-clover – ryegrass P-deficiency Rhizobium sp. T88 Herbaspirillum sp. AP21 – Inoculants induced beneficial effect on plants via increment of rock P use efficiency and growth promotion, which are associated with their phenotypic and genomic characteristics related with P solubilization and mineralization. P-Solubilization (RP and TCP)/ mineralization (phytate), symbiotic N-fixation, IAA and exopolysaccharides production Greenhouse experiment 149 Meanwhile, application of microbial consortia has been thoroughly deciphered at both fundamental and applied levels, and the benefits of these microorganisms on plant growth under harsh environmental conditions are well-documented. However, only few studies have reported the effect of microbial consortia on the growth of intercropped cereals and leguminous plants 27 , 39 . Yet, one of the main factors hindering the predictability and effective management of microbial inoculants for different cropping systems is the soil abiotic factors to which the response of microbial inoculants remained unstable and sometimes, inefficient. Therefore, it would be worthwhile to identify constraints associated with in-field application of microbial consortia, particularly in intercropping systems under drought and/or P-deficiency. In this review, shortcomings of the intricate interaction between microbial inoculants, cropping systems, and soil abiotic stresses - with focus on drought and P-deficiency – will be discussed. Therefore, first, (i) the effects of drought and P-deficiency on the plant-soil-microbe system will be highlighted. Then, the significance of shifts in resident microbial composition in response to cropping patterns, drought, and P-deficiency, as well as their potential to influence soil ecosystem processes, will be examined. In the second part, (ii) a comprehensive knowledge of the interrelationship between plant roots and the beneficial rhizobacteria communities is provided, at both fundamental and applied levels. The insights into these beneficial effects are based on a detailed presentation of relevant examples highlighting particularly at the applied level the ecological significance of drought-tolerant phosphate solubilizing rhizobacteria, with specific focus on multi-strains microbial inoculants as an essential component of the plant-soil system. Afterwards, (iii) the emerging technologies along with the main research gaps in the area of microbiome engineering are identified for incorporation into emerging agricultural practices, such as intercropping systems. Finally, (iv) current scopes and a detailed understanding of the consortia inoculants behavioral response within the plant-soil system are presented, along with the extent to which these inoculants could persist and induce long-lasting effects on subsequent cropping cycles. Plant microbe interactions affected by P deficiency and drought stress: a double edged sword for cereals and grain legumes The frequent occurrence of drought periods is a critical constraint to grain legumes and cereals production causing up to 80% of yield losses worldwide 40 , 41 . Climate change is predicted to bring about decreased winter rainfall in the range of 15% by 2030 and 30% by 2070 42 and an increased temperature of about 1.6 °C by 2050. The magnitude of plant production declines caused by the rainfall fluctuating patterns also relies on the soil fertility and varies with the different management techniques, such as fertilization. In fact, the reduced soil water availability due to recurrent drought scenarios typically leads to reduced diffusion rates of soluble nutrients mainly P, resulting in a low P-uptake and build up by the plants 9 , 43 . Grain legumes like faba bean are highly sensitive to stresses occurring during their growth cycle, especially drought stress that was reported to cause drastic effect on the crops’ growth and yield stability 3 , 4 . The detrimental effect of drought stress particularly, results from the photooxidative damage caused by ROS to nucleic acids, proteins, membrane lipids and photosynthetic pigments 10 , 44 . A decrease in photosynthesis, transpiration rate, membrane stability index, gas exchange, and leaf water potential were also noticed in drought and P-stressed, legume and cereal crops 45 – 47 , ultimately affecting the yield and its related parameters 45 (Fig. 2 ). Drought and P deficit were seen to cause poor nodule development and dysfunction in legume crops, which could have negative repercussions on the BNF performance 5 . Given that the symbiotic rhizobacteria relationship is likewise affected by both stresses - drought stress and P deficiency - the reduced nodulation may be an indirect outcome of both effects 3 , 11 . However, in contrast to grain legumes, research studies stated that cereal crops experience less growth and yield declines as a consequence of the bespoke stresses. Fig. 2 Water and P-deficiency impacts on intercropped plants and root bacteria. Schematic illustration of the effect of water and P-deficiencies on intercropped plants’ above- and below-ground processes. Upward and downward arrows represent the stimulation versus the repression of the plant’s physiological, morphological, and biochemical processes, respectively. Structural and functional diversity of root associated bacteria shows a negative response to both stressors. Of note, both stressors along with root exudates from intercropped species, favor the selection of resilient microbial species. This figure was created using BioRender.com. In addition to the drastic physiological and morphological growth alterations, some evidence demonstrates the direct link between cropping systems, soil microbial diversity and environmental constraints 28 , 48 , 49 . Indeed, differential responses of soil bacterial community were recorded under intercropping systems, and it was largely dependent on environmental changes. Drought has been reported to affect microbial communities either directly (desiccation) or indirectly (changes in exudates profiles), which further drive shifts in microbial community structure and diversity, consequently, microbial activities 38 , 50 . As an example, seasonal timing of drought significantly decreased bacterial community diversity and enzymes activities, notably N and P-acquisition enzymes 49 , yet, enzymes activities had mostly recovered, further demonstrating that structural and functional properties are not always synchronized 51 . Under water-deficient conditions, the growth of slow-growing, drought-adapted bacteria, such as Actinobacteria maybe favored 49 , 52 . Additionally, soil P status have been proven to drive shifts in rhizosphere microbial community composition and diversity 53 . Under wheat/ faba bean intercropping system, soil P availability was the main driver of the abundance of Proteobacteria, Actinobacteria and Firmicutes 28 . However, opposite trends were noticed in wheat-common bean intercropping as shifts in the root microbial community structure were species- rather than P-driven. More precisely, an increase in bacterial members (e.g., Hyphomicrobiaceae, Bradyrhizobiaceae, Comamonadaceae, and Rhodospirillaceae) associated with enhanced N nutrition of the host plant was noticed, irrespective of the P condition 32 . Generally, research studies on plant-microbe interactions indicate the enrichment of beneficial bacterial groups able to express specific plant growth promoting functions under drought or P-deficient conditions 54 . Temporal shifts in drought-tolerant PSB functionality and abundance were noticed over sole-cropped wheat growing seasons. In fact, drought tolerance potentials of the dominant species, notably those belonging to Phyllobacterium , Pseudomonas and Streptomyces genera were found to be increased at grain filling stage which coincides with heat seasons 22 . However, despite the ample knowledge of the belowground inter-species interactions and how shifts in root associated microbial communities are driven by crop species and environmental constraints, notably drought and P-deficiency, our understanding of these interactions, specifically in grain legume-cereal intercropping systems exposed to both stressors is still scarce. In fact, most research studies on legume-cereal crops and their associated microbial communities did not focus on studying the impact of drought and P-deficiency on the diversity and composition of the rhizo-microbiome as they rather focused on studying their associated activities. Indeed, drought and P-deficiency led to an optimal soil microbial biomass and activities in a maize-grass pea intercropping system 55 . In contrast to sole-cropped plants, low-P conditions boosted soil phosphatase activity and the acidification process. This tendency helped both intercropped species make better use of the few resources, with drought stress markedly strengthening this interspecific beneficial impact 55 . Similarly, under drought-stressed conditions and available P, microbial community promotion and soil acidification process were associated with enhanced facilitative interaction between maize-grass pea intercropped species. Also, P-deficiency stimulated phosphatase activity which in turn fostered the mineralization of soil organophosphorus, ultimately enhancing P nutrition in both crop species 56 . Based on the existing knowledge, one could conclude that the dynamic interaction within the soil microbial communities of intercropped plant species, particularly in reasonable legume-cereal based intercropping systems can contribute to ameliorating soil properties to a certain extent. Therefore, understanding the full extent of interaction between plant and associated rhizospheric microorganisms, and how these interactions are affected by drought and P-deficiency should offer new insights on how to improve crop resilience to the bespoke stresses via specific and stress-adapted microbial inoculants."
} | 5,710 |
35439346 | PMC9400955 | pmc | 4,637 | {
"abstract": "Abstract The oxidation of 5‐hydroxymethylfurfural (HMF) to 2,5‐furandicarboxylic acid (FDCA) is highly attractive as FDCA is considered as substitute for the petrochemically derived terephthalic acid. There are only few reports on the direct use of unrefined HMF solutions from biomass resources and the influence of remaining constituents on the catalytic processes. In this work, the oxidation of HMF in a solution as obtained from hydrolysis and dehydration of saccharides in chicory roots was investigated without intermediate purification steps. The amount of base added to the solution was critical to increase the FDCA yield. Catalyst deactivation occurred and was attributed to poisoning by amino acids from the bio‐source. A strong influence of amino acids on the catalytic activity was found for all supported Au, Pt, Pd, and Ru catalysts. A supported AuPd(2 : 1)/C alloy catalyst exhibited both superior catalytic activity and higher stability against deactivation by the critical amino acids.",
"conclusion": "Conclusion In this study, amino acids, which are significant ingredients from proteins in biomass, were identified as considerable poison for noble metal‐based catalysts during selective oxidation of 5‐hydroxymethylfurfural (HMF). We propose that the strong adsorption of sulfur and the functional guanidine group on the noble metal surface leads to the deactivation of the catalysts. The identification of specific amino acids (cysteine and arginine) as critical contaminants from biomass and the estimation of their concentration limits will help to control their level and adjust purification steps for removal of these compounds, if necessary, to achieve a better suited feedstock. Consequently, the results suggest that unnecessary purification steps can be avoided if suitable conditions are used, resulting in an overall greener process chain. Complementary to the optimization of the reaction conditions, the stability of the catalyst in presence of amino acids was significantly enhanced by alloying Pd with Au. This can be traced back to a change in the electronic and adsorption properties by alloying. Among the alloy‐based catalysts, AuPd(2 : 1)/C showed the highest stability and activity in the presence of amino acids with 2,5‐furandicarboxylic acid (FDCA) yields >90 %. The improved tolerance against amino acids facilitates the reusability and long‐term stability of the catalytic system, particularly promising for continuous flow applications. Furthermore, the present work leads to a better understanding of rational catalyst design for the direct use of sustainable bio‐derived solutions in a bio‐based chemical industry of the future. The direct conversion of more abundant agricultural by‐products instead of monosaccharides to base and fine chemicals will enable us to bridge the gap from fundamental research to application and potentially open up to a more sustainable and cheaper process.",
"introduction": "Introduction The production of bio‐based building blocks as alternative to fossil‐based resources has recently received a significant interest from industry as well as academia. One important platform molecule and promising substitute for the fossil‐derived terephthalic acid in the production of renewable polyesters is 2,5‐furandicarboxylic acid (FDCA), as identified by the US Department of Energy in 2004. \n [1] \n This potential building block can be obtained via selective oxidation of biomass‐derived 5‐hydroxymethylfurfural (HMF). Therefore, different approaches like heterogeneous, homogeneous, bio‐, and electro‐catalytic oxidation, as well as procedures without any catalyst, are presently discussed for industrial applications. \n [2] \n The easy separation and reusability of a solid catalyst makes this route more attractive than others, would contribute to a green conversion of HMF, and is very promising for industrial implementation. \n [2b] \n As introduced earlier, \n [8a] \n the oxidation of HMF starts with the oxidation of either the hydroxymethyl or aldehyde function (Scheme 1 ) to 5‐hydroxymethylfuran‐2‐carboxylic acid (HFCA) or 2,5‐diformylfuran (DFF), respectively. These intermediates are then further oxidized to 5‐formyl‐2‐furancarboxylic acid (FFCA) and FDCA (details on the reaction mechanism reported by Davis et al. \n [3] \n and Ardemani et al., \n [4] \n see the Supporting information). Scheme 1 Oxidation of HMF to FDCA (DFF formation not observed in this work). HMF can be synthesized from different bio‐based sources like monosaccharides (glucose and fructose) or polymers (e. g., cellulose and inulin). \n [5] \n Nevertheless, monosaccharides are mainly extracted from plants also used for food production. Therefore, using inedible plant parts, which are not competing with the food chain (e. g., agricultural waste material) is preferable and will lead to a cheaper and more sustainable process. \n [5a] \n Chicory root, which contains a high fraction of inulin, is a promising example. \n [6] \n Although one of the main criteria for the extensive research on green FDCA synthesis is the use of abundant and renewable biomass resources, investigations on the oxidation of HMF from real biomass to FDCA have hardly been reported in the literature. Thus, the influence of impurities in the feedstock from biomass on the catalytic system is often not considered. \n [7] \n Naim et al. \n [7] \n observed a major decrease in the FDCA yield using an Au/ZrO 2 catalyst in presence of 0.25 equiv. of levulinic and formic acid in the solution, which are among the main side products of HMF synthesis from fructose. This shows the necessity to investigate the influence of such feedstock ingredients. Moreover, purification steps in the production of highly pure HMF are a major reason for the high costs of the process chain due to multiple reaction and separation steps needed. New catalyst formulations are an essential tool for overcoming deactivation issues and increasing the FDCA yield. Various compositions for heterogeneous catalysts have been reported over the last years. These catalysts predominantly consist of noble metals such as Au,[ \n 3a \n , \n 8 \n ] Pt,[ \n 8c \n , \n 9 \n ] Pd,[ \n 8c \n , \n 10 \n ] Ru, \n [11] \n as well as alloys \n [12] \n of these metals as active component. For example, FDCA was obtained in a yield of >99 % with gold supported on hydrotalcite without the addition of any base to the solution. \n [8e] \n The elimination of a base makes this approach more sustainable, but serious concerns about the stability of hydrotalcite in water make it not so viable for large‐scale application. \n [4] \n On the other hand, the utilization of Pt or Ru as active metals allows for the use of weaker bases. Ait Rass et al. \n [9b] \n reported a procedure with a yield of 69 % FDCA with a Pt/C catalyst at 100 °C, 40 bar of synthetic air, and 2 equiv. of Na 2 CO 3 . The yield could even be improved to 98 % when adding 1 % Bi to the catalyst. Yi et al. \n [11a] \n investigated the activity of Ru/C catalyst in the presence of different bases and achieved a FDCA yield of 95 % with CaCO 3 as base. In an alternative approach, Gui et al. \n [12c] \n achieved a yield of >99 % FDCA with an AuPd( n / n 1 : 1) alloy‐based catalyst supported on zinc hydroxycarbonate by using NaHCO 3 as weak base. The use of AuPd alloy‐based catalysts also allows for base‐free conditions, as shown by Bonincontro et al. \n [12b] \n with AuPd(6 : 4) alloy supported on nanosized NiO. The authors attributed the activity to synergistic effects of Au, Pd, and the support material. Furthermore, improved activity and stability of Au‐rich alloys AuPd(8 : 2) supported on activated carbon, which showed quantitative conversion to FDCA after 2 h, was presented by Villa et al. \n [12a] \n A change in the electronic properties of the alloy was emphasized as the reason for the catalytic activity. In this work, the use of a raw HMF solution obtained by hydrothermal dehydration of fructose‐rich extract has been studied for the oxidation to FDCA. Therefore, forced chicory roots, which are an agricultural by‐product, were considered as attractive and sustainable feedstock. A greener process can be achieved by bridging HMF synthesis from bio‐resources and its oxidation to FDCA. Due to the broad scope of carbon as substrate being used for supporting different noble metals[ \n 8c \n , \n 9b \n , \n 11a \n , \n 12a \n ] we chose carbon black Vulcan as support material for most of the catalysts. The aim was to systematically study the influence of selected amino acids on the stability and activity of typical noble metals used for the heterogeneously catalyzed oxidation of HMF to FDCA. For this purpose, Au, Pt, Pd, and Ru catalysts supported on carbon black were prepared and tested in the oxidation of HMF in presence of varying concentrations of amino acids. Surprisingly, we discovered a significant influence of amino acids from the bio‐source on the catalytic activity and stability. Finally, Au and Pd alloys in different ratios were investigated with the aim to further improve the activity and stability of the catalysts.",
"discussion": "Results and Discussion Catalyst characterization The noble metal loading on the support was determined by inductively coupled plasma optical emission spectrometry (ICP‐OES) and the specific surface area was measured by the Brunauer‐Emmett‐Teller (BET) method. The noble metal loading for all catalysts is between 1.5 and 2 wt% making them well comparable for the catalytic tests. Two measurements were performed with ICP‐OES for each sample and the results are similar (±0.03 wt%), showing a good homogeneity of the catalysts. Moreover, the ratio of Au to Pd in the bimetallic catalysts is close to the intended ratio. The specific surface area of all catalysts is in a comparable range of around 200 to 220 m 2 g −1 (for details see Table S3), showing only small decrease upon the noble metal introduction compared to the pure carbon support with a surface area of 241 m 2 g −1 . Impregnation (ip) method for Pt/C results in a further decrease of the specific surface area due to the enhanced temperature applied during reduction. Powder X‐ray diffraction (XRD) patterns (Figure 1 ) showed reflections of Au and Pd in the monometallic catalysts, while no reflections of Pt and Ru particles (Pt: 39.8° 2 θ ; Ru: 43.5° 2 θ ) are visible. The reason for the latter is the presence of very small and highly dispersed nanoparticles with a size below the detection limit of XRD.[ \n 8b \n , \n 13 \n ] Indeed, the mean particle diameter was determined as 1.5±0.4 nm for Pt/C and 1.1±0.2 nm for Ru/C by transmission electron microscopy (TEM) (see Figures S12 and S14). In contrast, the mean particle diameter for Au/C and Pd//C was 3.3±1.2 nm and 4.2±1.8 nm, respectively (see Figures S11 and S13). For the bimetallic catalysts, reflections of the noble metal particles are between 2 θ =38.2 and 40.1° indicating the formation of an alloyed face‐centered cubic (fcc) phase of Au and Pd (Figure 1 b). \n [13] \n A nearly linear shift of the reflection from 39.1 to 39.5° with increasing Pd fraction was observed. The reflections of the carbon support at 24.9 and 43.9° are broad due to its mostly amorphous nature. For Au/ZrO 2 , no reflections of metallic Au were detected as already reported (see Figure S2). \n [8b] \n \n Figure 1 Powder XRD patterns of (a) Au/C, Pt/C, Pd/C, Ru/C, and carbon black support, and (b) AuPd(2 : 1)/C, AuPd(1 : 1)/C, AuPd(1 : 2)/C, and carbon black support. The Au and Pd reflections are shown from ICSD reference (ICSD reference code: Au – 00‐004‐0784, Pd – 00‐046‐1043) crystallographic data. The catalysts were further characterized by TEM, and the mean diameters were obtained by averaging over 150 particles, which were found to be in the range between 3.5 to 4.1 nm for bimetallic catalysts (see Figures S15–S17). In addition, energy‐dispersive X‐ray spectroscopy (EDX) mapping of AuPd(2 : 1)/C showed a homogeneous distribution of Au and Pd over the bimetallic nanoparticle (Figure 2 ). Thereby, no indication of the formation of segregated phases of Au and Pd (e. g., in a core‐shell like structure) \n [14] \n were found. This supports the conclusion from XRD that one dominant alloyed phase is formed.\n Figure 2 EDX‐mapping of AuPd(2 : 1)/C (green: Pd; red: Au). The small reflections of bimetallic catalysts in the XRD pattern, due to small particles, draw vague conclusions about the AuPd alloy formation. Therefore, X‐ray absorption spectroscopy (XAS) measurements of the bimetallic catalysts at the Au L 3 ‐ and Pd K‐edge were performed to determine the oxidation state of Au and Pd, as well as to confirm the alloy formation. The X‐ray absorption near edge structure (XANES) normalized spectra, which are the fingerprint of the oxidation state, of AuPd samples at the Au L 3 ‐edge show similarities of the catalysts with the AuPd(1 : 1) foil in the white line features indicating alloy formation (Figure 3 a). Similarly, spectra measured at Pd K‐edge for the three bimetallic catalysts, Pd foil, and PdO showed different features suggesting that Pd might be in an alloy state since the catalyst features are significantly different from Pd−Pd interaction and especially Pd−O interaction (Figure 3 b). There are changes in the near edge structure at both Au L 3 ‐edge and Pd K‐edge, depending on the ratio of Au/Pd in the bimetallic samples, which might be attributed to an interaction by electron transfer between Au and Pd as previously observed for AuAg alloys. \n [15] \n \n Figure 3 Normalized XANES spectra of the catalysts at (a) Au L 3 ‐edge and (b) Pd K‐edge, and Fourier‐transformed (FT) k 3 ‐weighted EXAFS spectra of bimetallic AuPd/C catalysts at (c) Au L 3 ‐edge and (d) Pd K‐edge. Standard reference oxide and foil (AuPd reference from ESRF) are shown. Fitting of the Fourier‐transformed extended X‐ray absorption fine structure (EXAFS) spectra (Figure 3 a) at Au L 3 ‐edge with Artemis software reveal scattering by Au−Au as well as Au−Pd in the first shell in all alloyed catalysts (for details see Table S4 and Figure S5). \n [16] \n A radial distance of about 2.8 Å (phase‐uncorrected) is expected for AuPd alloys for Au−Au as well as Au−Pd scattering, which was obtained as result of the fitting, proving the successful alloy formation. \n [17] \n The two peaks at around 2.2 and 2.7–2.9 Å are both assigned to first shell metal‐metal contribution. This peak is split into two due to an interference of Au−Au and Au−Pd backscattering because of a different phase shift and amplitude.[ \n 17a \n , \n 17c \n ] While ICP‐OES revealed Au/Pd ratios close to the intended ratio, fitting of EXAFS spectra showed Au‐enriched phases for all alloys. Note that ICP‐OES gives Au/Pd ratio in wt % while XAS refers to the molar ratio of Au/Pd. A reason for this observation might be a compositional gradient in the alloy with a Pd enriched shell due to lower CN for Au−Pd scattering compared to Au−Au scattering. \n [15] \n In addition, Fourier‐transformed EXAFS spectra measured at Pd K‐edge suggest the presence of non‐reduced PdCl 2 from the precursor on the catalyst surface. However, no reflections of PdCl 2 could be found in XRD indicating the presence of only minor amounts of PdCl 2 in the catalyst. Oxidation of chicory root‐derived HMF solution To examine the applicability of bio‐based HMF solutions for the catalytic oxidation with noble metal‐based catalysts, we tested the oxidation of HMF in a chicory root‐derived solution (HMF Chic; 0.004 m HMF) with an Au/ZrO 2 catalyst. Therefore, we used reaction conditions optimized in our previous study (125 °C, 30 bar pressure of synthetic air, 5 h reaction time, 8 equiv. NaOH, ratio of intended Au/HMF=1 : 100). \n [7] \n The low concentration of HMF in the solution, caused by a non‐optimized process for HMF synthesis, was compensated by adding commercial HMF to achieve a final HMF concentration of 0.067 m , which is comparable to other studies.[ \n 8a \n , \n 9b \n , \n 11b \n , \n 11c \n ] This bio‐derived solution modified to higher HMF concentration still contains the realistic amount of impurities, the influence of which we aim to study. We obtained a HMF conversion of 100 % and a yield of 7.1 % HFCA and 0 % FDCA (Table 1 , entry 2) with Au/ZrO 2 . Under the same reaction conditions, a quantitative conversion of HMF to FDCA was obtained in pure HMF solution (0.1 m ) with Au/ZrO 2 (entry 1), indicating the deactivation of the catalyst by impurities.\n Table 1 Screening of reaction conditions for the oxidation of HMF (0.067 m ) in chicory root‐derived solution HMF Chic with Au/ZrO 2 (entry 1–4) and Pt/C ip (entries 5–8; 5 h, ratio of intended Au/Pt to HMF=1 : 100; entries 1 and 5: 0.1 m pure HMF solution; entry 8: after 5 h addition of another 2 equiv. Na 2 CO 3 and continuation for 17 h). \n Entry \n \n Base equiv. \n \n \n T [ °C] \n \n \n p [bar] \n \n \n X (HMF) [%] \n \n \n Y (HFCA) [%] \n \n \n Y (FDCA) [%] \n \n C‐balance [%] \n \n 1 [a] \n \n \n 8 \n \n 125 \n \n 30 \n \n >99 \n \n 0 \n \n >99 \n \n >99 \n \n 2 [a] \n \n \n 8 \n \n 125 \n \n 30 \n \n >99 \n \n 7.1 \n \n 0 \n \n 7.1 \n \n 3 [b] \n \n \n 4 \n \n 125 \n \n 30 \n \n >99 \n \n 38.8 \n \n 2.3 \n \n 41.1 \n \n 4 [b] \n \n \n 2 \n \n 100 \n \n 10 \n \n 76.3 \n \n 27.6 \n \n 0 \n \n 36.2 \n \n 5 [b] \n \n \n 4 \n \n 125 \n \n 30 \n \n >99 \n \n 0.3 \n \n 81.7 \n \n 82.0 \n \n 6 [b] \n \n \n 4 \n \n 125 \n \n 30 \n \n >99 \n \n 11.8 \n \n 13.8 \n \n 25.6 \n \n 7 [b] \n \n \n 2 \n \n 100 \n \n 10 \n \n 96.2 \n \n 86.6 \n \n 2.4 \n \n 92.6 \n \n 8 [b] \n \n \n 2 \n \n 100 \n \n 30 \n \n >99 \n \n 17.2 \n \n 58.1 \n \n 75.3 \n [a] NaOH. [b] Na2CO 3 . Wiley‐VCH GmbH The formation of humins due to polymerization of HMF was a major side reaction and in order to increase FDCA yields, the formation of these by‐products has to be suppressed. \n [18] \n Therefore, we addressed this issue by testing at milder reaction conditions to improve the activity of our catalyst by decreasing the rate of the formation of humins. \n [19] \n For this purpose, we changed the base from NaOH to the weaker base Na 2 CO 3 and reduced the equivalents of the added base from 8 to 4. In this way, we could increase the FDCA yield to just 2.3 %; however, the C‐balance was enhanced to 41.1 % (entry 3). Note that only HMF and its oxidation products (HFCA, DFF, FFCA, and FDCA) were taken into account for the calculation of the presented C‐balance. This illustrates the considerable influence of the base and pH of the solution on the degradation of HMF to side products in presence of impurities of the chicory root‐derived solution. Due to the increase in the C‐balance by decreasing the alkalinity of the solution, we examined even milder conditions (100 °C, 10 bar synthetic air, and 2 equiv. Na 2 CO 3 ; entry 4). Surprisingly, we could not observe a further increase in the C‐balance under these reaction conditions with the Au/ZrO 2 catalyst. Next, we tested the oxidation of HMF in the bio‐derived solution with Pt/C ip (impregnated) catalyst to compare the degree of deactivation for Pt. While a lower C‐balance (25.6 %; entry 6) was observed for Pt/C ip with 4 equiv. Na 2 CO 3 , the FDCA yield increased to 13.8 %. Under milder conditions, Pt/C ip showed a further increase of the C‐balance to 92.6 % (entry 7). A simultaneous decrease in the FDCA yield was expected, mainly due to the decrease in dissolved oxygen at lower air pressure, which has a considerable influence on the FDCA yield.[ \n 8a \n , \n 8b \n ] Nevertheless, Pt/C ip was more tolerant to the impurities adsorbing on the catalyst surface, making it more promising for high FDCA yields. These findings show that the noble metal used for the oxidation process has to be optimized for the use of bio‐derived HMF solutions to increase its stability in presence of impurities. In addition, it is very important to adjust the pH of bio‐derived solutions with impurities, which can adsorb strongly on the catalyst surface. The high HFCA yields observed for Pt/C ip prompted us to test a two‐step process: (1) HMF oxidation to HFCA with a yield of 94.3 % with 2 equiv. Na 2 CO 3 added to the solution, and (2) oxidation of this HFCA in a second step in a one‐pot process with the addition of another 2 equiv. Na 2 CO 3 . In this way, we could increase the FDCA yield to 58.1 % (entry 8). This shows that the deactivation of the catalyst by impurities from the bio‐based solution can be compensated, at least to some extent, by carefully adjusting the reaction conditions, particularly the alkalinity of the solution leading to a greener process. Therefore, we can conclude that the FDCA yield can be improved by using a one‐pot two‐step or semi‐continuous process. Analysis of the amino acid content To unravel the cause of the deactivation of the catalyst in the chicory‐derived HMF solution, we studied the presence of the compounds in the solution. Thereby, we found a considerable content of free amino acids, deriving from decomposition of proteins during the hydrothermal dehydration process, in addition to saccharides and acids in the solution. Amine and thiol functional groups are known to adsorb strongly on noble metal surfaces by physisorption or chemisorption leading to blockage and deactivation of active sites. \n [20] \n To further study their influence, first the concentration of free amino acids in HMF solutions produced from forced and non‐forced chicory roots was determined (Table 2 ). A deviation in the concentration values before and after the oxidation reaction indicates decomposition of proteins leading to an increase in the concentration of some amino acids [e. g., cystine (denoted as Cys) and glutamic acid (GluA)]. Decrease in the concentration [e. g., for arginine (Arg) and aspartic acid (AspA)] is due to either side reactions of the amino acids or more likely due to adsorption on the noble metal surface. In addition, we observed substantial differences of the amino acid concentration depending on the use of forced or non‐forced chicory roots as feedstock.\n Table 2 Comparison of amino acid content in HMF solutions derived from forced and non‐forced chicory roots. \n HMF solution \n \n Amino acid content [μg mL −1 ] \n \n Ala \n \n Arg \n \n AspA \n \n Cys \n \n GluA \n \n Gly \n \n His \n \n Ile \n \n Leu \n \n Lys \n \n Met \n \n Phe \n \n Pro \n \n Ser \n \n Thr \n \n Tyr \n \n Val \n \n FCR [a] \n \n \n 0.4 \n \n <0.1 \n \n <0.1 \n \n <0.1 \n \n 2.4 \n \n 0.6 \n \n 0 \n \n <0.1 \n \n 0 \n \n 0.3 \n \n 0 \n \n 0 \n \n 0 \n \n <0.1 \n \n <0.1 \n \n 0 \n \n <0.1 \n \n NFCR [b] \n \n \n 5.0 \n \n 115.9 \n \n 46.3 \n \n 0.3 \n \n 19.6 \n \n 3.8 \n \n 3.0 \n \n 9.9 \n \n 3.9 \n \n 4.7 \n \n 0.3 \n \n 4.9 \n \n 27.8 \n \n 17.4 \n \n 10.2 \n \n 2.5 \n \n 8.6 \n \n HMF Chic [a] \n \n \n 1.2 \n \n 18.8 \n \n 5.7 \n \n <0.1 \n \n 1.9 \n \n 1.2 \n \n 0.3 \n \n 0.5 \n \n <0.1 \n \n 0.6 \n \n <0.1 \n \n <0.1 \n \n 1.2 \n \n 2.2 \n \n 2.2 \n \n <0.1 \n \n 0.6 \n \n HMF Chic [a,c] \n \n \n 2.0 \n \n <0.1 \n \n 4.8 \n \n 0.8 \n \n 83.8 \n \n 1.5 \n \n <0.1 \n \n 0.2 \n \n 0.1 \n \n <0.1 \n \n <0.1 \n \n <0.1 \n \n 0.8 \n \n <0.1 \n \n <0.1 \n \n <0.1 \n \n 0.5 \n [a] Forced roots. [b] Non‐forced roots. [c] Analyzed after use of the solution for oxidation of HMF [Au/ZrO 2 (ratio of intended Au loading to HMF=1 : 100), 125 °C, 30 bar synthetic air, 5 h, in 0.53 m NaOH]. Wiley‐VCH GmbH Among all amino acids found in the HMF solution, we chose an acidic (glutamic acid), an alkaline (arginine), and a sulfur‐containing (cysteine and dimer cystine) amino acid for further testing. We used the highest concentration values found in the solution HMF Chic as benchmark to investigate the influence of the amino acids on the catalysts. It is worth mentioning here that the actual amount of amino acids in the reaction mixture after the oxidation reaction can be even higher but cannot be analyzed due to adsorption of amino acids on the surface of the catalyst. Hence, higher concentrations have to be considered in the investigations. The significant decrease of the FDCA yield obtained with the chicory root‐derived solution, most likely due to poisoning, emphasizes the necessity to further investigate on their influence. In addition, the differences between the amino acid concentrations in the HMF solution obtained from different roots shows the importance of adjusting process and purification steps depending on the biomass source used in the process. Influence of amino acids present in the solution on the catalytic performance of HMF oxidation To investigate the influence of selected amino acids on the catalytic system, Au‐, Pt‐, Pd‐, and Ru‐based catalysts supported on carbon black Vulcan were prepared and tested in the HMF oxidation in presence of different concentrations of the amino acids starting with pure HMF solution. For glutamic acid (Figure 4 ), high concentrations >1000 μg mL −1 were necessary to observe any decrease of the FDCA yield or carbon balance for Au/C and Pt/C catalysts. Even at a concentration >3000 μg mL −1 , the FDCA yield was approximately 80 % for Au/C. In general, the catalyst deactivation due to glutamic acid was negligible since the interaction of the carboxylic acid groups with the metal surface is weak.\n Figure 4 Influence of glutamic acid (top) and arginine (bottom) on the oxidation of HMF with Au/C, Pt/C, Pd/C, and Ru/C (100 °C, 10 bar synthetic air, 5 h, 2 equiv. Na 2 CO 3 , M/HMF=1 : 100 or Ru/HMF=1 : 50). When arginine was used as an amino acid, results differed considerably (Figure 4 ). Especially for Au, there was a significant influence already at a concentration of about 19 μg mL −1 , a level which was present in the chicory root derived solution. For the other tested metals, the FDCA yield decreased steadily with increasing concentration. We assume that the strong interaction between the guanidine group and the noble metal surface leads to poisoning of the catalyst. For Au nanoparticles, a strong interaction with arginine was reported by Barbu‐Tudoran et al. \n [21] \n which, in their study, was used for the self‐assembly of nanoparticles. Since arginine has multiple functional groups available, it shows a high affinity to dissolve in water and can interact with other Au particles. \n [21] \n For Pt surfaces, chemisorption of amines under alkaline conditions was reported, which could only be removed under acidic conditions from the surface. \n [22] \n \n Interestingly, a pronounced deactivation was observed for cysteine, which contains a functional thiol group that can lead to poisoning of noble metal particles (Figure 5 ). For all catalysts, the FDCA yield decreased to below 30 % at a maximum cysteine concentration of about 50 μg mL −1 . For the adsorption of cysteine, the chemisorption via covalent bond with the Au surface in a monolayer was described in an NMR study while a weakly physisorbed second layer can form on top. \n [20c] \n \n Figure 5 Influence of cysteine (top) and cystine (bottom) on the oxidation of HMF with Au/C, Pt/C, Pd/C, and Ru/C (100 °C, 10 bar synthetic air, 5 h, 2 equiv. Na 2 CO 3 , M/HMF=1 : 100 or Ru/HMF=1 : 50). Cystine was only found in very low concentrations in the HMF solution compared to other amino acids. Since it was not found in the solution before the oxidation, the actual amount could be higher due to adsorption of cystine on the catalyst surface during the reaction. Cystine showed the strongest deactivating effect of all compounds with concentrations of <30 μg mL −1 being sufficient to decrease the FDCA yield to below 20 % (Figure 5 ), probably due to a strong interaction of the sulfur with the noble metal surface by chemisorption of the functional disulfide group. Apart from steric hindrance by the dimeric form cystine, electronic effects by polarization of the metal particle surface also play a vital role here. Furthermore, dissociation of disulfide bonds to form two covalent bonded thiol species is reported in literature. \n [23] \n This might also occur on the surface of the Au particles adsorbing cystine, which explains the roughly doubled concentration needed of the monomeric cysteine for a similar decrease in the FDCA yield. In contrast, surface‐enhanced Raman scattering of cystine adsorption on Au nanoparticles displayed an intact disulfide bond and the bonding via bidentate covalent bonds of both sulfur atoms to the Au surface. \n [20b] \n Hence, the use of monometallic Au‐based catalysts for the conversion of bio‐derived solutions containing such compounds should be avoided. For Pt, the binding of thiols is stronger than amines, which can show mobility on the surface as well as spillover to the support. \n [20a] \n \n Improving the activity and stability with alloyed catalysts To improve the stability and activity of noble metal‐based catalysts in presence of sulfur or nitrogen‐rich compounds, alloy catalysts are potential alternatives. Au and Pd were chosen for alloy formation as they showed the highest activity in pure HMF solution and a comparably stable activity in the reaction mixture, respectively. Hence, we prepared alloy catalysts with three different Au/Pd ratios to change the electronic properties of the surface like the d‐electron density or d‐band shape.[ \n 17a \n , \n 24 \n ] In this way, we aimed at a decrease in adsorption energies of poisoning compounds, which is crucial for the use of impure bio‐based solutions. The difference in the electron affinity of Au and Pd leads to an increase of s‐ and p‐electrons for Au, while for Pd the catalytically important d‐electrons increase. \n [25] \n This ultimately results in changes in the adsorbate‐metal interaction. Moreover, an improved activity and stability of the oxidation state of AuPd nanoparticles by a change in d‐electron density was shown by Liu et al. \n [17a] \n They could attribute the higher activity of a 25 % Au containing AuPd alloy to a higher degree of surface reduction as proven by X‐ray photoelectron spectroscopy (XPS) analysis. Furthermore, the rise in the activity compared to pure Pd is attributed to an Au−Pd interaction of very small Au clusters on the Pd shell observed by EXAFS fitting. A change in sulfur adsorption was reported for different alloys in literature; \n [26] \n for example, Lakhapatri and Abraham \n [26b] \n could show an increase of sulfur tolerance for a Ni‐based catalyst by Rh promotion. Furthermore, Ke et al. \n [26a] \n showed an improved sulfur tolerance of a PtCo bimetallic catalyst compared to a monometallic Pt catalyst. This behavior was attributed by XPS analysis to an electron transfer from Co atoms to Pt atoms leading to a decrease of the d‐band energy of Pt. This change of the electronic properties influences the interaction of sp 2 ‐orbitals from sulfur with the Pt electrons and ultimately leads to the change in the catalyst poisoning. All prepared AuPd alloy catalysts showed high FDCA yields ranging from 89.3 to 98.4 % (see Table S2), which is comparable to the yield obtained with the monometallic Au catalyst. Further investigation of the influence of amino acids for the alloyed catalysts showed that the adsorption of arginine on the surface leads to a similar decrease in the FDCA yield as for Pd/C and Pt/C catalysts (Figure 6 ). The best stability against deactivation was observed for the 2 : 1 ratio of Au/Pd.\n Figure 6 Influence of glutamic acid, arginine, cysteine, and cystine (from top to bottom) on the oxidation of HMF with AuPd‐based catalysts (100 °C, 10 bar synthetic air, 5 h, 2 equiv. Na 2 CO 3 , Au+Pd/HMF=1 : 100). For the HMF oxidation in presence of cysteine, a decrease of the FDCA yield with increasing concentration was observed for all alloyed catalysts (Figure 6 ). Nevertheless, considerably higher concentrations of the compound can be added to the solution compared to the monometallic catalysts. Interestingly, the Au‐rich catalyst AuPd(2 : 1)/C showed the best stability with an FDCA yield of 68.9 % at a cysteine concentration of 137 μg mL −1 although the monometallic Au catalyst was the most prone to deactivation by cysteine. Hence, the interaction of thiols with the Au surface could be weakened by alloying with Pd and the corresponding change in the electronic properties, which influences the adsorption properties. \n [17a] \n Moreover, the presence of cystine leads to a stronger decrease of the FDCA yield. Here, the AuPd(1 : 1)/C catalyst showed the best stability with a FDCA yield of about 72.3 % at a cystine concentration of 68.5 μg mL −1 . To compare the influence of the tested compounds on the HMF oxidation with noble metal‐based catalysts, functions were fitted to the obtained data points to calculate the concentration needed for a 10 % decrease of the FDCA yield compared to the pure HMF solution (Figure 7 ). It can be seen that by alloying Au with Pd, a clear increase in the resistance of the catalyst against deactivation for all tested amino acids could be achieved while high yields of FDCA are maintained. Surprisingly, the concentrations of cysteine and cystine tolerated in the solution could be increased more than twofold compared to Pt/C (Figure 7 ). This demonstrates the applicability of a change in the electronic properties of the surface for an improved stability of catalysts for the conversion of bio‐based resources. For arginine, which is the most commonly appearing amino acid in chicory root‐derived solution, only a slight improvement was observed when using AuPd(2 : 1)/C catalyst. In this context, the arginine concentration in bio‐derived HMF solution has to be monitored and, if it surpasses a critical value, it has to be regulated before performing the oxidation of HMF with noble metal based catalysts, in order to enable high FDCA yields. Moreover, AuPd(2 : 1)/C was tested to oxidize the chicory root‐derived solution HMF Chic (125 °C, 30 bar synthetic air, 5 h, 4 equiv. Na 2 CO 3 , Au+Pd/HMF=1 : 100) to check for the stability. Interestingly, a FDCA yield of 28.9 % at quantitative HMF conversion [ Y (HFCA)=3.5 %; Y (FFCA)=11.4 %] was achieved, which is a significant rise of more than twofold compared to the yield with Pt/C ip of 13.8 % (FDCA yield with Au/ZrO 2 : 2.3 %). This unravels an improved tolerance against sulfur compounds and demonstrates the superior properties of the alloy‐based catalyst.\n Figure 7 Concentration of amino acids needed to decrease FDCA yield by about 10 % for Au/C, Pt/C, Pd/C and AuPd(2 : 1)/C. The concentration values of the respective amino acids for AuPd(2 : 1) are given in the figure (cystine: 29.7 μg mL −1 , cysteine: 45.8 μg mL −1 , arginine: 30.6 μg mL −1 ). Investigation on deactivation mechanism over alloy catalyst The possible catalyst deactivation mechanisms upon the addition of specific amino acids to the HMF solution were investigated by considering leaching of noble metal species, particle sintering, or poisoning by strong adsorption of amino acids on the catalyst surface. \n [27] \n Firstly, we examined the stability of the catalysts with and without the addition of cysteine. Therefore, we determined the concentration of noble metal in the solution after the oxidation of HMF by ICP‐OES (for details see Table S6). For AuPd(2 : 1)/C, neither Au nor Pd could be detected in the solution, meaning no leaching of either Au or Pd into the solution occurred, indicating the alloy composition was highly stable in the reaction medium. The degree of leaching for all catalysts was almost the same in presence of cysteine, showing that leaching was not contributing to the observed deactivation of the catalysts. Moreover, sintering of noble metal particles influences their activity. For example, for Au/ZrO 2 and Au/C, the influence of the particle size on the activity of the catalysts was discussed in literature. \n [28] \n Naim et al. \n [7] \n observed sintering of Au particles after HMF oxidation reaction (as confirmed by powder XRD patterns), which were not visible in the as‐synthesized catalyst. To investigate the role of sintering, we determined the crystallite size of nanoparticles in AuPd(2 : 1)/C after preparation as well as after HMF oxidation by peak fitting and using the full width at half maximum (FWHM) in the Scherrer equation (details, cf. Table S7). However, we did not observe any influence of the reaction conditions or arginine on the sintering of AuPd (2 : 1) nanoparticles. Hence, sintering seems to be negligible for the observed catalyst deactivation. To further identify the role of the amino acids for the catalyst deactivation, we used ninhydrin to color different samples. Ninhydrin is used in the analysis of amino acids due to its ability to react with the primary amine function at elevated temperature by formation of so called Ruhemann's purple. \n [29] \n Due to side reactions involving the thiol function of cysteine, however, it is not possible to form Ruhemann's purple in these samples. \n [29] \n Adding ninhydrin to a solution of a blank test in HMF oxidation (100 °C, 10 bar air, 5 h, 2 equiv. Na 2 CO 3 , 375 μg mL −1 arginine) showed a change to a dark colored solution (see Figure S21). This is attributed to the reaction of ninhydrin with arginine remaining in the solution after 5 h. In addition, the formation of a shoulder at around 590 nm is observed in ultraviolet/visible spectroscopy (UV/Vis; for details see Figure S22), which is fitting to the peak by formation of Ruhemann's purple. A test with a solution produced under the same reaction conditions containing AuPd(2 : 1)/C catalyst did not show a color change by reaction with ninhydrin. Therefore, it can be inferred that no arginine was present anymore in the solution after 5 h. This is in agreement with the analysis performed for the experiments with chicory‐based solution (see Table 2 ). To judge whether arginine was weakly adsorbed on the catalyst surface and can be dissolved by water or DMSO, we washed the catalyst three times with water. Afterwards ninhydrin was added to the solution and the catalyst. However, none of the samples showed a color change that can be ascribed to the reaction of arginine with ninhydrin, meaning that the amine group has been either strongly adsorbed on the catalyst surface or decomposed to other products blocking the noble metal surface and thereby leading to the poisoning of the catalyst."
} | 9,493 |
25849889 | null | s2 | 4,638 | {
"abstract": "In Vibrio cholerae, the genes required for biofilm development are repressed by quorum sensing at high cell density due to the accumulation in the medium of two signaling molecules, cholera autoinducer 1 (CAI-1) and autoinducer 2 (AI-2). A significant fraction of toxigenic V. cholerae isolates, however, exhibit dysfunctional quorum sensing pathways. It was reported that transition state analogs of the enzyme methylthioadenosine/S-adenosylhomocysteine nucleosidase (MtnN) required to make AI-2 inhibited biofilm formation in the prototype quorum sensing-deficient strain N16961. This finding prompted us to examine the role of both autoinducers and MtnN in biofilm development and virulence gene expression in a quorum sensing-deficient genetic background. Here we show that deletion of mtnN encoding methylthioadenosine/S-adenosylhomocysteine nucleosidase, cqsA (CAI-1), and/or luxS (AI-2) do not prevent biofilm development. However, two independent mtnN mutants exhibited diminished growth rate and motility in swarm agar plates suggesting that, under certain conditions, MtnN could influence biofilm formation indirectly. Nevertheless, MtnN is not required for the development of a mature biofilm."
} | 301 |
37009568 | PMC10060342 | pmc | 4,639 | {
"abstract": "We study the microbiome of sea water collected from two locations of the Barbadian coral reefs. The two sites differ in several environmental and ecological variables including their endogenous benthic community and their proximity to urban development and runoffs from inland watersheds. The composition of the microbial communities was estimated using whole genome DNA shotgun sequencing with adjuvant measurements of chemical and environmental qualities. Although both sites exhibit a similar degree of richness, the less urbanized site (Maycocks reef at Hangman’s Bay) has a strong concentration of phototrophs whereas the more urbanized location (Bellairs reef at Folkstone) is enriched for copiotrophs, macroalgal symbionts and marine-related disease-bearing organisms from taxa scattered across the tree of life. Our results are concordant with previous profiles of warm ocean surface waters, suggesting our approach captures the state of each coral reef site, setting the stage for longitudinal studies of marine microbiome dynamics in Barbados. Supplementary Information The online version contains supplementary material available at 10.1007/s00338-022-02330-y.",
"introduction": "Introduction The capacity of fragile reef ecosystems to maintain key economic, social and environmental services for coastal human societies has been severely challenged by a global decline in coral reef cover of at least 50% (Eddy et al. 2021 ). The reef system of Barbados (Jackson et al. 2014 ) and many other small island states in the Caribbean (Burke et al. 2011 ) have also witnessed comparable coral loss since the 1970s. Documented drivers of coral loss in Barbados include local factors such as sedimentation, eutrophication (Hunte and Wittenberg 1992 ; Bell and Tomascik 1993 ) and overfishing (Gill et al. 2019 ). Regional factors include hurricane damage (Mah and Stearn 1986 ) and the loss of a keystone grazer (Hunte et al. 1986 ). Global factors include temperature-induced bleaching due to sea surface warning (Oxenford and Vallès 2016 ). The relative contribution of these factors to coral loss in Barbados remains unresolved (Wittenberg and Hunte 1992 ) and likely fluctuates as the reef deteriorates. The status of the Barbados reef system has been monitored since 1982, providing observational data of widely-used indicators of reef ecological integrity including coral diversity, percent coral and algal cover, and urchin and fish abundance at multiple sites along the west coast of the island (Cermes 2018 ). However funding constraints limit monitoring to 5 year cycles, a too infrequent interval to inform on reef change in a rapidly evolving global environment, for example, in response to the recent Sargassum invasions (Langin 2018 ). One motivation of our study here is the need for monitoring techniques that are accessible for small island states such as Barbados (Vallès et al. 2019 ). Since microbiomes influence and reflect the environment they inhabit, an understanding of the natural variability and shifts in community gradients of the coral reef waters may provide a more sensitive measure of reef health and allow for the precise identification of environmental disturbances, in turn suggesting prophylactic measures that could be taken to cull negative influences (Glasl et al. 2019 ; Weber et al. 2020 ). We profile for the first time the microbiome of reef water at two locations in the Barbadian reef system using whole genome DNA sequencing. These locations were chosen because they lie along a gradient of increasing eutrophication on the west coast of the island and because they have been previously regularly monitored (Bell and Tomascik 1993 ; Tosic et al. 2009 ). We ask whether it is possible to identify differences in microbiome composition that reflect differences in eutrophication and other variables that may be used to develop markers which accurately estimate overall reef health, and compare these sites to other oceanic microbial communities (Sunagawa et al. 2015 ).",
"discussion": "Results and discussion We found striking and consistent differences between Bellairs and Maycocks in many dimensions. Maycocks had higher concentrations of dissolved oxygen whereas Bellairs had higher concentrations of nitrate (Supplemental Table 1B). In total, 97% of the genera were identified at both sites through analysis of the sequencing data (Supplemental Table 1C and Supplemental Information 1 for richness analyses). However, the relative frequency of taxa is very different between the two sites. For example, Maycocks exhibits a relative enrichment of Bacteria and Chlorophyta whereas Bellairs contains more eukaryotic (primarily fungal) and archaeal taxa (Fig. 1 D; all p << 0.01, Pearson’s \\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}$$\\chi$$\\end{document} χ 2 ). Then within the bacterial superkingdom, there are more pronounced differences in the distribution across phyla (Fig. 1 E). For example, Maycocks has a strong preference for the Terrabacteria group (37% B vs 69% M; KW test, p << 0.01), whereas Bellairs has a preference for Proteobacteria, specifically the Alpha class. We explore these differences below. Maycocks is highly enriched for phototrophic microbes including the autotrophic genera Prochlorococcus and Synechococcus which account for over 98% of all Cyanobacteria identified in our study, a fact consistent with the oligotrophic nature of the Barbadian marine environment (Biller et al. 2015 ). This is consistent with our microscopy imaging which suggests that Maycocks has a tendency towards smaller organisms (Supplemental Fig. 1). Prochlorococcus is typically more prevalent in nutrient poor water while Synechococcus is more prevalent in eutrophic water and coastal plumes of rivers (Wawrik et al. 2003 ). This is consistent with our data where we observe a 3.7:1 Prochlorococcus to Synechococcus ratio at Bellairs, but a 5.3:1 ratio at Maycocks. The observed frequencies of both genera at Maycocks are more extreme than all other locations world-wide, including nearby Curacao (Weber et al. 2020 ). Candidatus Pelagibacter, which belongs to the ubiquitous marine SAR11 clade, is also significantly shifted towards Maycocks and is one of the most abundant genera identified in our study (KW test, p << 0.01, Fig. 2 A). These small free-living heterotrophic species thrive in low-nutrient environments and play a significant role in carbon cycling (Dinasquet et al. 2019 ). Fig. 2 A Here the x-axis is the log ratio of the fraction of all bacterial reads at Bellairs versus the fraction of all bacterial reads at Maycocks. Y -axis is the log of the sum of all reads across all genera within Bacteria, a measure of overall abundance. Light blue tickets on margins represent a rug plot to indicate distribution of genera across axes. This is done at the level of genera. B – D are analogous to panel A except for Proteobacteria, Archaea, and Eukaryota respectively. Vertical red lines indicate a 95% confidence interval the location of the mean. Blue lines indicate a 95% confidence interval on the distribution of the log-ratio for the genera. Light blue margin ticks represent a rug plot to indicate density distribution of genera along axes The enrichment of phototrophs at Maycocks also holds in the Archaea superkingdom. This includes euryarchaeota such as Candidatus Poseidoniales, which contains some of the most abundant planktonic archaeons in ocean surface waters. It is a motile photoheterotroph capable of degrading proteins and lipids (Rinke et al. 2019 ) (Fig. 2 C). Although the two sites have a similar percentage of Eukaryota, it is the Viridiplantae which exhibit the largest enrichment at Maycocks (21% B vs. 32% M). Some 99% of these reads map to the autotrophic green algae Chlorophyta (Fig. 2 D). This includes Micromonas pusilla and M. commoda which are photosynthetic picoeukaryotes known to thrive globally in tropical marine environments and play a key role in the primary production within the euphotic zone (Šlapeta et al. 2006 ). Bellairs is enriched for copiotrophs from taxa scattered across the tree of life, an observation consistent with its proximity to urban development and inland run-offs. There is a significant enrichment of Proteobacteria at Bellairs (Fig. 2 A), with emphasis on the Alpha class which are major contributors of bacterioplankton to ocean waters (Dunn’s test, p << 0.01; Fig. 2 B). The majority of genera within Rhodobacteraceae show a general trend towards the Bellairs site (KW test, p << 0.01), highlighting Dinoroseobacter, Tateyamaria and Jannaschia. Dinorosebacter are aerobic anoxygenic phototrophic bacteria, highly abundant in marine turf algae. Tateyamaria is a genus of coastal aerobic bacteria which can thrive under acidification conditions (Huggett et al. 2018 ). Jannaschia are aerobic anoxygenic phototrophic bacteria and some species play a role in nitrate reduction (Moran et al. 2007 ). Bellairs is also slightly enriched for several species of the highly abundant Vibrio genus, of which many species play a significant causative role in coral diseases (Munn 2015 ) (Supplemental Information 2). With respect to Archaea, Bellairs is strong enriched for thaumarchaeota involved in nitrification. (23% B versus 3% M, Dunn’s test, p << 0.01; Fig. 2 C, Fig. 3 A). The chemolithoautotrophic Nitrosopumilus maritimus species is a dominant contributor to nitrification in marine environments (Sánchez-Quinto and Falcón 2019 ) and Candidatus Nitrosopelagicus is a planktonic pelagic ammonia-oxidizing thaumarchaeon involved in nitrogen and carbon fixation (Santoro et al. 2015 ). Fig. 3 Analogous Fig. 1 E except for ( A ) Archae and ( B ) Eukaryota respectively. The Foraminifera, Stramenopiles (yellow star) and Ulvophyceae (red cross) are statistically enriched for the vast majority of genera identified only at Bellairs (blue) or Maycocks (red) sites; genera for the latter two are listed on the right-hand side With respect to Eukaryota, Bellairs is enriched for benthic epiphytes growing on coral and algae. Many species of Stramenopiles within the SAR clade are observed uniquely at Bellairs (Fig. 3 B denoted by a yellow star, 11 of all 34 uniquely identified organisms identified at Bellairs (hypergeometric binned by eukaryotic phyta, p << 0.01). This is not likely due to the depth of sequencing alone given that Maycocks in fact received 1.57 fold more reads than Bellairs and analysis in Supplemental Information 1). Several marine diatoms were identified including Licmophora, Cylindrotheca, Seminavis, and others which contribute species that grow on algae and corals or feed on diatoms in eutrophic environments and are causal in brown and red tides. Supplemental Information 3 provides a more detailed investigation of the SAR clade. Analogous to the Stramenopiles, the Foraminifera contribute a surprising number of uniquely identified genera (9 of 34; p << 0.01, hypergeometric binned by clades; Fig. 3 B). We asked how our sites compare to other ocean waters previously profiled by the Tara Oceans ( N = 68 epipelagic and mesopelagic locations) (Sunagawa et al. 2015 ). After normalizing our data with Tara Oceans’ and applying unsupervised clustering of genera frequencies, we see that both sites co-cluster with samples harvested from the surface and deep chlorophyll maximum layers (SRF, DCM respectively) of the trade and coastal biomes (Fig. 3 C). This is consistent with the finding from Sunagawa et al. that depth is the most important factor that determines species abundance. Both Bellairs and Maycocks are closest to Red Sea and Indian Ocean samples. Although concentrations of nitrates NO 2 and PO 4 at Maycocks are similar to levels of its neighbors in the clustering, nitrate levels of Bellairs are higher and more similar to mesopelagic layers. The right subtree of Fig. 4 is enriched for autotrophs consistent with the hot, oligotrophic waters of Barbados. Fig. 4 A heatmap of relative abundance of individual species, and associated measures of ecological diversity and physio-chemo-hydrographic attributes integrating the Barbadian coral reef profiles with the Tara Oceans’ data. DCM abbreviates deep chlorophyll maximum; SRF abbreviates surface water; MES abbreviates mesopelagic water Although it is infeasible to draw definitive conclusions from only two sites, the microbiome-level profiles are consistent with a shift from coral to macroalgae between Maycocks and Bellairs. This observation is further supported by our chemical/environmental and microscopy data, in addition to documentation of the benthic communities and with known differences in stressors between the two sites. However, our microbiome approach here provides potentially more mechanistic insight into the global effects of some stressors (e.g., run-off) on the state of reef health than standard monitoring efforts (e.g., benthic communities), as it identifies ~ 100 distinct differentially abundant genera which may interact with different dimensions of the reef ecology. As such, the findings here may influence, for example, the design and feasibility of coral rehabilitation projects and, more generally, our capacity to identify microbiome changes consistent with known stressors and states of eutrophication. This sets the stage for systematic studies using microbiome-based markers of the rate of change to reef health along many variables (e.g., geographic location, degree of urbanization, seasonal change). Our approach here should have relevance to other small islands in the vicinity of Barbados with similar reef systems and stressors."
} | 3,464 |
35542204 | PMC9080276 | pmc | 4,641 | {
"abstract": "In this study, a facile one-step dip-coating approach was reported for fabrication of superhydrophobic copper mesh by using PDMS, SiO 2 nanoparticles, PVDF microparticles and a couple agent 3-aminopropyltriethoxysilane (KH-550). It is found that undesirable SiO 2 agglomeration was obviously reduced by introducing KH-550 and PVDF microparticles. The KH-550 acts as the bridge-linker and binds SiO 2 , PVDF and PDMS together. The as-prepared superhydrophobic mesh exhibited a promising water contact angle of 160.1° and a small sliding angle of 2.5°. The coating displayed excellent resistance to various pollutants and retained its superhydrophobicity after abrasions (sandpaper abrasion or adhesive tape tear). The strong chemical stability was also observed when the mesh was immersed in various solutions, especially in neutral and alkaline solutions. The applications of the superhydrophobic mesh for quantitative water droplet manipulation and oil spill cleanup were also illustrated. The method is facile and economic, and could be used for large-scale fabrications for industrial applications.",
"conclusion": "4. Conclusions In summary, we provide a facile one-step dip-coating method to prepare a durable superhydrophobic mesh based on PDMS, SiO 2 nanoparticles and PVDF microparticles. Different from surface treatment of nanoparticles and microparticles, a coupling agent KH-550 was added to act as the bridge-linker to bind PDMS, SiO 2 and PVDF. The method could be applied to other organic polymers and inorganic spheres. The prepared mesh exhibited well dispersion of nanoparticles and microparticles in PDMS matrix, excellent superhydrophobicity, self-cleaning properties, adhesion resistance and wear resistance. Also, good stability was observed after immersion in different aqueous solutions. Furthermore, the probable applications of the superhydrophobic meshes in precise microdroplet-based reaction and oil spill cleanup were also illustrated.",
"introduction": "1. Introduction Wettability is an important property of solid surfaces. 1,2 A surface with a water contact angle (CA) greater than 150° and a sliding angle (SA) less than 10° is called superhydrophobic. Because of the limited contact area of the solid surface with water, superhydrophobic surfaces display various special properties, such as water repellency, 3,4 self-cleaning, 5,6 anti-icing, 7,8 anti-corrosion, 6,7 low drag for fluid flow, 9,10 and so on. Learned from nature, artificial superhydrophobic surfaces have been fabricated for applications in life and industry. 9,11–13 It was widely known that the superhydrophobic properties are the result of the combination of materials with low surface free energy and hierarchical micro- and nano-structures of the surface. The maximum achievable water contact angle is around 120° on a flat surface with a surface energy as low as 6.7 mJ m −2 , 14 thus various technologies have to be applied to construct rough surfaces. 15,16 Most of these superhydrophobic surfaces are fabricated by complicated procedures or expensive facilities, which restrict their extensive applications. Thus it is still highly demanded to fabricate superhydrophobic surfaces with facile and low cost approaches. Nanoparticles are promising materials used to construct roughness. 3,5,17,18 Based on solvent techniques, nanoparticles are readily introduced onto the material surfaces. In order to enhance the adhesion between the superhydrophobic coating and substrate, the bonding layer 5,17 and nanoparticle/polymer blends 19,20 have been applied. Nanoparticles always tend to agglomerate due to their high surface area and surface energy. The inter-particle forces within the agglomeration stem from the van der Waals, capillary, and electrostatic forces. 21 Nanoparticle aggregation usually form multi-scale roughness including the nano-scale roughness of primary nanoparticles and the micro-scale roughness of nanoparticle aggregates, 22 which is beneficial to form Cassie–Baxter contact state, 23 that a liquid droplet sits on the surface asperities with air pockets trapped in between, and therefore increases the water repellency. Double structured roughness was also constructed with both nano- and micro-particles recently. 3,24 These fabrications usually included complicated self-assembling techniques and multiple operation procedures. Usually, the more the nanoparticles deposit on it, the higher roughness the surface has, but the less the binding strength between nanoparticles and substrate surface becomes. 19,20 The resistance to mechanical tear and wear is a very important issue of superhydrophobic surfaces to their practical applications. Without proper design, the surface hydrophobic layers are prone to be damaged by mechanical abrasion, leading to undesired loss of water repellent properties. Polydimethylsiloxane (PDMS) is a typical elastomeric material with a low surface energy of about 20 mJ m −2 . PDMS and silica (SiO 2 ) nanoparticles have been used to fabricate superhydrophobic surfaces on various substrates, such as metal plates, 25 woods, 26 glass, 19,22 meshes, 27,28 fabrics, 29,30 polymer films, 31 etc. By applying a suspension of nanoparticles in PDMS solution, superhydrophobic surfaces coated with PDMS/SiO 2 nanocomposites have been realized with facile and economic methods, such as spin coating, 32 spray coating, 19,31 dip-coating, 27,29 etc. Among them, dip-coating was appropriate for large scale fabrications. For the lack of strong interactions between PDMS and SiO 2 particles, serious aggregations of nanoparticles and even cracks could be observed especially at high SiO 2 contents, 19 and the durability of superhydrophobic surfaces to mechanical wear and tear was usually weak. To improve particle dispersion and increase their compatibility with polymer matrices, surface modification of particles was usually adopted. 24,30 Polyvinylidene fluoride (PVDF) also has a low free energy. Because of its excellent heat resistance, weatherability, and chemical resistance, PVDF received extensive attentions. For the preparation of superhydrophobic surfaces, PVDF usually need to be dissolved into solvents and superhydrophobic films could be fabricated by inert solvent-induced phase-inversion techniques. 33,34 PVDF based coating techniques were also developed with the incorporation of nanoparticles. 35,36 One of the disadvantages of PVDF based coatings is that they have poor adhesion with most substrates. Therefore, pre-treatment of the surfaces is required. 35 As we know, there is no report about using commercial PVDF powder particles directly to construct superhydrophobic surfaces. In this paper, PDMS, SiO 2 nanoparticles, and PVDF microparticles were used to construct the superhydrophobic surface on the copper mesh through a simple one-step dip-coating method. The cross-linked PDMS acts as the adhesive agent, SiO 2 nanoparticles and PVDF microparticles were adopted to construct the surface roughness. A coupling agent 3-aminopropyltriethoxysilane (KH-550) was used to improve the interactions between particles and PDMS. The prepared coatings were well characterized by using field emission scanning electron microscopy (FESEM), Fourier transform infrared spectroscopy (FTIR), X-ray photo-electron spectroscopy (XPS), and energy dispersive spectroscopy (EDS). It is found that the nanoparticle agglomeration was reduced by introducing PVDF microparticles and KH-550. The as-prepared superhydrophobic mesh displayed excellent self-cleaning properties, good resistance to acid and base attack, and desirable mechanical stability against sandpaper abrasion damage and adhesive tape tear. The applications of the coated mesh for water droplet manipulation and oil collection were also illustrated. The method is facile and economic, and is suitable for the large-scale industrial fabrications.",
"discussion": "3. Results and discussion 3.1 Characterization of superhydrophobic meshes PVDF powder alone was insoluble in toluene and it was precipitated at the bottom of the bottle (Fig. S1a † ). When hydrophobic SiO 2 nanoparticles were added into the solution, after agitation, a homogenous and stable paint-like suspension solution was obtained (Fig. S1b † ). Because of the high specific surface area, fumed SiO 2 nanoparticles were prone to aggregate and form a suspension network in the solution, and PVDF particles would be suspended in the network. To verify the presence of PVDF on the copper mesh, the surface components of PDMS/SiO 2 and PDMS/SiO 2 /PVDF meshes were scratched down, and the FTIR spectra of the two samples as well as the pure PVDF powder were measured and shown in Fig. 1 . Appearance of the peak between 3300 cm −1 and 3700 cm −1 is due to the –OH stretching vibration. The peaks at around 2964 cm −1 , 1261.2 cm −1 and 802.2 cm −1 are the asymmetric –CH 3 stretching, symmetric –CH 3 deformation and –CH 3 rocking in Si–CH 3 of PDMS, respectively. A broad, multi-component peak ranging from 930 cm −1 to 1200 cm −1 is corresponding to the asymmetric Si–O–Si stretching vibration absorption 25,40,41 ( Fig. 1a ). Compared with the PDMS/SiO 2 sample, the absorption bands at around 761.2, 615.6, 535.6, 409.8 cm −1 , which can be assigned to the crystal vibration absorption peaks of PVDF, 42 could be clearly seen in the spectra of the PDMS/SiO 2 /PVDF sample ( Fig. 1b ). The weak PVDF crystal absorption peaks in the PDMS/SiO 2 /PVDF sample are due to the presence of the SiO 2 nanoparticles on the surfaces of the PVDF microparticles as disclosed in the following XPS and EDS measurements. Therefore, it is reasonable to infer that the mesh surface was effectively modified by SiO 2 nanoparticles and PVDF microparticles. Fig. 1 FTIR spectra of PVDF powder, PDMS/SiO 2 and PDMS/SiO 2 /PVDF samples. The optical images of the prepared meshes were shown in Fig. 2 . The surface of the dried PDMS/SiO 2 mesh displays many tiny cracks on it ( Fig. 2a ). The toluene evaporation induced the internal flow during the drying process, and the cross-linked PDMS network had relatively high viscosity, thus the light nanoparticles were prone to forming aggregations and attaching onto the substrate. 22 This led to the formation of cracks on the surface, especially at high SiO 2 content. 19 When PVDF powder was added into the solution, the cracks were more obvious ( Fig. 2b ) because of the incompatibility of PVDF and PDMS. Fig. 2a and b also indicated that both SiO 2 and PVDF particles have weak interactions with PDMS and the copper mesh. After adding KH-550 into the dipping solution, the formed PDMS/SiO 2 /KH-550 and PDMS/SiO 2 /PVDF/KH-550 meshes were neat and smooth, and no cracks were found by naked eyes on the surfaces ( Fig. 2c and d ). Obviously, KH-550 plays an important role in improving the dispersion of SiO 2 and PVDF particles in PDMS. Fig. 2 Optical images of superhydrophobic meshes: (a) PDMS/SiO 2 , (b) PDMS/SiO 2 /PVDF, (c) PDMS/SiO 2 /KH-550, and (d) PDMS/SiO 2 /PVDF/KH-550. The insets are corresponding CAs, respectively. When PVDF powder was added, the CA increased from 153.6° ± 3.6° of the PDMS/SiO 2 mesh to 157.8° ± 2.3° of the PDMS/SiO 2 /PVDF mesh ( Fig. 2a and b ), and the SA slightly decreased from 3.9° ± 1.0° to 3.4° ± 1.6° correspondingly. Both the low surface energy of PVDF and the improved roughness contributed to it. The addition of KH-550 further optimized the superhydrophobicity of the meshes, and the CAs and SAs were 156.9° ± 2.9° and 3.4° ± 1.4° for the PDMS/SiO 2 /KH-550 sample ( Fig. 2c ), and 160.1° ± 2.1° and 2.5° ± 0.6° for the PDMS/SiO 2 /PVDF/KH-550 mesh ( Fig. 2d ), respectively. Thus, the uniform distribution of the particles is beneficial to form a superhydrophobic surface. It is found that amines could form hydrogen bonds with PVDF. 43 Amines also enter into strong interactions with SiO 2 nanoparticles by three kinds of interactions ( Fig. 3b ): amine groups form hydrogen bonds with silanol groups (hydrogen bonding interactions), amines accept protons from isolated silanol groups (Brönsted interactions), and amines coordinate to SiO 2 nanoparticles (IV) when coordinative defect points are present on the surfaces (Lewis interactions). 37 Therefore, amine groups of KH-550 could react with SiO 2 and PVDF particles. The oxyethyl groups of KH-550, on the other end, would be hydrolyzed by absorbing water vapour in air, and the formed silanol groups could react with the hydroxyl groups of PDMS or form hydrogen bonds to PVDF. Therefore, KH-550 acts as the bridge-linker to bind SiO 2 nanoparticles, PVDF microparticles and PDMS. The multiple interactions among them were shown in Fig. 3a as a schematic chemical reaction process. The increased interactions among SiO 2 , PVDF and PDMS led to the formation of well-distributions of particles on the sample surfaces. Fig. 3 (a) The KH-550 acts as the bridge-linker to bind SiO 2 , PVDF and PDMS. (b) Possible interactions between an amine and the surface of SiO 2 nanoparticles. 37 The SEM images of PDMS/SiO 2 /KH-550 and PDMS/SiO 2 /PVDF/KH-550 meshes were shown in Fig. 4 . Compared with the surface of the PDMS/SiO 2 /KH-550 mesh, granular bulges with the diameters from 1 to 15 μm could be clearly seen on the PDMS/SiO 2 /PVDF/KH-550 mesh ( Fig. 4c ), which indicated the PVDF microparticles were successfully coated on the mesh surface. The agglomerations of SiO 2 nanoparticles were obviously seen on the surface of PDMS/SiO 2 /KH-550 mesh in the high magnification SEM image ( Fig. 4b ), while nanoparticles were distributed more homogenously on the surface of the PDMS/SiO 2 /PVDF/KH-550 mesh ( Fig. 4d ). These results indicated the microparticles, because of the much larger sizes and weights, would further prevent nanoparticles from forming agglomerations. The EDS elemental maps showed the uniform distribution of C, O and Si on the surface of PDMS/SiO 2 /PVDF/KH-550 mesh ( Fig. 5 ), while F was only observed on PVDF microparticles ( Fig. 5 , S2 and Table S1 † ). Because of the low content of KH-550, the N element was not detected by the EDS measurement. The Si element was also found on the surface of PVDF particles (Fig. S2 † ), which indicates SiO 2 adsorbed on the surfaces of PVDF microparticles. Fig. 4 SEM images of the coated surface. (a) and (b) are PDMS/SiO 2 /KH-550 mesh, (c) and (d) are PDMS/SiO 2 /PVDF/KH-550 mesh at different magnifications. Fig. 5 (a) SEM image and (b–f) EDS C, O, Si, F and N elemental maps of the PDMS/SiO 2 /PVDF/KH-550 mesh. XPS studies were further carried out to get the chemical compositions of the outmost layers ( Fig. 6 and Table 1 ). The C1s (285 eV), O1s (532 eV), Si2s (155 eV) and Si2p (103 eV) peaks can be seen clearly in the spectra of all samples. After introduction of PVDF, KH-550, or both of them, however, no clear peaks associated with the F and N elements could be found in the spectra. The elemental XPS spectra for F and N were further measured by using a pass energy of 20 eV (Fig. S3 † ). Only a very small F1s peak around 688 eV was observed in PDMS/SiO 2 /PVDF, and almost no fluorine element could be detected on the surface of PDMS/SiO 2 /PVDF/KH-550 mesh (Fig. S3a † ). The results indicated the SiO 2 nanoparticles and PDMS covered the surface of PVDF microparticles during the drying process. Also, a very weak N1s peak at 400 eV, which is attributed to nitrogen moieties with covalent N–H bonds in KH-550, could be seen in PDMS/SiO 2 /KH-550 and PDMS/SiO 2 /PVDF/KH-550 samples (Fig. S3b † ). The weak intensity is because of the low contents of KH-550 in them. Also, SiO 2 nanoparticles and PDMS are prone to moving to the surfaces to decrease the surface energy of the coatings, which further reduce the contents of N elements on the surfaces. After adding KH-550, the content of C element increases and that of the O element decreases ( Table 1 ), indicating that PDMS migrated to the coating surfaces. Therefore, the interaction between PDMS and particles became stronger. Fig. 6 XPS survey spectra of (a) PDMS/SiO 2 , (b) PDMS/SiO 2 /PVDF, (c) PDMS/SiO 2 /KH-550 and (d) PDMS/SiO 2 /PVDF/KH-550 mesh. Elemental compositions of different superhydrophobic meshes Mesh Compositions (at%) C Si O N F PDMS/SiO 2 23.97 32.41 43.62 — — PDMS/SiO 2 /PVDF 21.59 35.77 42.42 — 0.22 PDMS/SiO 2 /KH550 32.2 30.96 36.11 0.73 — PDMS/SiO 2 /PVDF/KH550 28.11 31.6 39.5 0.47 0.31 The PDMS/SiO 2 /PVDF/KH-550 mesh was chosen for further studies due to its outstanding superhydrophobicity. The optical images of water droplets (dyed by dark-blue ink) and oil droplets (dyed by Sudan red) on the pristine copper mesh and the superhydrophobic mesh were shown in Fig. 7a . Because of the porous structure, the pristine copper mesh is hydrophobic and has a CA around 120°. In comparison, the water droplet almost keeps a spherical shape on the surface of the modified mesh. The CA of oil droplet on the surface of the pristine mesh is small, while the oil droplet quickly spreads on the modified mesh, indicating the PDMS/SiO 2 /PVDF/KH-550 mesh is superoleophilic. A jet of water easily bounces off the superhydrophobic mesh due to the trapped air between the water and mesh ( Fig. 7b and Movie S1 † ). Moreover, the superhydrophobic mesh appears bright silver with a mirror-like phenomenon when it was pressed into water, further proving the air cushion between water and the superhydrophobic mesh ( Fig. 7d ). Fig. 7 (a) Optical images of water droplets (dyed by blue-black ink) and n -hexane droplets (dyed by Sudan red) on the surfaces of the pristine and coated mesh. (b) A jet of water bounces off the coated mesh. (c) The coated mesh is immersed in water by an external force. (d) The immersed mesh appears bright silver due to the existence of trapped air. 3.2 Self-cleaning properties of superhydrophobic meshes The coated mesh also exhibited excellent antifouling and self-cleaning properties. When a water droplet (dyed by dark-blue ink) was put onto the coated mesh for 10 min and then removed by tilting the mesh, no stain was found on it ( Fig. 8(a and b) and Movie S2 † ). When the mesh was immersed into ink dyed water and lifted up, no water droplets were adhered on the surface ( Fig. 7(c and d) and Movie S3 † ). Moreover, the model contaminant was also easily cleared away from the coated mesh by dropping water ( Fig. 7(e and f) and Movie S4 † ). These excellent self-cleaning properties were due to the air cushion between the solid and liquid interface. Fig. 8 (a) and (b) A water droplet (dyed by dark-blue ink) was put onto the coated mesh for 10 min, and then removed. (c) and (d) The mesh was inserted into dark-blue ink dyed water and lifted up. (e) and (f) The model contaminant (green chalk dust) was easily cleared away from the mesh. 3.3 Stability of superhydrophobic meshes The stability of the coating in long term contact with water should be taken into consideration for outdoor products. Therefore, the PDMS/SiO 2 /PVDF/KH-550 meshes were immersed in various solutions with different pH values, and the water CAs and SAs were measured to assess the chemical stability of the superhydrophobic meshes. As shown in Fig. 9 , CAs gradually decreased when the meshes were immersed in all solutions. In neutral solution, the CA became 151.4° after 8 days, and with the increase of the immersion time to 10 days, the CA decreased to 145.7°. The decrease of CAs was a little more obvious in acidic or alkaline solutions. Fig. 9 The CAs and SAs as a function of immersion time in pH solutions (pH = 1–13). The variations of SAs along with different pH levels are also illustrated in Fig. 9 . The SA increased with the immersion time, and the water droplets could roll off the coatings if the meshes were in neutral or alkaline solutions for 8 days. After 10 day immersion, the droplets were stuck on the coatings occasionally, which means the transition from the Cassie–Baxter state to the Wenzel state. 38 It was reported that the variation of hydrophobic performance after the water immersion is attributed to a weakening of the Si–CH 3 bond strength. 3 The results indicated the meshes have good resistance to neutral and alkaline aqueous solutions. For acidic treatments, though the CAs decreased gradually, the SAs were more sensitive to the damage of superhydrophobic surfaces. 38,44 The droplets became adhesive to the meshes when they were immersed in the solutions of pH 5, 3, 1 for more than 6, 4 and 3 days, respectively. This is because the acid would attack the amine groups and the multiple interactions among SiO 2 , PVDF and PMDS were gradually eliminated. Acid also reacted with the exposed copper wires. Consequently, a portion of the nanostructured area started to get damaged, resulting in increasing SAs. Such results indicated that the mesh has medium-good stability against acidic solutions. The mechanical stability of the coating was assessed by the repeated tear tests with adhesive tapes. 38,39 In this test, an adhesive tape was pasted onto the coated mesh, pressed with thumb, and then peeled off. 39 As shown in Fig. 10a , the PDMS/SiO 2 coating was easily peeled off, demonstrating the coating stability was poor. In comparison, CAs and SAs changed slowly with the increase of the tear cycles. After repetition for 35 cycles, the PDMS/SiO 2 /PVDF/KH-550 mesh remained superhydrophobic and the water droplets rolled off the tilted coating successfully ( Fig. 10b and c ). Thus the PDMS/SiO 2 /PVDF/KH-550 coating is firmly attached to the copper mesh. The results implied that KH-550 improved the adhesion strength of the coatings. The effect of KH-550 content on the adhesion strength was also tested. When KH-550 is 0.5 wt%, adhesion strength of the prepared mesh is less than that containing 1 wt% KH-550. This result is because of the less bonding strength between particles and PDMS matrix. When KH-550 is 1.5 wt%, the decreased tear stability may be caused by the more PDMS on the coating surface, which would reduce the roughness of the surface. The increased KH-550 content would induce more –NH 2 groups on the surface, which also decreased the superhydrophobicity of the surface. Fig. 10 An adhesive tape peeled off (a) from PDMS/SiO 2 mesh after the first time tear test and (b) from PDMS/SiO 2 /PVDF/KH-550 mesh after 35 times of tear tests, (c) water CAs and SAs of PDMS/SiO 2 /PVDF/KH-550 mesh after repeated tear tests. The sandpaper abrasion tests 5,39 were also carried out on the PDMS/SiO 2 /PVDF/KH-550 mesh. The mesh weighing 100 g was placed face-down to sandpaper (1500 meshes) and moved transversely and longitudinally for 10 cm respectively along the guided line as shown in Fig. 11a–d . This process is defined as one abrasion cycle. The water CAs and SAs after every five abrasion cycles are shown in Fig. 11e . When 1 wt% KH-550 was contained, the water CA decreased gradually to 151.6° and SA became larger than 10° after 30 cycles. But water droplets still could roll off the 14° tilted mesh after 35 cycles, which means that the water droplets are also in the Cassie–Baxter state. Therefore, the mesh has robust resistance to mechanical abrasions. The KH-550 content also influences the abrasion mechanical properties of the meshes, which was consistent with the results in the above tear test. Fig. 11 Sandpaper abrasion tests. (a–d) One cycle of the sandpaper abrasion test. (e) Water CAs and SAs of PDMS/SiO 2 /PVDF/KH-550 mesh after repeated abrasion tests. Surface modification of particles was often adopted to improve particle dispersion and increase their compatibility with polymer matrix. Recently, PDMS-coated silica nanoparticles prepared by a thermal vapor deposition method were used to provide a superhydrophobic characteristic to fabrics and meshes. 28 The prepared PDMS-coated SiO 2 /PDMS steel mesh showed negligible changes of CAs when it was exposed to acidic and basic environments for 40 min. And the superhydrophobic property was maintained when the mesh was impacted by 100 g of sands with velocity about 0.22 m s −1 . In comparison, more strong tolerance to chemical corrosions was observed in this study and excellent mechanical stability was also exhibited. The results is due to the multiple bridge-link interactions provided by the KH-550 couple agent. In a recent experimental report, improved mechanical properties and good adhesion of the PVDF/SiO 2 composites coating on the glass substrate were also found when the substrate was pre-treated with KH-550 molecules. 35 It was also reported that the additional cross-linking points provided by silane agents could improve the mechanical properties of superhydrophobic coatings considerably. 38 These results are consistent well with our findings. The hydrophobic SiO 2 nanoparticles, PVDF powder, PDMS and KH-550 were all commercially available and inexpensive, therefore can be utilized in real industrial applications. 3.4 The application of superhydrophobic mesh 3.4.1 Water droplet manipulation Based on the superhydrophobic mesh, we designed a device that uses negative pressure for the transfer of microdroplets ( Fig. 12a and S4 † ). As shown in Fig. 12b , without the assistant of negative pressure, a 5 μL water droplet can not be transferred on the superhydrophobic mesh. When a negative pressure (about 0.8 kPa) was applied to the back of the mesh, an obvious deformation of the droplet was observed when the syringe needle was slowly receded. Then the droplet was separated from the needle and successfully transferred onto the superhydrophobic mesh ( Fig. 12c ). With this droplet capture device and a quantitative syringe needle, we can rapidly prepare the micro/nanoliter droplets with controllable volumes ranging from 0.3 to 5 μL ( Fig. 12d ). Therefore the micro/nanoliter droplet could be manipulated by giving or repealing the negative pressure. Fig. 12 (a) Illustration of the microdroplet capture device. (b) A droplet of 5 μL water can not be transferred on the superhydrophobic mesh without the assistant of negative pressure. (c) A droplet of 5 μL water was successfully transferred onto the superhydrophobic mesh with the assistant of negative pressure. (d) With the droplet capture device and a quantitative syringe needle, the micro/nanoliter droplets with controllable volumes ranging from 0.3 to 5 μL could be rapidly prepared. With the above device, microdroplet-based microreactions can be achieved conveniently ( Fig. 13 ). A drop of 5 μL magnesium sulfate (1 M) and a drop of 5 μL sodium hydroxide (2 M) were transported to the superhydrophobic mesh, then the drop of magnesium sulfate was grabbed and put to the sodium hydroxide droplet, the white precipitate of magnesium hydroxide occurred later ( Fig. 13b ), demonstrating the potential strategy for the quantitative microdroplets reaction. Fig. 13 (a) A 5 μL droplet can be transferred conveniently by changing the negative pressure. (b) Microdroplet-based microreactions can be achieved easily with the device. 3.4.2 Oil collection The porous structure and superoleophilicity of the mesh surface ensure the infiltration of oils through the mesh, and superhydrophobic mesh have exhibited promising applications in oil spills cleanup. 10 We also tested the capability of the prepared mesh for oil collection. As shown in Fig. 14 , two mini boats with a size of 2.8 cm × 2 cm × 0.5 cm were fabricated from the pristine mesh and superhydrophobic mesh. Without loading, both of the two boats could float freely on water ( Fig. 14b and c ). However, the pristine mesh boat sunk after one day when a load of 0.39 g was added ( Fig. 14d ), while the superhydrophobic mini boat still float freely on water after five days and could hold a load of ∼4 times of the boat weight (the weight of boat and loading are 0.67 and 2.71 g, respectively; Fig. 14e ). As shown in Fig. 14f , there is an air cushion beneath the boat, and the deformation of the water surface occurs under the mini boat, yielding a force to support the weight of the boat and loads. Fig. 14 (a) Mini boats fabricated from the pristine mesh and superhydrophobic mesh. Both the pristine mesh boat (b) and superhydrophobic mesh boat (c) floating freely on water. (d) The pristine mesh boat cannot load weight and sink after a day. (e) The superhydrophobic mesh boat can load weight (2.71 g) and float on water for five days. (f) Overhead view of the superhydrophobic mesh boat with load floating on water. To demonstrate the ability of oil spill cleanup, xylene dyed with red oil (2.0 g) was used as a model oil spill, and the superhydrophobic mesh boat was put on the oil spill. As shown in Fig. 15 , the oil permeated and entered into the boat, and after 5 min, almost all the oil was automatically collected by the mini boat. During the whole process, no external force was needed. About 1.83 g of xylene was collected, which includes 0.17 g xylene absorbed by the mesh and 1.66 g xylene in the boat. The oil recovery was approximated as 91.5%. The collection process is self-driven, and no additional energy is required, displaying an energy-saving and efficient application for oil collection. And if a vacuum pump could be equipped with the boat, the collection could be continuous, unmanned and highly efficient. Fig. 15 The in situ collection of an oil spill by a superhydrophobic mesh boat."
} | 7,344 |
36936312 | PMC10018508 | pmc | 4,642 | {
"abstract": "In recent years,\nconductive hydrogels have received increasing\nattention as wearable electronics due to the electrochemical properties\nof conductive polymers combined with the softness of hydrogels. However,\nconventional hydrogels are complicated to prepare, require high temperature\nor UV radiation to trigger monomer polymerization, and are frozen\nat low temperatures, which seriously hinder the application of flexible\nwearable devices. In this paper, a conductive sensor integrating mechanical\nproperties, adhesion, UV shielding, anti-dehydration, and anti-freeze\nwas prepared based on Ca 2+ -initiated radical polymerization\nat room temperature using the synergy of sodium lignosulfonate, acrylamide\n(AM), and calcium chloride (CaCl 2 ). Metal ions can activate\nammonium persulfate to generate free radicals that allow rapid gelation\nof AM monomers at room temperature without external stimuli. Due to\nionic cross-linking and non-covalent interaction, the hydrogels have\ngood tensile properties (1153% elongation and 168 kPa tensile strength),\nhigh toughness (758 KJ·m –3 ), excellent adhesive\nproperties (48.5 kPa), high ionic conductivity (7.2 mS·cm –1 ), and UV resistance (94.4%). CaCl 2 can\ninhibit ice nucleation, so that the hydrogels have anti-dehydration\nand frost resistance properties and even at −80 °C can\nmaintain flexibility, high conductivity, and adhesion. Assembled into\na flexible sensor, it can sense various large and small movements\nsuch as compression, bending, and talking, which is a flexible sensing\nmaterial with wide application prospects.",
"conclusion": "4 Conclusions In summary, we report a rapid gelation strategy based on metal\nion catalysis. Ca 2+ can activate APS to produce free radicals\nand trigger environmental polymerization of AM monomers. Thanks to\nthe covalent, hydrogen, and ionic coordination bonds of the hydrogel\nnetwork, the resulting hydrogels had good mechanical properties (1153%\nelongation and tensile strength of 168 kPa), high electrical conductivity\n(7.2 mS·cm –1 ), and excellent adhesion to different\nsubstrates given by the catechol group in SL; profiting from the physicochemical\nproperties of CaCl 2 , the hydrogels were endowed with anti-freezing\nand moisturizing properties, which can be maintained even at −80\n°C. In addition, the hydrogels also exhibited excellent UV resistance\n(blocking UV by 95.1%) due to the polyphenolic structure of SL. SL-Ca 2+ /PAM hydrogels were made into flexible skin sensors, which\ncan sense small movements of swallow and speech and large movements\nof limbs such as wrists and knees. They have potential application\nprospects in flexible sensors. In short, this study opens up a broad\nprospect for hydrogel flexible electronics in extreme environments\nsuch as low temperatures and high-altitude areas.",
"introduction": "1 Introduction As a promising material,\nconductive hydrogels have attracted extensive\nattention in the fields of flexible wearable sensor, flexible supercapacitor, 1 , 2 ionic skin, 3 friction nano-generator\ncoating, 4 , 5 and so on. Conductive hydrogels can be divided\ninto electronic conductive hydrogels and ionic conductive hydrogels\naccording to the different transmission media. 3 The former is doped with conductive fillers such as carbon nanotubes, 6 , 7 silver nanowires, 7 , 8 graphenes (GO), 9 − 11 MXenes, 12 , 13 polypyrrole, 14 etc. The latter uses soluble\nmetal salt ions such as Al 3+ , Zn 2+ , Fe 3+ , Na + , Ca 2+ , K + , etc. 15 Efforts have been taken to develop conductive\nhydrogels with good properties, but the poor mechanical strength,\ntedious preparation process, and adhesion to other materials limit\ntheir practical applications. To avoid the effect of using additional\nbinders on the conductivity\nof hydrogels, there is an increasing demand for conductive hydrogels\nwith biocompatible and adhesive properties for flexible sensors, electronic\nskins, and conductive coatings. In recent years, research on adhesive\nhydrogels has focused on biomimetic mussels, 16 polysaccharides, 17 , 18 protein stickiness, 19 and base pair. 20 , 21 Self-adhesive\nproperties of hydrogels are conferred by non-covalent bonding interactions\nbetween specific functional groups in the hydrogel and the surface\nof the object, such as hydrogen bonding, metal–ligand complexes,\nπ–π stacking, hydrophobic interaction, and so on. 22 , 23 Thanks to the superb adhesion of marine mussels to various substrates, 24 the preparation of adhesive hydrogels based\non catechol compounds, 25 , 26 such as polydopamine, lignin,\nand tannins, has become a research hotspot recently. Due to the catechol\ngroups contained in the hydrogels, the covalent and non-covalent interactions\non various substrates confer self-adhesive properties. Lignin, as\nthe second most abundant natural polymer after cellulose, contains\nhydroxyl, methoxy, and carboxyl groups with properties such as antioxidant,\nantibacterial, UV shielding, low degradation, high strength, and high\nyield, 27 − 29 which make it a good choice for the preparation of\nhydrogels as components containing catechol groups. In addition,\nmost hydrogels cannot be used in extreme environments,\nsuch as low temperatures. Low temperatures can make the hydrogels\nfreeze. Even at ambient temperatures, hydrogels dry out and harden\ndue to water evaporation, which severely weakens the properties of\nhydrogels, 30 such as flexibility, electrical\nconductivity, and tensile properties, hindering their long-term usability.\nTherefore, it is a challenge to design a hydrogel that is frost resistant,\nhas moisture retention properties, and can be used for a long time.\nCaCl 2 is widely used to prevent ice formation on roads\nor house construction. Relying on the physicochemical properties of\nCaCl 2 , it lowers the freezing point of water, 31 − 33 thus imparting anti-freeze and anti-dehydration properties to hydrogels. The preparation process of conventional hydrogels requires high\ntemperature or UV initiation, or the introduction of toxic catalysts,\nwhich makes the preparation process tedious. Therefore, it is of great\nsignificance to develop hydrogels with excellent properties and simple\npreparation. It has been demonstrated that metal ions can activate\npersulfate to form free radicals through the electron transfer process 23 , 32 , 34 and can autocatalyze the gelation\nof hydrogels at room temperature or low temperature without the help\nof external stimuli. In this study, SL-Ca 2+ /PAM hydrogels\nwere prepared by a simple one-step method by co-blending SL, AM, and\nCaCl 2 . Ca 2+ activated the APS to produce SO 4 –• through the electron transfer\nprocess, triggering the polymerization of AM monomers within minutes\nat room temperature without UV radiation or high temperature initiation,\nwhile imparting electrical conductivity and antifreeze properties\nto the hydrogel. SL has good water solubility and contains functional\ngroups such as benzene ring, phenolic hydroxyl group, carbonyl group,\nand carboxyl group to give hydrogel adhesion and UV resistance. Furthermore,\nCa 2+ also forms metal ion coordination bonds with carboxyl\ngroups and catechol groups on SL, which act as sacrificial bonds, 35 , 36 effectively dissipating energy and enhancing mechanical properties.\nIn conclusion, we obtained the SL-Ca 2+ /PAM hydrogel, which\ncombines many properties of anti-freezing, moisture retention, good\nmechanical properties, high electrical conductivity, long-term stable\nadhesion, and UV shielding by a simple process. It was assembled into\na flexible sensor that can sense various large and small movements\nsuch as compression, bending, and talking, making it a flexible sensing\nmaterial with wide application prospects.",
"discussion": "3 Results and Discussion 3.1 Design Rationale of the\nSL-Ca 2+ /PAM Hydrogels The preparation process\nof traditional hydrogels\nis complicated due to high temperature heating or long-time UV irradiation\nand other reasons. 38 , 39 In addition, the hydrogels produced\nhave insufficient mechanical strength, weak bond performance, no resistance\nto low temperatures, no UV resistance, and so on, which hinder their\npractical application. In this study, an autocatalytic room temperature\nrapid gelation hydrogel was developed. By the simple and direct one-step\nrapid free radical polymerization of SL, AM, and CaCl 2 ,\nthe hydrogels were prepared with excellent mechanical properties,\nadhesion, low temperature resistance, conductivity, and UV resistance.\nMetal ions were widely used as functional components. Metal ions can\ninitiate free radical polymerization through the electron transfer\nprocess that reduces the activation energy of S 2 O 8 – homolysis to generate free radical SO 4 –• in monomer solutions, 40 , 41 to initiate monomer polymerization. No additional initiation conditions\nwere required, such as high temperature, UV radiation, etc. In addition,\nthe hydroxyl and carboxyl groups in SL can form coordination sites\nwith Ca 2+ , 35 , 36 , 42 giving the hydrogel tunable and enhanced mechanical properties.\nThe coordination reaction of SL with Ca 2+ was analyzed\nusing XPS. 43 , 44 It can be seen from Figure 1 a,b that the content\nof −C=O, C–O, C–OH, and −COOH decreased\nafter the addition of Ca 2+ to the SL solution, due to the\ndynamic ionic cross-linking between Ca 2+ and the −OH\nand −COOH groups on SL. Figure 1 d shows the FTIR spectra of SL, PAM hydrogel, and SL-Ca 2+ /PAM hydrogel. The two peaks at 3444 and 3193 cm –1 in the PAM spectrum represent N–H asymmetrical stretching\nvibration and symmetric stretching vibration. 45 Compared to the native PAM hydrogel, the absorption peak of −CO–NH 2 in the SL-Ca 2+ /PAM hydrogel shifted from 1670\nto 1649 cm –1 , confirming that the long polyacrylamide\nchains were entangled with each other through hydrogen bonds and formed\na strong interaction with SL. 46 The absorption\npeak at 2931 cm –1 in the spectra of SL was attributed\nto the stretching vibration of −CH 3 and −CH 2 . The peak at 2840 cm –1 was −OCH 3 stretching. Obviously, the intensity of these peaks decreased\nor disappeared, indicating that −OCH 3 groups were\nconsumed during SL oxidation. 47 The content\nof SL and CaCl 2 affected the gel speed, as shown in the Figure S5 ; the gelation time became longer as\nthe SL content increased, in contrast to the gel formation time which\nbecame shorter with the increase of CaCl 2 . This is due\nto the fact that the higher the CaCl 2 content, the easier\nit is to activate the APS to produce free radicals, which trigger\nthe monomer polymerization at room temperature, and therefore the\nfaster the gel formation speed. When the SL content increases, the\ngelation speed slows down, which is possibly because the SL would\nscour free radicals to inhibit intermolecular cross-linking. It has\nbeen reported that salt lowers the freezing point of water by preventing\nit from forming a solid phase. 33 , 41 , 48 , 49 When CaCl 2 was added\nto the hydrogels, the spaces between the water molecules were filled\nwith CaCl 2 particles, giving the hydrogels anti-freezing\nproperties. Based on this design mechanism, we have obtained hydrogels\nwith good mechanical properties, adhesion properties, frost resistance,\nand UV resistance, which have broad application prospects in the field\nof wearable sensors. Figure 1 Characterizations of the SL-Ca 2+ /PAM hydrogel.\n(a,b)\nHigh-resolution C 1s XPS for the SL and SL-Ca 2+ suspensions,\nrespectively; (c) SEM images of the freeze-dried SL-Ca 2+ /PAM hydrogel; (d) FTIR spectra of SL, PAM hydrogel, and SL-Ca 2+ /PAM hydrogel, respectively. 3.2 Mechanical Properties of the SL-Ca 2+ /PAM\nHydrogels In order to prolong the service life of wearable\nsensors, hydrogels must have certain extensibility and toughness.\nSL-Ca 2+ /PAM hydrogels can withstand mechanical deformation\nsuch as twisting and knotting to ensure the stability of wearable\nsensors ( Figure 2 i).\nThe mechanical properties of SL-Ca 2+ /PAM hydrogels were\nstudied in detail in Figures 2 and 3 . First, the effect of SL content\non the mechanical properties of hydrogel was studied ( Figure 2 a,b). The SL 0.3wt% hydrogel was elastic and could withstand a tensile strain of up\nto 1153%. The addition of SL caused a decrease in the cross-linking\ndensity of the hydrogel, as evidenced by the consistent decrease in\nthe modulus of elasticity. The hydrogel became softer and the strain\nincreased, with a consequent improvement in the stress. However, too\nmuch SL caused the hydrogel to be too soft, and their stress and strain\nwere reduced. This was because the complex polyphenol structure of\nSL deteriorates its mechanical properties. 40 It can be seen from Figure 2 c,d that the content of Ca 2+ in the system had\na significant impact on the tensile properties of hydrogels. With\nthe increase of Ca 2+ , the cross-linking density increased\nand the tensile strain and elongation at break increased. But too\nmuch Ca 2+ lead to excessive cross-linking and made the\nhydrogel brittle. Thus, both stress and strain again decreased. Figure S1a,b investigates the effects of monomer\ncontent and cross-linker dosage on the tensile properties of hydrogels.\nTo sum up, when SL, AM, MBA, and Ca 2+ were 0.3 wt %, 20\nwt %, 0.1 wt % ( MBA/AM) and 40 wt %, respectively, the breaking elongation\nand tensile stress were better, which was 1153% of breaking elongation\nand 168 kPa of breaking stress, and its toughness was also as high\nas 758 KJ·m –3 . It is much higher than those\nof most of the lignin-based hydrogels reported in the literatures\n( Table S5 ). In order to better evaluate\nthe properties of hydrogels, this formula hydrogel was selected as\na typical sample for subsequent testing (the other tests were the\nsame sample). Dissipation capacity 50 is\na common standard to measure the durability of flexible sensors. Figure 2 e–h shows\nthe cyclic tensile test of hydrogels. When the maximum strain increased\nfrom 100 to 500%, an obvious hysteresis loop appeared ( Figure 2 e), indicating that the destruction\nand reconstruction of the hydrogen bond network in the hydrogel effectively\ndissipated energy. 19 , 23 Five consecutive load–unload\ncycles were performed at a constant 500% strain ( Figure 2 g). It was worth noting that\nthe energy dissipated for the first time was large, and the energy\ndissipated for the next four times was almost stable, which reflected\nthe excellent fatigue resistance of hydrogels. The same cyclic phenomenon\nwas also reflected in compression. When the hydrogel was compressed\nto 90% strain, it can return to its original shape without obvious\ndamage ( Figure 3 a),\nwhich reflected its good toughness and mechanical strength. Under\n70% compression strain, there was a relatively large hysteresis loop\nfor the first time. In the last four compression cycles, the hysteresis\nloops almost overlap ( Figure 3 c,d). The SEM image in Figure 1 c confirmed the interwoven porous structure of SL-Ca 2+ /PAM hydrogels, which dissipated energy through breaking\nand reforming dynamic hydrogen bonds and coordination bonds. 41 Figure S2 shows the\nuniform distribution of C, O, N, and Ca elements in hydrogels. Figure 2 Tensile performance\nof SL-Ca 2+ /PAM hydrogel. (a) Stress–strain\ncurves of the hydrogels containing different amounts of SL; (b) corresponding\nelastic modulus and toughness of (a); (c) stress–strain curves\nof the hydrogels containing different amounts of Ca 2+ ;\n(d) corresponding elastic modulus and toughness of (b); (e) cyclic\ntensile curves of the SL-Ca 2+ /PAM hydrogels with the predetermined\nstrain increasing from 100 to 500%; (f) corresponding stress and dissipated\nenergy of (e); (g) cyclic loading–unloading curves of the SL-Ca 2+ /PAM hydrogels at 500% strain; (h) corresponding dissipated\nenergy of (g); (i) deformations of the hydrogels, including twist\nand knot; (j) images of the hydrogels in tensile test. Figure 3 Compression performance of the SL-Ca 2+ /PAM hydrogel.\n(a) Cyclic compression curves of the SL-Ca 2+ /PAM hydrogel\nwith the predetermined strain increasing from 50 to 90%; (b) corresponding\nstress and dissipated energy of (a); (c) cyclic loading–unloading\ncurves of the SL-Ca 2+ /PAM hydrogel at 70% strain; (d) corresponding\ndissipated energy of (c); (e) images of the hydrogels in compression\ntest. 3.3 Self-Adhesive\nProperties of the SL-Ca 2+ /PAM Hydrogel The adhesive\nproperty of hydrogels\nis an important factor for wearable sensors. Mussel 16 , 51 , 52 is a natural marine organism, which can\nfirmly adhere to the surface of many different materials in the sea.\nSL contains catechol groups and has mussel-like adhesive ability.\nSL-Ca 2+ /PAM hydrogels can adhere to a variety of substrates\nsuch as glass, plastic, rubber, paper, wood, metal, and polydimethylsiloxane\n( Figure 4 a). From Figure 4 b,c, it can be seen\nthat the hydrogels can adhere to the skin tightly, and there is no\nresidue after peeling. The main reasons for the adhesive property\nof hydrogels were the non-covalent bond interaction between specific\nfunctional groups in hydrogels and the surface of objects, such as\nhydrogen bond, metal–ligand complex, π–π\nstacking, hydrophobic interaction, and so on ( Figure 4 f). The adhesion test device is shown in Figure S3c . The influence of SL content and Ca 2+ content on adhesion strength is investigated in Figure S3a,b . It was obvious that the adhesion\nstrength improved with the increase of SL content, which was because\nthe enhanced SL content lead to the increase of catechol groups, the\ndecrease in cross-linking density, and the enhancement of hydrogen\nbond formation with other substrates. However, with the increase of\nCa 2+ content, the adhesive strength decreased, because\nCa 2+ cross-linking lead to a reduction of the catechol\ngroups. 53 Meanwhile, Ca 2+ formed\nionic coordination with −COOH, consuming the −COOH content,\nleading to the weakening of the interaction with the substrate surface.\nFurthermore, the increased cross-linking network inhibited the movement\nof the polymer chain at the adhesive interface. Figure 4 d investigates the adhesion of the SL-Ca 2+ /PAM hydrogel to different substrates. It can be seen that\nthe adhesion strength of the four materials followed the order: wood\n> glass > stainless steel > PDMS. The adhesion of SL-Ca 2+ /PAM hydrogel to wood was 50.6 kPa, and its high adhesion\nstrength\ncould be attributed to the rough porous structure and polyhydroxy\nstructure of wood, which was conducive to the infiltration of hydrogel\nand hydrogen bond formation. Figure S6a shows a comparison of the adhesion of lignin-based hydrogels to\nglass as reported in the literatures, which shows SL-Ca 2+ /PAM hydrogel’s excellent adhesion ability. The SL-Ca 2+ /PAM hydrogel also showed good repeatability and stability.\nAfter 5 consecutive peeling on different substrates, the hydrogel\nstill maintained high adhesion ( Figure 4 e). In summary, the excellent adhesion properties of\nthe hydrogel ensure the effectiveness of the device assembly process\nand the accuracy of human motion signal acquisition. Figure 4 Adhesiveness of the SL-Ca 2+ /PAM hydrogel. (a) Photographs\nof SL-Ca 2+ /PAM hydrogels adhered to various substrates\n(glass, plastic, rubber, paper, wood, and metal); (b) Hydrogel adhering\nto human skin; (c) Hydrogel detaching from human skin; (d) adhesion\nstrength of the hydrogels to various substrates; (e) repeated adhesion\nstrength of the hydrogels to various substrates; (f) adhesion mechanism\nbetween the hydrogel and various substrates. 3.4 Electromechanical Properties of the SL-Ca 2+ /PAM Hydrogels SL contained Na + , which\nalso made the hydrogel conductive. However, due to its low content\nand the addition of more CaCl 2 , it was mainly the addition\nof CaCl 2 that caused the hydrogel to be conductive. 54 − 56 Therefore, the SL-Ca 2+ /PAM hydrogel had high electrical\nconductivity, which can be glued to the human skin as a flexible sensor\nto monitor human movement. As shown in Figure 5 a, the small bulb was connected to the hydrogel\nto form a closed circuit. It can be seen that under the condition\nof no strain, the small bulb emitted bright light with a halo. When\nthe hydrogel was stretched to 400% strain, the brightness of the small\nbulb dimmed, which was caused by the narrowing of the ion channel\nby stretching, and the resistance was increased. With the increase\nof Ca 2+ concentration from 30 to 45 wt %, the conductivity\ndecreased from 11.6 to 6.8 mS·cm –1 ( Figure 5 b). This could be\nbecause with the increase of Ca 2+ , it formed more coordination\nwith −COOH, and the cross-linking density increased, leading\nthe ion channel to narrow. 40 When the Ca 2+ content reached 50 wt %, the conductivity increased slightly.\nSensitivity (GF) is an important index of the sensor, defined as the\nratio of the change of relative resistance to the applied strain.\nIn Figure 5 c, GF is\n1.31 in a wide strain range of 0–500%, and there is a highly\nlinear relationship between the relative resistance and strain ( R 2 = 0.995). Stability and repeatability are\nalso critical for sensors. Figure 5 d,e shows the curves stretched five times under small\nstrain (10–50%) and large strain (100–500%), and no\nsignificant deviation was observed in the resistance change rate,\nindicating that the hydrogel sensor had repeatability, stability,\nand a wide detection range. In order to observe the SL-Ca 2+ /PAM hydrogel as a sensor for monitoring various human deformations,\nthe hydrogel was cut into thin slices and adhered to different parts\nof the human body. The relative resistance curves can be observed\nas the sensor responds to motion in different parts of the body ( Figure 5 f–l). It can\ndetect human movement in real time. In addition to detecting large\nmovements, hydrogel sensors were sensitive enough to detect small\nmovements. As shown in Figure 5 f,g, when the throat made a swallowing motion and a sound,\nit could be seen that the change rate of the relative resistance reacted\nregularly with the action, which further indicated that the hydrogel\nsensor had good electrical stability and high sensitivity. The hydrogel\nsensor was also suitable for compressive strain. Figure S4a shows the electrical signals generated by pressing\nthe cylindrical hydrogel, and Figure S4b shows the electrical signals generated by sticking the hydrogel\nto the foot and walking slowly. Therefore, SL-Ca 2+ /PAM\nhydrogels have the characteristics of good stability, high sensitivity,\nand wide sensing range. Figure 5 Sensing performance of the SL-Ca 2+ /PAM hydrogel. (a)\nLuminance variation of the hydrogel at different elongations; (b)\nconductivity of the hydrogel with different calcium ion concentrations;\n(c) GF of the hydrogel in 500% strain range; (d) the relative resistance\nof the hydrogel varies from 10 to 50%; (e) the relative resistance\nof the hydrogel varies from 100 to 500%; detection of motions of (f)\npronunciation of “Hello”; (g) swallowing; (h) finger;\n(i) neck nodding; (j) wrist; (k) elbow; and (l) knee. 3.5 Anti-freezing and Anti-dehydration Properties\nof the SL-Ca 2+ /PAM Hydrogels Conventional hydrogels\nfreeze at low temperatures or dehydrate at room temperature, which\nseverely limit their applications as sensors. In this experiment,\nthe hydrogel was given anti-freezing and anti-dehydration properties\nby adding CaCl 2 , which can lower the freezing point the\nof aqueous phase, weaken the hydrogen bond inside the water molecules,\ndestroy the aggregation of water molecules, and effectively hinder\nthe formation of ice crystals in the hydrogel. Figure 6 a shows the comparison of the SL-Ca 2+ /PAM hydrogel, SL/PAM hydrogel, and PAM hydrogel placed at −20\n°C for 24 h. It was clear that the SL/PAM hydrogel and PAM hydrogel\nwithout CaCl 2 were frozen solid, becoming both opaque and\nhard. In contrast, the SL-Ca 2+ /PAM hydrogel still remained\nintact, soft, and tough. To study the low-temperature resistance of\nthe SL-Ca 2+ /PAM hydrogel, the shape was observed after\nbeing placed in an ultra-low temperature refrigerator at −80\n°C for 6, 12, 18, and 24 h. As can be seen from Figure 6 b, there was no change inside\nthe hydrogel when it was left at −80 °C for 6 h, and few\nice crystals were formed inside the hydrogel when it was left for\n12 h. Indeed, even after 24 h, the hydrogel can also be pressed down\ncompletely, showing excellent anti-freezing property. In order to\naccurately investigate the finite freezing temperature of the hydrogel,\nDSC 57 , 58 was carried out from −80 °C\nto 50 °C. As shown in Figure 6 c, the SL/PAM hydrogel had a sharp endothermic peak\nand a wide endothermic peak at −1.2 and −15.1 °C,\ncorresponding to the melting point of free water and bound water,\nrespectively. Notably, the SL-Ca 2+ /PAM hydrogel did not\nappear endothermic peak, indicating that it did not produce ice crystals\nin the test temperature range. It is difficult for other anti-freezing\ngels to reach ( Figure S6b ). To better evaluate\nthe application potential of hydrogels, the tensile properties, adhesion\nproperties, and electrical conductivity of hydrogels were investigated\nat 0, −20, and −80 °C ( Figure 6 d–f). The tensile stress–strain\ncurves illustrated that the mechanical properties of the hydrogels\ndecreased at low temperatures but still maintained 87.5 kPa tensile\nstrength and 775% stretching strain. This confirmed the outstanding\nflexibility of hydrogels at low temperatures. This was an unobvious\nchange in the adhesion of the hydrogel to the glass sheet in cold\nconditions and still reached 43.7 kPa, which meets the application\nof hydrogel electronics. Although the conductivity decreased with\nthe reduced temperature, an ultra-high ionic conductivity of 3.6 mS·cm –1 was also obtained at −80 °C, which ensured\nthe feasibility of the SL-Ca 2+ /PAM hydrogel application\nat low temperatures. Figure 6 Anti-freezing and anti-dehydration properties of the SL-Ca 2+ /PAM hydrogel; (a) comparison of PAM, SL/PAM, and SL-Ca 2+ /PAM hydrogels before and after putting into −20 °C\nfor 24 h; (b) comparison of SL-Ca 2+ /PAM hydrogels placed\nat −80 °C at different times; (c) DSC curves of SL/PAM\nand SL-Ca 2+ /PAM hydrogels; (d) tensile stress–strain\ncurves of SL-Ca 2+ /PAM hydrogels at different temperatures;\n(e) adhesion strength of SL-Ca 2+ /PAM hydrogels to glass\nat different temperatures; (f) conductivity of SL-Ca 2+ /PAM\nhydrogels at different temperatures; (g) comparison of the initial\nshape and morphology of PAM, SL/PAM, and SL-Ca 2+ /PAM hydrogels\nafter 30 days at room temperature; (h) relative changes of PAM, SL/PAM,\nand SL-Ca 2+ /PAM hydrogels with time at room temperature. The dehydration of hydrogels is another challenge\nfor flexible\nsensors. The SL-Ca 2+ /PAM, SL/PAM, and PAM hydrogels were\nstored at room temperature of about 30 °C and relative humidity\nof about 80% for 30 days to investigate the water retention capacity\nof the hydrogels. As shown in Figure 6 g, the SL/PAM hydrogels and PAM hydrogels were severely\ndehydrated and became particularly shriveled, while SL-Ca 2+ /PAM hydrogels had little change after 30 days. The PAM hydrogel\ndropped to 65% of its initial weight, the SL/PAM hydrogel could only\nmaintain 63% of its original mass, compared to the SL-Ca 2+ /PAM hydrogel, which was still 94% of its weight ( Figure 6 h). This phenomenon can be\nattributed to the fact that the vapor pressure inside the hydrogel\nwith CaCl 2 is lower than the ambient air pressure and that\nCaCl 2 has the ability to absorb water, thus making the\nhydrogel resistant to dehydration. In short, the anti-freezing and\nanti-dehydration ability of SL-Ca 2+ /PAM hydrogel guarantees\ntheir use in extreme conditions. 3.6 UV-Shielding\nPerformance of the SL-Ca 2+ /PAM Hydrogels Due to\nthe polyphenol structure of\nSL, the hydrogels have UV shielding properties. In Figure 7 a, at a wavelength of 365 nm,\nthe transmittance of the PAM hydrogel was about 70.7%, and after adding\nSL, the transmittance was about 28.5%. 56 When Ca 2+ was added again, the transmittance was only\n3.6%. This was due to the reduced transparency of the hydrogel after\nthe addition of Ca 2+ , which blocked most UV rays. At the\nsame time, the hydrogel was still visible. As shown in Figure 7 b, after being covered by 2\nmm hydrogel, the color and line of the flower can be clearly seen.\nThe combination of UV-blocking and transparency allowed conductive\nhydrogels to significantly expand their applications in flexible sensors. Figure 7 UV shielding\nperformance of the SL-Ca 2+ /PAM hydrogel.\n(a) UV–vis transmission spectrum of the PAM, SL/PAM, and SL-Ca 2+ /PAM hydrogels; (b) high transparency image of the SL-Ca 2+ /PAM hydrogel."
} | 7,160 |
22912712 | PMC3418255 | pmc | 4,643 | {
"abstract": "Background Quorum sensing (QS) in Sinorhizobium meliloti involves at least half a dozen different N -acyl homoserine lactone (AHL) signals. These signals are produced by SinI, the sole AHL synthase in S. meliloti Rm8530. The sinI gene is regulated by two LuxR-type transcriptional regulators, SinR and ExpR. Mutations in sinI , sinR and expR abolish the production of exopolysaccharide II (EPS II). Methodology/Principal Findings This study investigated a new type of coordinated surface spreading of Rm8530 that can be categorized as swarming. Motility assays on semi-solid surfaces revealed that both flagella and EPS II are required for this type of motility. The production of EPS II depends on AHLs produced by SinI. Of these AHLs, only C 16:1 - and 3-oxo-C 16:1 -homoserine lactones (HSLs) stimulated swarming in an ExpR-dependent manner. These two AHLs induced the strongest response in the wggR reporter fusions. WggR is a positive regulator of the EPS II biosynthesis gene expression. The levels of the wggR activation correlated with the extent of swarming. Furthermore, swarming of S. meliloti required the presence of the high molecular weight (HMW) fraction of EPS II. Within swarming colonies, a recombinase-based RIVET reporter in the wggR gene was resolved in 30% of the cells, indicating an enhanced regulation of EPS II production in the subpopulation of cells, which was sufficient to support swarming of the entire colony. Conclusions/Significance Swarming behavior of S. meliloti Rm8530 on semi-solid surfaces is found to be dependent on the functional QS regulatory cascades. Even though multiple AHL signals are produced by the bacterium, only two AHLs species, C 16:1 - and 3-oxo-C 16:1 -HSLs, affected swarming by up-regulating the expression of wggR . While EPS II is produced by Rm8530 as high and low molecular weight fractions, only the HMW EPS II facilitated initial stages of swarming, thus, suggesting a function for this polymer.",
"introduction": "Introduction \n S. meliloti is a soil α-proteobacterium, best known for its ability to establish nitrogen-fixing symbioses with plant hosts belonging to the genera Medicago , Melilotus and Trigonella . Signaling and regulatory events that take place during the early stages of the symbioses are studied and some of these pathways are well defined [1] – [3] . Less studied are the behaviors of this bacterium outside the hosts that influence the symbioses, such as bacterial quorum-sensing signaling in the rhizosphere [4] , biofilm formation [5] , [6] and the movement of the rhizobium on surfaces [7] – [9] . Bacteria use various types of motility to relocate their populations on surfaces in search for a more suitable environmental niche [10] . Types of surface motility include swarming, sliding, gliding, and twitching [11] . It is thought that motility in rhizobia is critical for the establishment of the symbiosis under natural conditions [8] because it helps the bacteria to gain better access to nutrients, expand into new inches and colonize hosts. Swarming motility is a multicellular bacterial movement across a surface. It is driven by rotating flagella and coupled to the production of a mucoid layer that facilitates the movement [11] , [12] . The latter serves as surfactants to reduce tension between the substrate and the bacterial cells at the swarming front [13] or as wetting agents to extract water from the surroundings [11] , [12] . Surfactants and wetting agents can be costly to synthesize, but once released, benefit other cells within the range, thus leading to their characterization as “public goods” [14] . The benefits (as well as costs) and mechanisms of such cooperative behaviors are a subject of research [15] – [17] . The productions of some of those public goods are controlled by quorum sensing (QS) systems [18] , [19] . Beside function as QS signals, AHLs with long N -acyl chains also function as surfactants in Rhizobium etli \n [20] . \n S. meliloti strain Rm8530 uses the AHL synthase SinI to produce at lease seven AHL molecules. They are C 12 -HSL, C 14 -HSL, 3-oxo-C 14 -HSL, C 16 -HSL, 3-oxo-C 16:1 -HSL, C 16:1 -HSL, C 18 -HSL [21] , [22] . At least two LuxR type transcriptional regulators, SinR and ExpR, regulate the expression of sinI \n [23] – [25] . In the presence of SinI AHLs, ExpR controls the accumulation of dozens of transcripts including those encoded by the EPS II gene cluster [24] , [26] , [27] . EPS II, a galactoglucan polymer, is one of the two symbiotically important exopolysaccharides produced by S. meliloti Rm8530 [23] , [28] , [29] . EPS II is secreted in two fractions, high and low molecular weights. A low molecular weight (LMW) EPS II fraction consists of 15–20 disaccharide subunits, it allows the rhizobial nodule invasion in Medicago sativa \n [30] , and it is also critical for the biofilm formation and autoaggregation under laboratory conditions [6] , [31] . The function for the high molecular weight (HMW) EPS II fraction has remained elusive. The EPS II gene cluster contains 22 genes. It is organized into wge (also called expE ), wga ( expA ), wgd ( expD ), wggR ( expG ) and wgcA ( expC ) operons [23] , [32] . WgcA is critical for the initiation of EPS II biosynthesis. Proteins encoded by wge ( expE ), wga ( expA ), and wgd ( expD ) operons are responsible for the polymerization of EPS II [33] . WggR, a member of MarR family of regulators, activates wga , wgd , wggR , wgcA and wgeA operons by interacting with the conserved palindrome motifs in the target promoter regions [34] , [35] . Disruption of wggR prevents the production of EPS II [33] . ExpR stimulates the expressions of wggR \n [24] and other EPS II genes in the presence of SinI AHL and WggR protein [27] . MucR, another regulatory protein, negatively affects the EPS II synthesis by repressing wgaA , wgdA , and wggR genes [8] , [36] , [37] . Disruption of mucR promotes synthesis of the HMW EPS II fraction [30] . In addition, the synthesis of EPS II in S. meliloti is also regulated by phosphate starvation [35] , [37] . In this study, we first describe the characterization of a flagella- and EPS II-dependent surface swarming behavior of S. meliloti Rm8530. We then investgated how AHL signals produced by Rm8530 contribute to the regulation of the bacterial swarming. We found that HMW EPS II is central for the intiation of swarm and that the production of EPS II is controlled by the specific SinI AHLs through stimilating the expression of regulatory gene wggR .",
"discussion": "Discussion Swarming behavior Soto et al. [7] first observed surface swarming in S. meliloti . Their G4 WT strain did not swarm under the conditions tested, but a fadD mutant did. Our results show that S. meliloti Rm8530 strain can swarm on very soft agar (0.4%). The fad mutant swarming cells were hyperflagellated, and they stopped their propagation in swarming colonies [7] . Rm8530 swarming cells were not hyperflagellated ( Figure 2A ), and they did not stop their propagation in swarming colonies ( Figure. S1 ). Social and cooperative behaviors are known to occur in swarming colonies in other bacteria [14] , [15] , and the swarming of Rm8530 was dependent on controlled secretion of EPS II, consistent with involvement of social organization in swarming colonies. Dual motility systems (A-motility and S-motility) in soil bacterium Myxococcus xanthus were reported [48] , and those motilities show different selective advantages on various surfaces [48] . Swarming of Rm8530 studied here is one of a few motility phenomena described in S. meliloti so far [7] , [9] , [27] , and likely to help the bacteria to adapt complex surface environments. Regulation of swarming behavior The need for ExpR/Sin QS system to initiate swarming colony in S. meliloti Rm8530 seems to be restricted to generating AHL signals perceiving the AHL signals, and regulating the EPS II production ( Fig. 4 and Fig. 7 ). The ability of SinI C 16:1 -HSL and oxo-C 16:1 -HSL to stimulate swarming of the sinI mutant and the sinR mutant but not the expR mutant, indicates that swarming colony initiation on the soft surface involves these specific SinI AHLs acting as signals mediated by the ExpR receptor. The inability of C 16:1 -HSL and oxo-C 16:1 -HSL to stimulate swarming of the wggR mutant indicates that these AHLs contribute little to swarm in the absence of WggR. Thus, it appears that sinI made C 16:1 -HSL and 3-oxo-C 16:1 -HSL activate the ExpR receptor and this directly or indirectly enhances expression of wggR and contributes to the regulation of the production of EPSII. This relationship is consistent with earlier transcriptional studies [24] , [26] , [27] , [33] . In addition to enhancing the expression of wggR , C 16:1 -HSL was shown to restore the expression of other EPS II genes at the presence of ExpR [26] . Current data show that positive regulation of EPS II genes by ExpR is dependent on WggR [27] . These explain why an overexpressed wggR is incapable to stimulate swarming in the sinI mutant ( Figure 4B ). The inability of C 14 –HSL and oxo-C 14:1 –HSL to stimulate swarming of the sinI , the sinR , the expR , or the wggR mutants ( Figure 7 ) suggests that those SinI AHLs normally do not act as signals for the initiation of Rm8530 swarming. The inability of C 14 and oxo-C 14 - AHL to stimulate wggR promoter in the presence of ExpR ( Fig. 9B ) strongly supports our finding that the expression of wggR is specifically stimulated by the C 16:1 - and oxo-C 16:1 -HSLs activated ExpR. Interestingly, earlier gel shift assays showed that oxo-C 14:1 –HSL did not enhance the relationship between ExpR and the wggR promoter [27] . The inability of overexpressed sinI and sinR genes ( Figure 4B ) and synthetic AHLs ( Figure 7 ) to stimulate swarming of EPS II mutants indicate that SinI AHLs do not normally act as surfactants and/or wetting agents in Rm8530 swarming cells. The levels of sinI expression in a sinI expR double mutant were not significantly affected by the addition of C 16:1 -HSL or oxo-C 16:1 -HSL added into soft agar ( Figure S2B ). These results support our conclusion that the initiation of swarming depends on the interaction of these AHLs with ExpR. The levels of sinI expression in the sinI expR double mutant were significantly increased by the addition of other AHLs (oxo-C 14 -HSL, C 12 -HSL and C 8 -HSL) ( Fig. S2A ), raising the question of whether or not these signal molecules interact with the SinR protein or other predicted LuxR-like proteins to affect the expression of sinI . The answer to that question remains unknown. EPSII is secreted in two major fractions: HMW and LMW. We have shown that the HMW fraction facilitated the initial stages of swarming and that LMW fraction is not critical for facilitating the initial stages of swarming ( Figure 10 ). This study demonstrates that swarming is a social behavior that can be encouraged or discouraged by changes in QS signaling input and the regulation in gene expression. While the influence of QS on swarming is studied in the aspect of wggR , other regulatory gene products may also contribute to the behavior through their effects on the production of EPS II and motility genes. For example, MucR, a RosR homolog, is a positive regulator of EPS I gene and a negative regulator of EPS II genes including wggR \n [35] . MucR mutants produce HMW EPS II exclusively [30] . ExpR/Sin QS system increases expression of the wggR . WggR derepresses EPS II production at the transcriptional level from MucR, while concurrently elevating the expression of wgeA , resulting in the synthesis of the LMW fraction [33] . The role of MucR in controlling swarming of Rm8530 remains to be investigated."
} | 2,968 |
25120534 | PMC4110509 | pmc | 4,645 | {
"abstract": "The effect of sulfate addition on the stability of, and microbial community behavior in, low-temperature anaerobic expanded granular sludge bed-based bioreactors was investigated at 15°C. Efficient bioreactor performance was observed, with chemical oxygen demand (COD) removal efficiencies of >90%, and a mean SO 2− 4 removal rate of 98.3%. In situ methanogensis appeared unaffected at a COD: SO 2− 4 influent ratio of 8:1, and subsequently of 3:1, and was impacted marginally only when the COD: SO 2− 4 ratio was 1:2. Specific methanogenic activity assays indicated a complex set of interactions between sulfate-reducing bacteria (SRB), methanogens and homoacetogenic bacteria. SO 2− 4 addition resulted in predominantly acetoclastic, rather than hydrogenotrophic, methanogenesis until >600 days of SO 2− 4 -influenced bioreactor operation. Temporal microbial community development was monitored by denaturation gradient gel electrophoresis (DGGE) of 16S rRNA genes. Fluorescence in situ hybridizations (FISH), qPCR and microsensor analysis were combined to investigate the distribution of microbial groups, and particularly SRB and methanogens, along the structure of granular biofilms. qPCR data indicated that sulfidogenic genes were present in methanogenic and sulfidogenic biofilms, indicating the potential for sulfate reduction even in bioreactors not exposed to SO 2− 4 . Although the architecture of methanogenic and sulfidogenic granules was similar, indicating the presence of SRB even in methanogenic systems, FISH with rRNA targets found that the SRB were more abundant in the sulfidogenic biofilms. Methanosaeta species were the predominant, keystone members of the archaeal community, with the complete absence of the Methanosarcina species in the experimental bioreactor by trial conclusion. Microsensor data suggested the ordered distribution of sulfate reduction and sulfide accumulation, even in methanogenic granules.",
"conclusion": "Conclusion COD removal can proceed at 15°C in anaerobic digesters exposed to sulfate. In situ methane production appears impacted only at COD: SO 2− 4 ratios ≤1:2; thus, higher COD: SO 2− 4 ratios would appear to support biogas production in cold anaerobic digesters. Hydrogenotrophic methanogens in low-temperature anaerobic sludge granules were more sensitive to sulfate than acetoclastic methanogens, but complex interactions of SRB, methanogens and homoacetogenic bacteria appear to underpin COD removal by sulfate reduction and methanogenesis. Conflict of interest statement The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.",
"introduction": "Introduction The application of anaerobic digestion (AD) is an efficient approach for the treatment of high-strength organic wastewater (Yu et al., 2005a ). AD does not require costly aeration and is thus considered more sustainable than aerobic systems (Rittmann and McCarty, 2001 ). Moreover, anaerobic systems generate re-usable biogases and significantly less nuisance, excess sludge (Yu et al., 2005a ). Furthermore, low-temperature (>20°C) AD (LtAD) has been demonstrated as a feasible approach for wastewater treatment (e.g., Connaughton et al., 2006 ; Akila and Chandra, 2007 ; Enright et al., 2009 ), allowing for further efficiencies by eliminating the need to heat AD bioreactors, and opening AD to new areas of environmental management, including for the digestion of raw sewage in temperate climates (Lew et al., 2004 ). AD has also been applied in the treatment of sulfate-rich wastewaters. Many industrial processes that use sulfuric acid (e.g., fermentation or seafood processing); or reduced sulfur compounds i.e., sulfide (e.g., in tanneries, kraft pulping), sulfite (e.g., sulfite pulping), thiosulfate (e.g., fixing of photographs) or dithionite (e.g., pulp bleaching) generate sulfate-contaminated wastewaters (Hulshoff Pol et al., 1998 ). In the absence of oxygen, sulfate-reducing bacteria (SRB) use sulfate as electron acceptor in the oxidation of an energy substrate with the production of hydrogen sulfide (H 2 S; Boshoff et al., 2004 ). Sulfate-rich wastewaters stimulate SRB growth, which can out-compete methanogens for substrates (e.g., H 2 , CO 2 and acetate) in anaerobic environments (Kristjanson et al., 1982 ; Schonheit et al., 1982 ), such as in AD bioreactors. Furthermore, SRB consume hydrogen below a minimum threshold for hydrogen metabolism by methanogens (Lovley, 1985 ; Lovley and Ferry, 1985 ). Thus, sulfate reduction can impair methane production in wastewater treatment systems. A particular problem arising from SRB activity is H 2 S production (Koschorreck, 2008 ). H 2 S is a potentially toxic gas, which is an industrial and municipal nuisance due to its flammability, as well as the corrosive effect on steel and concrete owing to sulfuric acid generation. Additionally, there is a negative effect on microbial cells due to the precipitation of essential trace metals as metal sulfides. Moreover—though depending on the charge of the sulfide ion—H 2 S can have a toxic effect on cellular mechanics as neutrally-charged H 2 S can be transported across the cell membrane, thus increasing the potential for toxicity (Tursman and Cork, 1989 ; Moosa and Harrison, 2006 ). The impact of sulfate ions on AD has been investigated using specific methanogenic activity (SMA) assays and toxicity assays. For instance, O'Flaherty et al. ( 1998a , b ) found competition between SRB and methanogens for available substrates, as well as impaired methanogenesis due to sulfide toxicity, which resulted in reduced methane production. In any case, sulfide toxicity is unlikely to be de-coupled from competition between SRB and methanogens, and due to their more favorable growth and thermodynamic properties, SRB are considered to out-compete other anaerobes in the presence of excess sulfate. O'Flaherty and Colleran ( 1999 ), O'Flaherty et al. ( 1999 ), and Pender et al. ( 2004 ) showed that acetoclastic methanogenesis was the most susceptible reaction to H 2 S inhibition. The outcome of the competition is important, as it determines the relative concentrations of biogas sulfide and methane (Hulshoff Pol et al., 1998 ). The chemical oxygen demand (COD): SO 2− 4 ratio in the influent wastewater is also important. For wastewater with a COD: SO 2− 4 ratio of 0.66, there is theoretically sufficient sulfate available to SRB to completely remove the organic matter (Rinzema and Lettinga, 1988 ); however, for lower COD: SO 2 4 ratios, the organic matter is insufficient for complete SO 2− 4 reduction. Similarly, for wastewaters with higher COD: SO 2− 4 ratios, complete removal of organic matter can only be achieved with concomitant methanogenesis and sulfidogenesis (Omil et al., 1997 ). In this study, expanded granular sludge bed (EGSB) bioreactors were used to investigate SRB activity in low-temperature anaerobic digesters. The impact of sulfate contamination on methanogenesis, as well as on community structure, and the distribution and abundance of SRB functional genes, was assessed at different COD: SO 2− 4 ratios.",
"discussion": "Discussion Bioreactor performance Low-temperature AD trials have previously demonstrated the potential of cold bioreactors for waste conversion (Collins et al., 2003 ; Enright et al., 2009 ; McKeown et al., 2012 ), including for the treatment of acidified, industrial wastewater similar to the feedstock used for this study (Nedwell and Reynolds, 1996 ; O'Flaherty and Colleran, 1999 ; O'Flaherty et al., 1999 ; Fukui et al., 2000 ). Similarly, successful COD removal (average, >80% efficiency) was achieved by both EGSB bioreactors in this study at 15°C during the start-up phase and throughout the trial. The impact of methanogenic and SRB activity on COD removal, and the interactions between methanogens and SRB, was apparent from bioreactor performance data. COD removal efficiency was not significantly different in R1 and R2, regardless of the presence of sulfate in the influent wastewater. The presence of sulfide indicated dissimilatory sulfate reduction by a sulfidogenic population. Based on the quality of the biogas produced, methanogenesis in the sulfate-amended bioreactor (R2) appeared to only be impacted during the final period (P5) of the bioreactor trial, when the COD: SO 2− 4 ratio was 1:2. Even then, the biogas methane concentration was reduced by only 10% compared with the periods before sulfate dosing. Although we do not present volumetric in situ methane yield data, and it is possible that methane production was depressed, the biogas quality data shown (Figure 1 ) indicate that methane concentrations were not diluted by sulfidogenic activity. O'Flaherty et al. ( 1998a ) and Pender et al. ( 2004 ) found that in mesophilic bioreactors treating sulfate-rich wastewaters, all of the methane produced originated from acetate, while H 2 was consumed by the SRB. These divergent pathways for acetate and hydrogen utilization can facilitate methanogenic and SRB populations to avoid competitive scavenging for available substrates. Moreover, this strategy also avoids impeding the growth of either population. In situ COD removal efficiency and biogas methane quality data, however, cannot alone be directly used to address questions on methanogenic-SRB competition, or on sulfide toxicity, in bioreactors. SRB activity can impact methanogenesis directly—through competition for available substrates—or indirectly—through toxicity from by-products, such as hydrogen sulfide. Therefore, the investigations using batch incubations, which were assayed under a range of specific and targeted conditions, are valuable to elucidate interactions along the methanogenic pathway. The assays cannot support differentiation between sulfide inhibition and interspecies competition, as these are largely interdependent i.e., sulfide toxicity in this system will arise from competition provided by the SRB; instead the assays are used to assess the competitive pressure on methanogens from SRB, rather than specific toxicity. Impact of sulfate on potential for methanogenic activity The higher SMAs at 37°C than in 15°C assays was expected, owing to the mesophilic origin of the seed biomass. Generally, due to the scarcity of full-scale, low-temperature anaerobic digesters, the use of biomass from mesophilic AD systems to seed new, cold systems is a likely option in most countries, and was thus the approach taken in this experiment. Although the route of methane production in AD bioreactors is usually through acetoclastic methanogenesis (Scully et al., 2006 ; Akila and Chandra, 2007 ; Enright et al., 2009 ), several previous studies have also found biomass in which hydrogenotrophic methanogenesis was dominant (McHugh et al., 2003 ; Enright et al., 2005 ; O'Reilly et al., 2010 ), as was the case with the seed sludge—and in R1 biomass throughout the trial (Table 4 ). The inhibition of hydrogenotrophic methanogenic activity in R1 (control) biomass at 15°C—and in 37°C assays by the conclusion of the trial—indicated the presence of, and competition from, SRB despite the absence of sulfate in R1 influent (Table 4 ). This was supported by DGGE fingerprinting, qPCR and FISH data (Figures 3 , 5 , 6 ). Indeed, it has been observed that in the absence of sulfate, many SRB ferment organic acids and alcohols, producing hydrogen, acetate, and carbon dioxide, and may even rely on hydrogen- and acetate-scavenging methanogens to convert organic compounds to methane (Plugge et al., 2011 ). Furthermore, whilst sulfate reducers can also grow without sulfate, in some cases they grow only in syntrophic association with methanogens or other hydrogen-scavengers. Thus, sulfate reducers may compete with methanogens or grow in syntrophy with methanogens depending on the prevailing environmental conditions (Muyzer and Stams, 2008 ). The dominant methanogens in R1 appeared to be Methanosaeta -like organisms (Figure 6 ), which are acetoclastic and are characterized by high affinity for acetate. Under conditions of low prevailing concentrations of acetate, therefore, Methanosaeta will out-compete acetoclastic methanogens with a lower affinity for acetate, such as Methanosarcina . SRB may have contributed to the maintenance of a low prevailing acetate concentration in R1, such that the dominant methanogen was Methanosaeta . Conversely, in R2, acetoclastic methanogenesis was the dominant route of methane production, at least at 15°C and at least until day 605 (Table 4 ). This may be due to a less active hydrogenotrophic methanogenic population owing to competition for H 2 from SRB, or syntrophic SRB aligning with acetoclastic methanogens resulting in this population shift (Bryant et al., 1967 ; Schink, 1997 ; Stams and Plugge, 2009 ; Plugge et al., 2011 ). Nonetheless, and interestingly, the assays indicated that sulfate impaired acetoclastic methanogenesis in R2 biomass (Table 4 ), but the high activity in sulfate-free assays suggests that the toxicity and/or competition was easily reversed, and supports the evidence from DGGE experiments indicating the persistence of acetoclastic methanogens (Figure 3 , Table 2 ). The findings indicate that acetoclastic methanogenesis was impaired even when the COD: SO 2− 4 ratio in the R2 influent was 8:1. Although increased methanogenic activity was observed on acetate in R2 by day 605 (>300 d after SO 2− 4 introduction to R2 influent) it was still strongly impaired—up to 69%—by SO 2− 4 , indicating continued competition from SRB at the lower COD: SO 2− 4 ratio at that time. The data also identify a rather complex situation in R2: SMA against H 2 in R2 assays was elevated with the addition of SO 2− 4 . This condition is reflective of R2 in situ conditions. This may be due to inhibited homoacetogenic activity, and hence inhibited acetoclastic methanogenic activity, which provides an opportunity for hydrogenotrophic methanogens. For instance, it is widely accepted that H 2 -utilizing SRB out-compete hydrogenotrophic methanogens and homoacetogens because of their lower Km values (higher affinity) (Chaganti et al., 2012 ). This, in turn, indirectly points to a syntrophic SRB lifestyle in collaboration with hydrogenotrophic methanogens, similar to observations from marine sediments (Plugge et al., 2011 ). By the conclusion of the trial, R2 assays (at 15°C) indicated reduced SMA on acetate and increased activity on H 2 , suggesting that the route of methane production had switched to predominantly hydrogenotrophic activity. Despite this, however, the hydrogenotrophic methanogens appeared outcompeted by SRB for H 2 . Intriguingly, SMA on the indirect substrate, propionate, increased when SO 2− 4 was present in assays (at 15 and 37°C), which suggests that propionate oxidation, coupled with SO 2− 4 reduction provided methanogenic substrates, which were otherwise unavailable in the absence of sulfidogenesis. Thus, it appears that non-sulfate-reducing propionate-oxidizers—i.e., obligate hydrogen-producing acetogens—were less abundant or less active in R2 biomass. Sulfate impacts on community structure and population dynamics, but not on the distribution of SRB, in anaerobic sludge granules The microbial communities of R1 and R2 diverged during the course of the trial, indicating that the addition of sulfate to R2 influent impacted community structure. Specifically, for example, Methanosarcina were undetected in R2 by the final sampling day. However, the physical distribution of microbial groups was not obviously different along the structure of the granular biofilms, with SRB clustering around the surface of sludge granules and with archaea located toward the core of the granules (Figure 5 ). During the trial, the abundance of dsrB genes was similar in R1 and R2, further indicating a persistent, background population of SRB even in the bioreactor without sulfate addition. Furthermore, little movement was observed in DGGE profile of the dsrB genes. However, the SRB populations detected by FISH experiments appeared to become more abundant in granules over the course of the trial. DGGE profiles and qPCR assays targeting the dsrB mRNA transcripts would provide greater insight; nonetheless, the FISH assays targeting rRNA from SRB do support the conclusion that, although a similar potential for sulfate reduction was present in R1 and R2 biomass, the active portion of the SRB community was more abundant in R2. Microsensor data supported the findings of FISH experiments, indicated an ordered distribution of sulfate reduction and the accumulation of sulfide in the low-temperature granules, as well as indicating the activity of SRB even in the previously unexposed R1 granules."
} | 4,213 |
36329932 | PMC9583627 | pmc | 4,646 | {
"abstract": "A triboelectric nanogenerator (TENG) provides an effective method to harvest mechanical energy from the environment. The morphology and structure of frictional electrode materials of this type of device affect the output performance significantly. Metal–organic coordination polymers (CPs) with special structure advantages offer a vast pool of materials enabling high performances. Two Co-CPs based on terephthalic acid and 2,5-dihydroxyterephthalic acid ligands, respectively, were used to fabricate TENGs. Detailed electrical characterizations of the TENG devices revealed that the introduction of the substituent groups in the organic ligands leads to the structural changes of CPs, which ultimately leads to significant differences in the output performance.",
"conclusion": "Conclusions In conclusion, two Co-CPs composed of an identical metal center and slightly different organic ligands were selected as electrode materials for the fabrication of TENG devices. The output performance of the TENGs was evaluated based on the capability of the friction electrode materials to gain or lose electrons. The BDC-TENG with a relatively strong tendency to lose electrons showed superior output performance, further confirming that the performance of the TENG electrode materials ( i.e. , their capability of losing and gaining electrons) affects the output performance of the TENG. This study shed light on the effect of different organic ligands (microstructural changes) on the output performance of TENGs when CPs were used as their electrode materials. Furthermore, it provides a simple method for designing new electrode materials of TENGs to improve their output performance in the future.",
"introduction": "Introduction Energy is one of the pillars of modern society, and the energy crises around the world have prompted the search for smart technologies to develop clean and sustainable energy sources. 1–4 A large amount of mechanical energy exists in the environment, such as tidal energy, 5 energy from falling raindrops, 6 energy generated during human movement, 7 and water wave energy, 8 which raises an important question in the field of energy harvesting to address the collection of mechanical energy in the environment. In 2012, triboelectric nanogenerators (TENGs) were designed by Prof. Wang for energy harvesting that exhibited advantages including simple structure, low cost, high energy conversion efficiency, and environmental friendliness, 9 which had far-ranging practical applications, such as cathodic corrosion prevention on metallic external electrodes, 10–13 clean-air, 14 portable power supply, 15 and self-powered sensors. 16,17 The output performance of TENGs is influenced by multiple factors, including device structure, frictional movement mode, and frictional electrode materials. 18–23 Previous reports have mentioned a variety of materials that can be used as frictional electrode materials: animal hair, 24 plant leaves, 25 hydrogels, 26 and coordination polymers (CPs). 19,27–29 CPs are an attractive kind of porous crystalline materials formed by self-assembly of metal ions and organic ligands through coordination bonds. 12,30 Owing to their large specific surface area, controllable regular spatial structure, and wide variety of building blocks, CPs have been rapidly developed over the last few decades. 18,31,32 Many applications of CPs have been reported. For example, MOF-5 for methane storage, 33 separation of CO 2 /CH 4 mixtures with the MIL-53(Al) metal–organic framework (MOF), 34 selective gas adsorption and separation in MOFs, 35 and catalysis and metal encapsulation using amine grafted MOFs on their coordinatively unsaturated metal centers. 36 Recently, MOFs or CPs have been explored as electrode materials of TENGs. 11,12,27–29 The use of CPs as friction electrode materials for TENGs has broadened not only the application area of CP materials, but also the source range of TENG electrode materials. The diverse metal centers and organic ligands in CPs result in a variety of materials, which have different effects on the output performance of TENGs. In this paper, we report two types of CPs formed from an identical metal center Co and different ligands, which were used as frictional electrode materials to assemble TENG devices. Friction nanogenerator is used to generate AC potential under the action of external driving force, which involves self-polarization; there is no additional power input in the whole process. The effect of organic ligand-modulated CPs as electrode materials on the output performance of TENGs was investigated. The electrochemical characterization showed that TENGs with higher dielectric constants achieved greater output performance, thus providing a search strategy for discovering suitable frictional electrode materials for TENGs.",
"discussion": "Results and discussion Compounds 1 and 2 were constructed with the same metal center (Co 2+ ) in combination with structurally different ligands (terephthalic acid and 2,5-dihydroxyterephthalic acid). The corresponding crystalline structures of compounds 1 and 2 are shown in Fig. 1 and 2 . Compound 1 crystalizes in triclinic space group P 1̄, in which the central Co ion is in a six-coordinate octahedral geometry ( Fig. 1a ), coordinating with six O atoms from four carboxyl groups of the ligand and two water molecules. Each ligand is connected to four Co ions through four O atoms from two carboxyl groups ( Fig. 1b ), generating a three-dimensional (3D) reticular structure with rhombic cavities ( Fig. 1e ). In the crystal structure of compound 2, the central Co ion is coordinated by six O atoms in a twisted octahedral fashion. And it can be seen from Fig. 2 that in addition to the carboxyl group, the hydroxyl group on the ligand has been also deprotonated during synthesis. In fact, all the O atoms in the ligand are involved in the coordination with Co. The central metal Co coordinates with six O atoms, five of which come from the ligand and the sixth from a water molecule. And the five oxygens connected to the metal center come from four ligands, two O atoms from the hydroxyl group in the two ligands, one O atom from the hydroxyl group in the ligand, and two O atoms from hydroxyl and carboxyl group in the same ligands ( Fig. 2a ). The coordination pattern of the organic ligands of compound 2 is one ligand connected to eight metal-center Co ions, of which six Co 2+ are monodentate coordinated with carboxyl hydroxyl groups, and two Co 2+ are chelated with carboxyl hydroxyl groups ( Fig. 2b ). Fig. 2c shows the planar structure of compound 2 along the c -axis, and Fig. 2d shows the 3D structure of compound 2. From Fig. 2c and d , it is clear that compound 2 is a CP with honeycomb topology. Fig. 1 (a) Coordination pattern of the metal center Co 2+ ion; (b) coordination pattern of the organic ligand terephthalic acid; (c) coordination chain fragment of the Co( ii ) octahedron; (d) planar structure of compound 1; (e) 3D reticular structure of compound 1. C, grey; O, red; Co, pink; all H atoms omitted for clarity. Fig. 2 (a) Coordination pattern of the metal center Co 2+ ion; (b) coordination pattern of the organic ligand 2,5-dihydroxyterephthalic acid; (c) planar structure of compound 2 along the c -axis; and (d) 3D structure of compound 2. C, grey; O, red; Co, pink; all H atoms are omitted in the structure diagram. The PXRD profile results for compounds 1 and 2 are shown in Fig. S1a and S1b, † which are consistent with previous reports, 37,38 verifying their phase purity. The peaks in the regions of 1457–1577 cm −1 in the FT-IR spectra of compounds 1 and 2 (Fig. S1c and S1d † ) could be assigned to the benzene ring. Fig. 3 shows the XPS spectra of the synthesized compounds 1 and 2, which were analyzed to determine the elemental composition and valence states on the surface of the ligand polymers. Fig. 3a shows the full XPS spectra of compounds 1 and 2, including C, O, and Co elements. Fig. 3b presents the energy level analysis of 2p in Co, with two main fitted peaks at 781.83 eV and 787.53 eV, and stable peaks at 786.1 eV and 803 eV, classified as Co 2p 1/2 and Co 2p 3/2 energy levels, respectively. Fig. 3 (a) Full XPS spectra of compounds 1 and 2; (b) XPS spectra of Co in compounds 1 and 2. There are many factors that affect the output performance of TENGs. The surface roughness, dielectric properties, and area of electrode material play a critical role for the output performance of TENGs. 18,39–43 Therefore, we fully ground the block crystals of 1 and 2 into powder and evenly coated on copper tape to improve the output performance of TENGs. Compounds 1 and 2 were used as friction electrode materials to prepare BDC-TENG and OH-BDC-TENG, respectively. The dielectric constants of compounds 1 and 2 were measured to determine the magnitude of their polarities (Fig. S2 † ). Test results clearly showed that the dielectric constant of compound 1 is higher than that of compound 2, implying a better output performance of BDC-TENG relative to that of OH-BDC-TENG. In addition to the properties of the electrode materials, the tightness of contact between the electrode materials also affects the output performance. Thus, we took the same amount of compounds 1 and 2, ground them into powder in different mortars, and then applied them uniformly as a coating on the copper tape to improve the output performance of TENGs. \n Fig. 4 shows the 3D structure of the CP-TENGs, which adopted a conventional operation mode with vertical contact and separation, where CPs and PVDF with different electronegativity were used as the friction materials on the electrode layers. Simulation of the mechanical energy in a real environment was achieved through the cyclic motion of a linear motor to apply external force to the TENGs. The working principle of TENGs is based on the coupling of triboelectrification and electrostatic induction as shown in Fig. 4 . According to the electronegativity difference, PVDF easily gains electrons, whereas CPs can donate electrons because of their conjugated structures. During the periodic contact and separation process, an electric current can flow in a reverse direction due to the existence of potential difference. Fig. 4 The working principle of a TENG. Under identical conditions, the I sc and V o values of the BDC- and OH-BDC-TENGs at 5 Hz were determined to be 76.47 μA and 522.97 V, and 40.67 μA and 388.74 V, respectively ( Fig. 5a and b ). Clearly, the BDC-TENG had higher I sc and V o compared to the OH-BDC-TENG. The charge density σ is the integral of current with respect to time, which measures charge transfer and serves as one of the parameters used to evaluate the performance of TENG materials. The σ values for the BDC- and OH-BDC-TENG were calculated as 143.48 μC m −2 and 74.78 μC m −2 , respectively ( Fig. 5c ), with the BDC-TENG clearly showing a relatively large charge density. As expected, the BDC-TENG exhibited a better output performance. The results show that the output performance of the TENGs prepared using Co-CPs was in the following order: BDC-TENG > OH-BDC-TENG. This further confirmed that their output performance was closely related to the variation in the electrode material structures. Due to the difference of chemical structure, the dielectric constant is different. In general, the greater the polarity difference of friction materials, the greater the dielectric constant, and one is more likely to gain electrons and the other is more likely to lose electrons. During the test, more charges will be transferred during the contact electrification process, resulting in more charge output by the TENG, the greater the potential difference between the two electrodes, the larger the TENG output. Fig. 5 (a) I sc , (b) V o , and (c) σ for TENGs made from compounds 1 and 2. (d) I sc of TENGs made from compounds 1 and 2 after being connected to a rectifier bridge. The stability and durability of BDC-TENG were tested at 5 Hz to evaluate the possibility of its practical application. After 35 000 cycles, its I sc and V o values remained stable, showing no significant changes ( Fig. 6c and d ). In addition, I sc and V o of the BDC-TENG were measured under different frequencies (1 Hz, 2 Hz, 4 Hz, 6 Hz, and 8 Hz) to investigate the effect of the test frequency on the output performance ( Fig. 6a and b ). The results indicated that the values of I sc and V o were highly related to the test frequency, both of which trended upwards with increasing test frequency, reaching maximum values of 121.52 μA and 608.51 V at 8 Hz, respectively. Additionally, the output performance of this work is equivalent to or higher than that reported for coordination compound TENG (Fig. S6 † ). Fig. 6 (a) I sc and (b) V o of BDC-TENG at different test frequencies. (c) I sc and (d) V o for BDC-TENG after 35 000 cycles. The surface morphology of compounds 1 and 2 and PVDF before and after the test was observed by FE-SEM and the elemental distribution of compounds 1 and 2 (Fig. S3–S5 † ) was investigated using EDS and the mapping software that came with the FE-SEM instrument, which further demonstrate the stability of the friction electrode materials. There was almost no change in the morphology of compounds 1 and 2, indicating that compounds 1 and 2 were relatively stable. \n Fig. 7a displays the power density and current under various load resistances, with the instantaneous power peaking at 2635.38 mW m −2 under a load resistance of 5 MΩ. Fig. 7b displays three cycles of charging and discharging of a capacitor with a capacity of 100 μF at 6 V. Fig. 7 (a) Power density and I sc of BDC-TENG under different loads. (b) Charging–discharging cycles of a 100 μF capacitor by BDC-TENG."
} | 3,451 |
35243445 | PMC8861581 | pmc | 4,647 | {
"abstract": "Highlights • An easy set-up of the co-cultures from 2 different microorganisms (filamentous fungi and bacteria) from different microbial domains resulting into a greater and more diverse metabolic and lignocellulolytic content. • An over expression of several key enzymatic lignocellulolytic activities is observed during the co-coculture due to elicitation. • An elicitation of some specific biosynthetic cluster genes is observed due to the activation of those the complexity of the carbon compounds present in the lignocellulose. • An elicitation of some specific biosynthetic cluster genes is observed only during the co-culture experiment. • A specific microbial crosstalk and interaction exists at the species level between the 3 Streptomyces and the fungi leading to a specific of lignocellulolytic enzyme and secondary metabolite production.",
"introduction": "1 Introduction Biorefining is dedicated to produce energy, molecules and materials from renewable feedstocks ( Kamm and Kamm, 2007 )). The use of lignocellulosic biomass such as agricultural and agro-industrial co-products does not compete with plants dedicated for food. The use of cheap and abundant co-products as substrates for fermentation allows to reduce the costs of the bioprocesses ( Sansinenea and Ortiz, 2011 ). Microbial cultures using lignocellulosic biomass as carbon sources are extensively studied for the production of enzymes and of various molecules of interest ( Mäkelä et al., 2014 ; López-Mondéjar et al., 2019 ). \"Top-down\" approaches, based on the study of natural ecosystems, where the degradation of lignocellulose is significant such as forest soils, compost, have enabled advances in the description and the multiple functions played by multiple microorganisms ( Jurado et al., 2014 ; López-Mondéjar et al., 2016 ). On the opposite, “bottom-up” approaches consisted in the implementation of co-cultures by a limited number of microorganisms. The use of synthetic microbial co-cultures has also been implemented in order to mimic natural processes and distribute the different functions to specific populations. These microbial co-cultures based on mutualistic relationships, besides the ability to perform different functions, have several advantages such as: the ability to prevent a nutritional deficiency due to the diversity of metabolic pathways present, the ability to exchange metabolites within community ( LaPara et al., 2002 ; Zuroff et al., 2013 ); stability and robustness within the microbial community ( Zuroff et al.; 2013 ). The proof of concept in co-culture has been demonstrated through various applications such as human health ( Steidler, 2004 ) or pollution control ( Chen et al., 2014 ) and has made it possible to increase the services provided compared to mono-cultures. The use of co-cultures increases the production of enzymes. Several explanations exist: (1) a greater diversity of enzymes is produced allowing a more efficient and more complete degradation of the substrate, this is the enzymatic synergy ( Taha et al., 2015 ), (2) a chemical interaction (interaction molecules, elicitors, secondary metabolites), as well as the sharing of metabolic pathways allows emulation of microbial development, this is the growth synergy ( Ren et al., 2015 ). In this study, co-cultures were performed between one fungal strain (belonging to the species Aspergillus niger ) and either one of three different actinobacteria (belonging to the Streptomyces genus; S. avermitillis ATCC 31,267, S. coelicolor A3(2) and S. griseorubens DSM 40,160). Naturally, the fungi play a role in the carbon cycle through extracellular hydrolytic and oxidative enzymes. This fungus is used in the fermentation industry to produce citric acid, cellulolytic enzymes by fermentation in solid media ( Pensupa et al., 2013 ), or even enzymatic cocktails to pre-treat lignocellulosic biomass ( Wang et al., 2019 ). Enzyme cocktails are easily recovered, since the fungus will secrete the enzymes in the extracellular environment. A.niger enzymes panel includes cellulases, endo/exoglucanases, β -glucosidases, xylanases ( Pensupa et al., 2013 ). Actinobacteria are Gram-positive bacteria characterized by a genome with high G + C ratio and are numerous and widely distributed group of soil microbes, constituting to about 10 – 50% of the soil microflora community and important producers of diversified secondary metabolites which can have several functions such as antifungal and antibacterial activities ( Challis, 2014 ). Streptomyces genus has produced approximately 67% of the natural antibiotics among the actinobacteria and could produce approximately around 7600 bioactive compounds ( Olanrewaju and Babalola, 2019 ). The genus Streptomyces , very widespread in soils, is a major player in the degradation of organic matter and lignocellulose ( Lu et al., 2014 ). This microbial genus has a large enzymatic arsenal encoding carbohydrate esterases, polysaccharide lyases, glycoside hydrolases and enzymes with auxiliary activities ( Book et al., 2014 ; Montella et al., 2017 ). They act in the catabolism of complex molecules and substances like lignocellulose, xylan, cellulose, and lignin, which are important in soil organic matter catabolism ( Malherbe and Cloete, 2002 ). The ability of Streptomyces to deconstruct lignocellulose has been studied in several ecosystems such as intestinal tracts of insects ( Book et al., 2014 ), grassland ( Yeager et al., 2017 ) and moreover, those bacteria can also act as promotor and increase rice-straw composting by other microorganisms ( Feng et al., 2020 ) . Due to their origin (mainly superficial layers of soil), Aspergillus and Streptomyces can present several interactions together. Indeed, the strain Streptomyces leeuwenhoekii C58 has been used to trigger and elicit secondary metabolites production of Aspergillus fumigatus MR2012 ( Wakefield et al., 2017 ). In the present study, we report on the setup and the co-cultivation of one A.niger strain DSM 1957 and three bacterial strains belonging to the actinomycetes for degrading wheat bran. The genomic sequencing of A. niger DSM 1957 allowed to evaluate the genomic potential of this strain for the production of enzymes such as CAZymes (Carbohydrate Active enZymes) involved in lignocellulose degradation. Genomic comparisons were performed between the four strains in order to highlight their CAZyme and secondary metabolites production potential. The interactions between the strains were first investigated with classical cultivation on Petri dishes. The efficiency of wheat bran degradation was investigated by quantifying the various enzymatic activities produced by the microorganisms and also by analyzing the various metabolites produced during the microbial growth onto wheat bran.",
"discussion": "3 Results and discussion 3.1 Defining the parameters of the co-cultures The growth of the two microbial partners was successfully observed whatever the condition culture used (liquid or solid). For the solid culture type, fungal growth was observed all over the Petri dish after inoculation of the spores in the middle; growth of the bacterial pellet was observed in the same area than the fungal mycelium (Supplementary Figure 3) . For the liquid culture type, fungal growth was characterized by the presence of a fungal pellet which could encompass the lignocellulosic substrate and potentially the typical bacterial pellet which confirms that the 2 partners are not antagonistic ( Supplementary Figure 4) . Concerning the time of inoculation, when the fungi were inoculated prior to the bacteria either 6 h, 24h30 or 30H, no growth of the bacteria was observed. On the opposite, when the bacteria was inoculated either in the same time of before compared to the fungi, both were able to develop and grow in the flasks. However, for the later times of the fungal inoculations (24h30 and 30 h), a lower amount of fungal mycelium was observed. The results were comparable for a fungal inoculation after 6 h or in the same time than the bacterial strains (data not shown). For a practical matter, a simultaneous inoculation was performed. 3.2 Genomic comparison of A.niger DSM 1957 The strain A.niger DSM 1957 was firstly studied by ( Steinberg, 1941 ) in order to describe the sulfur and trace-element nutrition among one strain of the A.niger species. This strain was later described for its ability to express xylanases and cellulases during growth onto different lignocellulosic subtrates ( Prasertsan et al., 1997 ; Prasertsan and Oi, 1992 ). The sequencing of the strain A.niger DSM 1957 generated 15,452,965 reads with an ultrasmall percentage of error (0.03%) with a Q30 of 91.4%. The assembly consisted into 138 (> 1000 bp) with a N 50 of 544,125 bp. The number of N was low in our contigs with a number of N's per 100 kbp of 1.84. In order to analyze the strain A.niger DSM 1957, we compared it to relatively closed genome from the strains A.niger An76, A.niger ATCC 1015, A.niger ATCC 13,496, A.niger CBS 513.88, A.niger CBS 101,883 and A.niger ATCC 64,974 N402. The strains have between 10,373 and 13,359 genes encoding proteins, the A.niger strain CBS 101,883 has the most SCP. The length of A.niger DSM 1957 was 35.6 Mbp and its GC content was 47% and was in the range of the compared A.niger strains. The genome content was annotated according to their CAZyme content. The diversity of structures, compositions and bonds of components forming lignocellulose has led microorganisms, during evolution, to produce large panels of enzymes capable of degrading it ( Manavalan et al., 2015 ). CAZymes are classified according to the CAZy database into five classes ( Cantarel et al., 2009 ): glycoside hydrolases (GH), glycosyltransferases (GT), polysaccharide lyases (PL), carbohydrate esterases (CE) and anxilliary activities (AA). GHs (EC 3.2.1.*) hydrolyze the glycosidic bonds between two carbohydrates, or between a carbohydrate and a non-carbohydrate residue. GTs (EC 2.4.*.*) are involved in the biosynthesis of saccharide chains and have debranching activities. PLs (EC 4.2.2.*) mainly cleave bonds between acids and polysaccharides. The CEs catalyze the hydrolysis of the carbohydrate esters. AAs group together enzymes that act on lignins and polysaccharides (LPMO or Lytic Polysaccharides MonoOxygenases) through redox mechanisms. Another class exists, that of proteins consisting in binding modules (CBM or Carbohydrate Binding Module), which are not enzymes, but increase the efficiency of the latter. The strain A.niger DSM 1957 has the greatest number of hypothetical CAZymes (581 enzymes), it should be noted that A.niger 1957 and A.niger ATCC 64,974 N402 which nevertheless have a smaller number of coding sequences have the same number of CAZymes. The strain with the best CAZymes /Single-coding proteins ratio is A.niger CBS 513.88, its CAZymes representing 5.43% of its total proteins. The strain A.niger 1957 has the most AA, CBM, CE and GH numbers among the compared dataset. The number of CAZymes in A.niger DSM 1957 was the most important (like in A.niger CBS 513.88 and A.niger CBS 101,883) with a total of 581 CAZymes ( Table 1 ). Table 1 Genomic characteristics and CAZyme content of the A.niger DSM 1957 strain compared to other Aspergillus strains (GH = Glycoside Hydrolase, CE = Carbohydrate Esterase, CBM = Carbohydrate Binding Module, PL = Polysaccharide lyase, GT = Glycoside Transferase, AA = Auxiliary activity). Table 1 Strain Length (MBp) Single-coding proteins CAZymes number CAZymes (%) AA CBM CE GH GT PL DSM 1957 35.6 10,798 581 5.38 106 17 88 262 99 10 An76 34.6 10,373 551 5.31 97 15 86 246 97 10 ATCC 1015 34.9 10,950 565 5.16 104 17 81 256 97 10 ATCC 13,496 35.7 12,194 576 4.72 103 16 86 261 101 9 CBS 513.88 34 10,609 576 5.43 104 15 83 249 107 18 CBS 101,883 35.9 13,359 581 4.35 106 15 88 262 100 10 ATCC 64,974 35.5 11,236 581 5.17 105 17 87 259 99 14 Among the 121 panCAZymes families present in the 7 fungal genomes analysed, a large common core was shared between them; indeed 115 families were present among all the strains. The strain A.niger DSM 1957 had 119 CAZymes families present. Among those families for that genome (with over 15 occurrences), the most important ones were CE10 (59 enzymes), AA7 (42), AA3 (33), GH13 (21), GH28 (21), GH3 (19), GT2 (18) and GH18 (14). Among the GH13 present, those enzymes encoded for several alpha-amylases and glucanotransferases. The GH28 encoded for several rhamnogalacturonases, exo-xylogalacturonan hydrolases and exopolygalacturonases. The GH3 encoded for β -glucosidases and β -xylosidases. The GH18 played a role into chitin degradation. The xylanase activity was carried by the GH10 and GH11. The GT2 were mainly involved in the chitin synthase process. The AA7 encoded for either glucooligosaccharideoxidase, chitooligosaccharide oxidase, cellooligosaccharide dehydrogenase and the AA3 for cellobiose dehydrogenase or glucose 1-oxidase. This result shows that the strain A.niger harbours a wide variety of genes encoding for xylanases, endo-glucanases and cellulases. The potential CAZyme secretome of the strain A.niger DSM 1957 was studied; 272 among the 581 CAZymes were potentially secreted. Among those secreted CAZymes, 51 AA among the 106 were present, CE 41 among the 88 present, 162 GH among the 262 present which represented more than 46% of each family. The PL was all secreted. On the opposite, only 4 GT and 4 CBM among the 99 and 17 are present in the secretome. 3.3 Genomic comparison of the actinobacterial strains In comparison to the Aspergillus genome, the actinobacterial strains have a shorter genome. The absolute number of CAZymes was lower compared to the fungal genomes (minimum of 255 and 336 respectively for S. griseorubens and S. avermitilis ) so do their relative abundance in CAZymes (between 3.35 and 4.13%). The main difference between the bacterial and fungal CAZyme content was the higher proportions of CBM (Carbohydrate-Binding-Module) in the bacteria (average of 48 per genome) and fungi (16 per genome) ( Table 2 ). Table 2 Genomic characteristics and CAZyme content of the actinobacterial strains. Table 2 Length (Mbp) Single-coding proteins CAZymes number CAZymes (%) AA CBM CE GH GT PL S.avermitillis 10.5 10,003 335 3,35 20 53 32 149 69 12 S.coelicolor 9.1 8128 336 4,13 15 57 37 156 59 12 S.griseorubens 7.7 6841 255 3,73 15 34 34 115 52 5 A Venn diagram ( Supplementary Figure 5 ) presents the common CAZymes genes that are present among the three Streptomyces analyzed. A large common core is shared between the 3 genomes with 80 CAZymes families similar. The strain S.avermitillis carried 13 specific CAZymes families which were S.avermitillis (AA6, CBM11, CBM50, CBM61, GH110, GH145, GH27, GH53, GH85, GH88, GH89, PL29, PL4). Among the GH CAZymes only present in S. avermitillis , the GH53, GH110, GH88 were mainly involved in hemicellulose degradation. For the S.coelicolor CAZome, 10 specific CAZymes families were present (CBM12, GH101, GH106, GH117, GH125, GH158, GH50, GH93, PL34, PL6) and involved in the hemicellulose and mannose degradation. The S.griseorubens had only 5 specific CAZymes families (GH136, GH81, GH84, GH97, GT84). The Streptomyces strains were thus well equipped in order to fractionate lignocellulose. 3.4 CAZyme comparison of the co-cultures A Venn diagram ( Fig. 1 ) describes the richness and diversity of the CAZymes brought by each organism during the mono and co-culture. To do so, CAZyme families with several iterations (such as the AA7 in A.niger which was present 42 times) were only considered once (reducing then the number of CAZymes in the genomes). For the co-culture S. avermitilis and A.niger DSM 1957, the fungal partner brought 60 unique CAZyme family into the genetic pool, whereas the bacteria partner brought 46; 59 were shared in common by the 2 partners. Similar results were obtained for the 2 other co-cultures. Among the CAZyme families only brought by A.niger DSM 1957, AA9 (lytic polysaccharide monooxygenases), AA1 (Laccase-like multicopper oxidase), GH71 ( β −1,3-glucanosyltransglycosylase) and GH28 (polygalacturonase) were the most represented. Among the CAZyme families only brought by the actinobacteria , AA10 (laccase-like multicopper oxidase), GH23 (chitinase) and GH42 ( β -galactosidase) were the most represented. The Venn diagram represents perfectly that diverse CAZymes were carried by each of the microbial partner and thus that the metabolic CAZymes diversity is drastically increased when a co-culture between Aspergillus and one of the three actinobacteria is performed. Fig. 1 Venn diagram representation of the CAZyme family content of each co-culture fungi-bacteria performed. Fig. 1 3.5 Secondary metabolite prediction and production Among the secondary metabolites predicted by fungal antismash ( https://fungismash.secondarymetabolites.org/#!/start ), A.niger harboured 66 regions encoding for secondary metabolites encoding majoritary for 19 T1PKS, 14 NRPS-like, 3 T1PKS/NRPS and 2 terpene. A.niger DSM 1957 was able to produce naphthopyrone, pyranonigrin E, clavaric acid and nidulanin. A majority of the secondary metabolites produced by this strain remains unknown and might represent thus a new source of secondary metabolites. For the actinobacteria, the strains S. coelicolor ( Bentley et al., 2002 ), S. griseorubens and S. avermitillis encoded respectively for 29, 22, 36 regions of secondary metabolite predicted by the Antismash software ( https://antismash.secondarymetabolites.org ). Among those secondary metabolites, terpenes, NRPS, siderophores, T1PKS and lassopeptides were the most predominant. Among the main secondary metabolites produced, the strain S.avermitillis was able to produce, albaflavenone, avermitilol, carotenoid, citrulassin D, ectoine, filipin, geosmin, informatipeptin, melanin, oligomycin and pentalenolactone. The strain S.coelicolor was able to produce coelichelin, ectoine, melanin, desferrioxamine B, actinorhodin ( Bystrykh et al., 1996 ), albaflavenone, curamycin, undecylprodigiosin, geosmin, hopene and germicidin ( Lautru et al., 2005 ). For the strain S. griseorubens , albaflavenone, alkylresorcinol, ectoin, antimycin and rhizomide were produced ( Supplementary Table 1 ). After UHPLC/Q-TOF HRMS analysis, a principal component analysis ( Fig. 2 ) was performed in order to describe the differential secondary metabolite expression by different mono and co-cultures when grown on WB. The results showed that the replicates from each group clustered together showing the reproducibility of the experiment. The groups hardly separate among the principal component axes (PC1 and PC2 axe had percentages of 18.9 and 15.3%). The results of the pcoA strain showed that the actinobacterial strains during their growth on wheat bran separated well from their homolog during their growth on glucose. This result is due do the large number of secondary metabolites produced by those actinobacterial strains and the co-culture A.niger/S.avermitilis on wheat bran compared to the other conditions. Indeed, the strain S.griseorubens was able to produce between 13 and 18 secondary metabolites when grown on wheat bran; however when grown on glucose, a production of 6 to 8 secondary metabolites was observed. For S.coelicolor, between 12 and 16 when grown on WB depending on the replicates; on the opposite, only 8 secondary metabolites were produced. For S.avermitilis , 17 secondary metabolites were produced whatever the number of replicate whereas only 2 or 3 were produced were grown when grown on wheat bran. For the A.niger strain, only one secondary metabolite was detected when growth was perfomed on glucose whereas 5 were detected whatever the replicate when growth was done on glucose ( Supplementary Table 1 ). By comparing those number of secondary metabolites depending on the carbon source, the results showed a lower production when grown on glucose; that result can be due to the carbon catabolite repression which guarantees the sequential utilization of carbon sources when more than one is simultaneously present in the culture media and would thus activate more biosynthetic clusters genes ( Romero-Rodríguez et al., 2016 ). For the strain S.coelicolor , several known compounds were produced and identified by mass spectrometry: coelibactin ( m/z = 481, elution time at 3.57), nogalamycin ( m/z = 788, elution time at 4.87) and actinorhodin which was clearly detected with a blue pigmentation ( m/z = 636, elution time at 3.19). For S.avermitilis and S.griseorubens , none of the predicted secondary metabolites were identified by mass spectrometry suggesting. For A.niger , one compound was produced with a predicted mass identified by mass spectrometry (fumonisin B1, m/z = 722, elution time of 1.1) when grown on wheat bran. The principal components analysis showed that the diversity of secondary metabolites obtained for the different conditions (growth on wheat bran or glucose) is different whatever the mono and co-culture; indeed few secondary metabolites were shared in common during the growth on wheat bran and glucose. Overall, this suggests that some components from WB (carbohydrates, proteins or lignin) could activate some silent biosynthetic cluster genes and will provide a new fingerprint of secondary metabolite production. Fig. 2 Principal component analysis of the metabolomics analysis for the different mono and co-cultures when grown on wheat bran (WB) and glucose (GLU). Fig. 2 Regarding the co-cultures, the A.niger/S.griseorubens co-culture, the number of secondary metabolites produced varied between 5 and 7 secondary metabolites (on WB) which were lower compared to the number of obtained for each member alone. By comparing with A. niger/S. griseorubens co-culture on glucose, less secondary metabolites was produced with a different diversity obtained compared to the one on WB. In the A.niger/S.griseorubens co-culture, the number of secondary metabolites produced varied between 5 and 7 secondary metabolites (on WB) which is lower compared to the number of obtained for each member alone. By comparing with the same co-culture on glucose, less secondary metabolites were produced with a different diversity obtained compared to the one on WB. The same trend was observed for the A.niger/S.coelicolor with a lower number of secondary metabolites produced compared to the growth on wheat bran (3 produced) to glucose (2 produced) and none shared together in the different carbon conditions ( Supplementary Figure 6 ). Contrary, the strain S.avermitilis was able to produce 17 compounds where grown in mono-culture. In the co-culture A.niger/S.avermitilis , 13 compounds in average were produced; 2 were in common with the S.avermitilis alone (at the elution times of 5.3 and 5.91 min) and none of the ones produced by A. niger alone was found in the co-culture. By comparing with A. niger/S. griseorubens co-culture on glucose, less secondary metabolites were produced with a different diversity obtained compared to the one on WB. Eleven new secondary metabolites which were not present in the mono-cultures were detected in the co-culture; among those eleven new secondary metabolites, only 2 were recovered in the coculture with A. niger/S. avermitilis grown on glucose which suggests thus an elicitation and activation of silent biosynthetic cluster genes. None of the molar masses detected in the co-cultures were close to the masses of the secondary metabolites predicted by the Antismash ( Blin et al., 2019 ) and MIBIG algorithms ( Kautsar et al., 2020 ) Previous co-cultures of Aspergillus fungi and Streptomyces showed a suppression of the production of the fungal metabolites ( Wakefield et al., 2017 ). The activation of silent of those silent biosynthetic cluster genes in a second microorganism may be stimulated through microbial crosstalk and may be interpreted as a defense mechanism triggered in response to a chemical signal from the other microorganism ( Wakefield et al., 2017 ) In our study, it is not possible to confirm which of the microbial partners was able to produce those new secondary metabolites in the co-culture. In order to prove which one the microbial partners is able to produce those new secondary metabolites, 1) either a spiking of the supernatant of one of the microbial partner could be added to the culture of the remaining one, 2) or an elicitation with one of the microbial lysate or the microbial cell components ( Abdelmohsen et al., 2015 ). Overall, the results of the metabolomic analysis showed a different relationship and crosstalk between the fungal strain and the different actinobacteria. In summary, an inhibition of the secondary metabolites produced by S.griseorubens and S.coelicolor was observed when grown with A.niger whereas when that fungal strain was grown with S. avermitilis , activation and a possible dual elicitation was observed whatever the carbon source (WB or glucose). However, the diversity of the secondary metabolites tend to be different between the growths on the carbon source type revealing the potential role of lignocellulose as elicitor of biosynthetic cluster genes. 3.6 Lignocellulolytic enzymatic activities during mono and co-cultures The enzymatic activities were quantified from the mono and co-cultures from wheat bran and not on glucose; indeed, previous preliminary experiments performed at the laboratory showed no lignocellulolytic enzymatic activity produced by the microorganism when grown on glucose on the opposite to wheat bran. Debranching intracellular enzymatic activities were measured for the 3 actinobacterial strains and A.niger in mono and co-cultures ( Fig. 3 ). The results showed that the intracellular β - d -glucosidase and b- d -xylosidase enzymatic activities were the most important with values higher than 1 IU/mg of protein; on the opposite, α- l -arabinosidase enzymatic activities were lower and never reached more than 200 mIU/mg of protein. In all the experiments performed in mono-culture, enzymatic activities were always higher for the fungi compared to the other actinobacteria. The enzymatic activities measured showed different patterns depending on the strain added in the co-culture: 1) more enzymatic activities were detected in the co-cultures with S.avermitilis and S.coelicolor 2) less enzymatic activities were detected in the co-culture with S.griseorubens . Indeed, for the monoculture, intracellular β- d -glucosidase activity was 371.74 ± 3.09 mIU/mg of protein whereas it was lower than 10 mIU/mg of protein for the other actinobacteria; in the co-cultures, β - d -glucosidase activities were 928.7 ± 648.6, 1082.4 ± 812.8 mIU/mg of protein respectively for A.niger/S.avermitilis and A.niger/S.coelicolor . None of the statistical tests were significant. On the opposite, the β - d -glucosidase activity was lower in the A.niger/S.griseorubens co-culture with a value of 205.9 ± 90.9 mIU/mg of protein (p-value < 0.05). The same patterns were also observed for the intracellular xylosidase activity: indeed, the activity quantified for the A.niger was 365.5 ± 112.3 mIU/mg of protein whereas it was much lower for the other actinobacterial strains. A decrease of the b- d -xylosidase activity was observed for the co-culture A.niger/S.griseorubens (303.4 ± 136.9 mIU/mg of protein) and an increase for the others co-culture with respectively 630.3 ± 392.4, 1720.6 ± 1309.2 mIU/mg of protein respectively for A.niger/S.avermitilis and A.niger/S.coelicolor . Fig. 3 Intracellular debranching activities of the mono and co-cultures at 144 h. Fig. 3 Regarding the α - l -arabinosidase activity, the enzymatic activities were less important compared to the b- d -xylosidase and β- d -glucosidase. The differences were less important between the activity produced by the fungi (83.25± 44.25 mIU/mg of protein) and the other actinobacteria respectively (26.9 ± 12.1, 23.9 ± 22.6 and 11.2 ± 2.2 S.avermitilis, S.griseorubens and S.coelicolor respectively ) compared to the other activities measured. In the A.niger/S.avermitilis and A.niger/S.coelicolor co-cultures, the measured α- l -arabinosidase enzymatic activities were 107.7 ± 58 and 100 ± 76.5 mIU/mg of protein and were superior to the enzymatic activity obtained for the fungi. On the opposite, the value observed for the A.niger/S.griseorubens co-culture was 24.9 ± 15.9 mIU/mg of protein. In all the co-cultures (exception of the α- l -arabinosidase for A.niger/S.avermitilis ) between A.niger with S.avermitilis or S.coelicolor, the enzymatic values obtained were superior to the sum of the enzymatic activity produced by each microbial partner analyzed in its own mono-culture (excepted for the α - l -arabinosidase activity in A.niger/S.avermitilis . Indeed, an overproduction up 1351 mIU/mg of protein was observed in the coculture A.niger/S.coelicolor (1720.6 mIU/mg of protein) for the xylosidase activity compared to the expected activity by the addition of each microbial partner (365.5 ± 4.07 respectively). For all those debranching activities, extracellular activities were also measured ( Supplementary Figure 7 ). For the strains A.niger and S.avermitilis , elicited enzymatic activities were observed for the arabinosidase and glucosidase. Indeed, the arabinosidase activity was 353 ± 31 mIU/mg of protein in the co-culture whereas they were respectively 7.6 and 34 mIU/mg of protein for S.avermitilis and A.niger . For the glucosidase activity in that same co-culture, the values reached up to 927 ± 198 mIU/mg of protein in the co-culture whereas they were respectively 97 and 60 mIU/mg of protein for S.avermitilis and A.niger . For A.niger/S.coelicolor coculture , higher enzymatic activities were observed for the xylosidase and glucosidase. For the xylosidase activity in that same co-culture, the values drastically reached up to 1610 ± 101 mIU/mg of protein in the co-culture whereas they were respectively 9 and 57 mIU/mg of protein for and A.niger . A lower elicitation was observed for the glucosidase activity; indeed the activity was 968 ± 400 mIU/mg of protein in the co-culture whereas they were respectively 92 and 60 mIU/mg of protein S.coelicolor for A.niger . For the A.niger/S.griseorubens coculture, a decrease of the enzymatic activities was observed (except for the arabinofuranosidase) overall for the extracellular activities. Intracellular peroxidase activities were also measured during mono and co-cultures ( Fig. 4 ). The peroxidase activities mainly responsible of the lignin degradation ( Dashtban et al., 2009 ). Peroxidases catalyze the oxidation of lignin in the presence of hydrogen peroxide as electron acceptor and can involve cytochrome c peroxidase ( Dashtban et al., 2009 ). On opposite to the other enzymatic activities before, all the peroxidase payload was not performed majority by the fungi but also by the bacteria. Indeed, A.niger DSM 1957 showed a peroxidase activity that reached 588 mIU/mg which superior only to the one observed in S.avermitilis (347 mIU/mg) whereas higher activities were present for the 2 remaining bacteria which were S.griseorubens and S.coelicolor (1314 and 3091 mIU/mg of protein respectively). Among all the co-cultures tested, no one showed a superior peroxidase activity compared to each microbial partner separately. This could be due to the low abundance of lignin in wheat bran which would explain this absence of difference. The utilization of more lignified agro-resources would maybe allow differences in term of peroxidase activity. A small decrease was observed for the A.niger/S.avermitilis and A.niger/S.griseorubens co-cultures whereas the decrease was more important for the A.niger/S.coelicolor (1726 mIU/mg of protein the co-culture compared to the sum of each microbial partner individually (3680 mIU/mg of protein). Fig. 4 Intracellular peroxidase activity of the mono and co-cultures at 144 h. Fig. 4 Xylanases catalyze the hydrolysis of xylans and have been widely studied in filamentous fungi such as Aspergillus ( Betini et al., 2009 ; Pal and Khanum, 2010 ). Aspergillus xylanases have been ( Paul et al., 2020 ) widely used in several industrial processes such as paper pulp biobleaching ( Sridevi et al., 2016 ). Waste management programs make use of xylanases so as to hydrolyze xylan found in industrial and municipal wastes ( Motta et al., 2013 ). Due to the importance of xylanase in the lignocellulolytic, secretion system present in Aspergillus , a dynamic study of the extracellular xylanase activity (at 48, 96 and 144 h) was performed on the compared to the other enzymatic activities ( Fig. 5 ). The results showed that xylanolytic activity load was carried by A.niger and not by the Streptomyces bacteria; indeed, the maximum xylanase activity for one of the Streptomyces member was at 144 h for S.coelicolor (3513 ± 980 mIU/mg of protein). For A.niger , xylanolytic activity increased continuously from 48 h to 144 h reaching up 49,251 ± 28,763 mIU/mg of protein. The same increasing trend was observed for the other actinobacteria through time. Fig. 5 Dynamic secreted xylanalytic activity of the mono and co-cultures at 48, 96 and 144 h. Fig. 5 The trend of dynamic xylanalytic activity of the co-culture A.niger / S.griseorubens confirmed the trends obtained from the others enzymatic activities depicted previously. Indeed, the enzymatic activity was 3.5 more time less important for the co-culture A.niger/S.griseorubens compared to the mono-culture of A.niger after 144 h. On the opposite, for the other actinobacterial strains, an increase factor up to 147% and 229% was found between A.niger and the co-cultures , A.niger/S.avermitilis and A.niger/S.coelicolor respectively. A different dynamic was observed between those two previous co-cultures; 1) indeed, the maximum xylanase activity was observed for A.niger/S.avermitilis was already important at 48 h reaching 21,804 ± 3265 mIU/mg (then slowly increased at 96 h and decreased at 144 h), 2) on the opposite, the xylanalytic activity of A.niger/S.coelicolor was low at 48 and 96 h but reached a peak at 144 h up to 59,226 ± 26,239 mIU/mg. The dynamic lignocellulolytic enzymatic activity was studied for all the co-cultures and showed that through time the xylanase was always over-expressed compared to the other enzymatic activities. Xylanase activity is thus necessary through all the growth and xylan represents consequently the main carbon and energy source of the microbial partners. Those results are correlated to previous studies regarding the dynamic secretion of A.niger An-76 where the expression of xylanase was detected on hydrolysates of lignocellulose polysaccharide at 24 h of inoculation until the end of growth (144 h) ( Xing et al., 2013 ). Debranching activities were detected at the final time point signifying that these enzymes play a crucial role for the hydrolysis of the oligosaccharides produced by the xylanases and other endo-enzymes. Overall, for all the enzymatic activities measured in that manuscript, some standard deviation could be due to the growth type of the microbial partners involved in those co-cultures. Indeed, those microorganisms form pellets of different size with different amount of cells and subsequent protein content. Despite those standard deviations, all the results confirm the different patterns where elicitation is observed between A.niger and S.coelicolor or S. avermitilis and a possible inhibition between A.niger and S.griseorubens . The co-culture growth observation was only conducted in a qualitative manner visually by assessing the presence of the two microbial partners depending on their morphological shape. The main point of our study was to describe the possible elicitation of enzymatic activities and production of secondary metabolites at a final time point when grown on lignocellulose. It is acknowledged that the metabolic profiles into a co-culture will depend on the distribution of the two partners ( Karuppiah et al., 2019 ; Romanens et al., 2020 ). The morphological patterns of the two microbial partners which form pellets do not allow a spectrophotometric quantification and thus as future experiments, we will develop an approach in follow-up studies about the metabolomic profiles obtained in those co-cultures at different sampling points allowing to generate data for both microorganisms. Here, the enzymatic activities were analyzed as mIU/mg of protein in order to normalize throughout the different samples (through time and the different consortia). The similar enzymatic activities and metabolomics profiles obtained showed that the experiments were reproducible and that the distribution was comparable among the replicates. In our experiment, the precise mechanism behind that microbial interaction is not clear and has not been investigated. The activation of those enzymatic activities can be due to several hypothesis: the presence of signaling molecules or direct contact between the two microbial partners. In the case of signaling molecules, the activation of enzymatic activities in Aspergillus can be due to several factors; indeed the activation can be due to the presence of different types of molecule: 1) previous study revealed indeed that 3 mM Cu 2+ supplementation in recombined xylanase A. niger US368 enhanced its activity by 54% ( Elgharbi et al., 2015 ); 2) the presence of secondary microbial metabolites which can over-express xylanase activity by 40% ( Andrioli et al., 2012 ). For the direct contact, previous experiments proved that this was necessary between bacteria/fungi to observe activation of cryptic metabolic pathways ( Scherlach and Hertweck, 2009 ). In order to prove that hypothesis of signaling molecules, further experiments could be performed 1) either by spiking only the secondary metabolites produced by the co-cultures fungi/actinobacteria to another fungal culture the fungi is grown alone, 2) use membrane reactors which would only allow the transfer of the secondary metabolites to each partner without direct contact between them. An accurate description of those molecules will be performed in the future which could be used for the improvement of fractionation by other microorganisms into other biotechnological processes by simple spike. Those activators which would improve lignocellulolytic activities would be then ready-to-use and less expensive compared to other approaches involving genome editing per example ( Liu et al., 2017 ). Overall, the results showed that the consortia tend to have a hemicellulolytic strategy compared to the cellulolytic one. This is related to the chemical structure and diversity of wheat bran which is mainly constituted of arabinoxylans (16% DM of arabinose and 26% DM of xylose) compared to cellulose (19% DM of glucose) which could hypothesis that more energy and carbon source could be available for the consortia from the hemicellulose and notably the arabinoxylans. Moreover, the cellulose degradation pathway requires the expression of more enzymes in A.niger (the main degrader in our consortia) compared to the xylan degradation pathway which could thus represent an energy save in the fungi metabolism ( Benoit-Gelber et al., 2017 ). The obtained results in that study are promising with: 1) an easy set-up of the co-cultures from 2 different microorganisms from different domains in the “Tree of life” resulting into a greater and more diverse metabolic and lignocellulolytic content, 2) an over expression of several key enzymatic activities, 3) an elicitation of some specific biosynthetic cluster genes observed only in the co-culture experiment, 4) a specific microbial crosstalk and interaction observed at the species level between the 3 Streptomyces and the fungi leading to a specific of lignocellulolytic enzyme and secondary metabolite production. Further experiments will be performed in order to: 1) decipher the regulatory and expression mechanisms at the gene level over-expressed in the co-culture, 2) describe the interaction type (chemical or physical) between the 2 microbial partners, 3) identify the secondary metabolites produced during the co-culture experiments."
} | 10,124 |
34879068 | PMC8654227 | pmc | 4,648 | {
"abstract": "The microbial and molecular characterization of the ectorhizosphere is an important step towards developing a more complete understanding of how the cultivation of biofuel crops can be undertaken in nutrient poor environments. The ectorhizosphere of Setaria is of particular interest because the plant component of this plant-microbe system is an important agricultural grain crop and a model for biofuel grasses. Importantly, Setaria lends itself to high throughput molecular studies. As such, we have identified important intra- and interspecific microbial and molecular differences in the ectorhizospheres of three geographically distant Setaria italica accessions and their wild ancestor S . viridis . All were grown in a nutrient-poor soil with and without nutrient addition. To assess the contrasting impact of nutrient deficiency observed for two S . italica accessions, we quantitatively evaluated differences in soil organic matter, microbial community, and metabolite profiles. Together, these measurements suggest that rhizosphere priming differs with Setaria accession, which comes from alterations in microbial community abundances, specifically Actinobacteria and Proteobacteria populations. When globally comparing the metabolomic response of Setaria to nutrient addition, plants produced distinctly different metabolic profiles in the leaves and roots. With nutrient addition, increases of nitrogen containing metabolites were significantly higher in plant leaves and roots along with significant increases in tyrosine derived alkaloids, serotonin, and synephrine. Glycerol was also found to be significantly increased in the leaves as well as the ectorhizosphere. These differences provide insight into how C 4 grasses adapt to changing nutrient availability in soils or with contrasting fertilization schemas. Gained knowledge could then be utilized in plant enhancement and bioengineering efforts to produce plants with superior traits when grown in nutrient poor soils.",
"introduction": "Introduction Setaria is a geographically widespread genus of C 4 grass that includes more than 100 species [ 1 ]. It is part of the larger Panicoideae subfamily that also includes important biofuel and commercial grain crops including maize, Miscanthus , switchgrass, sorghum, and others. Two model species of Setaria , S . viridis (common name: foxtail millet) and S . italica (common name: green foxtail) have been researched extensively to examine and understand abiotic and biotic stress and tolerance within C 4 grasses [ 2 , 3 ]. S . viridis is the wild ancestor of S . italica [ 4 ], and both exhibit drought and salt tolerance and optimized water-use efficiency, making them suitable for research pertaining to the cultivation of bioenergy crops in nutrient and water limited environments [ 5 – 13 ]. Additionally, Setaria in general are well-suited for growth chamber and laboratory research because of their optimal size and short generation time [ 14 ]. They also have a small, fully sequenced genome (~490 Mb) that enables high-throughput molecular analyses [ 15 ]. The characteristics that make S . viridis and S . italica important model C 4 grasses extend to their ectorhizospheres as well. Here, the ectorhizosphere is defined as the zone immediately surrounding, and influenced by, plant roots [ 16 ]. Ectorhizosphere studies associated with Setaria have thus far focused on understanding nitrogen fixation and general microbial taxonomic structure. For example, Okon et al. (1983) inoculated S . italica rhizospheres with Azospirillum brasilense , a known nitrogen fixer, to understand how it would impact the growth of Setaria . They found that the bacteria did not directly transport fixed nitrogen to plants but rather facilitated nutrient transport via nitrogen mineralization [ 17 ]. A study conducted by Jin et al. (2017) assessed microbial communities in the rhizosphere of S . italica and determined that plants selectively recruited microbes through exudation [ 7 ]. The studies above are important examples contributing to our understanding of the microbial composition and function of the plant-microbe relationship of C 4 grasses within horticultural, or uncharacterized agricultural soils. Less is known specifically about the microbial composition and function of S . italica and S . viridis ectorhizospheres within a nutrient poor soil. Further, how the microbial and molecular composition adjust with nutrient addition that often must take place for successful agricultural cultivation in such a soil. To our knowledge there has only been one reported study to investigate Setaria responses to nutrient addition in a nutrient-poor soil environment. Nadeem et al. (2018) investigated S . italica (L.) Beauv, variety Yugu 1, in response to a low nitrogen environment and found that the growth of roots was inhibited by this environment [ 18 ], molecular analyses were largely made on roots, but investigation of the ectorhizosphere in combination with the plant was not undertaken. Thus, it is not known how nutrient availability affects the corresponding ectorhizosphere and soil microbiome. Due to this knowledge gap, we chose to investigate how the Setaria ectorhizosphere responds to a nutrient poor soil receiving a nutrient addition (fertilizer). We measured leaf, root and soil metabolite and soil organic matter (SOM) profiles as well as the ectorhizosphere microbial responses of three S . italica accessions from three divergent geographical origins (Afghanistan, China, and India) as well as the S . viridis reference genotype (A10.1). The geographically diverse origins of the accessions led us to question how their ectorhizospheres would resemble each other before and after nutrient addition. To address this question, all accessions were grown within the same growth chamber and in the same nutrient-poor soil with (WNA; with nutrient addition) or without (NNA; no nutrient addition) periodic nutrient addition (i.e., fertilizer). Analyses of SOM, metabolites, and microbial taxa on soil from no plant controls and bulk soil were included to help differentiate the impact of Setaria on the native soil microbiome. Our results primarily focused on comparing the ectorhizospheres of those S . italica accessions exhibiting the largest or smallest response to nutrient addition in the context of SOM, metabolites and microbial taxa. However, we also measured and evaluated metabolites that are differentially produced by Setaria tissues (leaf and root) when comparing WNA to NNA and what, if any, associations could be made between these metabolic profiles, ectorhizosphere microbiome and plant phenotypic changes. We also hypothesized that based upon the methodology used here to separate the ectorhizosphere from bulk soil, SOM and microbial differences between ectorhizospheres of Setaria accessions WNA would be discernable.",
"discussion": "Discussion While most Setaria studies have focused on comparing genotypic responses of the rhizosphere to various environmental conditions [ 8 , 9 , 45 ] or only looked at the general taxonomic structure [ 17 ], our study used TOC/TNb, 16S & ITS sequencing, FTICR-MS, and GC-MS based metabolomics to characterize the plant and ectorhizosphere response to nutrient addition in a nutrient poor soil. To allow a multi-measurement comparison to be made on the ectorhizospheres of Setaria , it was crucial that the bulk and ectorhizosphere soil were successfully separated. The methods used here, and our diverse measurements, demonstrate that we were successful in performing this separation. For example, our TOC/TNb results show significantly higher values of organic C and total N in the ectorhizosphere than in the bulk soil ( Fig 2 ), suggesting an accumulation/concentration of these nutrients by the plant-ectorhizosphere interactome. Although we did not measure metabolic activity directly, our claim is supported by previous studies. Chaparro et al. (2013) and Zhu et al. (2016) have shown that greater metabolic activity in the ectorhizosphere compared to the bulk soil occurs because nutrient addition increases root exudation into the ectorhizosphere [ 46 , 47 ]. In addition, our 16S data also showed distinct separation between the ectorhizosphere and bulk soil with a large amount of overlap in OTUs between our bulk soil samples and no plant control samples ( Fig 5 ). Similar results were shown in a study done with switchgrass [ 48 ], which further supports that our separation of the ectorhizosphere has a distinct community composition compared to bulk soil as demonstrated when comparing beta-diversity (Bray-Curtis) ( S11 Fig ). The use of FTICR-MS to distinguish the ectorhizosphere from bulk soil represents a more recent application of this capability. Previous applications of FTICR-MS in soils have been used to characterize soil organic matter [ 28 ], dissolved organic matter [ 49 ], and determine how the rhizosphere can absorb metal pollutants [ 50 ]. By using FTICR-MS here, we not only identified separate soil fractions based upon organic chemical composition ( S3 Fig ), but also demonstrated that alterations to SOM chemical classes differs with Setaria spp . and within different accessions of the same species, as reported here for accessions A3 and A4. For example, under NNA the protein and amino sugar chemical classes could serve as N sources for A3, while for A4, a lower demand for N could be hypothesized based upon less alteration of these chemical classes. WNA, lignin decreases substantially in A3, yet not significantly for A4, while the opposite was observed for the tannin chemical class ( Fig 4 ). The alteration in these latter chemical classes leads us to hypothesize that this outcome is a result of differences in rhizosphere priming [ 51 ]. Dijkstra et al. (2013), defined rhizosphere priming as a change in the composition of SOM caused by root activity, which can be affected by nutrient availability [ 51 ]. If correct, our observations further suggest that differences in rhizosphere priming are a function of plant genotype (here accession, as the genotype of each Setaria was not investigated), with alterations in different chemical classes brought about by the interaction of the plant and its rhizosphere commensals; differences in root exudation for regulating these SOM chemical classes as sources and sinks of C and N. Further experimentation is needed to validate this hypothesis. The comparison of metabolites identified in our ‘no plant’ controls to those identified in the ectorhizosphere, bulk soils, leaves and roots provided examples of the enrichment of specific metabolites, which accumulated in either the roots, leaves and in the ectorhizosphere during nutrient deplete or replete conditions. Enrichment of specific metabolites in the roots could be exudated, which in turn could be used to add positive or negative growth pressures on specific ectorhizosphere microbes. For example, glutamic acid (Glu) and aspartic acid (Asp) where enriched in the leaves but the amine-enriched relatives to Glu and Asp, glutamine (Gln) and asparagine (Asn) were highly enriched in the roots. This might be indicative of the processes Setaria employ to balance source-sink relationships of C and N. Differential accumulation of distinct amino acids in distinct plant tissues supports the idea that amino acids play a central role in N incorporation after uptake from soil, along with the translocation, utilization, and metabolism within plants. All NH 4 + derived from soil or produced from NO 3 - reduction is first channeled through the glutamine synthetase (GS) reaction [ 52 ]. GS catalyzes the fixation of NH 4 + into Glu to form glutamic acid. Glutamine and glutamic acid then can be utilized as amino group donors as well as N transport molecules [ 53 ]. GS activity has been tied to metabolic and environmental changes and has been linked to the balance of C and N metabolism [ 54 – 56 ] where Glu levels act as a nutritional status sensor for plants. Further interconversions from Glu to Gln and to other amino acids, especially Asn, are then possible. Source-sink differences for C and N observed with Setaria could also come about through our observation of large fold increases of beta-cyano-L-alanine coupled with high abundances of L-phenylalanine (Phe) in the leaves and L-tyrosine (Tyr) in the roots (Figs 8 and 9 ). This observation suggests that WNA Setaria is utilizing the cyanoamino acid pathway to produce hydrogen cyanide (HCN) to act as a negative regulator of nitrate reductase (NR) [ 57 ]. NR controls nitrate to nitrite conversions enabling the downstream conversion of nitrite to hydroxylamine to ammonium in the plant cells. The ammonium is then coupled with carbon skeletons (i.e., 3-phosphoglycerate) produced in photosynthesis to produce amino acids. If production of ammonium ions is not stoichiometrically coupled with carbon skeleton production, then a toxic buildup of nitrate, hydroxylamine and ammonium can occur. This regulation however also needs to be controlled as HCN is also toxic to the plant in large enough quantities. To accomplish cyanide detoxification, higher plants convert the cyanide into beta-cyano-L-alanine, which is further converted enzymatically by two different pathways to either L-asparagine, which we found to be highly abundant in the WNA roots, or L-aspartate [ 58 ], which we found to be highly abundant in the WNA leaves ( Fig 8 ). The alkaloid synephrine may also play an important role in our observed nutrient acquisition differences in Setaria . We detected a large increase in synephrine in the WNA plants compared to the NNA plants. We hypothesize that this alkaloid may be an important response metabolite in nutrient-rich environments. Generally, synephrine research has focused on its use as a vasoconstrictor agent in animals and humans as a treatment to shock and sometimes asthma [ 59 , 60 ]. The limited research on synephrine in plant-soil interactions shows that Anthrobacter which is an Actinobacteria, can use synephrine as a sole carbon and nitrogen source [ 61 ]. Future studies should investigate further synephrine’s role in nitrogen utilization or possible microbiome remodeling in Setaria . A high abundance of glycerol observed in the ectorhizosphere WNA of all Setaria grown in this study indicates nutrient addition enhances glycerol uptake in a nutrient poor soil. Glycerol has been detected in other plant studies as a root exudate [ 46 , 62 ]. Miller et al. (2019) detected glycerol in a non-targeted metabolomics analysis of sorghum rhizospheres, in sand, clay or soil media [ 63 ] where they observed much higher levels of glycerol in their no-plant control compared to the soil growing sorghum. In our soil analysis here, we also had no-plant controls which did show glycerol levels to be higher when compared to the potted plants having NNA, but lower in the ectorhizosphere of plants WNA ( S10 Fig ). We observed that glycerol is significantly greater in the ectorhizosphere soil for both A3 and A4 while the bulk soil shows a much smaller change. This suggests that Setaria might utilize glycerol in a nutrient poor environment to mitigate stress. Miller et al. (2019) suggested that glycerol may act as both a plant root exudate and a rhizosphere-abated metabolite [ 63 ]. In this study, glycerol was considered an osmotic stress protectant for both plants and microorganisms, and it has been documented that glycerol can be used by bacteria such as Pseudomonas putida , a soil monoderm and Actinobacteria, as a sole carbon source [ 64 ]. It also has been shown to possess auxin-like activity, negatively affecting root growth [ 65 ]. In this study, exogenous glycerol applied to Arabidopsis inhibited primary root growth and altered lateral root development. This phenotype appeared concurrently with increased endogenous glycerol 3-phosphate (G3P), H 2 O 2 and decreased phosphate levels in roots. In plants with exogenously applied glycerol, free auxin content increased by 46%, suggesting that glycerol likely altered normal auxin distribution thus affecting root development. It is likely that the presence of glycerol in the plant and as a root exudate, is tied to nutrient availability and when Setaria is grown under nutrient limited conditions, the plant exudate composition might enable enrichment of microorganisms which favor glycerol as a carbon source. This alteration in the level of glycerol then might act to alter auxin levels and thus root architecture which are better suited to low nutrient soils (e.g., high surface area fine root formation). Other compounds found in the soil metabolite analysis revealed enrichments of benzoic and palmitic acid. Benzoic acid and palmitic acid are organic acids which have the capability of altering soil microbiomes. We found both compounds to be significantly enriched in the loose bound soil surrounding the roots of setaria grown WNA as compared to setaria grown NNA ( S10 Fig ). In prior work [ 66 ], benzoic acid added to soil with peanut plants have been shown to be quickly consumed and metabolized by bacteria such as Burkholderia, whereas AD3 and actinobacteria have been shown to be reduced in benzoic acid treated soils. In watermelon studies, palmitic acid addition to the soil, decreased the severity of Fusarium wilt, changed the bacteria microbiome composition, and overall promoted the growth of watermelon [ 67 ]. It has therefore been hypothesized that plants exude such organic acids as a mechanism to remodel the soil bacteria for species which metabolize the acids making plants less susceptible to fungal pathogens [ 68 ]. Our data suggest that Setaria with nutrient amendment might be exuding such organic acids to the soil as a mechanism to inhibit pathogenic fungi. In conclusion, our comparison of A3 and A4suggests that A3 responds better to nutrient amendment, while A4 is better adapted to nutrient poor soil conditions. While a greater analysis of metabolomics data (and SOM and community structure) did not extend to A1 and A2, measured global responses of these accessions showed similarity to A3. And, it is possible that for these accessions hydroquinone and serotonin (a tyrosine derived alkaloid like synephrine) may be responsible for this finding. Hydroquinone is known as a growth stimulator in small concentrations and as a growth inhibitor at high concentrations [ 69 ]. Hydroquinone might act as a growth inhibitor for some Setaria spp or accessions (here, accession A3) grown with limited nutrient availability. For example, growth indicators for A3 (above and below ground biomass, plant height; S12 Fig ) differed to a larger degree than for A4, supporting the observed enrichment of hydroquinone under NNA relative to WNA. However, WNA above ground growth may be promoted via serotonin, which was observed to be greater in the leaves of A3 WNA, but not measured for A4 ( Fig 8 ). Admittedly, the overall role of serotonin in plants is not well known, but it has been suggested to play a role in plant growth and root architecture [ 70 ], along with glycerol. While not undertaken here, future studies directly measuring which metabolites are actively exuded to the rhizosphere using stable isotope tracing would be an informative addition to this study as well as experiments to characterize what specific influence the alkaloids serotonin and synephrine have on soil microbiome dynamics, if any, would be interesting to pursue. A future genome-wide association experiment (GWAS) could also further elucidate why genetically, A3 responded differently than A4 with and without nutrient addition."
} | 4,935 |
38192869 | PMC10772173 | pmc | 4,649 | {
"abstract": "The paper was devoted to the results of the study of methods to obtain superhydrophobic film based on the plasma polymerisation of hexamethyldisiloxane (HMDSO) inside the plasma jet at atmospheric pressure. The 3D printing technology was intended for film deposition, which has the advantage of producing superhydrophobic surfaces over a wide range of scales. The effect of synthesis parameters on the hydrophobic properties of the film has been studied. The obtained superhydrophobic films demonstrated stability and resistance in chemical solutions, at high temperatures, under the influence of UV-irradiation and in various weather conditions. The results can be used in various fields, including automotive, construction, electronics, medicine and others, where surface protection against moisture, contamination and corrosion is required.",
"conclusion": "4 Conclusion In this work, superhydrophobic films were successfully produced using HMDSO plasma polymerisation method in an RF discharge plasma jet at atmospheric pressure. 3D printing technology was used to apply the films, allowing the superhydrophobic coating to be applied on a large scale. The study showed that the contact angle of the films was directly related to the RF discharge power. HMDSO concentration and number of cycles have no effect on the superhydrophobic properties of the film but do affect the optical properties, resulting in reduced light transmission. Under optimal conditions, a contact angle of about 165° is achieved, providing a highly hydrophobic surface. When the film was applied in a single layer, the transmittance at 700 nm was about 88 %, compared to 92 % for pure glass. Surface morphological and chemical characterisation was carried out using SEM, demonstrating that the plasma jet is able to create micro- and nanostructured coatings with high surface roughness, giving the surface a superhydrophobic property. The resulting superhydrophobic films also demonstrated stability and resistance to chemical solutions, high temperatures, UV irradiation and the weather. This method of producing stable and durable superhydrophobic films by HMDSO plasma polymerisation at atmospheric pressure can be applied to a variety of applications including automotive, construction, electronics, medical and others where protection of the surface against moisture, dirt and corrosion is required.",
"introduction": "1 Introduction The superhydrophobic surface has an extremely high water-repellency and is designed to prevent water from sticking, collecting and rolling off like mercury drops. Over the last decade, superhydrophobic coatings with a contact angle greater than 150° have been produced with the aim of achieving a ‘lotus effect’ on the surface of various materials. These coatings are of great interest due to their attractive properties such as water resistance [ 1 ], self-cleaning [ 2 ], corrosion protection [ 3 ], anti-fogging [ 4 ], anti-icing [ 5 ], biofouling protection [ 6 ], anti-friction properties [ 7 ] etc. Due to their unique properties, hydrophobic films have a great potential for practical applications and can be used in power engineering, construction, medicine, marine industry, aviation, textiles, etc. [ [8] , [9] , [10] , [11] ], for self-cleaning [ 11 ], flow resistance reduction [ 12 ], anti-icing [ 5 , 13 ], corrosion protection [ 14 ], oil and water separation [ 15 , 16 ]. Surface hydrophobicity depends on two factors: the surface topography of the material (micro-nanotextured roughness) and its chemical composition (low surface energy) [ 15 ]. The superhydrophobic effect arises from micro- and nanoscale features that create a rough textured surface and minimize the contact area between water droplets and the film, reducing the adhesive force between them [ 17 , 18 ]. As a result, water droplets are repelled from the surface and easily slip off, removing with them any dirt, dust or other contaminants that may be attended. At this stage of development, there are many methods of producing hydrophobic and superhydrophobic surfaces based on obtaining a rough topography. These methods can be classified under the conception of nanofabrication by ‘top-down’ and ‘bottom-up’ approaches [ 19 ]. Top-down methods include lithography and plasma treatment [ 20 , 21 ]. Bottom-up methods characterise the colloidal assembly, layer-by-layer (LbL) and chemical vapour deposition (CVD), plasma polymerisation [ [22] , [23] , [24] ]. Among the mentioned above methods, chemical methods and atmospheric pressure plasma polymerisation are economically feasible and suitable for large scale production. Undoubtedly, chemical methods can produce superhydrophobic coatings with good transparency and mechanical strength. However, most of these methods rely on the use of toxic solvents and liquid chemical compounds, mainly from organosilanes and fluorinated chemicals [ 25 ] which vapours are harmful to the human body and the environment. Additionally, these methods require multilayer deposition [ 26 ] to obtain a hydrophobic film, and immersion [ 27 ] or centrifugation [ 28 ] methods are commonly used to uniformly distribute the film, which also complicates the process of depositing the film into existing systems and increases the time to obtain a coating. Therefore, the study and development of a one-step coating method that is scalable, inexpensive, simple and environmentally friendly is a very urgent task. One of the most promising and environmentally friendly methods is atmospheric plasma polymerisation [ 29 , 30 ]. The advantage of using atmospheric pressure plasma compared with other existing plasma technologies for producing superhydrophobic surfaces [ 31 , 32 ] is rest on irrelevant for expensive vacuum facilities or the use of chemical compounds that may be harmful to human health, making it an economical and environmentally friendly technology. In addition, the plasma method allows for extensive control of the synthesis parameters [ 33 ], thereby influencing the properties of the resulting material. The production of hydrophobic surfaces using atmospheric pressure plasma is a relevant and prospective area of research. Despite the many potential applications of a superhydrophobic surface, there are still open issues that need to be solved. One of these is the durability, stability and resistance of hydrophobic surfaces to extreme conditions [ 14 , [34] , [35] , [36] ], as they can be damaged by critical temperatures, mechanical, chemical and UV exposure. Moreover, although many of the existing technologies for producing a superhydrophobic surface are time consuming and financially demanding [ [37] , [38] , [39] ], finding alternative methods and improving existing methods that are more compatible with large-scale industrial production is now becoming a hot topic [ 40 ]. This paper presents a method to produce a superhydrophobic surface based on HMDSO plasma polymerisation at atmospheric pressure with RF discharge using 3D printing technology for large scale surface treatment. The effect of plasma parameters, number of cycles, and precursor flow on the formation of the superhydrophobic surface was investigated. Characterisation of the obtained results was carried out by SEM, X-ray photoelectron spectroscopy (XPS) (to determine the elemental composition and having bonds on the surface of the obtained film). The optical emission spectrum of the OES plasma was investigated to determine the mechanism of hydrophobic coating formation. In addition, studies were carried out to determine the resistance, stability and durability of the resulting superhydrophobic coatings at critical temperatures and under extreme conditions. The ability of the resulting superhydrophobic films to act as self-cleaning surfaces has been demonstrated. These studies will allow the use of superhydrophobic coatings to be extended to various applications where effective protection of surfaces against moisture, dirt and corrosion is required.",
"discussion": "3 Results and discussion In order to determine the optimum plasma parameters for the formation of a superhydrophobic surface, experiments were first carried out to investigate the effect of discharge power on the contact angle of the film. The Ar and Ar + HMDSO gas flow rates were set constant at 40 sccm and 1.2 sccm, respectively, and the HMDSO temperature was set at 27 °C (room temperature). The discharge power was varied between 100 W and 200 W in increments of 25 W. Fig. 2 a shows the graph of contact angle versus discharge power. It can be seen from the graph that the contact angle increases as the power increases from 140° at 100 W to 165° at 200 W. This indicates that the surface becomes more hydrophobic as the power of the deposition process increases. A larger contact angle indicates that the liquid droplet on the sample surface is more spherical and less able to spread. This behaviour may be due to a change in the morphology and chemical composition of the film with increasing power, affecting its surface properties and interaction with water. Fig. 2 Graphs of contact angle versus discharge power (a) and HMDSO temperature (b). Fig. 2 Further studies were carried out to investigate the effect of HMDSO temperature on the contact angle at a constant discharge power (200 W). It should be noted that it can be stated that the HMDSO concentration also varies due to temperature change. The discharge gas flow rate of Ar and HMDSO were set constant: 40 sccm and 1.2 sccm respectively. The HMDSO temperature was varied between 35 °C and 60 °C. From the graph of contact angle versus HMDSO temperature in Fig. 2 b, it can be seen that the contact angle almost does not change with increasing HMDSO temperature. This indicates that changing the HMDSO temperature within the considered range has almost no significant effect on the hydrophobicity of the resulting film surface. However, increasing the HMDSO temperature will result in more vapour formation and therefore an increase in the concentration of HMDSO in the injected gas mixture. This can lead to an increase in the rate of film formation and therefore an increase in film thickness. Thus, in this experiment, increasing the HMDSO temperature mainly affects the evaporation process and precursor concentration rather than changing the contact angle and hydrophobicity of the film surface. The effect of the number of application cycles on the contact angle and transparency of the resulting film was also studied. The discharge gas flow rates of Ar and HMDSO were set constant: 40 sccm and 1.2 sccm respectively, the HMDSO temperature was 27 °C and the discharge power was 200 W. Fig. 3 a shows a graph of the contact angle versus the number of cycles. It can be seen from the graph that the contact angle does not change significantly as the number of application cycles increases. The variation of the contact angle is within the measurement error. This means that the number of cycles has no significant effect on the hydrophobicity of the film surface. Fig. 3 Graphs of contact angle versus number of cycles (a) and transmittance (b). Fig. 3 The spectra show that the film with 1 deposition cycle has a high transmittance of up to 90 % in the visible spectrum ( Fig. 3 b). However, as the number of deposition cycles increases, the transmittance gradually decreases. This may be due to an increase in film thickness or a change in optical properties with repeated deposition ( Fig. 4 ). Fig. 4 SEM images of the surface of films deposited on a silicon substrate by plasma polymerisation at atmospheric pressure after one (a) and ten (b) cycles. Cross section of thin films after one (c) and ten (d) cycles. Elemental composition of the films after one (e) and ten (f) cycles. (For interpretation of the references to colour in this figure legend, the reader is referred to the Web version of this article.) Fig. 4 Fig. 4 shows the results of SEM analysis of films deposited on the surface of a silicon substrate by plasma polymerisation at atmospheric pressure with one and ten cycles. From the SEM images of the surface ( Fig. 4 a and b ) it can be seen that the morphology of the films obtained at one and ten cycles is essentially the same. This explains the stability of the superhydrophobic properties and the consistency of the contact angle of the films at different cycles. However, the film thickness measurements show a significant difference between the samples obtained after one and ten cycles. At one deposition cycle, the film thickness is about 3 μm ( Fig. 4 c), whereas the sample obtained after ten cycles shows a thickness of about 24.5 μm ( Fig. 4 d). Thus, these results confirm that as the number of deposition cycles increases, the film thickness becomes thicker, which affects its optical transparency. The results of the elemental analysis of the thin films obtained at different cycles are shown in Fig. 4 e and f . The analysis shows that the composition of the films does not change with the number of deposition cycles. Furthermore, XPS analysis was performed to determine the chemical composition and available bonds on the surface of the obtained film. As the results of XPS analysis ( Fig. 5 a) show, the decomposition products of hexamethyldisiloxane in the plasma environment and the subsequent formation of organosilicon bonds are present on the surface of the samples. The percentages of atomic carbon, oxygen and silicon are given in Table 1 . The dominance of oxygen in the spectrum is due to the fact that the experiment is conducted under atmospheric conditions using HMDSO. In addition, it should be noted that the resulting film is porous and has a high degree of roughness, which can lead to large surface oxidation. Fig. 5 b shows the XPS spectra of the carbon region (C1s) plotted using the Gaussian approximation. Deconvolution of C1s gave the following peaks: SiCN, C \n \n\n<svg xmlns=\"http://www.w3.org/2000/svg\" version=\"1.0\" width=\"20.666667pt\" height=\"16.000000pt\" viewBox=\"0 0 20.666667 16.000000\" preserveAspectRatio=\"xMidYMid meet\"><metadata>\nCreated by potrace 1.16, written by Peter Selinger 2001-2019\n</metadata><g transform=\"translate(1.000000,15.000000) scale(0.019444,-0.019444)\" fill=\"currentColor\" stroke=\"none\"><path d=\"M0 440 l0 -40 480 0 480 0 0 40 0 40 -480 0 -480 0 0 -40z M0 280 l0 -40 480 0 480 0 0 40 0 40 -480 0 -480 0 0 -40z\"/></g></svg>\n\n N–O, C–O3 [ 44 ]. SiCN is probably the result of HMDSO decomposition into methyl groups CH3, Si–O–Si [ 45 ], which in turn are fragmented into components and interact with each other to form chemical bonds. The presence of nitrogen bonding in SiCN is attributed to the diffusion of air into the plasma environment. The C N–O and C–O3 peaks are presumably related to film oxidation or diffusion of oxygen and nitrogen from ambient air into the plasma environment and subsequent deposition on the film with the formation of chemical bonds [ 46 ]. The deconvolution of XPS spectra of Si2p and O1s is shown Fig. S3 in the supporting information. Fig. 5 XPS analysis of the thin film. (a) XPS spectra and (b) deconvoluted C1s peak of the XPS spectra. Fig. 5 Table 1 Quantitative analysis of the chemical composition of the surface. Table 1 Start BE Peak BE End BE FWHM eV Atomic % O1s 542.58 537.09 523.08 4.18 58.14 Si2p 110.08 107.4 94.08 4.13 21.88 C1s 296.08 289.16 281.58 4.03 19.98 Subsequently, an optical emission spectrum of OES was taken to better understand the film formation mechanism in Ar + HMDSO atmospheric pressure plasma. Fig. 6 shows the results of the optical emission spectrum of Ar plasma ( Fig. 6 a) and Ar + HMDSO plasma ( Fig. 6 b). Initially, when pure argon plasma is ignited, intense peaks of atomic argon and some peaks of oxygen and nitrogen are observed. Afterwards, the intensity of argon and oxygen peaks decreases with hexamethyldisiloxane injection. The main explanation is that part of the energy is spent on the dissociation of HMDSO into radicals and on the subsequent chemical reactions of film formation. In the decomposition of HMDSO molecules in RF discharge, the separation of the methyl group CH3 is the first step of HMDSO fragmentation due to the low bonding energy between Si and CH3 [ 46 ]. The dissociation of methyl groups can be observed by the peaks of SiH and CN which fragmented into H, C and with subsequent chemical reactions with ambient air (O2, N2) [ 47 ]. The presence of SiO emission lines and Si atomic spectrum (4s->3p, 3p->3s) [ 48 ] indicates the fragmentation of the Si–O–Si bond. The identified nitrogen-containing components in the plasma and in the film are due to the diffusion of ambient air into the active region of the plasma flow. Fig. 6 Optical emission spectrum of Ar plasma (a) and Ar + HMDSO plasma (b). Fig. 6 The films obtained were tested for stability and durability of hydrophobic properties under extreme conditions, in particular the effects of UV irradiation, high temperatures, chemical reagents and durability under weathering conditions. The concentration (temperature) of HMDSO and the number of cycles did not affect the superhydrophobic properties. For further studies, superhydrophobic surface experiments were carried out at room temperature and 1 cycle. The chemical test was carried out using three different chemicals: ethanol, acetone and 646 solvent. The chemical test procedure was as follows: three samples were taken and the contact angle of each was measured before the test. Each sample was then placed on a flask containing the appropriate chemical for 1 h. The film was then left outdoors for 24 h to allow the chemicals to evaporate completely and the contact angle was measured after the test. Fig. 7 a shows that the hydrophobic properties of the films obtained remained almost unchanged after the chemical test. This indicates that chemical reagents such as ethanol, acetone and 646 solvent do not have a destructive effect on the superhydrophobic films. It can therefore be concluded that these films are resistant to chemical influences. Fig. 7 Contact angle graphs of obtained films from chemical tests (a), UV tests (b), thermal tests (c), time (d). Fig. 7 UV resistance was tested using a 100 W UV lamp with a wavelength of 250 nm. The film was exposed to UV irradiation for 1 h at 10-min intervals and the contact angle is measured before and after each irradiation step. The change in contact angle before and after irradiation is shown in Fig. 7 b. As can be seen from the graph, UV irradiation has no visible effect on the hydrophobic properties of the films obtained. The contact angle is largely unchanged after irradiation, indicating that the films are resistant to UV irradiation. This indicates that the films have good UV resistance and retain their hydrophobic properties even with prolonged irradiation. The thermal test was carried out using a heater. After reaching a certain temperature, we left the film on the heater for 30 min. The contact angle was measured before and after the test. The dependence of the contact angle on temperature is shown in Fig. 7 c. It can be seen from the graph that high temperatures, even up to 400°, have almost no effect on the hydrophobic properties of the obtained film. An insignificant decrease of the contact angle is observed which is within the measurement error of the goniometer. This indicates high thermal stability and preservation of the hydrophobic properties of the film at critical temperatures. The obtained superhydrophobic films were subjected to a 16-month durability test in Almaty weather conditions. The average temperature in the region ranges from −11 °C in winter to 30 °C in summer, rarely dropping below −18 °C or rising above 34 °C [ 49 ]. The contact angle was measured before the start of the experiment and monthly thereafter. The sample was exposed to a variety of weather conditions, including heavy rain, snow, frost, direct sunlight and heat. Fig. 7 d shows a graph of the contact angle versus time of exposure to weather conditions. The graph shows that the obtained superhydrophobic films show their stability in extreme weather conditions. The contact angle is virtually unchanged throughout the test period, indicating that the films retain their hydrophobic properties. This indicates that the films are highly resistant and are able to retain their functionality even when exposed to prolonged exposure to aggressive weather conditions. Thus, the results of the study confirm that the superhydrophobic films obtained by atmospheric plasma polymerisation are weather resistant and can be successfully applied in real climatic conditions, including extreme temperatures and exposure to solar radiation. Finally, the self-cleaning property of the resulting superhydrophobic films was investigated. The self-cleaning property of the surface plays an important role in practical applications such as solar panels and architectural glass exterior walls, as it allows the automatic removal of contaminants without additional energy input. As shown in Fig. 8 b, when a drop of water rolls over the superhydrophobic coating, contaminants on the surface are easily removed by the water drop, leaving the surface clean. Moreover, as the number of drips increases, the water removes more and more contaminants, indicating the superior self-cleaning characteristics of the superhydrophobic coating. In contrast, when the same test is performed on clean glass ( Fig. 8 a), traces of water droplets remain on the surface and contamination is not removed, indicating the inadequate self-cleaning ability of clean glass. The difference in the self-cleaning characteristics of superhydrophobic film and clear glass can be explained by their different adhesion properties to water. The surface of clear glass contains a large number of hydrophilic hydroxyl groups, which gives the surface a strong affinity to water. As a result, a drop of water remains on the surface of pure glass. In turn, films formed by HMDSO plasma polymerisation at atmospheric pressure are superhydrophobic and have very low adhesion to water. Water droplets can therefore easily roll off the superhydrophobic coating, taking the dirt with them (a video of testing the self-cleaning properties of the coating is shown in video S2 in the accompanying information). These results show that the superhydrophobic coating obtained in this work can be used as a self-cleaning surface. This opens up the prospect of applications in various areas where maintaining a clean surface is important, such as solar panels and architectural glass. Fig. 8 Self-cleaning property test without deposition (a) and with deposition (b). Fig. 8 In order to investigate the capability of plasma technology at atmospheric pressure in a variety of applications, we have applied hydrophobic coatings on a variety of materials including polyvinyl chloride (PVC) ( Fig. 9 a), plastic ( Fig. 9 b), glass ( Fig. 9 c), corundum ( Fig. 9 d), fabric ( Fig. 9 e) and paper ( Fig. 9 f). The figures show that the treated samples have the ability to repel water, providing long-term protection against moisture. Water droplets on these surfaces roll off easily without leaving traces or penetrating the material. The resulting hydrophobic coatings have a wide range of applications in various fields such as construction, industry, textile production and many others where reliable protection of materials against moisture and water is crucial. Thus, this technology can be successfully applied in various practical fields. Fig. 9 Obtaining a hydrophobic coating on different materials: PVC (a), plastic (b), glass (c), corundum (d), fabric (e), paper (f). Fig. 9"
} | 5,917 |
33910908 | PMC8081362 | pmc | 4,651 | {
"abstract": "Metal ion tracking allows monitoring of the healing process in supramolecular polymers on the nanoscale.",
"introduction": "INTRODUCTION Because the possibility to heal defects in polymeric objects is of substantial technological relevance, the design of materials that enable this function and the development of a fundamental understanding of the underlying processes are of considerable interest ( 1 – 5 ). In thermoplastic polymers, healing of severed surfaces can be achieved by heating above the glass transition or melting temperature ( 6 – 8 ), and the process involves the steps of surface rearrangement, wetting, diffusion of macromolecules across the interface, chain reentanglement, and randomization ( 6 ). According to the reptation model, the terminal relaxation time of entangled macromolecules ( T r ) is related to molecular weight M by T r ∝ M 3 ( 9 , 10 ), and healing rates consequently decrease with increasing M in a power law dependency. The ensuing problem that healing of high–molecular weight polymers is slow can be mitigated in polymers featuring covalent or noncovalent dynamic bonds ( 4 , 5 , 11 ). For example, the reversible association of binding motifs causes the temporary disassembly of supramolecular polymers upon exposure to suitable stimuli. The resulting decrease in the apparent molecular weight of linear polymers or the cross-link density of networks increases chain mobility and reduces viscosity ( 12 ), and healing processes are accelerated. When the stimulus is removed, supramolecular polymers reassemble ( 4 , 5 ). This approach has been exploited in a range of polymers ( 2 , 3 ), many of which were assembled through hydrogen bonds or metal-ligand interactions ( 13 – 18 ). If the interphase created by the healing process is indistinguishable from the pristine material, then the original properties can be fully restored ( Fig. 1A ) ( 1 ). Quantitative studies of such interphase formation are, however, rare. Schnell et al. ( 19 , 20 ) have shown that an interfacial width of 11 to 22 nm is required for self-adhesion in polystyrene. This was possible by combining fracture mechanics tests and neutron reflectivity experiments on nanoscale bilayers in which scattering contrast was established by using one deuterated layer. The results agree with computational and interdiffusion studies on other glassy polymers ( 8 ). Similar investigations with chemically more complex polymers are exceedingly difficult, and healing of polymers containing dynamic bonds is therefore typically probed by macroscopic experiments, such as the qualitative observation of the disappearance of scratches ( 3 , 4 , 21 ), or by comparing the mechanical properties of damaged and healed samples ( 14 – 18 , 22 ). For example, the recovery of the elongation at break, tensile strength, and/or toughness is established by tensile tests ( 14 , 21 , 23 – 25 ) or fracture mechanics ( 26 – 28 ). Efforts to examine healing processes on a microscopic level include in situ Raman (micro)spectroscopy ( 29 , 30 ), internal reflection infrared imaging ( 31 , 32 ), and laser speckle imaging ( 33 ). These studies corroborate that polymer diffusion is essential for efficient healing, but a quantification of the interphase formation is challenging, and it remains unclear what extent of diffusion is necessary to achieve complete healing in supramolecular and many other polymers. Fig. 1 The healing process in polymers. ( A ) The final stages of the healing process in polymers involve wetting, interdiffusion with reentanglement, and randomization. ( B ) To investigate the healing process on a length scale of a few nanometers, metallosupramolecular polymers (MSPs) assembled from telechelic PEB with terminal Mebip ligands ( M n = 3800 g mol −1 ; m ≈ 0.32, n ≈ 0.68, p ≈ 55) and either Eu(ClO 4 ) 3 or Tb(ClO 4 ) 3 were studied. The two metallosupramolecular polymers display similar properties, but the different ion types can be monitored in a spatially resolved manner. Here, we report an approach that allows monitoring of the interphase formed upon healing actual or simulated defects with a spatial resolution of a few nanometers. The approach relies on mending two otherwise identical metallosupramolecular polymers (MSPs) that were assembled with different metal ions. The two different ion types can be distinguished in a spatially resolved manner by energy-dispersive x-ray (EDX) spectrum imaging in scanning transmission electron microscopy (STEM). This allows monitoring of their diffusion across the interface ( Fig. 1B ) and thereby facilitates a correlation between the contact time, the depth of the interphase, and the macroscopic mechanical properties. We find notable differences to self-adhesion in glassy polymers, including that the required interphase thickness for complete healing in MSPs and, by extension, of similar healable polymers is an order of magnitude larger.",
"discussion": "DISCUSSION The interphase thickness required to restore the MSPs’ mechanical properties is considerably higher than the 11 to 22 nm reported for the welding of glassy homopolymers ( 19 , 20 ). Since the latter studies involved smooth interfaces and testing in an asymmetric double cantilever beam geometry, the considerable differences may be related to the roughness of the separately cut MSP films used herein, geometric factors of the mechanical testing, the interfacial structure, and wetting kinetics. Perhaps more important, however, are the different responses of the materials to mechanical deformation. The MSPs studied here are rubbery polymer networks with barely any entanglements ( M e of PEB ca. 2000 g mol −1 ) ( 52 ) in which the domains formed by metal-ligand complexes act as effective cross-linking points ( 36 ). The effectiveness of these cross-links for the mechanical properties of the MSPs may hence be considerably different than the entanglements of glassy, high–molecular weight polymers (typically more than 10 M e ), and more substantial interdiffusion is apparently necessary in the case of the supramolecular polymers to restore the effective cross-link density and bulk mechanical properties. Experimental work and theoretical modeling have corroborated that high molecular mobility and efficient diffusion across polymer-polymer interfaces are the key requirements for healing, and supramolecular interactions therefore represent an attractive basis for the design of healable polymeric materials. The MSPs investigated here can be readily healed, and the efficiency and time dependence of the process correlate with the relaxation time of the metal-ligand complexes. The design of the study allowed a rare insight into how the recovery of mechanical properties relates to interphase formation. Quite unexpectedly, the interphase thickness required to achieve complete healing of cuts applied to the MSPs greatly exceeds the previously reported values for the adhesion of flat films of glassy polymers, indicating that this parameter is not universal. Since most self-healing or healable polymers are—similar to the presently investigated materials—soft and feature reversible bonds and typical healing scenarios involve defects such as the ones studied here, our findings appear to be highly relevant. Our data show unequivocally that in this case, microscopic techniques with a resolution of the order of tens of nanometers or less may be suitable to probe the underlying process. Moreover, recent direct studies of diffusion in metal-coordinated transient polymer networks suggest complex and unexpected superdiffusive behavior that promotes polymer “hopping” rather than “walking” ( 53 – 55 ). Knowledge gained through such studies should be useful to develop a better understanding of the healing process and to guide the further design of new polymers with improved healing characteristics."
} | 1,967 |
33304029 | PMC7116457 | pmc | 4,653 | {
"abstract": "Rapid changes in species composition, also known as ecotones, can result from various causes including rapid changes in environmental conditions, or physiological thresholds. The possibility that ecotones arise from ecological niche construction by ecosystem engineers has received little attention. In this study, we investigate how the diversity of ecosystem engineers, and their interactions, can give rise to ecotones. We build a spatially explicit dynamical model that couples a multispecies community and its abiotic environment. We use numerical simulations and analytical techniques to determine the biotic and abiotic conditions under which ecotone emergence is expected to occur, and the role of biodiversity therein. We show that the diversity of ecosystem engineers can lead to indirect interactions through the modification of their shared environment. These interactions, which can be either competitive or mutualistic, can lead to the emergence of discrete communities in space, separated by sharp ecotones where a high species turnover is observed. Considering biodiversity is thus critical when studying the influence of species–environment interactions on the emergence of ecotones. This is especially true for the wide range of species that have small to moderate effects on their environment. Our work highlights new mechanisms by which biodiversity loss could cause significant changes in spatial community patterns in changing environments.",
"introduction": "Introduction Whether species composition changes gradually, or forms discrete zones along environmental gradients has been the subject of a long-standing debate in ecology ( Clements 1916 , Gleason 1926 , Braun-Blanquet 1928 , Hedberg 1955 , McIntosh 1967 ). Observational studies have found both gradual ( Whittaker 1956 , Lieberman et al. 1996 , Vazquez and Givnish 1998 , Ellison et al. 2010 ) and discrete patterns ( Kitayama 1992 , Tuomisto and Ruokolainen 1994 , Kessler 2000 , Hemp 2006 ). Rapid changes in community composition along gradients, also termed ecotones ( Kent et al. 1997 ), have been observed in a wide range of ecosystems, such as alpine treelines ( Germino et al. 2002 ), tropical mountain forests ( Martin et al. 2007 ) and coastal environments ( Walker et al. 2003 , Sternberg et al. 2007 ). Hereafter, a transition will be termed ‘rapid’ when its scale is much smaller than the spatial scale of the landscape, even though the transitional area may show mixing of species. While rapid changes can be blurred by species dispersal ( Liautaud et al. 2019 ) or stochasticity in nature, it is important to understand the theoretical conditions under which rapid community changes can emerge. These rapid changes in species composition can coincide with rapid changes in environmental conditions, such as the frost line ( Kitayama and Mueller-Dombois 1992 ) or discontinuities in edaphic conditions ( Tuomisto and Ruokolainen 1994 , Kessler 2000 ). In these cases, it is often assumed that changes in abiotic conditions are responsible for the change in species composition ( McIntosh 1967 , Kent et al. 1997 ). This assumption is supported in many cases, but it may obscure the possibility that, in other settings, the two boundaries emerge together from the influence of species on their abiotic environment. The mechanisms that can lead to such transitions are poorly known, and in particular the respective contributions of species–environment feedbacks and interspecific interactions. Species that are able to modify their abiotic environment are often called ‘ecosystem engineers’ ( Jones et al. 2010 ). Classical examples range from beavers that impact water flow and habitat heterogeneity ( Wright et al. 2002 ), to cushion alpine plants that buffer extreme temperatures and increase soil moisture ( Badano et al. 2006 ). Ecological niche construction is a particular case in which engineers modify the environment to their own benefits ( Kylafis and Loreau 2008 , 2011 ), creating a feedback with the environment (an example in which engineers can instead create succession is presented in the Supplementary material Appendix 1–5 ). This ecological process should be distinguished from the related concept of niche construction in evolutionary theory in which we would also expect species traits to evolve over time ( Odling-Smee et al. 1996 , 2003 ). Examples of ecological niche construction range from plant–water feedbacks in arid environment ( Dekker et al. 2007 ) to increases in nutrient inputs by trees in tropical ecosystems ( De longe et al. 2008 ). Such feedbacks can govern species distributions ( Wilson and Agnew 1992 ), particularly under harsh environmental conditions ( von Hardenberg et al. 2001 , Gilad et al. 2004 , Meron et al. 2004 , Kéfi et al. 2007 ), and lead to the emergence of ecotones ( Jiang and DeAngelis 2013 , Bearup and Blasius 2017 ). Classical studies on ecosystem engineers, however, have generally focused on the effects of a particular species having strong effects on the abiotic environment ( Bouma et al. 2010 , Jones et al. 2010 , Prugh and Brashares 2012 ). But many more species have small or moderate impacts on their environment. Such species, which are often neglected individually, might substantially affect their environment when aggregated. Furthermore, previous studies have scarcely explored what types of interactions can arise between multiple species that engineer their shared environment. We thus propose to focus on the role of diversity and species interactions in the emergence of ecotones through ecological niche construction. Biodiversity can have two main effects on the emergence of species–environment feedbacks: a cumulative effect of species number, and a heterogeneity effect due to variations in species’ preferences and engineering ability. Cumulative effects are similar to complementarity in biodiversity–ecosystem functioning relationships ( Loreau and Hector 2001 , Hooper et al. 2005 ). The fact that species coexist with weak or no competition implies the existence of different niches, i.e. other factors beyond the environmental preference modelled here. This cumulative effect is most important when there is no single identifiable engineer, but where a community acts collectively to create an ecotone. A potential example is the occurrence of ecotones between mangroves and hardwood forests, where several mangrove tree species can modify water salinity in synergy ( Sternberg et al. 2007 ). In contrast, the heterogeneity effect of biodiversity arises when there are differences in species’ preferred environmental states. We investigate the effect of these differences on emergent competition or facilitation between ecosystem engineers, and how this could play a role in ecotone emergence. In this study, we build a theoretical model that couples the dynamics of a community and of its abiotic environment to assess the role of ecosystem engineers and of their diversity in the emergence of ecotones in space. In our model, ecotones are represented by abrupt changes, including discontinuities. In the presence of multiple interacting species, we show that ecological niche construction can lead to the emergence of indirect interspecific interactions – which can be either positive or negative – through environmental modifications. Similarly, we show that even species with different preferences can act synergistically as a single community. We then assess the consequences of these different interaction types for community patterns in space, and identify the conditions under which ecotone formation is predicted to occur.",
"discussion": "Discussion In this work, we investigated the role of biodiversity and species interactions in the emergence of ecotones through ecological niche construction. In particular, we studied the respective contributions of niche construction strength ( γ ), similarity in the environment optimum of the species ( ΔC ) and diversity ( S ). Our results show that, depending on the engineering strength γ , the contribution of biodiversity to ecotone emergence will be either through the similarity of species’ environmental optima ΔC , or through the diversity of engineering species S . In the case of a single ecosystem engineer acting on the environment, discontinuities occur when a high niche construction rate ( γ ) allows the engineer to control its environment. These abrupt shifts are explained by the presence of two alternative stable states in the system that correspond to: 1) a modified state, with the environment close to the engineer’s optimum, and 2) a non-modified state, corresponding to the baseline value of the environment. A small change in the environmental conditions can thus lead to an abrupt shift from one attractor to the other. In the case where species are strong ecosystem engineers, the difference in environmental optima ( ΔC ) is the main contribution of biodiversity to the emergence of ecotones. The presence of various engineers with distinct environment optima leads to the emergence of indirect interactions that influence the community patterns. We showed in a two-species system that these indirect interactions can be competitive or mutualistic, depending on the value of the diference ΔC . When engineers have distant environmental optima and strong engineering abilities, their net interaction is competitive. At a given location, a species has a lower abundance when associated to a second engineer, as compared with its abundance when alone. Indirect competition through the environment can be observed in cases where there is multistability in the system, but also when a single equilibrium exists. In the extreme case where the modified environmental conditions are outside the other species’ fundamental niche, the latter can be excluded. By contrast, when the species’ environmental optima are close, with weak engineering abilities, we observe the emergence of net mutualistic interactions. In these cases, the two species are able to improve their carrying capacities, by modifying the environment to their mutual benefit. The abundance of a species is thus higher when associated with another engineer. In our study, the more species differ in their environmental optima, the stronger the negative effect they have on each other. This differs from classical limiting similarity theory ( MacArthur and Levins 1967 , Abrams 1983 ). Considering limiting resources such as water or light, limiting similarity theory predicts an increase in competition strength as the similarity in the resource requirements of the various species increases. By contrast, when species modify the abiotic environment to their own benefit, we showed that competition decreases, and then can turn into a net mutualistic interaction as the similarity of species’ environmental optima increases. With more than two strong engineers along the gradient, engineers with close optima will tend to modify the environment to their collective benefit. When the ability of a community to modify the environment becomes higher than the ability of another one, the former will replace the latter along environmental gradients. This can be interpreted as a situation where there is competition between communities. In this case, the community shows a high level of integration ( Clements 1916 , Wilson and Sober 1989 ). This type of community organization tends to create particular species abundance patterns in space, with discrete communities separated by sharp boundaries. In the case where the species are weak ecosystem engineers, the main contribution of biodiversity to community organization is through the number of engineering species. In this case, a weak ecosystem engineer alone is not able to substantially modify the environment and create a species– environment feedback. But when numerous weak engineers with similar optima are present, we do observe the emergence of species–environment feedbacks. In these cases, species jointly modify the environment to their collective benefit, as described above. In our model, an increase in species diversity can lead to an increase in each species’ biomass, through facilitation. The collective action of a large number of different ecosystem engineers can thus lead to the emergence of discrete communities along an environmental gradient, associated with sharp changes in the environment. In this study, the effect of several weak ecosystem engineers on the environment is not qualitatively different from the effect of a single strong engineer, but the spatial extent of the environmental change may be larger. The existence of several species may indeed broaden the spectrum of abiotic conditions under which the environment is modified, as seen in the case of positive interactions between two engineers. Biodiversity is potentially a key factor influencing the emergence of species– environment feedbacks in nature, and thus the emergence of sharp ecotones separating discrete communities. This might be the case in mangrove ecosystems, where several species can have similar effect on water salinity ( Sternberg et al. 2007 ). As shown in this study, a certain level of biodiversity in ecosystem engineers might be necessary to maintain species–environment feedbacks. Likewise, Gonzalez et al. (2008) showed that the accumulation of small environmental changes by weak engineers can ultimately lead to a substantial change in the abiotic environment, and thus allow an ecosystem engineer to invade. A decrease in biodiversity, as currently observed worldwide ( Pimm et al. 2014 , Ceballos et al. 2015 ), might thus have important consequences, not only for community composition and organization, but also for the abiotic environment and for ecosystem functioning. Species that do not modify their environment can also be influenced by ecological niche construction. By changing the environment, ecosystem engineers can promote species that benefit more from the modified state than the baseline conditions. In this case, ecosystem engineers indirectly facilitate other species through environmental modification. Facilitation has been shown to occur, particularly under harsh environmental conditions, such as in arid ecosystems ( Armas and Pugnaire 2005 , Soliveres and Maestre 2014 , Vega-Álvarez et al. 2018 ) or in cold environments ( Choler et al. 2001 , Callaway et al. 2002 ). When an engineer facilitates another species, it can be considered as a ‘nurse species’ ( Niering et al. 1963 ) that modifies the environment and allows the growth of species that would not have the ability to grow otherwise. Nevertheless, ecosystem engineering can also have negative effects on other species. For example, van Breemen (1995) showed how Sphagnum species can depress the growth of vascular plants by changing the environmental conditions in peat bogs ecosystems. A sharp ecotone can thus be explained by the appearance or disappearance of an engineer along the gradient, facilitating or preventing the growth of other species. In the case where species do not modify the environment to their own optimum, succession in time can be observed. In this case, the engineer can foster the growth of its successors, thus having a negative impact on its own performances ( Supplementary material Appendix 4 ). Species interactions – such as competition or mutualism – have been identified as drivers of species abundance along environmental gradients ( Terborgh and Weske 1975 , Choler et al. 2001 ). We have shown in this paper that interactions between species and the abiotic environment can have unexpected consequences on species interactions themselves. These interactions can lead to the emergence of discontinuities in the environment, associated with sharp ecotones where important species turnover are observed. Explicit consideration of species–environment feedbacks is thus likely to increase our understanding of species distributions along environmental gradients. It may similarly be essential when studying the responses of species or communities to temporal changes in their environment. Finally, we have also shown that biodiversity can influence community organization along an environmental gradient. Current biodiversity loss can have major consequences for species distributions, abiotic environmental conditions and ecosystem functioning."
} | 4,093 |
27863495 | PMC5116212 | pmc | 4,654 | {
"abstract": "Background Poly-3- d -hydroxybutyrate (PHB) that is a promising precursor for bioplastic with similar physical properties as polypropylene, is naturally produced by several bacterial species. The bacterial pathway is comprised of the three enzymes β-ketothiolase, acetoacetyl-CoA reductase (AAR) and PHB synthase, which all together convert acetyl-CoA into PHB. Heterologous expression of the pathway genes from Cupriavidus necator has enabled PHB production in the yeast Saccharomyces cerevisiae from glucose as well as from xylose, after introduction of the fungal xylose utilization pathway from Scheffersomyces stipitis including xylose reductase (XR) and xylitol dehydrogenase (XDH). However PHB titers are still low. Results In this study the acetoacetyl-CoA reductase gene from C. necator (CnAAR), a NADPH-dependent enzyme, was replaced by the NADH-dependent AAR gene from Allochromatium vinosum (AvAAR) in recombinant xylose-utilizing S. cerevisiae and PHB production was compared. A. vinosum AAR was found to be active in S. cerevisiae and able to use both NADH and NADPH as cofactors. This resulted in improved PHB titers in S. cerevisiae when xylose was used as sole carbon source (5-fold in aerobic conditions and 8.4-fold under oxygen limited conditions) and PHB yields (4-fold in aerobic conditions and up to 5.6-fold under oxygen limited conditions). Moreover, the best strain was able to accumulate up to 14% of PHB per cell dry weight under fully anaerobic conditions. Conclusions This study reports a novel approach for boosting PHB accumulation in S. cerevisiae by replacement of the commonly used AAR from C. necator with the NADH-dependent alternative from A. vinosum . Additionally, to the best of our knowledge, it is the first demonstration of anaerobic PHB synthesis from xylose. Electronic supplementary material The online version of this article (doi:10.1186/s12934-016-0598-0) contains supplementary material, which is available to authorized users.",
"conclusion": "Conclusions This work demonstrated the advantages of using a NADH-dependent AAR for PHB synthesis from xylose in S. cerevisiae compared to the regularly used NADPH-dependent AAR of C. necator. The NADH-dependent AAR from A. vinosum not only improved the conversion of xylose into PHB under aerobic conditions but it also enabled anaerobic PHB production from xylose when combined with redox engineered XR enzyme in recombinant S. cerevisiae . Further optimisation for higher titers, volumetric yields and productivities will notably require to engineer the central carbon metabolism in order to boost the production of the acetyl-CoA pathway precursor.",
"discussion": "Discussion The present study proposes a metabolic engineering strategy to enhance the production of PHB from xylose in recombinant S. cerevisiae . The bacterial pathway for PHB production from C. necator has previously been integrated into xylose-utilising S. cerevisiae strains as part of a strategy to convert xylose into PHB [ 16 ]. Here we show that replacing the NADPH-dependent acetoacetyl-CoA reductase (AAR) from C. necator with the NADH-dependent AAR from A. vinosum is essential for boosting PHB production from xylose under aerobic and oxygen-limiting conditions. We also obtain anaerobic PHB production from xylose and the highest ever reported PHB content per cell in S. cerevisiae , in a strain combining the new AAR with a co-factor balanced xylose pathway. These results offer interesting insights for the potential of industrial anaerobic ethanol-PHB co-production from lignocellulosic feedstocks and other agricultural/industrial residues because during the anaerobic processes the metabolic flux for synthesis of ethanol and PHB is favored. In addition the need for sparging the reactor with air is no longer necessary, leading to a reduction of investments in equipment and energy supply that are necessary to increase oxygen transfer and cooling [ 35 ]. In native organisms PHB is a storage carbohydrate generated under starvation conditions, which implies that its production is growth-uncoupled [ 36 ]. This may explain why most PHB pathways can use NADPH-dependent AAR as it does not have to compete with the NADPH-demanding biosynthetic reactions. In S. cerevisiae, however, this is a drawback because the deregulated PHB pathway competes with biosynthesis for NADPH. Also, cytosolic availability of NADPH is known to be lower than for NADH [ 37 ]. Altogether this may contribute to the limited PHB yields that have been observed in this organism [ 13 , 15 , 16 ]. Using instead a NADH-dependent AAR should enable PHB production that is less dependent from biomass formation than with the regular C. necator pathway. Indeed, we were able to show that PHB volumetric titers, yields and PHB content per cell could be improved by using the NADH-dependent AAR from A. vinosum . It cannot be ruled out that the improvement may result from the overall higher NAD(P)H acetoacetyl-CoA conversion rate in the novel AAR. However the increasing PHB content per cell under oxygen-limiting conditions suggests that the availability of intracellular NADH plays a key role in the overall improvement. A growth-coupled PHB production also implies that part of the cytosolic acetyl-CoA that is needed for biosynthesis has to be directed towards PHB formation. This can explain why PHB production is found to be inversely proportional to biomass formation and growth rate under all tested conditions. For example, TMB4425 carrying the NADH-favoring XR mut has a lower growth rate than TMB4445 (carrying the NADPH-favoring XR wt ) under aerobic conditions, as a result of limiting NADH availability under respiratory conditions, and it accumulates 1.4 times more PHB per cell; instead, when the oxygen availability is limiting, TMB4425 grows faster than TMB4445 (XR wt ) due higher availability of NADH but the PHB content per cell is one-third lower than the one observed in TMB4445 (XR wt ). More generally a reduction of the carbon flux towards biomass synthesis via the TCA cycle is expected to result in an increase in the flow towards ethanol and acetate, thereby increasing the levels of PHB precursors. The choice of XR is also critical for anaerobic PHB production from xylose. We show that a combination of XR mut and NADH-dependent AAR, i.e. TMB4425, should be used under these conditions to produce significant amounts of PHB because they favour NADH excess, which benefits both XR mut and AvAAR activity, as well as a shift towards the fermentative pathway, thereby the PHB precursors, as a result of the absence of mitochondrial respiration. Still ethanol remains the major by-product of xylose fermentation, therefore it will be necessary to further increase the carbon flow towards the acetate and acetyl-CoA precursors, for instance by combining the current changes with the reported acetyl-CoA pool engineering strategies consisting in expressing heterologous pathways [ 38 – 41 ] or modifying the native pathways [ 39 , 42 – 44 ]. Under oxygen-limiting conditions the strains carrying the PHB pathway (TMB4425 and TMB4445) were found to have higher glycerol yields and lower ethanol yields than the corresponding reference strains (TMB4424 and TMB4444). This can be expected when a part of acetaldehyde, i.e. the precursor of that contributes to the re-oxidation of NADH from glycolysis, is redirected towards acetate formation for PHB production. Indeed we observed that the difference in ethanol molar yield corresponded to the increase in glycerol molar yield (Additional file 3 ). This would mean that PHB synthesis cannot per se act as an alternative redox sink to ethanol, since its synthesis only regenerates 0.5 NAD + per pyruvate, i.e. half of the NAD + regenerated via ethanol formation. Therefore glycerol formation is preferred. We also hypothesize that the redirected flux from ethanol was used for PHB production; however the produced PHB level per g xylose was much lower than the potentially redirected acetyl-CoA precursor (Additional file 3 ); so a part of the redirected flux might be used for other purposes, which could for instance also explain the increase in biomass yield in the PHB-producing strains as compared to their controls."
} | 2,065 |
39108310 | PMC11300351 | pmc | 4,657 | {
"abstract": "Introduction The integration of self-attention mechanisms into Spiking Neural Networks (SNNs) has garnered considerable interest in the realm of advanced deep learning, primarily due to their biological properties. Recent advancements in SNN architecture, such as Spikformer, have demonstrated promising outcomes. However, we observe that Spikformer may exhibit excessive energy consumption, potentially attributable to redundant channels and blocks. Methods To mitigate this issue, we propose a one-shot Spiking Transformer Architecture Search method, namely Auto-Spikformer. Auto-Spikformer extends the search space to include both transformer architecture and SNN inner parameters. We train and search the supernet based on weight entanglement, evolutionary search, and the proposed Discrete Spiking Parameters Search (DSPS) methods. Benefiting from these methods, the performance of subnets with weights inherited from the supernet without even retraining is comparable to the original Spikformer. Moreover, we propose a new fitness function aiming to find a Pareto optimal combination balancing energy consumption and accuracy. Results and discussion Our experimental results demonstrate the effectiveness of Auto-Spikformer, which outperforms the original Spikformer and most CNN or ViT models with even fewer parameters and lower energy consumption.",
"conclusion": "6 Conclusion In this work, we are the first to propose a one-shot spiking transformer architecture search method for spiking-based vision transformers, named Auto-Spikformer. Auto-Spikformer optimizes both energy consumption and accuracy by incorporating critical parameters of SNN and transformers into the search space. We introduce two novel methods: Discrete Spiking Parameters Search (DSPS), which optimizes SNN parameters, and the Accuracy and Energy Balanced Fitness Function F A E B , designed to balance energy consumption and accuracy objectives. Extensive experiments demonstrate that the proposed algorithm significantly enhances the performance of Spikformer and uncovers numerous promising architectures. As part of our future work, we plan to extend our experiments to larger benchmark datasets.",
"introduction": "1 Introduction Spiking neural networks (SNNs) show promise for the next generation of artificial intelligence, owing to their biological inspiration and appealing features such as sparse activation and temporal dynamics. The performance of SNNs has improved by employing advanced architectures from ANNs, such as ResNet-like SNNs (Fang et al., 2021a ; Hu et al., 2021a , b ; Zheng et al., 2021 ), or Spiking Recurrent Neural Networks (Lotfi Rezaabad and Vishwanath, 2020 ). Transformer, originally developed for natural language processing (Vaswani et al., 2017 ), has proven successful in various computer vision applications, including image classification (Dosovitskiy et al., 2020 ; Yuan et al., 2021a ), object detection (Carion et al., 2020 ; Zhu et al., 2020 ; Liu et al., 2021 ), and semantic segmentation (Wang et al., 2021 ; Yuan et al., 2021b ). The self-attention mechanism, a crucial component of the Transformer model, selectively attends to relevant information and is analogous to an important feature of the human biological system (Caucheteux and King, 2022 ; Whittington et al., 2022 ). The integration of self-attention into SNNs for advanced deep learning has gained attention due to the biological properties of both mechanisms. Spikformer (Zhou et al., 2022 ), a recent SNN architecture, has demonstrated promising results on both static and neuromorphic datasets using its Spiking Self-Attention (SSA) and Spiking Patch Splitting (SPS) modules. While SNNs are known for their low energy consumption compared to ANNs, our observations revealed that the energy consumption of Spikformer can be significantly reduced as it contains potentially redundant channels and blocks. In Figure 1 , we observed suboptimal architecture parameters in the original Spikformer, with redundancy channels, particularly in higher-order channels (See Section 3 for more details). These phenomenons motivates us to search to design a better Spikformer architectures. Nevertheless, designing and training such hybrid models remains a challenging task (Dosovitskiy et al., 2020 ; Touvron et al., 2021 ). Figure 1 Analysis of redundancy of Spikformer (A) Relationship among energy consumption, number of parameters, and accuracy for various Spikformer candidates. Original Spikformer candidates are obtained from Zhou et al. ( 2022 ). We select 100 candidates from the Spikformer large search space S T s using our proposed Auto-Spikformer method and random selection method, then plot their Pareto frontier onto the figure. Note that larger circles represent a higher number of parameters. Detailed results can be found in Section 5.3. (B) The Structural Similarity (SSIM) matrix between channels after embedding (also called SPS) in Spikformer. Both the X and Y axes represent channels. The color indicates the SSIM value: yellow denotes higher similarity, while green denotes lower similarity. The matrix reveals significant redundancy in channels, particularly in higher-order channels, after embedding. We address the Spikformer search problem by dividing it into two main parts: the Transformer part and the SNN neuron part. Transformer Architecture Search (TAS) (Chen B. et al., 2021 ; Chen M. et al., 2021 ; Su et al., 2022 ) has gained attention as an automated way to search for multiple configurations of Vision Transformer (ViT) architectures. The one-shot NAS scheme (Dong and Yang, 2019 ; Chen M. et al., 2021 ), leveraged in TAS, obtains reliable performance estimations on various ViT architectures. We choice weight entanglement supernet training strategy (Chen M. et al., 2021 ) as base search method to optimize the Transformer architecture. However, directly applying TAS may not be the most optimal solution for Spiking Transformers. The original TAS method does not consider the SNN search space and the energy consumption, which is vital in the field of SNN. To optimize the internal parameters of SNN neurons, we propose a method that leverages the concept of natural selection and evolutionary algorithms. While previous studies have focused on improving SNN performance through network structure exploration, the significance of individual neuron parameters has also been identified (Che et al., 2022 ; Kim et al., 2022 ; Na et al., 2022 ). We draw inspiration from Darwin's theory of evolution, which suggests that organisms adapt to their environment through natural selection over time (Slowik and Kwasnicka, 2020 ; Jordan et al., 2021 ). Similarly, SNN neurons can evolve and optimize their internal parameters to enhance network performance. By treating traits such as the threshold, decay, and time-step parameters of a neuron as candidate solutions and the input stimuli as the environment, we can apply simulated evolution to find optimal parameter sets that improve accuracy and efficiency. This novel approach, referred to as Discrete Spiking Parameters Search (DSPS), utilizes an evolutionary algorithm to search for the internal parameters of SNN neurons. Our study is the first to apply the evolutionary algorithm to search for the internal parameters of SNN neurons. Our method for optimizing Spikformer explores the optimal combination of key factors but doesn't ensure lower energy consumption. To address this, we introduce a joint fitness function, F A E B , balancing energy consumption and accuracy. This allows us to achieve a Pareto optimal combination, striking a balance between these two objectives. We summarize our contributions are as follows: We provide the first systematic and in-depth analysis of the channel redundancy in SNNs by analysing the performance curve and Structural Similarity (SSIM), which are crucial to the high energy efficiency. To the best of our knowledge, this study is the first to use NAS for spiking-based ViT, namely Auto-Spikformer. By employing Discrete Spiking Parameters Search (DSPS) and the weight entanglement supernet training method, Auto-Spikformer enhances the efficiency and accuracy of spiking-based ViT architectures. Auto-Spikformer integrates an accuracy and energy balanced fitness function F A E B to optimize the Spikformer search space by considering both energy consumption and accuracy simultaneously."
} | 2,101 |
33596647 | PMC8915166 | pmc | 4,658 | {
"abstract": "This Review presents\nand discusses the current state of the art\nin “exchangeable liquid crystalline elastomers”, that\nis, LCE materials utilizing dynamically cross-linked networks capable\nof reprocessing, reprogramming, and recycling. The focus here is on\nthe chemistry and the specific reaction mechanisms that enable the\ndynamic bond exchange, of which there is a variety. We compare and\ncontrast these different chemical mechanisms and the key properties\nof their resulting elastomers. In the conclusion, we discuss the most\npromising applications that are enabled by dynamic cross-linking and\npresent a summary table: a library of currently available materials\nand their main characteristics.",
"conclusion": "13 Conclusions and Outlook The field of liquid\ncrystal elastomers and their applications has\nbeen exploding in the past few years, driven by the robust and accessible\nchemistries leading to a set of standard “benchmark”\nmaterials, and inviting real application development. The latter development\nis happening in several directions but very noticeably in the 3D printing\nusing the direct writing of LC “ink”. After permanent\ncross-linking (often induced by UV, after extrusion), the LCE structures\nbecome fully reversible shape-morphing 3D objects, where the actuation\nis controlled by the local alignment. On this background, dynamic\nxLCE networks represent the next turn\nof this development—a radical departure from the idea of permanent\nnetworks, leading to a different approach to precision shaping and\nlocal programming of alignment, and as a result offering a different\nspectrum of applications. The inherent ability to be remolded and\nrecycled is the key appeal of xLCEs. This Review attempted to present\nthe main directions of materials exploration, where a variety of exchangeable\nchemistries have been introduced with the bond exchange induced/triggered\nby a range of stimuli (temperature, light, solvent). Table 1 below gives a summary of these\nmaterials and methods. However, we feel the standard “benchmark”\nin the xLCE field is not yet set. The field of xLCEs continues to\ngrow, and new chemistries are being introduced all the time. Table 1 Examples of DCC Reactions That Have\nBeen Used to Produce xLCEs a DCC reactions (reference) activation stimulus activation temperature\n(°C) LC phase temperature (°C) T g (°C) actuation (%) transesterification from epoxy–acid 86 , 93 , 140 thermal or solvent 150–180 SmC-100-I 55 30:80 transesterification from thiol–acrylate 92 thermal or solvent 80 N-50-I 18 10:30 transesterification from hydrosilylation 121 thermal 120 N-150-I 4 transesterification of epoxy–thiol 141 , 142 thermal 170–210 SmC/N-40:140-I 4–14 7:25 boronic transesterification 95 thermal 40 N-90-I –5 90 transcarbamoylation of urethane 94 thermal 150 SmA-42-N-80-I –8 100 siloxane exchange\nfrom thiol–ene 97 thermal 250 N-75:32-I –35 60 siloxane exchange hydrosilylation 98 thermal 125 SmC-72-I –5 55 Diels–Alder dynamic networks 96 thermal 125 SmA-88-I 25 48 disulfide exchange\nfrom epoxy acid 99 thermal,\nphoto, or solvent 160 or 22 SmC-100:135-I 10–48 40 disulfide exchange from\nthiol acrylate 132 thermal\nor photo 180 or 22 N-56-I –5 38 reversible [4 + 4] cycloaddition\nof anthracene 101 thermal\nor photo 200 or 22 N-63-I 22 reversible [2 + 2] cycloaddition\nof cinnamon 100 , 137 photo 22 SmC-50-N-60-I 22 50 radical-mediated addition–fragmentation chain transfer 87 photo 22 N-80-I 0 60 a The bond-exchange types, activation\nconditions (e.g., temperature, light), thermal transition temperature,\nliquid crystalline phase type, and actuation performance. The actuation\nis recalculated using the following formula: actuation strain (%)\n= ( L / L iso – 1)\n× 100. In the range\nof different chemistries explored, each have demonstrated\ntheir own unique appeal, advantages, and also limitations. When considering\npractical applications, these have to be factored in for the choice\nof dynamic chemistry for the xLCE network. The constraints of the\napplication envisaged will determine the specifications the xLCE has\nto meet, from the type of stimulus for the actuation and for the activation\nof the bond exchange (which can be identical or orthogonal), the presence\nor not of a catalyst and its nature, the reprogramming conditions\nand methods, the value of T i , etc. The\nmechanism of the exchange reaction is important when considering the\noverall properties desired for a material. In the case of reactions\nsuch as transesterification, an associative bond exchange occurs:\nthe new bond forms before the old one breaks, guaranteeing a conservation\nof the structural integrity of the network at all times and resulting\nin a material that is insoluble even when the bond exchange is activated.\nThis is excellent to guarantee a robust material under all conditions.\nIn the case of reactions such as the disulfide exchange, the DA reaction,\nand other cycloadditions, the exchange occurs through a dissociative\nmechanism, when the bond rupture and the bond formation are two independent\nphenomena. Despite a bond dissociation resulting in a decrease in\nnetwork connectivity (often undesirable in general polymer networks),\nthis mechanism has proven quite appealing for xLCEs, as it enabled\nsample reprogramming at room temperature and can also enable solvent\nuse to facilitate reprocessing if desired. Finally, in other reactions,\nsuch as transcarbamoylation, either mechanism is possible, and the\npredominant one depends on a range of factors, such as the structure\nof the chemical groups involved and the nature of the catalyst used\nin the material if any is used. These factors influence the material\nbehavior and properties during and after network exchange. Overall,\nthe conditional network malleability, leading to the reprogramming,\nreprocessing, and recycling the materials, opens new doors for xLCE\napplications; a range of innovative processing methods have been demonstrated\nto enable this. xLCEs could be used to fabricate aligned composites\nthat are responsive to temperature, light, electricity, or magnetic\nfield to generate mechanical actuation. In the near future, we hope\nto see xLCEs utilized in 3D printing applications based on the fused\nfilament fabrication (FFF) technique. xLCEs with thermally induced\nbond exchange can plastically flow under stress at high temperature,\nwhich allows them to be extruded into aligned filaments and printed\ninto active 3D objects. FFF is a better and cheaper 3D printing technique\ncompared to direct ink writing methods, because it does not rely on\nUV cross-linking (as the current used methods do). In addition\nto the actuation response, the damping and adhesion\napplications promise better results with xLCEs, since the material\nhas an additional energy dissipation mechanism due to the dynamic\ncross-linking (separate from the independently mobile nematic director,\nwhich is already making the damping of LCEs anomalous compared to\nstandard elastomers). It is possible that the impact and vibration\ndamping efficiency, and the strength of the dynamic adhesion associated\nwith it, could increase in xLCEs compared to permanent thermosets.\nHowever, this depends on how significant the bond-exchange rate is\nat an operating temperature—and in some dynamic networks with\na higher activation energy of exchange, this increase could be small.\nMuch additional research is needed into these dynamic properties of\nxLCEs. Similarly, the modulation of the adhesive powers of xLCE materials\nthrough the modulation of the number of loose dangling chains generated\nthrough reversible dissociative bond cleavage is an additional direction\nof interest for these materials, compared to standard LCE thermosets.\nIt would be interesting to see whether the damping efficiency is enhanced\nby the presence of potentially dynamic bonds within the network. Indeed,\ndynamicity is expected to confer to the material an additional dissipation\nmechanism due to the stress relief through the bond exchange. In surface\nmodification applications, xLCEs offer reliable ways to imprint surface\ntopography or composite structures that would reversibly alter the\nsurface behavior. Interestingly, the theoretical understanding,\nmodeling, and predictions\nof xLCE behavior are somewhat behind, which is in contrast to the\nclassical field of LCEs where the theory went hand in hand, or even\nahead of experiments and the material development. Active fully recyclable\npolymers, which are doing mechanical work by themselves, is certainly\na future of plastics in the 21st century.",
"introduction": "1 Introduction on LCEs and\nTheir Alignment The idea of a large-strain reversible mechanical\nactuator based\non the intrinsic material properties of liquid crystalline elastomers\n(LCEs) has been understood for over 30 years. 1 − 3 The key characteristics\nof LCE actuation are remarkable: fully reversible action, 4 large amplitude, with a stroke of up to 500%, 5 , 6 and the stress–strain–speed response matching or exceeding\nthe human muscle. 7 The origin of this effect\nlies in the direct coupling of the macroscopic shape of a cross-linked\nnetwork and the underlying anisotropic order of its polymer strands,\ne.g., the length of a sample contracts along the director axis when\nthe internal liquid crystal order is altered by heating into the isotropic\nphase (although other ways of altering the order parameter exist,\nextensively reviewed in the literature 3 , 4 ). At the same\ntime, separately from the natural order–shape connection leading\nto actuation, LCEs have a unique mechanical property of “soft\nelasticity” (when elastic deformation may occur at low or zero\nstress), 8 − 10 which leads to a different strand of potential applications.\nThese properties make LCEs highly attractive in biomedical engineering, 11 , 12 robotics, 11 , 13 smart textiles, 14 , 15 damping, 16 , 17 adhesive systems, 11 , 18 surface modifications, 14 , 19 and many other fields\nof modern engineering. 20 − 23 After initially being envisioned as thermotropic mechanical\nactuators\n(artificial muscles) by de Gennes in 1975, 24 the first LCEs were synthesized and investigated by Finkelmann in\n1981. 25 Finkelmann and co-workers were\nable to first make these new materials in the form of a nematic side-chain\nelastomer employing a hydrosilylation reaction to graft a vinyl mesogenic\npendant groups onto a siloxane polymer backbone. 25 A parallel approach used mesogenic monoacrylates to polymerize\ninto a different kind of side-chain LCEs. 26 , 27 In side-chain elastomers, the mesogens are attached to the flexible\npolymer backbone and their orientational order imposes anisotropy\non the backbone chains linked into the rubbery networks. To\nachieve actuation, the uniformly aligned LCE needs to be, e.g.,\nheated above its isotropic transition temperature (although there\nare other ways of changing internal order). However, unlike in ordinary\nliquid crystals, the natural equilibrium configuration of LCE networks\nis a polydomain state with a characteristic domain size of ∼1\nμm, unless special precautions are taken to cross-link it in\nan aligned configuration. A large body of knowledge exists on the\norigins of the polydomain state, which is due to the randomly quenched\ndisorder in the liquid crystalline system, 28 , 29 as well as on the polydomain–monodomain transition that occurs\non uniaxial stretching of a polydomain LCE. 30 − 32 Polydomain\nLCEs cannot produce actuation without external stress due to their\nlack of overall anisotropy. Therefore, LCEs with uniform equilibrium\nmolecular alignment, i.e., the monodomain or the “single crystal”\nLCEs, were created to enable load-free reversible actuation. 1 The average molecular alignment must be fixed\npermanently by network cross-linking, which is most commonly achieved\nthrough one of the following three methods: mechanical stretching\nwith two-step cross-linking, 1 , 33 , 34 surface alignment on a substrate, 35 , 36 or cross-linking\nafter shear extrusion 5 , 37 ( Figure 1 ). Figure 1 Schematic illustration of the three key methods\nof alignment in\nmonodomain LCEs. Top line: alignment by two-step cross-linking. 1 , 34 Middle line: surface alignment. 36 , 48 Bottom line:\nalignment by shear on extrusion. 5 , 37 Some graphics are adapted\nfrom ref ( 34 ), copyright\n2015 Royal Society of Chemistry, from ref ( 5 ), copyright 2006 John Wiley and Sons, with permissions\nfrom ref ( 36 ) copyright\n2015 AAAS, from ref ( 48 ) copyright 2017 Springer Nature. Mechanical stretching via two-step cross-linking was the first\ntechnique to achieve the permanent uniform molecular alignment in\nLCEs, and it is this achievement that has “ignited”\nthe whole field of LCE research and applications. Küpfer and\nFinkelmann originally utilized a two-step reaction to enable the stress-aligning\nof the weakly cross-linked LCE gel between the steps, based on different\nreaction rates. In the first step, a fast hydrosilylation reaction\nwas used to attach side-chain vinyl mesogens to a siloxane backbone.\nThe second step was to cross-link the network via a much slower reaction\nof siloxane with acrylate cross-links, the long window of time to\nfull cross-linking allowing for the mechanical stretching and aligning\nof the partially cross-linked gel. The second reaction was allowed\nto complete in the sample under load to lock in the new aligned conformation\n( Figure 1 ). This technique\nhas been extensively used by many research groups, in both side-chain\nand main-chain LCEs (in the latter, the mesogens are placed within\nthe polymer backbone). 1 , 2 , 33 , 38 − 40 The two-step hydrosilylation\nmethod produces lightly cross-linked elastomers with tunable thermomechanical\nproperties and LC phase behavior and full actuation range. 4 , 41 The hydrosilylation-based two-step cross-linking method had\nenormous\nsuccess during the 1990s and early 2000s, and as a result, LCEs have\nbecome a relevant field in material science. 4 , 42 − 46 However, this cross-linking method suffered from fundamental problems,\nwhich restricted the real-world applications for these materials.\nFor example, the difficulty of making monodomain structures and producing\nscalable samples remained a major problem of the field of LCEs. The\ndifficulties originated from the lack of control over the reaction\nkinetics during the alignment step: applying the load too soon to\na still weak gel results in fracture, while applying the load too\nlate in the continuously ongoing cross-linking process results in\npoor alignment and strong random disorder. The concept of two-step\ncross-linking has been significantly improved\nin the past few years, after adapting thiol-based click chemistry\nto the production of LCEs (e.g., two-step thiol–acrylate reaction). 34 Unlike for hydrosilylation, this chemistry relies\non two independent reactions (nucleophilic Michael addition of thiol–acrylate\nand acrylate photopolymerization, which can be photoinitiated for\nadditional control). 34 , 47 Therefore, the process allows\nfor much better separation of the reaction steps and the process parameters.\nMoreover, this chemistry uses well-established commercially available\nreacting monomers, which allows for producing scalable samples. The second method to align LCEs is the surface alignment technique\nwhich has been used to align liquid crystalline (LC) molecules in\nLC displays since the 1970s. 36 , 48 , 49 It was introduced to LC polymers in the 1980s by Broer and co-workers. 50 − 52 This method of photopolymerization of diacrylate mesogenic monomers,\naligned on a substrate, produces highly cross-linked and aligned networks\nwith the glass transition ( T g ) above 100\n°C and an actuation strain of less than 5%. Polymerization of\ndiacrylates was demonstrated as useful in many applications where\ninduced surface bending could be utilized. 53 Recently, White et al. added small difunctional isotropic monomers\n(e.g., primary amine or dithiol) to these diacrylate networks to form\naligned elastomeric films with their T g just below room temperature. The actuation of these new elastomers\nwas about 40–100% strain. 36 , 54 It is important\nto note that surface alignment techniques are only effective to produce\nplanar systems (films). On the other hand, it could allow complex\npatterns of alignment compared to the conventional two-step alignment\napproach only giving a uniform uniaxial director. Nonetheless, the\nmethod remains limited to very few polymerization chemistries and\nto films of less than 100 μm in thickness. The third method\nto align LCEs for actuation is by shear stress\nduring extrusion. Shear has been traditionally employed to align fibers\neven in isotropic polymer composites. The method was introduced to\nthe field of LCEs in 2006 when an extruder was used to extract well-aligned\nLCE fibers with high actuation strain (>400%) from physically cross-linked\nLC polymers. 5 Recently, this technique\nwas dramatically improved by the use of 3D printing, where printed\nLCE objects can be produced by extruding LC oligomers into filaments\nand then subsequently photo-cross-linking them to create complex shapes\nand structures. 37 , 55 − 58 It is important to point\nout that all of these approaches to produce\npermanently aligned monodomain LCEs can be difficult to use in practice,\nespecially when it comes to producing complex geometries and shapes\nof the elastomer, because the required molecular alignment must occur\nbefore the final cross-linking reaction is complete. This presents\nthe unavoidable competition between the alignment (which needs low\ncross-linking to avoid quenched disorder) and the cross-linking (which\nis needed to give the material mechanical stability but prevents further\nalignment). Also, as with all thermosets, there is a problem of recycling\nor reprocessing. This Review is organized as following. In the\nnext two sections,\nwe offer an overview of dynamically cross-linked LCE networks—which\ndiffer greatly from a “thermoplastic LCE” concept based\non physical cross-linking (although thermoplastic elastomers are common\nin polymer science and technology, their application in the LCE field\nhas not yet been successful, mainly because there is unavoidable creep\nat high temperatures). Section 2 gives a brief discussion of how the dynamically exchangeable\ncovalent bonds can be used in forming the network, the idea that has\nled to the foundation of exchangeable liquid crystalline elastomers. Section 3 offers an overview\nof key physical properties that distinguish these materials from permanently\ncross-linked LCEs, which are common to all types of dynamic networks.\nFollowing that, the subsequent sections 4 – 12 list and\ndiscuss different chemistry advances (types of bond exchange and the\nresulting properties of the materials) achieved in the past few years—since\nthis field has taken off."
} | 4,701 |
29435256 | PMC5792611 | pmc | 4,660 | {
"abstract": "Abstract Despite the importance of coral microbiomes for holobiont persistence, the interactions among these are not well understood. In particular, knowledge of the co‐occurrence and taxonomic importance of specific members of the microbial core, as well as patterns of specific mobile genetic elements (MGEs), is lacking. We used seawater and mucus samples collected from Mussismilia hispida colonies on two reefs located in Bahia, Brazil, to disentangle their associated bacterial communities, intertaxa correlations, and plasmid patterns. Proxies for two broad‐host‐range (BHR) plasmid groups, IncP‐1β and PromA, were screened. Both groups were significantly (up to 252 and 100%, respectively) more abundant in coral mucus than in seawater. Notably, the PromA plasmid group was detected only in coral mucus samples. The core bacteriome of M. hispida mucus was composed primarily of members of the Proteobacteria, followed by those of Firmicutes. Significant host specificity and co‐occurrences among different groups of the dominant phyla (e.g., Bacillaceae and Pseudoalteromonadaceae and the genera Pseudomonas , Bacillus, and Vibrio ) were detected. These relationships were observed for both the most abundant phyla and the bacteriome core, in which most of the operational taxonomic units showed intertaxa correlations. The observed evidence of host‐specific bacteriome and co‐occurrence (and potential symbioses or niche space co‐dominance) among the most dominant members indicates a taxonomic selection of members of the stable bacterial community. In parallel, host‐specific plasmid patterns could also be, independently, related to the assembly of members of the coral microbiome.",
"introduction": "1 INTRODUCTION Corals can harbor complex microbial ecosystems, which frequently result in the development of both specific and variable host‐associated microbial communities (reviewed in Webster & Reusch, 2017 ), which can benefit host fitness (Peixoto, Rosado, Leite, Rosado, & Bourne, 2017 ; Webster & Reusch, 2017 ). Despite the close relationship between corals and their associated microbiomes, which can include organisms that have effects that vary from beneficial (Damjanovic, Blackall, Webster, & van Oppen, 2017 ; Krediet, Ritchie, Paul, & Teplitski, 2013 ; Peixoto et al., 2017 ; Webster & Reusch, 2017 ) to pathogenic (Meistertzheim, Nugues, Quéré, & Galand, 2017 ; Sweet & Bulling, 2017 ; Wright et al., 2017 ), knowledge of these intrinsic symbiotic, or dysbiotic, that is, disrupted symbiotic relationships (Bosch & Miller, 2016 ; Egan & Gardiner, 2016 ; Petersen & Round, 2014 ), interactions, and associated mechanisms is sparse. It has been proposed that important mechanisms associated with the holobiont, that is, the host and its associated microbial community (Margulis & Fester, 1991 ), can be regulated through microbiome shuffling (i.e., shifts in microbial abundance) and/or switching (i.e., acquisition of the microbial strains from the surrounding environment) (reviewed in Webster & Reusch, 2017 ). The acquired microorganisms could also be passed on from parental to offspring generations (Leite et al., 2017 ; Padilla‐Gamiño, Pochon, Bird, Concepcion, & Gates, 2012 ). This microbiome‐mediated transgenerational acclimatization (MMTA) (proposed by Webster & Reusch, 2017 ) could lead to the rapid adaptation (and evolution) of corals to adverse environmental conditions. This natural acclimatization could be boosted in the face of environmental stresses (Damjanovic et al., 2017 ; Peixoto et al., 2017 ), for example, through the manipulation of specific key members of the microbiome, which have recently been termed “beneficial microorganisms for corals” (BMCs) (Peixoto et al., 2017 ). However, several questions remain, namely who are these key beneficial players, is there a taxonomic selection of the dominant microbes, and how do they interact within the holobiont? Knowledge of the patterns of variation and interactions within the coral microbiome is limited. Other microbial‐community studies have shown that evaluation of co‐occurrence patterns in microbiomes may offer a more comprehensive view of complex microbial communities, constituting a complementary approach to estimates of alpha and beta diversity (Barberán, Bates, Casamayor, & Fierer, 2012 ; Dini‐Andreote et al., 2014 ). Identifying microbial patterns (Andrade et al., 2012 ; Peixoto et al., 2011 ; Rachid et al., 2013 ; Santos, Cury, Carmo, Rosado, & Peixoto, 2010 ) and potential interactions among microorganisms may reveal stable populations and shared niches, indicating preferences for certain resources, and consequently, microbial groups that are more competitive for such niches, or even elucidating potential direct symbiotic relationships between these microorganisms (as suggested by Barberán et al., 2012 ). This approach may be especially promising in coral microbiome studies because the close relationship between the host and its microbial community reported in several studies (Ainsworth, Thurber, & Gates, 2010 ; Cárdenas, Rodriguez‐R, Pizarro, Cadavid, & Arévalo‐Ferro, 2012 ; Ceh, Keulen, & Bourne, 2013 ; Ceh, Raina, Soo, van Keulen, & Bourne, 2012 ; Kelly et al., 2014 ; Lema, Bourne, & Willis, 2014 ; Lins‐De‐barros et al., 2010 , 2013 ; Mouchka, Hewson, & Harvell, 2010 ; Sharp, Ritchie, Schupp, Ritson‐Williams, & Paul, 2010 ; Thompson, Rivera, Closek, & Medina, 2014 ). We believe, in particular, that exploring the taxonomic diversity of the bacterial part of the microbiome core (the bacteriome) as well as relevant ecological rules shaping these communities could provide valuable tools to guide BMC and MMTA surveys. Another potential key aspect of coral microbiomes that has not received much attention is horizontal gene transfer (HGT) and the presence of specific patterns to support gene exchange. HGT plays important roles in bacterial evolution and gene exchange (Bhattacharya et al., 2016 ; van Elsas, Turner, & Bailey, 2003 ; Heuer & Smalla, 2007 ). Conjugation, for instance, which is mediated by different classes of mobile genetic elements (MGEs), allows the acquisition of novel genes (Heuer & Smalla, 2012 ). Plasmids, which are the main vectors for this genetic exchange, can act in the acquisition of genes or genetic pathways (such as for antibiotic resistance, pollutant degradation, and others) (Dealtry et al., 2014 ; Heuer & Smalla, 2012 ; Izmalkova et al., 2006 ). This HGT could be advantageous for holobiont resilience under environmental disturbance and, therefore, constitute a key component for MMTA (Webster & Reusch, 2017 ). Despite their possible essential role, plasmid patterns are largely unexplored in corals. In this study, we present a survey of proxies for two broad‐host‐range (BHR) plasmid groups, IncP‐1B and PromA, in Mussismilia hispida coral mucus and the surrounding seawater. These plasmids can efficiently transfer their genetic material to a wide range of hosts and have been widely used as proxies to evaluate the potential spread of genes in several environments (van der Auwera et al., 2009 ; Heuer & Smalla, 2007 , 2012 ; Zhang, Pereira e Silva, Chaib De Mares, & Van Elsas, 2014 ) and as providers of bacterial HGT capacities in some soil environments (Zhang et al., 2014 ). We also describe the bacterial diversity in these samples, as well as the co‐occurrence patterns of the coral bacteriome. We discuss the potential impact of these results in the context of the MMTA.",
"discussion": "4 DISCUSSION Here, we describe the diversity and intercorrelations of the bacterial diversity, as well as the prevalence of MGEs associated with mucus from M. hispida and the surrounding seawater. We focused particularly on the BHR plasmid groups IncP‐1 and PromA, widely used as BHR plasmid proxies and indicated as the major providers of bacterial HGT in some soil environments (van der Auwera et al., 2009 ; Heuer & Smalla, 2012 ; Zhang et al., 2014 ), as well as on integron1, a good proxy for pollution (Gillings et al., 2015 ). The use of these plasmid groups was also based on their hypothesized importance, which is related to resistance to antibiotics and heavy metals and to the efficient mobilization among Gram‐negative bacteria. Our main findings indicate that key groups of bacteria, that is, Proteobacteria, followed by Firmicutes, mainly represented by members of Rhodobacteraceae and the genera Pseudomonas and Bacillus associated with M. hispida , were present in the coral mucus. These dominant groups and members of the entire M. hispida microbial core have also been described as being vertically transmitted from parent to offspring in the same coral species (Leite et al., 2017 ). In addition, this core microbiome and some other groups in the mucus bacteriome have a positive relationship of co‐occurrence, especially the families Desulfovibrionaceae and Flavobacteriaceae, the genera Pseudoalteromonas and Arcobacter , and Rugeria and other members of Rhodobacteraceae, suggesting that these microorganisms could be selected by the holobiont. Our data also indicated that the holobiont selects (i.e., contains a higher abundance or even a specific persistence) the IncP‐1 and PromA groups of BHR plasmids, as these were significantly more abundant in coral mucus or absent in the seawater samples, respectively. Studies on coral microbiomes have shown the importance of these organisms for host health, fitness, maintenance (Musovic, Oregaard, Kroer, & Sørensen, 2006 ; Peixoto et al., 2017 ; Santos et al., 2014 , 2015 ; Sweet & Bulling, 2017 ; Webster & Reusch, 2017 ), and evolution (Bhattacharya et al., 2016 ). Microbial surveys have contributed to our understanding of how microbial communities can promote the resilience of coral reefs to environmental stress (Bhattacharya et al., 2016 ; Peixoto et al., 2017 ; Sweet & Bulling, 2017 ; Webster & Reusch, 2017 ), and have generated knowledge of the beneficial potential of the microbiome and its potential future manipulations (Damjanovic et al., 2017 ; Peixoto et al., 2017 ; Sweet & Bulling, 2017 ; Webster & Reusch, 2017 ), thereby improving the health of reef ecosystems. Knowledge of key coral microbiome microbial groups and potential intertaxa correlation patterns can improve and guide such BMC manipulations, by indicating stable, well‐adapted populations that could be involved in beneficial mechanisms and would be, at the same time, competitive, and well established in manipulative approaches. The seawater bacteriome observed here was more diverse than the coral bacteriome, as reported in other studies (Castro et al., 2010 ; Garcia et al., 2013 ; Reis et al., 2009 ; Rojo, 2010 ; Rosenberg, Kellogg, & Rohwer, 2007 ). Mussismilia hispida bacterial communities from the mucus samples were composed mainly of Proteobacteria, a phylum that is widely found in M. hispida microbiomes (Castro et al., 2010 ; Leite et al., 2017 ; Lins‐De‐barros et al., 2010 ; Musovic et al., 2006 ) as well as in other species of the genus Mussismilia (Fernando et al., 2015 ; Garcia et al., 2013 ; Santos et al., 2015 ) and in other coral genera (Bourne & Munn, 2005 ; Kimes, van Nostrand, Weil, Zhou, & Morris, 2010 ; Kimes et al., 2013 ; Mouchka et al., 2010 ; Vega Thurber et al., 2009 ). Moreover, the phylum Proteobacteria is quite abundant in a range of Mussimilia microhabitats such as the mucus, tissue, and skeleton, compared with other bacterial groups (Castro et al., 2010 ; Fernando et al., 2015 ; Garcia et al., 2013 ; Leite et al., 2017 ; Lins‐De‐barros et al., 2010 ; Reis et al., 2009 ; Santos et al., 2015 ). Recent studies have suggested that the coral microbial community is composed of both a stable and a variable fraction. The stable fraction is proposed to be directly involved in basic host requirements (i.e., the microbial core, which is also composed of two components, a host‐specific ubiquitous community, and a niche‐specific community). The variable fraction is proposed to vary rapidly with environmental shifts (Hernandez‐Agreda, Leggat, Bongaerts, & Ainsworth, 2016 ). For the maintenance of the coral holobiont, the host can acquire its symbionts directly, via parental gametes/eggs (i.e., vertical transmission (Musovic et al., 2006 ; Sharp, Distel, & Paul, 2011 ; Padilla‐Gamiño et al., 2012 )) or through acquisition from the surrounding environment (i.e., horizontal transmission) (Apprill, Marlow, Martindale, & Rappé, 2009 ; Knowlton & Rohwer, 2003 ). The early acquisition and maintenance of a microbiome may ensure the establishment of key mechanisms to protect and foster the settlement and development of coral larvae (Lema et al., 2014 ; Sharp & Ritchie, 2012 ). Leite et al. ( 2017 ) have indicated that members of the core bacteriome of M. hispida (i.e., the genera Burkholderia, Pseudomonas, Acinetobacter, Ralstonia, Inquilinus and Bacillus, and unclassified Rhodobacteraceae) were transmitted vertically to offspring, through the gametes, reinforcing the potential importance of the coral bacteriome core members. Therefore, we have focused on the core bacteriome from the M. hispida mucus samples from different sampling points. Our data have also indicated core members that have been described by Leite et al. ( 2017 ) at early life stages, such as Pseudomonas, Bacillus, and Rhodobacteraceae members, in all coral mucus samples from the two sampling sites, and showing a high level of intertaxa relationships. In addition, considering the BHR that were screened, the IncP‐1 plasmid group was the most abundant plasmid group in the coral mucus bacteriome. These plasmids have a wide distribution and are highly efficient for Gram‐negative bacteria (Popowska & Krawczyk‐Balska, 2013 ), but have also been reported mobilizing Gram‐positive bacteria (Musovic et al., 2006 ). This group of plasmids can exchange a wide range of potentially advantageous genes, such as genes for antibiotic resistance and degradation of different carbon sources (Popowska & Krawczyk‐Balska, 2013 ; Shintani et al., 2010 ; Zhang et al., 2014 ), which, given the abundance of “enriched” plasmids, could suggest a key role of HGT in the coral–microbiome interactions. The second most abundant group of plasmids, PromA, proposed by van der Auwera et al. ( 2009 ), was detected only in coral samples. Previous studies have found that IncP‐1 and PromA, BHR groups of plasmids, are extremely important gene carriers in other systems, such as for soil bacterial communities (van der Auwera et al., 2009 ; Heuer & Smalla, 2012 ; Zhang et al., 2014 ), and are both efficient plasmids for gene exchanges between members of the Proteobacteria group (Zhang et al., 2014 ). We find it interesting that this group was detected only in coral mucus samples. Although there are multiple possible explanations, this could also indicate that the holobiont can indeed select and concentrate a specific diversity of MGEs. When considering the network analyses from the total mucus samples, that is, not considering only the bacteriome core, we have found a large number of related OTUs, mainly based on co‐occurrence among Proteobacteria and Firmicutes members. More specifically, separate clusters harboring core microbiome members, previously described as vertically transmitted in M. hispida ( Pseudomonas , Bacillus and Rhodobacteraceae members) (Leite et al., 2017 ), were observed. There are multiple possible explanations for these patterns of co‐occurrence and dominance, and we discuss a few of them below. One possibility is that taxonomic relationships, at the OTU level, are indeed relevant for the coral microbiome assembly. In this case, it could suggest that something about these specific taxa (e.g., key functions), that is best, or exclusively, provided by these members, could not be replaced by other taxa or HGT. This could explain the stable taxonomic selection observed. Alternatively, or complementarily, interactions between taxa, or between the host and these taxa, maintain their presence or absence and the observed correlations. In addition, these patterns could also be merely a consequence of history, that is, successive vertical transmission of specific groups that leads to correlations. The observed co‐occurrence and specific taxonomic persistence indicate that these taxa are potential key players in coral health, give that their presence in the offspring is ensured. This co‐occurrence and taxonomic persistence could also suggest that these members might be actively involved in the persistence of other bacterial groups through symbiotic relationships. On the other hand, these data could indicate that these coexisting and dominant groups are independently influenced by environmental factors. Thus, these groups would be selected as the most able to survive in this environment (Barberán et al., 2012 ), due to their potential key irreplaceable functions. In this case, they are only sharing the M. hispida mucus niche. Nevertheless, this would mean that these are the most competitive groups within this niche, which clearly indicates them as important targets for M. hispida BMC manipulative studies. In parallel, positive correlations were observed between the coral mucus and the abundance and/or the specific presence of the screened plasmids. Although plasmid abundance is not supported by the observed stable bacterial taxonomic diversity, as, in this case, the relevant role seems to be related to the taxonomic level rather than to transferrable functions, it could be related to the variable fraction of the coral microbiome. Thus, it is possible that these “holobiont‐enriched” plasmids could be in fact associated with the noncore, non‐co‐occurring taxa. The “enriched” presence of these MGEs within the holobiont could indicate that advantageous genes could be eventually exchanged between all members of the coral bacteriome. This advantageous exchange of genes could eventually support the transient (and even the stable) members under adverse conditions, which can, in turn, contribute to the resilience of the host in the face of environmental shifts. However, Hall, Williams, Paterson, Harrison, and Brockhurst ( 2017 ) have recently suggested that conjugation can be reduced by positive selection, indicating that HGT can be inhibited by those beneficial elements. The conjugative mobilization would be more related to infections and parasitic elements. Thus, the remaining questions are as follows: To what taxa do these “enriched” plasmids belong? And is there active HGT, mediated by these plasmids, occurring? Taken together, the high prevalence of co‐occurrence between core bacterial groups and the specific plasmid‐pattern data could suggest separate roles in the coral bacterial assembly. It is also possible that both mechanisms could be correlated, as a random consequence of the high prevalence of the dominant microbial diversity, Proteobacteria. This could, in turn, randomly select those plasmids that can be established by the abundance of this dominant group, though not being necessarily relevant for this dominance. This suggestion is supported by the correlations between Proteobacteria OTUs and plasmids in the mucus samples. On the other hand, this role could be associated with specific mechanisms, evolved to selectively permit the persistence of the dominant components of the bacteriome and associated plasmids, which could allow eventual cooperation between other (and transient) members, mediated by gene exchange. Both hypotheses are somehow driven by the holobiont and its microbial diversity."
} | 4,922 |
26712847 | PMC4937784 | pmc | 4,662 | {
"abstract": "Although the photosynthetic reaction center is well conserved among different cyanobacterial species, the modes of metabolism, e.g. respiratory, nitrogen and carbon metabolism and their mutual interaction, are quite diverse. To explore such uniformity and diversity among cyanobacteria, here we compare the influence of the light environment on the condition of photosynthetic electron transport through Chl fluorescence measurement of six cyanobacterial species grown under the same photon flux densities and at the same temperature. In the dark or under weak light, up to growth light, a large difference in the plastoquinone (PQ) redox condition was observed among different cyanobacterial species. The observed difference indicates that the degree of interaction between respiratory electron transfer and photosynthetic electron transfer differs among different cyanobacterial species. The variation could not be ascribed to the phylogenetic differences but possibly to the light environment of the original habitat. On the other hand, changes in the redox condition of PQ were essentially identical among different species at photon flux densities higher than the growth light. We further analyzed the response to high light by using a typical energy allocation model and found that ‘non-regulated’ thermal dissipation was increased under high-light conditions in all cyanobacterial species tested. We assume that such ‘non-regulated’ thermal dissipation may be an important ‘regulatory’ mechanism in the acclimation of cyanobacterial cells to high-light conditions.",
"introduction": "Introduction Cyanobacteria are the first life form performing oxygenic photosynthesis and are the evolutionary origin of the chloroplast in plants. Although the photosynthetic machinery of photosynthetic reaction center complexes is almost identical between cyanobacteria and chloroplasts, metabolic interaction of photosynthesis with other cellular processes is fundamentally different. As a photosynthetic prokaryote, cyanobacteria do not have organelles and all the metabolic pathways could directly interact with one another. In particular, photosynthetic electron transport and respiratory electron transport shared several electron transfer components such as plastoquinone (PQ), the Cyt b 6 / f complex and Cyt c ( Aoki and Katoh 1982 , Peschek and Schmetterer 1982 ). This direct interaction between photosynthesis and respiration makes cyanobacterial photosynthesis different from photosynthesis of land plants. In the case of land plants, the PQ pool is oxidized in the dark, and gradually reduced along with the elevation in photon flux densities (PFDs). In the case of cyanobacteria, however, the PQ pool is already reduced in the dark due to respiratory electron transfer (Mullineaux and Allen 1986, Schreiber et al. 1995 , Campbell and Öquist 1996 ). Low light illumination oxidizes, not reduces, the PQ pool in the case of cyanobacteria ( Campbell and Öquist 1996 , Campbell et al. 1998 ). A further increase in the PFD reduces the PQ pool again. The PQ pool is one of the regulatory sites of photosynthetic electron transfer and energy dissipation. The redox state of the PQ pool or neighboring electron carriers is known to regulate state transition both in land plants and in cyanobacteria. Light energy absorbed by the PSII antenna complex may be directed either to the PSII reaction center complex or to the PSI reaction center complex, depending on the redox state of the PQ pool during the process of state transitions, both in land plants and in cyanobacteria. There are at least two different mechanisms in cyanobacterial state transition. In low-light-acclimated cells of cyanobacteria, the RpaC-dependent state transition is induced to maximize the efficiency of photosynthesis ( Emlyn-Jones et al. 1999 ). On the other hand, the PsaK2-dependent state transition is induced to avoid photoinhibitory damage in high-light-acclimated cells ( Fujimori et al. 2005 ). State transition seems to have a protective role in addition to a regulatory role in land plants too ( Bellafiore et al. 2005 ). Since redirection of light energy to PSI decreases the yield of fluorescence from PSII, the state transition could be viewed as the quenching of Chl fluorescence. The origin of such quenching is also different between land plants and cyanobacteria. In the case of land plants, the most conspicuous non-photochemical quenching (NPQ) is energy-dependent quenching (qE) due to the xanthophyll cycle, which plays an important protective role in avoiding photoinhibitory damage ( Demmig-Adams and Adams 1996 ). qE could be distinguished from the quenching due to state transition (qT) or that due to photoinhibition (qI) by the rate of recovery from quenching in the dark (Quick and Stitt 1989, Krause and Weis 1991 ). The situation is somewhat different in algae, which have a very diverse antenna system for photosynthesis. Different algal group might have different mechanisms of qE ( Goss and Lepetit 2015 ). Even within one class of green algae (e.g. Trebouxiophyceae or Chlorophyceae), the existence of the different mechanisms in qE among species has been implied ( Quaas et al. 2015 ). On the other hand, the mechanism of cyanobacterial NPQ is totally different from that of land plants and algae. Cyanobacteria do not have a xanthophyll cycle ( Stransky and Hager 1970 ) and NPQ is mainly ascribed to state transition ( Campbell and Öquist 1996 ). Under strong blue light conditions, orange carotenoid protein (OCP) could dissipate excess energy in the phycobilisome antenna to protect the machinery of the photosystem ( Kirilovsky 2007 ). However, the division of roles between state transition and OCP in protection from photoinhibitory damage has not been examined. Although the photosynthetic reaction center is well conserved among different cyanobacterial species, the modes of metabolism and their mutual interaction are quite diverse. There are few studies comparing the interaction of photosynthesis and other metabolic pathways or light energy dissipation among different species of cyanobacteria grown under certain conditions. Here we examined the effect of respiration of photosynthetic electron transfer and energy dissipation in six cyanobacterial species using Chl fluorescence measurements. We found that the redox state of the PQ pool in the dark is totally different among different cyanobacterial species. Furthermore, the correlation between the redox state of PQ and the yield of energy dissipation in the dark to the growth light range is different from that in growth light to the high light range. Apparently, some process of energy dissipation was induced only under high-light conditions. The results obtained here demonstrate species differences in the redox state and species commonality in energy dissipation in cyanobacteria.",
"discussion": "Discussion Cyanobacterial diversity observed in the PQ reduction in the dark It is well known that the PQ pool is mostly reduced in dark-acclimated cells of cyanobacteria ( Campbell et al. 1998 ), which is quite different from the case of land plants. Apparently, such a dark reduction of the PQ pool is not observed in all cyanobacterial species. Among the six cyanobacterial species examined in this study, the PQ pool was reduced to different extents in four species but it is oxidized in the dark-acclimated cells of N. punctiforme ATCC29133 and A. marina MBIC11017. All the cells are grown at the same temperature and under the same PFD, so the difference among species could not be ascribed to the difference in growth conditions, but to the intrinsic nature of the cyanobacterial species. Since the two Nostoc species, Nostoc punctiforme ATCC29133 and Nostoc sp. HK-01, showed totally different redox conditions of their PQ pool in the dark, the diversity could not be explained solely by phylogenetic factors. Nostoc punctiforme ATCC29133 was first isolated as a symbiont from a root of a gymnosperm cycad in Australia ( Rippka and Herdman 1992 ). A. marina MBIC11017, another cyanobacterium with an oxidized PQ pool in the dark, was also originally isolated from inside of an ascidian in Palau ( Miyashita et al. 1996 ). The light environments of the original habitats of these cyanobacteria should be rather weak, and adaptation to such environments may be the evolutionary cause of the PQ pool characteristics. The life style as a symbiont may be also considered as the cause of PQ oxidation in the dark, but it is difficult to assume such a concrete connection between them. As for the actual mechanism of the variation in the redox condition of the PQ pool, there are three possibilities: first, the redox state of the cytosol may affect the condition of the PQ pool. For example, Mi et al. (1994) showed that dark-starved cells of Synechocystis sp. PCC6803 had an oxidized PQ pool that might have resulted from starvation of the respiratory substrates in the cytosol. In our experiments, however, the cells were grown photoautotrophically under continuous light so that the concentration of respiratory substrates should not be particularly different among species. Secondly, the difference in the activities of the NDH complex upstream of the PQ pool may result in the variation in the PQ redox. Since the inactivation of the NDH complex resulted in the oxidation of the PQ pool ( Ogawa et al. 2013 ), the difference in the activity of the NDH complex would be a plausible candidate for the cause of the species difference of PQ redox. Thirdly, the difference in Cyt c oxidase (COX) activity in the dark may contribute to the variation in PQ redox. It was reported that Synechocystis sp. PCC6803 could grow heterotrophically in the dark when a mutation was introduced in the Cyt c M gene ( Hiraide et al. 2014 ). It was implied that Cyt c M would act as a negative regulator for COX activity in cyanobacteria lacking the ability to grow heterotrophically in the dark. In cyanobacteria with the ability to grow heterotrophically in the dark, e.g. N . punctiforme , COX activity would be kept relatively high in the dark, resulting in the oxidation of the PQ pool in the dark. The actual mechanism, however, should be tested in the near future. Regulatory energy dissipation is not limited to NPQ in cyanobacteria As discussed above, the PQ pool is in a reduced condition which could be monitored as a low qP level in the dark-acclimated cyanobacterial cells, and the PQ reduction induced the State 2 condition which could be monitored as high Φ NPQ . Upon the increase of actinic illumination, the PQ pool is gradually oxidized (i.e. qP is gradually increased), inducing transition to State 1 that could be monitored as the decrease in Φ NPQ . Until the actinic light reached the growth light level, the relationship between qP and Φ NPQ is linear, suggesting that this process is a simple state transition to State 1 induced by the oxidation of the PQ pool. No other energy dissipation process was induced under the growth condition, since Φ f, D was constant during this process. Although the initial levels of the PQ redox state in the dark varied among cyanobacterial species, the qP–Φ NPQ relationships and Φ PSII –Φ NPQ relationships were more or less similar, with the same slope between the dark condition and growth light condition. In this sense, Synechocystis sp. PCC 6803 is somewhat peculiar, showing a decrease in Φ NPQ in the dark to growth light transition without apparent PQ oxidation. A similar change was observed by Campbell et al. (1998) , but the mechanism of this change is unknown. Apparently, Synechocystis sp. PCC 6803 is not a typical cyanobacterium from the view of the relationship between PQ redox and state transition. The reverse change was observed in the range from the growth light to high light, i.e. the level of qP decreased while the level of Φ NPQ increased again. Although the change in this range could be partly attributed to the reverse transition to State 2 induced by the reduction of the PQ pool, the slope of the qP–Φ NPQ relationships (or Φ PSII –Φ NPQ relationships) was different from that in the range from the dark condition to the growth light condition. The relationship between Φ PSII and Φ NPQ indicates that Φ f, D was increased by high light together with the state transition induced by the reduction of the PQ pool. In many land plant species, the level of Φ f, D was reported to be relatively constant with a wide range of actinic PFD, and thus Φ f, D was regarded as ‘non-regulatory’ energy dissipation as fluorescence or as heat ( Hendrickson et al. 2004 , Zhang et al. 2011 , Park 2013 ). In cyanobacteria, however, an increase of Φ f, D was observed in all the species tested here, and the rates of the increase of Φ f, D (i.e. the slopes of the data lines in the Φ PSII –Φ NPQ relationships) were similar among species. Hendrickson et al. (2004) showed that Φ f, D in Vitis vinifera increased along with the increase of actinic light to 500 µmol m −2 s −1 at 25°C, but not at 10°C. Since Φ f, D decreased, not increased, in the higher actinic light range (750–2,000 µmol m −2 s −1 ) in their experiments, the mechanism of Φ f, D regulation in land plants would be different from the one in the cyanobacteria observed here. Judging from the full recovery of F m ′ in the dark within 5–10 min ( Fig. 1 ), significant photoinhibition was not induced by high light, which could be the cause of the apparent increase in Φ f, D . Furthermore, no persistent photoinhibition under the growth condition was induced, since the change in F v / F m was mostly explained by the change in PC contents. Thus, photoinhibition is not the cause of the increase in Φ f, D . It was reported that a passive increase of Φ f, D under high light was observed in the M55 mutant of NDH-1 complexes, which is incapable of state transition ( Schreiber et al. 1995 ). Similarly, an increase in Φ f, D was observed in the PsbS mutant of rice plants, which is deficient in some mechanism of NPQ ( Ishida et al. 2011 , Ikeuchi et al. 2014 ). These results may suggest that the increase in Φ f, D is induced in the absence of physiological energy dissipation. Since red actinic light was used in our study, OCP was not induced in our experimental conditions. Furthermore, the increase of Φ f, D was reported under strong blue light in the OCP deletion mutant of Synechocystis sp. PCC6803, resulting in the decrease of ϕ NPQ but not in the change of Φ PSII ( Kusama et al. 2015 ). In our preliminary result, however, the V-shape relationship between Φ PSII and Φ NPQ was observed even when blue light was employed as actinic light. Unfortunately, blue light also acts as PSI light, resulting in oxidation of the PQ pool that complicated the interpretation of the results. The involvement of the OCP in the change of Φ f, D should be further explored in the near future. It should also be noted that we employed rather a high PFD (200 µmol m −2 s −1 ) for the growth light. It was reported that there are at least two types of state transition in cyanobacteria. PsaK2-dependent state transition was only induced in high-light-acclimated cells ( Fujimori et al. 2005 ), while RpaC-dependent state transition is functional under low-light conditions ( Emlyn-Jones et al. 1999 ). The lack of the RpaC-dependent state transition in high-light-acclimated cells may also bring about the increase of Φ f, D in the absence of physiological energy dissipation. In any event, the mechanism of the induction of Φ f, D could not be ascribed solely to a passive increase, since the increase in Φ f, D was observed in the actinic light range where the induction of NPQ was not saturated. The increase of Φ f, D should lead to smaller energy allocation to photosynthetic electron transfer. Similarly to the case of NPQ, the induction of Φ f, D that has been defined as ‘non-regulatory’ in the past may also contribute to the protection of the photosynthetic machinery from photoinhibition. Alternatively, the increase in Φ f, D might protect PSI from photoinhibition that could be caused by the excessive electron flow from PSII. The observed increase in Φ f, D must be some regulatory mechanism or at least some acclimatory response generally observed in cyanobacteria."
} | 4,084 |
36645348 | PMC9893811 | pmc | 4,663 | {
"abstract": "Rapid and sustained\ncondensate droplet departure from a surface\nis key toward achieving high heat-transfer rates in condensation,\na physical process critical to a broad range of industrial and societal\napplications. Despite the progress in enhancing condensation heat\ntransfer through inducing its dropwise mode with hydrophobic materials,\nsophisticated surface engineering methods that can lead to further\nenhancement of heat transfer are still highly desirable. Here, by\nemploying a three-dimensional, multiphase computational approach,\nwe present an effective out-of-plane biphilic surface topography,\nwhich reveals an unexplored capillarity-driven departure mechanism\nof condensate droplets. This texture consists of biphilic diverging\nmicrocavities wherein a matrix of small hydrophilic spots is placed\nat their bottom, that is, among the pyramid-shaped, superhydrophobic\nmicrotextures forming the cavities. We show that an optimal combination\nof the hydrophilic spots and the angles of the pyramidal structures\ncan achieve high deformational stretching of the droplets, eventually\nrealizing an impressive “slingshot-like” droplet ejection\nprocess from the texture. Such a droplet departure mechanism has the\npotential to reduce the droplet ejection volume and thus enhance the\noverall condensation efficiency, compared to coalescence-initiated\ndroplet jumping from other state-of-the-art surfaces. Simulations\nhave shown that optimal pyramid-shaped biphilic microstructures can\nprovoke droplet self-ejection at low volumes, up to 56% lower than\nsuperhydrophobic straight pillars, revealing a promising new surface\nmicrotexture design strategy toward enhancing the condensation heat-transfer\nefficiency and water harvesting capabilities.",
"conclusion": "Conclusions Through detailed simulations,\nwe show that droplet removal through\njumping can be enhanced by increasing and optimizing the confinement\neffect from the surrounding microstructures. This ensures that the\nconversion of surface energy to kinetic (removal) energy is optimally\nutilized. We show that this effect can be exploited by combining a\npyramid-shaped microstructure, a superhydrophobic surface, and hydrophilic\nspots placed at the bottom, among the pyramid elements. Such a texture\ncauses “slingshot-like” droplet ejection from the surface\ndue to additional deformation on the droplet, followed by a sudden\n“snapping” ejection event. In particular, the 20°\npyramids combined with hydrophilic spots show the smallest droplet\nejection volume across all the considered geometries. Compared to\nthe superhydrophobic pillar structure, where self-ejection was observed,\nthe ejection volume could be reduced by 56%. A smaller droplet\nejection volume increases the frequency of the\ncondensation cycle improving the heat-transfer efficacy. The introduction\nof hydrophilic spots on hierarchically structured superhydrophobic\nsurfaces represents therefore a novel, unexplored approach to reducing\nthe droplet departure volume and increasing the condensation efficiency.\nThe concept of out-of-plane biphilic surfaces is relevant for enhancement\nof other processes that involve condensation as well, such as water\nharvesting. To this end, the improvement of droplet mobility can lead\nto larger amounts of condensate that leave the surface at potentially\nlonger distances from the cooled surface due to the “slingshot\neffect”.",
"introduction": "Introduction Condensation is a necessary step of the\nnatural water cycle, but\nit is also of fundamental importance to the energy sector, for example,\nin thermal power generation 1 and thermal\nmanagement of microprocessors, 2 as phase\nchange can drastically increase the heat transfer. Traditionally,\nthe functionality of industrial condensers involves condensation on\nmetallic tubes made from aluminum or copper, which are inherently\nhydrophilic and thus promote filmwise condensation, limiting the heat-transfer\nefficiency. On the other hand, hydrophobic surfaces usually enable\ndropwise condensation wherein water droplets nucleate, grow, coalesce,\nand depart periodically. This leads to a significantly higher overall\nheat-transfer coefficient. 3 − 5 For this reason, several works\nin recent years have investigated surface characteristics enabling\npassive, controlled droplet departure during dropwise condensation\non metallic surfaces. 6 − 10 Texturing a hydrophobic surface can result in superhydrophobicity,\nwhich is characterized by ultralow contact angle hysteresis (typically\nless than 10°) and, therefore, small droplet departure diameters.\nThis can only be achieved with the presence of a hydrophobic micro-/nanotexture. 11 Although the nanoscale texture is critical for\nsuperhydrophobicity during condensation to efficiently remove the\nsmall condensate droplets, the microscale texture can be added to\nsynergistically assist droplet departure through the generation of\nLaplace pressure imbalance. 11 − 14 This principle can be utilized to generate passive\nwetting transitions of the condensed droplets from Wenzel to Cassie\nstate at the length scale of the surface texture 13 through rational design of the microtopography. Superhydrophobicity\nat the nanoscale also enables droplet departure through coalescence-induced\ndroplet jumping (CIDJ) during condensation. 7 This is a gravity-independent droplet departure mechanism which\nallows passive and rapid removal of condensed water to enhance anti-icing, 15 defrosting, 16 and\nself-cleaning 17 properties of surfaces.\nThe low adhesion force on superhydrophobic surfaces allows the conversion\nof a larger amount of excess surface energy into kinetic energy due\nto total surface area reduction during coalescence, enabling jumping\nevents. This phenomenon can potentially lead to superior condensation\nheat transfer 6 , 7 , 18 since\nit affects much smaller droplets compared to the size threshold requirement\nfor the gravitational removal of droplets. Given that it is a rather\nrandom occurring phenomenon, research has been directed toward designing\nrational textures that tune the size, velocity, and departure direction\nof jumping droplets. 18 − 20 A variety of microfeature geometries that can\nenable pressure gradients\nwithin the droplets and manipulate the droplet movement, such as micropillars\nor microcones, have already been experimentally investigated. 13 , 18 , 21 − 24 In particular, it has been shown\nthat diverging microcavity geometries, defined by their half-opening\nangle β, enhance droplet ejection by creating a favorable Laplace\npressure imbalance and, additionally, increase the surface area available\nfor vapor condensation and overall heat transfer. 12 , 21 , 22 , 25 Theoretical\nstudies have shown that the highest Laplace pressure imbalance is\nachieved for β ≈ 7°. 12 Optimizing the microcavity geometry experimentally is challenging\nand not cost-effective due to the inherent nature of the fabrication\nprocess. Thus, simulating condensation over surfaces with different\nmicrocavity opening angles can give new insights into the ideal microscale\nstructure, avoiding long and costly trial-and-error experimental parametric\ninvestigations. Previous computational studies have been able\nto successfully show\nhow the droplet growth behavior strongly depends on the number of\nnucleation sites, 26 as well as to derive\na mathematical model that can predict individual droplet evolution,\ndroplet coalescence, and droplet departure. Additionally, contact\nangle and hysteresis variations have also been included, and the results\nof the simulations have been validated with experimental data. 27 Subsequently, detailed droplet dynamics and\nheat-transfer performance of different wettability patterns have been\nnumerically analyzed. In particular, the effects of a pillar microstructure\non droplet dynamics (especially droplet coalescence jump, pillar squeezing\ndroplet jump, and droplet dragging by wettability gradients) were\ninvestigated. 28 In this work, we\ngo beyond analyzing well-known condensation modes,\nand we explore sophisticated surface designs that can lead to unique\ncondensation outcomes. Using a computational framework coupling a\nthree-dimensional (3D) volume of fluid (VOF) model with the continuity\nand momentum equations, 29 , 30 we propose a novel\nstrategy for enhanced condensate droplet jumping. It involves combining\nsuperhydrophobic divergent microstructures in the form of micropyramids\nwith local hydrophilic spots at the bottom of the microcavities. We\nfind that this out-of-plane biphilic texture can trigger enhanced\njumping of individual condensate droplets due to a synergistic combination\nof the Laplace pressure imbalance induced by diverging cavity and\nlocal pinning induced by the hydrophilic spots. This results in a “slingshot-like”\nself-ejection of droplets from the microtexture at lower volumes compared\nto the existing approaches for droplet removal from surfaces. Ultimately,\nthese surface designs open new possibilities to tune droplet ejection\nduring condensation, which is critical for both heat transfer and\nwater collection applications.",
"discussion": "Results and Discussion Single-Droplet Jumping\non Biphilic Microcavities The\nworking principle of the biphilic microcavities is presented in Figure 1 . Our approach consists\nof investigating droplet jumping due to droplet growth in different\nregular surface microstructures composed of micropillars and micropyramids\n( Figure 1 a). In the\nfirst (control) case, a superhydrophobic surface is applied ( Figure 1 b), while in the\nsecond case, we introduce a hydrophilic spot in the cavities surrounded\nby otherwise superhydrophobic micropyramids, resulting in out-of-plane\nbiphilic surface structures (or biphilic cavities, Figure 1 c). We investigate the effect\nof surface textures toward removal of condensate droplets from surfaces\nby utilizing Laplace pressure imbalance and the addition of hydrophilic\nspots that induce the \"slingshot effect”. To this end,\nwe focus\non the dynamics of individual condensate droplets as they grow in\ndiverging superhydrophobic microcavities formed by arrays of micropillars\nor micropyramids. We simulate the growth of a single condensate droplet\nin one unit cell of such a regular microtexture and investigate the\neffect of texture geometry on droplet mobility. Five different texture\ngeometries are simulated: pyramids with a half-opening angle β\nof 0 (pillars), 7, 14, 20, and 27°, respectively. The microelements\nhave a constant height ( H ) of 56 μm, and the\npitch ( P , center to center) distance is 81 μm. Figure 1 (a) Cross\nsection of the microelements. H = 56\nμm and P = 81 μm are kept constant. The\nhalf-opening angle is denoted by the Greek letter β. Comparison\nbetween superhydrophobic (b) and biphilic (c) microcavities. The biphilic\nstructures induce controlled droplet pinning at the bottom, enabling\npressure gradients within the droplet. Detachment from the hydrophilic\nspot results in conversion of surface energy into kinetic energy,\ncausing surface clearing droplet jumping. The goal of this analysis is to investigate how\nindividual droplets\nare affected by the microtexture per se, excluding the influence of\ncoalescence-induced droplet depinning. To simulate the growth of an\nindividual microdroplet in this geometry, a user-defined mass transfer\nmodel is used (see Section S1 in the Supporting Information ). The initial condition for this simulation consists\nof a small droplet (0.001 nL) placed at the base of the microcavity.\nSubsequently, the model “grows” the droplet through\nmass sources of liquid water in every cell of the droplet (where the\nliquid volume fraction, see Section S2 in the Supporting Information for the definition of the volume fractions,\nis equal to 1). The mass source is configured such that the droplet\nradius growth rate follows a power law and such that the rate reduces\nwith increasing flow time. 11 The\ndomain is discretized using a uniform mesh structure. Mesh\nresolution is chosen so as to allow the use of the dynamic contact\nangle model 31 (see Section S3 in the Supporting Information ) in order to account for\nthe effect of nanostructuring on microstructures. To simulate a superhydrophobic\nsurface, the advancing contact angle θ a is set to\n167.8°, the receding θ r to 165.9°, and\nthe static θ to 166.9°. The contact angle hysteresis Δθ\n= θ a – θ r is therefore 1.9°.\nThese values stem from experimental measurements on a perfluorodecanethiol\nfunctionalized copper hydroxide nanostructured surface. 32 Such contact angle values are typical also for\nother types of superhydrophobic surfaces, such as the TiO 2 -based surface which also gives a contact angle value greater than\n165° and a contact angle hysteresis value smaller than 2°. 33 Additionally, the hydrophilic spot has a size\nof 85 μm 2 , and the contact angle on the spot is set\nto 20°. Figure 2 a depicts\nthe results from five different microstructures [half-opening angles\n0 (pillars), 7, 14, 20, and 27°]. For the first set of simulations,\nthe microfeatures are simulated with a uniform superhydrophobic surface\nwithout hydrophilic spot, characterized by the aforementioned wetting\nparameters. For every texture geometry, a four-frame sequence during\nthe growth of a single droplet in the superhydrophobic microcavity\nis shown. Figure 2 (a) Single droplet growing at the center of the computational domains.\nFive different microgeometries have been simulated, including uniform\nsuperhydrophobic surface in all cases with θ = 166.9°,\nθ r = 165.9°, and θ a = 167.8°.\nThe number following the text “Pyramids” or “Pillars”\nindicates the angle β. For the β = 7° case, the droplet\nbarely clears the microtexture, while for pillars, the droplet jumps\nout of the computational domain. (b) The same as (a), but with a hydrophilic\nspot (θ = 20°, A = 85 μm 2 ) at the center of the domain. Surface clearing jumping events are\nobserved in all cases. The inset figure illustrates the top view of\nthe texture along with location of the hydrophilic spot. Each four-frame\nsequence shows the growth of the droplet, representing the detachment\nand/or ejection of the droplet. During growth, the droplet is detached from the\nbase of the microcavity\nas a consequence of the spatial constriction by the microelements.\nAfter detachment (due to low superhydrophobic surface adhesion), in\nall cases, the droplets move upward and out of the microcavity driven\nby the increase in volume and the action of Laplace pressure imbalance\ninduced by the sidewall confinement. Droplet jumping is observed for\nthe case of the pyramids with 7° inclined walls and the pillars.\nIn particular, the 7° pyramids cause a small jump, enough to\nbarely move the droplet out of the microcavity, but not with high\nenough velocity to completely remove the droplet from the computational\ndomain. In fact, the droplet returns and sits on top of the micropyramids\n(gravity is acting against the droplet movement). The pillars, on\nthe other hand, allow for a jump that completely removes the droplet\nfrom the surface. This can be explained by the fact that the vertical\nwalls cause the droplet to remain inside the microcavity up to a much\nhigher volume, and the pillars are able to exert a higher constriction\nand pressure on the droplet compared to the pyramids. Evidently, this\nresults in a more pronounced squeezing of the droplet and a higher\nrelease of excess surface energy in the form of kinetic energy, therefore\ncausing a jump with higher departure velocity. On the other hand,\nthe shape of the pyramid sidewalls causes a Laplace pressure imbalance\nthat drives the droplet gradually upward, thus forcing it to stay\nless time inside the cavity and therefore accumulate less water mass\ncompared to pillars. Eventually, this first set of simulations interestingly\nshows how vertical walls can induce a higher constriction on the droplet\nduring the growing phase, compared to the inclined walls as in the\npyramid case. Going beyond the aforementioned findings, we introduce\nhere a new\napproach that has the potential to further enhance the droplet ejection\nfrom diverging microgeometries. Instead of a uniform superhydrophobic\nsurface at the nanoscale, we introduce a hydrophilic spot at the center\nof the microcavity at its bottom ( Figure 2 b). The hydrophilic spot, surrounded by the\nsuperhydrophobic background, can induce controlled droplet pinning\nat the droplet bottom, without altering the low adhesion due to high\ncontact angle on the remaining contact area between the droplet and\nthe microgeometry. Therefore, during growth and for a certain time,\nthe droplet can remain pinned to the hydrophilic spot, thereby delaying\nits detachment from the base of the microcavity. As a result, the\npyramids can deform (stretch) the droplet to a greater extent, increasing\nthe capillary pressure gradient inside the droplet to a critical point\nwhere the droplet “snaps” and detaches itself from the\nspot. The intensity of the pinning force can be adjusted by the size\nof the hydrophilic spot and/or θ. Following the above\nargument, another set of simulation runs are\nperformed for biphilic microcavities consisting of the same five superhydrophobic\n(θ = 166.9°) microgeometries as shown in Figure 2 a but with an additional hydrophilic\nspot ( Figure 2 b). The\neffect of the hydrophilic spot on the droplet ejection behavior can\nbe immediately observed especially for the pyramids with half-opening\nangles of 20 and 14°. The droplet is held inside the microcavity\ndue to contact line pinning at the hydrophilic spot, allowing the\ndroplet to deform. This deformation is kept up until the droplet finally\nsnaps away from the hydrophilic spot. At that moment, the drop returns\nto a spherical shape and the released surface energy is converted\ninto kinetic energy, thus allowing the droplet to jump suddenly in\nthe vertical direction away from the surface, resembling a slingshot\n(see also Section S4 in the Supporting Information ). Figure 2 b highlights\nhow this new “slingshot effect” can cause surface clearing\njumping events on all five microgeometries, and this is a key difference\ncompared to the cases without a hydrophilic spot. Figure 3 a presents\na comparison of the geometrical droplet aspect ratio [ h /(2 R 0 ), h is the distance\nfrom the lowest to the highest point of the droplet on the y axis and R 0 is the radius\nof a perfectly spherical droplet at the given volume] for the 20°\npyramids, with (green curve) and without (red curve) the hydrophilic\nspot. The results are plotted as a function of the droplet volume\nand show how the hydrophilic spot can increase the aspect ratio (thus\ninduce larger deformation). At the moment of detachment from the hydrophilic\nspot, the aspect ratio is abruptly reduced, indicating the release\nof excess surface energy resulting in the droplet jump. Figure 3 (a) Calculated\ngeometrical aspect ratio h /(2 R 0 ) as a function of the droplet volume for biphilic\n(green) and superhydrophobic (red) surface (for 20° pyramids). h represents the distance from the lowest to the highest\npoint of the droplet, while R 0 represents\nthe radius of a perfectly spherical droplet at the given volume. (b)\nVolume at which the droplet clears the surface (loses contact) represented\nfor five different microgeometries with biphilic surface (green) and\nsuperhydrophobic surface (red). An “X” instead of a\nbar indicates that the droplet was not completely ejected from the\nsurface. Inset figure illustrates the moment of detachment from the\nsubstrate, which was taken as an ejection condition. (c) Vertical\nforce on, and (d) vertical acceleration of the droplet from the moment\nof detachment from the hydrophilic spot to complete lift off from\nthe surface. To further compare these two surfaces\nquantitatively, the surface\nclearing volumes (the volume at which the droplet loses contact with\nthe microcavity surface) are calculated and compared in Figure 3 b. The biphilic surface gives\na significant advantage on all diverging microgeometries: the droplet\nis self-ejected due to the “slingshot effect”. On the\nuniformly superhydrophobic surface (no hydrophilic spot), the droplet\nis gradually dragged out of the microcavity and ends up sitting on\ntop of the microelements. Without the stretching effect of the hydrophilic\nspot, individual droplets are not able to clear the surfaces with\ndiverging microgeometry. The 20° pyramids with the hydrophilic\nspot achieve overall\nthe smallest droplet ejection volume across all the geometries, 56%\nlower than the superhydrophobic straight pillars. This result indicates\nthat the opening angle and hydrophilic spot can be optimally combined\nto reduce the droplet departure size through gravity-independent individual\ndroplet jumping. This in turn can enable the highest frequency of\ncondensation cycle among all geometries considered in this study.\nThe introduction of a hydrophilic spot on superhydrophobic surfaces\nrepresents therefore a new approach to reducing the droplet departure\nvolume, reducing the risk of surface flooding, and potentially increasing\nthe condensation heat-transfer efficiency. To further characterize\nthe surfaces equipped with a hydrophilic\nspot, an analysis on the droplet ejection force is carried out. Specifically,\nthe reaction force from the substrate on the droplet is calculated,\nfrom the moment of detachment from the hydrophilic spot to the complete\nlift-off of the droplet from the surface. The magnitude of this reaction\nforce reflects the sum of external forces acting on the droplet. In\nthis case, only the vertical component of this reaction force is considered\nsince the horizontal components are cancelled out due to symmetry.\nThe reaction force from the surface reduces to zero at the moment\nthe droplet loses contact with the microcavity walls (i.e., lifts-off).\nBy dividing this vertical reaction force by the mass of the droplet,\nwe obtain the vertical acceleration of the droplet. Figure 3 c,d shows both the vertical\nreaction force and vertical acceleration as a function of time. The\ngraphs show that the more the pyramid walls are inclined (higher β),\nthe quicker the force reduces to zero. This means that less time is\nelapsed from the moment of detachment from the spot to complete lift-off\nof the droplet from the surface. This can be seen for the cases of\nthe 27 and 20° pyramids. The magnitude of the peak force is approximately\nthe same for all cases. However, the size of the droplet at the moment\nof jumping is not the same for these cases: the droplet is smallest\nin the case of the 20° pyramids and is the largest in the case\nof the pillars. It can be seen that the 20° pyramids with hydrophilic\nspots exert by far the highest acceleration on the droplet (see Figure 3 d). From this point\nof view, the hydrophilic spot brings the highest benefit when the\ninclination angle of the microgeometry is optimized: not only it can\nprovoke droplet jumping for the geometries where droplet jumping is\nnot observed otherwise ( Figure 2 ) but it can also maximize the droplet acceleration, enabling\nenhanced self-ejection. Multiple Droplet Interaction The\nprevious analysis\nhas shown how the hydrophilic spot increases the tendency for individual\ndroplet jumping on all surfaces except for the pillars ( Figure 2 b). In this section, an additional\nset of simulations on a six-element microstructure is performed. Here,\nwe are interested to see if using a larger surface, where more droplets\nare present and can interact with each other, can possibly affect\nthe phenomena that were observed before for smaller domains where\nthe focus was on single droplets. The simulations have been performed\nfor the 20° pyramids, which showed the best results in the single-droplet\nanalysis, and for the straight pillars, where the surface with the\nhydrophilic spot showed worse performance compared to the uniformly\nsuperhydrophobic surface ( Figure 3 b). In Figure 4 a, it is shown that two droplets are grown in neighboring\nunit cells of 20° micropyramids. Interestingly, the two droplets\nare self-ejected before they reach a sufficient size to coalesce.\nWhile CIDJ is known to reduce the droplet removal size threshold compared\nto gravity-driven techniques, the out-of-plane biphilic surfaces show\nthe potential to reduce this size threshold even more compared to\nCIDJ. This confirms the capability to increase the frequency of condensation\ncycle compared to conventional superhydrophobic surfaces. Figure 4 Comparison\nbetween two double domains of (a) 20° pyramids\nand (b) straight-walled pillars, both equipped with hydrophilic spots.\nThe three-frame sequence shows the effect of increasing droplet volume.\nIn the case of the 20° pyramids, the droplets are stretched inside\nthe microcavity, similar to what is observed on the single-droplet\ndomain. The droplets are self-ejected, without interacting with the\nneighboring droplet. On the pillar domain, coalescence with the neighbor\ndroplet results in ejection at lower individual volume compared to\nwhat was observed on the single-droplet domain. Figure 4 b shows\ntwo droplets growing in straight-walled pillar biphilic cavities.\nThe droplets are pinned to the hydrophilic spot; however, when coalescence\noccurs, the released surface energy is large enough to induce depinning\nfrom both hydrophilic spots and trigger coalescence-driven ejection\nof the droplets. The volume of the individual droplets right before\ncoalescence is 0.3 nL, which is lower than the single-droplet ejection\nvolume on the superhydrophobic pillar domain and the biphilic pillar\ndomain (see Figure 3 b) and closer to the droplet ejection volume 0.18 nL for micropyramids\nwith β = 20°. Therefore, the seeming performance disadvantage\nof the biphilic pillar surface reduces significantly, and it can be\nseen how droplet coalescence can work synergistically with the “slingshot\neffect”, when passive removal of droplets without the need\nfor gravity is desired. In summary, the hydrophilic spot improves\nthe droplet ejection\nbehavior on all pyramid-shaped microgeometries: due to contact line\npinning, the droplets are kept inside the microcavities, where they\nexperience a higher degree of deformation compared to similar surfaces\nwithout the hydrophilic spot. Depinning through snapping away from\nthe hydrophilic spot allows the droplets to regain a spherical shape,\nand the resulting release of surface energy induces surface clearing\njumping events. The half-opening angle of 20° combined with a\nhydrophilic spot showed the smallest surface clearing volume, more\nthan 49% lower than any other investigated surface geometry here.\nThe droplets are self-ejected, reducing the jumping volume threshold\ncompared to CIDJ. We envisage that the rapidly developing microfabrication\ntechniques\ncan be explored toward realizing this biphilic texture for the improvement\nof condensation heat-transfer efficiency in realistic applications.\nIn fact, superhydrophobic micropyramid structures that can trigger\nLaplace pressure imbalances in condensation can be fabricated using\na combination of laser microstructuring and chemical etching. 12 On such platforms, microprinting techniques\ncan be used to design the biphilic patterns, such as laser printing 34 or electrohydrodynamic printing. 35 Depending on the selected fabrication method,\nvarious surface defects might be introduced that deviate from the\nmodel surface design studied here. This would obviously alter to a\ncertain degree the droplet departure behavior. However, here we will\ndescribe the ideal defect-free case."
} | 6,877 |
34919672 | PMC8892540 | pmc | 4,665 | {
"abstract": "ABSTRACT Fungi form a major and diverse component of most ecosystems on Earth. They are both micro and macroorganisms with high and varying functional diversity as well as great variation in dispersal modes. With our growing knowledge of microbial biogeography, it has become increasingly clear that fungal assembly patterns and processes differ from other microorganisms such as bacteria, but also from macroorganisms such as plants. The success of fungi as organisms and their influence on the environment lies in their ability to span multiple dimensions of time, space, and biological interactions, that is not rivalled by other organism groups. There is also growing evidence that fungi mediate links between different organisms and ecosystems, with the potential to affect the macroecology and evolution of those organisms. This suggests that fungal interactions are an ecological driving force, interconnecting different levels of biological and ecological organisation of their hosts, competitors, and antagonists with the environment and ecosystem functioning. Here we review these emerging lines of evidence by focusing on the dynamics of fungal interactions with other organism groups across various ecosystems. We conclude that the mediating role of fungi through their complex and dynamic ecological interactions underlie their importance and ubiquity across Earth's ecosystems.",
"conclusion": "CONCLUSIONS AND FUTURE DIRECTIONS Unique combinations of fungal traits such as diverse dispersal mechanisms as well as versatile genomic, lifestyle and ecophysiological traits could have contributed to their success in spanning multiple scales and dimensions of time, space, and biological interactions, that is not rivalled by other organism groups (Figs. 1 , 2 ). In addition, fungi ubiquitously form complex multikingdom and multispecies interactions, with important implications for ecosystem and host functioning. Several lines of evidence also suggest that fungi act as mediating links between a wide range of organisms and ecosystems, and that both symbioses, parasitism, and indirect interactions have strongly influenced the evolution and distribution patterns of fungi and their interacting taxa (Fig. 3 ). We conclude that the versatility, complex interactions, and mediating role of fungi further underscores their importance within and across various ecosystems. Yet, the exact underlying mechanisms of the role of fungi in many systems warrant further investigation. In this respect, the key questions that need to be addressed are: How are interactions between fungi with other organisms established? How does evolutionary history influence these interactions and what functional traits facilitate them? How do fungal interactions with other organisms persist over time and space and what conditions facilitate these interactions? What are the relative roles of bacteria and viruses—including HGT and HVT—in fungal interactions with eukaryotic hosts? Further field and controlled experiments are needed to examine to what extent the environment and spatiotemporal variation in fungal communities contributes to the dynamics of fungal interactions. Deciphering links between fungal diversity, fungal and host functional traits and their interactions will not only improve our understanding of the resilience of ecosystems against environmental perturbation, but also help predict future responses to global change including potential range expansions of both fungi and their interacting partners. Studies of fungal ecology and biogeography, and inferred interactions, have been greatly facilitated by recently developed tools and databases for assigning fungal traits (Nguyen et al . 2016 ; Põlme et al . 2020 ; Zanne et al . 2020 ). Yet, we still need tools and analytical approaches that account for the flexibility in fungal lifestyles and interactions, embrace the complexity of fungi and their influence in ecosystems, and which can reveal realized interactions in mixed fungal communities (Fig. 2 ). Recent developments in single cell microbial genomics and droplet microfluidics hold great promise for unravelling microbe–microbe and microbe–environment interactions, including communication, competition, cooperation, and food-web dynamics and formation in constructed and controlled micro ecosystems (Nagy et al . 2018 ). Although these methods are not currently well-suited for studying filamentous fungi and macro eukaryotes due to technical limitations (Millet et al . 2019 ), there is great potential to adapt these approaches to build custom made systems that mimic small scale heterogeneous soil environments and also microcosms involving seedlings and root symbionts (Aleklett et al . 2018 ). Recent studies have made exciting progress in this regard, where co-culturing of a plant-associated fungus and bacterium isolated from a host was combined with a novel microfluidics system, comparative metabolomics and analytical methods to identify events of metabolic exchange that initiate a symbiosis between these organisms (Uehling et al . 2019 ; Büttner et al . 2021 ). Genomics data combined with transcriptomics and epigenomics approaches (to study all expressed genes and epigenetic factors in environmental samples, respectively) can provide unique opportunities in the next few years for studying fungal interactions at an unprecedented scale and resolution. Yet, perhaps of key interest to study fungal activity and interactions with hosts, viruses, microbes and ecosystems is metabolomics. In this respect, unravelling metabolic interactions by integrating different omics methods (metagenomics, transcriptomics and metabolomics) and mathematical modelling may be a promising approach to relate species-species interactions, genotypes and phenotypes and to gain mechanistic insights into fungal interactions (Blasche et al . 2021 ). Increasing genetic, transcriptomic and genomic studies on molecular host-fungus interactions and biomolecule degradation pathways provide comprehensive characterisation of functional gene families and activity centres of enzymes, transporters, signalling molecules and transposable elements. By moving towards the study of the phenotypic expression of fungi, host systems and their interactions, and ecosystems, can we begin to collect cause and effect information on fungal interactions and functional outcomes. By exploring fungi beyond traditional disciplinary and ecosystem boundaries, can we further unravel how communities of fungi and their interacting organisms may shift in response to changes in environmental conditions and how this may affect host and ecosystem functioning and health.",
"introduction": "INTRODUCTION Fungi are ubiquitous and form diverse communities temporally and spatially spanning multiple scales across many ecosystems (Fig. 1 ). We have only begun to scratch the surface of the importance of fungi in our lives and environment owing to their great diversity and challenges involved in studying fungal biology and ecology. Of an estimated 1.5–5 million fungal species only around 120 000 have been described (Hawksworth and Lücking 2017 ), largely due to the difficulty of isolating and culturing most fungi (Kõljalg et al . 2013 ), which has also been a barrier to assigning them functional roles. Furthermore, it is difficult to distinguish where an individual of a fungal species begins and ends. This is highlighted by the tight association of fungi with other organisms e.g. through lichen, mycorrhizal, endophytic and mixed biofilm associations, but also by potentially frequent cross-kingdom horizontal gene transfer (HGT) between fungi and other organisms (Venice et al . 2020b ), and in the heterogeneity of genetic and phenotypic intraspecific and intracellular variation that occurs in fungi (Ehinger et al . 2012 ; Taylor et al . 2017 ). With recent advances in molecular methods for gene-based identification of microbes over the last 20 years, some of these challenges have been overcome, leading to a rapid growth in our understanding about the importance of fungi in various ecosystems (reviewed in Peay, Kennedy and Talbot 2016 ; Nilsson et al . 2019 ). In terrestrial habitats, while relying on their hosts for carbon resources, mutualistic and pathogenic fungi play key roles in driving the physicochemical and biological properties of their environment as well as host population dynamics (Alan Pounds et al . 2006 ; Clemmensen et al . 2013 ; Bagchi et al . 2014 ; Chen et al . 2019 ; Tedersoo, Bahram and Zobel 2020 ). Meanwhile saprotrophic fungi are the key decay agents of organic material, playing a central role in carbon and nutrient cycling in many ecosystems (Averill, Turner and Finzi 2014 ; Fernandez and Kennedy 2016 ; Netherway et al . 2021 ). In aquatic habitats, fungi can regulate food-web dynamics and biogeochemical cycling by rendering resources more available to higher order consumers and increasing the efficiency of trophic transfer through aquatic ecosystems (reviewed in Grossart et al . 2019 ). Figure 1. Fungi form diverse entities that are distributed across different aspects and scales of space and time. Fungal diversity can be viewed across different levels of biological organization from the gene level to the level of complex communities, as well as from a taxonomic, phylogenetic or functional perspective. Even at the individual level, fungi exhibit different life cycle stages, that all have their unique biogeographical patterns e.g. the growth phase of fungi (yeast or filamentous) is embedded within or on a substrate, whereas the reproductive and dormant phases of fungi usually bridge different substrates/habitats. This is exemplified by a soil dwelling fungus from the family Agaricaceae that exists as a mycelium (growth phase) within the soil matrix, which then produces a fruitbody in the form of a mushroom that bridges habitats between the soil and the atmosphere. Finally, the fungus releases spores that may make their way into the atmosphere before landing in a new patch of soil, each growth phase is subjected to unique environmental factors, biological interactions, and other processes that lead to distinct biogeographic patterns. As fungi bridge the gap between micro- and macro-organisms, they also exhibit spatial patterns at the microhabitat to macro habitat level, including across different depths of a substrate, between different substrates, between different organs of a host, through to macroecological scales such as across landscapes to biomes and the global level. Fungi also exhibit distinct biogeographic patterns across different temporal scales from days to seasons, through to years and geologic time. With large and varying phylogenetic and functional diversity as well as great variation in dispersal modes, fungi can act as important pathogens, commensals, and mutualists of macro-eukaryotic hosts, and interact with and influence the activity and functions of microbial prokaryotes and eukaryotes in most ecosystems (Fig. 2 ). They bridge the divide between micro- and macroorganisms, from single celled yeasts to complex macro-fungi that produce structures rivalling plants and animals in terms of size. Recent studies have provided unprecedented insights into the ecology and biogeography of fungi. There is growing evidence pointing towards commonalities and differences in the community assembly patterns and processes of fungi compared to those traditionally considered for microorganisms such as bacteria, but also from macroorganisms such as plants and animals (Tedersoo et al . 2014 ; Davison et al . 2015 ; Tisthammer, Cobian and Amend 2016 ; Kivlin et al . 2017 ; Bahram et al . 2018 ; Cameron et al . 2019 ). The characteristics of fungi and their ecology, including their associations with other organisms, may have enabled fungi to become central agents of ecosystem, ecological, and evolutionary processes (Fig. 2 ), and to span multiple scales and dimensions of time, space, and biological interactions (Figs 1 and 3 ). The combined and increasing evidence suggests that fungi can mediate links between different organisms and ecosystems, and potentially alter ecological, evolutionary and biogeographic relationships of the organisms they interact with. Here we review this evidence by exploring the current knowledge around the dynamics of fungal ecological interactions and underlying mechanisms in various ecosystems and discuss how these affect the evolution, ecological fitness and distribution of their associated organisms. We further present a perspective for embracing the importance of fungal interactions in ecosystems. Figure 2. Proposed triangle of fungal traits that are integral to their success as organisms and as links between organisms and across ecosystems. Fungal reproductive and dispersal traits are central to adaptation and dispersal to new environments, whereas fungal ecophysiological and morphological traits are central to their nutrient acquisition, stress tolerance, and interactions with other organisms. These two groups of traits then feed into the major fungal ecological strategies that transition along a three-dimensional continuum from symbiotrophism, saprotrophism to parasitism depending on environmental conditions. All groups of traits both influence and are influenced by interactions with other organisms, and these collections of traits allow fungi to be key modulators of ecological, ecosystem and evolutionary processes. Figure 3. Examples of mediating role of fungi in different ecosystems. Fungi through their unique morphological and ecophysiological properties act as mutualists, commensalists, and antagonists with plants, animals, microbes and other fungi, mediate the health, performance, population dynamics and biogeography of these organisms. Meanwhile, through their affinity for and ability to break down complex substrates (notably plant derived) and even contribute to mineral weathering, fungi mediate carbon and nutrient cycles in both terrestrial and aquatic ecosystems, and through their enormous production and release of spores into the atmosphere fungi may even mediate rainfall. For example, in terrestrial systems mycorrhizal fungi mediate nutrient acquisition of plants as well as their interactions with antagonists such as plant pathogens and herbivores, while saprotrophic fungi mediate the cycling of complex plant derived substrates, and fungal pathogens mediate the population dynamics of eukaryotic hosts. In aquatic systems fungi mediate food web dynamics by controlling resource fluxes to higher order consumers in the process also mediating the efficiency of transfer across trophic levels. In human habitats such as skin and gut, the mycobiome, through complex interactions with other microbes, can play a key mediating role in human health and dysbiosis."
} | 3,722 |
35557786 | PMC9088944 | pmc | 4,666 | {
"abstract": "Introducing exogenous species to an indigenous microbial community is an effective way to reveal the connections between metabolic processes, ecological function and microbial community structure. Herein, three different functional consortia (ferrous oxidizers, sulfur oxidizers and ferrous/sulfur oxidizers) were added to a natural leaching solution system derived from Zijin copper mine, China. The leaching experiment showed that the copper extraction rate of the community invaded by a sulfur-oxidizing consortium was 50.40% higher than that of the indigenous leachate at the endpoint of bioleaching. The variations of ferrous content, total iron, pH and redox potential in leachates also provided evidence that the community with exogenous sulfur oxidizers was more efficient. XRD analysis demonstrated that a proper addition of the sulfur-oxidizing consortium could eliminate sulfur passivation, promote production of chalcocite and enhance leaching. Furthermore, an exogenous ferrous-oxidizing consortium and a sulfur-oxidizing consortium greatly changed the community structure and microbial succession and promoted the cell growth rate during the bioleaching process, while ferrous/sulfur oxidizers showed no obvious effects on the indigenous community. Exogenous ferrous oxidizers, mainly L. ferriphilum , and sulfur oxidizers, mainly A. thiooxidans , successfully established and colonized in the indigenous community. However, only colonized A. thiooxidans , rather than L. ferriphilum , showed advantageous enhancement in the dissolution of chalcopyrite. Results indicated that exogenous sulfur oxidizer A. thiooxidans , which was scarce in the indigenous community, could easily colonize in the indigenous community, significantly change the community structure, sufficiently execute its function, and greatly enhance copper dissolution.",
"conclusion": "Conclusions The introduction of acidophilic consortia with different oxidizing functions was performed prior to the rapid growth phase of an indigenous community. Exogenous ferrous oxidizers and sulfur oxidizers greatly changed the community structure and microbial succession and promoted cell growth rate during the bioleaching process, while ferrous/sulfur oxidizers showed no significant effects on the indigenous community. Exogenous ferrous oxidizers, particularly L. ferriphilum , and sulfur oxidizers, particularly A. thiooxidans , successfully established and colonized in the indigenous community. However, only colonized A. thiooxidans , rather than L. ferriphilum , showed the advantage of enhancing the dissolution of chalcopyrite. At the end of bioleaching, the copper extraction rate of the system invaded by sulfur oxidizers was 50.40% higher than that of the indigenous leachate. These results indicated that exogenous sulfur oxidizer A. thiooxidans , which was scarce in the indigenous community, could colonize easily, execute its function sufficiently, and greatly enhance copper dissolution.",
"introduction": "Introduction With the continuous consumption of high-grade minerals, the demand for exploitation of low-grade ores has been increasingly urgent. In recent decades, bioleaching has been considered as a successful metallurgical technology to extract precious and base metals from low-grade ores, 1 such as secondary copper sulfides, pyritic gold ores or nickel-copper sulfide ores. 2,3 Bioleaching systems constitute suitable habitats and niches for the colonization of a diversity of microorganisms. To date, more than 40 types of acidophilic species have been detected in bioleaching systems. 4 Based on different functional performances, bioleaching microorganisms can be assigned as ferrous-oxidizing acidophiles (mainly Leptospirillum ferriphilum and Ferroplasma thermophilum ), sulfur-oxidizing acidophiles (mainly Acidithiobacillus caldus and Acidithiobacillus thiooxidans ) and ferrous/sulfur-oxidizing acidophiles (mainly Acidithiobacillus ferrooxidans and Sulfobacillus thermosulfidooxidans ). 5 Ferrous-oxidizing acidophiles can dissolve sulfide ores to produce ferric iron, which further attack minerals to generate sulfur or polysulfide on the ore surface. Complementary to this, sulfur-oxidizing acidophiles can consume elemental sulfur or polysulfide to drive the bioleaching process. 6 Currently, many researchers have turned their attention on microbial communities owing to the close relationships between functional oxidizers, environmental factors and bioleaching performances. 7,8 Sulfur and iron oxidizers are often jointly used to improve the bioleaching efficiency. 9,10 Due to the ever-changing physical and chemical parameters in leachates, the types of microorganisms in different bioleaching systems or in different phases under the same bioleaching condition are always differential. 8,11 Under two subsystems in the Zijin Shan copper mine (Fujian province, China), Acidithiobacillus was the dominant genus in the leaching heap system, while Leptospirillum owned the competitive advantage in the leaching solution system. 12 In a successive process of chalcopyrite bioleaching, At. caldus , Sulfobacillus acidophilus and F. thermophilum sequentially dominated the system from the initial to the end phase. 13 The dynamic changes in different microorganisms played different roles in chalcopyrite dissolution and their synergistic functions were tightly related to the efficiency of chalcopyrite bioleaching. 10 However, in a natural ecosystem, microbial invasion is an extremely common scenario. As a dynamic and open environment, such a bioleaching system could be more frequently affected by exotic species. Alien or exotic species can in some cases change the structure and functioning of an entire ecosystem. 14 Our previous study has demonstrated that when an exogenous strain At. thiooxidans A01 was introduced to a consortium of pyrite bioleaching, the functional gene expression, structure and function of the indigenous consortium changed, and 10.7% increase in bioleaching rate was observed. 15 It has also been reported that the introduction of F. thermophilum into the defined microbial consortium ( At. caldus and L. ferriphilum ) accelerated the dissolution of the mineral and caused significant differences in the planktonic and attached population dynamics. 16 These examples provided more information for the study of exogenous acidophiles on artificial microbial communities in a bioleaching system. However, to date, only a few studies have reported the effects of exogenous acidophiles on the community structure and function in an in situ copper mine system. A study on an in situ copper mine system could be more practical and applicable for understanding natural bioleaching systems. 17 In our previous study, the differences in microbial structure and function between leaching heap (LH) and leaching solution (LS) in Zijin Shan copper mine (Fujian province, China) were investigated. Bioleaching experiment showed that microbial communities in LH had stronger leaching ability, while mineral extraction efficiency was significantly positively correlated with sulfur-oxidizing microbes. 12 Therefore, we suspected that additional oxidizing microbes could enhance the bioleaching efficiency of LS. To explore the effects of exogenous microorganisms on chalcopyrite bioleaching, acidophiles with different oxidative functions (ferrous oxidizers, sulfur oxidizers and ferrous/sulfur oxidizers) were introduced into in situ microbial communities of LS. Through observing the changes in environmental properties and microbial communities, this study can further reveal the mechanism of chalcopyrite bioleaching upon coupling with exogenous functional oxidizers.",
"discussion": "Results and discussion Variation in main physicochemical parameters in different leachates As shown in Fig. 2 , exogenous acidophiles had a great influence on the variation in physicochemical parameters in indigenous leachate. The whole course in leachate could be divided into three phases: strain-adaptive growing phase (SAG, 0–9th d), rapidly increasing phase (RIC, 9–18th d) and stationary phase (STA, 18–36th d). During the bioleaching process, ferrous concentrations increased rapidly in the early stage for all four treatments, and then decreased sharply until zero ( Fig. 2a ). Compared with the abiotic control, system D first reached to its peak ferrous concentration (549 ± 24 mg L −1 ) at day 9, followed by system B (602 ± 24 mg L −1 ) and system C (675 ± 26 mg L −1 ), and system A was the last one reach its highest ferrous concentration (603 ± 21 mg L −1 ) at day 15. It has been reported that a high content of introduced ferrous oxidizers could enhance the iron metabolism during bioleaching and yield more Fe from the mineral. 10 However, the ferrous concentration in system D decreased first, followed by systems B, C and A, which indicated that the ferrous oxidative ability of system D was much stronger than that of the other systems. Herein, the stronger ferrous oxidative ability of ferrous/sulfur oxidizers ( A. ferrooxidans and S. thermosulfidooxidans ) might be attributed to the fact that the ferrous oxidative genes were stimulated and sufficiently activated by long-term ferrous substrate domestication in our laboratory. Moreover, it is generally accepted that iron metabolism could have been enhanced via the synergistic effect with sulfur metabolism. 28,29 Therefore, the ferrous-oxidizing ability of ferrous/sulfur oxidizers could be more efficient under the stimulation of their sulfur oxidation. Ferrous oxidative ability has been related to several factors, such as microbial types, cell growth rate and environmental parameters, rather than a single variable. Fig. 2 Variation (mean ± SD) in physicochemical parameters in leachates by introducing different functional consortia ((a) ferrous ions, (b) total iron, (c) pH, (d) ORP). During the whole process, total iron concentration in abiotic control slowly and gradually increased, but the variation in the experimental treatments was high, particularly for system C ( Fig. 2b ). Total iron concentration in the four experimental systems increased rapidly from day 6, suggesting that the dissolution of chalcopyrite was significantly accelerated with the introduction of exogenous cells. 30 Differences among the four systems appeared from the RIC phase onwards. In this period, total iron concentration increased rapidly to a maximum value and then decreased. Remarkably, the concentration of total iron in system C decreased at a much faster rate than that in systems B, A and D from the 12 th day. Finally, the concentration of total iron in system C declined to 224 ± 9 mg L −1 , while that in systems B, A and D declined to 433 ± 11 mg L −1 , 451 ± 14 mg L −1 and 509 ± 11 mg L −1 , respectively. In the leaching solution, a decrease in total iron was mainly due to the transformation of Fe 3+ to jarosite. System C holds more sulfur-oxidizing bacteria and produced more SO 4 2− , which might drive the reaction between Fe 3+ and SO 4 2− . pH of the abiotic control increased gradually with the leaching process. However, in the four experimental leachates with inoculated microorganisms, pH declined in general, although with fluctuations ( Fig. 2c ). In the early stage, pH slightly increased before the 6 th day, which might be caused by the consumption of protons through the direct chalcopyrite bio-oxidation during initial bioleaching. pH of systems B, C and D appeared to rapidly decrease from the 6 th day, while the pH of system A began to decrease rapidly from 9 th day, which might be attributed to the addition of bacteria that accelerated the dissolving process of the mineral and produced more acid products. Among the introduced systems, C and D showed lower pH than B because the exogenous cells in C and D exhibited a sulfur-oxidizing function. This observation was in accordance with the consideration that more active sulfur oxidizers can produce more acid and cause a decrease in pH by enhancing the oxidation of elemental sulfur or sulfide ores. 31 In the STA phase, the pH value maintained a modest downtrend and the value in system C was always significantly lower than that in other systems . At the endpoint of bioleaching, pH in systems A, B, C and D dropped to 1.62, 1.58, 1.48 and 1.60, respectively. Compared with the abiotic control, ORP of the four experimental groups increased continuously and then reached a plateau (about 660 mV) in the later stage of the bioleaching process ( Fig. 2d ). The solution potential is a determinant of the chalcopyrite biohydrometallurgy system. 32 The variation of ORP in different leaching systems was closely related to the increase in ratio of Fe 3+ /Fe 2+ during the process. 33 Ferrous concentration of system D decreased from 549 ± 24 mg L −1 to 8 mg L −1 from day 9 to day 12 and finally to 0 mg L −1 at day 24, during which ORP of system D increased from 483 ± 13 mV to 609 ± 9 mV and finally to 666 ± 7 mV. Similarly, the variation of ORP in the other three systems also showed a steadily inverse correlation with ferrous concentration, but with changed times, exhibiting varying degrees of latency: system B from day 12 to day 15, system C from day 12 to day 18 and system A from day 15 to day 18. Many studies have shown that the passivation of chalcopyrite occurred quickly if the initial solution potential was too high. 32 Similarly, in the STA phase, ORP of the four treatments reached a stable value of about 670 mV, which indicated that iron started to precipitate and consequently inhibit copper dissolution. Bioleaching efficiency of different leachates and XRD analysis of bioleaching residues As shown in Fig. 3 , copper extraction by all the four treatments continually increased during the entire leaching time. In the SGA phase, there was no significant difference among the four treatments. After exogenous consortia were introduced, chalcopyrite in the introduced systems showed a faster dissolution rate, particularly for system C. Over the course of bioleaching, the differences between systems A, B and D diminished gradually, while system C always maintained a higher dissolution rate. In the latter stage, the extraction rate of copper ions became slower owing to higher jarosite yield, which further formed a passivation layer on the ore surface to block the continuous copper extraction. 29,34 After leaching for 36 days, a maximum of 49.48% of copper was released from chalcopyrite in system C, followed by system B (34.94%), system A (32.90%) and system D (30.81%). These results indicated that the oxidative dissolution of chalcopyrite was more extensive with exogenous sulfur-oxidizing bacteria Acidithiobacillus . In our previous studies, it has been demonstrated that the ability of pyrite bioleaching was significantly and positively correlated with Acidithiobacillus , 12 which suggested that higher abundance of Acidithiobacillus could enhanced mineral bioleaching, which is in line with our results. Fig. 3 Differences in copper extraction efficiency in different systems during the whole bioleaching process. XRD analysis was performed to investigate the composition of the ore residues after bioleaching for 15 and 30 days ( Fig. 4 ). At day 15, more distinct diffraction peaks of chalcopyrite were detected in systems A and D, confirming that chalcopyrite was leached faster in systems B and C. In addition, the peak ascribed to elemental sulfur was observed in the XRD patterns of residues of systems A, B and D, but no such peak appeared in the XRD pattern of the residue of system C, which indicated that most of the sulfur was consumed by the diverse sulfur-oxidizing bacteria in system C. At day 30, jarosite was observed in the residues of all four systems, which was the main factor that inhibited further copper dissolution. Moreover, fewer peaks of elemental sulfur and chalcopyrite were detected in the XRD pattern of the residue of system C, demonstrating its high ability for mineral dissolution and sulfur oxidization. Interestingly, an inconspicuous peak of chalcocite was also observed in system C residue at day 30, implying the reduction of chalcopyrite. A two-step model of chalcopyrite reduction has been proposed in the past. 35 This model suggested that chalcopyrite could be reduced by ferrous ions in the presence of cupric ions to form chalcocite at relatively low redox potentials. Then, chalcocite, which was more amenable to leaching than chalcopyrite, was oxidized to cupric ions and elemental sulfur by ferric ions or oxygen. Combined with the redox potential variation, systems A and C seemed to be more liable to the reduction of chalcopyrite. However, no chalcocite was observed in system A residue, which might be owing to small concentration in the feed or fast oxidation by cupric ions. 36 Fig. 4 XRD patterns of chalcopyrite residues at different time by introducing different functional consortia. (a) Indigenous community; (b) indigenous + L. ferriphilum & F. thermophilum ; (c) indigenous + A. caldus & A. thiooxidans ; and (d) indigenous + A. ferrooxidans & S. thermosulfidooxidans . Microbial community succession in leachates As important members of the bioleaching systems, the dynamics of acidophilic microorganisms in a community were closely related to the dissolution process of minerals. Therefore, microbial community succession was investigated by 16S rDNA sequencing and RT-qPCR. A total of 10 000 rarified 16S rRNA gene sequences were obtained and clustered into 289 operational taxonomic units (OTUs) at a 97% similarity threshold. On this basis, the effects of exogenous species on microbial structure and function and the relationships between microbial structure and bioleaching performance were analyzed. Detrended correspondence analysis (DCA) was performed to portray the overall succession of microbial communities in response to different exogenous oxidizing bacteria ( Fig. 5a ). At the beginning, the distribution of communities at day 6 showed noticeable deviation from the original communities (day 0) due to intensified competition among the bacteria. After the exogenous consortia were introduced, the samples at different time points in the same system clustered together neatly, but the distance between the samples in system A and system D was less pronounced. Compared with system B, the samples in system C clustered more closely during the whole bioleaching process, indicating a more stable community in system C. Furthermore, cluster analysis was conducted for the 19 abundant OTUs (relative abundance >1.0%) in leachates ( Fig. 5b ). The obtained results also indicated that exogenous ferrous/sulfur oxidizers did not cause significant changes to the microbial structure, but the structures of microbial communities invaded by sulfur or ferrous oxidizers were significantly different from that of the indigenous leachate. It was noticeable that there exists unique abundant OTUs in different systems; e.g. , OTU_3 was more abundant in system A and D, OTU_2 was more abundant in system B and OTU_212 was more abundant in system C. These unique species might have contributed to the dissolution of mineral by different mechanisms. Fig. 5 Community structures of different systems during the process of bioleaching. (a) Detrended correspondence analysis (DCA) of different communities. (b) Heat map of the 19 abundant OTUs of the four communities. System A: indigenous community; system B: indigenous + L. ferriphilum & F. thermophilum ; system C: indigenous + A. caldus & A. thiooxidans ; and system D: indigenous + A. ferrooxidans & S. thermosulfidooxidans . The succession of microbial community is shown in Fig. 6 . On the one hand, the cell growth rate showed significant differences due to different exogenous species. During the SAG phase, minor amounts of nutrient substrates were released from the ore for cell growth. 37 Therefore, cell growth rate was still weak. With the production of ferrous ions, elemental sulfur and other metabolic intermediates (such as CuS, CuS 2 , polysulfide and extracellular secretions), 38 bacteria with different functions were stimulated to accelerate the bioleaching process; thus, microbial growth increased significantly in the RIC phase. As expected, the introduction of exogenous consortia accelerated the processes of cell growth and mineral dissolution. System C maintained the maximum cell number during the whole bioleaching process. At the endpoint of bioleaching, cell density of the four systems reached 7.1 × 10 8 , 7.9 × 10 8 , 8.2 × 10 8 and 7.6 × 10 8 cells per mL. The cell density of system C was approximately 15% higher than that in the indigenous community. These results indicated that exogenous species, particularly sulfur-oxidizing species, had stronger growth activity and produced a competitive advantage to occupy their living niche. Fig. 6 Microbial community succession of acidophilic cells in the four systems. (a) Indigenous community; (b) indigenous + L. ferriphilum & F. thermophilum ; (c) indigenous + A. caldus & A. thiooxidans ; and (d) indigenous + A. ferrooxidans & S. thermosulfidooxidans . On the other hand, the proportion of the microbial sequences could be assigned to different taxonomic ranks with distinct taxonomic resolution ( Fig. 6 ). The introduction of exogenous species caused drastic changes in the microbial composition. During the first 6 days, the predominant genus in leachates changed from Leptospirillum (72.63%) to A. caldus (85.33%). Compared with the indigenous community, the microbial compositions of systems B and C changed significantly after the exogenous oxidizers were introduced. In system B, the proportion of Leptospirillum increased rapidly and finally became the dominant species. In contrast, a higher amount of sulfur oxidizers ( A. caldus and A. thiooxidans ) and Sulfobacillus were detected in system C. Different community succession for systems B and C indicated that exogenous species performed successful colonization in the indigenous leaching system. During the STA phase, the amount of Leptospirillum began to increase by different degrees in all the four treatments till the end of the bioleaching process, which was consistent with the previous studies which showed that L. ferriphilum had higher tolerance to high ORP and low pH conditions. 39 Moreover, due to the accumulation of organics from microbial death and extracellular metabolites with time, facultative chemolithoautotroph Sulfobacillus became an important genus in the latter stage to accelerate the consumption of organics and thus lessen the toxic effect of organics on Acidithiobacillus and Leptospirillum . 40 Furthermore, the variation in community succession was also related to copper recovery efficiency. Copper extraction was much higher in system C, which might be related to its special community. Similar to other systems, A. caldus played an important role in sulfur oxidization. In addition to A. caldus , exogenous A. thiooxidans performed its oxidizing function in system C. Sulfur oxidizers diminished the accumulation of sulfur on the surfaces of mineral particles and provided sulfuric acid for proton attack and maintenance of low pH. 41,42 Before introducing the sulfur consortium ( A. caldus and A. thiooxidans ), the leachate was mainly composed of A. caldus . Therefore, it was difficult for exogenous A. caldus to settle in the indigenous community because of a high niche overlap and repulsive interaction. In contrast, A. thiooxidans seemed to present stronger ability to establish its presence in the indigenous community. In summary, microbial community structure and composition were greatly altered by exogenous acidophiles. High amounts of sulfur oxidizing bacteria, particularly A. thiooxidans , could enhance the leaching efficiency. Mechanism of different exogenous consortia on chalcopyrite bioleaching A mechanism model coupling community succession and main biochemical reactions ( eqn (1)–(10) ) is proposed according to the different effects of the exogenous consortia on the indigenous leachate ( Fig. 7 ). As shown in Fig. 7 , the intensity of the colour (aquamarine blue) filled in the leaching systems represented the concentration of copper ions. In other words, darker the colour, higher would be the copper ion concentration. In the SAG phase, both the sulfur and iron metabolisms were still weak due to low number of cells ( L. ferriphilum and A. caldus ) and chemical ions. Due to limited substrates, competition among bacteria was intensified. Thus, the structure of communities significantly changed in the beginning ( Fig. 5a ). When microorganisms colonized on the mineral surface, chalcopyrite was dissolved through a direct mechanism, as shown in eqn (1) , which was in line with increasing trend of pH during the initial bioleaching process ( Fig. 2c ). After a short adaptive phase, exogenous consortia with different oxidizing ability were introduced to the indigenous leachate. During the RIC phase, with the accumulation of ferric ions (according to eqn (1) ), chalcopyrite was dissolved rapidly by the ferric ions ( eqn (2) ) and proton attack ( eqn (3) ). With the production of more ferrous ions and elemental sulfur ( Fig. 4 ), iron and sulphur metabolisms were greatly enhanced in the presence of oxidizing bacteria to produce more ferric ions ( eqn (4) ) and sulfuric acid ( eqn (6) ), respectively. More detailed reaction processes for iron and sulfur metabolisms are shown below. Fig. 7 A model of the process of chalcopyrite bioleaching upon introducing different oxidizers. SAG: strain adaptive-growing phase; RIS: rapidly increasing phase; STA: stationary phase. (A) indigenous community; (B) indigenous + L. ferriphilum & F. thermophilum ; (C) indigenous + A. caldus & A. thiooxidans ; and (D) indigenous + A. ferrooxidans & S. thermosulfidooxidans . Numbers ①–⑩ represent eqn (1)–(10) . Ferrous and ferric ions in bioleaching systems were the main reactants of iron metabolism. It has been suggested that the adsorption behavior of attached cells at the early stage is beneficial to further concentrate ferric ions and attack the chalcopyrite, according to eqn (2) . 43,44 Subsequently, ferrous ions formed from the former reactions ( eqn (2) and (3) ) were oxidized by iron oxidizers to promote the dissolution of chalcopyrite. 30 Compared with the indigenous leachate, increased ferrous concentration in systems B, C and D indicated stronger iron reactions in exogenous systems ( Fig. 2a ). This is because the exogenous bacteria greatly increased the cell density and changed the community composition. The mechanism model showed that in the RIC phase, the iron oxidizers in systems A, C and D were mainly A. ferrooxidans and S. thermosulfidooxidans and in system B, the iron oxidizers were A. ferrooxidans and L. ferriphilum . During the STA phase, S. thermosulfidooxidans and L. ferriphilum successively performed an increasingly important role in all the four systems. During this period, the yield of tawny jarosite precipitation was greatly enhanced ( Fig. 4 ) by the excess ferric ions ( eqn (5) ), which inhibited further copper dissolution. 1 2CuFeS 2 + 17/2O 2 + 2H + = 2Cu 2+ + 2Fe 3+ + 4SO 4 2− + H 2 O 2 CuFeS 2 + 4Fe 3+ = Cu 2+ + 5Fe 2+ + 2S 0 3 CuFeS 2 + O 2 + 4H + = Cu 2+ + Fe 2+ + 2S 0 + 2H 2 O 4 4Fe 2+ + O 2 + 4H + = 4Fe 3+ + 2H 2 O 5 M + + 3Fe 3+ + 2SO 4 2− + 6H 2 O = MFe 3 (SO 4 ) 2 (OH) 6 + 6H + Sulfur metabolism was closely related to iron metabolism. With the dissolution of chalcopyrite, according to eqn (2) and (3) , elemental sulfur was released, which covered the ore surface. Moderate community structure was important to maintain a better balance between iron and sulfur metabolism. 34 With the introduction of different oxidizing consortia, the indigenous community could automatically adjust its structure to a new balanced state and continue the oxidizing function. The reduced inorganic sulfur compounds (RISCs) could be effectively consumed by sulfur-oxidizing bacteria, such as A. caldus and A. thiooxidans , according to eqn (6) and (7) . The model showed that the sulfur-oxidizing bacteria were more abundant and diverse in system C, and the copper extraction rate of system C was 50.40% higher than that of system A. Thus, the exogenous A. thiooxidans established in the indigenous community successfully enhanced the sulfur oxidizing function. Furthermore, XRD analysis showed an inconspicuous peak of chalcocite in the pattern of system C, which indicated that chalcopyrite was partly dissolved by cupric ions and ferrous ions ( eqn (8) ). Chalcocite had better leaching features than chalcopyrite; thus, it was oxidized by ferric ions or oxygen to cupric ions ( eqn (9) and (10) ) more easily. 6 2S 0 + 2H 2 O + 3O 2 = 2SO 4 2− + 4H + 7 S x O y n − + H 2 O + O 2 → SO 4 2− + H + 8 CuFeS 2 + 3Cu 2+ + 3Fe 2+ = 2Cu 2 S + 4Fe 3+ 9 Cu 2 S + 4Fe 3+ = 2Cu 2+ + 4Fe 2+ + S 0 10 Cu 2 S + O 2 + 4H + = 2Cu 2+ + S 0 + 2H 2 O With the synergistic effect of iron and sulfur metabolisms, the copper ions were gradually dissolved, as according to eqn (2) , (3) and (9) . By introducing different oxidizing consortia, the final copper concentrations in the four systems reached 2308 ± 55, 2383 ± 46, 3276 ± 59 and 2040 ± 50 mg L −1 , respectively. Compared with the indigenous system (32.90%), exogenous sulfur oxidizers increased the copper extraction rate to 49.48%, which indicated that exogenous sulfur oxidizers could establish in the new environment and hold stronger leaching ability for chalcopyrite than other exogenous cells."
} | 7,456 |
38053584 | PMC10693988 | pmc | 4,671 | {
"abstract": "By directly converting solar energy and carbon dioxide into biobased products, cyanobacteria are promising chassis for photosynthetic biosynthesis. To make cyanobacterial photosynthetic biosynthesis technology economically feasible on industrial scales, exploring and engineering cyanobacterial chassis and cell factories with fast growth rates and carbon fixation activities facing environmental stresses are of great significance. To simplify and accelerate the screening for fast-growing cyanobacteria strains, a method called Individual Cyanobacteria Vitality Tests and Screening (iCyanVS) was established. We show that the 13 C incorporation ratio of carotenoids can be used to measure differences in cell growth and carbon fixation rates in individual cyanobacterial cells of distinct genotypes that differ in growth rates in bulk cultivations, thus greatly accelerating the process screening for fastest-growing cells. The feasibility of this approach is further demonstrated by phenotypically and then genotypically identifying individual cyanobacterial cells with higher salt tolerance from an artificial mutant library via Raman-activated gravity-driven encapsulation and sequencing. Therefore, this method should find broad applications in growth rate or carbon intake rate based screening of cyanobacteria and other photosynthetic cell factories.",
"conclusion": "4 Conclusions Traditional culture-based screening paradigm usually requires plotting of the growth curve based on OD, which would take at least 7–10 days and thus is time-consuming, of low throughput, and consuming a large amount of medium. On the other hand, natural chlorophyll autofluorescence can be monitored to track growth rate by the microdroplet platform, however, this is also dependent on the time-consuming cell culture and moreover growth-phenotype sorting of the microdroplet-based cyanobacterial cultures has not been realized [ 9 ]. In contrast, iCyanVS, by exploring the rich information content in a SCRS, can quickly screen highly active single-cells (0.1 s/single-cell) after only hours of stable isotope incubation. In addition, ability to probe growth rate-related or carbon fixation-related phenotypes at single-cell resolution is highly valuable for tackling genetically heterogeneous samples such as a mutant library, as this would allow the mutants to be phenotypically screened under their in situ conditions, regardless of their relative growth competitiveness among mutant cells in the consortium. Furthermore, as carotenoids are widely found in photosynthetic organisms, this approach is broadly applicable to not just cyanobacteria but to other types of microalgae and higher plants. In this example, using PCC 7942 and the highlight-tolerant mutant strain HS199 as a model whose growth rates differ in bulk cultures, the iCyanVS system discriminated between cells of the two species after just 12 h of incubation, and then identified the salt-tolerant strain JC19 under salt stress from the mutant library via single-cell RAGE-Seq. The results represent an advance over previous cyanobacteria-phenotyping studies since the phenotyping measurement is directly based on the metabolic activity properties of every single cell. Therefore, the advantages of iCyanVS, including being culture-free, identifying single-cell metabolic activity, high throughput, and high accuracy, suggest its promising use as a broadly applicable approach for efficiently mining fast-growing autotrophic chassis cells or for dissecting phenotype-genotype links of cyanobacteria at single-cell resolution.",
"introduction": "1 Introduction Cyanobacteria are the only group of prokaryotes that perform oxygen-generating photosynthesis, thus they serve as an important source of primary productivity in the biosphere [ 1 ]. In recent years, cyanobacteria are considered promising chassis for photosynthetic biosynthesis, due to their capability of directly converting solar energy and carbon dioxide into various biobased products [ 2 , 3 ]. Compared with higher plants and eukaryote algae, cyanobacteria possess shorter life cycles, faster growth, simpler structures, and more efficient photosynthesis, thus are attractive microbial photosynthetic platforms for biotechnological and industrial applications [ 4 ]. For example, cyanobacterial strains have been cultivated to produce high-value secondary metabolites, such as carotenoid, mycosporine-like amino acids, and scytonemin [ 5 , 6 ]. Moreover, synthetic biology approaches have been adopted to improve the photosynthesis productivity of cyanobacteria chassis cells and cell factories and to remodel the photosynthetic metabolism network for the photosynthetic production of multiple natural or nonnatural metabolites [ 7 ]. However, industrial application of cyanobacterial photosynthetic biosynthesis is still hindered by multiple factors, e.g., the weak cellular robustness of cyanobacterial chassis cells and cell factories when facing environmental stresses in industrial systems and processes [ 8 ]. Cyanobacterial strains of rapid growth and efficient carbon fixation are the foundation for large-scale cultivation in actual industrial environments. Therefore, rapid and reliable methods to identify strains with such phenotypes from the natural environment or from mutant libraries are of keen interest [ 9 ]. The traditional paradigm for phenotypic screening of cyanobacteria is based on culture, e.g., plate-based colony cultivation followed by liquid culture. Due to the lower growth rate of such photoautotrophic cells than heterotrophic cells such as Escherichia coli or yeast, this paradigm is very time-consuming and of low throughput, especially when cultivated under stress conditions. Single-cell-based selection methods provide a solution to tackle these challenges [ [10] , [11] , [12] ]. Single-cell is the basic unit of life forms and the atomic step of biological evolution on Earth, thus single-cell technologies for phenotypic or genotypic analysis at single-cell resolution can dissect and mine biological devices, modules or chasses with unprecedented precision [ 13 , 14 ]. On the other hand, for the purpose of phenotype screening, single-cell technologies can greatly shorten or even completely skip the tedious and time-consuming cultivation and propagation of cells [ 15 ]. Therefore, single-cell technologies have supported phenotype-based profiling and screening of cyanobacteria [ 16 ], particularly for product-related phenotypes (i.e., yield of target chemicals [ [17] , [18] , [19] , [20] ]). For instance, as cyanobacteria contain chlorophyll which exhibits some level of fluorescence and can serve a measure of the number of cells, a microdroplet platform was introduced for screening fast-growing cyanobacteria from two cyanobacterial species which monitors intracellular natural chlorophyll autofluorescence after 4 days of in-droplet growth, however it is unable to sort cyanobacteria due to their small cell size [ 9 ]. Meanwhile, a droplet-based microfluidic workflow was proposed that combines several microfluidic devices to encapsulate, assay, and sort l -lactate-producing strains of the cyanobacterium Synechocystis sp. PCC 6803 (hereafter PCC 6803) based on fluorescence monitoring after addition of lactate assay enzyme solution [ 18 ]. These approaches are all intrinsically growth-based, thus remain slow and inefficient. Here we ask whether it is possible to screen cyanobacteria for growth rate at single-cell resolution, i.e., to predict the growth rate of cyanobacteria without actually tracking its cellular multiplication. A single-cell Raman spectrum (SCRS) can serve as an intrinsic biochemical fingerprint of individual cells that is label-free and non-invasive features [ [21] , [22] , [23] ]. Moreover, SCRS can be exploited to track substrate intake, as the incorporation of stable isotopes in substrate into intracellular macromolecules would lead to the “red-shift” of corresponding Raman bands in SCRS [ 24 , 25 ]. Such Stable Isotope Probing based Raman Microspectroscopy (Raman-SIP) was employed to probe substrate-specific metabolism at the single-cell resolution [ 20 , [26] , [27] , [28] ]. For example, to tackle the challenges associated with methods that are based on cellular growth, deuterium-probing based SCRS was proposed for quantitative assessment of vitality [ 29 ] and antimicrobial susceptibility [ 20 , 25 , 30 ]. However, although cellular growth is clearly dependent on substrate consumption by the cell, it is not clear whether, to what degree, or under what circumstances the substrate intake rate revealed at SCRS at the single-cell resolution is correlated with the growth rate of the strain. In this study, we introduce a culture-free, one-cell-resolution, phenome-genome-combined strategy called Individual Cyanobacteria Vitality Tests and Screening (iCyanVS). We show that the 13 C incorporation ratio of carotenoids in individual, genetically heterogeneous cyanobacterial cells can be used to measure differences of their growth rate and carbon fixation rate in bulk cultivations. The potential and robustness of this approach were further demonstrated by identifying cells with higher salt tolerance from an artificial mutant library by Raman-activated gravity-driven encapsulation and sequencing. The iCyanVS strategy has the advantages of being culture-free, identifying single-cell metabolic activity from single-cell accuracy, high throughput, and high accuracy.",
"discussion": "3 Results and discussions 3.1 Overview of the iCyanVS strategy for fast-growing individual screening directly from an artificial mutant library To screen the fast-growing phenotypes of cyanobacteria from an artificial mutant library, the iCyanVS strategy was proposed which include two steps: the vitality test via single-cell Resonance Raman-SIP, and the Raman-activated cell sorting coupled to single-cell sequencing via the RAGE-Seq technique ( Fig. 1 ). The vitality test aims to establish the relationship between specific Raman signals and the metabolic and growth activities of cyanobacteria. Specifically, the cyanobacterial mutant library was pretreated for stress exposure under a certain stress condition and then incubated in medium containing NaH 13 CO 3 under the same stress conditions for a duration. The resulted sample which includes cells exhibiting distinct SCRS due to their different metabolic activities in stress response was then analyzed using Raman Microspectroscopy. In a SCRS, the Raman bands for carotenoids are proper biomarkers for the red-shifts that quantify the extent of 13 C incorporation, due to their characteristic Raman signal, their relatively high intracellular contents, and their resonance Raman signals that are much stronger than other metabolites. The carotenoid signal intensity was associated with the incubation time of NaH 13 CO 3 and the metabolic and growth activity of cyanobacteria. Based on the red-shift of Raman bands for carotenoids in a SCRS, the 13 C incorporation ratio [ 13 C/( 12 C+ 13 C)] was employed to model the metabolic activity and the growth rate of each individual cyanobacteria cell. To decode the single-cell genotype or complete genome sequence that is associated with the SCRS-modeled phenotype, RAGE-Seq was employed to screen and then sort the single cells with targeted phenotype, due to its advantages in maximally preservation of metabolic vitality [ 36 ] and in production of high-coverage one-cell genome sequences [ [37] , [38] , [39] , [40] ]. Specifically, the individual cells are screened via SCRS in an aquatic, vitality-preserving environment, and then the individual cells with targeted SCRS are precisely packaged in a picoliter microdroplet and readily exported in a precisely indexed, “one-cell-one-tube” manner. Such integration of picoliter microdroplet encapsulation into Raman-activated sorting ensures low-bias amplification and thus high-coverage sequencing of the one-cell genome that is directly linked to the cell's metabolic phenome ( Fig. 1 ). 3.2 Metabolic activity labeling and vitality tests via Raman-SIP Different cyanobacterial cells exhibit distinct growth rates and metabolic activities under levels of stress [ 41 ]. Fed with NaH 13 CO 3 after stress exposure, cells that exhibit higher vitality would carry a strong 13 C incorporation ratio, suggesting their stronger stress tolerance and correspondingly, higher growth rate under stress ( Fig. 2 A). To establish the relationship between specific Raman signals and the metabolic and growth activity of cyanobacteria, first, the growth curves of PCC 7942 with different concentrations of NaHCO 3 as the only carbon source were derived. Under NaHCO 3 concentrations that are greater than 50 mM, the growth of PCC 7942 can be maintained for at least 48 h ( Fig. S1 ), thus 100 mM NaHCO 3 was chosen as the carbon source in all subsequent experiments. The resonance Raman spectra of carotenoids display characteristic Raman bands ( Fig. 2 B): v 1, v 2, and v 3. The sharp and strong v 1 (stretching mode of the C \n \n\n<svg xmlns=\"http://www.w3.org/2000/svg\" version=\"1.0\" width=\"20.666667pt\" height=\"16.000000pt\" viewBox=\"0 0 20.666667 16.000000\" preserveAspectRatio=\"xMidYMid meet\"><metadata>\nCreated by potrace 1.16, written by Peter Selinger 2001-2019\n</metadata><g transform=\"translate(1.000000,15.000000) scale(0.019444,-0.019444)\" fill=\"currentColor\" stroke=\"none\"><path d=\"M0 440 l0 -40 480 0 480 0 0 40 0 40 -480 0 -480 0 0 -40z M0 280 l0 -40 480 0 480 0 0 40 0 40 -480 0 -480 0 0 -40z\"/></g></svg>\n\n C bonds at ∼1515 cm −1 ), v 2 (stretching mode of the C–C bonds at ∼1155 cm −1 ), and v 3 (deformation of the methyl groups at ∼1003 cm −1 ) peaks can be unambiguously assigned to carotenoids [ 42 ]. In addition, the Raman bands of carotenoids displayed a red-shift due to the active 13 C incorporation by cells: the v 1, v 2, and v 3 bands shifted to 1485, 1130, and 990 cm −1 from the original 1520, 1160, and 1005 cm −1 in SCRS of 12 C cells after growth in NaH 13 CO 3 , which was similar to the phenomena reported in previous work with Synechococcus sp. PCC 7002 grown in NaH 13 CO 3 [ 26 ]. In addition, the signal intensity of 13 C incorporation was closely related to the culture time with NaH 13 CO 3 as the sole carbon source ( Fig. 2 C), in that the proportion of 13 C incorporation ratio rapidly increased from 2 h and reached a stable labeling state after 36 h ( Fig. 2 B). Hence, in subsequent experiments, we used the 13 C incorporation ratio in a certain incubation time before reaching a steady state as criteria, so as to identify and sort those cyanobacteria cells that actively take in NaH 13 CO 3 . 3.3 Performance of Raman-SIP in evaluating and screening a previously known fast-growing cyanobacteria strain under high-light conditions To validate the performance of the Raman-SIP system in screening strains with fast-growing characteristics, the cyanobacterial strains PCC 7942, Synechococcus elongatus UTEX 2973 (hereafter UTEX 2973), and Synechocystis sp. PCC 6803, and a mutant strain HS199 with different growth rates under high light conditions were evaluated. Among them, UTEX 2973 is a recently isolated cyanobacterial strain that has a faster growth rate and better tolerance to high temperature and high light than PCC 7942 and PCC 6803 [ 43 , 44 ]. HS199 is a mutant strain that enhances the high light tolerance of Synechococcus by upregulating shikimate kinase expression [ 41 ]. All the strains were determined under high light conditions (1400 μmol photons m −2 s −1 ) with NaH 13 CO 3 as the sole carbon source. The results indicated that the mutant strains HS199 and UTEX 2973 maintained rapid growth under high light culture conditions, and their OD 730nm values were increased by 2.7-fold and 1.8-fold compared with PCC 7942 after 12 h of culture under high light conditions ( Fig. S2 ). In addition, the OD 730nm of the mixed culture of PCC 7942 and HS199 (initial inoculation amount PCC 7942: HS199 = 1:1) was intermediate between PCC 7942 and HS199. The SCRS spectral analysis of 1000 single cells from each sample indicated that the metabolic activity of HS199 was significantly higher than that of the other strains ( Fig. 3 ). The metabolic activity of UTEX 2973 was significantly higher than that of PCC 7942 and PCC 6803, which was consistent with the growth phenotype. In addition, there was no significant difference in growth between PCC 6803 and PCC 7942, but the metabolic activity of PCC 7942 was still slightly higher than that of PCC 6803, which is also in agreement with previously reported growth characteristics [ 45 ]. Interestingly, the Raman ratio of 1000 single cells were diverse, especially for PCC 7942 and PCC 6803, suggesting that different single cells of the same species had different metabolic activity under high light condition. The reason might be that different single cells responded differently to high-light stress at the transcription and translation levels. Early study also showed that the PCC 6803 had an increasing gene-expression heterogeneity after a certain period of nitrogen-starvation stress [ 46 ]. In the near future, it is expected to explore its internal regulatory mechanism through single-cell transcriptome. Moreover, the dispersion degree of the mixed culture of PCC 7942 and HS199 was relatively large, because the metabolic activity of PCC 7942 and HS199 was different under high light conditions. Taken together, the Raman-SIP system can distinguish the growth rate and metabolic activity of different strains by the 13 C incorporation ratio of carotenoids, which could be applied to screen fast-growing strains under stress conditions. Fig. 3 Single-cell metabolic activity of the cyanobacterial strains of PCC 7942, PCC 6803, UTEX 2973, HS199, and PCC 7942 mixed with HS199 (PCC 7942: HS199 = 1: 1) in a mock mutant community. The ratios of v 1 and v 2 were used to represent the metabolic activity, and the red to blue gradient represents the density from high to low. The dashed line indicates the threshold for distinguishing the metabolic activity of different strains. Fig. 3 3.4 Identifying the fastest growing strain by RAGE-Seq from the artificial mutant library To further screen high vitality cyanobacteria from non-model cyanobacteria strains and different mutants artificially constructed by RAGE-Seq, a Raman-SIP mock mutant library was built. Sigma (σ) factors are key regulators of global gene expression patterns that dictate the fate of metabolic pathways in cyanobacteria [ 47 ]. In recent years, an important strategy based on regulating the levels of σ factors, termed transcription factor engineering, has already been developed and adopted for engineering the complex metabolism and physiological phenotypes of diverse microorganisms [ 48 ]. The use of this approach alters the global expression of specific subsets of genes that can result in a positive impact on cell robustness [ 49 ]. Meanwhile, large-scale cultivation of cyanobacteria with seawater is necessary for industrial biotechnological applications [ 8 ]. Thus, it is urgent to improve the salt tolerance of freshwater PCC 7942 for the future application of this promising chassis for green fuels and chemical production. Therefore, all the σ factors in PCC 7942 were overexpressed, and mutant libraries including PCC 7942 and JC19-JC27 were constructed to screen the fast-growing mutant under salt stress ( Table 1 ). Furthermore, the growth curves of all mutant strains and PCC 7942 (WT) were analyzed under normal and salt stress conditions ( Fig. S3 ). The growth of JC19 was increased by 44 %, which was significantly higher than that of the wild type ( Fig. 4 ). Fig. 4 Growth and metabolic activity of the cyanobacterial mutant library. A) The OD 730nm of WT and different mutants (JC19, JC20, JC22, JC25, JC26, JC27) under salt stress conditions; B) The metabolic activity of 130 randomly selected cells. The dashed line indicates the threshold for screening the high vitality cyanobacteria; C) Source-tracking of the selected cells with high metabolic activity. Fig. 4 To screen salt-tolerant strains by RAGE from the mutant library more efficiently, the growth in bulk culture and metabolic activity of PCC 7942 at the single-cell level under different NaCl concentrations were tested ( Fig. S4 ). The metabolic activity of PCC 7942 at the single-cell level decreased with increasing salt concentration, which had a positive correlation with the growth phenotype. Herein, the 0.35 M NaCl was chosen to screen the salt-tolerant strains in the following experiments. All mutant strains and WT were mixed in a certain proportion to build a mutant library (1/60 for each mutant, 54/60 for WT). A total of 130 single cells were selected and analyzed by Raman-SIP ( Fig. 4 B). According to the metabolic activity threshold set of different strains under the high light condition, the individual cells in the top 8 % of metabolic activity were selected for MDA amplification and targeted gene verification ( Fig. 4 C): 72 % of the single cells were identified as strain JC19 (overexpressing the σ factor rpoD1 ) ( Table 2 ), suggesting an enrichment ratio of JC19 (from the 1.6 % before sorting to the 72 % after sorting). During the process, two situations could lead to false positives: one is the result of PCR amplification with no product, and the other is the result of sequencing showing a wild-type phenotype. In the first case, after single-cell sorting, the experiments involving MDA and PCR had a certain success rate, meaning not all single cells could obtain valid PCR results, such as No. 18 and 23 in Table 2 . In the second case, where sequencing results showed a wild-type phenotype, it could be that single-cell phenotypic heterogeneity resulting from transcriptional variations. Wild-type strain with higher single-cell activity might also be sorted due to the fact that the reference threshold is not absolutely accurate. JC19 exhibits higher metabolic activity and a faster growth rate under salt stress. In addition, in the process of preculture and labeling, strain JC19, as the dominant strain, had a certain degree of enrichment, which was consistent with the enhancement of HS199 under high-light stress conditions. Table 2 Identification of the individual cyanobacterial cells with high metabolic activity. Table 2 Number V1_ratio V2_ratio Identification 39 0.509 0.353 JC19 4 0.488 0.387 JC19 29 0.544 0.424 JC19 6 0.532 0.411 JC19 7 0.505 0.391 WT 8 0.607 0.419 JC19 34 0.499 0.388 JC19 15 0.488 0.384 JC19 18 0.480 0.356 - a 22 0.488 0.362 JC19 23 0.536 0.376 - a a False-positive: No PCR products were amplified. Furthermore, the metabolic activities of JC19 and WT at the single-cell level under different salt concentrations were also analyzed. No significant difference between the two strains under normal conditions ( Fig. 5 ). The metabolic activity of JC19 was significantly higher than that of WT at 0.35 M NaCl, and slightly higher at 0.5 M NaCl. In addition, the overall metabolic activity decreased with increasing salt concentration. Thus, strain JC19 grew faster under salt stress. In conclusion, mutant strains with high metabolic activity identified by RAGE at the single-cell level also had obvious growth advantages in the population-level growth phenotype. These findings are of great significance for the screening of fast-growing strains of cyanobacteria and provide references for high-throughput screening of other phenotypes such as growth, resistance, and metabolite synthesis. Fig. 5 Growth and single-cell metabolic activity analysis of PCC 7942 and mutant strain JC19 under different salt concentrations. A) OD 730nm for PCC 7942 under 0 M, 0.35 M, and 0.5 M NaCl concentration conditions; B) The metabolic activity of PCC 7942 and JC19 at the single-cell level under different concentrations of NaCl. Fig. 5"
} | 5,989 |
36182956 | PMC9526701 | pmc | 4,674 | {
"abstract": "Corynebacterium glutamicum is the major host for the industrial production of amino acids and has become one of the best studied model organisms in microbial biotechnology. Rational strain construction has led to an improvement of producer strains and to a variety of novel producer strains with a broad substrate and product spectrum. A key factor for the success of these approaches is detailed knowledge of transcriptional regulation in C. glutamicum . Here, we present a large compendium of 927 manually curated microarray-based transcriptional profiles for wild-type and engineered strains detecting genome-wide expression changes of the 3,047 annotated genes in response to various environmental conditions or in response to genetic modifications. The replicates within the 927 experiments were combined to 304 microarray sets ordered into six categories that were used for differential gene expression analysis. Hierarchical clustering confirmed that no outliers were present in the sets. The compendium provides a valuable resource for future fundamental and applied research with C. glutamicum and contributes to a systemic understanding of this microbial cell factory. Measurement(s) Gene Expression Analysis Technology Type(s) Two Color Microarray Factor Type(s) WT condition A vs. WT condition B • Plasmid-based gene overexpression in parental strain vs. parental strain with empty vector control • Deletion mutant vs. parental strain Sample Characteristic - Organism Corynebacterium glutamicum Sample Characteristic - Environment laboratory environment Sample Characteristic - Location Germany"
} | 402 |
25727314 | PMC4677816 | pmc | 4,675 | {
"abstract": "Seagrass meadows are a crucial component of tropical marine reef ecosystems. Seagrass plants are colonized by a multitude of epiphytic organisms that contribute to broadening the ecological role of seagrasses. To better understand how environmental changes like ocean acidification might affect epiphytic assemblages, the microbial community composition of the epiphytic biofilm of E nhalus acroides was investigated at a natural CO 2 vent in Papua New Guinea using molecular fingerprinting and next-generation sequencing of 16S and 18S rRNA genes. Both bacterial and eukaryotic epiphytes formed distinct communities at the CO 2 -impacted site compared with the control site. This site-related CO 2 effect was also visible in the succession pattern of microbial epiphytes. We further found an increased relative sequence abundance of bacterial types associated with coral diseases at the CO 2 -impacted site ( F usobacteria , T halassomonas ), whereas eukaryotes such as certain crustose coralline algae commonly related to healthy reefs were less diverse. These trends in the epiphytic community of E . acroides suggest a potential role of seagrasses as vectors of coral pathogens and may support previous predictions of a decrease in reef health and prevalence of diseases under future ocean acidification scenarios.",
"conclusion": "Conclusion: does epiphyte composition change due to OA? We detected a highly diverse bacterial and eukaryotic community on the leaves of E . acroides . Although OTU richness seemed unaffected, our results overall suggest a pronounced and interconnected shift in bacterial and eukaryotic community composition of the epiphytic biofilm of E. acroides with changes in the carbonate system of the surrounding water. Besides organisms well known to respond to elevated p CO 2 , this shift may also include taxa that have not been identified in OA research before. In some cases, a potential response to elevated p CO 2 was only visible at a very high level of taxonomic resolution. We further detected an increased prevalence of microbial sequence types associated with coral diseases at the vent site under elevated p CO 2 conditions. This agrees with the hypothesis that coral reefs experiencing elevated p CO 2 levels will be more susceptible to diseases than reefs not yet exposed to OA (Hoegh-Guldberg et al ., 2007 ). It further highlights the potential of seagrasses as vectors of coral pathogens (Sweet et al ., 2013 ) and stresses the point that seagrasses should be viewed as a holobiont when making predictions about OA effects and ecological consequences in coral reefs. Given the high diversity of the epiphytic community on seagrass leaves, an accurate assessment of the interaction of seagrasses with other components of reef ecosystems will also require further knowledge of their epiphytic community composition.",
"introduction": "Introduction Tropical marine reef ecosystems are hotspots of biodiversity and productivity in an otherwise desert-like marine system. Apart from corals, seagrass meadows are a crucial component of these reef ecosystems. As fish nurseries, nutrient cyclers, organic carbon producers and sediment stabilizers, seagrass meadows contribute substantially to ecosystem functioning (Orth et al ., 2006 ). Similar to corals (Mouchka et al ., 2010 ), seagrasses are colonized by microorganisms that form epiphytic biofilms on the seagrass leaves (Michael et al ., 2008 ). These biofilms have been shown to affect seagrass physiology as well as their interactions with other reef organisms by e.g. regulating light availability (Sand-Jensen, 1977 ), influencing the settlement of secondary epibionts and biofouling (Wahl, 1989 ) or the production of antimicrobial substances (Marhaeni et al ., 2011 ). As such, a seagrass plant and its epiphytic biofilm can be referred to as a seagrass holobiont. Ocean acidification (OA), defined as a decrease in ocean water pH caused by increased atmospheric CO 2 concentrations, is among the most worrisome threats to coral reef ecosystems (Hoegh-Guldberg et al ., 2007 ). The impacts of OA on corals range from a decrease of skeletal integrity (Hoegh-Guldberg et al ., 2007 ) to changes in the composition of the microbial biofilm associated with the coral reducing larval settlement and probably coral health (Meron et al ., 2011 ; Webster et al ., 2013 ). Seagrasses, on the other hand, are generally thought to benefit from OA because of the increased availability of CO 2 and bicarbonate for photosynthesis (Koch et al ., 2013 ; Brodie et al ., 2014 ). However, data on how the epiphytic biofilm on seagrass leaves might respond to OA and on the behaviour of the seagrass holobiont in future OA scenarios are still sparse. Several studies have investigated the epiphytic community on seagrass leaves giving detailed information on the composition of bacterial or eukaryotic epiphytes (Uku et al ., 2007 ; Medina-Pons et al ., 2009 ; Hamisi et al ., 2013 ). The effect of OA on epiphytic communities on seagrass leaves is far less well documented. Previous studies reported a decrease of calcifying epiphytes such as crustose coralline algae (Martin et al ., 2008 ; Donnarumma et al ., 2014 ) as already seen elsewhere in coral reefs (Fabricius et al ., 2011 ). Donnarumma and colleagues ( 2014 ) also highlighted a decrease in epiphyte diversity with decreasing pH. However, both studies only visually identified epiphytes by using light microscopy and did not address the multitude of cryptic epiphytes detectable only with the increased sensitivity and taxonomic resolution provided by molecular tools. Our study aims (i) to provide a first overview of both bacterial and eukaryotic epiphytes at a molecular level and (ii) to estimate how the epiphytic community on seagrass leaves may change in response to OA. This may thus help increase our understanding of the part the seagrass holobiont may play in the reef ecosystem under future OA scenarios. Recent research has turned to naturally CO 2 -rich systems as models for future OA scenarios (Hall-Spencer et al ., 2008 ; Fabricius et al ., 2011 ; Lidbury et al ., 2012 ; Kerfahi et al ., 2014 ). Unlike laboratory experiments, which are usually restricted to short-term studies, natural sites offer the opportunity to predict OA effects in long-term adapted systems that can be studied in their entirety without the need for experimental manipulation (Hall-Spencer et al ., 2008 ). However, the inherent complexity of natural systems can also confound OA effects, and caution is needed in selecting natural CO 2 -rich sites for OA research (Vizzini et al ., 2013 ). Here, the epiphytic biofilm on the leaves of the seagrass Enhalus acroides was investigated at a natural CO 2 vent and a control site in Papua New Guinea (PNG; Fig. S1 ). The sites were previously described as potential sites to study long-term effects of OA on coral reef communities because the prevailing environmental conditions are assumed to have been stable for up to 100 years (Fabricius et al ., 2011 ). The diversity and composition of both bacterial and eukaryotic microbial epiphytic communities were assessed using molecular community fingerprinting and next-generation sequencing of amplicon libraries. Besides the site-related CO 2 impact, the factor leaf age was included in the analysis to account for different developmental stages of the epiphytic biofilm as well as potential interactions of biofilm development with OA effects. To further characterize the seagrass leaves and their epiphytes, additional data were collected on total epiphyte cover and carbon and nitrogen content of the seagrass leaves.",
"discussion": "Results and discussion Logger deployments over approximately 44 h at the vent site at Dobu Island ( Fig. S1 ) recorded median pH values of 7.8 in the water column (K. Fabricius, pers. comm.). At the control site, pH values of 8.3 were measured. These values were consistent with previous data on the carbonate system at Dobu Island (Fabricius et al ., 2011 ). Apart from the carbonate system, the physicochemical characteristics of the water at the two sampling sites were very similar, suggesting that changes in the carbonate system between the vent and the control site were not confounded by any other of the observed parameters (Fabricius et al ., 2011 ). Enhalus acroides shoots were collected at approximately 4 m water depth at each of the two sampling sites in May 2013. 18S ribosomal DNA sequences confirmed that the seagrass shoots belonged to one species and did not show any pattern by sampling site (data not shown). Each shoot consisted of three to five leaf pairs that were ranked by their order of budding, i.e. leaf age, with the youngest leaf pair being assigned the first rank. When possible, we sampled ranks one to four (youngest to oldest). On average, E. acroides is expected to produce a new pair of leaves approximately every month (Johnstone, 1979 ; Brouns and Heijs, 1986 ; Agawin et al ., 2001 ). The time covered in this study would then amount to 4–5 months of settlement, although it is possible that growth rates were higher under low pH conditions (Koch et al ., 2013 ). During that time, carbon (C) content of the seagrass leaves decreased with leaf age from approximately 33% to 26% dry weight [analysis of variance (ANOVA), F 1,38 = 25.986, P < 0.001] and nitrogen (N) content from 2% to almost 1% (Kruskal–Wallis, χ 2 = 21.262, df = 3, P < 0.001; Table 1 ). Carbon and nitrogen measurements matched previous measurements of leaves of E. acroides (Yamamuro et al ., 2004 ) and were not affected by sampling site, suggesting that the substrate type, i.e. the seagrass leaf, was not confounded between sampling sites. Table 1 Carbon (C) and nitrogen (N) content in percentage dry weight, C:N ratio and epiphyte cover of the leaves of E . acroides , the number of bacterial and eukaryotic OTUs obtained through ARISA and amplicon sequencing (bacteria: 16S rRNA gene, Illumina sequencing; eukaryotes: 18S rRNA gene, 454 sequencing); values constitute mean ± standard error where applicable; for the bacterial sequencing data set Chao1 richness estimates are given in italics with 95% confidence intervals in brackets ARISA Amplicon sequencing N [%] C [%] C : N ratio Epiphyte cover [%] Bacteria Eukaryotes Bacteria (16S) Eukaryotes (18S) Control All ages 1.42 ± 0.08 28.87 ± 0.8 20.98 ± 0.71 18.08 ± 2.45 96.52 ± 3.27 86.14 ± 2.56 507.5 ± 31.45 520.75 ± 84.25 917.31 ± 49.13 Youngest 1.98 ± 0.18 34.63 ± 2.21 17.89 ± 2.5 3.25 ± 3.25 111.25 ± 6.02 72.5 ± 3.8 585 277 913.76 (822.52/1040.05) Second youngest 1.54 ± 0.1 29.68 ± 0.73 19.56 ± 0.88 14.3 ± 5.5 96.83 ± 7.49 86.67 ± 4.97 532 664 991.53(863.52/1168.96) Third youngest 1.2 ± 0.04 28.07 ± 0.8 23.47 ± 0.42 22.33 ± 2.72 94.83 ± 2.4 93.17 ± 3.36 462 561 984.34 (827.90/1207.66) Oldest 1.19 ± 0.04 25.83 ± 0.89 21.87 ± 1.31 22.54 ± 3.56 86.4 ± 6.31 88 ± 4.69 451 581 779.63 (680.69 /921.19) Vent All ages 1.41 ± 0.12 26.93 ± 1.8 19.96 ± 0.79 6.61 ± 1.6 135.95 ± 2.82 89.68 ± 2.71 619.67 ± 48.22 517.75 ± 23.24 1044.09 ± 187.94 Youngest 2.05 ± 0.05 32.02 ± 0.42 15.68 ± 0.42 2.5 ± 1.5 139.6 ± 7.64 97.4 ± 7.06 594 459 933.61 (839.61/1063.58) Second youngest 1.25 ± 0.21 24.87 ± 3.83 20.15 ± 0.74 8.7 ± 2.2 133.5 ± 5.94 87.33 ± 4.36 NA 553 Third youngest 1.07 ± 0.18 24.26 ± 3.91 22.65 ± 1.3 8.55 ± 3.84 133.83 ± 2.7 84.33 ± 3.19 552 502 788.18 (718.60 /886.80) Oldest 1.29 ± 0.04 28.36 ± 0.1 22.04 ± 0.76 3.75 ± 0.75 140.5 ± 7.5 93.5 ± 7.5 713 557 1410.49 (1240.43/1635.38) Total 1.41 ± 0.07 27.92 ± 0.97 20.48 ± 0.53 12.81 ± 1.77 408 329 2179 3928 3811.43 ± 142.83 Epiphyte cover increased with leaf age (Table 1 ). At the vent site, this increase reached only about threefold lower values than under control conditions, most likely due to a lower abundance of pH-sensitive organisms such as crustose coralline algae (Corlett and Jones, 2007 ; Martin et al ., 2008 , Fabricius Kluibenschedl, Harrington, Noonan, and De’ath, unpublished). However, regardless of the trend in epiphyte cover, at the high taxonomic resolution provided by 16S and 18S amplicon sequencing, epiphyte communities seemed to be as diverse at the vent site than at the control site (Table 1 ). Molecular fingerprinting using automated ribosomal intergenic spacer analysis (ARISA) As a first step to assessing the composition of the epiphytic biofilm of E . acroides , the epiphytic community was screened using the molecular fingerprinting technique ARISA (Ramette, 2009 ; Wolf et al ., 2013 ). ARISA identified 408 bacterial and 321 eukaryotic operational taxonomic units (OTUs). Non-metric multidimensional scaling ordination plots based on Bray–Curtis dissimilarity coefficients revealed three prominent patterns in the bacterial and eukaryotic community structure (Fig. 1 ). Figure 1 Non-metric multidimensional scaling (NDMS) plot based on the Bray–Curtis dissimilarity matrix for bacteria (A) and on the Jaccard dissimilarity matrix for eukaryotes (B) on leaves of E . acroides ; both bacterial and eukaryotic communities were assessed using ARISA; dashed hulls representing a minimum of 30% shared OTUs between samples within the hull; labelled points: samples selected for 16S/18S amplicon sequencing. First, there was a strong separation of the communities sampled at the vent and the control site, which tended to cluster away from each other (bacteria: analysis of similarity (ANOSIM), R = 0.775, P < 0.05; eukaryotes: R = 0.692, P < 0.05; Table S1 ). Only about 30% of the bacterial and eukaryotic OTUs were shared between any two samples from the vent and the control site. Redundancy analysis further confirmed that both sampling site and leaf age significantly explained part of the variation in the microbial community structure ( Table S2 ). Of the observed parameters, sampling site was the dominant factor responsible for the patterns in epiphytic community structure (bacteria: adjusted R 2 = 27.3%; eukaryotes: adjusted R 2 = 12.4%), with about four times more variation being explained by sampling site than leaf age ( Table S2 ). This pronounced shift in the epiphytic community structure on seagrass leaves between vent and control site further supports previous results, which found a response of bacterial as well as eukaryotic microbes to OA in other habitats (Johnson et al ., 2011 ; Lidbury et al ., 2012 ; Kerfahi et al ., 2014 ). Second, at each site, there appeared to be a successive shift in epiphyte communities from the youngest to older leaves ( Table S1 ). Despite the differences in epiphytic community composition between the vent and the control site, a successional pattern in community composition from younger to older leaves was observed at both sites regardless of CO 2 impact (Fig. 1 ). Because organic matter has been shown to be transferred from the seagrass leaves to the epiphytes (Michael et al ., 2008 ), changes in carbon and nitrogen content with leaf age as documented here may contribute to the influence of leaf age in shaping epiphyte communities. Third, apart from the general response to the factors sampling site and leave age, patterns in community structure between samples, i.e. the pairwise similarity between samples, correlated strongly between the bacterial and eukaryotic data sets (Mantel test, r = 0.64, P < 0.05). The strong correlation seemed unlikely to be caused exclusively by changes in abiotic parameters. A more likely explanation may be that both communities influence and shape each other as previously suggested by Steele and colleagues ( 2011 ) and Sawall and colleagues ( 2012 ). Amplicon sequencing of epiphytic communities To taxonomically classify the epiphytic communities on E . acroides , eight samples were selected for amplicon sequencing of 16S and 18S rRNA genes for bacterial and eukaryotic communities respectively (ENA accession PRJEB7181). From each sampling site, one sample was chosen for each leaf age. OTU clustering was performed at 97% sequence identity and SILVAngs was used for the taxonomic classification of the OTUs (Quast et al ., 2013 ). A more detailed description of the sequence-processing workflow can be found in Text S1. Amplicon sequencing of the V4–V6 variable region of the bacterial 16S rRNA gene recovered 2179 OTUs with about 600 OTUs per sample. Approximately 62% of the OTUs were singletons (47%) or doubletons (15%), which accounted for 8–16% of the total sequence counts per sample. This percentage of rare bacterial types did not significantly vary between sampling sites (Welch’s t -test, t = −0.944, df = 3.817, P > 0.05). The Chao1 index of total OTU richness yielded estimates almost twice as high as the raw counts. There was no significant difference in OTU richness between the sampling sites (Welch’s t -test, t = −0.819, df = 2.204, P > 0.05; Table 1 ). Previous reports on bacterial richness and rare bacterial types using next-generation sequencing technology showed inconsistent responses to OA (Kerfahi et al ., 2014 ; Raulf et al ., 2015 ), which might be explained by the difference in environments being investigated. As such, the lack of change in bacterial richness and rare bacterial types on seagrass leaves at the vent site should not be generalized beyond the scope of this study. Amplicon sequencing of the V4 variable region of the eukaryotic 18S rRNA gene recovered 3928 OTUs. OTU number per sample ranged from 277 (C1) to 664 (C2; Table 1 ). Similar to the bacterial OTU richness, there was no significant trend in the OTU number between sampling sites (Welch’s t -test, t = 0.034, df = 3.454, P > 0.05). This result was consistent with that of Lidbury and colleagues ( 2012 ) who did not detect a response of eukaryotic microbial richness on settlement tiles to OA using a molecular fingerprinting technique. However, the scarcity of OA studies on eukaryotic microbes applying next-generation sequencing technology does not allow for a more comprehensive discussion on how eukaryotic epiphyte richness may respond to OA. Taxonomic composition of bacterial epiphytes Most of the bacterial sequences belonged to the phylum Proteobacteria (51%), with Gammaproteobacteria (38%) and Alphaproteobacteria (11%) constituting the majority. The next most abundant phyla were Cyanobacteria (30%, chloroplast sequences 27%), Bacteroidetes (12%, Flavobacteria : 8%) and Fusobacteria (4%), which were especially abundant on older leaves at the vent site (Fig. 2A ). The high percentage of Gammaproteobacteria and Alphaproteobacteria was consistent with previous observations on bacterial epiphytes of tropical seagrasses (Weidner et al ., 2000 ; Uku et al ., 2007 ). The high percentage of chloroplast sequences may be explained by the origin of the samples, which were taken in the photic zone from a chloroplast-containing substratum that was also colonized by algae. We identified several taxa that may potentially be influenced by sampling site and/or age of the seagrass leaves ( Table S3 ). Notice that taxa that seemed to be predominantly affected by leaf age are not further discussed here, because the main objective of our study was to describe potential OA effects on epiphytic microbes. Figure 2 Taxonomic composition of the epiphytic biofilm on leaves of E . acroides : (A) bacterial community based on the relative abundance of OTUs (16S rRNA gene sequences, 454 sequencing); (B) eukaryotic community based on the presence/absence of OTUs (18S rRNA gene sequences, Illumina sequencing). Bars are coloured by bacterial class or eukaryotic phylum, separated by genus. Hatched areas: examples of genera potentially influenced by site and/or leaf age. Bold: bacterial classes or eukaryotic phyla potentially influenced by sampling site ( Tables S3 and S5 ). Samples are ordered by leaf age (left: youngest, right: oldest) within sampling site. Cyanobacteria appeared to have a higher relative abundance at the control site than at the CO 2 -impacted vent site. Predictions of OA effects on free-living cyanobacteria are controversial and range from no effect on metabolic rates (Gradoville et al ., 2014 ) to an increase in carbon and nitrogen fixation (Hutchins et al ., 2007 ; Lomas et al ., 2012 ). In microbial biofilms, OA seemed to decrease in cyanobacterial abundance and diversity (Witt et al ., 2011 ; Russell et al ., 2013 ). In complex assemblages, cyanobacteria are supposed to benefit less from OA than other photosynthetic organisms such as chlorophytes, and may thus be outcompeted by them (Low-Décarie et al ., 2014 ). In agreement with this hypothesis, cyanobacteria seemed to decrease in relative abundance with decreasing pH in this study: e.g. the two nitrogen-fixing genera Leptolyngbya and Lyngbya , which are known epiphytes of seagrasses (Uku et al ., 2007 ; Hamisi et al ., 2013 ), were more abundant at the control site, the latter even being unique to the control site. In the case of Leptolyngbya , this response has been documented before in a temperate system (Russell et al ., 2013 ), whereas Lyngbya is expected to react more to changes in temperature and nutrient availability than to OA (Paerl and Huisman, 2009 ). Contrarily to Cyanobacteria , Deltaproteobacteria , Bacilli , Fusobacteria and Clostridia seemed to increase in relative abundance at the vent site. Within the Deltaproteobacteria , this increase was mostly due to an increase in the relative abundance of OTUs of the order Bdellovibrionales at the vent site as also observed by Raulf and colleagues ( 2015 ) in sediments from PNG. The responses of Bacilli and Fusobacteria were mostly due to an increase in the relative abundance of only one OTU belonging to the genus Paenibacillus and to the family Leptotrichiaceae respectively. For Paenibacillus , this response has previously been observed in sediments under elevated pCO 2 (Kerfahi et al ., 2014 ). The fusobacterial OTU was among the most abundant OTUs in the data set (3.5% of all sequences) and was further identified as a relative of Propionigenium sp. with a sequence identity of 93% to the latter (NCBI accession number KC918186). Fusobacteria are a group of strictly anaerobic bacteria, which have been associated with tidal flat sediments, where they contribute to organic matter degradation (Graue et al ., 2012 ), and are present in the gut microflora of marine invertebrates (Li et al ., 2012 ; Dishaw et al ., 2014 ; Rungrassamee et al ., 2014 ) and coral biofilm (Morrow et al ., 2012 ). There is evidence that Fusobacteria associated with corals increase in abundance under OA (Vega Thurber et al ., 2009 ), which might support our results; although the exceptionally high-sequence abundance of Fusobacteria at the vent site was restricted to the two oldest leaves. Noticeably, Fusobacteria as well as Clostridia have been implicated in coral diseases (Vega Thurber et al ., 2009 ; Sweet et al ., 2013 ). Alphaproteobacteria , Gammaproteobacteria and Flavobacteria did not show a response to sampling site on class level. However, at a higher level of taxonomic resolution, several taxa appeared to be affected by sampling site ( Table S3 ). Among the most abundant OTUs in the data set, those potentially influenced by sampling site belonged to the Gammaproteobacteria , i.e. Thalassomonas (1.6%) and Marinomonas (3.8%), which were more abundant at the vent site, and Reinekea (7.2%) and Melitea (2.3%), which were more abundant at control site. Sequence comparison of the OTU belonging to Thalassomonas showed a high-sequence identity (99%) to the sequence retrieved by Webster and colleagues ( 2013 ; NCBI accession number JQ178640), which was associated with the crustose coralline algae Hydrolithon at low pH. It was further closely related (96% sequence identity) to Thalassomonas loyana (NCBI accession number NR043066), the causative agent of white plague-like disease in corals (Thompson et al ., 2006 ), suggesting a potentially pathogenic role. The OTU of Marinomonas was related to Marinomonas poseidonica (99% sequence identity, NCBI accession number NR074719), which has been reported to be beneficial to seagrass (Celdrán et al ., 2012 ) and may contribute to increased growth rates at the vent site. Reinekea is a genus that might play an important role in the degradation of organic matter after phytoplankton blooms (Teeling et al ., 2012 ). Its reduced abundance at the vent site may be caused by the decreased availability of degradable material presumably due to the lower percentage of epiphyte cover. However, it also belongs to the order Oceanospirillales , which are common in coral biofilms and expected to decrease in abundance in diseased corals (Mouchka et al ., 2010 ). Hardly anything is known about the genus Melitea and, although it has been mentioned before in OA research, its response to elevated pCO 2 remains largely unknown (Meron et al ., 2011 ). The direction of potential changes (i.e. the increase or decrease) in relative OTU abundance from control to vent site or vice versa appeared to be related to total OTU abundance. Whereas approximately equal numbers of abundant OTUs (defined by more than 1% total sequence abundance) increased towards either the vent or control site, more OTUs of intermediate abundance level (defined by more than two sequence occurrences, but less than 1% total sequence abundance) tended to increase towards the vent site than towards the control site ( Table S4 ). Although not seen in the rare bacterial types as previously discussed, this trend might be comparable with the increase in rare types with decreasing pH observed in marine sediments at PNG (Raulf et al ., 2015 ). Among these increasing OTUs, sulfur oxidizers were overrepresented, some of which – but not all – were unique to the vent site. This suggests that a higher concentration of sulfur compounds that can be metabolized by bacteria might be present in the water column at the vent compared with the control site, although so far, no direct evidence exists for that matter (Fabricius et al ., 2011 ). H 2 S was detected in the sediment (A. Fink, pers. comm.) and gas, but H 2 S levels in the water column did not exceed values typically observed for seawater (Fabricius et al ., 2011 ). On the other hand, sulfur-oxidizing bacteria might also constitute a contamination from the sediment and might not even be active on the seagrass leaves. Furthermore, apart from their biogeochemical function, sulfur-oxidizing bacteria have also been associated with coral diseases (Frias-Lopez et al ., 2002; 2004 , ; Bourne et al ., 2013 ). Their increased relative abundance may therefore not only be attributable to sulfide seepage. Other OTUs of intermediate abundance, which increased at the vent site, belonged to genera such as Shewanella and Vibrio , which again have been related to coral diseases (Mouchka et al ., 2010 ; Meron et al ., 2011 ; Garcia et al ., 2013 ; Sweet et al ., 2013 ). This general trend of an increase in disease-associated bacterial OTUs at the vent site has also been observed at PNG in corals (Morrow et al ., 2014 ). Taxonomic composition of eukaryotic epiphytes Eukaryotic OTUs were dominated by Florideophycidae , which mostly consisted of crustose coralline algae ( Corallinophycidae , 2282 OTUs) and Rhodymeniophycidae (235 OTUs), followed by diatoms (695 OTUs), Ulvophyceae (171 OTUs) and dinoflagellates (145 OTUs, Fig. 2B ). This composition conforms with the findings of microscopy-based work on tropical seagrasses, which also reported a prevalence of crustose coralline algae (Corlett and Jones, 2007 ; Martin et al ., 2008 ). Potential changes in OTU richness were related to genera of the taxa Corallinophycidae , Dinoflagellata and Diatomea ( Table S5 ). Corallinophycidae were slightly less diverse at the vent site, especially on the older leaves where they only retained about 65% of their OTUs. As calcifying organisms, crustose coralline algae are likely to suffer from OA (Martin et al ., 2008 ; Fabricius et al ., 2011 ; Donnarumma et al ., 2014 ). However, some genera appear to be more vulnerable to elevated pCO 2 than others. Here, Hydrolithon the most diverse genus of crustose coralline algae on the leaves of E. acroides lost about two thirds of its OTUs, and Lithophyllum , which disappeared completely at the vent site, seemed especially susceptible to acidified conditions. Severe declines in Hydrolithon have also been observed on settlement tiles in PNG (Fabricius et al ., unpublished). The calcite deposits of Hydrolithon and Lithophyllum contain a high percentage of magnesium, whereas e.g. Spongites , which was the only crustose coralline algae unique to the vent site, deposits calcite with little magnesium content – a form that is less susceptible to reduced pH than high-Mg calcite (Smith et al ., 2012 ). These differences in calcite composition may contribute to the resilience of crustose coralline algae under OA (Ries, 2011 ; Ragazzola et al ., 2013 ). The genus Galeidinium ( Dinoflagellata ) was more diverse at the vent compared with the control site. However, the impacts of OA on dinoflagellates, in general, and Galeidinium , in particular, are not very well studied, so that potential implications of an increased diversity of Galeidinium under elevated pCO 2 cannot yet be predicted. Diatoms showed a variable response to sampling site with Navicula and Grammatophora being more diverse at the vent and Cyclophora and Cylindrotheca at the control site. These changes in the diversity of diatoms largely concurred with previous findings, which predicted an increase in the genera Grammatophora and Navicula under OA with a coinciding decrease of Cyclophora and Cylindrotheca (Johnson et al ., 2011 ; Singh and Singh, 2014 ), which was also the case here. Although photosynthetic organisms in general are expected to benefit from OA, species-specific responses depend on the respective ability of each organism to utilize inorganic carbon during photosynthesis and on their comparative competitiveness (Koch et al ., 2013 ). Conclusion: does epiphyte composition change due to OA? We detected a highly diverse bacterial and eukaryotic community on the leaves of E . acroides . Although OTU richness seemed unaffected, our results overall suggest a pronounced and interconnected shift in bacterial and eukaryotic community composition of the epiphytic biofilm of E. acroides with changes in the carbonate system of the surrounding water. Besides organisms well known to respond to elevated p CO 2 , this shift may also include taxa that have not been identified in OA research before. In some cases, a potential response to elevated p CO 2 was only visible at a very high level of taxonomic resolution. We further detected an increased prevalence of microbial sequence types associated with coral diseases at the vent site under elevated p CO 2 conditions. This agrees with the hypothesis that coral reefs experiencing elevated p CO 2 levels will be more susceptible to diseases than reefs not yet exposed to OA (Hoegh-Guldberg et al ., 2007 ). It further highlights the potential of seagrasses as vectors of coral pathogens (Sweet et al ., 2013 ) and stresses the point that seagrasses should be viewed as a holobiont when making predictions about OA effects and ecological consequences in coral reefs. Given the high diversity of the epiphytic community on seagrass leaves, an accurate assessment of the interaction of seagrasses with other components of reef ecosystems will also require further knowledge of their epiphytic community composition."
} | 7,964 |
37227629 | PMC10293441 | pmc | 4,678 | {
"abstract": "An exponential rise in global pollution and industrialization has led to significant economic and environmental problems due to the insufficient application of green technology for the chemical industry and energy production. Nowadays, the scientific and environmental/industrial communities push to apply new sustainable ways and/or materials for energy/environmental applications through the so-called circular (bio)economy. One of today’s hottest topics is primarily valorizing available lignocellulosic biomass wastes into valuable materials for energy or environmentally related applications. This review aims to discuss, from both the chemistry and mechanistic points of view, the recent finding reported on the valorization of biomass wastes into valuable carbon materials. The sorption mechanisms using carbon materials prepared from biomass wastes by emphasizing the relationship between the synthesis route or/and surface modification and the retention performance were discussed towards the removal of organic and heavy metal pollutants from water or air (NO x , CO 2 , VOCs, SO 2 , and Hg 0 ). Photocatalytic nanoparticle–coated biomass-based carbon materials have proved to be successful composites for water remediation. The review discusses and simplifies the most raised interfacial, photonic, and physical mechanisms that might take place on the surface of these composites under light irradiation. Finally, the review examines the economic benefits and circular bioeconomy and the challenges of transferring this technology to more comprehensive applications.",
"conclusion": "Conclusions and challenges This review summarizes the main applications of valorized lignocellulosic biomass for environmental remediation. Lignocellulose biomass wastes can be converted into activated carbon via several ways and activation processes. The use of some techniques, i.e., microwave carbonization, might lead to obtaining highly activated carbon, having excellent porosity and functionality for enhanced adsorption of water and air pollutants. In addition, the incorporation of photocatalytic nanoparticles on the surface of lignocellulose biomass-based materials aims to solve several challenges associated with the conventional photocatalysis, such as a significant enhancement in the kinetics due to the adsorb and shuttle process, reduction in toxic-by products generation because of the highly adsorptive ability, and the improvement of light absorption and charges separation (interfacial interaction, i.e., C–O–Ti bridge, and photosensitizing). The separation of photocatalyst/carbon composites is quite easier than the recovery of nanosized naked photocatalysts. In addition, the cost of photocatalysts/lignocellulose biomass would be cheaper. From the eco-environmental point of view, the valorization of lignocellulosic biomass into carbon materials to be used as adsorbents for water and air pollutants, or to support photocatalytic particles suits very well the concepts of circular bioeconomy in terms of waste recycling, use bio-resource and sustainability. To better scale up lignocellulose biomass waste for large environmental remediation applications, some challenges need to be addressed. In terms of activated carbon from lignocellulosic biomass, sustainable approaches for the activation and carbonization with the use of harmful chemicals are required to be investigated to avoid secondary water and air pollution, e.g., the production of toxic gas of pyrolysis and use of highly toxic chemical for the activation (highly corrosive agents, i.e., KOH or H 3 PO 4 ). Self-activation approaches to produce carbon materials instead of chemical or physical routes require more investigation because of its sustainability and low cost. For photocatalytic applications, certainly, most of the studies reported that synergetic effects take place to promote the removal of pollutants as compared to simple adsorption and photocatalysis primarily due to the adsorb and shuttle process . In addition, the oxidation of organic contaminants on the surface of adsorbents is regarded as a self-regeneration, allowing a more prolonged use of the material. However, up to date, this approach is not considered at large scale because of other technical issues, such as the type of irradiation and photoreactor that allows better light penetration if the black photocatalyst/carbon material is used. Self-oxidation of biomass by photoproduced ROSs should be estimated. Since ROSs could oxidize the biomass, it is highly recommended to check the release of products from the biomass during photocatalytic experiments and the stability of the structure by some surface characterization. Further studies to test adsorbents and photocatalysts should be conducted in real conditions and pilot scale. Techno-economic and sustainability factors should be estimated. Standardized protocols for carbon materials fabrication from lignocellulosic biomass should be developed based on the type of raw materials and the targeted application. In fact, even though chemical activation leads to highly active activated materials, it would be better to seek for advanced green approaches to design activated carbon–based materials in order to avoid the over usage of harmful and toxic chemicals. Most of research studies have been carried out on the valorization of a selected biomass waste; however, real municipal biomass wastes contain a variety of biomass materials. At a large scale, it is hard to separate different biomass wastes from each other. Therefore, it would be interesting to investigate the conversion of real municipal biomass wastes as a total into carbon materials to widen the valorization of real biomass wastes in to valuable products.",
"introduction": "Introduction Production process through the linear economy for many decades has led to serious environmental, economic, and ecosystem perturbations (Jørgensen et al. 2018 ; Turner 2018 ). Linear economy generates huge quantities of waste in the aquatic and atmospheric environment. Hence, the scientific and industrial communities worldwide actively search for green approaches to decrease the quantity and hazardous of generated waste and their possible recycling (Nabgan et al. 2023 ). The concept of “zero waste theory” is based on using green and recyclable products and valorizing natural and available resources (Romano et al. 2019 ). As a result, the bioeconomy European Union must include sustainability and circularity at the core of its industrial makeup to effectively transition from a linear to a circular economy. By 2060, international energy agencies anticipated that the world’s need for bioenergy would multiplied (Brahma et al. 2022 ), suggesting a need to switch to renewable feedstock for heat generation, chemicals, fuels, and electricity, such as lignocellulosic biomass, is gaining more and more attention (Djellabi et al. 2022 ). Lignocellulosic biomass is a carbon-rich material that can be employed in the sustainable route for bioenergy production and high-value products as well as multifunctional materials for environmental remediation (Bhatnagar et al. 2015 ; Nouacer et al. 2015 ; Khelaifia et al. 2016 ; Wang et al. 2021 ). The production of carbon materials from lignocellulosic biomass wastes has gained attention in recent years, particularly due to its low cost and relative earth abundance (Ziati et al. 2012 ; Yahya et al. 2015 ; Ikram et al. 2022 ). Lignocellulosic biomass mainly contains three components: lignin, cellulose, and hemicellulose, wherein lignin is the central part. Lignin is rich in carbon atoms compared to the other components, and therefore, it is regarded as the best precursor to produce activated carbon (Ikram et al. 2022 ). The synthesis of high-quality activated carbon significantly depends on different factors, such as the starting carbonaceous precursor, carbonization and activation approaches, and other synthesis conditions (Carrott and Carrott 2007 ). In fact, an extensive list of biomass wastes was converted into activated carbon, such as date stones (Abderrahim et al. 2022a ), olive pits (Redondo et al. 2015 ), coconut shells (Gratuito et al. 2008 ), lignin (Carrott and Carrott 2007 ), barley straw (Pallarés et al. 2018 ), orange peels (Köseoğlu and Akmil-Başar 2015 ), soybean oil cake (Tay et al. 2009 ), tomato (Sayğılı and Güzel 2016 ), chestnut shell (Duan et al. 2021 ), wood (Danish and Ahmad 2018 ), palm shell (Hamada et al. 2020 ), and walnut shell (Plaza et al. 2010 ). Different approaches are used for the activation to ensure effective carbonization, including the chemical, physical, and physicochemical ones. In terms of chemical activation, several agents can be used, such as KOH (Khalil et al. 2013 ), FeCl 3 (Bedia et al. 2020 ), ZnCl 2 (Caturla et al. 1991 ), H 3 PO 4 (Yorgun and Yıldız 2015 ), NaOH (Islam et al. 2017 ), K 2 CO 3 (Sayğılı and Sayğılı 2019 ), and K 2 B 4 O 7 (Gao et al. 2015 ). In addition, the synthesis method significantly affects several features, such as the surface area, internal porosity, oxygenated functional groups, and physical properties (González-García 2018 ). Different approaches were reported to assist the activation and carbonization, such as hydrothermal (Hoekman et al. 2011 ), microwave (Paramasivan 2022 ), ultrasonic (Xu et al. 2019 ), and vacuum (Yousaf et al. 2021 ). Lignocellulosic biomass has been extensively used as support for photocatalytic nanoparticles to improve the pollutant removal efficiency during environmental remediation. TiO 2 /carbon composites are some of the successful photocatalytic materials for water purification because of the synergistic effects in terms of enhanced light absorption, excellent adsorptive ability, and promoted charges transferred (Puma et al. 2008 ; Lisowski et al. 2018 ; Djellabi et al. 2019a , b , c , d ; Djellabi et al. 2019a , b , c , d ; Djellabi et al. 2021a , b , c ). Several photocatalytic mechanistic pathways have been reported regarding oxidation and reduction of pollutants on the surface of titania-carbon materials (Matos et al. 2010 ; Asiltürk and Şener 2012 ; Djellabi et al. 2019a , b , c , d ). Besides, a wide list of photocatalysts has been supported on the surface of lignocellulosic biomass or lignocellulosic biomass–based activated carbon such as ZnO (Raizada et al. 2014 ), Ag 3 PO 4 (Abderrahim et al. 2022b ), CuO (Khaled et al. 2020 ), g-C 3 N 4 (Pi et al. 2015 ), and MoS 2 (Ye et al. 2019 ). The present review discusses the valorization of biomass from both fundamental and applied research. It includes most of recent data on the synthesis of carbon based materials to be used for environmental application. Differently to previous reported reviews, we stressed in this review the mechanistic pathways that take place during the synthesis of carbon materials from biomass wastes. In addition, it provides the most a summary regarding the mechanistic pathways taking place for the removal of materials through simple adsorption or photocatalytic route from air and water. The adsorption mechanisms towards the removal of organic pollutants, heavy metals, CO 2 , VOCs, NO x , and mercury are systematically reviewed. In-depth discussion was held on the photocatalytic behavior of semiconductor-supported lignocellulosic biomass. The most important uses of valorized lignocellulosic biomass–based materials for adsorption and photocatalytic removal of pollutants from the aquatic and atmospheric environment are outlined in the paper."
} | 2,891 |
37857824 | PMC10873200 | pmc | 4,679 | {
"abstract": "Molecular engineering seeks to create functional entities for modular use in the bottom-up design of nanoassemblies that can perform complex tasks. Such systems require fuel-consuming nanomotors that can actively drive downstream passive followers. Most artificial molecular motors are driven by Brownian motion, in which, with few exceptions, the generated forces are non-directed and insufficient for efficient transfer to passive second-level components. Consequently, efficient chemical-fuel-driven nanoscale driver–follower systems have not yet been realized. Here we present a DNA nanomachine (70 nm × 70 nm × 12 nm) driven by the chemical energy of DNA-templated RNA-transcription-consuming nucleoside triphosphates as fuel to generate a rhythmic pulsating motion of two rigid DNA-origami arms. Furthermore, we demonstrate actuation control and the simple coupling of the active nanomachine with a passive follower, to which it then transmits its motion, forming a true driver–follower pair.",
"conclusion": "Conclusion We describe the bottom-up construction of a biohybrid DNA-origami-based nanomachine that performs tasks fundamental for any device requiring automated motion: an autonomous, fuel-driven, rhythmically pulsing DNA nanoengine that can be coupled as a module with a passive DNA-origami-based ‘follower’ entity to which it transmits its motion and force, thus constituting a driver–follower pair. Since DNA-origami technology permits modular bottom-up construction of robust nanostructures with diverse properties that span from mechanically rigid to mechanically compliant structures 12 , 23 , 24 , 50 , all allowing for combination into a single architecture, the versatility of mechanical power transmission by the nNE to other devices is high. Although the passive follower structures used here are fairly simple, the prototypical design of D–F suggests that the D should be applicable to other DNA nanostructures as a driving engine to achieve larger and more complex structural rearrangements as exemplified before in non-autonomous systems 51 (Supplementary Text 8 ). For future applications, one may envision introducing a clutch mechanism that allows the driver to be disconnected from one coupled follower and instead connected to another ‘on the fly’ while the engine is still running; for example, by controlling the hybridization of the D–F connecting oligodeoxynucleotides with light-switchable isomers, as demonstrated in other DNA nanomachines 11 , 52 – 54 . Similarly, photoresponsive molecules could be introduced into the promoter region 55 to stop active movement by light even in the presence of fuel (Supplementary Text 9 )."
} | 663 |
23861321 | PMC3859321 | pmc | 4,681 | {
"abstract": "Because cyanobacteria directly harvest CO 2 and light energy, their carbon metabolism is important for both basic and applied sciences. Here, we show that overexpression of the sigma factor sigE in Synechocystis sp. PCC 6803 widely changes sugar catabolism and increases production of the biodegradable polyester polyhydroxybutyrate (PHB) during nitrogen starvation. sigE overexpression elevates the levels of proteins implicated in glycogen catabolism, the oxidative pentose phosphate pathway, and polyhydroxyalkanoate biosynthesis. PHB accumulation is enhanced by sigE overexpression under nitrogen-limited conditions, yet the molecular weights of PHBs synthesized by the parental glucose-tolerant and sigE overexpression strain are similar. Although gene expression induced by nitrogen starvation is changed and other metabolites (such as GDP-mannose and citrate) accumulate under sigE overexpression, genetic engineering of this sigma factor altered the metabolic pathway from glycogen to PHB during nitrogen starvation.",
"introduction": "1. Introduction Cyanobacteria can utilize light energy via two photosystems. Carbon dioxide is fixed via the Calvin-Benson cycle, producing sugar phosphate, followed by production of metabolites including glycogen as a carbon and energy sources. 1 , 2 Stored glycogen is oxidized by glycogen catabolic enzymes such as glycogen phosphorylases (encoded by glgP ) or isoamylases (encoded by glgX ) ( Supplementary Fig. S1 ). 3 Under heterotrophic conditions, the glucose (or glucose-phosphate) are degraded mainly through the oxidative pentose phosphate (OPP) pathway. 4 Under mixotrophic conditions, glucose is degraded via glycolysis and CO 2 is concomitantly fixed via the Calvin cycle, providing organic acids such as pyruvate. 4 The pyruvate is used to biosynthesize organic acids (including amino acids), to generate reducing power, or to assist nitrogen assimilation through the tricarboxylic acid cycle. From previous studies, it is known that many sugar catabolic genes are activated by the sigma factor SigE in Synechocystis 6803. 5 \n Synechocystis genome contains nine sigma factors (SigA–I), and SigE belongs to Group 2 sigma factor that consists of four sigma factors (SigB–E). 5 Overexpression of sigE enhances metabolite levels of acetyl-CoA and organic acids such as citrate, 6 implying utilization of this sigma factor for metabolic engineering. The expression of sigE is increased by nitrogen depletion; 7 , 8 however, the implication of SigE into metabolite profiling during nitrogen starvation have not been described. Polyhydroxyalkanoates (PHAs) are a class of polyesters, stored by many bacteria as carbon and energy sources. PHAs have attracted industrial interest, because they are naturally biodegradable. 9 PHAs, consisting of short-chain-length or medium-chain-length monomers, are the constituents of >150 identified hydroxyalkanoates. 10 , 11 Several PHAs, such as poly[( R )-3-hydroxybutyrate] (PHB, a homopolymer of 3-hydroxybutyrate) and poly[( R )-3-hydroxybutyrate- co -( R )-3-hydroxyhexanoate] (a co-polymer of 3-hydroxybutyrate and 3-hydroxyhexanoate), are industrially produced today (for example, KANEKA Biopolymer AONILEX). PHA granules are subcellular complex, consisting of a polyester core, associated proteins such as PHA synthases, phasins, PHA depolymerases, and regulatory proteins. 11 – 14 Life cycle assessment studies have suggested that using PHB in place of conventional petrochemical polymers (such as film products and disposable items) lowers environmental impacts. 15 PHAs are potentially applicable to medical research such as surgical sutures, vein valves, and targeted drug delivery, 12 though their expensive production remains a major obstacle to their universal development. PHAs, particularly PHB, accumulate in several cyanobacterial strains including Synechocystis sp. PCC 6803 (we herein designate this strain Synechocystis 6803). 16 Levels of PHB in cyanobacteria increase under nitrogen or phosphorus starvation. 17 Addition of external carbon sources such as acetate enhances PHB accumulation in Synechocystis 6803 and other cyanobacteria. 18 – 20 The biosynthetic pathway of PHB in Synechocystis 6803 is currently well understood. Acetoacetyl-CoA is formed from two molecules of acetyl-CoA by the enzyme β-ketothiolase (encoded by phaA ). Then, d -3-hydroxybutyryl-CoA is synthesized, catalysed by acetoacetyl-CoA reductase (encoded by phaB ). 20 \n d -3-hydroxybutyryl-CoA is also polymerized to PHB by a PHA synthase, comprising PhaC and PhaE. 16 PHA synthase activity in cyanobacteria is up-regulated in the presence of acetyl phosphate. 18 Reverse genetic analysis has revealed that a protein designated Sll0783, which is conserved among PHB-producing bacteria and which is expressed under nitrogen depletion stress, is essential for retaining PHA synthase activity during prolonged nitrogen starvation. 21 Disruption of sll0461, encoding gamma-glutamyl phosphate reductase, or sll0565, encoding a hypothetical protein, increases PHB accumulation in standard growth media. 22 Transcription levels of phaA , B , C , and E have been shown to increase following nitrogen depletion. 8 Expression of all four genes peaks 6 h after nitrogen depletion, then gradually decreases, but remains above the nitrogen-replete levels for a minimum of 120 h. 21 To date, transcriptional regulator(s) for controlling PHA biosynthetic gene expression have not been found in cyanobacteria. It is noteworthy that introducing external pha regulons into Synechocystis 6803 increases enzymatic activities of PHA synthases, but does not enhance PHB accumulation. 19 Here, we show that genetic engineering of sigE resulted in large change of sugar metabolism and increased PHB accumulation during nitrogen starvation, indicating that sigma factor is useful for metabolic engineering of cyanobacteria.",
"discussion": "4. Discussion In this study, metabolome analysis revealed that sigE overexpression modified the metabolism from glycogen to PHB during nitrogen starvation in Synechocystis 6803 ( Supplementary Fig. S5 ). Genetically engineered Synechococcus sp. PCC 7942 containing PHB biosynthetic enzymes of Alcaligenes eutrophus accumulates 25% PHB of cellular dry weight under 2-week cultivation in nitrogen-limited conditions with 10 mM acetate. 30 However, direct use of CO 2 is desirable from an environmental point of view. In the conditions of our experiments, cells grown photoautotrophically and supplied with 1% (v/v) CO 2 accumulate up to 1.4% PHB per cellular dry weight (Fig. 5 ). In a recent study, genetically engineered microalgae Phaeodactylum tricomutum , in which the bacterial PHB pathway of Cupriavidus necator had been incorporated, accumulated up to 10.6% PHB per algal dry weight after 7 days of nitrogen depletion. 31 These results, supplementary to ours, suggest that genetically engineered microalga and cyanobacteria are useful for PHB production. The molecular weight and monomer units of PHB are unaltered by sigE overexpression ( Supplementary Fig. S4 ). In general, increased PHA synthase activity decreases the molecular weight of PHA. 32 However, the molecular weight of PHB in sigE- overexpressing strain kept similar with that of GT strain, indicating that carbon supply for PHB biosynthesis is enough due to activation of the sugar catabolism by sigE overexpression. Type-III PHA synthase, which produces short-chain-length PHAs (including PHB), is present in most of the cyanobacteria, 33 rendering our NMR results consistent with those of previous studies. Obtaining a range of polyesters, such as other PHAs or polythioesters, is the next goal in cyanobacterial PHA production. In Synechocystis 6803, PHA biosynthetic genes and sugar catabolic genes are co-regulated by SigE, showing that the pathway from glycogen to PHB is regulated at the transcription level ( Supplementary Fig. S5 ). The transcripts of PHA biosynthetic genes peak 6 h after nitrogen starvation, while PHB synthase activity peaks at 48 h after nitrogen depletion. 21 PHB continuously increases at least 120 h, 21 indicating translational and post-translational regulation of PHA biosynthetic enzymes. In this way, elucidation of the regulatory mechanism of enzymes in PHA biosynthesis and sugar catabolism is important for PHB production in cyanobacteria. The glycogen levels were similar between the GT and sigE overexpression strains after nitrogen starvation (Fig. 2 A), indicating that the increase in PHB and the decrease in glycogen are not correlated. We also found that the mRNA of phaABCE were less induced by sigE overexpression (Fig. 1 ), however, the proteins of PhaABCE were similarly increased in the sigE overexpression strain after nitrogen depletion (Fig. 4 ). These results suggested that PhaABCE proteins are post-transcriptionally regulated in Synechocystis 6803. In terms of four glycogen catabolic genes ( glgX (slr0237), glgX (slr1857), glgP (sll1356), and glgP (slr1367)), the mRNA and protein levels are also not correlated (Fig. 1 and 2), indicating post-transcriptional regulation of these glycogen catabolic enzymes. Metabolome analysis suggests that amounts of GDP-mannose and citrate also increased by sigE overexpression; thus, reducing the metabolic fluxes of these compounds by genetic modification may further enhance PHB production ( Supplementary Fig. S5 ). In other bacteria, repression of lipopolysaccharide biosynthesis leads to elevated PHB synthesis in C. necator . 34 In addition, multiple regulators are involved in transcriptional control under nitrogen-limited conditions. 35 \n In vitro studies have shown that Group 1 sigma factor (SigA in Synechocystis 6803) possesses stronger transcriptional activity than the Group 2 sigma factors, irrespective of promoter sequences. 36 Overexpression of sigE regulates not only sugar catabolic and PHA biosynthetic genes, but also nitrogen-related genes ( Supplementary Fig. S3 ), suggesting that nitrogen-starvation-induced transcriptional activation depends on the correct balance of transcriptional regulators. Our results also suggest that manipulating multiple transcriptional regulators is important for optimizing PHB production in cyanobacteria."
} | 2,580 |
22121345 | PMC3219917 | pmc | 4,682 | {
"abstract": "For neural network simulations on parallel machines, interprocessor spike communication can be a significant portion of the total simulation time. The performance of several spike exchange methods using a Blue Gene/P (BG/P) supercomputer has been tested with 8–128 K cores using randomly connected networks of up to 32 M cells with 1 k connections per cell and 4 M cells with 10 k connections per cell, i.e., on the order of 4·10 10 connections (K is 1024, M is 1024 2 , and k is 1000). The spike exchange methods used are the standard Message Passing Interface (MPI) collective, MPI_Allgather, and several variants of the non-blocking Multisend method either implemented via non-blocking MPI_Isend, or exploiting the possibility of very low overhead direct memory access (DMA) communication available on the BG/P. In all cases, the worst performing method was that using MPI_Isend due to the high overhead of initiating a spike communication. The two best performing methods—the persistent Multisend method using the Record-Replay feature of the Deep Computing Messaging Framework DCMF_Multicast; and a two-phase multisend in which a DCMF_Multicast is used to first send to a subset of phase one destination cores, which then pass it on to their subset of phase two destination cores—had similar performance with very low overhead for the initiation of spike communication. Departure from ideal scaling for the Multisend methods is almost completely due to load imbalance caused by the large variation in number of cells that fire on each processor in the interval between synchronization. Spike exchange time itself is negligible since transmission overlaps with computation and is handled by a DMA controller. We conclude that ideal performance scaling will be ultimately limited by imbalance between incoming processor spikes between synchronization intervals. Thus, counterintuitively, maximization of load balance requires that the distribution of cells on processors should not reflect neural net architecture but be randomly distributed so that sets of cells which are burst firing together should be on different processors with their targets on as large a set of processors as possible.",
"introduction": "Introduction Fast simulations of large-scale spike-coupled neural networks require parallel computation on large computer clusters. There are a number of simulation environments that provide this capability such as pGENESIS (Hereld et al., 2005 ), NEST (Gewaltig and Diesmann, 2007 ) SPLIT (Djurfeldt et al., 2005 ), NCS (Wilson et al., 2001 ), C2 (Ananthanarayanan and Modha, 2007 ), and others. Interprocessor spike exchange is, of course, an essential mechanism in parallel network simulators. All simulators employ the standard and widely available Message Passing Interface (MPI) and most utilize the non-blocking point-to-point message passing function, MPI_Isend. NEURON (Migliore et al., 2006 ) chose the simplest possible spike distribution mechanism which directly distributes all spikes to all processors. This “Allgather” method uses MPI_Allgather, and occasionally MPI_Allgatherv if there are more spikes to be sent than fit in the fixed size MPI_Allgather buffer. They note that this provides a baseline for future comparison with more sophisticated point-to-point routing methods and that supercomputers often provide an optimized vendor implementation of MPI_Allgather(v) that yields hard to match performance. For example, Eppler et al., ( 2007 ), using NEST, noted that Allgather performs better on their 96 core cluster with Infiniband switch than the Complete Pairwise Exchange (Tam and Wang, 2000 ) algorithm. However, simulations are sometimes now being carried out on many more processors than the number of connections per cell (Markram, 2006 ; Ananthanarayanan et al., 2009 ). Also, the Blue Gene/P (BG/P) architecture adds a Direct Memory Access (DMA) engine to facilitate injecting packets to the network and receiving packets from the torus network over its predecessor BG/L. This allows the cores to offload packet management and enables better overlap of communication and computation. Therefore, much of MPI point-to-point messaging no longer uses processor time. Finally, much less overhead than MPI is obtainable through direct use of the underlying Deep Computing Messaging Framework (DCMF) (Kumar et al., 2008 ), which introduces a family of non-blocking asynchronous collective calls on lists of processors (vs pre-created communicators) called Multisend. The family member that generalizes the non-blocking point-to-point MPI_Isend by allowing a source processor to send the same message to a user specified list of target processors is the non-blocking point-to-many DCMF_Multicast function. For these reasons, we decided to compare performance of the Allgather, MPI_Isend, and DCMF_Multicast methods. Note that DCMF is extensible to architectures other than BG/P. Four considerations suggest that at some point Allgather scaling will fall behind the others when number of processors is much larger than number of connections per cell. First, MPI_Allgather itself requires twice the time when the number of processors doubles. Second, all incoming processor buffers must be examined for spikes, even if the spike count for a given source processor is 0. Third, every incoming spike requires a search in a table for whether or not the spike is needed by at least one cell on the processor. Fourth, it is not possible, at least on the BG/P, to overlap computation and communication. None of these issues apply with MPI point-to-point and Multisend methods. From the viewpoint of communication, large-scale spiking neural networks consist of computational units, neurons, which are connected by one-way delay lines to many other neurons. Neurons generate logical events, spikes, at various moments in time, to be delivered to many other neurons with some constant propagation delay which can be different for different connections. Neurons generally send their spikes to thousands of neurons and receive spikes from thousands of neurons. A spike or logical event is uniquely identified by the pair ( i, t s ) where, the value of i or global identifier is an integer that labels the individual neuron sending the spike and t s is the time at which the spike is generated. If there is a source to target connection between neurons i and j with connection delay d ij , then neuron j receives the spike at time t s + d ij which causes an ij connection dependent discontinuity in one of neuron j 's parameters or states. During time intervals between input events, the neuron is typically defined by a system of continuous ordinary differential equations along with a threshold detector which watches one of the states and determines when the output event is generated.",
"discussion": "Discussion All the Multisend methods we tested exhibit complete overlap of communication within the computation interval except for Record-Replay with 10 k connections per cell. In particular, the MPI_Isend implementation of multisend also exhibits complete overlap. The difference between computation time and total time is almost entirely due to load imbalance with respect to the number of incoming messages per processor within integration intervals. Load imbalance due to high variance of cell firing within an interval is greatly alleviated, either through the use of Record-Replay operations of DCMF_Multicast, or with a two-phase message propagation strategy which uses processors to send messages to the N t target processors of the spiking cell. The fact that overlap is complete, even for one-sub-interval Multisend methods with the largest number of processors used, seems a particularly impressive property of the BG/P communication system. That is, a message sent during a single time step to 1000 processors distributed randomly over the machine topology, reaches all its destinations before the beginning of the next time step. On even the largest partition sizes used, there is no reason yet to consider more sophisticated distribution schemes that would attempt to minimize the total message propagation distance. Although MPI_Allgather is a blocking collective, overlap between computation and spike exchange may be possible through the use of threads. With two sub-intervals for each minimum network connection delay interval, a high priority spike exchange thread would initiate MPI_Allgather at the end of one sub-interval. While the spike exchange thread was waiting for completion of the MPI_Allgather, a low priority computation thread could continue until the end of the other sub-interval. During computation, completion of the MPI_Allgather would occasionally initiate an MPI_Allgatherv asynchronously in the spike exchange thread within that other sub-interval. Finally, the computation thread would also wait for completion of the MPI_Allgather(v) at which point, spike exchange would be initiated for the spikes generated during the other sub-interval. Unfortunately, this strategy cannot be tested on the BG/P since only one thread per core is allowed. For machines without the single thread per core limitation, it would be an experimental question whether, during MPI_Allgather processing/communication, the CPU is actually available for computation and whether the savings would be greater than the cost of thread switching. In any case, the rate of increase in MPI_Allgather time as the number of processors increases, means that MPI_Allgather time will eventually dominate computation time. An alternative to repeated use of MPI_Allreduce within a conservation loop is to explicitly receive the count of spikes that should arrive at each individual target processor by using MPI_Reduce_scatter (Ananthanarayanan and Modha, 2007 ) and then calling MPI_Recv that many times to receive all the messages destined for that target processor (or, for the DCMF_Multicast method, entering a DCMF_Messager_advance loop until that many messages arrive). Since conservation loop timing results show that the time charged to the MPI_Allreduce portion, and indeed, usually the entire conservation loop itself, is negligible on up to 128 K processors, we have not compared MPI_Reduce_scatter to MPI_Allreduce performance. Note that the use of MPI_Reduce_scatter would vitiate some of the load balance benefits we saw with the Record-Replay method. That is, a spike generated on the source processor would require an iteration over the processor target list of the source cell in order to increment the count of sent spikes to those processors and thus increase dynamic load imbalance. The excessive number of conservation iterations for Record-Replay with 10 k connections per cell is most likely attributable to the fact that the current implementation of Record-Replay only permits messages for one cell's spikes to be sent out at a time. A call to DCMF_Messager_advance after the current cell's spikes have been sent out is needed to trigger the data transfer for the next cell. In the future, we plan to explore techniques that can accommodate cell spike data transfer in the same way as the standard DCMF_Multicast implementation, and that should further improve the performance of the Record-Replay technique. Our first weak scaling run attempted on 128 K processors (32 K nodes) did not succeed because 184 of the processes raised a “can't open File” error on the Hoc file specified at launch. Since the expected boot time of that size partition is 12 minutes, a minimal test run which exits immediately uses at least 26,000 CPU hours. In fact, the job waited until it timed out after an hour thus wasting almost 140 k CPU hours. To scale-up to larger processor numbers it was necessary to avoid the huge numbers of replicated reads by having rank 0 read the Hoc file and broadcast it to all ranks, which then execute the buffer contents. There exists a DCMF_Multicast operation which can send different data to its list of targets. This would allow very significant reduction in the lookup time on the destination processor for lookup of the source object associated with the source GID. That is, instead of a hash table lookup, the index into a list of source objects on the destination processor would be sufficient. Prior to a simulation run, the proper destination list indices would be stored along with each cell's list of destination processors. This optimization is used, for example, by MUSIC (Djurfeldt et al., 2010 ) in its spike exchange algorithm. However, this optimization may be vitiated by the loss of an existing optimization that we use where the user portion of the message is empty and the ( i, t s ) information is encoded in the header so only a single packet arrives—thus, software overhead of waiting for and assembling the packets is avoided. The Table 1 strong scaling results on 8–32 K processors for the 1/4 M cell, 10 k connections per cell simulations are from two to three times faster than the results shown in Table 2 of Kumar et al. ( 2010 ). The largest part of the improvement for DCMF_Multicast comes from the use of two-phase method, and for Record-Replay, the use of DCMF_Messager_advance for each time step. The size of networks used in this study has been limited due to the memory needed by NEURON's network connection objects. These objects use reciprocal pointers to their source and target objects as well as to wrapper objects at the interpreter level. Those pointers are required only for purposes of maintaining consistency when the user modifies a cell or eliminates a connection and the wrapper objects themselves are only needed transiently during setup time. NEURON's use of double precision delay and weights for each connection is another place where the memory needed by connections can be reduced. There exist artificial spiking cell simulators such as NEST whose network connection memory footprint has been minimized (Kunkel et al., 2009 ). In those simulators, networks with many more cells on the same number of processors could be simulated and it would be interesting to see if the Multisend method's complete overlap we see between computation and spike communication would be maintained in those environments. That would be expected on the grounds of the almost linear relation between computation time and number of cells. And with more cells, spike input balance is even less of a problem. Record-Replay and the two-phase Multisend method solve the load balance problems due to high processor variance in cell firing within an interval. However, it is clear that load balance in terms of spike input to the sets of cells on processors will become a much more vexing issue with more realistic neural networks where populations of cells having a large dynamic spiking range have multiple projections to other populations. That is, input activity is generally also correlated in time and space. For example, Ananthanarayanan et al. ( 2009 ) report that for their extremely large-scale simulations (0.9·10 9 cells, 0.9·10 13 synapses, 144 K cores, and using MPI_Isend) 60–90% of the time was consumed in an MPI_Barrier due to the variability in firing rate. Our results supply strong evidence that one should neglect efforts to minimize spike exchange time through any kind of matching of populations to subsets of nearby processors in favor of promoting load balance by distributing these populations over as wide a number of processors as possible. Thus, networks in which connections are to nearest neighbor cells and all cells fire uniformly, give only a two-fold performance improvement when this connection topology is exploited through the use of placing neighbor cell sets on the minimal set of adjacent processors. But this performance improvement disappears as soon as cell groups begin to burst. Thus, in the same way that the very simple Allgather method provides a baseline for comparison with alternative spike exchange methods, the simple random distribution of cells evenly on all processors provides a performance baseline for comparison with more sophisticated distribution algorithms."
} | 4,032 |
22171825 | null | s2 | 4,683 | {
"abstract": "We present the case of a two-component collagen peptide hydrogel that self-assembles through noncovalent electrostatic interactions. Natural collagen materials, such as those of connective tissue or the basement membrane, assemble in a hierarchic fashion. Similarly, the synthetic peptides presented here proceed from monomer to trimer to fiber and, finally, to a hydrogel. By varying stoichiometry and concentration, we are able to dissect the stages of higher order assembly. Insight gained from this study will improve the molecular design of biomimetic materials."
} | 141 |
28217092 | PMC5289985 | pmc | 4,687 | {
"abstract": "In the context of the dynamical system approach to cognition and supposing that brains or brain-like systems controlling the behavior of autonomous systems are permanently driven by their sensor signals, the paper approaches the question of neurodynamics in the sensorimotor loop in a purely formal way. This is carefully done by addressing the problem in three steps, using the time-discrete dynamics of standard neural networks and a fiber space representation for better clearness. Furthermore, concepts like meta-transients, parametric stability and dynamical forms are introduced, where meta-transients describe the effect of realistic sensor inputs, parametric stability refers to a class of sensor inputs all generating the “same type” of dynamic behavior, and a dynamical form comprises the corresponding class of parametrized dynamical systems. It is argued that dynamical forms are the essential internal representatives of behavior relevant external situations. Consequently, it is suggested that dynamical forms are the basis for a memory of these situations. Finally, based on the observation that not all brain process have a direct effect on the motor activity, a natural splitting of neurodynamics into vertical (internal) and horizontal (effective) parts is introduced.",
"introduction": "1. Introduction From a neurocybernetics perspective the dynamical systems approach to embodied cognition can be traced back to the work of Ashby (Ashby, 1960 ) and von Foerster (Von Foerster, 1960 ). The assumption is that a living organism, in order to survive, must be able to develop internally some stable “entities” (von Foerster) which refer to or classify objects and situations in the physical world. These “entities” are the result of cognitive and sensorimotor processes developing through continuous interactions of an individual with its specific environment. On the other hand, cognitive and sensorimotor processes, relevant for the behavior of the individual, depend on the formation of these stable structures; i.e., they are complementary in the sense that one defines or implies the other. The assumption was, that an organism must be able to relate discrete internal structures to relevant aspects of its own interaction with its environment. Although, the underlying processes are continuous these internal “entities” have to be discrete because the referenced objects or situations are discrete features of the environment. They also have to be “stable” in a certain time domain. On the other hand, due to changing sensorimotor or cognitive processes, they have to get “unstable” in the sense that different references have to be built up; i.e., new “stability domains” have to be visited or formed. To pursue the dynamical systems approach to embodied cognition in this spirit, this paper will consider an individual as an autonomous system called an animat . An animat Dean ( 1998 ) and Guillot and Meyer ( 2001 ) is a simulated or physical robot equipped with sensors and actuators, and a neural network for behavior control. The neural controllers then have to operate in the so called sensorimotor loop, getting inputs from sensory signals and generating motor signals, which in turn will lead to new sensor inputs. The essential role of these closed loop processes for living or live-like systems has been discussed over several decades now from various points of view (Bishop, 1960 ; Beer, 1995 ; Di Paolo, 2003 ; Philipona et al., 2004 ; Hülse et al., 2007 ; Zahedi et al., 2010 ; Sándor et al., 2015 ). Here we use a purely formal approach and carefully analyze the dynamical description by making successive approximations to these processes. Neurocontrollers, mimicking their biological counterparts, are considered as recurrent neural networks which in general allow for dynamical properties. That is, for fixed synaptic weights, bias terms and inputs such a network can be described as a dynamical system. Then, assuming that a neurocontroller is driven by slow sensor inputs, it will be properly described as a parametrized family of dynamical systems, where sensor inputs (and proprioceptive signals as well) are considered in a first approximation as parameters of such a family. Furthermore, for every parameter value the corresponding dynamical system may have a manifold of different attractors. The postulated internal “entities” then will be identified with the basins of attraction of parametrically stable neurodynamical systems. The interaction with the environment then may change the references to situations in the external world by changing parameter values given, for instance, by the sensor signals. This process of changing references will be described by so-called bifurcations . For theoretical reasons, parameters are assumed to change so slowly that the system can approach its asymptotic states. This is often not the case for realistic sensor inputs. So, in a second step we will introduce sequences of neural states called meta-transients as for instance in Negrello and Pasemann ( 2008 ) Negrello ( 2011 ), and Toutounji and Pipa ( 2014 ). In general these meta-transients can not be given an interpretation as trajectories of a dynamical system, mainly because the inducing sequence of sensor signals is not a trajectory of a dynamical system on sensor space. Instead, because of the closed loop, it is superposition of movements in the environment and the result of motor actions. The case where one has access to controlling parameters has often been discussed in geometric control theory (Gardner, 1983 ; Sussmann, 1983 ; Respondek, 1996 ; Kloeden et al., 2013 ). There then one can generalize the concept of attractors and the like. Although we do not find this approach applicable for the dynamics in the sensorimotor loop we will work with a comparable view. Finally, these meta-transients have to be mapped to motor neurons, inducing then actions of the animats body; i.e., its behavior. Due to this projection not all elements of the neural system will be involved directly in the generation of motor signals. This leads naturally to a fiber structure over the motor space allowing to introduce the concepts of vertical or internal neurodynamics, having no direct effect on behavior, and a horizontal or effective neurodynamics, the projection of which generates the movements of the animat. To clarify concepts, the paper will address the discrete-time neurodynamics of networks composed of standard sigmoidal neurons of additive type. Using this simplifying setup, it is assumed that the aspects described in the following are transferable also to neural systems employing more biologically plausible or other types of neurons. The basic concern here is to specify the role of, for example, attractors, basins of attraction, transients, bifurcations and stability properties in the context of systems acting in a sensorimotor loop. Approaching the description of neurodynamics in the sensorimotor loop in three steps, we will first define the type of neurodynamics studied in this paper (Section 2), exemplifying it by some well known results. Assuming that sensor inputs are slow when compared to the activity dynamics of the neural system, we argue in Section 3 that neural systems in the sensorimotor loop are effectively described by parametrized families of dynamical systems, were parameters correspond to the sensor inputs. Other parameters, not considered here, are, for instance, signals coming from proprioceptors and the synaptic weights of the network, the change of which usually is associated with learning. Referring to the more realistic situations, meta-transients are introduction in Section 4. Finally, Section 5 discusses the generation of motor signals resulting from a projection of attractor transients or meta-transients, respectively, to the motor space; this then allows to differentiate between so called effective and internal neurodynamics. Finally the sensorimotor loop is closed through the environment by a formal mapping from motor space M to sensor space S . The paper concludes with a discussion of the possible role the introduced concepts can play for understanding neural representations of behavior relevant situations in the external world and, correspondingly, for a notion of memory which is not based on specific attractors like, for instance, fixed point attractors in Hopfield networks.",
"discussion": "6. Discussion The description of biological brains as dynamical systems is often assumed to be an appropriate approach to describe cognition and the behavior of animals (Port and Van Gelder, 1995 ; Thelen and Smith, 1996 ). Based on the observation that the typical activity of an animat is a reaction to its environment, we used the sensorimotor loop to carefully approach the dynamics hypothesis in three steps. Relying on experiences in the field of evolutionary robotics (Nolfi and Floreano, 2000 ) we used discrete-time neurodynamics to, first, describe the (isolated) brains as dynamical systems. Having realized that (living) brains are always driven by sensor inputs, we made clear that the description of brains as parametrized families of dynamical systems is more appropriate. This allowed to introduce the concept of parametric stability which helped to formalize the general observation that a certain behavior is robust against “noise,” and can be classified as “the same,” although the initializing sensor inputs vary over a larger domain. In a third step, assuming that sensor inputs may change so fast that they can not be assumed to serve as parameters in the mathematical sense (compare for instance Manoonpong et al., 2005 ), we were compelled to introduce the concept of meta-transients to describe the brains activity in a sensorimotor loop. These meta-transients in general will not be describable as orbits of a dynamical system. Finally, we used the fact that not all of the brains activity is directly reflected in the motor performance to discern between the brains effective (horizontal) and internal (vertical) activations. In a more general sense the horizontal part is associated more with the sensorimotor pathways, whereas the vertical part is assigned to the higher centers of the brain, associated with cognitive faculties of a system. Of course horizontal and vertical processes are not decoupled and depend on each other; they are processes on one and the same highly recurrent network. As usual, higher centers are assumed to check the adequacy of the activities along the sensorimotor pathways; they are modulating the sensorimotor flow of signals. On the other hand, the vertical processes are permanently restricted by the “horizontal” flow of signals; otherwise, that is, without sensor inputs, they will run freely into perhaps noxious states of brain and body. Following a purely formal approach to neurodynamics, we introduced in Section 3.2 the concept of parametric stability and the associated concept of a dynamical form. We think that these concepts may help to discuss questions concerning the representation of objects or, in this context better, behavior relevant situations in the external world. From the dynamical point of view certain patterns of sensor inputs will be associated with the existence of certain attractors in activation space A ; or otherwise stated, with the existence of a certain attractor-landscape. Because one has to assume that the brains dynamics is always driven by sensor inputs (including proprioception) it is more plausible to refer to a basin of attraction as a candidate for representing an external situation. Taking our argument for meta-transients serious it becomes obvious, that a dynamical form, associated with a certain type of behavior, is a reasonable representative for behavior relevant situations in the external world. Thus, taking parametric stability as essential for the reproducible identification of “the same” situations gives a reasonable conceptual basis for treating brain dynamics induced by an ever changing complex environment. If one approves this interpretation then it will also allow for a less restrictive dynamical view on memory. Neural memories usually are represented by asymptotically stable fixed points, like in Hopfield's associative-memory model, or are conceived as periodic, quasiperiodic, or even chaotic attractors of neural networks. In fact, the correspondence between attractors and memories is one of the fundamental aspects of neural networks. But, as we have seen, situated in a sensorimotor loop and driven by sensor inputs, the best we can expect is that attractors of a neural network serve as kinds of symbols, while the system always runs on transients to these attractors (or on meta-transients). So in a first step memory should be associated with the basins of certain attractors. Taken that the natural situation is such that neural systems in the sensorimotor loop run on meta-transients, we have to assume that the union of all basins of attraction, belonging to the possibly morphing attractors of a dynamic form, should be identified with the memory of certain behavior relevant external situations. We will call this kind of memory model a blurred memory. The relation between learning and blurred memory will be the subject of further research."
} | 3,329 |
39499080 | PMC7616799 | pmc | 4,688 | {
"abstract": "ABSTRACT The ability of bacteria to interact with and respond to their environment is crucial to their lifestyle and survival. Bacterial cells routinely need to engage with extracellular target molecules, in locations spatially separated from their cell surface. Engagement with distant targets allows bacteria to adhere to abiotic surfaces and host cells, sense harmful or friendly molecules in their vicinity, as well as establish symbiotic interactions with neighboring cells in multicellular communities such as biofilms. Binding to extracellular molecules also facilitates transmission of information back to the originating cell, allowing the cell to respond appropriately to external stimuli, which is critical throughout the bacterial life cycle. This requirement of bacteria to bind to spatially separated targets is fulfilled by a myriad of specialized cell surface molecules, which often have an extended, filamentous arrangement. In this review, we compare and contrast such molecules from diverse bacteria, which fulfil a range of binding functions critical for the cell. Our comparison shows that even though these extended molecules have vastly different sequence, biochemical and functional characteristics, they share common architectural principles that underpin bacterial adhesion in a variety of contexts. In this light, we can consider different bacterial adhesins under one umbrella, specifically from the point of view of a modular molecular machine, with each part fulfilling a distinct architectural role. Such a treatise provides an opportunity to discover fundamental molecular principles governing surface sensing, bacterial adhesion, and biofilm formation.",
"introduction": "INTRODUCTION Bacteria are often compelled to rapidly sense and adapt to changes in their environments, which is a requirement critical to their survival in diverse settings ( 1 – 3 ). Environmental sensing is often performed by filamentous (long, thin, and thread-like) appendages emanating from the surface of bacteria. The extended arrangement of these filamentous appendages means that they can engage with stimuli (external molecules) at locations distant from the cell surface ( 1 – 4 ). These appendages are usually adhesive, allowing them to directly bind to their targets, which can range from signaling molecules, abiotic surfaces, other bacterial cells in biofilms, or even host cells during infection ( 5 ). Despite the diversity of putative targets of surface appendages, there is an underlying similarity in architecture and arrangement of filamentous appendages present on bacterial cells. In this review, we consider different contexts of bacterial environmental sensing and adhesion, focusing on a few examples along the way to highlight similarities between bacterial surface filamentous appendages. Rather than discussing specific molecular mechanisms in one system, the main goal of this article is to consider surface appendages from a “molecular machines” perspective, to showcase common architectural principles. For more comprehensive reviews about specific types of adhesins and surface molecules, please refer to authoritative previous works by others ( 6 , 7 ). We also highlight cases where the same appendage is utilized by bacteria in multiple scenarios, showing how the underlying molecular architecture of filamentous appendages is sufficient for multiple use cases."
} | 845 |
39499080 | PMC7616799 | pmc | 4,688 | {
"abstract": "ABSTRACT The ability of bacteria to interact with and respond to their environment is crucial to their lifestyle and survival. Bacterial cells routinely need to engage with extracellular target molecules, in locations spatially separated from their cell surface. Engagement with distant targets allows bacteria to adhere to abiotic surfaces and host cells, sense harmful or friendly molecules in their vicinity, as well as establish symbiotic interactions with neighboring cells in multicellular communities such as biofilms. Binding to extracellular molecules also facilitates transmission of information back to the originating cell, allowing the cell to respond appropriately to external stimuli, which is critical throughout the bacterial life cycle. This requirement of bacteria to bind to spatially separated targets is fulfilled by a myriad of specialized cell surface molecules, which often have an extended, filamentous arrangement. In this review, we compare and contrast such molecules from diverse bacteria, which fulfil a range of binding functions critical for the cell. Our comparison shows that even though these extended molecules have vastly different sequence, biochemical and functional characteristics, they share common architectural principles that underpin bacterial adhesion in a variety of contexts. In this light, we can consider different bacterial adhesins under one umbrella, specifically from the point of view of a modular molecular machine, with each part fulfilling a distinct architectural role. Such a treatise provides an opportunity to discover fundamental molecular principles governing surface sensing, bacterial adhesion, and biofilm formation.",
"introduction": "INTRODUCTION Bacteria are often compelled to rapidly sense and adapt to changes in their environments, which is a requirement critical to their survival in diverse settings ( 1 – 3 ). Environmental sensing is often performed by filamentous (long, thin, and thread-like) appendages emanating from the surface of bacteria. The extended arrangement of these filamentous appendages means that they can engage with stimuli (external molecules) at locations distant from the cell surface ( 1 – 4 ). These appendages are usually adhesive, allowing them to directly bind to their targets, which can range from signaling molecules, abiotic surfaces, other bacterial cells in biofilms, or even host cells during infection ( 5 ). Despite the diversity of putative targets of surface appendages, there is an underlying similarity in architecture and arrangement of filamentous appendages present on bacterial cells. In this review, we consider different contexts of bacterial environmental sensing and adhesion, focusing on a few examples along the way to highlight similarities between bacterial surface filamentous appendages. Rather than discussing specific molecular mechanisms in one system, the main goal of this article is to consider surface appendages from a “molecular machines” perspective, to showcase common architectural principles. For more comprehensive reviews about specific types of adhesins and surface molecules, please refer to authoritative previous works by others ( 6 , 7 ). We also highlight cases where the same appendage is utilized by bacteria in multiple scenarios, showing how the underlying molecular architecture of filamentous appendages is sufficient for multiple use cases."
} | 845 |
39499080 | PMC7616799 | pmc | 4,689 | {
"abstract": "ABSTRACT The ability of bacteria to interact with and respond to their environment is crucial to their lifestyle and survival. Bacterial cells routinely need to engage with extracellular target molecules, in locations spatially separated from their cell surface. Engagement with distant targets allows bacteria to adhere to abiotic surfaces and host cells, sense harmful or friendly molecules in their vicinity, as well as establish symbiotic interactions with neighboring cells in multicellular communities such as biofilms. Binding to extracellular molecules also facilitates transmission of information back to the originating cell, allowing the cell to respond appropriately to external stimuli, which is critical throughout the bacterial life cycle. This requirement of bacteria to bind to spatially separated targets is fulfilled by a myriad of specialized cell surface molecules, which often have an extended, filamentous arrangement. In this review, we compare and contrast such molecules from diverse bacteria, which fulfil a range of binding functions critical for the cell. Our comparison shows that even though these extended molecules have vastly different sequence, biochemical and functional characteristics, they share common architectural principles that underpin bacterial adhesion in a variety of contexts. In this light, we can consider different bacterial adhesins under one umbrella, specifically from the point of view of a modular molecular machine, with each part fulfilling a distinct architectural role. Such a treatise provides an opportunity to discover fundamental molecular principles governing surface sensing, bacterial adhesion, and biofilm formation.",
"introduction": "INTRODUCTION Bacteria are often compelled to rapidly sense and adapt to changes in their environments, which is a requirement critical to their survival in diverse settings ( 1 – 3 ). Environmental sensing is often performed by filamentous (long, thin, and thread-like) appendages emanating from the surface of bacteria. The extended arrangement of these filamentous appendages means that they can engage with stimuli (external molecules) at locations distant from the cell surface ( 1 – 4 ). These appendages are usually adhesive, allowing them to directly bind to their targets, which can range from signaling molecules, abiotic surfaces, other bacterial cells in biofilms, or even host cells during infection ( 5 ). Despite the diversity of putative targets of surface appendages, there is an underlying similarity in architecture and arrangement of filamentous appendages present on bacterial cells. In this review, we consider different contexts of bacterial environmental sensing and adhesion, focusing on a few examples along the way to highlight similarities between bacterial surface filamentous appendages. Rather than discussing specific molecular mechanisms in one system, the main goal of this article is to consider surface appendages from a “molecular machines” perspective, to showcase common architectural principles. For more comprehensive reviews about specific types of adhesins and surface molecules, please refer to authoritative previous works by others ( 6 , 7 ). We also highlight cases where the same appendage is utilized by bacteria in multiple scenarios, showing how the underlying molecular architecture of filamentous appendages is sufficient for multiple use cases."
} | 845 |
39499080 | PMC7616799 | pmc | 4,689 | {
"abstract": "ABSTRACT The ability of bacteria to interact with and respond to their environment is crucial to their lifestyle and survival. Bacterial cells routinely need to engage with extracellular target molecules, in locations spatially separated from their cell surface. Engagement with distant targets allows bacteria to adhere to abiotic surfaces and host cells, sense harmful or friendly molecules in their vicinity, as well as establish symbiotic interactions with neighboring cells in multicellular communities such as biofilms. Binding to extracellular molecules also facilitates transmission of information back to the originating cell, allowing the cell to respond appropriately to external stimuli, which is critical throughout the bacterial life cycle. This requirement of bacteria to bind to spatially separated targets is fulfilled by a myriad of specialized cell surface molecules, which often have an extended, filamentous arrangement. In this review, we compare and contrast such molecules from diverse bacteria, which fulfil a range of binding functions critical for the cell. Our comparison shows that even though these extended molecules have vastly different sequence, biochemical and functional characteristics, they share common architectural principles that underpin bacterial adhesion in a variety of contexts. In this light, we can consider different bacterial adhesins under one umbrella, specifically from the point of view of a modular molecular machine, with each part fulfilling a distinct architectural role. Such a treatise provides an opportunity to discover fundamental molecular principles governing surface sensing, bacterial adhesion, and biofilm formation.",
"introduction": "INTRODUCTION Bacteria are often compelled to rapidly sense and adapt to changes in their environments, which is a requirement critical to their survival in diverse settings ( 1 – 3 ). Environmental sensing is often performed by filamentous (long, thin, and thread-like) appendages emanating from the surface of bacteria. The extended arrangement of these filamentous appendages means that they can engage with stimuli (external molecules) at locations distant from the cell surface ( 1 – 4 ). These appendages are usually adhesive, allowing them to directly bind to their targets, which can range from signaling molecules, abiotic surfaces, other bacterial cells in biofilms, or even host cells during infection ( 5 ). Despite the diversity of putative targets of surface appendages, there is an underlying similarity in architecture and arrangement of filamentous appendages present on bacterial cells. In this review, we consider different contexts of bacterial environmental sensing and adhesion, focusing on a few examples along the way to highlight similarities between bacterial surface filamentous appendages. Rather than discussing specific molecular mechanisms in one system, the main goal of this article is to consider surface appendages from a “molecular machines” perspective, to showcase common architectural principles. For more comprehensive reviews about specific types of adhesins and surface molecules, please refer to authoritative previous works by others ( 6 , 7 ). We also highlight cases where the same appendage is utilized by bacteria in multiple scenarios, showing how the underlying molecular architecture of filamentous appendages is sufficient for multiple use cases."
} | 845 |
19280184 | PMC2690845 | pmc | 4,690 | {
"abstract": "The solvent-tolerant bacterium Pseudomonas putida S12 was engineered to efficiently utilize the C 1 compounds methanol and formaldehyde as auxiliary substrate. The hps and phi genes of Bacillus brevis , encoding two key steps of the ribulose monophosphate (RuMP) pathway, were introduced to construct a pathway for the metabolism of the toxic methanol oxidation intermediate formaldehyde. This approach resulted in a remarkably increased biomass yield on the primary substrate glucose when cultured in C-limited chemostats fed with a mixture of glucose and formaldehyde. With increasing relative formaldehyde feed concentrations, the biomass yield increased from 35% (C-mol biomass/C-mol glucose) without formaldehyde to 91% at 60% relative formaldehyde concentration. The RuMP-pathway expressing strain was also capable of growing to higher relative formaldehyde concentrations than the control strain. The presence of an endogenous methanol oxidizing enzyme activity in P. putida S12 allowed the replacement of formaldehyde with the less toxic methanol, resulting in an 84% (C-mol/C-mol) biomass yield. Thus, by introducing two enzymes of the RuMP pathway, co-utilization of the cheap and renewable substrate methanol was achieved, making an important contribution to the efficient use of P. putida S12 as a bioconversion platform host.",
"introduction": "Introduction The solvent-tolerant Pseudomonas putida S12 is used as a platform for the bioconversion of sugars into substituted aromatic compounds (Nijkamp et al. 2005 ; Nijkamp et al. 2007 ; Verhoef et al. 2007 ; Wierckx et al. 2005 ). These compounds are produced via central metabolite intermediates such as l -tyrosine and l -phenylalanine, the formation of which is directly linked to cellular growth. In such a process, a significant proportion of the available substrate is not used to form biomass and product, but utilized for the generation of free energy (ATP and proton gradient) and reducing equivalents (NAD(P)H). Since the sugar substrate represents an important cost factor in bioproduction processes (Schmid et al. 2001 ), the addition of a cheap auxiliary catabolic substrate to generate free energy and/or reducing equivalents may significantly improve the economy of the production process. Previous studies have shown that co-utilization of thiosulfate or C 1 compounds like formate and formaldehyde leads to an increased yield on the primary carbon source (Baerends et al. 2008 ; Bruinenberg et al. 1985 ; Harris et al. 2007 ; Masau et al. 2001 ). Another C 1 compound that can be used as auxiliary substrate is methanol. Being more reduced than formate or formaldehyde, methanol can yield more reducing equivalents per C-mole. Since methanol can be derived from biomass via synthesis gas (Chmielniak and Sciazko 2003 ) it is a promising renewable auxiliary substrate for biotechnological processes. The first step in methanol metabolism is the oxidation to formaldehyde, via dehydrogenases or oxidases (Anthony 1986 ; Sahm 1977 ). Formaldehyde is extremely toxic due to non-specific reactivity with proteins and nucleic acids. Therefore, rapid and efficient formaldehyde metabolization is a crucial step in the utilization of methanol. Several pathways for the metabolism of formaldehyde have been described. Methylotrophic yeasts may assimilate formaldehyde by the xylulose-5-phosphate cycle. In this pathway, formaldehyde is coupled to xylulose-5-phosphate and converted into dihydroxyacetone and glyceraldehyde-3-phosphate (GA3P) by a specialized transketolase-dihydroxyacetone synthase (Yurimoto et al. 2005a , b ). Alternatively, formaldehyde is oxidatively dissimilated to form formate and eventually carbon dioxide and water (Yurimoto et al. 2005a , b ). In bacteria, three formaldehyde metabolic pathways are known. Like yeasts, also bacteria may oxidatively dissimilate formaldehyde to formate and CO 2 . Alternatively, formaldehyde may be assimilated via the serine cycle. In this pathway formaldehyde is coupled to l -glycine to form l -serine ( l -ser). l -ser is subsequently metabolized via a cyclic pathway that ensures replenishment of l -glycine and permits a drain on glycerate-3-phosphate to generate biomass (Chistoserdova et al. 2003 ). The third bacterial formaldehyde metabolic route is the RuMP pathway (Fig. 1 ) (Kato et al. 2006 ). In this pathway, formaldehyde is coupled to ribulose-5-phosphate (Ru5P) forming hexulose-6-phosphate (Hu6P). Hu6P is isomerized to fructose-6-phosphate (F6P), that can be further metabolized via the Embden–Meyerhof–Parnas (EMP) pathway, the Entner–Doudoroff (ED) pathway, or the pentose phosphate pathway (PPP). When F6P enters the oxidative part of the PPP (after isomerization to glucose-6-phosphate (G6P)), the RuMP pathway constitutes a cyclic oxidation pathway for formaldehyde: G6P yields Ru5P and CO 2 while generating two NADPH. This cyclic oxidative dissimilation of formaldehyde has been described in, a.o., Methylobacillus flagellatus KT (Chistoserdova et al. 2000 ). The RuMP pathway may also constitute a formaldehyde assimilation pathway (Fig. 1 ). In this case one-third of the F6P formed enters either the EMP or ED pathway and is converted into GA3P and pyruvate. Pyruvate is used for the production of cell constituents whereas GA3P and two-thirds of the produced F6P are used to regenerate Ru5P by a combination of transketolase, transaldolase, and isomerization reactions (Jakobsen et al. 2006 ; Kato et al. 2006 ; Large and Bamforth 1988 ).\n Fig. 1 Assimilation and dissimilation pathways for formaldehyde in P. putida S12 pJNNhp(t). 1 Hexulose phosphate synthase, 2 hexulose phosphate isomerase, 3 hexose phosphate isomerase, 4 glucose-6-phosphate dehydrogenase, 5 6-phosphogluconate dehydratase, 6 2-dehydro-3-deoxy-phosphogluconate aldolase, 7 transketolase, 8 transaldolase, 9 pentose phosphate isomerase, 10 pentose phosphate epimerase; 11 6-phosphogluconate dehydrogenase, 12 formaldehyde dehydrogenase, 13 formate dehydrogenase. Ru ribulose, Hu hexulose, F fructose, G glucose, Xu xylulose, R ribulose, E erythrose, Su sedoheptulose, KDPG 2-dehydro-3-deoxy-6-phospho- d -gluconate, HCHO formaldehyde. Black arrows indicate the assimilatory RuMP pathway, dashed arrows indicate the dissimilatory RuMP pathway, gray arrows indicate the linear oxidation of formaldehyde to carbon dioxide \n Previously, formaldehyde has been successfully applied as auxiliary substrate in yeast; however, its high toxicity hindered efficient co-utilization (Baerends et al. 2008 ). Expression of formaldehyde dehydrogenase (Fld) and a formate dehydrogenase (Fmd) from the methylotrophic yeast Hansenula polymorpha in Saccharomyces cerevisiae resulted in an increased biomass yield with formaldehyde as auxiliary substrate (Baerends et al. 2008 ). An increased tolerance towards formaldehyde was obtained by introducing the fld and fmd genes. The aim of the present study was to develop and optimize a P. putida S12 strain capable of efficiently utilizing formaldehyde as auxiliary substrate. Since P. putida S12 possesses genes encoding formaldehyde and formate dehydrogenase, it was expected that P. putida S12 has a basic endogenous capacity to oxidize formaldehyde to formate and CO 2 . To further improve this capacity, additional formaldehyde metabolic pathways were constructed by expressing the two key enzymes of the RuMP pathway, 3-hexulose-6-phosphate synthase (Hps) and 6-phospho-3-hexulose isomerase (Phi). Biomass yields were determined in chemostat cultures for the engineered strain on different mixtures of glucose and formaldehyde. The RuMP pathway strain showed significantly improved performance over the control strain, achieving a biomass yield-on-glucose of 91% when using formaldehyde as auxiliary substrate. Replacing formaldehyde with the renewable auxiliary substrate methanol resulted in a biomass yield-on-glucose of 84%.",
"discussion": "Discussion P. putida S12 was shown to have an innate capability to co-utilize formaldehyde and glucose, in relative concentrations of up to 50% in C-limited chemostats. This ability is indicative of the presence of an endogenous formaldehyde metabolic pathway in P. putida S12. Database searches revealed the presence of several genes coding for formaldehyde and formate dehydrogenases in the P. putida S12 genome (manuscript in preparation). Therefore, this pathway probably consists of a linear route that oxidizes formaldehyde, via formate, to CO 2 . The observed yield increase of the control strain may be attributed to the reducing equivalents generated during formaldehyde oxidation. Although P. putida S12 disposes of an endogenous formaldehyde detoxification pathway, the introduction of an additional, heterologous formaldehyde metabolic pathway clearly had a positive effect in chemostat cultures grown on a mixture of glucose and formaldehyde. This was reflected by a consistently improved biomass yield on glucose and the ability to grow at elevated relative concentrations of formaldehyde. The presence of an additional formaldehyde metabolic route may allow for growth at higher relative formaldehyde concentrations, since accumulation of the highly toxic formaldehyde is prevented more efficiently. This unlikely explains, however, the higher biomass yield gain for the strain expressing the RuMP pathway enzymes. Possibly, the improved yield gain relates to a better cofactor balance: when constituting a cyclic oxidation pathway, the RuMP cycle generates 2 mol of NADPH per mol formaldehyde (Fig. 1 ), whereas the linear oxidation pathway yields 2 mol of NADH. It should be noted, however, that the introduction of the hps and phi genes will also constitute an assimilatory RuMP pathway (Fig. 1 ). Thus, (co-)assimilation of formaldehyde may occur, in which case formaldehyde not only serves as a catabolic auxiliary substrate, but also as an assimilatory substrate. It was shown that the presence of the RuMP pathway results in a yield increase that is twice the yield increase attained by linear formaldehyde oxidation. Considering that the same amount of formaldehyde was metabolized in both strains, it may be concluded that the RuMP pathway strain utilizes formaldehyde twice as efficient as the empty vector control strain. Since the linear and the cyclic oxidation pathway yield an equal amount of reducing equivalents (Fig. 1 ; although of different nature), co-assimilation of formaldehyde is the most likely explanation for the observed additional yield increase found with the RuMP pathway strain. Thus, the biomass yield expressed as yield-per-glucose likely is an overestimation of the actual biomass yield-per-assimilated carbon for the RuMP-pathway strain grown on formaldehyde-glucose mixtures. The exact relative contributions of linear oxidation, cyclic oxidation, cofactor balance, and assimilation of formaldehyde to biomass yield cannot be determined as it is not possible to selectively shut down either of the pathways. Attempts to knock-out formaldehyde dehydrogenase-encoding genes in order to abolish linear oxidation in P. putida S12 have consistently failed (unpublished). No viable double cross-over recombinants could be obtained, suggesting that the linear oxidation pathway apparently is crucial for the detoxification of formaldehyde. Also in non-methylotrophs, formaldehyde is an endogenous metabolite that is produced by oxidative demethylation of DNA (Aas et al. 2003 ; Falnes et al. 2002 ) and during methionine, histidine, and choline metabolism (Arnstein 1954 ). Moreover, the contributions of the cyclic oxidation pathway and the assimilatory pathway cannot be separated since the expression of Hps and Phi constitutes both pathways at a time. Despite the likely ability of P. putida S12pJNNhp to utilize formaldehyde as sole C-source, chemostat cultures washed out when deprived of glucose while maintaining the formaldehyde feed. The activity of the first two enzymes of the RuMP pathway as measured in mid-log phase batch cultures should suffice to assimilate the formaldehyde administered to the low-D (0.03 h −1 ) cultures. The net Hps/Phi activity amounted to approximately 6,800 U/g protein. Assuming a protein content of 50%, this activity should allow 1 g of cells (dry weight) to cope with a formaldehyde feed of about 200 mmol h −1 . However, in the low-D cultures 270 mg of CDW was not able to fully metabolize formaldehyde that was administered at a rate of 0.54 mmol h −1 . This observation suggests that the metabolic flux through the endogenous part of the formaldehyde assimilatory cycle may be the bottleneck in preventing formaldehyde accumulation, and subsequent wash-out of cells, when deprived of other C-sources. Alternatively, the activity of the RuMP-pathway enzymes in batch cultures may not be representative for chemostat cultures. Replacement of formaldehyde with methanol in C-limited chemostat cultures at a low dilution rate also resulted in improved biomass yield. Methanol was not completely utilized, however, and the biomass yield on glucose was slightly lower than for the corresponding formaldehyde experiments. The accumulation of methanol but not formaldehyde/formate suggests that the endogenous methanol dehydrogenase activity is the bottleneck for methanol utilization. This was also indicated by the 50-fold lower activity with methanol as the substrate in the Hps/Phi enzyme assay. From the P. putida S12 genome sequence (manuscript in preparation) no close homologues to established methanol dehydrogenases could be identified. Therefore, it is likely that the low methanol dehydrogenase activity of P. putida S12 results from a side activity of a broad-specificity alcohol dehydrogenase. In conclusion, the enhanced ability of P. putida S12pJNNhp(t) to utilize C 1 compounds as auxiliary substrate can be used to substantially improve raw feedstock utilization efficiency. Since both primary and auxiliary substrates can be obtained from biomass, process economy can be improved without compromising sustainability. Although somewhat less efficient than formaldehyde, methanol is the preferred auxiliary substrate as it is less toxic, easier to handle, cheap and renewable. Currently, possibilities are explored to improve the utilization of methanol by increasing the endogenous methanol dehydrogenase activity or by co-expressing a heterologous methanol dehydrogenase."
} | 3,622 |
38168087 | PMC10761962 | pmc | 4,691 | {
"abstract": "Microbial communities are shaped by complex metabolic interactions such as cooperation and competition for resources. Methods to control such interactions could lead to major advances in our ability to better engineer microbial consortia for synthetic biology applications. Here, we use optogenetics to control SUC2 invertase production in yeast, thereby shaping spatial assortment of cooperator and cheater cells. Yeast cells behave as cooperators ( i.e ., transform sucrose into hexose, a public good) upon blue light illumination or cheaters ( i.e ., consume hexose produced by cooperators to grow) in the dark. We show that cooperators benefit best from the hexoses they produce when their domain size is constrained between two cut-off length-scales. From an engineering point of view, the system behaves as a bandpass filter. The lower limit is the trace of cheaters’ competition for hexoses, while the upper limit is defined by cooperators’ competition for sucrose. Cooperation mostly occurs at the frontiers with cheater cells, which not only compete for hexoses but also cooperate passively by letting sucrose reach cooperators. We anticipate that this optogenetic method could be applied to shape metabolic interactions in a variety of microbial ecosystems.",
"introduction": "Introduction Metabolic interactions, such as competition and cooperation to metabolize nutrients, are central to the development of microbial colonies and biofilms 1 – 4 . These metabolic interactions participate in the establishment of complex, spatially structured multicellular systems in which cells located at different positions experience varied microenvironments and can compete or cooperate with each other. Cooperating cells (cooperators) are defined by their capacity to invest resources to promote the proliferation (i.e., increase the fitness) of other cells 5 – 7 . Conversely, cells that benefit from other cells without contributing to their metabolic efforts are defined as cheaters 8 – 11 since they compete for resources without paying the cost required to produce the resources. Cooperation and competition dynamics have been studied in various contexts 12 , 13 , particularly using the canonical example of sucrose utilization by the budding yeast Saccharomyces cerevisiae 14 – 18 (Fig. 1 ). Briefly, yeast cells can produce the invertase Suc2p, which is retained in the periplasmic space and can catalyze the hydrolysis of sucrose into glucose and fructose. Both of these hexoses (glucose and fructose) can then be taken up by cells and metabolized intracellularly (Fig. 1a , Supplementary Fig. 1 ). Since hydrolysis of sucrose occurs in the periplasm, the hexoses produced by hydrolysis are not only taken up but also leaked into the extracellular space and become public goods as these sugars can also diffuse away and be consumed by adjacent cells (including cheater cells). This situation both favors interactions between individual cells and also represents a fitness cost for the cells expressing Suc2p 16 . Note that although sucrose can alternatively be taken up via sucrose-proton symporters (Mal11p, Mal31p, and Mph2/3p) 19 to then be hydrolyzed internally (a cytosolic form of the invertase, maltase, and isomaltase), usually external hydrolysis is the dominant sucrose uptake process in wild-type yeast 20 , 21 . Fig. 1 Optogenetic control of yeast sucrose catabolism. a Blue light illumination induces transcription of the SUC2 gene and the production of the invertase Suc2p, which is secreted by exocytosis and retained in the periplasm. There, Suc2p catalyzes the hydrolysis of sucrose into two hexoses (glucose and fructose). These hexoses can be imported by cells via specific transporters (HTX1–4, 6–7, and Gal2) to support the growth of yeast cells through glycolysis. Alternatively, glucose and fructose can also diffuse away from the producing cell into the extracellular environment. b If the optogenetic system is tightly controlled, only cells stimulated by light can produce Suc2p, while cells in the dark cannot produce Suc2p. Projecting patterns of light on a yeast assembly induces well-separated spatial domains of cooperators and cheaters: illuminated cells behave as cooperators (i.e., they produce hexoses as public goods), while cells in the dark behave as cheaters (i.e., they rely on cooperators’ production of public goods to grow). c Illumination induces the local production of hexoses and the establishment of hexose gradients through diffusion and uptake by both cooperators and cheaters. While it is now well accepted in the literature that spatial structure plays a determinant role in natural communities’ fate 4 , 22 – 25 ; most controlled laboratory experiments pursuing a quantitative understanding of microbial competition/cooperation mechanisms have focused on well-mixed or small-scale populations. For example, the seminal works by Maclean et al. 26 compared the stability of cooperation in yeast cooperator-cheater cocultures and further used a 96-well plate to mimic the spatial structure of a metapopulation . Although this experimental system was key to studying the impact of the cheater/cooperator ratio on the global population fitness, it cannot be used to create spatially extended and interacting domains of cheaters and cooperators. In fact, it is hard to experimentally create, in a controlled manner, a microbial community of cheaters and cooperators with a user-defined spatial assortment 24 that would allow us to explore quantitatively its impact on microbial cooperation. Here, we propose to use optogenetics to solve this issue and further experimentally explore the microbial interactions over scales of spatial assortment. In the case of sucrose utilization by structured yeast populations, the key parameters that define the length scales at which cooperation and competition occur are the diffusion and consumption of both hexoses and sucrose. Simple estimates based on the diffusion–reaction equation indicate that the typical length scale over which the concentration decreases from a point source at steady state varies as the square root of the diffusion coefficient of the metabolite of interest and the inverse of the square root of the rate at which the metabolite is absorbed by cells. In expanding microbial colonies, this distance over which metabolic gradients are formed is of the order of a few hundred micrometers 27 . The absorption rate ( k ) depends on the local cell density, which also varies in time as hexoses are progressively transformed into biomass. Hence, the cell density of cooperators and cheaters is likely to be a key parameter that defines the cooperation potency and indeed, this parameter has been shown to be critical in the case of sucrose cooperation dynamics. For example, in liquid culture, higher population growth was observed when yeast formed multicellular clumps 16 . Other researchers showed that—in silico—static growth assortment (where daughter cells stay close to their mother) stabilizes yeast cooperation for sucrose catabolism 28 . Taken together, these results suggest that cooperation through the production of a diffusible public good appears generally more cost-efficient if the cooperating cells stay close together 3 . However, most experimental studies have not considered the impact of the spatial organization of cheating and cooperating domains on the dynamics of cooperation, and, so far, very few experimental quantitative studies 29 , 30 have investigated this problem, mostly due to technological limitations. Optogenetics, because it permits the achievement of quantitative, spatial control of gene expression over a population of microorganisms, sounds like a tool of choice to address those questions. To date, optogenetics has been reported in only a few works as a tool to spatially control public goods, such as the production of extracellular matrix in Sinorhizobium meliloti 31 , of adhesin in Escherichia coli 32 bacteria or of the SUC2 invertase in budding yeast 29 in recent work by Moreno Morales et al. 29 . These studies used the potential of spatial patterning of optogenetics to explore the impact on cooperation and diffusion of public goods in microbial systems. In this work, we use optogenetics and spatial light patterning to activate the expression of the invertase SUC2 at selected locations within populations of yeast cells. Yeast cells can therefore selectively be switched from cheater to cooperator phenotypes upon light stimulation (Fig. 1b ), creating spatially structured landscapes of cooperators and cheater cells (Fig. 1c ). Combined with a dedicated experimental system to track the growth of cell populations with time and a numerical model, we show the existence of two characteristic length-scales of cooperation/competition that involve both cheaters and cooperators and drive the emergence of the spatial landscape within a cooperator/cheater yeast consortium.",
"discussion": "Discussion In this study, we built a yeast strain that produces the sucrose invertase Suc2p upon illumination with light and tested the population growth dynamics and the benefits of cooperation in various artificially shaped landscapes of cooperators and cheaters. The OptoSuc2 strain indeed acts as a cooperating cell when illuminated but remains a cheater when kept in the dark because non-illuminated cells cannot metabolize sucrose and instead rely on the hexose produced by adjacent cooperating cells. We were, therefore, able to quantitatively explore the spatial metabolic interactions between cooperators and cheaters. Our main finding is the existence of two typical length scales that set the domain size of both cooperators and cheaters. Both length scales are defined by the diffusion and uptake properties of hexose and sucrose. The first length scale is that of competition between cheaters and cooperators and can easily be understood as the typical length over which hexose diffuses away from cooperators. As exemplified in Fig. 7e , if cooperator domains are smaller than this wavelength, the number of cheaters that benefit from the produced hexose is comparable to the number of cooperators, and cooperating provides no clear benefit. In other words, cooperator domains need to be larger than λ - to be distinguishable from cheater cells: too-small domains are equivalent to what would be obtained with a homogenous mixture of cooperators and cheaters. The existence of the second upper length scale, λ + , was unexpected and demonstrates the benefit of grouping cooperators decreases when their domain is too large. We attribute this decrease to the fact that cooperators not only interact by producing hexose that benefits their neighbors (cooperation) but also by competing for the basal carbon source, in this case, sucrose. This competition means that cells far away from the sucrose source obtain less sucrose and, as such, produce less hexose. This is similar to the growth dynamics observed in any extending colony, for which growth occurs mostly at the edge of the colony where nutrients are abundant. Therefore, in a spatially structured cheater/cooperator system, the existence of large domains of cheater cells (which cannot hydrolyze sucrose) ensures the presence of secured pools of sucrose that can diffuse toward cooperator islands and be used first by the cooperator cells located at the frontier between cheater and cooperator domains. Competition for sucrose takes place within a cooperator domain, and the cells closest to the cheater domains are at an advantage. Said differently, cooperating cells benefit from proximity to cheater cells, and cheater cells not only function as cheaters but also as key actors that facilitate the growth of cooperating cells at the domain frontiers. Therefore, cheater cells also help the cooperators to grow faster in the vicinity of the cheaters’ domain of existence. Furthermore, as exemplified by our study, this beneficial role of cheater cells is only apparent when the domains of both cheaters and cooperators are large enough. As we proposed in the Results, this relationship can be summarized by an analogy with a spatial bandpass filter, in which critical wavelengths are linked to the typical distances of the metabolic interactions in the SUC2 yeast system: a lower cut-off wavelength (λ − ~5 mm) due to cheater-cooperator competition for hexoses and a higher cut-off wavelength (λ + ~20 mm) due to cooperator self-competition for sucrose. We confirmed this analysis with our numerical model, which even though it is only a minimal model with several limitations (e.g., it does not capture well the dependency with light intensity), is able to reproduce the bandpass filter behavior, indicating that it has the main ingredients to explore spatial interactions between cooperators and cheaters. Thus, thanks to the ability to artificially create cooperator/cheater landscapes with light, we defined the optimal range of domain sizes that create cooperating microbial niches. We anticipate that our approach could be applied to other microbial ecosystems to explore the parameters that define the landscape of metabolic interactions. Our study illustrates the power of optogenetics and spatial patterning to decipher the metabolic interactions at play in spatially complex multicellular assemblies such as colonies, biofilms, and engineered consortia. There is a growing interest in engineering microbial consortia 43 – 45 , in which different types of cells cooperate to more efficiently achieve specific biological functions (bioproduction 46 , living materials 47 , or live therapeutics 48 , 49 ). Thus, it is essential to better grasp the physical limitations of such systems—in particular, the impacts of chemical diffusion, the composition of consortia, and their metabolic interdependence—on the dimensions of microbial niches in such applications. We anticipate that optogenetics could be used to locally change the cellular metabolic capabilities of microbial consortia by controlling the size of the domains of the species. Such experiments will help to better understand cooperation and competition mechanisms in microbial ecosystems and how to control complex synthetic microbial consortia in real-time. Importantly, we showed that intrinsic dimensions exist for microbial niches and can be played with to optimize cheating and/or self-competition for resources. This study can guide synthetic biologists to appropriately set the dimensions of engineered living materials 50 , 51 (ELM) in microbial niches to sizes that are compatible with the desired properties of the ELM, which is a crucial step required to obtain precise functionalities and efficient external control. We extrapolate that such spatial constraints could also be considered when studying the spatial organization of imbalanced microbiomes (dysbiosis), which are linked with major problems such as human obesity, diabetes, skin disease, and a myriad of other diseases due to alterations in the human gut microbiome 52 , or unsustainable farm soil fertility associated with a high need for nitrogen fertilization 53 ."
} | 3,783 |
26242625 | PMC4526712 | pmc | 4,697 | {
"abstract": "ABSTRACT It is well known that rhizosphere microbiomes differ from those of surrounding soil, and yet we know little about how these root-associated microbial communities change through the growing season and between seasons. We analyzed the response of soil bacteria to roots of the common annual grass Avena fatua over two growing seasons using high-throughput sequencing of 16S rRNA genes. Over the two periods of growth, the rhizosphere bacterial communities followed consistent successional patterns as plants grew, although the starting communities were distinct. Succession in the rhizosphere was characterized by a significant decrease in both taxonomic and phylogenetic diversity relative to background soil communities, driven by reductions in both richness and evenness of the bacterial communities. Plant roots selectively stimulated the relative abundance of Alphaproteobacteria , Betaproteobacteria , and Bacteroidetes but reduced the abundance of Acidobacteria , Actinobacteria , and Firmicutes . Taxa that increased in relative abundance in the rhizosphere soil displayed phylogenetic clustering, suggesting some conservation and an evolutionary basis for the response of complex soil bacterial communities to the presence of plant roots. The reproducibility of rhizosphere succession and the apparent phylogenetic conservation of rhizosphere competence traits suggest adaptation of the indigenous bacterial community to this common grass over the many decades of its presence.",
"introduction": "INTRODUCTION Plant roots supply a significant amount of carbon (C) to adjacent rhizosphere soil, resulting in growth and interactions among populations present in the resident soil microbial community ( 1 , 2 ). Root activities modify the physiochemical properties of the surrounding soil (e.g., water and oxygen availability and pH), which also shape microbial communities ( 3 , 4 ). Through their interactions with plant roots, rhizosphere microbial communities influence terrestrial C and nutrient cycling, as well as plant growth and health ( 5 – 7 ). The ecological processes that control the assembly of the rhizosphere microbiome and drive the succession of the rhizosphere community are of fundamental importance to terrestrial ecosystem functioning. Previous studies using microbial fingerprinting techniques (e.g., denaturing gradient gel electrophoresis [DGGE] and terminal restriction fragment length polymorphism [TRFLP]) have shown that plant species and soil types have measurable effects on rhizosphere microbial communities ( 8 , 9 ). However, the responses of microbial populations to plant growth are less well known ( 10 ). High-throughput sequencing (e.g., Illumina and 454 pyrosequencing) of 16S rRNA gene amplicons enables exploration of the phylogenetic/taxonomic composition and structure of microbial communities at a much higher resolution than previous techniques ( 11 , 12 ) and has increased our capacity to describe the diversity and evolutionary basis of succession in root-associated soil microbial communities ( 10 , 13 ). For example, two studies that characterized rhizosphere communities and endophytes associated with the model plant Arabidopsis thaliana (1,000 to 4,000 operational taxonomic units [OTUs]) using 454 pyrosequencing showed that A. thaliana roots were preferentially colonized by Proteobacteria , Bacteroidetes , and Actinobacteria ( 14 , 15 ). More recently, Chaparro et al. ( 16 ) reported that rhizosphere bacterial communities associated with A. thaliana at the seedling stage were significantly different from other developmental stages (vegetative, bolting, and flowering) based on over 30,000 454 sequencing reads; some phyla (e.g., Acidobacteria , Actinobacteria , and Bacteroidetes ) followed distinct patterns as the plant grew. However, it remains unclear how microbial communities change after plant senescence and when a new growing season begins. In this study, we followed the responses and succession of the bacterial community in the rhizosphere of a common annual grass, Avena fatua , growing in a California annual grassland soil. A. fatua is a common, naturalized exotic inhabitant of California Mediterranean grasslands and a globally important agricultural weed. The composition of the rhizosphere bacterial community was followed for two growing seasons separated by a dry season, as occurs annually in the California Mediterranean-type climate. Soil microbial communities were analyzed from preplanting through four plant growth stages (i.e., seedling, vegetative, flowering, and senescent) in both seasons ( Fig. 1 ) using Illumina MiSeq sequencing of 16S rRNA gene amplicons and quantitative PCR (qPCR). Plants were grown in a greenhouse for 12 weeks per growth cycle, using grassland soil from the University of California Hopland field station in which Avena spp. have been resident for over a century. This investigation was designed to answer two questions: (i) how does the composition of the soil bacterial community associated with growing roots of an annual grass, Avena fatua , change with growth of the plant, and is this pattern repeatable across seasons? and (ii) do bacteria indigenous to a soil in which A. fatua has grown for many years exhibit adaptation to the soil environment created by roots, as evidenced by phylogenetic clustering? FIG 1 Experimental design and definition of soils sampled for analysis.",
"discussion": "DISCUSSION The effects of plant roots on soil bacterial communities are well documented ( 8 , 18 , 19 ). Our study demonstrated that the “rhizosphere effect” was present as early as 3 weeks after grass germination and that the effect increased throughout the plant growth cycle. This suggests that plant growth drove succession in the rhizosphere community, while the bulk/residual soil communities remained relatively stable. Li et al. ( 20 ) reported that maize rhizosphere communities changed between early and late growth stages; however, in their research it was difficult to distinguish between plant growth and seasonal effects. Chaparro et al. ( 16 ) showed that rhizosphere bacterial communities at the seedling stage of Arabidopsis thaliana were distinct from vegetative, bolting, and flowering stages; the communities associated with the latter three stages were not significantly different. It is commonly hypothesized that plant-driven changes in the rhizosphere microbial community composition could result from alterations of plant root exudates (quality and quantity) at different growth stages ( 21 , 22 ). Using a sterile hydroponics system, we have documented a gradual shift in the exudate profile of A. fatua over time (S. Shi, R. Estera, S. Jenkins, T. Northen, M. Firestone, unpublished data). Interestingly, our study here clearly demonstrates that the rhizosphere succession pattern was reproducible from season to season. The significant difference observed between samples collected from two seasons may be due to the presence of root debris in residual soil in season 2. We found that the presence of living Avena roots increased the dispersion of the bacterial community significantly in both seasons. This was likely due to variability in the effects of live roots on the soil community. The microbial community variance at the end of season 1 disappeared in the absence of live roots during the dry season between season 1 and season 2. Interestingly, the presence of Avena roots decreased bacterial diversity in the rhizosphere. Soil is thought to be among the most diverse microbial habitats on Earth ( 23 , 24 ). This high diversity is commonly attributed to microsite niche heterogeneity ( 25 , 26 ). If the presence of roots reduces extant niche heterogeneity (albeit temporarily), then the microbial community richness (and diversity) could be reduced in the rhizosphere soil. As roots move through a heterogeneous array of soil microsites, the impacts of the root on C availability, pH, water, and soil atmosphere could overwhelm and homogenize differences among soil microsites, at least until other environmental drivers, such as summer dry-down, reset the system. By 12 weeks, taxon richness in our A. fatua rhizosphere soil was significantly reduced and showed continued reduction in the second season ( Fig. 3 ). Alternatively, a reduction in community diversity could result from altered species abundance distributions over time. If the presence of roots substantially reduces the evenness of taxon distribution (enrichment of select members or loss of detectable low-abundance taxa), then a decrease in taxon-based univariate diversity indices would likely result. In our study, the presence of Avena roots did significantly reduce Pielou’s evenness in the rhizosphere samples from week 12 in the first season and later in the second season ( Fig. 3 ). Interestingly, the reductions in bacterial richness and Faith’s PD indices that were observed at the end of season 1 persisted over the dry, nongrowing season and were apparent at the beginning of the second season. Thus, the observed reduction in bacterial community diversity in the rhizosphere soil likely resulted from a combination of reduced taxon richness and evenness. Reductions in bacterial community diversity in rhizosphere soils have been observed in other plant systems ( 13 , 20 , 27 – 29 ). We do not yet know whether reductions in taxonomic and phylogenetic diversity are accompanied by reductions in functional diversity. Reducing functional diversity could impact microbial community redundancy and/or biogeochemical processes. The response of taxa to Avena roots was generally consistent at the phylum/class level and between the two seasons ( Fig. 5 ; see also Fig. S3 in the supplemental material). For example, Proteobacteria and Bacteroidetes commonly respond positively to plant roots ( 2 , 13 – 15 , 18 , 30 ). Members of the Proteobacteria , especially Alphaproteobacteria , Betaproteobacteria , and Gammaproteobacteria , are well-known rhizosphere colonizers and have generally been characterized as fast-growing r-strategists, which respond positively to low-molecular-weight substrates ( 31 , 32 ), which are abundant in plant root exudates ( 1 , 35 ). Studies using 13 CO 2 pulse-labeling of host plants have demonstrated that Proteobacteria were the major microbial group utilizing root exudates ( 33 , 34 ). Apart from stimulating these soil microbial groups by releasing a variety of exudate compounds ( 1 , 5 , 35 , 36 ), plant root activities can also modify the physicochemical environment (e.g., water content, pH, and nutrient availability) of surrounding soil ( 3 ), thus acting as a habitat filter to select for or against microbial populations. In general, our results show that populations affiliated with the phyla of Acidobacteria , Actinobacteria , and Firmicutes decreased in relative abundance in the rhizosphere as the plant grew. The depletion of these populations in the rhizosphere could be due to a decrease in their absolute abundance (actual depletion) or a decrease in relative abundances as the result of increased abundances of other microbes (apparent depletion). Indeed, we did detect a significant increase of overall bacterial 16S copy number in the rhizosphere. Although it is difficult to distinguish between these two situations, rank order shifts of different microbial groups within a community can reflect survival/functional ability within available niches ( 32 ). The decrease of these populations could be due to a negative impact of the changing environment in soil near plant roots (e.g., pH) or due to competition from fast-growing microbes (e.g., Alphaproteobacteria and Betaproteobacteria ) for resources or microbe-microbe inhibition. For example, the “depletion” of Acidobacteria in our study may be due to their typically low growth rates and classical k-strategist lifestyle ( 31 , 37 , 38 ). Significantly, rhizosphere-stimulated taxa were phylogenetically clustered across all four growth stages in both seasons, suggesting that these bacterial taxa share ecological traits (i.e., rhizosphere competence) that are phylogenetically conserved ( 17 ). Our greenhouse-based study corroborates a study of bacterial populations associated with Avena spp. growing in three California annual grasslands; Nuccio reports strong phylogenetic clustering of the core Avena rhizosphere microbiome ( 39 ). Apart from C utilization traits, other traits associated with habitat (e.g., growth strategy, optimum pH, and soil moisture) may play important roles in rhizosphere colonization. Past studies have shown that microbes with habitat preference traits are ecologically coherent at high taxonomic ranks and thus potentially phylogenetically clustered ( 31 , 40 – 43 ). As the rhizosphere presents a complex and dynamic environment, rhizosphere competence likely requires a coordination of multiple phenotypic traits. Thus, our observation of phylogenetic clustering of rhizosphere competence suggests that the complex suite of traits necessary to proliferate in the root environment may have deep evolutionary origins. Our results provide a detailed picture of the temporal succession of the soil bacterial community in response to growing roots of the common annual grass Avena fatua . The pattern of succession is repeatable and highly consistent over two growing seasons. We show that this annual grass exerts selection pressure from early stages of growth and throughout its life span, resulting in a rhizosphere community substantially changed from that of the background soil. Previous field-based research suggested that components of the Avena microbiome provide nutrient-based benefits to the plant ( 44 , 45 ). Thus, this strong and consistent successional pattern of the Avena rhizosphere may represent a fitness trait of the plant as it consistently recruits soil microbes with similar functions to its root environment. Such an annual cycle of selection would be expected to repeat each year with variation in selection associated with different grasses and forbs growing in the field. The indigenous soil community encountered by a growing root in this annual grassland soil would thus result from integration of repeated and varied annual plant selection nested in soil and climatic controllers of the extant soil community."
} | 3,601 |
37537681 | PMC10401788 | pmc | 4,698 | {
"abstract": "Background Plants rely on their root microbiome as the first line of defense against soil-borne fungal pathogens. The abundance and activities of beneficial root microbial taxa at the time prior to and during fungal infection are key to their protective success. If and how invading fungal root pathogens can disrupt microbiome assembly and gene expression is still largely unknown. Here, we investigated the impact of the fungal pathogen Fusarium oxysporum ( fox ) on the assembly of rhizosphere and endosphere microbiomes of a fox- susceptible and fox- resistant common bean cultivar. Results Integration of 16S-amplicon, shotgun metagenome as well as metatranscriptome sequencing with community ecology analysis showed that fox infections significantly changed the composition and gene expression of the root microbiome in a cultivar-dependent manner. More specifically, fox infection led to increased microbial diversity, network complexity, and a higher proportion of the genera Flavobacterium , Bacillus , and Dyadobacter in the rhizosphere of the fox -resistant cultivar compared to the fox -susceptible cultivar. In the endosphere, root infection also led to changes in community assembly, with a higher abundance of the genera Sinorhizobium and Ensifer in the fox -resistant cultivar. Metagenome and metatranscriptome analyses further revealed the enrichment of terpene biosynthesis genes with a potential role in pathogen suppression in the fox -resistant cultivar upon fungal pathogen invasion. Conclusion Collectively, these results revealed a cultivar-dependent enrichment of specific bacterial genera and the activation of putative disease-suppressive functions in the rhizosphere and endosphere microbiome of common bean under siege. Supplementary Information The online version contains supplementary material available at 10.1186/s40793-023-00524-7.",
"conclusion": "Conclusions Our multi-‘omics approach allowed us to identify the most responsive bacterial groups in the common bean rhizosphere and endosphere to invasion by the fungal root pathogen Fusarium oxysporum . We found that the genera Flavobacterium , Dyadobacter , Bacillus , Pedobacter, Pseudomonas , and Paenibacillus were enriched in the rhizosphere and endosphere of the fox -resistant cultivar under siege. Interestingly, the genus Flavobacterium showed up as the most responsive species, increasing in abundance and identified as keystone species and a specialist group. These responsive species may display different mechanisms in disease suppression, including competition for nutrition and ecological niches, production of antibiotics, and induction of plant systemic resistance [ 13 , 98 ]. Our metatranscriptome analysis showed that the root microbiome of the fox -resistant cultivar was more responsive to the pathogen invasion, with a higher expression of biosynthetic gene clusters classified as terpenes, NRPS-like, NRPS, betalactone, and arylpolyene. Whether the enriched members and traits of the root microbiome reinforce the resistance of the fox -resistant cultivar or if the changes in the microbiome are a consequence of the fungal invasion remains to be investigated. For this, a comprehensive study would involve the isolation of antagonistic microbial groups, as pointed by metagenome approach and selected from the rhizosphere of the fox -resistant cultivar. This would be combined with the use of site-directed mutagenesis to identify and confirm specific microbial antagonistic traits responsible for the soil borne pathogen antagonism. Additionally, a metabolomic approach would be instrumental for identifying plant compounds responsible for both microbial recruitment and/or pathogen antagonism. Lastly, we emphasize that next-generation sequencing coupled with a community ecology approach is pivotal to help disentangle the link between plant defense and root-associated microbial communities.",
"introduction": "Introduction The rhizosphere and root endosphere are hotspots for a myriad of microorganisms that, upon expression of specific functional traits, can provide a range of benefits for the plant, including nutrient acquisition [ 1 , 2 ], abiotic stress tolerance [ 3 , 4 ], and protection against pathogens [ 5 – 7 ]. Plants and microbes have co-evolved beneficial relationships and a tightly regulated defense system for protection against diseases [ 8 , 9 ]. Several rhizospheric and endophytic bacteria are able to prevent pathogen infections by producing antimicrobial compounds or inducing systemic resistance in the host plant [ 7 , 10 , 11 ]. Studies on disease-suppressive soils further revealed that plant protection is conferred by a subset of the microbiota selected from the indigenous soil microbiome following a pathogen attack on the root system [ 7 , 12 , 13 ]. Hence, microbiome assembly and activation of specific beneficial traits prior to, during, or after infection is key to the protective success of the microbiome. Recent studies indicated that plant domestication [ 14 – 17 ] and plant breeding for disease resistance [ 18 , 19 ] have affected the assembly of rhizosphere and endosphere microbiomes [ 20 ]. Moreover, plant defense also impacts the rhizosphere microbiome composition as was exemplified with mutants disrupted in specific defense pathways [ 21 , 22 ] and studies on microbiome analyses of crop cultivars with different levels of resistance to a specific pathogen [ 18 , 19 , 23 ]. If and how root pathogens affect microbiome assembly has been much less documented. The study by Chapelle et al. [ 5 ] showed that an invading root pathogenic fungus induces stress responses in the rhizobacterial community and the host plant with concomitant shifts in the microbiome resulting in plant protection. Recently, Zhou et al. [ 98 ] also showed that the infection of plants by Fusarium impacts the associated microbiome by changing the microbiome structure, decreasing diversity and network complexity. However, how the interplay between pathogen infection and plant resistance affects the assembly and gene expression of the root microbiome, i.e. rhizosphere and endosphere, is still poorly understood. In this study, we investigated the impact of the fungal root pathogen Fusarium oxysporum ( fox ) on the assembly of rhizosphere and endosphere microbiomes of a fox- susceptible and fox- resistant common bean cultivar. Common bean ( Phaseolus vulgaris L.) is the most important legume crop for low-income farmers in Latin America and Africa and the second in the world [ 24 , 25 ]. Fusarium oxysporum ( fox ) is a major disease of common bean worldwide and the most efficient strategy for its control is the use of resistant cultivars [ 26 ]. In resistant cultivars, structural and chemical defense mechanisms restrict pathogen invasions, such as vascular occlusion, tyloses, deposition of additional wall layers, and infusion of phenols and other metabolites [ 27 ]. Although fox -resistance in common bean has a genetic basis, we previously demonstrated that the fox -resistant common bean cultivar has a different rhizosphere microbiome composition than its fox -susceptible counterpart with a higher frequency of beneficial rhizobacterial genera [ 6 , 28 , 29 ]. More specifically, the results showed that beneficial taxa such as Pseudomonas , Bacillus , and Paenibacillus , and antifungal traits such as protein secretion systems and biosynthesis of phenazines, rhamnolipids, and colicin V were enriched in the rhizosphere of the fox -resistant bean accession. However, our previous community-based analyses were limited to the rhizosphere and performed in the absence of the fungal root pathogen. To provide a more comprehensive understanding of the impact of the fungal root pathogen on the assembly and gene expression of the root microbiome, we integrated 16S rRNA amplicon, metagenomic, and metatranscriptome sequencing to assess taxonomic and functional differences between the root microbiomes of these two common bean cultivars with contrasting levels of fox resistance. We hypothesized that assembly and gene expression in the rhizospheric and endophytic microbiomes of the fox -resistant common bean cultivar is more responsive to pathogen invasion than the root microbiome of the fox -susceptible cultivar.",
"discussion": "Discussion In this study, we showed that root pathogenic F. oxysporum ( fox ) had a significant impact on the taxonomic and functional diversity of the rhizosphere and endosphere microbiome of common bean cultivars with distinct levels of fox -resistance. First, we observed marked differences in the structure and composition of the microbial communities associated with each niche, i.e. bulk soil, rhizosphere, and endosphere. Remarkably, Proteobacteria exceeded 95% of the endosphere microbiome, with 99% of these sequences belonging to the Rhizobium genus, a well-known endosymbiotic nitrogen-fixing microbe associated with roots of leguminous species. Interestingly, the microbiome assembly in each niche followed distinct patterns, with the rhizosphere samples being dominated by niche-based mechanisms, while bulk soil and endosphere followed a neutral process. This result confirms and extends previous results that the rhizosphere microbiome is influenced more by selection processes associated with biotic and abiotic factors in this niche [ 73 ]. Indeed, we showed that pathogen infection led to significant changes in the rhizosphere community composition and structure, extending results from previous studies on banana [ 74 ], barley [ 75 ], citrus [ 76 ], cotton [ 77 ], and sugar beet [ 5 , 13 ]. We also observed that the rhizosphere community responded to pathogen invasion by enhancing diversity, community complexity (i.e., number of interactions in the network), and a higher proportion of specialists. Enhanced microbial diversity together with higher community complexity could diminish pathogen invasion success due to a more efficient competition for resources and niche occupancy [ 78 , 79 ]. Although both common bean cultivars showed an enhanced microbial diversity upon pathogen invasion, we found a higher number of specialists and more complexity in the fox -resistant cultivar. Previous studies have demonstrated that specialists have a narrow niche but the highest fitness in that niche [ 80 , 81 ]. Also, specialists are more responsive to environmental disturbances [ 82 , 83 ], such as pathogen invasion. Paenibacillus , Saccharibacteria , Chitinophagaceae, and Flavobacterium were found as specialists in the rhizosphere of the fox -resistant cultivar. Although several of these genera have been previously reported for their antagonistic activities toward pathogenic Fusarium species of different crops [ 84 , 85 ], future experiments will be needed to validate this assumption. Also, the mechanisms underlying the observed microbiome changes are to be elucidated. The fox -resistance of the resistant cultivar is genetically and physiologically based, where the pathogen invasion is restricted by vascular occlusion, tyloses, deposition of additional wall layers, and infusion of phenols and other metabolites [ 27 ]. This genetic change can alter plant exudation patterns and the assembly of the rhizosphere community, differentiating the microbiome assembly between the cultivars with distinct levels of resistance to fox [ 6 , 28 , 29 ]. Those microbiome members that are differentially enriched in the microbiome of the fox -resistant cultivar in absence of the pathogen may have complementary protective activities to the intrinsic genetic fox -resistance. In the current study, we showed that fox infection also had a significant impact on the microbial communities of the rhizosphere and endosphere of these cultivars. These microbiome shifts can be caused directly by the pathogen itself or indirectly via plant physiological changes induced by the pathogen. The latter mechanism has also been referred to as the ‘cry for help’ [ 86 ], where plants under siege secrete specific exudates or signaling compounds that recruit and or activate specific members of the root microbiome for protection against subsequent infections. Liu et al. [ 87 ] showed that local root infection by F. oxysporum in cucumber altered the concentration of 89 mostly primary metabolites in exudates, which correlated with root colonization by beneficial Bacillus amyloliquefaciens . Whether the changes we found in the community composition in the fox -resistant cultivar under the pathogen infection are the results of the induced excretion of antimicrobial compounds by the infected roots remains to be investigated. Most studies on plant microbiome have focused more on microbial diversity rather than on gene function [ 88 ]. Microbes living on and in plant roots may induce known and yet unknown biosynthetic pathways in plants leading to alterations in the plant chemistry [ 89 ]. On the other hand, changes in plant metabolomics may affect the functional profile of the associated microbiome. Thus, we assessed the effect of the pathogen infection on the functional profiles of the microbiome. A common strategy used by microbes against other competitors includes limiting resources and producing antimicrobial compounds [ 90 ]. Interestingly, the fox -resistant cultivar presented an enrichment of sequences affiliated to ‘defense mechanisms’ after pathogen infection. The increase of sequences affiliated to this category could reflect the more diverse and dynamic the community becomes after the pathogen infection (based on niche occupancy and network analysis). Also, there is an increase of genes belonging to the pathway classified as ‘signal transduction mechanism’. This pathway can act to amplify the cellular response to an external signal, which could lead to a prompt response of the community towards the pathogen infection. Carrión et al. [ 7 ] found an enrichment of genes affiliated to signal transduction mechanisms in the endophytic community of sugar beet grown in suppressive soils in the presence of the pathogen Rhizoctonia solani. They also noted that specific bacterial families were associated with this enrichment, namely Chitinophagaceae, Flavobacteriaceae, and Pseudomonadaceae, groups that also increased in abundance in the rhizosphere and endosphere of the infected fox -resistant cultivar in our experiment. Later, the BGC analysis revealed several clusters enriched in both the rhizosphere and endosphere in presence of the pathogen. An important means of microbial protection are secondary metabolites, which are a very broad group of compounds or peptides with a wide range of biological activities, e.g., antimicrobial or iron chelation [ 91 , 92 ]. Our analysis obtained five candidate BGCs, with terpenes as the most representative BGC class in both rhizosphere and endosphere. Although terpenes have mostly been isolated from plants and fungi, they are also widely distributed in bacteria [ 93 ]. It has been shown that the biosynthesis of terpenes by plants [ 94 , 95 ] and bacteria [ 96 ] suppress fox infection. Thus, the high abundance of terpenes in the rhizosphere microbiome could indicate its role in the suppression of fox infection in the resistant bean cultivar. Interestingly, the BGC arylpolyene was highly expressed after fox invasion and a previous report has shown its function in the control of banana fusarium wilt [ 96 ]. Interestingly, the fox -resistant cultivar presented an increased expression of arylpolyene genes, which were affiliated to Flavobacteriaceae family, a group of bacteria that was the most responsive to pathogen infection in this cultivar. The genus Flavobacterium is reported to suppress fox in several plant species [ 84 , 97 ]. It’s worth noting that the rhizosphere of the fox -resistant cultivar was more responsive to the pathogen infection. On the other hand, the endosphere of the susceptible cultivar presented more overrepresented BGCs, suggesting that this cultivar is more affected by the pathogen infection, revealing less efficiency of the susceptible rhizosphere microbiome to protect the plant against the pathogen infection. Together, our analysis of the functional profile indicates a pathogen-induced activation of disease-suppressive functions in the rhizosphere and endosphere of the fox -resistant cultivar, suggesting that breeding for fox resistance in common bean may have co-selected for unknown plant traits that reinforce microbiome-assisted plant defense."
} | 4,138 |
32545472 | PMC7356612 | pmc | 4,699 | {
"abstract": "Here, a 12-liter tubular microbial electrolysis cell (MEC) was developed as a post treatment unit for simultaneous biogas upgrading and ammonium recovery from the liquid effluent of an anaerobic digestion process. The MEC configuration adopted a cation exchange membrane to separate the inner anodic chamber and the external cathodic chamber, which were filled with graphite granules. The cathodic chamber performed the CO 2 removal through the bioelectromethanogenesis reaction and alkalinity generation while the anodic oxidation of a synthetic fermentate partially sustained the energy demand of the process. Three different nitrogen load rates (73, 365, and 2229 mg N/Ld) were applied to the inner anodic chamber to test the performances of the whole process in terms of COD (Chemical Oxygen Demand) removal, CO 2 removal, and nitrogen recovery. By maintaining the organic load rate at 2.55 g COD/Ld and the anodic chamber polarization at +0.2 V vs. SHE (Standard Hydrogen Electrode), the increase of the nitrogen load rate promoted the ammonium migration and recovery, i.e., the percentage of current counterbalanced by the ammonium migration increased from 1% to 100% by increasing the nitrogen load rate by 30-fold. The CO 2 removal slightly increased during the three periods, and permitted the removal of 65% of the influent CO 2 , which corresponded to an average removal of 2.2 g CO 2 /Ld. During the operation with the higher nitrogen load rate, the MEC energy consumption, which was simultaneously used for the different operations, was lower than the selected benchmark technologies, i.e., 0.47 kW/N·m 3 for CO 2 removal and 0.88 kW·h/kg COD for COD oxidation were consumed by the MEC while the ammonium nitrogen recovery consumed 2.3 kW·h/kg N.",
"conclusion": "4. Conclusions The experimental study demonstrated the feasibility of the bioelectrochemical process for nitrogen recovery and simultaneous COD and CO 2 removal with the utilization of a 12-L tubular geometry MEC. The MEC was operated under three different nitrogen load rates, maintaining the same organic load rate, to study the influence of the process with respect to the ammonium nitrogen content. The nitrogen load rate increase resulted in a progressive increase of the anodic COD removal, which increased from 1.8 ± 0.3 g COD/day during the first operating period to 6.3 ± 0.6 g COD/day during the third operating period; the COD removal increase was probably due to the acclimation of non-electroactive microorganisms along the process operation. The increase of the COD removal did not promote a consequent electric current increase, i.e., a slight decrease in terms of the current output was observed by increasing the nitrogen load rate. As a consequence, the coulombic efficiency of the anodic reaction decreased from 77% to 18%. The cathodic bioelectrochemical reduction of CO 2 into CH 4 increased during the explored operating conditions, giving almost a recovery of the current into methane (i.e., the cathode capture efficiency) during the second and third operating periods, with average values of 98% and 81%. The nitrogen load rate increase promoted ammonium migration and recovery, with a nonlinear magnitude, i.e., by increasing 5 and 30 times the nitrogen load rate with respect to the first operating period. The ammonium recovery and the corresponding ammonium contributed to the electroneutrality maintenance being increased by 10 and 100 times. An interesting effect of the nitrogen load rate increase resulted in the increase of the electro osmotic diffusion of the liquid phase from the anode to the cathode chamber. The CO 2 removal from the cathodic chamber was slightly increased by the nitrogen load rate. By the analysis of the CO 2 removal mechanisms, the role of the alkalinity generation resulting from the electroneutrality maintenance was underlined by the fact that almost 50% of the removed CO 2 was promoted by the migration of ammonium or other cations (such as calcium and magnesium). The analysis of the energetic consumption of the bioelectrochemical process showed a lower energy consumption for COD and CO 2 removal with respect the benchmark technologies: 1.2 kW·h/kg COD for activated sludge and 0.8 kW·h/Nm 3 CO 2 for the water scrubbing biogas upgrading technology. The ammonium nitrogen recovery energetic cost was interesting, particularly during the third operational period in which an energy consumption of 2.3 kW·h/kg N was used for the nitrogen recovery. It is also important to underline the fact that the energy consumption in the bioelectrochemical process was simultaneously utilized for COD removal, CO 2 removal, and nitrogen removal. Moreover, additional energy recovery is offered by CH 4 production, which is described by the energy efficiency of the process, which was 52% during the third operating period. Finally, by comparing the performances of the tubular MEC with a previous bench-scale MEC, similar performances were obtained in terms of the CO 2 removal rate and ammonium recovery rate; however, due to the considerably lower current densities obtained in the tubular MEC, the good potential of the upscaled tubular MEC can be assessed, indicating the necessity of a current density increase.",
"introduction": "1. Introduction Biogas, the main product of the anaerobic digestion (AD) process, is a gas mixture mainly composed of carbon dioxide and methane [ 1 , 2 ]. To obtain biomethane with a high percentage of methane (>95%), an upgrading operation to increase the CH 4 content through CO 2 removal and a purification step aimed at impurity removal (NH 3 , H 2 S) are necessary to increase the gas mixture calorific power [ 3 , 4 , 5 ]. Due to the investment and operations costs required for the purification and upgrading steps, biogas is commonly utilized for the cogeneration of electricity and heat through the CHP (Combined Heat Power) unit; however, due to the recent emission reduction goals stated by the European Union for 2050 [ 6 , 7 ], an incentive plan for biogas conversion into methane has recently been activated in different European countries [ 8 ]. Biomethane can be considered as a renewable carbon neutral fuel with high added value, which can be used in automotive engines or injected into the natural gas grid [ 9 , 10 ]. In order to couple CO 2 emission mitigation and renewable energy storage [ 11 ], several approaches for biogas upgrading have been proposed in the literature [ 12 ]; basically, the biological approach for biogas upgrading consists in the supply of renewable hydrogen to methanogens, which are able to convert CO 2 into CH 4 [ 13 , 14 ]. Along the different hydrogen supply techniques, which includes in situ [ 15 ] and ex situ approaches [ 16 ], the use of bioelectrochemical systems to supply the reducing power resulted in a more sustainable approach due to the utilization of mild reaction condition as well as the use of a robust and low-cost catalytic material widely present in the AD processes [ 17 ]. The bioelectrochemical system exploits the ability of the electroactive microorganisms to exchange electrons with solid electrodes by the extracellular electron transfer mechanism (EET) [ 18 ]. The interphase constituted by an electroactive biofilm on an electrode can be named a bioelectrode [ 19 ]; in more detail, when the electroactive biofilm uses the electrode as an electron acceptor, the electrochemical interphase acts as a bioanode [ 20 ]; on the contrary, if the electroactive biofilm uses the electrode as an electron donor, the interphase is defined as a biocathode [ 21 ]. The electron exchange between the electroactive biofilm and the electrodic material can be directly performed by specialized membrane proteins or by the utilization of mediators, which have the function of electrons shuttles between the biofilm and the electrode surface [ 22 , 23 ]. Biocathode utilization has been investigated for several environmental applications, which includes biofuel production [ 24 , 25 ], CO 2 fixation into VFA (Volatile Fatty Acid) [ 26 , 27 ], and groundwater bioremediation [ 28 , 29 ]. The bioelectrochemical reduction of CO 2 into CH 4 , named the bioelectromethanogenesis reaction, is obtained by using an electrodic material for the reducing power supply to mixed methanogenic consortium, which adopts the electrodic material as an electron donor. Two limit mechanisms regulate the electrode–microorganisms interaction, i.e., a direct electron uptake [ 30 ] and a hydrogen-mediated [ 31 ] mechanism have been identified; however, specifically for the bioelectromethanogenesis reaction, several intermediate steps for the electrode–microorganisms interaction have recently been reported in the literature [ 32 ]. The utilization of the bioelectromethanogenesis reaction requires the utilization of a microbial electrolysis cell (MEC), in which, by the application of an external potential, partial energy support is supplied by the anodic bioelectrochemical oxidation of organic waste streams [ 33 , 34 ]. Several authors proposed the utilization of MECs for biogas upgrading into biomethane with different configurations, including the direct treatment of biogas [ 35 , 36 ] or separate conversion of the residual CO 2 from the upgrading step in the biocathode [ 37 ]. Moreover, in an MEC biocathode, the main CO 2 removal mechanism along with the bioelectromethanogenesis reaction is represented by the CO 2 sorption as HCO 3 − promoted by alkalinity generation, which directly depends on the transport of ionic species different from protons and hydroxyls for the maintenance of electroneutrality [ 38 ], i.e., the alkalinity generation in an MEC biocathode permits the removal of up to 9 moles of CO 2 for each mole of CH 4 produced [ 39 ]. When using a cation exchange membrane as a separator in an MEC, which receives an anolyte with a physiological pH, the electroneutrality maintenance is ensured by several cations different from protons, such as the ammonium ion, which is considerably present in the anaerobic digestion liquid effluents [ 40 ]. In an MEC, it is possible to exploit the migration of the ammonium ion caused by the electroneutrality maintenance as a mechanism to recover ammonium nitrogen [ 41 ]. Ammonium nitrogen is usually present at high concentrations in manure and digestate due to the proteins’ hydrolysis [ 42 ]. The integration of the AD process and an MEC has been tested by using real effluents as anodic substrates of a methane-producing MEC [ 43 ], which allowed ammonium recovery and CO 2 removal in the biocathode. Moreover, a new three-chamber configuration MEC with a two-sided cathode configuration was successfully tested [ 44 ]. Even if the bioelectromethanogenesis reaction and ammonium recovery are well known at the laboratory scale, with several configurations, few studies have reported scale-up attempts of the MEC process. In the present study, a 12-L micro-pilot MEC [ 45 ] with a tubular geometry was designed for the integration of the process with a two-stage anaerobic digestion process in which the biogas upgrading through the bioelectromethanogenesis reaction is coupled with the oxidation of COD and nitrogen recovery in the anodic chamber. A synthetic feeding solution containing a mixture of VFA was used as the substrate of the anodic bioelectrochemical oxidation, while three different ammonium nitrogen load rates (73, 365, and 2229 mg·N/Ld) were tested to assess the process performances and possible poisoning effects of the high ammonium concentration. The three nitrogen load rates were chosen following previous experiments performed in a bench-scale filter press MEC [ 40 , 43 ]. The bioelectrochemical process was evaluated by the analysis of the COD removal, CH 4 production and CO 2 removal, and ammonium nitrogen migration and recovery.",
"discussion": "3. Results and Discussion 3.1. Electrodic Reaction’s Performances After the inoculation and the consequent start-up period (which lasted 15 days), characterized by the polarization of the anode chamber at +0.20 V vs. SHE, the increase in current generation indicated the electroactive biofilm’s formation, which oxidized the organic substrates using the graphite granules as final electron acceptors. During all the operating periods, the anodic chamber was continuously fed with the VFAs synthetic mixture with an average flow rate of 6.9 ± 0.2 L/day, corresponding to a hydraulic retention time (HRT) of 0.52 days. During the first operating period, the theoretical ammonium concentration was 32 mg·N/L corresponding to a nitrogen load rate of 73 mg·N/Ld. As reported in Figure 2 , the average electric current was 190 ± 14 mA, which was generated by an average COD removal of 1.8 ± 0.3 g COD/day ( Figure 3 ), corresponding to a COD removal efficiency of 29 ± 11%. The fraction of COD transformed into electric current, named the coulombic efficiency (CE), was on average 77 ± 18%. The main product detected in the cathodic chamber, as reported in Figure 4 , was methane, with an average production rate of 9 ± 1 mmol/day. The fraction of electric current converted into methane, defined as the cathodic capture efficiency (CCE), was on average 42 ± 8%, which indicated the, low activity of the methanogens probably due to their long acclimatization time. During the first run, as reported in Figure 2 , the average cell voltage measured between the anode and cathode was −2.66 ± 0.25 V. After 25 days, the nitrogen concentration inside the feeding solution was increased five times, giving a theoretical nitrogen loading rate of 365 mg·N/Ld. During this second run, as reported in Figure 2 , an average electric current of 166 ± 10 mA was obtained with a consumption of 4.0 ± 0.3 g COD/day, giving a CE average value of 30 ± 4%. As showed in Figure 3 , the COD removal efficiency increased to 65 ± 17% probably due to non-electroactive microorganisms’ activity, which were previously inhibited by the low concentration of nitrogen in the anodic chamber. Inside the cathodic chamber, as reported in Figure 4 , an increase in the methane production rate to 18 ± 1 mmol/day was obtained, giving an average value of CCE 98 ± 11%, indicating an almost complete utilization of the current for CO 2 reduction into CH 4 . The cell voltage applied between the anode and cathode was on average −2.00 ± 0.13 V during the second operating condition of the MEC. After 40 days, the nitrogen concentration inside the feeding solution was raised to a theoretical concentration of 1000 mg·N/L, corresponding to a nitrogen load rate of 2229 mg·N/Ld. During the run with the higher ammonium concentration, reported in Figure 2 , an average value of electric current of 157 ± 7 mA was obtained, while the COD removal shown in Figure 3 increased to 6.3 ± 0.6 g COD/day. The increase in COD removal, without a corresponding increase in terms of the electrical current, affected the CE of the process, which was only 18 ± 2%. The increase of the COD removal efficiency up to 70% probably indicated an underestimation of the biomass growth in the anodic chamber or the presence of non-electroactive COD removal pathways like COD sorption or entrapment in the biofilm matrix [ 47 ]. As reported in Figure 4 , the methane production rate was almost stable, with an average value of 14 ± 2 mmol/day, which resulted in an average CCE value of 81 ± 14%. No inhibition effect of the high ammonium concentration was detected during the last run of the MEC. The cell voltage ( Figure 2 ) obtained in the last operating period was −1.48 ± 0.08 V. Table 1 summarizes all the main parameters describing the performances of the bioelectrochemical reactions in the three different operating periods. 3.2. NH 4 + Removal and Nitrogen Mass Balance The ammonium nitrogen concentration was monitored in all of the reactor streams. As reported in Figure 5 , during the first operating period, the average influent ammonium concentration was 37 ± 2 mg·N/L, while the average effluent ammonium concentration was 25 ± 2 mg·N/L; on average, 89 ± 31 mg·N/day were removed, giving a corresponding nitrogen removal efficiency of 33 ± 13%. The ammonium was mainly removed through its migration through the CEM membrane, i.e., an ammonium concentration of 101 ± 9 mg·N/L in the cathodic chamber underlined the migration of ammonium ions, which result in a 4 times higher concentration with respect to the anodic concentration. The steady-state achievement was underlined by the stable concentration of ammonium, which was caused by the daily catholyte spill performed to counterbalance the electroosmotic diffusion phenomenon. During the second operating period, the influent and effluent ammonium concentration in the anodic chamber was 241 ± 14 and 148 ± 9 mg·N/L, respectively. A nitrogen removal efficiency of 45 ± 12% was obtained by the daily removal of 713 ± 150 mg·N/day. The concentration of the ammonium in the cathodic chamber ( Figure 5 ) was 674 ± 48 mg·N/L, a 4.5 times higher value with respect to the anodic ammonium concentration. By applying the higher nitrogen load rate, which corresponded to an average anodic influent ammonium concentration of 1341 ± 28 mg·N/L, the daily nitrogen removal was on average 3246 ± 558 mg·N/d due to an anodic effluent concentration of 1013 ± 66 mg·N/L. The resulting nitrogen removal efficiency was quite like the previous operating periods, with an average value of 36 ± 7%. The concentration of the ammonium ion in the catholyte during the last operating condition was 2094 ± 78 mg·N/L. Table 2 summarizes all the average ammonium concentrations observed in the different MEC streams during the three different operating conditions. An average VSS cathodic concentration of 62 ± 2 mg VSS/L was also determined, which resulted in a negligible amount of fixed nitrogen. The ammonia content in the outcoming gas was occasionally monitored by an acid trap, which was placed at the end of the gas pipeline. No ammonia was ever detected in the outcoming gas, during all of the experimental period. The nitrogen mass balance, which is summarized in Table 2 , reports the two main removal mechanisms detected for the removal of the ammonium ion from the anodic feeding solution. The biomass formation, evaluated by the determination of the volatile suspended solids (VSSs) in the anodic effluent, and the migration and the consequent daily spill of the cathodic liquid phase. This last procedure was the main ammonium removal mechanism involved in the process. During the three different operating periods, the daily spill of catholyte permitted the recovery of 31 ± 3, 281 ± 20, and 2445 ± 9 mg·N/day. A significant increase of the cathodic spill flow rate was observed during the three operational periods, i.e., the cathodic spill flow rates were 0.31 ± 0.02, 0.42 ± 0.08, and 1.17 ± 0.20 L/day. The change in the cathodic spill flow rate was reasonably explained by the analysis of the ammonium contribution to the electroneutrality maintenance, i.e., while in the first two conditions, 1% and 22% of the current was counterbalanced by the ammonium ion; in the third operating condition, almost all the current was transported by the ammonium ion. Interestingly, the increase of the ionic current transported by the ammonium ion was not linear to the influent concentration, i.e., the 5-fold concentration increase in the second period corresponded to an increase of 22-fold while, by increasing the influent concentration 30 times, a percentage increase of 100%. The ammonium migrates from the anodic chamber to the cathodic one through the cation exchange membrane against the concentration gradient to maintain the electroneutrality of the chambers. The average concentration reached inside the cathodic chamber was 101 ± 9 mg·N/L. Moreover, the ammonium migration transported only1% of the ionic charge. Concerning the second stream, the nitrogen concentration was raised by five times, giving as a result an average concentration of 674 ± 48 mg·N/L inside the cathodic chamber. Furthermore, the total nitrogen removal was of 45% with a transported charge of 22 ± 2 mA (13%). During the third stream, considering a cathodic daily spill of 1.17 ± 0.20 L/day, the ammonium recovery was 2.5 ± 0.1 g·N/day, which was responsible for 195 ± 7 mA of transported charge, giving a 124% contribution to electroneutrality maintenance. 3.3. CO 2 Removal and Inorganic Carbon Mass Balance The daily CO 2 removal obtained in the cathodic chamber was on average 443 ± 40, 453 ± 19, and 481 ± 38 mmol/day during the three different MEC operating periods. Those values were calculated by measuring the CO 2 concentration difference between the influent and the effluent gas flow of the cathodic chamber. The bicarbonate concentration in the different reactor streams, reported in Figure 6 , showed the effect of the alkalinity generation in the cathodic liquid phase, which promoted the sorption of bicarbonate at a higher concentration with respect to the anode chamber. As a result, the average cathodic pH value was 7.5 ± 1. As reported in Table 3 , during the three different operating periods, the cathodic bicarbonate concentration showed similar concentrations of 10.94 ± 1.20, 0.72 ± 0.56, and 11.39 ± 2.10 gHCO 3 − /L, while, due to the utilization of a CEM membrane which avoids bicarbonate diffusion, the influent and the effluent concentrations in the anodic chamber ( Figure 6 ) were considerably lower with respect to the cathodic chamber. A slight increase in the bicarbonate concentration in the anodic effluent solution ( Table 3 ) was detected during the second and third operational period due to the slight modification of the feeding solution preparation, which caused a decrease of its bicarbonate content; the bicarbonate increase was caused by the VFA oxidation. The analysis of the mechanisms involved in the CO 2 removal during the three operating periods is reported in Table 3 . The two mechanisms characterized during the operation resulted in methane production and bicarbonate removal within the daily cathodic liquid phase spill. In the first operating period, the methane production was 9 mmol/day, which corresponds only to 2% of the CO 2 removal, while a bicarbonate spill of 55 ± 5 mmol/day resulted in 12% of the removed CO 2 . Similar results were obtained during the second and the third operating period, with higher methane production rates of 18 ± 1 and 14 ± 2 mmol/day, which contributes 4% and 3% of the CO 2 removal, respectively. Moreover, during the latter two operating periods, the bicarbonate spill was 73 ± 4 and 218 ± 40 mmol/day, which corresponded to 16% and 45% of the removed CO 2 . As reported in the previous chapter, the substantially higher contribution of the bicarbonate spill during the third operating period resulted from the substantial increase in the cathodic spill flow rate, which increased the daily bicarbonate removal. The high percentage of unjustified CO 2 removed clearly indicates that other mechanisms contributed to the overall CO 2 removal in the process. In this sense, a hypothesis can be elaborated for the CO 2 removal justification. The CO 2 removal in the cathodic chamber could be increased by the precipitation of low-soluble carbonates with alkaline earth metals, such as calcium and magnesium, which are present in the synthetic feeding solution and can be transported from the anode to the cathode by migration for the electroneutrality maintenance. By taking into account the current, which is not justified by the ammonium migration, previously reported in Table 2 , a daily migration of 94 and 72 mmol/day of calcium and magnesium was evaluated for the first and the second operating period, respectively. Moreover, by assuming the complete precipitation of low-soluble carbonate salts, calcium and magnesium migration accounted for 21% and 16% of the CO 2 removal in the first and second operating periods. During the third operating period, ammonium migration was the only cation responsible for the electroneutrality maintenance. Considering the calcium and magnesium migration and carbonate precipitation in the cathodic chamber, an overall recovery of 42%, 45%, and 48% of the removed CO 2 was obtained in the three different operating periods. Moreover, even if the cathodic biomass concentration in the catholyte was only 62 ± 2 mg VSS/L, resulting in a negligible contribution to the cathodic CO 2 removal, a possible underestimation of this mechanism can be present due to the high surface area of the cathodic chamber of the tubular MEC. 3.4. Energetic Consumption and Evaluation of the Process The MEC energy consumption was calculated for the COD removal inside the anodic chamber, the CO 2 removal in the cathodic chamber, and the energetic cost of the nitrogen recovery form the cathodic phase spill. The energetic consumption for each MEC operation was compared with a selected benchmark technology, i.e., activated sludge for COD removal and water scrubbing for biogas upgrading (expressed as CO 2 removal). Concerning the energetic consumption for nitrogen recovery, the process of energy consumption was compared to the sum of the energetic cost for ammonium production (Haber Bosch process) and the nitrification/denitrification process in a wastewater treatment plant, which accounted for 8.5 and 12.5 kW·h/kg N, respectively. The energy consumption for the different operations, as reported in Table 4 , decrease within the increase of the nitrogen load rate; this energy consumption reduction was caused by the increase of the reactor performances concerning COD and CO 2 removal and nitrogen recovery. The lowest energy consumption for COD and CO 2 removal was obtained during the third operating period characterized by the higher nitrogen load rate, with an average value of 0.88 ± 0.08 kW·h/kg COD for COD removal and 0.47 ± 0.02 kW·h/Nm 3 CO 2 for the CO 2 removal. The energetic cost of the nitrogen recovery was 2.3 ± 0.5 kW·h/kg N, a considerably lower value with respect to the production and removal of ammonium. It is also noteworthy to mention that the energy consumption in an MEC is adopted for the simultaneous operation of the COD and CO 2 removal as well as for the nitrogen recovery, i.e., during the third operational period, with 0.47 kW·h/d 1 m 3 of CO 2 removed by the biocathode, while, at the same time, the MEC oxidized 0.53 kg/day of COD in the bioanode and 0.21 kg N/day was recovered as concentrated ammonium solution. Finally, at the biocathode, methane production resulted in additional energy recovery given by its energetic content, i.e., a theoretical energy efficiency of 59 ± 1% was also obtained during the third operating condition. 3.5. Comparison of the Upscaled Process with the Previous Bench-Scale Reactor The tubular MEC performances regarding the CO 2 removal and ammonium recovery were compared with previous experiments [ 39 , 40 , 43 ] performed on a bench-scale MEC, which adopted a simple filter press configuration, in which the same bioelectrochemical reactions and similar operating conditions were adopted. The filter press MEC configuration presented a cathodic and anodic with an empty volume of 0.86 L while the tubular MEC presented in this study resulted in an empty volume of 3.14 and 8.86 L for the anodic and cathodic chamber, respectively. Regarding the cathodic CO 2 removal, the tubular MEC was capable of removing on average 50 mmol CO 2 /Ld while in a previous study, 100 mmol/Ld of CO 2 were removed by the biocathode of the filter press MEC [ 39 ]. The higher CO 2 removal rate reached in the filter press MEC was justified by the current density of 91 A/m 3 , which resulted in a higher value with respect to the 19 A/m 3 reached in the tubular MEC. The volumetric current density referred to the empty cathodic volume due to the fact that in each reactor, the same graphite granules were adopted as electrodic material. Moreover, the higher current density promoted a higher energetic consumption of the filter press MEC, which resulted in 2.36 kW·h/Nm 3 CO 2 being removed while the tubular reactor allowed for a consumption of 0.8 kW·h/Nm 3 . As reported in Table 5 , the specific CO 2 removal parameter, normalized for the cathodic volumetric current density, showed a higher performance was obtained by the tubular MEC, which allowed a 2.5 times higher CO 2 removal with respect to the filter press MEC. Regarding the nitrogen recovery performances, the comparison of the filter press MEC and the tubular MEC was performed considering the applied nitrogen load rate (NLR) applied to the anodic chamber in two different previous experiments [ 40 , 43 ]. As reported in Table 6 , the filter press MEC showed a higher ammonium recovery rate at the lower NLR applied to the anode. On the contrary, by applying higher NLR, the ammonium recovery rate was similar in the two MEC, with a slightly higher ammonium recovery rate obtained by the tubular MEC. Moreover, even if in the filter press a higher current density was reached, the ammonium recovery rate was influenced mainly by the applied NLR to the anodic chamber, i.e., the NLR directly influences the availability of ammonium ions for the electroneutrality maintenance. The performances of the tubular reactor are comparable with the performance obtained through the smaller scale filter press MEC; however, the obtained current densities obtained in the tubular MEC are considerably lower, indicating the good potential of the reactor at higher current densities."
} | 7,401 |
35336243 | PMC8951542 | pmc | 4,701 | {
"abstract": "Roots hold complex microbial communities at the soil–root interface, which can affect plant nutrition, growth, and health. Although the composition of plant microbiomes has been extensively described for various plant species and environments, little is known about the effect of wheat straw return (WSR) on the soybean root microbiota. We used Illumina-based 16S rRNA and ITS amplicon sequencing to track changes in bacterial and fungal microbiota in bulk soil and soybean rhizosphere, rhizoplane, s1and endosphere during the third and fourth years after implementing WSR in a wheat–soybean rotation system. The results revealed that WSR had a greater impact on fungal communities than bacterial communities, particularly in bulk soil, rhizosphere, and rhizoplane. WSR enriched the relative abundance of cellulose-degrading fungi (e.g., Acremonium , Trichoderma , and Myrmecridium , among which Trichoderma also had antimicrobial activity), saprotroph (e.g., Exophiala ), and nitrogen cycling bacteria (e.g., Chryseolinea ). Furthermore, WSR depleted the relative abundance of pathogenic fungi (e.g., Fusarium and Alternaria ). These data revealed for the first time that WSR had diverse effects on soybean root-associated microbial community composition, not only in soil but also in the rhizosphere, rhizoplane, and endosphere.",
"conclusion": "5. Conclusions Our study demonstrated that the effect of short-term WSR on the microbial community composition was different among different soybean root compartments, and the fungal community responded more strongly than the bacterial community. Many WSR-affected microbiota was associated with the functions such as cellulose degradation, nitrogen cycling, and plant infection, representing some major effects of short-term WSR on the soil ecology and plant health. Further investigations on the effects of long-term WSR on the microbial community, as well as the responses of the endophyte community of underground and aboveground organs of soybean to WSR would be interesting.",
"introduction": "1. Introduction The roots of soil-grown plants are the main site of interactions with soil microbes and are in direct proximity to the largest known reservoir of microbial diversity [ 1 , 2 ]. Plants rely on interactions between roots and microbes for various beneficial effects, such as improved stress resistance, improved nutrient availability, promoted plant growth, and suppressed pathogen infection [ 2 , 3 , 4 , 5 ]. Roots have distinct compartments (rhizosphere (soil close to the root surface (RS)), rhizoplane (root surface (RP)), and endosphere (root interior (ES))) with varying microbial diversity at the soil–root interface [ 2 , 4 , 6 , 7 ]. Wheat straw return (WSR), i.e., wheat stalk, was crushed into pieces and added directly to the soil surface. WSR provides a valuable source of carbon for soil microbes, and a series of studies have investigated the effects of WSR on soil microbial communities using high-throughput sequencing. For example, wheat–rice or wheat–maize straw return boosted the variety of soil bacterial and fungal communities substantially [ 8 , 9 , 10 , 11 ]. In a wheat–soybean rotation system, WSR also altered nitrogen cycling and pathogen-associated soil microbiota [ 12 ]. Rice straw return has also been shown to have an impact on rhizosphere microbial communities of subsequently planted maize [ 13 ]. However, little is known about how WSR affects crop-root-associated microbiomes, which are more intimately linked to plant health than field soil. The current study consisted of a two-season field experiment conducted in 2017 and 2018 in a wheat–soybean cropping system in three sites of the Huang-Huai region of China. We compared differences in bacterial and fungal communities among the RS, RP, and ES of soybean roots, and bulk soil (BS) from soybean fields, and evaluated the influence of WSR upon soybean root-associated microbiota. We hypothesized that WSR could not only affect the microbial community in BS but also in RS, RP, and ES. Further, some nitrogen cycling bacteria and pathogenic fungi would also be affected by WSR. The results are expected to facilitate the implementation of effective agricultural microbial community management strategies and the development of sustainable agriculture.",
"discussion": "4. Discussion Studies of plant microbiomes in different environments are an active area for research, but the work conducted to systematically characterize the root microbiome in soybean is still largely lacking [ 21 , 22 ]. Similarly, the effect of WSR on soybean root microbiota has not been previously investigated. This work provides the first detailed characterization of the short-term (third and fourth years) response of the soybean-associated microbial community to WSR in three locations in the Huang-Huai region of China. Our study demonstrated that the microbial community composition was significantly influenced by all these factors. In order to extract high-quality and quantity DNA from bulk soil, rhizosphere, rhizoplane, and soybean root tissue samples, and reduce the deviation caused by low DNA recovery, we used two different kits to extract DNA. Although the use of different kits will affect the investigation of the microbiome to some extent, we cannot use one DNA extraction kit to extract high-quality and quantity DNA from all compartments. Previous research papers also used different kits to extract DNA from different samples. For example, Cregger et al. (2018) used MoBio PowerSoil DNA Isolation Kit (MoBio Laboratories, Inc., Carlsbad, CA, USA) to extract rhizosphere, phyllosphere, and bulk soil samples DNA and MoBio PowerPlant Pro DNA Isolation Kit (MoBio Laboratories, Inc., Carlsbad, CA, USA) to extract plant tissues samples DNA; Beckers et al. (2017) extracted rhizosphere soil DNA with Power Soil DNA Isolation Kit (MoBio, Carlsbad, CA, USA) and plant samples DNA with Invisorb Spin Plant Mini Kit (Stratec Biomedical AG, Birkenfeld, Germany) [ 23 , 24 ]. Nevertheless, our results show that the microbial community varies greatly between compartments. We evaluated the effects of WSR in different geographical locations, rhizocompartments, and years, as these factors are known to affect soil and rhizosphere microbial communities [ 13 , 15 , 25 , 26 ]. Our data suggest that microbial diversity and community composition in soybean rhizocompartments were moderately affected by WSR. Overall, community composition was most affected by root compartment and geographical location and least affected by straw return ( Figure 2 A,B). It was consistent with an earlier study in Italy and the Philippines, where straw treatment had a minor impact on soil and rhizosphere microbial community composition when compared to crop rotation, field location, niche (bulk soil or rhizosphere), and time. [ 13 ]. It is worth noting that in this study, we used UPARSE (v. 7.0.1090) ( http://www.drive5.com/uparse/ ) (accessed on 18 May 2020) to cluster OTU based on 97% sequence similarity, which was called the “gold standard” of clustering methods of amplicon sequencing. This method not only simplified the workload but also improved the analysis efficiency. However, recently, a new method has been developed, that is, using DADA2 to cluster amplicon sequence variables (ASV) based on 100% sequence similarity [ 27 ]. However, ASV also has disadvantages. For example, some rich and very low species in the sample may be eliminated due to its more strict judgment method. Therefore, OTU clustering is still used in this study. The rhizocompartments (bacterial R = 0.614, fungal R = 0.362) and geographical locations (bacterial R = 0.242, fungal R = 0.385) described the largest source of variation in the composition of microbial communities sampled. This conclusion is consistent with the fact that there are significant differences in the composition of microbial communities from different geographical and climatic regions. [ 7 , 13 , 25 , 28 ]. Moreover, root-associated compartments also significantly affected microbial community composition and diversity, as observed in previous studies [ 4 , 7 ]. The above-observed differences may be due to the variation in soil environmental factors, as well as the dynamic acquisition process of the root microbial community. Compared with other factors, the short-term response to WSR was relatively mild. ANOSIM analysis showed that WSR had a stronger impact on fungal community composition than bacterial community composition, and the effects occurred mainly in the RP, RS, and BS ( Figure 3 A,B). Fungi play an important role in straw degradation, which can decompose more recalcitrant material, and decomposition products released by fungi may be an important nutrient source for bacteria [ 29 , 30 , 31 , 32 ]. This is consistent with a previous study showing that the effect of rice straw return on fungal communities in maize RS and BS was greater than the influence on bacterial communities [ 13 ]. Notably, the rhizosphere-priming effects can improve plant nutrition status by mineralizing nutrients released from refractory organic carbons [ 33 ]. These processes may lead to significant alterations in microbial community composition in the RS and RP after WSR. Members of some of the WSR-enriched taxa (showing a consistently increasing trend) may belong to cellulose-degrading fungi. These genera have the ability to degrade plant residue [ 30 , 34 , 35 , 36 , 37 , 38 , 39 , 40 , 41 ]. Most of these fungal genera belong to the classes Sordariomycetes and Dothideomycetes, which is consistent with previous reports that genera belonging to the two classes were significantly enriched in Italian soil after rice straw return [ 13 ]. Furthermore, the relative abundance of Pyrenochaetopsis similarly increased following WSR. These fungi exhibit proteolytic activity and are also associated with elevated atmospheric carbon dioxide levels [ 8 , 42 , 43 , 44 ]. The relative abundance of some plant pathogen-associated fungal genera decreased after WSR, mainly in BS, RS, and RP, e.g., Fusarium , and Alternaria , while the relative abundance of Mycosphaerella increased ( Figure 5 B and Figure 6 B,C). This is in line with our previous results, which revealed that the relative abundances of Fusarium and Alternaria were significantly lower in WSR soils, while that of Mycosphaerella was significantly higher [ 12 ]. These genera are known to contain many species of soil-borne soybean and/or wheat pathogens [ 45 , 46 , 47 , 48 , 49 ]. This effect may result from the presence of a large number of organic substrates in WSR treatment, which promote nutrient cycling and improve the ecological environment and nutrient status of soil and soybean root-associated microbial communities. It may also be related to the accumulation of antagonistic microbes after WSR, such as Trichoderma, a well-known biocontrol fungus. Such microbes could play an important role in the inhibition of soil-borne diseases caused by Fusarium oxysporum and F. graminearum [ 50 , 51 ]. For bacteria, more genera showed increased abundance under WSR, compared to those showing reduced abundance ( Figure 5 A). Among the enriched genera under WSR, we identified many nitrogen cycling-associated bacterial genera (e.g., Chryseolinea , Pseudoduganella , Ensifer , and Nitrospira ) in BS, RS, or RP. This is in line with our previous findings, where the relative abundances of the nitrogen cycling-associated bacterial genera, such as Nitrospira , were significantly higher in soils treated with WSR within three years [ 12 ]. Thus, WSR exhibited an impact on the nitrogen cycling-associated microbiota in soil and distinct root-associated compartments."
} | 2,927 |
40277678 | PMC12026868 | pmc | 4,703 | {
"abstract": "Ion-conductive gels (ICGs) are essential for achieving human–machine interfaces, bioelectronic applications, or durable wearable sensors. However, traditional solvent-dependent ICGs face bottlenecks such as dehydration-induced failure and challenges in achieving a balance between conductivity and mechanical properties. Here, this work developed a novel ternary ion-conductive xerogel (PEM-Li ICXG) system based on polyethylene glycol (PEG), poly (2-methoxyethyl acrylate) (PMEA), and LiTFSI. PEM-Li ICXGs exhibit high conductivity (2.7 × 10 −2 S/m), high adhesive capability (0.34 MPa), and solvent-free characteristics. Remarkably, the incorporation of ions into ICXGs simultaneously optimizes their mechanical performance. We demonstrate the application of ICGs in flexible sensors for strain or temperature sensing. The proposed synthesis strategy is straightforward and may further inspire the design of novel high-performance ICXGs.",
"conclusion": "3. Conclusions In summary, this study successfully constructed PEM-Li ICXGs that leverage the dynamic coordination effects of LiTFSI to achieve synergistic regulation of mechanical, electrical, and interfacial properties. The PEM50-Li5% ICXGs have good flexibility with damage resistance while maintaining robust adhesion and high conductivity. More importantly, the PEM50-Li5% ICXGs exhibit dual-mode temperature/strain-sensing capabilities, with rapid response characteristics and cyclic stability that provide a reliable solution for human motion monitoring. The unique dynamic ionic network endows the materials with self-recovery properties and long-lasting antibacterial performance, offering a novel design strategy for the development of intelligent flexible electronic devices. Despite these promising results, challenges remain that must be addressed to facilitate real-world applications. Long-term stability under varying environmental conditions, such as humidity and temperature fluctuations, is critical for practical use. In addition, improving the environmental robustness and scalability of the fabrication process and seamless integration with existing electronic systems are essential for further advancement. Future work should focus on optimizing the material’s durability and adaptability in complex environments, while enhancing its compatibility with diverse technologies for commercialization and biomedical applications. Addressing these challenges will not only solidify the current contributions, but also pave the way for broader practical implementation and future innovations.",
"introduction": "1. Introduction Against the backdrop of the rapid advancement of Internet of Things technology, the growing demand for wearable devices, miniaturized instruments, and attachable sensors has significantly driven innovative breakthroughs in flexible electronics [ 1 , 2 , 3 ]. The field has successfully developed novel solutions applicable to cutting-edge scenarios, such as biomedical monitoring [ 4 ] and bio-inspired robotics [ 5 , 6 , 7 , 8 ]. Notably, current electronic systems predominantly employ electrons as information carriers [ 9 , 10 , 11 , 12 ], which fundamentally differs from the ion-mediated conduction mechanisms in biological organisms [ 13 , 14 , 15 ]. Flexible polymer materials with ion-conductive properties through dynamic ion–polymer interactions (i.e., ion-conductive gels, ICGs) demonstrate immense potential in bridging the electromechanical performance gap [ 16 , 17 , 18 , 19 ]. To meet the application requirements of next-generation intelligent soft devices, such materials must integrate exceptional electrical conductivity, reliable mechanical properties, as well as environmentally responsive functionalities and other comprehensive performance metrics [ 20 , 21 ]. Previous research focused on constructing highly ionically conductive gels by integrating flexible polymer networks with liquid electrolytes, such as ionic hydrogels or ionic liquid gels. For instance, polyacrylamide/lithium chloride (PAAm/LiCl)-based ionic hydrogels achieve high conductivity (>0.1 S m −1 ), approaching biological tissue conductivity levels, and demonstrate potential in flexible sensing applications [ 22 ]. However, such systems heavily rely on solvation to maintain ion transport channels. Prolonged environmental exposure leads to water evaporation, causing conductive network collapse and conductivity decay exceeding 80% [ 23 ]. More critically, under dynamic cyclic strain (e.g., 100% stretching amplitude over 1000 cycles), the modulus mismatch between the liquid electrolyte and polymer network exacerbates interfacial slippage, forming millimeter-scale conductive phase-separation regions and resulting in resistance fluctuations exceeding 200% [ 24 ]. To eliminate solvent dependency, solvent-free ICXGs chemically anchor electrolyte salts within polymer networks [ 25 ]. A representative strategy involves metal-coordination systems based on polyethylene oxide and zinc bis (trifluoromethanesulfonyl)imide, where dynamic coordination between Zn 2+ and ether oxygen groups forms crosslinked networks. This enables the material to retain an elastic modulus of 0.15 MPa at 60 °C without leakage risks [ 26 ]. However, strong coordination interactions significantly suppress Li + mobility, yielding a room-temperature conductivity of only 0.017 S m −1 , which is two orders of magnitude lower than liquid-based systems [ 27 ]. Additionally, constrained by the solubility threshold of salts in polymer matrices (typically <30 wt%), synergistic optimization of network crosslinking density and ion transport capability faces fundamental challenges [ 28 ]. Recent breakthrough studies have attempted to merge the advantages of solvent-based and solvent-free systems, such as composite electrolytes employing polyethylene glycol (PEG) and dual-network designs [ 29 ]. By dissolving lithium bis (trifluoromethanesulfonyl)imide (LiTFSI) salts in PEG solvents while forming hydrogen bonds with poly (urethane acrylate) networks, a conductivity of 0.08 S m −1 at 25 °C is achieved, alongside a 40% improvement in anti-swelling performance compared to conventional hydrogels [ 30 ]. Nevertheless, such systems still face mechanical limitations: when PEG content exceeds 50 wt%, the elastic modulus drops below 0.5 MPa. Although fracture elongation reaches 800%, plastic deformation readily occurs under high-frequency loading scenarios (e.g., >10 N cyclic loads in soft robotic joints), with residual strain accumulation reaching 35% after 100 cycles [ 31 ], where these limitations severely hinder the application of ICXGs [ 32 ]. This study developed high-performance ICXGs by constructing a PEG/poly (2-methoxyethyl acrylate) (PMEA)/LiTFSI ternary system, which is abbreviated as PEM-Li. The chemical structures and compositions of PEM-Li ICXGs are presented in Scheme 1 . We discovered that Li + dynamic coordination establishes a reversible ion-dipole energy dissipation network [ 33 ], endowing it with a high work of extension at the fracture of 0.36 MJ/m 3 . The presence of PMEA imparts universal adhesion to wood, ceramic, and metal substrates (0.28–0.33 MPa), and LiTFSI’s dual antibacterial mechanism achieves an inhibition zone diameter of 7 ± 0.4 mm. Electrically, as an organic salt widely used in solid polymer electrolytes, LiTFSI was employed in this work to enhance conductivity. Considering the good compatibility between LiTFSI and the polymer matrix PEM, LiTFSI can dissociate as the cations Li + and anions TFSI − within PEM-Li ICXGs to provide charge carriers [ 34 , 35 , 36 ]. PEM-Li yields a conductivity of 2.7 × 10 −2 S/m with dual-temperature–strain response characteristics: a temperature coefficient of resistance (TCR) of −1.54% °C −1 in the physiological range (30–45 °C), a gauge factor (GF) of 1.78 under 60% strain, and a response time <535 ms. After 200 cycles, the dynamic network demonstrates efficient energy dissipation and self-recovery capabilities [ 37 ]. As a multifunctional sensor, it successfully achieves synchronized monitoring of joint motion and temperature signals. We believe that this work opens a new pathway for developing high-performance ion-conductive intelligent materials [ 38 , 39 ].",
"discussion": "2. Results and Discussion We successfully constructed ternary PEM x -Li y ICXGs through a free radical copolymerization strategy, where “ x ” represents the mass ratios of MEA ( ω MEA ) to the total MEA and PEG monomers, and “ y ” denotes the mass ratios of Li ( ω Li ) to the total precursor solution. The system employs PEG4000 as a flexible backbone, MEA as a functional building unit, and LiTFSI as a dynamic ionic regulation medium. As shown in Figure 1 , when ω Li increases from 0 to 20%, both the optical appearances of the PEM50 and PEM50-Li systems are turbid, suggesting distinct phase-separation phenomena formed by the PEG crystals. Of note is that the PEM50-Li samples with ω Li ≥ 30% fail to form stable films and are excluded from further consideration. Subsequently, by systematically investigating the uniaxial tensile behavior of PEM50-Li ICXGs with ω Li from 0 to 20% ( Figure 2 a), we can see that the elastic modulus ( E ) drastically decreases from 16.9 to 2.04 MPa with increasing ω Li . On the contrary, the work of extension at fracture ( W extf ) increases from 0.03 to 0.36 MJ/m 3 , showing a 12-fold enhancement [ 40 ]. In terms of adhesive performance, lap-shear testing results show that the PEM elastomer without LiTFSI exhibits an initial adhesive strength of 0.8 MPa ( Figure 3 a) due to the methoxyethyl (-OCH 2 CH 2 O-) and ester (-COO-) groups in MEA molecules forming a multi-noncovalent bonding network with the glass substrates [ 41 ]. As ω Li increases to 5%, 10%, and 20%, the adhesive strength progressively declines in a stepwise manner from 0.34 to 0.22 MPa and finally to 0.1 MPa ( Figure 3 b). This degradation likely stems from two factors: (1) the competitive coordination between Li + and the polar functional groups of MEA reduces the density of effective adhesive sites, and (2) the steric hindrance effect of TFSI − anions weakens the physical entanglement capability of polymer chains [ 42 , 43 ]. As shown in Figure 4 a, when ω Li is 5%, the conductivity of PEM50-Li ICXG achieves the peak conductivity of 2.7 × 10 −2 S/m, surpassing that of various ICGs reported before. It is noted that the electrical conductivity measured in this study by the DC electricity reflects the combined contributions of ionic and electronic conductivity in the material. Future work will include impedance measurements (e.g., electrochemical impedance spectroscopy) to further distinguish ionic conductivity from electrical conductivity. Specifically, its conductivity not only far exceeds that of traditional ionogels (e.g., polyurethane ionogels ~1.5 × 10 −2 S/m, starch/gelatin-based systems ~1 × 10 −2 S/m), glycerol hydrogels (~1.2 × 10 −2 S/m), and silicone rubber elastomers (~3 × 10 −3 S/m, Figure 4 b) [ 44 , 45 , 46 , 47 , 48 , 49 , 50 ]. This optimization arises from the uniform dispersion of Li + in the polymer matrix, forming a continuous conductive network, and the synergistic enhancement of carrier mobility through moderate ion–polymer interactions [ 51 ]. When the ω Li exceeds 5%, it may trigger ion cluster aggregation, obstructing ion migration pathways and causing conductivity decline ( Figure 4 a). Based on the study of multi-parameter synergistic evolution, when the ω Li is 5%, the PEM50-Li ICXGs achieve the relative optimal balance among mechanical, adhesive, and conductive properties. Then, we examined the effects of the mass ratio of PEG and PMEA ( ω PMEA ) on the tensile and adhesion properties of PEM-Li5% ICXGs. As shown in Figure 5 a, by investigating the influence of ω PMEA from 40% to 70% on the uniaxial tensile behavior of ICG samples, it is found that the fracture elongation rate achieves a 40-fold leap from 15% to 580%, but the elastic modulus E decreases from 12.5 to 2.8 MPa, with a 77.6% reduction. This drastic mechanical transformation originates from structural reorganization involving PEG crystalline domain dissociation and PMEA dynamic network formation [ 52 ]; the former weakens the strong interchain constraints, while the latter enhances the motional freedom through chain slippage mechanisms [ 53 ]. In addition, the work of extension at fracture W extf exhibits stepwise growth, rising from 0.05 to 4.92 MJ/m 3 , whereas the fracture stress follows a nonmonotonic trend, where it is decreasing first and then increasing as ω PMEA >50%. The initial decline likely stems from the rigidity loss due to the PEG crystalline network dissociation, while the subsequent recovery may be dominated by phase-separation-induced crack blunting effects [ 54 ]. After comprehensive performance evaluation, the PEM50-Li5% ICXGs are ultimately selected as the optimal ratio. The cyclic tensile testing results in Figure 6 a demonstrate that the PEM50-Li5% ICXGs exhibit good energy dissipation characteristics during 50 cycles at 40% strain. The first cycle generates a significant hysteresis loop due to molecular chain reorganization and energy dissipation, but the hysteresis rate rapidly stabilizes around 3.98% after subsequent cycles ( Figure 6 b), attributed to the dynamic entanglement network achieving rapid equilibrium of energy dissipation pathways via reversible disentanglement mechanisms [ 55 ]. A residual strain of 14.33% was observed, confirming that the segmental slippage effect within the entropy–elastic network endows the materials with excellent deformation recovery [ 56 ]. Quantitative analysis of stabilized hysteresis rates and residual strain reveals the dual advantages of efficient energy dissipation and structural self-recovery in the PEM50-Li5% ICXGs. Notably, the PEM50-Li5% sample demonstrates exceptional interfacial adhesion adaptability across diverse materials. As shown in Figure 7 a, experimental comparisons of its adhesive performance on substrates including wood, ceramic, rubber, glass, and metal reveal stable adhesion strengths within the range of 0.28–0.33 MPa ( Figure 7 b,c). This universal adhesion capability arises from the dual mechanisms of methoxyethyl and ester groups in MEA molecules: for highly polar substrates (e.g., metal, glass), ether oxygen (-O-) and ester (-COO-) groups form strong interactions with surface hydroxyls or oxides via hydrogen bonding; for low-polarity substrates (e.g., rubber, wood), the conformational adaptability of flexible polyether chains enables the accommodation of surface roughness, enhancing the interfacial bonding through chain penetration and mechanical interlocking effects [ 57 , 58 , 59 ]. The retention of such broad-spectrum adhesion, combined with the optimized electrical and mechanical properties discussed earlier, further highlights the unique advantages of PEM50-Li5% ICXGs for integration with flexible sensors and complex surfaces (e.g., bio-inspired skins). Leveraging the synergistic effects of free Li + and TFSI − ions in the PEM50-Li5% ICXG, it exhibits a pronounced temperature-responsive characteristic. To validate its temperature-sensing performance, we quantitatively analyzed the temperature coefficient of resistance (TCR) as a key metric [ 60 ]. Experimental data show an outstanding TCR value of −1.54% °C −1 at 36 °C (near human body temperature, Figure 8 a), strongly confirming its potential as a temperature sensor. Further studies reveal that under varying heating/cooling rates (e.g., 10, 25, 40 °C min −1 ) within the 30–40 °C range, the final relative resistance change (ΔR/R 0 ) variation remains constant ( Figure 8 b), demonstrating reliable electrical response characteristics. To address practical application requirements for sensor devices, we systematically evaluated the cyclic stability of PEM50-Li5% ICXGs in the 35–40 °C range. After 100 consecutive heating–cooling cycles, the ΔR/R 0 signal amplitude remains stable ( Figure 8 c), with long-term stability tests fully verifying the performance reliability of PEM50-Li5% ICXGs under repeated usage conditions. The combined validation of reproducibility and long-term stability provides the critical experimental evidence for assessing the suitability of PEM50-Li5% ICXGs in biomedical temperature monitoring and related fields. Likewise, systematic studies on strain-resistance response characteristics reveal that PEM50-Li5% ICXGs also exhibit exceptional strain-sensing performance ( Figure 9 ). The PEM50-Li5% ICXGs present a high-strain responsive window from 0 to 60% ( Figure 9 a). The results of the gauge factor (GF = 1.78) and correlation coefficient (R 2 = 0.993), calculated by linearly fitting the ∆R/R 0 with loading strain, indicate the good linearity of sensing signals. Notably, this gauge factor significantly surpasses those of conventional strain-sensitive materials [ 61 , 62 , 63 , 64 ]. To further evaluate the practical device performance, multi-parameter cyclic testing was conducted. Under varying fixed strain levels (5–50%, Figure 9 b) and loading rates (5–100 mm/min, Figure 9 c), the material exhibits stable resistance responses during continuous stretch–release cycles. This high repeatability primarily stems from the three-dimensional crosslinked network structure, which endows the ICG with superior elastic recovery and microstructural stability [ 65 ]. Dynamic mechanical response testing highlights the superior sensing properties of PEM50-Li5% ICXGs. As illustrated in Figure 10 a, under 40% strain, the material displays exceptional dynamic responsiveness, with stretching and recovery response times of 535 and 360 ms, respectively. Notably, after 200 cyclic loading tests at 50% strain, the sensor retains stable resistance output ( Figure 10 b), which is a critical durability feature for dynamic monitoring applications. Combined with its sensitive temperature/strain response ( Figure 9 and Figure 10 ), the material demonstrates significant advantages as a multifunctional wearable sensor, capable of addressing multi-parameter detection requirements in complex operational scenarios. The application potential of PEM50-Li5% ICXGs in biomedical sensing is particularly remarkable, as its unique temperature–strain-coupled response characteristics are fully demonstrated under human body surface temperature conditions (~36 °C). As shown in Figure 11 , the PEM50-Li5% ICXGs achieve synchronous capture of multimodal biomechanical signals through flexible device integration, with core advantages manifested in two aspects: (1) stable adhesion at the material–skin interface ensures signal transmission reliability; (2) the linear mapping relationship between dynamic deformation and electrical signals enables precise motion resolution. Specifically, when deployed as a finger joint sensor (inset in Figure 11 a), the device can record bending angle variations of the finger in the 15°–90° range in real time. The ΔR/R 0 value exhibits a monotonic increasing trend with angle increments and rapidly returns to baseline within 1.2 s after reverting to the initial state (State I). Furthermore, these PEM50-Li5% ICXGs were also applied to monitor other body joints, such as the elbow, wrist, and knee joint. Figure 11 b–d show that the PEM50-Li5% ICXGs can discern the flexure of the elbow joint and the bending of the wrist and knee joint. In biosafety evaluation, the antibacterial efficacy of PEM50-Li5% against Staphylococcus aureus was systematically investigated via inhibition zone assays ( Figure 12 a). The results show that the samples with ω Li = 5% form inhibition zones of 7 ± 0.4 mm diameter on agar plates, which is significantly larger than that of the control group without LiTFSI (3 ± 0.2 mm). After 5 days of continuous culture, the inhibition zone area of LiTFSI-containing samples remains at 108% of the initial value, while the control group maintains stable antibacterial efficacy ( Figure 12 b). This long-lasting antimicrobial property likely stems from the dual mechanisms of fluorinated species in LiTFSI: (1) TFSI − anions penetrate bacterial cell membranes to inhibit activity; (2) gradually released Li + competitively binds with phosphate groups of phospholipid head groups, disrupting membrane integrity [ 66 ]. These mechanisms collectively endow PEM50-Li5% with robust antibacterial performance."
} | 5,107 |
26388938 | PMC4574612 | pmc | 4,704 | {
"abstract": "Background Switchgrass is a prime target for biofuel production from inedible plant parts and has been the subject of numerous investigations in recent years. Yet, one of the main obstacles to effective biofuel production remains to be the major problem of recalcitrance. Recalcitrance emerges in part from the 3-D structure of lignin as a polymer in the secondary cell wall. Lignin limits accessibility of the sugars in the cellulose and hemicellulose polymers to enzymes and ultimately decreases ethanol yield. Monolignols, the building blocks of lignin polymers, are synthesized in the cytosol and translocated to the plant cell wall, where they undergo polymerization. The biosynthetic pathway leading to monolignols in switchgrass is not completely known, and difficulties associated with in vivo measurements of these intermediates pose a challenge for a true understanding of the functioning of the pathway. Results In this study, a systems biological modeling approach is used to address this challenge and to elucidate the structure and regulation of the lignin pathway through a computational characterization of alternate candidate topologies. The analysis is based on experimental data characterizing stem and tiller tissue of four transgenic lines (knock-downs of genes coding for key enzymes in the pathway) as well as wild-type switchgrass plants. These data consist of the observed content and composition of monolignols. The possibility of a G-lignin specific metabolic channel associated with the production and degradation of coniferaldehyde is examined, and the results support previous findings from another plant species. The computational analysis suggests regulatory mechanisms of product inhibition and enzyme competition, which are well known in biochemistry, but so far had not been reported in switchgrass. By including these mechanisms, the pathway model is able to represent all observations. Conclusions The results show that the presence of the coniferaldehyde channel is necessary and that product inhibition and competition over cinnamoyl-CoA-reductase (CCR1) are essential for matching the model to observed increases in H-lignin levels in 4-coumarate:CoA-ligase (4CL) knockdowns. Moreover, competition for 4-coumarate:CoA-ligase (4CL) is essential for matching the model to observed increases in the pathway metabolites in caffeic acid O -methyltransferase (COMT) knockdowns. As far as possible, the model was validated with independent data. Electronic supplementary material The online version of this article (doi:10.1186/s13068-015-0334-8) contains supplementary material, which is available to authorized users.",
"conclusion": "Conclusions The model proposed in this article captures all available data and performed well in independent PvMYB4 validation experiments. This good match with data is reason for cautious optimism, which however is to be supported with further experimental confirmation. Indeed, work is in progress to generate and analyze additional transgenic switchgrass lines and to incorporate further lignin compositional and enzyme activity and kinetic data into the model. If the model fares well in these additional validation studies, the results from the present study suggest that one might use the model for predictions, for instance, with respect to double knock-downs, and for optimization studies that could potentially affect the lignin-based recalcitrance in switchgrass in a favorable manner.",
"discussion": "Discussion In this work, we developed an ensemble of models of lignin biosynthesis in stem and tiller tissue in switchgrass, P. virgatum . The model reflects the consequences of various enzyme knock-downs quite well and performed satisfactorily in two validation studies with experimental data that had not been used in the model design or implementation. We used as the modeling framework the generalized mass action (GMA) format within biochemical systems theory (BST) [ 21 – 25 ]. The power-Law representation, which is the hallmark of this type of model, is arguably the least biased default formulation and by its mathematical nature avoids problems due to possibly invalid assumptions that may cast doubt on traditional Michaelis–Menten models in vivo [ 26 ]. Parameter values were, as always, difficult to obtain in a direct manner. We used for this purpose experimental knock-down data and a sophisticated Monte Carlo sampling strategy that has been used very successfully for similar systems before [ 14 ]. As a particular sub-goal, we investigated the regulatory mechanism of the pathway and the possible co-localization or coupling of the pair of enzymes, CCR1/CAD that was previously suggested for Medicago [ 5 ]. To elucidate the co-localization or coupling of these enzymes in switchgrass, we studied multiple configurations that seemed a priori plausible and identified those natural designs that were consistent with the experimental data. The consistent designs were further examined under different regulation scenarios. The main result from this study is a very robust model of lignin biosynthesis in switchgrass that is consistent with all available data. The model was, at least to some degree, validated with a formerly unused dataset. If this validation can be confirmed and expanded experimentally, the model proposed here may be used to predict responses of the natural pathway system to alterations that are difficult to assess with experimental means. For instance, a further validated model will allow the prediction of responses to combinatorial knockdowns that could be the basis for future designs of more sophisticated transgenic lines than are currently available. The computational analysis suggests the co-localization or functional coupling of the two enzymes CCR1 and CAD. Metabolic channeling and compartmentalization in plants have been identified in many biochemical pathways [ 27 ]. Of importance here, it has been suggested that enzymes catalyzing early reactions in the monolignol pathway may be co-localized in their binding to the ER. For instance, a multi-protein complex has been identified between PAL and C4H, and it seems that most of the substrates use these channels, but that some substrate undergoes the metabolic conversion in two steps [ 28 – 30 ]. C4H can also form a complex with C3′H [ 31 ], and it has been suggested that different forms of 4CL form a complex in poplar [ 32 ]. Independent computational work on alfalfa came to a similar conclusion for channeling of enzymes associated with coniferaldehyde, which were proposed to form a metabolic channel [ 14 ]. Our results on switchgrass, presented in this article, are in line with the latter result and suggest moreover that channeling around coniferaldehyde is necessary to capture the available data. The comparative study of different configurations revealed that consistency with the available experimental data was most difficult to achieve for transgenic 4CL down-regulated lines, in which, surprisingly, the H lignin concentration is increased. This observation is at first counterintuitive because 4CL is located directly upstream of the H lignin precursors, which would lead to the a priori expectation of a decrease in H lignin. The combination of two postulated types of regulatory mechanisms was able to explain this observation. The first is product inhibition, which is observed quite frequently in biochemical systems. While improving the data compatibility, this mechanism turned out to be insufficient, thus requiring additional signaling. Arguably the simplest explanation is a regulatory structure that works in either of the mechanisms below: An intermediate in the pathway is increased in response to the 4CL knockdown and activates the precursors of H lignin synthesis. The most likely candidates for this scenario appear to be p -coumaric acid, caffeic acid, and ferulic acid (Fig. 8 a). Fig. 8 Two plausible explanations for an increase in the H lignin concentration in 4CL transgenic lines. a represents a putative increase in an activator located upstream of the enzyme 4CL, whereas b shows a putative decrease in an inhibitor located downstream of 4CL There exists an inhibitor for the H lignin branch. This metabolite would have to be located such that its concentration is decreased due to the 4CL knockdown, which means that the inhibitor activity is inhibited and therefore exerts a net positive effect on the system (Fig. 8 b). Feruloyl-CoA could be a good candidate for this scenario. The current literature does not support the first hypothesis. By contrast, multiple candidates are available for the second scenario. A reasonable scenario arises from the fact that the lignin pathway in switchgrass includes parallel fluxes that share the same enzymes. Indeed, 4CL, CAD, COMT, F5H and CCR1 all catalyze multiple reactions, and it is likely that the substrates exert competitive inhibition for the shared enzyme, as it was also suggested in [ 33 ]. Supporting this scenario, a targeted numerical analysis demonstrated that competition over CCR1 perfectly matches the results of the 4CL knockdown line in the model with product inhibition. One could surmise that the latter mechanism would suffice to represent the increase in H lignin concentration. To test this hypothesis, we simulated the model with enzyme competition but without product inhibition. The results showed that competitive inhibition by itself could not satisfactorily resolve the issue. By contrast, the combined model containing product inhibition and competitive inhibition matches the experimental results very well. One should also recall that the product inhibition and substrate competition mechanisms only work properly if the proposed metabolic channel is present (Fig. 3 , Configuration 1). Another aspect of the experimental data that was not captured well by the original model, even when product inhibition and substrate competition over CCR1 were taken into account, is the accumulation of 4CL substrates in COMT transgenic plants. Particularly counterintuitive appears to be the accumulation of ferulic acid as a product of a reaction catalyzed by COMT. The observed concomitant decrease in the steady-state concentration of coniferaldehyde supports the possible explanation that the observation is due to regulation that begins to inhibit the conversion of ferulic acid into coniferaldehyde, when 4CL substrates are in excess. The simultaneous accumulation of p -coumaric acid and caffeic acid provides additional evidence that reactions catalyzed by 4CL are inhibited in COMT knockdown plants. Accounting for this feature to our model, all experimental data are represented well. The mechanism of the regulation remains a subject of further experimental investigations. Figure 9 shows the pathway including all inferred regulatory signals. Fig. 9 Full scheme of the lignin biosynthetic pathway in switchgrass suggested by the computational results of this study. All regulatory signals, i.e., universal product inhibition, substrate competition over CCR1, and 4CL inhibition are shown. The 4CL inhibiting agent is unknown and therefore denoted with X. 5-OH-ferulic acid might be a candidate for this role"
} | 2,799 |
39145926 | PMC11348155 | pmc | 4,706 | {
"abstract": "Significance There is a trade-off between the mobility and cross-linking structure of covalent adaptable networks (CANs), making it challenging to develop CANs with excellent mechanical properties and high self-healing efficiency. This work presents a pioneering approach by introducing internal catalysis into the cross-linking units to fabricate elastomers. The incorporation of neighboring groups significantly enhances the reversibility of dynamic bonds, resulting in a remarkable improvement in the dynamic behavior of the polymer network. By synergistically combining this strategy with the multiple-cross-linking-in-one site topological design, the resulting i-Canogel exhibits outstanding mechanical strength and self-healing properties at room temperature.",
"conclusion": "Conclusion This work represents an instance where internal catalysis has been incorporated into the cross-linking units to develop a generation of elastomer with both high self-healing performance and superior mechanical properties. The presence of neighboring groups significantly enhances the reversibility of dynamic bonds, leading to a substantial improvement in the network’s dynamic behavior. Combined with the multiple-cross-linking-in-one site topological design, the resulting i-Canogel exhibits both remarkable strength and self-healing properties at room temperature. The incorporation of active neighboring bonds provides an efficient approach toward regulating emerging dynamic oxime-urethane and other groups. Considering the extensive application of polyurethanes, this work holds widespread significance. The essence of the four-arm cross-linking unit combining electronic effects, spatial effects, and topological structures in a synergistic manner provides a powerful chemical pathway for tuning material properties.",
"discussion": "Results and Discussion Model Study of Neighboring Urea Bonds Promoting Oxime-Urethane Exchange. For the synthesis of model small molecules, N-hydroxyacetamidine was reacted with phenethyl isocyanate to obtain the neighboring bond model compounds DBD ( Fig. 2 A and SI Appendix , Scheme S1 ). The NMR spectra ( 1 H NMR and 13 C NMR) were utilized to confirm the structure of DBD ( SI Appendix , Figs. S1 and S2 ). Acetone oxime was reacted with phenethyl isocyanate or propyl isocyanate to obtain the DC or AC ( Fig. 2 A and SI Appendix , Schemes S2 and S3 ), which lack a neighboring urea bond. The structure of DC and AC was confirmed by 1 H NMR and 13 C NMR ( SI Appendix , Figs. S3–S6 ). At room temperature, the production of ABD or AC and D obtained through the reaction of compound DBD or DC with A was monitored in real-time using 1 H NMR ( Fig. 2 B and C ). In 1 H NMR spectra, the signals at 1.915 or 1.929 ppm of DC, 1.918 or 1.933 ppm of AC, and 1.737 ppm of DBD corresponded to the protons of the methyl group on the oxime. In the control group, the intensity of 1.915 or 1.929 ppm proton peaks did not change with time, while the proton peaks at 1.918 or 1.933 ppm almost did not appear, indicating that compound AC was not significantly produced ( Fig. 2 B ). Significant signal changes were observed in the [DBD]+[A] experimental group with time. Fig. 2 C shows that the appearance and gradual intensification of the 1.750 ppm proton peaks, along with the decrease in the 1.737 ppm peaks, are indicative of the formation of compound ABD . To quantitatively evaluate the reaction equilibrium rate, the peak area of the methyl group in AC and ABD was integrated. The conversion rate of AC and ABD can be determined using the equations [AC]/([DC]+[AC]) and [ABD]/([ABD]+[DBD]) , respectively, where [DC] , [AC] , [DBD] , and [ABD] represent the concentrations of DC , AC , DBD , and ABD at a given time. At 25 °C after 96 h, the conversion rate of AC was nearly negligible, whereas that of an internal catalyst was approximately 35.38%. Fig. 2 D shows that the rate of exchange reaction in the presence of an internal catalyst is significantly greater compared to that without the internal catalyst. To better understand the effect of temperature on the exchange reaction of oxime-urethane bonds, the exchange reaction of the small-molecule model was conducted at 50 °C and 100 °C. At 50 °C, the DC peak in the control group remained stable ( SI Appendix , Fig. S7 A ), and the product AC peak barely appeared, consistent with the results at room temperature. When the temperature increased to 100 °C, the AC peak gradually emerged over time ( SI Appendix , Fig. S7 B ). In contrast, the experimental group responded quickly to temperature changes. At 50 °C, the ABD product peak appeared shortly ( SI Appendix , Fig. S7 C ). At 100 °C, the exchange rate significantly increased ( SI Appendix , Fig. S7 D ). SI Appendix , Fig. S7 E showed the conversion rate in the experimental group at 50 °C and 100 °C, indicating the reaction reached equilibrium after 60 min at 100 °C. These results demonstrated that the reversibility of the oxime-urethane bond was substantially enhanced by internal catalysis and the exchange rate increased as the temperature rose. These results indicated that the exchange reaction rate of the experimental group was accelerated in comparison to the control group, thereby demonstrating that the presence of neighboring urea bonds played a facilitating role in the dynamic exchange reactions of oxime-urethane groups. This provides clear evidence of the effect of NGP. Fig. 2. Model study of neighboring urea bonds promoting oxime-urethane exchange. ( A ) Exchange reaction between model compounds DBD or DC and A produced ABD or AC and D at 25 °C. ( B ) Real-time 1 H NMR spectra of the mixture of DC and A . ( C ) Real-time 1 H NMR spectra of the mixture of DBD and A . ( D ) Conversion rate of the molecular model with or without internal catalyst. Design of Ionic Covalent Adaptable Networks with Superior Self-Healing Properties. After observing the enhanced dynamic exchange reactions of oxime-urethane bonds in the model study, we proceeded to incorporate a reversible topological molecular structure of tetrafunctional diaminoglyoxime into polymer networks as the cross-linking unit, aiming to enable self-healing ( Fig. 3 A ). The procedure for synthesizing i-Canogel was detailed in SI Appendix . The synthesis of i-Canogel n:m was accomplished through a one-step polycondensation process using commercially available materials: poly(butylene glycol adipate) diols (PBGAD), fluorinated tetraethylene glycol (FTEG), IPDI, AMG, and the ionic liquid 1-ethyl-3-methylimidazolium bis(trifluoromethylsulfonyl) imide ([EMI][TFSI]). The reaction was catalyzed by dibutyltin dilaurate. PBGAD was chosen as the soft segment due to its high compatibility with [EMI][TFSI] ( 30 ). FTEG was selected as the hard and hydrophobic segment as it primarily contains carbon-fluorine (C-F) bonds which are poor hydrogen donors and acceptors, leading to strong dipole-dipole interactions between FTEG segments ( 31 ). IPDI was picked as the hard segment because of its unique structure, which not only prevents crystallization but also promotes the exchange of dynamic structures through its steric hindrance effect ( 32 ). [EMI][TFSI] was selected as the functional component because it not only enhances the chain mobility through its physical lubrication effect but also imparts ionic conductivity to the material, resulting in the formation of an ionogel. The design and chemical structure of the proposed i-Canogel, denoted by the molar ratio of AMG, PBGAD, and FTEG, is illustrated schematically in Fig. 3 B and SI Appendix , Scheme S4 . We conducted a multidimensional comparison of the formulations, with a constant IPDI/AMG ratio of 10:3, changed the ratio of soft and hard segments: PBGAD/FTEG ratio of 2.5:1.5, 2:2, and 1.5:2.5 (i-Canogel 2.5:1.5, i-Canogel 2:2, and i-Canogel 1.5:2.5). Constant molar ratio of PBGAD/FTEG (2:2) was employed along with changing the content of cross-linker AMG: IPDI/AMG ratio of 10:4 (i-Canogel AMG 4), 10:3 [i-Canogel AMG 3 (i-Canogel 2:2)], and 10:2 (i-Canogel AMG 2). Fig. 3. The multifunctional integrated cross-linking structure design of i-Canogel with outstanding mechanical and self-healing properties. ( A ) Chemical structure of i-Canogel. ( B ) Schematic molecular structure of i-Canogel. The structure of i-Canogel was analyzed using attenuated total reflectance Fourier transform infrared spectroscopy (ATR-FTIR) ( SI Appendix , Fig. S8 ). The peaks observed at 3,375 cm −1 and 1,725 cm −1 were attributed to the stretching vibrations of N–H and C=O, respectively, which suggested the formation of urethane groups. Notably, no peaks were observed at 2,264 cm −1 corresponding to N=C=O skeleton stretching vibrations, indicating the complete reaction of the IPDI monomer ( 6 ). Accordingly, the self-healing ability was not caused by residual monomers. The formation of the cross-linked structure was demonstrated by immersion of i-Canogel 2:2 in tetrahydrofuran for 24 h without dissolution ( SI Appendix , Fig. S9 ). Compared with CAN 2:2, which lacks [EMI][TFSI], the N–H peak of i-Canogel 2:2 shifted from 3,375 cm −1 to 3,352 cm −1 , indicating that the ionic liquid forms hydrogen bonds with the N–H in the polymer chain. This result suggests a partial replacement of the hydrogen bonds formed by N–H and C=O in the network ( 30 ) ( SI Appendix , Fig. S10 ). Additionally, the four vibrational bands corresponding to the symmetric stretch of S–N–S and O=S=O, as well as the asymmetric stretch of CF 3 and O=S=O from the [TFSI] anion, which are located at 1,049, 1,132, 1,176, and 1,346 cm −1 , respectively, exhibited a higher wavenumber shift in i-Canogel 2:2 ( SI Appendix , Fig. S11 ). Therefore, the ionic liquid served as a lubricant, which promoted chain mobility and reduced the energy required to form five-membered rings by the NGP effect. Moreover, owing to the presence of FTEG chain segments, the [EMI][TFSI] could interact with the polymer chains via ion–dipole interactions. This meant the ionic liquid could disperse evenly in the CAN and would not leak out of the network ( 31 ). Elemental mapping of the cross-section of i-Canogel 2:2 revealed a uniform distribution of the elements C, O, N, F, and S, indicating the even distribution of [EMI][TFSI] in the CAN, without any noticeable phase separation ( SI Appendix , Fig. S12 ). The excellent miscibility of the ionic liquid with the polymer contributed to the good transparency of the i-Canogel composite ( SI Appendix , Fig. S13 ) ( 31 ). Neighboring Bond Participation in Covalent Adaptable Networks. The properties of various i-Canogel formulations were evaluated by adjusting the monomer ratio. The molar ratios of 2.5:1.5, 2:2, and 1.5:2.5 PBGAD/FTEG were used, and due to the lower molecular weight of FTEG, the content of the hard segment was found to have the most significant influence on the material properties. This accounts for the wide range of observed properties. To compare the impact of the PBGAD/FTEG molar ratio on the material properties, the molar ratio of IPDI and AMG was kept constant at 10:3 and the ionic liquid content was fixed at 50% for all formulations. The uniaxial tensile tests were performed to investigate the mechanical properties of the i-Canogel. As the content of the hard segment in the i-Canogel increased, the mechanical properties consequently improved. The tensile strength of the i-Canogel n:m (2.5:1.5, 2:2, and 1.5:2.5) at a stretching rate of 50 mm min −1 increased from 3.49 ± 0.3 MPa to 6.48 ± 0.14 MPa, and toughness rose from 11.2 ± 1.58 MJ m −3 to 20.88 ± 1.8 MJ m −3 ( Fig. 4 A ). Correspondingly, Young's moduli of the materials were also greatly improved from 0.45 ± 0.03 MPa to 0.96 ± 0.44 MPa. Moreover, because hard segments hindered the movement of molecular chains, the elongation at break decreased from 908.91 ± 35.47% to 714.87 ± 37.14%. These desired properties of i-Canogel were achieved through the incorporation of noncovalent interactions and a reversible four-arm cross-linking unit, which resulted in high toughness and good elasticity, respectively. Herein, the cyclic tensile tests were performed to demonstrate these performances. First, the cyclic tensile tests with different strain multiples of i-Canogel showed good stretch resilience ( SI Appendix , Figs. S14–S16 ), and after 100 times tensile cycle test with a strain of 200%, i-Canogel 2:2 still maintained good stability ( SI Appendix , Fig. S17 ). Second, the cyclic tensile tests with gradually larger strains ( Fig. 4 B ), which performed with no waiting time between two consecutive loadings, showed great elasticity of i-Canogel 2:2. The dynamic covalent network and unbroken noncovalent interaction, including hydrogen and dipole–dipole interaction, entropically drove the stretched networks almost back to the initial state, after the i-Canogel 2:2 was stretched 100% strain on the first cycle. The result proved that i-Canogel 2:2 was a good elastic conductor within the strain up to 100% ( 6 , 33 ). In the subsequent cyclic increasing strain (>100%), the residual strain and hysteresis loop area of the obtained stress–strain curve gradually increased, which indicated that a large number of noncovalent bonds break as sacrifices in the process of continuous stretching, successfully dissipating energy ( 6 ). Moreover, to demonstrate the elastic properties, repeated cyclic tensile tests at a large strain of 300% were performed on i-Canogel 2:2, with no waiting time between two consecutive cycle tests for ten cycles ( Fig. 4 C ). The large hysteresis loop in the first cycle indicated significant energy dissipation due to the breaking of noncovalent bonds. The dissipation of energy during the second cycle of cyclic tensile testing was significantly lower than the first cycle due to insufficient time for the noncovalent bond to return to its original state. In subsequent tests, the hysteresis decreased slightly with increasing cycles, indicating continuous reorganization of sacrificial bonds. After relaxing for 2 h at 25 °C, the i-Canogel 2:2 showed good elastic recovery during the second cycle test, exhibiting a similar loading-unloading curve to the first cycle. Fig. 4. Mechanical properties of the i-Canogel. ( A ) The tensile stress–strain curves of pristine i-Canogel. ( B ) The tensile stress–strain curves of cyclic tensile tests with gradually larger strains of the prepared i-Canogel 2:2. ( C ) Repeated cyclic tensile curves of i-Canogel 2:2 at 300% strain. The samples were then left to rest for 2 h at 25 °C to allow for relaxation before the second cyclic tensile test. ( D ) The determined relaxation times of i-Canogel fitted to the Arrhenius equation. To gain insights into the dynamic behavior of i-Canogel, stress–relaxation tests were conducted on all samples at different temperatures to determine the network relaxation times accurately. These tests involved applying a 5% torsional strain and recording the relaxation modulus over time. The stress relaxation test results demonstrated that the i-Canogel exhibited significant relaxation between 25 and 55 °C, which indicated the dynamic dissociation of the oxime-urethane bonds ( 6 ) ( SI Appendix , Figs. S18–S20 ). Furthermore, the Maxwell model for viscoelastic fluids was used to determine the relaxation time (τ*), which was found to be 37% (G/G 0 = 1/e = 37%) of the normalized relaxation modulus. In addition, the temperature-dependent behavior of the relaxation time can be described by the Arrhenius equation τ ( T ) = τ 0 exp( E a / RT ), where τ represents the characteristic relaxation time, τ 0 represents the pre-exponential factor, and E a represents the activation energy for stress relaxation ( 34 ). The activation energy ( E a ) of stress relaxation for i-Canogel 1.5:2.5 was found to be significantly higher, at 77.92 ± 3.99 kJ mol –1 , compared to the E a values of i-Canogel 2:2 and i-Canogel 2.5:1.5, which were 55.56 ± 2.51 kJ mol –1 and 50.57 ± 1.99 kJ mol –1 , respectively, indicating that the material with the lower molar ratio of PBGAD to FTEG had a stronger network structure and higher thermal stability ( Fig. 4 D ). These results indicated that the molar ratio of PBGAD to FTEG had a significant impact on the thermal stability and network relaxation of i-Canogel. To assess the thermal stability of i-Canogel, thermogravimetric analysis (TGA) was performed, as the characterization and processing of i-Canogel involve multiple heating steps at high temperatures ( SI Appendix , Fig. S21 ). The results of TGA tests showed that all three networks had a similar temperature at 5% weight loss (T deg,5% ) in the nitrogen atmosphere, with values around 260 °C. Temperature-rising infrared testing was used to evaluate the generation of isocynanate group ( SI Appendix , Fig. S22 ). At room temperature, the characteristic peak of N=C=O was negligible, but as the temperature increased, the characteristic peak of N=C=O became increasingly higher. This indicated that the oxime-urethane bond had dissociated the N=C=O group, so it was a dissociative dynamic bond ( 30 ). When the temperature reached a certain degree, the dissociation of the oxime-urethane bond far exceeded the restructuring, and the thermosetting material could exhibit a melting point like a thermoplastic material. It was proved by the rheological experiments that the melting point of the i-Canogel increased gradually with the decrease in the molar ratio of PBGAD to FTEG ( SI Appendix , Figs. S23–S25 ). The melting points of i-Canogel n:m (1.5:2.5, 2:2, and 2.5:1.5) were found to be 138.9, 135.9, and 129.5 °C, respectively. Different contents of the cross-linking agent significantly impact the performance of material. ATR-FTIR confirmed the successful synthesis of i-Canogel AMG 2 and i-Canogel AMG 4 ( SI Appendix , Fig. S26 A ). TGA results indicated that both i-Canogel AMG 2 and i-Canogel AMG 4 exhibited excellent thermal stability ( SI Appendix , Fig. S26 B ). The dynamic mechanical analysis (DMA) showed that the T g of i-Canogel AMG 2 and i-Canogel AMG 4 was lower than −60 °C ( SI Appendix , Fig. S26 C and D ). The rheological test revealed that the melting point increased with higher AMG content ( SI Appendix , Fig. S26 E and F ). Uniaxial tensile test results revealed that as the AMG content increased, the material’s tensile strength is enhanced ( SI Appendix , Fig. S26 G – I ). However, due to the high cross-linking agent content, the elongation at break for i-Canogel AMG 4 was limited to approximately 300%. i-Canogel AMG 2 with relatively low cross-linking agent content unsatisfied elasticity and lower tensile strength. Accordingly, we selected i-Canogel AMG 3 (i-Canogel 2:2) with a moderate IPDI/AMG ratio of 10:3 for the following studies. Mechanical Self-Healing Properties of the Prepared i-Canogel. The results of the stress relaxation experiment indicated that the activation energy of the polymer network could significantly vary depending on the content of hard and soft segments. This could result in variations in the polymer network’s dynamic properties and affect its ability to recover from stress, with higher activation energies indicating a slower rate of network rearrangements and a decreased ability to self-heal. On the other hand, lower activation energies correspond to more facile network rearrangements and improved self-healing capabilities. Theoretically, the low E a in i-Canogel 2:2 and i-Canogel 2.5:1.5 resulted in more effortless network rearrangements, enabling enhanced self-healing properties in these i-Canogel formulations. The mobility of the segments in the prepared i-Canogel was evaluated using a differential scanning calorimeter (DSC) ( SI Appendix , Fig. S27 ). The DSC test results revealed that the temperature range of −70 to 200 °C did not show any glass transition point ( T g ). However, the melting point was in agreement with the results obtained from the rheometer. To determine the T g of the i-Canogel, DMA tests were performed ( SI Appendix , Figs. S28–S30 ). The data indicated a decrease in the T g of i-Canogel with an increase in the molar ratio of PBGAD to FTEG. The T g values for i-Canogel n:m (1.5:2.5, 2:2, and 2.5:1.5) were −59.6, −63.9, and −67.8 °C, respectively. The T g characterized the mobility of polymer chains at room temperature. At room temperature, i-Canogel exhibited enhanced elasticity and self-healing ability, as a result of its distinctive structure that involves the participation of neighboring bonds. The self-healing ability of i-Canogel was evaluated using a uniaxial tensile experiment ( Fig. 5 A ). Prior to the experiment, a strip of i-Canogel was cut into two pieces and then reconnected under ambient conditions. Large number of hydrogen bonds in the polymer network were expected to promote the self-healing of the i-Canogel ( 35 , 36 ). The self-healing ability was assessed after 24 h and the results showed that the tensile strength, elongation, toughness, and Young’s moduli of i-Canogel 1.5:2.5 and i-Canogel 2:2 had recovered to 1.58 ± 0.2 MPa, 228.89 ± 52.26%, 2.52 ± 0.77 MJ m −3 , and 0.93 ± 0.06 MPa, and 1.09 ± 0.15 MPa, 328.89 ± 60.05%, 2.29 ± 0.67 MJ m −3 , and 0.53 ± 0.05 MPa, respectively ( Fig. 5 A and C and SI Appendix , Fig. S31 ). The self-healing efficiencies of the i-Canogel 1.5:2.5 and i-Canogel 2:2 were determined and found to be lower than 50% for all tests except for Young’s modulus, where it was found to be greater than 90%. However, the i-Canogel 2.5:1.5 showed outstanding self-healing capabilities. The self-healing efficiencies were remarkable, with a value of 98.9% for tensile strength, 97.4% for elongation, 94.1% for toughness, and 96.6% for Young’s modulus, respectively ( Fig. 5 A and SI Appendix , Fig. S32 ). This indicated that the i-Canogel 2.5:1.5 had a high ability to recover its mechanical properties after being damaged. This was likely due to the good compatibility of the [EMI][TFSI] with the polymer and the special molecular topological cross-linking structure that allows for neighboring bond participation, which contributes to the excellent self-healing properties of the material. The mechanical properties of i-Canogel 2:2 exhibited excellent self-healing properties when the healing time was extended to 48 h. The self-healing efficiency of tensile strength, elongation, toughness, and Young’s moduli were 92.1%, 89.5%, 85.7%, and 98.8%, respectively ( Fig. 5 B and C ). The self-healing efficiency of i-Canogel 1.5:2.5 was only slightly higher after 24 h, which could be attributed to its high content of hard segments ( SI Appendix , Figs. S31 and S33 ). This conclusion was supported by the results of the stress relaxation experiment, which is consistent with the activation energy data. The self-healing behavior of polymers is often closely related to the properties of their surfaces. Additionally, the hydroxyl groups at the polymer chain ends likely accelerated the exchange of oxime-urethane bonds. We employed X-ray photoelectron spectroscopy (XPS) to quantify the hydroxyl groups on the material surfaces ( SI Appendix , Fig. S34 ). In the high-resolution O 1s XPS spectra, the hydroxyl content for i-Canogel n:m (1.5:2.5, 2:2, and 2.5:1.5) was 8.54%, 10.65%, and 19.94%, respectively. The test results were consistent with the trends observed in self-healing performance evaluated by mechanical tests. Fig. 5. Self-healing properties of the i-Canogel. ( A ) The stress–strain curves of pristine i-Canogel and healed samples for 24 h at room temperature. ( B ) The typical stress–strain curves of pristine and healed i-Canogel 2:2, confirming its self-healing ability for 48 h at room temperature. ( C ) The statistics of mechanical properties of the prepared i-Canogel 2:2. ( D ) Scatter plot of “Toughness,” “Strength,” and “Self-healing time” of i-Canogel 2:2 and other room temperature self-healing ionogels reported in the literature ( 37 – 44 ). To compare the effects of with or without neighboring group catalyzed dynamic bonds on the self-healing property of materials, we designed the following CANs ( SI Appendix , Scheme S5 ). We replaced the four-arm cross-linking unit AMG with dynamic chain extender without neighboring bonds participation (diacetylmonoxime, DMG) and nondynamic cross-linkers (glycerol) ( 6 , 30 ). Consequently, we synthesized counterpart ionogel (i-Canogel DMG 2:2) containing the same content of ionic liquid as in i-Canogel 2:2. ATR-FTIR results indicated successful synthesis of the compounds ( SI Appendix , Fig. S35 A ). Additionally, Fig. 5B and SI Appendix , Fig. S35 B showed that i-Canogel DMG 2:2 swelled but did not dissolve after being soaked in THF for 24 h, proving the cross-linked structure of i-Canogel DMG 2:2. Furthermore, using TGA, DMA, and rheological tests, the T deg, 5% , T g , and melting point of i-Canogel DMG 2:2 were determined to be 245 °C, −65 °C, and 154 °C, respectively ( SI Appendix , Fig. S35 C – E ). The mechanical evaluations were also conducted ( SI Appendix , Fig. S35 F ). The tensile strength, elongation, toughness, and Young’s moduli of i-Canogel DMG 2:2 were 0.56 ± 0.04 MPa, 801.1 ± 136.3%, 1.47 ± 0.09 MJ m −3 , and 32.7 ± 7.9 kPa, respectively. The tensile strength, toughness, and Young’s moduli of i-Canogel 2:2 were 8.1, 9.2, and 19.3 times greater than those of i-Canogel DMG 2:2 ( Fig. 3 A ). The results revealed that i-Canogel DMG 2:2 exhibited significantly lower tensile strength and room-temperature self-healing efficiency than i-Canogel 2:2 ( SI Appendix , Fig. S35 G ). After 48 h, the self-healing efficiencies of i-Canogel DMG 2:2 based on tensile strength, elongation, toughness, and Young's modulus were 62.5%, 76.3%, 53.3%, and 78.8%, respectively. All results demonstrated that i-Canogel 2:2 with internally catalyzed four-arm dynamic cross-linking points has good mechanical properties and room-temperature self-healing capability superior to couterpart i-Canogel DMG 2:2. The temperature sweep curves from the rheological experiments clearly showed that as the temperature increased, the storage modulus of i-Canogel rapidly decreased ( SI Appendix , Figs. S23–S25 and S35 E ). Compared to exchangeable dynamic bonds, dissociative oxime-urethane bonds were more conducive to extrusion and injection molding. When the temperature decreased, the recombination rate exceeded the dissociation rate, leading to a liquid–solid transition of the polymer network. The neighboring group participation enhanced the dissociation rate of oxime-urethane bonds, resulting in significantly lower melting point of i-Canogel 2:2 than that of i-Canogel DMG 2:2. At the same time, the four-arm cross-linked structure endowed i-Canogel 2:2 with excellent mechanical strength much higher than i-Canogel DMG 2:2. The mechanical self-healing performance of the ionogels, prepared using the neighboring bond participation effect, surpassed most of room temperature self-healing ionogels ( 37 – 44 ) ( Fig. 5 D ). Electrical Properties of the Prepared i-Canogel. Stretchable conductors play key roles in flexibility electronics, which have significant advantages in terms of flexibility, transparency, and elasticity ( 45 ). Compared to most existing electron-conductive materials, ion conductors (hydrogels and ionogels) exhibit softness, high stretchability, and transparency, leading to increasing attention. Furthermore, compared to hydrogels, ionogels are more stable as they are not susceptible to the issues caused by water freezing and evaporation. Therefore, ionogels have better application potential. The presence of [EMI][TFSI] in the i-Canogel imparted both physical lubrication, which improved the mobility of the polymer chains, and ionic conductivity, thus yielding an ionogel ( 46 ). The [EMI][TFSI] was stable and did not evaporate with time owing to anions being rich in C-F bonds and their negligible vapor pressure. The electrochemical impedance spectra revealed that as the hard segment content increased in the system, the resistance decreased ( Fig. 6 A ). The ionic conductivities of i-Canogel 1.5:2.5, i-Canogel 2:2, and i-Canogel 2.5:1.5 at 25 °C were 4.9 × 10 −2 , 5.1 × 10 −2 , and 5.7 × 10 −2 S m −1 , respectively ( Fig. 6 B ). The increase in hard segment content resulted in reduced flexibility of the chain segments, which in turn hindered the mobility of ions within the network. The decrease in ion mobility directly contributed to a reduction in electrical conductivity ( 47 ). The addition of the four-arm cross-linking units and ionic liquid in the i-Canogel led to its remarkable elasticity, enabling it to function as a strain sensor. The ionic conductivity of i-Canogel was temperature-dependent. As the temperature increased, the ionic conductivity of the material also increased. This can be attributed to the enhanced mobility and easier transport of ions at higher temperatures ( Fig. 6 C ). To assess the temperature-sensing capability of i-Canogel, i-Canogel 2:2 was chosen for evaluation ( Fig. 6 D ). The temperature-sensing performance of i-Canogel 2:2 showed a linear region from 25 to 60 °C. In addition, the sensitivity of the i-Canogel 2:2 was determined by the gauge factor (GF), which was calculated as GF = (ΔR/R 0 )/ε, where ΔR is the change in resistance relative to the nominal resistance R 0 at zero strain, and ε represents the tensile strain applied to the sensor. The resistance of the ion-conductive i-Canogel 2:2 exhibited a linear relationship with the applied strain. Linear equations were fitted to the data for practical applications of the i-Canogel 2:2. The relative change in resistance (ΔR/R 0 ) was observed to have a proportional relationship with the applied small strain, with a GF of 2.91 within the strain range of 0-75%. For larger strains, the GF increased to 4.57 within the strain range of 100 to 300% ( Fig. 6 E ). Strain sensors typically require conductors with exceptional elasticity and resistive responsiveness ( 48 , 49 ). By employing such stretchable and conductive materials, an expanding array of strain sensors has been developed for real-time monitoring of human motions. We utilized i-Canogel 2:2 to fabricate a strain sensor. The variation in sensor resistance was observed during cyclic tensile and release cycles at various strain levels, encompassing both small strains (5 to 10%) ( SI Appendix , Fig. S36 A ) and large strains (50 to 100%) ( SI Appendix , Fig. S36 B ). In addition to excellent elasticity and resistive responsiveness, instant self-healing properties were critical for the strain sensor. This capability was demonstrated in SI Appendix , Fig. S37 A , where i-Canogel 2:2 was incorporated into a circuit featuring a green LED connected to a power source. Cutting i-Canogel 2:2 into two segments caused the LED to turn off. Upon rejoining the two pieces of i-Canogel 2:2, the LED immediately illuminated again. Real-time monitoring of the resistance of the cut and healed i-Canogel 2:2 was conducted using a multimeter ( SI Appendix , Fig. S37 B ). Upon cutting, the resistance sharply increased to infinity, indicating a breakdown in the conductive path. Rejoining the two cut sections of i-Canogel 2:2 rapidly restored the conductive path, resulting in a corresponding decrease in resistance. After the initial cut-healing cycle, the electrical resistance recovered to its original value. Due to the nonvolatile and hydrophobic properties of [EMI][TFSI] and the hydrophobic nature of the hard segment in the polymer chain ( 50 , 51 ), the weight of i-Canogel remained stable even after being stored for 43 d under ambient conditions with varying relative humidity (33 to 82%) and temperatures (−1 to 30 °C) ( Fig. 6 F ). This observation suggested that i-Canogel exhibited environmental insensitivity, making it suitable for use in open-air and moist environments. Fig. 6. Electrical performance of the prepared i-Canogel. ( A ) Electrochemical impedance spectra of i-Canogel. ( B ) Ionic conductivity results of i-Canogel. ( C ) Temperature dependence of the ionic conductivities of i-Canogel in the temperature range of 25 to 60 °C. ( D ) Change in the resistance of i-Canogel 2:2 vs. temperature. ( E ) The resistance-strain curve of i-Canogel 2:2 strain sensor. ( F ) Weight retention of i-Canogel vs. time without specific encapsulation in ambient conditions."
} | 8,131 |
28878756 | PMC5572346 | pmc | 4,708 | {
"abstract": "Advances in metagenomics enable high resolution description of complex bacterial communities in their natural environments. Consequently, conceptual approaches for community level functional analysis are in high need. Here, we introduce a framework for a metagenomics-based analysis of community functions. Environment-specific gene catalogs, derived from metagenomes, are processed into metabolic-network representation. By applying established ecological conventions, network-edges (metabolic functions) are assigned with taxonomic annotations according to the dominance level of specific groups. Once a function-taxonomy link is established, prediction of the impact of dominant taxa on the overall community performances is assessed by simulating removal or addition of edges (taxa associated functions). This approach is demonstrated on metagenomic data describing the microbial communities from the root environment of two crop plants – wheat and cucumber. Predictions for environment-dependent effects revealed differences between treatments (root vs. soil), corresponding to documented observations. Metabolism of specific plant exudates (e.g., organic acids, flavonoids) was linked with distinct taxonomic groups in simulated root, but not soil, environments. These dependencies point to the impact of these metabolite families as determinants of community structure. Simulations of the activity of pairwise combinations of taxonomic groups (order level) predicted the possible production of complementary metabolites. Complementation profiles allow formulating a possible metabolic role for observed co-occurrence patterns. For example, production of tryptophan-associated metabolites through complementary interactions is unique to the tryptophan-deficient cucumber root environment. Our approach enables formulation of testable predictions for species contribution to community activity and exploration of the functional outcome of structural shifts in complex bacterial communities. Understanding community-level metabolism is an essential step toward the manipulation and optimization of microbial function. Here, we introduce an analysis framework addressing three key challenges of such data: producing quantified links between taxonomy and function; contextualizing discrete functions into communal networks; and simulating environmental impact on community performances. New technologies will soon provide a high-coverage description of biotic and a-biotic aspects of complex microbial communities such as these found in gut and soil. This framework was designed to allow the integration of high-throughput metabolomic and metagenomic data toward tackling the intricate associations between community structure, community function, and metabolic inputs.",
"introduction": "Introduction The biology of individual organisms is linked to their community and ecosystems via metabolic activity. Organisms take up energy and resources from the environment, convert them into other forms, and excrete altered forms back into the environment ( Brown et al., 2004 ; Perez-Garcia et al., 2016 ; Zomorrodi and Segre, 2016 ). Metabolic activity is a key determinant of interaction patterns between micro-organisms ( Klitgord and Segre, 2011 ; Widder et al., 2016 ). Microbial species not only compete for the available resources, but in many cases work together toward the degradation of complex polymers into simpler compounds ( Schink, 2002 ; Fuhrman, 2009 ; Marx, 2009 ; Koropatkin et al., 2012 ; Grosskopf and Soyer, 2014 ). Degradation chains shape the structure of the community as a primary degrader mediates the accessibility of energy sources to other members of the community. Secondary degraders rely on the presence of the primary mediators, and the final excretion products are determined according to the identity of the downstream chain members. The perception of ecosystems as a trinity of environment (specific resources) – community (possible conversion repertoire) – and function (excretion of altered forms), provides a conceptual framework for the study of microbial activity in ecological habitats. Shifts in community structure are hence assumed to reflect changes in either one or both adjacent edges in the community-environment-function trinity. High-resolution mapping of shifts in bacterial community structure has become widely accessible with the development of massive, low-cost, sequencing techniques. Together with biodiversity detection in environmental samples, metagenomics projects allow the construction of community-level gene catalogs ( Zengler and Palsson, 2012 ; Franzosa et al., 2015 ; Guo et al., 2015 ; Widder et al., 2016 ). A considerable effort has been invested in the development of computational approaches for a functional-oriented interpretation of such data and specifically in deciphering the variations in metabolic activity between treatments ( Stolyar et al., 2007 ; Freilich et al., 2011 ; O’Dwyer et al., 2012 ; Segata et al., 2013 ; Roling and van Bodegom, 2014 ; Zomorrodi et al., 2014 ; Bowman and Ducklow, 2015 ; Guo et al., 2015 ; Hanemaaijer et al., 2015 ; Roume et al., 2015 ; Zelezniak et al., 2015 ; Granger et al., 2016 ). Metagenomics driven gene catalogs are typically two dimensional, i.e., genes can be classified according to both functional annotation and taxonomic affiliation ( Greenblum et al., 2012 ). Functional annotations allow the construction of community level metabolic networks, similarly to the construction of species-specific networks, based on the content of enzyme coding genes in their respective genomes ( Abubucker et al., 2012 ; Levy and Borenstein, 2013 ; Roume et al., 2015 ; Tobalina et al., 2015 ). Subsequently, predictions for network-specific sets of source-metabolites can be inferred through computational approaches, providing an approximation of the relevant metabolic content of an environment ( Borenstein et al., 2008 ; Handorf et al., 2008 ). Computational simulations can then address the influence of environmental inputs (nutritional resources) on network dynamics. At the species (genome) level, metabolic-activity simulation allows predicting the effects of environmental and genetic perturbations through iterative modifications of the available metabolic inputs and/or network structure, respectively ( Freilich et al., 2009 , 2010 ). At the community level, a similar approach can be applied for delineating functional division between community members. By overlaying the taxonomic dimension over network edges (functional annotation), metabolic capacities contributed exclusively by specific taxa can be grouped. The communal network functional performances can then be tested by simulating the iterative removal or addition of corresponding network edges specifically associated with key taxonomic groups. Such iterations can, first, describe the metabolic hierarchy where different taxonomic groups are expected to vary in their contribution for converting complex nutrients into widely accessible ones; second, reveal variations between treatments in network performances. The main goal of this study is to use metabolic-network approach to explore the environment-function-structure associations in the complex microbial communities of the rhizosphere microbiome (rhizobiome). The rhizosphere is the soil known as the area that is directly under the influence of living roots. The rhizobiome is known to be strongly influenced by plant roots activity. These act as selective nutritional sources for phytochemicals that stimulate and support enrichment of specific groups of soil microorganisms ( Larkin et al., 1993 ; Smith et al., 1997 , 1999 ; Berg et al., 2002 ; Mazzola, 2004 ; Cook, 2006 ; Ikeda et al., 2006 ; Micallef et al., 2009 ; Ofek-Lalzar et al., 2014 ; Ofek et al., 2014 ; Haldar and Sengupta, 2015 ). Advances in sequencing technologies promoted the extensive characterization of community structures in rhizosphere compared with the more distant soil, not under the direct effect of the root ( Mirete and González-Pastor, 2010 ; Turner et al., 2013 ; Bouffaud et al., 2014 ; Lakshmanan et al., 2014 ; Ofek et al., 2014 ; Roume et al., 2015 ). A published gene catalog, constructed from genomic DNA that was extracted from the root and respective soil samples of cucumber and wheat, was used for characterizing a core set of functional genes associated with root colonization ( Ofek-Lalzar et al., 2014 ). Here, we hypothesized that analyzing this gene catalog using a metabolic network based framework will further allow associating specific functions with taxonomic groups and external metabolic signals (such as those induced by root plants). Starting from this gene catalog, we constructed four environment-specific metabolic networks (cucumber root and soil; wheat root and soil) and predicted specific externally consumed metabolites associated with each environment, as well as network functions dominated by specific taxonomic groups (order level). The impact of each taxonomic group was assessed through the dynamic removal of the enzymatic functions of specific groups, one by one and all at once. Similarly, complementation potential of bacterial combinations – that is, the ability of taxonomic groups to co-produce metabolites that are not synthesized by any of the individual entities, was explored in the four different niches.",
"discussion": "Discussion “Omics” approaches are moving toward describing the full picture of host–microbe interactions requiring integration and systems-level modeling ( Nayfach and Pollard, 2016 ). Here, we suggest a framework for the functional interpretation of metagenomic data providing predictions for the contribution of key taxonomic groups to the overall community performances. Identification of such group-specific functions is typically not trivial and is hampered by the complex nature of microbial communities. Our approach addresses three key challenges. First, almost all functions are associated, to different degrees, with multiple taxonomic groups. Hence, the definition of unique vs. core enzymes requires quantitative estimates for the phylogenetic representation of reads assigned. This need for a measure of the degree of taxonomic dominance over function can be viewed as an extension of the long-discussed concept of taxonomic dominance over ecological environments. The Simpson index is the conventional measure used by ecologists to describe species dominance in a habitat ( Heip et al., 1998 ; Zhang et al., 2014 ). The innovative application of this well-established index in the current study produced quantified links between functions and taxonomic groups, tackling an unsolved challenge in functional analysis. Once such link is established, a second challenge is predicting the impact of taxa-dominated functions on overall community performance. It is well-established that environments populated by highly complex bacterial communities show high functional robustness ( Monard et al., 2011 ; Stenuit and Agathos, 2015 ; Bordron et al., 2016 ). The contextualizing of discrete enzymes into functional networks, as done here, allows directly assessing robust functions vs. these relying on specific groups/group combinations. Third, taxonomic structure and functional variations are often induced by environmental inputs. Our framework allows an approximation of environmental effect through simulating activity in different natural-like environments. Here, we demonstrate the application of the framework for the analysis of a metagenomics derived gene catalog from the complex microbial communities of plant roots ( Ofek-Lalzar et al., 2014 ). The rhizobiome is a central determinant of crop health and yield, hence understanding how to manipulate rhziobiome communities toward desired function is a major agricultural concern ( Mazzola and Freilich, 2017 ). Rhizobiome communities are strongly influenced by root activity where plant secretion is a key determinant of their structure ( De-la-Pena and Loyola-Vargas, 2014 ; Jha et al., 2015 ). Our framework was applied for tackling the intricate associations between community structure, community function, and metabolic inputs in this important ecosystem. The metabolic context created by these associations extends previous findings of functional capabilities in root systems and allows testing the significance of individual taxonomic groups within their community. We simulated environment specific communal performances, associated functions with specific taxonomic groups, and identified potential co-exchange patterns leading to the production of complementary metabolites. The simulations and the resulting predictions are environment-specific, based on computational approximation of the key available nutrients in different treatments. The simulated observations are in accordance with common ecological and network concepts. First, the communal networks are highly robust where the large majority of basic metabolism functions are conserved between environment and do not rely on specific groups. Yet, despite this inherent robustness, the analysis succeeded in pointing at several taxonomic-associated functions. Many of these functions are unique to the root-like environment (vs. soil) and are in agreement with reported observations. Examples for such predictions made include utilization of plant exudates as linoleic-acid, flavonoids, and geraniol by Rhizobiales, Sphingomonadales , and Burkholderiales . Finally, the predictions for the profiles of complementary metabolites, formed between specific taxonomic combinations in specific environments, may suggest a possible functional significance for observed co-occurrence patterns. For example, Burkholderiales and Xanthomonadales activity can possibly compensate for the low levels of tryptophan secreted in by the cucumber’s root. Overall, the presented approach was successful in predicting root-specific effects that link the utilization of specific environmental nutrients (here, plant exudates) with specific taxonomic groups, pointing at the impact of each such compound as a determinant of the microbial community structure. Caveats of the current analysis, reflecting both data-driven and conceptual limitations should be acknowledged. Most notably, data-driven limitations include the partial coverage of metagenomic sequence data. The dataset used here, as in most data collected from complex environments (such as the root and soil), does not provide a full coverage description of the corresponding communities. Future projects are expected to provide a rapidly increasing coverage; such coverage will allow the detection of the less abundant functions and assembly guided taxonomic classification of sequence reads. In parallel to the advent of sequencing technologies, metabolomics technologies are now rapidly emerging ( El Amrani et al., 2015 ; Daliri et al., 2017 ; Parmar et al., 2017 ). Though in the current analysis environmental approximations are based on computational predictions, we expect that in the very near future a growing number of ecosystems will be subject to an extensive profiling by metabolomics technologies. The framework was designed to allow the future integration of such data in concert with ultra-high coverage metagenomic sequencing. Finally, the inclusion of transcriptomic data, produced together with metabolomics and higher coverage metagenomic information will allow a more comprehensive and more accurate description of community function. Information on transcriptomics/metabolomics paves the way for quantitative predictions of metabolic fluxes ( Heinken and Thiele, 2015 ; Sajitz-Hermstein et al., 2016 ; Valgepea et al., 2017 ). To date, quantitative modeling using for example, Constraint-Based Modeling is typically applicable to relatively simplistic communities and consortia ( Ye et al., 2014 ; Koch et al., 2016 ; Budinich et al., 2017 ). Recent works attempt to apply quantitative models toward the study of complex microbial communities ( Bauer et al., 2017 ; Magnusdottir et al., 2017 ). The partiality of data (metagenomics, metatranscriptomics, and metabolomics) from highly diverse ecosystems, together with the computational complexity associated with community-level genome scale metabolic modeling and biases stemming from automated and semi-automated model curation approaches makes topological-based qualitative approaches, as applied here, a powerful and relatively straightforward framework for the analysis of genome-wide ‘omics’ data ( Heinken and Thiele, 2015 ; Taxis et al., 2015 ; Charitou et al., 2016 ). Furthermore, it has been suggested that ecological dynamics, as predicted by network topology based frameworks, are of great impact on the metabolic capacity of complex bacterial communities and provide insights on the drivers of species-metabolite dynamics ( Noecker et al., 2016 ). Though predictions derived from the framework might include biases introduced due to the limitations of the current data, many of our simulated observations correspond with the documented role of bacterial groups, supporting the biological relevance of the analyses. Such predictions should be treated as educated ‘leads’ that are useful for the formulation of testable hypotheses. Predictions from the framework used here allow researchers to delineate biological signal from complex data and to rationally design possible manipulation strategies that will induce optimized function. Predictions-based design of agricultural practice can include (i) the identification of microorganisms carrying desired or undesired functions and (ii) the characterization of the effect of the introduction of environmental treatments (that is, adding/depleting specific compounds) ( Mazzola and Freilich, 2017 ). In the absence of appropriate analysis tools and considering the volume of data produced in metagenomics studies, identification of meaningful associations resembles finding a needle in a haystack. Hence, despite limitations, metabolic models can serve as a starting point for generating experimentally testable hypotheses ( Magnusdottir et al., 2017 ). In summary, this work contributes to the current efforts in the field of Systems Biology for developing new conceptual approaches for the analyses of metagenomic data allowing delineating biological processes and integrating testable predictions. More generally, the framework addresses key ecological challenges regarding the intricate associations between community structure, community function and metabolic inputs and is applicable to a wide array of systems including the human gut, biofilms biotechnological production and bioremediation."
} | 4,685 |
22065989 | PMC3204971 | pmc | 4,710 | {
"abstract": "Background The Symbiodinium community associated with scleractinian corals is widely considered to be shaped by seawater temperature, as the coral's upper temperature tolerance is largely contingent on the Symbiodinium types harboured. Few studies have challenged this paradigm as knowledge of other environmental drivers on the distribution of Symbiodinium is limited. Here, we examine the influence of a range of environmental variables on the distribution of Symbiodinium associated with Acropora millepora collected from 47 coral reefs spanning 1,400 km on the Great Barrier Reef (GBR), Australia. Methodology/Principal Findings The environmental data included Moderate Resolution Imaging Spectroradiometer (MODIS) satellite data at 1 km spatial resolution from which a number of sea surface temperature (SST) and water quality metrics were derived. In addition, the carbonate and mud composition of sediments were incorporated into the analysis along with in situ water quality samples for a subset of locations. Analyses were conducted at three spatio-temporal scales [GBR (regional-scale), Whitsunday Islands (local-scale) and Keppel Islands/Trunk Reef (temporal)] to examine the effects of scale on the distribution patterns. While SST metrics were important drivers of the distribution of Symbiodinium types at regional and temporal scales, our results demonstrate that spatial variability in water quality correlates significantly with Symbiodinium distribution at local scales. Background levels of Symbiodinium types were greatest at turbid inshore locations of the Whitsunday Islands where SST predictors were not as important. This was not the case at regional scales where combinations of mud and carbonate sediment content coupled with SST anomalies and mean summer SST explained 51.3% of the variation in dominant Symbiodinium communities. Conclusions/Significance Reef corals may respond to global-scale stressors such as climate change through changes in their resident symbiont communities, however, management of local-scale stressors such as altered water quality is also necessary for maintenance of coral- Symbiodinium associations.",
"introduction": "Introduction Unicellular photosynthetic symbionts ( Symbiodinium spp.) play a vital role in the energy budget, metabolism and secretion of the calcium carbonate skeleton of scleractinian corals [1] , [2] . The Symbiodinium community associated with scleractinian corals is widely considered to be influenced by host identity and environmental factors and has been shown to shape the coral's tolerance to environmental extremes [3] , [4] . Symbiodinium is a diverse dinoflagellate genus comprising nine phylogenetic clades (A–I), which are subdivided into numerous types based on ribosomal and chloroplast DNA [5] , [6] . Of the nine known clades, six (A–D, F and G) have been identified from scleractinian corals with clades C and D being dominant in the Indo-Pacific. Functional differences among clades and types are known to confer competitive advantages to their host leading to increased resistance to thermal stress (e.g. [4] , [7] , [8] ) and diversification into low light mesophotic habitats [9] , [10] . Sea surface temperature (SST) is an important influence on the coral- Symbiodinium association under natural conditions [11] – [13] and as a driver of Symbiodinium community shifts under bleaching conditions [8] Light is also known to exert important controls on the structure of Symbiodinium communities (e.g. [14] ). Studies conducted throughout the Caribbean and Indo-Pacific have found that while some clade C types occur abundantly under a variety of thermal and light conditions, clade D is generally found in warmer water or turbid environments [4] , [15] , [16] . However, the affinity of clade D to certain environments is host-specific and several studies have found clade D types to be more abundant in both shallow, high light environments [10] , [13] , [17] and low light or turbid environments (Great Barrier Reef, GBR; [18] – [20] ). Studies of the genetic diversity of Symbiodinium over large-scale spatial gradients have shown geographically distinct populations that differ with latitude or inshore to offshore conditions (e.g. [15] , [21] ). The general conclusion from these studies is that Symbiodinium diversity is driven by acclimatization to local environments (e.g. latitudinal changes in SST; inshore to offshore conditions). Over smaller spatial scales, variation in Symbiodinium community composition across depth gradients [7] , [22] has been shown to be greater than over larger horizontal spatial scales [13] , further suggesting that light is an important driver of Symbiodinium diversity. Thus, local-scale patterns cannot be extrapolated to regional scales given that environmental drivers of coral- Symbiodinium associations operate at different spatial scales [23] . In addition to variability in their spatial distributions, Symbiodinium associations are flexible over temporal scales with evidence of shuffling from thermo-sensitive to tolerant types in adult corals during SST anomalies on time scales of months and reverting to their post-bleaching consortia within months or several years [8] , [24] . The influence of other environmental drivers such as water quality, nutrient levels and sediment-types on Symbiodinium biogeography remains poorly understood [25] . Nutrient levels may play a role given their importance in symbiont metabolism and biomass dynamics [26] , while sediment type may be important as the free-living stage of Symbiodinium associate predominantly with the benthos [27] . A better understanding of the specific environmental drivers of Symbiodinium biogeography is fundamental for prediction of coral community responses to a changing climate. To achieve this, comprehensive studies that examine the influence of multiple predictors are required. The scleractinian coral Acropora millepora (Ehrenberg, 1834) has been shown to host a variety of Symbiodinium clades and types including C1, C2, A and D (based on ITS1-SCCP or ITS1-QPCR) either individually or simultaneously [4] , [8] , [19] , [20] , [28] . Symbiont populations may undergo change [5] in response to transplantation to a different environment [4] or following a natural bleaching event [8] . The flexibility of A. millepora - Symbiodinium associations makes it an ideal model to test the importance of environmental drivers in shaping the distribution of Symbiodinium . This study reports on the correlation between a range of environmental variables and the distribution of Symbiodinium associated with A. millepora collected at different spatial scales: i) along 13° latitude spanning approximately 1,400 km on the GBR, ii) along a persistent water quality gradient in the Whitsunday Islands spanning approximately 65 km [29] , and iii) on populations sampled repeatedly over a number of years at Davies and Trunk Reefs, and the Keppel Islands. The environmental predictors combine satellite data for the regional-scale analysis with water quality samples collected in the Whitsunday Islands for the local-scale analysis. Our results suggest that Symbiodinium distribution in A. millepora is not always primarily driven by temperature, but that it is dependent on the combination of a variety of environmental variables. Further, depending on the thermal history, the spatial and temporal scales over which a study is conducted are likely to influence the patterns observed.",
"discussion": "Discussion One of the fundamental gaps underlying studies of Symbiodinium biogeography is the lack of highly resolved spatial and temporal biological and environmental data. A combination of meta-analysis and field data for coral samples, coupled with large-scale satellite data as well as in situ water quality and sediment data provided a high resolution spatio-temporal dataset for this study. This has deepened our understanding of environmental controls of the patterns of Symbiodinium diversity and distribution in A. millepora by establishing that factors other than SST influence Symbiodinium biogeography and local- and regional-scale patterns are influenced by a variety of factors. Regional-scale drivers of symbiont communities Our results show that spatial and temporal scales are important for determining the environmental drivers that have the strongest influence on Symbiodinium associations in A. millepora on the GBR. At regional scales, the patterns of variability in Symbiodinium distribution were best explained by a combination of mud and carbonate content as well as SST anomalies and mean summer SST. Sediment types can influence the level of resuspension in the water column and therefore light levels but may also act as a reservoir of Symbiodinium for uptake by corals such as A. millepora that acquire new symbionts from their environment every generation. It is unknown how sediments influence the symbiont community in corals, but since the free-living stage of Symbiodinium is predominantly sediment-associated [27] , it is conceivable that sediment type and particle size could influence the ecology of the free-living symbionts. While the spatial distribution of mud and carbonate broadly co-vary with distance from shore and water clarity (as a proxy for light), our data show that water clarity per se (here measured as Secchi depth) correlates weakly with symbiont type suggesting that factors other than distance from shore are implicated. Anomalies in SST and mean summer SST that exceed the thermal tolerance of corals are conducive to bleaching conditions and may drive the shuffling of Symbiodinium types as observed by [4] and [8] for A. millepora . The GBR-wide distribution of the most abundant symbiont type, Symbiodinium C2, appears to be driven by long-term SST, mean summer SST as well as long-term and 3-month Secchi depth and mud content of the sediment, suggesting that it is adapted to a broad range of mean, non-anomalous, temperature regimes and water quality conditions. The fact that the remaining Symbiodinium types associated strongly with the optical properties of the water column and sediment composition suggests that water clarity and possibly nutrient levels may play a role in less abundant Symbiodinium consortia, with anomalous thermal regimes an important co-driver. The distribution of Symbiodinium D1 was strongly related to many of the environmental predictors that were examined. Previous studies have focused predominantly on temperature and have found a strong association between temperature extremes and D1 prevalence [16] , [23] . However, our results suggest that water clarity and sediment type may also be important factors governing the distribution of D1. Although the association of D1 with water clarity is generally consistent with the conclusion of LaJeunesse et al. (2010) [15] , we suggest that caution be applied in interpreting results where the standard chlorophyll- a concentration algorithm [30] is used as a measure of water clarity in shallow coral reef waters, especially at low (e.g. 24 sq km) resolution. The empirical chlorophyll- a concentration algorithm was developed for open ocean Case-1 waters [31] and is unable to correct for bottom reflectance or the presence of scattering particles resulting in significant contamination of the signal in shallow or turbid (Case 2) waters. \n Symbiodinium clade D is pandemic but uncommon on a global-scale, occurring predominantly on reefs that are subjected to periodic stress or that have a history of bleaching. As such, clade D types have been described as opportunistic endosymbionts that are able to out-compete other types in health-compromised corals [32] . This view is inconsistent with the observation of stable D1 symbiont communities in A. millepora at locations such as Magnetic Island, which is difficult to classify as “health-compromised” in the context of coral cover, juvenile coral recruitment, or coral species diversity [33] . Here, D1 prevalence has been shown to be governed by a combination of environmental predictors, hence it is unlikely that stress conditions alone drive their abundance. Rather, responses to environmental conditions coupled with possible local adaptation operate in concert to fulfil their physiological requirements [5] , [23] , [32] . Influence of local-scale variation in environmental drivers (water quality) on Symbiodinium distribution Aside from SST and light levels, environmental variables such as water quality have generally not been considered previously as environmental controls on the distribution of Symbiodinium , despite having known effects on symbiont density (e.g. [26] , [34] ) and photo-physiology [35] , [36] . The environmental gradients observed across the Whitsunday Island region are stronger and more persistent than those observed along the GBR [29] , [37] and have been linked to changes in photo-physiology and coral assemblages with increasing distance away from the discharge of two local rivers [36] , [38] , [39] . The pattern of variability of Symbiodinium types was best explained by the mud content of sediment, the gradient of which is particularly strong in the Whitsunday Islands. Our local-scale results also show that the distribution of Symbiodinium C2 was best explained by long-term Secchi depth, highlighting again that water quality, and possibly nutrient levels, are important drivers of Symbiodinium distribution. Further, Secchi depth has previously been shown to correlate with large-scale differences in Mesoamerican symbiont communities [40] . As such, water clarity can exert strong local structuring on the marine environment due to terrestrial runoff [41] , and wind driven resuspension of sediments [42] , that in turn can drive changes at all trophic levels on coral reefs. It is unclear why Secchi depth related more strongly to Symbiodinium C2 at a local-scale compared to the regional GBR-scale. It may simply be a function of higher local-scale sampling intensity of the environmental gradients, which could produce stronger statistical associations. On the other hand, Oliver and Palumbi (2009) [23] also found that regional-scale thermal correlates could not be demonstrated in the west Pacific. They attributed their findings to other factors such as host responses, other environmental drivers, or within-type physiological diversity. Given the high potential for local adaptation of Symbiodinium as a result of large population sizes, significant existing heritable genetic variation in physiological performance [43] , [44] , and restricted gene flow among GBR populations [45] , it should not be surprising that responses differ between regional and local scales. The possibility that local-scale environmental conditions may structure Symbiodinium associations in contrasting ways to those observed at larger spatial scales warrants further investigation. Temporal variability & acclimatization The occurrence of temporally variable symbiont communities at some locations and not at others is an issue that remains poorly understood. Three of the four sites where substantial temporal changes took place occurred in the Keppel Island group. Symbiont shuffling in this area has been documented following a natural bleaching event in early 2006 [8] but has clearly also taken place previously as a result of bleaching in 2002 [46] . Similarly, A. millepora at Trunk Reef in the central GBR underwent a shift from Symbiodinium C1 towards the end of a warm summer in 2005 to Symbiodinium C2 in 2009. This is the first documented case of temporal symbiont variability on an offshore reef on the GBR indicating that shuffling is not restricted to the more environmentally variable inshore reefs. It is worth noting that all of the temporal changes in symbiont communities presented can be directly correlated to thermal stress (Keppels: 2002 and 2006 bleaching events; Trunk Reef: 2005). This highlights the importance of thermal stress as the driver of symbiont change and the need for standardizing data to non-stressful periods prior to undertaking large-scale analyses to avoid extra sources of variation. The stronger association of Symbiodinium C1 with Secchi depth than with temperature is probably an artefact of the data since it is influenced substantially by a successional change in symbiont types at North Keppel and Miall Islands after bleaching. At these sites symbiont communities changed from Symbiodinium C2 to Symbiodinium D1 and C1 dominance during and after bleaching, and remained as a mix of predominantly Symbiodinium C1 and Symbiodinium D1 over following months, eventually drifting back to C2 dominance over a period of 1–2 years. Symbiodinium C1 is considered thermo-tolerant [47] with a photo-physiology that allows it to out-compete Symbiodinium D1 over a period of time until it is itself out-competed by Symbiodinium C2 [47] – [49] . Conversely, symbiont communities remained stable during, and following, the 2002 bleaching event at Magnetic Island (100% D1 dominant) and Davies Reef (100% C2* dominant). These results highlight our limited understanding, not only of the factor(s) that influence symbiont shuffling, but also of the prerequisite symbiont makeup and densities that are required for shuffling to take place. In summary, given the importance of Symbiodinium for holobiont metabolism and health, understanding the environmental controls on the distribution of symbiont types is crucial if we are to manage the resilience of coral reefs in an era of rapid environmental change. Modelling of Symbiodinium community structure as well as accumulation of large-scale data in space and time through meta-analysis and satellite imagery is important to allow reliable predictions of biological responses to environmental changes. Our findings highlight that drivers of specific associations between Symbiodinium and A. millepora are multiple and varied depending on the spatial and temporal scale at which investigations took place. At larger scales, SST variables are important drivers whereas local-scale patterns can be explained by variables that are affected by environmental gradients caused by local events. Importantly, this study is unique among other large-scale studies in that the samples all come from the same coral species, which avoids possible confounding of patterns due to host identity, thus reducing the risk of spurious environmental correlations."
} | 4,666 |
36828821 | PMC9958194 | pmc | 4,711 | {
"abstract": "Lytic polysaccharide monooxygenases (LPMOs) catalyze oxidative cleavage of crystalline polysaccharides such as cellulose and are crucial for the conversion of plant biomass in Nature and in industrial applications. Sunlight promotes microbial conversion of plant litter; this effect has been attributed to photochemical degradation of lignin, a major redox-active component of secondary plant cell walls that limits enzyme access to the cell wall carbohydrates. Here, we show that exposing lignin to visible light facilitates cellulose solubilization by promoting formation of H 2 O 2 that fuels LPMO catalysis. Light-driven H 2 O 2 formation is accompanied by oxidation of ring-conjugated olefins in the lignin, while LPMO-catalyzed oxidation of phenolic hydroxyls leads to the required priming reduction of the enzyme. The discovery that light-driven abiotic reactions in Nature can fuel H 2 O 2 -dependent redox enzymes involved in deconstructing lignocellulose may offer opportunities for bioprocessing and provides an enzymatic explanation for the known effect of visible light on biomass conversion.",
"introduction": "Introduction Every year, 100 billion tons of CO 2 are converted to cellulose by photosynthetic organisms 1 , making lignocellulosic plant biomass the most abundant natural material on Earth and a large reservoir of renewable carbon that can be transformed to chemicals and fuels. However, plant cell walls have evolved to become recalcitrant co-polymeric structures to provide mechanical strength and rigidity and to provide resistance against pathogen attack, and are, thus, hard to break down 2 . Plant cell wall-degrading microorganisms have solved this challenge by developing multi-component enzymatic tools that act synergistically to process this highly complex and recalcitrant biomass. Selective oxidation of non-activated C-H bonds in crystalline cellulose by lytic polysaccharide monooxygenases (LPMOs) is crucial for efficient aerobic decomposition of plant biomass 3 – 6 . LPMOs are abundant in Nature and classified, based on their sequences, in the auxiliary activity (AA) families 9–11 and 13–17 of the Carbohydrate Active enZymes (CAZy) database 7 . LPMOs are mono-copper enzymes 4 , 5 that catalyze oxidative cleavage of glycosidic bonds in insoluble polysaccharides such as cellulose 5 , 6 and chitin 3 , as well as in certain hemicelluloses 8 , 9 . LPMOs were first considered monooxygenases as the activity was shown to depend on the presence of molecular oxygen, but recent studies have demonstrated that H 2 O 2 is the kinetically relevant co-substrate making these enzymes peroxygenases rather than monooxygenases 10 – 14 . The oxidative action of LPMOs disrupts the crystalline polysaccharide surface 15 , 16 thus promoting depolymerization by hydrolytic enzymes 3 , 17 . It is generally accepted that LPMOs are the C1 factor hypothesized by Elwyn Reese and co-workers in 1950 18 and that LPMOs explain why Eriksson et al. found, in 1974, that oxygen promotes biomass conversion by a fungal secretome 19 . LPMO catalysis was first thought to require delivery of two electrons, two protons and molecular oxygen per catalytic cycle in what would be a monooxygenase reaction (R-H + 2e − + 2H + + O 2 → R-OH + H 2 O), whereas in the peroxygenase reaction, a reduced LPMO can catalyze multiple turnovers with H 2 O 2 (R-H + H 2 O 2 → R-OH + H 2 O) 20 . A standard monooxygenase reaction set-up involves incubating the LPMO with substrate and a reductant under aerobic conditions and it has been shown that a wide variety of reducing compounds and reducing equivalent-delivering enzymes can drive LPMO reactions 4 , 21 – 27 . It is currently being debated whether observed monooxygenase reactions are in fact peroxygenase reactions that are limited by the in situ generation of H 2 O 2 by LPMO-catalyzed or abiotic oxidation of the reductant (e.g., Bissaro et al. 28 ). Importantly, like for other redox enzymes, high levels of H 2 O 2 combined with low levels of substrate will lead to autocatalytic oxidative damage in the catalytic center of the enzyme 10 , 17 , 29 . H 2 O 2 -driven LPMO catalysis is a double-edged sword, enabling high enzymatic activity at the possible cost of enzyme inactivation. Light represents an abundant and cheap source of energy that can be harvested by a photoredox catalyst to tailor H 2 O 2 levels to enzymatic reactions 30 , 31 . Light-driven LPMO reactions were first described in 2016. Cannella et al. 32 showed that the activity of a fungal LPMO acting on amorphous cellulose (PASC) could be boosted dramatically by adding chlorophyllin, a photosynthetic pigment, and light, next to the reductant, ascorbic acid (AscA). Light-driven activity of a bacterial LPMO from Streptomyces coelicolor ( Sc AA10C) on crystalline cellulose (Avicel) using irradiated vanadium-doped titanium dioxide (V-TiO 2 ) was demonstrated later the same year 33 . Both studies discussed molecular mechanisms for the observed LPMO activity, but neither considered light-induced formation of H 2 O 2 from O 2 as the primary driver for LPMO activity, which, later, was shown to be the key driver of LPMO activity in these light-fueled reaction systems 23 . The impact of light on biomass conversion is of great interest, with repercussions spanning from the global carbon cycle to industrial biorefining. Light has been demonstrated to facilitate microbial decomposition of plant litter by increasing the accessibility of cell wall polysaccharides to enzymatic conversion 34 – 38 . Since secondary plant cell walls, the natural substrates of LPMOs, are rich in lignin, and since lignin is photoactive and can promote formation of H 2 O 2 39 , 40 , we hypothesized that light-driven redox processes involving lignin and LPMO activity can help explain the observed photofacilitation of biomass decomposition. Of note, possible effects of light may also be relevant for reactor design in industrial biorefining of lignocellulosic biomass, since pretreated feedstocks that are subjected to enzymatic saccharification with LPMO-containing cellulolytic enzyme cocktails usually contain large amounts of lignin. Here we report a detailed biochemical study of cellulose degradation by Sc AA10C, a well-studied model LPMO from the soil actinomycete Streptomyces coelicolor , using light-exposed lignin to fuel the LPMO reaction. We show that light-exposure of lignin has a large effect on LPMO activity and that this effect is driven by the ability of lignin to promote generation of H 2 O 2 . We also show that the necessary priming reduction of the LPMO may be achieved through direct interactions with polymeric lignin and that LPMOs, thus, can oxidize lignin. Using NMR spectroscopy, we demonstrate the impact of visible light on the lignin structure, revealing effects on olefinic structures. Next to providing insight into how lignin and light-exposed lignin affect LPMO activity, this study offers an alternative, enzyme-based explanation for the effect of light on biomass turnover in the biosphere.",
"discussion": "Discussion Biotic degradation of recalcitrant carbohydrates in plant litter is promoted by sunlight. This effect is believed to stem from photodegradation of lignin in secondary plant cell walls, which would increase the availability of cell wall carbohydrates for enzymatic degradation 34 – 36 , 38 . LPMOs are key to aerobic solubilization of cellulose and other polysaccharides 55 , 56 from plant cell walls and, in the present study, we show that the impact of light on biomass degradation may relate to the activity of these enzymes. We show that irradiation of lignin promotes lignin oxidation and formation of H 2 O 2 , which fuels the LPMO reaction. Notably, abiotic generation of H 2 O 2 in the biomass may also promote the activity of other biomass-converting and H 2 O 2 -consuming enzymes, for example lignin peroxidases. This study provides further evidence for H 2 O 2 -driven LPMO activity and adds to the notion that LPMOs are peroxygenases, and that the monooxygenase activity of these enzymes, if existing at all, is of minor importance, kinetically. We demonstrate that LPMO activity is improved in conditions generating higher H 2 O 2 levels and is inhibited by HRP, supporting the notion that the LPMO reaction is H 2 O 2 -dependent. Since LPMOs are susceptible to autocatalytic inactivation 10 , 57 , as also demonstrated here, in Fig. 1 and Supplementary Fig. 1 , regulating the amount of H 2 O 2 available to the LPMO is important. The use of lignin and light not only offers a cheap and abundant source of reducing power for LPMO reactions, but could also be used to obtain better control and regulation, as previously shown for light-driven LPMO reactions with chlorophyllin 32 , 42 , 58 . It should be noted that the use of light to control LPMO activity in commercial bioreactors operating at high dry matter concentrations with for instance lignocellulose will be challenging as light is attenuated in reaction slurries. Still, light will penetrate to some extent and it is thus worth noting that the present results suggest that the outcome of lignocellulose saccharification experiments with LPMO-containing cellulase cocktails may depend on the vessel type (glass or steel) and the light conditions in the laboratory or the industrial plant. These light attenuation issues will not apply in light/lignin fueled reaction with other H 2 O 2 -dependent enzymes, for example the oxyfunctionalization of hydrocarbons recently reported by Kim et al. 40 . LPMO catalysis depends on reducing equivalents that are needed to bring the enzyme in its reduced, catalytically competent state. Since a once reduced LPMO can catalyze multiple peroxygenase reactions 14 , 17 , 59 and since most LPMO reactions likely are limited by available H 2 O 2 , the amount of LPMO reduction needed to maintain optimal reaction speed is somewhat unclear but is certainly much lower than the need for in situ generation of H 2 O 2 . We show here that LPMOs can oxidize polymeric lignin directly to recruit electrons and do so at an appreciable rate. The rates determined in our stopped-flow experiments are one order of magnitude lower than those observed for lignin oxidation by manganese peroxidase 60 , between two and three orders of magnitude lower than the most efficient lignin peroxidases 61 , and two orders of magnitude lower than LPMO reduction by one of the most efficient small molecule reductants, AscA 12 . While photoyellowing and photobleaching of lignin are well-known phenomena 50 , and studies on the impact of visible light on lignin model compounds and lignin combined with (non-lignin) photoredox catalysts have been reported 62 , 63 , to our knowledge not much is known about the structural modifications that may occur when polymeric lignin is exposed to visible light ( λ = 400–700 nm). Our NMR analysis reveals that visible light-exposure of lignin results in oxidation of ring-conjugated carbon-carbon double bonds with a concomitant increase in cinnamaldehyde end groups (Fig. 6 , Supplementary Figs. 8 – 11 ). Following light-exposure, the lignin hydroxyl groups experience an increase in hydrogen bonding, an effect that is opposite of what was found when the lignin was incubated in the presence of an LPMO, in the dark. This indicates that light-induced oxidation of lignin and LPMO-catalyzed lignin oxidation are distinct reactions Importantly, while the structural studies of lignin show effects of both irradiation and LPMO action and clearly point at the chemical processes involved, further studies are needed to fully unravel structural changes in lignin. We used the highest practical sample concentrations in the NMR analyses, to maximize sensitivity. The complexity and heterogeneity of the lignin structures requires high sensitivity, while achieving complete dissolution of samples is challenging. It is likely that the structural changes in lignin observed in this study only provide part of the picture, due to low signal-to-noise ratios, particularly for the 1D carbon spectra. Of note, the apparent lack of an effect of LPMO treatment on the 1D carbon spectra of lignin (Fig. 6 and Supplementary Fig. 8 ) could to some extent be due to the lower signal-to-noise ratio in these spectra (compared to the spectra obtained in the experiments with light). Thus, we cannot fully exclude that LPMO action also leads to lignin oxidations similar to those occurring upon treatment with light. Further in-depth studies of treated and untreated lignin are needed to unravel the full impact of light and LPMO action of lignin. Such studies may eventually allow the determination of quantitative correlations between the degree of lignin oxidation, the amount of hydrogen peroxide produced and LPMO activity. Of note, revealing such correlations would require accurate quantitative detection of all LPMO products and hydrogen peroxide levels under relevant conditions, which is challenging for reactions with lignin. The present findings show that LPMO reactions can be fueled by light-exposed lignin and may have wide implications for how we understand biological processes related to biomass conversion in Nature. Lignin is abundant in plant biomass, which could make many processes involving biomass light sensitive. Interestingly, LPMO action was recently shown to be a major contributor to the infectivity of the potato pathogen Phytophtora infestans 64 and one may wonder if infectivity is affected by light. On another note, our findings suggest that changes in access to light may contribute to the well-known impact of tillage regimes on the turnover and sequestration of organic matter in soil 65 . It would be of interest to investigate whether the interplay between light, redox-active structural components, and enzymes such as LPMOs has had an impact on the (co-)evolution of lignin-rich materials and the enzyme systems that degrade these. While these are interesting possible implications and while the impact of light on biomass conversion in Nature is indisputable, the magnitude and relative importance of light/lignin-fueled catalysis by LPMOs and other H 2 O 2 -dependent biomass degrading enzymes remains to be established. No matter the width and magnitude of these implications, the present study provides important insight into the complex roles of lignin and light in Nature and the catalytic potential of LPMOs."
} | 3,611 |
38366066 | PMC10881299 | pmc | 4,712 | {
"abstract": "Abstract Microorganisms living in soil maintain intricate interactions among themselves, forming the soil microbiota that influences the rhizosphere microbiome and plant growth. However, the mechanisms underlying the soil microbial interactions remain unclear. Streptomyces and Mesorhizobium are commonly found in soil and serve as plant growth-promoting rhizobacteria (PGPR). Here, we identified an unprecedented interaction between the colonies of red-soil-derived Streptomyces sp. FXJ1.4098 and Mesorhizobium sp. BAC0120 and referred to it as “ p roximity- b ased d efensive m utualism (PBDM).” We found that metabolite-mediated iron competition and sharing between the two microorganisms were responsible for PBDM. Streptomyces sp. FXJ1.4098 produced a highly diffusible siderophore, desferrioxamine, which made iron unavailable to co-cultured Mesorhizobium sp. BAC0120, thereby inhibiting its growth. Streptomyces sp. FXJ1.4098 also released poorly diffusible iron-porphyrin complexes, which could be utilized by Mesorhizobium sp. BAC0120, thereby restoring the growth of nearby Mesorhizobium sp. BAC0120. Furthermore, in ternary interactions, the PBDM strategy contributed to the protection of Mesorhizobium sp. BAC0120 close to Streptomyces sp. FXJ1.4098 from other microbial competitors, resulting in the coexistence of these two PGPR. A scale-up pairwise interaction screening suggested that the PBDM strategy may be common between Mesorhizobium and red-soil-derived Streptomyces . These results demonstrate the key role of iron in complex microbial interactions and provide novel insights into the coexistence of PGPR in soil.",
"conclusion": "Conclusion In this study, we demonstrated an unprecedented and widespread PBDM interaction between Streptomyces and Mesorhizobium via sequestration and release of iron. We found that Streptomyces inhibited the growth of distant Mesorhizobium by creating an iron-deficient environment via DFO secretion. Streptomyces also released iron-porphyrin complexes around its colonies, thereby restoring the growth of nearby Mesorhizobium . Ternary interactions showed that Streptomyces simultaneously inhibited both Mesorhizobium and plant pathogens indiscriminately via iron sequestration by DFO, but spared the nearby Mesorhizobium via iron sharing by iron-porphyrin complexes. These findings suggest that the PBDM strategy facilitates the coexistence of diverse pathogen-suppressive and plant growth-facilitating microorganisms in the soil.",
"introduction": "Introduction The soil microbiome is populated by taxonomically diverse bacteria and fungi, giving rise to numerous intra- and inter-kingdom interactions that affect the rhizosphere microbiome and plant health [ 1–3 ]. Microorganisms that provide life support and/or aid in the defense of their host plants are called plant growth-promoting rhizobacteria (PGPR), which are involved in a variety of activities that promote plant growth, such as nutrient absorption, environmental stress tolerance, and pathogen inhibition [ 4 , 5 ]. Streptomycetes, well-known producers of bioactive metabolites [ 6 ], are validated as PGPR that protect plants from pathogen infections and promote their growth [ 7–10 ]. In turn, the plants can reciprocally benefit the Streptomyces strains with nutrients through root exudates [ 11 ]. Rhizobia represent another major group of PGPR that can colonize inside the roots of legumes to form root nodules and fix nitrogen for their host plants [ 12–14 ]. Although streptomycetes and the rhizobia are from different phyla, Actinomycetota (earlier synonym: Actinobacteria ) and Pseudomonadota (earlier synonym: Proteobacteria ), respectively [ 15 ], they may synergistically promote plant growth. For example, co-inoculation of selected Streptomyces strains with Mesorhizobium ciceri has been shown to significantly enhance chickpea growth by promoting nodulation, increasing nitrogen fixation, and improving the host resistance against Botrytis gray mold disease [ 8 ]. However, the mechanism underlying this synergistic effect remains unclear. In addition to their relationships with host plants, various interactions occur among the soil microorganisms. Streptomyces generally secrete secondary metabolites including antibiotics and siderophores into the environment, thereby exerting antagonistic effects on their neighbors [ 16–18 ]. We previously reported that the secondary metabolites produced by Streptomyces play a pivotal role in interference competition with other co-occurring soil microorganisms [ 19 ]. A recent study revealed that Streptomyces spores can hitchhike on the flagella of motile bacteria to be transported to plant roots, where the spores may germinate and produce antibiotics to ward off the plant pathogens [ 20 ]. Unlike Streptomyces , interactions of rhizobia with other microorganisms have long been underestimated. Zhang et al . reported that the mycelia of Phomopsis liquidambaris constitute ideal dispersal networks to facilitate rhizobial enrichment in the peanut rhizosphere from bulk soil, thereby triggering peanut–rhizobium nodulation [ 21 ]. However, interactions between Streptomyces and rhizobia and their effects on other organisms in the same habitat have rarely been studied. Moreover, antagonistic Streptomyces can indiscriminately kill other PGPR in the same niche, raising questions on how Streptomyces and rhizobia can coexist in the rhizosphere of legumes. Microorganisms inhabiting the same niche often compete for limited resources. Although iron is one of the most abundant elements on Earth, it is normally present in its poorly soluble ferric ion (Fe[III]) form under aerobic environmental conditions, making it a limited resource in soil [ 22 ]. Thus, iron competition plays an important role in the soil microbial interactions, influencing the composition and function of soil microbiomes [ 23 ]. To effectively utilize poorly bioavailable iron, many soil microorganisms produce siderophores, a group of small-molecule chelators with high affinity for Fe(III) [ 24 ]. Siderophores play a well-established role in iron homeostasis within cells and exert two opposing social effects on microbial community members. Siderophores can be “public bads” [ 25 ], inhibiting the growth of microorganisms lacking matching receptors required for iron uptake by sequestering environmental iron. Many PGPR, including Streptomyces , suppress phytopathogen growth by producing siderophores [ 7 , 23 , 24 ]. In contrast, siderophores can also be “public goods” [ 25 ], facilitating the growth of microorganisms with matching receptors by delivering iron to the cells. For example, desferrioxamine E (DFOE) produced by Streptomyces coelicolor can be readily pirated by the DFO-deficient Amycolatopsis sp. AA4 [ 26 ], and Sinorhizobium meliloti can acquire iron from xenosiderophores, ferrichrome, and ferrioxamine B [ 27 ]. In addition to siderophores, heme and heme-containing proteins are another major source of iron for microorganisms. The ability to use heme or heme complexes as iron source was previously believed to be restricted to animal-pathogenic bacteria until Noya et al . discovered this ability in some non-pathogenic bacterial genera in 1997 [ 28–30 ]. Many bacteria have developed specialized receptors and uptake systems to acquire heme- or iron-containing porphyrins from their hosts or surroundings [ 31 , 32 ]. In Rhizobia, such as Bradyrhizobium japonicum [ 29 ], Rhizobium leguminosarum [ 33 ], and S. meliloti [ 34 ], heme can be recognized by specific TonB-dependent outer membrane receptors and subsequently transported into the cell via ATP binding cassette (ABC) transporters with the energy provided by the TonB–ExbB–ExbD complex [ 35 ]. However, the combined effects of siderophores and heme uptake strategies for iron competition on the interactions among soil microorganisms remain obscure. Red soil (equivalent to Ultisol in the US soil classification system or Acrisol and Ferralsol in the soil classification of the Food and Agriculture Organization of the United Nations) is characterized by high iron oxide content, low organic matter, and acidity, and is widespread in tropical and subtropical regions [ 36 ]. Previous studies have demonstrated that red soil contains abundant Streptomyces and rhizobia [ 19 , 37 ]. In this study, unlike classical microbial interactions represented by growth inhibition and facilitation, we discovered an unprecedented interaction that some red-soil-derived streptomycetes inhibited the growth of Mesorhizobium spp. at a certain distance, while not affecting those in the vicinity. We termed this phenomenon “ p roximity- b ased d efensive m utualism (PBDM)” between Streptomyces and Mesorhizobium and demonstrated that this interaction is mediated by a combination of iron sequestration and sharing, rather than physical contact. DFOE and iron-porphyrin complexes produced by Streptomyces are responsible for the sequestration and sharing of iron, respectively, resulting in simultaneous antagonism and amity between different bacterial phyla. In addition, to evaluate the ecological significance of the interaction, we carried out ternary interactions in which a bacterium co-isolated from the same red-soil sample or a common plant pathogen was added to the Streptomyces versus Mesorhizobium co-culture. The results showed that Streptomyces could partially protect Mesorhizobium from microbial competitors. Our findings provide new insights into the coexistence of PGPR of different microbial phyla in soil and highlight the importance of iron in microbial interactions.",
"discussion": "Discussion We describe an unprecedented PBDM interaction between red-soil-derived Streptomyces and Mesorhizobium , in which Streptomyces protect the nearby Mesorhizobium from microbial inhibition. Mutually beneficial coexistence can be widely observed among species with positive interactions, whereas competitive coexistence among species is dependent on their dynamic metabolic adaptation and niche differentiation [ 58 , 59 ]. PBDM is a complex phenomenon, not limited to classical facilitation (positive) or inhibition (competitive), indicating that microbial interactions are far more complex than previously expected. Phenotypes of Mesorhizobium sp. BAC0120 colonies in the PBDM interaction varied according to their distance to Streptomyces sp. FXJ1.4098, resulting in spatial structure-dependent coexistence, which is in line with that in the original soil habitat. Red soil has a high content of iron, but most of which is in biologically unavailable iron oxides form. Therefore, microorganisms in the red soil compete with each other intensively for the biologically limited iron resource [ 19 ]. All PBDM-S strains were isolated from the red soil [ 19 , 37 ], whereas streptomycetes from other habitats failed to trigger this interaction. This finding implies that habitat may play an indispensable role in the evolution of inter-phylum interactions. However, a systematic study involving more strains from diverse habitats is needed to determine whether the PBDM phenomenon is widespread between rhizobia and Streptomyces derived from other habitats. We found that PBDM-S and PBDM-M strains displayed complementary metabolic characteristics absent in non-PBDM bacteria. PBDM-S strains are capable of synthesizing and secreting siderophores and iron-porphyrin complexes (heme) into the extracellular environment, whereas PBDM-M strains maintain an iron-porphyrin uptake system instead of siderophore production. DFOs, and perhaps some other siderophores, can scavenge ferric ions from some iron-containing proteins [ 60 ], but not from heme or heme-containing proteins [ 61 ]. Given that Streptomyces produce a wide variety of siderophores [ 62 ], other highly diffusible siderophores besides DFOs may also serve as inhibitors in the PBDM interaction. Moreover, we examined 323 soil-derived pre-assembled metagenomes retrieved from Integrated Microbial Genomes and Microbiomes ( https://img.jgi.doe.gov ) and found that all of them contain heme biosynthesis and heme uptake genes from bacteria (unpublished data). These suggest that the siderophore- and iron-porphyrin-mediated PBDM identified in our study may be an unneglectable component in soil microbial interactions. Unlike the other five PBDM-M strains, M . muleiense CGMCC 1.11022 T exhibited diverse interaction types with Streptomyces ( 6A ), which may be partially attributable to its dual capability of siderophore biosynthesis and iron-porphyrin uptake. Another possible explanation may be that the inducing effects of M. muleiense CGMCC 1.11022 T on Streptomyces iron-porphyrin secretion vary among different Streptomyces strains. Sinorhizobium meliloti 2011 has outer membrane receptors to recognize and pirate DFOs; hence, it can coexist with DFO-producing Streptomyces ( 6A ). Our findings reveal the diverse strategies adopted by co-existing microorganisms to overcome the iron competition in the soil. Despite attempts for several porphyrin extraction methods [ 53 , 63 , 64 ], we did not detect iron-porphyrin in the extracts even from the medium containing exogenous iron-porphyrin complexes such as hemin. In a recent study on cryptic metabolite-driven exploratory growth of Streptomyces venezuelae , the authors also detected the secretion of unmetallated and zinc-bound CPs, but not iron-bound CP or other iron-porphyrins [ 65 ]. Therefore, it is likely that iron-porphyrin secreted by Streptomyces sp. FXJ1.4098 forms insoluble complexes with the components of the medium, making direct extraction and detection impossible. This may explain why the extracts from area b cannot restore the growth of Mesorhizobium under iron-limited conditions. Nevertheless, the experiments of heme uptake gene disruption and compound-simulated PBDM provided circumstantial evidence for the release of iron-porphyrin complexes as antidotes from Streptomyces . Few extracellular secretion systems of iron-porphyrin are known in microorganisms, except for the HrtAB system. This system is an ABC heme-dedicated efflux pump that plays a critical role in heme detoxification in many Gram-positive pathogenic and commensal bacteria, including Corynebacterium diphtheriae [ 66 ], Lactococcus lactis [ 67 , 68 ], and Staphylococcus aureus [ 67 , 68 ]. However, the HrtAB system has not been found in Streptomyces . Alternatively, Streptomyces can secrete extracellular vesicles that carry many molecules, including bacterioferritin, which stores iron and iron-porphyrins [ 69 , 70 ]. Also, Wang et al . discovered that a Dietzia strain achieves cross-species iron source delivery via vesicles [ 71 ]. Therefore, we speculate that the delivery of iron-porphyrins during PBDM may be mediated by vesicles. However, isolation of vesicles from co-culture plates remains challenging. We thus isolated and purified the vesicles of Streptomyces sp. FXJ1.4098 cultured in liquid medium, but these vesicles failed to restore the growth of Mesorhizobium sp. BAC0120 under iron-limited conditions (data now shown). Furthermore, to confirm whether bacterioferritin is involved in PBDM, we constructed a bacterioferritin deletion mutant of Streptomyces sp. FXJ1.4098, but the mutant still retained PBDM interaction with Mesorhizobium sp. BAC0120 (data not shown). These results suggest that vesicles from Streptomyces sp. FXJ1.4098 may not induce PBDM, although the contribution of vesicles to PBDM in the solid plates cannot be ruled out. In contrast, the iron-porphyrin-containing protein bovine hemoglobin relieved the growth inhibition of DFOB on Mesorhizobium sp. BAC0120 ( Fig. 3B ), suggesting that hemoproteins secreted by Streptomyces sp. FXJ1.4098 are likely the antidote in PBDM. Hence, future analysis of the extracellular proteome in the co-culture to identify proteins with iron-porphyrin cofactors is warranted. In addition, the construction and screening of random mutation libraries of PBDM-S strains for non-PBDM mutants would also help to elucidate the mechanism of iron-porphyrin delivery among bacteria. Understanding interspecies interactions in complex polymicrobial coexistence is important for the utilization of PGPR to synergistically facilitate plant growth and inhibit phytopathogens. This is exemplified by the growth-facilitating effect of M. ciceri on chickpeas, which is greatly enhanced when co-inoculated with Streptomyces [ 8 ]. However, the interaction between Mesorhizobium and Streptomyces and the mechanism underlying their coexistence are not known. Our findings demonstrate that Mesorhizobium species can coexist with antimicrobial streptomycetes via the PBDM strategy. Moreover, the PBDM strategy protects nonantagonistic PGPR from antagonistic microorganisms including plant pathogens ( 5 and Fig. S17 ), which provides insights into the ecological implications of this unprecedented interaction strategy. That is, by Streptomyces conferring benefit upon Mesorhizobium , they may synergistically promote the growth of plant that they are living on. The plant may in turn increase the fitness of Streptomyces by providing nutrients through root exudates. This may explain how protecting other PGPR from microbial competition via PBDM is beneficial to streptomycetes themselves. These results highlight the potential role of Streptomyces in regulating rhizosphere communities, which usually harbor both PGPR and pathogens. Nevertheless, further experiments are required to elucidate the functional significance of the PBDM interaction for host plant growth and health."
} | 4,433 |
35245502 | PMC8978274 | pmc | 4,713 | {
"abstract": "Photosynthetic organisms have evolved light-harvesting antennae over time. In cyanobacteria, external phycobilisomes (PBSs) are the dominant antennae, whereas in green algae and higher plants, PBSs have been replaced by proteins of the Lhc family that are integrated in the membrane. Red algae represent an evolutionary intermediate between these two systems, as they employ both PBSs and membrane LHCR proteins as light-harvesting units. Understanding how red algae cope with light is not only interesting for biotechnological applications, but is also of evolutionary interest. For example, energy-dependent quenching (qE) is an essential photoprotective mechanism widely used by species from cyanobacteria to higher plants to avoid light damage; however, the quenching mechanism in red algae remains largely unexplored. Here, we used both pulse amplitude-modulated (PAM) and time-resolved chlorophyll fluorescence to characterize qE kinetics in the red alga Porphyridium purpureum . PAM traces confirmed that qE in P. purpureum is activated by a decrease in the thylakoid lumen pH, whereas time-resolved fluorescence results further revealed the quenching site and ultrafast quenching kinetics. We found that quenching exclusively takes place in the photosystem II (PSII) complexes and preferentially occurs at PSII’s core antenna rather than at its reaction center, with an overall quenching rate of 17.6 ± 3.0 ns −1 . In conclusion, we propose that qE in red algae is not a reaction center type of quenching, and that there might be a membrane-bound protein that resembles PsbS of higher plants or LHCSR of green algae that senses low luminal pH and triggers qE in red algae.",
"discussion": "Discussion qE in P. purpureum is ΔpH-dependent While it has been well documented that in green algae, mosses, and higher plants, the qE process is activated by low luminal pH, it remains controversial in red algae ( 37 , 38 ). Delphin et al . found that in the presence of the membrane uncouplers NH 4 Cl or nigericin, the fluorescence of the R. violacea and P. purpureum cells is no longer quenched by high light ( 37 ). However, Fattore et al . ( 38 ) later found that nigericin-treated D. giordanoi cells still performed quenching in response to high light, and their NPQ rose with the increase of the actinic light intensity. Note that in their experiments, the far-red light was turned off during the measurements, which should be kept on to keep the cells in state I. Here, we performed the same experiment again using P. purpureum and found that the qE process is indeed activated by a low lumen pH, in line with previous results ( 37 , 46 , 57 ). We speculate that in the study of D. giordanoi , State II may have been induced under high light illumination, even in the presence of nigericin, which then led to qE-like fluorescence quenching. Nevertheless, biodiversity in qE mechanisms among different species within the Class of Rhodophyceae should not be excluded. Phycobilisome and PSI are not quenched Here, we provided direct evidence to show that qE in red algae takes place predominantly in the PSII complexes. In principal, all pigment-binding photosynthetic subunits including PSII core complex, PSI core complex, and their peripheral antenna LHCs could potentially be the initial target of quenching, and indeed in the green lineage, it was found that the quenching site is nonspecific ( 33 , 35 , 39 , 58 , 59 , 60 ). In contrast, in cyanobacteria, the qE occurs specifically in phycobilisomes, and the orange carotenoid protein (OCP) is indispensable for this process. Red algae also contain phycobilisomes; however, the gene encoding OCP protein is lacking ( 61 ). This rules out this phycobilisome-OCP type of quenching. This is consistent with our observation that the emission at 660 nm from phycobilisomes was not changed upon quenching. Another question is if other photosynthetic complexes, such as PSII core, PSI (PSI core plus its antenna LHCRs), are quenched. Our ultrafast fluorescence spectroscopic results precisely separated the kinetics of PSI and PSII. These measurements clearly demonstrated that only PSII was affected by qE, at least in cells that were kept in state I. The quenching does not occur in the RC PSII core complex is further composed of the reaction center (RC) and two core antenna proteins, CP43 and CP47. Kirilovsky and colleagues ( 37 ) suggested that the PSII fluorescence quenching might occur in the reaction center other than its core antenna because the minimal fluorescence was not quenched during qE. A similar opinion was expressed by Krupnik et al . ( 57 ). They observed a reaction center-based quencher in red algae by measuring isolated PSII complexes at low pH. However, our data favor the view that qE occurs in the antenna and not in the RC itself. In the first place, we would like to emphasize that using F 0 quenching as an indicator for the presence of an RC-based quencher should be reconsidered. An interesting study on Arabidopsis has pointed out that qE might function differently in RCs that are in the open and closed state, respectively ( 62 ), making F 0 a less reliable indicator. Moreover, the F 0 after high light illumination might not represent the true F 0 anymore, since the PQ pool is likely to remain partially reduced considering the additional electron feed-in via NADPH dehydrogenase in darkness (nonphotochemical reduction of the PQ pool). Secondly, in an RC-based quenching model, the quencher was assigned to the Chl cation P680 + ( 57 , 63 ), a radical formed during charge separation, but in our case cells were kept in complete darkness, and addition of a weak acid won’t create excitons. One may doubt if weak-acid-induced quenching equates the biological process of qE. Regarding this question, in a previous study we have established a solid correlation between weak-acid-induced quenching and the expression of LHCSR proteins in the Chlamydomonas system ( 27 ). It was, however, not practical to use this approach for red algae since no qE-defective mutant of red algae was available. Nevertheless, the chance that these two quenching processes, light-induced and weak-acid-induced, are different is very small in our opinion. The quencher is highly effective but unknown The quenching rate was estimated to be 17.6 ± 3.0 ns −1 , which corresponds to a lifetime of 55 ps, PSII core complexes contain 35 chlorophylls, if we count one quencher per dimer, that would mean under the scheme of ultrafast equilibrium, the slowest quenching time per Chl is about 55 ps/70 = 0.78 ps, which is extremely fast. On the other hand, this fast quenching rate is not surprising, several reported rates on the single molecule level in different quenching systems are even faster ( 27 , 64 , 65 ). It seems that this high rate of quenching is essential for the qE process to outcompete photochemical quenching, which is equally fast (subpicosecond). And only in this way it could guarantee an effective photoprotection, even for open RCs. One question that arises here is: what exactly is the nature of this ultrafast quencher? Photoprotective proteins such as PsbS, LHCSR, and LHCX are the key players of qE in vascular plants, green algae, moss, and diatoms, respectively ( 20 , 66 ). Given the fact that qE in red algae can also be triggered by a low luminal pH and that it belongs to an antenna type of quenching, we suggest that there may be a membrane-bound protein that resembles PsbS in higher plants or LHCX/SR in branches of green algae and mosses being responsible for the observed energy dissipation in PSII."
} | 1,906 |
38755131 | PMC11099027 | pmc | 4,714 | {
"abstract": "Mechanical energy harvesting using triboelectric nanogenerators is a highly desirable and sustainable method for the reliable power supply of widely distributed electronics in the new era; however, its practical viability is seriously challenged by the limited performance because of the inevitable side-discharge and low Coulombic-efficiency issues arising from electrostatic breakdown. Here, we report an important progress on these fundamental problems that the spontaneously established reverse electric field between the electrode and triboelectric layer can restrict the side-discharge problem in triboelectric nanogenerators. The demonstration employed by direct-current triboelectric nanogenerators leads to a high Coulombic efficiency (increased from 28.2% to 94.8%) and substantial enhancement of output power. More importantly, we demonstrate this strategy is universal for other mode triboelectric nanogenerators, and a record-high average power density of 6.15 W m −2 Hz −1 is realized. Furthermore, Coulombic efficiency is verified as a new figure-of-merit to quantitatively evaluate the practical performance of triboelectric nanogenerators.",
"introduction": "Introduction Sustainable and reliable power sources that are not detrimental to the environment are essential and in great demand in the new era of distributed sensor networks, artificial intelligence (AI), and internet of things (IoTs). The potential power sources are expected to enable continuous, long-term, and self-powering the widely distributed electronics and to facilitate future remote monitoring for intelligent sensing 1 , 2 . Due to clean, renewable, abundant, and ubiquitous advantages, mechanical energy is considered to be a particularly attractive source 3 , 4 . An advanced energy harvesting device that generates electrical power from mechanical motion could be an important step towards next-generation self-powered systems 5 . Among various mechanical energy harvesting technologies, triboelectric nanogenerator (TENG), has attracted much more attentions due to their merits of low cost, simple structure, wide choice of materials, easy adaptative and even high efficiency at low frequency, showing promising applications in energy and sensor fields 6 – 8 . To realize long-term operation and enlarge the application scenario, improving the charge density of TENG is the key direction because of the square relationship between charge density and power density 9 – 12 . However, it has been recently revealed that electrostatic breakdown leads to a substantial charge decline on the dielectric surface and then the square loss of power density of TENGs 13 , 14 , and that’s why the output charge density of TENG can be greatly improved by restricting the electrostatic breakdown in many conventional approaches, including strict environmental control 15 – 19 and thinner triboelectric layer design 20 – 22 . Besides, to evaluate and compare the performance of TENGs, the standards for quantifying the performance of TENGs were established by the cycle for maximized energy output 23 – 25 (denoted as CMEO). However, this context makes it difficult to understand that the actually measured average power density is largely smaller than the theoretical result calculated based on CMEO and short-circuit output charge. The core issue is that the charge loss from electrostatic breakdown (especially side-discharge) at large load or for energy storage is neglected, making the high charge density at short-circuit conditions meaningless whether for practical power management or energy storage (Supplementary Figs. 1 and 2 and Supplementary Note 1 ), i.e., with the electrostatic breakdown, the charge utilization efficiency under the condition of external load is dramatically lower than that without electrostatic breakdown, that’s why the practical output energy is largely smaller than the theoretical result calculated based on CMEO (Supplementary Fig. 3 and Supplementary Note 2 ). Overall, these have presented enormous challenges: how to avoid the serious side-discharge problem to increase the output energy density while maintaining high charge utilization efficiency of TENG, and correctly evaluate its practical performance. Here, we propose a simple but effective strategy to address the serious and troublesome side-discharge problem by introducing the SEREF on the insulator between the electrode and triboelectric layer in TENGs. Our theoretical and experimental results indicate that the additive of an insulator alongside the edge of metal electrode limits the electric field intensity around the electrode edge below the threshold of electrostatic breakdown, therefore the side-discharge problem is avoided owing to the SEREF on the insulator during the sliding of TENGs. Meanwhile, we introduce a new figure-of-merit, named Coulombic efficiency (the charge utilization efficiency of TENG under a fixed load or voltage) to correctly evaluate TENG’s performance considering the issue of electrostatic breakdown, which also can be used for guiding the optimization of TENG’s performance. With the demonstration of direct-current TENG (DC-TENG) devices, our strategy not only improves the short-circuit charge ( Q SC ) and open-circuit voltage ( V OC ), but also solves the issue of low charge utilization efficiency, and then a substantial enhancement of average power density of 2.3 W m −2 Hz −1 (increased by 54 times) is achieved owing to the improvement of Coulombic efficiency from 28.2% to 94.8%. More importantly, our strategy and the proposed Coulombic efficiency are feasible and universal for conventional alternate-current TENG (AC-TENG), and a record-breaking average power density of 6.15 W m −2 Hz −1 is realized (increased by 22 times). The strategy and Coulombic efficiency provided here set the foundation for realizing a high-performing TENG device capable of producing electricity approaching the Coulombic efficiency limit and the further industrialization of TENG technology.\n\nIntroducing Coulombic efficiency as a figure-of-merit for accurately evaluating the performance of TENG In general, to evaluate and compare the performance of TENGs, the standards for quantifying the performance of TENGs were established by the cycle for maximized energy output (CMEO) 23 – 25 . The CMEO’s V-Q curve describes the relationship of the SCD of triboelectric layer ( σ 0 ), ideal V OC ( V OC, ideal ), and the inherent capacitor of TENG ( C T ): 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}$${V}_{{{{{\\rm{OC}}}}}},\\, _{{{{{\\rm{ideal}}}}}}={\\sigma }_{0}\\times {S/C}_{{{{{{\\rm{T}}}}}}}$$\\end{document} V OC , ideal = σ 0 × S / C T where S is the effective area of triboelectric layer, the reciprocal of absolute value of the V-Q curve slope is the C T . Ideally, the output energy at the fixed voltage V n ( n = 1, 2, 3) can be obtained by multiplying the horizontal and vertical coordinates of the intersection between the voltage curve ( V = V n ) and the CMEO’s V - Q curve (Fig. 3d , the shaded area represents the output energy.). Actually, considering the existence of electrostatic breakdown, the increase in output voltage will enhance the electric field strength around the electrode edge, leading to electrostatic breakdown, and then the SCD of triboelectric layer will decrease. This means that the V-Q curve drawn by formula (1) is not static, and it is a dynamic curve with the increase of TENG’s output voltage. We assume that, when the output voltage increases to V 1 , the SCD of triboelectric layer will decrease to σ 1 . It is noteworthy that C T is not affected by electrostatic breakdown. Therefore, substitute σ 1 into formula (1), a new V - Q curve is obtained (the green dashed line in Fig. 3e ), and the intersection of the voltage curve ( V = V 1 ) and the new V - Q curve shift left. Obviously, the actual output charge ( Q ’ 1 ) will be lower than the ideal output charge ( Q 1 ) (Fig. 3e ). When the output voltage further increases to V 2 and V 3 , SCD will decrease to σ 2 and σ 3 , respectively, and the intersection continuously shifts left. In addition, when the output voltage increases to V 4 , and V 4 is equal to σ 4 × S / C T , the output charge will decrease to zero. By connecting these intersections, a new curve for real energy output (CREO) can be obtained, and the product of the horizontal and vertical coordinates of each point on the curve represents the actual output energy of TENG (Fig. 3e , Supplementary Fig. 21 and Supplementary Note 9 ). Obviously, the difference between CREO and CMEO is caused by electrostatic breakdown, that’s also why the actual energy output is much lower than the energy calculated by CMEO. From this perspective, our strategy utilizes the SEREF on insulator to suppress the electrostatic breakdown, which reduces the difference between CMEO and CREO, and makes CREO continuously close to CMEO to enhance the actual energy output (Fig. 3f ). To clearly understand the relationship of output energy, Q SC , and V OC , the Q - V curve of TENG with voltage as the horizontal axis and charge as the vertical axis is plotted (Fig. 3g , Supplementary Fig. 22 and Supplementary Note 5 ), in which the output charge ( Q ( V )) as a function of the output voltage V : 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}$$Q(V)=(-a\\times V+1)\\times {{{{{{\\rm{Q}}}}}}}_{{{{{{\\rm{SC}}}}}}}$$\\end{document} Q ( V ) = ( − a × V + 1 ) × Q SC Here, the ratio of Q ( V ) to Q SC is defined as Coulombic efficiency η ( V ) to represent the charge utilization ratio at a fixed voltage: 3 \\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}$$\\eta (V)=Q(V)/{Q}_{{{{{{\\rm{SC}}}}}}}=(-a\\times V+1)$$\\end{document} η ( V ) = Q ( V ) / Q SC = ( − a × V + 1 ) Here, a is a constant, which is influenced by TENG’s inherent capacitor, TENG’s parasitic capacitor, and electrostatic breakdown (Supplementary Note 10 ). The larger a is, the lower η ( V ) is. Therefore, the output energy can be calculated as: 4 \\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}$$E(V)=\\eta (V)\\times {Q}_{{{{{{\\rm{SC}}}}}}}\\times V$$\\end{document} E ( V ) = η ( V ) × Q SC × V According to formula (4) and Supplementary Fig. 23 , even if TENGs with the same Q SC and V OC , the maximum output energy is different due to differences in η ( V ) (Supplementary Note 10 ). Based on above analysis, we consider that η ( V ) should be a parameter that is as important as Q SC or V OC , for evaluating the output power of TENG. The testing circuit in Fig. 2g <i> is utilized to obtain the Q - V curve and η ( V ). The Coulombic meter measures the total output charge ( \\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}$$\\mathop{\\sum }\\nolimits_{{{{{\\rm{i}}}}}=1}^{{{{{\\rm{n}}}}}}{Q}_{{{{{\\rm{n}}}}}}$$\\end{document} ∑ i = 1 n Q n ) of DC-TENG, where Q n represents the output charge in the nth motion cycle (Fig. 3h ). The output voltage after the nth motion cycle ( V n ) can be calculated by C test (Supplementary Fig. 24 ): 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}$${V}_{{{{{\\rm{n}}}}}}=\\frac{{\\sum }_{{{{{\\rm{i=1}}}}}}^{{{{{\\rm{n}}}}}}{Q}_{n}}{{C}_{{{{{{\\rm{test}}}}}}}}$$\\end{document} V n = ∑ i=1 n Q n C test By linearly fitting the experimental data ( V n , Q n ), the curve shown in Fig. 3i can be obtained. The results of DC-TENG with different structures in Fig. 3j demonstrate that η ( V ) will be much higher than that of DC-TENG without insulator under the same voltage (Supplementary Fig. 25 and Supplementary Table 1 ). In addition, we calculated the maximum energy output of TENG by Coulombic efficiency, and compared them with the maximum output energy tested in the experiment. The comparison results are shown in the Supplementary Fig. 26 . After introducing Coulombic efficiency, the calculated results are closer to the experimental results, which also demonstrate our strategy improving the performance of DC-TENG from three aspects: the output charge, the output voltage and Coulombic efficiency.",
"discussion": "Discussion Here, we propose a universal and effective strategy to solve the troublesome side-discharge issue in TENGs by the spontaneously established reverse electric field, which is carried out only by pasting an insulator at the electrode edge to accumulate static charges and to establish a reverse electric field for suppressing electrostatic breakdown. Then, it is demonstrated that our strategy not only can improve short-circuit charge ( Q SC ) and open-circuit voltage ( V OC ) of TENGs like other strategies (increasing electrostatic breakdown threshold), but also can improve the performance of TENGs under load conditions by improving Coulombic efficiency. With verified devices, the enhanced average power density of 2.3 W m −2 Hz −1 (increased by 54 times) in DC-TENG and a record-breaking average power density of 6.15 W m −2 Hz −1 in AC-TENG (increased by 22 times) strongly demonstrate the universality and effectiveness of the strategy. It is noteworthy that our strategy for modulating the electric field intensity to suppress electrostatic breakdown in the breakdown domain has not been previously reported in the research field of TENG. Furthermore, we hypothesize that in addition to utilizing surface charge for electric field regulation, adjusting the terminal voltage of the electrode may also serve as a viable method (Supplementary Fig. 36 and Supplementary Note 13 ). More importantly, the performance of TENGs could be further optimized by the synergetic enhancement of improved triboelectric charge density, as many previous works reported, and improved Coulombic efficiency as our strategy in the future. It is noteworthy that the troublesome side-discharge problem widely exists in sliding mode AC/DC-TENGs with high SCD and limits the highest achievable output power density of TENGs. The SEREF on insulators presented in this work provides a promising solution to substantially enhance the output performance of TENGs. In addition, a new figure-of-merit, Coulombic efficiency, is proposed and demonstrated for correctly quantifying the output performance of TENGs, overcoming the issue of SCD decline dynamically caused by electrostatic breakdown. More importantly, Coulombic efficiency provides a clear direction for guiding the design of high-performance TENGs and could be a standardized parameter for quantifying the performance of TENGs. Moreover, the Q-V (or I-V ) curve of TENGs drawn by Coulombic efficiency, V OC , and Q SC can intuitively determine the performance of TENGs and provide the most direct data reference for subsequent power management circuit design. Overall, the strategy for improving performance and methods for evaluating the performance of TENG provided here set the foundation for the further applications and industrialization of TENG technology."
} | 4,051 |
38940599 | PMC11288006 | pmc | 4,715 | {
"abstract": "ABSTRACT Skeletonema costatum , a cosmopolitan diatom primarily inhabiting coastal ecosystems, exhibits a typically close yet variable relationship with heterotrophic bacteria. The increasing temperature of surface seawater is expected to substantially affect the viability and ecological dynamics of S. costatum , potentially altering its relationship with bacteria. However, it remains unclear to what extent the elevated temperature could change these relationships. Here, the relationship between axenic S. costatum and natural seawater bacteria underwent a dramatic shift from mutualism to antagonism as the co-culture temperature increased from 20°C to 25°C. The co-occurrence network indicated significantly increased complexity of interaction between S. costatum and bacteria community after temperature elevation, especially with Flavobacteriaceae , implying their potential role in eliminating S. costatum under higher temperatures. Additionally, a Flavobacteriaceae isolate, namely MS1 identified as Tamlana genus, was isolated from the co-culture system at 25°C. MS1 had a remarkable ability to eliminate S. costatum , with the mortality rate at 25°C steadily rising from 30.2% at 48 h to 92.4% at 120 h. However, it promoted algal growth to some extent at 20°C. These results demonstrated that increased temperature promotes MS1 shifts from mutualism to antagonism with S. costatum . According to the comparative genomics analysis, changes in the lifestyle of MS1 were attributed to the increased gliding motility and attachment of MS1 under elevated temperature, enabling it to exert an algicidal effect through direct contact with alga. This investigation provided an advanced understanding of interactions between phytoplankton and bacteria in future warming oceanic ecosystems. IMPORTANCE Ocean warming profoundly influences the growth and metabolism of phytoplankton and bacteria, thereby significantly reshaping their interactions. Previous studies have shown that warming can change bacterial lifestyle from mutualism to antagonism with phytoplankton, but the underlying mechanism remains unclear. In this study, we found that high temperature promotes Tamlana sp. MS1 adhesion to Skeletonema costatum , leading to algal lysis through direct contact, demonstrating a transition in lifestyle from mutualism to antagonism with increasing temperature. Furthermore, the gliding motility of MS1 appears to be pivotal in mediating the transition of its lifestyle. These findings not only advance our understanding of the phytoplankton-bacteria relationship under ocean warming but also offer valuable insights for predicting the impact of warming on phytoplankton carbon sequestration.",
"conclusion": "Conclusions S. costatum often forms blooms in coastal waters, significantly affecting marine ecosystems. The elevated surface seawater temperature could affect the dynamics of S. costatum and its interactions with bacteria. The findings of the present study indicated that natural seawater bacteria can accelerate the mortality of S. costatum under high-temperature conditions. The LDA and co-occurrence network analyses validated that members of Flavobacteriaceae promote algal mortality under elevated temperatures. Then, we demonstrated that elevated temperature could activate the “Jekyll-and-Hyde” mode of a Flavobacteriaceae isolate, MS1, making its lifestyle transition from mutualism to antagonism with S. costatum . This transition may be attributed to the increased gliding motility and attachment of MS1 under elevated temperature, enabling it to exert an algicidal effect through direct contact. Therefore, with rapid ocean warming, the phycosphere bacteria may undergo a temperature-induced lifestyle transition, thereby significantly influencing the phytoplankton biomass. This study provides new insights into phytoplankton-bacteria interaction during temperature elevation, which will increase the reference and knowledge in this field.",
"introduction": "INTRODUCTION Ocean warming poses a significant threat to the stability of marine ecosystems. According to climate models, by the end of this century, surface seawater temperature is expected to increase as high as 6.4°C ( 1 ). This warming trend is critically involved in altering the structure of marine food webs and the biogeochemical cycles of the marine ecosystem ( 2 , 3 ). Over the past century, it has been observed that the global decline of phytoplankton diversity is closely associated with the rise in surface seawater temperatures ( 4 ). The impact of ocean warming on phytoplankton is expected, as temperature exerts indirect effect by affecting stratification and nutrient flux ( 5 ) and direct effect by altering community composition and metabolic rates ( 6 ). Considering the pivotal role of phytoplankton in marine food webs, the impact of elevated temperatures on phytoplankton has gained significant attention. Diatoms, constituting 20% of the global net primary production, are essential for carbon flow within marine food webs and biogeochemical cycles ( 7 ). Approximately 50% of the carbon fixed by diatoms in the marine ecosystem is released into the surrounding environment, where it is subsequently absorbed and utilized by heterotrophic bacteria ( 8 ). A thin layer, known as phycosphere, surrounds the diatoms and serves as the region where molecules disperse along diffusion gradients ( 9 ). Bacteria settle in the phycosphere through random encounters, chemotactic movements, or vertical propagation ( 10 , 11 ). The interactions between diatoms and bacteria typically encompass mutualism, antagonism, and parasitism ( 11 ). Extensive research has found the mutualistic or antagonistic mechanisms of bacteria against diatoms in the phycosphere ( 12 , 13 ). Within the phycosphere, mutualistic bacteria, like Ruegeria pomeroyi , can stimulate the growth of diatom by secreting Vitamin B 12 (VB 12 ). Furthermore, they engage in a symbiotic exchange where the bacteria provide VB 12 to diatoms and acquire organic sulfur compounds, such as 2,3-dihydroxypropane-1-sulfonic acid from diatoms ( 14 ), whereas antagonistic bacteria often inhibit the growth of phytoplankton by competition or secreting algicidal agents ( 12 ). Furthermore, it has been reported that bacteria in the phycosphere have cooperative alliances and work together to decompose and utilize algal metabolites ( 15 , 16 ), suggesting a prominent role of bacterial community around the phycosphere. However, the interaction of phytoplankton with bacteria may not always be stable. For instance, it has been reported that the release of dimethylsulfoniopropionate (DMSP) from diatoms may mediate the transition of some bacteria from mutualism to antagonism ( 17 ). Furthermore, in controlled laboratory conditions, the relationship between Synechococcus and heterotrophic bacteria also changes from antagonism to mutualism, ultimately evolving into symbiosis ( 18 ). To date, only one reported instance found that an elevation in cultivation temperature can alter bacteria from mutualism to antagonism with Emiliania huxleyi ( 19 ). Detailed reports regarding the changes in diatom-bacteria interactions under warming conditions are currently lacking. Nevertheless, elevated temperatures can exert a significant influence on diatom growth. For instance, the growth of Skeletonema dohrnii and Thalassiosira pseudonana increases with rising temperatures, while that of S. costatum and Phaeodactylum tricornutum decreases ( 20 ). For phycosphere bacteria, the increase in temperature can enhance their attachment to diatoms ( 11 ), as well as the carbon and nitrogen flux between them ( 21 ). Moreover, elevated temperature was never observed to result in a greater abundance of attached bacteria compared to free-living bacteria ( 21 , 22 ). However, further assessment is still needed to evaluate how elevated temperatures induced alterations in bacterial community compositions and functional features associated with diatom. Skeletonema is a ubiquitous diatom genus, which is broadly distributed from the Antarctica and the Arctic to tropical waters, and it can form large-scale blooms in coastal regions ( 23 ). To deepen our understanding of the interactions between diatom and bacteria under ocean warming conditions, we set up experiments with an axenic diatom, S. costatum , and investigated its interactions with bacterial community from natural seawater. We recently obtained an axenic S. costatum from a eutrophic bay, Xiangshan Bay (XSB). To test the hypothesis that the interactions between diatom and bacteria exhibit distinct patterns under different temperatures, natural bacteria communities from XSB and S. costatum were cultured together at 20°C and 25°C, enabling the investigation of the roles of algal attached and free-living bacterial community compositions and functional potentials. Moreover, S. costatum was also cultured with bacteria isolated from the above co-culture system at 25°C, to elucidate primary mechanisms that alter the interactions between S. costatum and bacteria under different temperature conditions. The results of this study will increase our understanding of how global warming alters interactions between phytoplankton and bacteria.",
"discussion": "DISCUSSION In marine ecosystems, phytoplankton and their associated bacterial community collectively participate in several key biological processes. In the phycosphere, heterotrophic bacteria employ diverse mechanisms to establish symbiotic relationships with phytoplankton cells and engage in metabolite exchange. Temperature could significantly alter the metabolic characteristics of phytoplankton, consequently influencing bacterial community compositions in the psychosphere ( 47 ). Our findings revealed that during increased temperatures, the natural seawater bacterial community exerts an inhibitory effect on the growth of S. costatum . Furthermore, these elevated temperatures can stimulate the growth of bacteria as well as S. costatum , hastening nutrient depletion and ultimately leading to algal collapse ( 48 ). However, this phenomenon was not observed in the present study, as the growth of S. costatum was inhibited after only 48 h. Therefore, it was speculated that the death of algae might be linked with high temperatures-induced lifestyle alterations of bacteria. Rhodobacteraceae and Flavobacteriaceae were observed as dominant bacteria in the co-culture system at both 20°C and 25°C, consistent with previous studies on algal-associated bacterial communities ( 49 , 50 ). Members of Rhodobacteraceae can secrete VB 12 , which supports the growth of diatoms ( 51 ). Therefore, Rhodobacteraceae can rapidly establish symbiotic relationships with diatoms. Because of their abilities to utilize algal polysaccharides, members of the Flavobacteriaceae frequently occupy advantageous positions in algal-associated environments ( 52 , 53 ). However, after the algal cell lysis, a notable decrease was observed in the relative abundance of Flavobacteriaceae , indicating a close association with the growth of S. costatum . This phenomenon is frequently observed during algal bloom, where Flavobacteriaceae thrive amidst extensive phytoplankton proliferation, displaying a preference for high-molecular-weight dissolved organic matter (DOM) ( 54 ). Although Rhodobacteraceae employ various survival strategies, from streamlined oligotrophs to metabolically versatile opportunists. They can degrade phytoplankton-derived low-molecular-weight DOM when interacting with phytoplankton ( 55 ). This effectively clarifies why the abundance of Rhodobacteraceae was markedly higher in AA than the FL. Furthermore, due to their diverse survival strategies, Rhodobacteraceae can still maintain high abundance after the lysis of the S. costatum . Additionally, LDA analysis revealed prominent discriminative bacteria from Rhodobacteraceae in the AA at 25°C. High temperature could improve the demand of diatom for VB 12 ( 56 ), thereby favoring Rhodobacteraceae selection in the phycosphere. The discriminative bacteria, including Rueger and Nautella , have been identified as VB 12 producers ( 57 ), which were selected by S. costatum . Moreover, bacterial communities in both AA and FL demonstrated a significant connection with the growth of S. costatum , as evidenced by NMDS analysis, indicating distinct divergence in bacterial community structures upon algal demise. This further highlighted that DOM from S. costatum shapes both AA and FL bacterial communities. Elevated temperature increases the intensity of interactions among bacteria, as well as between bacteria and S. costatum . Temperature cannot only increase the release of DOM by diatoms ( 58 ) but also enhance the attachment of heterotrophic bacteria to algal cells, promoting carbon-nitrogen flux ( 21 , 59 ). This was confirmed by LSCM, which found intensive attachment of bacteria to S. costatum at 25°C ( Fig. 1B ). Interestingly, no interactions between Flavobacteriaceae and algae were observed at 20°C, yet their interactions increased remarkably at 25°C, surpassing those between Rhodobacteraceae and algae. This indicated that the interaction between Flavobacteriaceae and algae was highly sensitive to elevated temperature than those with Rhodobacteraceae. Flavobacteriaceae adeptly utilize new DOM released by diatoms under warming conditions ( 22 ). It was also speculated that the decline of S. costatum at high temperatures may be closely related to Flavobacteriaceae given its increased interactions with S. costatum . Members of Flavobacteriaceae are commonly reported as algicidal bacteria capable of lysing multiple algal species ( 60 , 61 ), including S. costatum . At high temperatures, bacteria belonging to Flavobacteriaceae may alter their lifestyle to become more lethal to S. costatum . The ST ratios are widely employed to assess the impact of ecological stochasticity on microbial community assembly ( 38 ). Here, stochastic processes predominantly modulated the process of entire community assembly; however, it was notably decreased in stochasticity at the time of S. costatum mortality ( Fig. 2C and D ). By combining redundancy ( 62 ) and the lottery hypotheses ( 63 ), within groups of species sharing similar ecological characteristics, the first arrival secures the “lottery” of niche. However, it was also found that bacterial community associated with Thalassiosira rotula was driven more by deterministic processes because the ecological niche surrounding diatoms can offer bacteria species-specific metabolic features, leading to a highly stable and repeatable core bacterial community ( 49 ). The results of the present study more evidently align with the former perspective. However, stochasticity was lower in the AA than in the FL community, and this difference became more pronounced under elevated temperatures. This suggests the constraining effect of phycosphere around diatoms on community assembly, which was amplified under high temperatures. Moreover, with the mortality of S. costatum , a convergence effect occurred on community assembly, delineating bacterial community structures into two phases attributed to the death of S. costatum ( Fig. 2A and B ). The released DOM from S. costatum can strongly alter the nutrient levels in the co-culture system, thus decreasing the stochasticity of the bacterial community. Furthermore, at high temperatures, the relatively high stochasticity in FL was also linked with the motility of bacteria. FL community was more significantly impacted by higher temperatures, resulting in significant differences in the relative abundance of a large number of genes, particularly those related to motility and chemotaxis. At high temperatures, the FL community motility shifted from being dominated by pili to being governed by flagella, thus exhibiting enhanced motility ( Fig. 4 ). Regarding diatom proliferation and algal bloom collapse, increased motility bacteria have advantages over non-motile ones to acquire more DOM ( 64 ). Additionally, the co-culture system exhibited relatively higher instability during the early (24 h) and decline stages (96 h) of S. costatum compared to the stability observed at 48 h, consequently resulting in more variations in gene abundance at 24 and 96 h. Moreover, the alteration of DOM may affect the behavior in the FL community. Due to the relatively stable environment in the phycosphere, the AA community had few differential motility genes at 24 and 48 h. Altogether these data revealed that alterations in stochasticity in this co-culture system were closely linked with S. costatum growth and bacterial motility. Tamlana sp. MS1, isolated from the co-culture system at 25°C, exhibited high algicidal effects on S. costatum under elevated temperatures ( Fig. 5 ), further indicating the essential activity of Flavobacteriaceae interacting with S. costatum . Research has indicated that Ruegeria sp. R11 displays temperature-enhanced virulence against E. huxleyi ; however, the underlying mechanisms remain undetermined ( 19 ). Furthermore, the lifestyle of Sulfitobacter D7 switches from coexistence to pathogenicity after it interacts with E. huxleyi ( 65 ), and this mode is known as the “Jekyll-and-Hyde” phenotype. Algal DMSP has been identified as a key chemical component that mediates the transition between lifestyles ( 17 ). In its pathogenic phase, the flagellar motility and various transport systems of Sulfitobacter D7 were significantly enhanced, likely aiming to maximize assimilation of metabolites originating from algae following cell death. Moreover, a similar phenomenon has also been identified in the Phaeobacter genus when it is co-cultured with E. huxleyi ( 66 , 67 ). However, these transitions were mainly triggered by algal senescence, which is different from the findings of this research, where transitions are induced by temperature. When MS1 was co-cultured with S. costatum , a considerable amount of MS1 adhered to the surface of algal cells at 25°C, as evidenced by SEM revealing substantial aggregation of MS1 at damaged sites of algal cells. Conversely, only a small quantity of MS1 was observed on algal cells in the co-culture system at 20°C. This suggests that an elevation in temperature augments the motility of MS1, thereby facilitating its attachment to the surface of algal cells. Numerous investigations have already substantiated the intimate association between bacterial virulence and their motility and attachment ( 68 , 69 ). Studies on coral pathogenic bacteria have elucidated the pivotal role of motility in positioning and initiating infection within the host during the initial stages of Vibrio infection, with heightened motility at elevated temperatures correlating with increased infectivity ( 68 ). Conversely, mutants with impaired motility display diminished infectivity, while non-motile mutants fail to infect corals ( 68 ). Thus, bacterial motility and attachment are intricately linked to their pathogenicity and are significantly influenced by temperature. The gliding motility exhibited by Flavobacteria serves as a paradigm for studying bacterial gliding, with genes associated with gliding being widely dispersed throughout the Flavobacteria ( 70 ). The gliding motility system encompasses two systems: (i) the motility apparatus composed of membrane Gld subunits B, D, H, and J; (ii) the T9SS constituted by GldK/L/M/N and SprA, SprE, PorV, and others ( 70 – 72 ). These two systems collaboratively orchestrate the transition from bacterial motility to attachment. The T9SS demonstrates remarkable efficacy in secreting CAZymes and various extracellular proteins, while also playing a role in facilitating surface-associated gliding motility ( 73 ). Numerous investigations found the indispensability of gliding motility and T9SS in attachment, virulence, and extracellular protease secretion in Flavobacteria ( 71 , 73 , 74 ). In pathogenic species such as Flavobacterium psychrophilum and F. columnare , mutants deficient in gldD , gldN , and porV not only lack attachment capabilities but also exhibit markedly reduced protease activity and pathogenicity ( 73 ). Comparative genomic analysis has unveiled the presence of a complete set of gld and spr genes encoding gliding motility and T9SS in the MS1 genome, indicative of the possession of a comprehensive gliding motility and T9SS system by MS1. Furthermore, the filtrate from MS1 fermentation did not notably affect algal growth, implying that MS1-induced algal death requires direct attachment to the surface of algal cells. Consequently, we propose that elevated temperatures can potentially enhance the gliding motility of MS1, promoting its attachment to algal cells, and ultimately resulting in the death of S. costatum . In general, bacteria induce algal death through two primary modes: indirect or direct (or a combination thereof, depending on the host) ( 75 ). In the indirect mode, bacteria release algicidal compounds that result in algal death, with documented instances of algicidal microbes predominantly employing this mechanism ( 75 – 77 ). Direct mode entails bacterial contact and attachment for effective algal demise, as exemplified by Streptomyces globisporus encircling Microcystis aeruginosa cells, leading to direct algal death ( 78 ). The marine bacterium Saprospira sp. SS98-5 can directly lyse Chaetoceros ceratosporum by utilizing gliding motility to approach diatoms, thereby inducing diatom aggregation and subsequently rupturing diatom cells through the production of microtubule-like structures ( 79 ). It is apparent that bacteria possessing gliding motility, such as MS1, have the potential to induce algal death through direct contact. Conclusions S. costatum often forms blooms in coastal waters, significantly affecting marine ecosystems. The elevated surface seawater temperature could affect the dynamics of S. costatum and its interactions with bacteria. The findings of the present study indicated that natural seawater bacteria can accelerate the mortality of S. costatum under high-temperature conditions. The LDA and co-occurrence network analyses validated that members of Flavobacteriaceae promote algal mortality under elevated temperatures. Then, we demonstrated that elevated temperature could activate the “Jekyll-and-Hyde” mode of a Flavobacteriaceae isolate, MS1, making its lifestyle transition from mutualism to antagonism with S. costatum . This transition may be attributed to the increased gliding motility and attachment of MS1 under elevated temperature, enabling it to exert an algicidal effect through direct contact. Therefore, with rapid ocean warming, the phycosphere bacteria may undergo a temperature-induced lifestyle transition, thereby significantly influencing the phytoplankton biomass. This study provides new insights into phytoplankton-bacteria interaction during temperature elevation, which will increase the reference and knowledge in this field."
} | 5,795 |
25335577 | PMC4205843 | pmc | 4,716 | {
"abstract": "Under high light (HL) stress, astaxanthin-accumulating Haematococcus pluvialis and β -carotene-accumulating Dunaliella salina showed different responsive patterns. To elucidate cellular-regulating strategies photosynthetically and metabolically, thylakoid membrane proteins in H. pluvialis and D. salina were extracted and relatively quantified after 0 h, 24 h and 48 h of HL stress. Proteomic analysis showed that three subunits of the cytochrome b 6 /f complex were greatly reduced under HL stress in H. pluvialis , while they were increased in D. salina . Additionally, the major subunits of both photosystem (PS) II and PSI reaction center proteins were first reduced and subsequently recovered in H. pluvialis , while they were gradually reduced in D. salina . D. salina also showed a greater ability to function using the xanthophyll-cycle and the cyclic photosynthetic electron transfer pathway compared to H. pluvialis . We propose a reoriented and effective HL-responsive strategy in H. pluvialis , enabling it to acclimate under HL. The promising metabolic pathway described here contains a reorganized pentose phosphate pathway, Calvin cycle and glycolysis pathway participating in carbon sink formation under HL in H. pluvialis . Additionally, the efficient carbon reorientation strategy in H. pluvialis was verified by elevated extracellular carbon assimilation and rapid conversion into astaxanthin.",
"discussion": "Discussion Thylakoid membrane of H. pluvialis might be more extensively protected Although HL caused quite similar morphological and growth changes in the two microalgae, for example, the loss of flagella and cell density declines ( Figures 1 and 2 ), thylakoid membranes under HL were different in the two algae. The thylakoid membrane was very much vulnerable to HL induced photooxidative damage 12 . Thus, the consequence of HL irradiation on the thylakoid membrane can be fatal and even directly lead to cell death. Before HL induction, the thylakoid membranes of H. pluvialis and D. salina , which stacked between the 20% and 50% layers in a sucrose gradient, possessed structural integrity and homogeneityin ( Figure 4 ). Subsequently, 24 h of HL illumination caused some of the thylakoid fragments to float above the 20% sucrose layer, but still left the main partition between 20% and 50% layers for both cells. This phenomenon revealed that either the fragmentation of the thylakoid membranes, or an increase of lamellae thylakoid was induced by HL 23 , based on the fact that more lamellae could lead to a lower density of the total thylakoid membrane 24 . It has also been reported that HL can lead to the reduction of the appressed thylakoid domain (grana) so that thylakoid lamellae would be relatively increased 25 , resulting in the variation of thylakoid band distribution patterns 24 . As shown ( Figure 4 ), 48 h of HL induction caused a higher percent of low density thylakoids in D. salina , but less in H. pluvialis . In H. pluvialis , the thylakoid membrane grana structure may have been more extensively protected under HL compared with in D. salina . However, HL could lead to the alteration of the fatty acid composition in the membrane, which in turn might affect the density of membrane protein. For example, HL could lead to the increase of saturated fatty acid and decrease of unsaturated fatty acid in Chlorella vulgaris 26 . H. pluvialis was more efficient in HL acclimation compared to D. salina By applying 1,000 μmol photons/m 2 /s irradiance, HL stress was created for both microalgae, which could also be confirmed by the decline of cell density ( Figure 2 ) and Fv/Fm ( Figure 3 a ). HL stress could lead to higher energy absorbance by LHCII. This resulted in the decrease of vulnerable proteins constituting the PSII complex, including OEC, CP43, CP47, D1 and D2 for both H. pluvialis and D. salina at 24 h ( Figures 7 and 8 ). Since reaction center proteins were decreased, while the LHCII was relatively increased, at 24 h ( Figures 5 and 7 ), the relatively over-reduced state at PSII needed to be regulated. For photosynthetic organisms, it is critically important to maintain a coordinated electron transfer rate so that the redox poise will be stabilized at a balanced state when encountering HL stress 27 . To accomplish this, the ETR(I) and ETR(II) declined ( Figure 3 b, c ) synchronously for both H. pluvialis and D. salina at 24 h. The decline of ETR(I) and ETR(II) could be related to the cytochrome b 6 /f complex. Subunits of cytochrome b 6 /f complex work in tandem patterns, thus the decline of any subunit could lead to inefficient functioning of the photosynthetic electron transport (PET). Consequently, PSI received far less PSII-generated electrons via linear PET for both H. pluvialis and D. salina . Under such circumstances, PSI activity could be well protected 9 , but PSII would still suffer from an over-reduction. With HL stress continued at 48 h, D. salina increased ETR(I) while ETR(II) was still suppressed ( Figure 3 b, c ). This stress-responsive strategy is typical and a cyclic PET pathway around PSI was able to funnel excess electrons to generate ATP without increasing oxygen evolution. The widened gap between ETR(I) and ETR(II) enabled a more efficient electron diversion from PSII to PSI, defending PSII from further damage 28 . This responsive mechanism was fully supported by the increase of the electron carrier protein, the cytochrome b 6 /f complex, at 48 h ( Figure 6 a ). Additionally, the increased PsbS protein in D. salina could be involved in PSII protection via feed-back de-excitation (qE), because the overexpression of PsbS could increase qE capacity 29 . Nevertheless, D. salina did not acclimate well to HL stress, which could be concluded from the persistent decline of F v /F m , qP and the PSI and PSII reaction center proteins ( Figures 3 a and 10 ). However, H. pluvialis seemed to employ another different strategy by narrowing down the gap between ETR(I) and ETR(II) at 48 h, which could be concluded from the declining ratio of ETR(I) to ETR(II) ( Figure 3 d ). And the strategy turned out to be more effective as Fv/Fm, PSI and PSII reaction center proteins were recovered at 48 h while those of D. salina didn't ( Figures 3 , 7 and 10 ). Those facts indicated that there was a different electron diversion mechanism to relieve the over-reduction stress caused by HL in H. pluvialis . However, one phenomenon should be noted, that PetB and PetC proteins decreased and fell below the instrument detection sensitivity after 24 h HL stress. Also, the rapid decline of cytochrome f under HL stress in H. pluvialis found in this research ( Figure 6 a ) was consistent with results by Tan et al 9 . It is known that increased PSI can enhance the cyclic PET pathway to relieve the tension. Yet, the conventional cyclic PET pathway heavily relies on a functional linear PET, involving cytochrome b 6 /f , to transfer PSII-generated electrons. Based on our results, the cytochrome b 6 /f complex was nonfunctional and unable, or at least quite inefficient, to contribute to the linear PET pathway. Consequently, it was highly unlikely that H. pluvialis employed a cyclic PET strategy at 48 h. As shown in Figure 3 b , there was still moderate ETR(I) at 48 h, raising the question of the origin of the electrons that drive PSI. Additionally, the high efficiency of HL acclimation in H. pluvialis could be reflected in the recovery of qP at 48 h ( Fig. 3 F ). The qN parameter also showed a significant increase at 48 h, indicating an efficient ability to dissipate excessive energy. Comparatively, no such phenomenon was observed in D. salina . The pool size for the xanthophyll-cycle and conversion ratio were both relatively higher in D. salina compared with H. pluvialis ( Table 1 ). Yet, the decreasing qN suggested that it might not be sufficient enough for the xanthophyll-cycle alone to cope with HL. Consequently, the higher HL tolerance in H. pluvialis could be attributed to more than the xanthophyll-cycle when compared with D. salina . Decrease of cytochrome b 6 /f was beneficial for astaxanthin synthesis HL-induced oxidative stress was directly caused by reactive oxygen species (ROS), including H 2 O 2 , O 2 − , -OH* and 1 O 2 * 30 , and the chloroplasts were believed to be the primary site of photooxidative damage 16 . Han et al . also found that 24 h of HL caused high amounts of ROS accumulation in the motile cells of H. pluvialis 16 . The great decline of cytochrome b 6 /f observed in this research and the results by others 9 demonstrated that this could lead to the decrease of the electron transfer from PSII to PSI. The over-excited P680* could not transfer energy to the photosystem, but to oxygen, forming singlet oxygen ( 1 O 2 *) 31 . Although the direct fatal damage of singlet oxygen to H. pluvialis cells was unlikely, as has been discussed 16 , singlet oxygen was indeed a qualified plastid-generated signal that might be involved in nuclear gene regulation 32 . Yet, how singlet oxygen regulates HL-induced secondary carotenoid synthesis has not been well explained. The decrease of the cytochrome b 6 /f complex also caused an over-reduction of PSI by breaking the cyclic PET around PSI. Meanwhile, there was an increase of LHCI in H. pluvialis ( Figure 5 a ), creating an over-reduction stress for PSI in H. pluvialis . Consequently, the generation of superoxide and H 2 O 2 by PSI would be favored 31 . The combined stress caused by both the cytochrome b 6 /f decline and the LHCI increase could lead to high amounts of superoxide and H 2 O 2 accumulation. As has been depicted, stressor-dependent carotenogenesis, induced by HL, improved the cell survival rate 12 . ROS accumulation was also due to astaxanthin synthesis requiring more oxygen in the oxidation and hydroxylation steps when compared with β -carotene synthesis 8 . Those oxygen-consuming reactions would remove the excess intracellular oxygen produced during ROS detoxification. Overall, while the cytochrome b 6 /f complex decline might appear to negatively affect photosynthesis, it was actually an early preparation for astaxanthin synthesis during long-term acclimation. Differences between H. pluvialis and D. salina in response to HL For both H. pluvialis and D. salina , normal light provides moderated levels of irradiation energy to drive photosynthesis, thus the linear PET dominated, generating ATP and NADPH. Such a conventional PET pattern provided sufficient reduction power to fuel the Calvin cycle for carbon fixation, as depicted in Figure 12 . Under such circumstance, cell proliferation metabolism would be favored and biomass was accumulated. However, H. pluvialis showed a dramatic decline in the cell proliferation rate and increase of cyst cells under HL stress 33 . Secondary carotenoid accumulation occurred in cyst cells under unfavorable environments, including HL, nutrient deprivation, salt stress and oxidative stress 34 35 . Mostly, astaxanthin accumulation was accompanied cell enlargement 14 and adding carbon sources, like acetate, CO 2 or NaHCO 3 enhanced the astaxanthin accumulation 36 . Although the photosynthetic activity was compromised under stress in H. pluvialis , the carbon source was still assimilated, and Hagen et al . believed stressed cells store carbon and energy for recovery after stress 37 . When encountering HL stress, although many physiological and photosynthetic proteome differences existed between H. pluvialis and D. salina , there was a common purpose of cellular survival. For D. salina , the increased cytochrome b 6 /f complex could help transfer PSII generated electrons and increase cyclic PET around PSI while producing ATP under HL. For H. pluvialis , the great decline in the cytochrome b 6 /f complex broke the linear PET, thus the reduction power (NADPH) and ATP production were restrained. In fact, H. pluvialis was able to accumulate astaxanthin, starch and oil globules under HL 9 16 , and in those cases the reduction power, ATP and carbon material (i.e. acetyl-CoA) were all heavily required. Many widely accepted theories have been established to explain the HL-responsive mechanism in H. pluvialis 8 10 11 16 . A dominant one is that H. pluvialis and other higher plants employ a plastid terminal oxidase (PTOX)-involved mechanism to regulate the internal redox poise under HL stress 15 16 28 38 39 . However, a conventional carbon assimilation process would not be driven efficiently due to the decreased cytochrome b 6 /f complex. Consequently, H. pluvialis needs to balance the reduction power and to make the most of the carbon source available. H. pluvialis must manage HL stress while fuelling a carbon sink at the same time. Based on our results and previous research, we propose a possible systematic metabolic network that covers both photosynthetic acclimation and carbon flow reorientation ( Figure 12 ). For H. pluvialis , the reduction power could be provided by the photosynthesis process and cellular metabolism. Due to the partially destroyed PET pathway, less reduction power was generated by regular photosynthesis. Thus, other complementary pathways would be required for first starch, and subsequent astaxanthin and fatty acid synthesis. Wang et al . reported the up-regulation of proteins involved in glycolysis and the pentose phosphate pathway under oxidative stress 11 . Results by Chen et al . 40 showed that astaxanthin synthesis was accompanied by significant up-regulation of both Rubisco activity and rbc L gene expression, indicating possible Rubisco participation in astaxanthin synthesis. However, it seemed unnecessary to maintain a high Rubisco activity while photosynthesis was compromised by HL, unable to provide sufficient reduction power for carbon fixation. The reduction power requirement constraint could break the Calvin cycle by suspending the regeneration of RuBp. In fact, it has been reported that Rubisco could participate in metabolite conversion without the Calvin cycle 41 , in which case the RuBp requirement by Rubisco could be fulfilled by the pentose phosphate pathway. Photosynthetic proteome results and previous conclusions suggested it is both necessary and possible that a reoriented carbon flow pathway exists in H. pluvialis under HL. Under HL stress, conventional linear PET was greatly compromised due to a decrease in the cytochrome b 6 /f complex. Insufficient reduction power would not be able to drive the Calvin cycle. However, the pentose phosphate pathway diverted RuBp to the Calvin cycle to maintain the carbon fixation process. The 3-phosphoglycerate (3PGA) produced from the fixation step of the Calvin cycle would be mostly diverted to glycolysis for acetyl-CoA production to meet the great demand for carbon skeleton buildup during fatty acid and carotenoid synthesis. The shared tank of ATP and reduction power during carbon sink construction can also be complementarily fuelled by the TCA cycle. Overall, it is an economic and effective pathway for carbon assimilation when photosynthesis is inhibited by HL. It has been experimentally verified that carbon sources, like HCO 3 − or CO 2 , and acetate enhanced astaxanthin accumulation 34 42 . Consequently, it is theoretically possible for H. pluvialis to quickly form intracellular carbon sinks with minimized restrictions caused by the photosynthetic decline, even though the hypothesis requires experimental verification. The maintenance of increased efficiency of carbon incorporation in H. pluvialis As proposed in Figure 12 , the occurrence of carbon sink formation, mainly through astaxanthin synthesis, was possible, and the enlargement of cell diameters ( Figure 1 ) suggested that extracellular inorganic carbon were required. By using isotope-labelled inorganic carbon, the inorganic carbon-fixation capability was measured under HL stress. The decrease in fixed organic carbon ( Figure 11 a ) confirmed the negative influence by HL stress. However, the increased conversion ratio to astaxanthin ( Figure 11 b ) implied that a carbon flow reorientation occurred under HL in H. pluvialis . The carbon flow regulation could also be reflected by the increase of CA activity at 48 h of HL. Although total fixed carbon dropped at 48 h compared with the level at 24 h, CA activity was increased at 48 h. Moreover, the elevated astaxanthin conversion ratio and the increase of CA activity were more than just coincidental occurrences at 48 h of HL. In fact, the increase of CA activity should be considered a prerequisite for the rapid carbon sink formation under HL. Additionally, there remained another unknown mechanism that regulated the distribution of carbon flux fuelled into the carbon sink of starch, astaxanthin and fatty acids. It has been reported that pyruvate kinase was down-regulated under oxidative stress 11 . In the proposed metabolic pathway, whether pyruvate kinase played a control role in carbon flux distribution remains to be verified."
} | 4,288 |
31060509 | PMC6501319 | pmc | 4,717 | {
"abstract": "Background Obligate sulfur oxidizing chemolithoauthotrophic strains of Hydrogenovibrio crunogenus have been isolated from multiple hydrothermal vent associated habitats. However, a hydrogenase gene cluster (encoding the hydrogen converting enzyme and its maturation/assembly machinery) detected on the first sequenced H. crunogenus strain (XCL-2) suggested that hydrogen conversion may also play a role in this organism. Yet, numerous experiments have underlined XCL-2’s inability to consume hydrogen under the tested conditions. A recent study showed that the closely related strain SP-41 contains a homolog of the XCL-2 hydrogenase (a group 1b [NiFe]-hydrogenase), but that it can indeed use hydrogen. Hence, the question remained unresolved, why SP-41 is capable of using hydrogen, while XCL-2 is not. Results Here, we present the genome sequence of the SP-41 strain and compare it to that of the XCL-2 strain. We show that the chromosome of SP-41 codes for a further hydrogenase gene cluster, including two additional hydrogenases: the first appears to be a group 1d periplasmic membrane-anchored hydrogenase, and the second a group 2b sensory hydrogenase. The region where these genes are located was likely acquired horizontally and exhibits similarity to other Hydrogenovibrio species ( H. thermophilus MA2-6 and H. marinus MH-110 T ) and other hydrogen oxidizing Proteobacteria ( Cupriavidus necator H16 and Ghiorsea bivora TAG-1 T ). The genomes of XCL-2 and SP-41 show a strong conservation in gene order. However, several short genomic regions are not contained in the genome of the other strain. These exclusive regions are often associated with signs of DNA mobility, such as genes coding for transposases. They code for transport systems and/or extend the metabolic potential of the strains. Conclusions Our results suggest that horizontal gene transfer plays an important role in shaping the genomes of these strains, as a likely mechanism for habitat adaptation, including, but not limited to the transfer of the hydrogen conversion ability. Electronic supplementary material The online version of this article (10.1186/s12864-019-5710-5) contains supplementary material, which is available to authorized users.",
"conclusion": "Conclusions As previously assumed from the comparison of the 16S rRNA genes (identity ≥99%; [ 8 ]), the genome of Hydrogenovibrio sp. SP-41 is closely related to that of Hydrogenovibrio crunogenus XCL-2. Despite a low average nucleotide identity (87.7%), which would suggest an assignment of the two strains to different species, the alignment of their genomes shows a highly conserved gene order. However, additional sequences are present in both genomes, in short non-homologous regions, or insertions to one of the two genomes in traits where the rest of the sequence is collinear. Two hydrogenase gene clusters were found in SP-41. Cluster I is homologous to the hydrogenase gene cluster in XCL-2 and codes for a group 1b hydrogenase. Cluster II is absent in XCL-2 and codes for two hydrogenases: a group 1d periplasmic membrane-anchored hydrogenase and a group 2b sensory hydrogenase. Their genomic proximity might indicate interplay of these two hydrogenases, such as regulation of the group 1d hydrogenase by the sensory hydrogenase in response to different hydrogen concentrations in the environment. Hydrogenase gene cluster II has been likely derived from horizontal gene transfer, as it is surrounded by DNA modification and mobilization genes, and is predicted as genomic island. The closest relatives of this region are found in members of the same genus ( H. thermophilus MA2-6, H. marinus MH-110 T ). As previously observed [ 21 ], it is likely that the region has been acquired multiple times in the lineage, as it is located in different genomic contexts in the different strains. If this is the case, all these strains acquired the hydrogenase from a similar source. However, horizontal acquisition of this gene cluster might be common, well beyond this lineage. Similar regions were found in hydrogen oxidizers, phylogenetically distant from SP-41 and isolated from very different and geographically distant habitats: the Betaproteobacterium Cupriavidus necator H16 (isolated in Germany from soil samples [ 58 ]) and two strains of the Zetaproteobacterium Ghiorsea bivora . The latter were isolated from similar habitats (iron mats of hydrothermal vents) but far away from each other (TAG-1 T : TAG vent site, Mid-Atlantic Ridge; SV-108: Snail Vents site, Mariana back-arc; [ 44 ]). Also in these genomes, genes related to DNA mobility were found in proximity of their hydrogenase gene clusters. Both MH-110 and MA2-6 are able to grow on hydrogen, as SP-41, thus likely the presence of this region explains the difference in this ability from XCL-2, which lacks the region. We showed that both the large subunit genes of cluster II group 1d hydrogenase and of cluster I group 1b hydrogenase are expressed during H 2 consumption, and their expression is higher when H 2 is the only available electron donor. As both hydrogenases are expressed, we hypothesize that elements of both hydrogenase gene clusters could interact in SP-41 during the observed hydrogen oxidation activity. This could explain the differences in activity rate and hydrogen affinity of SP-41 to MA2-6, both containing cluster II. However, if this is the case, this is probably not the only activity mechanism of cluster I, as interaction cannot explain its presence and conservation in XCL-2, where the cluster II is absent. Besides the ability of growing on hydrogen, horizontal transfer of genetic material appears to play an important role in shaping the genome of SP-41. This is reflected by the higher number of transposases (compared to XCL-2) located in multiple small regions not present in the XCL-2 genome. These regions often contain signs of possible DNA mobilization, such as the presence of genes for transposases, integrases and DNA modification, or a genomic position next to common insertion points, such as tRNA genes. In an environment where DNA mobility is likely very high, the necessity may also arise to protect against unwanted sequences, such as invading plasmids or viruses; this might explain the presence of a CRISPR locus, not present in XCL-2. In a group of autotrophic Protobacteria genomes the average number of transposases appears to be higher where CRISPR loci are present. However, we found also several counter-examples, where presence of CRISPRs and abundance of transposases are not correlated. Thus, further studies are necessary to understand the observed correlation and which other factors may play a role. The inserted DNA confers to SP-41 features absent in XCL-2, such as a urease and transport system for urea, a transport system for ferrous iron and a detoxification system for mercury. These may be important for the survival in the specific environment. Adaptations to the habitat are a common feature of Thiomicrospira , Hydrogenovibrio and Thiomicrorhabdus species, explaining their prevalence in multiple heterogeneous environments [ 21 ]. Besides this, as postulated for other hydrothermally influenced habitats, high levels of horizontal gene transfer may confer an advantage to the bacterial community as a whole [ 28 ].",
"discussion": "Results and Discussion Sequencing and annotation of the SP-41 genome The genome assembly of the isolated SP-41 was obtained using the Pacific Biosciences platform. The reads (total length of 1.24 Gbp, 503X coverage) were assembled into a single 2.47 Mbp long contig. Illumina sequences previously obtained from the enrichment culture from which SP-41 was isolated were mapped to the assembly and used for 192 corrections (mostly of single nucleotides). The final assembly is 2’453’259 bp long. The genome includes 9 rRNA genes (3 copies of 23S, 16S and 5S), 44 tRNA genes (for all canonical amino acids), a tmRNA gene and 2293 protein coding genes (Fig. 1 ). 87.3% of the protein products had a specific annotation (i.e. not “hypothetical protein”) and 37.5% were assigned an EC number by Prokka [ 22 ]. COG annotations by CD-search [ 23 ] were assigned to 64.0% of the proteins (Additional file 1 ), and KO annotations by Blastkoala [ 24 ] to 64.2% of the proteins (Additional file 2 ).\n Fig. 1 Circos plot of the genome of Hydrogenovibrio sp. SP-41. From the outside to theinside, the plot contains the following tracks: CDS (plus strand); CDS (minus strand); RNA genes (both strands); G+C content; genomic islands. The CDS are colored according to the COG annotation (functional categories, see legend on the right) Phylogenetic relation to other strains Analysis of the average nucleotide identity (ANI), shows that the strain most closely related to SP-41, for which a genome sequence is available, is XCL-2 (Additional file 3 ). Most proteins encoded by the SP-41 genome (2019 or 88.1%) have a homolog in XCL-2, in most cases (1986) encoded by a gene in the same position of the alignment of the two genomes (Additional file 4 ). In both strains, a similar proportion of proteins have no ortholog in the other strain (11.9% of proteins of SP-41; 11.4% XCL-2). In SP-41, these exclusive proteins are more often uncharacterized: 28.8% of them are hypothetical proteins in SP-41, 10.6% in XCL-2. The relatedness of SP-41 and XCL-2 was already suggested by a previous partial sequencing of the 16S rRNA gene of SP-41 (Genbank KJ573628), in which only 3 differences were found compared to the 16S rRNA gene of XCL-2 (which itself has 3 identical 16S copies) [ 9 ]. The 16S rRNA gene sequences obtained from the genome sequencing of SP-41 confirm one of the 3 differences, located in the V7 hypervariable region, present in all three 16S rRNA gene copies of SP-41 (Additional file 5 ). The further two differences previously observed are not confirmed by the genome sequencing (they are located in the forward sequencing primer region and were likely a sequencing artefact). However, the genome sequence of SP-41 also highlighted the intragenomic heterogeneity of its 16S rRNA genes. Through the genome sequencing, it became clear that the previously published 16S rRNA gene sequence of SP-41 actually represented a consensus sequence, as each of the copies have additional differences, compared to XCL-2, not present in the other two copies (and not present in the previously published sequence). The differences in single copies were masked in the sequencing by the other two copies and lead to wrong base callings (Additional file 6 ). These are located in the V1 region (1 difference, 1st 16S rRNA gene copy) and V2 region (4 differences, 2nd 16S rRNA copy; 2 differences, 3rd 16S rRNA gene copy). The high level of 16S rRNA gene identity between SP-41 and XCL-2 is thus confirmed, ranging from 99.7% to 99.9% depending on which SP-41 16S rRNA gene copy is considered. Phylogeny reconstruction based on the 16S rRNA genes [ 8 ] assigned several strains to the H. crunogenus species: TH-55 T , SP-41, XCL-2, L-12, EPR75, 37-SI-2, MA-3, HY-62. However, the comparison of the genome sequences of SP-41 and XCL-2 shows that these two strains belong to two different species, as their ANI of 87.7% is significantly below the 95% threshold suggested for species definition [ 25 ]. Despite this, the two strains are the closest relative of each other for which a genome sequence is available (Additional file 3 ). For the other strains mentioned before, only the sequences of the 16S rRNA gene and in some cases of the hynL gene are available [ 8 ]. Without further genome sequences it is thus not possible to accurately reconstruct the phylogeny of the lineage and, in particular, to understand, if the other strains previously assigned to H. crunogenus should be assigned to the same species as SP-41. Genomic structure and plasticity The genome of SP-41 is slightly larger (25.5 kbp or 1.0% more) than that of XCL-2. The dot plot of their alignment of the genome sequence of SP-41 to that of XCL-2 (Fig. 2 ) shows that most regions of the two genomes are homologous and collinear (in total 2.17 Mbp, 88.6% of the SP-41 genome; 89.7% XCL-2). The remaining parts of the alignment (Additional file 7 ) include (a) 61 exclusive regions, where one genome contains a sequence with at least one annotated feature (36 regions in SP-41 and 25 in XCL-2), and the other genome contains either no sequence or a non-homologous sequence with no features; (b) 12 divergent regions, non-homologous and containing at least one feature in both genomes; (c) a single translocation, i.e. homologous region of 12.1 kbp which is located 50 kbp ahead in the SP-41 sequence, with respect to the rest of the aligned sequences.\n Fig. 2 Dotplot representation of the alignment of the genomes of Hydrogenovibrio crunogenus XCL-2 and Hydrogenovibrio sp. SP-41. The regions of the genome predicted to be genomic islands are highlighted in green (for XCL-2) and red (for SP-41). Counting from the lowest coordinate, islands 1, 3, 4, 5, 7, 8 of XCL-2 and islands 1, 2, 3, 5 of SP-41 contain mostly exclusive sequences, thus were likely acquired after the divergence of the two strains. An island is in common (Island 6 of XCL-2 / 4 of SP-825 41). Island 2 of XCL-2 is partially common to SP-41, but not predicted as an island in that strain. Island 9 of XCL-2 partially overlaps islands 6 and 7 of SP-41, in a region of lower similarity, compared to the rest of the alignment (a translocation is also located there) The alignment does not reveal if the additional sequences found in exclusive and divergent regions represent sequences lost in the other strain, compared to the last common ancestor, or acquired by horizontal transfer. Thus, we analyzed the two genomes using IslandViewer [ 26 ] to identify genomic islands. In total, 7 islands were found in SP-41 (Additional file 8 ), between 4.5 kbp and 51.6 kbp in length, coding for a total of 148 protein-coding genes (Additional file 9 ). For comparison, XCL-2 contains 9 islands, between 7.6 kbp and 64.8 kbp, with a total of 377 protein-coding genes (Additional file 10 ). In the dot plot in Fig. 2 , we highlighted the coordinate ranges of the islands in SP-41 (green) and XCL-2 (red), to allow a visual identification of the overlap of islands of the two genomes and of the islands with exclusive regions. The results show that only part of the islands overlap with exclusive regions of the two genomes: 13 exclusive regions in SP-41 and 12 regions in XCL-2. This appears to be more consistent to a loss of sequences after strain divergence for the remaining exclusive regions. All genomic island prediction software is based on heuristics, which might fail to find the exact island boundaries or even miss entire islands in some cases. The island annotation by IslandViewer is based on the combination of two programs: IslandPath-DIMOB which looks for dinucleotide biases in a region of at least 8 consecutive genes, including a mobility gene [ 27 ]; Islander which looks for regions flanked by a tRNA gene or a tRNA gene fragment and containing an integrase gene. The limitations in predicting genomic islands are shown by the second island of XCL-2, which is largely homologous to SP-41, where, however, no island is predicted in the region. Furthermore, proteins related to transposition are present in other 5 of SP-41 exclusive regions not overlapping predicted islands (while in XCL-2 they are present only in predicted genomic islands), indicating possible further islands not recognized by the prediction software. As discussed in [ 14 ], XCL-2 contains a prophage sequence, which is not present in SP-41, and represents the largest exclusive region of XCL-2 (38.7 kbp) in the alignment to SP-41. However, in general, SP-41 shows a trend towards more genome plasticity: It contains more exclusive regions (36 vs 25), although of slightly smaller average size (5.6 vs 6.4 kbp) than the exclusive regions of XCL-2. Furthermore, 30 SP-41 but only 3 XCL-2 proteins were annotated with transposase/putative transposase KO (K07483;K07497) and/or COGs (COG2801;COG2963;COG3328). In environments associated with hydrothermal venting, a high prevalence of transposases has been previously observed in the biofilm coating the carbonate chimneys of Lost City [ 28 ]. There it has been hypothesized to serve as a generator of phenotypic diversity as counterpart to the low organismal diversity of the biofilm community, and possibly contributing to its overall fitness. Both strains XCL-2 and SP-41 were isolated from hydrothermally influenced samples. Thus it remains unclear, which other factors explain the presence and abundance of transposases in one, but not in the other strain. A 6.0 kbp exclusive region of SP-41 contains a CRISPR array, with 22 repeat units, and the associated proteins Cas1, Cas2 and Cas9. CRISPRs are thought to confer immunity towards invading DNA, such as plasmids and viruses, matching the spacers’ sequences [ 29 ]. This could be more useful in habitats where DNA mobility is more common. To understand if the presence of a CRISPR could be correlated to the abundance of transposases observed in SP-41, we counted the number of transposases and CRISPRs in a group of autotrophic Proteobacteria genomes previously analysed by [ 21 ]. We found that, in these organisms, the average number of transposases is significantly higher (p-value 0.02) in genomes with annotated CRISPRs (27.3 transposases in average) than in those where no CRISPR is present (14.6 transposases in average) (Additional file 11 ). However, e.g. within the Thiomicrospira / Hydrogenovibrio / Thiomicrorhabdus lineage this is not always the case. Hydrogenovibrio sp. Milos-T1 and Thiomicrorhabdus sp. Milos-T2 have a high number of transposases [ 21 ], but a CRISPR was annotated only in Milos-T1. Other members of the lineage containing a CRISPR ( Hydrogenovibrio halophilus DSM 15072 T , Hydrogenovibrio marinus MH-110 T , Hydrogenovibrio sp. MA2-6, Thiomicrorhabdus sp. Kp2, Thiomicrospira aerophila AL3 T , Thiomicrospira microaerophila ASL8-2 T ) do not generally show a high number of transposases. Hydrogenase gene clusters The hydrogenase gene cluster (encoding the structural hydrogenases, catalyzing H 2 ⇔2 H + +2 e − as well as accessory, assembly and maturation proteins) of XCL-2 is also found in SP-41 (genes GHNINEIG_02156 to GHNINEIG_02165). For ease of reading we name it hydrogenase gene cluster I. The hydrogenase belongs to group 1b [ 30 ]. The gene for the large subunit had been previously cloned and characterized [ 8 ]. The small subunit has the same unusually large size, as in XCL-2 (813 aa). The entire cluster is present also in SP-41, with the same gene order as in XCL-2. Besides the XCL-2 resembling hydrogenase gene cluster, a further hydrogenase gene cluster is located on the SP-41 genome (here named hydrogenase gene cluster II). It is part of the largest exclusive region of the SP-41 genome (relative to XCL-2) with 62.6 kbp (starting at position 808620). In total, this exclusive region contains 63 protein-coding genes. Up- and downstream of this region are genes involved in DNA mobilization and modification. A horizontal acquisition of this region is supported by the genomic island prediction, which covers a large part of the area (the last 50.4 kbp). The hydrogenase gene cluster and some related genes (described below) are contained in the central part of the region (27 genes, from gene GHNINEIG_00794 to gene GHNINEIG_00820). The first of the two hydrogenases from the hydrogenase gene cluster II is encoded by genes GHNINEIG_00797 (large subunit) and GHNINEIG_00798 (small subunit). The small subunit contains the Tat motif RRXFXK important for the translocation to the periplasm [ 31 ]. This motif is absent in the other two small subunits from the hydrogenase gene cluster I encoded on both SP-41 and XCL-2 genomes. Furthermore, the presence of a cytochrome b subunit gene (gene GHNINEIG_00796) on the hydrogenase gene cluster II suggests anchoring of the hydrogenase to the membrane [ 32 ]. In contrast, this gene is not present in hydrogenase gene cluster I and in its homolog in XCL-2. SP-41 hydrogenase activity was shown to be localized in the membrane and not in the soluble fraction [ 9 ]. The lack of the Tat motif and Cytochrome b subunit was postulated to be a possible reason for the hydrogenase inactivity, under the tested conditions, of the XCL-2 hydrogenase [ 8 ]. Their presence here could explain why SP-41 is able to consume hydrogen, while XCL-2 is not. Sequence motifs of the hydrogenases encoded by genes GHNINEIG_00797 and GHNINEIG_00798 resemble hydrogenases assigned to group 1d [ 30 ]. In particular, the large subunit (gene GHNINEIG_00797) contains L1 (VERICGVCTGCH) and L2 (SFDPCLACSTH) motifs compatible with the group 1d classification [ 30 ]). Interestingly, the L3 (HDHIVHFYHLHALD) and L4 motifs (GTVAAPRGALAH) are the canonical motifs, i.e. those not found in the XCL-2 hydrogenase large subunit and its ortholog in SP-41. The small subunit (gene GHNINEIG_00798) contains proximal and distal cluster binding motifs typical of group 1d, while the 5th position of the medial binding motif (FPIQAGHGCIGCS) contains an Alanine instead of a Serine of the described motif for group 1d (xPIxSGHxCxGCx) and is compatible to group 1f. The second hydrogenase in the hydrogenase gene cluster II is encoded by genes GHNINEIG_00818 (large subunit) and GHNINEIG_00819 (small subunit). Its small subunit does not contain a Tat-motif. Its medial and distal cluster binding motifs are compatible with group 2b, while the proximal cluster binding motif contains a Serine instead of Glycine at its third position, when compared to the motif described for group 2b (xCGGCx—xCxxxGG—xCP). The large subunit contains L1 (APRICGICSVSQ) and L2 (SFDPCMVCTVH) motifs compatible with this group assignment. This suggests a sensory function for this hydrogenase [ 30 ]. The following gene (GHNINEIG_00820) is an homolog of the Escherichia coli K12 zraS / hydH gene. This is a sensory protein kinase [ 33 ], originally described as regulating the labile hydrogenase activity in E. coli K12 [ 34 ] and is also homologous to the HoxJ component of the hydrogen-sensing system of Cupriavidus necator (formerly Alcaligenes eutrophus ) [ 35 ]. Function of the hydrogenase clusters In order to test the expression of the two group 1 [NiFe]-hydrogenases (group 1b, encoded by hydrogenase cluster I, and group 1d, encoded by hydrogenase cluster II), we performed qRT-PCR experiments with RNA extracts of SP-41, grown with an atmosphere of H 2 :CO 2 :O 2 :He(2:20:1:78 % ( v / v )). Both cluster I and cluster II [NiFe]-hydrogenases are expressed in SP-41 under the tested cultivation conditions, i.e. with (MJ-T medium) and without (MJ medium) thiosulfate addition (Fig. 3 ). For both hydrogenases the highest expression levels are observed after 24 h and if hydrogen is the only available electron donor. If thiosulfate is available in the medium, the relative expression of both hydrogenase genes is significantly lower. In contrast to the MJ incubation, in the thiosulfate supplemented MJ-T medium the highest expression levels of both hydrogenases is observed after 8 h incubations. For the cluster I hydrogenase, this was already shown in [ 9 ]. This effect is most obvious for the cluster II hydrogenase, which altogether exhibits considerably lower expression levels than the cluster I hydrogenase in the MJ-T incubations.\n Fig. 3 Expression of SP-41’s hydrogenase genes under different cultivation conditions. Relative expression of the hynL genes for the large subunit group 1 [NiFe]-hydrogenases in hydrogenase gene cluster I a and cluster II b , normalized to the housekeeping gene rpoD . The expression was analyzed for SP-41 grown in MJ medium for 8 h (light blue bars) and 24 h (dark blue bars), in MJ-T medium grown for 8 h (light orange bars) and 24 h (dark orange bars) It was previously observed that membrane fractions of hydrogen-oxidizing Hydrogenovibrio strains containing a group 1b hydrogenase (e.g. SP-41) display a higher hydrogenase activity rate than those containing a group 1d hydrogenase (MA2-6, MH-110) [ 8 ]. However, the presence of the group 1b hydrogenase alone (in XCL-2) does not confer the ability to oxidize hydrogen under all tested conditions. Also the presence of a group 1d hydrogenase alone does not necessarily explain all aspects of the observed hydrogenase activity. E.g. although both strains contain a group 1d hydrogenase, the hydrogen affinity of the hydrogenases of MA2-6 and SP-41 appears to be different: MA2-6 consumes initially more H 2 , but its activity ends at higher H 2 concentrations [ 8 ]. The degree of sequence conservation, and gene expression in SP-41, suggest that also cluster I genes do actually encode a structural hydrogenase [ 8 ]. As both hydrogenases are expressed simultaneously during the observed hydrogen consumption activity, it is possible that some components of hydrogenase gene cluster II, missing or not functional in cluster I, affect the other cluster. As no hydrogenase activity was detected in soluble fractions [ 8 ], this would require anchoring of the hydrogenase of cluster I to the membrane by components of cluster II, e.g. by its cytochrome b subunit. Some chaperons and maturation proteins of cluster II might as well affect the cluster I hydrogenase. Also, the group 2b sensory hydrogenase might regulate the expression of the cluster I hydrogenase. A similar regulation mechanism appears to affect both hydrogenases, as the expression levels of both hydrogenases correlate well (Fig. 3 ). Further experiments will be necessary to test these hypotheses. Despite a possible interaction, the cluster I hydrogenase is likely to function also independently from cluster II as a soluble hydrogenase and/or with a different regulation mechanism, under other currently undetermined environmental conditions. Cluster I is located far on the genome from cluster II, and is absent in XCL-2, where it is still well conserved. Other strains exhibiting high hydrogenase oxidation activity in their membrane fraction, such as TH-55 T , MA-3 and L-12 have a group 1b hydrogenase. However, their genomes have not yet been sequenced, thus it is unknown if these strains also contain further hydrogenases, as SP-41. Both XCL-2 and SP-41 are microaerophiles, suggesting that they are able to use O 2 as electron acceptor. Similar to the other members of the lineage [ 21 ], including XCL-2, SP-41 carries the genes for a cbb 3 -type cytochrome oxidase (EC 1.9.3.1). This enzyme is found mostly in Proteobacteria, but with representants spread across all bacterial phyla [ 36 ], and is typically expressed under microaerophilic conditions [ 37 ]. Besides oxygen, hydrogen oxidation can be coupled to the reduction of several other molecules [ 38 ]. Therefore, we tried to identify genes, which could suggest the potential use of alternative electron acceptors. A nitrate reductase gene is annotated in SP-41 and XCL-2, but it is not likely to have a respiratory function: denitrification tests on H. crunogenus TH-55 T were negative [ 2 ]. Five genes are homologs of dsrE , a component of dissimilatory sulfite reductase systems. However, DsrE may as well be involved also in sulfate oxidation [ 39 ], and no further component of a Dsr system is found in the genome. Thus, similar to what was previously noted for XCL-2 [ 14 ], we conclude that no other known terminal oxidase, besides cbb 3 is present in SP-41. Homologs of the hydrogenase gene cluster II proteins The genomic region containing the hydrogenase gene cluster II is not present in the XCL-2 genome: accordingly, only 3 of the 27 genes in the central part of the region have homologs in XCL-2 (thioredoxin; elongation factor-1-alpha; YeeE/YedE family protein; average blast hit coverage: 95.6%, similarity: 64.9%). However, several of the proteins have homologs in other organisms (Fig. 4 ; Additional file 12 ). The highest number of homologs in the region is found in the genome of Hydrogenovibrio thermophilus MA2-6, which has homologs of all 27 proteins in the region (average blast hit coverage: 97.1%, similarity: 80.0%). With the exception of the carbonic anhydrase, which has a homolog (coverage: 99.5%, similarity: 90.6%) elsewhere in the MA2-6 genome (cds372), most of the genes are in two regions of the MA2-6 genome (cds1576 to cds1580, cds1590 to cds1610). The genome alignment of MA2-6 to SP-41 shows that the gene order in the two genomes is mostly conserved (Fig. 5 ). However, the hydrogenase gene cluster is inserted in the MA2-6 genome at a different position and inverted and a region around the cluster in SP-41 is missing in MA2-6 (Fig. 5 ). The gene order in the hydrogenase region itself is also conserved, although some rearrangements are apparent (Fig. 6 ). The two groups of hydrogenase-related genes in MA2-6 are separated by 9 genes, mostly related to sulfur assimilation (sulfate adenylyltransferase; phosphoadenosine phosphosulfate reductase; sulfite reductase; cysteine desulfuration protein SufE; siroheme synthase). Of these, only SufE (encoded by MA2-6 cds1588) has a SP-41 homolog (cov: 91.8%; sim: 81.5%), encoded by a gene located elsewhere on the genome (SP-41 gene GHNINEIG_01718). The presence of genes for enzymes related to assimilatory sulfate reduction next to the hydrogenases has been postulated to be possibly assisting the synthesis of the hydrogenases iron sulfur clusters [ 21 ]. However, their absence in SP-41 shows that they are not essential for the hydrogenase activity.\n Fig. 4 Presence of homologs in other organisms of the proteins encoded by Hydrogenase gene cluster II of SP-41. Diagram showing for which genes in the SP-41 hydrogenase gene cluster not present in XCL-2 (genes GHNINEIG_794 to GHNINEIG_820) homologs were found (full circle) or not (empty circle) in several organisms using BlastP. The results for XCL-2, bacteria with 20 or more homologs and the bacterium outside of Proteobacteria with the highest number of homologs ( Nostoc punctiforme ) are shown \n Fig. 5 Alignment of the SP-41 and MA2-6 genomes. Dot plot of the alignment of the SP-41 and MA-2 genomes, for the whole genome a and detail around the hydrogenase gene cluster b . The orientation and starting point of the MA2-6 sequence was changed to match that of SP-41 \n Fig. 6 Gene order in Hydrogenase gene cluster II of SP-41 compared to homologous regions in other Proteobacteria. Comparison of the gene order in the hydrogenase gene cluster II of SP-41 with homologous regions in Hydrogenovibrio thermophilus MA2-6, Hydrogenovibrio marinus MH-110 T , Cupriavidus necator H16 and Ghiorsea bivora TAG-1 T . Color background rectangles are used to highlight gene syntheny. For MA2-6 and TAG-1 the reverse-complementary strand to the reference sequence is visualized, in order to maximize the number of homologs aligned to the other genomes. For MH-110, the region is different in the two available genome sequences: a gene duplication is present in the assembly by Scott et al. [ 21 ] but not in that by Jo et al. [ 17 ] Another member of the same genus, Hydrogenovibrio marinus MH-110, contains homologs of 25 of the 27 proteins in the region (average blast hit coverage: 97.6%, similarity: 78.5%), missing the hybE/rubredoxin and the carbonic anhydrase (which is not present in the region also in MA2-6). Two genome sequencings were performed independently by two groups ([ 21 ] and [ 17 ]). In both sequences the order of the genes in the region is very similar to that of MA2-6. The sulfur assimilation genes are also present, but not the Ton-B receptor. The MH-110 genome sequence described by [ 21 ], contains some additional genes, including a transposase and a duplication of the first hydrogenase and some of the related genes, homologous to SP-41 genes GHNINEIG_00796 to GHNINEIG_00800. However, these genes are not present in the sequence described by [ 17 ]. Besides this, the adenylyltransferase small subunit gene is not annotated by [ 17 ]. As the two sequencing projects target the same strain, it is unclear if the differences in the sequences represent a genuine rearrangement or if they are sequencing or assembling artifacts. As XCL-2 is more closely related to SP-41, than the other two strains (Additional file 3 ), different reconstructions of the evolutionary history of the region remain possible. It might have been acquired by an ancestor of these bacteria and then lost by XCL-2 and other strains of H. crunogenus ; in this case, it remains unclear for which reason the region was not maintained, as it confers a larger metabolic flexibility. Alternatively, the region might have been acquired horizontally multiple times; this was considered the most likely explanation to explain the presence of the region in strains of H. thermophilus and H. marinus [ 21 ], but not in related strains, and could also hold for SP-41. This would explain, why the island is present in different genomic surroundings. It is not known, why this particular hydrogenase island appears so well-suited for members of Hydrogenvibrio . A possible reason could be the presence of the group 2b sensory hydrogenase, which could confer an advantage in the regulation of hydrogenase activity in response to rapid changes in H 2 availability. Outside of the Thiomicrospira / Thiomicrorhabdus / Hydrogenovibrio , 9 other bacterial genomes contain 20 or more homologs of the region, mostly Gammaproteobacteria. The organisms with the next highest number of homologs (Fig. 4 ) are Gammaproteobacteria living as symbionts, i.e. the Chromatiaceae strain 2141T.STBD.0c.01a (23 homologs), symbiont of the giant shipworm Kuphus polythalamia [ 40 ] and Candidatus Endolucinida thiodiazotropha (21 homologs), symbiont of the shallow water bivalve Codakia orbicularis [ 41 ]. The highest number of homologs outside of the Gammaproteobacteria is found in Thiomonas sp. FB-Cd (Betaproteobacteria; 21 homologs). Only a few proteins have homologs in organisms outside of Proteobacteria (Additional file 12 ), with the highest value (7 homologs) found in the cyanobacterium Nostoc punctiforme . Several genomes of known hydrogen oxidizers from hydrothermal vents have been sequenced. Among the Proteobacteria, outside of the Thiomicrospira / Thiomicrorhabdus / Hydrogenovibrio clade, these include the complete genomes of Nitratifractor salsuginis E9I37-1 T [ 42 ] (Iheya field, Mid-Okinawa Trough), and the draft genomes of Caminibacter mediatlanticus TB-2 T (Mid-Atlantic Ridge) [ 43 ], Ghiorsea bivora TAG-1 T (TAG site, Mid-Atlantic Ridge) and SV-108 (Snail Vents, Mariana back-arc) [ 44 ], Nitratiruptor tergarcus MI55-1 T (Iheya field, Mid-Okinawa Trough) [ 45 ] and Hydrogenimonas thermophila EP1-55-1 T (Karei field, Central Indian Ridge) [ 46 ]. Among these genomes, homologs of the SP-41 proteins of the region were found only in Ghiorsea bivora TAG-1 T . The hydrogenase gene cluster of TAG-1 is almost identical to that of the other strain of the species, SV-108 [ 44 ] and is surrounded by an integrase and a recombinase. Only a few differences were found in the gene arrangement, compared to SP-41 (Fig. 6 ), suggesting a common origin. Among known hydrogen-oxidizing Proteobacteria isolated from other habitats, an homologous region was found to the megaplasmid pHG1 of Cupriavidus necator H16, which codes for four different hydrogenases [ 47 ]. The homologous region of pHG1 has a gene arrangement similar to that of Ghiorsea bivora TAG-1 T (Fig. 6 ). Also, in both strains, differently from SP-41 and the other Hydrogenovibrio strains, all genes in the region have the same orientation. Also for the H16 strain, signs of possible DNA integration are present: a transposase gene is found in close proximity (Fig. 6 ). Comparison of the functional potential of SP-41 and XCL-2 Besides the regions discussed in the previous sections (i.e. hydrogenase gene cluster II, CRISPR array, prophage) the genomes of SP-41 and XCL-2 contain several other exclusive and divergent regions. In order to assess their potential role in conferring additional metabolism abilities and other environment adaptations, we compared the COG and KO annotations of the two genomes. COG annotations (Additional file 13 ) and KO annotations (Additional file 14 ) were assigned to an amount of XCL-2 proteins (62.7% and 63.9%, respectively) very similar to that of SP-41 (64.0%; 64.2%). We identified regions coding for proteins with COG and/or KO annotations not present in the genome of the other strain. In total (without considering transposases), SP-41 contains 17 such regions, with 47 exclusive KO and 59 exclusive COG annotations (Additional file 15 ). Conversely, the SP-41 genome lacks 30 KO and 22 COG annotations, present in 14 exclusive or divergent regions of the XCL-2 genome (Additional file 16 ). Next, we describe these regions, generally following their order in the genome. No differences to XCL-2 were observed in the citric acid cycle enzyme: i.e. as XCL-2 [ 14 ], SP-41 is also lacking 2-oxoglutarate dehydrogenase and malate dehydrogenase. SP-41 carries, similar to MA2-6 and other members of the genus, but not XCL-2 [ 21 ] enzymes for the phosphate acetyltransferase-acetate kinase pathway. These are encoded by a small insertion (genes GHNINEIG_00105 and GHNINEIG_00106) to the XCL-2 genome, together with a gene for a putative mobility protein (also present in MA2-6). The number of membrane transporters is low in XCL-2, reflecting its obligate autotrophic lifestyle [ 14 ]. SP-41 has a similar number of KEGG orthology protein annotations included in the KEGG Brite hierarchy “Transporters” (ko02000) (172 in SP-41 and 171 in XCL-2). However, the SP-41 transport proteome covers a wider range of functions (133 KO groups vs. 123 for XCL-2). The transport systems exclusive of SP-41, described below with further detail, are those for urea (UrtABCDE), iron (AfuABC), and mercury (MerRTP), and are located in regions of the genome, which have likely been horizontally acquired. For the uptake of nitrogen, SP-41 has, in common with XCL-2, nitrate transporter and assimilation proteins NasFED and NasA, the nitrite reductase NirBD, as well as 3 of the 4 Amt ammonia transporters of XCL-2. However, SP-41 contains also an additional region of the genome, including a gene cluster ureDABCEFGH for urease and its accessory proteins, genes for amidase (EC 3.5.1.4) and formamidase (EC 3.5.1.49), nitric oxide reductase activation protein NorD, and a urea transport system (genes urtABCDE ). Some members of the genus Hydrogenovibrio are able to use urea as nitrogen source, e.g. H. marinus [ 48 ]. Urease and urea transport genes are also present in other genomes: The closest known relative to the SP-41 region is found in the Hydrogenovibrio kuenenii genome, which contains the urt , amidase and ure genes in the same order (although lacking the NorD and formamidase genes). Genes for urease are also found in the genome of Hydrogenovibrio sp. Milos-T1. Despite the presence of three NorD genes in SP-41, no other components of a nitric oxide reductase operon were found. Instead, in another single-gene spanning exclusive region of the genome, SP-41 codes for a nitric oxide dioxygenase (EC 1.14.12.17) with a potential role in nitric oxide detoxification [ 49 ]. Nitric oxide dioxygenase genes have been previously shown to be particularly prone to horizontal gene transfer [ 50 ]. Recently, an iron-oxidizing strain of Hydrogenovibrio , SC-1 has been isolated [ 51 ]. It is unknown, if other related strains exist, which share this ability. [ 21 ] reported that none of the genomes of Hydrogenovibrio and related genera analyzed in their study contained genes associated with iron oxidation or reduction ( cyc2 , mtoA , ompB , omcB ). This also holds for SP-41. However, SP-41 and XCL-2 differ in their iron transport systems. Both genomes code for the ferrous iron transporters FeoAB, and the ABC transport system TroABCD capable of transporting Zn 2+ and Mn 2+ , but also Fe 2+ and potentially Fe 3+ [ 52 ]. XCL-2 contains a gene for the high affinity iron transporter EfeU [ 53 ], while SP-41 codes for the iron (III) transport proteins AfuABC. The SP-41 afuABC genes (genes GHNINEIG_01422 to GHNINEIG_01424) are located in a short, divergent region followed by two tRNA genes, which are common recombination and insertion points. In the corresponding position of the genome, XCL-2 contains unrelated genes, including a sarcosine oxidase (EC 1.5.3.1) operon. The efeU gene in XCL-2 (cds2153) is instead located in a predicted genomic island, a region of the genome, which contains multiple non-homologous sequences in the two genomes and a translocation. A further exclusive region of SP-41 contains the merRTPA operon (genes GHNINEIG_02228 to GHNINEIG_02231), coding for a mercury transport system and the mercury reductase MerA. It is surrounded by transposase genes, indicating a likely horizontal acquisition. MerA has been previously described as a mercury adaptation system in other deep-sea hydrothermal vents organisms [ 54 ]. Functional mer operons have been characterized in several members of the Actinobacteria, Firmicutes, Beta- and Gammaproteobacteria and in Thermus thermophilus [ 55 ]. A thiosulfate dehydrogenase (KO K19713, EC 1.8.2.2) was annotated by KEGG BlastKoala in a genomic island region of the XCL-2 genome not present in SP-41 (cds2156, annotated in the reference sequence as “cytochrome c”). Like XCL-2, SP-41 carries two genes encoding sulfide:quinone oxidoreductase enzymes ( sqrA and sqrF ) reflecting its ability to consume hydrogen sulfide at different sulfide levels. Homologs to all Sox genes of XCL-2 were found in SP-41. Like in XCL-2, their arrangement differs from that typical of facultatively autotrophic sulfur-oxidizers, as the system is encoded by three groups of genes ( soxXYZA , soxB and soxCD ), located in different regions of the genome [ 14 ], which may be indicative of a differential regulation of these components [ 21 ]. A feature missing in SP-41 is the system for tRNA seleno-modification. This consists in the two genes selD (seledine, water dikinase, EC 2.7.9.3) and selU / ybbB (tRNA 2-selenouridine synthase), which are located in a 2-gene exclusive region of XCL-2 (cds 1052-1053). Seleno-modification occurs at tRNAs for Glu, Gln and Lys. The function of this modification is not completely understood, although it is thought to be related to the codon-anticodon interaction [ 56 , 57 ]. Both genomes contain a region of the genome coding for sugar/nucleotide metabolism enzymes related to cell wall, membrane and flagellum. The region is partly non-homologous in the two genomes, although functionally related: Products of the genes in the region include 8 exclusive KO annotations for SP-41 (FlaA1, WbbJ, RmlA1, RfbB, RfbX, GlpA, OafA, MviM) and 8 exclusive KO in XCL-2 (WcaJ, ManC, Tld, Gmd, RfbC, AscC, RfbG and FbF)."
} | 10,820 |
37984885 | PMC10885666 | pmc | 4,718 | {
"abstract": "Abstract The exponential growth of research on artificial cells and organelles underscores their potential as tools to advance the understanding of fundamental biological processes. The bottom–up construction from a variety of building blocks at the micro‐ and nanoscale, in combination with biomolecules is key to developing artificial cells. In this review, artificial cells are focused upon based on compartments where polymers are the main constituent of the assembly. Polymers are of particular interest due to their incredible chemical variety and the advantage of tuning the properties and functionality of their assemblies. First, the architectures of micro‐ and nanoscale polymer assemblies are introduced and then their usage as building blocks is elaborated upon. Different membrane‐bound and membrane‐less compartments and supramolecular structures and how they combine into advanced synthetic cells are presented. Then, the functional aspects are explored, addressing how artificial organelles in giant compartments mimic cellular processes. Finally, how artificial cells communicate with their surrounding and each other such as to adapt to an ever‐changing environment and achieve collective behavior as a steppingstone toward artificial tissues, is taken a look at. Engineering artificial cells with highly controllable and programmable features open new avenues for the development of sophisticated multifunctional systems.",
"conclusion": "6 Conclusion and Outlook Artificial cells constructed through bottom–up approaches hold great potential in advancing the understanding of life as well as in technological and biomedical applications, including compound production, detoxification from harmful substances, or protein replacement therapy. The building blocks span a large variety of micro‐ and nanoscale assemblies of different chemical nature (lipids, peptides, polymers) in combination with biomolecules and active compounds. In this review, we focused on approaches to develop synthetic cells whose compartments have polymers as main constituents. We took this perspective due to the broad spectrum of polymers and resulting assemblies with fine‐tuned or even new‐to‐nature properties and functionalities that are expected to support the development of advanced multifunctional systems. However, the complexity of natural cells poses challenges in developing fully autonomous materials that can function as equal counterparts to biological cells. Here the advantages of the bottom–up strategy come into effect; cellular phenomena and processes can be simplified and studied under user‐defined conditions without the bustling environment of natural cells. At the same time, by incorporating different nano‐ and microcompartments into artificial cells, a multicompartment architecture can be created that promotes communicative reaction networks. Although the collection of functional modules used as building blocks for artificial cells has been rapidly expanding over the past years, they represent mainly model systems with simple architectures and/or reactions. Both, artificial organelles and cells are meant to provide insight into basic bio‐processes and cell‐related features. They are essential as an early‐stage research to prepare the next generation of artificial cells with more intricate metabolic pathways, intracellular interactions or the ability to repeatedly react stimuli and adapt to the respective environment. Strategies favoring a controlled arrangement of various organelles in a specific spatial context within cell‐like compartments are not yet in place. In addition, with compartments‐in‐compartment and coacervates more attention has been devoted to the biomimicry of 3D cell architecture than to the properties of individual polymer compartments aiming at medical applications, as for example biocompatibility and biodegradability. In this respect, synthesis routes providing polymers and assemblies that are able to more closely recapitulate in vivo features should be enlarged and optimized. More systematic studies are necessary to explore the fate of such polymer‐based artificial cells and move from single‐cell assays to animal models and beyond. Yet, the construction of synthetic prototissues, which involves assembling these protocells into complex micro‐architectures like spheroidal clusters and sheet‐like aggregates that can exhibit collective behavior, is still at its infancy. All current synthetic tissue‐mimics exhibit very limited stability, only few basic activities (linear communication and macroscopic deformation) and lack reversible responsiveness to external signals. A key strategy for achieving close‐to‐nature multifunctionality would be integrating a larger selection of more adept organelles and biologically relevant reactions inside artificial cells which then hierarchically organize into spatially defined multicellular entities. Advanced protocell materials that can interface with living cells and influence their behavior will break the ground for novel biomedical applications such as organoid formation, vasodilation, drug synthesis, and release. A step in this direction has been achieved by photo‐activation of conidiation in the fungus Trichoderma atroviride with Gaussia luciferase (Gluc)‐expressing synthetic cells. [ \n \n 1 \n \n ] \n Despite significant technical progress in replicating features of living systems in artificial cells, integrating these features in life tissues remains challenging. Moreover, mimicking processes key to life are still riddled with bottlenecks, for example replicating information and ensuring cellular content following cell division. The ultimate vision is to create synthetic cells capable of identifying and controllably treating diseases without adverse or off‐target effects. This approach holds great promise for repairing disturbed cell functions or compensating erroneous organelle division associated with diseases. To achieve these goals, an interdisciplinary approach is necessary, integrating basic and applied research in polymer chemistry, synthetic and molecular biology, cell physiology, biotechnology, and pharmacology. Concerted efforts will hopefully accelerate the clinical realization of many potential applications, ushering in a new era where polymeric artificial cells combined with advances in biotechnology and molecular biology lead to groundbreaking applications.",
"introduction": "1 Introduction The complexity and multifunctionality of cells and the changes that arise under pathologic conditions inspired researchers to create artificial organelles and cells. They are intended as models to understand bio‐processes, signaling and communication, [ \n \n 1 \n , \n 2 \n , \n 3 \n , \n 4 \n , \n 5 \n \n ] to provide diagnostics and treatment solutions, [ \n \n 6 \n , \n 7 \n \n ] and to develop new materials with close‐to‐nature functionality. [ \n \n 7 \n , \n 8 \n , \n 9 \n , \n 10 \n \n ] By means of a bottom–up strategy cellular processes are broken down into simpler pathways and reactions to eventually recapitulate them in user‐defined compartments under controlled conditions, removed from the intricate environment of natural cells. [ \n \n 11 \n , \n 12 \n , \n 13 \n , \n 14 \n , \n 15 \n \n ] Similarly, artificial organelles as sub‐compartments of cells are designed in a bottom–up approach by encapsulating/entrapping active compounds (enzymes, proteins, DNA, mimics of thereof) inside nanoassemblies (micelles, vesicles, nanoparticles, hydrogels) that will be sequestered and protected from the surrounding while performing their intrinsic activity/functionality. [ \n \n 12 \n , \n 16 \n , \n 17 \n , \n 18 \n \n ] Another key aspect to be taken into account when designing reactive artificial organelles and cells is the transmission of molecules and signals between compartments. Lipid‐based compartments (liposomes and giant unilamellar vesicles, respectively) have been largely used for the development of artificial cells and organelles because the phospholipid composition of their membrane compares to that of natural cells. [ \n \n 19 \n , \n 20 \n , \n 21 \n \n ] Many excellent reviews have been published recently that address the bottom–up construction of artificial cells based on giant unilamellar lipid vesicles (l‐GUVs). [ \n \n 22 \n , \n 23 \n , \n 24 \n , \n 25 \n , \n 26 \n , \n 27 \n \n ] However, the intrinsic mechanical instability and presence of defects limits the use of l‐GUVs for advanced applications and motivated researchers to search for more robust compartments. Synthetic amphiphilic polymers are the building blocks of choice because they allow formation of nano‐ and micro‐compartments with, [ \n \n 28 \n \n ] improved properties compared to the lipid‐based ones (e.g., mechanical stability, thickness, and permeability of the membranes). In addition, polymer building blocks can be tuned to confer special features such as stimuli‐responsiveness, shape plasticity, or self‐organization into clusters upon the ensuing compartments. [ \n \n 23 \n , \n 24 \n , \n 25 \n , \n 26 \n , \n 27 \n \n ] While the overall hollow spherical shape is similar for lipid and polymer compartments, the difference between their membrane properties arises from the divergent molecular weight of their hydrophilic and hydrophobic domains. Importantly, this difference governs the combination with biomolecules and presents many challenges regarding the preservation of biomolecular integrity and/or functionality in synthetic compartments. To create cell‐like compartments, amphiphilic copolymers self‐assemble into micrometer‐sized compartments (synthetic or p‐GUVs) with simultaneous encapsulation of nanoassemblies (lipid‐ or polymer‐based) and active biomolecules. [ \n \n 14 \n , \n 15 \n , \n 33 \n \n ] The result is a “compartments‐in‐compartment” architecture that mimics in a simplified manner the presence of organelles inside a cell. This multi‐compartmentalization allows for active compounds within different nanocompartments to participate in simple reactions as part of metabolic pathways for longer periods of time than is the case for lipid‐based artificial cells. In addition, the synthetic cells with polymer components allow to introduce stimuli‐responsive features or even new‐to‐nature reactions between the compartments. [ \n \n 20 \n , \n 34 \n , \n 35 \n \n ] While many compartments‐in‐compartment polymer systems are membrane enclosed, a membrane‐less confinement is becoming popular as different type of artificial cell. [ \n \n 36 \n , \n 37 \n , \n 38 \n , \n 39 \n \n ] \n In this review, we first explore nano‐ and microscale polymer assemblies and how they are used as building blocks for artificial cells ( Figure \n \n 1 \n ). For the purpose of this review, we use artificial cell, synthetic cells, and protocell interchangeably. We mostly focus on cell‐sized compartments where polymers are the major constituent of the assembly because owing to their improved properties and an unprecedented spatiotemporal control of a desired function, they uniquely support advanced applications over long time periods. An additional benefit is that they can be furnished with new‐to‐nature behavior. For nanosized compartments, we consider mainly bottom–up produced assemblies of nonbiological and/or biological components that are designed to recapitulate one or more features of biological cells. In particular, we present different membrane‐bound and membrane‐less compartments and supramolecular structures based on polymers, and briefly summarize how they are produced, before describing how they turn into functional modules mimicking cellular processes. Nanosized assemblies that are customized to mimic features of natural cells but also those that, once internalized, provide living cells with a distinct activity we define as artificial organelles. We address how artificial organelles convert polymer‐based giant compartments into sophisticated multifunctional synthetic cells. We further explore special cell behaviors, properties, and communication that provide first insights into collective features and support development of multifunctional materials. The large variety of polymer‐based artificial organelles and cells lend themselves to exploring the function of biomolecules in synthetic environments, and eventually offer cutting‐edge solutions in various domains, including medicine, catalysis, technology. However, due to the incredible complexity of natural cells, there are still a plethora of challenges to overcome and we will point at some of them as an outlook for new avenues for the field to explore. Figure 1 The artificial cell possesses the capacity to emulate the structure of a biological cell in a simplified manner. Its hierarchical multi‐compartmentalization enables active compounds within distinct nanocompartments to engage in straightforward cascade reactions and communication when organized within cell consortia."
} | 3,218 |
23469896 | null | s2 | 4,720 | {
"abstract": "The availability of extensive genome information for many different microbes, including unculturable species in mixed communities from environmental samples, has enabled systems-biology interrogation by providing a means to access genomic, transcriptomic, and proteomic information. To this end, metaproteomics exploits the power of high-performance mass spectrometry for extensive characterization of the complete suite of proteins expressed by a microbial community in an environmental sample."
} | 123 |
25992838 | PMC4437649 | pmc | 4,721 | {
"abstract": "Eukaryotic marine microalgae like Dunaliella spp . have great potential as a feedstock for liquid transportation fuels because they grow fast and can accumulate high levels of triacylgycerides with little need for fresh water or land. Their growth rates vary between species and are dependent on environmental conditions. The cell cycle, starch and triacylglycerol accumulation are controlled by the diurnal light:dark cycle. Storage compounds like starch and triacylglycerol accumulate in the light when CO 2 fixation rates exceed the need of assimilated carbon and energy for cell maintenance and division during the dark phase. To delineate environmental effects, we analyzed cell division rates, metabolism and transcriptional regulation in Dunaliella viridis in response to changes in light duration and growth temperatures. Its rate of cell division was increased under continuous light conditions, while a shift in temperature from 25°C to 35°C did not significantly affect the cell division rate, but increased the triacylglycerol content per cell several-fold under continuous light. The amount of saturated fatty acids in triacylglycerol fraction was more responsive to an increase in temperature than to a change in the light regime. Detailed fatty acid profiles showed that Dunaliella viridis incorporated lauric acid (C12:0) into triacylglycerol after 24 hours under continuous light. Transcriptome analysis identified potential regulators involved in the light and temperature-induced lipid accumulation in Dunaliella viridis .",
"introduction": "Introduction Marine microalgae have been investigated for their great potential as feedstocks for renewable liquid transportation fuels because they could replace petroleum-derived fuels without competing for resources like land or fresh water required for food and feed production [ 1 , 2 ]. Dunaliella spp . grow in salt lakes and marine environments worldwide and have adapted to their specific environments [ 3 , 4 ]. Several Dunaliella spp . grow well under high levels of salt, which reduces culture contamination with other microorganisms or pathogens [ 5 , 6 ]. Dunaliella spp . grow in media containing an extremely wide range of NaCl concentrations from 0.05 M to 5.5 M NaCl [ 7 ]. The ability of D . salina to produce high levels of carotenoids made it commercially viable as a nutritional supplement [ 8 ]. In contrast to D . salina , D . viridis does not accumulate large amounts of β-carotene, but produces oxygenated carotenoids [ 9 ]. Dunaliella spp . lack a cell wall [ 4 ], which enables the extraction of oil droplets by osmotic shock in freshwater as one of the most inexpensive and environmental friendly ways to extract oil from algae cultures [ 10 ]. Despite all these useful traits of Dunaliella for bioenergy production, production of algae oil for liquid transportation fuels is commercially not viable. Technoeconomic analysis identified algae oil content and growth rates as the two top cost modifiers of fuel production [ 11 ]. We therefore focused our research on the regulation of cell division rates and oil content in Dunaliella by environmental control. Photosynthetic organisms accumulate carbohydrates, sugars and/or triacylglycerides (TAGs) during the light period to store assimilated carbon and energy, which is then used for consumption during the night, or when environmental conditions limit their growth [ 12 , 13 ]. Cell division rates and TAG accumulation exhibit an inverse relationship because cell division rates are maximal under optimal growth conditions while TAG as carbon and energy storage component accumulates during conditions that limit cell division [ 14 ]. Nitrogen deprivation, salt or light stress and high CO 2 concentration have been shown to increase oil accumulation in different algal species [ 15 – 19 ]. The response of different Dunaliella species or even strains to oil-inducing growth conditions varies greatly. Nitrogen deprivation can induce increases in lipid content in D . viridis and D . tertiolecta [ 15 , 19 ] or can have no effect on lipid content of the cells of D . salina [ 20 ]. Because regulation of oil accumulation and cell division are diurnally regulated in algae [ 21 – 23 ], we studied the responses of Dunaliella spp . to changes in photoperiod as well as temperature, and the combination of both environmental effects. A shift from light:dark cycles to continuous light should eliminate the need of the cells to degrade their carbon and energy reserves during the dark period. The aim of this research was to characterize the time-resolved physiological, metabolic and genomic response of Dunaliella viridis to photoperiod, growth temperature and the integration of changes in both environmental conditions. Our results show that changes in photoperiod and temperature have both, specific and combined effects on gene expression and metabolism. While cell division is synchronized by the dark period, the cellular protein content only responds to temperature, not light period. Starch metabolism on the other hand is controlled via transcriptional regulation of its degradation enzymes and does not respond to temperature changes under light:dark cycles, but is sensitive to temperature under continuous light conditions. Accumulation of TAG has two distinct and independent components—a photoperiod responsive component that increased oil content more than five-fold, and a temperature-dependent component responsible for a doubling of oil content. These effects were independent, so that the combination of continuous light and elevated temperature led to a tenfold increase in oil content. Transcriptome analysis showed that fatty acid (FA) biosynthesis for increases in oil content under elevated temperature is not transcriptionally regulated, while TAG biosynthesis is driven by transcriptional up-regulation of lipases involved in the recycling of FAs from membrane lipids.",
"discussion": "Discussion When we tested the most commonly described Dunaliella species for their responses to different environmental stresses including light duration and temperature, we found a wide spectrum of physiological and metabolic differences among those species. This is not very surprising, because of their large evolutionary distances and adaptation to different marine and salt lake habitats worldwide [ 4 ]. We focused on a strain of Dunaliella viridis that was identified when we attempted to confirm the identity of cultures we received from another lab. Our strain, Dunaliella viridis dumsii, differs by three nucleotides in the internal transcribed spacer 2 (ITS2) region from the closest established Dunaliella genotype, which necessitated the unique name of our strain. Sequence information of ITS2 region of Dunaliella viridis dumsii available at Genbank (KP057241). This D . viridis ecotype accumulates TAG under elevated temperature without a large drop in growth rate when grown under LL. To characterize the underlying mechanisms which enable these algae to accumulate storage components like TAGs at a fast growth rate, we analyzed the time-resolved metabolic and genomic responses to each environmental change separately as well as in combination. Interestingly, the accumulation of TAGs is increased by either stress independently, which results in an additive effect of TAG accumulation when both stresses are applied. We are discussing here the specific effects of either stress and the integration of both stresses. Cell cycle regulation by photoperiod Exposure to continuous light (LL) had a significant effect on increasing the cell division rate compared to LD, while temperature changes had no significant effect ( Fig 2A ) during the time period of this experiment. The algae cells divided during the dark period ( S2 Fig ), but acquired an apparently constant cell division rate when grown under LL, indicating that cell division is not under circadian control but light responsive (diurnal). GO term enrichment analysis of differentially expressed genes shows the cell cycle category as enriched under LL ( Table 1 ). The algal cell cycle has been found to be closely connected to photoperiod with cells being in the G 1 phase during the light period and then advancing through S, G 2 , and M phases during the dark period. The diurnal pattern of cell growth in the light and cell division in the dark is thought to allow for maximum collection of energy [ 35 ]. The synchrony of cell cycle progression in D . viridis can be seen from the transcript levels of canonical S and M phase-expressed genes, showing high abundances in the dark, but not in the light ( Fig 12 and S8 Table ). The diurnal pattern of expression of these genes fades into a continuous expression pattern under LL suggesting that the population of cells is no longer dividing synchronously. The loss of synchronous growth most likely stems from the lack of the dark induced pause in the cell cycle, which allows cells to resume cell cycle progression as soon as cell division is complete. The increased cell density under LL indicates that the time required for completion of a cell cycle is less than 24 hours under sufficient nutrient supply and that the cells have the ability to progress through the cell cycle constantly. 10.1371/journal.pone.0127562.g012 Fig 12 Cell cycle regulated genes show diurnal regulation under LD, but constant transcript levels in LL. Lines represent the changes in transcript abundance (RPKM) for each of the genes. Genes are: H2A, histone H2A; CYCA, A-type cyclin; CYCB1, B-type cyclin 1; CYCB2, B-type cyclin 2; cyclin dependent kinase B. While there was no significant effect of temperature on cell division rate, the cell size was affected by temperature only under LL. Cells grown at 35°C were significantly larger than 25°C ( Fig 2B and S3 Fig ). The increased cell size in D . viridis correlates with a significant increase in PL as it would be expected from the need for more membrane in cells with larger surface ( Fig 7 ). Photosynthesis under heat stress The most abundant polar lipids are DGDG and MGDG, which are exclusively found in the chloroplast envelope and thylakoid membranes [ 36 ]. In our experiment, changes in cellular DGDG and MGDG quantities occurred in response to light duration and temperature ( Fig 7 ), and strongly correlated with the chlorophyll contents under the respective growth conditions ( Fig 3A ). In response to elevated temperature, cellular chlorophyll content and MGDGs increased, suggesting that the photosynthetic machinery of D . viridis cells acclimate to heat stress when switched from 25°C to 35°C as a result of enhanced thermal stability. A similar response was observed in Chlamydomonas [ 37 ]. The photosystem II complex is especially susceptible to heat [ 38 – 40 ]. Most of the photosystem I and II subunit related genes at 35°C had about 2-fold greater RPKM at 30 hrs in comparison to 25°C under LL ( S11C and S11D Fig). Chlorophyll is coordinated in the membrane by the light-harvesting complex proteins (LHCP). Transcript levels of those major LHCPs were transiently increased 3- to 4-fold within 6 hours after transfer of cells to elevated temperatures, but returned to similar levels as in cells grown at 25°C thereafter ( S11B Fig ). Membrane lipid remodeling has been shown to be a mechanism in Arabidopsis and other vascular plants to modify the physical properties of membranes associated with temperature stress. Higher temperature increases membrane fluidity which can be countered by an increase in the degree of FA saturation to maximize hydrophobic interactions and thereby counteract the increase in fluidity under higher temperature. The increase in growth temperature of Arabidopsis resulted in a decrease in the trienoic FAs and an increase in C16:0 and C18:2 levels in the membrane lipid composition in leaves [ 41 ]. Similar to Arabidopsis leaf cell membranes, a significant decrease in the saturation levels in membrane lipid composition were observed when Dunaliella cultures were transferred from 25°C to 35°C under LL ( S6 Fig ). The changes in the FA composition of membrane lipids suggest the possibility of membrane remodeling in D . viridis under heat stress. Chlamydomonas mutants defective in ω-3 desaturase (homolog of FAD7 in plants) have a significant reduction in trienoic membrane lipid FAs under short term heat stress contributing to increased photosynthetic thermotolerance compared to wild type [ 42 ]. Similar to Chlamydomonas FAD7 mutants, we found that under heat stress there was a down-regulation of FAD7 in D . viridis ( Fig 10 ). Increased levels of DGDG and/or the ratio of DGDG to MGDG play an important role in basal as well as acquired thermotolerance in Arabidopsis [ 43 ], which contributes to increased thylakoid membrane stability at elevated temperature [ 44 ]. Arabidopsis mutants defective in DGD1, the key enzyme in the conversion of MGDG to DGDG, were sensitive to temperature stress due to their inability to increase their DGDG levels and DGDG:MGDG ratios in response to elevated temperature [ 43 ]. These Arabidopsis experiments were carried out with plants grown under a LD cycle. We found that in Dunaliella , this response to elevated temperature is dependent on the light period conditions. Under LD cycle a temperature increase resulted in an increase in steady-state levels of MGDG and a decrease in DGDG levels ( S1 Table ), suggesting a reduction or inhibition in the conversion of MGDGs to DGDGs. However, under LL, the elevated temperature increased the steady-state levels of both, DGDG and MGDG. These results show a strong interaction of light duration and temperature stress on the composition of chloroplast membrane lipids. It is noteworthy to mention that D . viridis possess PC, the most common membrane lipid in plants [ 45 ], while Chlamydomonas does not [ 46 ]. HSPs and carotenoids participating in the xanthophyll cycle have been shown to play a role in protecting the photosystem II complex against heat stress [ 37 , 47 – 50 ]. Our transcriptome analysis identified several HSP genes that were up-regulated in response to elevated temperature ( S5 Table ). This is consistent with a proteomics analysis on Chlamydomonas grown at 42°C [ 51 ]. Metabolic acclimation to continuous light Cells under LL at 25°C divided the fastest ( Fig 2A ) and did not shown an increase in their cellular chlorophyll content ( Fig 3A ) or polar lipid content ( Fig 7 ) as they did when grown under a LD regime at 25°C. These algae cells (LL, 25°C) also had decreased levels of chloroplast membrane lipids ( S1 Table ), but increased amounts of the storage components starch ( Fig 4 ) and TAGs ( Fig 5A ). The lower chlorophyll content might be due to irradiance stress as observed in D . salina , where cells grown under high-light exhibit lower chlorophyll levels than those grown under low-light [ 52 ]. Cells acclimate to high levels of irradiance by reducing the chlorophyll antenna size of the photosystems as shown in D . tertiolecta [ 53 ], D . salina [ 54 ] and C . reinhardtii [ 55 ]. Light harvesting chlorophyll antenna size varies according to variable amounts of LHC I and LHC II with the respective photosystems in D . salina [ 56 – 58 ]. GO term analysis showed enrichment in transcripts of chloroplast and photosynthesis as top categories for light factor ( Table 1 ). Additionally, several photosystem transcripts were the most differentially regulated in response to light duration ( S4 Table and S11B – S11D Fig) which might explain the cells strategies to acclimate to LL. Chlamydomonas cells have been shown to have increase carbonic anhydrase (CA) activity when grown photo-autotrophically and exhibit higher affinity for inorganic carbon uptake [ 59 ]. The most dramatic effect in response to LL at either temperature (25°C or 35°C) was observed in the transcript abundance changes of specific carbonic anhydrase isoforms ( S11A Fig ). When light absorption exceeds CO 2 -fixation rates, the excitation energy can cause photoinhibition. To adjust their photosynthesis to excess light energy (as under LL), the cells can either increase photoprotective mechanisms for non-photochemical quenching or increase their CO 2 fixation rates. Based on our transcript data, Dunaliella cells use both strategies to compensate for LL exposure. It has been shown in Chlamydomonas that the major extracellular carbonic anhydrase (HLA5) is induced by light or low CO 2 [ 60 ]. Increasing the carbonic anhydrase as part of the carbon concentrating mechanism, enables the cell to productively use the excitation energy for the uptake of C i against a concentration gradient, and for the fixation of CO 2 . The increase in cell number and storage components (starch and TAGs) requires a net increase in CO 2 fixation in cells under LL exposure. Photoprotection by non-photochemical quenching is apparent from the differential expression of transcript 10594 encoding chloroplast carotene biosynthesis-related (CBR) protein ( S4 Table ). Under LD conditions, expression varied from ~1500 RPKM to no expression in the dark (~5–10 RPKM), while under LL, transcript abundance remained above 400 RPKM during the course of the experiment. CBR is a homolog of vascular plant ELIP proteins (early light-induced proteins), shown to bind zeaxanthin to form photoprotective complexes within the light-harvesting antennae for non-photochemical quenching [ 61 ]. CBR proteins accumulate in D . salina cells under high light stress and are thought to be indicative of irradiance stress [ 61 – 63 ]. The role of this protein may explain its up-regulation in response to LL stress. Cells grown under LL contained 25% less total membrane lipids compared to cells grown LD under 25°C at 54 hours ( S1 Table ). This decrease was due to the reduction of the chloroplast membrane lipids MGDG and DGDG ( Fig 7 ), and strongly correlates with the low content of chlorophyll under LL at 25°C ( Fig 3A ). While the content of total membrane lipids decreased under LL at 25°C ( Fig 7 ), TAG content increased 10-fold, accumulating oleic acid (C18:1) ( Fig 5A ). In Nannochloropsis , galactolipids (MGDG and DGDG) decrease and TAG increases, as irradiance level was shifted from low to high [ 53 ]. Significant transcriptome analysis Several transcripts encoding for HSPs were up-regulated in response to heat stress ( S5 Table ). This is consistent with the results from a proteomics analysis of Chlamydomonas grown at 42°C [ 51 ]. Protein degradation was one of the enriched categories in our GO term enrichment analysis for heat stress ( Table 1 ). This is in agreement with our metabolic data, because the cells grown under 35°C had a sudden increase in protein content directly after the increase in temperature. This is possibly due to production of HSPs, while the decreased protein content under constant elevated temperature, and is possibly due to peptidase activity ( Fig 3B and S5 Table ). In several cases, we had more than one transcript encoding for the same enzyme (different isoforms). Interestingly, the transcripts that showed differential expression (up-regulation or down-regulation) were actually the transcripts with the lowest relative abundance (low RPKMs) of the two isoforms. For example, FAB2 has one isoform which maintains steady state expression and the other isoform is lowly expressed, but shows differential expression in response to high temperature ( S7A Table ). This was also found in Chlamydomonas : the expression of DGTT1, one of the 5 type-2 DGAT from Chlamydomonas showed the highest fold change but had the lowest transcript abundance [ 31 ]. This could be an important mechanism for the regulation of specific pathways. Mechanism of starch accumulation under photoperiod We identified enzymes from the transcriptome analysis to reconstruct the classical starch biosynthesis and degradation pathways ( Fig 11 ) [ 64 ]. Transcript abundances for several enzymes of the synthesis and degradation pathways were under strong diurnal control: SS, DPE1, α-AMY, O-1,6G, and α-GWD had lower transcript levels in the dark and higher levels in the light, while the BE, ISA, BAM, MEX, and the cytosolic DPE2 show the opposite cycling with high transcript levels in the dark and lower levels in the light ( S11O and S11P Fig). We did not see any changes in starch quantities during those time points under LD, but it is possible that these time points did not reflect times of maximum difference, but mid-points in accumulation and degradation and therefore fail to show diurnal changes. Starch is accumulated in leaves of vascular plants during the light period and degraded during the subsequent dark phase [ 12 ]. Several light regulated enzymes have been shown to be under control of the circadian clock [ 65 , 66 ]. However, starch metabolism in algae has been shown to have some distinct differences compared to vascular plants. In Chlamydomonas starch biosynthesis and degradation is controlled by a circadian rhythm, but the levels of starch content peaked in those cells during the dark phase and were lowest during the light period [ 67 ]. The authors of that study hypothesize that the difference in starch accumulation is due to the difference in the cells developmental state. Regulation of starch accumulation in mature cells in source tissues may be under a different control mechanism than in fast dividing cells. However, these Chlamydomonas cells were grown heterotrophically on acetate, which might have a major impact on the regulation of carbon storage metabolism. In the green pico-algae, Ostreococcus tauri , starch accumulated similar to vascular plants during the light period and was degraded during the dark phase [ 68 ]. The first rate-limiting step for starch biosynthesis is the formation of ADP-glucose by AGPase, a heterotetramer of 2 large and 2 small subunits [ 69 ]. The activity of AGPase in vascular plants is regulated allosterically by triosephosphates and phosphate as well as via posttranslational redox modification. Reversible formation of intermolecular cysteine bridges between the two catalytic small subunits of AGPase is formed by a NADP-thioredoxin reductase C complex leading to changes in enzyme activity [ 69 – 71 ]. Reversible redox activation has been shown for other enzymes in the starch biosynthesis or degradation pathways including ISA, Limit Dextrinase, SS, GWD, and the BE in Arabidopsis [ 72 ]. This conserved mechanism of posttranslational regulation of enzyme activity in vascular plants is apparently lacking in green unicellular algae. AGPase activity in Ostreococcus tauri is not regulated by redox modification [ 73 ], and the conserved cysteine residues in AGPase, GWD, and ISA that are required for redox regulation are replaced by other amino acids in Chlamydomonas , Ostreococcus and Micromonas [ 68 ]. We analyzed the respective sequences from the four Dunaliella species described here, that were either sequenced in our lab or through the oneKP project [ 74 – 76 ]. Sequence alignment of the transcriptome data for the identified cysteine motifs, which are responsible for the redox activation of their respective enzymes showed that D . viridis , D . tertiolecta , D . salina and D . primolecta enzymes do not contain the conserved cysteine residues and therefore are likely not regulated via redox modification ( S10 Fig ). Due to the lack of redox modification of key starch metabolic enzymes, the authors of the respective studies in Chlamydomonas and Ostreococcus concluded that the major regulatory mechanism is on the transcriptional level [ 67 , 68 ]. This hypothesis is the most likely scenario in Dunaliella species as well. While our sampling time points were not designed to capture full diurnal cycles, we did ensure that harvesting was carried out at the same intervals after transition to light (6 hrs) or after transition to dark (4 hrs). While the time points cannot represent diurnal changes, some transcripts show significant differences under LD that are not apparent when grown under LL. In D . viridis , overall starch only accumulated under LL, not under LD ( Fig 4 ). The overall decreasing RPKM values for the transcripts of starch degradation enzymes under LL ( Fig 11 ) support the fact that cells under LL at 25°C might accumulate starch by repressing degradation. Two distinct pathways for starch degradation, the hydrolytic and phosphorolytic routes, were observed in D . viridis [ 77 ]. The hydrolytic enzymes, α-AMY and oligo-1,6-glucosidase (O 1,6G), catalyze the hydrolysis of starch to α-D-glucose. The released α-D-glucose can be exported to the cytosol and undergo glycolytic conversion to pyruvate, or be phosphorylated by hexokinase (HXK) into glucose-6-phosphate for reentry into starch synthesis pathway [ 77 ] ( Fig 11 ). The phosphorolytic pathway enzyme, α-GWD phosphorylates starch prior to degradation by BAM into maltose which is exported via the plastidial transporter MEX1 into the cytosol [ 78 ]. The fact that the D . viridis cells accumulated starch when grown under LL implies that starch biosynthesis occurs during the light period. In order to accumulate, the rate of starch degradation must be lower than the rate of synthesis. This is not reflected in the changes in transcript levels of both degradation pathways—phosphorolytic (α-GWD) and hydrolytic enzymes (α-AMY and O 1,6G) as well as DPE1 ( Fig 11 ). Despite the loss of diurnal variation, transcript levels for the degrading enzymes were higher under LL compared to LD. Effects of light duration and temperature on de nov o FA synthesis The three major lipid fractions (TAG, polar membrane lipids, FFA) accumulated when cells were grown at 35°C compared to 25°C under LL (Figs 5A and 7 , S7 Fig ). Because FAs are the building blocks of the different lipid fractions, an increase in total lipids requires an increase in FA synthesis or a decrease in their catabolic process (β-Oxidation). We identified the transcripts coding for the enzymes of the FA synthesis pathway, the prokaryotic and eukaryotic lipid biosynthesis pathways, as well as those for lipid and fatty acid catabolism ( S11E – S11N Fig). ACCase is a key regulatory enzyme controlling the first committed step of plant FA synthesis [ 79 ]. The transcript levels of the four ACCase subunits: Alpha-carboxyltransferase subunit (α-CT), Beta-carboxyltransferase subunit (β-CT), Biotin carboxylase subunit (BC) and Biotin carboxyl carrier protein subunit (BCCP) in D . viridis were under diurnal control with higher levels observed mostly down-regulated in response to heat ( Fig 9 and S11G Fig ). A similar expression pattern was observed in Chlamydomonas under N-deprivation; although TAG accumulated, the transcript abundances of most of the genes in the de novo fatty acid synthesis pathway (ACCase subunits in particular) were down-regulated [ 80 ]. Many biochemical pathways are controlled by a feedback mechanism where the end product inhibits the activity of the regulatory enzyme which is often the first committed step of the pathway. In oilseed plants, there is evidence for feedback regulation of plastidic ACCase by 18:1- acyl-carrier protein (C18:1-ACP) [ 81 ]. Additionally in tobacco, ACCase protein levels did not change during the feedback inhibition, indicating that inhibition of FA synthesis occurred through biochemical or posttranslational modification of ACCase [ 82 ]. Based on our transcriptome data, the transcript abundance of the FAT that releases ACP from 16:0-ACP, 18:0-ACP or 18:1-ACP to yield FFAs is up-regulated under high temperature conditions ( Fig 9 ). We hypothesize that the increased FAT under higher temperature, would lead to the significant decrease in the pool of acyl-ACP in the plastid, thereby releasing the negative feedback regulation on ACCase. This may lead to increased flux through ACCase and explains the increased contribution of more FAs to total lipids under higher temperature. Overexpression of a thioesterase gene in Phaeodactylum tricornutum led to an increase in total FA content by 72% while the FA composition did not change [ 83 ]. FAB2-2 also showed increased expression at higher temperature ( Fig 9 ), which could provide increased amounts of 18:1, a precursor of polyunsaturated FAs. Mechanism of TAG accumulation under heat stress and light duration TAG biosynthesis through the acyl-CoA dependent (Kennedy) pathway TAG is the most important lipid for biofuel production [ 13 ]. We observed a more than 15-fold increase in cellular TAG content when the cells were grown at 35°C under LL than at 25°C under LD ( Fig 5A ). At 54 hrs, the cells grown under LD at 25°C had a relatively lower TAG content than other conditions tested ( Fig 5A ). The low TAG content result is similar to the results of a TAG content survey done of various Dunaliella spp . grown under a light:dark cycle of 14/10 hr at 24°C/20°C with a new and sensitive UPLC-MS analytical technology [ 84 ]. GPAT and LPAT which form PA are common to both the acyl-CoA dependent and acyl-CoA independent pathways. Transcript levels of both enzymes showed strong diurnal variation to LD but were expressed at a constant medium level during LL and were down-regulated at 35°C ( Fig 10 and S11L Fig ). DGATs, the main enzymes in the acyl-CoA dependent TAG synthesis pathway ( Fig 10 ), are responsible for TAG formation by addition of an acyl-CoA to the sn -3 position of the glycerol backbone. The majorities of algal species encode at least one type 1 DGAT (DGAT1) and encodes multiple type 2 DGATs (DGGT) [ 85 ]. In Chlamydomonas , there are 5 type-2 DGATs: DGTT1-DGTT5 and DGTT1 have been shown to be up-regulated in response to N-starvation as is DGAT1 [ 31 , 80 ]. We found two DGAT1 gene and 4 DGTT genes in D . viridis based on sequence similarity to Chlamydomonas proteins, which were differentially regulated at 35°C ( Fig 10 and S11L Fig ). The two DGAT1 genes had opposite pattern of regulation. DGAT1 (transcript 6208) was down-regulated 6 hours after the temperature switch to 35°C, but then was up-regulated at the higher temperature whereas DGAT1 (transcript 3780) was initially up-regulated after the temperature shift to 35°C and then down-regulated at 40 and 54 hrs ( Fig 10 and S7A Table ). One of the DGTT genes (transcript 9514) had slight up-regulation with increasing time in response to higher temperature. In Chlorella vulgaris , DGAT had the largest increase in abundance of all analyzed proteins from the FA and TAG biosynthesis pathway under N-deprivation [ 86 ]. We hypothesize that either the enzymes of the acyl-CoA dependent pathway for TAG synthesis are not transcriptionally regulated in D . viridis , or the pathway is not up-regulated under high temperature and thus the acyl-CoA dependent TAG synthesis pathway does not have a major role in TAG accumulation under high temperature. TAG biosynthesis through membrane lipid recycling of fatty acids When considering TAG synthesis through the acyl-CoA independent pathway ( Fig 10 ), we need to consider the recycling of the chloroplast membrane lipids for TAG formation. Under higher temperature, all enzymes involved in membrane lipid formation were up-regulated except for sulfoquinovosyldiacylglycerol (SQD1) and MGD1 ( Fig 10 ). At 25°C, MGDG is the second most abundant chloroplast membrane lipid after DGDG, but upon growth at 35°C its content more than doubles and it becomes the most abundant membrane lipid ( Fig 7 ). The accumulation of MGDG at 35°C and the down-regulation of the MGD1 gene suggest this process is either not transcriptionally regulated, but alternatively regulated, or an unknown enzyme/pathway for the biosynthesis of MGDG exists in D . viridis . In Arabidopsis , MGD1 is post-transcriptionally regulated by PA and PG [ 87 ]. At 35°C, increases in both PA and PG ( S1 Table ), suggest a possibility for the post-transcriptional control of MGD1 activity in D . viridis . In Chlamydomonas , PDAT and PGD1 were found to be important in membrane lipid recycling for TAG accumulation [ 31 , 34 , 80 ]. Under N-starvation in Chlamydomonas , PDAT1 is a protein found in purified lipid bodies [ 88 ], although its expression for TAG accumulation is not clear. PDAT1 was found to be up-regulated under N-starvation [ 80 ]. However it was also shown that if PDAT contributed appreciably to the TAG formation under favorable growth conditions, its contribution under N deprivation was minor [ 89 ]. In D . viridis at 35°C, PDAT1 is down-regulated at all-time points suggesting that its contribution to recycling of membrane lipids for TAG accumulation is only minor unless the enzyme is not transcriptionally regulated ( Fig 10 ). PGD1 was up-regulated at 35°C ( Fig 10 ), which is similar to TAG accumulation in Chlamydomonas under N-deprivation [ 31 , 34 ]. Presumably, the mechanism of TAG accumulation by membrane lipid recycling using PDAT1 and PGD1 is similar for higher temperature in D . viridis and for N-deprivation in Chlamydomonas . In addition, lipases are thought to be involved in membrane lipid recycling. We found several lipases highly up-regulated at 35°C ( Fig 10 and S11N Fig ), similar to lipases upregulated by N-starvation in other algae for TAG accumulation [ 31 , 90 ]. Another enzyme involved in membrane remodeling in Arabidopsis under freezing stress is galactolipid: galactolipid galactosyltransferase (GGGT), which uses MGDG as both a donor and acceptor molecule and forms DGDG and DAG [ 28 ]. This enzyme was up-regulated in D . viridis at 35°C ( Fig 10 ). Based on our findings and insights from other research, we hypothesize that TAG accumulation in D . viridis under heat stress could be mainly due to the membrane lipid recycling of FAs. In summary, environmental conditions enabled the production of high levels of oil during fast growth in D . viridis . The effects of eliminating the dark period in the growth cycle of D . viridis resulted in a loss of cell cycle synchronicity and faster growth rates. The accumulation of TAG and starch in D . viridis cells were apparently due to the lack of degradation that usually occurs during the dark period. Temperature changes did not have any effect on short-term growth rates, but did lead to increases in chlorophyll, starch and lipid content likely due to higher photosynthesis rates under elevated temperature. The most interesting aspect was that the effects of light duration and temperature increase were independent but additive with respect to TAG accumulation. The higher temperature also increased the degree of saturation in the TAG fatty acids. Saturated fatty acids in the TAG fraction are advantageous, because it reduces the cost for hydrogenation during refining for fuel production. Transcriptome analysis indicated that TAG accumulation in D . viridis under heat stress could be mainly due to the membrane lipid recycling of FAs."
} | 8,792 |
32348298 | PMC7213742 | pmc | 4,722 | {
"abstract": "Uncertainty in the structure and parameters of networks is ubiquitous across computational biology. In constraint-based reconstruction and analysis of metabolic networks, this uncertainty is present both during the reconstruction of networks and in simulations performed with them. Here, we present Medusa, a Python package for the generation and analysis of ensembles of genome-scale metabolic network reconstructions. Medusa builds on the COBRApy package for constraint-based reconstruction and analysis by compressing a set of models into a compact ensemble object, providing functions for the generation of ensembles using experimental data, and extending constraint-based analyses to ensemble scale. We demonstrate how Medusa can be used to generate ensembles and perform ensemble simulations, and how machine learning can be used in conjunction with Medusa to guide the curation of genome-scale metabolic network reconstructions. Medusa is available under the permissive MIT license from the Python Packaging Index ( https://pypi.org ) and from github ( https://github.com/opencobra/Medusa ), and comprehensive documentation is available at https://medusa.readthedocs.io/en/latest .",
"introduction": "Introduction Hypothesis-driven computational models of biological systems are being increasingly applied to guide experimentation [ 1 ]. In hypothesis-driven modeling, in contrast to data-driven modeling [ 2 ], hypothesized biological parts, functions, and interactions are mathematically formalized to allow in silico experimentation. These models take many forms, ranging in complexity from a single linear equation relating two quantities to systems of nonlinear differential equations describing dynamic systems. Across all hypothesis-driven modeling frameworks, the choice of model scope and parameter values may strongly influence simulation results. For some types of hypothesis-driven models in biology, approaches from other fields have been applied to quantify the influence of parameter values on simulation outcomes, such as sensitivity analysis of dynamical models [ 3 ]. For network-based models of biological systems such as metabolic or signaling networks, the presence or absence of a network component may be uncertain due to lack of characterization or uncertainty in data itself. Traditional sensitivity analysis methods have recently been reformulated for these systems to analyze sensitivity to topological variation, but these methods have not seen wide adoption [ 4 ]. While uncertainty in network structure poses analytical difficulties, it also presents an actionable framework to accelerate biological discovery. Alternative network structures can guide experimental design, allowing comparison of simulation results for alternative networks to experimental data to identify the network structure most consistent with biological behavior (i.e. model selection) [ 5 ]. This uncertainty can also be used to prioritize experiments that will maximally improve confidence in the simulations performed with a model (i.e. uncertainty reduction) [ 6 ]. In studies of metabolism, genome-scale metabolic network reconstructions (GENREs) have emerged as a useful formalism for hypothesis-driven modeling [ 7 ]. In conjunction with biological objective functions, such as maximization of growth rate, GENREs can be used to construct genome-scale metabolic models (GEMs). In addition to topological uncertainty (e.g., presence/absence of reactions in a network), simulations with GEMs generally yield many alternative solutions. Even the simplest simulations that can be performed with GEMs exhibit this behavior. This is the case for flux balance analysis (FBA), in which a pseudo-steady state is assumed, and flux values are found for all reactions in a GEM such that an objective function is optimized [ 8 ]. While a single global maximum value for the objective is guaranteed to be found, flux through every other component of the network is only constrained within a solution space, not to a single value. As a result, even though performing FBA yields a single value for the flux through reactions in a network, there are an infinite number of feasible flux values within the range determined by the solution space for some reactions. Techniques such as flux variability analysis and flux sampling have been developed to explore the space of alternative solutions in this scenario [ 9 , 10 ]. A myriad of additional algorithms have been developed for the analysis of GEMs for strain engineering, contextualization of experimental data, and building cell- and tissue-type specific GEMs [ 11 – 13 ]. In addition to optimization problems that can be solved using linear programming such as FBA, problems have been formulated to take advantage of mixed integer linear programming (MILP; see [ 14 ] for a review of optimization problems in systems biology). MILP employs binary state variables during optimization to solve problems that involve discrete activation or inactivation of variables. MILP problems are particularly well-suited to network-based models, since they allow switch-like behavior that can include or exclude network components (e.g., shutting reactions off/on). MILP has been used widely for gap-filling of GEMs, a process in which constraints or objectives are set to recapitulate a known phenotype by adding biochemical functions from a universal set of reactions [ 15 ]. In MILP problems used for gap-filling, the objective function is generally minimization of the number of modifications to a GEM that must be made to satisfy the constraints imposed (e.g., metabolite uptake or secretion, production of biomass). One consequence of this formulation is that alternative solutions, which contain unique sets of reactions which need to be altered in the network or added, are common for large networks that have a large space of potential solutions to draw from (e.g., a large universal set of reactions). These alternative optima in MILP problems are increasingly being considered and leveraged to understand redundancy in solutions and whether or not portions of a solution may be spurious [ 16 – 18 ]. It has been shown that the order in which separate instances of gap-filling are applied to the same network (e.g., gap-filling for growth on individual carbon sources iteratively) strongly influences which reactions are included in the resulting network [ 19 ]. In this same study, the alternative solutions generated during this process were used to improve gene essentiality predictions using EnsembleFBA, a technique in which sets of alternative GEMs are used to perform FBA to determine gene essentiality. Using the entire ensemble, performance can be tuned by varying the voting threshold required to make a specific prediction. This is analogous to the threshold-based voting procedure used to construct receiver operating characteristic curves for ensemble-based machine learning models such as random forest [ 20 ]. This approach is likely to be highly beneficial for studies of organisms for which little biochemical data are available, which typically have many gaps in their GENRE and thus have many highly-variable alternative gap-filling solutions. Although a nascent approach for studying GENREs, we have built on these observations, and ensemble generation and analysis have been applied in several cases [ 6 , 19 , 21 , 22 ]. Here, we present Medusa, a Python package for the generation and analysis of ensembles of GENREs. Medusa provides a framework for compactly representing ensembles of GENREs, avoiding the redundancy of storing many separate models while still being flexible enough to represent variation in any component within a GENRE. Medusa manages ensemble storage and indexing during simulation, allowing users to interact with an entire ensemble in the same way they would interact with an individual GENRE using any constraint-based reconstruction and analysis (COBRA) method. Furthermore, by standardizing the representation of ensembles and their interface with existing COBRA methods, Medusa enables the application of supervised and unsupervised machine learning to gain insight into the influence of varying components within an ensemble of GENREs on the predictions they make. The architecture and functionality of Medusa were designed to make ensemble analyses as accessible and usable as COBRA methods applied to single networks."
} | 2,100 |
27722504 | PMC5314688 | pmc | 4,723 | {
"abstract": "We recently developed capillaric circuits (CCs) – advanced capillary microfluidic devices assembled from capillary fluidic elements in a modular manner similar to the design of electric circuits (Safavieh & Juncker, Lab Chip , 2013, 13 , 4180–4189).",
"conclusion": "Conclusions Taken together, our results indicate that 3D-printing allows rapid and inexpensive fabrication of reliable capillaric valves and circuits. We established design rules for CCs, TVs, and RBVs (see Table 3 ). These design rules are specific to our 3D-printer and the PDMS replicas with a hydrophobic top surface (advancing and receding contact angles of 114° and 89°, respectively) and hydrophilic bottom and side surfaces (advancing and receding contact angles 45° and 31°, respectively). The resolution reliably achievable with the consumer grade 3D printer used here was limited to ∼200 μm. The design of a CC must consider multiple, sometimes competing, conditions for achieving the desired number of sequential events, flow rates, and time of delivery. With further improvements and better 3D-printers and resolution, higher capillary pressures could be generated, and more RBVs and liquid delivery steps could be included in the CC, thus increasing the possibilities of CCs. Table 3 Summary of calculated and empirical design rules for CCs with TVs and RBVs printed using the EnvisionTEC MicroEDU 3D-printer, and replicated into PDMS with surface properties described in the text and under the condition that all solutions have the same surface tension Design rules for capillaric circuits made from 3D-printed molds \n Maximal conduit size to stay within capillary microfluidic regime: Minimal feature size imposed by EnvisionTEC MicroEDU 3D-printer: Channel width: w ≤ 1 mm Channel width: w > 200 um Channel height: h ≤ 1 mm Channel height: h ≥ 50 μm Max. device footprint: 75 × 100 mm 2 \n Min. step between two widths: Δ w = 100 μm Min. step between two heights: Δ h = 50 μm Trigger valve (TV) design rules: Retention burst valve (RBV) design rules: 1. Height difference between TV and release channel: Δ h ≥ 300 μm 1. Difference in capillary pressure between successive RBVs: Δ P > 40 Pa 2. Condition for sequential delivery of liquids in CC with side branches with high-resistance retention valves connected to a main channel with pressure P \n J : | P \n J | < | P \n i +1 | during drainage of branch i \n The skill and resources needed to make CCs from 3D-printed molds lies between paper microfluidics and cleanroom-fabricated capillary microfluidics. The replication step into PDMS that was used here only adds a few hours to the iteration time. Whereas direct printing is desirable, a replication step also has benefits as the mold of the best working CC is preserved, and could serve as a master mold for subsequent mass production of CCs by hot embossing or injection molding. With the widespread adoption of 3D-printers, CCs could be readily printed by many researchers, and the design rules presented here will facilitate the fabrication of functional circuits. 3D-printing of CCs is especially appealing as a way to rapidly iterate through multiple designs and test new functions. In the future, it would be desirable to replace PDMS – which only retains its hydrophilicity for a few hours after plasma treatment 34 – with alternate polymers with more stable hydrophilic surfaces, 35 either by directly 3D printing them, or by replication into stable polymers. 3D-printed autonomous CCs may be developed for large-volume and multi-step biochemical assays to be used for point-of-care diagnosis, for research in a lab, as well as for educational purposes.",
"introduction": "Introduction Capillary-driven microfluidic devices move liquids using capillary forces defined by the geometry and surface chemistry of microchannels. This allows liquid delivery without using external pumps and valves. A wide range of capillary fluidic control elements were developed over the years including: stop valves, 1 retention valves, 2 trigger valves, 3 and capillary pumps. 2 , 4 , 5 Autonomous capillary microfluidic systems capable of self-powered and self-regulated completion of biochemical assays were also developed. 2 , 6 – 8 Yet these autonomous capillary microfluidic systems were fabricated using silicon wafers and cleanroom processes with multiple photomasks, thereby increasing their cost and complexity. Paper-based microfluidics and lateral flow assays were also re-discovered as inexpensive approaches to autonomous capillary-driven flow; 9 , 10 nevertheless, paper-based methods rely on heterogeneous porous substrates with statistical flow paths and cannot accomplish some of the valving capabilities that require the deterministic and predictable flow paths of microchannel-based devices. As such, there is a need for rapid and inexpensive fabrication of microchannel-based capillary microfluidics. Capillaric circuits for autonomous liquid delivery More recently, advanced capillary microfluidic devices capable of pre-programmed delivery of multiple liquids were developed to enable autonomous multi-step processes, for instance to incorporate wash or signal amplification steps for improved bioassay sensitivity and specificity. 11 – 13 Our research group proposed capillaric circuits (CCs) – advanced capillary circuits that are assembled from individual capillaric elements in the same way that electric circuits are assembled from individual electric components. 13 CCs operate in a walk-away format where the operator pre-loads each reservoir, without worrying about the timing or sequence of these operations – instead, capillary microfluidic elements choreograph liquid delivery operations with minimal user intervention. This makes CCs a desirable platform for automating biochemical assays in point-of-care settings with minimal instrumentation. The words capillary and capillaric are meant to emulate the distinction between electric and electronic whereas the former pertains to basic principles and the latter is used in the context of advanced circuits integrating multiple functionalities. In addition, the term capillary is ambiguous, as it is both used in reference to physical capillaries (including artificial and natural capillaries such as blood vessels) and in reference to surface tension-driven flow either within capillaries, microfluidic conduits or porous media, which can lead to confusion. The term capillaric is restricted to surface-tension driven microfluidic circuits, and thus helps resolve the ambiguity. Our group introduced two new fluidic elements to enable deterministic flow control with CCs. First, we developed two-level trigger valves (TVs) that stop liquids for over 30 minutes using an abrupt geometry change and a hydrophobic PDMS cover, thereby enabling pre-loading of reservoirs and subsequent liquid release when flow is triggered by a connected channel. 13 We also developed retention burst valves (RBVs) that have a burst pressure encoded by their geometry. When integrated with other capillary fluidic elements within a CC, RBVs allow autonomous delivery of liquids in a pre-programmed sequence according to increasing order of RBV capillary pressure. 13 \n Rapid prototyping of passive microfluidic devices Although CCs enable sophisticated and automated fluidic operations, the prevailing view is that deterministic capillaric microfluidics require high-precision and small-scale (∼10 μm) features for proper operation. As such, fabrication of CCs was dependent on cleanrooms, and was resource-intensive, time-consuming and expensive. Coupled with the need of photomasks for photolithography, a high cost and slow turnaround time for new design iterations limits the development of new devices and their widespread adoption. To overcome the limitations of cleanroom fabrication, rapid and inexpensive prototyping of capillary microfluidic valves and integrated devices has been explored. Rapid prototyping techniques used for developing capillary microfluidic devices include micromilling 14 and laser cutting. 15 These techniques have successfully been used for making capillary stop valves using primarily hydrophobic surface coatings that greatly relax the design constraints on the valve, but at the expense of autonomy and thus require syringe pumps or centrifugal forces to move the liquids within the microchannels. 16 – 19 More recently, a simple, autonomous self-filling capillary system comprising a capillary TV was fabricated by CO 2 laser cutting. 15 These results suggested that larger scale capillary circuits may be possible, however the laser cutting created triangular shaped conduits with limited control over the channel dimension, thus preventing the integration of more advanced elements such as retention burst valves for making more advanced capillaric circuits. 3D-printed microfluidics Lately, there has been a surge of interest in 3D-printing for microfluidics applications due to the speed, accessibility, and low cost required to fabricate multilayer microfluidic structures. Recent reviews describe state of the art 3D-printing for microfluidics applications. 20 , 21 All demonstrations of 3D-printed microfluidics so far employ active flow control (usually pneumatic or centrifugal pumps). The resolution currently available with consumer grade 3D-printers is typically ≥200 μm ( ref. 22, 23 ) with ∼1 μm surface roughness. 24 Capillary microfluidics however have traditionally been made with channels in the 1–100 μm range because the capillary pressure is inversely proportional to the smallest dimension, and becomes very small for large microchannels. Moreover, valving and flow control depend on the surface topography and abrupt geometric changes and low surface roughness are considered necessary to prevent pre-wetting and creeping flows. Hence the prevailing perception is that current 3D-printing technology may not be suitable for making capillary microfluidics because the smallest dimensions are too large to obtain adequate capillary pressure, the resolution and precision insufficient for making abrupt changes needed for reliable valves – notably due to the layered structure of stereolithographic printing forming steps that lend themselves to corner flow – and the high surface roughness may lead to creeping of liquid. Capillaric circuits from 3D-printed molds Here we present microfluidic capillaric circuits made from 3D-printed molds fabricated by stereolithographic 3D-printing with geometries scaled up >20-fold compared to cleanroom-fabricated circuits. 3D-printing allows rapid and inexpensive fabrication of CCs. This enables investigation and engineering of CCs with greater capabilities and increased accessibility in research and point-of-care settings. First, we 3D-print molds for TVs and characterize their performance as a function of geometry and surfactant concentration. Then we investigate design rules for CCs composed of TVs, RBVs, flow resistors, and capillary pumps using a proof of principle circuit with four reservoirs. Finally, we demonstrate the capabilities of our CCs by developing a circuit for autonomous delivery of eight liquids in <7 minutes.",
"discussion": "Results and discussion CCs operate using a series of functional elements including inlets, channels, flow resistors, capillary pumps, trigger valves (TV), capillary retention valves, and retention burst valves (RBVs) that can be combined for encoding the autonomous delivery of multiple liquids. 13 The capillary pressure of each RBV is calculated using the Young–Laplace equation: 1 where P is the capillary pressure, γ is the surface tension of liquid in the microchannel, and h , w , are the channel height and width respectively. θ \n t , θ \n b , θ \n r , θ \n l , are the top, bottom, right, and left channel wall contact angles, respectively. Contact angle hysteresis must be taken into account when designing RBVs since the advancing contact angles are relevant when a channel is filled while the receding contact angles are relevant when a channel is drained. Likewise, the resistance R for a conduit with a rectangular cross-section is given by: 27 \n 2 where η is the viscosity of liquid in the channel, and L is the length of the microchannel. The cross section of channels and various elements for microfabricated CCs reported by Safavieh et al. \n 13 ranged from 15 × 100 μm 2 to 200 × 200 μm 2 . Thus, assuming receding contact angles of 89° and 31° for the hydrophobic top PDMS surface and hydrophilic side and bottom surfaces respectively, and a surface tension γ of 72 N m –1 , the capillary pressures of microchannels (calculated using eqn (1)) in the cleanroom-fabricated circuits ranged from –7948 Pa to –1264 Pa. These capillary pressures would correspond to water column heights of 810 mm and 129 mm respectively in capillary rise experiments. Since our microchannel lengths were on the order of 5 mm, capillary forces dominated gravity in our microfabricated CCs and our devices could be operated without considering gravity effects. We first tested whether 3D-printed channels and capillary pumps replicated into PDMS could be filled by a liquid, and found that this worked reliably up to 1000 × 1000 μm 2 constituting the upper limit for capillary elements in this study. The lower size limit for fluidic elements was set by the resolution of the 3D printer. The vertical resolution was set by the thickness of each printed layer and was 50 μm. The lateral resolution was 100 μm under the best circumstances, but was limited to 200 μm when taking into consideration fabrication yield. Hence, for 3D-printed circuits, the cross-sectional dimensions range from 50 × 200 μm 2 to 1000 × 1000 μm 2 , and the capillary pressure ranges from –955 Pa to –188 Pa, or a water column height from 97 mm to 19 mm. These type of conduits filled spontaneously with aqueous solutions and remain in a microfluidic regime where gravity and inertia within the conduits are negligible. Next, we set out to test whether critical functional elements such as the TV and the RBV could also be 3D printed, whether surface roughness might affect their functionality, and to determine the design rules for making them. Trigger valves In order to develop functional CCs, the first step is to have functional and reliable trigger valves (TVs) to robustly hold liquids in reservoirs. 13 Consequently, we first characterized TVs on a standalone basis, before developing more complex CCs. Cleanroom-fabricated versus 3D-printed trigger valves Cleanroom fabrication is generally considered the gold standard for manufacturing capillary stop valves and TVs because of the small feature sizes and smooth channel surfaces attainable. 1 , 3 , 8 , 13 , 29 Cleanroom-fabricated TVs have small features (∼20 μm) and smooth, vertical channel walls ( Fig. 1a and c ). Meanwhile, 3D-printed trigger features have larger minimum widths (≥100 μm) and rough, layered channel walls ( Fig. 1b and d ). These stark geometry differences call into question the functionality and reliability of 3D-printed TVs. Fig. 1 Comparison between cleanroom-fabricated and 3D-printed TVs. a) Top view of TV fabricated by photolithography in the cleanroom showing smooth, high-precision features. b) Top view of TV fabricated by stereolithography-based 3D printing showing rough, large features. c) Scanning electron micrograph of TV fabricated by deep reactive ion etching of silicon showing vertical channel walls with sub-micron roughness. d) Front view of PDMS replica of 3D-printed TV showing 50 μm thick ridges on the channel wall due to the layer-by-layer printing process. We 3D-printed TV molds and tested a wide range of geometries and surfactant concentrations to assess their functionality and reliability. Previously, the success rate of capillary stop valves and TVs was only reported over a 5 minute period. 15 , 28 Here we defined TV success as when a valve holds liquid for at least 30 minutes without leakage. This allowed autonomous microfluidic operations in a walk-away format where the user pre-loads samples and reagents onto the chip and subsequently starts the assay at a time of their choosing, without needing to fit their operations to a strict 5 minute window. Effect of trigger valve geometry on success rate The geometry of TVs influences their success rate. 18 , 28 , 29 \n Fig. 2a shows the geometric parameters known to affect the performance of capillary TVs: the height of the TV, width of the TV, and the height difference between the TV and its release channel. To determine which geometries provide high TV success rates, we tested valves with widths of 96 μm, 192 μm, 288 μm, 480 μm, 672 μm, 960 μm, and 2016 μm. TV heights were fixed at either 400 μm or 1000 μm to obtain different height-to-width ratios for these experiments. As summarized in Fig. 2b , all TVs tested were at least 75% successful ( N = 8). The few failures were due to difficulties while loading valves with low (<1) or high (>5) aspect ratios ( i.e. height-to-width ratios) that required the user to apply additional positive pressure when filling the valves. We found that 3D-printed TVs were reliable with dimensions up to 3 times larger than reported with CO 2 laser cutting 30 and up to 80 times larger than typical cleanroom-fabricated valves. 13 , 28 \n Fig. 2 Effect of geometry and surfactant concentration on success rate of TVs. a) Front view of food dye solution stopped at TV showing the TV height ( h ), width ( w ), and the height difference between TV and release channel (Δ h ). b) Success rates for TVs over a wide range of widths and heights. N = 8. c) Above a height difference (Δ h ) of 300 μm, TVs were 100% successful. N = 6. d) TVs were 100% successful at Tween® 20 concentrations ≤0.0650% weight/volume in 1 × PBS. N = 3. Since the minimum z -layer thickness of the microchannels was limited to 50 μm by the 3D-printer resolution, we tested height differences of 100 μm, 150 μm, 200 μm, 250 μm, 300 μm, 400 μm, and 500 μm between the TV and the release channel. The TVs used for these height difference tests were 300 μm wide and 50 μm deep, since our TV characterizations showed reliable functionality over a wide range of geometries ( Fig. 2b ). As seen in Fig. 2c , the height difference between the TV and the release channel had a threshold effect on TV success. When the height difference was ≥300 μm, TVs were 100% successful ( N = 6). Effect of surfactant concentration on trigger valve performance Despite the fact that the most common application of capillary microfluidics is to automate biological assays that often require the use of surfactant-containing reagents, the effect of surfactant concentration on TV performance is not well reported in the literature. To determine the effect of surfactant on TVs, we tested aqueous solutions with different concentrations of Tween® 20, a surfactant commonly used in immunoassay wash buffers and for cell lysis. The critical micelle concentration of Tween® 20, is 0.0074% w/v. Consequently, we tested the following concentrations of Tween® 20: 0.0074, 0.0110, 0.0650, 0.1100, and 0.2750% weight/volume. As shown in Fig. 2d , we found that the TVs were 100% reliable when Tween® 20 concentrations were ≤0.0650% weight/volume ( N = 3), a suitable surfactant concentration for use in wash buffers during immunoassays that commonly use 0.05%. 31 , 32 \n Retention burst valves Retention burst valves (RBVs) retain liquid in a conduit up to a threshold, or bursting, pressure which if exceeded leads to bursting of the valve and draining of the liquid held downstream in a reservoir. It is thus possible to drain a series of reservoirs connected to a main channel in a predetermined sequence by terminating each of them with a RBV with increasing burst pressure. The burst pressure of a RBV can be calculated using eqn (1) and using the receding contact angles for the liquid which were found to be 95° for the hydrophobic PDMS cover, and 31° for the hydrophilic bottom and side walls. Capillaric circuit for autonomous delivery of four liquids As a proof of principle that we could 3D-print molds for capillaric circuits, we designed a circuit with 4 RBVs ( Fig. 3a and b ). PDMS replicas of the 3D-printed mold were made ( Fig. 3c ), plasma-treated for hydrophilicity, and sealed with a hydrophobic PDMS cover ( Fig. 3d ). The expected pre-programmed operation of the CC is illustrated in Fig. 3e . First reservoirs were filled and TVs held each liquid in place. Next, a solution was added to the release channel, connecting the reservoirs to the pump and starting the pre-programmed liquid delivery sequence. Subsequently, the RBVs burst sequentially according to increasing capillary pressure. Fig. 3 Design, mold, PDMS replica and operation of CCs for autonomous sequential delivery of four liquids. a) Symbolic representation of CC with main fluidic elements labelled. b) Schematic of CC. c) 3D-printed mold of the CC and d) PDMS replica with transparent PDMS cover and clean room wipe contacting the capillary pump. e) Schematic illustrating expected operation of RBVs. Solutions loaded into the reservoirs are delivered in pre-programmed manner according to RBV capillary pressure. The TVs in the CC were designed to have the smallest cross section in the circuit and the highest capillary pressure in the CC since they play a dual role – stopping liquids during initial filling of reservoirs, and acting as retention valves with higher capillary pressure than the capillary pump during reservoir drainage (see Fig. 3a ). These retention valves ensure that the side branches are not completely emptied (with minimal dead volume), thereby allowing sequential liquid delivery without bubble trapping. 2 , 13 \n In cleanroom-fabricated devices, multiple masks are needed for making structures with multiple depths; hence only the microchannel widths were used as a free parameter to adjust the RBV threshold. 13 RBVs were typically 100 μm deep and had widths of 200 μm, 130 μm, 110 μm, and 90 μm corresponding to capillary pressures of –1264 Pa, –1601 Pa, –1847 Pa, and –2028 Pa respectively. With 3D-printing both the width and depth can be adjusted independently and fabricated in one shot. Consequently, we encoded the capillary pressure differences between RBVs by modifying both the height and widths of the microchannels. The lower size limit of our microchannels was set by 3D-printer resolution. The pixel size for the EnvisionTEC Perfactory MicroEDU 3D-printer is listed as 96 μm, the smallest features that we were able to print with a high yield were 200 μm wide and 50 μm deep open channels. This resolution obtained is similar to that reported for other state of the art stereolithographic 3D-printers in the literature. 20 \n The reservoirs were 960 μm wide, 1000 μm deep, and 6250 μm long with a volume of 6 μL, corresponding to 60 times the volume of typical microfabricated reservoirs. 13 Due to the change in the size of the RBV, there were minor changes in volume for each reservoir ( Fig. 3c and d ) that could be compensated for by adjusting the reservoir size. To accommodate the large volumes in the reagent reservoirs without significantly increasing device footprint, we placed a cleanroom wipe made of paper (Durx 670, Berkshire Corporation, USA) atop the capillary pump. 33 The combination of 3D-printing and off-the-shelf, low cost paper saves costs without compromising performance. The driving capillary pressure in the circuit is defined by capillary pump because the gap between the edge of the PDMS and the paper forms an open microchannel that can be drained. Hence, the capillary pressure is dictated by the capillary pressure of the capillary pump, allowing use of paper pumps with higher, but sometimes ill-defined capillary pressures, without impacting the accuracy and functionality of the CCs. Requirements for sequential RBV bursting It is not sufficient to simply increase the burst pressure of the RBV to achieve sequential drainage and in fact the architecture of the CC must be designed to ensure that an RBV only bursts after complete drainage of the reservoir connected to the previous RBV. To illustrate this point, the proof of concept CC shown in Fig. 3a when filled with liquid is modeled by an electrical equivalent circuit shown in Fig. 4a . Fig. 4 Design and experimental validation of CC for autonomous delivery of four liquids. a) Electric circuit analogue showing the flow resistances and capillary pressures in the CC. b) Graphs showing the calculated junction pressures during bursting of each RBV. Junction pressures were designed to ensure that RBVs burst sequentially. c) Time-lapse images showing autonomous and sequential drainage of reservoirs in the CC. Arrows represent sequence and flow direction. Text labels show time during liquid delivery. A video of the autonomous liquid delivery operation is provided in movie S1. † d) Flow rates for different branches of the capillaric circuit. N = 3 devices from different 3D-printed molds. Error bars represent standard deviation. Considering the circuit at the instant when all reservoirs are filled, but still under static conditions, without flow, the junction pressure P \n J will be equal to the pressure P \n C of the capillary pump. Given that the capillary pressure of the pump is larger than the capillary pressure of the side branches, liquid will be drawn towards the junction P \n J , leading to flow in the CC. The first side branch to be drained in the CC is the one connected to the RBV with the lowest burst pressure, which here is RBV1 with pressure P \n 1 . As liquid drains from side branch 1, there is a pressure drop across R \n 1 and R \n RV on one hand and across the main resistor R \n M on the other hand, which will lead to a reduction of the pressure P \n J at the juncture between the side-branch and the main channel. The high resistance of R \n RV and R \n M compared to the low resistance of the release channel ensures that pressure P \n J is replicated across all 4 junctions (red dot in Fig. 4a ). To avoid bursting of RBV2 while branch 1 is draining, it is imperative that | P \n J | < | P \n 2 | at all times. Assuming a single branch drains at any given time, the pressure P \n J during drainage of the RBV can be calculated from the electrical circuit analogue using Kirchhoff's law and Ohm's law yielding: 3 where the index i represents the side branch that is being drained in the capillary circuit, P \n i is the capillary pressure of the liquid meniscus on the end of the side branch, R \n i is the flow resistance of the RBV and reservoir of the side branch, R \n RV is the resistance of the retention valve, R \n M is the flow resistance of the main resistor, and P \n c is the pressure of the capillary pump (see Fig. 4a ). As the liquid drains, the resistance in the side branch is expected to change. However, the retention/trigger valve structure has the smallest cross-section (300 × 50 μm 2 ) in the side branch and its associated resistance R \n RV > 100 R i . Consequently, changes in the resistance of the side branch during drainage are negligible and do not need to be considered in the calculation of P \n J . Moreover, after the RBV drains, P \n J decreases since the capillary pressure at the end of the side branch now becomes the capillary pressure of the reservoir rather than the capillary pressure of the RBV. This drop in P \n J does not adversely affect sequential liquid delivery since the condition for sequential liquid delivery is still met; in fact, during reservoir drainage one expects liquid delivery to be even more sequential since the junction pressure is lower. Hence the most stringent condition on P \n J is given by the situation described by the electrical circuit with fully filled conduits, which can thus be used to establish the conditions for sequential drainage of each of the side branches. The required condition for junction pressure to ensure that only one RBV in the CC bursts can be generalized as follows: 4 P J < P i +1 during drainage of branch i where P \n i +1 is the capillary pressure of the next RBV to burst in the circuit. This condition can be satisfied by balancing the flow resistance in the circuit, and in particular adjusting the main flow resistance ( R \n M ) in front of the capillary pump to ensure that P \n J during drainage is lower than the pressure of the subsequent RBVs (see Fig. 4a ). This calculation is applicable to CCs where the resistance of the channel linking the side branches is negligible compared to R \n RV , or else that resistance must also be considered and the appropriate analogous electrical model derived and resolved. The calculation holds for the model CC and can be used to calculate the pressure P \n J during drainage of branch i and ensure that it is smaller than the retention pressure P \n i +1 of branch i + 1 ( Fig. 4b ). The geometries of the RBVs in the CC are summarized in Table 1 . We designed our proof-of-principle device to obtain uniform capillary pressure differences of 80 ± 5 Pa between successive valves. All RBVs were 2.6 mm long. We designed a 4.2 mm long, 290 μm wide, and 100 μm deep main resistor so that the junction pressure during each liquid delivery step satisfied our condition for sequential liquid delivery (see Fig. 4b ). Table 1 Geometry of retention burst valves (RBVs) for autonomous delivery of four liquids. Junction pressures during drainage of RBVs were calculated using eqn (3) \n RBV1 RBV2 RBV3 RBV4 Width (μm) 960 670 480 380 Height (μm) 1000 750 650 550 RBV pressure P \n i (Pa) –194 –271 –358 –441 \n P \n J during drainage (Pa) –238 –309 –390 –443 Contact angle hysteresis must be taken into account when designing the capillary pump to ensure that the capillary pressure threshold for all RBVs can be overcome. Since the filling of the capillary pump is dictated by the advancing contact angles on the microchannel walls while the bursting of the RBVs is dictated by the receding contact angles on the microchannel walls, the dimensions of the capillary pump must be significantly smaller than the smallest dimension of RBVs to ensure drainage. 13 Thus, the capillary pump in our proof-of-principle 4-valve circuit was using microchannels that were 200 × 100 μm 2 , providing a wicking capillary pressure of –736 Pa that is large enough to drain each RBV in the circuit. To experimentally validate our design, we 3D-printed the CC mold with our calculated dimensions for the main resistor and made PDMS replicas as described earlier (see Fig. 3 ). Then we tested liquid delivery using aqueous food dye solutions. As expected, each side branch drained sequentially without drainage of the other RBVs ( Fig. 4c and movie S1 † ). Pre-programmed drainage of the side branches was completed within 4 min. The sequence of RBV drainage was 100% successful over four repeated tests with devices made from three different 3D-printed molds. As shown in Fig. 4d , the flow rates for liquid drainage from branches 1, 2, 3, and 4 were 0.21 ± 0.02, 0.21 ± 0.01, 0.20 ± 0.01, and 0.10 ± 0.01 μL s –1 respectively. Next we tested whether reproducibility of flow rate could be further improved by using three replicates from a single mold, but the variability remained comparable, suggesting that user manipulations and other parameters, but not the 3D-printer imprecision, are the main source of variability. Capillaric circuit for autonomous delivery of eight liquids After establishing general guidelines for designing RBVs to obtain sequential liquid delivery, we designed a CC with eight liquid delivery steps, double the number in our proof-of-principle CC and exceeding the number of sequentially-encoded, self-regulated microfluidic drainage events in our previous work with cleanroom-fabricated CCs. 13 \n As described earlier, the smallest microchannel width that we could print without a high incidence of defects was 200 μm. We designed the capillary pump region of the CC to be 300 μm wide and 50 μm deep to ensure reliable printing since the capillary pump has a larger pressure than all the RBVs in the circuit. To encode capillary pressure differences, we systematically varied the heights and widths of microchannels in each side branch (see Table 2 ). We designed RBVs according to the junction pressure criterion (see eqn (4)) to ensure that valves were drained sequentially. Although in theory, very small differences in capillary pressure between successive RBVs should ensure serial drainage, empirical tests yield that designed capillary pressure differences of ∼40 Pa provided reliable sequential drainage of RBVs. This empirical value depends on the resolution and accuracy of features produced by the 3D printer and might be reduced with a more accurate printer, or conversely might need to be increased for experiments that require solutions with different surface tensions that will affect the contact angle and the capillary pressure P \n i of the RBV and branch loaded with this solution. Table 2 RBVs designed for autonomous delivery of eight liquids \n RBV1 RBV2 RBV3 RBV4 RBV5 RBV6 RBV7 RBV8 Width (μm) 770 670 580 480 380 380 380 380 Height (μm) 900 750 650 600 600 400 300 200 Valve pressure P \n i (Pa) –233 –270 –314 –365 –430 –483 –536 –642 \n P \n J during drainage (Pa) –250 –286 –329 –379 –442 –493 –544 –643 The main resistor was 18.5 mm long, 300 μm wide, and 50 μm deep to obtain a calculated drainage time of ∼10 min for all 8 liquid delivery steps based on the capillary pressures, resistances, and volumes of the microchannels in the circuit. The smallest RBV in the circuit was 380 μm wide and 200 μm deep. Since this valve was much shallower than the reservoir (960 μm wide and 1000 μm deep), we connected the valve to the reservoir using a gently sloped staircase with 50 μm height increments to prevent from liquid stopping due to the formation of an undesired stop valve. We set the maximum channel height in our CCs to 1 mm to stay within a regime where capillary forces are dominant. These geometric constraints limited the number of RBVs, and by extension the number of liquid delivery steps that we could automate in our CCs. We experimentally validated the operation of the 8-step circuit by 3D-printing a mold and making PDMS replicas as described previously. Fig. 5 shows time-lapse images of autonomous and sequential delivery of 8 liquids in the CC. The autonomous sequential liquid delivery is shown in movie S2. † Liquids were initially pre-loaded into the reagent reservoirs, and then the central release channel was filled with 10 μL of liquid to start the autonomous drainage operations. Following drainage of the solution from inlet 8 and pinning of the air–liquid interface at RBV8 in the trigger channel, RBV1 is the first to start bursting at t = 3 min 11 s. Each RBV with its attendant reservoir take ∼50 s to drain and the autonomous drainage of the 8 solutions was completed in <7 min. Fig. 5 CC for autonomous delivery of eight liquids. a) Symbolic representation of CC showing modular assembly of fluidic elements. b) Schematic representation of CC. c) Time-lapse images showing autonomous delivery of 8 liquids in the CC. Arrows indicate flow direction and numbers highlight the time and sequence of liquid delivery. A video of liquid delivery is provided in movie S2. † \n Comparison between 3D-printed and cleanroom-fabricated capillaric circuits Cleanroom fabrication allows microchannel height and width specifications down to 1 μm and less, whereas with the 3D printer used here the resolution was limited ∼200 μm in XY and 50 μm in Z . Consequently, one can more finely vary the capillary pressures of microfabricated CCs, which in theory could allow sequential drainage of more channels. However, sequential drainage is constrained by the whole circuit architecture to ensure that the condition for sequential drainage of all the retention burst valves in the capillary circuit is still met (eqn (3) and (4) and Fig. 4a ). A strength of 3D printing is the capability to print multi-height features in a single run, whereas when using classical photolithography each different depth level would require a photolithographic and processing step with precise alignment which would make fabrication excessively slow, costly and at the same time reduce the yield. For example, the CC for the autonomous delivery of eight liquids used seven different depths on the same mold (see Fig. 5 ). Capillary forces are dominant over gravitational and inertial forces at the scale of the conduits used for the CCs shown here. However, the 3D-printed conduits extend over several tens of millimetres in some cases, and if a chip is not horizontal, or in the most extreme case if it is positioned such that the channel is vertical, then the hydrostatic pressure could disrupt the functionality of the CC and notably the pre-programmed drainage order. For the 8-valve CC the difference between two sequential retention burst valves is 40 Pa, which corresponds to the pressure of a water column height of 4 mm. The footprint of the 8-valve CC is much larger, and by placing it on one of the sides at a 90° tilt, the sequence of drainage was disrupted, as predicted. Hence, for the reliable operation of CCs with large conduits and incremental differences in capillary pressure it is important to consider the position of the CC, and ideally to position the devices horizontally."
} | 9,389 |
39628613 | PMC11610949 | pmc | 4,724 | {
"abstract": "Exploiting light to drive redox reactions is currently a hot topic since light is considered as an environmentally friendly source of energy. Consequently, cyanobacteria, which can use light e.g., for generating NADPH, are in the focus of research. Previously, it has been shown that various heterologous redox enzymes could be expressed in these microorganisms. Here we demonstrated the successful inducer-free expression of α-keto-acid dehydrogenases (L-HicDH and D-HicDH) from Lactobacillus confusus DSM 20196 and Lactobacillus paracasei DSM 20008 in Synechocystis sp. PCC 6803 Δ hoxYH mutant using replicative plasmids. While the L-HicDH showed poor activity limited by the amount of expressed enzyme, the D-HicDH was applied both in vivo and in vitro , transforming the selected α-keto acids to the corresponding optically pure ( R )-α-hydroxy acids ( ee >99%) in up to 53% and 90% conversion, respectively.",
"introduction": "1 Introduction Cyanobacteria are microorganisms that rely on oxygenic photosynthesis for growth and survival. Besides some minerals they require only sunlight and carbon dioxide as energy and carbon source, respectively. Compared to higher plants and eukaryotic microalgae, prokaryotic cyanobacteria are well-known for rapid growth and a relatively simple genetic background amenable to manipulation. Thus, the simple nutritional requirements of these organisms, combined with the autotrophic lifestyle and metabolic plasticity, as well as the availability of molecular and synthetic biology tools make them promising ‘low-cost’ solar-driven microbial cell factories ( Abed et al., 2009 ; Carroll et al., 2018 ; Knoot et al., 2018 ; Lau et al., 2015 ; Santos-Merino et al., 2019 ; Thajuddin and Subramanian, 2005 ). In recent years, the development of molecular tools enabled the engineering of new recombinant photoautotrophic strains by expressing heterologous oxidoreductive enzymes that benefit from the steady photosynthetic production of NADPH ( Jodlbauer et al., 2021 ; Schmermund et al., 2019 ). This paved the way to develop new approaches for the recycling of the costly cofactors NADH and NADPH. Such phototrophic regeneration of NADPH uses only light and water as stoichiometric reagents, providing superior atom economy in comparison to traditional industrially applied approaches ( Mordhorst and Andexer, 2020 ). Examples include an ene reductase ( Assil-Companioni et al., 2020 ; Köninger et al., 2016 ), a Baeyer-Villiger monooxygenase ( Böhmer et al., 2017 ), a carboxylic acid reductase ( Yunus and Jones, 2018 ), the AlkBGT hydroxylation-system ( Hoschek et al., 2019a ), a cytochrome P450 monooxygenase ( Hoschek et al., 2019b ), an alcohol dehydrogenase ( Sengupta et al., 2019 ) and imine reductases ( Büchsenschütz et al., 2020 ). However, strategies for tightly controlled gene expression, that are widely available and routinely used in E. coli and other heterotrophic hosts, are still very limited for cyanobacteria ( Hitchcock et al., 2020 ; Jodlbauer et al., 2021 ; Till et al., 2020 ). Foreign DNA instability and particularly expression of toxic enzymes can therefore pose a particular challenge ( Borirak et al., 2015 ; Jones, 2014 ; Keasling, 2008 ; Wu et al., 2020 ; Yunus and Jones, 2018 ). The best-studied cyanobacterial strain to date is the unicellular non-nitrogen-fixing Synechocystis sp. PCC 6803, widely acknowledged as the ‘green E. coli ’ ( Branco dos Santos et al., 2014 ). The vast amount of physiological and molecular data available, together with a relatively small genome, make Synechocystis sp. PCC 6803 suitable to be used as a photoautotrophic biotechnological platform ( Santos-Merino et al., 2019 ; Vermaas, 1996 ). To investigate strengths and limitations of cyanobacterial α-keto-acid reduction, we introduced α-keto acid dehydrogenases (KADH) in Synechocystis sp. PCC 6803 wild type, as well as a markerless deletion mutant (Δ hoxYH ) lacking the functional Hox electron sink. In light-fluctuating conditions of natural habitats, the photosynthetic apparatus and cell metabolism of cyanobacteria are protected from an overflow of reactive electrons by action of several natural electron sinks such as the flavodiiron proteins, the type I NADH dehydrogenase (NDH-1) ( Santana-Sanchez et al., 2019 ) and the bidirectional hydrogenase Hox ( Tamagnini et al., 2007 ). However, these electron sinks are not needed in controlled laboratory conditions and enhanced activity of a heterologous ene-reductase and a cytochrome P450 were demonstrated upon the deletion of flavodiiron in Synechocystis sp. PCC 6803 ( Assil-Companioni et al., 2020 ) and NDH-1 in Synechococcus sp. PCC 7002 ( Berepiki et al., 2018 ), respectively. The Δ hoxYH was originally constructed as a chassis for hydrogen production and characterized as a robust photoautotrophic host due to adjusted metabolism ( Pinto et al., 2012 ). The reduction of stereodiverse α-keto acids, phenylpyruvic acid 1a and 4-methyl-2-oxovaleric acid 1b , to the corresponding α-hydroxy acids 2a-b , was chosen as the benchmark reaction ( Scheme 1 ). α-Hydroxy acids can be found in various natural products, as well as in pharmaceutical and plant-protection agents ( Coppola and Schuster, 1997 ; Hammerer et al., 2018 ). Furthermore, they are essential constituents of depsipeptides where they act as mimetics for the corresponding natural amino acids like 2a - b for phenylalanine and leucine, respectively ( Bunyajetpong et al. 2006 ; Lemmens-Gruber et al., 2009 ; Tripathi et al., 2010 ; Scherkenbeck et al., 2012 ). Scheme 1 Reduction of the of the α-keto acids, phenylpyruvic acid 1a and 4-methyl-2-oxovaleric acid 1b , to the chiral α-hydroxy acids, phenyllactic acid 2a and 2-hydroxy-4-methylvaleric acid 2b , using Synechocystis sp. PCC 6803 Δ hoxYH heterologously expressing keto acid dehydrogenases. The required reduced NAD(P)H is regenerated by the metabolism. Scheme 1:",
"discussion": "3 Results and discussion In an initial screening, we tested the activity of five keto acid dehydrogenases towards the target substrates 1a and 1b in the presence of NADH and NADPH to determine the suitability of the enzymes to catalyze the target reaction in cyanobacteria, which provide NADPH as reducing agent ( Tamoi et al., 2005 ). The enzymes were expressed in E. coli BL21(DE3) and the whole cell lyophilizates were then investigated in a spectrophotometric assay (Table S5). The best performing enzymes were a pair of enantio-complementary keto-acid dehydrogenases, namely L- and D-2-hydroxyisocaproate dehydrogenase derived from Lactobacillus confusus DSM 20196 (UniProtKB ID: P14295) ( Schutte et al., 1984 ) and Lactobacillus paracasei DSM 20008 (UniProtKB ID: P17584) ( Hummel et al., 1985 ), respectively (hereafter L-HicDH and D-HicDH). It is noteworthy that, in contrast to literature reports ( Hummel et al., 1985 ; Schutte et al., 1984 ; Busto et al., 2016 , 2014 ; Gourinchas et al., 2015 ), where both enzymes were described as NADH-dependent, they displayed reasonable activity, although reduced, also in the presence of NADPH ( Table 1 ). Furthermore, in end-point biotransformations supplied with NADPH, good conversions in the range of 84% to 90% and perfect stereoselectivity towards both, the ( R )- or the ( S )-products, depending on the applied enzyme, were reached (Table S6). Table 1 Specific activity of L- and D-HicDH cell lysates from E. coli and Synechocystis sp. PCC 6803 in the presence of NAD(P)H, measured photometrically as decrease in NAD(P)H absorbance at 340 nm. Table 1 enzyme substrate cofactor specific activity [U mg −1 ] a E. coli b Synechocystis sp. PCC 6803 Δ hoxYH c L-HicDH 1a NADH 130.8 ± 7.5 0.3 ± 0.1 NADPH 12.8 ± 1.0 0.0 ± 0.1 1b NADH 31.5 ± 2.0 0.1 ± 0.1 NADPH 12.8 ± 2.9 0.1 ± 0.1 D-HicDH 1a NADH 520.1 ± 13.6 14.1 ± 0.4 NADPH 30.4 ± 2.6 1.1 ± 0.1 1b NADH 31.8 ± 0.1 0.8 ± 0.1 NADPH 6.2 ± 4.5 0.3 ± 0.0 Reaction conditions: substrate (1.8 mM), NADH or NADPH (0.9 mM), potassium phosphate buffer (100 mM, pH 7.5, 30°C). Average and standard deviation of technical triplicates, background activity of control reactions without substrate subtracted. a Units per mg total protein b L-HicDH 0.3 µg mL −1 , D-HicDH 0.4 µg mL −1 total protein c L-HicDH 13.8 µg mL −1 , D-HicDH 15.7 µg mL −1 total protein These results motivated us to express both HicDH enzymes in Synechocystis sp. PCC 6803. Synthetic genes encoding L- and D-HicDH, codon optimized for expression in the cyanobacterium (for details see Supporting Information), were cloned into the pET21a(+) vector and overexpressed in E. coli BL21(DE3). Expression of active enzymes using the synthetized genes was confirmed by remeasuring the activity towards 1b (Fig. S1). The codon optimized genes were then subcloned in the pSEVA251 replicative vector ( Silva-Rocha et al., 2013 ), under the regulation of different synthetic medium-strength constitutive promoters (P trc.x.tetO2 or P trc.x.lacO ) ( Ferreira et al., 2018 ) and the RBS BBa_B0030 from the Registry of Standard Biological Parts ( Englund et al., 2016 ; Registry of Standard Biological Parts parts.igem.org). The construct with D-HicDH under the control of the P trc.x.lacO promoter showed notable genetic instability due to which the plasmid with the correct sequence could not be produced in a sufficient yield for cyanobacterial transformation. pSEVA251_P trc.x.lacO ::L-HicDH , pSEVA251_P trc.x.tetO2 ::L-HicDH and pSEVA251_P trc.x.tetO2 ::D-HicDH were introduced in the Synechocystis sp. PCC 6803 wild type and the Δ hoxYH strain, however only two transformants, both in the Δ hoxYH genetic background, were successfully identified: the L-HicDH under the control of P trc.x.lacO promoter (plasmid pSEVA251_P trc.x.lacO ::L-HicDH ; hereafter S. L-HicDH) and D-HicDH under the control of the P trc.x.tetO2 promoter (plasmid pSEVA251_P trc.x.tetO2 ::D-HicDH ; hereafter S. D-HicDH). The troublesome cloning suggests a harmful effect of constitutive expression of the KADHs on the host cell metabolism. However, growth rates and chlorophyll a content of the strains harbouring the HicDHs were comparable to the Synechocystis sp. PCC 6803 wild-type and Δ hoxYH (Fig. S3). Additionally, even though the P trc.x.lacO is reported as a stronger promoter than P trc.x.tetO2 ( Ferreira et al., 2018 ), the fact that L-HicDH was only expressed under the control of P trc.x.lacO supports reports on greater complexity of the regulation in Synechocystis sp. PCC 6803, depending also on genetic context ( Thiel et al., 2018 ). Measuring the activity of cyanobacterial cell lysates in a spectrophotometric assay using substrates 1a and 1b confirmed active expression of D-HicDH. However, the activity of L-HicDH was detectable only with 1a in the presence of NADH, which was also the preferred combination when expressed in E. coli ( Table 1 ). In addition, the activity of the cyanobacterial cell lysate was generally more than one order of magnitude lower compared to E. coli lysate. The difference in activity of cyanobacterial and E. coli enzyme preparations suggest that the amount of the active D-HicDH expressed per weight of total soluble protein was roughly 30-fold lower in the cyanobacteria while this difference in case of L-HicDH was more than 400-fold. The lesser amount of the target enzyme reflects our choice of a regulatory system in Synechocystis , that is based on a relatively low-level constitutive expression in order to minimize the metabolic burden on the cyanobacterial host, as compared to the strong inducible system that was used in E. coli . We investigated whether the living cyanobacterial cells, upon illumination with white light, could provide the reducing equivalents needed for the ketoreduction of 1a - b ( Fig 1 AB). Remarkably, the S. D-HicDH at OD 750 of 10 was able to convert 26% of 1a and 28% of 1b with perfect stereoselectivity to the corresponding optically pure products ( R )- 2a or ( R )- 2b (ee >99%). Interestingly, when the substrate loading was increased from the initial 2 mM to 5 and 10 mM, the percentage of formed product stayed in the same range, effectively demonstrating a higher productivity while retaining the perfect ee of >99% ( Fig. 1 AB and Fig. S4). This trend might be explained by a limited uptake of the polar substrates, which is increased at a higher substrate concentration. The overall conversion could be even boosted by increasing the cell density to an OD 750 of 20, reaching 45% at a concentration of 10 mM of 1a and 53% at 10 mM of 1b . In contrast, the S. L-HicDH at OD 750 of 10 was less active, as it only converted less than 0.3 mM of 10 mM substrate to ( S )- 2a or ( S )- 2b . Fig. 1 In vivo biotransformations of ( A ) phenylpyruvic acid ( 1a ) and ( B ) 4-methyl-2-oxovaleric acid ( 1b ) (2-10 mM) using cyanobacterial cells. Substrate loading and cell loading of S. D-HicDH were investigated. End-point measurements, reaction time 16 h. Enantiomeric excess of reactions with S. D-HicDH was >99% for ( R )-phenyllactic acid ( R )- 2a and ( R )-2-hydroxy-4-methylvaleric acid ( R )- 2b and was not determined for compounds under 0.5 mM. Left = light, right = dark. Average and standard deviation of three independent experiments. Fig 1: Another option to increase the conversion by S. D-HicDH at OD 750 of 10 was by increasing the reaction time ( Fig. 2 ). Under light, 49% of 1b was converted to ( R )- 2b after 24 hours and after 48 hours the conversion remained at 52%. Still, 1b continued to be depleted at a constant rate even between 24 and 48 hours. Initially, the dark reaction proceeded at a slower pace, reaching 39% product after 24 hours, but catching up with the light reaction after 48 hours at 49%. Fig 2 Time course of the in vivo biotransformation of 1b (10 mM) by cyanobacterium S. D-HicDH (OD 750 10). Fig 2: The amount of α-hydroxy acids 2a - b that was produced with S. D-HicDH under irradiation with white light, was comparable to the results obtained under dark conditions ( Fig. 1 AB, Fig. 2 ). As the photosynthetic machinery directly produces NADPH and is therefore considered the major reducing equivalent ( Park and Choi, 2017 ), the comparable activity in the dark suggests that reducing equivalents may also originate from other parts of the metabolism, such as glycolysis (upon glycogen degradation) and in the form of NADH. Therefore, the presented process is light-dependent in the sense that the growth of the biocatalyst and provision of reducing equivalents are autotrophic, but light is not a direct driving force of the targeted activity, as it can also take place in the dark ( Löwe et al., 2018 ). A similar dark reaction has been found previously when imine reductases were heterologously expressed in Synechocystis sp. PCC 6803 ( Büchsenschütz, 2020 ). No background keto-acid dehydrogenase activity of the Δ hoxYH was detected, however, we observed lower recovery, particularly with 1a as substrate. The cells did not demonstrate activity towards rac- 2a or \n rac- 2b (Fig. S5A); therefore, the mass balance loss can be attributed to 1a and 1b . Partly, the loss of 1a is due to spontaneous decomposition, as less than 80% of 1a is recovered in both light and dark conditions upon incubation of 1a only in the reaction buffer for 16 h (Fig. S5B). However, in the presence of cyanobacterial cells ( Fig. 1 A), the recovery was always notably lower under irradiation, regardless of the presence of recombinant enzyme. This loss of substrate may not be surprising knowing that phenylpyruvate 1a is one of the central metabolites in phenylalanine metabolism of Synechocystis sp. PCC 6803 , that is connected to other pathways such as biosynthesis of tropane, piperidine and pyridine alkaloids (pathway syn00360 in the KEGG, Kyoto Encyclopedia of Genes and Genomes ( Kanehisa, 2000 )). Unlike the targeted keto-reduction, the substrate degradation showed clear light-dependence. Similarly, α-ketoisocaproic acid 1b is also a natural metabolite in branched amino acid biosynthesis (KEGG pathway syn00290) but it generally has less of a central role, therefore the side-reactivity is less pronounced. When cyanobacterial cell lysates corresponding to an OD 750 of 10 were exogenously supplied with an equivalent of NAD(P)H, nearly 90% conversion of both substrates by S. D-HicDH lysate was observed regardless of the cofactor, demonstrating the application of the strain for the photosynthetic production of the target enzymes ( Fig. 3 ). This also suggests that the limiting factor of in vivo biotransformations is not the amount of D-HicDH expressed but rather the availability of reduced cofactor NADH or the transport of the substrate into the cell. On the other hand, S. L-HicDH lysate converted roughly 50% of 1a-b in the presence of NADH and <5% in the presence of NADPH, confirming our hypothesis that for this strain, the amount of active enzyme is another limiting factor. Control reactions with the cyanobacterial lysate free of the recombinant enzymes (Δ hoxYH ) resulted in decrease of substrate concentrations. The loss of 1a was especially pronounced in reactions supplied with NADH and, to a lesser extent, NADPH. This result suggests that some of the competing reactions are dependent of NAD(P)H and explains their light-dependency in vivo . Fig. 3 In vitro biotransformations of phenylpyruvic acid ( 1a ) and 4-methyl-2-oxovaleric acid ( 1b ) (10 mM) using cyanobacterial cell lysates in presence of 1 equivalent of NAD(P)H. Enantiomeric excess for the corresponding products was not determined for concentrations below 0.5 mM. Error bars correspond to standard deviation of biological triplicates. Fig 3: In conclusion, the keto acid dehydrogenases D-HicDH and L-HicDH, initially expressed in E. coli , showed reasonable activity also with NADPH although NADH is the preferred cofactor. Both enzymes were successfully expressed under constitutive regulation only in the Synechocystis sp. PCC 6803 Δ hoxYH mutant and their activity was demonstrated, albeit the S. L-HicDH showed very poor expression and consequently activity. We hypothesize that the interference of heterologous HicDHs in the metabolic pathways of Synechocystis sp. PCC 6803 imposed a heavy metabolic burden to the host organism which only the Δ hoxYH mutant was able to alleviate, indicating a greater degree of metabolic plasticity of this mutant. The cyanobacterium expressing the D-HicDH was able to convert up to 46% of 1a and 53% of 1b at a substrate concentration of 10 mM giving the corresponding α-hydroxy acid in optically pure form ( ee >99%). One limitation in the cell's productivity might be the uptake of the polar α-keto acid substrates at the conditions used. Experiments in the dark and with the cell lysates indicated that the HicDHs most likely consumed NADH from the cell's metabolism instead of the photosynthetically produced NADPH, which can be attributed to the enzymes’ preference for NADH. Nevertheless, as the cells grow only using CO 2 as carbon source and light as energy source, the overall process is still light dependent. The use of the photosynthetically produced enzymes in form of a lysate with externally added NADH allowed to reach conversions of up to 90% for 1a and 89% for 1b . This study indicates that the observed cofactor promiscuity of the studied enzymes towards NADPH is not sufficient for exploiting the light-driven cyanobacterial metabolism, which might be mitigated by using strictly NADPH-dependent enzymes or by increasing internal NADH supply by co-expressing a transhydrogenase ( Angermayr et al., 2012 ; Niederholtmeyer et al., 2010 ) and engineering the electron transport ( Meng et al., 2021 ). Moreover, the activity may be improved via inducible regulation of enzyme expression. Furthermore, the reaction might be additionally tuned by supplying the substrates in a form that can more easily cross the cell wall (e.g. as esters) or by providing a CO 2 rich environment for the whole cell catalysts. Overall, we demonstrated the successful inducer-free expression of D-HicDH and L-HicDH from replicative plasmids in Synechocystis sp. PCC 6803 Δ hoxYH mutant, confirming its robustness as a host in comparison to the wild-type."
} | 5,094 |
36650835 | PMC9840862 | pmc | 4,725 | {
"abstract": "Background Mining deposits often contain high levels of toxic elements such as mercury (Hg) and arsenic (As) representing strong environmental hazards. The purpose of this study was the isolation for plant growth promoting bacteria (PGPBs) that can improve phytoremediation of such mine waste deposits. Methods We isolated native soil bacteria from the rhizosphere of plants of mine waste deposits and agricultural land that was previously mine tailings from Tlalpujahua Michoacán, Mexico, and were identified by their fatty acid profile according to the MIDI Sherlock system. Plant growth promoting traits of all bacterial isolates were examined including production of 3-indoleacetic acid (IAA), siderophores, biofilm formation, and phosphate solubilization. Finally, the response of selected bacteria to mercury and arsenic was examined an in-vitro assay. Results A total 99 bacterial strains were isolated and 48 identified, representing 34 species belonging to 23 genera. Sixty six percent of the isolates produced IAA of which Pseudomonas fluorescens TL97 produced the most. Herbaspirillum huttiense TL36 performed best in terms of phosphate solubilization and production of siderophores. In terms of biofilm formation, Bacillus atrophaeus TL76 was the best. Discussion Most of the bacteria isolates showed high level of tolerance to the arsenic (as HAsNa 2 O 4 and AsNaO 2 ), whereas most isolates were susceptible to HgCl 2 . Three of the selected bacteria with PGP traits Herbispirillum huttiense TL36, Klebsiella oxytoca TL49 and Rhizobium radiobacter TL52 were also tolerant to high concentrations of mercury chloride, this might could be used for restoring or phytoremediating the adverse environmental conditions present in mine waste deposits.",
"conclusion": "Conclusions Throughout the world, various anthropogenic activities, in particular mining, have directly or indirectly deposited enormous amounts of PTEs in the soil, resulting in negative consequences for the environment and human health. Consequently, identifying bacterial strains that can improve phytoremediation or could facilitate the establishment of native vegetation in the generally uncovered tailing soils offers a promising option for their restoration. In this study, we found genera that have been widely reported in soils contaminated with heavy metals, such as Bacillus and Pseudomonas , also bacteria that are resistant to Hg and As and at the same time present plant growth promotion properties such as H. huttiense TL36, R. radiobacter TL52 and K. oxytoca TL49. It is important to mention that Herbaspirillum is reported for the first time as a genus that has tolerance to high concentrations of heavy metals and participates in the solubilization of phosphates. The identification using the FAME method allowed us to have an approach to the isolated species in our study, however, in future perspectives these isolates that were promising will be identified through phylogenetic analyzes based on the 16SrRNA gene. Our results suggest that isolates TL36, TL52 and TL49 can be an excellent alternative in the remediation of mine tailings; however, some tests are still needed to evaluate the ability of these isolates to remove or transform heavy metals that allow us to corroborate their potential as bioremediation agents. Additionally, the advantage of obtaining cultivable microorganisms allows us to think of the use of inoculants as a plant-microorganism interaction strategy to optimize bioremediation processes. Therefore, in future perspectives, isolates TL36, TL52 and TL49 will be inoculated in representative crops of the municipality of Tlalpujahua to validate their results under greenhouse conditions as growth promotion and bioremediation agents.",
"introduction": "Introduction In the past few decades, soil pollution caused by heavy metals has increased in prevalence, especially in developing Countries ( Samuel et al., 2021 ). Activities such as rapid industrialization, poor agricultural practices, and mine tailings runoff are often detrimental to soil health and can distribute hazardous metals into the environment, with harmful health consequences ( Raklami et al., 2022 ). In particular, the intensive extraction of minerals taking place in mining zones has produced a large volume of wastes and tailings, which release potentially toxic elements (PTE) to the environment, including heavy metals. Contamination with heavy metals, particularly arsenic (As) and mercury (Hg), is frequent throughout the planet ( Hering et al., 2017 ). Soil contamination by heavy metals has become a serious environmental issue, since most metals exert harmful effects at low concentrations (1–10 mg/mL), while some metals such as Hg have a toxic effect at even lower concentrations (0.001–0.1 mg/mL) also, being a mobile element in the environment, its negative impacts are considerable ( Kurniati et al., 2014 ; Devi et al., 2022 ; Zhang et al., 2010 ). Arsenic pollution is not only a result of mining activities, but it also enters ecosystems using pesticide and/or herbicides based on this chemical element ( Kumar et al., 2016 ). One way of reducing the environmental impact of contaminated soils is the use of remediation strategies, some are chemical and physical in nature; however, these methods can be expensive and not always effective ( Kunito et al., 2001 ). Biological techniques, such as phytoremediation and PGPB bioremediation, have emerged as environmentally sound approaches to heavy metal removal ( Devi et al., 2022 ). Microbial communities also play an important role during the recovery of soils by improving their structure and fertility and are excellent indicators because of their rapid response to environmental changes ( Xiao et al., 2017 ). However, high concentrations of heavy metals might alter the diversity and structure of microbial communities ( Quadros et al., 2016 ), their vital microbiological features (like growth, adhesion, and morphology), and essential biochemical traits such as respiration, nitrogen fixation, and mineralization of nitrogen and phosphorus ( Rajapaksha, Tobor-Kapłon & Bååth, 2004 ). In consequence, soil microbial mediated phytoremediation requires choosing soil bacteria capable of growing in the presence of heavy metals, sometimes in high concentrations. Also, these bacteria have to show plant growth promotion features such as: synthesis of 3-indoleacetic acid (IAA), phosphate solubilization, biofilm formation, and production of siderophores that are important for plant growth in contaminated soils ( Khan et al., 2015 ; Yu et al., 2014b ; Santoyo et al., 2016 ). In Tlalpujahua, Michoacán, located in west-central Mexico, an over 400-year history of mining activity in the region has generated tailings on which a partial natural regeneration of the vegetation has been taking place in the last 60 years since the mines in the region ceased to operate ( Corona-Chávez et al., 2010 ). In the mining zone of Tlalpujahua-El Oro, little work has been carried out to remediate contaminated soils. Osuna-Vallejo et al. (2019) , evaluated the capability of the conifers Juniperus deppeana and Pinus pseudostrobus for extracting mercury from soils contaminated by mining deposits, and found that both species accumulated the metal in their wood, representing excellent candidates for phytoremediation by bioacumulation of mercury. PGPR can improve this type of phytoremediation processes ( Raklami et al., 2019 , 2021 ). The objective of this study was to select cultivable bacteria from tailing soils in Tlalpujahua, with traits of plant growth promotion and tolerance to mercury and arsenic for future use as inoculants in phytoremediation processes. We hypothesized that, in altered soils and substrates (that result from long term mining), bacteria have evolved and developed tolerance mechanisms as a response to the selection pressure imposed by heavy metals, and some also have plant growth promotion mechanisms that make them potentially useful as phytoremediation agents. Our study is relevant due to the small number of studies that have been carried out in this area and the limitation of available strategies to restore soil conditions, so offering alternatives through native microorganisms represents an interesting biological restoration option.",
"discussion": "Discussion One the alternatives for mine tailings or contaminated soil restoration is the use of phytoremediation in combination with inoculation with PGPBs, which, through alteration of the mobility and bioavailability of the metals, play an essential role in facilitating plant growth in conditions of high heavy metal concentrations in the soil ( Rehman et al., 2019 ; Tara et al., 2019 ; Yahaghi et al., 2019 ). Currently, many microbial genera have been reported for their ability to reduce the toxic effects caused by heavy metals in the environment, being the most frequent; Aeromonas , Rahnella , Ochrobactrum , Microbacterium , Azospirillum , Rhizophagus , Klebsiella , Enterobacter , Ralstonia , Rhizobium , Bacillus , and Pseudomonas ( Mishra, Singh & Arora, 2017 ; Tiwari & Lata, 2018 ). Molecular studies such as metagenomics that allow us to study genetic material from environmental samples are extremely useful ( Datta et al., 2020 ). However, the use of cultivable microorganisms allows us to identify and reproduce bacteria with potentially useful characteristics for phytoremediation by promoting plant growth. In this study, we applied a culture-dependent approach which consisted in the identification of microbial communities based on the groupings of fatty acids. The FAME method allows to differentiate the main taxonomic groups within a community ( Kirk et al., 2004 ). However, one of the limitations of this technique is that in some cases the differences between fatty acid profiles cannot be contrasting enough to accurately establish the different species that make up a community ( Bing-Ru et al., 2006 ). Being aware of this limitation, we identified 34 species of bacteria belonging to 23 genera. It is important to consider that the diversity of bacterial groups in soils is conditioned by soil conditions and by the identification techniques, so our data might under-represent the true diversity or the sampled soils. The most abundant identified genera were Bacillus , Pseudomonas , Paenibacillus , Herbaspirillum , and Acinetobacter . Using culture-dependent methods, Hamood et al. (2020) found eleven isolates tolerant to arsenic that were native to gold mining tailings in Malaysia, among which the dominant genera were Bacillus , Pseudomonas , Lysinibacillus , and Micrococcus . The first two also found in our study. In a study of the bacterial community growing in soils contaminated with heavy metals, Tipayno et al. (2018) found that the structure of the community at the level of phylum depended on the general soil properties. At lower taxonomic levels, the concentrations of arsenic and lead were significant; and species of the genus Bacillus were positively correlated with the concentration of arsenic. Using a culture-independent metagenomic approach, Hemmat-Jou et al. (2018) addressed the biodiversity of the microbial community in soils contaminated with lead and zinc and found ten most abundant bacterial genera: Solirubrobacter , Geobacter , Edaphobacter , Pseudomonas , Gemmatiomonas , Nitrosomonas , Xanthobacter , Sphingomonas , Pedobacter , and Ktedonobacter . This study differs from ours in the contaminating heavy metals and the identification methodology. However, in general, the latter previous reports agree with our results in that Bacillus and Pseudomonas predominate in soils contaminated with heavy metals and with the review by Fakhar et al. (2020) , that concluded that Bacillus and Pseudomonas are two of the most frequent genera responsible of bioremediation of contaminated soils. The presence of microorganisms with bioremediation and phytoremediation potential by producing plant growth promotion compounds, are essential for the search of strategies for the for restoration of soils contaminated with heavy metals. In our study, the isolate with the best plant growth promotion and heavy metal tolerance was H. huttiense TL36, outstanding for its performance in phosphate solubilization and siderophore production. Siderophore producing bacteria play a major role in the survival and growth of plants present in tailings soils by alleviating the toxicity of metals and providing nutrients, which is due to the combination of bacterial siderophores with metals other than iron, that might explain why microorganisms are able to survive in tailings contaminated with heavy metals ( Adler et al., 2012 ). As mentioned above, the TL36 isolate stood out for its ability to solubilize phosphates. In habitats such as mine tailings, the ability of microorganisms to solubilize recalcitrant substances such as phosphorus is a trait that determines their ability to adapt to these environments ( Jones & Oburger, 2011 ). Additionally, phosphate solubilizing bacteria in these environments help in the establishment of vegetation. It is known that bacteria can reduce HAsNa 2 O 4 to AsNaO 2 the latter accumulates in aerial parts of plants. To overcome the stress caused by arsenic, plants require an adequate supply of phoshorus, therefore bacteria capable of phosphate solubilization are essential for an adequate remediation of soil contamination with HAsNa 2 O 4 ( Alka et al., 2020 ). PGPBs not only mitigate metal toxicity but also act as plant growth promoters. The production of IAA emitted by bacteria alters various physiological processes in plants related with stress tolerance and growth. Ahemad & Kibret (2014) reported that IAA, in addition to stimulating root growth, facilitates water movement, controls vegetative growth, and initiates formation of adventitious and lateral roots. In our study the best producers of IAA were P. fluorescens TL97 (131.02 mg/L) and P. putida TL80 (74.20 mg/L). IAA producing bacteria have proven their usefulness due to their role in plant-bacteria interactions and plant growth in heavy metal contaminated soils ( Chiboub et al., 2016 ). Previous reports have highlighted the species in the genus Pseudomonas particularly P. fluorescens and P. putida as outstanding producers of IAA ( Saharan & Nehra, 2011 ). In a review of the role of exopolysaccharides in metal removal, Gupta & Diwan (2017) describe the mechanisms by which biofilms can immobilize or modify the redox state of metals, thus reducing their toxic effects on plants. Therefore, this ability is important for the restoration of soils contaminated with heavy metals. Our results showed that the best biofilm producer was Bacillus atrophaeus TL76. Several species in the genus Bacillus have the capability of responding to stress by producing biofilm ( Bais, Fall & Vivanco, 2004 ; Kasim et al., 2016 ). Biofilm production becomes essential for bacteria growing in heavy metal contaminated soils. Because biofilms are negatively charged, they can adhere to surfaces by electrostatic attraction and to normally positively charged heavy metals, the biofilm layered bacteria thus acting as biosorbents of metals in the soil, therefore reducing their bioavailability to plants ( Kalita & Joshi, 2017 ). Because tolerance to heavy metals is one of the key factors of bioremediation and restoration of tailings soils using microorganisms, we evaluated tolerance to As (III), As (V), and Hg (I) in eleven isolates selected for their performance in assays of plant growth promotion and bioremediation features. If the bacteria isolated from tailings soils in Tlalpujahua are adapted to withstand heavy metal contamination in soils, we demonstrated their heavy metal tolerance. All assayed isolates showed adaptation to the presence of 1,000 mg kg −1 of pentavalent arsenic (HAsNa 2 O 4 ), possibly due to associated mechanisms including bioaccumulation, oxidation-reduction reactions, efflux mechanisms, and others ( Hamood et al., 2020 ). In the presence of trivalent arsenic (AsNaO 2 ), the only isolates that did not tolerate the medium concentration we tested (600 mg/kg) were P. putida TL80 and P. fluorescens TL97. The toxicity of As (III) is tenfold that of As (V), which is due to the former reacting with thiols of small molecules and sulfhydryl residues of cysteine in proteins thus inhibiting essential biochemical processes in organisms including bacteria ( Rosen & Liu, 2009 ). Román-Ponce et al. (2018) isolated 27 bacterial strains from the rhizosphere of Prosopis laevigata and Sphaeralcea angustifolia growing in mine tailings in Santa María, San Luis Potosí, Mexico. The authors determined the minimum inhibitory (MIC) concentration of the isolates that they identified as belonging to the genera Arthrobacter, Bacillus, Brevibacterium, Kocuria, Microbacterium, Micrococcus, Pseudomonas , and Staphylococcus by culturing them in variable concentrations of arsenic, finding MIC values from 20 to over 100 mM for As (V) and between 10–20 mM for As (III), corroborating that the latter has toxic effects limiting bacterial growth. In our assays for tolerance to HgCl 2 , none of the tested isolates was able to tolerate the minimum concentration we used (200 mg/kg). The toxic effect of relatively low concentrations of mercury that we observed on bacteria isolated from soils in Tlalpujahua agrees with the results of Harries-Hellal et al. (2009) the first to report the consequences on the soil microbiota of the presence of mercury, who observed that 0.1 mg kg −1 of mercury changed the structure and abundance of the microbial soil communities, a change that became more noticeable at a concentration of 20 mg kg −1 . The performance of the isolates H. huttiense TL36, K. oxytoca TL49, and R. radiobacter TL52 in our heavy metal tolerance assays was outstanding. The tolerance of species in the genera Rhizobium and Kleibsella to several heavy metals had previously been recognized by authors like Deepika et al. (2016) , Meena et al. (2018) , Mohan et al. (2019) , Kumar et al. (2021) , and Chakraborty et al. (2021) . Some species of the genus Klebsiella have been isolated from contaminated soils, and it has been reported that strains of K. pneumoniae y K. Oxytoca tolerate high concentration of cadmium and arsenic ( Shakoori et al., 2010 ; Shamim & Rehman, 2012 ). Kumar et al., 2021 , obtained 108 isolates of arsenic resistant bacteria from mining sites in India. Among them strain RnASA11 of Klebsiella pneumonía was resistant to 600 mM As (V) y 30 mM As (III), and was capable under controlled conditions of reducing the concentration of arsenate in 44% and arsenite in 38.8% when compared with a control. Rhizobium has been studied mostly for its nitrogen fixation and symbiotic capabilities ( Masson-Boivin & Sachs, 2018 ), but its role as a bioindicator of heavy metal presence in the soil has been also studied ( Stan et al., 2011 ). Deepika et al., 2016 , isolated Rhizobium from nodules of Vigna radiata . Isolate VBK102 that was identified as Rhizobium radiobacter , produced exopolysacharides that sequestered arsenic (10% of total cell weight). This species tolerates several heavy metals such as: As(V) (10 mM), Cu (1.5 mM), Pb (0.18 mM), Cr (0.1 mM), Ni (0.08 mM), and Cd (0.004 mM). Finally, althogh Herbaspirrillum is not frequent in heavy metal contaminated soils, Govarthanan et al. (2014) , found that Herbaspirillum sp. GW103 played a role in the biolixiviation of copper in mine deposits. This strain (GW103) besides its effect on Cu, was resistant to As (550 mg/l), Cu (350 mg/L), zinc (Zn) (300 mg/L) and plomo (Pb) (200 mg/L)."
} | 4,951 |
22922732 | PMC3566019 | pmc | 4,726 | {
"abstract": "Nannochloropsis species have emerged as leading phototrophic microorganisms for the production of biofuels. Several isolates produce large quantities of triacylglycerols, grow rapidly, and can be cultivated at industrial scales. Recently, the mitochondrial, plastid and nuclear genomes of Nannochloropsis gaditana were sequenced. Genomic interrogation revealed several key features that likely facilitate the oleaginous phenotype observed in Nannochloropsis, including an over-representation of genes involved in lipid biosynthesis. Here we present additional analyses on gene orientation, vitamin B12 requiring enzymes, the acetyl-CoA metabolic node, and codon usage in N. gaditana . Nuclear genome transformation methods are established with exogenous DNA integration occurring via either random incorporation or by homologous recombination, making Nannochloropsis amenable to both forward and reverse genetic engineering. Completion of a draft genomic sequence, establishment of transformation techniques, and robust outdoor growth properties have positioned Nannochloropsis as a new model alga with significant potential for further development into an integrated photons-to-fuel production platform.",
"conclusion": "Conclusions Algae can produce large quantities of lipid, have high photon to biomass conversion efficiencies, and can grow in a variety of water sources, but the lack of a genetically tractable, industrially relevant alga previously limited progress. The genomic DNA sequence of Nannochloropsis gaditana , in addition to efficient transformation protocols, will permit the rapid development of this organism into a biofuel production platform. Interrogation of the N. gaditana genome has revealed many features that contribute to our understanding of the oleaginous phenotype observed, but these findings are just a starting point for further investigations. The genomic sequence provides the basis for systems biology investigations and will serve as a platform for transferring knowledge attained from other algal systems to Nannochloropsis to improve biofuel phenotypes. 31 An informed understanding of the metabolic pathways and their regulation in Nannochloropsis will allow for metabolic engineering strategies to reroute metabolites to biofuel precursors. The tools already developed for Nannochloropsis have positioned it for rapid strain improvements and advances are likely to emerge in the near future."
} | 605 |
33335679 | PMC7720022 | pmc | 4,727 | {
"abstract": "Graphical abstract",
"introduction": "1 Introduction Electron transfer reactions are at the core of numerous biological processes, in particular in respiration. During respiration most microorganisms are able to convert biochemical energy into ATP. This usually involves a cascade of reactions where electrons are transferred, via several redox proteins, from an electron donor to an electron acceptor. Most forms of respiration involve a soluble compound as an electron acceptor (e.g. nitrate, oxygen, and sulfate), however there are others where solid compounds (e.g. metal oxides, electrodes) act as the electron acceptor [1] . In this case, the terminal electron acceptor is insoluble and cannot enter the cell, and the microorganisms must perform extracellular electron transfer (EET) to connect their electron transport chain to the solid electron acceptor [2] , [3] . Today, it is well recognized that the reduction of solid electron acceptors occurs through two different mechanisms: directly, (i) through cell-surface proteins that interact with the solid electron acceptor (ii) or through cellular appendages, including electrically conductive pilus that form a bridge between the cell and the electron acceptor; or indirectly, (iii) through the use of chelators or siderophores that solubilize the solid electron acceptor and introduce them into the cell, or (iv) using soluble shuttles, such as organic compounds with quinones moieties, that interact with the electron acceptor outside of the cell [3] , [4] , [5] ( Fig. 1 ). Fig. 1 Mechanisms of extracellular electron transfer (EET) processes. EET may occur through direct contact using cell-surface proteins, including multiheme c -type cytochrome (process i) or electrically conductive pilus (process ii), or through indirect electron transfer where chelators or siderophores solubilize the solid electron acceptor and transfer the electrons to the bacteria (process iii), or with soluble electron shuttles that mediate electron transfer between the cell and the solid electron acceptor (process iv). In this figure, the solid electron acceptor is represented in grey and bacteria are represented in red. Chelators/siderophores and electron shuttles are represented in green and in yellow, respectively. (For interpretation of the references to colour in this figure legend, the reader is referred to the web version of this article.) Microorganisms that possess EET capabilities play a fundamental role in the geochemical cycle of several elements, including carbon and iron, and are also potential targets for numerous biotechnological applications, such as for the bioremediation of metal contaminated environments, production of energy and added-value compounds, or for biosensing [6] , [7] , [8] , [9] , [10] . Some of these organisms are also termed electroactive given their ability to exchange electrons with an electrode, in the so called bioelectrochemical systems (BES) [11] , [12] . Microbial fuel cells (MFC) are one of the most studied BES [13] . A typical MFC is an electrochemical cell arranged in two chambers separated by a proton exchange membrane, containing an anode and a cathode. In the anode compartment, the microorganisms oxidize organic matter and use the electrode as the terminal electron acceptor [6] . Typically, the electrons collected at the electrode are then transferred to the cathode through an external wire, and are combined with oxygen to generate water. The electron flow between the anode and cathode enables the electrical power harvesting [6] , [13] . The increased interest of this type of technology has boosted the application of BES, being currently explored for the production of electrical power, treatment of wastewaters, for electrosynthesis of added-value compounds and biofuels and for water desalination [14] , [15] . Electroactive organisms can be found in all three domains of life, being ubiquitous in distinct environments, including lakes, soils as well as in deep-sea hydrothermal vents [11] , [12] . Recently, is has been demonstrated that these organisms are also present in the human digestive system [16] , [17] , in the mouse gut microbiome [18] , [19] , [20] and oral plaque [21] , with some of them associated with infectious diseases [22] , [23] . Gram-negative mesophilic bacteria are one of the most studied class of electroactive organisms, with most of the knowledge being confined to the model organisms Geobacter sulfurreducens and Shewanella oneidensis MR-1 [2] , [3] , [24] . Nonetheless, Gram-positive bacteria have recently attracted the scientific attention, given their capacity in producing high levels of current in MFC [25] , [26] , and by being associated with infectious diseases in humans [16] , [17] , [22] , [23] . Given the importance of these organisms in BES, research has been dedicated in exploring their use as catalysts in BES and in the understanding of their EET processes [27] , [28] , [29] . Only by understanding how electroactive organisms perform EET and exchange electrons to electrodes, it is possible to use them in biotechnological processes and start implementing BES in real-world applications. Indeed, knowledge on the molecular mechanisms of the EET processes performed by Gram-negative bacteria allowed to modify these organisms and improve their performance in BES [30] , [31] , [32] , [33] , [34] , [35] , [36] , showing that synthetic biology field has the potential to advance the implementation of BES in the real-world [37] . As several reviews have been published on Gram-negative organisms [2] , [3] , [24] , this review will focus on Gram-positive electroactive bacteria, in particular on the developments on the understanding of their EET mechanisms and on the cellular components involved in these processes."
} | 1,458 |
35496444 | PMC9043983 | pmc | 4,728 | {
"abstract": "Bio-based ionogels with versatile properties are highly desired for practical applications. Herein, we designed a novel self-healing, anti-freezing, and self-adhesive ionogel with excellent sensor capability. The ionogel was obtained by cross-linking amino groups (chitosan) and aldehyde groups (dextran oxide) to form Schiff-base bonds in the ionic liquids (EMIMOAc) with TA. Ionogels inherited the superior electrical conductivity of ionic liquids (IG 2 , 1.1 mS cm −1 ). Due to the dynamic reaction of Schiff-base bonds, the obtained IG 2 possessed self-healing properties (self-healing efficiency = 89%). The presence of TA also provided the ionogel with excellent self-adhesive properties (IG 2 /TA, adhesive strength to hogskin = 8.05 kPa). Owing to the low freezing point and low vapor pressure of ionic liquids, ionogels were endowed with anti-freeze properties and resistance to solvent volatility. Moreover, the ionogel can act as a strain sensor, and exhibited excellent sensitivity and sensing performance. Our work provided a green and effective method in preparation of the high performance ionogel sensor, which could accommodate future practical industrial applications.",
"conclusion": "Conclusion In this paper, we synthetized a new type of dynamic chemical cross-linked ionogels based on bio-macromolecular polymers (chitosan and dextran) in ionic liquid. The ionogel obtained by Schiff base reaction had good mechanical properties (strain strength and elongation at break of IG 2 were 342 kPa and 414%), and was endowed with a better self-healability. TA enabled the ionogel to have excellent adhesion to different surfaces. The DMA tests indicated that the ionogels could maintain outstanding flexibility when the temperature was below zero. What's more, owing to the good conductivity of ionogel at different temperature, it was used as a flexible ionogel strain sensor. The ionogel showed good sensing performance under different conditions and environment (GF = 0.19 to 0.44). Therefore, the prepared self-healable, self-adhesion and anti-freezing ionogels are promising as an ideal flexible strain sensor under different conditions.",
"introduction": "Introduction With the development of science and technology, wearable smart devices have been widely introduced into people's vision. The demand for smart sensors has also risen dramatically, and various smart bionic devices have been developed to be widely used at the human–machine interface, in electronic skin, and personalized medicine. Among them, gel-based sensors have received wide attention due to their excellent electrical conductivity and good flexibility and tensile properties. 1–6 Nowadays, common hydrogels are widely studied because of their simple preparation, but they all inevitably suffer from evaporation of solvent water or freezing of water at temperatures below zero, which would seriously affect the performance of the sensor. Ionogels, unlike common hydrogels, are mainly composed of ionic liquids and polymers. Due to the inherent properties of ionic liquids (high ionic conductivity, low freezing point, high boiling point, etc. ) being retained, the resulting ionogels have excellent ionic conductivity and are resistant to both volatility and freezing. This is a great improvement over conventional hydrogels. Meanwhile, due to the shortage of fossil resources and environmental degradation, scientists are gradually turning their interest to renewable and eco-friendly biomass materials. Natural polymers such as cellulose, 7–12 sodium alginate, 13–16 dextran 17,18 and chitosan 19,20 have been widely used in the preparation of polymer products to replace synthetic polymers. Wang et al. 12 fabricated an ionogel by cellulose and ionic liquid, which had obvious advantages over the existing equivalent products in terms of mechanical and electrical conductivity. And it can perform well under high and low temperature conditions. The self-healing sensors would automatically repair mechanical and electronic damage under environmental conditions. As a result, the durability and reliability of gel-based sensors could be enhanced by introducing the self-healing property. A variety of self-healing mechanisms have been used to synthesize self-healing sensors, such as covalent bonds (borate bonds, 21–23 Schiff base bonds, 24 etc. ) and non-covalent bond interactions (ionic bonds, 25,26 hydrogen bonds 27,28 and host–guest interaction 29,30 etc. ). Among them, covalent bonds are widely preferred due to the superiority of the mechanical properties of the prepared materials over those of non-covalent bonds. For example, the self-healing sensors based on Schiff-bond by Peng et al. had excellent self-healing properties. 31 Although promising progress has been made, current flexible sensors are usually difficult to fit well with the human body, and require tape to be fixed on human skin or clothing, leading to an unstable signal as a human motion sensor. Therefore, good adhesion performance will be one of the necessary properties of future sensors. Inspired by nature, dopamine, a derivative of mussel material, can be used as an adhesion agent to develop hydrogels with special tissue adhesion properties. 32 But the high cost of dopamine also makes it highly limited in practical applications. Tannic acid (TA) is an inexpensive polyphenolic substance that has a similar structure to dopamine and has significant adhesion to different surfaces, making it an ideal substitute for dopamine. 33 Herein, based on the concept of environmental protection, we tried to develop a fully bio-based ionogel with excellent adhesion properties and self-healing properties. Dextran oxide (containing aldehyde groups) was co-dissolved with chitosan in IL, and TA was also uniformly dispersed in it. The ionogels were obtained through the reaction of aldehyde groups with amino groups (Schiff base bond structure). The uniform distribution of IL in ionogels would give them excellent conductivity, anti-volatility, and anti-freeze properties. Meanwhile, the dynamic Schiff base bonds in the cross-linked network would also confer good self-healing properties to the ionogels. The addition of TA increases the adhesion properties of the ionogel and allows for a perfect fit directly on the object. We believe that this fully bio-based ionogel sensor has potential for a wide range of applications.",
"discussion": "Results and discussion Synthesis of chitosan/dextran–TA ionogels Dextran oxide was chemical crosslinked with chitosan via Schiff base reaction in homogeneous ionic liquid solution to obtain chitosan/dextran–TA ionogels. First, partial hydroxyl groups of dextran were oxidized to aldehyde groups. Subsequently, self-healable and self-adhesive ionogel was prepared by chemically cross-linked between chitosan and dextran. The FTIR spectra of the chitosan, original dextran, dextran oxide, EMIMOAc, and chitosan/dextran–TA ionogel were clearly exhibited in Fig. 2a . Fig. 2 The characterizations of ionogel: (a) FT-IR spectrum; (b) photos of the ionogel (IG 2 ) with different shape (twisted and stretched); (c) SEM photos of IG 2 . The strong band at 3350 cm −1 was assigned to the –OH of these bio-based polymer (chitosan, dextran, and dextran oxide). The characteristic peaks at 1664 cm −1 and 1593 cm −1 can be attributed to the twisting and wagging vibration of the –NH 2 (chitosan). For dextran oxide, a band compared to dextran at 1732 cm −1 was assigned to the C \n \n\n<svg xmlns=\"http://www.w3.org/2000/svg\" version=\"1.0\" width=\"13.200000pt\" height=\"16.000000pt\" viewBox=\"0 0 13.200000 16.000000\" preserveAspectRatio=\"xMidYMid meet\"><metadata>\nCreated by potrace 1.16, written by Peter Selinger 2001-2019\n</metadata><g transform=\"translate(1.000000,15.000000) scale(0.017500,-0.017500)\" fill=\"currentColor\" stroke=\"none\"><path d=\"M0 440 l0 -40 320 0 320 0 0 40 0 40 -320 0 -320 0 0 -40z M0 280 l0 -40 320 0 320 0 0 40 0 40 -320 0 -320 0 0 -40z\"/></g></svg>\n\n O group. As for the crosslinked ionogel, the characterization peaks of chitosan (–NH 2 ) and dextran oxide (C O) was decreased or disappeared. Meanwhile, the characteristic peak of C N groups can be observed at 1724 cm −1 . Due to the small amount of TA, its characteristic peak cannot be observed in this spectrum. Morphology of ionogels Since the prepared ionogels are flexible, the ionogel can be stretched and twisted into different shape. Fig. 2b showed the photos of the ionogels with different states of ionogels. To image the morphology of the ionogel, the samples were first subjected to a series of pre-treatments and freeze-dried for the observation of SEM. As shown in Fig. 2c , the inner of the samples was extremely coarse and contains a large number of micron-sized pores. This special porous structure provided a good ion channel for ion transport, allowing the ionogel to possess excellent ionic conductivity. Rheological and mechanical properties of ionogels The changes of storage modulus ( G ′) and loss modulus ( G ′′) as a function of angular frequency for different ionogels was shown in Fig. 3a . The values of G ′ were always higher than that of G ′′, and both the G ′ and G ′′ were almost independent with the frequency. These results indicated a typical gel behavior of the ionogels. Low thermo-mechanical stability is a common problem for gels in general, but this problem can be better solved by chemically cross-linked gels. As with the obtained IG 2 , they were quite stable and the value of G ′ could be maintain over a wide range of temperature (25 to 120 °C, Fig. 3b ). Importantly, the gel state structure would not be destroyed at temperature up to 150 °C, which can be demonstrated by dynamic temperature sweep measurements. The typical stress–strain curves of the tensile tests for the obtained ionogels. Fig. 3 Rheological properties of ionogels: (a) the relationship between storage modulus ( G ′), loss modulus ( G ′′) and angular frequency for different ionogels; (b) the relationship between storage modulus ( G ′), loss modulus ( G ′′) and temperature for IG 2 . As shown in Fig. 4a , the strain strength increased from 120 kPa to 435 kPa, which was corresponding with the polymer contents. The above results were mainly attributed to the cross-linking densities. While the strain strength and elongation at break decreased with the polymer contents. The strain strength and elongation at break of IG 2 were 332 kPa and 443%, respectively. As for IG 3 , the strain strength increased to 435 kPa and the elongation at break decreased to 334%. The outcomes can be explained by the cross-linking density increased with increasing polymer contents. The information of the storage modulus and tan delta for IG 2 was provided by dynamic mechanical analysis. The change of storage modulus and tan delta of IG 2 was shown in Fig. 4b . Besides, the mechanical properties of IG 2 with and without TA were conducted, and the results revealed that the tensile strength of the ionogel became slightly stronger after the addition of TA, but the elongation at break decreased slightly (Fig. S1 † ). This may be due to the increased hydrogen bonding within the ionogel due to the addition of TA, resulting in an increase in tensile strength and a decrease in elongation at break. In the temperature range between −90 °C and 100 °C, the storage moduli of IG 2 first sharply decreased, and then almost kept the same. Furthermore, the T g of IG 2 was around −60 °C, indicating that the obtained ionogels can work at extremely conditions (temperature at sub-zero). The ionogel at −20 °C also can keep flexible ( Fig. 4c and d ), which was further proved the anti-freezing ability of ionogels. Therefore, ionogel with such excellent properties (good mechanical strength, outstanding thermal stability) over a wide temperature range demonstrated that it can work as a superior sensing material in extreme environments. Fig. 4 The mechanical properties and thermal stability: (a) the mechanical properties of different ionogels; (b) dynamic mechanical properties of ionogel from −90 to 110 °C; (c) photo of the ionogel at −20 °C; (d) the weight of ionogel in air varies with the days. Self-healing properties of ionogels To maximize the service life of materials and reduce maintenance costs, the self-healing feature of materials is highly desirable in practical applications. The dynamically reversible C N double bonds endow the chemically cross-linked ionogels with excellent self-healing properties as illustrated in Fig. 5 . A rectangular ionogel sample strip was cut and then tightly contacted together. After one day at room temperature, or 5 hours at 50 °C, the fracture disappeared and the healed strip can be stretched again to several times its original length ( Fig. 5a ). POM was further used to demonstrate the self-heal process of the ionogel, as shown in the Fig. 5b , where the cracks were gradually healed with time. It was attributed to the re-bonding of the broken C N double bonds, resulting the repair of the ionogel. The infrared spectroscopy characterization of the ionogels before and after self-healing demonstrated that the intensity of the C N peak at 1724 cm −1 gradually increased with increasing healing time, indicating that the bond was being re-generated (Fig. S2 † ). The self-healability of these ionogels was also investigated by comparing their mechanical properties original and healed, and the results were illustrated in Fig. 5c . The repair efficiency of the ionogel containing TA was initially significantly higher than that of the ionogel without TA, due to the strong adhesive properties of TA in the ionogel, which caused a certain force between the fractured ionogels. When the repair procedure was completed, the repair efficiency of the two ionogels was almost identical, as the effect of TA on the adhesion between the two broken ionogels was negligible once the C N was re-bonded together. The self-healing efficiency ( η ) of ionogels grows with increasing healing time, and can exceed 89% when the self-healing time is over 1 day at room temperature, or over 5 hours at 50 °C. The variation of the time of self-healed versus mechanical properties in the Fig. S3 † exhibited that the mechanical properties of the healed ionogel gradually decrease as the time of self-healed increases. And after 5 times, the efficiency was already below 60%. (Self-healing efficiency is defined as η = healed strength/original strength.) Fig. 5 The self-healing ability of ionogels: (a) the photos of self-healed ionogel; (b) the POM photos of self-healing process; (c) the mechanical properties of ionogel before and after self-healed. Self-adhesive ability of ionogels To ensure accurate sensor monitoring, it is essential to improve the adhesion between human skin and wearable devices adhesion between electronic devices. A good adhesion between the skin and the sensor can avoid interface delamination of the sensor during repeated dynamic deformations, which can lead to inaccurate detection. To this end, TA was introduced to endow the ionogel with the unique self-adhesive ability. The TA was added into IG 2 as the example (IG 2 /TA). As displayed in Fig. 6 , the IG 2 /TA exhibited strong adhesion to diverse objects, including inorganic materials and organic materials ( Fig. 6a ). More interestingly, the ionogel can be completely and easily peeled off from the attached objects without any residues ( Fig. 6b ). The adhesive strength of the ionogel to plastic, glass, PTFE and hogskin was up to 22.63, 19.72, 10.41 and 8.05 kPa, respectively ( Fig. 6c ). These exciting results demonstrated its great potential for wearable applications. Fig. 6 Adhesive ability of ionogels: (a) ionogel adhered to different materials (inorganic materials and organic materials); (b) the ionogel could easily peel off from the attached objects without any residues; (c) the adhesive strength of the ionogel to plastic, glass, PTFE and hogskin. Sensor capability of ionogels Since the vast majority of the components of ionogels are composed of ionic liquids, ionogels should have the excellent electrical conductivity that ionic liquids possess. Numerous literature reports on ionogels prepared based on ionic liquids also support this view. The EMIMOAc was closed to use as the matrix owing to its excellent solubility for chitosan and dextran, and non-toxic to humans. 34,35 Hence, a strain sensor with a size of 2 × 1 × 0.3 cm 3 was fabricated. The correlation between the strain length and the resistance variations was measured. As exhibited in Fig. 7a , the resistance of the sensor increased as it gradually stretched. The gauge factor (GF) was then calculated by the ratio of relative change in electrical resistance R to the mechanical strain The GF for the IG 2 /TA sensor was increased from 0.19 to 0.44, as the strain was varied from 0% to 290% ( Fig. 7b ). These values were much better than many previously reported sensors. Since the ionogel had a T g of −60 °C and remained flexible at −20 °C, the ionogel as a sensor with excellent freeze resistance should exhibit stable sensing performance at low temperatures. Therefore, the sensing performance of ionogel at different temperatures was compared. As shown in Fig. 7c , the ionogel presented almost the same resistance change at −20 °C, 0 °C, 20 °C. All the results discussed above indicated that the obtained ionogel sensor can provide reliable sensing performance under different conditions. Due to the wide working range, excellent mechanical, and adhesion properties of ionogel sensor, the feasibility of the ionogel sensor as wearable sensor was discussed for diverse human motions. Fig. 7d showed the ionogel sensor was used to monitor the wrist bending state. The relative resistance change of the sensor was clear and stable under different states (bending up, original state, bending down). Moreover, the different states of the elbow ( Fig. 7e ) could also be detected by the relative variations of the resistance of the ionogel sensor. Similarly, the ionogel sensor also be attached to the knee to monitor the movement of the joint ( Fig. 7f ). To demonstrate the sensing stability of ionogel after storage for a period of time, the strain sensing performances were conducted (Fig. S4 † ). Obviously, the results exhibited that the sensing performance of the ionogel remained unchanged with time. Fig. 7 The sensor capability of ionogel: (a) the relationship between Δ R / R 0 and strains; (b) the value of GFs from 0 to 400% strains; (c) the sensor capability of the ionogel at different temperature; (d) the changes of resistance for bending wrist; (e) the changes of resistance for bending arm; (f) the changes of resistance for bending leg."
} | 4,690 |
38645396 | PMC11026667 | pmc | 4,729 | {
"abstract": "Arsenic (As) accumulation in plants is a global concern. Although the application of arbuscular mycorrhizal fungi (AMF) has been suggested as a potential solution to decrease As concentration in plants, there is currently a gap in a comprehensive, quantitative assessment of the abiotic and biotic factors influencing As accumulation. A meta-analysis was performed to quantitatively investigate the findings of 76 publications on the impacts of AMF, plant properties, and soil on As accumulation in plants. Results showed a significant dose-dependent As reduction with higher mycorrhizal infection rates, leading to a 19.3% decrease in As concentration. AMF reduced As(V) by 19.4% but increased dimethylarsenic acid (DMA) by 50.8%. AMF significantly decreased grain As concentration by 34.1%. AMF also improved plant P concentration and dry biomass by 33.0% and 62.0%, respectively. The most significant reducing effects of As on AMF properties were seen in single inoculation and experiments with intermediate durations. Additionally, the benefits of AMF were significantly enhanced when soil texture, soil organic carbon (SOC), pH level, Olsen-P, and DTPA-As were sandy soil, 0.8%–1.5%, ≥7.5, ≥9.1 mg/kg, and 30–60 mg/kg, respectively. AMF increased easily extractable glomalin-related soil protein (EE-GRSP) and total glomalin-related soil protein (T-GRSP) by 23.0% and 28.0%, respectively. Overall, the investigated factors had significant implications in developing AMF-based methods for alleviating the negative effects of As stress on plants.",
"conclusion": "5 Conclusions and prospects Meta-analysis of 1,362 data records retrieved from 76 published studies evaluated the effects of various abiotic and biotic factors on AMF-related As accumulation in plants by subgroup and regression analyses. The findings of this research were as follows: AMF infection significantly lowered plant As levels in a dose-dependent manner ( p < 0.05) and enhanced total P concentration and plant dry weight. Notably, As(V) levels decreased and DMA levels increased with AMF inoculation, but As(III) levels were unaffected. Single AMF inoculation was more effective at reducing As in plants than mixed inoculation. The optimal soil conditions for AMF are a SOC range of 0.8%–1.5% and a pH of ≥7.5 for maximum As reduction in plants. AMF inoculation increased the P/As ratio in plants, with a significant negative correlation ( p < 0.05) between this ratio and As grain concentration. Among common AMF species, R. intraradices and F. mosseae were effective in reducing As in plants, with R. intraradices showing particular promise in Leguminosae plants, making them strong candidates for further research. AMF also increased EE-GRSP and T-GRSP concentrations in As-contaminated soils, offering a potential strategy to reduce As exposure and intake through the human diet, thereby enhancing human health. For a further understanding of how AMF affects As accumulation in plants, future research should focus on 1) conducting field trials to optimize AMF use in biofortification, considering crop–AMF interactions, inoculation methods, soil properties, agricultural practices, and AMF’s long-term impact on As dynamics; and 2) investigating the mechanisms of AMF–plant synergy in As tolerance, including variability in AMF efficacy, environmental interactions, underlying metabolic pathways or signaling molecules, and key enzymes or proteins. Further in-depth research is needed to comprehensively understand the mechanisms by which AMF improves the As tolerance of plants.",
"introduction": "1 Introduction Arsenic (As) contamination in agricultural soils is a common environmental issue that poses remarkable risks to human health and ecosystem sustainability ( Wan et al., 2024 ). As a toxic metalloid, As can naturally occur in soils or be introduced through various human activities such as mining, industrial processes, and application of arsenical pesticides ( Mawia et al., 2021 ; Gui et al., 2023 ). As contamination is a global issue in countries such as the United States, China, Argentina, Australia, Bangladesh, Chile, India, Mexico, Thailand, and Vietnam ( Baruah et al., 2021 ; Wang et al., 2023 ; Jahandari and Abbasnejad, 2024 ). The As pollution in China is of grave concern, with the nation’s farmland soil arsenic levels averaging a concerning 11.83 mg/kg, significantly surpassing the global average of 7.20 mg/kg ( Ren et al., 2022 ). As uptake by plant roots and its subsequent translocation to edible parts of plants can result in potential health risks throughout the food chain. According to the U.S. Agency for Toxic Substances and Disease Registry (ATSDR), As was classified as a top hazardous substance in the United States ( Escudero-Lourdes, 2016 ). As exposure has been found to contribute to different cancer types such as skin, lung, liver, kidney, and bladder cancers ( Zakir et al., 2022 ). Worldwide, the threat of As poisoning affects over 230 million people, with 180 million in Asia being especially vulnerable ( Shaji et al., 2021 ). Hence, there is a growing attention to research on soil As pollution issues among researchers. Arbuscular mycorrhizal fungi (AMF) are beneficial soil microorganisms, which form mutualistic symbiotic associations with roots in most land plant species ( Hawkins et al., 2023 ). These symbiotic relationships play crucial roles in enhancing plant nutrient uptake, stress tolerance, and overall growth and development. Recently, several research works have highlighted the key role of AMF in the absorption, translocation, and accumulation of As in plants ( Li et al., 2016 ; Alam et al., 2020 ; Mitra et al., 2022 ). AMF involvement may be crucial in decreasing As phytoavailability, which achieves this by stabilizing As through mycelium and glomalin ( Wu et al., 2015 ; Zhang et al., 2020 ; Riaz et al., 2021 ). AMF mycelium forms a network in the soil, effectively immobilizing and trapping As to prevent its uptake by plants. In addition, glomalin, a glycoprotein produced by AMF, binds to and sequesters As to further reduce its availability for plant uptake ( Maldonado-Mendoza and Harrison, 2018 ). These mechanisms, which are adopted by AMF to stabilize As, significantly contribute to lowering its potential impact on plants, enhancing agricultural system sustainability, and mitigating the risks associated with As contamination. The interaction of AMF colonization and As accumulation holds great promise for ensuring safe food production and implementing bioremediation programs ( Gupta et al., 2022 ). AMF inoculation effect on As concentration in soil–plant systems has been extensively studied by scholars all over the world. Singh et al. (2023) found that applying 10 g/kg Glomus mosseae significantly reduced arsenic in wheat grains ( Singh et al., 2023 ). Gupta et al. (2021) reported that Rhizophagus intraradices inoculation counteracted As-induced changes in sugar metabolism, affecting enzyme activities related to starch phosphorylase, α-amylase, β-amylase, acid invertase, sucrose synthase, and sucrose phosphate synthase in leaves ( Gupta et al., 2021 ). Under As stress of 25 mg/kg, the synergistic effect of Funneliformis mosseae and iron can reduce the toxic effects of arsenic by enhancing phosphorus uptake and increasing the activity of antioxidant enzymes in rice ( Zhou et al., 2023 ). However, a comprehensive review of previous experimental studies revealed significant inconsistencies in the published results. Some research works have reported that AMF inoculation resulted in significant As concentration reductions in both roots and shoots of maize ( Yu et al., 2009 ), while others have found increases in As concentration in maize roots ( Long et al., 2021 ). Similarly, conflicting results have been reported in rice, with some research works showing no significant effect of AMF inoculation on total As concentration in rice grains ( Zhang, X. et al., 2016 ), while some others have reported increased total and inorganic As concentrations in rice grains ( Li et al., 2016 ). Several qualitative literature reviews have focused on the complex interaction frameworks of environmental and biological factors influencing AMF-mediated As concentration ( Cabral et al., 2015 ; Neidhardt, 2021 ; Mitra et al., 2022 ; Tan et al., 2023 ). However, no comprehensive meta-analysis has been performed to quantitatively determine and evaluate the relationship between AMF and As concentration in plants under various environmental and biological conditions. Meta-analysis is a powerful method aimed at deriving broad generalizations by pooling and analyzing outcomes from multiple studies. Its fundamental goal is to provide a more comprehensive understanding than what can be obtained from individual primary studies alone. However, it is important to note that meta-analysis has limitations. The quality of each study included and the potential publication bias can influence the results of the meta-analysis. Additionally, the inclusion of new studies in the future may lead to updates and modifications in the conclusions of the meta-analysis. It is a dynamic process that requires continuous evaluation and consideration of new evidence ( Gurevitch et al., 2018 ). Therefore, this research aims to 1) verify the impact of AMF inoculation on As accumulation in plants grown in As-contaminated soils; 2) quantify the effects of AMF on plant As concentration, considering various biotic factors (e.g., AMF root colonization rate, AMF inoculum type, AMF species, and plant family) and abiotic factors [e.g., soil texture, soil pH, and soil organic carbon (SOC)]; and 3) investigate the mechanisms supported by data as proposed in the literature. Additionally, potential future research directions regarding the interaction between AMF and As will be discussed.",
"discussion": "4 Discussion 4.1 How much does AMF treatment decrease total As concentration in plants and grains? The results of the meta-analyses performed on the entire dataset demonstrated significant and dose-dependent effects of AMF inoculation on decreasing As concentration in plants ( \n Figure 7A \n ). In addition, the analysis showed that AMF treatment significantly decreased the overall As concentration of plants by 19.3% and significantly reduced As concentration in grains by 34.1% ( \n Figure 3A \n ). These results revealed the potential of AMF inoculation as a promising strategy for the mitigation of As contamination in plants. A conceptual flow diagram outlining the AMF-induced reduction of As accumulation in plants has been specifically discussed in the last paragraph of the Discussion section ( \n Figure 10 \n ). In fact, AMF reduced As accumulation while increasing P, N, K, Mg, Ca, Fe, Zn, Mn, Ni, and Se levels in grains, indicating that AMF may assist in overcoming mineral deficiencies in populations that consumed wheat-based diets, especially in As-contaminated areas ( Gupta et al., 2022 ). AMF can play a key role in enhancing nutritional value and addressing mineral deficiencies in affected populations. Hence, AMF-based strategies were found to have the potential to provide multiple nutritional benefits in As-contaminated regions. Through the promotion of the availability and uptake of essential minerals, mycorrhizal colonization improved the nutritional quality of grains and potentially contributed to addressing mineral deficiencies for populations with wheat-based diets, especially in As-contaminated areas. However, AMF can be potentially utilized in a conventional biofortification strategy to provide appropriate levels of minerals in grains and, thereby, help to overcome mineral deficiencies for populations with grain-based diets, especially in As-contaminated areas. Figure 10 A conceptual flow diagram of the role of arbuscular mycorrhizal fungi (AMF) in regulating As bioavailability and accumulation in the plants. The red arrow represents an increase in content or effect, and the blue arrow represents a decrease. This research revealed that AMF treatment significantly decreased As(V) concentration but presented no significant effect on As(III) concentration ( \n Figure 3E \n ). Research indicates that AMF inhibited As uptake through plant roots by specifically inhibiting the high-affinity transport system, which was responsible for P uptake. This inhibition ultimately decreased As(V) absorption ( Chan et al., 2013 ; Anawar et al., 2018 ). Our findings were supported by previous research studies. For example, Cattani et al. (2015) reported a significant reduction in As(V) concentration in the shoots and roots of AMF-inoculated maize, while As(III) concentration remained unaffected. Similarly, Yu et al. (2009) concluded that AMF inhibited As(V) accumulation in maize shoots, significantly lowering its uptake compared to non-mycorrhizal plants. However, its effect on As(III) uptake was not statistically significant. Indeed, AMF inoculation suppressed the activities of peroxidase, superoxide dismutase, and As(V) reductase, indicating that AMF colonization can prevent As(V) reduction to As(III). Consequently, As toxicity to plants was alleviated due to the decreased conversion of As(V) to more toxic As(III). AMF inoculation was found to play a key role in mitigating As toxicity in plants ( Yu et al., 2009 ). In addition, AMF facilitated As methylation and volatilization, increasing the concentration of DMA and other organic substances. These findings provided valuable insights into the potential of AMF in mitigating As contamination in plants and highlighted its contribution to As detoxification processes ( Li et al., 2022 ). Organic As toxicity is generally considered to be lower than that of inorganic As ( Bali and Sidhu, 2021 ). Inoculation of plants with AMF could also lead to a 50.8% increase in DMA concentration ( \n Figure 3E \n ). This finding was consistent with the results obtained by Li et al. (2016) , who reported a significant DMA concentration increase in rice grains following AMF inoculation. Others have reported that AMF contributed to detoxifying microbial As through processes such as methylation and volatilization ( Li et al., 2018b ). 4.2 How do soil factors affect As concentration in AMF-inoculated plants? Although our meta-analysis revealed a significant dose-dependent AMF vaccination effect on decreasing As concentration in plants ( \n Figure 7A \n ), relatively poor data fitting the model were explained by large differences in experimental conditions (different plant species and varieties, growing periods, soil conditions, etc.) ( \n Figures 4 \n – \n 6 \n ). According to our previous research findings ( \n Figure 4A \n ), soil-mediated AMF with moderate SOC levels (ranging from 0.8% to 1.5%) presented the maximum ability to decrease As concentration in plants. It is noteworthy that the amount of SOC can greatly influence AMF community structure ( Qin et al., 2015 ). Specifically, high SOC levels increased the germination of AMF spores and mycelium and ultimately impacted AMF community composition in rhizosphere soils or roots ( Luo et al., 2019 ). However, excessive levels of soil nutrients decreased the diversity and mycorrhizal benefits of AMF, hindering mycorrhizal symbiosis and weakening its ability to inhibit As uptake ( Qin et al., 2020 ; Ma et al., 2021 ). AMF exhibited a maximum As concentration reduction effect on plants in sandy soils or soils containing ≥50% sand, accounting for a plant As concentration reduction of 21.5% ( \n Figure 4A \n ). In contrast to sandy soils, non-sandy soils possessed greater adsorption capacities for As, and As was stabilized in the soil, which resulted in the reduction of its fluidity and solubility ( Suriyagoda et al., 2018 ). Therefore, AMF inhibitory effect on As uptake was much more apparent in non-sandy soils. By increasing soil pH, AMF significantly reduced As concentration in host plants, which became especially pronounced under weak alkaline conditions ( \n Figure 4A \n ). As uptake by plants mainly depends on As bioavailability in soil ( Kumarathilaka et al., 2018 ). As solubility and availability were increased as soil pH increased, leading to the release of large amounts of As in weakly alkaline soils ( Yao et al., 2022 ). However, soil pH was the main environmental factor affecting the composition of the AMF community ( Qin et al., 2015 ). Acidic soils inhibited AMF growth and spore germination, hindering AMF function ( Liu et al., 2020 ). Therefore, we can conclude that the mycorrhizal effect was more pronounced in alkaline soils. AMF exhibited the maximum reduction effect on the As concentration in plants at a medium As level. However, at higher levels of As in the soil, this inhibitory effect was weakened ( \n Figure 4A \n ). High concentrations of heavy metals and metalloids in soils can be toxic to plants, bacteria, and fungi ( Parvin et al., 2019 ). Although AMF inoculation improved the tolerance of plants to As exposure, excessive concentrations of As in soils decreased the AMF colonization rate, negatively affecting their physiological activities and decreasing the host plant’s resistance to As symbiosis ( Zhang et al., 2020 ). The physical and chemical properties of soils had significant effects on the AMF community, which in turn affected the germination of AMF spores and infection of hyphae, ultimately alternating As absorption and accumulation processes in plants ( Tian et al., 2017 ; Xue et al., 2018 ). Strong interactions can occur between soil physicochemical properties and AMF. Therefore, further research is required to understand and optimize soil conditions under which AMF inoculation can have the most significant impact on the reduction of As concentration in plants and ecosystem protection. Meanwhile, further research is required to optimize AMF inoculation techniques, investigate genotype-specific responses, and evaluate long-term effects of AMF on As dynamics in different soil environments. 4.3 How do AMF inoculation methods modify As concentration in plants? Our research revealed that several factors can influence the AMF effect on As concentration of plants. These factors included AMF inoculation species ( \n Figure 6A \n ), inoculation timing ( \n Figure 5A \n ), AMF colonization rate ( \n Figure 7A \n ), and host plants ( \n Figure 6B \n ). This was because various AMF species had distinct morphological properties, nutritional status, symbiotic efficiencies, and gene expression patterns during symbiotic interactions with plants. This research revealed a very interesting phenomenon: the interaction of AMF and crop plants affected As concentration. Maximum As reduction effects were obtained by G. geosporum and R. intraradices in Poaceae and Leguminosae ( \n Figure 6B \n ). From a food safety point of view, a significant reduction in As concentration of grains was highly desirable. This research also showed that certain varieties of Leguminosae exhibited significant suitability for AMF inoculation. Considering the significant importance of Leguminosae as a major crop plant, it was necessary to prioritize efforts aimed at decreasing As absorption and concentration in grains, specifically through AMF treatments, to improve plant health and decrease potential risks associated with As exposure. Interestingly, the inhibitory effect of single AMF inoculation on total As levels in host plants was stronger than that of mixed AMF inoculation ( \n Figure 5A \n ). Chan et al. (2013) showed that rice grains inoculated with G. mosseae alone had remarkably lower total As concentrations than those inoculated with both G. versiforme and G. mosseae . Therefore, this observation suggested that fungi competed in roots and that one infection unit can prevent adjacent fungal infections, thereby avoiding secondary infections ( Hepper et al., 1988 ; Buil et al., 2022 ). However, mixed inoculation presented a synergistic effect, which increased biomass and P concentration ( \n Figures S4 \n , \n S6 \n ). In fact, Pteris vittata co-colonized by both indigenous mycorrhizas and G. mosseae contained higher P concentrations than those colonized by either of the two AMFs ( Leung et al., 2013 ). Time plays a very important role in the biological process of AMF–plant symbiosis. Inhibitory effects of AMF on As concentration in host plants were gradually weakened with time ( \n Figure 5A \n ). This observation was consistent with the findings of Li et al. (2013) . The colonization rates of the two rice cultivars exhibited significant decreases, with colonization rates ranging from 12% to 23% on day 7, from 7.3% to 11% on day 35, and from 1.3% to 4.9% on day 63. Correspondingly, shoot As concentrations appeared to be decreased on D63 when compared to D35 ( p < 0.05) ( Li et al., 2013 ). Initially, it was expected that longer experiments would promote the enhanced development of symbiosis, especially in situations where resources such as rooting space and nutrients were decreasing ( Schroeder and Janos, 2004 ). However, contrary to these expectations, our findings showed that long duration levels (≥112 days) of the experiment did not result in higher mycorrhizal effects compared to short and intermediate experiments. Additionally, there was no significant difference among the three duration levels ( \n Figure 5A \n ). This is likely because As does not become limiting over time. Additionally, the results of the meta-analysis indicated a significant, dose-dependent effect of AMF infection rate on the reduction of As concentration in plants ( \n Figure 7A \n ). The duration of an experiment has a significant impact on the determining of AMF-mediated As tissue concentrations. Luo et al. (2017) found that the AMF colonization rates fluctuated with growth stages, reaching their peak at the jointing stage and then decreasing at flowering and ripening stages, but flowering and ripening stages were critical periods for AMF to limit grain Cd uptake. Wang et al. (2012) also observed a similar trend in alfalfa ( Medicago sativa L.) that the root colonization rates by R. intraradices increased from 17% at day 25 to 69% at day 60 and then decreased to 43% after 80 days. This suggests that AMFs constitute an important functional component of the soil–plant system and the mechanisms cannot be explained by root colonization rates simply. Inoculation with G. intraradices at planting did not result in a higher root mycorrhizal colonization level than that found in non-inoculated control plants at the end of 18 months in the field. This highlights the presence of competitive processes between the natural AMF taxa and the introduced AMF strain that occurred over 18 months ( Bissonnette et al., 2010 ). Based on the published literature, the AMF–plant symbiotic system generally exhibits ecological functions throughout the entire life span of a plant. Meanwhile, different symbiotic systems have varying time periods during which they exhibit maximum functionality. As the plant’s life cycle comes to an end, the colonization rate of AMF significantly decreases. However, it should be noted that the time period during which AMF exerts maximum functionality does not necessarily coincide with the time of maximum colonization rate. Although we have obtained the above general conclusion, we must acknowledge that the colonization rate alone cannot adequately explain the mechanism of AMF tolerance to As. Feddermann et al. (2010) suggested that AMF inoculated with different host plants may possess different nutritional status, symbiotic efficiency, and gene expression patterns leading to distinct colonization rates. Liu et al. (2005) found that the shoot As concentration of rice with AMF inoculation decreased at lower rates of As application to the soil (<50 mg/kg), with the opposite trend at higher soil As levels. There was a break point at the added As level of 50 mg/kg at which the lowest proportion of As was distributed in the shoots ( Liu et al., 2005 ). Long et al. (2021) also found that when adding 10 g/kg iron tailings (IT), AMF significantly increased root As concentration ( p < 0.05), while at 40 g/kg iron tailings (IT), AMF decreased As concentration in shoots by reducing As absorption efficiency. This suggests that soil arsenic levels also influence AMF-mediated uptake and translocation of As in host plants. In fact, the effect of As concentration in plants mediated by AMF symbiosis was influenced by many factors, including plant species ( AbdElgawad et al., 2022 ), plant growth stage ( Chen et al., 2006 ), AMF species ( Bhalla and Garg, 2021 ), physical and chemical properties of soil ( Coutinho et al., 2015 ), nutritional status ( Hajiboland et al., 2009 ), and soil amendment Bissonnette. Our findings also supported this viewpoint ( \n Figures 4 \n – \n 6 \n ). The relatively poor fits of the data to the model were explained by the large difference in experimental conditions (different plant species and varieties, AMF species, growing period, soils, etc.) of the 76 studies that matched the selection criteria ( \n Figure 7A \n ). The contradictions in the literature regarding the effects of AMF on plant As response can be attributed to the complex soil–AMF–plant mechanisms involved, which cannot be solely evaluated based on root colonization rates. 4.4 How do P fertilizers and AMF modify As concentration in plants? Our meta-analysis presented compelling quantitative evidence demonstrating a significant decrease in As concentration with the increase of the P/As ratio in the plant. In addition, a significant negative correlation ( p < 0.05) was observed between the P/As ratio and As concentration in total plants and grains ( \n Figures 8C, D \n ). This finding was important since P competed with As for plant uptake, limiting As absorption and translocation in plants ( Li et al., 2016 ). The symbiotic relationship between AMF and plants can effectively reduce As influx into roots and enhance the accumulation of plant nutrients, resulting in a “dilution effect” on As in plant tissues. Consequently, As concentration in crops was effectively decreased, mitigating the risk of As exposure to humans through dietary intake ( Cheema and Garg, 2022 ). In addition, AMF facilitated As translocation from the host plants to their hyphae, enabling the fungi to effectively remove As from plant systems and prevent its accumulation in host tissues. This translocation process further helped to decrease As concentration within plants ( Li et al., 2018a ). However, under low and medium P supply conditions (P levels of below 60 mg/kg), AMF inoculation did not significantly affect As levels in plants ( \n Figure 5A \n ). This finding was interesting because it suggests that the AMF effect on decreasing As concentration in plants depended on the availability of P in soil. In other words, when P was limited, AMF might be unable to fully exert its potential in decreasing As accumulation. However, under high P conditions (P levels equal to or above 60 mg/kg), AMF inoculation was found to significantly decrease As concentration in plants ( \n Figure 5A \n ). This result showed that when plants had sufficient P supply, AMF could effectively increase the uptake and utilization of P, improving plant growth and diluting As accumulation. This finding highlighted the importance of considering P availability in soil when implementing AMF as a strategy for mitigating As contamination. Optimizing soil P levels through fertilization or soil amendment can potentially improve AMF effectiveness in decreasing As accumulation in plants. AMF inoculation alleviated As toxicity to plants through two mechanisms. First, it upregulated the levels of low-affinity P transporters, thereby improving P absorption efficiency and assisting host plants in acquiring more P ( Paszkowski et al., 2002 ). Second, it downregulated the expression levels of high-affinity P transporters on the root surface and hair, decreasing As uptake ( Gupta et al., 2022 ). Both of the above mechanisms contributed to As toxicity mitigation in plants. Overall, the shoot P/As ratio served as a valuable indicator for evaluating the beneficial effects of AMF species in enhancing As tolerance in various plant species ( Mitra et al., 2022 ). In fact, the shoot P/As ratio was found to be a critical indicator for investigating As concentrations in both plants and grains ( \n Figures 8C, D \n ). In addition, the shoot P/As ratio can help identify plants or varieties exhibiting higher As tolerance or lower As accumulation, assisting in breeding programs and agricultural practices aimed at minimizing As uptake in crops. 4.5 What are the general mechanisms of AMF regulation on the bioavailability and accumulation of As in plants, as proposed in literature and presented data? This research showed that inoculating AMF significantly enhanced the concentrations of EE-GRSP and T-GRSP in As-contamination soils ( \n Figure 9 \n ), which was consistent with the findings of previous studies ( Li et al., 2018a ; Zhang et al., 2020 ). GRSP, a glycoprotein produced by AMF in soil, has been extensively recognized for its ability to bind to toxic metals, thus contributing to metal stabilization ( Chen et al., 2022 ). Previous research studies have revealed a significant correlation between GRSP and mycorrhizal root volume ( Bedini et al., 2009 ). Others have observed that higher glomalin contents corresponded to a greater expansion of AMF extraradical hyphae ( Zhang, H. et al., 2016 ). This observation suggested that increased GRSP contents might contribute to longer AMF extraradical hyphae and faster turnover of those hyphae compared to no-inoculation treatment ( Zhang et al., 2020 ). In fact, this consequence explained the potential reasons why AMF inoculation enhanced plant tolerance to toxic As in soils. However, further research is still required to determine whether glomalin can directly bind to As, decrease As bioavailability in soil, and subsequently decrease As uptake by plants. Finally, by integrating previous analysis results and literature, we outlined a conceptual flow diagram depicting the role of AMF in regulating the bioavailability and accumulation of As in plants ( \n Figure 10 \n ). \n Figure 10 \n shows the potential of AMF–plant symbiosis systems to enhance dry biomass, increase P concentration, and subsequently decrease As concentration in plants. Tolerance of host plants to toxic As mainly relies on As uptake reduction in AMF plants, which is accomplished through different mechanisms, including the following. 1) Downregulation of As transporters: As(III) is taken up by plants through the silicon transporter (Lsi1), and As(V) is taken up and transported by phosphate transporters (PhTs) ( Ma et al., 2008 ; Zhao et al., 2009 ). Inoculation with AMF led to the downregulation of Lsi1 and Lsi2 in rice roots, resulting in reduced As(III) uptake and transport ( Chen et al., 2012 ). In rice, inoculation with AMF downregulated the expression of several OsPT genes ( OsPT1–3 , OsPT6 , and OsPT9–10 ) ( Chen et al., 2013 ). 2) Upregulation of P transporters: Inoculation with AMF significantly enhanced P nutrition in plants and limited As uptake through the upregulation of the AM-induced PhT gene, MsPT4 ( Li et al., 2018a ). Additionally, the activity of Pht1;5 and Pht1;6 increased and exhibited higher selectivity toward P than As, effectively reducing the absorption of As in AMF symbiosis ( Christophersen et al., 2012 ). 3) Sequestration, methylation, and volatilization of As: Mycorrhizal association can lead to involvement in sequestration (storage), deposition (in external mycelium), chelation (form stable metal–organic complexes), excretion [As(III) excretion to external media], methylation (conversion to less toxic forms), and volatilization (release into the air) of As ( Liu et al., 2005 ; Li et al., 2016 ; Li et al., 2018a , b ; Xing et al., 2024 ). These processes helped in decreasing the overall As load in plants. 4) Improved physiological function: AMF can mitigate the oxidative stress induced by As, enhance the activity of the antioxidant enzyme system, and improve photosynthesis. By reducing the production of hydrogen peroxide (H 2 O 2 ) and lipid peroxidation, AMF effectively countered oxidative damage in host plants ( Shukla et al., 2023 ; Zhou et al., 2023 ). This symbiotic relationship significantly increased the activity of essential antioxidant enzymes, including catalase (CAT), peroxidase (POD), and superoxide dismutase (SOD) ( Cheema and Garg, 2022 , 2024 ; Mitra et al., 2022 ). AMF facilitated the restoration of pigment levels, including chlorophyll a , chlorophyll b , and carotenoids, and enhanced photosynthetic efficiency, transpiration rates, and water use efficiency under conditions of As stress ( Zhang et al., 2022 ; Shukla et al., 2023 ). 5) Increased the accumulation of nutrients: AMF not only facilitated the enhanced uptake of vital macronutrients but also significantly boosted the accumulation of critical nutrients, including nitrogen (N), P, potassium (K), calcium (Ca), and magnesium (Mg), in the plant grains. By increasing the host plants’ biomass, AMF also contributed to a reduction in the As concentration through a dilution effect, effectively mitigating the toxic effects of As exposure ( Gupta et al., 2022 ). To obtain a comprehensive understanding of the mechanisms applied by AMF to improve As tolerance in plants, further biochemical and physiological characterization is necessary. This knowledge will guide strategies to improve plant resilience to As contamination and contribute to developing sustainable and effective solutions for the management of As in agricultural systems."
} | 8,441 |
21426537 | PMC3129676 | pmc | 4,730 | {
"abstract": "Background Combined metagenomic and metatranscriptomic datasets make it possible to study the molecular evolution of diverse microbial species recovered from their native habitats. The link between gene expression level and sequence conservation was examined using shotgun pyrosequencing of microbial community DNA and RNA from diverse marine environments, and from forest soil. Results Across all samples, expressed genes with transcripts in the RNA sample were significantly more conserved than non-expressed gene sets relative to best matches in reference databases. This discrepancy, observed for many diverse individual genomes and across entire communities, coincided with a shift in amino acid usage between these gene fractions. Expressed genes trended toward GC-enriched amino acids, consistent with a hypothesis of higher levels of functional constraint in this gene pool. Highly expressed genes were significantly more likely to fall within an orthologous gene set shared between closely related taxa (core genes). However, non-core genes, when expressed above the level of detection, were, on average, significantly more highly expressed than core genes based on transcript abundance normalized to gene abundance. Finally, expressed genes showed broad similarities in function across samples, being relatively enriched in genes of energy metabolism and underrepresented by genes of cell growth. Conclusions These patterns support the hypothesis, predicated on studies of model organisms, that gene expression level is a primary correlate of evolutionary rate across diverse microbial taxa from natural environments. Despite their complexity, meta-omic datasets can reveal broad evolutionary patterns across taxonomically, functionally, and environmentally diverse communities.",
"conclusion": "Conclusions Microbial metagenomes and metatranscriptomes are amalgams of thousands of taxonomically and functionally diverse microorganisms, each of which experiences unique evolutionary pressures. Such complexity might be expected to preclude the detection of bulk evolutionary signals in meta-omic data. Here, we show broad trends in protein coding sequence conservation that transcend variation in both taxonomic composition and habitat type. Specifically, we confirm that the hypothesized positive relationship between gene expression level and sequence conservation, which has been well established for individual taxa under experimental conditions [ 11 ], is a universal trend across diverse microbial communities in both marine and terrestrial environments. Detecting this trend required binning genes into broad categories (expressed versus non-expressed) based on the detection of transcripts, which depended, in part, on the depth of sequencing per sample. Deeper sequencing would reveal a greater proportion of the expressed gene pool and potentially lead to more accurate measurements of expression level for low frequency genes [ 22 , 23 ]. However, the tremendous taxonomic diversity inherent in microbial communities, as well as the temporal heterogeneity of the environment in which these communities exist, likely confounds any attempt to predict protein conservation based on transcript abundance on a gene-by-gene basis using meta-omic data. Nonetheless, the broad discrepancy in sequence conservation between expressed and non-expressed gene fractions is significant, operates consistently across diverse taxa (Figure 4 ), and confirms that expression level is a primary determinant of evolutionary rate in naturally occurring microorganisms. The mechanism linking evolutionary rates and expression level is still debated. Sequence conservation in highly expressed proteins has been hypothesized to be driven by selection acting to minimize the costs of protein misfolding, which should increase in tandem with expression level (protein copy number per cell) [ 14 ], though the harmful effects of misfolding have been brought into question [ 43 ]. This selection for 'translational robustness/accuracy' is predicted to be largely decoupled from a protein's functional importance [ 13 , 14 , 24 ]. Here, using mRNA abundance as a proxy for expression level, our results demonstrate broad commonalities in expressed gene content across communities in widely different habitats (ocean versus soil). These data indicate a trend toward genes of protein synthesis and energy metabolism in the more actively expressed gene fraction and toward genes of cell replication and growth in the less expressed fraction. Additionally, this finding, in the context of our results demonstrating enhanced sequence conservation among expressed genes, indirectly suggests that the expression-conservation relationship may partially be constrained by protein function. However, these data cannot be used to justify this conclusion, since both gene expression level and functional importance may independently co-vary with protein evolution rates, as has been demonstrated for isolates of Pseudomonas aeruginosa [ 33 ]. Though characterizing the mechanism linking gene expression level and evolutionary rate is beyond the scope of this study, metatranscriptomic data may inform future studies exploring the relative effect of protein function on sequence conservation. We show that the expressed gene set, compared to the non-expressed set, is more likely to contain genes that belong to an orthologous core genome shared across closely related sister taxa. This pattern was broadly consistent across the marine samples and the soil sample. Interestingly, the overrepresentation of expressed genes within the core set was not observed in the HOT 500 m sample (Figure 4 ), which we hypothesize may be related to an overall decline of metabolic activity at deeper depths within the water column. Microbial transcriptomes can vary significantly in response to the growth phase of the organism [ 44 - 46 ]. In less actively growing communities (for example, stationary phase), expression level might be more uniform across the genome (that is, background expression), with both core and non-core genes having a relatively equal probability of detection. In contrast, in actively growing communities, the distribution of transcripts might become dominated by a subset of highly expressed genes (for example, genes mediating energy metabolism, membrane transport), as we have observed in other samples. If such genes fall within the core genome, core genome representation in the expressed gene set would be predicted to be greater in more active communities. Our datasets highlight similarities in gene expression and sequence evolution across very different microbial habitats, but differ markedly in other attributes. Notably, the soil community was a clear outlier with respect to functional gene content (Figures 6 , 7 , and 9) and amino acid usage (Figures 12 and 13 ), likely due to the distinct community composition of this habitat (Figure 4 ). However, given our analysis of a single soil metatranscriptome, and the use of different RNA extraction kits for soil versus marine samples (see Materials and methods), we urge caution when comparing microbial community composition between soil and marine datasets. A more comprehensive comparison of taxonomy and functional gene expression would involve extended metatranscriptome sampling across multiple soil types (and locations), as well as optimization of RNA extraction protocols to ensure unbiased lysis of all microorganisms. Such an analysis was not the focus of this study. However, the inclusion of the soil sample confirmed a positive relationship between expression level and sequence conservation at both the genome and community levels (Figures 3 and 4 ), as well as an overrepresentation of core genes within the highly expressed gene set (Figure 4 ). Though it is possible that such patterns may not be observed in other sample types, or following different extraction protocols, our results provide strong evidence for universal features of protein-coding gene evolution in natural microbial communities. The composition of metatranscriptomic and metagenomic sequence datasets depends not only on intrinsic biological factors (for example, community composition, metabolic state) but also on the physical and chemical environment at the time of sampling. Furthermore, interpretation of the resulting data can vary based on the analytical method (for example, database-dependent versus -independent analyses, as shown here) and on the availability and biases of the reference sequences to which the data are compared. Here, we attempt to rule out potential database artifacts by analyses at both the community and genome level. In so doing, our results suggest that environmental meta-omic datasets, despite their inherent complexity, can inform theoretical evolutionary predictions and reveal universal trends across ecologically and phylogenetically diverse microbial communities.",
"discussion": "Results and discussion Expressed genes evolve slowly The relationship between gene expression (transcript abundance) and sequence conservation was examined for protein-coding genes in coupled metagenome and metatranscriptome datasets generated by shotgun pyrosequencing of microbial community DNA and RNA, respectively. These datasets represent varied environments, including the oligotrophic water column from two subtropical open ocean sites in the Bermuda Atlantic Time Series (BATS) and Hawaii Ocean Time Series (HOT) projects, the oxygen minimum zone (OMZ) formed in the nutrient-rich coastal upwelling zone off northern Chile, and the surface soil layer from a North American temperate forest (Tables 1 and 2 ). Prior studies have experimentally validated the metatranscriptomic protocols used here (RNA amplification, cDNA synthesis, pyrosequencing; see Materials and methods), confirming that estimates of relative transcript abundance inferred from pyrosequencing accurately parallel measurements based on quantitative PCR [ 15 , 17 , 19 ]. Here, amino acid identity relative to a top match reference sequence identified by BLASTX against the National Center for Biotechnology Information non-redundant protein database (NCBI-nr) is used to estimate sequence conservation. Table 1 Read counts and accession numbers of pyrosequencing datasets Sequences \n a \n Site Depth (m) Data Total Non-rRNA \n b \n Coding \n c \n Accession OMZ 50 DNA 393,403 340,117 204,953 SRX025906 RNA 379,333 117,760 42,327 SRX025907 85 DNA 595,662 567,772 341,350 SRX025908 RNA 184,386 69,200 16,960 SRX025909 110 DNA 403,227 380,057 215,217 SRX025910 RNA 557,762 268,093 81,492 SRX025911 200 DNA 516,426 485,044 274,463 SRX025912 RNA 441,273 149,699 39,218 SRX025913 BATS 216 20 DNA 357,882 343,370 223,563 SRX008032 RNA 511,525 334,507 124,832 SRX016882 50 DNA 464,652 423,258 244,638 SRX008033 RNA 365,838 263,811 91,489 SRX016883 100 DNA 525,606 498,222 305,260 SRX008035 RNA 519,951 334,037 129,369 SRX016884 HOT 186 25 DNA 623,559 596,902 331,347 SRX007372 RNA 561,821 252,586 113,664 SRX016893 75 DNA 995,747 654,106 363,459 SRX007369 RNA 557,718 199,416 55,545 SRX016897, SRX016896 110 DNA 473,166 458,260 237,759 SRX007370 RNA 398,436 135,452 34,644 This study, SRA028811 500 DNA 673,674 972,967 540,042 SRX007371 RNA 479,661 83,795 38,913 This study, sra028811 Soil Surface DNA 1,439,445 1,392,745 976,899 This study, sra028811 RNA 1,188,352 985,305 445,479 This study, sra028811 a Generated on a Roche 454 GS FLX instrument. b All non-rRNA reads; duplicate reads (reads sharing 100% nucleotide identity and length) excluded. c Reads matching (bit score >50) protein-coding genes in the NCBI-nr database. BATS, Bermuda Atlantic Time Series; HOT, Hawaii Ocean Time Series; OMZ, oxygen minimum zone; rRNA, ribosomal RNA. In all the samples, amino acid identities, averaged across all genes per dataset, were significantly higher for RNA-derived sequences (metatranscriptomes) compared to DNA-derived sequences (metagenomes), with an average difference of 8.9% between paired datasets (range, 4.4 to 14.7%; P < 0.001, t -test; Table 2 ). Further analysis of a representative sample (OMZ, 50 m) showed that RNA identities remained consistently elevated across a gradient of high-scoring segment pair (HSP) alignment lengths (Figure 1 ). This pattern suggests that the DNA-RNA difference was not driven by the (on average) shorter read lengths in the RNA transcript pool (length data not shown), which could have imposed selection for reads with higher identity in order to meet the bit score cutoff (see Materials and methods). This pattern was not observed in the highest alignment length bin (>100 amino acids), likely due to the small number of genes ( n = 53) detected among the RNA reads falling into this category (for example, 0.4% of those in the 40 to 50 amino acid bin; see error bars in Figure 1 ). Table 2 Mean percentage amino acid identity of 454 reads matching database reference genes (NCBI-nr) shared between and unique to DNA and RNA samples Percentage identity to reference genes present in \n a \n Depth (m) Data DNA+RNA \n b \n DNA only \n c \n RNA only All \n d \n OMZ 50 DNA 71.0 59.8 NA 60.8 RNA 73.8 NA 72.2 72.7 85 DNA 67.3 59.5 NA 59.8 RNA 68.4 NA 67.9 68.1 110 DNA 65.7 58.7 NA 59.7 RNA 68.5 NA 71.1 70.2 200 DNA 64.3 58.5 NA 59.1 RNA 67.0 NA 65.9 66.4 BATS 216 20 DNA 72.5 59.5 NA 62.7 RNA 75.6 NA 71.6 72.9 50 DNA 76.4 61.5 NA 64.4 RNA 78.3 NA 71.2 74.1 100 DNA 76.8 60.5 NA 63.9 RNA 78.6 NA 71.6 74.8 HOT 186 25 DNA 75.3 63.7 NA 65.7 RNA 76.4 NA 69.1 72.0 75 DNA 77.3 64.1 NA 65.6 RNA 77.5 NA 69.1 72.9 110 DNA 80.0 60.7 NA 62.4 RNA 81.3 NA 73.0 77.1 500 DNA 63.1 59.4 NA 59.6 RNA 64.0 NA 66.0 65.0 Soil Surface DNA 58.9 55.0 NA 56.1 RNA 59.8 NA 61.1 60.5 a Mean percentage identity across all genes (unique accession numbers) identified via BLASTX against NCBI-nr (HSP alignment regions only; bit score cutoff = 50). b Genes present in both DNA and RNA datasets, that is, 'expressed' genes. c Genes present only in the DNA dataset, that is, 'non-expressed' genes. d Genes shared between datasets (in DNA + RNA) plus genes unique to a dataset. BATS, Bermuda Atlantic Time Series; BLAST, Basic Local Alignment Search Tool; HOT, Hawaii Ocean Time Series; HSP, high-scoring segment pair; NA, not applicable; NCBI-nr, National Center for Biotechnology Information non-redundant protein database; OMZ, oxygen minimum zone; rRNA, ribosomal RNA. Figure 1 Discrepancies in DNA (blue) and RNA (red) amino acid identities over variable high-scoring segment pair alignment lengths . Reads were binned by HSP alignment length, with identities averaged across all genes identified per bin. Error bars are 95% confidence intervals. To further rule out that the DNA-RNA discrepancy was due to methodological differences in DNA- and RNA-derived samples (for example, error rate variation due to differential sample processing; see Materials and methods), we examined amino acid identities in expressed and non-expressed genes derived from the DNA dataset only. Hereafter, we operationally define 'non-expressed' genes as those detected only in the DNA datasets, whereas 'expressed' genes are those detected in both the DNA and RNA datasets (gene counts per fraction are provided in Table 3 ). Across all datasets, mean identities for DNA-derived non-expressed genes were significantly lower (mean difference, 10.6%; range, 3.7 to 19.4%; P < 0.001, t -test; Table 2 ) than those of DNA-derived expressed genes, whose values were similar to those of RNA transcripts that matched expressed genes (Table 2 ). This trend was consistent across all samples (Table 2 ) and independent of the database used for identifying reads, as comparisons against the Kyoto Encyclopedia of Genes and Genomes (KEGG) and Global Ocean Sampling (GOS) protein databases for a representative sample (OMZ, 50 m) revealed a similar RNA-DNA incongruity (Table 4 ). Furthermore, this pattern was unchanged when ribosomal proteins were excluded from the datasets (Table 4 ), as has been done previously to avoid bias due to the high expression and conservation of these proteins [ 14 ]. These data confirm a significantly higher level of sequence conservation in expressed versus non-expressed genes, broadly defined based on the presence or absence of transcripts. Table 3 Unique reference genes shared between and unique to DNA and RNA datasets Reference genes present in \n a \n Depth (m) DNA+RNA \n b \n DNA only \n c \n RNA only OMZ 50 11,374 113,747 21,445 85 5801 172,055 6766 110 17,843 109,924 31,697 200 12,688 126,574 17,408 BATS 216 20 29,841 90,866 60,287 50 26,954 110,131 38,145 100 31,416 119,795 36,871 HOT 186 25 28,459 135,390 44,243 75 18,098 142,892 21,800 110 12,148 125,882 12,315 500 14,345 248,534 13,573 Soil Surface 104,453 283,180 107,475 a Number of unique NCBI-nr reference genes (accession numbers) identified as top matches to query reads via BLASTX (bit score > 50); in instances when a read matched multiple genes with equal bit scores, all genes were counted. b Genes present in both DNA and RNA datasets, that is, 'expressed' genes. c Genes present only in the DNA dataset, that is, 'non-expressed' genes. BATS, Bermuda Atlantic Time Series; BLAST, Basic Local Alignment Search Tool; HOT, Hawaii Ocean Time Series; NCBI-nr, National Center for Biotechnology Information non-redundant protein database; OMZ, oxygen minimum zone. Table 4 Mean percentage amino acid identity of OMZ 50-m reads with top matches to distinct reference databases (GOS, KEGG, NCBI-nr) and with ribosomal proteins removed Percentage identity to reference genes present in \n b \n Database \n a \n Data DNA+RNA \n c \n DNA only \n d \n RNA only All \n e \n All data GOS DNA 89.3 82.1 NA 82.8 GOS RNA 90.8 NA 87.5 89.3 KEGG DNA 67.8 58.3 NA 59.7 KEGG RNA 71.0 NA 69.4 69.6 NR DNA 71.0 59.8 NA 60.8 NR RNA 73.8 NA 72.2 72.7 Without ribosomal proteins f NR DNA 70.7 59.6 NA 60.6 NR RNA 73.6 NA 71.9 72.5 a BLAST database against which reads were compared. b Mean percentage identity across all genes identified via BLASTX against NCBI-nr (HSP alignment regions only; bit score cutoff = 50). c Genes present in both DNA and RNA datasets, that is, 'expressed' genes. d Genes present only in the DNA dataset, that is, 'non-expressed' genes. e Genes shared between datasets (in DNA + RNA) plus genes unique to a dataset. f Ribosome-associated proteins removed manually from datasets. BATS, Bermuda Atlantic Time Series; BLAST, Basic Local Alignment Search Tool; GOS, Global Ocean Sampling; HOT, Hawaii Ocean Time Series; HSP, high-scoring segment pair; KEGG, Kyoto Encyclopedia of Genes and Genomes; NA, not applicable; NR, National Center for Biotechnology Information non-redundant protein database (NCBI-nr); OMZ, oxygen minimum zone. Given the differences observed between expressed and non-expressed categories, a positive correlation between conservation and the relative level of gene expression may also be anticipated [ 9 ]. Here, per-gene expression level was measured as the ratio of gene transcript abundance in the RNA relative to gene abundance in the DNA, with abundance normalized to dataset size. Correlations between amino acid identity and expression ratio were not observed in any of the samples when all genes representing all taxa were combined (r 2 = 0 to 0.02; see Figure 2 for a representative dataset). This pattern suggests that for a substantial portion of the metatranscriptome, transcriptional activity cannot be used as a predictor of evolutionary rate. This is likely due in part to the difficulty of accurately estimating expression ratios for low frequency genes, which constitute the majority of the metatranscriptome at the sequencing depths used in this study [ 22 , 23 ]. However, across all samples, mean amino acid identity consistently increased with expression ratio when genes were binned into broad categories: all genes, top 10%, top 1%, and top 0.1% most highly expressed (Figure 3 ). These data indicate that while transcript abundance is a poor quantitative indicator of sequence conservation on a gene-by-gene basis in community datasets, the most highly expressed genes are, on average, more highly conserved than those expressed at lower levels. Figure 2 Percentage amino acid identity as a function of expression level in the Bermuda Atlantic Time Series 20 m sample . Per gene expression level is measured as a ratio - (Transcript abundance in RNA sample)/(Gene abundance in the DNA sample) - with abundance normalized to dataset size. Per gene percentage amino acid identity is averaged over all reads with top BLASTX matches to that gene. Figure 3 Sequence conservation increases with mRNA expression ratio . Genes are binned by rank expression ratio: all genes, top 10%, 1%, and 0.1% most highly expressed. Amino acid sequence identity is averaged across all DNA reads per gene (HSP alignment regions only), and then across all genes per bin. Error bars are 95% confidence intervals. Genome-level corroboration It is possible that differences in the relative representation of genes in the BLAST databases may cause the incongruity in sequence conservation between expressed and non-expressed genes. Specifically, if expressed genes are more abundant in the database (which may be likely if these genes are also more abundant in nature), an expressed gene sampled from the environment will have a higher likelihood of finding a close match in the database, relative to a non-expressed gene. We therefore examined the discrepancy between expressed and non-expressed gene sets only for DNA reads whose top hits match the same reference genome. Under a null hypothesis of uniform evolutionary rates across a genome, all genes in a sample whose closest relative is the same reference genome should exhibit uniform divergence from the reference. The link between expression level and sequence conservation was observed at the level of individual genomes. Figure 4 (left panel) shows the discrepancy in amino acid identity between expressed versus non-expressed genes that match the top five most abundant reference taxa (whole genomes) in each sample. In all genomes, excluding Bradyrhizobium japonicum from the soil sample, the mean amino acid identity of expressed genes was significantly greater than that of non-expressed genes ( P < 0.001, t -test). These taxon-specific patterns argue against an overall bias due to varying levels of gene representation in the database. Rather, assuming that the sequences that match the expressed and non-expressed gene fractions of a given reference genome are indeed present in the same genome in the sampled environment (an assumption that might be unwarranted if these two gene fractions experience varying rates of recombination or horizontal transfer among divergent taxa - see below), these results suggest that differential conservation levels, and not sampling artifacts, are driving the overall discrepancy between expressed and non-expressed genes. Figure 4 Expressed and non-expressed genes differ in amino acid identity (left) and core genome representation (right) . Data are from DNA sequence sets and include the five most abundant taxa per sample, with taxon abundance determined by the proportion of total reads with top matches to protein-coding genes in each genome (BLASTX of all DNA reads against NCBI-nr). 'Core genome representation' is calculated as the percentage of each gene set (that is, expressed or non-expressed genes) falling within the core genome of each taxon, as defined in the text. All differences (left and right panels) are significant ( P < 0.001), unless marked with an asterisk. Core genes are overrepresented in the expressed gene fraction Our data confirm an inverse relationship between expression level and evolutionary rate in natural microbial communities. However, it remains unclear to what extent gene expression level depends on a gene's functional importance to organism fitness (that is, essentiality) versus other potential explanations, such as 'translational accuracy or robustness' [ 24 ]. It has been argued that orthologous genes retained across divergent taxa ('core' genes) may mediate basic cellular functions and that such genes are more likely to be more essential than non-core (taxon-specific) genes [ 25 - 27 ]. Here, we calculated the proportional representation of expressed and non-expressed genes in the core genome, determined separately for each of the top five most abundant organisms in each of the samples (18 taxa total). Each taxon's core genome is composed of a relative orthologous gene set determined from comparison to a closely related sister taxon (or taxa; Table 5 ). The exact number of genes within each core set would likely vary if different sister taxa were used for comparison [ 28 ]. Here, the proportion of each genome that fell within the core set varied widely, from 17 to 80% (Table 5 ), reflecting natural variation and variation in the availability of whole genomes from different taxonomic groups. Table 5 Proportion of reference taxon genes shared with sister taxon (that is, core gene set) Taxon \n a \n Number of CDS \n b \n Sister taxon \n c \n Percentage of core \n d \n Alpha Proteobacterium HIMB114 1,425 Pelagibacter ubique HTCC1062 63 Ca. Kuenenia stuttgartiensis 4,787 Planctomyces limnophilus DSM 3776 17 Nitrosopumilus maritimus 1,796 Cenarchaeum symbiosium 49 Ca. Pelagibacter sp. HTCC7211 1,447 Pelagibacter ubique HTCC1062 75 Ca. Pelagibacter ubique HTCC1002 1,423 Pelagibacter sp. HTCC7211 80 Ca. Pelagibacter ubique HTCC1062 1,354 Pelagibacter sp. HTCC7211 80 Prochlorococcus marinus AS9601 1,920 All Pro. strains 68 Prochlorococcus marinus CCMP1375 1,883 All Pro. strains 69 Prochlorococcus marinus MIT 9312 1,810 All Pro. strains 72 Prochlorococcus marinus MIT9301 1,906 All Pro. strains 67 Prochlorococcus marinus NATL1A 2,193 All Pro. strains 59 Prochlorococcus marinus NATL2A 2,162 All Pro. strains 59 Uncultured SUP05 cluster bacterium 1,456 Ca. Ruthia magnifica 52 Solibacter usitatus Ellin6076 7,826 Acidobacterium capsulatum ATCC 51196 22 Ca. Koribacter versatilis Ellin345 4,777 Acidobacterium capsulatum ATCC 51196 36 Acidobacterium capsulatum ATCC 51196 3,377 Solibacter usitatus Ellin6076 51 Bradyrhizobium japonicum USDA 110 8,317 Bradyrhizobium sp. BTAi1 49 Bacterium Ellin514 6,510 Verrucomicrobium spinosum DSM 4136 24 a Representative taxon at high abundance in each sample. b Number of CDS is the number of protein-coding genes in the sequenced reference genome of each taxon. c Sister taxon used for identification of core genome (see main text). d Percentage of core is the percentage of protein-coding genes in each taxon that are shared with the sister taxon. CDS, coding sequence. Expressed genes were significantly more likely to fall within a core gene set shared across taxa. Figure 4 (right panel) shows the difference in core genome representation (percentage of genes within core set) between expressed and non-expressed gene fractions for each reference organism. In 52 of the 60 comparisons (87%), the percentage of expressed genes falling within the core set was greater than that for the non-expressed gene fraction; of these differences, 38 (73%) were significant ( P < 0.0009, chi-square). In some taxa, such as Prochlorococcus marinus str. NATL2A, core genome representation was over 30% greater among expressed genes relative to non-expressed genes. In contrast, for the HOT 500 m dataset, expressed genes were not enriched in core genes, which we speculate may be due to the activity of the microbial community at this depth (see Conclusions section below). Overall, however, the data support the broad trend that highly expressed genes are more likely to belong to an orthologous set shared across multiple taxa. The differential representation of core genes within expressed and non-expressed genes may influence the relative sequence conservation levels of these two gene fractions. Gene acquisition from external sources (for example, homologous recombination, horizontal gene transfer (HGT)) is an important source of genetic variation in bacteria [ 29 ]. A conserved core genome is traditionally thought to undergo lower rates of recombination and HGT relative to more flexible genomic regions (for example, genomic islands) [ 30 ], though the horizontal transfer of core genes may also be common in some taxa [ 31 ]. A central limitation to shotgun sequencing datasets is that disparate sequences cannot be definitively linked to the same genome, making it challenging to evaluate the relative contributions of HGT, homologous recombination, and mutation to sequence divergence. Consequently, it is possible that the higher levels of sequence divergence observed in the non-expressed gene set are due in part to enhanced rates of HGT among the non-core genes that predominate in this gene set. Surprisingly, within the expressed gene fraction, non-core genes were more highly expressed than core genes. Among the datasets representing the five most abundant taxa per sample ( n = 60, as above), 80% showed higher expression levels (expression ratio) of non-core genes relative to core genes (Figure 5 ). Averaged across all of these taxa, the expression ratio was 34% higher in non-core genes relative to core genes (2.5 versus 1.9; n = 13,324 and 30,096, respectively; P < 0.00001). This pattern seemingly conflicts with studies based on cultured organisms. For example, a prior comparative survey of 17 bacterial proteomes showed a relative enrichment of peptides representing proteins encoded within the core genome [ 28 ]. Also, essential proteins necessary for organism survival have been shown to be expressed at higher abundances than nonessential proteins in cultures of both Escherichia coli [ 32 ] and Pseudomonas aeruginosa [ 33 ]. This observation indirectly links core genome representation and gene expression, as essential orthologs have been shown to be more broadly represented among diverse taxonomic groups than nonessential genes [ 34 ]. Our data, representing diverse taxa from the natural environment, raise the hypothesis that core genes are more likely to be expressed (above the level of detection at the sequencing depths used here). However, non-core genes, when expressed, are more likely to be expressed at higher levels. The high expression of non-core genes, also observed previously for Prochlorococcus [ 19 ], may reflect the importance of taxon-specific genes for adaptation to individual niches in a heterogenous environment [ 30 ]. Figure 5 Mean expression level of core and non-core genes across the five most abundant taxa per sample . Per gene expression level is measured as a ratio - (Transcript abundance in RNA sample)/(Gene abundance in the DNA sample) - with abundance normalized to dataset size. 'Core' genes are determined individually for each taxon based on orthology with a closely related sister taxon, as described in the main text. Asterisks mark taxa for which expression ratios differed significantly between core and non-core genes ( P < 0.001, t -test). Functional patterns in expressed gene sets The degree to which expressed gene sets share functional similarity across microbial communities from diverse habitats is unclear. Hewson et al. [ 16 ] observed shared functional gene content among metatranscriptome samples taken from the same depth zone (upper photic layer) at eight sites in the open ocean. Also, the four OMZ metatranscriptome datasets analyzed in this study have been shown to cluster separately from the corresponding metagenome datasets based on functional category abundances, suggesting similar expressed gene content across depths [ 35 ]. However, this clustering was likely influenced in part by variation in per-gene sequence abundance (evenness) between the metagenomes and metatranscriptome, and did not explicitly compare expressed and non-expressed gene fractions. Here, we explored functional differences between expressed and non-expressed genes (as defined above) within metagenome (DNA) samples, for which the relative read copy number per gene is more uniform than for metatranscriptome samples. To do so, the proportional abundance of KEGG gene categories and functional pathways was examined for five samples representing contrasting environments: the oxycline and lower photic zone of the coastal OMZ (50 m), the suboxic, mesopelagic core of the OMZ (200 m), the upper photic zone in the oligotrophic North Pacific (HOT 25 m), the deep, mesopelagic zone (HOT 500 m), and the soil from Harvard Forest. Hierarchical clustering based on correlations in gene category and functional pathway abundances indicated clear divisions among datasets. Not surprisingly, both the expressed and non-expressed fractions from the soil sample grouped apart from the ocean samples, highlighting functional differences between ocean and soil communities (Figures 6 and 7 ). Among the four ocean metagenomes, expressed gene sets clustered together to the exclusion of the non-expressed genes from the same samples (Figure 6 ). Indeed, shifts in functional gene usage between expressed and non-expressed fractions were broadly similar across all samples (Figures 8 and 9 ). Instances in which all five samples showed the same direction of change (increase or decrease) in KEGG gene category abundance occurred in 14 of the 25 functional categories shown in Figure 8 (marked by open stars), significantly higher (nine times) than random expectations if ignoring potential covariance between categories ( P < 0.0002, chi-square). Notably, across all five samples, the expressed gene set was significantly enriched in genes involved in energy and nucleotide metabolism, transcription, and protein folding, sorting, and degradation (Figure 8 ). In contrast, the non-expressed gene set was enriched in genes mediating lipid metabolism and glycan biosynthesis and metabolism; in all ocean samples but not the soil sample, DNA replication and repair was also significantly overrepresented among non-expressed genes ( P < 0.0004, chi-square). At the finer resolution provided at the KEGG pathway level, genes involved in oxidative phosphorylation, chaperones and protein folding catalysis, translation factors, and photosynthesis were consistently and significantly ( P < 0.0001, chi-square) overrepresented among expressed genes in all samples, whereas genes of peptidoglycan biosynthesis, mismatch repair, and amino sugar and nucleotide sugar metabolism were proportionally more abundant in the non-expressed fraction (Figure 9 ). These data indicate broad similarities in functional gene expression across diverse microbial communities, with expressed gene pools biased towards tasks of energy metabolism and protein synthesis but relatively underrepresented by genes of cell growth (for example, lipid metabolism, DNA replication). Figure 6 Clustering of samples based on gene function . Samples are hierarchically clustered based on the proportional abundance of KEGG gene categories and metabolic pathways in expressed (DNA+RNA; red) and non-expressed (DNA-only; blue) gene fractions in the DNA data from five representative samples. Figure 7 Relative abundance of KEGG k2 functional categories (25 most abundant) across nr-reference genes identified in five representative DNA datasets . Reference genes detected among the DNA reads were classified as unique to the DNA dataset (non-expressed) or shared between the DNA and RNA datasets (expressed). Figure 8 Proportional change in KEGG category abundance between expressed and non-expressed gene fractions . Data are shown for DNA datasets from five representative samples. Unfilled stars mark KEGG k2 categories in which the direction of change was consistent across all five samples. Small filled stars mark categories that did not differ significantly in relative abundance between expressed and non-expressed gene fractions ( P > 0.05, chi-square). Only the 25 most abundant KEGG categories are shown. Figure 9 Heat map showing the proportion increase (green) or decrease (red) of KEGG k3 functional pathway abundance between expressed and non-expressed gene fractions in DNA data from five representative samples . Colored gene names mark KEGG pathways in which the direction of change was consistent across all five samples. All differences (abundance in expressed fraction versus abundance in non-expressed fraction) are significant ( P < 0.0001, chi-square), unless marked with an asterisk. Only the 75 most abundant KEGG k3 pathways are shown. Genes are grouped by hierarchical clustering of Pearson correlation coefficients for each pairwise dataset comparison. Database-independent analysis Our characterization of relative evolutionary rates in expressed versus non-expressed genes is based on sequence divergence relative to closest relatives in the sequence database (NCBI-nr). It is unclear to what extent this same trend may be detected within clusters of related sequences within our samples, independent of comparison to an external reference database. We therefore examined variability in amino acid divergence within clusters of expressed and non-expressed protein-coding sequences for five representative samples, including shallow and deep depths from the OMZ and HOT oceanic sites, and the surface soil sample (Table 6 ). Table 6 Counts and mean percentage identity of amino acid sequence clusters for four representative samples Cluster counts \n a \n Mean percentage identity \n d \n Sample Total Singleton DNA+RNA \n b \n DNA only \n c \n RNA only \n c \n DNA+RNA DNA only OMZ 50 m 213,683 180,311 1804 26,505 5063 77.0 85.2 OMZ 200 m 257,388 209,564 2712 40,401 4711 79.4 83.7 HOT 75 m 353,573 297,850 5681 44,163 5879 80.3 82.9 HOT 500 m 500,413 425,524 4677 66,151 4061 73.7 79.7 Soil 1,277,816 1,046,744 29,980 141,158 59,934 72.6 87.5 a CD-HIT clustering parameters: sequence identity 55% over local aligned region, with a length difference cutoff of 90%, and clustering to the most similar cluster (g = 1). b Clusters containing both DNA- and RNA-derived sequences. c Clusters containing only DNA- or RNA-derived sequences. d Mean amino acid identity (relative to the cluster reference sequence) across all DNA sequences per cluster. BATS, Bermuda Atlantic Time Series; HOT, Hawaii Ocean Time Series; OMZ, oxygen minimum zone. Mean identity per cluster was consistently higher for DNA sequences in non-expressed clusters compared to DNA sequences from expressed clusters (mean difference 5.3%; Table 6 ). This pattern is opposite to that observed in comparisons of sequences to external reference databases (above). However, we argue that this inverse pattern is indeed consistent with our hypothesis that expressed genes are more likely to be part of a core set shared across taxa (Figure 4 ). If this hypothesis is true, then the DNA-only cluster set (non-expressed genes) will be relatively enriched in non-core genes, including those present in only one taxon/genome and lacking any known homologs (for example, orphans) [ 36 , 37 ]. In environmental sequence sets, if these sequences appear multiple times, they are more likely to be identical, or nearly so, because they come from a single taxon population and therefore cluster only with themselves (homologs from other taxa are by definition absent and will not fall into the cluster). In contrast, if expressed genes are more likely to fall within the core genome, clusters containing both DNA- and RNA-derived sequences (that is, expressed sequences) will be relatively enriched in homologs that occur across multiple divergent taxa. By definition, therefore, DNA+RNA clusters will be relatively enriched in sequences differing at both the population level and at higher taxonomic levels (for example, 'species'), while DNA-only clusters will be enriched in sequences differing only at the population level. Given this explanation, we would predict that DNA+RNA clusters (with RNA sequences excluded) are larger than DNA-only clusters and that the DNA-only cluster set as a whole is enriched in high identity clusters. Indeed, DNA+RNA clusters are, on average, approximately 20 to 33% larger than DNA-only clusters (RNA sequences not included in counts) and DNA-only cluster sets, notably those of the OMZ samples, are enriched in clusters with identities greater than 98% (Figure 10 ). These data indicate that expressed gene clusters recruit a larger and more diverse set of sequences, consistent with the hypothesis that expressed genes are more likely to represent core genes shared across taxa. More generally, the contrast between this self-clustering approach and the BLAST-based comparisons (above) demonstrates how divergence measurements taken relative to an external top match reference can differ from those relative to a top match internal reference from the same dataset, with the latter more likely to involve comparisons between highly related sequences from the same strains/populations. Figure 10 Database-independent cluster statistics . (a) Size and (b) percentage identity of clusters containing amino acid sequences present only in DNA datasets or in both DNA + RNA datasets from five representative samples. Cluster sizes are based on counts of only the DNA-derived sequences within each cluster type. Numbers in legends indicate mean cluster size (a) and mean amino acid identity (b). Amino acid sequences were clustered above a threshold identity of 55%. GC content and amino acid usage differ between expressed and non-expressed genes The discrepancy in sequence conservation between expressed and non-expressed genes coincided with differences in nucleotide composition and amino acid usage between these two sequence pools. GC content was substantially higher in the soil compared to the ocean samples (approximately 20 to 25% enrichment) and consistent between the DNA and RNA pools (Table 7 ). In contrast, across all 11 ocean samples, RNA-derived protein-coding sequences were significantly elevated in GC relative to those from the DNA (mean RNA-DNA difference, 6%; Table 7 ), suggesting a broad shift towards GC enrichment in the expressed gene pool. Surprisingly, however, DNA sequences corresponding to expressed genes consistently had a lower GC content than DNA reads matching non-expressed genes (mean difference, 1.9%). These data suggest that the DNA versus-RNA discrepancy in GC content may be driven by a subset of transcripts in the RNA pool, likely those at high abundance. Indeed, analysis of the RNA reads from one sample (OMZ 50 m) showed a progressive increase in GC content with transcript abundance (when transcripts are subdivided into four categories (top 10%, 1%, 0.1% 0.01%) based on the rank abundance of the genes they encode (data not shown). Table 7 GC percentages (averaged over all reads) in open reading frames identified using Metagene DNA reads RNA reads Site Depth (m) All \n a \n DNA only \n b \n DNA+RNA \n c \n All \n a \n OMZ 50 37.6 38.2 36.0 42.8 85 38.2 38.4 36.7 44.9 110 41.1 42.6 38.1 42.8 200 40.9 41.9 38.6 45.6 BATS 216 20 34.5 35.4 33.8 42.8 50 35.2 36.4 33.9 40.3 100 33.7 34.7 32.9 37.8 HOT 186 25 35.5 36.0 34.8 44.0 75 34.9 35.2 34.4 41.4 110 36.0 36.4 35.0 40.0 500 43.2 43.1 43.5 50.6 Soil Surface 62.7 63.1 62.5 62.6 a All reads identified as 'protein-coding' via significant BLASTX matches to NCBI-nr (bit-score > 50), with GC content then estimated for Metagene-called open reading frames within this read set. b Reads matching genes detected only in the DNA data. c Reads matching genes detected in both the DNA and RNA data. BATS, Bermuda Atlantic Time Series; BLAST, Basic Local Alignment Search Tool; GC, guanine-cytosine; HOT, Hawaii Ocean Time Series; HSP, high-scoring segment pair; NCBI-nr, National Center for Biotechnology Information non-redundant protein database; OMZ, oxygen minimum zone. Consistent with the GC pattern, amino acid usage of protein-coding sequences differed significantly between the DNA and RNA samples (Table 8 , Figures 11 , 12 , 13 , and 14 ). Notably, with the exception of three ocean samples (HOT 500 m, OMZ 110 m and 200 m) and the outlying soil sample, RNA datasets from diverse regions and depths grouped separately from DNA samples when clustered based on amino acid frequencies (Figure 12 ), suggesting a global distinction between the metagenomic and metatranscriptomic amino acid sequence pools in marine microbial communities. Indeed, of 240 comparisons of amino acid proportions in DNA versus RNA datasets (12 DNA/RNA samples × 20 amino acids), 227 (95%) involved a significant change in amino acid frequency, with 114 involving an increase and 113 involving a decrease in frequency from DNA to RNA ( P < 0.0002, chi-square; Table 8 , Figure 13 ). (The high proportion of significant changes is due to the large sample sizes in the analysis.) On average, alanine, glycine, and tryptophan (high GC content) underwent the largest proportional increases from DNA to RNA, while lysine, isoleucine, and asparagine (low GC content) all decreased substantially in frequency. These shifts were largely consistent among ocean samples, but clearly distinct from the pattern observed in soil, where several amino acids changed in frequency in the direction opposite to that in the ocean samples. Table 8 Proportional change a in amino acid usage in RNA datasets compared to DNA datasets Amino acid OMZ BATS 216 HOTS 186 GC \n b \n 50 m 85 m 110 m 200 m 20 m 50 m 100 m 25 m 75 m 110 m 500 m Soil Ala 0.83 31.5 30.2 21.8 20.1 35.5 27.6 28.0 45.4 44.2 26.2 26.8 5.5 Gly 0.83 26.8 24.7 17.8 16.4 18.7 16.1 16.7 19.0 23.4 14.4 -10.2 0.0 Pro 0.83 11.0 11.7 3.3 7.4 13.1 6.6 8.7 10.1 2.4 4.0 6.8 5.1 Arg 0.72 -3.3 -3.2 -11.7 1.8 23.2 14.2 9.7 11.9 0.9 4.1 -6.7 1.3 Trp 0.67 34.0 49.5 27.0 21.2 12.2 9.2 11.9 18.2 25.7 17.1 -9.3 16.3 Cys 0.50 -0.5 -5.1 -6.9 2.1 -2.6 -6.6 -9.5 -10.3 -8.9 -11.8 17.5 3.9 Asp 0.50 3.3 5.6 0.9 8.0 3.0 -0.8 -0.2 -0.1 0.4 -2.0 -2.9 -3.6 Glu 0.50 -7.0 -11.1 -2.4 2.8 0.7 -1.2 -1.0 -6.6 -8.9 -5.6 -21.7 -9.0 His 0.50 -4.1 -5.4 -7.8 -0.6 4.1 -1.4 -3.4 9.7 0.9 -4.5 -28.3 2.9 Gln 0.50 -1.3 1.3 -2.9 2.5 9.3 6.4 6.8 9.3 3.8 4.2 -10.8 -2.7 Ser 0.50 -5.4 -2.8 -3.4 -2.9 -8.5 -7.4 -5.2 -7.6 -6.3 -2.0 6.4 4.1 Thr 0.50 14.9 17.8 11.3 7.5 7.9 10.0 10.7 14.2 14.6 9.8 -17.4 1.3 Val 0.50 17.7 18.8 12.2 11.5 16.0 15.0 13.2 18.1 20.2 11.4 7.5 -4.1 Leu 0.39 -9.7 -11.0 -10.4 -8.1 -6.6 -6.7 -5.3 -7.2 -6.9 -2.2 7.8 4.4 Met 0.33 14.2 20.0 7.6 6.6 13.0 14.5 6.1 17.5 20.3 5.3 -0.5 -1.6 Phe 0.17 -10.5 -9.1 -6.9 -10.2 -15.9 -11.3 -9.6 -8.4 -5.8 -1.6 -3.6 4.3 Lys 0.17 -22.3 -28.6 -9.5 -17.8 -13.7 -8.4 -6.6 -25.1 -23.4 -11.5 15.9 -26.3 Asn 0.17 -23.6 -20.5 -14.8 -18.0 -25.1 -20.2 -19.2 -24.9 -23.0 -17.4 13.6 0.9 Tyr 0.17 -8.4 -6.7 -5.3 -4.8 -15.2 -9.3 -11.0 -1.9 -5.6 -7.7 25.7 5.4 Ile 0.11 -19.3 -21.8 -11.8 -18.2 -22.5 -18.2 -18.5 -23.0 -20.6 -17.5 -3.7 0.4 a ((Proportion in RNA) - (Proportion in DNA))/(Proportion in DNA) × 100. b GC content = (Number of guanine or cytosine bases among synonymous codons)/(Number of total bases among synonymous codons). BATS, Bermuda Atlantic Time Series; HOT, Hawaii Ocean Time Series; OMZ, oxygen minimum zone. Figure 11 Amino acid usage changes based on GC content of synonymous codons in ocean communities . (a,b) Charts include only those amino acids whose frequency significantly increased (blue) or decreased (red) in the RNA relative to the corresponding DNA reads (a) or in the expressed genes relative to the non-expressed genes in the DNA reads only (b). Significant increases/decreases are categorized based on the GC content (x-axis) of the amino acid, where GC content = (Number of GC bases among synonymous codons)/(Number of total bases among synonymous codons). Data from all 11 DNA/RNA ocean samples are pooled; these charts exclude the soil data, in which GC enrichment in the RNA was not observed. Figure 12 Relatedness of DNA (blue) and RNA (red) datasets as determined by amino acid proportions . Dendrograms are based on hierarchical clustering of Pearson correlation coefficients for each pairwise comparison of amino acid proportions. Figure 13 Clustering of sample datasets based on proportional amino acid change . Heat maps show the relative magnitudes of the proportional change (from DNA to RNA) in each sample set. Red = increase, green = decrease, black = zero. Dendrograms are based on hierarchical clustering of Pearson correlation coefficients for each pairwise dataset comparison. Numbers beside amino acid names indicate GC content, calculated as: (Number of GC bases among synonymous codons)/(Number of total bases among synonymous codons). Figure 14 Proportional changes in amino acid usage decrease with depth . Data include the seven amino acids with the greatest proportional increase from DNA to RNA. Proportional change = ((Amino acid proportion in RNA) - (Amino acid proportion in DNA))/(Amino acid proportion in DNA) × 100. Among the ocean datasets, DNA-RNA shifts in amino acid frequency were strongly related to amino acid GC content (Figure 11a ; see Materials and methods). Amino acids with an intermediate GC content (0.5) constituted equivalent fractions, 40% and 36%, of the total number of amino acid frequency increases and decreases, respectively. Strikingly, amino acids with GC content below 0.5 were significantly less abundant in the RNA, being involved in 61% of all decreasing DNA-RNA amino acid frequency changes. In contrast, frequency increases were dominated by amino acids enriched in GC: 50% of increases involved amino acids with GC greater than 0.5, significantly higher than the representation of these amino acids in changes involving a decrease (3%). A similar, but less dramatic, shift in amino acid usage is observed when the DNA reads were binned into expressed and non-expressed gene sets (Figure 11b ). In general, the magnitude of the proportional shift in amino acid usage decreases with depth in the water column (Figure 14 ). This pattern may reflect an overall decrease in microbial activity with depth, such that the transcriptome, less weighted by highly expressed and highly conserved genes, more closely resembles the metagenome as activity declines. Together, these results suggest a significant shift towards GC-rich amino acids in the expressed gene pool. Prior studies describe a relationship among gene expression level, sequence conservation, and amino acid usage [ 38 - 42 ]. Specifically, significant enrichment in GC-rich amino acids among highly expressed genes has been demonstrated for individual bacterial taxa, including Prochlorococcus [ 38 , 39 ]. GC richness in expressed genes is potentially driven by a combination of factors, including selection against metabolically costly amino acids (for example, AT-enriched phenylalanine and tyrosine) [ 40 ], or selection against AT-richness in highly expressed genes. Alternatively, this pattern may stem from an overall enhanced conservation level in highly expressed genes [ 12 ]. Assuming an underlying GC-to-AT mutational bias, which may be a universal trend in bacteria [ 41 , 42 ], selectively constrained genes are predicted to retain a GC-rich signature relative to less-constrained genes. Therefore, the proportional increase in GC-enriched amino acids in expressed genes compared to non-expressed genes in this study is consistent with our observation of enhanced sequence conservation in the expressed community gene pool, and confirms a fundamental distinction in amino acid usage related to gene expression level."
} | 13,323 |
35558008 | PMC9090853 | pmc | 4,734 | {
"abstract": "Oil/water separation has been a challenge in chemical engineering for various applications. There are numbers of studies on using coated metal meshes as a filter for oil/water separation. However, water resistance, chemical (such as: acid, base, and fouling) resistance and heat resistance for coating materials need further exploration, especially in terms of the durability of the coating materials. In this study, we synthesized a new coating material, hydrophilic polycarbonate polyurethane (HPCPU). We used HPCPU to chemically modify a steel mesh, and the mesh exhibits superhydrophilic and underwater superoleophobic properties. The HPCPU coated mesh shows excellent capacity for oil/water separation with a separation efficiency higher than 99.99% even after 40 cycles of separation. The coating material also exhibits excellent properties of water resistance, heat resistance, and chemical resistance. Moreover, the HPCPU-coated mesh exhibits a strong durability. For example, the separation efficiency for various oil/water mixtures remains higher than 99.7% after the HPCPU-coated mesh has been soaked in water for 30 days, hot water for 5 days, oils for 5 days, 0.5 M HCl solution, 0.5 M NaOH solution and 0.5 M NaCl solution for 24 hours.",
"conclusion": "4. Conclusions We have successfully synthesized the superhydrophilic and underwater superoleophobic HPCPU-coated mesh, which exhibits excellent water/oil separation capacity. The HPCPU-coated mesh was tested to separate water from the various water/oil mixtures with excellent separation efficiency. Here, we want to emphasize the durability and the fouling resistance of HPCPU. When the mesh was pre-treated with the acidic, alkaline, and salt solutions, the separation efficiency was kept up to 99.7%. When the mesh was treated with hot water and pre-soaked in various oils, the separation efficiency maintained on the same level. With such property, the HPCPU-coated mesh could be used in very harsh environments, and real application in industrial scale.",
"introduction": "1. Introduction Oil/water separation has become an indispensable and pressing challenge due to the growing waste of oil/water mixtures caused by the frequent oil spills and the emission of industrial waste water. 1 Effective technologies are needed to remove, recover and clean up oil spills or oil slicks from the surface of water. 2 So far, a number of methods are applied in handling the water/oil separation in real oil spill accidents, such as shimming, using dispersants, in situ burning, and other manpower-intensive technologies. 3 The environmental and economic concerns of treating oil spills have encouraged many researchers to search for and find eco-friendly solutions to separate oil/water mixtures, such as adsorption, bioremediation, and filtration. 4–11 Among these technologies, filtration is the most frequently-used method due to certain advantages, such as high flux, non-pollution, low energy consumption, eco-friendliness and easy-operation. 6 With regard to wettability, the coating materials used in filtration are mainly divided into two categories. One is the ‘oil-removing’ type with the properties of superoleophilicity and superhydrophobicity; 12–21 the other is ‘water-removing’ type with the properties of superhydrophilicity and superoleophobicity, 22–25 or the properties of superhydrophilicity and underwater superoleophobicity. 26–32 In recent studies, the coating materials for the ‘water-removing’ type have attracted great attention due to the advantages of outstanding stain resistance and industrial applicability, in particularly, the coating materials with superhydrophilicity and underwater superoleophobicity. The superhydrophilic and underwater superoleophobic material is usually prepared, then covering the hydrophilic material on the surface of the porous material by physical coating or chemical grafting. To achieve the superhydrophilicity and underwater superoleophobicity, many materials have been used as membrane substrates of filtration, including metal meshes, fabrics, 33–35 foams, 13,36 nylon meshes, 37 aerogel, 38 sponges 39–41 and etcetera. Among them, metal meshes, such as stainless-steel mesh and copper mesh, exhibit superior mechanical properties and feasibility. Whereas, the surface of metal meshes is difficult to modify chemically to achieve the hydrophilicity. Often, the surface is modified physically to obtain the hydrophilicity by coating with hydrophilic hydrogels, 42,43 graphene oxide, 44,45 titania, 46 zinc oxide, 47 palygorskite, 30 chitosan 48 and etc. Due to the superhydrophilic property and lacking the chemical bonds between the metals and the coating materials, the coating easily falls off from the mesh after being in the water for long time. Consequently, the water-resisting property of such meshes is very poor. For copper meshes, the surface could be chemically modified to yield the hydrophilicity. The hydrophilic compounds, generated on the surface of the copper mesh by chemical reactions, such as: copper hydroxide, copper oxide, etc. , 49–52 are easy to react with acids, leading to the damage of the copper mesh, implying a deficiency of acid-resistance. The practical application calls for new coating materials with the new properties, such as remarkable chemical stability and water-resistance, in addition to high flux, low environment pollution, low energy consumption, and simple operation. There are extremely few relevant reports on promising metal meshes with superhydrophilic and underwater superoleophobic properties. In the previous studies in our group, we found that the polycarbonate polyurethane (PCPU) exhibits excellent water resistance, heat resistance, and weatherability. 53–56 Here, we prepared two layers of coating coated mesh with superhydrophilicity and underwater superoleophobicity. First, we synthesized inner layer that PCPU with terminal group of C \n \n\n<svg xmlns=\"http://www.w3.org/2000/svg\" version=\"1.0\" width=\"13.200000pt\" height=\"16.000000pt\" viewBox=\"0 0 13.200000 16.000000\" preserveAspectRatio=\"xMidYMid meet\"><metadata>\nCreated by potrace 1.16, written by Peter Selinger 2001-2019\n</metadata><g transform=\"translate(1.000000,15.000000) scale(0.017500,-0.017500)\" fill=\"currentColor\" stroke=\"none\"><path d=\"M0 440 l0 -40 320 0 320 0 0 40 0 40 -320 0 -320 0 0 -40z M0 280 l0 -40 320 0 320 0 0 40 0 40 -320 0 -320 0 0 -40z\"/></g></svg>\n\n C and then coated it onto the stainless steel mesh (PCPU-coated mesh). Next, outer layer, poly-hydroxyethyl acrylate (PHEA), was grafted on PCPU-coated mesh by free radical polymerization to obtained hydrophilic PCPU-coated (HPCPU-coated) mesh. Under the joint effect of strong adhesion between water resistant, weatherable inner-layer and stainless steel mesh and chemical crosslinking between outer layer and inner layer, the coating material exhibits strong water resistance, heat resistance and acid/base-resistance in addition to the high separation efficiency (>99.7%) for various oil/water mixtures. The high separation efficiency was maintained after the coated meshes being treated in water for 30 days, in hot water for 5 days, in 0.5 M HCl, 0.5 M NaOH, and 0.5 M NaCl solutions for 24 hours, and in various oils for 5 days. To our knowledge, this is the first time that the metal meshes for oil/water separation exhibits such properties. In addition, the synthetic route for the coating material is very simple, depicting a promising application in large-scale production."
} | 1,872 |
35743847 | PMC9224936 | pmc | 4,741 | {
"abstract": "Manganese (Mn) oxides are widespread on the surface environments of the modern Earth. The role of microbial activities in the formation of Mn oxides has been discussed for several decades. However, the mechanisms of microbial Mn oxidation, and its role in complex microbial communities in natural environments, remain uncertain. Here, we report the geochemical, mineralogical, and metagenomic evidence for biogenic Mn oxides, found in Japanese hot spring sinters. The low crystallinity of Mn oxides, and their spatial associations with organic matter, support the biogenic origin of Mn oxides. Specific multicopper oxidases (MCOs), which are considered Mn-oxidizing enzymes, were identified using metagenomic analyses. Nanoscale nuggets of copper sulfides were, also, discovered in the organic matter in Mn-rich sinters. A part of these copper sulfides most likely represents traces of MCOs, and this is the first report of traces of Mn-oxidizing enzyme in geological samples. Metagenomic analyses, surprisingly, indicated a close association of Mn oxides, not only in aerobic but also in anaerobic microbial communities. These new findings offer the unique and unified positions of Mn oxides, with roles that have not been ignored, to sustain anaerobic microbial communities in hot spring environments.",
"conclusion": "4. Conclusions Geochemical, mineralogical, and metagenomic analyses were performed on Mn-oxide-rich sinters in Japan. Sub-micron scale spherical aggregates of Mn oxides were observed. HAADF analyses revealed that the Mn oxides were composed of poorly crystalline phyllomanganates, including δ-MnO 2 (vernadite), hausmannite (Mn 2+ Mn 3+ 2 O 4 ), and birnessite ((Na,Ca,K) × (Mn 4+ , Mn 3+ 2 ) 2 O 4 ·1.5H 2 O). Nanoscale layers of Mn oxides in each sphere were, often, intercalated with layers of organic matter, which are, rarely, found in marine Mn crust or nodules. The low crystallinities of the spherical Mn oxides and their close associations with organic matter support the biogenic origin of Mn oxides. Several putative Mn-oxidizing genes encoding MCOs were identified, using metagenomic analyses. The predominant putative Mn-oxidizing genes were moxA and mcoA . Nanoscale nuggets of copper sulfides were, also, discovered in the layers of organic matter. Thermodynamic calculations indicated that conditions in the examined hot spring environment were not favorable for the abiotic precipitation of copper sulfides. In addition, other mineralogical and geochemical data excluded the possibility of the product, by microbial sulfate reduction or simple adsorption and enrichment on the surfaces of Mn oxides. Therefore, the novel copper sulfides are, most likely, degradation products of MCO-bearing proteins. Enzymatically produced Mn oxides, most likely, acted as electron acceptors or helped in the degradation and storage of organic matter. These actions would help sustain and develop the overall aerobic and anaerobic microbial communities. Nine MAGs of putative Mn-oxidizing bacteria were detected. In particular, four of them appeared to be close associations of Mn-oxidizing genes with anaerobic bacteria, including SRB, although there was high uncertainty regarding whether anaerobic bacteria anaerobically oxidized Mn(II). The findings of the present study suggest that Mn oxides became a part of meso to thermophilic microbial mats and offer essential roles to sustain anaerobic microbial communities.",
"introduction": "1. Introduction 1.1. General Background Manganese (Mn) is ubiquitous in the Earth’s lithosphere and hydrosphere. Mn(II) is stable in solution under relatively acidic or anoxic conditions, whereas Mn(III) and Mn(IV) are favored under oxic or high pH conditions and, mainly, exist as Mn hydroxides, oxyhydroxides, or oxides [ 1 ]. The Mn cycling on the modern Earth is operated by shuttling between soluble Mn(II) and insoluble Mn(III) and Mn(IV). Mn(IV) oxides are found in diverse environments, including metal-contaminated streams [ 2 , 3 ], submarine hydrothermal fields [ 4 , 5 ], the ocean floor, where they occur as ferromanganese nodules and crusts [ 6 , 7 , 8 ], and terrestrial hot springs [ 9 , 10 , 11 ] ( Table S1 ). Microbial Mn(II) oxidation is, generally, faster than abiotic Mn(II) oxidation processes [ 1 ]. This kinetic advantage implies that biological Mn(II) oxidation is thought to be widespread and significant, in natural environments over time [ 12 , 13 , 14 ]. However, little is known about the mechanisms of microbial Mn oxidation. Direct mineralogical evidence of the biogenesity of Mn(IV) oxides is, still, obscure, due to the difficulties in distinguishing biogenic Mn(IV) oxides from abiotic Mn(IV) oxides. In contrast, several genetic pathways have been proposed for biogenic Mn(IV) oxides, including step-by-step enzymatic oxidation [ 15 , 16 , 17 ] and disproportionation of early biotic oxide [ 18 ]. Hence, more case studies of coupled examination of mineralogy, physiology, and enzymatic genomics, using natural samples, are required to, further, understand microbial Mn oxidation. A total of 227 16S rRNA sequences of Mn-oxidizing bacteria have been reported from natural samples, through September 2021 ( Table S2 ). It has become possible, in the last several years, to synthesize Mn (IV) oxides under environmentally relevant conditions, by incubating Mn(II)-oxidizing bacteria, and to compare their properties with those of synthetic Mn oxides [ 2 , 3 , 5 ]. Based on these experimental works, the understanding of the mechanisms of biological Mn oxidation has been well advanced. In addition, previous studies have discussed the benefits of biological Mn oxidation for Mn-oxidizing bacteria, in terms of protection from UV [ 19 ], oxidative stress [ 20 ], toxic heavy metals, reactive oxygen species, predators, and viral attack [ 21 , 22 ]. However, the significance of Mn oxidation in complex microbial communities is uncertain. Furthermore, Mn(IV) oxides have been the focus of researchers because of the chemical similarities between Mn oxides and the Mn-containing complex of photosystem II [ 23 , 24 ]. Therefore, studies on biogenic Mn oxides can, also, contribute to the understanding of the mechanisms of photosynthesis. 1.2. Mn Oxidizing Enzyme Several enzymes involved in the biological Mn(II) oxidation and their essential roles have been previously described, e.g., [ 25 , 26 , 27 , 28 , 29 , 30 ]. Mn oxidases belong to two families of proteins in general: the animal heme peroxidases (AHPs) and the multicopper oxidases (MCOs). However, AHPs with Mn-oxidation capacity have been reported only in the marine bacteria Erythrobacter sp. SD-21, Aurantimonas manganoxydans SI85-9A1 [ 26 ], and Roseobacter AzwK-3b [ 29 ]. Recently, some algae have been confirmed to utilize extracellular proteins or superoxides for Mn oxidation [ 30 ]. MCOs include protein families, such as laccases, ferroxidases, and ascorbate oxidase. Crystal structures of more than ten MCOs have been determined, which have contributed to the understanding of their functions e.g., [ 31 , 32 ]. MCOs contain at least four copper (Cu) atoms that bind to specific amino acids. MCO particles were directly observed using transmission electron microscopy (TEM), and the size of the individual MCO particle was identified as 6 nm to 8 nm in diameter [ 33 ]. MCOs are prominent in all kingdoms and play a critical role in iron metabolism and copper homeostasis [ 34 ]. The activities of MCOs not only promote the oxidative metabolic cycle but also influence some diseases in animals [ 35 ]. In bacteria, some MCOs regulate the concentrations of Cu in cells, to avoid copper toxicity [ 36 , 37 ]. Previous experiments have demonstrated a link between genes encoding MCOs and Mn(II)-oxidizing enzymes [ 25 , 27 , 28 ]. In the past decades, Mn(II) oxidation experiments were performed using MCOs or putative Mn-oxidizing bacteria with MCOs (e.g., [ 25 , 26 , 27 , 28 ]). On the other hand, direct evidence of MCOs utilization is, still, missing for microbial Mn oxidation in natural environments. 1.3. Purpose of the Present Study Mn(IV) oxides are actively precipitating in several terrestrial Mn-rich hot springs [ 38 ]. Meso to thermophilic Mn-oxidizing bacteria are believed to be involved in Mn(IV)-oxide formation. These Mn-rich hot springs are ideal for examining the physiology and enzymatic activities of Mn-oxidizing bacteria and their mineral products. In addition, Mn oxides occur deep in the hot spring microbial mat. This feature implies the certain role of Mn oxides (e.g., energy conservation) to sustain not only aerobic but also anaerobic microbial communities [ 39 , 40 ]. Here, we present our novel genomic analyses, coupled with mineralogical and geochemical analyses, of the Mn-rich precipitates at an Fe- and bicarbonate-rich hot spring in Japan ( Figure 1 ), to address the problems of the biogenesity of Mn oxides, enzymatic evidence of microbial Mn oxidation, and roles of Mn oxides to sustain hot spring microbial communities.",
"discussion": "3. Results and Discussion 3.1. Biogenic Mn Oxides in Fe- and CO 2 -Rich Hot Spring Assembled spherical Mn oxides (<5 µm in diameter, Figure 2 A) in each sample were observed by FE-SEM. The spheres consisted of alternating layers of Mn oxides and organic matter ( Figure 2 B–F). Such a spatial relationship between organic matter and Mn oxides is unique and has, rarely, been reported in modern marine Mn nodules or crusts. Organic matter is encrusted by linear, folded, and fibrous forms of Mn oxides, at the nanoscale ( Figure 3 A,B,D,E and Figure S1 , HKs-Mn, HKm, and HKd). Most Mn oxides in the examined samples were amorphous in phases, but some of the Mn oxides showed randomly stacked lattices in the TEM images ( Figure 3 B,E). High-angle annular dark field scanning (HAADF) TEM analyses revealed that the examined samples were composed of poorly crystalline phyllomanganates. δ-MnO 2 (vernadite, Figure 3 C), hausmannite (Mn 2+ Mn 3+ 2 O 4 , Figure 3 F), and birnessite ((Na,Ca,K) × (Mn 4+ , Mn 3+ 2 ) 2 O 4 ·1.5H 2 O) were identified in the examined samples. TEM images ( Figure 3 A,B,D,E and Figure S1 ) are, apparently, different from synthetic triclinic MnO 2 [ 88 ]. The biogenesity and genetic sequences from vernadite, hausmannite, and birnessite have been discussed, by previous investigators [ 12 , 89 , 90 ]. Incubation experiments of Mn-oxidizing bacteria, also, produced poorly crystalline birnessite [ 39 ]. Our observations are consistent with the previously proposed biogenic origin models of these phyllomanganates [ 12 , 89 , 90 , 91 ]. Previous studies have reported the enzymatic or biogenic formation of Mn-oxide nanoparticles as a nascent phase [ 33 , 92 ]. However, nanoparticles of Mn oxides were not found in the examined samples, suggesting early rapid merging of nanocrystals into larger polycrystals, in the hot spring environments. Tunnel-structured manganates, such as todorokite, are common in Mn nodules on the modern ocean floor [ 93 , 94 , 95 ], but these were not present in the examined samples in the present study. 3.2. Complex Microbial Community at HK Metagenomic analyses for biogenic Mn oxides indicated different phylogenies at each sampling point. The phylum- and class-level community compositions are illustrated in Figure 4 and Table S3 . One-third of the operational taxonomic units (OTUs) at each site were, generally, composed of members of Gammaproteobacteria and Alphaproteobacteria . The abundance of Proteobacteria was the same in HKs-Mn and HKs-Fe. Actinobacteriota and Bacteroidota are abundant in HKm and HKd, compared to HKs-Mn and HKs-Fe. Acidobacteriota and Chloroflexi were more abundant in HKm than in HKd. OTUs of Pastescibacteria , which was referred to as candidate phyla radiation, were abundant in HKd, but were not found in HKm. OTUs of Gallionellaceae were not found in HKm and HKd, corresponding to a lower abundance of Fe-(hydro)oxides. Cyanobacteria accounted for 6.5% and 2.3% of the microbial community in HKs-Fe and HKd, respectively. HKs-Mn and HKm, which were not exposed to the surface, did not show OTUs of cyanobacteria. The genera Pedobacter sp., Candidatus Kaiserbacteria sp., and Massilia sp. were also found in the Mn-rich samples (HKd). HKs-Mn, uniquely, contained a considerable proportion of anaerobic microorganisms (red in Figure 4 ). The class Thermoanaerobaculia , accounted for a large proportion (10.0% in Figure 4 ). The class of Thermoanaerobaculia includes two genera, namely TPD-58 and Thermoanaerobaculum , in HKs-Mn. The following classes are, also, found: Desulfomonilia (3.1%), the phylum of Desulfobacterota (5.3%), and Thermodesulfovibrionia (1.6%). Thermodesulfovibrionia belongs to phylum Nitrospirae , which contained putative Mn-oxidizing bacteria [ 39 ]. 3.3. Bacteria Associated with Mn Oxidation The taxonomy of MAGs was determined, based on 120 concatenated single-copy bacterial genes ( Table S4 ). Nine MAGs possessed Mn-oxidizing genes, indicating the presence of putative Mn-oxidizing bacteria ( Table S5 ). In the HKs-Mn, MAGs of putative Mn-oxidizing bacteria, genus Rhizobiaceae_RCIO01 (HKs107), previously reported as Mn-oxidizing bacteria [ 96 ], was successfully constructed. In the HKm, MAGs of the putative Mn-oxidizing bacteria Ramlibacter sp. (HKm46) was constructed ( Table S5 ). The genus Ramlibacter sp. Is, generally, an aerobic heterotroph and has not been recognized as a Mn-oxidizing bacteria. Other MAGs of putative Mn-oxidizing bacteria were assigned to thermophile, the class of Blastocatellia (HKm2), which, also, has not been reported to have Mn-oxidation capacity. Sample HKd showed different characteristics. MAGs of the putative Mn-oxidizing bacteria, Herminiimonas sp. (HKm161) and Hydrogenophaga sp. (HKd102) were constructed ( Table S5 ). Herminiimonas sp. has not been recognized as Mn-oxidizing bacteria, but Hydrogenophaga sp. was, previously, reported as Mn-oxidizing bacteria [ 46 ]. These are candidates for major Mn-oxidizing bacteria at each site. Beside those common Mn-oxidizing bacteria, some anaerobic bacteria were found to have Mn-oxidizing genes (see Section 3.5 ). 3.4. MCOs Utilization for Biological Mn Oxidation in Nature Our metagenomic data indicated the prevalence of putative Mn-oxidizing genes encoding MCOs in the examined samples ( Table S5 ). Among the nine MAGs in the present study, moxA (locus ID; CAJ19378) was the top hit, with an identity of approximately 70% of the HKs-Mn, HKm, and HKd (HKs107, HKm46, HKd161), respectively. Other putative Mn-oxidizing genes in HKs-Mn (HKs85, 166, 176, 177) were moxA , mcoA (locus ID; ABY98562), and mnxG (locus ID; PputGB1_2447), while those in HKm and HKd were moxA and mcoA (HKm2 and HKd102). These data confirm that MCOs were the dominant Mn-oxidizing genes around venting and downstream sites. Nanoscale textures and chemistry of organic matter in Mn-oxide spherules were analyzed, using high-resolution transmission electron microscopy (HR-TEM) and STEM. Cu-bearing nuggets (<300 nm, mostly 100–200 nm Figure 5 A) have been, newly, found in the spherules. Such nuggets only occurred in organic matter in the spherules, and Mn oxides or carbonates in the same spherules never contained the Cu-bearing nuggets. Quantitative analyses by HR-TEM indicated that the nuggets were mostly made of Cu x S y ( Figure 5 B–F), although the determination of specific stoichiometry was difficult. Cu-bearing nuggets are relevant for natural covellite (CuS) or chalcocite (Cu 2 S). Such Cu-bearing nuggets in organic matter have not been reported, previously, in terrestrial hot spring environments. The stability field of Cu x S y was estimated, using the chemical data of HK hot spring water ( Figure S3 ). The stability field was incompatible with the conditions of the samples, in which aragonite and goethite precipitate. These facts suggest that abiotic precipitation of Cu x S y from hot spring water is not thermodynamically favored. In addition, FeS 2 or FeS were not found in the examined samples, although hot spring water contains significant amounts of Fe 2+ . This suggests that Cu x S y was not a simple product of microbial sulfate reduction (e.g., [ 97 ]). In nature, Mn oxides act as sponges to adsorb trace elements (e.g., [ 98 ]). Mn nodules or crusts on the modern ocean floor are known to abiotically accumulate Cu and other heavy metals, and they are comparable to several hundreds to thousands parts per million (ppm) (e.g., [ 99 ]). On the other hand, the Mn oxides in the examined samples did not show the enrichment of Cu (1.5 ppm) and other heavy metals ( Table S8 ). Such observations suggest a unique mechanism to form Cu x S y in the examined samples, rather than simple adsorption and enrichment on the surfaces of Mn oxides. We interpret this to mean that the novel Cu nuggets are traces of MCOs, after significant diagenetic modification from their original forms. Nano-scale aggregations of biogenic metal sulfides within organic matter were reported, previously, from natural samples [ 100 ]. Metals in metal-binding proteins are bound with sulfur in amino acids, proteins, and polypeptides belonging to the sulfhydryl group. Nano-particles of metal sulfides were formed, after degradation of the original protein–metal compounds [ 97 ]. Similar nano-particles of various metal sulfides (e.g., Zn, Hg, Fe, Cd) have been found, in natural organic-rich samples. It is interpreted that Cu-binding proteins (MCOs) were degraded after cell death, and Cu and sulfur from organic molecules were trapped in non-permeable organic layers cemented in carbonates. H 2 S from deep sulfate reduction might join, as a part of sulfur, in this closed system. Aggregations were promoted by binding protein-rich organic matter with metal sulfides. In particular, cysteine stimulates large aggregations, up to ~100 nm diameter [ 100 ]. MCOs, generally, contain cysteines bound with Cu. Such high concentrations of Cu and organic molecules, including cysteine in closed systems, were responsible for Cu x S y formation in the examined samples. Other organic sulfurs, also, contribute to form Cu sulfides. The finding of Cu x S y is consistent with the detection of genes encoding MCOs in the same samples, supporting that MCOs were the major Mn-oxidizing genes in the venting area and downstream sites. 3.5. Role of Mn Oxidation in the Sinter Ecosystem Phylogenetic analyses indicated that the microbial communities in Mn oxides differed at the sampling locality. Mn oxides in the venting site harbored a remarkable proportion of anaerobic microorganisms, such as sulfate-reducing bacteria (SRB) and Mn-reducing bacteria. In contrast, Mn oxides at the downstream harbored the aerobic heterotrophs. Putative Mn-oxidizing bacteria at the venting site were different from those at downstream sites. The temperature of hot spring water was lower and more oxic at downstream sites, compared to the venting site. These factors are considerable reasons for the differences in the microbial community structures and Mn-oxidizing bacteria at each site ( Figure 4 , and Table S3 ). At both sites, biological Mn oxidation benefits the entire microbial community, and Mn oxides are utilized as electron acceptors. Alternatively, Mn oxides are utilized for the degradation and storage of organic matter in the microbial community [ 101 , 102 ]. Our analyses indicate that the following anaerobic bacteria have putative Mn-oxidizing genes: Thermodesulfovibrionales (HKs177), Desulfobacterota (HKs166), Thermoanaerobaculia (HKs85), and Chloracidobacteriales (HKs176) ( Table S5 ) . Desulfobacterota and Thermodesulfovibrionales were SRB. Finding putative Mn-oxidizing genes in those anaerobic bacteria is enigmatic, and it is still uncertain whether those bacteria are actively oxidizing Mn(II) at the examined site. Recent incubations of anaerobic phototrophs [ 40 ] and aerobic chemolithoautotrophs [ 39 ] showed microbial Mn(II) oxidation, with a help from other aerobic and anaerobic microbial communities. These studies indicate the importance of biogenic Mn oxides, for developing microbial communities at the interface of oxic and anoxic environments. Yu and Leadbetter (2020) suggested that the class of putative Mn-oxidizing bacteria are phylogenetically closed to the phylum of Nitropsirae , which contains SRB classes. Our findings and previous results, further, imply that SRB, potentially, acquired the anaerobic Mn-oxidizing ability during its evolution, although conclusive evidence is, still, unavailable. Yu and Leadbetter (2020) proposed metabolic pathways for chemoautotrophic Mn oxidation and emphasized the postulation of Fe-S clusters with Cu-bearing protein, to transfer electrons in vitro. This model, further, implies the necessity of sulfur for Mn oxidation. The presence of SRB might be beneficial for Mn-oxidizing bacteria in the same microbial community, so that Mn-oxidizing bacteria could uptake essential sulfur species easily from SRB. This could be alternative explanation for detection of Mn oxides in SRB-bearing complexed microbial community. The presence of Mn oxides was also suggested to be beneficial to SRB for energy conservation (i.e., buttery) through metabolic electron transfers. This, further, implies that the inorganic Mn oxides are unified with microbial mats and have essential roles to sustain anaerobic microbial communities."
} | 5,371 |
26663479 | PMC4767432 | pmc | 4,743 | {
"abstract": "Abstract The bacterium Corynebacterium glutamicum is utilized during industrial fermentation to produce amino acids such as l ‐glutamate. During l ‐glutamate fermentation, C . glutamicum changes the flux of central carbon metabolism to favor l ‐glutamate production, but the molecular mechanisms that explain these flux changes remain largely unknown. Here, we found that the profiles of two major lysine acyl modifications were significantly altered upon glutamate overproduction in C . glutamicum ; acetylation decreased, whereas succinylation increased. A label‐free semi‐quantitative proteomic analysis identified 604 acetylated proteins with 1328 unique acetylation sites and 288 succinylated proteins with 651 unique succinylation sites. Acetylation and succinylation targeted enzymes in central carbon metabolic pathways that are directly related to glutamate production, including the 2‐oxoglutarate dehydrogenase complex ( ODHC ), a key enzyme regulating glutamate overproduction. Structural mapping revealed that several critical lysine residues in the ODHC components were susceptible to acetylation and succinylation. Furthermore, induction of glutamate production was associated with changes in the extent of acetylation and succinylation of lysine, suggesting that these modifications may affect the activity of enzymes involved in glutamate production. Deletion of phosphotransacetylase decreased the extent of protein acetylation in nonproducing condition, suggesting that acetyl phosphate‐dependent acetylation is active in C . glutamicum . However, no effect was observed on the profiles of acetylation and succinylation in glutamate‐producing condition upon disruption of acetyl phosphate metabolism or deacetylase homologs. It was considered likely that the reduced acetylation in glutamate‐producing condition may reflect metabolic states where the flux through acid‐producing pathways is very low, and substrates for acetylation do not accumulate in the cell. Succinylation would occur more easily than acetylation in such conditions where the substrates for both acetylation and succinylation are limited. This is the first study investigating the acetylome and succinylome of C . glutamicum , and it provides new insight into the roles of acyl modifications in C . glutamicum biology.",
"introduction": "Introduction Nε‐lysine acetylation is one of the most common post‐translational modifications in both eukaryotes and prokaryotes (Hu et al. 2010 ; Jones and O'Connor 2011 ; Kim and Yang 2011 ; Choudhary et al. 2014 ; Huang et al. 2014 ; Hentchel and Escalante‐Semerena 2015 ). Recent proteomic studies in diverse bacterial species have identified hundreds of lysine‐acetylated proteins that function in various cellular processes (Yu et al. 2008 ; AbouElfetouh et al. 2014 ; Castano‐Cerezo et al. 2014 ; Kuhn et al. 2014 ; Liu et al. 2014 ; Hentchel and Escalante‐Semerena 2015 ; Kosono et al. 2015 ; Schilling et al. 2015 ). New acyl‐modifications of lysine residues, such as propionylation (Chen et al. 2007 ; Garrity et al. 2007 ; Cheng et al. 2009 ; Zhang et al. 2009 ; Okanishi et al. 2014 ), butyrylation (Chen et al. 2007 ; Zhang et al. 2009 ), succinylation (Zhang et al. 2010 ; Xie et al. 2012 ; Colak et al. 2013 ; Weinert et al. 2013b ; Hirschey and Zhao 2015 ; Kosono et al. 2015 ; Pan et al. 2015 ; Yang et al. 2015 ), malonylation (Peng et al. 2011 ; Xie et al. 2012 ; Hirschey and Zhao 2015 ), crotonylation (Tan et al. 2011 ), 2‐hydroxyisobutyrylation (Dai et al. 2014 ), and glutarylation (Tan et al. 2014 ; Hirschey and Zhao 2015 ) have been recently discovered, and they often target the same lysine residues as acetylation. Of these acyl modifications, succinylation, as well as acetylation, are considered to frequently occur in both eukaryotes and prokaryotes (Weinert et al. 2013b ; Kosono et al. 2015 ). \n Corynebacterium glutamicum is an aerobic, gram‐positive bacterium that grows on a variety of sugars, organic acids, and alcohols, and it is utilized for the industrial production of amino acids, particularly l ‐glutamate and l ‐lysine (Eggeling and Bott 2005 ). During glutamate fermentation, the depletion of biotin, the addition of a detergent such as Tween 40, or the addition of lactam antibiotics such as penicillin triggers glutamate overproduction. These triggers open mechano‐sensitive channels to excrete glutamate from the cell (Nakamura et al. 2007 ; Hashimoto et al. 2010 , 2012 ) and simultaneously change the flux of central carbon metabolism to favor glutamate production (Shimizu et al. 2003 ; Shirai et al. 2007 ). In glutamate overproduction, the decrease in 2‐oxoglutarate dehydrogenase complex (ODHC) activity, which is positioned at the branch point where the citrate cycle and glutamate biosynthesis pathways diverge, is a well‐characterized phenomenon (Kawahara et al. 1997 ; Shimizu et al. 2003 ). OdhI (encoded by NCgl1385) is known to negatively regulate the ODHC by binding to the E1o component (encoded by NCgl1084), depending on its phosphorylation status (Niebisch et al. 2006 ; Schultz et al. 2007 ; Kim et al. 2011 ). However, the molecular mechanisms underlying other flux changes remain unknown. Increasing evidence indicates that acyl modifications play a role in controlling metabolic enzymes (Wang et al. 2010 ; Guan and Xiong 2011 ; Choudhary et al. 2014 ; Hirschey and Zhao 2015 ). Lysine acyl modifications may provide an elegant mechanism to coordinate metabolic processes by utilizing metabolic intermediates such as acyl‐CoA and nicotinamide adenine dinucleotide (NAD + ) as sensors (Wellen and Thompson 2012 ). Recently, enzyme modification has emerged as important mechanism to control metabolic enzymes and flux (Chubukov et al. 2013 ). Thus, we speculate that lysine acyl modifications may underlie metabolic flux change during glutamate overproduction in C . glutamicum . To explore this possibility, we performed a label‐free semi‐quantitative proteomic analysis of lysine acetylation and succinylation substrates in glutamate‐producing and nonproducing C . glutamicum . We found that lysine acetylation and succinylation targeted most enzymes in central carbon metabolic pathways that are directly linked to glutamate production, and furthermore that the extent of modification changed in response to glutamate overproduction. To our knowledge, this is the first to report the acetylation and succinylation profiles of proteins in C . glutamicum .",
"discussion": "Discussion In this study, we provided multiple lines of evidence for significant changes in the two major acyl modifications, lysine acetylation and succinylation, during glutamate overproduction in C . glutamicum . Decreased acetylation and increased succinylation in response to glutamate production were demonstrated by western blot and MS‐based semi‐quantitative proteomic analyses (Figs. 1 , 2 , and Table 1 ). Analyses of potential substrate motifs in 1D and 3D (Fig. 3 and Table S5) and the few overlaps observed between acetylation and succinylation sites (Figs. 2 , 4 , 5 ) suggested that the two acyl modifications likely target different lysine residues, as recently reported in B . subtilis (Kosono et al. 2015 ). Dynamic changes in acyl modifications were observed in proteins of the central carbon metabolism pathway, which is directly linked to glutamate production (Fig. 4 ). The flux through the central carbon metabolic pathway was found to be largely influenced by the addition of Tween 40, which leads to glutamate production: increased flux was observed in glycolysis, anaplerotic pyruvate carboxylase activity, and glutamate synthesis from 2‐oxoglutarate, whereas a decreased flux was observed in the pentose phosphate pathway and the 2‐oxoglutarate‐to‐oxaloacetate step of the citrate cycle (Shirai et al. 2007 ). Our proteomic data show that the abundance of most enzymes in these pathways did not change or rather decreased under glutamate‐producing conditions (Table 4 , Table S4), which was consistent with the transcriptome analysis reported previously (Kataoka et al. 2006 ). Thus, we must consider qualitative changes in metabolic enzymes to explain the increased flux in spite of the decreased protein abundance during glutamate production, and we infer that protein modifications including the acyl modifications identified in this study might contribute to the change in metabolic flux in glutamate‐producing C . glutamicum . Our structural mapping suggested that some of the acyl modifications may affect ODH and/or PDH activities (Fig. 6 ). We are currently determining the impact of acyl modifications on the activities of ODH and PDH. To investigate why acyl modifications change in response to glutamate overproduction, we examined the impact of deletion of the two KDAC homologs, the AckA‐Pta pathways involved in acetyl‐P metabolism, and the glyoxylate bypass on global protein acylation status. We observed that protein acetylation in the nonproducing condition was decreased in the pta mutant relative to the wild type (Fig. 7 ), suggesting that acetyl‐P dependent acetylation is active in C . glutamicum . However, the ackA mutant did not show great enhancement of acetylation, which was different from the effect observed in E . coli (Weinert et al. 2013a ; Kuhn et al. 2014 ) and B . subtilis (Kosono et al. 2015 ). This result suggests that other acetyl‐P degradation pathways might exist. According to the KEGG pathway database, NCgl1987 annotated as acylphosphatase might be a candidate to convert acetyl‐P to acetate. Further studies are necessary to determine this possibility. In contrast to the nonproducing condition, the pta mutation had no effect in the glutamate‐producing condition (Fig. 7 ). Studies have shown that fluxes and enzymatic activities at the pyruvate node are changed in glutamate‐producing C . glutamicum (Shirai et al. 2007 ; Hasegawa et al. 2008 ). In the glutamate‐producing condition induced by Tween 40, the flux through the lactate and acetate‐producing pathways from pyruvate and acetyl‐CoA was smaller (Shirai et al. 2007 ). We propose that the pta deletion had no effect on protein acetylation in the glutamate‐producing condition because the flux through the acetyl phosphate‐producing pta ‐ ackA pathways is very low under these conditions. Furthermore, the decreased acetylation status in the glutamate‐producing condition probably resulted from the carbon flux associated with anaplerotic pathways rather than acid‐producing pathways, and thus substrates for protein acetylation (acetyl‐CoA and acetyl phosphate) do not accumulate in the cell. Recently, it has been reported that deletion of the global carbon regulator CRP causes a dramatic loss of protein acetylation in E . coli (Schilling et al. 2015 ). C . glutamicum possesses a CRP‐like cAMP‐activated global transcriptional regulator known as GlxR (NCgl0286), which is suggested to regulate the expression of genes involved in central carbon metabolism, aromatic compound degradation, and others (Kohl et al. 2008 ). The protein amount of GlxR was unchanged between the nonproducing and glutamate‐producing conditions (protein ratio = 1.1, Table S3). It will be interesting to determine if a GlxR‐dependent mechanism to regulate acetylation exists in C . glutamicum , as suggested in E . coli (Schilling et al. 2015 ). In contrast to acetylation, succinylation increased with glutamate overproduction (Figs. 1 , 2 , and S4). Deletion of the glyoxylate bypass did not affect the global succinylation status (Fig. 7 ), suggesting that the glyoxylate bypass is not a critical pathway for supply of succinyl‐CoA. Succinyl‐CoA can be supplied from oxaloacetate in a reverse reaction of the citrate cycle, but the increased flux from oxaloacetate to succinate was not observed in a previous metabolic flux analysis (Shirai et al. 2007 ) (H. Shimizu, pers. comm.). We therefore conclude that succinyl‐CoA may be supplied by ODHC; the activity of ODHC decreases but it still operates in the glutamate‐producing condition. As mentioned above, the concentrations of acetyl‐CoA and acetyl‐P are likely low in the glutamate‐producing condition. If both acetyl‐group and succinyl‐group donors are present at low levels, protein succinylation would occur more easily than acetylation, because nonenzymatic succinylation occurs at low (micromolar) concentrations of succinyl‐CoA, whereas nonenzymatic acetylation occurs at millimolar concentrations of acetyl‐CoA or acetyl‐P (Wagner and Payne 2013 ; Weinert et al. 2013a , 2014 ; Kuhn et al. 2014 ). Deletion of the two KDAC homologs (NCgl0078 and NCgl0616) did not apparently affect global acylation status in both conditions (Fig. 7 and Fig. S4). Though we cannot exclude the possibility that unknown KDACs still exist, we likely consider that KDAC‐dependent deacetylation may not significantly contribute to the reduced acetylation observed with glutamate overproduction; rather, acetylation is probably downregulated. Our proteome data indicate that the protein abundance of NCgl0616 increased in the glutamate‐producing condition (protein ratio = 0.37, Table S3). We can imagine that deacetylation of several acetylated proteins may be enhanced, though it was not detected by our western blot analysis. Further studies are necessary to determine whether the KDAC homologs function as protein deacetylase and whether acetylation level in some sites is elevated in the KDAC homolog‐deleted KS13 strain by a MS‐based proteomic analysis. Our results suggest that the changes in acyl modifications observed in this study reflected metabolic states that preferentially produced the substrates utilized for acyl modifications, such as acetyl‐CoA, acetyl phosphate, and succinyl‐CoA. Our study demonstrated flux switching between the acid‐producing and anaplerotic pathways, as previously reported (Shirai et al. 2007 ; Hasegawa et al. 2008 ). Because acyl modifications depend on the metabolic state and vice versa, the change in acyl modifications found in this study would be both the cause and effect of changes in metabolic states. To distinguish their role more clearly, it will be necessary to determine acylomes with cells at an earlier time point or over time to identify acyl modifications that cause changes in metabolic flux. We also should evaluate our acylome data in combination with metabolome and/or flux analysis in a future study. Our results provide a foundation to uncover novel mechanisms regulating metabolic flux changes through acyl modifications and new targets for metabolic engineering during glutamate fermentation in C . glutamicum ."
} | 3,682 |
28086091 | null | s2 | 4,748 | {
"abstract": "Bacteria residing within biofilm communities can coordinate their behavior through cell-to-cell signaling. However, it remains unclear if these signals can also influence the behavior of distant cells that are not part of the community. Using a microfluidic approach, we find that potassium ion channel-mediated electrical signaling generated by a Bacillus subtilis biofilm can attract distant cells. Integration of experiments and mathematical modeling indicates that extracellular potassium emitted from the biofilm alters the membrane potential of distant cells, thereby directing their motility. This electrically mediated attraction appears to be a generic mechanism that enables cross-species interactions, as Pseudomonas aeruginosa cells also become attracted to the electrical signal released by the B. subtilis biofilm. Cells within a biofilm community can thus not only coordinate their own behavior but also influence the behavior of diverse bacteria at a distance through long-range electrical signaling. PAPERCLIP."
} | 256 |
27881075 | PMC5121958 | pmc | 4,751 | {
"abstract": "Background In their natural environment, bacteria face a wide range of environmental conditions that change over time and that impose continuous rearrangements at all the cellular levels (e.g. gene expression, metabolism). When facing a nutritionally rich environment, for example, microbes first use the preferred compound(s) and only later start metabolizing the other one(s). A systemic re-organization of the overall microbial metabolic network in response to a variation in the composition/concentration of the surrounding nutrients has been suggested, although the range and the entity of such modifications in organisms other than a few model microbes has been scarcely described up to now. Results We used multi-step constraint-based metabolic modelling to simulate the growth in a complex medium over several time steps of the Antarctic model organism Pseudoalteromonas haloplanktis TAC125. As each of these phases is characterized by a specific set of amino acids to be used as carbon and energy source our modelling framework describes the major consequences of nutrients switching at the system level. The model predicts that a deep metabolic reprogramming might be required to achieve optimal biomass production in different stages of growth (different medium composition), with at least half of the cellular metabolic network involved (more than 50% of the metabolic genes). Additionally, we show that our modelling framework is able to capture metabolic functional association and/or common regulatory features of the genes embedded in our reconstruction (e.g. the presence of common regulatory motifs). Finally, to explore the possibility of a sub-optimal biomass objective function (i.e. that cells use resources in alternative metabolic processes at the expense of optimal growth) we have implemented a MOMA-based approach (called nutritional-MOMA) and compared the outcomes with those obtained with Flux Balance Analysis (FBA). Growth simulations under this scenario revealed the deep impact of choosing among alternative objective functions on the resulting predictions of fluxes distribution. Conclusions Here we provide a time-resolved, systems-level scheme of Ph TAC125 metabolic re-wiring as a consequence of carbon source switching in a nutritionally complex medium. Our analyses suggest the presence of a potential efficient metabolic reprogramming machinery to continuously and promptly adapt to this nutritionally changing environment, consistent with adaptation to fast growth in a fairly, but probably inconstant and highly competitive, environment. Also, we show i) how functional partnership and co-regulation features can be predicted by integrating multi-step constraint-based metabolic modelling with fed-batch growth data and ii) that performing simulations under a sub-optimal objective function may lead to different flux distributions in respect to canonical FBA. Electronic supplementary material The online version of this article (doi:10.1186/s12864-016-3311-0) contains supplementary material, which is available to authorized users.",
"conclusion": "Conclusions Here we have used constraint-based metabolic modelling to provide a time-resolved, systems-level scheme of Ph TAC125 metabolic re-programming following nutrients switching in a nutritionally complex medium. Such features have been analysed using the metabolic model and growth data of the Antarctic bacteria P. haloplanktis TAC125. Previous experimental tests revealed a number of nutrients switches in this microorganism when grown in a complex medium [ 14 ], consistent with the fact that this Antarctic organism is adapted to fast growth in a fairly rich (but probably inconstant and highly competitive) environment (plankton debris) [ 36 ]. Indeed, sequential uptake of nutrients is thought to emerge when competition for nutrients is present [ 32 ]. Our modelling framework identified the central phase of the growth curve as a probable key reprogramming point, with more than 400 reactions predicted to adjust their flux in these time points. This corresponds ( in vivo ) to the exhaustion of most of the first metabolized C sources (Ser, Thr, Asn and Asp, most likely the preferred ones), a time step in which only Glu is used as the sole C source, and the final part of the growth curve in which Ph TAC125 stops using Glu and relies on a completely different set of nutrients. Our model highlights the occurrence of such an adaption and the need for reprogramming a large set of reactions to maintain an efficient metabolic network. A similar scenario is observed at the end of the growth, when His is used as the sole C source. This transition is the most demanding, requiring a change in the predicted flux of almost 450 reactions. Taken together, our simulation indicates the presence of an almost constant number of reactions (501, on average) that are required to sustain life across all the time points examined. Interestingly, this set of reactions resembles, in percentage, the size of the minimal metabolic network predicted to be active in E. coli [ 37 ] (37% and 28% of the reactions embedded in the corresponding models). To maintain such a constant trend, however, a deep reprogramming of the whole cellular metabolism appears to be necessary during the entire growth period, as up to more than 400 biochemical reaction, display at least one change in the predicted carried flux. According to our simulations, these changes do not only involve peripheral metabolic pathways (e.g. amino acid catabolism) but also a number of central pathways, e.g. TCA cycle, glycolysis and PP pathway. TCA cycle, for example, displays an increasing trend in the number of flux-carrying reactions parallel to the exhaustion (in the medium) of the amino acids whose degradation provide key TCA cycle intermediates. This is in line with the assumption that fluxes distribution within the cell are influenced by the entry-point of the a given C-source into the metabolic network [ 38 ]. Indeed, the C sources provided in our simulations have different distances from the Ph TAC125 central metabolism (e.g. TCA cycle) and thus are expected to cause a re-wiring of an important fraction of the network. This, in turn, suggests the presence of an efficient metabolic reprogramming machinery (that includes the regulation of the expression of the corresponding genes) to continuously and promptly adapt to this nutritionally changing environment and/or to the exhaustion of the preferred carbon source(s). Modelling and dividing the growth curve in discrete time points has allowed us to infer common trends in the predicted flux patterns of the reactions of the model. We have shown that such coupled patterns likely correspond to reactions and enzymes that work in a concerted fashion and that, in some cases, represent functional partners and/or be encoded by co-regulated genes. The modelling framework we have set up here has allowed gaining insights on the (co-)regulation of Ph TAC125 metabolic genes and, as in the case of the Arg regulon, to expand the current knowledge on commonly regulated genes. Furthermore, we have assembled a dataset of putatively co-regulated genes that can be used for further manual curation and/or subject to experimental validation. Despite FBA-based predictions led to the identification of biologically consistent trends, the nutritional-MOMA approach we have implemented here suggests that care needs to be taken when choosing the objective function for constraint-based metabolic modelling. Indeed, when we accounted for the possibility that cells could be in a near-optimal or sub-optimal state (i.e. minimizing the reprogramming required at each nutritional transition) the two modelling frameworks predicted quite a different topological and functional reprogramming of Ph TAC125 cells. As it is currently not possible to unambiguously discern among these two alternative solutions provided by FBA and nutritional-MOMA, further experimental evidence (e.g. time-resolved transcriptomics) of Ph TAC125 cells grown in complex medium will allow deriving a clearer picture of those pathways that are really active during the growth and, consequently, which of the two approaches outperforms the other.",
"discussion": "Results and discussion Modelling procedures We have recently reconstructed a genome-scale metabolic model of Ph TAC125, using it for inferring the metabolic adjustments of this bacterium induced by changes in gene expression following a temperature downgrade in this bacterium [ 17 ]. Here we used this model to investigate the metabolic rearrangements occurring during growth in a nutritionally rich medium. A detailed analysis of the data reported by Wilmes et al. [ 14 ] allowed the identification of twelve distinct phases in the growth of Ph TAC125 on peptone medium, each of them corresponding to a time step of one hour. As during the last couple of phases almost no nutrients uptake was recorded, we limited our analyses to the first ten phases (P1 to P10). For each of these phases we identified the specific uptake rates of the different compounds (amino acids) present in the growth medium (as detailed in Methods ) and/or (possible) switches in the use of the available C-sources. These values were used as input for ten different FBA simulations (selecting biomass production as the objective function) to derive the most likely fluxes distribution in the Ph TAC125 metabolic model in each time step. This allowed a system-level characterization of the metabolic changes occurring after variations in the usage (uptake fluxes) of the different carbon sources. A schematic representation of the nutrients provided to the model in each of the time steps is reported in Fig. 1a . Fig. 1 Summary of PhTAC125 genome-scale reprogramming following nutrients switching. a. The nutrients provided to the model in each different growth phase according to [ 14 ] b. Heat map with log values of fluxes across all the phases. c. Number of flux carrying reactions in each growth phase. d. Number of flux-changing reactions in each growth phase. The dashed line represents the average number of reactions carrying flux over all time points. e. Number of reactions whose flux is predicted to increase ( blue line ) and decrease ( red line ) following each shift in the nutrients provided; the black line accounts for those reactions whose flux is predicted to decrease not as an effect of an imposed reduced growth rate during simulations. f. functional annotation of reactions varying their flux across all the phases \n Prediction of core and switching reactions First, we evaluated the predicted flux for each reaction in the model, across all the time points. Overall, we found that 710 reactions did not carry flux in our model in any of the growth phases; these reactions are probably not essential to sustain Ph TAC125’s growth in the simulated conditions and were discarded from the following analyses. Conversely, 612 reactions were predicted to carry flux in at least one time point (Fig. 1b ) and were considered for further analyses. On average, excluding the last time point in which all the reactions are predicted to be turned off [corresponding to growth rate ( μ ) close to zero], 501 reactions are predicted to carry flux for each of the analysed phases (Fig. 1c ). Interestingly, this value is quite constant throughout the time points (standard deviation, s.d. = 13.8, Fig. 1c ), revealing that, according to our simulations, the number of reactions necessary to support Ph TAC125 growth is predicted to be somehow independent from the carbon sources (amino acids) used as C and energy sources. Despite this apparently constant trend in the number of metabolic reactions employed by Ph TAC125 in a complex medium, the shift among the different growth phases is presumably characterized by a relatively high number of reactions that are predicted (on the basis of our simulation and the biomass-based objective function) to change their flux (Fig. 1d ). On average, 392 reactions display at least a single variation in their predicted flux across all the time points (Fig. 1d ). However, in this case, a greater variability across all the time points is observed in our simulations (s.d. = 53). Gene-protein-reaction (GPR) rules of our model indicate that this latter set of reactions is encoded, on average, by 438 genes. Interestingly this number resembles that found when modelling the metabolic switch of S. coelicolor (549 enzyme-coding genes, 7% of S. coelicolor genes) [ 10 ] and represents around 12% of the Ph TAC125 coding sequences (55% of the metabolic genes embedded in the metabolic reconstruction). According to our simulations, switching among the available carbon sources in a complex medium may have an impact on the overall metabolic network of Ph TAC125 with more than half of flux-carrying reactions influenced by a change in the utilized C-source or in its uptake rate. More in detail, we found that, on average, 276 reactions are predicted to decrease their flux throughout the growth period (s.d. = 74.8), whereas 69 (s.d. = 30.1) are predicted to increase it. However, the number of reactions whose flux decreases might be biased due to a general decrease, across the time steps, of the growth rate. More specifically, this systemic bias is related to the amino acid uptake rates, which have been derived from the physiological data by Wilmes et al. [ 14 ], whose constant decrease lead, for some reactions, to a flux reduction in each time step. Thus, we adjusted the set of “decreasing reactions”, by removing those for which we observed a consistent decrease for all the growth phases. Although the normalization did not affect the general trend, in that the normalized set (grey curve in Fig. 1e ) and the not normalized one (red curve in Fig. 1e ) have similar trends, the number of decreasing reactions is (for the adjusted set) comparable to that of the increasing reactions. Furthermore, we also computed a normalized flux distribution for each of the modelled growth phases, expressing them as a fraction of the predicted growth rate and evaluated whether this procedure led to different results (in terms of number of reactions carrying and changing flux in each phase and flux increase/decrease patterns). Results (shown and described in Additional file 2 : Figure S1) revealed no major differences in the overall trends compared to the original calculation of fluxes distribution, suggesting that our results hold true regardless of the normalization procedure adopted to account for the different (decreasing) growth rates predicted by the model across the growth period. Not all Ph TAC125 metabolic pathways are impacted by this switching of nutrients in the same manner, according to our simulations. Figure 2a shows the hypothetical number of flux-carrying reactions for five pathways, i.e. TCA cycle, Lys biosynthesis, Glu metabolism, Val, Leu and Ile biosynthesis and degradation (a complete overview is presented Additional file 2 ). TCA cycle, for example, displays and increasing trend in the number of flux-carrying reactions according to our modelling framework; this is consistent with the exhaustion of amino acids (Asp, Asn and Glu) whose degradation provides important TCA cycle intermediates, i.e. oxaloacetate, fumarate and 2-oxo-glutarate and, consequently, with the necessity to activate those reactions leading to the biosynthesis of such compounds. Conversely, Lys and Glu metabolic routes display an overall constant trend (Fig. 2a ), with a similar number of active reactions across the different simulated growth phases. This is in line with i) the necessity to use (part) of the lysine biosynthetic route to synthesize diaminopimelic acid, an essential component of bacterial cell wall (see below) and ii) with the importance of Glu metabolism for Ph TAC125 (see below and [ 14 ]). Finally, Val, Leu and Ile biosynthesis and degradation pathways display an opposite trend one another (Fig. 2a ). Intuitively, this reflects the necessity to synthesize these molecules in the first part of the growth phase (when they are not used from the medium) and the necessity to catabolise them once Ph TAC125 is using those amino acids as carbon sources, respectively. Fig. 2 Changes in the central metabolism of PhTAC125. a. The number of active (flux-carrying) reactions for five major pathways across all the time points is shown. b. A simplified representation of the interconnections in the central metabolism of Ph TAC125. Dashed lines indicate the presence of more than one reaction between the connected compounds. Modified from [ 39 ] \n We next evaluated whether changes in the kind and number of utilized substrates across all the time points was also parallel to major structural changes at the whole metabolic network level. We used Flux Variability Analysis (FVA) to perform a comprehensive exploration of alternate optimal routes in Ph TAC125 metabolic network, in each of the predicted growth phases (as described in Methods ). This analysis revealed that the number of “flexible reactions” is predicted to remain constant throughout all the growth phases (data not shown), suggesting that the plasticity of Ph TAC125 metabolic network is not particularly dependent on the set of metabolized substrates. This suggests the presence of an efficient adaptation of Ph TAC125’s metabolic network to face a nutritionally rich but probably variable environment. Overall, according to our model, the central phases of the growth curve are characterized by the largest set of reactions changing their flux (T3 to T5 in Fig. 1d ), consistently with the major changes in the set of utilized nutrients observed experimentally by Wilmes et al. [ 14 ] (Fig. 1a ). The first important switch in the panel of utilized compounds occurs between phase 3 and 4 (Fig. 1a ). According to experimental evidence from Wilmes et al. [ 14 ], in this time point Ph TAC125 stops using Ser, Asn and Asp as C and N sources, relying only on glutamate for sustaining growth. The importance of glutamate was previously observed from fed-batch cultivation experiments as it was the most strongly metabolised amino acid in all growth experiments [ 14 ]. Also, Glu (together with Pro and Gln) is the amino acid allowing the fastest growth rate (0.11 h −1 ) among all the 20 amino acids when growth in twenty minimal growth media (embedding each amino acid the sole C and N source and an arbitrary uptake rate of 1 mmol * g (−1) * h (−1) ) was simulated (data not shown). During P4, Ph TAC125 relies only on glutamate for sustaining growth and needs to utilize this compound to derive its main building blocks. L-Glutamate:NADP+ oxidoreductas (KEGG id: R00248) is predicted to be a key reaction in this phase as it allows the conversion of Glutamate to 2-Oxoglutarate to be used as energy source feeding the TCA cycle (Fig. 2b ). More in general, our modelling framework predicts a deep re-programming at the whole metabolic network level following this shift, with 394 reactions changing their predicted flux; more in detail, 264 reactions are predicted to decrease their flux (124 if we exclude those whose decrease may be due to the reduced growth rate imposed to the model for the fitting with experimental values), whereas 57 are predicted to carry an increased flux (Fig. 1e ). The remaining (73) reactions are predicted to operate in the reverse direction compared to the previous growth phase following the switch. Among the first set of reactions, 12 are predicted to be turned on in respect to the previous phase, whereas 31 are predicted to be turned completely off. This latter set of reactions includes, as it might be expected, the transporters of serine, asparagine and aspartate, i.e. those compounds that are no longer available in the medium and that cannot be internalized anymore. The cell faces the absence of some of these compounds by redirecting (part) of the glutamate available in the medium to their synthesis. Indeed, for example, the reaction encoded by L-aspartate:L-glutamine amido-ligase (R00578) leading to the formation of asparagine from Gln (derived from Glu) is predicted to double its flux after this metabolic transition (Fig. 2b ). The exhaustion of Asp is probably compensated by reversing reaction R00035 (L-Aspartate2-oxoglutarate aminotransferase), generating Asp from Glu (Fig. 2b ). Furthermore, our simulation indicated a drop to no flux in the reaction encoded by threonine dehydratase (R00996) and catalysing the conversion of Thr to production of 2-oxobutanoate (and ammonium), a precursor of Val, Leu and Ile (Fig. 2b ). A decrease in the availability of Asp (from which Thr is usually synthesized) might impair the biosynthesis of 2-oxobutanoate following this pathway. Our model predicts that Ph TAC125 faces this perturbation by reversing the flux of reaction R00999 (cystathionine gamma-synthase), leading to the production of 2-oxobutanoate (and succinate) from O-Succinyl-L-homoserine. After the following switch (from P4 to P5, T4), Wilmes et al. observed that Ph TAC125 starts using Ala, Leu, Gly and Tyr (together with Glu) (Fig. 1a ). According to our modelling framework, this involves a predicted change in the flux of 421 reactions (Fig. 1d ), with 260, 68 and 93 of them decreasing, increasing or changing the direction of their flux, respectively. Consistently with this new set of amino acids, the model predicts that most of the biosynthetic routes leading to the production of such compounds result turned “off”. This is the case of the path leading to the synthesis of Leu in the branched-chain amino acid biosynthetic route (from 3-isopropylmalate dehydratase to 2-oxoglutarate aminotransferase, R01213, R03968, R01652, R04001, R04426). The same occurs for Ala biosynthesis (accordingly, the reactions catalyzed by L-asparaginase (R00485) and L-Aspartate 4-carboxy-lyase (R00397) are predicted to be turned off) and Lys biosyntheses. Interestingly, in the latter case, only the final step of the whole pathway (Diaminopimelate decarboxylase, R00451) is predicted to carry no flux, consistently with the observation that the rest of the pathway is required for the synthesis of meso-2,6-Diaminopimelate, an essential precursor for peptidoglycan assembly (Fig. 2b ). Amino acid biosynthetic routes are not the only pathways affected by this metabolic switch, according to our simulated growth model. Two TCA cycle reactions are predicted to be turned on following the utilization of Ala, Leu, Gly and Tyr, i.e. those leading to the production of 2-Oxoglutarate from Isocitrate (R01899 and R00268, Fig. 2b ). This raises the intriguing question on the source of 2-Oxoglutarate in the previous growth phases given that those TCA reactions were predicted to carry no flux. A possible explanation is provided by the observation that 2-Oxoglutarate can be obtained from the carbon skeletons of several five-carbon amino acids through a first conversion into Glu , which is then oxidatively deaminated by glutamate dehydrogenase to yield α-ketoglutarate. However, the presence of Glu in the medium would not favour this solution as the major fraction of Glu can be obtained without additional energy expenses. Alternatively, 2-Oxoglutarate can be obtained from the conversion of Glu (the only carbon source in P4) to Tyr (required for sustaining growth in P4). In our model this reaction (R00734) is predicted to be “on” during P5 thus allowing the synthesis of 2-oxoglutarate (Fig. 2b ). However, starting to use Tyr present in the medium during P5 might cause reaction R00734 to be turned off and, consequently, the necessity to synthesize 2-oxoglutarate from succinate to maintain the functioning of TCA cycle. Similarly, the production of fumarate from malate (R01082) is predicted to carry no flux in the shift to P5 (Fig. 2b ). Again, this is consistent with the degradation of amino acids as a major source of important metabolic intermediates, as fumarate can be obtained from the degradation of aromatic amino acids as Tyr, available in the medium during P5 and then tunnelled into the TCA cycle. The time-resolved growth data from Wilmes et al. [ 14 ] show that, after other minor transition in which few additional amino acids are degraded (Phe, Ile, Val), the final switch involved the utilization of histidine as the only C source and causes a major drop in the growth rate of Ph TAC125 [ 14 ]. In our model, this corresponds to the highest number of flux-changing reactions (with the exclusion of the last time point in which all the reactions are turned off) and thus, to the deepest reprogramming encountered by Ph TAC125 during this growth curve. As it might be expected, histidine biosynthetic reactions (R03013, R01163, R03012, R04035, R04640, R03457, R03243, R01071, R04558, and R04037) are predicted to carry zero flux following this transition. Despite the entire pathway is predicted to carry no flux, AICAR (1-(5'-Phosphoribosyl)-5-amino-4-imidazolecarboxamide), one of the intermediates of histidine biosynthesis and a crucial precursor in purine metabolism, might still be synthesized through reaction R04559 (adenylosuccinate lyase). This compound, thanks to the flux predicted to be carried in this phase by reaction R01049, leads to the synthesis of 5-Phospho-alpha-D-ribose 1-diphosphate from D-Ribose 5-phosphate. Furthermore, the switch to the utilization of His as the sole C-source is predicted to be responsible for the stop of the catabolic routes of the previously degraded amino acids (e.g. Ala, Leu, Lys, Gly, Phe, Ile, Tyr) and, consequently, in the stop of the production of important cellular intermediates (such as 2-oxoglutarate, fumarate). According to our simulation, this causes the re-activation of key metabolic reactions that cannot rely on many degradation intermediates as in the previous phase. These include those belonging to the TCA cycle (e.g. Isocitrate dehydrogenase and Fumarate hydratase, R00268 and R01082, respectively) and purine metabolism (R01049, see before). Finally, the reactions involved in the conversion (degradation) of His into Glu (Formiminoglutamase, Imidazolonepropionase, Histidine ammonia-lyase, R02285, R02288, R01168, respectively) are predicted to be turned on following this final transition, allowing the production of glutamate and, from these, all the major pathways found to carry flux also in phase P4 (Fig. 2b ). A sub-optimal objective function predicts alternative fluxes distribution It is worth noticing that all the simulations described up to now were conducted under the FBA canonical assumption of biomass optimality, i.e. assuming that all metabolic fluxes in the cell are geared towards the production of biomass in each moment of the growth curve. However, situations in which cells invest substantial resources in alternative metabolic processes at the expense of optimal growth rates have been analysed and described quite extensively. According to this scenario, cells may invest substantial (energy) resources in a specific metabolic process at the expense of optimal growth, this being reflected by sub-optimal flux distributions [ 12 , 29 , 30 ]. This situation might be observed, for example, when microbes allocate energetic resources for anticipating changing environmental conditions at the expense of optimal growth [ 31 ]. Also, when cells are exposed to nutrients fluctuations they might respond with a minimal metabolic adjustment, to avoid the waste of protein synthesis and degradation necessary to reprogram the entire metabolic network and to simultaneously achieve two objectives, i.e. rapid and minimal adjustments. This latter scenario may indeed resemble the actual competition for nutrients that emerges in natural environments and that has been proposed to be reflected by the sequential uptake of nutrients [ 32 ]. To explore this alternative scenario, we have accounted for the possibility that the Ph TAC125 biomass objective function could not be fully optimized but, instead, in a near-optimal or sub-optimal state. Since FBA classically assumes the optimization of the biomass production flux to compute the most likely fluxes distribution inside the cell, it cannot per se provide hints concerning alternative (sub-optimal) fluxes distribution. For this reason, we have used MOMA [ 18 ] to study the hypothetical fluxes distribution when minimizing the metabolic adjustments required at each (metabolic) transition of the entire growth period analysed (“nutritional-MOMA”, see Methods ). This differs from the canonical formulation of MOMA in which the effects of a gene knock-out are evaluated by providing an approximate solution for a sub-optimal growth flux state (the mutant strain), nearest in flux distribution to the unperturbed state (wild type strain). Applying this modelling strategy to our study case revealed that the choice of the optimization criterion (i.e. biomass vs. minimal adjustment following nutrients-switching) has a great influence on the predicted fluxes distribution, both in terms of the fraction of the entire network required to sustain Ph TAC125 growth and on the set of active pathways. First, a higher number of active reactions compared to the original FBA predictions were predicted for each of the growth phases, when the implemented nutritional-MOMA approach was used (Fig. 3 and Additional file 2 : Figure S2). This gap is even more evident in the first seven growth phases, whereas the difference between the two approaches becomes negligible in the last three phases. However, despite the two approaches predict a similar number of active reactions in these two final time points, the two sets of reactions appear to be quite different (as shown by the number of shared reactions reported in Fig. 3 ). Fig. 3 Comparison between nutritional-MOMA and FBA. Here we show the number of predicted flux carrying reactions in each growth phase for FBA ( red ) and nutritional-MOMA ( blue ) optimization on the Ph TAC125 model. Also, the number of shared reactions identified by the two approaches is shown (in yellow ) \n The fact that nutritional-MOMA always predicts a higher number of active reactions might be explained by considering that to optimize the model using FBA identifies the flux distribution maximizing the objective function (biomass optimization), regardless of the metabolic states in the previous time steps. Conversely, nutritional-MOMA will seek for the solution (fluxes distribution) that is closer to the one of each previous time step (i.e., the previous nutritional condition). Therefore, the results of MOMA will likely include a higher number of reactions in the model to produce biomass, since it will minimize the changes (i.e. the active reactions) with respect to the previous time steps, while activating novel reactions to cope with the changes in the nutrient composition. We then investigated whether these differences in the sets of predicted flux-carrying (active) reactions between the two approaches were involved in specific pathways or, rather, spanned over a larger part of the entire Ph TAC125 metabolic network. Thus, for each metabolic pathway in the model, we computed the fraction of reactions predicted to be active by nutritional-MOMA, FBA and both methods (Fig. 4 ). Results of this analysis revealed that the choice of the optimization method impacts the predicted fluxes distribution not only for what concerns the activity of the peripheral (degradation) pathways, i.e. those pathways that start the degradation of amino acids and then tunnel them into the central metabolism. Indeed, we found many central processes (e.g. TCA cycle, Glycolysis, Fatty acids metabolism) in which the proportion of active reactions is (more or less) specific for each of the two optimization strategy (Fig. 4 ). Notably, for the first six growth phases, most of the reactions predicted to be active under FBA optimizations (and previously described) are carrying flux also adopting the nutritional-MOMA approach. In some cases, however, entire pathways are differently predicted to be active/inactive by the two approaches (e.g. glutathione and tyrosine metabolism in growth phase 3 (Fig. 4 ). The last two phases of the growth nutritional-MOMA and FBA predict a similar number of active reactions but, from a functional viewpoint, deep differences exist in that entire pathways are predicted to be active only under a specific optimization method (either nutritional-MOMA or FBA, Fig. 4 ). Fig. 4 Functional differences between nutritional-MOMA and FBA predictions. Here we show the proportion of reactions predicted to be active by nutritional-MOMA ( blue ), FBA ( red ) and both methods ( yellow ) for each main functional category represented in the Ph TAC125 reconstruction \n To summarize, using our MOMA-based approach we identified a higher number of reactions predicted to be carrying flux in respect to the FBA-based optimization. The nutritional-MOMA approach predicted larger active metabolic networks throughout the analysed phases possessing, on average, about 150 flux-carrying reactions more than the one simulated with FBA. Furthermore, despite most of the active reactions identified through FBA were also identified by the nutritional-MOMA approach, in some cases these predictions differed significantly from a functional viewpoint (as shown in Fig. 5 ). At present, further experimental evidences are needed to shed light on the real number and function of reactions used by Ph TAC125 in each time point (i.e. using each particular nutrients set) and, in other words, to infer how far from the actual fluxes distribution our in silico predictions are. Fig. 5 Flux correlation analysis. Heatmap accounting for the Pearson correlation of all the flux difference vectors across all the time points. The metabolic process of each reaction is also reported \n Flux correlation analysis identifies functionally associated genes In this section we analyse more in depth the flux matrix (schematically represented by the heatmap of Fig. 1b ). This matrix contains the FBA-predicted flux of the reactions (rows) for each of the time steps (columns). As such, it allows capturing the co-variation of reactions in the model; in other words, we identified as paired reactions those whose predicted fluxes displayed a similar trend throughout the time points. These, in turn, may represent functional partners in the cell and/or may belong to the same metabolic pathway/module. We represented co-varying reactions (sharing flux Pearson correlation ≥ 0.7, see Methods ) in the form of a heatmap as shown in Fig. 5 . In most cases, the small clusters of reactions showing high Pearson correlation values are involved in the same metabolic process. This is compatible with a scenario in which, following a nutrient(s) switch, entire metabolic pathways are activated (or deactivated) to face the novel environmental condition (as shown in the previous section). Nevertheless, grouping reactions with a lower but still significant threshold (i.e. down to 0.7), clusters start to embed reactions belonging to other metabolic processes. These, in turn, may represent previously undetected functional associations between genes and/or entire pathways. Overall, our method led to the identified 28 different clusters Fig. 6a , comprising 203 reactions. According to the GPR of our model, these reactions were encoded by 223 genes. Fig. 6 \n a . Co-varying reactions clusters identification. Common flux trends (expressed as the normalized difference between the absolute value of fluxes across each growth phase and the following one) for the reactions embedded in each of the 28 clusters. b. The distribution of STRING combined scores among all the genes embedded in each cluster of genes (primary y axis) and the number of genes embedded by each cluster (secondary y axis, red line). The grey line represents the median of the combined score computed for each possible pair of genes in the model \n We analysed these clusters to answer two biological questions, namely whether the embedded genes i) are de facto functionally related and ii) share conserved upstream motifs (which, in turn, would suggest a common regulation mechanism). Groups of co-varying genes display higher combined evidence scores To address the first point, we exploited the combined evidence score provided by the STRING database. As shown in Fig. 6b , the median of the combined score for the genes embedded in the same cluster is, in most cases, well above the median of all the possible combinations of the genes embedded in the model, suggesting that flux coupling in the model occurs among bona fide functional patterns. Accordingly, this result corroborates the capability of our modelling framework to identify functional metabolic modules and the functional associations of their encoding genes in Ph TAC125. Interestingly, in some cases apparently un-related genes (showing low combined score values in respect to the other genes) seem to be embedded in the clusters. Despite these instances may represent erroneous predictions of our model, they might also suggest the presence of still undetected functional associations and/or the use of common pools of chemical intermediates by the corresponding reactions (see below). We here present a description of the functional relationships retrieved for genes belonging embedded in three of these clusters (Fig. 7 ). A detailed list of the reactions and genes embedded in all the clusters is provided as Additional file 3 to allow further experimentation and/or in silico analyses. Fig. 7 Sample STRING clusters. Evidence network, co-expression instances and co-occurrence patterns for clusters 23 ( a , b , and c , respectively), 11 ( d , e and f , respectively) and 2 ( g , h and i , respectively). Red asterisks in g indicate those genes known to belong to the same regulon according to RegPrecise database (ArgR regulon) \n \n Cluster 23. This cluster is composed of seven reactions encoded by 19 genes. Three of these reactions are predicted to be catalysed by enzymes encoded by multiple genes, i.e. cytochrome-c oxidase encoded by PSHAa2869-71, Ferrocytochrome-coxygen oxidoreductase encoded by PSHAa1842-45 and ATP synthase encoded by PSHAa3007-15. According to the GPR rules of our model, the simultaneous presence of all the corresponding genes is required to carry flux of each of these reactions. The functional association network obtained probing the STRING database with all the genes of Cluster 23 is reported in Fig. 7a . Genes encoding proteins involved in the same enzymatic reaction appear to be highly interconnected among themselves with evidences that included co-expression, gene fusions, and experimental evidences (see the sub-clusters in the network of Fig. 7a ). This is not surprising, as it might be expected that genes encoding proteins that participate in the same enzymatic complex should display coupled regulation and co-occurrence patterns in other organisms. Links, however, are also found among members of different enzymatic complexes, consistent with the fact that these reactions belong to the overall process of oxidative phosphorylation. The ndk gene (encoding a nucleoside diphosphate kinase) is also found connected to one of the sub-cluster Fig. 7a ). This protein plays a major role in the synthesis of nucleoside triphosphates and is involved in purine and pyrimidine metabolism. The coupling of this reaction’s flux with those belonging to oxidative phosphorylation can thus be explained by their common final products (nucleotide triphosphates and ATP for Ndk and pentose phosphate pathway, respectively), as optimizing the model for biomass production would probably force the flux through both these pathways. Finally, the fact that orthologs of these genes are found to be co-expressed and located in physical proximity in other (related) genomes (Fig. 7b and c , respectively) support the functional association predicted by our model. \n Cluster 11. We found eight reactions (encoded by 10 genes) embedded in this cluster (Fig. 7d ), involved in the following metabolic pathways: Glycerolipid metabolism, Pentose phosphate pathway and Glycolysis. Half of the reactions of this cluster are involved in the conversion of glycerol into glycerate 3-pshosphate that, in turn, is connected to glycolysis/gluconeogenesis. As shown by the STRING analysis output, many evidences exist of a functional association among the genes of this cluster, in most cases including instances of co-expression and physical proximity Fig. 7e and f , respectively). Two genes of the cluster remained disconnected in the STRING network (PSHAa0190 and PSHAb0551), both encoding a glycerol kinase and assigned to the same reaction in our metabolic reconstruction (R00847, encoding the transformation of glycerol into sn-Glycerol 3-phosphate). Accordingly, despite no previously detected functional associations were retrieved between these two genes and the other embedded in the same cluster, their presence could be accounted for by the use of common precursors (i.e. glycerol) with the reactions encoded by Cluster 11 genes. Indeed, fluctuations in the availability of such compound (and its derivatives) during the simulated growth transitions explains the coupling of such reactions. \n Cluster 2. This cluster embeds nine reactions encoded by 10 genes involved in Arg biosynthetic pathway argAH, B, C, D, E and in the metabolism of Pro ( putA and Orn cyclodeaminase (PSHAb0543)). The STRING-based evidence network in Fig. 7g revealed hints on the functional associations existing among the genes of the cluster including co-occurrence of orthologs of these genes in other (closely related) genomes, as well as co-expression and conserved genomic neighbourhoods Fig. 7h and i, respectively). The genes embedded in this cluster encode enzymes involved in the the formation of Arg and Pro from Glu, using Asp and then Orn as intermediates. These reactions appear to carry flux especially in the first three phases of the growth (P1 to P3), that is when aspartate (or Asn from which aspartate can be easily obtained) is present in the medium. Afterwards, a decrease in the flux carried by these reactions is observed (the peak in the “Cluster 2” panel of Fig. 7 ), followed by an overall constant flux trend in the next transitions. Accordingly, the correlation among the fluxes of these reactions could be explained by the need to turn these pathways “on” (or “off”) when glutamate and required intermediates are present (or absent) in the growth medium, respectively. A gene encoding an acetyl-CoA synthetase ( acsA ) is also embedded in this cluster, despite it has not connections with arginine and proline metabolism-related genes. However, its presence might reflect the necessity to synthesize acetyl-CoA from acetate during the first four growth phases identified, i.e. in the absence of ketogenic amino acids. Once Ph TAC125 starts metabolising the second set of amino acids, acetyl-CoA can be obtained from the degradation of specific amino acids (e.g. Leu, Val, Ile) and thus the reaction encoded by acsA (PSHAa0698) is predicted to stop carrying flux. \n Analysis of upstream sequences identifies putative regulation mechanisms In the previous section we reported a list of gene sets which are, presumably, functional partners and whose activity is concerted during the different metabolic switches. If this holds, it is reasonable to hypothesize that some of these clusters might embed co-regulated genes that, in turn, may share conserved upstream motifs (implying for common transcriptional regulatory mechanisms). Thus, to address this point we applied an ad hoc computational pipeline (implementing tools from the MEME suite) to analyse the upstream regions of the genes (or arrays of genes in case of operons) embedded in each cluster. This resulted in the identification of motifs that are shared in the upstream regions of all the genes for each cluster. Upstream regions were identified using DOOR (Database of prOkariotic OpeRons) [ 28 ]. These have been annotated using three different motif databases (i.e. CollecTF [ 33 ], PRODORIC [ 34 ] and RegTransBase [ 35 ]), to identify motifs with putatively related functions and that might be recognized by putatively related transcription factors (TFs). The whole set of annotations has been manually curated in a conservative way to obtain high-confidence motif annotations. Finally, the obtained putative TFs have been cross-validated by comparing their associated functions with those related to the genes embedded in the clusters. The whole set of data related to the identified motifs and their annotations (before manual curation), for each database, is provided as Additional file 4 . On average, we identified for each cluster a large number of annotations according to the different databases (10.9, 8.9 and 29.4 annotations for CollecTF, PRODORIC and RegTransBase, respectively). However, after manual curation, the number of annotations was reduced to 31, divided in 15 clusters (2.1 on average). Most of the remaining annotations (74%) come from Prodoric, while the other databases have a minor contribution of high-quality annotations (16% CollecTF; 10% RegTransBase). For each cluster, we eventually compared the putative TFs with the functions of the genes embedded in the cluster, using both literature information and the RegPrecise database. With few exceptions that will be described in details, we found no correlation between the putative TFs and the regulation/biological function associated to the genes embedded in the clusters, suggesting that the majority of the co-varying groups share a common regulation but are regulated by a number of different TFs. We are describing below the clusters for which we found agreement between the predicted TFs and the function of the embedded genes (Table 1 ). Table 1 Main features of the putatively co-regulated clusters found during flux-correlation analysis. In this table, for each cluster, we report its number, the name of the regulator identified, the genes embedded in it and the conserved motif found upstream of its genes Cluster name Motif name Genes Weblogo Cluster 2 ArgR PSHAa0194, PSHAa0698,PSHAa2175, PSHAa2287, PSHAa2290, PSHAa2291, PSHAa2292, PSHAb0333, PSHAb0428, PSHAb0543 \n \n Cluster 3 CcpA PSHAa0189, PSHAa0609, PSHAa0740, PSHAa1167, PSHAa1648, PSHAa1649, PSHAa1650, PSHAa1651, PSHAa2167, PSHAb0082, PSHAb0345 \n \n Cluster 6 GalR PSHAa0603, PSHAa0871, PSHAa1364, PSHAa1767, PSHAa2301, PSHAb0295 \n \n \n \n Cluster 2. This cluster, as described previously, is involved in the metabolism of Arg and Pro. A search on the RegPrecise regulon database [ 25 ] revealed that, at least in the case of Arg, the genes embedded in the cluster of Fig. 7g are part of a common regulon (ArgR regulon). The only exception is represented by argD which, according to the RegPrecise database is not part of the arginine regulon in Ph TAC125, and the other genes involved in the biosynthesis of Pro. However, both the computational approach described above and a manual search for putative transcription factor binding sites upstream of these genes detected the already known conserved motif shared by the other arg genes of the regulon (Table 1 ). This suggests that the all the genes are part of the arginine regulon in Ph TAC125. More in general, this finding confirms that, starting from our modelling outcomes it is possible to infer biologically consistent patterns of gene co-expression. \n Cluster 3. This cluster includes genes from the sdh operon ( sdhABCD ), encoding the succinate dehydrogenases system, as well as other enzymes involved in energy conversion, such as other dehydrogenases ( glpD , bcd ) and other enzymes with a role in nucleotide modifications ( hpt , ushA and mazG ). Our computational pipeline predicted two different regulator genes associated with this cluster, i.e. ccpA and psrA (Table 1 ). These regulators encode proteins are involved in the carbon and fatty acid metabolism, respectively. Comparing the functional role of the TFs with the cluster annotation, we can easily relate the set of genes encoding dehydrogenases with ccpA and psrA , which is also confirmed by RegPrecise database. The other three genes are indirectly involved in the oxidative metabolism, in that they are involved in the metabolism of FAD. \n Cluster 6. This cluster includes five genes, with three of them involved in the synthesis of folate ( folP and two copies of folK ), while the remaining two encode a galactokinase ( galK ) and a galactose-1-epimerase ( galM ). All these genes are involved in the metabolism of nucleotides, since folate is necessary to synthesize TDP sugars from UDP, while the other genes encode enzymes that are involved in the synthesis of UDP. The analysis of the upstream sequence of these genes revealed a conserved motif similar to the GalR binding site, thus suggesting that these genes are being co-regulated (Table 1 )."
} | 12,317 |
23434798 | PMC3640398 | pmc | 4,752 | {
"abstract": "Genome sequences of the reef-building coral, Acropora digitifera , have been decoded. Acropora inhabits an environment with intense ultraviolet exposure and hosts the photosynthetic endosymbiont, Symbiodinium . Acropora homologs of all four genes necessary for biosynthesis of the photoprotective cyanobacterial compound, shinorine, are present. Among metazoans, these genes are found only in anthozoans. To gain further evolutionary insights into biosynthesis of photoprotective compounds and associated coral proteins, we surveyed the Acropora genome for 18 clustered genes involved in cyanobacterial synthesis of the anti-UV compound, scytonemin, even though it had not previously been detected in corals. We identified candidates for only 6 of the 18 genes, including tyrP , scyA , and scyB . Therefore, it does not appear that Acropora digitifera can synthesize scytonemin independently. On the other hand, molecular phylogenetic analysis showed that one tyrosinase gene is an ortholog of vertebrate tyrosinase genes and that the coral homologs, scyA and scyB , are similar to bacterial metabolic genes, phosphonopyruvate ( ppyr ) decarboxylase and glutamate dehydrogenase ( GDH ), respectively. Further genomic searches for ppyr gene-related biosynthetic components indicate that the coral possesses a metabolic pathway similar to the bacterial 2-aminoethylphosphonate (AEP) biosynthetic pathway. The results suggest that de novo synthesis of carbon-phosphorus compounds is performed in corals.",
"conclusion": "4. Conclusion We have previously identified environmental response genes in corals. These included genes unique to metazoans, such as fluorescent proteins [ 52 ] and enzymes involved in shinorine synthesis [ 15 ]. The present gene survey does not support the hypothesis that A. digitifera can synthesize scytonemin independently. Although the A. digitifera genome contains homologs of several genes that function in scytonemin synthesis in Nostoc , these genes may have acquired new functions in Acropora that remain to be elucidated. The homologs of scyA and scyB , ppyr decarboxylase , gdh-1-1 , and gdh-1-2 are similar to genes involved in general bacterial metabolic pathways. Our genome-wide surveys for genes of enzymes involved in synthesis of photoprotective compounds indicate that corals retain genes for some enzymes not found in Homo and Drosophila . Therefore, it is likely that not only marine bacteria, but also marine invertebrates produce many unknown natural compounds, as suggested by the presence of the AEP pathway. Genomic surveys will undoubtedly provide more clues regarding natural product synthesis.",
"introduction": "1. Introduction Reef-building corals (Class Anthozoa) typically inhabit shallow and relatively clear tropical waters; therefore, they are constantly exposed to high levels of ultraviolet radiation. Since corals are particularly susceptible to bleaching when exposed to both rising temperatures and high solar radiation [ 1 , 2 ], one intriguing question is how corals protect themselves against ultraviolet damage. UV-absorbing substances potentially act as photoprotective compounds. These include mycosporine-like amino acids (MAAs), scytonemin, carotenoids, and other compounds of unknown structure [ 3 , 4 ]. These photoprotective compounds have been isolated from various marine organisms, including corals [ 5 , 6 ]. However, since reef-building corals maintain symbiotic dinoflagellates, such as Symbiodinium , in the gastrodermal tissue layer [ 7 , 8 ], and since dinoflagellates can independently synthesize photoprotective compounds [ 9 ], the origins of these compounds are often uncertain [ 10 ]. Following the sequencing of the genome of the sea anemone (anthozoan) Nematostella vectensis [ 11 ], Starcevic et al. [ 12 ] investigated whether the Nematostella genome contains genes for enzymes of the shikimic acid pathway, which contributes to the biosynthesis of MAAs. They found that the Nematostella genome contains genes encoding aroB (dehydroquinate synthase (DHQS)) and other genes from the same pathway. The Nematostella genes are closely related to those of dinoflagellates, suggesting that the Nematostella genes were acquired via horizontal gene transfer (HGT) [ 12 ]. Recently, the genome of the hydrozoan, Hydra magnipapillata , was also sequenced [ 13 ], and the presence of retained genes in cnidarians, not found in the other animal genomes, has been reported [ 14 ]. We have now sequenced the genome of the coral, Acropora digitifera , using Roche 454 GS-FLX and Illumina GAIIx sequencers, obtaining approximately 110-fold coverage with whole-genome shotgun, paired-end and mate-pair methods [ 15 ]. The coral genome was estimated to be 420 Mbp in size. We identified 23,668 gene models in the coral genome; 16,434 of these are complete gene models with both start and stop codons. Approximately 93% of the coral gene models have counterparts in other metazoan genomes [ 15 ]. Recently, Balskus and Walsh [ 16 ] identified a four-gene cluster (encoding DHQS-like, O -MT ( O -methyltransferase), ATP-grasp, and NRPS-like (nonribosomal peptide synthetase-like) enzymes) that is required for conversion of pentose-phosphate metabolites into shinorine (an MAA) in the cyanobacterium, Anabaena variabilis . We scanned the Acropora gene models for homologs of the shinorine gene cluster, and found that this four-gene pathway is present in both Acropora and Nematostella , but not in Hydra [ 15 ]. This strongly suggests that both Acropora and Nematostella can synthesize shinorine, which may be used to produce photoprotective compounds. In addition, by molecular phylogenetic analyses, we showed that the homologous putative proteins in Acropora had more sequence similarities to those of bacteria and dinoflagellates than to those of humans and Drosophila [ 15 ]. The indole-alkaloid, scytonemin, is a UV-blocking compound, found exclusively in cyanobacteria, and has been evaluated for biomedical applications [ 17 ]. Recently, Soule et al. [ 18 , 19 ] showed that scytonemin synthesis is controlled by an 18-gene cluster in the cyanobacterium, Nostoc punctiforme ( Figure 1 ). The Nostoc operon includes scyA , scyB , scyC , scyD , scyE , scyF , NpR1270 ( glycosyltransferase ), tyrA , dsbA and aroB . Although scytonemins have not been found in corals, the presence of symbiotic cyanobacteria in coral species has been reported [ 20 ]. Furthermore, some cyanobacteria have been implicated in coral disease [ 21 ] and the roles of microbial communities associated with coral are being discussed [ 22 ]. Therefore, in this study, we investigated whether the coral genome contains genes encoding proteins that are homologous to cyanobacterial enzymes involved in scytonemin synthesis. In relation to the homologs of scyA , we surveyed the Acropora genome for genes encoding enzymes of the 2-aminoethylphosphonate (AEP) pathway. AEP is a natural carbon-phosphorus compound, first reported by Horiguchi & Kandatsu [ 23 ]. This study will provide a basis for natural product surveys of anthozoans. Figure 1 Distribution of genes associated with biosynthesis of scytonemin in cyanobacteria, cnidarians, and other metazoans. ( a ) Pathways of biosynthesis of the photoprotective molecule, scytonemin, in the cyanobacterium, Nostoc punctiforme [ 6 , 16 ]. Gene homologs encoding enzymes indicated with asterisks were identified in the A. digitifera genome. ( b ) Schematic showing the organization of the scytonemin gene cluster. Genes indicated by red arrows encode enzymes involved in the biosynthesis of aromatic amino acids. The presence of corresponding genes in various organisms is indicated by “+”, indicating that a TBLASTN search against N. punctiforme as query showed significant hits. Anthozoan genomes encode a gene homologous to aroB , involved in aromatic amino acid metabolism, which is not found in higher metazoans.",
"discussion": "2. Results and Discussion The UV-blocking compound, scytonemin, is produced exclusively by cyanobacteria ( Figure 1 ). The probable biosynthetic pathway has been reported [ 24 ] ( Figure 1 a). The scytonemin gene cluster in Nostoc punctiforme consists of one subcluster of genes involved in aromatic amino acid biosynthesis, but the functions of many novel genes in another subcluster are unknown [ 19 ]. The former subcluster includes tyrA , dsbA , aroB , trpE , trpC , trpA , tyrP , trpB , trpD and aroG ( Figure 1 b, red arrows). The latter includes scyA , scyB , scyC , scyD , scyE , and scyF ( Figure 1 b, black arrows). Screening of the A. digitifera genome via BLAST and domain structure comparisons led to the identification of candidates for six of the 18 genes involved in scytonemin synthesis: scyA , scyB , scyF , dsbA , aroB , and tyrP ( Figure 1 b). Analysis of aroB ( DHQS ) in a previous study identified an aroB homolog in the Acropora genome [ 15 ]. Molecular phylogenetic analyses group the aroB-like sequences of Acropora and Nematostella with those of several dinoflagellates,ss, consistent with the possibility that the aroB-like genes of cnidarians originated by horizontal transfer from dinoflagellates [ 12 ]. Here we describe results of molecular phylogenetic analyses of scyA , scyB , dsbA , and tyrP. Detailed analyses of scyF homologs were not performed for reasons that will be explained subsequently (See Section 2.3 ). 2.1. scyA (TPP-Dependent Enzyme) scyA encodes a TPP (thiamine pyrophosphate)-dependent enzyme [ 25 ], a protein similar to human 2-hydroxyacyl-CoA lyase, which has close homologs in a variety of organisms, including Drosophila and Arabidopsis ( Figure 1 b; Table 1 ). It is also similar to acetolactate synthase which is found in plants and micro-organisms. Both 2-hydroxyacyl-CoA lyase and acetolactate synthase are involved in synthesis of the essential amino acids, valine, leucine, and isoleucine [ 26 ]. Biosynthesis of 2-aminoethylphosphonate (AEP) from phosphoenolpyruvate (PEP) requires just three enzymes: PEP mutase, phosphonopyruvate decarboxylase, and AEP transaminase, collectively known as the AEP biosynthetic pathway [ 27 ] ( Figure 2 ; See Section 2.6 ). Phosphonopyruvate (ppyr) decarboxylase is also similar to both 2-hydroxyacyl-CoA lyase and acetolactate synthase. marinedrugs-11-00559-t001_Table 1 Table 1 Putative enzyme genes in the Acropora digitifera genome that are similar to enzymes involved in biosynthesis of the cyanobacterial sunscreen, scytonemin. Gene name Gene model ID Intron number All PFAM domains (in order) * corresponding to ESTs scaffold References \n phosphonopyruvate decarboxylase \n aug_v2a.20271 6 TPP_enzyme_N, TPP_enzyme_C + 12471 Figure S1 \n 2-hydroxyacyl-CoA lyase 1 \n aug_v2a.06817 13 TPP_enzyme_C − 2544 Figure S1 \n glutamate dehydrogenase1-1 (gdh1-1) \n aug_v2a.22675 0 ELFV_dehydrog_N, ELFV_dehydrog + 15779 Figure S2 \n glutamate dehydrogenase1-2 (gdh1-2) \n aug_v2a.23483 1 ELFV_dehydrog_N, ELFV_dehydrog + 16875 Figure S2 \n glutamate dehydrogenase2-1 (gdh2-1) \n aug_v2a.13667 6 ELFV_dehydrog_N, ELFV_dehydrog + 5605 Figure S2 \n glutamate dehydrogenase2-2 \n (gdh2-2) \n aug_v2a.16277 7 ELFV_dehydrog_N, ELFV_dehydrog − 7525 Figure S2 \n DSBA domain containing gene-1 \n aug_v2a.12085 21 Dynein_Heavy, DSBA, DSBA + 4763 Figure S3 DHQS-like ( aroB-like ) aug_v2a.14548 2 DHQ_synthase + 6105 [ 15 ] \n TyrP1 \n aug_v2a.08070 2 TSP_1, TSP_1, TSP_1, TSP_1, Tyrosinase + 3066 Figure S4 \n TyrP2 \n aug_v2a.10437 12 Tyrosinase + 4001 Figure S4 * Search parameters: E -value of 1.0. Figure 2 Metabolic pathways unique among metazoans and found in corals. The 2-aminoethylphosphonate (AEP) biosynthetic pathway was first discovered in Tetrahymena pyriformis . Phosphoenolpyruvate decarboxylase, shown in Table 1 , is uncommon in metazoans. Homologs of the other two enzyme genes involved, indicated by asterisks, are also found in coral; see Table 2 for details. Molecular phylogenetic analysis showed that two Acropora proteins containing a TPP enzyme domain were separated into two clades, one containing PEP decarboxylase, with orthology to the Bacteroides fragilis enzyme and the other, 2-hydroxyacyl-CoA lyase, with orthology to the human protein ( Table 1 ; Figure S1 ). Both enzymes have Nematostella counterparts, and these were closely related to each other ( Figure S1 ). In contrast, the latter group formed a clade that includes Homo , Drosophila , and Arabidopsis orthologs. PEP decarboxylase was not found in other metazoan genomes. The Acropora PEP decarboxylase gene has six introns and was located at the 5′ terminus of scaffold 12471. Its neighbor was a gene for an ephrin-like protein, which belongs to the tyrosine kinase receptor subfamily. mRNA corresponding to ppyr decarboxylase , but not hydroxyacyl-CoA lyase , was present in EST databases ( Table 1 ). The gene for acetolactate synthase was not found. Neither of the two Acropora genes formed a clade with scyA of the cyanobacteria, Nostoc and Nodularia . 2.2. scyB (GDH Subfamly) scyB encodes a protein that resembles glutamate dehydrogenase (GDH) [ 17 ]. GDH enzymes are divided into four classes [ 28 , 29 ]. Searches for GDH genes in the Acropora genome revealed four genes, gdh-1-1 , -1-2 , -2-1 , and -2-2 ( Table 1 ). Molecular phylogenetic analysis indicated that gdh-2-1 and gdh-2-2 form a clade with Nematostella and Hydra orthologs ( Figure S2 ). This clade also includes orthologs of Drosophila and Homo , suggesting that gdh-2-1 and gdh-2-2 encode metazoan GDH. The presence of gdh-2-1 and gdh-2-2 in one clade implies that they were duplicated within the lineage ( Table 1 ). On the other hand, gdh-1-1 and gdh-1-2 form another clade with the corresponding Nematostella genes ( Figure S2 ). This group includes bacterial and Arabidopsis genes, but not those of metazoans ( Figure S2 ). All trees (Bayesian inference, Neighbor joining, and Maximum likelihood) supported the clade ( Figure S2 ). gdh-1-1 has no introns while gdh-1-2 has one. The expression of gdh-1-1 was confirmed in the EST database. gdh-1-2 was located at the 5′ terminus of scaffold 16875 and the neighboring gene is similar to human caseinolytic peptidase B, a hexameric chaperone. This analysis indicates that corals have two GDH class 1 and two GDH class 2 enzymes. Because GDH class 1 has not been found in metazoans [ 29 ], corals may have unknown GDH metabolic pathways. 2.3. scyF (NHL Repeat Containing) NHL is a conserved structural motif present in a large family of growth regulators. Many NHL-containing proteins also possess additional domains, e.g., RING fingers, B-box zinc fingers, and coiled-coil motifs. According to structural model analysis, the NHL domain-containing genes could be involved in protein−protein interactions and/or protein-nucleic acid interactions [ 30 ]. scyF encodes a protein that contains an NHL repeat (Ncl-1, HT2A and Lin-41), which is defined by amino acid sequence similarities to Ncl-1, HT2A, and Lin-41 proteins [ 30 ]. Most animal and plant genomes contain scyF -like genes ( Figure 1 b). A Pfam domain search of the NHL domain revealed that the Acropora genome contains 107 genes encoding NHL-containing proteins. In addition, the three Acropora genes most similar to Nostoc scyF , aug-v2a.11071, aug-v2a.01011, and aug-v2a.06686, included other domains such Filamin, SGL, and zf-B Box. Therefore, it was difficult to clarify the relationship among NHL-repeat-containing genes. Only three genes encode proteins with one NHL repeat each. Some of these may be members of novel metabolic pathways. 2.4. dsbA DsbA (disulfide bond A) is a subfamily of the thioredoxin family [ 31 , 32 ]. Efficient, correct folding of bacterial disulfide-bonded proteins in vivo is dependent upon a class of periplasmic oxidoreductase proteins called DsbA. The bacterial protein-folding factor DsbA is the most oxidizing member of the thioredoxin family. dsbA genes with high similarities to Nostoc dsbA have been identified in each of the cnidarians ( A. digitifera , Nematostella vectensis and Hydra magnipapillata ) and in Trichoplax (Phylum Placozoa), but are not found in Drosophila and Homo ( Table 1 ). Metazoan dsbA genes have greatly diverged from bacterial DsbA genes; therefore, it was difficult to align the sequences. Such low similarities may be due to selenoproteins, in which it is difficult to predict the open reading frame [ 33 ]. By domain search, we found three candidates, aug-v2a.12085, aug-v2a.05997, and aug-v2a.00764 in the Acropora genome. However, the gene models, aug-v2a.05997 and aug-v2a.00764, were likely partial, and were excluded from further analyses. These models may be artifacts of insufficient assembly or inaccurate gene prediction. The four cnidarian dsbA sequences formed discrete clades in molecular phylogenetic analyses ( Figure S3 ), suggesting diversification of these genes in the cnidarian lineage. In addition, DSBA domain-containing gene-1 was positioned in a subgroup different from the cyanobacterium dsbA . 2.5. TyrP TyrP (Tyrosinase-related Protein) has a well-established role in melanin biosynthesis in mammals, and is involved in several biological functions [ 34 ]. We found six candidate tyrosinases, but four of them were partial sequences. Therefore, we used only the two complete candidates for molecular phylogenetic analysis. Interestingly, TyrP 1 forms a clade with its vertebrate equivalents ( Figure S4 ), although we could not find any Nematostella and Hydra orthologs in this clade ( Figure S4 ). On the other hand, TyrP 2 is a member of a group that included the tyrosinase-related proteins of cnidarians ( Figure S4 ). No Acropora tyrosinase genes form a clade with cyanobacterium TyrP, but further studies will be needed to understand the relationships of the four unknown, partial genes. 2.6. Genes for AEP Pathway Because it has been reported that PEP decarboxylase is an enzyme for one of three steps in the AEP biosynthetic pathway in protists and bacteria [ 35 ], we surveyed homologs of enzyme genes for the other two steps. Interestingly, we found candidate genes for phosphoenolpyruvate mutase and aminoethylphosphonate transaminase ( Table 2 ; Figures S5 and S6 ). Our gene survey suggests that Acropora digitifera has a complete AEP biosynthetic pathway from phosphoenolpyruvate (PEP) ( Figure 2 , Figures S1, S5 and S6 ), which is the shortest known pathway for construction of natural phosphonate [ 35 ]. Therefore, corals may be important producers of carbon-phosphorus compounds in marine ecosystems. It is possible that reported draft genome sequences of metazoans could include sequences from other organisms, resulting from contamination. However, the coral A. digitifera genome sequences from the purified sperm genomic DNA of one individual did not contain contaminated sequences [ 15 ]. The following observations indicate that all of the annotated genes in this study are encoded by the A. digitifera genome: (1) Orthologs of these genes, which formed a clade in molecular phylogenetic analysis, were found in Nematostella ; (2) Expression of most genes was confirmed by embryonic transcriptome analysis; and (3) Some of the gene orders, including annotated genes, were conserved between A. digitifera and N. vectensis . marinedrugs-11-00559-t002_Table 2 Table 2 Orthologs of genes for the AEP biosynthetic pathway in the Acropora digitifera genome. Gene name Gene model ID Intron number All PFAM domains (in order) * corresponding to ESTs scaffold References \n phosphoenolpyruvate mutase \n aug_v2a.19072 7 PEP_mutase + 11028 Figure S5 \n 2-aminoethylphosphonate transaminase \n aug_v2a.21804 4 − + 14440 Figure S6 * Search parameters: E -value of 1.0."
} | 4,972 |
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