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22529839 | PMC3328120 | pmc | 5,082 | {
"abstract": "The bioavailability of iron to microorganisms and its underlying mechanisms have far reaching repercussions to many natural systems and diverse fields of research, including ocean biogeochemistry, carbon cycling and climate, harmful algal blooms, soil and plant research, bioremediation, pathogenesis, and medicine. Within the framework of ocean sciences, short supply and restricted bioavailability of Fe to phytoplankton is thought to limit primary production and curtail atmospheric CO 2 drawdown in vast ocean regions. Yet a clear-cut definition of bioavailability remains elusive, with elements of iron speciation and kinetics, phytoplankton physiology, light, temperature, and microbial interactions, to name a few, all intricately intertwined into this concept. Here, in a synthesis of published and new data, we attempt to disassemble the complex concept of iron bioavailability to phytoplankton by individually exploring some of its facets. We distinguish between the fundamentals of bioavailability – the acquisition of Fe-substrate by phytoplankton – and added levels of complexity involving interactions among organisms, iron, and ecosystem processes. We first examine how phytoplankton acquire free and organically bound iron, drawing attention to the pervasiveness of the reductive uptake pathway in both prokaryotic and eukaryotic autotrophs. Turning to acquisition rates, we propose to view the availability of various Fe-substrates to phytoplankton as a spectrum rather than an absolute “all or nothing.” We then demonstrate the use of uptake rate constants to make comparisons across different studies, organisms, Fe-compounds, and environments, and for gaging the contribution of various Fe-substrates to phytoplankton growth in situ . Last, we describe the influence of aquatic microorganisms on iron chemistry and fate by way of organic complexation and bio-mediated redox transformations and examine the bioavailability of these bio-modified Fe species.",
"introduction": "Introduction By virtue of its flexible redox chemistry, iron (Fe) plays an integral role in many biological processes such as photosynthesis, respiration, processing of reactive oxygen species, and nutrient acquisition. In view of these functions, it is not surprising that iron inputs and bioavailability in aquatic environments have far reaching repercussions for many natural systems and diverse areas of study as briefly outlined in Box 1 . At the basis of these lies the role of iron in controlling phytoplankton growth. Photosynthetic life on Earth originated in reduced, low oxygen aquatic environments where the soluble ferrous iron – Fe(II) – was abundant and freely available. The rise of oxygenic photosynthesis favored the oxidized ferric form – Fe(III) – which rapidly precipitates out of oxic solutions as iron oxides or hydroxides. Modern day oceans and lakes thus cater poorly to the Fe requirements of phytoplankton with surface waters bearing picomolar to nanomolar concentrations of dissolved unchelated inorganic iron, Fe′ (Johnson et al., 1997 ), the most readily available form of Fe to phytoplankton, be it in ferrous Fe(II)′ or ferric form Fe(III)′ (Morel et al., 2008 ). Box 1 Scope of iron influence on natural systems and research fields . Aquatic iron biogeochemistry has been in the limelight over the past three decades with numerous studies linking Fe to carbon cycling and global climate (Martin et al., 1990 ; Watson et al., 2000 ; Blain et al., 2007 ; Martinez-Garcia et al., 2011 ). A particular emphasis has been placed on Fe availability to phytoplankton since over 45% of global photosynthesis occurs in aquatic environments (Falkowski et al., 1998 ) and photosynthetic systems are heavily dependent on iron (e.g., Raven, 1990 ; Greene et al., 1991 ). It is now well established that limited iron availability lowers phytoplankton pigment content and light harvesting capabilities, hinders photosynthesis and growth rates, and subsequently diminishes the production of organic matter and biogenic minerals (CaCO 3 and opal) and curtails CO 2 drawdown in vast ocean regions (Figure 2 ; Boyd et al., 2007 ). Many biogenic gases other than CO 2 are important determinants of atmospheric chemistry and climate, but far less is known about the controls iron exerts on the sea-atmosphere fluxes of such gases (Figure 2 ; Liss, 2007 ; Buesseler et al., 2008 ). What is known however, is that phytoplankton growth, death, and decomposition, all of which may be controlled by Fe availability, result in emissions of dimethylsulfide (DMS) and isoprene (cloud formation promoters), N 2 O and CH 4 (potent greenhouse gases), and CO and OH (reactive species influencing the atmosphere oxidation potential; Law and Ling, 2001 ; Meskhidze and Nenes, 2006 ). The combined effects of these emissions on atmospheric radiative forcing remain largely unknown (Lampitt et al., 2008 ). By controlling phytoplankton standing stocks, Fe availability may also influence the surface ocean light field, and subsequently play a role in the surface ocean heat budget (Figure 2 ; Manizza et al., 2005 ). In addition to constraining primary productivity, iron deficiency impedes biogenic element cycling since phytoplankton cannot synthesize the enzymes required for utilizing major nutrients such as nitrate and N 2 (Figure 2 ; Milligan and Harrison, 2000 ; Kustka et al., 2002 ; Sohm et al., 2011 ). Low iron availability may also alter ecosystem structure and function: under Fe limitation smaller phytoplankton are favored, resulting in rapid carbon regeneration and lowered carbon export flux to the deep ocean (Figure 2 ; Price et al., 1994 ; Finkel et al., 2010 ). As limited Fe availability alters nutrient assimilation ratios and phytoplankton species composition, it bares implications for the reconstruction of ocean paleo-productivity and paleo-nutrient distributions. Examples include the intensively studied sedimentary records of diatoms, whose abundance, morphology, and composition is strongly regulated by Fe (Figure 2 ; Strzepek and Harrison, 2004 ; Marchetti et al., 2006 ; Marchetti and Cassar, 2009 ). An additional, less explored example, is the recently reported effect of Fe limitation on cadmium (Cd) drawdown from seawater by phytoplankton (Lane et al., 2009 ), Subsequently, Fe limitation may alter seawater Cd:P ratios (Cullen et al., 1999 ) and thus bias past reconstruction of PO 4 3 - distributions which is based on seawater Cd:P ratios (Figure 2 ; Boyle, 1988 ; Elderfield and Rickaby, 2000 ). Recent attention has also been drawn to the effect of iron inputs and availability on toxic algal species occurrence and toxin production in oceans and lakes (Figure 2 ; e.g., Trick et al., 2010 ; Alexova et al., 2011 ). Extensive research on iron bioavailability to phytoplankton has been conducted over recent decades, yet a clear-cut definition of this term remains elusive. Bioavailability may be defined as the degree to which a certain compound can be accessed and utilized by an organism. However this definition may be oversimplistic as elements of iron speciation and kinetics, phytoplankton physiology, light, temperature, and microbial interactions, to name a few, are all intricately intertwined into what we term “bioavailability” (Wells et al., 1995 ; Worms et al., 2006 ). Given the complex and interdisciplinary nature of Fe bioavailability, progress in understanding this concept depends on addressing its sub-aspects by means of well-defined questions and multiple analytical techniques. In this contribution, rather than seeking a definition capable of encompassing the multiple aspects and scales of Fe bioavailability to phytoplankton, we attempt to disassemble this concept into its composing facets and explore them further. At a fundamental level, cellular Fe acquisition or uptake rates are indicative of the availability of any single Fe-substrate to a specific phytoplankton species (Figure 1 ). Fe uptake rate, in turn, is a function of the uptake pathways expressed by an organism and the chemical compatibility or exchange kinetics of the Fe-substrate with the transport systems (Figure 1 ). Rates of Fe acquisition can be determined experimentally using model or naturally occurring phytoplankton and Fe-substrates. In the next two sections we discuss the experimental evaluation of Fe uptake pathways and rates and suggest the use of uptake rate constants as a means of comparing between organisms, Fe species, and environments, and gaging the relative contribution of specific Fe-compounds to phytoplankton in a natural setting. Needless to say, the availability of Fe to natural phytoplankton assemblages in oceans and lakes is influenced by many chemical, biological, and physical factors outside the experimental beaker (see Figure 1 for an outline of some of these factors). For example, both Fe speciation and phytoplankton physiology are dynamic in time and space and to complicate matters even further, these two factors are interconnected. Moreover, interactions among the various organisms in the ecosystem, in addition to a host of environmental variables, can strongly impact the ability of phytoplankton to meet their Fe requirements. While a complete description of this added complexity to bioavailability is beyond the scope of this contribution, in the last section we describe how aquatic microorganisms influence iron chemistry and fate by way of organic complexation and bio-mediated redox transformations, emphasizing the resulting effects on Fe bioavailability to phytoplankton. Figure 1 A conceptual diagram disassembling the multivariable concept of iron bioavailability to phytoplankton . The figure outlines the composing facets of Fe bioavailability where green text highlights topics elaborated in the paper. At the most basic level, the availability of an iron species to a phytoplankton species is determined by the rate at which it is acquired by the organism. Fe uptake rate, in turn, is a function of the uptake pathways expressed by an organism and the chemical compatibility or exchange kinetics of the Fe-substrate with the transport systems (upper box). In Sections “Fundamentals of Fe Bioavailability: Phytoplankton Fe Acquisition Systems” and “Fundamentals of Fe Bioavailability: Phytoplankton Fe Acquisition Rates” we discuss the experimental evaluation of Fe uptake rates by laboratory cultures and natural populations. In the environment, many other chemical, biological, and physical factors are important for determining Fe availability to phytoplankton, some of which are detailed in the lower box. In Section “Added Complexity to Bioavailability: Bio-Mediated Transformations of Fe Speciation” we turn to organism–Fe interactions and discuss how secretion of organic compounds and bio-mediated redox processes alter Fe speciation and influence Fe availability. Figure 2 Summary of processes, systems, and research fields which are influenced by iron inputs and bioavailability (see text for details) ."
} | 2,762 |
23209744 | PMC3510198 | pmc | 5,083 | {
"abstract": "Home is a special location for many animals, offering shelter from the elements, protection from predation, and a common place for gathering of the same species. Not surprisingly, many species have evolved efficient, robust homing strategies, which are used as part of each and every foraging journey. A basic strategy used by most animals is to take the shortest possible route home by accruing the net distances and directions travelled during foraging, a strategy well known as path integration. This strategy is part of the navigation toolbox of ants occupying different landscapes. However, when there is a visual discrepancy between test and training conditions, the distance travelled by animals relying on the path integrator varies dramatically between species: from 90% of the home vector to an absolute distance of only 50 cm. We here ask what the theoretically optimal balance between PI-driven and landmark-driven navigation should be. In combination with well-established results from optimal search theory, we show analytically that this fractional use of the home vector is an optimal homing strategy under a variety of circumstances. Assuming there is a familiar route that an ant recognizes, theoretically optimal search should always begin at some fraction of the home vector, depending on the region of familiarity. These results are shown to be largely independent of the search algorithm used. Ant species from different habitats appear to have optimized their navigation strategy based on the availability and nature of navigational information content in their environment.",
"introduction": "Introduction Path integration (PI) is a strategy used by many animals to return home by the shortest possible route. In path integration, animals compute a home vector (HV) by integrating the angles steered and distances travelled on the outward journey [1] , [2] , [3] , [4] . The most conclusive evidence of an animal’s ability to path-integrate comes from experiments where individual animals returning home are displaced to a distant location where familiar visual landmark information is absent. If an animal continues to travel in the direction where the nest would have been it can be concluded that it has a path integrator. The path integrator accumulates both systematic and random errors and hence often leads animals to the vicinity of the home, rather than the home itself [5] , [6] , [7] , [8] . It is perhaps to overcome such errors in the path integrator, animals rely on visual landmarks [9] , [10] and use distinct search strategies [11] , [12] , [13] , [14] , [15] , [16] , [17] , [18] to locate home. Desert ants and most likely other ants too possess a path integration system. In landmark-poor habitats ants return home by taking the shortest possible route, thus relying on path integration (e.g., [5] ). In landmark-rich habitats ants return home by establishing idiosyncratic paths using visual landmark information (for Cataglyphis fortis see [9] , for Melophorus bagoti see [19] ). Typically for a homing ant, both the path integrator and visual landmarks provide the same directional information. But when the two strategies are put in conflict, then either the path integration information is fully suppressed (e.g., [20] , [21] ), or ants follow a direction intermediate to that indicated by the path integrator and the visual landmarks (e.g., [21] , [22] , [23] ). In principle, ants could find their way to the nest or back to the familiar route by moving to match the current view on their retina to a previously stored image either from a location along the route or from the nest (e.g., [24] ). Such views can guide individual ants to return to the nest from long distances [25] , [26] . When ants are displaced to distant locations where familiar visual landmarks are absent, their initial path is guided solely by the path integrator. During such distant displacements, the distance an ant travels following the home vector varies with the complexity of the landscape. For instance, in landmark-dense habitats of French Guiana ants travel only about 50 cm before beginning a search [27] , [28] , in semi-arid Central Australian deserts ants travel about 40% of their HV ( Figure 1 , [29] ) and in landmark-poor habitats of North Africa ants travel nearly 90% of their HV [6] . The distance travelled by individual ants ( Melophorus bagoti ) relying on their HV differs even within the same species: fractional use of the HV increases from 40% in landmark-rich habitats to 70% in landmark-poor habitats [30] . Furthermore, when the outward and return journeys are restricted to homogeneous linear channels ants travel the entire distance of outbound path prior to initiating search [28] , demonstrating the availability of a full HV. 10.1371/journal.pone.0050451.g001 Figure 1 Distance travelled using the path integrator by M. bagoti ants. Homing trajectories of ants caught at feeder placed 6 m, 12 m, 20 m and 35 m from the nest. End point of the trajectories indicates the start of search. Fictive nest position (N*) and release point (R) is indicated. Inset: means±se of distance travelled before the start of search for each of the four distances from the nest to feeder. Dashed line indicates predicted path integration if animals had travelled the entire HV. Modified from Narendra 2007a [29] . Given these differences in the distance travelled relying on the HV, we here ask what the theoretically optimal balance between PI-driven and landmark-driven navigation should be. In the presence of a familiar route, we investigate the possibility that initiating search prior to running off the entire HV may be a robust solution to minimize the expected cost and maximize the probability of success in finding the familiar route. 10.1371/journal.pone.0050451.g002 Figure 2 Positional uncertainty at the start of homing. A. Positional uncertainty due to errors accumulated during outbound foraging beyond the familiar route. B. Positional uncertainty due to sudden displacement from the end of the familiar route.",
"discussion": "Discussion The distance individual ants travel relying on their path integrator in an unfamiliar terrain decreases from landmark-poor saltpans, to landmark-rich desert scrub and to landmark-dense rainforest. The fractional read-out of HV information shown by ants occupying landmark rich and dense habitats may be related to the range over which these ants know the visual scene around their nests. Here, we used two theoretical analyses to find the optimal start of search, given an accurate HV obtained through PI, and a familiar route. The first analysis assumed that the cost of search is monotonically dependent on the distance to the search target. For a homogeneous set of points along the familiar route, the optimal position was shown to be the midpoint of the familiar route according to the PI system. The second analysis assumed that the start of optimal search should begin at the mode of the optimal search density function. For any symmetrical unimodal positional uncertainty distribution, the modal position is the midpoint of the familiar route according to the PI system. The start of search according to both the cost minimization and optimal search distribution models is in agreement, and corresponds to the midpoint of the familiar region according to the PI system. As a first approximation, the familiar region was assumed to be a thin, linear region extending from the nest. This scenario is a geometric approximation of the experimental conditions where Melophorus bagoti trained to a feeder 20 m away from the nest returned home in a narrow 0.5 m wide corridor of landmarks (see Fig. 3 in [19] , Fig. 2b in [21] for landmark corridors encountered during natural foraging). Much like observations in M. bagoti \n [29] , the optimal ant should run off about half of its HV prior to initiating search ( Figure S1B ). Conversely, the theoretical results predict that an ant which runs off most of its HV prior to searching is likely to have a relatively small familiar range compared with the foraging range ( Figure S1A ). This is consistent with observations in Cataglyphis fortis where its nest is typically a tiny hole in a large, cue-poor and wide open environment. Recent evidence from M. bagoti that inhabits landmark-poor habitats indicates that upon displacement they travel nearly 70% of their HV [30] . Does this mean that M. bagoti ants dynamically optimise their homing strategy depending on the available landmark information? When both the foodward and nestward routes of M. bagoti ants were restricted to linear tunnels ants were guided by HV information over nearly the full home distance [29] . This is most likely because the visual context during both the outward and nestward trips was similar. If the homing mechanism of M. bagoti is truly dynamically optimized, then it should be testable in an experiment where ants familiar with a long landmark corridor (e.g., 20 m) is provided later with a food source at midway (i.e., 10 m) for a few trials. If these ants are displaced, a dynamically optimized homing system should begin search immediately to minimize expected cost and maximize expected probability of finding the familiar route of 20 m. Alternatively, if M. bagoti runs off approximately half its HV (i.e., about 5 m) prior to searching, this may suggest a strategic optimization for commonly encountered conditions rather than dynamic adaptation from one foraging trip to the next ( Figure S1C ; see [30] ). In contrast, it is possible that in some landmark-rich environments the familiar range extends beyond the typical foraging route. This might explain the observation that tropical rainforest ants such as Gigantiops destructor travel only about 5–25% of the true distance towards home before starting their search [27] . If the familiar range is close to double the distance between nest and foraging zone, then the foraging zone is close to the midpoint along the familiar region. Hence the optimal search should begin almost immediately after release, as observed. It is also possible that G. destructor begins homing along the theoretical feeder-to-nest vector for an obligate distance, e.g. 0.5 m [27] , rather than as a fraction of the HV. Such a result would argue against the use of a fractional HV as a general adaptive mechanism across all ant species. To test this possibility, it is necessary to collect more information on the start of search from a wide range of nest-feeder distances in the natural environment of G. destructor . It will be equally important to test whether these ants rely on their HV when their foodward and nestward trips are restricted to linear channels. Search is a crucial component in the ant’s navigation toolkit (e.g., [15] , [34] ). One possible trigger for the activation of search may simply be that some cumulative level of unfamiliarity is reached, independent of the HV or size of the familiar region. Computationally, this could be mediated by a familiarity network (e.g., [35] ) which has learnt views along the familiar route. When displaced, an accumulation of novel views could perhaps cross some threshold for initiating search. Under this hypothesis, ants should travel different distances along the HV before beginning their search, depending on visual unfamiliarity. Experimentally, however, the HV distance in both ‘slightly familiar’ (ants displaced laterally from the nest-feeder route) and ‘unfamiliar locations’ (ants displaced to distant locations) have been shown to be similar [21] , arguing against a simple unfamiliarity threshold model. More work is needed to rigorously quantify the view differences between the ‘slightly familiar’ and ‘unfamiliar locations’. Although this work focused on the initiation of search rather than the search algorithm itself, it is clear that the search strategy affects the cost and effectiveness of search. Vickerstaff & Merkle [36] recently showed that a Bayesian model of systematic search for home is better able to cope with continually accruing positional uncertainty than other models. It may be possible to extend the Vickerstaff and Merkle model to incorporate familiar routes, so that predicted search path characteristics may be tested experimentally. The home of a central place foraging animal is a special location, often explored more frequently than other foraging areas. The ease of detection and/or value of detection may vary according to the position along a familiar route. If known, these functions of position may be incorporated explicitly into the formulation of the optimal effort distribution, using a change of variable method [32] . Qualitatively, it would be expected that if the value and ease of finding home is significantly higher than other parts of the familiar route, search initiation should be biased towards home. On the other hand, since positional uncertainty increases for the entire duration away from the familiar route, delaying the initiation of search may increase the total number of steps needed to find the familiar route, partially negating the benefit of the former. Finally, the familiarity of the route, and hence ease of detection of the route, may vary depending on the orientation of the animal, not just its position [37] . Combining detailed experimental and theoretical studies of these factors will be required to determine how an ant may fine tune its search initiation point. To further complicate matters, experimental evidence suggests that the available visual information may directly influence the search strategy per se . Individual homing ants ( M. bagoti ) caught close to the nest (zero-vector ants), when displaced far away from their familiar region, search more or less symmetrically around the release location [21] , [38] . In contrast, zero-vector ants released only 10 m laterally to their familiar route engage in a search which shows a clear bias towards the nest (Fig 6 in [21] ), suggesting that familiar visual cues influence the search trajectory. Similarly, when animals with full vector information are displaced 10 m laterally from their familiar route, they run off nearly half their HV and then engage in a progressive search with a bias towards the nest (Fig 5 in [21] ). To fully understand the complex interplay between PI and search, it is therefore critical to characterise the complete range of search strategies along with the information content of the environment, together with the state of the PI system. The experimental and theoretical results described here also have implications on the nature of the neural networks subserving path integration. From a computational perspective, there needs to be an accurate path integration system and, if the familiar route is to be (approximately) bisected, then there needs to be metric properties associated with the familiar region. It is unclear at present whether a decentralized neural architecture such as Cruse & Wehner [34] suffices, or whether a single coherent representation of the spatial world is required. Possible neural models of path integration able to replicate fractional HV use are currently under investigation."
} | 3,819 |
36298205 | PMC9610894 | pmc | 5,085 | {
"abstract": "Convolutional neural networks (CNNs) play a key role in deep learning applications. However, the high computational complexity and high-energy consumption of CNNs trammel their application in hardware accelerators. Computing-in-memory (CIM) is the technique of running calculations entirely in memory (in our design, we use SRAM). CIM architecture has demonstrated great potential to effectively compute large-scale matrix-vector multiplication. CIM-based architecture for event detection is designed to trigger the next stage of precision inference. To implement an SRAM-based CIM accelerator, a software and hardware co-design approach must consider the CIM macro’s hardware limitations to map the weight onto the AI edge devices. In this paper, we designed a hierarchical AI architecture to optimize the end-to-end system power in the AIoT application. In the experiment, the CIM-aware algorithm with 4-bit activation and 8-bit weight is examined on hand gesture and CIFAR-10 datasets, and determined to have 99.70% and 70.58% accuracy, respectively. A profiling tool to analyze the proposed design is also developed to measure how efficient our architecture design is. The proposed design system utilizes the operating frequency of 100 MHz, hand gesture and CIFAR-10 as the datasets, and nine CNNs and one FC layer as its network, resulting in a frame rate of 662 FPS, 37.6% processing unit utilization, and a power consumption of 0.853 mW.",
"conclusion": "7. Conclusions This article proposed a software and hardware co-design to design a CIM-aware model quantization algorithm and an SRAM-based CIM accelerator. In the design, the CIM-aware algorithm with 4-bit activation and 8-bit weight is examined on hand gesture and CIFAR-10 datasets, and determined to have 99.70% and 70.58% accuracy, respectively. A profiling tool to analyze the proposed design is also developed to measure how efficient our architecture design is. The proposed design system utilizes the operating frequency of 100 MHz, hand gesture and CIFAR-10 as the datasets, and nine CNNs and one FC layer as its network, resulting in a frame rate of 662 FPS, 37.6% processing unit utilization, and power consumption of 0.853 mW.",
"introduction": "1. Introduction DEEP neural networks (DNNs) have highly flexible parametric properties, and these properties are being exploited to develop artificial intelligence (AI) applications in various domains ranging from cloud computing to edge computing. However, the high computational complexity and high-energy consumption of CNNs trammel their applications, particularly in terms of hardware. Regarding hardware, various CNN accelerators have been proposed to address computing needs, but most of them are still based on the Von Neumann architecture, which requires substantial amounts of energy to transfer massive amounts of data between memory and processing elements. Transferring a DNN to an edge device remains challenging because of the high storage, computing, and power requirements. To overcome this challenge, numerous high-throughput, low-power devices have been proposed in recent years to reduce the time complexity of matrix–vector multiplications. Computing-in-memory (CIM) reduces the massive data movement by performing computation on the memory to avoid the Von Neumann bottleneck issue. Nevertheless, CIM-based accelerators still need to overcome challenges. To reduce the storage and computational costs, many different model compression algorithms have been proposed. In this particular model, a quantization algorithm is used, which is one of the most used compression algorithms. In the quantization algorithm, the input and weight bit width is limited to reduce the computational complexity by using different types of quantizers. These types include binary [ 1 ], ternary [ 2 ], uniform [ 3 , 4 , 5 ], and non-uniform quantizers [ 6 , 7 , 8 ]. Our SRAM-based CIM accelerator design is proposed to detect the event with ultra-low-power consumption. A hierarchical AI architecture shown in Figure 1 below, and is promising to save system power in AIoT applications. In the low-power sensor module, information captured from the peripheral sensor such as the imager is pre-processed to 32 × 32 image size and then sent into the CIM-based accelerator for event detection to trigger the precision inference in the next stage. Therefore, the end-to-end system power can be optimized and saved by at least a 30% reduction. The adopted SRAM CIM macro [ 9 ] in this paper can accommodate 8192 × 8 bit (64 Kb) weights, and contains 8 partitions. The 16 input data are shared in 8 partitions. These perform the inner product with the activation of the weight group at the same time, and then eight results are generated in the next cycle. This article is organized as follows. Section 2 introduces the background of model quantization, the SRAM CIM macro, and the CIM-based accelerator. Section 3 describes the proposed SRAM-based CIM accelerator architecture design. Section 4 describes the equation-based profiling tool. Section 5 presents the experimental results, and Section 6 concludes this article.",
"discussion": "6. Discussion We use the CIM (computing-in-memory) mechanism to overcome the Von Neumann bottleneck issue. The bottleneck is mainly summed up by three aspects: data movement between memory arrays and the processing unit results in non-negligible latency; data movement in memory hierarchies is greatly limited by bandwidth; high energy consumption, such as the power consumption of moving data between computing and off-chip memory units, is 100 times more than floating point computing. To overcome such problems, CIM technology is proposed. The key idea of the proposed technology is to bring memory and computing closer instead of separating them, therefore improving the efficiency of the data movement. Our proposed model is based on the difference between the ideal CIM macro and the taped-out one. The ideal CIM macro usually has a large capacity that does not bring multiple reloading during calculation and is able to perform high-precision calculations. However, the taped-out CIM macro has a limited capacity, has low-precision calculations, and must regard the analog-to-digital converter (ADC) number and its variation caused by the BL current. The proposed CIM-based architecture in this work is aimed at optimization based on the limitation of the taped-out CIM macro by proposing a hardware and software co-design. Our profile tool is very simple and restricted (narrowed to our architecture). One of the well-developed profilers for the CIM hardware accelerator is NeuroSim [ 32 ]. However, the reason why we do not use the existing profiling tool is that our architecture design focus is on CIM macro development. Moreover, several inputs to the simulator are different, including memory types, nonideal device parameters, transistor technology nodes, network topology and sub-array size, and training datasets and traces. None of the other CIM simulators have been validated with the actual silicon data (although NeuroSim has been validated with SPICE simulations using the PTM model and FreePDK. It is known that the PTM model and FreePDK are for educational purposes rather than for foundry fabrication purposes). We can explore further the algorithm, data movement, and circuit design perspective to reduce the computational cost in the future. From the algorithm perspective, in the current state, we only applied the quantization method. In the future, we are planning to use the pruning algorithm to enable the sparsity of connections. Therefore, we can reduce the data movement due to the lesser connections. This also makes it possible to achieve a reduction in area and energy consumption in the circuit design [ 33 ]."
} | 1,938 |
30953787 | PMC6557310 | pmc | 5,086 | {
"abstract": "Understanding how plants respond to nitrogen in their environment is crucial for determining how they use it and how the nitrogen use affects other processes related to plant growth and development. Under nitrogen limitation the activity and affinity of uptake systems is increased in roots, and lateral root formation is regulated in order to adapt to low nitrogen levels and scavenge from the soil. Plants in the legume family can form associations with rhizobial nitrogen-fixing bacteria, and this association is tightly regulated by nitrogen levels. The effect of nitrogen on nodulation has been extensively investigated, but the effects of nodulation on plant nitrogen responses remain largely unclear. In this study, we integrated molecular and phenotypic data in the legume Medicago truncatula and determined that genes controlling nitrogen influx are differently expressed depending on whether plants are mock or rhizobia inoculated. We found that a functional autoregulation of nodulation pathway is required for roots to perceive, take up, and mobilize nitrogen as well as for normal root development. Our results together revealed that autoregulation of nodulation, root development, and the location of nitrogen are processes balanced by the whole plant system as part of a resource-partitioning mechanism.",
"introduction": "Introduction Legumes benefit from symbiotic association with soil nitrogen (N)-fixing rhizobia for N uptake. Nodulation relies on two closely coordinated processes: the infection process, including the colonization of the bacteria inside the host plant, and the organogenic process, in which the nodule tissue is formed to accommodate the bacteria ( Madsen et al., 2010 ). Signal exchange for bacterial entry takes place between rhizobia that release nod factors (NF) and host plant roots that release flavonoids. NF perception by receptor-like kinases such as NFP in Medicago truncatula activates nodulation specific genes including DMI1 , DMI2 , and DMI3 and NSP2 ( Amor et al., 2003 ) and downstream calcium signaling ( Peiter et al., 2007 ). Nodulation signal transduction involved in initiating calcium spiking ultimately results in the activation of calcium/calmodulin-dependent protein kinase (CCaMK) ( Tirichine et al., 2006 ) and subsequent physiological and morphological changes ( Kosuta et al., 2008 , Capoen et al., 2009 ) including root hair curling, infection thread formation, and root nodule primordial development ( Oldroyd and Downie, 2008 ). These developmental processes are coordinated across different root cell types by a number of transcription factors including NIN , NSP1 , NSP2 , ERN1 , and ERN2 ( Kaló et al., 2005 , Cerri et al., 2012 ; Vernié et al., 2015 ). This developmental molecular coordination also involves induction of early nodulin-like proteins including ENOD11 , ENOD12 , and RIP1 in the root epidermis, and induction of ENOD20 and ENOD40 in the root cortex and pericycle ( Catoira et al., 2000 ). In the M. truncatula - Sinorhizobium meliloti symbiosis, host legume root cells undergo repeated rounds of genome duplication and increase in volume 80-fold, internalizing the rhizobia in a specialized compartment called “symbiosome”. Within this structure rhizobia differentiate to form N-fixing-specialized polyploid bacteroids unable to replicate ( Maroti and Kondorosi, 2014 ). These bacteroids exchange biologically fixed N for an allocation of photosynthate from the host legume ( Jones et al., 2007 ) with exchange at the symbiosome membrane through specific channels ( Weaver et al., 1994 ). Nitrogen responses in plants have been well studied, most commonly in the model plant Arabidopsis thaliana (reviewed in, e.g., O'Brien et al., 2016 ). Nitrogen responses in roots involve coordinated regulation of metabolic and cellular pathways that modulate N uptake and root system architecture ( Walker et al., 2017 ). The nitrate transporter/sensor NRT1 . 1 plays a key role in modulating root responses in response to varying external N. These include repression of lateral root branching in response to deplete nitrate by diverting accumulating auxin from lateral root primordia ( Bouguyon et al., 2015 ). In Medicago , nitrate transporters similarly play a key role in signaling as well as the distribution of internal N (nodulation-sourced) and external N (taken up from the environment) for growth and development ( Pellizzaro et al., 2017 ). In addition, major intrinsic proteins, and more specifically those expressed specifically in nodules, nodule intrinsic proteins, appear to play an important role in the movement of symbiosome-sourced N in the form of ammonia ( Benedito et al., 2008 ). This is one example of the many regulatory controls that exist to balance photosynthate payout with N payback in order to optimize whole plant growth, and the ability of the root system to develop lateral roots for other nutrient-harvesting purposes. Peptide and amino acid transporters also play important roles as part of regulation of cellular N metabolism during different stages of plant development ( Miranda et al., 2003 ). In legume species, high concentrations of N inhibit nodulation ( van Noorden et al., 2016 ) and there are differing inhibitory effects depending on whether the source is nitrate or ammonium ( Barbulova et al., 2007 ). These inhibitory effects can occur at a very early stage of NF signaling ( Heidstra et al., 1997 ), and plant N status affects symbiotic competence ( Omrane et al., 2009 ). Nitrogen status can also act later to modulate nodule functioning and activity. Repressing nodulation when the N status of the plant is sufficient involves a series of mobile signals. In soybean, Nitrogen-Induced CLE1 (NIC1), a CLE (CLV3/EMBRYO SURROUNDING REGION) peptide induced by nitrate, is involved in the local inhibition of nodulation ( Reid et al., 2011 ). Grafting experiments show that NIC1 is perceived by a root-localized CLAVATA1-like Leucine-Rich Repeat Receptor-Like Kinase (LRR-RLK) called Nodulation Autoregulation Receptor Kinase (GmNARK) ( Searle et al., 2003 ). In Lotus japonicus this gene is encoded by LjHAR1 ( Hypernodulation Aberrant Root Formation 1 ) ( Wopereis et al., 2000 ) and in M . truncatula by SUperNumary Nodules , MtSUNN ( Penmetsa et al., 2003 , Schnabel et al., 2005 ). These mutants display disruption in the autoregulation of nodulation (AON) pathway, which consists of at least two systemic regulatory circuits to control nodule numbers and activity ( Kassaw et al., 2015 ). AON uses a feedback-suppression mechanism from a root-derived signal that is thought to move via the phloem ( Oka-Kira and Kawaguchi, 2006 ). Along with SUNN , RDN1 ( Root-Determined Nodulation ) controls AON responses in M . truncatula , and CLE peptides are thought to be involved as both the local and systemic nodulation status signal ( Mortier et al., 2012 ). In L . japonicus it was shown that Nitrate Unresponsive Symbiosis 1 ( NRSYM1 ), a NIN -like gene, regulates nodule numbers and nodule development in response to nitrate levels ( Nishida et al., 2018 ). Hypernodulating mutants developing excess nodules escape autoregulation even in the presence of high levels of nitrate, indicating that nitrate exerts at least part of its effect via the autoregulatory pathway. The set of genes controlling AON also seem to affect nitrate perception and signaling; for example, nodulation in Lotus LjHAR1 mutants is not downregulated by high nitrate because the mutant is unable to recognize LjCLE-RS2 peptides ( Okamoto et al., 2009 ). There are a number of genetic links between the AON and other N-responsive root development pathways, including lateral root development ( Huault et al., 2014 ). In a similar regulatory mechanism another mobile small peptide, MtCEP1 (C-terminally Encoded Peptide 1) is recognized by an LRR-RLK, MtCRA2 ( Compact Root Architecture 2 ) to antagonistically regulate nodulation and lateral root architecture ( Mohd-Radzman et al., 2013 ). Accumulating evidence suggests an N-status-dependent molecular dialog between long-distance signaling of AON pathway and nodulation. The effect of N on nodulation has been investigated in a variety of studies (reviewed in Nishida et al., 2018 ) but the effect of nodulation on N responses is much less well known. Following a systems biology-integrated approach, here we have used a combination of phenotypic data and transcriptomic analyses of wild type and the hypernodulating sunn - 1 mutant to examine the interaction of rhizobial responses and N resources.",
"discussion": "Results and Discussion Analysis of Rhizobia and Nitrogen Responses in M. truncatula We measured root system architecture (RSA) in M. truncatula plants grown on deplete-N (0.1 mM NH 4 NO 3 ) conditions treated with either S. meliloti (“rhizobia”) or mock for 14 days and then either treated with deplete N or replete N (5 mM NH 4 NO 3 ) for 16 more days to study their individual and combinatorial effects on RSA (see Methods ). Deplete and replete levels were chosen based on tests that showed inhibition of nodulation at levels higher than 2 mM NH 4 NO 3 ( Supplemental Table 1 A), and in accordance with this we only saw nodulation in the N-deplete condition ( Figure 1 A and 1B; Supplemental Table 1 B). Figure 1 A17 Root System Architecture Is Altered Differently upon N Treatment if Plants Are Inoculated with Rhizobia. (A) Images of A17 seedlings that were mock or rhizobia inoculated and then grown in N-deplete (0.1 mM NH 4 NO 3 ) or N-replete (5 mM NH 4 NO 3 ) conditions. Scale bars, 1 cm. (B–E) Root size and features were measured. (B) Number of nodules; (C) primary root (PR) length; (D) number of lateral roots (LRs); (E) average LR length. Data are presented as mean ± SD. Different letters denote statistically different groups for pairwise comparisons using Wilcoxon’s rank-sum test; n ≥ 11. P < 0.05; N.S.D., no significant difference. See also Supplemental Table 1 . The primary root (PR) was significantly shorter when plants were inoculated with rhizobia, independently of the N treatment ( Figure 1 C), suggesting that investment in nodules is at the expense of RSA. Additionally, the shorter PR under rhizobia-inoculated conditions is also evident on replete N ( Figure 1 C), despite the fact that nodulation does not take place. This suggests a more complex regulatory effect at play, driven by external N availability. When comparing mock-treated plants it was observed that PR is shorter in replete N ( Figure 1 C), suggesting that PR growth might be driven by an N-scavenging response. Such an effect has previously been described in legumes ( Mohd-Radzman et al., 2013 ) and maize ( Gao et al., 2014 ). Moreover, rhizobia inoculation and N treatments seem to be additive in their effects on PR length, as PR in the rhizobia- and replete-N condition was shorter than in any other condition studied ( Figure 1 C). Between these conditions there was no significant difference in lateral root (LR) number or length ( Figure 1 D and 1E; Supplemental Table 1 B and 1C). Rhizobia-Inoculated Plants Show a Different Nitrogen Response Compared with Mock-Inoculated Plants To investigate the early stages of the combinatorial effects of rhizobia and N responses on RSA, we performed the same experiments as for phenotyping by growing plants under N-deplete conditions and then transferring to N-replete conditions. Thus, we harvested roots at 0 h (at the moment of N addition), 2 h, and 6 h after the replete-N (5 mM NH 4 NO 3 ) treatment ( Figure 2 A), carried out transcriptomic expression using microarrays (see Methods ), and determined differentially expressed genes (DEGs) ( Supplemental Table 2 ). Principal component analysis (PCA) was used to ask whether there were major sources of variation over the samples ( Figure 2 B). Earlier (0 h) time points in both rhizobia- and mock-inoculated samples had a greater degree of variation than later time points, and the variation over time was structured such that principal component 1 (PC1)/PC2 captured around 52% of the variation, PC1/PC3 38%, and PC2/PC3 34%. This time effect was also clear when plotting DEG heatmaps for mock- and rhizobia-inoculated experiments separately ( Supplemental Figure 1 ). This analysis also showed that there was a greater change over time in the gene expression of samples that had been mock inoculated, suggesting that external N treatment has a greater impact in mock-inoculated plants. Figure 2 Nitrogen Responses in Rhizobia- and Mock-Inoculated Roots Suggests that A17 Seedlings Treated with Rhizobia Are More Responsive to External Nitrogen Than Mock-Inoculated Roots. (A) Experimental design for transcriptomics experiment. (B) Principal component analysis (PCA) reveals a greater difference between rhizobia- and mock-inoculated plants at time 0, and greater changes over time in the gene expression in mock-inoculated (M) than rhizobia-inoculated (R) roots. (C) Number of DEGs in rhizobia versus mock at each time point and their associated gene ontology terms. (D) Venn diagram representing the distribution of DEGs between rhizobia-inoculated and mock-inoculated roots over time after N treatment. Genes that are differentially regulated in rhizobia-inoculated and mock-inoculated roots; if other genes in the same family are also in the DEG list, the number is given in parentheses; n = 3 except for Rhizobia-6hNitrogen where n = 2. See also Supplemental Tables 2 and 3 . When we compared rhizobia and mock-inoculated roots we found 1030 DEGs at at least one time point ( Figure 2 C). We then queried the lists of genes that change between these conditions (R0–M0, R2–M2, and R6–M6) to ask whether there were any gene ontology (GO) terms associated with genes regulated over time in each experiment. Genes related to defense, redox processes, and stress had a higher expression in mock-inoculated roots treated with replete N, suggesting that roots in these conditions have higher levels of stress than those inoculated with rhizobia. In contrast, genes that had higher expression in rhizobia-inoculated roots treated with replete N were associated with many GO terms related to the enriched term of “transport” ( Figure 2 C and Supplemental Table 3 ), which in the context of these experiments (rhizobia-inoculated plants with supplied external N treatment) could reflect alteration in N transport. Between R0 and M0, DE genes related to nodulation were found, including members of the nodule-specific small peptide (CCP/NCR) gene family and nodulins ( Figure 2 D). Genes that were expressed more highly in mock-inoculated roots included functions that could be part of N-scavenging responses including transmembrane amino acid transporter (Medtr5g023260) and Medtr2g097530, an IAA-amino acid hydrolase ILR1-like protein associated with auxin regulation and dormancy in Medicago ( Du et al., 2017 ). There was a decrease in the number of nodulation-associated DE genes in rhizobia-inoculated roots over time (compared with mock) from 28 genes at 0 h to 26 genes at 2 h, and finally to three genes at 6 h of N treatment, suggesting that these regulated genes could be part of the N-repression of nodulation mechanism that has a greater impact over time ( Figure 2 D). These changes could be part of the regulation of nodulation system dynamics, whereby plants that have established a symbiotic relationship with rhizobia are more sensitive to the external N levels as one part of the mechanism to keep resource use for nodulation in balance with N demand. We compared changes over time between rhizobia and mock-inoculated roots and found 251 genes to be different at all time points; among these are key nodulation DEGs such as Nodule Inception NIN (Medtr5g099060) ( Vernié et al., 2015 ) (as validated using qPCR; Supplemental Table 4 ), ENOD18 (Medtr7g065770) ( Hohnjec et al., 2003 ), as well as a range of nodulins and NCRs ( Figure 2 D). We then compared the effect of N on mock-inoculated roots and found that 3126 genes change expression over the 6 h. We used a MapMan analysis ( Thimm et al., 2004 ) to gain more detail into the processes that change and found that redox responses and biotic and abiotic stresses were more prevalent among genes that change in mock-inoculated roots, in accordance with the GO-term analysis described above ( Figure 2 C and Supplemental Figure 1 A–1D). Finally, we compared the effect of N on rhizobia-inoculated roots and found that 857 genes change expression ( Supplemental Figure 1 E–1H and Supplemental Table 2 ). Despite the fact that rhizobia-inoculated roots have a much smaller sized response to N, the number of genes categorized as being involved in N transport and metabolism changing over time in the rhizobia-inoculated roots was similar to that in mock-inoculated roots ( Supplemental Figure 1 ), thus the rhizobia N response seemed to relatively enriched. These results suggest that external N treatment affects both mock- and rhizobia-inoculated roots, showing interplay between N- and rhizobia-plant root responses. Genes Controlling Nitrogen Transport Are Differently Regulated after Mock or Rhizobia Inoculation To understand the combined effect of N and rhizobial inoculation, and investigate the hypothesized reduction in nodulation-associated genes ( Figure 2 D), we used hierarchical clustering using silhouette plots to assess the expression patterns of all 3986 DEGs over all conditions (union of 3126 genes that are N-responsive over time in mock-inoculated roots, 857 genes that are N-responsive over time in rhizobia-inoculated roots, and 1030 genes that differ between mock- and rhizobia-inoculated roots) ( Figure 3 A and Supplemental Table 2 ). There were 13 clusters, with four predominant patterns (clusters 1, 2, 5, and 11) ( Figure 2 A). Genes in the largest cluster (cluster 11 with 1317 genes) were found to be N repressed in both rhizobia- and mock-inoculated roots, including the MYB transcription factor MYB164 ( Figure 3 B). This cluster also includes transport inhibitor response 1 (Medtr7g083610), an ortholog of A. thaliana AFB3, an auxin receptor involved in primary and LR growth inhibition in response to nitrate and a target of miR393 ( Vidal et al., 2010 ) as well as lateral organ boundaries (LOB) domain protein Medtr1g095850, a member of the plant organ development key regulators ( Xu et al., 2016 ). These genes could be involved in regulating root architecture in both nodulating and non-nodulating plants. Figure 3 Change in Gene Expression in Plant Roots Inoculated with Rhizobia and in Response to Nitrogen Treatment Shows that Regulation of Nitrogen Mobilization Genes Varies upon Nodulation Status. (A and C) Heatmap and number of genes in each cluster for genes clustered over “raw” log 2 values (A) and log 2 fold N responses (C) . Cluster sizes and membership is specified and also depicted by the colored triangles. (B) Overview of main effect in clusters 2, 5, and 11, with representative genes (as referred to in text) and examples of the expression levels of representative genes. For each gene the cluster in the “response” data (C) is given in parentheses. n = 3 except for Rhizobia-6hNitrogen where n = 2. See also Supplemental Tables 2 and 3 . Cluster 2 genes (1070 genes) were N-induced in both rhizobia-inoculated and mock-inoculated roots with greater N induction in mock-inoculated roots. The cluster includes range of N-response genes such as nitrate reductases and nitrate transporters ( Figure 3 B). Cluster 5 genes (517 genes) were much more highly expressed in rhizobial-inoculated roots than mock, and were N repressed. This cluster includes a range of N transporters and amino acid transporters (including Medtr8g089360, Figure 3 B), which could be related to N-mobilization and N-metabolism rearrangement in plants undergoing nodulation. The changes in gene expression in these clusters indicate altered dynamics of N responses, potentially underlying the variation in root phenotypes under rhizobial inoculation compared with mock inoculation. We also clustered the same genes by N response to directly compare the scale of N responses not dependent on the basal expression level ( Figure 3 C). Among the 14 N-response clusters there was enrichment of genes annotated with terms including nitrogen, transport, cysteine, redox, kinase, and jasmonate ( Supplemental Tables 2 , 3 A, and 3B). The difference in N responses between rhizobia- and mock-inoculated roots suggested that N transport was activated in plants that had been inoculated with rhizobia. Transcriptomic Analysis Shows that Altered Autoregulation of Nodulation Affects the Pathways Controlling Root Development, Nitrogen Perception, Uptake, and Transport To investigate whether plants that were taking up fixed N in the root had altered N uptake, we used transcriptomic analysis of M . truncatula A17 compared with the SUPERNUMERARY NODULES (Medtr4g070970) sunn-1 mutant, since it has an extreme, hypernodulating phenotype. sunn-1 mutants continue to form nodules when A17 (wild-type) plant nodulation is repressed and thus sense that the N environment is perturbed ( Jin et al., 2012 ). Previous work including use of split roots has shown that the altered N regulation occurs at both local and systemic levels ( Jeudy et al., 2010 ). We used an experimental space with A17 and sunn-1 where we varied both N level and S. meliloti inoculation. We inoculated plants with S . meliloti (or mock inoculated) and then 14 days later carried out a 6-h replete-N (5 mM NH 4 NO 3 ) treatment or left on deplete N (0.1mM NH 4 NO 3 , control) before harvesting roots ( Figure 4 A). PCA was used to identify major sources of variation over the samples ( Figure 4 B). PC1/PC2 captured 59% of the variation, reflecting that the greatest differences observed come from the mock-rhizobia comparisons in both genotypes. The differences between sunn - 1 and A17 genotypes are greater in rhizobia-inoculated plants. In terms of N responses, there seemed to be a stronger impact in mock-inoculated sunn-1 plants. These results suggested a possible crosstalk between AON pathway and N transport in mock-inoculated plants. Figure 4 The sunn-1 Mutant Does Not Respond to the Combination of Rhizobia + N as Strongly as A17 and Could Be Transporting N Less Efficiently. (A) Experimental design for transcriptomic experiment. Schematic indicating root architecture at point of N treatment for rhizobia-/mock-inoculated A17/ sunn-1 . (B) PCA shows that samples are separated principally on genotype and to a greater extent by inoculation status. (C) Clustering heatmap of the DEGs alongside cluster number and number of genes in the cluster. Cluster sizes and membership is depicted by the colored triangles. (D) Wordclouds representing the most significant terms for the three largest clusters that together represent 91.4% of the total DEGs. Enriched terms were found to be: cluster 4, cysteine; cluster 6, kinase, transport, redox, calcium, nitrogen, and UDP. (E) Examples of the expression levels of representative genes (as referred to in the text). n = 3. See also Supplemental Tables 3 and 5 . We carried out gene expression analysis using microarrays as before (see Methods ) ( Supplemental Table 5 ) and identified a set of 6910 regulated genes. Hierarchical clustering was used to assess gene expression patterns ( Figure 4 B). There were 11 clusters, with three predominant patterns (clusters 4, 6, and 9) representing 91% of DEGs ( Figure 4 C). Cluster 4 (1283 DEG) contained genes that have increased expression in rhizobia-inoculated roots compared with mock, both in sunn-1 and A17; however, rhizobia induction is stronger in sunn-1 than in A17, independent of the N treatment. As would be expected, many genes in this rhizobia-enhanced cluster are involved in the nodulation pathway, e.g., NIN (Medtr5g099060, Figure 4 E) ( Vernié et al., 2015 ), MtNSP2 (Medtr3g072710) ( Kaló et al., 2005 ) (both validated by qPCR), nodule-specific cysteine-rich peptides (159 genes), leghemoglobins (10 genes), late nodulins (25 genes), and glycine-rich proteins (19 genes). Cluster 4 also includes a number of genes involved in N metabolism and transport, including the amino acid transporter Medtr8g089360, also found to be strongly expressed in rhizobia-inoculated roots in the previous experiment ( Figure 3 B). This gene, as well as other N transporters (e.g., Medtr3g069420) are downregulated by N specifically in sunn-1 mock-inoculated plants, and could be part of the crosstalk between the AON pathway and N transport in mock-inoculated plants. The fact that the induction of these genes was stronger in sunn-1 than in wild-type A17 is likely part of the molecular alteration underlying the hypernodulation phenotype of sunn-1 . As well as nodulation genes in cluster 4, there were calmodulin binding proteins (four genes) required for the recruitment of ubiquitous Ca 2+ for endosymbiotic N fixation and cytochromes P450 (12 genes) that interact with calmodulin binding proteins to act as mediators in multiple catalytic pathways ( Yamada et al., 1998 , Li et al., 2012 ). We also found regulatory genes such as LRR receptor-like kinases (16 genes), MYB (11 genes), GRAS (4 genes), MADS-box transcription factors (3 genes), zinc finger proteins (19 genes), and members of the F-box protein family (17 genes) as well as transport genes including peptide transporters (10 genes) and peptide/nitrate transporters (6 genes) ( Figure 4 B and Supplemental Table 5 ). Cluster 6 (3137 DEGs) was enriched for genes with annotation terms including kinase (276 genes), transport (142 genes), redox (26 genes), calcium (28 genes), nitrogen (35 genes), and UDP (37 genes) ( Figure 4 C and 4D; Supplemental Table 3 A and 3C). In this cluster, A17 and sunn-1 exhibited opposite gene expression responses to replete-N treatment when rhizobia inoculated ( Figure 4 C), with upregulation in A17 but not in sunn-1 . Interestingly, we found N-related genes in this cluster including nitrate transporters (4 genes), peptide/nitrate transporters (8 genes), nitrogen fixation proteins (2 genes), and ammonium transporters (2 genes out of 12 ammonium transporters in the whole genome). These findings suggested that N transport may be more efficient in A17 than in sunn-1 in the presence of rhizobia. Within cluster 6, differentially N-responsive between sunn-1 and A17 there was Medtr7g092930, an ortholog of the squamosa promoter-binding-like protein SPL9 , involved in LR development in A. thaliana ( Yu et al., 2015 ). This cluster also contains 10 lateral organ boundaries (LOB-domain) genes. One of these LOB-domain genes, Medtr6g005080, is specifically downregulated in sunn-1 compared with A17 in rhizobia-inoculated roots, but only in N-deplete conditions, that typically have essential roles in integrating development in response to environmental changes ( Xu et al., 2016 ) as well as two MYB transcription factors (including MYB164, Figure 4 E). The altered N regulation of these genes suggests that LR growth might also be different in A17 compared with sunn-1 . Cluster 9 (1896 DEGs) was more strongly expressed in sunn-1 with rhizobia inoculation than in any other experiment and independent of the N treatment. This cluster had many regulatory gene annotations, including kinase (95 genes), transmembrane (86 genes), and transcription (91 genes) ( Figure 4 C and 4D). Upregulation of the 35 LRR kinases and 11 LysMs in this cluster could be related to the altered perception of rhizobia in the sunn-1 mutant. Overall, using transcriptomic analysis we found evidence that the SUNN and N-transport pathways are integrated. It has been previously hypothesized that SUNN perceives the N/carbon (C) ratio in the shoot and then sends a signal to the root to control nodule number ( Jin et al., 2012 ). From our experiments, we hypothesized that SUNN is involved in root perception of external N levels and is responsible for uptake and transport of N to the shoot. To test this hypothesis, we analyzed the RSA phenotype of sunn-1 under different levels of N after mock or rhizobia inoculation and its putative involvement in N transport in an N-uptake assay followed by mineral analysis. Nodule Numbers, Root Development, and Nitrogen Uptake Are Balanced Differently in A17 and sunn-1 We grew sunn-1 seedlings and inoculated them with S. meliloti (or carried out mock inoculation). Fourteen days later we treated them with deplete or replete levels of N (as used in the microarray experiments) for 16 days, then measured RSA ( Figure 5 and Supplemental Table 6 ). As found previously, sunn-1 has significantly more nodules than A17, even on replete levels of N (5 mM NH 4 NO 3 ) when wild type consistently shuts down nodulation ( Figure 5 A and 5B). We found that sunn-1 mutants had a significantly longer PR than A17 on replete N, either with or without rhizobia inoculation, although the PR was longer when mock inoculated ( Figure 5 B). On deplete N all plants had similar numbers of lateral roots but on replete N, sunn-1 mutants had significantly more LRs and greater lateral root density than A17 when rhizobia or mock inoculated ( Figure 5 D and Supplemental Table 6 ). Figure 5 sunn-1 Root System Architecture Changes after N Treatment, with Effects Dependent on Rhizobia-Inoculation Status. (A) Images of sunn-1 seedlings that were mock or rhizobia inoculated and then grown in N-deplete or N-replete conditions. Scale bar, 1 cm. (B–E) number of nodules (B) , PR length (C) , number of LRs (D) , and average LR length (E) ; data are presented as mean ± SD. Different letters denote statistically different groups for pairwise comparisons using Wilcoxon’s rank-sum test, P < 0.05; n ≥ 11. See Supplemental Table 6 . sunn-1 mutants that were rhizobia inoculated had shorter LRs than mock-inoculated plants only in deplete-N conditions ( Figure 5 E); in fact, sunn-1 LRs in these conditions are also significantly shorter than in A17 ( Supplemental Figure 2 and Supplemental Table 7 ). Shorter LRs in sunn-1 when rhizobia inoculated could be explained by the regulation of LOB-domain genes (including Medtr6g005080 that is specifically downregulated in sunn-1 in these conditions) as described earlier ( Figure 4 C). These genes could be key in the regulation of LR length, integrating the internal and external N signals to mount an appropriate developmental response. Autoregulation mutants have previously been found to have nodulation-independent phenotypes, such as the increased LR density and a shorter root system in the ljhar1 mutant ( Wopereis et al., 2000 ). In our experiments we observe that this LR phenotype is even more significant under rhizobia inoculation. These results suggest that the RSA phenotypes in AON mutants are also under the control of a long-distance signaling system. Auxin has been implicated in the shoot-to-root signaling regulating nodule and LR development ( Jin et al., 2012 ), and we found “auxin” to be an enriched term in our regulated genes; DEGs related to auxin are mostly in clusters 6 and 9. The presence of auxin genes in cluster 6 (upregulated in A17 but not in sunn-1 ) is consistent with the phenotypic differences in A17 and sunn-1 in rhizobia + replete-N conditions ( Figure 4 C): shorter PR phenotype in A17 when compared with sunn-1 ( Supplemental Figure 2 and Supplemental Table 7 ). Based on the differing N transcriptome response of the sunn-1 hypernodulating mutant, we then asked whether sunn - 1 mutants mobilized N to the shoot differentially, and if this affected whole plant size. We measured the shoot dry weight, free nitrate, and percentage of total N and total C shoot content of A17 and sunn-1 plants grown in perlite pots. As with the study for transcriptomics, we inoculated plants with S . meliloti (or mock inoculated) and then 14 days later carried out a replete-N (15mM NH 4 NO 3 ) treatment before harvesting shoots after 6 and 24 h as well as at 0 h to assess N uptake. After 24 h of growth with N supply, dry weight of all shoots was increased ( Figure 6 A and Supplemental Table 8 ). sunn-1 rhizobia-inoculated plants appeared to increase in dry weight more quickly than all other plants (they are significantly different in dry weight at 6 h, but then similar at 24 h; Figure 6 A and Supplemental Table 8 ). Figure 6 A17 Mock-Inoculated Plants Mobilize More N to Shoots Than sunn-1 . (A) Root and shoot fresh weight is larger in N-replete conditions but even more so when plants are rhizobia inoculated. This is not a rhizobia-alone effect as fresh weight is the same between mock- and rhizobia-inoculated conditions in N-deplete levels. (B) Free NO 3 − content is higher in N-replete than N-deplete conditions and higher in the shoots of rhizobia-inoculated plants; free NO 3 − is similar in the roots of mock- and rhizobia-inoculated plants. (C) Total N content is higher in N-replete than N-deplete conditions but is similar in mock- and rhizobia-inoculated plants. Data in (A) and (B) are presented as mean ± SD. Letters denote statistically different values: a–b, P < 0.05; a–c, P < 0.01. See also Supplemental Table 8 . We measured shoot free NO 3 − content and found that it increased in all plants over the 24-h period ( Figure 6 B and Supplemental Table 8 ). This shows that transport from root to shoot occurs rapidly, within the first 6 h. However, by 24 h of N supply, free NO 3 − is higher in A17 mock-inoculated plants than sunn-1 , suggesting that sunn-1 plants are less efficient at moving N to the shoots. This difference is not apparent for rhizobia-inoculated plants. We then measured total percent N content and found it increases from 0–6 h to 24 h in A17 (mock inoculated) but not in other conditions ( Figure 6 C and Supplemental Table 8 ). This again suggests that A17 mock-inoculated plants are more efficient at taking up NO 3 − and transporting it to the shoot. We also measured total percent C and found no significant changes between plants ( Supplemental Table 8 ); regulation of photosynthesis upon higher N uptake could occur later than this 24-h time period. Interactions between Nodulation and Nitrogen Regulatory Pathways Put together, our transcriptomic and phenotypic analyses suggest that not only does external N treatment regulate the outcome of nodulation, but also that the AON signaling pathway regulates N uptake and metabolism in the absence of rhizobia. A key regulator of nodulation, NIN , was found not to respond to N in A17 wild type, but to be N repressed in sunn-1 , only in mock conditions. A similar response pattern was found for many NCRs and N transporters. Most of these genes also seem to be more highly expressed in sunn-1 compared with A17 only in mock-inoculated and N-deplete conditions. This differential N responsiveness of sunn-1 , implicating SUNN in control of N mobilization even when plants are not nodulating, is supported by our mineral analysis results. A17 appears to be able to transport more N to the shoot, compared with sunn-1 , in mock conditions. Our new data enable us to propose a model to help elucidate the regulatory links between nodulation, root development, and plant nutritional status ( Figure 7 ). Plants with a functional SUNN protein that are subject to rhizobia inoculation are able to perceive external levels of N and mobilize this to the shoot. N perception, uptake, and transport could thus be the first step required to trigger signaling that contributes to the AON. Figure 7 Model Showing the Interactions between Rhizobia and N Treatment on Medicago Root Architecture, and the Function of SUNN in Mediating These Interactions. Nitrogen and N transport affects distal responses in leaves and mediates root architecture, including the balance between lateral and PR growth. SUNN was previously implicated in controlling the lateral–PR balance. With our new phenotypic and transcriptomic data, we hypothesize that the SUNN impact on root architecture (and ultimately the ability to nodulate) is partly due to playing a role in transport of N to the shoot. Plants with a functional SUNN protein are able to perceive external levels of N and mobilize this to the shoot more rapidly. Nitrogen perception, uptake, and transport could be the first step required to trigger signaling that contributes to the autoregulation of nodulation."
} | 9,159 |
39976164 | PMC11883240 | pmc | 5,087 | {
"abstract": "ABSTRACT The desert ant Cataglyphis fortis inhabits the harsh and featureless North African saltpans. Individuals forage long distances and return to their inconspicuous nest entrance using path integration, but also rely on visual and olfactory landmarks. Here, we investigated the navigational decision making of these ants in differently structured environments. While individual ants show consistent route preferences, significant variability exists between individuals. Furthermore, the ants favor repetitive routes, suggesting that vision-based learning mechanisms and motor responses guide their navigation, with similar visual cues leading to similar egocentric decisions. This formation of idiosyncratic routes, seen in other ant species, appears to be conserved in C. fortis despite its usually flat habitat.",
"introduction": "INTRODUCTION Desert habitats impose ecological challenges, demanding adaptations in resident species such as Cataglyphis fortis . This ant species has developed morphological (e.g. elongated limbs for rapid locomotion), physiological (e.g. thermophilic traits) and cognitive adaptations to navigate the arid, exposed landscapes of North African saltpans ( Wehner, 2020 ). Efficient foraging and homing strategies, supported by robust memory ( Huber and Knaden, 2018 ) and neuronal plasticity ( Rössler, 2019 ), are fundamental for its survival. Cataglyphis fortis ants forage individually for dead arthropods in the featureless saltpans. Probably because of the high ground temperatures inhibiting chemical trails and the uneven distribution of food, the ants do not use trail pheromones for navigation ( Dietrich and Wehner, 2003 ; Wehner, 2003 ) as other ants do ( Barrie et al., 2023 ). Instead, they rely on path integration (PI), using a ‘sky compass’ for direction and step integration for distance ( Knaden and Graham, 2016 ; Müller and Wehner, 1988 ; Wittlinger et al., 2006 ). Despite the inhospitable environment, C. fortis can also use environmental cues, including visual ( Knaden and Wehner, 2005 ), olfactory ( Steck et al., 2009 ), and tactile ( Seidl and Wehner, 2006 ) stimuli, with visual cues best studied because of the experimental practicality of working with visual cues ( Zeil, 2012 ). Understanding the navigation strategies of C. fortis in featureless environments has provided valuable insights into the complexity of ant navigation. Cataglyphis fortis can develop distinctive routes even when introduced to artificial landmarks, emphasizing their cognitive processing of spatial information ( Buehlmann et al., 2015 ; Collett, 2010 ). Studies confirm that C. fortis foragers follow individual-specific routes without added landmarks, and GPS tracking has revealed distinct initial heading directions ( Buehlmann et al., 2015 ). The consistency of these routes, similar to behaviors in Melophorus bagoti and Cataglyphis velox , suggests a well-developed navigational strategy ( Kohler and Wehner, 2005 ; Mangan and Webb, 2012 ; Schwarz and Cheng, 2010 ). Research on M. bagoti shows how ants navigate obstacle courses using views, landmarks and routes, demonstrating their adaptability to structured environments ( Wystrach et al., 2011a ). These studies exemplify how ants incorporate visual landmarks and panoramic views into their navigation, shaping their foraging routes. Building on this background, our study investigated how C. fortis adapts its navigational strategies to structured landscapes. We focused on interactions with artificial landmarks to assess the flexibility of the ants' navigation system and to explore the cognitive processes guiding decision making in altered terrains. To do this, we introduced artificial landmarks with varying complexity and configuration to measure their impact on route formation and modification.",
"discussion": "RESULTS AND DISCUSSION Idiosyncrasy in an artificially complex environment The first experiment investigated whether the idiosyncratic navigation observed in C. fortis in featureless environments ( Buehlmann et al., 2015 ) extends to more structured settings. An artificial maze was constructed by placing barriers between the ants' nest and a food source. The movement patterns of five individual ants were monitored across five consecutive runs through this maze. Each ant demonstrated a strong preference for specific routes, consistently choosing the same or similar paths during multiple trials. Despite the complexity introduced by the artificial barriers, individual ants exhibited distinct and repeatable navigation choices ( Fig. 1A ). Analysis revealed minimal variability in the routes chosen by individual ants across different runs. Most ants had low deviation scores from their initial paths, indicating high route fidelity. The standard deviation of path deviations for each ant was notably low, varying between 0.0 and 0.5 ( Fig. 1B ). The mean intraindividual variability across all observed ants was 0.2, suggesting a strong tendency to repeat navigation decisions. In contrast, routes chosen by different ants varied significantly. Higher deviation scores were observed when comparing the most common path of each ant against those of their peers, with standard deviations ranging from 1.9 to 2.9 ( Fig. 1B ). The mean interindividual variability was 2.44, indicating significant differences in the chosen paths. Thus, the intraindividual variability was significantly smaller than the interindividual variability ( t =−7.697 d.f.=4, P =0.002), suggesting that while individual ants are consistent in their navigation choices, considerable variation exists between individuals. We conclude that C. fortis , which usually inhabits a flat and featureless environment, like other ants ( Barrie et al., 2023 ; Kohler and Wehner, 2005 ; Mangan and Webb, 2012 ; Wystrach et al., 2011b ) follows idiosyncratic routes when facing an artificial complex environment. Decision-making dynamics in structured environments Having shown that C. fortis navigates along idiosyncratic routes, in a second experiment we investigated the basic rules underlying the formation of such routes. To address this, we created a simplified maze with two visually identical decision points, requiring the ants to choose between turning L or R. Individual ants were tracked within the maze ( Fig. 2A ), recording their choices for both outbound and inbound paths during the same foraging bout (for example, outbound LR, inbound LR, resulting in forage bout LRLR; Fig. 2B,C ). We subsequently asked whether certain choice combinations occurred more frequently than others. Routes were categorized into three different types: symmetrical routes, asymmetrical routes and identical routes ( Fig. 2C ). Symmetrical routes involved the same directional choice on both outbound and inbound runs (e.g. RRRR); identical routes had opposite decisions (e.g. LLRR) and asymmetrical routes showed no similarity (e.g. RLLL). Out of 300 foraging runs made by 30 individuals, 167 were symmetrical, 55 displayed identical routes and only 78 were asymmetrical ( Fig. 2C,D ). Notably, over 55% of instances involved a preference for either RRRR or LLLL sequences ( Fig. 2D ). These frequencies significantly deviated from what would have been expected under equal likelihood, with RRRR and LLLL routes being more common (χ 2 =529.55, d.f.=15, P <0.001, Chi-square test). The prevalence of symmetrical routes indicates a consistent choice of either left or right at each decision point during foraging. This consistency suggests that uniform visual cues within the maze lead to repetitive egocentric decision making, regardless of whether the ant is approaching the first or second decision point and irrespective of the direction of travel. While previous studies have shown that wood ants can retrieve different memories from a single landmark based on their travel direction ( Fernandes et al., 2018 ; Graham and Collett, 2002 ; Harris et al., 2005 ), many tested desert ants displayed repeated reactions to a landmark, regardless of the timing or direction of approach. Interestingly, another group of ants showed consistency within inbound and within outbound runs, but varied between them, resulting in identical routes such as LLRR and RRLL ( Fig. 2C ). We then asked whether the ants, despite the general preference to perform symmetrical or identical runs, would still follow idiosyncratic routes within the maze. To assess this, we computed the Shannon diversity index, a metric typically used for evaluating species diversity ( Shannon, 1948 ), to determine whether individual route combinations (e.g. LRLR or RRRR) were overrepresented in a single ant, indicating a stable route. With the 16 possible combinations ( Fig. 2C,D ), the Shannon diversity index could reach values from zero (the ant always took the same route) to 2.77 (the ant always took a different route). Among the 30 ants followed on 10 consecutive foraging runs, five consistently followed the same path in all trials, another five altered their route only once, while the other ants exhibited different levels of route flexibility ( Fig. 3A ). Next, we performed a permutation test using the observed frequencies of different route combinations to determine whether the variability within individual ants was lower than expected from the overall frequencies observed across all ants ( Fig. 3B ). Our analysis revealed that the observed Shannon indices were significantly lower than those obtained from simulations assuming random decision making (two-sample t -test: t =−12.48, P <0.001). Thus, we conclude that ants consistently follow idiosyncratic routes, even within a simplified maze, with some ants sharing similar routes and others following different ones. In desert settings, C. fortis predominantly relies on its path integrator, whereas other desert ant species from more cluttered environments tend to emphasize learning and using visual landmarks more heavily ( Bühlmann et al., 2011 ; Schwarz and Cheng, 2010 ). For decades, C. fortis and other desert ant species have been studied for their navigational capabilities, both in their natural environment and in artificial mazes ( Barrie et al., 2023 ; Bega et al., 2019 , 2020 ; Kohler and Wehner, 2005 ; Mangan and Webb, 2012 ; Saar et al., 2017 ; Wehner, 2019 ). This body of work has provided a robust framework for exploring the navigational decision-making processes of C. fortis . Our experiments demonstrated that ants exhibit remarkable consistency in their choices at successive binary decision points, corroborating previous studies on visually driven choices and individual lateralization in ant navigation ( Collett, 2010 ; Frasnelli and Vallortigara, 2018 ). This consistency suggests fundamental navigational mechanisms may involve view-based learning and motor responses that guide identical choices at visually similar junctions, regardless of whether the ants are on inbound or outbound journeys. Given Cataglyphis ants' extensive memory (e.g. Bisch-Knaden and Wehner, 2003 ; Huber and Knaden, 2018 ), it is plausible that they can remember multiple decision points along a complex journey, benefiting from consistent handling of similar scenarios. Although individual ants tend to follow repetitive routes, these routes can vary between individuals. Future experiments would need to control for external factors to determine whether L/R choices are fully individually determined and consistent across more contexts. They should determine whether ants that, for example, prefer L in one type of maze will exhibit a similar preference under different conditions. This could indicate some form of navigational handedness, which might explain the significant overrepresentation of LLLL sequences in our dataset ( Fig. 2D ). It would also be interesting to investigate whether ants that consistently navigate the same routes belong to specific demographic or experiential groups within the colony's forager force, potentially linking run stability to foraging experience. Our data show the existence of stable routes when the salt pan-adapted C. fortis is experimentally exposed to a complex environment. In using such routes, the ants display a strategy similar to that observed in other desert ants from cluttered environments ( Wystrach et al., 2011b ). Our study emphasizes the need for experimental designs that can test specific cognitive rules. By analyzing ant navigation in well-defined structured environments (e.g. mazes, where consecutive decision points are visually identical), we lay the groundwork for future research to isolate the factors influencing navigation decisions. For example, manipulating the visual aspects of junctions or altering setups to assess the impact of prior choices on subsequent decisions could provide concrete evidence of the cognitive processes involved. One could imagine, for example, that making the two decision points in our setup more visually distinct would result in fewer ants that continuously turn either LL or RR during their inbound or outbound runs. Initially, we aimed to identify ants that consistently chose LR or RL patterns in either inbound or outbound routes, as these ants would be ideal candidates for testing additional decision-making rules along idiosyncratic routes. For instance, we wanted to see how an LR ant would have behaved at a second junction if the first one were removed in a test scenario. However, we found that the tendency of ants to repeat the same behavior when encountering the same junction twice was so predominant that it did not allow us to successfully carry out these experiments. The idiosyncratic navigation patterns observed in C. fortis are not unique to ants: similar behaviors have been documented across various taxa, including other insects such as bumblebees and vertebrates such as pigeons and elephants, all of which navigate complex environments using learned and adaptive strategies ( Baker, 1978 ; de Silva and Wittemyer, 2012 ; Guilford and Biro, 2014 ; Ohashi et al., 2007 ). This cross-species prevalence of idiosyncratic navigation accentuates its evolutionary significance and the adaptive advantage in efficiently locating and exploiting resources in dynamic environments. In conclusion, while our study advances the understanding of C. fortis ' navigational abilities in structured environments, it also exposes the need for caution in interpreting these results as definitive proof of specific cognitive rules. Rather, our findings should serve as a foundation for more detailed investigations aimed at experimentally dissecting and confirming the cognitive underpinnings of ant navigation. Future studies might explore whether navigational ‘handedness’ or preference for specific directions correlates with other behavioral traits, potentially revealing deeper links between navigation, cognition and ecological success in desert ants."
} | 3,734 |
38972887 | PMC11228038 | pmc | 5,088 | {
"abstract": "This study addresses the challenges in large-scale unmanned aerial vehicle (UAV) clusters, specifically the scalability issues and limitations of using reactive routing protocols for inter-cluster routing. These traditional methods place an excessive burden on cluster heads and struggle to adapt to frequently changing topologies, leading to decreased network performance. To solve these problems, we propose an innovative inter-cluster routing protocol (ICRP), which is based on a hybrid ant colony algorithm. During the route establishment phase, ICRP uses this algorithm to identify the optimal relay node. This approach is inspired by the foraging behavior of Physarum polycephalum, combining factors such as the number of hops from the source node, the load condition of the node, and its weight in the pheromone calculation. In the route maintenance phase, ICRP uses a predictive repair and contraction mechanism to dynamically maintain routes, accommodating the high mobility of UAVs. Comparative simulations in OMNeT + + showed that this protocol surpasses ad-hoc on-demand distance vector (AODV), fuzzy-logic-assisted-AODV, and Enhanced-Ant-AODV routing protocols in packet delivery rate and end-to-end transmission delay. Furthermore, it showed superior adaptation to network environments with high-speed node mobility.",
"conclusion": "Conclusion To address the problems of excessive burden on cluster-head nodes and poor adaptability to highly dynamic topologies in existing inter-cluster routing protocols, this study proposed an inter-cluster routing protocol based on a hybrid ant colony algorithm. During the route establishment phase, the node pheromone is calculated by comprehensively considering the number of hops from the source node, node load, and node weights. Additionally, the foraging behavior of Physarum polycephalum is integrated as a heuristic function to evaluate the stability of links between nodes. This approach optimizes relay node selection and enhances routing stability. In the route maintenance phase, the protocol employs predictive repair and contraction mechanisms to dynamically adjust established routes. This adjustment reduces route disconnections and the number of redundant relay nodes, further improving link stability. Experimental results showed that our proposed routing protocol performs better in stability and reliability than the existing routing protocols. However, the potential issue of information leakage inherent in wireless communication has not been addressed in this study. Future research will focus on enhancing the secure transmission of routing information while ensuring the stability of network routing links. In the future, we plan to further explore and integrate advanced encryption technologies suitable for wireless network environments, such as quantum encryption and AES encryption, and authentication mechanisms, such as certificate-based PKI systems, to ensure the confidentiality and integrity of data transmission. Additionally, we will focus on the development of future network technologies, including 5G/6G and IoT, to ensure that our protocols can adapt to new network technology frameworks, support cross-platform and cross-technology interoperability, and lay the foundation for building a more secure, stable, and widely applicable network communication environment.",
"introduction": "Introduction In recent years, unmanned aerial vehicles (UAVs) have shown significant potential in both military and civilian applications. Military uses include reconnaissance 1 , target identification, and attack operations. In civilian context, UAVs are utilized for disaster monitoring 2 , agricultural plant protection 3 , and as aerial base stations 4 . However, the limitations of a single UAV in terms of energy, capability, and payload capacity make it unsuitable for complex missions. Therefore, multi-UAV cooperative operations have emerged as a solution, where rapid and reliable communication is a major challenge. The concept of the flying ad-hoc network (FANET) has been introduced in response to this need. FANET, a dynamic formation network, introduces ideas from mobile and vehicle ad-hoc networks into UAV cooperative communication 5 . This concept enables UAVs to spontaneously form a communication network, facilitating information sharing with self-repair capabilities. FANET architectures are generally of two types: planar and hierarchical. In a planar structure, all nodes are at the same level and communicate directly, making it ideal for small networks. By contrast, large-scale UAV clusters often adopt a hierarchical structure, divided into sub-networks through clustering strategies for efficient, hierarchical management. Within these sub-networks, proactive routing protocols are preferred because of frequent node communication, necessitating quick route establishment. However, for inter-cluster communication, reactive routing protocols are mostly used in the backbone network, as demonstrated by existing research and protocols such as cluster based routing protocol (CBRP) 6 , which uses the dynamic source routing (DSR) protocol. Researchers 7 – 9 have considered factors such as UAV residual energy and inter-UAV distances in creating multiple routes along the backbone network, selecting the most suitable for data forwarding. Despite their effectiveness, traditional reactive routing protocols face challenges and limitations when applied to cluster-structured networks with highly dynamic topologies. Current research on reactive routing protocols primarily aims to enhance link stability during route discovery. The widespread use of flooding to propagate routing information often leads to network congestion and unstable links. To mitigate this issue, Mei et al. 10 proposed a high-stability protocol that combines load prediction with a pseudo-gossip mechanism for balanced network load and reduced network overhead, thereby improving route establishment reliability. Chen et al. 11 developed a method to calculate dynamic forwarding probability based on local neighboring nodes, incorporating cross-layer ideas such as node speed to determine link weights for route selection. Lee et al. 12 suggested a novel approach where each node, during route initiation, assesses its suitability to forward a route request (RREQ) based on factors such as direction, residual energy, link quality, and stability. Typically, the destination node receives multiple RREQs, necessitating the selection of an optimal multi-hop route for the route reply (RREP) unlike the case in reported existing research. An et al. 13 defined link retention probability to assess link connectivity stability. Similarly, Lee et al. 14 proposed a routing mechanism focusing on path reliability and stability, evaluating links through duration and node congestion likelihood. The paths with high stability are identified by combining the two key metrics. Xufeng et al. 15 introduced a link quality prediction-based on-demand routing algorithm, predicting link stability and node congestion using a gray-WNN model. This method aims to minimize the impact of UAV dynamics. Li et al. 16 developed a fuzzy logic-assisted ad-hoc on-demand distance vector (AODV) routing algorithm that considers latency, stability, and residual energy, selecting the most reliable node for data transmission. Studies 17 – 21 have introduced firefly and ant colony algorithms into routing protocols, optimizing node selection for the next hop based on factors such as energy consumption, buffer congestion, and hop count. To address the low fault tolerance in data transmission, an interference-resistant multipath routing protocol, considering link quality, traffic load, and spatial distance, has been proposed to identify optimal neighboring links 22 , 23 . For route maintenance, the fast repair of broken links is crucial. Qing et al. 24 presented a protocol that selects the highest-energy node from common neighbors at a disconnection to re-establish the link, thus enhancing repair efficiency, reducing delay, and lowering routing overhead. In summary, while these reactive routing protocols offer various solutions for inter-cluster routing, they still present challenges in application, particularly in networks with highly dynamic topologies. Excessive load on cluster-head nodes: in a cluster-structured network, cluster-head nodes often bear excessive computational and communication tasks. Reactive routing protocols typically fail to effectively distribute these tasks between the cluster-head nodes and other nodes, resulting in an imbalanced load. Evaluation metrics are not comprehensive and efficient enough: current link stability metrics mainly focus on node movement, neglecting communication quality. Additionally, some metrics are computationally complex and inefficient, reducing their practicality in distributed network applications. Consequently, the computational and communication resource overhead increases, limiting scalability and efficiency. Poor network fault tolerance: given the dynamic nature of node positions in inter-cluster routing, the network topology is prone to unpredictable changes, emphasizing the need for robust fault tolerance in routing algorithms. Reactive routing protocols often lack such fault-tolerant strategies, making them ill-equipped to handle topology changes. To address these problems, this study proposes an inter-cluster routing protocol (ICRP), a novel protocol grounded in a hybrid ant colony algorithm and inspired by both ant colony behaviors and the foraging model of Physarum polycephalum. ICRP aims to reduce the load on cluster-head nodes by considering the weight of each node within the cluster network structure during the relay node selection process. This approach, coupled with a streamlined design of evaluation indices, improves the protocol’s adaptability in large-scale network scenarios. Furthermore, ICRP incorporates predictive repair and contraction mechanisms to improve its fault tolerance. These mechanisms account for potential link disconnections and bypasses, allowing for dynamic routing adjustments and efficient reduction in data transmission delays. In the route establishment phase, the protocol utilizes node pheromones and heuristic functions to select optimal relay nodes, thus constructing stable and efficient transmission links. During the route maintenance phase, ICRP effectively manages high UAV mobility and network topology changes, thereby improving transmission reliability. The innovative aspects of this algorithm, as presented in this paper, offer significant advancements compared to existing approaches. In the route establishment phase, ICRP, during this phase, uses node pheromones and heuristic functions to select relay nodes. Pheromone concentration considers the number of hops from the source node, the node’s load, and its weight in the network structure. This approach aims to minimize the involvement of key nodes, such as cluster heads, in the route. In the heuristic function design, inspired by the foraging behavior of Physarum polycephalum, to evaluate link communication, optimizing the selection of relay nodes and enhancing route stability. In the route maintenance phase, a predictive repair and contraction mechanism is incorporated to dynamically adjust routing, accommodating high UAV mobility and network topology changes. This approach aims to reduce route disconnections and eliminate unnecessary relay nodes, thereby enhancing data transmission reliability. The rest of the paper is organized as follows. In Section \" Hybrid ant colony algorithm \", we present an improved hybrid ant colony algorithm. In Section \" Inter-cluster routing protocols \", we provide a detailed explanation of the proposed ICRP. In Section \" Simulation results and analysis \", we compare and analyze the performance of the ICRP scheme with existing routing protocols, such as AODV, fuzzy-logic-assisted-AODV (FL-AODV), and Enhanced-Ant-AODV, through simulation experiments. Finally, in Section \" Conclusion \", we summarize our work and provide an overview of future research directions and potential work."
} | 3,040 |
25462851 | null | s2 | 5,089 | {
"abstract": "In the present study, an artificial spider silk gene, 6mer, derived from the consensus sequence of Nephila clavipes dragline silk gene, was fused with different silica-binding peptides (SiBPs), A1, A3 and R5, to study the impact of the fusion protein sequence chemistry on silica formation and the ability to generate a silk-silica composite in two different bioinspired silicification systems: solution-solution and solution-solid. Condensed silica nanoscale particles (600-800 nm) were formed in the presence of the recombinant silk and chimeras, which were smaller than those formed by 15mer-SiBP chimeras, revealing that the molecular weight of the silk domain correlated to the sizes of the condensed silica particles in the solution system. In addition, the chimeras (6mer-A1/A3/R5) produced smaller condensed silica particles than the control (6mer), revealing that the silica particle size formed in the solution system is controlled by the size of protein assemblies in solution. In the solution-solid interface system, silicification reactions were performed on the surface of films fabricated from the recombinant silk proteins and chimeras and then treated to induce β-sheet formation. A higher density of condensed silica formed on the films containing the lowest β-sheet content while the films with the highest β-sheet content precipitated the lowest density of silica, revealing an inverse correlation between the β-sheet secondary structure and the silica content formed on the films. Intriguingly, the 6mer-A3 showed the highest rate of silica condensation but the lowest density of silica deposition on the films, compared with 6mer-A1 and -R5, revealing antagonistic crosstalk between the silk and the SiBP domains in terms of protein assembly. These findings offer a path forward in the tailoring of biopolymer-silica composites for biomaterial related needs."
} | 470 |
26379660 | PMC4552172 | pmc | 5,090 | {
"abstract": "In Pseudomonas aeruginosa the Gac signaling system and the second messenger cyclic diguanylate (c-di-GMP) participate in the control of the switch between planktonic and biofilm lifestyles, by regulating the production of the two exopolysaccharides Pel and Psl. The Gac and c-di-GMP regulatory networks also coordinately promote the production of the pyoverdine siderophore, and the extracellular polysaccharides Pel and Psl have recently been found to mediate c-di-GMP-dependent regulation of pyoverdine genes. Here we demonstrate that Pel and Psl are also essential for Gac–mediated activation of pyoverdine production. A pel psl double mutant produces very low levels of pyoverdine and shows a marked reduction in the expression of the pyoverdine-dependent virulence factors exotoxin A and PrpL protease. While the exopolysaccharide-proficient parent strain forms multicellular planktonic aggregates in liquid cultures, the Pel and Psl-deficient mutant mainly grows as dispersed cells. Notably, artificially induced cell aggregation is able to restore pyoverdine-dependent gene expression in the pel psl mutant, in a way that appears to be independent of iron diffusion or siderophore signaling, as well as of recently described contact-dependent mechanosensitive systems. This study demonstrates that cell aggregation represents an important cue triggering the expression of pyoverdine-related genes in P. aeruginosa , suggesting a novel link between virulence gene expression, cell–cell interaction and the multicellular community lifestyle.",
"introduction": "Introduction Pseudomonas aeruginosa is a metabolically versatile Gram-negative bacterium and an opportunistic pathogen in cystic fibrosis (CF) and otherwise critical patients, causing both chronic and acute infections ( Driscoll et al., 2007 ). The ability of P. aeruginosa to rapidly adapt to diverse ecological niches and to switch from acute to chronic infections is related to the tightly regulated expression of specific sub-sets of genes in response to environmental cues ( Stover et al., 2000 ). Characteristic traits of P. aeruginosa chronic infection are the microcolony and biofilm mode of growth ( Parsek and Singh, 2003 ). Microcolonies are small aggregates of cells that open the way to the communal organization of biofilms, which are surface-associated communities of bacteria encased in a self-generated polymeric matrix ( Zhao et al., 2013 ). Extracellular polysaccharides represent a key component of the P. aeruginosa biofilm matrix, and are involved in surface attachment and cell–cell interaction ( Ma et al., 2009 ). Pseudomonas aeruginosa strains can produce three main exopolysaccharides, namely alginate, Pel, and Psl ( Colvin et al., 2012 ). Alginate confers the typical mucoid phenotype to producing strains ( Sherbrock-Cox et al., 1984 ), and plays a crucial role in CF lung colonization. Psl and Pel are normally produced by non-mucoid strains, and failure to produce Pel and/or Psl exopolysaccharides impairs biofilm formation in vitro ( Colvin et al., 2012 ). The exopolysaccharide Psl consists of repeating pentamers of D -mannose, L -rhamnose, and D -glucose ( Ma et al., 2007 ). The helical distribution of Psl on the cell surface promotes cell–cell and cell–surface interactions in microcolonies ( Zhao et al., 2013 ). Psl also plays an essential role in the maintenance of the mature biofilm structure ( Jackson et al., 2004 ; Ma et al., 2009 ). The exopolysaccharide Pel was originally identified by a transposon mutagenesis screening for the loss of surface pellicle formation in P. aeruginosa PA14 ( Friedman and Kolter, 2004 ). Pel structure has not yet been determined, although carbohydrate analysis suggested a glucose-rich composition ( Friedman and Kolter, 2004 ). The production of the Pel and Psl exopolysaccharides is controlled by many regulatory networks at the level of transcription, translation, and biosynthesis ( Goodman et al., 2004 ; Ventre et al., 2006 ; Lee et al., 2007 ; Sakuragi and Kolter, 2007 ; Gilbert et al., 2009 ). The Gac system and the signaling molecule cyclic diguanylate (c-di-GMP) are the best characterized regulatory networks involved in the regulation of pel and psl gene expression and, consequently, in the switch from the planktonic to the biofilm lifestyle. The Gac system relies on the sensor kinase GacS that, in response to a still unknown signal, activates the transcriptional regulator GacA, which in turn promotes the transcription of two small non-coding RNAs (sRNAs), RsmZ and RsmY ( Brencic et al., 2009 ). These sRNAs bind to and sequester the mRNA binding protein RsmA, thereby inhibiting its activity as translational repressor ( Heeb et al., 2006 ). While psl gene expression is directly repressed by RsmA at the translational level ( Irie et al., 2010 ), there is no evidence of a direct effect of RsmA on the pel genes, although transcriptomic analysis showed that the active state of the Gac network (i.e., rsmA mutation) also promotes transcription of the pel operon ( Brencic and Lory, 2009 ). The Gac system has recently been shown to positively affect the intracellular levels of the signaling molecule c-di-GMP, which also induces exopolysaccharides production ( Moscoso et al., 2011 , 2014 ; Irie et al., 2012 ; Frangipani et al., 2014 ). This second messenger regulates Pel production both at the transcriptional and post-transcriptional level, by inhibiting the activity of the transcriptional repressor FleQ ( Hickman and Harwood, 2008 ), and activating the Pel biosynthesis protein PelD ( Lee et al., 2007 ). Although the molecular mechanism has not been elucidated yet, high intracellular levels of c-di-GMP also increase the expression of psl genes ( Hickman et al., 2005 ). Besides exopolysaccharides production and biofilm formation, Gac and c-di-GMP also act in a concerted way to promote the expression of pyoverdine genes ( Frangipani et al., 2014 ), and it has recently been reported that the exopolysaccharides Pel and Psl are important for c-di-GMP regulation of pyoverdine production ( Chen et al., 2015 ). Pyoverdine is a green fluorescent siderophore which plays a prominent role in P. aeruginosa pathogenicity ( Visca et al., 2007 ; Cornelis and Dingemans, 2013 ). Pyoverdine acts not only as a high-affinity iron scavenger, but also serves as a signal molecule to promote the expression of important P. aeruginosa virulence factors, via a cell–surface signaling cascade which involves the other membrane ferri-pyoverdine receptor FpvA and the cytoplasmic membrane-spanning antisigma factor FpvR, ultimately leading to the activation of the extracytoplasmic function (ECF) sigma factor PvdS ( Lamont et al., 2002 ; Llamas et al., 2014 ). PvdS directs the transcription of almost thirty P. aeruginosa genes, including those involved in pyoverdine biosynthesis and transport, the gene for the extracellular protease PrpL and, indirectly, the exotoxin A gene toxA ( Ochsner et al., 2002 ; Llamas et al., 2014 ). The dual function of pyoverdine in iron uptake and virulence renders this siderophore essential for P. aeruginosa infectivity, as demonstrated in different mouse models ( Meyer et al., 1996 ; Takase et al., 2000 ; Imperi et al., 2013 ). As for any iron-uptake system, pyoverdine production needs to be promptly shut down when intracellular iron levels are sufficiently high. This iron-mediated control occurs through the ferric uptake regulator Fur, an iron-sensing transcriptional repressor which binds to its co-repressor Fe 2+ and inhibits transcription of the sigma factor gene pvdS ( Ochsner et al., 2002 ). Besides Fur-Fe 2+ mediated repression, pyoverdine production was found to be influenced by a number of environmental signals and regulatory pathways, including oxygen and nutrient availability, cellular communication and oxidative stress (reviewed in Llamas et al., 2014 ). In the present study we demonstrate that the extracellular polysaccharides Pel and Psl are also essential for Gac-mediated regulation of pyoverdine production, which appears strongly repressed in a pel psl double mutant. We also provide evidence that artificially induced cell aggregation is able to restore pyoverdine-dependent gene expression in the Pel and Psl-deficient mutant. Our findings support the hypothesis that cell aggregation, rather than polysaccharide production per se , is an important cue triggering production of pyoverdine and pyoverdine-controlled virulence factors in P. aeruginosa .",
"discussion": "Discussion In the last decades, the old vision of bacteria as strictly unicellular organisms living in a planktonic single-cell status was swept away by the finding that bacterial cells in natural, industrial and many clinical settings predominantly exist as biofilms, i.e., structured microbial communities attached to a surface and encased in an extracellular matrix ( Mann and Wozniak, 2012 ). Also during planktonic growth in liquid cultures bacteria can assemble into aggregates of densely packed cells, and it is believed that planktonic aggregation can play a role in resistance to stresses and antibiotics ( Schleheck et al., 2009 ; Blom et al., 2010 ; Haaber et al., 2012 ), as well as in microbe–host cell interaction ( Lepanto et al., 2011 ). Although hundreds of studies have investigated the physiology of biofilm-living bacterial cells, very little is known about the effects of cell aggregation during planktonic growth. Here we provide evidence that growth as planktonic aggregates promotes production of three major virulence factors in the opportunistic pathogen P. aeruginosa , namely pyoverdine, extracellular protease PrpL and exotoxin A. Indeed, we observed that a Pel and Psl-null mutant unable to aggregate in liquid cultures is also defective in the expression of virulence factor genes, and that this effect can be rescued by artificially induced cell aggregation ( Figure 4 and Supplementary Figure S3 ). Although in this study, we did not elucidate the mechanism by which cell aggregation is perceived by bacterial cells and translated into a transcriptional response which affects pyoverdine-related genes, two hypotheses can reasonably be made to explain the observed effect of planktonic aggregation on gene expression. First, a sensory machinery could transduce a contact signal deriving from the cell envelope into a cytoplasmic response. Our experiments lead to exclude the involvement of physical changes in the cell envelope due to contacts with abiotic surfaces, as well as any role of the two mechanosensors PilY and type IV pili ( Figure 5 ), which have recently been found to induce P. aeruginosa virulence in response to surface contacts ( Siryaporn et al., 2014 ; Persat et al., 2015 ). Also the P. aeruginosa Wsp system, a chemotaxis-like signal transduction complex which promotes c-di-GMP production by activating the diguanylate cyclase WspR, is stimulated by growth on surfaces ( Güvener and Harwood, 2007 ). However, it has recently been shown that exopolysaccharides depletion in a c-di-GMP overproducing P. aeruginosa strain abrogated the ability of c-di-GMP to promote pyoverdine production ( Chen et al., 2015 ). Accordingly, we found that the overexpression of a constitutively active WspR variant, which results in almost 100-fold increase in intracellular c-di-GMP levels, is unable to induce pyoverdine production in Pel and Psl-deficient cells (Supplementary Figure S4 ), reasonably excluding also the involvement of the Wsp system. However, other mechanosensitive factors or contact-dependent systems, which likely respond to specific cell-to-cell interactions, could exist in P. aeruginosa . For instance, the P. aeruginosa PAO1 genome is predicted to encode 26 methyl accepting chemotaxis proteins ( Whitchurch et al., 2004 ), 13 cell–surface signaling systems ( Llamas et al., 2014 ), up to 50 canonical two-component systems and more than 10 orphan sensor kinases ( Gooderham and Hancock, 2009 ), many of them being implicated in virulence gene regulation. The alternative hypothesis is that growth as aggregates determines changes in the cell surrounding environment which could influence virulence gene expression. Studies on the physiology of planktonic aggregates are still at an early stage, but it has recently been reported that localized oxygen depletion occurs in aggregates of P. aeruginosa cells grown in a gelatin-based microtrap ( Wessel et al., 2014 ). Notably, oxygen is a well known inducer of pyoverdine gene expression ( Ochsner et al., 1996 ; Llamas et al., 2014 ); thus oxygen limitation cannot explain the observed induction of pyoverdine production in bacterial aggregates. On the other hand, since exotoxin A expression is induced under microaerobic conditions through a PvdS-independent mechanism ( Gaines et al., 2007 ), we cannot exclude that the increased exotoxin A expression levels in planktonic aggregates ( Figures 1 and 4 ) could be related, at least in part, to oxygen depletion. It has also been reported that pyoverdine concentration is heterogeneous in P. aeruginosa microcolonies, with a maximum at the colony center ( Julou et al., 2013 ). Although this heterogeneity could influence the efficiency of pyoverdine signaling and/or iron uptake in cell aggregates, we have demonstrated that the aggregation-mediated effect on pyoverdine production is independent of pyoverdine signaling and the iron sensor Fur ( Figure 2 ), ruling out that increased virulence gene expression in aggregated cells may be due to altered pyoverdine signal transduction or dysregulated iron homeostasis. Thus, it is plausible that still uncharacterized chemical and/or physiological changes occurring in densely packed bacterial cells are responsible for the observed activation of pyoverdine-related virulence genes in P. aeruginosa planktonic aggregates. In summary, our work provides the first evidence that formation of planktonic aggregates stimulates the production of pyoverdine-dependent virulence factors in P. aeruginosa . Further studies are necessary to clarify the mechanistic link between cell aggregation and activation of virulence gene expression. Such a kind of cell contact- or aggregation-dependent induction of virulence could represent a further strategy to modulate bacterial pathogenicity in response to population density, additional or complementary to chemical signaling via quorum sensing. Cellular aggregation also represents the first committed step of biofilm formation. Although some recent transcriptomics and proteomics studies highlighted an overall attenuation of virulence gene expression in mature P. aeruginosa biofilms ( Li et al., 2014 ; Park et al., 2014 ), which include many slowly growing or quiescent cells, our finding could imply that the virulence potential of P. aeruginosa is increased during the first stages of biofilm formation, when siderophores, extracellular enzymes, and toxins would provide cells with essential nutrients for the energy-demanding process of biofilm development. This hypothesis is in line with the evidence that the gene expression profile of developing P. aeruginosa biofilms is more similar to that of exponential phase cultures rather than mature biofilms ( Waite et al., 2006 ). Finally, since planktonic aggregation seems to be widespread among bacteria ( Fazli et al., 2009 ; Schleheck et al., 2009 ; Blom et al., 2010 ; Haaber et al., 2012 ), it would be interesting to verify whether the correlation between cell aggregation and virulence is conserved in other bacterial pathogens."
} | 3,904 |
33240241 | PMC7680738 | pmc | 5,092 | {
"abstract": "Artificially stimulated, high-yield microbial production of methane from coal is a challenging problem that continues to generate research interest. Decomposition of organic matter and production of methane from coal are the results of multiple redox reactions carried out by different communities of bacteria and archaea. Recent work by our group ( Beckmann et al., 2015 ) demonstrated that the presence of the redox-mediating molecule neutral red, in its crystalline form on a coal surface, can increase methane production. However, hydrolysis and the acetogenesis of the coal surface are essential precursor steps for methane production by archaea. Acetogenesis is the preparation phase of methanogenesis because methanogens can only assimilate acetate, CO 2 and H 2 among the products formed during this process. In the present study, the surface chemical analysis of neutral red treated coal using attenuated total reflectance-fourier transform infrared (ATR-FTIR) spectroscopy and X-ray photoelectron spectroscopy (XPS) demonstrate that the acetate production and resulting oxidation of the coal only occurred at few nanometers into the coal surface (at the nanoscale <5 nm). We observed that in the presence of neutral red and groundwater microbes, acetate signals in coal surface chemistry increased. This is the first evidence suggesting that neutral red enhances the biological conversion of coal into acetate. Microscopy demonstrated that neutral red crystals were co-localize with cells at the surface of coal in groundwater. This is consistent with neutral red crystals serving as a redox hub, concentrating and distributing reducing equivalents amongst the microbial community. In this study, the chemical changes of neutral red treated coal indicated that neutral red doubles the concentration of acetate over the control (coal without neutral red), emphasizing the importance of maximizing the fracture surface coverage of this redox mediator. Overall, results suggested that, neutral red not only can benefit acetoclastic methanogens, but also the fermentative and acetogenic bacteria involved in generating acetate.",
"introduction": "Introduction Capturing methane from coal is a growing industry that involves naturally produced methane extraction from coal seam beds. Conversion of coal into methane ( Hofrichter and Fakoussa, 2001 ) involves four different steps; (1) biofragmentation of coal to molecules of lower molecular mass, (2) anaerobic oxidation of biofragmented products to produce volatile fatty acids including acetate, methylated compounds, CO 2 and H 2 , (3) conversion of volatile fatty acids into CO 2 , H 2 and acetate, and, (4) conversion of acetate, methylated compounds or H 2 /CO 2 to methane ( Figure 1 ; Szeinbaum, 2009 ). FIGURE 1 Conversion of coal into methane (adapted from Stra̧poć et al., 2008 ). Hydrolytic bacteria involved in step 1, decompose and convert complex organic compounds of coal into polyaromatic hydrocarbons, aromatic carboxylate and ketone derivatives, single-ring aromatics and long-chain alkanes include Thauera, Azonexus, Geobacter, Acidovorax, Acinetobacter , and Pseudomonas . In step 2, Actinobacteria, Firmicutes, Bacteroidetes, and Spirochetes, are responsible for the fermentation of hydrolyzed substrate to produce CO 2 and H 2 . In step 3, homoacetogenic bacteria convert carbon dioxide (CO 2 ) and molecular hydrogen (H 2 ) into acetyl-CoA, through the reductive acetyl-coenzyme A (acetyl-CoA) pathway, also known as the Wood–Ljungdahl pathway. Homoacetogens associated with coal seams include Acetobacterium, Treponema, Desulfobacterium, Syntrophomonas, Clostridium, Eubacterium, Desulfuromonas, Syntrophobacter, Acetonema, Desulfobacter, Syntrophothermus, Pelobacter, Syntrophobacter , and Desulfovibrio . In step 4, methanogenic archaea utilize acetate, methylated compounds or H 2 as energy sources and generate methane as a bi-product. Most methanogens in coal seam groundwater carry out acetoclastic or hydrogenotrophic methanogenesis, such as Methanosarcina, Methanolobus, Methanobacteria, Methanocorpusculum, Methanosaeta, Methanococci, Methanoculleus, and Methanoregula. The use of electron mediators to transport reducing equivalents to or from microorganisms has received considerable attention due to their potential to accelerate energy metabolism for growth and to alter the terminal metabolite production toward profitable products (for example: L-glutamate) during the process of fermentation ( Nerdahl and Weimer, 2015 ). The delivery of electrons to fermentative bacteria for biocommodity production at the lab-scale has been successfully implemented using several different mediators including neutral red, benzyl viologen, methyl viologen and humic substances such as anthraquinone 2,6-disulfonate ( Hongo and Iwahara, 1979 ; Park et al., 1999 ; Clark et al., 2012 ). Among these, neutral red has shown promising results due to its low toxicity and low standard reduction potential (Eo = −375 mV) ( Hongo and Iwahara, 1979 ). Neutral red (3-amino-7-dimethylamino-2-methylphenazine, Figure 2 ) is an amino derivative of the heterocyclic phenazine. Phenazine derivatives have highly reversible redox behavior and act as reducing equivalents in transfer mediating systems ( Laviron and Roullier, 1983 ; Mugnier et al., 1991 ). Neutral red is a cationic dye and has been used as a histological stain because of its optical sensitivity to pH in the relevant range of pH 6.0–8.0 ( Halliday and Matthews, 1983 ). The molecular structure of neutral red differs under different pH and redox potentials. These structures include NRH + , reduced neutral red (NRH 2 ) and leuco neutral red (NR). Electrochemical reduction of neutral red yields a yellow solution with a green fluorescence ( Park et al., 1999 ). Reduction of NRH + and NR occurs via two-electron transfer ( Azariah et al., 1998 ). NRH + is reduced to NRH 2 by a number of bacterial species ( Makgill, 1901 ; Gage and Phelps, 1902 ; Chamot and Sherwood, 1915 ). Neutral red has been observed to crystallize as a second crystalline form (a polymorph) ( Labine-Romain et al., 2017 ) at higher temperatures (40–80°C, depending upon the depth of the coal reserves) that may occur in coal seams. FIGURE 2 Neutral red. In a previous study by Beckmann in 2015, neutral red crystals (NRCs) were found to increase acetoclastic methanogenesis with coal as a substrate (last step in the conversion of coal to methane), by increasing the phenazine pool in the membrane of methanogens. Whilst methane production was stimulated there was no evidence presented that coal bioconversion increased as would be expected. The aim of this study therefore was to, find out the changes in the surface chemistry of coal in the presence of neutral red and coal seam associated microbes using FTIR and XPS. Coal samples were incubated under anaerobic conditions in groundwater with its incumbent microbial community in the presence and absence of neutral red for 6 months. Coal samples were then subject to imaging and surface chemical analysis. Imaging revealed the formation of a fine mesh of crystalline neutral red associated with the surface of coal with embedded cells. Acetogenesis was detected by XPS (surface sensitivity ca. 5 nm) and shown to be stimulated by neutral red though these changes were not detected by ATR-FTIR (surface sensitivity ca. 3 micrometers) confirming that acetogenesis was a nanoscale surface phenomenon.",
"discussion": "Discussion Acetogens are strictly anaerobic bacteria and ubiquitous in nature. With methane-forming archaea, they constitute the final step in the production of methane from coal. Acetogens utilize the Wood–Ljungdahl ( Ragsdale, 2008 ) pathway for the production of acetyl-CoA and also for biofuel production such as ethanol, butanol or hexanol. Bio-commodities including acetate, lactate, butyrate, hexanoate, 2,3-butanediol and acetone are produced by acetogens, by using syngas as the carbon and energy source. All acetogens that produce organic acids like acetic, butyric or other acid are described as acetogenic ( Schiel-Bengelsdorf and Dürre, 2012 ) while the acetogens that produce solvents like ethanol, butanol, and hexanol could be described as solventogenic ( Bengelsdorf et al., 2018 ). Acetogens also employ a type of chemolithoautotrophic metabolism and ( Müller, 2003 ) provide capabilities to perform; (a) the oxidation of CO into CO 2 , (b) using reducing equivalents derived from the above reaction for their growth, (c) assimilate the part of CO 2 formed via ribulose-bisphosphate cycle, and (d) to withstand CO inhibition. A total of 61 strains were described to be acetogens, 56 are reported to grow using H 2 + CO 2 , three strains are known to use CO, not H 2 + CO 2 , and the remaining two are included because of having the gene cluster of Wood-Ljungdahl pathway ( Bengelsdorf et al., 2018 ). The Wood-Ljungdahl pathway has two branches, the methyl and carbonyl branch, with both contributing to the formation of central intermediate acetyl-CoA. During heterotrophic growth, with sugar as energy and carbon source, pyruvate by glycolysis, enters the Wood-Ljungdahl pathway ( Bengelsdorf et al., 2018 ). Ferredoxin oxidoreductase catalyzes and convert pyruvate into acetyl-CoA with the production of CO 2 . Then, this CO 2 is reduced to produce acetyl-CoA in the Wood–Ljungdahl pathway, with the reducing equivalents produced during the process of glycolysis, however, the methyl branch is involved in the stepwise reduction of produced CO 2 into methyl group, which is needed to produce acetyl-CoA. A different mechanism of extracellular electron transfer (EET) has been proposed for acetogens ( Figure 8 ; Bengelsdorf et al., 2018 ). Direct EET involves outer-membrane bound cytochromes, transporting extracellular electrons from outside to inside of the cells and the other way around. This mechanism is well studied in Geobacter sp. The species of Moorella and Sporomusa , have cytochromes but most other acetogens are lacking them. Shewanella oneidensis and Pseudomonas aeruginosa use flavins and phenazines to mediate the transport of electrons ( Bengelsdorf et al., 2018 ). Another possibility is an indirect electron exchange based on the evolution of H 2 . FIGURE 8 Overview of proposed extracellular electron transfer mechanism ( Lovley, 2011 ): (A) direct, (B) electron transfer mediated by dissolved redox mediators, and neutral red (proposed hypothesis), indicating that chemical processes occurring at nanoscale, (C) dependent on the H 2 generation catalyzed by extracellular hydrogenase. During methanogenesis in Methanosarcina species, multiple reduction-oxidation reactions take place, mediated partly by a membrane bound phenazine molecule called methanophenazine. In Methanosarcina mazei , methanophenazine relays electrons through the cytoplasmic membrane from a membrane bound F 420 H 2 dehydrogenase and membrane bound heterodisulphide reductase ( Deppenmeier, 2004 ). Neutral red and methanophenazine are structurally related phenazines with similar mid-point potentials (E 0 −300 to −400 mV), and Beckmann et al. , demonstrated that NR mimics methanophenazine, in the respiratory chain of Methanosarcina mazei . In addition, NR forms crystals, at the concentration of 250 μM in anoxic culture media and these crystals accept electrons more efficiently than the soluble form of neutral red. NRCs accept electrons more efficiently because its midpoint potential is 444 mV, more positive at +69, than the soluble neutral red of −375 mV. This makes the crystalline form more amenable to attract electron from organic and inorganic electron carriers present in environment and make them available to the microbes. Neutral red molecules solubilized in the reduced state by protonation at the point of methanogen cell contact, with the crystal surface deliver electrons to the methanogens at a negative midpoint potential. Reduced neutral red delivers electrons to the heterosulfide reductase (HDrED) in the membrane, resulting in proton translocation and liberation of CoM-SH and CoB-SH enhancing the rate of methanogenesis ( Beckmann et al., 2015 ). With this phenomenon, increased methane production was recorded, but nothing is known about the impact of neutral red on the microbial communities involved in the other stages in the bioconversion of coal to methane. Light microscopy of coal surfaces, in the presence of the coal-seam groundwater with neutral red indicated that the crystals distribute widely over the surface of coal and have coating on them. The deposition over crystals are cells, and mineral deposition. The backscattered electron microscopy of crystals provided a clearer picture of the crystals. The crystals, because of their midpoint potential, have higher efficiency to attract reducing equivalents from microbes or coal or reduced minerals, and behave like an electron distribution hub for microbes. The microbial communities, involved in different steps of biodegradation of coal, could have accepted the electrons for their growth and metabolism from these crystals ( Park et al., 1999 ; Park and Zeikus, 1999 ), and have attached themselves on the crystalline surfaces of these crystals. However, the electron microscopy of the coal, treated with groundwater, in the absence of neutral red, revealed limited colonization of the coal surface. Electron microscopy of coal samples, prepared by the HMDS method ( Hazrin-Chong and Manefield, 2012 ) which preserves cell structure, showed different cell morphologies widely distributed over the surface of coal. This also indicates that most of the microbial communities tend to associate with the neutral red crystals and thus take benefit from it, if is this provided in the environment. The HMDS method involves the use of strong solvents that dissolve the crystals which is why no crystals are visible over the coal surface. The acetate production occurs at the interface where microbes are localized on a coal surface coated with neutral red crystals. The surface analysis demonstrates that the acetate production resulting from the oxidation of the coal which was only found a few nanometers into the coal surface. The surface of coal was analyzed by ATR-FTIR and XPS spectroscopy, to find out the changes caused by coal seam water associated microbial communities with and without neutral red. ATR-FTIR spectroscopy did not show differences between treatments because of the limited depth sensitivity of the ATR-crystal. However, XPS, which provides ca. 5 nm surface sensitivity, showed that acetate O = C-O groups and C-O group on the coal surface increased in the presence of neutral red with microbes. The origin of the O = C-O and C-O signal in the XPS is assumed to be from near-surface microbial oxidation, however, it is possible that there is a contribution from carbonyl-containing biomolecules originating from the organisms. The acetate production indicates that the microbial communities involved in conversion of coal into acetate, react with the very top and exposed surface of the coal, and that this reaction benefited from the presence of neutral red crystals. The XPS analysis of the groundwater sample found to be contain organic matter with C-O, C = O and O = C-O groups which could be associated with dissolved fatty acids and other organic material, possibly derived from humic leachates of soil organic matter, algal extracellular polymeric substances (EPS), and dead microbial matter. The adsorption of this material on the surface would enhance the XPS signals for the coal with microbe spectrum. The XPS data for the coal with microbe experiment cannot be definitively ascribed to microbial oxidation processes at the surface. However, the relative enhancement of the O = C-O and C-O signals in the XPS when neutral red is present can only be explained if the microbes are consuming the coal surface. No measurable oxidation product (acetate) is produced when only neutral red is present in the groundwater (no coal provided). The XPS analysis of NR with groundwater data did not show any acetate and is provided as a separate Supplementary Excel File. The acetate analysis by GC, indicated the production of acetate, was double in the presence of neutral red, than the cultures without neutral red. The microbial communities involved in acidogenesis and acetogenesis during the conversion of coal into methane, might have taken benefits from NRCs by giving or consuming the reducing equivalents instead of just acetoclastic methanogens ( Beckmann et al., 2015 ) and these electrons may have transported inside the cells through various channels, present in the cell membrane or by following direct interspecies electron exchange ( Figure 8B ) however, the hypothesis of accepting electrons by acetogens and other microbial communities from NRCs still needs to be proven by redox potential-based analytical methods including cyclic voltammetry."
} | 4,246 |
33597941 | PMC7882529 | pmc | 5,094 | {
"abstract": "The relationship between biodiversity and ecosystem functioning (BEF) is a central issue in soil and microbial ecology. To date, most belowground BEF studies focus on the diversity of microbes analyzed by barcoding on total DNA, which targets both active and inactive microbes. This approach creates a bias as it mixes the part of the microbiome currently steering processes that provide actual ecosystem functions with the part not directly involved. Using experimental extensive grasslands under current and future climate, we used the bromodeoxyuridine (BrdU) immunocapture technique combined with pair-end Illumina sequencing to characterize both total and active microbiomes (including both bacteria and fungi) in the rhizosphere of Trifolium pratense . Rhizosphere function was assessed by measuring the activity of three microbial extracellular enzymes (β-glucosidase, N-acetyl-glucosaminidase, and acid phosphatase), which play central roles in the C, N, and P acquisition. We showed that the richness of overall and specific functional groups of active microbes in rhizosphere soil significantly correlated with the measured enzyme activities, while total microbial richness did not. Active microbes of the rhizosphere represented 42.8 and 32.1% of the total bacterial and fungal taxa, respectively, and were taxonomically and functionally diverse. Nitrogen fixing bacteria were highly active in this system with 71% of the total operational taxonomic units (OTUs) assigned to this group detected as active. We found the total and active microbiomes to display different responses to variations in soil physicochemical factors in the grassland, but with some degree of resistance to a manipulation mimicking future climate. Our findings provide critical insights into the role of active microbes in defining soil ecosystem functions in a grassland ecosystem. We demonstrate that the relationship between biodiversity-ecosystem functioning in soil may be stronger than previously thought.",
"conclusion": "Conclusion Soil, the rich ecosystem, includes numerous and diverse microorganisms. Some microbes were active and responsible for ecosystem function, while other are non-active and may serve as a backup of functional redundant microbes and/or a reservoir of the genetic information. We found that the composition of the active and total microbial community compositions were distinct from each other. Moreover, the active communities were more accurately to reflect the correlation between tested soil function and microbial richness. Furthermore, we provide evidence on the factors shaping active community compositions in the rhizosphere soil, which were totally different from those that shape the total community composition. Finally, our results showed that soil microbes in the rhizosphere of T. pratense (both active and inactive portions) were highly adaptable to the future climate changes, and thus, they can provide soil ecosystem functions nowadays and in the future.",
"introduction": "Introduction Microbial communities in soil exhibit high phylogenetic ( Tringe et al., 2005 ; Hug et al., 2016 ), taxonomic ( Fierer et al., 2007 ), and functional ( Prosser, 2002 ; Escalas et al., 2019 ) diversity and contribute to several ecosystem services ( Saccá et al., 2017 ) by maintaining multiple functions ( Jurburg and Salles, 2015 ; Delgado-Baquerizo et al., 2016 ; Soliveres et al., 2016 ). Most soil functions are more likely to be driven by the active microbial diversity at each specific time point ( Bastida et al., 2016 ). However, in soils, large proportions of the microorganisms are not active at each time point ( Blagodatskaya and Kuzyakov, 2013 ). Therefore, linking diversity of microbes to their contribution to soil functions during biodiversity-ecosystem functioning (BEF) studies remains controversial, as it requires distinguishing metabolically active microbes within the belowground ecosystem compartment ( Carini et al., 2016 ). The advent of high-throughput sequencing technology resulted in revealing the taxonomic diversity and composition of soil microbial communities. Most of these approaches detect the total genomic DNA persisting in soils, which includes a mixture of (i) relic or “extracellular” DNA able to persist many years in soils ( Nielsen et al., 2007 ), (ii) DNA in non-intact cells, (iii) DNA of potentially active microbes that may trigger into activity within minutes to few hours by trace quantities of specific nutrients ( De Nobili et al., 2001 ; Blagodatskaya and Kuzyakov, 2013 ), (iv) DNA of dormant microbes, and (v) DNA of living cells—intact, capable of reproduction, and metabolically active ( Emerson et al., 2017 ). Not differentiating between these fractions leads to biased estimates of really active soil microbial diversity ( Carini et al., 2016 ). To avoid such biases and only capture the active microbial communities, many approaches have been proposed, including RNA sequencing ( Bowsher et al., 2019 ), metaproteomics ( Williams et al., 2010 ), active cell staining ( Bowsher et al., 2019 ), stable isotope probing ( Dumont and Murrell, 2005 ), quantitative multi-isotope imaging mass spectrometry (MIMS) ( Lechene et al., 2006 ), bio-orthogonal non-canonical amino acid tagging (BONCAT) ( Hatzenpichler et al., 2016 ), and the viability indicator propidium monoazide (PMA) ( Carini et al., 2016 ). An alternative approach for distinguishing active microbes within an ecosystem is the incorporation of the thymidine analog, bromodeoxyuridine (BrdU), into replicating cells during DNA synthesis, labeling actively growing microorganisms, and isolating the BrdU-labeled DNA by BrdU immunocapture using specific anti-BrdU antibodies ( Borneman, 1999 ; Urbach et al., 1999 ). BrdU incorporation has been shown to successfully detect active bacteria in microcosms ( Bravo et al., 2013 ; Kelly et al., 2016 ) and in natural habitats such as freshwater lakes ( Grubisic et al., 2017 ), marine water ( Galand et al., 2013 ; Taniguchi et al., 2015 ), sewage ( Walters and Field, 2006 ), crop lands ( Hjort et al., 2007 ), forest soil ( Goldfarb et al., 2011 ), arctic soil ( Mcmahon et al., 2011 ), fallow soil, and bacterial association with arbuscular mycorrhizal hyphae ( Artursson and Jansson, 2003 ) as well as active fungi in forest soils ( Allison et al., 2007 ; Allison and Treseder, 2008 ) and leaf litters ( Allison et al., 2007 ). One potential disadvantage of BrdU-immunocapture technique is that not all microbial taxa are able to take up and incorporate BrdU ( Mcmahon et al., 2011 ); however, a previous study ( Hellman et al., 2011 ) showed that 18 out of 23 studied bacterial strains are able to incorporate BrdU, and generally, all bacterial phyla can be detected. No information regarding BrdU uptake capacity by fungal taxa is available. Comparisons of active and total microbial communities have been done in many ecosystems including terrestrial and aquatic habitats ( Baldrian et al., 2012 ; Romanowicz et al., 2016 ; Cardoso et al., 2017 ; Nawaz et al., 2018 ; Li et al., 2019 ). They revealed significant differences between these two communities, which are shaped partly by similar ( Romanowicz et al., 2016 ; Nawaz et al., 2019 ) but also different ( Rajala et al., 2011 ; Baldrian et al., 2012 ; Zhang et al., 2014 ) environmental factors. Soil physicochemical factors, climate change factors such as warming and altered precipitation patterns are known to modify ecosystem properties and processes and may affect the rhizosphere microbial diversity and community composition ( Wieland et al., 2001 ; Garbeva et al., 2007 ; Drigo et al., 2008 ; Philippot et al., 2013 ; Alkorta et al., 2017 ). However, we still do not fully understand how the total and the active microbial communities in rhizosphere respond to such changes. Soil functions can be assessed by various indicators. One of those biological indicators is extracellular enzyme activity (EEA) ( Sinsabaugh et al., 2008 ; Steinauer et al., 2015 ; Bastida et al., 2016 ; Creamer et al., 2016 ). Production of microbial extracellular enzymes is higher in the biologically active rhizosphere zone as compared to the bulk soil ( Joshi et al., 2018 ). Microbial extracellular enzymes are considered the proximate agents of organic matter break down and mineralization. Also, they have a protective function through oxidizing toxic substances ( Gianfreda, 2015 ; Joshi et al., 2018 ). Consequently, EEA contributes to the supporting and regulating ecosystem services carried out by active microbes ( Bodelier, 2011 ; Joshi et al., 2018 ). Although previous studies proved that the active diversity more accurately reflects ecosystem functionality than total diversity ( Bastida et al., 2016 ; De Vrieze et al., 2018 ), to our knowledge, no studies to date have compared the links between total and active microbial diversity and soil ecosystem functions in the rhizosphere soil in grasslands. In the present study, we used BrdU immunocapture combined with Illumina rRNA operon amplicon sequencing to characterize the total and active rhizosphere soil microbiome of Trifolium pratense (red clover) in a grassland ecosystem. Trifolium pratense is one of the most important forage legumes in the world and is adapted to many edaphic and climatic conditions ( Taylor and Smith, 1979 ). It maintains high pasture quality under low fertilization by providing significant nitrogen input via symbiotic nitrogen fixation ( Thilakarathna et al., 2018 ). To investigate the edaphic and climatic factors that shape the total and active rhizosphere microbiomes, we conducted the experiment at the Global Change Experimental Facility (GCEF) under both ambient conditions and a future climate scenario expected in 50–70 years from now in Central Germany ( Schädler et al., 2019 ). In addition, we measured the activity of three microbial extracellular enzymes (β-glucosidase, N-acetyl-glucosaminidase, and acid phosphatase) in the same rhizosphere soil to be considered as indicators of ecosystem functions displayed by rhizosphere microbial communities for C, N, and P acquisition in this system. To get insights into the relationship between biodiversity–ecosystem functioning, we linked the measured EEA with two indices of microbial biodiversity: total microbial richness and active microbial richness and with the compositions of active and total communities. Our specific goals were to (i) estimate the proportion of active microbes relative to the total rhizosphere microbiome, (ii) study the responses of total and active microbiome to a manipulated future climate, and (iii) identify the possible links between total and active microbiomes and the soil ecosystem function. We expected differences in richness and community composition as well as the environmental drivers of total and active rhizosphere soil microbiomes. We hypothesized that the relationship between biodiversity–ecosystem functioning obtained from active rhizosphere soil microbiome to be stronger than the one from the total rhizosphere microbiome.",
"discussion": "Discussion Estimation of the Active Microbial Fraction in the Rhizosphere Soil Using BrdU-Immunocapture Approach Several studies have already estimated the proportion of active soil microorganisms in different ecosystems. The ratio of active microbes in soil based on a microbial cultivation approach and direct microscopic estimations after cell staining revealed that 10–40% (up to 60%) of the total microbial biomass was potentially active microbes ( Blagodatskaya and Kuzyakov, 2013 ). Another study reviewed that approx. 80% of total cell counts as determined by fluorescence in situ hybridization (FISH) or staining with CTC, and approx. 50% of OTUs of ribosomal RNA–ribosomal DNA terminal restriction fragment length polymorphism (TRFLP) in bulk soil may be inactive ( Lennon and Jones, 2011 ). In our study, 43 and 35% of the detected bacterial and fungal community richness, respectively, represented active members (based on BrdU immunocapture and Illumina sequencing) in the rhizosphere soil of Trifolium pratense of a grassland ecosystem. The majority of bacterial and fungal phyla and ecological functions were represented in the active fraction, indicating that a broad microbial spectrum was capable of BrdU uptake and that its detection by immunocapture technique works. The experiment was performed at the Global Change Experimental Facility, a field infrastructure with a realistic scenarios to compare ecosystem effects on biological systems, so the incubation conditions with BrdU was not artificial, no additional substrates (isotopically labeled) were added, and therefore, the results reflect a realistic image of the soil microbial community in an active status. Despite the general assumption that the active soil community represents a subset of the total soil community, reported results showed that these two fractions are quite independent from each other and that the active community is similar in richness as or even more taxonomically diverse than the total community ( Baldrian et al., 2012 ; Romanowicz et al., 2016 ; Nawaz et al., 2019 ). In our approach, the active bacterial and fungal community composition could, however, be considered as a subsets of the total microbial community composition, as we detected a low proportion of active bacterial (10.2%) and fungal (6.5%) OTUs unique to the active fraction. These unique active communities were masked by the high abundant inactive taxa. Application of the BrdU-immunocapture has removed the interference of inactive highly abundant microbes. Active and Total Microbial Communities Composition Are Different and Are Shaped by Soil Physicochemical Factors, but Not by Climatic Factors Similar to previous studies in rhizosphere and bulk soils of other ecosystems ( Freedman et al., 2015 ; Ragot et al., 2016 ; Cardoso et al., 2017 ; Li et al., 2019 ), our results indicated that there are significant differences between the total and the active microbial community compositions. We found that the total community was more diverse than the active one. This could be explained by the fact that rhizosphere is a highly chemically dynamic compartment with tremendous microbial interactions ( Singh et al., 2004 ; Zhalnina et al., 2018 ). Thus, at any given time only some specific substrates are available and only some microbes with ability to use these substrates have a chance to become active ( Vieira et al., 2019 ). The rest of the microbes may stay inactive until suitable substrates are present in the rhizosphere ( Lennon and Jones, 2011 ). In addition, appropriate conditions (i.e., edaphic, climatic, and biotic factors) may also play very important roles to activate microbes ( Li et al., 2015 ; Birgander et al., 2018 ). Our results revealed that total and active microbial communities are shaped by different soil factors. There appears to be no study comparing active and total microbial community in the rhizosphere soil in comparable systems with our study, thus we compare our results to studies in bulk soils. Studies in the bulk soil of temperate grassland and forest ecosystem emphasized that the same environmental factors (soil moisture, pH as well as soil C and N) shaped the total and active bacterial communities ( Ragot et al., 2016 ; Romanowicz et al., 2016 ). Also, the same environmental factors shaped the total and active fungal community in bulk soil of a forest ecosystem ( Romanowicz et al., 2016 ). We do find that these factors shape the total microbial communities in the rhizosphere soil, but not the active microbial communities ( Table 1 ). We found that the active bacterial communities were correlated with Ca 2+ while active fungal communities were correlated with Ca 2+ and P. This could be explained by the importance of these elements for active microorganisms because of their contribution to active physiological processes. Ca 2+ is involved in a number of bacterial processes such as maintenance of cell structure, motility, adhesion, cell division, gene expression and cell differentiation processes such as sporulation, heterocyst formation, fruiting body development and biofilm formation ( Norris et al., 1996 ; Torrecilla et al., 2004 ; Das et al., 2014 ). Also, Ca 2+ is involved in hyphal tip growth of fungi ( Jackson and Heath, 1993 ). Previous studies reported that Ca 2+ content in soil influence bacterial community structure ( Rezapour, 2013 ; Xue et al., 2017 ). Furthermore, P is essential as part of many cellular compounds, such as DNA and the energy carrier adenosine triphosphate (ATP). Available P in our system (extensively managed grassland) is limited as we do not supply any fertilizer, thus P mineralization and assimilation are important processes in the rhizosphere. Members of the active fungal community composition were capable to assimilate the dissolved phosphate in soil ( Caldwell, 2005 ). Similar to these results we found a positive correlation between active fungal community composition and phosphatase activity, indicating that some active fungi hydrolyze P from organic compounds and thereby making P bioavailable in the rhizosphere soil. In this study of a microbial hot spot, we demonstrated that active microbial community composition was very differently organized compared to the total community with no overlapping factors shaping respective community compositions. In less active environments such as bulk soil, the total and active community compositions were different but they were shaped by similar soil physicochemical properties ( Ragot et al., 2016 ; Romanowicz et al., 2016 ). At the Global Change Experimental Facility (GCEF), the future climate scenario regime was started in 2014 and included altered precipitation patterns during the year and an increase of mean annual temperatures by ∼2°C ( Schädler et al., 2019 ). Our sampling was performed after four years of climate manipulation, however, our results indicated that both bacterial and fungal communities, except for specific few taxa, have not been influenced by the future climate scenario. Microbial communities may have been resistant to the changing environmental factors, enabled by microbial trait plasticity. Another possibility is that microbial communities showed some resilience and returned to its original composition after being disturbed during the drought periods of summer months ( Allison and Martiny, 2008 ). In addition, it is possible that microbial communities changed at the genomic level that were not visible from 16S and ITS sequencing data. Also, investigation of the microbial extracellular enzymes production revealed mostly a resistance toward the future climate scenario. The recovery of enzymes has been also reported during the rewetting after drought periods ( Pohlon et al., 2013 ). In addition, it is interesting to find one of the most important bacterial function, atmospheric N-fixation, has not been altered by changing climate. T. pratense is an efficient atmospheric N fixer plant because of the symbiosis with nitrogen fixing bacteria ( Fustec et al., 2010 ) suggesting that T. pratense could be considered as a soil fertility supporting crop in the future. Ecosystem Functions Are Linked With Active Rather Than Total Microbial Diversity The relationship between microbial diversity and ecosystem functioning (BEF) is complex and understanding this elusive link is one of the most demanding scientific challenges ( Cardinale et al., 2012 ). In soil, a large portion of the microbial diversity detected may not contribute to functions at a given point in time, obscuring microbial BEF studies. We found that the studied ecosystem function (enzymes activity) was correlated with active but not total microbial communities. In addition, the enzymes’ activity was linked with the richness of specific functional classes (metabolic function or functional guilds) of the active community. For instance, we found that the richness of active chemoheterotrophic and aerobic chemoheterotrophic communities, the two dominant trophic modes, positively correlated with β-glucosidase and N-acetyl-glucosaminidase activity. This can be explained by the fact that the bulk of the enzyme activity is contributed by microbes that can be characterized by their high occurrence and large biomass, comparatively higher metabolic activity and larger quantities of secretion of extracellular enzymes into the soil ( Joshi et al., 2018 ). It is also interesting that active bacteria associated with N cycling (N-fixing) were positively linked with activity of N acquisition enzyme (N-acetyl-glucosaminidase). Due to the high activity of N-fixing microbes in our rhizosphere soil, Nitrogen fixed by T. pratense microbes is released into the soil mainly through N-containing exudates as well as root decomposition ( Thilakarathna et al., 2018 ). As a result, the N content in the form of NO 3 – -N, NH 4 \n + -N and dissolved organic N is increased in the rhizosphere ( Thilakarathna et al., 2018 ) leading to increase in the activity of N-acetyl-glucosaminidase enzyme ( Schleuss et al., 2019 ). Moreover, richness of active fungi was positively correlated with phosphatase activity. Our study field is characterized by no application of fertilizers, which resulted in limitation of P. We detected many fungal genera capable of solubilization and mineralization of insoluble soil phosphate to release soluble P and making it available to plants ( Alori et al., 2017 ), including Alternaria, Arthrobotrys, Aspergillus, Cladosporium, Curvularia, Fusarium, Myrothecium, Oidiodendron, Paecilomyces, Saccharomyces, Schwanniomyces, Torula , and Trichoderma. Our findings thus provide experimental evidence that soil ecosystem function can be reasonably predicted by the overall and function specific richness and the community composition of active microbial community. We performed our study in a short time scale, and hence, large-scale ecological studies are needed to assess our findings, including other ecosystems and time scales."
} | 5,523 |
37303745 | PMC10251114 | pmc | 5,095 | {
"abstract": "Combining engineering and biology surely must be a route to delivering solutions to the world's most pressing problems in depleting resources, energy and the environment. Engineers and biologists have long recognized the power in coupling their disciplines and have evolved a healthy variety of approaches to realizing technologies. Yet recently, there has been a movement to narrow the remit of engineering biology. Its definition as ‘the application of engineering principles to the design of biological systems' ought to encompass a broad church. However, the emphasis is firmly on construction ‘…of novel biological devices and systems from standardized artificial parts’ within cells. Thus, engineering biology has become synonymous with synthetic biology, despite the many longstanding technologies that use natural microbial communities. The focus on the nuts and bolts of synthetic organisms may be deflecting attention from the significant challenge of delivering solutions at scale, which cuts across all engineering biology, synthetic and natural. Understanding, let alone controlling, every component of an engineered system is an unrealistic goal. To realize workable solutions in a timely manner we must develop systematic ways of engineering biology in the face of the uncertainties that are inherent in biological systems and that arise through lack of knowledge.",
"discussion": "1 . Discussion The genesis of the concept of controlling biological systems is often attributed to Loeb [ 1 ]. He stood back from the reductionist paradigm that pervaded biology in the late nineteenth century and believed, and indeed demonstrated that empirically derived rules could be used to control generic biological mechanisms across multiple organisms based on environmental cues. He was interested in outputs and believed that biological complexity need not be totally unravelled to achieve them [ 2 ]. This was described as the ‘engineering ideal in biology’ by commentators [ 3 ]. Detractors were numerous and persistent in their demands to see the phenomena fractured into precise descriptions of their fundamental biochemical component parts [ 3 ]. While Loeb did influence the thinking of several influential biologists, reductionism continued to dominate most biological disciplines during the last century, and it has undoubtedly delivered fundamental understanding of biology. Advances in molecular biology and in genetic manipulation, in particular, fuelled renewed interest in control that ultimately led to synthetic biology. Despite synthetic biology often citing Loeb as inspiration, it has layered a reductionist perspective on top of the desire for control to deliver something subtly different from Loeb's more pragmatic engineering ideal. Synthetic biology assumes, or at least aspires to, a control from the molecule up and thus an absolute, deterministic knowledge of how changes made in the genome will affect an organism's response to the environment [ 4 ]. This is the ultimate reductionism and, as such, it is ambitious. Indeed, it is more ambitious than most established engineering disciplines in that, like the Feynman quote, ‘what I cannot create, I do not understand’, it equates engineering with perfect understanding. At the same time as Loeb was advocating, and defending, his engineering ideal, engineers had got their hands on biology and were, albeit crudely, enacting it. Most notably, to help solve the prosaic problem of preventing pollution of our coasts and rivers from organic industrial and domestic wastewaters [ 5 ]. They engineered systems where complex microbial communities are forced to grow in biofilm structures by metabolizing the organic waste and producing CO 2 and other less harmful molecules. The biofilms are engineered to be retained on fixed structures or to settle out of the water so that clean water can be discharged into the environment. A suite of biologically informed but necessarily empirical rules have allowed engineers to build wastewater treatment works all over the world and transform sewage treatment into one of the most economically and environmentally important biotechnologies. In this case, the necessity of delivering a timely solution to the pressing problem of urban squalor in the nineteenth century was the mother of invention. The empirical design rules were, and still are, far from perfect. Technologies sometimes fail for inexplicable reasons and the crudeness of the rules makes further invention slow, but the biotechnologies have saved countless lives. Other technologies, such as biorefineries [ 6 ] and bioremediation [ 7 ], have similarly benefited from engineering biology built on a combination of knowledge of the natural history of organisms and pragmatic empiricism. Synthetic biology has promised to deliver solutions to the current environmental, energy and agricultural problems in high-profile reports and papers over the last two decades [ 8 ]. Genetically modified organisms certainly have had an enormous influence on a variety of fields. Take, for example, the production of recombinant proteins by genetically modified bacteria. These are already synthesizing vital drugs, like insulin. Moving from gene editing through to a workable technology is still, however, a major challenge that many potential synthetic biology solutions fail to overcome [ 9 ]. Even for the expression of a single protein, the interactions between the modified genetic code, environmental cues and the vagaries of the host cell throw up unanticipated problems that can take years of trial and error, rather than deep biological knowledge-based interventions, to overcome. The resulting organisms then typically operate in a small window of environmental conditions so that they need to be grown over short periods of time and cosseted in pristine, highly controlled conditions that are expensive to operate, and only make economic sense for these high-value products. As the gene circuits become more complicated the potential suite of interactions grows nonlinearly. For some of the world's most pressing problems like pollution control or conversion of waste into bioenergy, where organisms need to persist in open environments, synthetic biology solutions have yet to be successfully scaled-up. So, for both the engineered consortia of natural organisms in open systems and for the current, successful, high-value synthetic biology technologies Loeb's engineering ideal of control has been achieved, but not through a complete understanding and/or standardization of every component in the biological system. There is a general consensus that for both open biological systems and highly constrained white biotechnologies that the control and the design processes need to be improved significantly if we are to accelerate innovation [ 9 , 10 ]. However, taking a dogmatic approach to delivering a solution to a complex problem has been shown in other fields, like software engineering, to slow progress and increase the likelihood of failure [ 11 ]. The reductive vision of synthetic biology has its place but should not be allowed to obscure a broader, more pragmatic, perspective on engineering biology. All engineering design from the construction of enormous infrastructure, like skyscrapers, bridges and dams to the smallest computer chips relies on mathematical models. Engineering biology is no different and more control will be delivered by improved models. However, improving a model for design does not necessarily occur by adding complexity. Take for example the design of a water distribution system to a large city [ 12 ]. These were being designed and implemented in the late nineteenth century when the most comprehensive description of fluid flow was the Navier–Stokes equations coupled to a turbulence model. Yet no engineer deployed the Navier–Stokes equations in design at the time; they could neither parameterize nor solve them. Rather they relied then, and still do today, on Bernoulli's equation; an elegant simplification of the physics that eschews much of the complexity of momentum transfers and encapsulated the flow in terms of energy. Had we waited for Navier–Stokes to be useable our cities would have been without water until the 1970s. Deterministic mathematical modelling that integrates all of the biological component parts is the holy grail in terms of synthetic biology design, but it requires a level of knowledge that we do not currently have. The most comprehensive descriptions are genome-scale metabolic models, which certainly serve as excellent tools for generating hypothesis on the metabolism of organisms [ 13 ]. However, if reaction kinetics are included then parameterizing the models becomes an extremely daunting prospect and to include the spatial distribution and dynamics of molecules in the cell would yield a model so complex that it could not be validated using existing experimental methods. Thus, while this reductionist modelling is important in corroborating our understanding, for the models to be used effectively in a timely manner at least some components of the system will be gross approximations [ 9 ]. Even then these models are essentially of the internal cell biology of a single organism. To deliver solutions at scale then the biology beyond the cell wall has to be considered. In focusing the remit of engineering biology too keenly on the construction component of synthetic biology we may fail to reward efforts to quantify and predict the interactions of cells within and between populations and with the chemical and physical environment, all of which are vital to the design of any biotechnology [ 10 ]. Modelling the internal biology of every cell in massive populations of synthetic or natural microorganisms would be overwhelming even if it were computationally feasible, which it is not. So, like Bernoulli's hydraulic model, a simpler description of the biology is required that eschews much of the internal biology and, also like Bernoulli's hydraulic model, energy-based approaches have proved extremely useful. Thus, in engineering, for the microbial communities used in environmental applications, design is guided by theory based on chemical thermodynamics [ 14 ]. The growth and yield of a population of microbes growing on particular electron acceptors and donors are governed by the free energy in the redox reaction they exploit [ 15 ]. In individual-based modelling of microbial populations, the energy balance has been applied to the metabolism and division of individual cells such that the spatial distribution of cells in a complex consortia can be explicitly simulated [ 16 , 17 ]. The energy approach, while operating at scale, does treat the microbes as omnipresent catalysts, such that if chemical conditions are supportive then functional groups of organisms will thrive. And yet there is little consensus on how the diversity of functional groups and the resilience of the community, or key members in it, can be predicted, which should be vital considerations in design. Thus, for models at all resolutions from the intracellular to whole bioreactors, our incomplete knowledge of the biological systems that we try to engineer, whether synthetic or natural, is one of the main uncertainties in designing solutions that will work at scale. It is imperative that we improve models, but it is also crucial that we are pragmatic. Pragmatism is a hallmark of engineering where solutions need to be delivered on time, even when some of the fundamental science is still obscure. So, in structural engineering the deformation of materials like concrete in response to stress depends on the intricate exchange of forces between the fibres, aggregates and cements, the details of which are fascinating but obscure, therefore, in design they are captured in a single macroscopic property, Young's modulus, which all engineers can interpret. Hydraulic engineers designing everything from pipe networks to flood embankments use Manning's n to characterize the roughness of complex, heterogeneous surfaces and estimate energy dissipation. Although the tortuous flow of water through porous media may be explicitly characterized in expensive laboratory experiments, for design, engineers use an effective conductivity, which they have documented for different rocks and soils. There is a plethora of such macroscopic parameters used routinely to deliver engineering solutions. Their derivation and the equations they are deployed in are often as ingenious as any literal, reductionist, full description of the system. We need more ways of wrapping up unresolved biology in parameters like Young's modulus, Manning's n and effective conductivity in engineering biology if we are to deliver solutions quickly. Furthermore, if we are honest about the uncertainty in these parameters then engineering biologists can begin to adopt systematic methods of dealing with uncertainty, such as limit state design, that will define the bounds within which their technologies will safely operate. Engineers, unlike Feynman, by necessity routinely create things that they do not ‘fully’ understand. Lack of knowledge is only one uncertainty that engineering biologists ought to confront. A recent review of the challenges facing engineering biology [ 9 ] acknowledged an issue that has perhaps not received the attention it deserves in engineering biology: ‘Currently, most synthetic biology projects work to design and construct a cell or strain and then put it to work, performing a task like biosynthesis or biosensing. We either just hope that mutation and selection will not act upon our ‘finished product’ once it is in operation, or in some cases, we design it as best as we can to reduce this chance [ 18 ]’. The efforts to design organisms that can resist evolution have, for example, involved the creation of stable chassis organisms with minimal genomes comprising what are considered essential genes that are shared by several wild-type strains. These chassis can then be used as stable platforms into which synthetic biological parts can be spliced. However, they have only ever been used in highly controlled laboratory environments. When exposed to fairly innocuous stresses that they might experience in real biotechnologies, such as starvation, it has been shown that the chassis organisms accumulate mutations equally as fast as wild-type organisms and up to 1000 times faster than the background mutation rate. Furthermore, the mutations were more likely to be deleterious in the chassis organism [ 19 ]. So, for application where populations of synthetic organisms need to be long lived or are exposed to environmental fluctuations or biological competition, at the very least, the effects of evolution should be built into the design process from the start. Building in feedbacks, such as kill switches [ 20 ] and more sophisticated circuits [ 21 ], can stop well-characterized detrimental mutations gaining a foothold, but anticipating evolutionary trajectories is almost impossible. One surprising idea that seems to run counter to the reductionist, control paradigm in synthetic biology, is to let evolution control the design of laboratory organisms, but it has been suggested that this would need a whole new ‘engineering theory of evolution’ [ 22 ]. It may be that exploring the natural biodiversity of microbes would reveal organisms that can already do components of the desired biological transformation and that using them in an engineered system would be a more prudent route to a solution. It would be a shame to see this equally valid approach to engineering biology sacrificed by adopting a restricted, synthetic biology, perspective on the field. For open systems, it is not only the uncertainty from evolution that engineering biologists must cope with. Random events such as immigration, emigration, deaths and births are manifest in a contribution to the dynamics of the abundance of species in the system [ 23 ]. So, species may be lost or gained, or their abundance amplified purely by chance. Our ability to quantify the risk of these processes derailing a biotechnology may make the difference between its widespread acceptance or ultimate failure. Again, simple engineering models with macroscopic parameters whose values can index typical behaviours and be used directly in the calculation of risk, such as the effective community size [ 23 ], are an imperative. Environmental extremes, biogeography, evolution and our lack of knowledge of much of the detail of a biological system mean that engineering biology solutions will always be pursued in the face of uncertainties. These will never be totally eradiated, but they can be quantified and, in some cases, reduced. Engineering biology needs to find ways of delivering solutions that are robust and resilient within the bounds of expected variability. Loeb's original engineering ideal was about effecting control without necessarily resolving every aspect of the biology and achieving this means bringing to bear all of the most up-to-date science and mathematics. Synthetic biology and the tools it has developed have a huge role to play, but the idea that engineering biology solutions must involve genetically modified organisms is dangerously restrictive."
} | 4,328 |
35678574 | PMC9241951 | pmc | 5,097 | {
"abstract": "ABSTRACT Methanotrophs, which help regulate atmospheric levels of methane, are active in diverse natural and man-made environments. This range of habitats and the feast-famine cycles seen by many environmental methanotrophs suggest that methanotrophs dynamically mediate rates of methane oxidation. Global methane budgets require ways to account for this variability in time and space. Functional gene biomarker transcripts are increasingly studied to inform the dynamics of diverse biogeochemical cycles. Previously, per-cell transcript levels of the methane oxidation biomarker pmoA were found to vary quantitatively with respect to methane oxidation rates in the model aerobic methanotroph Methylosinus trichosporium OB3b. In the present study, these trends were explored for two additional aerobic methanotroph pure cultures grown in membrane bioreactors, Methylocystis parvus OBBP and Methylomicrobium album BG8. At steady-state conditions, per-cell pmoA mRNA transcript levels strongly correlated with per-cell methane oxidation across the three methanotrophs across many orders of magnitude of activity ( R 2 = 0.91). The inclusion of both type I and type II aerobic methanotrophs suggests a universal trend between in situ activity level and pmoA RNA biomarker levels which can aid in improving estimates of both subsurface and atmospheric methane. Additionally, genome-wide expression data (obtained by transcriptome sequencing [RNA-seq]) were used to explore transcriptomic responses of steady-state M. album BG8 cultures to short-term CH 4 and O 2 limitation. These limitations induced regulation of genes involved in central carbon metabolism (including carbon storage), cell motility, and stress response. IMPORTANCE Methanotrophs are naturally occurring microorganisms capable of oxidizing methane, having an impact on global net methane emissions. Additionally, they have also gained interest for their biotechnological applications in single-cell protein production, biofuels, and bioplastics. Having better ways of measuring methanotroph activity and understanding how methanotrophs respond to changing conditions is imperative for both optimization in controlled-growth applications and understanding in situ methane oxidation rates. In this study, we explored the applicability of methane oxidation biomarkers as a universal indicator of methanotrophic activity and explored methanotroph transcriptomic response to short-term changes in substrate availability. Our results contribute to better understanding the activity of aerobic methanotrophs, their core metabolic pathways, and their stress responses.",
"introduction": "INTRODUCTION Methanotrophs are microbes capable of getting all their carbon and energy from methane, the second most abundant greenhouse gas after carbon dioxide ( 1 , 2 ). Methanotrophs represent the only known biological methane sink and play a pivotal role in the global methane cycle ( 3 ). Methanotroph-based biotechnologies, coupling methane oxidation with production of value-added products like methanol, biopolymers, single-cell proteins, lipids, and enzymes, have been gaining interest ( 4 – 6 ). Therefore, they represent a possible lynchpin in biorefinery scenarios for conversion of methane and/or biogas to bioproducts. Methanotrophic bacteria are found in the phyla Proteobacteria and, more recently, Verrucomicrobia ( 7 , 8 ) and NC10 ( 9 ). The most widely studied methanotrophs are from the classes Gammaproteobacteria and Alphaproteobacteria , and based on physiological characteristics, they were historically divided into type I and type II methanotrophs, respectively ( 10 , 11 ). However, methanotrophs can exhibit considerable metabolic flexibility even among methanotrophs of the same type. For example, they are capable of using nitrate or ammonium as a nitrogen source ( 12 , 13 ), nitrogen fixation ( 14 ), carbon accumulation under nutrient-limited conditions ( 15 , 16 ), and long-term survival under starvation conditions ( 17 – 21 ). A defining characteristic shared by almost all methanotrophic bacteria is methane monooxygenase (MMO) enzymes, which initiate methane oxidation ( 22 ). Methane is converted to methanol, followed by formaldehyde, which can be either incorporated as biomass or ultimately converted to CO 2 . Two distinct MMOs can be found in methanotrophs, particulate methane monooxygenase (pMMO) and soluble methane monooxygenase (sMMO). Methanotrophs that possess both MMOs express pMMO under high copper-to-biomass ratios and sMMO at low copper-to-biomass ratios ( 23 ). Gene and gene expression amounts of pMMO and sMMO subunits, genes pmoA and mmoX , respectively, are used to quantify populations of methanotrophs, their activities, and their transcriptional and phenotypic response to changes in conditions ( 24 – 26 ). As nearly all aerobic methanotrophs possess pMMO, gene pmoA is the preferred biomarker to determine both their abundance and methane oxidation activity. Approaches that quantify both biomarker mRNA transcripts and/or enzymes in addition to microbial amounts have been suggested as estimators of microbial contributions in biogeochemically relevant processes ( 27 ). The utility of protein and mRNA biomarkers in microbial communities has been demonstrated previously, in both identifying microbial function and determining in situ activities of specific community members ( 28 – 31 ). Periods of temporal variations affecting microbial activity would also be reflected by changes in their respective mRNA or enzyme pools. In methanotrophs, increases in pmoA gene copies and pmoA transcript levels correlate with increased methane oxidation ( 6 , 32 – 34 ) and have been proposed as quantitative indicators of their activity ( 35 , 36 ). Recently, a strong correlation between steady-state per-cell pmoA transcript levels and per-cell methane oxidation rate was demonstrated in the type I methanotroph Methylosinus trichosporium OB3b grown in membrane bioreactors ( 37 ). If similar relationships between biomarker amounts and activity are shared across different methanotrophs, they could allow dynamic inference of in situ methane oxidation rates. The goal of this study was to explore correlations between methane oxidation biomarker amounts and methanotrophic activity across aerobic methanotrophs species and obtain further insight into their transcriptomic response to dynamic conditions of substrate availability. To assess this, aerobic methanotrophs Methylocystis parvus OBBP (type II) and Methylomicrobium album BG8 (type I) were grown in membrane bioreactors under different conditions where biomarker pmoA gene and transcript amounts (via quantitative PCR [qPCR] and reverse transcription-qPCR) were measured. Additionally, effects of short-term (less than one retention time [RT]) substrate (O 2 and CH 4 ) limitation and recovery on RNA biomarker expression were explored in M. album BG8 using a targeted qPCR approach and RNA sequencing to determine temporal expression patterns under dynamic conditions influenced by environmental factors.",
"discussion": "RESULTS AND DISCUSSION Steady-state reactor performance. Reactor flow rates were monitored, and retention times (RTs) were consistent throughout operation. Reactors had average RTs of 2.8, 4.3, 5.7, and 9.1 days for M. album BG8 reactors and 2.8, 4.3, 5.8, and 9.0 days for M. parvus OBBP (see Fig. S1 in the supplemental material). RTs ensured that the methanotroph cultures in these reactors could reach distinct steady-state growth conditions. Reactors were considered steady state when biomass, oxygen, and methane levels were not changing significantly. All reactors reached steady-state conditions at all four RTs ( Fig. 1 ); duration, average biomass and substrate levels, and methane oxidation rates for steady-state periods are provided in Tables S1 and S2. FIG 1 Performance of M. album BG8 and M. parvus OBBP reactors. (A) M. album BG8, 2.8-day RT, CH 4 limitation, transition to 5.7-day RT; (B) M. album BG8, 4.3-day RT, O 2 limitation, transition to 9.1-day RT; (C) M. parvus OBBP, 2.8-day RT, 5.8-day RT; (D) M. parvus OBBP, 4.3-day RT, 9.0-day RT. Aqueous concentrations of CH 4 and O 2 , left y axis, and biomass, right y axis. Data are means ± standard deviations from triplicate reactors. Steady-state periods of M. album BG8 reactors are provided in Table S1 and shown in Fig. 1A and B . Dissolved-methane concentrations ranged between 0.21 and 1.68 mg L −1 , and oxygen concentrations ranged from 3.77 to 5.26 mg L −1 , decreasing with increasing retention time. Steady-state periods of the M. parvus OBBP reactors are provided in Table S2 and shown in Fig. 1C and D . Dissolved-methane concentrations ranged between 0.42 and 2.16 mg L −1 , while oxygen concentrations varied only slightly across all RTs, ranging from 4.19 to 5.41 mg L −1 . Both cultures followed the trend of increasing biomass with increasing retention time. Overall, biomass levels for M. album BG8 reactors were higher than for M. parvus OBBP for reactors with equivalent retention times. Differences in biomass amounts could be from differences in growth rate, internal carbon storage, and intracytoplasmic membrane (ICM) amounts, which differ between type I and type II methanotrophs ( 38 ). Methane levels observed during substrate limitation periods for both cultures were not always below reported methanotroph half-saturation constant ( K S ) values reported in the literature, which range from 0.06 to 1.48 mg L −1 ( 39 – 41 ). However, the observed levels were enough to affect methanotroph growth and activity. It is known that K S values are system specific (e.g., impacted significantly with stirring rate) and could be higher in the case of the methanotrophs grown in the membrane bioreactors. Volume-normalized methane oxidation rates were consistent across retention times and cultures (0.095 to 0.105 mg CH 4 mL −1 day −1 ) (Tables S1 and S2). Biomass-normalized methane oxidation rates decreased with increasing retention times for both cultures, reflecting the increase in biomass with retention time. Reactors had similar overall methane consumption rates while having distinct biomass levels. Differences in biomass-normalized methane oxidation rates implied different activities of the reactor methanotroph populations. Methane oxidation rates in this study agreed with reported literature values of 0.00014 to 0.14 mg CH 4 mL −1 day −1 ( 42 ) and 0.48 to 3.85 mg CH 4 mg cell (dry weight) −1 day −1 ( 37 , 43 ) for aerobic methanotrophs. Biomarker amounts and oxidation rates under steady-state conditions. Correlations between steady-state methane oxidation activity and cell and pmoA transcript amounts for M. album BG8 and M. parvus OBBP reactors were determined for the sample times indicated in Tables 1 and 2 and Fig. S2. For both methanotrophs, reactor methane oxidation rates and pmoA transcript amounts were normalized by corresponding genome amounts to obtain per-cell methane oxidation rates and per-cell pmoA transcript levels ( Fig. 2 ). FIG 2 Steady-state per-cell pmoA transcript amounts and methane oxidation rates for aerobic methanotrophs. Data are averages from individual reactors from distinct sampling dates. Error bars represent standard deviations of biomarker amounts ( y axis) from replicate reactors. Power law trend and R 2 value are shown. M. trichosporium OB3b data were obtained from the work of Tentori and Richardson ( 37 ). TABLE 1 Detailed operating conditions for M. album BG8 reactors Retention time (days) Condition Time (days) Operational change Times when samples were collected 2.8 ± 0.1 Steady state 0–15.2 Days: 12, 13, a and 15 CH 4 limitation 15.2–16.2 CH 4 off Hours after CH 4 off: 0.5, 1, 2, a 8, 12, and 24 Recovery 16.2–17.2 CH 4 on Hours after CH 4 on: 8 and 24 5.7 ± 0.1 Steady state 17.2–38.8 RT increase Day: 30 4.3 ± 0.1 Steady state 0–15.2 Days: 12, 13, a and 15 O 2 limitation 15.2–16.2 O 2 off Hours after O 2 off: 0.5, 1, 2, a 8, 12, and 24 Recovery 16.2–17.2 O 2 on Hours after O 2 on: 8 and 24 9.1 ± 0.2 Steady state 17.2–38.8 RT increase Day: 30 a Samples were selected for RNA-seq. TABLE 2 Detailed operating conditions for M. parvus OBBP reactors Retention time (days) Condition Time (days) Operational changes Days when samples were collected 2.8 ± 0.1 Steady state 0–19.5 18, 19 5.8 ± 0.2 Steady state 19.5–36.2 RT increase 32, 34 4.3 ± 0.2 Steady state 0–19.5 17, 18, 19 9.1 ± 0.4 Steady state 19.5–44.0 RT increase 36, 43 The range of per-cell pmoA transcript levels across oxidation rates suggests a wide range of activities for the methanotroph cultures in this study. M. album BG8 cultures exhibited a wider range of both per-cell transcript amounts (0.0004 to 60.84) and per-cell methane oxidation rates (0.005 to 1.681) than did M. parvus OBBP, 0.0002 to 0.150 and 0.003 to 0.045, respectively. The observed per-cell pmoA transcript levels for these cultures are similar to those in M. trichosporium OB3b cultures (0.25 to 120.74) grown in the same type of reactor ( 37 ). They are also comparable to other reported per-cell pmoA transcript amounts under controlled ( 21 , 44 – 46 ) and in situ ( 35 , 36 , 47 ) conditions and reported per-cell methane oxidation rates ( 17 , 22 , 48 , 49 ). Higher per-cell pmoA transcript levels matched well with cultures with shorter RTs for both M. album BG8 and M. parvus OBBP. Increased per-cell pmoA transcript amounts are hypothesized to correspond to more active cells with increased activities. For the three aerobic methanotroph cultures in Fig. 2 , a strong positive correlation (Pearson’s R 2 = 0.91) between per-cell pmoA transcript levels and per-cell methane oxidation rates was observed across several orders of magnitude. Strong correlations were maintained when pure methanotroph cultures were examined individually (Fig. S3). Cell amounts alone and methanotrophic activity have been shown to be poorly correlated previously in methanotrophs ( 37 , 50 ). Identical populations in terms of cell abundances can display vastly different activities, which could explain the stronger relationship when looking at cell amounts in conjunction with transcript amounts. The strong correlation observed for per-cell pmoA transcript levels and per-cell methane oxidation rate is due to the regulation of pMMO in response to available methane for oxidation. These results, spanning three aerobic methanotroph species, including both type I and type II methanotrophs, demonstrate that per-cell pmoA transcript levels may serve as a universal quantitative biomarker of extant bacterial methanotrophic activity. To date, this correlation has only been demonstrated under controlled lab conditions using pure cultures and requires more robust testing in complex microbial bioreactors and ecosystems. M. album BG8 reactor response to substrate limitation. At 15.2 days of operation, effects of a 24-h methane and oxygen limitation on M. album BG8 cultures were explored. The limitation period was followed by a 24-h recovery period in which membrane pressures were turned back on ( Fig. 3 and Fig. S2A and B). Methane limitation caused reactor oxygen levels to increase from 5.5 to 9.0 mg L −1 due to the lack of incoming methane, while biomass levels decreased by about half ( Fig. 3A ). The decrease in biomass was reflected in both cell genome and pmoA mRNA amounts ( Fig. 3B ). Within 2 h of methane limitation, genome copies were about a third of steady-state amounts, while pmoA mRNA amounts decreased by about 3 orders of magnitude. During steady state, per-cell pmoA transcript levels ranged from 9.02 to 11.60 transcripts per cell. A sharp decrease in the pmoA transcriptional activity 0.5 h after the onset of methane limitation was observed, reaching its minimum 8 h postlimitation, with a log 2 fold decrease of 8.2 in per-cell pmoA transcript levels compared to average steady-state levels ( Fig. 3C and Table S3). Per-cell pmoA transcript levels stayed significantly depleted through the 24 h following the onset of methane limitation. Resumption of methane saw sharp increases in pmoA transcript amounts, with per-cell pmoA transcript levels approaching steady-state levels 24 h after recovery, while biomass (as both genome copies and dry cell weight) lagged ( Fig. 3B and C and Table S3). FIG 3 M. album BG8 reactors during substrate limitation and recovery. (A) 2.8-day RT reactor response during CH 4 limitation; (B) 2.8-day RT reactor genome and pmoA mRNA amounts during CH 4 limitation; (C) 2.8-day RT reactor pmoA mRNA copies per cell during CH 4 limitation; (D) 4.3-day RT reactor response during O 2 limitation; (E) 4.3-day RT reactor genome and pmoA mRNA amounts during O 2 limitation; (F) 4.3-day RT reactor pmoA mRNA copies per cell during O 2 limitation. Data are means ± standard deviations from triplicate reactors. Asterisks indicate samples selected for RNA-seq. Data following RT switch are not shown. In oxygen-limited reactors, a sharp decrease in biomass levels was also observed, which continued into the recovery period ( Fig. 3D ). Steady-state pmoA transcript levels for the 4.3-day reactors ranged between 24.19 and 47.52 for the days preceding oxygen limitation. During oxygen limitation, oxygen levels decreased from 4 to 2 mg L −1 and methane levels increased from 1 to 3 mg L −1 . Genome copies dropped slowly throughout both the limitation and recovery periods, while pmoA mRNA amounts decreased by 2 orders of magnitude 0.5 h after oxygen limitation, with a more gradual decline observed in the subsequent hours during oxygen limitation ( Fig. 3E ). The decrease pattern in pmoA transcript amounts was reflected in per-cell pmoA transcript levels, with a log 2 fold decrease of 4.3 in compared to average steady-state levels ( Fig. 3F and Table S3). The oxygen recovery period also saw increases in pmoA transcript amounts which led to per-cell pmoA transcript levels comparable to steady-state levels after 24 h following oxygen repletion ( Fig. 3B and C and Table S3). The M. album BG8 biomarker response observed due to methane and oxygen limitation differed compared to that in steady state. Genome copies in the methane-limited reactors exhibited a sharp decrease within the first hour, with no further change observed through 24 h until recovery, when a small increase in genome copies was observed, while oxygen-limited reactors exhibited a steady decrease throughout both the limitation and recovery periods ( Fig. 3B and E ). Both methane and oxygen limitation had the largest decrease of pmoA transcript levels in the first hour, with a larger overall decrease observed with methane limited. Differences between per-cell pmoA transcript levels during both methane and oxygen limitation and their preceding steady-state levels were statistically significant ( P < 0.001) (Table S4). Both sets of reactors reached per-cell transcript levels comparable to steady-state averages within the 24-h recovery period. Differences in per-cell pmoA transcript levels were statistically significant during methane limitation and recovery ( P < 0.01) and during oxygen recovery and the preceding limitation period ( P < 0.05) (Table S4). The impact of oxygen limitation may have been less than that of methane limitation, as oxygen was present in the reactor influent. Similar pmoA expression patterns have been observed in mixed-consortium bioreactors, where cultures grown under feast conditions showed 2- to 3-fold per-cell pmoA transcript decreases within 1 h of famine conditions ( 51 ). Rodríguez et al. ( 51 ) also observed that per-cell pmoA transcripts of methanotroph cultures regularly exposed to recurring feast-famine conditions were not affected by the onset of famine, seeing no change in the first 12 h. Overall pmoA mRNA per-cell levels observed in the current study were orders of magnitude higher than in the study by Rodríguez et al. (0.00005 to 0.002) ( 51 ) and in line with those of M. trichosporium OB3b cultures ( 37 , 52 ) and Methyloprofundus sedimenti ( 21 ). The observed decrease in per-cell pmoA transcripts during the first hours of methane limitation suggest a fast pmoA regulation in response to methane levels. This is also supported by the increase observed in per-cell pmoA transcripts when methane was available during the recovery period, where a response occurred in the first 8 h and reached steady-state levels after 24 h ( Fig. 3C ). The observed rapid response of methanotrophs to changing methane and oxygen conditions suggests that they can quickly adapt to changes in local environmental conditions. In addition to cell amounts decreasing, the observed changes in biomass could be due to decreases in protein mass, as starvation under aerobic conditions has been demonstrated to induce protein biomass loss in methanotrophs ( 17 ). Rapid changes at the transcript level due to changes in local conditions are well documented for aerobic methanotrophs. The “copper switch,” a well-established link between copper levels and expression of pMMO or sMMO, has been shown to occur in the order of minutes to hours ( 23 , 53 , 54 ). Aerobic methanotrophs have also been shown to regulate expression of methane oxidation pathways in response to the type and amount of carbon source available (e.g., methane versus other one-carbon compounds) ( 55 – 57 ). This suggests that decreased methane amounts during periods of limitation would lead to a decrease in pmoA expression and a decrease in pmoA transcripts per cell and the opposite effect during the recovery period. Rapid (<2-h) responses from methanotrophs to starvation and recovery periods when grown in different biofilters and stirred tank reactors have been observed ( 51 , 58 ). Rodríguez et al. grew methanotroph reactors under different operational methods, feast-famine growth, and continuous growth and examined the influence of operational period on performance (methane consumption) and pmoA expression levels. Following a famine period, reactors under both operational methods were able to recover to their prelimitation performance within 2 h; however, the pmoA expression levels were influenced by operational mode, with a more drastic decrease observed in pmoA expression levels for the continuous-growth reactors ( 51 ). However, different trends have been observed in the methanotroph M. sedimenti , for which short-term methane starvation periods led to increased pmoA expression throughout the starvation period, with the opposite trend observed during recovery ( 21 ). Differing expression patterns between methane oxidation and the methanol dehydrogenase (MDH) genes mxaF and xoxF have been observed during starvation, suggesting independent regulation ( 21 ). The differences in expression observed between methanotrophs could serve as different survival strategies during starvation periods. M. album BG8 transcriptomic samples and gene expression response to short-term substrate limitation. Transcriptome sequencing (RNA-seq) samples from M. album BG8 under short-term substrate limitation yielded 170.4 million reads across the 8 multiplexed samples from the four conditions (Table S5). Gene counts showed agreement after normalization, with medians consistent across samples and between biological replicates (Fig. S4). Biological replicates were examined using principal-component analysis (PCA) of normalized logarithmic transformed read counts using DESeq2 ( 59 ). Similarity was observed between duplicate biological replicates for the substrate-limiting conditions. Steady-state samples showed less uniformity due to one replicate from the 4.2-day RT (Fig. S5). Despite the variability observed in PCA of the M. album BG8 transcriptome samples under steady state, samples were not considered outliers, as no significant differences in transcripts were detected. Coverage and normalized counts were similar across replicates, and replicates for all conditions were included in subsequent analyses. Collectively across all transcriptomes, expression was observed for 3,931 out of 3,984 (98.7%) of predicted protein-coding genes in the published M. album BG8 genome ( 60 ). Significant differential gene expression (DGE) (log 2 fold change [FC] > | 1.0 |; adjusted P value [ P adj ] < 0.05) was observed for CH 4 - and O 2 -limited samples compared to corresponding steady-state samples (Table S6 and Data Set S2). Methane limitation and oxygen limitation resulted in 444 and 282 genes, respectively, with significant DGE compared to reference steady-state samples. Reference steady-state samples had similar expression profiles regardless of 2.8- or 4.2-day RT, with only 4 genes showing significant DGE. EggNOG 5.0 was used to categorize genes showing DGE between substrate-limited and corresponding steady-state samples, categorizing 74% and 81% of differentially expressed genes of the methane- and oxygen-limited samples, respectively ( Fig. 4 ). FIG 4 Classification of significant differential gene expression (DGE) in M. album BG8, based on COG classification from the eggNOG 5.0 database. Positive and negative values on the x axis indicate numbers of genes in that COG category that were upregulated and downregulated, respectively, compared to steady-state conditions. Unclassified, category S (“Function Unknown”) and categories with fewer than two genes with DGE are not shown. Both CH 4 - and O 2 -limited conditions triggered upregulation of several of genes related to cell motility, flagella, and chemotaxis (identifier in Clusters of Orthologous Groups [COG] database: N) ( Fig. 4 and Data Set S2). Upregulation of motility genes has been linked to nutrient limitation, with motile cells seeking more favorable conditions ( 61 ). In methanotrophs, growth conditions influence expression of motility and adhesion genes. Similar expression patterns have been observed in M. album BG8 and Methylocystis sp. strain Rockwell grown with methanol ( 62 ) in which flagellum genes were upregulated. In this study, both limitation of CH 4 and limitation of O 2 resulted in a strong consistent downregulation of translation, ribosomal structure, and biogenesis (COG: J) genes, with effects on ribosomal protein genes more pronounced in methane limitation, while oxygen limitation mostly affected translational genes ( Fig. 4 and Data Set S2). The changes in expression observed, with an increase in cell motility, posttranslational modifications, and signal transduction genes and a decrease in carbohydrate, amino acid, and lipid metabolism and transport, and translation genes, are similar to those observed for M. album BG8 grown with methanol compared to methane ( 62 ). For all conditions, genes involved in methane oxidation and methanol oxidation were among the most highly expressed genes (16.6 to 28.9% of all transcripts [Data Set S2]), following expression trends observed in aerobic methanotrophs ( 63 – 65 ). The general trend was downregulation in C 1 metabolism genes upon short-term limitation. Exceptions were upregulation of alcohol dehydrogenase gene xoxF under both conditions and formate dehydrogenase ( fdh ) specifically during methane limitation. However, the C 1 metabolism gene transcript remained among the most highly abundant reads detected even during stress. A period of substrate limitation longer than 2 h might be required to observe an effect due to elevated starting transcript amounts for these pathways and the half-life of RNA. Partially degraded RNAs may still being “readable” by the RNA-seq method, unlike with qPCR, in which full-length transcripts are required for detection. A strong effect was observed in genes involved in energy production and conversion (COG: C) ( Fig. 4 ). For methanotrophs, these changes in expression of genes for energy production and conversion are hypothesized to be in response to the available substrate, to compensate for differences in energy available and minimize effect on core metabolic pathways ( 66 ). A visual representation of the response of central metabolic pathway genes and other genes of interest to methane and oxygen limitation is shown in Fig. 5 . NADH oxidoreductase and cytochrome genes were upregulated under methane-limited conditions, while ATP synthases were downregulated, with stronger effects observed under CH 4 limitation. Upregulation of cytochrome oxidase genes in response to O 2 limitation was observed, and such upregulation has been previously observed in Methylomicrobium buryatense 5GB1C ( 20 ); however, a stronger response was observed for CH 4 limitation. Different carbon sources have been found to affect transcriptional responses of both alpha- and gammaproteobacterial methanotrophs ( 55 , 67 ). FIG 5 Changes in central metabolic pathways in M. album BG8 during short-term substrate limitation of CH 4 (top) and O 2 (bottom). Upregulated (green) and downregulated (purple) pathways and pathways with no change (gray) compared to steady-state conditions are highlighted. Multiple colors indicate that different genes in that pathway were both up- and downregulated. Abbreviations and corresponding intermediates and compounds are provided in Table S7. M. album BG8 upregulated pentose phosphate (PP) pathway genes and electron transport chain (ETC) genes, namely, NADH oxidoreductase ( nuo ) and cytochrome genes, to adapt methane limitation while downregulating other metabolic activity ( Fig. 5 and 6 ). During oxygen limitation, Entner-Doudoroff (ED) pathway genes, in addition to PP pathway genes, were upregulated, while Embden-Meyerhof-Parnas (EMP; glycolysis) pathway genes were downregulated and electron transport chain genes saw both slight up- and downregulation. Genes involved in oxidation of methane ( pmo ) and methanol ( mxa ) were generally downregulated under both conditions. A homolog of methanol dehydrogenase, XoxF, was highly upregulated under both limiting conditions. XoxF-type methanol dehydrogenases, however, are lanthanide dependent ( 68 ), and reactor medium was unchanged, indicating that this could be a survival response upon nutrient limitation or to shift metabolism entirely during stress conditions. These could be potential strategies for maintaining activity and growth upon substrate limitation. Research on M. buryatense 5GB1 and M. album BG8 grown on methanol suggests that electrons obtained from methanol dehydrogenase are directly transferred to cytochromes and the electron transport chain, bypassing the electron requirements from NADH oxidoreductase complex for ATP production ( 67 ). Some studies have shown that type I methanotrophs potentially perform fermentation-based methanotrophy, in which methane-derived formaldehyde can be used for formation of formate, acetate, succinate, lactate, and hydroxybutyrate under low-oxygen conditions ( 69 ). In Methylomicrobium alcaliphilum 20Z, low oxygen induced increased expression of EMP genes, predicted fermentation pathway enzyme genes and O 2 carrier hemerythrin, and decreased expression of NADH:ubiquinone oxidoreductase and cytochrome c oxidase in addition to detection of possible fermentation products and H 2 ( 69 ). The genome of M. buryatense 5GB1 also contains homologs of enzymes required for fermentation, and its metabolism under O 2 limitation is a combination of fermentation and respiration ( 20 ). Another potential strategy during substrate limitation is use of internal carbon storage. M. album BG8 has genes for glycogen biosynthesis, just as does “ Candidatus Methylacidiphilum fumariolicum” SolV from phylum Verrucomicrobia , which consumes stored glycogen during periods of substrate limitation ( 19 ). In this study, periods of both methane and oxygen limitation led to significant downregulation of glycosyltransferase and glycogen synthase genes and an upregulation in glycogen debranching and cleaving genes (COG: G), which are potentially involved in glycogen synthesis and degradation, respectively ( Fig. 6 ). FIG 6 Gene expression profiles of M. album BG8 under short-term substrate (O 2 and CH 4 ) limitation compared to steady-state conditions. Statistically significantly differentially expressed genes (log 2 FC > | 1.0 |, P adj < 0.05) are indicated by asterisks. Genes are grouped by orthology and/or function. Subset of genes shown; see Data Set S2 for complete differential expression analyses. Short-term substrate limitations led to transcriptional changes in ETC enzyme genes, preference for the PP pathway, and, in the case of O 2 limitation, upregulation of ED pathway genes. These responses show potential strategies where pathways are shifted to use internal carbon reserves and, potentially, fermentation for survival. In addition to the changes in carbon metabolism, observed changes in gene expression in both methane- and oxygen-limited samples were indicative of methanotrophs with decreased activity ( 70 ), and short-term substrate limitation was sufficient to elicit a stress response. DGE genes and additional genes of interest grouped by COG for substrate-limited samples are shown in Fig. 6 . Additionally, both limitation conditions had downregulation of the carbon storage regulator, csrA , and significant upregulation of sigma factor rpoE , an expression pattern indicative of stress response ( 71 ). DGE was observed for other genes indicative of stress response as seen for stressed M. album BG8 grown with methanol ( 67 ) ( Fig. 6 ). Methane limitation led to upregulation of oxidative stress genes, including superoxide dismutase (log 2 FC = 1.8; P adj < 0.05), peroxiredoxin (log 2 FC = 2.5; P adj < 0.05), and thioredoxin (log 2 FC = 1.6; P adj < 0.05) genes, likely to deal with increased oxidative stress as dissolved-oxygen levels increased after methane limitation ( Fig. 6 ). Under O 2 -limited conditions, a 2-fold increase was observed for hemerythrin, a putative O 2 -scavenging protein ( 72 ) which is upregulated in Methylomicrobium buryatense 5GB1C ( 20 ) and Methylomicrobium alcaliphilum 20Z ( 69 ) under conditions of oxygen limitation. Additional stress indicators were downregulation of DNA polymerase genes and upregulation of competence and DNA-protecting protein genes (COG: L) ( Fig. 6 ). In addition to transcriptional regulation, shifts in core metabolic processes could be regulated via translational and posttranslational mechanisms as well ( 66 ). The 2-h substrate limitation period was enough to observe a transcriptional response in M. album BG8 under both conditions. Experimental and modeling results have shown that transcriptional responses in bacteria due to substrate limitation are fast, being elicited within 1 h, while translational changes may continue for more than 10 h ( 73 ). When compared to steady-state levels, RNA-seq data showed pmoA transcript amounts decreased about 2-fold during substrate-limiting conditions. However, the effects were not as drastic as observed in reverse transcription-qPCR (~100-fold decrease). The observed difference could be due to partially decayed transcripts being read in RNA-seq, whereas for reverse transcription-qPCR, only the nondegraded full pmoA target region would be amplified and detected. This work highlights mRNA biomarkers as indicators of methanotrophic activity as well the transcriptomic response of M. album BG8 to short-term substrate limitations, demonstrating a fast response to dynamic conditions and potential strategies for growth under periods of limited substrate availability. The results presented suggest a cross-genus, potentially universal trend between steady-state per-cell activity and per-cell pmoA transcript levels in aerobic bacterial methanotrophs across several orders of magnitude. Universal trends would only be possible if these correlations are explored in anaerobic, bacterial methanotrophs from phylum NC10 and Verrucomicrobia . This work holds promise for assessing methanotroph growth and activity in diverse environments and informs operational strategies in biotechnological applications of methanotrophs, for example, in biorefinery scenarios where biogas is upcycled into more valuable bioproducts."
} | 9,112 |
38076074 | PMC10709053 | pmc | 5,098 | {
"abstract": "The rapid development of society and industry as well as the frequent occurrence of oil spills cause the shortage of fresh water resources, which not only affects human safety and life, but also impedes the world-wide sustainable development. To address these challenges, novel membrane materials with unique wettability properties have gained significant attention, particularly in the field of oil/water separation. In this research, we modified the hydrophobic PET fabric to achieve superhydrophilic characteristics using impregnation method. Subsequently, we electrospun hydrophobic PVDF fibers onto the superhydrophilic fabric surface, and PVDF/Ca 10 (PO 4 ) 6 (OH) 2 @PET Janus membrane with asymmetric wettability was obtained. The membrane has an excellent unidirectional liquid transport capacity, and can effectively separate heavy oil or light oil, the separation efficiency is more than 90 %. The results also show that the Janus membrane can be used under alkaline conditions and has satisfactory tensile resistance and re-use performance. This work provides a new idea for Janus membrane design and effectively improves the application potential of the Janus membrane in the field of oil/water separation.",
"conclusion": "4 Conclusions In summary，using PET fabric as the substrate, we successfully prepared hydrophobic/superhydrophilic polyvinylidene fluoride/hydroxyapatite@PET (PVDF/Ca 10 (PO 4 ) 6 (OH) 2 @PET) Janus membrane by electrospinning and impregnation methods. The contact angle showed that the Ca 10 (PO 4 ) 6 (OH) 2 @PET fabric exhibited superhydrophilic/underwater oleophobic. Therefore, the oil/water separation performance of PVDF-12G/Ca 10 (PO 4 ) 6 (OH) 2 @PET Janus membrane was explored based on the unidirectional liquid transport capacity of Janus membrane. The results showed that Janus membrane could effectively separate six kinds of oil/water mixtures with different surface tension, and all of the separation efficiency were above 90 %. Unlike other reported membranes used for oil-water separation, our prepared Janus membrane can simultaneously satisfy the separation of oil and water mixtures of heavy oil or light oil with high efficiency. Moreover, our Janus membrane also has re-usability, alkaline resistance, and excellent mechanical properties. This work provides insights into Janus membrane design and broadens its applications in oil/water separation.",
"introduction": "1 Introduction Oily wastewater discharges and frequent oil spills are global environmental concerns [ [1] , [2] , [3] ], resulting in water pollution that not only disrupts ecosystems but also poses significant risks to human health [ [4] , [5] , [6] , [7] ]. Incomplete statistics reveal that over the past few decades, more than 20 oil spill incidents have occurred, releasing tens of thousands of tons of oil into our oceans, causing extensive harm to marine life and indirectly affecting human well-being [ 8 , 9 ]. Despite ongoing efforts to mitigate oil pollution, addressing oil spills remains a formidable challenge. In the field of material research, achieving effective and affordable oil/water separation has emerged as a pivotal focus for scientists [ [10] , [11] , [12] , [13] ]. Traditional methods for oil/water separation can be divided into three categories: chemical, biological, and physical methods. Chemical methods include flocculation, dispersion, and incineration, with incineration being an unwise choice due to the associated environmental hazards, including the generation of toxic gases and haze [ 14 , 15 ]. Biological methods primarily rely on biodegradation for oil/water separation [ 16 ]. Physical methods, on the other hand, encompass adsorption, containment, and separation techniques [ 17 ]. Notably, physical methods offer distinct advantages, including simple equipment, cost-effectiveness, and rapid separation rates, making them highly applicable in both industrial and everyday settings. In recent years, researchers have discovered that materials with unique wettability properties offer distinct advantages, including high separation efficiency and exceptional reusability [ [18] , [19] , [20] ]. These materials can be transformed into filter membranes and have garnered significant attention from researchers for their low energy consumption and the reduction of secondary pollutant production [ 21 , 22 ]. This discovery has spurred the development of new, cost-effective methods for separating oil and water, characterized by their simplicity and high efficiency [ [23] , [24] , [25] , [26] ]. Shi et al. [ 27 ] manipulated surfactant-induced wetting to fabricate a Janus membrane with a controllable thickness of the hydrophilic layer, which exhibits outstanding anti-oil-fouling and anti-oil-wetting abilities. Raj et al. [ 28 ] were the first to achieve oil-water separation using successfully synthesized Sr-MOF. This material has a high oil rejection rate and flux, and can be used in most harsh environments. In addition, emulsified mixtures tend to present in multiple states under different conditions and are therefore more difficult to separate compared to oil-water mixtures [ 29 ]. Nevertheless, by adjusting the composition of the membrane, the researchers have not only achieved separate oil-water emulsions, but can also give the membrane additional functions [ 30 ]. Zhang et al. [ 31 ] prepared CPA composites consisting of Ag@TiO 2 nanoparticles, lignocellulosic nanofibril, and polyvinyl alcohol under crosslinking of glutaraldehyde, which can simultaneously treat bacteria, dyes, and oils in complex wastewater. Additionally, researchers also prepared Janus PPy@Ag/AgCl@Wood membrane by loading Ag/AgCl and PPy NPs in/on the Balsa wood matrix, hydrophobic modifying the as-obtained nanocomposite by stearic acid [ 32 ]. This Janus membrane exhibited exceptional performance, including high pollutant degradation efficiency, efficient solar energy conversion, excellent separation of oil-in-water emulsions, remarkable oil absorption capacity, and outstanding antibacterial properties. So as to achieve the purpose of oil and water separation, it was found that the oleophilic/superhydrophobic membrane material could penetrate the oil and block the water, successfully achieving the separation of the heavy oil/water mixture, but could not separate the light oil/water mixture. Conversely, the superhydrophilic/underwater oleophobic membrane only allows water to transport and blocks oil to achieve effective separation of light oil/water mixtures. This membrane, however, was not suitable for separating heavy oil/water mixtures [ 33 ]. Therefore, it is crucial to develop membrane materials capable of separating both heavy and light oil/water mixtures. Janus material with asymmetric wettability was awarded as the “excellent candidate material for oil/water separation” [ 34 ]. The Janus membrane, which consists of the superhydrophilic/underwater oleophobic layer and the oleophilic/hydrophobic layer, separation of heavy oil/water mixture and light oil/water mixture can be achieved [ 35 ]. In this study, we prepared a hydrophobic/superhydrophilic Janus membrane with oil/water separation capacity by combining electrospinning technology and impregnation method. First of all, the original hydrophobic PET fabric was modified by the impregnation method, and the superhydrophilic Ca 10 (PO 4 ) 6 (OH) 2 @PET fabric was obtained. Then, a layer of hydrophobic PVDF fibrous membrane was electrospinning on the superhydrophilic Ca 10 (PO 4 ) 6 (OH) 2 @PET fabric by electrospinning technology, and finally PVDF/Ca 10 (PO 4 ) 6 (OH) 2 @PET Janus membrane with asymmetric wettability was obtained. Due to the asymmetric wettability of the PVDF/Ca 10 (PO 4 ) 6 (OH) 2 @PET Janus membrane, it exhibits excellent unidirectional liquid transport. Consequently, the Janus membrane achieves an oil/water separation efficiency of over 90 %. The efficient oil/water separation performance of the PVDF/Ca 10 (PO 4 ) 6 (OH) 2 @PET Janus membrane makes it a promising candidate for oil pollution removal, which is of great significance in addressing fresh water shortage and environmental pollution.",
"discussion": "3 Results and discussion Fig. 1 a illustrates the fabrication process of the hydrophobic/superhydrophilic polyvinylidene fluoride/hydroxyapatite@PET (PVDF/Ca 10 (PO 4 ) 6 (OH) 2 @PET) Janus membrane. The PET fabric was sequentially impregnated in Na 3 PO 4 , anhydrous ethanol, CaCl 2 , and anhydrous ethanol to obtain the superhydrophilic Ca 10 (PO 4 ) 6 (OH) 2 @PET fabric, which served as the substrate for electrospinning the hydrophobic PVDF fibrous membranes. This process successfully produced the hydrophobic/superhydrophilic PVDF/Ca 10 (PO 4 ) 6 (OH) 2 @PET Janus membrane. In Fig. 1 b and c, the surface structure of the original PET fabric and the Ca 10 (PO 4 ) 6 (OH) 2 @PET fabric are presented. It can be found that the surface of the original PET fabric was relatively smooth, while the surface of Ca 10 (PO 4 ) 6 (OH) 2 @PET fabric had a large number of obvious rough structures, and the rough structures were evenly distributed on the fabric surface from the local magnified picture. The results indicate that Ca 10 (PO 4 ) 6 (OH) 2 particles generated by the reaction of Na 3 PO 4 and CaCl 2 were uniformly deposited on the surface of PET fabric after impregnation, and the Ca 10 (PO 4 ) 6 (OH) 2 @PET fabric was successfully prepared. To investigate the wettab ility change of the PET fabric before and after impregnation treatment, we characterized the WCA of the original PET fabric and the Ca 10 (PO 4 ) 6 (OH) 2 @PET fabric obtained by impregnation modification ( Fig. S1 , Supporting information). The water contact angle of untreated PET fabric is ∼107.8°, while that of modified Ca 10 (PO 4 ) 6 (OH) 2 @PET fabric becomes ∼0° in 0.72 s, which indicates that the modified fabric is superhydrophilic. Furthermore, Fig. 1 d presents optical photos of water droplets on the PET fabric and Ca 10 (PO 4 ) 6 (OH) 2 @PET fabric. When the water dropped on the original PET fabric, it took on an oval shape, whereas on the Ca 10 (PO 4 ) 6 (OH) 2 @PET fabric, it was rapidly absorbed and diffused across the surface. This observation confirms the superhydrophilic nature of the Ca 10 (PO 4 ) 6 (OH) 2 @PET fabric. Fig. 1 Preparation and characterization of PVDF/Ca 10 (PO 4 ) 6 (OH) 2 @PET Janus membrane. (a) Schematic illustration of the fabrication process of Ca 10 (PO 4 ) 6 (OH) 2 @PET fabric; (b,c) SEM image and local magnification image of PET fabric and Ca 10 (PO 4 ) 6 (OH) 2 @PET fabric; (d) Optical photo-graph and corresponding water contact angle of water droplet dripped in PET fabric and Ca 10 (PO 4 ) 6 (OH) 2 @PET fabric; (e) FTIR of untreated PET fabric, Ca 10 (PO 4 ) 6 (OH) 2 powder, and Ca 10 (PO 4 ) 6 (OH) 2 @PET fabric; (f,g) EDS element distribution of untreated PET fabric and Ca 10 (PO 4 ) 6 (OH) 2 @PET fabric. Fig. 1 The chemical composition of the material plays an important role in surface wettability. In order to explore the chemical composition changes of the PET fabric after impregnation treatment, Fourier transform infrared spectroscopy (FTIR) was used to analyze the PET fabric before and after impregnation treatment. Fig. 1 e showed the FTIR of PET fabric, Ca 10 (PO 4 ) 6 (OH) 2 powder and Ca 10 (PO 4 ) 6 (OH) 2 @PET fabric. Compared with pristine PET fabric, the Ca 10 (PO 4 ) 6 (OH) 2 @PET fabric had an obvious wide peak in the range of 3697–2759 cm −1 , corresponding to the stretching vibration peak of –OH [ 36 , 37 ]. The peaks at 1024 cm −1 and 583 cm −1 belonged to the absorption of PO 4 3− [ 38 ]. These results indicated that Ca 10 (PO 4 ) 6 (OH) 2 particles were successfully anchored on the surface of PET fabric after impregnation treatment, and the hydroxyl group interacted with the rough texture of the fabric, the Ca 10 (PO 4 ) 6 (OH) 2 @PET fabric was superhydrophilic. Further, we analyzed the element distribution of the two fabrics by Energy Dispersion Spectroscopy (EDS). Fig. 1 f and g showed EDS for elements C and O of original PET and EDS for elements C, O, Ca and P of Ca 10 (PO 4 ) 6 (OH) 2 @PET fabrics, respectively. It can be seen that the original PET fabric contained only C and O elements. For Ca 10 (PO 4 ) 6 (OH) 2 @PET fabric, in addition to the original C and O, Ca and P were found on the surface of PET fabric after impregnation, with the content of 3.57 wt% and 2.09 wt%, respectively. Compared with Fig. 1 f and g, the content of C decreased from 61.57 wt% to 53.04 wt%, and the content of O increased from 38.43 wt% to 41.30 wt%. The results further proved that Ca 10 (PO 4 ) 6 (OH) 2 was successfully anchored on the surface of PET fabric after impregnation treatment, which was consistent with the results of SEM and FTIR. Furthermore, it can be seen that the Ca 10 (PO 4 ) 6 (OH) 2 particles are uniformly distributed on the surface of the PET fabric, and there was no obvious particle aggregation or clustering. Those results further indicated the superhydrophilic Ca 10 (PO 4 ) 6 (OH) 2 @PET fabric was successful prepared. The schematic diagram of the PVDF/Ca 10 (PO 4 ) 6 (OH) 2 @PET Janus membrane was outlined in Fig. 2 a. In the above study, we successfully prepared the superhydrophilic Ca 10 (PO 4 ) 6 (OH) 2 @PET fabric. Further, we needed to determine the electrospinning conditions of PVDF fibrous membrane. As shown in Fig. S2 , Supporting information, a series of PVDF fibrous membranes were obtained by changing the concentration of PVDF electrospinning solution. By comparison, it was found that when the concentration of electrospinning solution was 27.5 wt%, the fibrous membrane obtained was pure fiber structure, and there were no “spheres” or \" knot-in-fiber \" structures. Based on this electrospinning condition, the next experiment was carried out. Fig. 2 (a) Schematic diagram of Janus membrane composition; (b) SEM images of four nozzle types of PVDF fibrous membranes and corresponding fiber diameter statistics; (c) Water contact angles of PVDF fibrous membrane electrospun by four nozzles. Fig. 2 The inner diameter of electrospinning nozzles is another critical factor affecting the morphology and diameter of fibrous membranes. Fig. 2 b showed the SEM diagram and diameter distribution of PVDF fibrous membranes prepared with four different nozzle types in 27.5 wt % PVDF electrospinning solution system. It can be seen from the figure that with the increase of the inner diameter of the nozzles, the diameter of the obtained PVDF fibrous membrane increased, with the average diameter being 155 nm, 207 nm, 225 nm and 287 nm, respectively. This phenomenon can be attributed to the difference in nozzle inner diameter A nozzle with a smaller inner diameter results in a reduced liquid flow rate, which leads to the formation of finer fibers and, consequently, fibrous membranes with a smaller diameter. After further stretching and refining, the average diameter of the fibrous membrane obtained was smaller. Fig. 2 c showed the relationship between WCA and nozzle types of PVDF fibrous membrane. As can be seen that the WCA corresponding to four different fibrous membranes were 143.5 ± 0.7°, 140.8 ± 1.1°, 137.2 ± 0.8° and 133.8 ± 1.3°, respectively. Those results indicated that PVDF fibrous membranes exhibited hydrophobic properties. Furthermore, it can be seen that the WCA decreased with the increase of electrospinning nozzle diameter. The reason for this phenomenon was that the diameter of PVDF fibers obtained by using different nozzles in the electrospinning process directly affects the roughness of the material surface. Larger roughness will make hydrophobic materials more hydrophobic and hydrophilic materials more hydrophilic [ 39 ]. Therefore, the smaller diameter fibrous membrane has a larger surface roughness and thus a larger WCA compared to the larger diameter hydrophobic PVDF fibrous membrane. We also measured the influence of droplets of different pH on the water contact angle of the hydrophobic layer of Janus membrane, and the results were shown in Fig. S3 , Supporting information. Due to the stable nature of PVDF itself, its resistance to acid-base environment is high, and the water contact angle is basically maintained at the normal environment. The thickness of the hydrophobic layer plays an important role in the process unidirectional liquid transport. In this experiment, the thickness of the hydrophobic layer was adjusted by controlling the electrospinning time, we selected two groups of PVDF/Ca 10 (PO 4 ) 6 (OH) 2 @PET Janus membranes with large differences in diameter for testing, that is, the Janus membranes prepared under the condition of 6G and 12G electrospinning nozzles, and named PVDF-6G/Ca 10 (PO 4 ) 6 (OH) 2 @PET Janus membrane and PVDF-12G/Ca 10 (PO 4 ) 6 (OH) 2 @PET Janus membrane. The unidirectional liquid transport capacity of the PVDF-6G/Ca 10 (PO 4 ) 6 (OH) 2 @PET and PVDF-12G/Ca 10 (PO 4 ) 6 (OH) 2 @PET Janus membranes prepared at different electrospinning times were studied ( Fig. 3 a and b). According to Fig. 3 a, for PVDF-6G/Ca 10 (PO 4 ) 6 (OH) 2 @PET, when the electrospinning time was less than 30 s, the thickness of the Janus membrane was less than 151.8 ± 2.49 μm and presented bidirectional transport. When the electrospinning time increased from 40 to 120 s, the thickness of the Janus membrane increased from 152.2 ± 1.79 μm to 156.7 ± 2.08 μm, the Janus membrane exhibited unidirectional liquid transport. However, if the electrospinning time was over 120 s, the thickness of the Janus membrane exceeded 158 ± 0.51 μm, the Janus membrane revealed bilateral obstruction behavior. Similarly, for PVDF-12G/Ca 10 (PO 4 ) 6 (OH) 2 @PET Janus membrane, when the electrospinning time increased from 60 to 180 s, the thickness of the Janus membrane ranged from 157.3 ± 2.55 to 162.7 ± 2.08 μm, Janus membrane had unidirectional liquid transport. When the thickness of the Janus membrane was less than 157.3 ± 2.55 μm or exceeded 162.7 ± 2.08 μm (the electrospinning time was less than 60 s or over 180 s), it cannot realize the unidirectional liquid transport. Fig. 3 Water permeability of Janus membrane. (a) The relationship between electrospinning time, membrane thickness and unidirectional liquid transport; (b) Diagram of a hydrostatic pressure experiment; (c) PVDF-6G/Ca 10 (PO 4 ) 6 (OH) 2 @PET and (d) PVDF-12G/Ca 10 (PO 4 ) 6 (OH) 2 @PET relationship between hydrostatic pressure and electrospinning time on both sides of Janus membrane; (e) Water vapor transmittance of different samples at three temperature conditions; (f,g) Transport behavior of water droplets on both sides of Janus membrane. Fig. 3 In order to further study the unidirectional liquid transport performance of Janus membranes, the hydrostatic pressure of Janus membranes was investigated. The schematic diagram of the hydrostatic pressure test device as shown in Fig. 3 b. The Janus fabric was fixed between the two glass tubes with a flange, and the liquid was slowly dripped into the upper glass tube, recording the maximum height of the water column when the water began to penetrate the Janus membrane which was the hydrostatic pressure. Fig. 3 c and d exhibited the hydrostatic pressure of Janus membranes. It can be seen that, for both PVDF-6G/Ca 10 (PO 4 ) 6 (OH) 2 @PET Janus membrane and PVDF-12G/Ca 10 (PO 4 ) 6 (OH) 2 @PET Janus membrane, the hydrostatic pressure of Janus membrane increased with the electrospinning time. The reason was that the thickness of hydrophobic PVDF layer increased with electrospinning time, and the resistance of liquid transport from the hydrophobic layer to the hydrophilic layer increased. Moreover, when the electrospinning time was 90 s, the PVDF-6G/Ca 10 (PO 4 ) 6 (OH) 2 @PET Janus membrane had the best unidirectional liquid transport. When the electrospinning time was 150 s, the PVDF-12G/Ca 10 (PO 4 ) 6 (OH) 2 @PET Janus membrane had the best unidirectional liquid transport. In addition, the hydrostatic pressure of PVDF-12G/Ca 10 (PO 4 ) 6 (OH) 2 @PET Janus membrane was lower than that of PVDF-6G/Ca 10 (PO 4 ) 6 (OH) 2 @PET Janus membrane under the same electrospinning time, indicating that larger fiber pore size can promote unidirectional liquid transport. On the basis of these hydrostatic tests, the water vapor transmittance of the original PET fabric, Ca 10 (PO 4 ) 6 (OH) 2 @PET fabric, PVDF-6G/Ca 10 (PO 4 ) 6 (OH) 2 @PET Janus membrane (electrospinning time 90 s) and PVDF-12G/Ca 10 (PO 4 ) 6 (OH) 2 @PET Janus membrane (electrospinning time 150 s) were tested, as shown in Fig. 3 e. It can be found that the water vapor permeability of PET fabric was slightly higher than that of Ca 10 (PO 4 ) 6 (OH) 2 @PET fabric, which may be due to the existence of Ca 10 (PO 4 ) 6 (OH) 2 @PET particles leading to the lower porosity of modified fabric than PET fabric. In addition, the water vapor transmission of Janus membrane was higher than that of the PET fabric, because the Janus membrane had excellent unidirectional liquid transport. The water vapor transmission of PVDF-12G/Ca 10 (PO 4 ) 6 (OH) 2 @PET Janus membrane was better than that of PVDF-6G/Ca 10 (PO 4 ) 6 (OH) 2 @PET Janus membrane, which was consistent with the results of unidirectional liquid transport and hydrostatic pressure. In addition, with the increase of temperature, the water vapor transmittance of all four samples showed an increasing trend, because the higher the water temperature, the faster the transmittance rate of water vapor. Fig. 3 f and g was the real-time unidirectional liquid transport performance of the Janus membranes. Liquid droplets were dropped onto both sides of the Janus membrane. For both PVDF-6G/Ca 10 (PO 4 ) 6 (OH) 2 @PET Janus membranes and PVDF-12G/Ca 10 (PO 4 ) 6 (OH) 2 @PET Janus membranes, When the liquid dropped on the hydrophobic PVDF layer, the droplet penetrated and contacted the superhydrophilic Ca 10 (PO 4 ) 6 (OH) 2 @PET layer. After that, the liquid was gradually unidirectionally transported from the hydrophobic layer to the hydrophilic layer. Nevertheless, when the Janus membrane was overturned, instead of penetrating the Janus membrane, the liquid spread and blocked the superhydrophilic layer. This was because when the droplet dropped on the hydrophobic layer, it slowly sank down, and when it contacted the hydrophilic layer, it suffered a large capillary force, which promoted the liquid to pass through the Janus membrane. On the contrary, when the liquid was added to the superhydrophilic side of Janus membrane, the water droplet diffused to form a water membrane under the action of the capillary of the hydrophilic layer, and the Laplace force was close to 0, which hindered the transport of the droplet. Furthermore, for PVDF-12G/Ca 10 (PO 4 ) 6 (OH) 2 @PET Janus membrane, the complete unidirectional transport time of liquid was 40 s, which is shorter than that of the other Janus membrane (45 s), and the spread of droplets on PVDF-12G/Ca 10 (PO 4 ) 6 (OH) 2 @PET Janus membrane was larger (as shown by the red dotted lines in Fig. 3 g), demonstrating that the unidirectional liquid transport capacity of PVDF-12G/Ca 10 (PO 4 ) 6 (OH) 2 @PET Janus membrane was superior to that of PVDF-6G/Ca 10 (PO 4 ) 6 (OH) 2 @PET Janus membrane. Therefore, we selected PVDF-12G/Ca 10 (PO 4 ) 6 (OH) 2 @PET Janus membrane in the follow-up experiment and named it as PVDF/Ca 10 (PO 4 ) 6 (OH) 2 @PET Janus membrane. As shown in Fig. 4 a, the Ca 10 (PO 4 ) 6 (OH) 2 @PET fabric was immersed in aqueous media, and six kinds of oils with different densities and surface tension (Oil properties are listed in Table 1 , Supporting information) were used to evaluate the underwater oil wettability of the fabric. Fig. 4 b showed the underwater oil contact angles of a series of typical oil droplets on the Ca 10 (PO 4 ) 6 (OH) 2 @PET fabric, and the shapes of these oil droplets were also presented as the insets. The test results showed that the oil contact angles of CCl 4 , 1, 2-dichloroethane, toluene, n-hexadecane, diesel and gasoline on the surface of Ca 10 (PO 4 ) 6 (OH) 2 @PET fabric were 144.7 ± 3.5°, 151.4 ± 4.1°, 135 ± 5.9°, 148.1 ± 5.3°, 122 ± 6.5° and 118 ± 5.3°, respectively. All of the oil contact angles on the Ca 10 (PO 4 ) 6 (OH) 2 @PET fabric surface were larger than 90°, which proves that the fabric has underwater oleo-phobic property. Based on the above studies on the unidirectional liquid transport, hydrostatic pressure and water vapor permeability of Janus membrane, PVDF/Ca 10 (PO 4 ) 6 (OH) 2 @PET Janus membrane was selected to carry out the separation experiment of oil/water mixture. In order to observe the separation of oil and water, water and oil were dyed with water-soluble and oil-soluble dyes respectively, and then CCl 4 , 1, 2-dichloroethane, toluene, n-hexadecane, diesel and gasoline were mixed with water in a certain proportion in order to obtain six different types of oil/water mixtures. Fig. 4 c was the schematic diagram of the separation phenomenon. For heavy oil/water mixture, the superhydrophilic Ca 10 (PO 4 ) 6 (OH) 2 @PET layer needs to be facing up. At this point, the water will be blocked in the upper glass tube, and the heavy oil will transport through the Janus membrane [ 40 , 41 ]. For light oil/water mixture, the hydrophobic layer needs to face up. When the mixture comes into contact with the Janus membrane, water will quickly transport the Janus membrane, while the light oil will be blocked. The Janus membrane thus achieves efficient separation of the oil/water mixture. In Fig. 4 d, we used CCl 4 and dichloroethane to represent heavy oil, and toluene, n-hexadecane, diesel and gasoline to represent light oil, demonstrating the efficient oil/water separation processes of light oil/water and heavy oil/water mixture by PVDF/Ca 10 (PO 4 ) 6 (OH) 2 @PET Janus membrane, respectively. To further evaluate the oil/water separation performance of Janus membrane, we calculated the separation efficiency and flux of the Janus membrane for different types of oil by Eqs. (1) , (2) ), and the results were shown in Fig. 4 e, f. As can be seen from Fig. 4 e, the separation efficiencies of Janus membrane for CCl 4 /water, dichloroethane/water, toluene/water, n-hexadecane/water, diesel/water and gasoline/water were 90.31 %, 92.99 %, 94.99 %, 97.02 %, 95.14 % and 94.10 %, respectively, and the separation efficiencies were all above 90 %. Fig. 4 f showed the separation flux in oil/water separation process, and it can be seen that the corresponding oil/water separation flux was 295.87 ± 13, 248.41 ± 21, 153.29 ± 16, 172.32 ± 9, 168.09 ± 14 and 324.27 ± 15 L m −2 h −1 , respectively. It was worth noting that the separation flux of Janus membrane for different kinds of oil varies greatly, and the analysis was mainly caused by the difference in oil density or surface tension. Compared to membranes in other references, the Janus membrane prepared in this experiment exhibits a slightly lower efficiency and flux in oil-water separation. However, it can efficiently separate both heavy and light oils and remain capable of meeting practical requirements. Fig. 4 Oil/water separation experiment of Janus membrane. (a) Schematic diagram of under-water oil CA test; (b) Underwater oil contact Angle of different types of oil droplets on Ca 10 (PO 4 ) 6 (OH) 2 @PET fabric (illustrated with optical micrograph of underwater oil contact angle); (c) Oil/water separation schematic diagram and (d) Oil/water separation experiment of Janus membrane; (e) Oil/water separation efficiency and (f) separation flux of Janus membrane for different oil/water mixtures. Fig. 4 In addition, we also studied other properties of PVDF/Ca 10 (PO 4 ) 6 (OH) 2 @PET Janus membrane. First, we repeated the oil-water separation experiment with the same Janus membrane, and washed the surface of the membrane with deionized water after the completion of one experiment to test the separation efficiency and flux each time ( Fig. S4 , Supporting information). The separation efficiency and flux of the Janus membrane remained basically unchanged for five cycles, which proves that Jauns membrane can be reused. Then, we immersed Janus membrane in H 2 SO 4 solution with pH = 4 and NaOH solution with pH = 10 for 4 h and 8 h, respectively. Then we cleaned the Janus membrane with deionized water and dried it, and the separation efficiency and flux of Janus membrane were measured ( Fig. S5 , Supporting information). The separation efficiency and flux of Janus membrane immersed in alkaline solution decreased slightly, while that of Janus membrane immersed in acidic solution decreased significantly. The reason is that Ca 10 (PO 4 ) 6 (OH) 2 is prone to H + reaction in acidic solution, resulting in the failure of hydrophilic modification. Therefore, PVDF/Ca 10 (PO 4 ) 6 (OH) 2 @PET Janus membrane can be maintained for a longer time in alkaline solutions, while they are unstable in acidic solutions. Finally, we tested the tensile properties of Janus membrane with different weights. According to Fig. S6 , Supporting information, it can be seen that Janus film does not show obvious deformation when suspended with 500 g heavy objects, which proves that its tensile resistance is great. Therefore, according to the above results, our prepared Janus membrane can not only meet the oil-water mixture of separating heavy oil or light oil, but also has re-usability, use in alkaline environments, and excellent mechanical properties. Fig. 5 showed the mechanism of oil/water separation. For the heavy oil with ρ o i l > ρ w a t e r (CCl 4 and dichloroethane), the superhydrophilic Ca 10 (PO 4 ) 6 (OH) 2 @PET layer of Janus membrane should be turned upward during the oil/water mixture separation experiment, as shown in Fig. 5 a. When the heavy oil/water mixture was slowly poured into the glass tube above, the oil will first contact the superhydrophilic layer of the Janus membrane, which can quickly pass through the Janus membrane and form an oil film on the surface of the superhydrophilic layer [ 42 , 43 ]. Then, when the water touched the upper surface of the Janus membrane, the water was blocked in the glass tube above due to the presence of the oil film and the hydrophobic layer on the back, successfully separating the heavy oil/water mixture, the entire process was shown in Fig. 5 b. When separating light oil/water mixture with ρ o i l < ρ w a t e r , the hydrophobic PVDF fibrous membrane of the Janus membrane should face up, as shown in Fig. 5 c. Fig. 5 d shows the entire process of separation of the light oil/water mixture. When the light oil/water mixture was slowly poured into the transparent glass tube above, the water quickly passes through the Janus membrane once it comes into contact with the hydrophobic PVDF fibrous membrane of the Janus membrane. Meanwhile, the existence of water film and superhydrophilic/underwater hydrophobic layer obstructed the oil in-filtration and finally realized the separation of light oil/water mixture. Fig. 5 Oil/water separation mechanism diagram. (a, b) Oil/water separation of heavy oil/water mixture; (c, d) Oil/water separation of light oil/water mixture. Fig. 5"
} | 7,816 |
22817690 | PMC3521176 | pmc | 5,099 | {
"abstract": "Background The positive relationship between habitat area and species number is considered a fundamental rule in ecology. This relationship predicts that the link number of species interactions increases with habitat area, and structure is related to habitat area. Biological invasions can affect species interactions and area relationships. However, how these relationships change at different spatial scales has remained unexplored. We analysed understory plant–pollinator networks in seven temperate forest sites at 20 spatial scales (radius 120–2020 m) to clarify scale-associated relationships between forest area and plant–pollinator networks. Results The pooled data described interactions between 18 plant (including an exotic) and 89 pollinator (including an exotic) species. The total number of species and the number of interaction links between plant and pollinator species were negatively correlated with forest area, with the highest correlation coefficient at radii of 1520 and 1620 m, respectively. These results are not concordant with the pattern predicted by species–area relationships. However, when associations with exotic species were excluded, the total number of species and the number of interaction links were positively correlated with forest area (the highest correlation coefficient at a radius of 820 m). The network structure, i.e., connectance and nestedness, was also related to forest area (the highest correlation coefficients at radii of 720–820 m), when associations with exotics were excluded. In the study area, the exotic plant species Alliaria petiolata , which has invaded relatively small forest patches surrounded by agricultural fields, may have supported more native pollinator species than initially expected. Therefore, this invasive plant may have altered the original relationships between forest area and plant–pollinator networks. Conclusions Our results demonstrate scale-dependent effects of forest area on the size and structure of plant–pollinator networks. We also suggest that a single exotic plant species can impact plant–pollinator networks, even in temperate continental habitats.",
"conclusion": "Conclusions Since the original publication of the equilibrium theory of island biogeography\n[ 1 ], species–area relationships have been extensively studied for various groups of organisms\n[ 3 , 4 ]. Furthermore, the theory has been applied to the conservation of focal species in continental habitats\n[ 4 , 5 ]. Sugiura\n[ 9 ] analysed the relationships between island area and plant–ant mutualistic interactions and suggested an extension of this basic species–area relationship to more specifically address “species interactions–area relationships”. Interaction networks on continental habitats as well as oceanic islands can be considered with a view to the species interactions–area relationships. Indeed, Sabatino et al.\n[ 11 ] indicated that the number of interaction links among flowering plants and their pollinators increases with habitat area in the continental environment. Valladares et al.\n[ 15 ] also indicated that the network structure of plant–leafminer–parasitoid webs is related to fragmented forest area. In this study, we determined that network metrics of plant–pollinator interactions are related to forest area and the relationships depend on spatial scale. Each species has a particular spatial scale of habitat area that most strongly affects the abundance because body size and mobility differ among species\n[ 19 , 44 ]. Therefore, the spatial scales of habitat area that most strongly affected the abundance generally differed among species\n[ 19 ]. In this study, we determined the spatial scales that most strongly affected the structure of plant–pollinator interactions, which suggests the presence of a spatial scale that most strongly influences the structuring and maintenance of the species interaction network. Finally, exotic species appear to alter the relationship between habitat area and interaction network. Although many researchers have reported that exotic species impact interaction networks, particularly on oceanic islands\n[ 9 , 14 , 57 - 59 ], we suggest that a single exotic plant species can impact this relationship, even in temperate continental habitats.",
"discussion": "Discussion Some of our results did not support our hypotheses when exotic species were included (Figures \n 3 e,\n 5 c,e,\n 6 a,\n 7 a,c). However, almost all of the hypotheses were verified when associations with exotic species were excluded from the networks (Figures \n 3 f,\n 5 b,d,\n 6 b,\n 7 b,d,f). In particular, we determined that the relationship between forest area and plant–pollinator network depended on spatial scale (Figures \n 2 ,\n 6 ). To the best of our knowledge, this is the first study to demonstrate scale-dependent effects of habitat area and exotic species on interaction networks. Scale-dependent effects of forest area on plant–pollinator networks The species–area relationships were vastly different between plant and pollinator species when exotic species were included in the analyses (Figures \n 3 a,c). Additionally, the spatial scale at which the highest correlation coefficient was found differed between plant and pollinator species (Figure \n 2 a). Responses to habitat area are generally different between plant and pollinator species\n[ 19 ]. However, the scale-dependent relationship was similar among the number of plant species, pollinator species, and interaction links, when exotic species as well as native species only observed interacting with exotics were excluded (Figure \n 2 b\n 6 b). The similarities among the relationships between native plant and pollinator species are not likely to have been caused by our sampling scheme (i.e., insect collection on plants), because the results using a different sampling method (pan traps) also showed that native bee abundance and diversity increased with increasing forest area (with the highest correlation coefficients at 500–750 m radius\n[ 46 ]). The study\n[ 46 ] was conducted in the same region, but in different study forests (where A. petiolata did not flower). Therefore, the similar responses of native plant and native pollinator species to forest area caused a positive relationship between forest area and the numbers of links at the same spatial scale (i.e., 820 m; Figures \n 6 b\n 7 b). Previous studies have indicated that network metrics, connectance and nestedness are related to the total number of species involved in mutualistic interactions\n[ 12 - 14 , 32 , 33 ]. Our results partly supported this pattern (Figures \n 5 a,b,d; but Figure \n 5 c,e,f). Although previous studies have used data sets composed of mutualistic networks at different sampling areas as well as different geographical regions\n[ 12 - 14 , 32 , 33 ], all of our data were collected from within the same sampling area (234 m 2 ) in the same region. Because the total number of species was related to the forest area (Figures \n 3 e,f), so was the network structure (Figures \n 7 a,b,d,e,f). More redundant networks with highly asymmetric interactions were found in relatively large forest areas (Figure \n 7 d,e,f), suggesting that the stability of plant–pollinator networks might increase with forest area. However, these relationships may partly be explained by non-biological factors. For example, connectance may decrease with increasing numbers of possible interaction links, as the number of observations per species declines when the same absolute effort is made to sample networks of different sizes\n[ 31 , 34 ]. In this study, however, connectance did not decrease with the total number of species when exotic species were included (Figure \n 5 c). Also, nestedness was not related to the total number of species (Figures \n 5 e,f), although it increased with increasing forest area (Figures \n 7 e,f). These results suggest that the effects of forest area on connectance or nestedness were caused not only by changes in the total number of species, but also by other factors. For example, changes in the population densities of some species might affect the structure of interaction networks relative to habitat area because the population densities of various species are known to be related to habitat area\n[ 53 ]. Further studies are needed to clarify the mechanism driving the relationships between habitat area and network structure. Impacts of exotic species The scale-dependent relationships among forest area, total number of species, and interaction networks were different when interactions were considered with and without exotic species (Figures \n 2 3 5 6 7 ), suggesting impacts of exotic species. Plant–pollinator interactions included only two exotic species, garlic mustard A. petiolata and the honeybee A. mellifera (Additional file\n 2 and Additional file\n 3 ). Although the contribution of A. mellifera to the interactions was not insignificant (Additional file\n A3 ), A. petiolata was central to the interactions in at least two forests ( Additional file\n 2 ). Although exotic plants are only rarely thought to invade temperate natural forests\n[ 24 , 54 ], A. petiolata has frequently been reported to invade the forest edge and understory in North America\n[ 38 ]. Flowers of A. petiolata produce rich nectar, attracting a variety of native bees and flies\n[ 55 ]. Nectar-rich flowers of invasive plants can disturb native plant–pollinator interactions\n[ 56 ]. In the study areas of the present study, A. petiolata , which has invaded relatively small forest patches surrounded by agricultural fields (Figure \n 4 b), may have supported more native pollinator species than initially expected ( Additional file\n 3 ). The presence of exotic species strongly influenced the scale-dependent relationships between forest area and the number of interaction links and connectance (Figures \n 7 a–d). Thus, this invasive plant may have altered the original relationships between forest area and plant–pollinator networks and their scale-dependency. However, nestedness showed the same trend for both networks, with and without exotics (Figures \n 7 e,f). Vilá et al.\n[ 23 ] hypothesised that invasive plants by being supergeneralists, both interacting with generalists and specialists, would increase the nestedness of the plant–pollinator network. Our results showed that excluding associations with exotic species increased the values of nestedness in some study sites (Figures \n 7 e,f). However, this increase did not change the relationships between forest area and nestedness (Figures \n 7 e,f)."
} | 2,635 |
26779881 | null | s2 | 5,101 | {
"abstract": "Polyelectrolyte complexation is critical to the formation and properties of many biological and polymeric materials, and is typically initiated by aqueous mixing followed by fluid-fluid phase separation, such as coacervation. Yet little to nothing is known about how coacervates evolve into intricate solid microarchitectures. Inspired by the chemical features of the cement proteins of the sandcastle worm, here we report a versatile and strong wet-contact microporous adhesive resulting from polyelectrolyte complexation triggered by solvent exchange. After premixing a catechol-functionalized weak polyanion with a polycation in dimethyl sulphoxide (DMSO), the solution was applied underwater to various substrates whereupon electrostatic complexation, phase inversion, and rapid setting were simultaneously actuated by water-DMSO solvent exchange. Spatial and temporal coordination of complexation, inversion and setting fostered rapid (∼25 s) and robust underwater contact adhesion (Wad ≥ 2 J m(-2)) of complexed catecholic polyelectrolytes to all tested surfaces including plastics, glasses, metals and biological materials."
} | 282 |
29390143 | PMC5808792 | pmc | 5,102 | {
"abstract": "Abstract The genus Methylocystis belongs to the class Alphaproteobacteria , the family Methylocystaceae , and encompasses aerobic methanotrophic bacteria with the serine pathway of carbon assimilation. All Methylocystis species are able to fix dinitrogen and several members of this genus are also capable of using acetate or ethanol in the absence of methane, which explains their wide distribution in various habitats. One additional trait that enables their survival in the environment is possession of two methane-oxidizing isozymes, the conventional particulate methane monooxygenase (pMMO) with low-affinity to substrate (pMMO1) and the high-affinity enzyme (pMMO2). Here, we report the finished genome sequence of Methylocystis bryophila S285, a pMMO2-possessing methanotroph from a Sphagnum -dominated wetland, and compare it to the genome of Methylocystis sp. strain SC2, which is the first methanotroph with confirmed high-affinity methane oxidation potential. The complete genome of Methylocystis bryophila S285 consists of a 4.53 Mb chromosome and one plasmid, 175 kb in size. The genome encodes two types of particulate MMO (pMMO1 and pMMO2), soluble MMO and, in addition, contains a pxmABC -like gene cluster similar to that present in some gammaproteobacterial methanotrophs. The full set of genes related to the serine pathway, the tricarboxylic acid cycle as well as the ethylmalonyl-CoA pathway is present. In contrast to most described methanotrophs including Methylocystis sp. strain SC2, two different types of nitrogenases, that is, molybdenum–iron and vanadium–iron types, are encoded in the genome of strain S285. This unique combination of genome-based traits makes Methylocystis bryophila well adapted to the fluctuation of carbon and nitrogen sources in wetlands.",
"introduction": "Introduction Aerobic methanotrophs (methane-oxidizing bacteria, MOB) are a unique subset of methylotrophic bacteria that can utilize methane (CH 4 ) as their sole source of energy. They use methane monooxygenase (MMO) enzymes to oxidize methane to methanol ( Hanson and Hanson 1996 ; Trotsenko and Murrell 2008 ). Methanotrophic capabilities relying on MMO activity are currently recognized in members of the bacterial phyla Proteobacteria , Verrucomicrobia , and the candidate division NC10 ( Stein et al. 2012 ). Of these, methanotrophic Proteobacteria are represented by the greatest number of characterized isolates. Belonging to the classes Gamma - and Alphaproteobacteria , they are classified as type I and type II MOB, respectively. Members of these two groups differ in their cellular ultrastructure, C1-utilization pathway, fatty acid composition, and other physiological and biochemical characteristics. The genus Methylocystis is one of the first described and historically recognized genera of aerobic methanotrophic bacteria ( Whittenbury et al. 1970 ). It belongs to the class Alphaproteobacteria , the family Methylocystaceae , and encompasses obligate and restricted facultative methanotrophic bacteria with the serine pathway of carbon assimilation ( Belova et al. 2013 ). All members of this genus possess a membrane-bound or particulate methane monooxygenase (pMMO), whereas some also contain a soluble form of this enzyme (sMMO). Representatives of this genus inhabit a wide variety of terrestrial and aquatic ecosystems and display a number of environmental adaptations. Thus, in the absence of methane, some species of this genus are capable of slow growth on acetate and ethanol ( Belova et al. 2011 ; Im et al. 2011 ; Belova et al. 2013 ). Another ecologically important adaptation of these methanotrophs is their ability to produce two pMMO isozymes, the conventional form with low affinity to methane (pMMO1), and the high-affinity enzyme (pMMO2) ( Baani and Liesack 2008 ). The isozymes are encoded by pmoCAB1 and pmoCAB2 , respectively. To date, the complete genome sequence has been reported for only a single pMMO2-possessing member of the genus Methylocystis , Methylocystis sp. strain SC2 ( Dam et al. 2012 ). In this study, we obtained the complete genome sequence of another pMMO2-possessing methanotroph, Methylocystis bryophila S285. The species Methylocystis bryophila accommodates facultative methanotrophs, which were isolated from acidic Sphagnum -dominated wetlands and are capable of slow growth on acetate in the absence of methane ( Belova et al. 2011 ). Members of this species account for 20–50% of all the methanotroph cells detectable in acidic peat by fluorescence in situ hybridization ( Belova et al. 2011 ). The taxonomic description of Methylocystis bryophila was based on the characterization of two isolates, strains H2s T and S284 ( Belova et al. 2013 ). One additional representative of this species, strain S285, was later obtained from the same peat sample as strain S284. These two isolates share identical 16S rRNA gene sequences, which also match the 16S rRNA gene sequence of strain H2s T , the type strain of Methylocystis bryophila (GenBank accession number FN422003). Our further analysis of pMMO-encoding genes revealed that, in contrast to strain S284, only the pmoA2 gene fragments could be PCR-amplified from DNA of strain S285 by using the primer combination A189f–A682b ( Holmes et al. 1995 ). These primers are routinely employed for pmoA1 detection in methanotrophs. To conclusively verify the absence or presence of pMMO1 and to get an insight into genome-encoded features of a pMMO2-possessing methanotroph from wetlands, we have determined and analyzed the complete genome sequence of Methylocystis bryophila S285.",
"discussion": "Results and Discussion Finished Genome of Methylocystis bryophila S285 A total of 50,623 PacBio reads were obtained with a mean length of 3,228 bp. These reads enabled the assembly of the complete genome sequence of Methylocystis bryophila S285. The finished genome consists of one circular chromosome of 4,532,950 bp and a single plasmid of 175,021 bp. The chromosome contains two identical rrn operon copies (16S-23S-5S rRNA), a full complement of 47 tRNA genes and 4,387 CDS ( table 1 ). The distribution of protein-coding genes into SEED subsystem is shown in supplementary table S1 , Supplementary Material online.\n Table 1 General Genomic Features of Methylocystis bryophila S285, Methylocystis sp. SC2, and Methylocella silvestris BL2 Features S285 SC2 BL2 Accession number CP019948 HE956757 CP001280 Size (Mb) 4.53 3.77 4.3 G+C (%) 63 63 63 Genes (total) 4,444 3,677 4,014 CDS (total) 4,387 3,623 3,956 Genes (coding) 4,285 3,583 3,875 Pseudogenes 102 40 81 Genes (RNA) 57 54 58 rRNAs (5S, 16S, 23S) 2, 2, 2 1, 1, 1 2, 2, 2 tRNAs 47 47 48 ncRNAs 4 4 4 pmoCAB1 operon 2 2 Absent pmoCAB2 operon 1 1 Absent Monocistronic pmoC 2 3 (1 in plasmid) Absent pxmABC operon 1 Absent Absent sMMO operon 1 Absent 1 Serine pathway genes Present Present Present RuMP pathway genes Absent Absent Absent Plasmid(s) 1 2 NR ncRNAs, noncoding RNAs; NR, not reported. Genome Alignment The complete genome map ( fig. 1 ) of the three alphaproteobacterial methanotrophs suggests that the genomes of Methylocystis bryophila S285 and Methylocystis sp. SC2 display high synteny. In fact, the comparison of average nucleotide identities (ANIs) between the seven Methylocystis genomes and the eight genomes from other methanotrophic taxa revealed that Methylocystis bryophila S285 shares highest ANI values with the genomes of the other six Methylocystis spp. and Methylosinus trichosporium OB3b (72–74%) ( supplementary table S2 , Supplementary Material online). The further comparison of particular gene traits between Methylocystis bryophila S285 and the other six Methylocystis genomes provided additional evidence for high genomic similarity between Methylocystis bryophila S285 and Methylocystis sp. SC2. These two type II MOB share the largest number of common features (88.2%) and differ in the least number of unique traits (5.2%) among the seven Methylocystis genomes ( supplementary table S3 , Supplementary Material online). By contrast, Methylocella silvestris BL2 shows low similarity to Methylocystis bryophila S285 and Methylocystis sp. SC2, with regard to both complete genome ( fig. 1 ) and particular gene traits ( table 1 and supplementary tables S1 and S4 , Supplementary Material online).\n Fig . 1. —Genomic map of Methylocystis bryophila S285 in comparison to those of Methylocystis sp. SC2 and Methylocella silvestris BL2. The concentric circles denote the following features (from inside to outside): genome of Methylocystis bryophila S285 with coordinates (chromosome region, black circle), the GC content, GC skew (green and red indicate that the nucleotides Guanine and Cytosine are over-respectively underrepresented) and the genome alignment of Methylocystis sp. SC2 and Methylocella silvestris BL2 against Methylocystis bryophila S285, where color indicates a BLAST match of nucleotide sequence identity of 70–100% (based on BLASTn) between central genome ( Methylocystis bryophila S285) and comparative genomes, Methylocystis sp. SC2 (brown) and Methylocella silvestris BL2 (blue). Diverse Methane Monooxygenase Genes Despite the inability to detect the pMMO1-encoding genes in Methylocystis bryophila S285 by PCR, our genomic analysis revealed that this strain possesses both types of pmoCAB : two copies of pmoCAB1 and one copy of pmoCAB2 ( table 1 and supplementary fig. S1 , Supplementary Material online). The reason for the failure to detect pmoA1 is not fully clear but may be due to the fact that, if using primer set A189f–A682b, pmoA2 of strain S285 is more efficiently PCR-amplified than its pmoA1 counterpart. Although primer A189f is perfectly matching to its target site in either pmoA1 or pmoA2 , primer A682b has one mismatch in its target site of both pmoA1 and pmoA2 ( supplementary table S5 , Supplementary Material online). However, this mismatch position differs between pmoA1 (G–G mismatch in primer position 14 from 3′-end) and pmoA2 (C-T mismatch in position 13 from 3′-end). In addition, adjacent primer positions are defined by N (position 12 from 3′-end) and S (position 15 from 3′-end). Thus, our experimental results indicate that the G–G mismatch ( pmoA1 ) in primer position 14 is more detrimental for efficient pmoA amplification than the C–T mismatch ( pmoA2 ) in position 13. Notably, Radajewski et al. (2002 ) also detected only pmoA2 but not pmoA1 in their study on metabolically active methanotrophs in an acidic forest soil. This group of researchers also used the primer set A189f–A682b for their analysis. The inferred peptide sequences of pmoA clones retrieved by means of stable isotope probing (SIP) technique (accession numbers AY080950, AY080958, AY080959) showed high similarity (97.7–98.3%) to PmoA2 from Methylocystis bryophila S285, whereas no pmoA1 fragments were obtained from Methylocystis bryophila -like methanotrophs ( Radajewski et al. 2002 ). In addition to pMMO, strain S285 is able to produce the soluble form of MMO. Although pMMO is encoded by three genes ( pmoCAB ), the sMMO operon encompasses five consecutive genes ( mmoYZXBC ). Two monocistronic pmoC genes were also identified ( table 1 ). Based on our survey of currently available genomes (including complete and draft genomes), Methylocystis bryophila S285 and Methylocystis sp. strain LW5 are the only type II MOB which harbor all three types of MMO: pMMO1, pMMO2, and sMMO. Strain SC2 does not produce sMMO ( table 1 and supplementary table S4 , Supplementary Material online). A particular trait of strain S285 is the presence of a pxmABC- like gene cluster. The pxmABC operon is predicted to encode a member of the copper-containing membrane-bound monooxygenase (Cu-MMO) protein family, pXMO. Its function and substrate are not yet known ( Tavormina et al. 2011 ). Recent evidence for pxmABC expression in response to hypoxia suggests that pXMO is important for survival of methanotrophs under O 2 limitation ( Kits et al. 2015 ). The pxmABC -like gene clusters are widely distributed among gammaproteobacterial type I MOB, including strains of the genera Methylomonas , Methylobacter , and Methylomicrobium ( Tavormina et al. 2011 ). Among type II MOB, the pxmABC -like gene cluster has previously been detected only in Methylocystis rosea SV97 and Methylocystis sp. strain SB2 ( Knief 2015 ) ( supplementary table S4 , Supplementary Material online). Thus, Methylocystis bryophila S285 is the only third type II MOB shown to possess pxmABC genes. Notably, its PxmA fragment clusters together with a large group of environmental pxmA transcripts obtained from a subarctic peatland (AFY11631.1 and AFY11641.1 in supplementary fig. S1 , Supplementary Material online; Liebner and Svenning 2013 ) as well as with PxmA fragments from two type I MOB, Methylomonas sp. strain M5 and Methylococcaceae bacterium M200. Like Methylocystis bryophila S285, these two strains were isolated from a Sphagnum wetland ( Kip et al. 2011 ). This wide array of different MMO types, including MMO-like enzymes, may ensure survival of strain S285-like methanotrophs in environments with fluctuating methane concentrations as well as under copper or O 2 limitation. Multiple Carbon Assimilation Pathways All the genes required for the serine pathway are present in the genome of Methylocystis bryophila S285 and those of two key enzymes of the ribulose monophosphate cycle (RuMP) are absent. The serine pathway is the main carbon assimilation pathway of type II MOB. In addition to the complete tricarboxylic acid (TCA) cycle, the genomes of Methylocystis bryophila S285 and Methylocystis sp. strain SC2 encode the complete ethylmalonyl-CoA (EMC) pathway. This pathway is also present in Methylocystis sp. strain SB2, which has proven ability to grow on acetate or ethanol ( Im et al. 2011 ; Vorobev et al. 2014 ). We also identified the genes encoding alcohol dehydrogenase and aldehyde dehydrogenase in the S285 genome. These two enzymes convert ethanol to acetate. Thus, presence of the ethanol-converting enzymes coupled with EMC pathway provides the possibility for growth of Methylocystis bryophila S285 on ethanol or acetate. Strain S285 may convert ethanol or acetate via acetyl-coenzyme A synthetase (gene locus: B1812_19345) to acetyl-CoA, which is then funneled into the TCA cycle for energy generation or incorporated into biomass via the EMC pathway. Utilization of these two-carbon compounds was confirmed for strains H2s T and S284, the two taxonomically characterized—but not genome-sequenced—representatives of Methylocystis bryophila ( Belova et al. 2013 ). Thus, facultative methanotrophy represents an important alternative strategy for life in peatlands, allowing survival if no methane is available ( Belova et al. 2011 ). Even to date, the exact metabolic basis for facultative methanotrophy is not known, but it has been suggested that a key determinant of obligate methanotrophy is the restricted ability to transport potential substrates across the membrane ( Tamas et al. 2014 ). Correspondingly, the total number of membrane transporters encoded in the genome of strain S285 exceeds that in strain SC2. Notably, the genome of Methylocella silvestris BL2 even encodes a greater repertoire of membrane transporters than the two Methylocystis spp., strains S285 and SC2 ( supplementary table S1 , Supplementary Material online). This finding corresponds well to the fact that acetate is the preferred substrate for growth of Methylocella spp. In addition, M. silvestris BL2 also grows on ethanol, pyruvate, succinate, malate, propanol, propanediol, acetone, methyl acetate, acetol, glycerol, propionate, tetrahydrofuran, gluconate, ethane, and propane ( Dedysh and Dunfield 2016 ). The repertoire of membrane transporters encoded by the S285 genome also includes acetate permease ActP (cation: acetate symporter; gene locus: B1812_15125). ActP has proved function for acetate transportation in E. coli ( Gimenez et al. 2003 ). However, the mere genomic presence of actP is not a valid indicator for facultative methanotrophy. Genes encoding putative ActP are also present in Methylocystis spp. hitherto characterized as obligate methanotrophs such as, for example, Methylocystis sp. strain SC2, Methylocystis rosea SV97, and Methylocystis sp. strain Rockwell ( supplementary fig. S2 and table S4, Supplementary Material online). Alternatively, it may be that these methanotrophs can uptake and utilize acetate for growth but that appropriate growth conditions have yet to be identified. The genome of M. silvestris BL2 also encodes ActP, but its sequence clusters on a branch separate from those of the Methylocystaceae spp. ( supplementary fig. S2 , Supplementary Material online). Nitrogen Fixation Nitrogenase is a metalloprotein complex that comprises two components, a nitrogenase iron protein and a dinitrogenase reductase ( McGlynn et al. 2012 ). At least three genetically distinct but homologous nitrogenase systems have been identified until now ( Hu and Ribbe 2015 ). These O 2 -sensitive nitrogenases are primarily distinguished by the metal composition of their active-site metallocluster: conventional molybdenum–iron nitrogenase and the alternative vanadium–iron type, and iron-only nitrogenase ( Eady 1996 ; Hu and Ribbe 2015 ). Genes encoding the molybdenum–iron (Mo) and vanadium–iron (V) types of nitrogenase were identified in the S285 genome ( supplementary table S4 , Supplementary Material online). The Mo-nitrogenase is the most universally distributed nitrogenase in nature. The V-nitrogenase is present only in a limited number of microorganisms such as, for example, Azotobacteriaceae . The V-nitrogenase, encoded along with the Mo-nitrogenase, is expressed in the case of Mo-deficiency and has therefore been considered an alternative or “back-up” system ( Rehder 2000 ; Zhao et al. 2006 ). However, despite the high structural similarity between the two nitrogenases, V-nitrogenase can also reduce CO ( Hu et al. 2012 ). The ability of V-nitrogenase to catalyze the reduction of both CO and N 2 suggests a potential link between the evolution of carbon and nitrogen cycles ( Lee et al. 2010 ). We conducted a survey (August 10, 2017) on the distribution of V-nitrogenase-coding gene clusters among all 49 public methanotroph genomes that are available in NCBI GenBank. The survey showed that the genes encoding V-nitrogenase are present in only two type II MOB: Methylocystis bryophila S285 and Methylocystis parvus OBBP, but not in any type I MOB. The V-nitrogenase is presumably a primitive form that in ancient microbes, functioned in both nitrogen and carbon fixation ( Lee et al. 2010 ). Thus, the rare inherited trait of V-nitrogenase in methanotrophs, but identified in Methylocystis bryophila S285, may indicate that in peatlands, the fluctuation of both carbon and nitrogen sources might retard the evolution of carbon fixation system."
} | 4,791 |
25108218 | null | s2 | 5,104 | {
"abstract": "Advances in synthetic biology and metabolic engineering have enabled the construction of novel biological routes to valuable chemicals using suitable microbial hosts. Aldehydes serve as chemical feedstocks in the synthesis of rubbers, plastics, and other larger molecules. Microbial production of alkanes is dependent on the formation of a fatty aldehyde intermediate which is converted to an alkane by an aldehyde deformylating oxygenase (ADO). However, microbial hosts such as Escherichia coli are plagued by many highly active endogenous aldehyde reductases (ALRs) that convert aldehydes to alcohols, which greatly complicates strain engineering for aldehyde and alkane production. It has been shown that the endogenous ALR activity outcompetes the ADO enzyme for fatty aldehyde substrate. The large degree of ALR redundancy coupled with an incomplete database of ALRs represents a significant obstacle in engineering E. coli for either aldehyde or alkane production. In this study, we identified 44 ALR candidates encoded in the E. coli genome using bioinformatics tools, and undertook a comprehensive screening by measuring the ability of these enzymes to produce isobutanol. From the pool of 44 candidates, we found five new ALRs using this screening method (YahK, DkgA, GldA, YbbO, and YghA). Combined deletions of all 13 known ALRs resulted in a 90-99% reduction in endogenous ALR activity for a wide range of aldehyde substrates (C2-C12). Elucidation of the ALRs found in E. coli could guide one in reducing competing alcohol formation during alkane or aldehyde production."
} | 395 |
31361464 | null | s2 | 5,105 | {
"abstract": "Synthetic microbial consortia consist of two or more engineered strains that grow together and share the same resources. When intercellular signaling pathways are included in the engineered strains, close proximity of the microbes can generate complex dynamic behaviors that are difficult to obtain using a single strain. However, when a consortium is not cultured in a well-mixed environment the constituent strains passively compete for space as they grow and divide, complicating cell-cell signaling. Here, we explore the temporal dynamics of the spatial distribution of consortia cocultured in microfluidic devices. To do this, we grew two different strains of "
} | 166 |
26101545 | PMC4476231 | pmc | 5,108 | {
"abstract": "Background Large-scale algal biofuel production has been limited, among other factors, by the availability of inorganic carbon in the culture medium at concentrations higher than achievable with atmospheric CO 2 . Life cycle analyses have concluded that costs associated with supplying CO 2 to algal cultures are significant contributors to the overall energy consumption. Results A two-phase optimal growth and lipid accumulation scenario is presented, which (1) enhances the growth rate and (2) the triacylglyceride (TAG) accumulation rate in the oleaginous Chlorophyte Chlorella vulgaris strain UTEX 395, by growing the organism in the presence of low concentrations of NaHCO 3 (5 mM) and controlling the pH of the system with a periodic gas sparge of 5 % CO 2 ( v / v ). Once cultures reached the desired cell densities, which can be “fine-tuned” based on initial nutrient concentrations, cultures were switched to a lipid accumulation metabolism through the addition of 50 mM NaHCO 3 . This two-phase approach increased the specific growth rate of C. vulgaris by 69 % compared to cultures sparged continuously with 5 % CO 2 ( v / v ); further, biomass productivity (g L −1 day −1 ) was increased by 27 %. Total biodiesel potential [assessed as total fatty acid methyl ester (FAME) produced] was increased from 53.3 to 61 % (FAME biomass −1 ) under the optimized conditions; biodiesel productivity (g FAME L −1 day −1 ) was increased by 7.7 %. A bicarbonate salt screen revealed that American Chemical Society (ACS) and industrial grade NaHCO 3 induced the highest TAG accumulation (% w / w ), whereas Na 2 CO 3 did not induce significant TAG accumulation. NH 4 HCO 3 had a negative effect on cell health presumably due to ammonia toxicity. The raw, unrefined form of trona, NaHCO 3 ∙Na 2 CO 3 (sodium sesquicarbonate) induced TAG accumulation, albeit to a slightly lower extent than the more refined forms of sodium bicarbonate. Conclusions The strategic addition of sodium bicarbonate was found to enhance growth and lipid accumulation rates in cultures of C. vulgaris , when compared to traditional culturing strategies, which rely on continuously sparging algal cultures with elevated concentrations of CO 2(g) . This work presents a two-phased, improved photoautotrophic growth and lipid accumulation approach, which may result in an overall increase in algal biofuel productivity. Electronic supplementary material The online version of this article (doi:10.1186/s13068-015-0265-4) contains supplementary material, which is available to authorized users.",
"conclusion": "Conclusions A study was conducted comparing the influence of various bicarbonate salts on growth and lipid accumulation in the model Chlorophyte Chlorella vulgaris sp. strain UTEX 395. An optimized, two-phase enhanced growth and lipid accumulation scenario was developed, which uses strategic additions of sodium bicarbonate to enhance growth rates and lipid accumulation rates in cultures of C. vulgaris , as compared to traditional growth regimes which usually supply elevated concentrations of CO 2(g) as the sole inorganic carbon substrate. From an industrial perspective, the transport, storage, and delivery of gaseous CO 2 has been suggested to be costly for large-scale algal production [ 7 , 48 – 50 ]. However, competitive growth rates in microalgae cultures may only be achievable when elevated concentrations of dissolved inorganic carbon are present in the medium. By supplementing cultures with low doses of bicarbonate to first enhance the specific growth rate, and secondly by adding elevated concentrations of bicarbonate in concert with medium nitrogen depletion, both growth and lipid accumulation rates were increased for the optimized scenario by 69 and 27 %, respectively, above cultures which received 5 % CO 2 continuously. In addition, it was found that the raw, unprocessed form of bicarbonate (sesquicarbonate) could be an adequate source of inorganic carbon for both enhanced growth and lipid accumulation in cultures of C. vulgaris . These data indicate that the type and strategy ( e.g ., timing, concentration, and purity) of inorganic carbon addition may have a significant influence on lipid production in C. vulgaris . In summary, the strategies presented here could contribute towards a potentially cost-competitive approach to optimizing dissolved inorganic carbon supply in algal biofuel production through alkalinity control via the strategic addition of bicarbonate salts. The feasibility of this technology might be location specific and should be assessed using a techno-economic analysis to compare costs associated with bicarbonate addition compared to gaseous CO 2 addition. Beyond further optimization of inorganic carbon supply, additional optimization can perceivably be achieved via improved strain selection and optimized light parameters ( i.e ., photosynthetic flux available to the algae due to culture density). Much of this work is in progress in our laboratories and elsewhere.",
"discussion": "Results and discussion Part 1: carbonate salt screen for lipid production To the best of our knowledge, only ACS grade NaHCO 3 has been used to induce TAG accumulation in microalgae [ 33 – 35 , 38 ]. From an industrial cost perspective, a less refined and hence less expensive source of inorganic bicarbonate will be preferable. Five different bicarbonate salts and one carbonate salt (Table 1 ) were evaluated regarding their potential to induce TAG accumulation relative to ambient air sparged cultures using C. vulgaris . Briefly, batch cultures of C. vulgaris were grown in Bold’s basal medium until just prior to nitrogen depletion, at which time the cultures were pooled, harvested by centrifugation, and transferred into medium supplemented with inorganic carbon and deplete of any nitrogen source. Seven experimental conditions were designed to compare extractable lipid accumulation as monitored by gas chromatography-flame ionization detection (GC-FID) and total biodiesel potential via gas chromatography-mass spectroscopy (GC-MS). Extractable lipids are defined here as intracellular lipids such as TAGs, free fatty acids (FFAs), monoacylglycerides (MAGs), or diacylglycerides (DAGs), which can be liberated from lysed cells using non-polar solvents. Our previous work has shown that GC-FID analysis can be effectively used for the characterization of extractable lipids from microalgae cultures [ 39 ]. Total biodiesel potential is defined here as the mass of all fatty acids (polar and non-polar), which are quantified by transesterification into FAMEs. The values are usually expressed in weight FAME per weight biomass and are assessed based on carbon chain length and saturation via GC-MS [ 39 ]. FAMEs can originate from TAG, DAG, MAG, FFA as well as membrane and glycolipids [ 39 , 40 ]. Table 1 C. vulgaris culture characteristics during nitrogen limited growth when supplemented with various bicarbonate salts Treatment Cell concentration (×10 7 cells mL −1 ) Cell doublings a \n Dry weight (g L −1 ; DCW) b \n Endpoint pH Total chlorophyll (mg L −1 ) Total chlorophyll per cell (pg) Dry weight per cell (ng) 0 mM HCO 3 \n − \n 3.48 ± 0.17 2.47 ± 0.23 0.37 ± 0.03 8.08 ± 0.07 1.82 ± 0.08 0.052 ± 0.004 0.011 ± 0.001 50 mM ACS grade NaHCO 3 \n 2.15 ± 0.17 * \n 1.35 ± 0.19 * \n 0.58 ± 0.06 ** \n 9.82 ± 0.03 ** \n 2.00 ± 0.34 0.093 ± 0.011 ** \n 0.027 ± 0.001 ** \n 50 mM industrial grade NaHCO 3 \n 1.78 ± 0.13 * \n 0.64 ± 0.40 * \n 0.60 ± 0.02 ** \n 9.82 ± 0.06 ** \n 1.72 ± 0.18 0.097 ± 0.016 ** \n 0.034 ± 0.003 ** \n 50 mM KHCO 3 \n 1.56 ± 0.13 * \n 0.88 ± 0.27 * \n 0.54 ± 0.03 ** \n 9.77 ± 0.03 ** \n 2.01 ± 0.21 0.129 ± 0.013 ** \n 0.035 ± 0.003 ** \n 50 mM NH 4 HCO 3 \n 0.62 ± 0.04 * \n −0.25 ± 0.47 * \n 0.15 ± 0.04 * \n 9.31 ± 0.05 ** \n 0.31 ± 0.17 * \n 0.031 ± 0.014 0.016 ± 0.005 50 mM Na 2 CO 3 \n 1.21 ± 0.22 * \n 0.83 ± 0.15 * \n 0.35 ± 0.01 9.98 ± 0.02 ** \n 1.37 ± 0.15 * \n 0.115 ± 0.024 ** \n 0.029 ± 0.006 ** \n 25 mM NaHCO 3 ∙Na 2 CO 3 \n 1.21 ± 0.16 * \n 0.38 ± 0.42 * \n 0.49 ± 0.04 ** \n 9.83 ± 0.03 ** \n 1.77 ± 0.22 0.149 ± 0.036 ** \n 0.041 ± 0.008 ** \n Values are reported for the completion of the experiment (5.75 days) ( n = 3) \n * \n p value <0.05 as determined by a two-tailed t test, statistically significantly lower [difference between treatment and control group (0 mM HCO 3 \n − )] \n ** \n p value <0.05 as determined by a two-tailed t test, statistically significantly higher [difference between treatment and control group (0 mM HCO 3 \n − )] \n a Cell doublings are calculated as n = log 2 (C f /C i ); where n is the number of cell doublings and C \n f and C \n i are final and initial cell concentrations, respectively \n b Dry cell weight (DCW) determined gravimetrically with lyophilized biomass Six experimental groups in triplicate received the following sources of dissolved inorganic carbon (DIC, 50 mM C final concentration): (1) ACS grade NaHCO 3 , (2) industrial grade NaHCO 3 , (3) KHCO 3 , (4) NH 4 HCO 3 , (5) Na 2 CO 3 , and (6) NaHCO 3 ∙Na 2 CO 3 . These treatments were compared with each other and a control, which received no additional inorganic carbon. Table 1 presents culture characteristics for each treatment at the conclusion of the experiment. As expected based on our previous work [ 33 , 34 , 41 ], the cultures that did not receive any bicarbonate or carbonate increased in cell concentration from 6.3 × 10 6 cells mL −1 until reaching stationary growth at 3.5 × 10 7 cells mL −1 (Table 1 ), whereas cultures supplemented with 50 mM DIC (as carbonate or bicarbonate) did not increase as much, remained stagnant, or even decreased in cell numbers (NH 4 HCO 3 ). However, even though cell numbers did not increase as much in bicarbonate supplemented treatments, cell dry weights in all bicarbonate supplemented cultures, except the NH 4 HCO 3 supplemented treatment, increased. This resulted in higher dry weights per cell in the bicarbonate- and carbonate-amended treatments compared to the control treatments (Table 1 ). Conversely, analysis of the concentration of chlorophyll indicates no statistical difference between the control treatments and those that received bicarbonate additions, with exception of those that received NH 4 HCO 3 and the treatments which received carbonate. When normalized to the cell number, there was significantly more chlorophyll per cell in all salt amendments with exception to those that received NH 4 HCO 3 (Table 1 ). Again, this is due to the delay or arresting of the cellular division, and this phenomenon has been well documented and is believed to be the result of a fundamental metabolic shift from growth metabolism to lipid accumulation metabolism after bicarbonate addition [ 33 – 35 ]. Figure 1 presents the extractable lipids (FFA, MAG, DAG, TAG) and in situ transesterified FAMEs for each culture after maximum TAG accumulation (5.75 days of N-limited culturing) [ 39 ]. Cultures supplemented with 50 mM ACS or industrial grade bicarbonate accumulated the most TAG (22.9 ± 1.5 and 24.3 ± 0.7 % w / w , respectively), as compared to the cultures which did not receive any additional DIC (13.3 ± 3.4 % w / w ). Cultures supplemented with 50 mM KHCO 3 or 25 mM Na 2 CO 3 ·NaHCO 3 also accumulated a significant amount of TAG (17.7 ± 1.7 and 19.5 ± 0.797 % w / w , respectively), albeit less than cultures supplemented with ACS or industrial grade NaHCO 3 (Fig. 1 ). Sodium sesquicarbonate is the raw material ( i.e ., trona) used for the production of the more refined grades of carbonates/bicarbonates. Trona typically contains high concentrations of silicates and other compounds and is likely one of the least expensive forms of bicarbonate available. Fig. 1 Extractable lipid class and FAME profiles for cultures of C. vulgaris re-suspended into medium depleted of nitrogen and supplemented with various bicarbonate salts. Final concentrations of bicarbonate salts per experimental condition: 0 mM HCO 3 \n − (control), 50 mM ACS grade NaHCO 3 (grade 1), 50 mM industrial grade NaHCO 3 (grade 2), 50 mM KHCO 3 , 50 mM Na 2 CO 3 , and 25 mM NaHCO 3 ∙Na 2 CO 3 (25 mM of sesquicarbonate was used to provide equimolar carbon). Values are reported for the completion of the experiment ( n = 3). All values expressed as weight percent (% weight extractable lipid or weight FAME/weight biomass) Cultures treated with 50 mM NH 4 HCO 3 did not accumulate TAG (data not shown), declined in cell concentration after 2 days of incubation (Additional file 1 ) and reached cell concentrations and cell dry weights significantly lower than all the other cultures at the end of the experiment (Table 1 ). The final pH of the NH 4 HCO 3 amended system was approximately 9.3 (Fig. 2 , Table 1 ), which is equal to the pKa for the NH 4 + /NH 3 equilibrium, indicating that high concentrations of NH 3 were present. The decline in cell concentration is presumably due to the toxic effects of ammonia, which has been shown previously to inhibit photosynthesis in microalgal cultures [ 42 ]. Fig. 2 pH for cultures of C. vulgaris re-suspended into medium depleted of nitrogen and supplemented with various bicarbonate salts. Final concentrations of bicarbonate salts per experimental condition: 0 mM HCO 3 \n − (control), 50 mM ACS grade NaHCO 3 (grade 1), 50 mM industrial grade NaHCO 3 (grade 2), 50 mM KHCO 3 , 50 mM NH 4 HCO 3 , 50 mM Na 2 CO 3 , and 25 mM NaHCO 3 ∙Na 2 CO 3 (25 mM of sesquicarbonate was used to provide equimolar carbon) ( n = 3) Finally, cultures supplemented with 50 mM Na 2 CO 3 accumulated a significantly lower percentage of TAG (6.2 ± 1.3 % w / w ) compared to all other treatments (Fig. 1 , Table 2 ). There are no known metabolic pathways through which C. vulgaris can assimilate CO 3 2− into biomass and at the initial pH 11.4 (Fig. 2 ) virtually all of the dissolved inorganic carbon would have been present as carbonate (the pKa for the HCO 3 − /CO 3 2− equilibrium is approximately at pH 10.3). However, throughout the experiment, the pH continuously decreased and reached a final pH of 9.98 (Fig. 2 , Table 1 ); thereby, speciation was shifted to predominantly bicarbonate which became bioavailable to the alga. It is presumed that the higher sodium concentration in the CO 3 2− amended systems may also have influenced lipid metabolism in C. vulgaris . However, Gardner et al. (2013) showed that the addition of up to 50 mM sodium ions did not have a negative effect on the lipid accumulation activity of a Scenedesmus sp. [ 41 ]. This effect will have to be investigated further in the future for C. vulgaris . Table 2 FAME profiles of C. vulgaris when supplemented with various bicarbonate salts just prior to nitrogen depletion Treatment C16:0 C16:1 C18:0 C18:1 C18:2 C18:3 Other a \n Total biodiesel potential (%) b \n Potential biodiesel productivity (g L −1 day −1 ) b \n 0 mM HCO 3 \n − \n 7.51 ± 0.69 1.59 ± 0.16 4.72 ± 0.5 16.82 ± 2.97 3.92 ± 0.43 9.3 ± 0.75 0.4 ± 0.1 44.19 ± 5.42 0.029 ± 0.004 50 mM ACS grade NaHCO 3 \n 7.78 ± 0.46 2.33 ± 0.18 2.46 ± 0.14 31.82 ± 1.73 5.61 ± 0.35 9.06 ± 0.43 0.58 ± 0.06 59.63 ± 3.34 * \n 0.061 ± 0.008 * \n 50 mM industrial grade NaHCO 3 \n 7.38 ± 0.37 2.11 ± 0.05 2.28 ± 0.18 30.06 ± 1.39 5.16 ± 0.13 8.43 ± 0.74 0.61 ± 0.05 56.04 ± 2.9 * \n 0.058 ± 0.001 * \n 50 mM KHCO 3 \n 6.8 ± 0.22 1.79 ± 0.05 2.64 ± 1.11 27 ± 2.08 5.15 ± 0.07 7.74 ± 0.3 0.54 ± 0.13 51.62 ± 2.25 * \n 0.048 ± 0.012 * \n 50 mM Na 2 CO 3 \n 5.12 ± 0.11 1.26 ± 0.03 2.56 ± 0.04 14.47 ± 0.12 3.4 ± 0.02 5.84 ± 0.05 0.2 ± 0.05 32.82 ± 0.28 0.02 ± 0.001 25 mM NaHCO 3 ∙Na 2 CO 3 \n 6.69 ± 0.08 1.84 ± 0.03 3.82 ± 0.09 23 ± 0.62 4.67 ± 0.09 7.72 ± 0.12 0.47 ± 0.08 53.33 ± 1.35 * \n 0.041 ± 0.003 * \n Values are reported for the completion of the experiment (5.75 days) ( n = 3). All values expressed as weight percent (% weight FAME/weight biomass) unless indicated otherwise \n * \n p value <0.05 as determined by a two-tailed t test, statistically significant higher [difference between treatment and control group (0 mM HCO 3 \n − )] \n a Sum of other compounds detected \n b Total FAMEs Free fatty acid, MAG and DAG contents were statistically the same in all treatments (6.5 ± 0.3, 0.24 ± 0.06, 6.1 ± 0.34 % w / w , respectively, Fig. 1 ); thus, the total extractable lipid content (sum of FFA, MAG, DAG, and TAG) for each culture followed the same trend as the TAG content per cell. Total biodiesel potential (as % w / w FAME) and potential biodiesel productivity (as g L −1 day −1 FAME) [ 43 ] mirrored the trend of the TAG contents. Cultures treated with 50 mM ACS and industrial grade NaHCO 3 accumulated the most FAME at 59.6 ± 3.3 and 56.0 ± 2.9 % w / w and had the highest potential biodiesel productivity with 0.061 ± 0.008 and 0.058 ± 0.001 g L −1 day −1 , respectively (Table 2 ). FAME profiles are further expanded upon in Table 2 , which presents a number of FAMEs separated by carbon chain length and saturation. Overall, C. vulgaris primarily synthesized C 18 fatty acids, specifically mono-unsaturated C 18 as reported previously [ 38 , 39 , 44 ]. The cultures which did not receive additional inorganic carbon, however, produced only approximately half (16.8 ± 3 % w / w ) as much C18:1 fatty acids compared to the treatment which received 50 mM ACS grade NaHCO 3 (31.8 ± 1.7 % w / w ) or the industrial grade NaHCO 3 (30.1 ± 1.4 % w / w ). This indicates that a large fraction of the C18:1fatty acids were synthesized as a result of elevated concentrations of DIC (Table 2 ). Interestingly, the cultures which did not receive additional inorganic carbon produced equivalent concentrations of fully saturated C16:0 fatty acids and poly-unsaturated C18:3 fatty acids compared to treatments which received sodium bicarbonate (Table 2 ). These results are consistent with previous reports describing changes in the fatty acid profiles of C. vulgaris UTEX 395 when cultured under nitrogen deprivation [ 44 , 45 ]. For example, Guarnieri et al. (2011) reported a 9-fold increase in C18:1 fatty acid when UTEX 395 was cultured in nitrogen-free medium. Here, the addition of sodium bicarbonate resulted in a 2-fold increase in C18:1 fatty acid content based on cell dry weight over the bicarbonate-free control, which suggests that bicarbonate can enhance fatty acid synthesis in concert with nitrogen depletion. The results from evaluating the effect of (bi)carbonate salts on lipid accumulation provided evidence that the timely addition of bicarbonate, when coupled with nitrogen depletion, can induce significant lipid accumulation and promote the preferential production of C18:1 fatty acid in C. vulgaris . Not only was the lipid content per biomass (biodiesel potential in % w / w ) higher in the bicarbonate triggered cells, but due to the higher dry weight for the bicarbonate triggered cells, the potential biodiesel productivity (g FAME L −1 d −1 ) was also higher in the bicarbonate-triggered cultures. However, as noted, the bicarbonate addition affects cell division and actually decreases the number of newly produced cells during the lipid accumulation phase. Therefore, in part 2 of this study, a strategy was developed that allowed for faster biomass production through the addition of small amounts of bicarbonate during the initial culturing before lipid accumulation was triggered. Implementation of such an optimized strategy may further improve lipid productivity of algal cultures. Part 2: optimized growth and lipid accumulation Part 1 of this manuscript described that the lipid content (in % lipid per biomass) can be increased through bicarbonate supplementation of C. vulgaris cultures. However, cell replication was largely arrested at the time of bicarbonate addition (Table 1 ). Based on these as well as previously reported observations [ 33 – 35 ], it was hypothesized that enhanced cell growth and thus higher culture productivity might be achievable by adding low concentrations of bicarbonate before inducing lipid production using a second addition of bicarbonate salt at a higher concentration near nitrogen depletion. Industrial and laboratory systems often use CO 2 (1–5 % up to 100 % v / v ) to control the pH of algal cultures between 8.0 and 8.7 [ 44 , 46 , 47 ]. This process can be automated via pH controllers, which initiate a CO 2 gas sparge when the pH values in the culture medium reach undesirably high levels and discontinue the addition of CO 2 once the pH has been lowered sufficiently. In an attempt to increase biomass production prior to inducing lipid accumulation, 5 mM ACS grade NaHCO 3 (final concentration at inoculation) was added to the medium at the beginning of cultivation and a pH controller maintained the medium pH between 8.4 and 8.7 by automatically sparging the system with 5 % CO 2 ( v / v ) when the pH reached 8.7 and turning off when the pH had declined to 8.4. These cultures were also supplemented with 50 mM ACS grade NaHCO 3 at day 4, just prior to nitrogen depletion, to induce TAG accumulation. This optimized growth and lipid accumulation strategy (optimized scenario) was compared to the following four culture conditions: (1) cultures sparged with atmospheric air only, (2) cultures sparged with atmospheric air and the medium supplemented with 5 mM NaHCO 3 prior to inoculation, (3) cultures sparged with atmospheric air and pH regulated with 5 % CO 2 ( v / v ) during the daylight hours to maintain a pH range of 8.4–8.7, and (4) cultures aerated with 5 % CO 2 ( v / v ) continuously during the daylight hours. In this way, each inorganic carbon regime ( i.e ., 5 mM NaHCO 3 , 5 % CO 2 , pH regulated, and atmospheric air only) was evaluated individually, and the optimized scenario could be compared to those individual systems. This part of the study was not intended to be a full factorial design study, but rather synthesized the knowledge of previous work (reported here and elsewhere) to devise a strategy for improved growth and lipid accumulation without the need for using high concentration and possibly high-purity sources of CO 2 for algal growth and lipid accumulation. The initial addition of 5 mM bicarbonate increased the average specific growth rate and biomass productivity (in g CDW L −1 day −1 ) for the optimized scenario by 69 and 27 %, respectively, compared to the other treatments (Fig. 3 , Table 3 ). Furthermore, the addition of 50 mM bicarbonate to the optimized scenario on day 4 resulted in the expected cessation of cellular replication (Fig. 3 , black arrow and Table 3 ) and increased lipid accumulation (discussed below) [ 33 – 35 ]. Fig. 3 Growth (cells mL −1 ) of cultures of C. vulgaris cultured under various inorganic carbon regimes. ( Square ) Continuous sparge of atmospheric air, ( triangle ) continuous sparge of atmospheric air and supplemented with 5 mM NaHCO 3 at inoculation, ( circle ) continuous sparge of atmospheric air supplemented periodically with 5 % CO 2 ( v / v ) to maintain pH between 8.4 and 8.7, ( diamond ) continuous sparge of atmospheric air supplemented with 5 % CO 2 ( v / v ) during daytime hours, and ( right pointing triangle ) the optimized scenario of a continuous sparge of atmospheric air supplemented periodically with 5 % CO 2 ( v / v ) to maintain pH between 8.4 and 8.7 and an initial addition of 5 mM NaHCO 3 at inoculation plus an additional 50 mM NaHCO 3 just prior to nitrogen depletion to stimulate TAG accumulation ( n = 3). Arrow indicates time of 50 mM NaHCO 3 addition just prior to nitrogen depletion of the culture medium Table 3 C. vulgaris culturing characteristics when grown under various inorganic carbon regimes Treatment Cell concentration (×10 7 cells mL −1 ) Specific growth rate ( μ \n max day −1 ) Biomass productivity (g L −1 day −1 ; DCW) a \n Maximum chlorophyll (mg L −1 ) Air only 2.31 ± 0.09 * \n 0.61 ± 0.09 * \n 0.04 ± 0.00 * \n 3.52 ± 1.0 * \n Air + 5 mM NaHCO 3 \n 3.53 ± 0.2 * \n 0.82 ± 0.02 * \n 0.06 ± 0.01 * \n 5.72 ± 1.0 * \n Air + 5 % ( v / v ) CO 2 pH control 14.21 ± 4.67 ** \n 0.76 ± 0.05 * \n 0.11 ± 0.02 * \n 8.01 ± 1.23 * \n 5 % ( v / v ) CO 2 continuous 23.92 ± 1.55 ** \n 1.02 ± 0.02 * \n 0.11 ± 0.01 * \n 8.13 ± 1.46 * \n Optimized scenario 5.93 ± 0.61 b \n 1.72 ± 0.06 0.14 ± 0.00 10.7 ± 0.84 Experimental conditions: (1) continuous sparge of atmospheric air, (2) continuous sparge of atmospheric air and supplemented with 5 mM NaHCO 3 at inoculation, (3) continuous sparge of atmospheric air supplemented periodically with 5 % CO 2 ( v / v ) to maintain pH between 8.4 and 8.7, (4) continuous sparge of atmospheric air supplemented with 5 % CO 2 ( v / v ) during daytime hours, and (5) the optimized scenario of a continuous sparge of atmospheric air supplemented periodically with 5 % CO 2 ( v / v ) to maintain pH between 8.4 and 8.7 and an initial addition of 5 mM NaHCO 3 at inoculation plus an additional 50 mM NaHCO 3 just prior to nitrogen depletion to stimulate TAG accumulation ( n = 3). All growth yields are calculated for the exponential growth phase ( i.e ., from inoculation until depletion of nitrogen) \n * \n p value <0.05 as determined by a two-tailed t test, statistically significantly lower (difference between treatment and optimized scenario) \n ** \n p value <0.05 as determined by a two-tailed t test, statistically significantly higher (difference between treatment and optimized scenario) \n a Dry cell weight (DCW) determined gravimetrically with lyophilized biomass \n b 50 mM NaHCO 3 addition results in cessation of cellular division The total chlorophyll content of the cultures was monitored throughout the experiment to estimate the photosynthetic potential of each culture (Fig. 4 ). The optimized scenario exhibited an early increase in chlorophyll concentration, as compared to the other cultures, indicating a higher degree of culture health and photosynthetic potential during the exponential growth phase. Similarly, the concentration of chlorophyll was higher in the optimized scenario during the nitrogen limitation induced lipid accumulation phase (after day 4). This increased chlorophyll content along with the higher carbon fixation rates indicates a higher photosynthetic performance of the alga when cultured under the optimized scenario; however, additional photosynthetic parameters will need to be monitored in the future to verify this interpretation. Fig. 4 Total chlorophyll concentration (mg L −1 ) for cultures of C. vulgaris cultured under various inorganic carbon regimes. ( Square ) Continuous sparge of atmospheric air, ( triangle ) continuous sparge of atmospheric air and supplemented with 5 mM NaHCO 3 at inoculation, ( circle ) continuous sparge of atmospheric air supplemented periodically with 5 % CO 2 ( v / v ) to maintain pH between 8.4 and 8.7, ( diamond ) continuous sparge of atmospheric air supplemented with 5 % CO 2 ( v / v ) during daytime hours, ( right pointing triangle ) the optimized scenario of a continuous sparge of atmospheric air supplemented periodically with 5 % CO 2 ( v / v ) to maintain pH between 8.4 and 8.7 and an initial addition of 5 mM NaHCO 3 at inoculation plus an additional 50 mM NaHCO 3 just prior to nitrogen depletion to stimulate TAG accumulation ( n = 3) To evaluate the extent of lipid accumulation, lipid profiles were compared for cultures sparged continuously with 5 % CO 2 , during the daylight hours, against cultures grown under the optimized scenario (Fig. 5 , Table 4 ). Additionally, to assess how cultures grown under the optimized scenario would perform when using a lower grade of bicarbonate, a third set of cultures was supplemented with 50 mM NaHCO 3 ∙Na 2 CO 3 (sesquicarbonate), instead of 50 mM ACS grade NaHCO 3 , to induce lipid accumulation. Lipid profile data for cultures grown on only atmospheric air (with and without 5 mM NaHCO 3 ) or air with pH regulated with 5 % CO 2 ( v / v ) have been omitted for clarity because these systems did not accumulate lipid to a larger degree than the cultures which received 5 % CO 2 continuously. Fig. 5 Extractable lipid class and FAME profiles for cultures of C. vulgaris when cultured under various inorganic carbon regimes. Experimental conditions: (1) the optimized scenario of a continuous sparge of atmospheric air supplemented periodically with 5 % CO 2 ( v / v ) to maintain pH between 8.4 and 8.7 and an initial addition of 5 mM NaHCO 3 at inoculation plus an additional 50 mM of ACS grade NaHCO 3 just prior to nitrogen depletion to stimulate TAG accumulation. (2) The same culture conditions as scenario 1 listed above, except TAG accumulation was induced by adding 25 mM NaHCO 3 ∙Na 2 CO 3 (sesquicarbonate). (3) Continuous sparge of atmospheric air supplemented with 5 % CO 2 ( v / v ) during daytime hours. Values are reported for the completion of the experiment ( n = 3). All values expressed as weight percent (% weight extractable lipid or weight FAME/weight biomass) Table 4 C. vulgaris lipid characteristics for cultures grown under various inorganic carbon regimes Treatment C16:0 a \n C16:1 a \n C18:0 a \n C18:1 a \n C18:2 a \n C18:3 a \n Other b \n Total biodiesel potential (%) c \n Potential biodiesel productivity (g L −1 day −1 ) c \n Optimized Scenario 10.66 ± 0.3 2.76 ± 0.03 5.17 ± 0.09 24.42 ± 0.44 7.17 ± 0.14 10.21 ± 0.33 0.64 ± 0.06 61.04 ± 1.31 0.098 ± 0.002 50 mM NaHCO 3 ∙Na 2 CO 3 \n 9.27 ± 0.34 2.64 ± 0.1 3.97 ± 0.11 19.85 ± 1.2 6.56 ± 0.18 8.45 ± 0.15 0.68 ± 0.09 51.41 ± 2.14 * \n 0.07 ± 0.005 * \n 5 % ( v / v ) CO 2 Continuous 9.45 ± 0.7 1.85 ± 0.14 2.36 ± 0.2 26.59 ± 2.1 4.58 ± 0.4 7.88 ± 0.76 0.63 ± 0.12 53.33 ± 1.35 * \n 0.091 ± 0.005 * \n Experimental conditions: (1) the optimized scenario of a continuous sparge of atmospheric air supplemented periodically with 5 % CO 2 ( v / v ) to maintain pH between 8.4 and 8.7 and an initial addition of 5 mM NaHCO 3 at inoculation plus an additional 50 mM of ACS grade NaHCO 3 just prior to nitrogen depletion to stimulate TAG accumulation. (2) The same culture conditions as scenario 1 listed above, except TAG accumulation was induced by adding 25 mM NaHCO 3 ∙Na 2 CO 3 (sesquicarbonate). (3) Continuous sparge of atmospheric air supplemented with 5 % CO 2 ( v / v ) during daytime hours ( n = 3). Biodiesel productivity is calculated for the stationary growth phase ( e.g ., from depletion of nitrogen until termination of experiment) \n * \n p value <0.05 as determined by a two-tailed t test, statistically significantly lower (difference between treatment and optimized scenario) \n a All values expressed as weight percent (% weight FAME/weight biomass) \n b Sum of other compounds detected \n c Total FAMEs Figure 5 presents extractable lipid profiles for the three treatments that accumulated significant lipids as well as total extractable lipid (sum of FFA, MAG, DAG, and TAG) and total biodiesel potential (% FAME w / w biomass). No statistical difference was observed in TAG content between cultures sparged with 5 % CO 2 (23.2 ± 0.77 % w / w ) and cultures supplemented with 50 mM NaHCO 3 ∙Na 2 CO 3 (23.5 ± 1.5 % w / w ). Cultures supplemented with 50 mM ACS grade NaHCO 3 on day 4 (Optimized Scenario) increased in TAG content by 3.3 % ( w / w ) over the 5 % CO 2 control ( t test p < 0.05). Free fatty acid, MAG and DAG contents were statistically equivalent between all three cultures. Total biodiesel potential (% FAME biomass −1 ) was increased from 53.3 ± 1.34 (% w / w ) (5 % CO 2 continuously) to 61 ± 1.3 (% w / w ) under the optimized scenario (Fig. 5 ), and the averaged biodiesel productivity over the 7-day culturing time was increased by 7.7 % (Table 4 ). The increase in total biodiesel potential is partially attributed to increased concentrations of TAG in the optimized scenario; however, a portion also appears to have been derived from membrane-bound lipids or glycolipids. Table 4 presents FAME profiles for each of the three cultures, separated by carbon chain length and saturation. As observed in the first part of this work, cells supplemented with ACS grade sodium bicarbonate accumulated slightly, yet statistically significantly ( t test, p < 0.05) more of each fatty acid (in % weight FAME/weight biomass) except for the mono-unsaturated C 18 FAMEs, which reached the highest concentration in cultures grown on 5 % CO 2 , albeit not significantly higher ( t test, p = 0.15). As discussed in part 1, changes in lipid profiles during nitrogen starvation or bicarbonate addition have been reported previously and the observations described here largely agree with those reports [ 38 , 39 , 44 ]."
} | 8,160 |
36615996 | PMC9824178 | pmc | 5,109 | {
"abstract": "The antifogging coating based on super-hydrophilic polymer is regarded as the most promising strategy to avoid fogging but suffers from short-term effectiveness due to antifogging failure induced by water invasion. In this study, a black phosphorus nanosheets (BPs) hybrid polymer hetero-network coating (PUA/PAHS/BPs HN) was prepared by UV curing for the first time to achieve long-term antifogging performance. The polymer hetero-network (HN) structure was composed of two novel cross-linked acrylic resin and polyurethane acrylate. Different from physical blending, a covalent P-C bond between BPs and polymer is generated by UV initiated free radical reaction, resulting in BPs firmly embedded in the polymer HN structure. The BPs enriched on the coating surface by UV regulating migration prevent permeation of water towards the inside of the coating through its own good water-based lubricity and water absorption capacity. Compared with the nonhybrid polymer HN, PUA/PAHS/BPs HN not only has higher hardness and better friction resistance properties, but also exhibits superior water resistance and longer antifogging duration. Since water invasion was greatly reduced by BPs, the PUA/PAHS/BPs HN coating maintained antifogging duration for 60 min under a 60 °C water vapor test and still maintained long-term antifogging performance after being immersed in water for 5 days.",
"conclusion": "4. Conclusions In summary, for the first time, a BPs hybrid super-hydrophilic polymer HN coating (PUA/PAHS/BPs HN) was prepared by UV curing for long-term antifogging performance. Different from physical blending, covalent P-C bonds between BPs and polymer were generated by UV-initiated free radical reaction, resulting in BPs firmly embedded in the polymer HN structure. The BPs enriched on the coating surface by UV regulating migration can effectively prevent the permeation of water towards the inside of the coating through its own good water-based lubricity and water absorption capacity. Compared with nonhybrid polymer HN, PUA/PAHS/BPs HN not only has higher hardness and better friction resistance properties, but also exhibits superior water resistance and longer antifogging duration. Since water invasion is greatly reduced by BPs, PUA/PAHS/BPs HN maintained 60 min antifogging duration under the 60 °C water vapor test and still maintained long-term antifogging performance after being immersed in water for 5 days. After exposure to air for 6 weeks, the antifogging performance of PUA/PAHS/BPs HN did not decline, showing its outstanding stability. This study provides not only a method for fabricating BP hybrid polymer materials through the generation of P–C bonds induced by free radical reaction, but also a new way for regulating the directional enrichment of BP towards the surface of a composite structure, which presents new approaches for long-term antifogging in humid environments.",
"introduction": "1. Introduction When saturated water vapor condenses on a surface with a temperature lower than the dew point of the vapor, fog forms as tiny droplets that can scatter visible light [ 1 , 2 , 3 ]. For those transparent materials, surface fogging greatly reduces light transmittance, resulting in undesirable failure when applied in optical-related fields. For example, surface fog largely impacts the precision of optical and analytical instruments, such as infrared microscopes and clinical laparoscopy [ 4 ]. Many strategies, referring to super-hydrophobic and super-hydrophilic surfaces, have been developed for effective antifogging. Super-hydrophobic surfaces eliminate the effect of fog droplets by rolling down from the surface, but the light transmission tends to be compromised by the surface’s own micro/nano-structured roughness [ 4 , 5 , 6 , 7 ]. Different from super-hydrophobic surfaces, the antifogging coating based on super-hydrophilic polymer avoids fogging by quickly spreading fog droplets into a continuous water film, so as to effectively prevent the light scattering. Therefore, in the field of antifogging, super-hydrophilic surfaces are easier, more reliable and more promising than super-hydrophobic surfaces [ 8 , 9 , 10 , 11 , 12 ]. However, as the water film on a super-hydrophilic surface grows to a certain thickness, polymer-based coatings suffer from loss of water-soluble components and swelling-induced peeling and cracking, which results in the antifogging failure of the coatings [ 13 ]. Therefore, polymer-based coatings are usually limited to short-term antifogging effectiveness due to their insufficient water resistance capability. To improve the water resistance of polymer-based coatings, some water-resistant organics such as hydrophobic components are usually added into the coatings. Unfortunately, these organics usually weaken the hydrophilicity of the coating, resulting in a decline in its antifogging ability. To date, it is still a huge challenge to balance the hydrophilicity and water resistance of polymer-based coatings [ 14 ]. Generally, hydrophilic inorganic nanomaterials are able to absorb and store water without swelling, thus bringing a new strategy to regulate the balance between the hydrophilicity and water resistance of polymer-based coatings [ 15 ]. To improve the comprehensive properties of these organic–inorganic composites, more strategies for regulating compatibility, dispersion and stability of inorganic nanomaterials in the polymer-based coatings are required [ 16 ]. As a new hydrophilic two-dimensional inorganic nanomaterial [ 17 , 18 ], black phosphorus (BP) can improve the wear resistance and drainage capacity of the coating due to its excellent water-based lubrication performance [ 19 , 20 , 21 ]. On the other hand, BP possesses excellent compatibility with hydrophilic polymers, leading to universally good stability of the BP/polymer complex materials [ 22 , 23 ]. Unfortunately, the combination mode of BP with polymer reported at present is mostly weak physical blending rather than strong chemical combination [ 24 , 25 , 26 , 27 ], which greatly affects various performance attributes of BP-based polymer coatings. Therefore, although the BP-based polymer coating is a promising material for antifogging, some huge challenges, especially the chemical combination and microstructure regulation between BP and polymer, still need to be overcome. In this study, a long-term antifogging coating based on BP nanosheets (BPs) hybrid super-hydrophilic polymer hetero-network (HN) was designed and prepared by construction of P-C bond under UV curing. The polymer hetero-network (HN) structure [ 28 , 29 ] was composed of two novel cross-linked compounds, acrylic resin and polyurethane acrylate. Different from physical blending, a covalent P-C bond between BPs and polymer is generated by UV-initiated free radical reaction, resulting in BPs firmly embedded in the polymer HN structure. The BPs enriched on the coating surface by UV regulating migration can effectively prevent the permeation of water towards the inside of the coating through its own good water-based lubricity and water absorption capacity.",
"discussion": "3. Results and Discussion 3.1. Material Synthesis and Characterization The polymer HN consisted of polyurethane acrylate (PUA) and acrylic resin (poly (acrylic acid (AA)-2-hydroxyethyl methacrylate (HEMA)-sulfobetaine methacrylate (SBMA), short for PAHS)), prepared by UV-initiated cross-linking. Both polymers have outstanding hydrophilicity and prominent adhesion on plastic substrates [ 11 , 30 ]. The UV-curable six functional PUA was prepared by polymerization of polyethylene glycol (PEG) with diisocyanate and end-capping with pentaerythritol triacrylate (PETA) ( Supporting Information, Figure S1 ). PAHS was obtained by radical polymerization of three hydrophilic monomers AA, HEMA, SBMA, which contained generous hydrophilic groups ( Figure S2 ). The 1 H nuclear magnetic resonance ( 1 H NMR) spectra and the Fourier transform infrared (FTIR) spectra of PUA and PAHS illustrated the successful synthesis of the two polymers ( Figure S3–S5 ). The BP nanosheets (BPs) were obtained by a liquid exfoliation method reported by our group [ 31 ]. The scanning electron microscope (SEM) image ( Figure S6 ) shows BPs about 200–300 nm in size. The UV-curable solution prepared by dissolving the as-obtained PUA, PAHS, BPs and photo initiator in solvent was spin-coated on a plastic substrate and irradiated by UV to synthesize the BPs hybrid polymer HN coating (denoted as PUA/PAHS/BPs HN). As shown in Figure 1 a, the hybridization of BPs and the cross-linking of PUA and PAHS occurred simultaneously under UV irradiation. The photo initiator produced free radicals to initiate polymerization of acrylate groups and generation of P-C bonds, thus embedding BPs in the polymer HN structure. In addition to the generation of P-C bonds, the interactions between P atoms and hydrophilic groups also enhances the stability of BPs. PUA/PAHS/BPs HN showed not only long-term stability but also high mechanical strength due to the crosslinking of polymers. When water vapor condensed on the surface of PUA/PAHS/BPs HN, a hemi-wicking phenomenon occurred on the hydrophilic rough surface ( Figure 1 b) [ 32 ]. The hybridization of BPs increased the surface roughness, which made the water spread more rapidly on the surface of the coating, thus realizing the effective antifogging performance of PUA/PAHS/BPs HN. The SEM images of PUA/PAHS HN and PUA/PAHS/BPs HN are shown in Figure 2 a,b, respectively. The network structure formed by the cross-linking of PUA and PAHS had a diameter of about 300–500 nm, with micron-scale hole diameter and about 70% porosity. The morphology of the network structure remained basically unchanged with the addition of BPs. The cross-section SEM image of PUA/PAHS/BPs HN in Figure S7 shows its good homogeneity and tight combination with the substrates. The AFM diagram of PUA/PAHS/BPs HN showed that BPs were relatively evenly distributed in the coating ( Figure S8 ). The FTIR peaks of PUA at 3000–3100 cm −1 and 1650 cm −1 were attributed to C–H and C=C, respectively ( Figure 2 c and Figure S9 ). Compared with PUA, the C–H and C=C peaks of PUA/PAHS/BPs HN disappeared, and some new peaks such as those at 1039 cm −1 and 1240 cm −1 associated with the characteristic peak of PAHS and BPs, respectively, appeared. This result shows that the free radical polymerization of C=C and the hybridization of BPs were realized synchronously. High-resolution XPS (HR-XPS) spectra of PUA/PAHS/BPs HN were acquired and analyzed ( Figure 2 d,e and Figure S10 ). As shown by the C 1s XPS spectrum, P–C, C–O, and C=O peaks at 284.1, 286.1, and 288.4 eV, respectively, confirmed the existence of P-C bonding and carbon oxygen covalent bonding of the polymers. The P 2p spectrum showed the P 2p 3/2 and P 2p 1/2 doublets at 129.6 and 130.5 eV, respectively, characteristic of crystalline BP. In addition, the broad peak at 133.3 eV corresponded to P-C covalent bonds, corroborating chemical binding between BP and PUA by free radical reaction. Compared with PUA/PAHS HN, the Raman spectrum of PUA/PAHS/BPs HN shows three prominent peaks of BPs related to A 1 g at 361 cm −1 , B 2g at 438 cm −1 and A 2 g at 466 cm −1 , respectively [ 33 ], indicating preservation of BPs structure during hybridization ( Figure 2 f). These results imply successful preparation of the HN structure and hybridization of BPs. 3.2. Mechanical Properties of Coatings The mechanical properties of PUA/PAHS HN and PUA/PAHS/BPs HN were further evaluated. Compared with PUA/PAHS HN, the pencil hardness of PUA/PAHS/BPs HN increased from HB to 3H, which illustrates that BPs improved the hardness of the coating ( Figure 3 a). Meanwhile, the WCA of PUA/PAHS/BPs HN decreased with the increase in BPs content, indicating that the introduction of BPs improved the roughness of the coating. The simultaneous improvement of roughness and hardness through BPs hybridization was beneficial to the scratch resistance of the coating. Since adhesion strength of the coating determines its peeling resistance, pull-off adhesion strength tests of PUA/PAHS/BPs HN and PUA/PAHS HN on polyethylene terephthalate (PET), polycarbonate (PC), polymethyl methacrylate (PMMA) and acrylonitrile-butadiene-styrene terpolymer (ABS) were performed. All substrates were transparent and smooth plates (size 50 mm × 50 mm, thickness 3 mm), and the thickness of the coatings obtained on the different substrates was 50 μm by default. As shown in Figure 3 b, the adhesion strength of PUA/PAHS HN was between 1.7–3.5 MPa, illustrating that topological entanglement and covalent bonding with substrates of polymer HN made the coating firmly adhere on different plastic substrates [ 28 ]. The introduction of BPs further enhanced the adhesion of the coating, indicating that hybridization of BPs improves the topological entanglement between polymers. The coefficient of friction (COF) continued to decline with the increase in BPs content. Compared with the HN coating without BPs, the COF of PUA/PAHS/BPs HN decreased by 68%, indicating that successful hybridization of BPs greatly improved the lubricity of the coating ( Figure 3 c). Benefiting from the lubrication of BPs, the WCA of PUA/PAHS/BPs HN did not rise significantly after 1000 friction test cycles, showing its excellent friction resistance property ( Figure 3 d). These results indicate that the hybridization of BPs can effectively improve the hardness, roughness, adhesion strength and friction resistance of polymer HN. 3.3. Antifogging Performances of Coatings When BPs content of the HN coating was less than 6 wt%, PUA/PAHS/BPs HN had a light transmittance higher than 90% ( Figure S11 ). Considering the transmittance and mechanical properties of the coating, we chose to add 6 wt% BPs into the HN coating thereafter. The sustained antifogging performance of PUA/PAHS/BPs HN was evaluated by the 60 °C hot water vapor test for 60 min. Compared with bare PMMA slide, the PMMA slide coated with PUA/PAHS/BPs HN presented a super-hydrophilic state (WCA = 8°) and had a light transmittance higher than 90% over the 300–800 nm wavelength range under 60 min continuous antifogging test ( Figure 4 a). The optical photographs in Figure 4 b show that the coated PMMA slide did not fog when exposed to hot water vapor (60 °C) for 60 min, while the bare PMMA slide fogged even in the first minute. These results indicate that the BPs hybrid polymer HN coating possesses sustained antifogging ability. In addition, PUA/PAHS/BPs HN on PMMA slides remained highly transparent (optical transmittance over 90%) during seven wet–dry cycles of antifogging tests, illustrating its long-term and stable antifogging performance ( Figure 4 c). To further analyze the influence of BPs on the antifogging performance of the coating, sustained antifogging tests of PUA/PAHS HN and PUA/PAHS/BPs HN with different thicknesses were conducted. As the increase in coating thickness could delay water invasion, thicker PUA/PAHS HN yielded antifogging performance for a longer duration ( Figure 4 d). Unfortunately, it was still difficult to maintain 60 min antifogging duration with the PUA/PAHS HN even with increased thickness. In contrast to the PUA/PAHS HN, the PUA/PAHS/BPs HNs with 5 μm, 20 μm and 50 μm thicknesses were able to maintain over 90% light transmittance when exposed to hot water vapor (60 °C) for 60 min. The optical photographs in Figure 4 e show that antifogging performance of PUA/PAHS HN declined after being immersed in water for 1 day, and completely failed after 5 days. However, the PUA/PAHS/BPs HN still maintained long-term antifogging ability even after being immersed in water for 5 days. These results demonstrate the outstanding water resistance and long-term antifogging performance of PUA/PAHS/BPs HN. To further study the stability of PUA/PAHS/BPs HN, its hydrophilicity was tested after long-term exposure to humid air. The WCA of PUA/PAHS/BPs HN on different substrates did not rise significantly after exposure to air for 6 weeks ( Figure 4 f), while the WCA of PUA/PAHS HN rose significantly after exposure to air for 1 week. In addition, PUA/PAHS/BPs HN had better high and low temperature cycling resistance than PUA/PAHS HN ( Figure S12 ), suggesting that the hybridization of BPs enhanced the thermal stability of HN. These results illustrate that the BPs hybrid polymer HN coating has excellent water-resistant and antifogging performance and outstanding stability, thus achieving its long-term antifogging capability. 3.4. Long-Term Antifogging Mechanism of Coatings Based on these results, the long-term antifogging mechanism of PUA/PAHS/BPs HN is proposed in Figure 5 a. Due to the different surface tensions of BPs and organic polymers, solvent evaporation drives Bénard Marangoni convection during the process of UV curing [ 34 ]. BPs tend to migrate towards the coating surface due to Bénard Marangoni convection, resulting in the generation of microstructure and increased roughness on the surface. Due to excellent water-based lubricity and water absorption capacity, BPs on the surface make it difficult for water to penetrate towards the inside of the coating, preventing antifogging failure induced by water invasion. To verify this assumption, WCA tests, microscopic observations and water penetration tests of PUA/PAHS/BPs HN were conducted. With the increase in UV curing time, the WCA of the HN coating decreased from 23° to 8°, indicating increased roughness of the coatings during UV curing ( Figure 5 b). Optical microscopic observation of the coating surface (about 1 μm thick) before and after UV curing was further conducted. The microscopic photographs of PUA/PAHS/BPs HN in Figure 5 c show that BPs migrated towards the coating surface during UV curing, resulting in enrichment of BPs on the surface. These results prove that Bénard Marangoni convection of BPs and polymers occurred in this study. The improvement of hydrophilicity is attributed to the increase in coating roughness after enrichment of BPs on the surface. To further analyze the water resistance of PUA/PAHS/BPs HN, its water permeability was tested after being immersed in water for 5 days. The water penetration rate of PUA/PAHS/BPs HN decreased with the addition of BPs content and promotion of the mass ratio of PUA to PAHS ( Figure 5 d). The crosslinking density of polymers increased with the addition of PUA content, resulting in a denser HN structure and lower water penetration rate. In addition, the introduction of BPs contributed to blocking the permeation of water, thus enhancing the water resistance of PUA/PAHS/BPs HN in a humid environment. The above results demonstrate the long-term antifogging performance mechanism of the proposed coating."
} | 4,698 |
24860649 | PMC4018179 | pmc | 5,110 | {
"abstract": "Microorganisms are rarely found in isolation. Frequently, they live as complex consortia or communities known as biofilms. The microbes within these complex structures are typically enmeshed in a matrix of macromolecules collectively known as the extracellular polymeric substances (EPS). The last decade has seen enormous growth in the breadth and depth of biofilm-related research. An important area of focus has been the study of pure culture biofilms of different model species. This work has informed us about the different genetic determinants involved in biofilm formation and the environmental conditions that influence the process. These studies have also highlighted both species-specific aspects of biofilm development and common trends observed across many different organisms. This report highlights some exciting findings in recent biofilm-related research.",
"conclusion": "Conclusions Biofilm research continues to reveal interesting aspects of bacterial life on a surface. In this commentary, we have highlighted some of the recent interesting work that indicates that biofilm formation is an intricate process involving the sensing of surface-related stimuli and the production of an extracellular matrix that is surprisingly complex in its structure and function. The early days of biofilm research questioned whether the process involved a developmental program. It is now clear that it does indeed and that there are several general trends emerging that are important for a number of different species. The role of biofilms in disease is becoming clearer, and several newly identified features of biofilms contribute to the process. The next few years should be illuminating as researchers continue to unravel important aspects of biofilm development.",
"introduction": "Introduction Recent work in biofilm research has brought certain topics to the fore. Several labs have made key contributions to our understanding of how surfaces are sensed by bacteria (highlighted in Figure 1 ). These reports relate the signal transduction events involved in linking adherence to a specific physiological response. Scientists are very interested in discerning the molecular mechanisms involved in responding to surfaces as a way to combat biofilm formation for pathogenic species. Figure 1. New events involved in biofilm development A traditional figure depicting different stages of biofilm development. Detailed in letters below are recent discoveries important for this process discussed in the text of the report. This includes the appearance of colony morphology variants that have been isolated from mature biofilms of Pseudomonas aeruginosa . Abbreviations: eDNA, extracellular DNA; PS, polysaccharide; RSCV, rugose small-colony variant. The composition and function of biofilm EPS vary significantly among organisms and this has been the topic of several recent reviews [ 1 - 6 ]. Here, we will summarize recently described, exciting properties of one biofilm matrix component, exopolysaccharides (PSs). Three interrelated topics will be discussed: (a) linking PS structure with biological function; (b) PS signaling properties and (c) the roles of PSs in promoting biofilm morphology and behavior. In addition, the importance of the secondary messenger, cyclic dimeric guanosine monophosphate (cyclic-di-GMP), in controlling the transition between biofilm and planktonic lifestyles is becoming increasingly apparent for many Gram-negative species [ 7 - 9 ]. The appearance of genetic variants with altered colony morphologies has been reported for a number of bacterial species and these variants have been linked to disease, cyclic-di-GMP signaling, and altered PS expression profiles. This short report will also address these themes with a focus on the model species Pseudomonas aeruginosa ."
} | 943 |
34522307 | PMC8386638 | pmc | 5,111 | {
"abstract": "The molecular level transfer of stress from a stiff percolating filler to a stretchable matrix is a crucial and generic mechanism of toughening in soft materials. Yet the molecular details of how this transfer occurs have so far been experimentally unreachable. Model multiple network elastomers containing spiropyran (SP) force sensors incorporated into the stiff filler network or into the stretchable matrix network are used here to detect and investigate the mechanism of stress transfer between distinct populations of polymer strands. We find that as the filler network progressively breaks by random bond scission, there is a critical stress where cooperative bond scission occurs and the macroscopic stretch increases discontinuously by necking. Surprisingly, SP molecules reveal that even in the necked region both filler and matrix chains share the load, with roughly 90% of the SPs force-activated in the filler chains before necking still being loaded in the necked region where significant activation of the SP incorporated into the matrix chains occurs. This result, where both networks remain loaded upon necking, is qualitatively consistent with the model proposed by Brown, where holes or microcracks are formed in the stiff regions and are bridged by stretched matrix chains. Detection of merocyanine ( i.e. activated SP) fluorescence by confocal microscopy shows that such microcrack formation is also active at the crack tip even for materials that do not exhibit macroscopic necking. Additionally, we demonstrate that when the ethyl acrylate monomer is replaced by hexyl methacrylate in the first network, preventing molecular connections between the two networks, the stress transmission is less efficient. This study outlines the different roles played by these multiple networks in the onset of fracture and provides molecular insights for the construction of molecular models of fracture of elastomers.",
"conclusion": "3. Conclusion The incorporation of a force-sensitive probe into multiple network elastomers has been used to shed light on the toughening mechanisms of double network gels and multiple network elastomers. Four groups of multiple network elastomers that exhibit yielding in uniaxial extension have been synthesized by incorporating SP into the first, second, and third networks, respectively. The color change of SP observed under strain confirms that the filler network first sustains the full load and only transfers into the second network at the point where macroscopic necking is observed. However, this stress transfer is only partial as roughly 9% of the filler network chains are broken at the yield point despite the large jump in stretch, i.e. the first network still holds the main load. In quadruple networks, a high level of stress in the third network is not observed. This result points at a scenario of microcracks as proposed by Brown, rather than a scenario of islands as proposed by Gong for double network gels. It should be pointed out, however, that these details may be dependent on the network architecture. Moreover, when the interconnection between the first and the second networks owing to the chain transfer reaction is altered, by replacing ethyl acrylate with hexyl methacrylate, the stress transmission is delayed and the second network does not activate the SP until the whole specimen is necked. Finally, since MC is fluorescent, the activation of SP into MC can also be detected by confocal microscopy revealing that the stress transfer does occur over a localized region in front of the crack tip even in materials that do not undergo macroscopic necking in uniaxial extension. This novel picture of fracture of tough elastomers, revealing the existence of a damage zone at the crack tip with alternating soft and stiff regions, may be much more general than for our model system and may also provide some helpful ideas for more conventional elastomers filled with percolating nanofillers.",
"introduction": "1. Introduction Elastomers are widely used in industrial applications because of their unique combination of fully reversible deformation and high fracture toughness. The classic strategy to increase their stiffness and improve their fracture toughness while maintaining a high reversible elasticity involves incorporating nanofillers, 1–5 mainly carbon black 6,7 and nanosilica 8,9 in industry and more recently graphene 10–12 and carbon nanotubes 10 at the academic level. Yet, these nanofillers aggregate and scatter light, making the preparation of tough, transparent, and highly elastic elastomers (with the notable exception of strain-crystallizing natural rubber) still challenging. In the last 20 years new materials have been designed that might just fill that gap, 13–16 and some generic multiscale toughening strategies have been proposed. 1,17 These strategies involve the incorporation of sacrificial bonds, covalent 14,15,18,19 or more commonly non-covalent, 16,20–24 to increase the energy needed to grow a crack, 25 as well as the extensibility and fracture toughness. Microscopic 26,27 and continuum mechanical models 28,29 based on interpenetrated networks have been proposed to describe the toughening mechanism but while these models are qualitatively consistent with the observed behavior, they are based on untested molecular hypotheses. The key question can be appreciated in the following way: for the toughening of interpenetrated networks to be effective there needs to be a mechanism to transfer the load from a stiff to a stretchable structure capable of sustaining the load without catastrophic failure. Intuitively, the more delocalized and random the process of failure of the stiff structure is, the less likely is the creation of large weak spots that eventually nucleate cracks. Yet the molecular requirements to obtain that random weakening without creating macroscopic cracks remain unknown for lack of suitable experimental techniques and model systems. The design of tough unfilled elastomers based on multiple networks, 19,30–32 synthesized via an approach pioneered in hydrogels by Gong and coworkers, 14,33–35 bears some clear resemblance to the design of nanofilled elastomers (where the role of the stiff network is played by the percolating network of nanofillers 2 ) and may be a good model system to investigate their toughening at the molecular level. In multiple network elastomers, the first network is highly crosslinked and acts as a percolating filler ( i.e. , stiff structure) into a loosely crosslinked matrix ( i.e. , stretchable structure). Thus, the covalent bonds in the first network serve as sacrificial bonds that dissipate energy when ruptured during loading. In a particularly insightful model, 26 Brown proposed the following idea to explain why such a molecular design would lead to toughening: at a critical “yield stress” internal cracks nucleate in the stiff structure creating damaged zones but these zones do not coalesce, like crazes in glassy polymers. 36 At the critical point, final failure occurs because the damage zone becomes sufficiently wide to create a stress concentration at the damage zone tip and grow a crack in the damaged region. Such a model is based on the growth of cracks into a craze (a plasticity driven damage zone) into a polymer glass 37,38 and is qualitatively consistent with the observed toughening but details remain difficult to verify experimentally. Thanks to the development of mechanochemistry as an effective tool to detect the force and scission of polymer chains, we can revisit the question of molecular fracture in elastomers. The molecules used in mechanochemistry, called mechanophores, are force-sensitive and can convert mechanical stimuli into observables like luminescence, 39–41 fluorescence, 42 and color. 43 Spiropyran (SP), a classic mechanochromic mechanophore, has in particular attracted lots of attention due to its distinct change in color and fluorescence upon activation and has been used as a molecular probe to sense stress and show damage inside polymeric materials by many groups, including those of Moore and Sottos, 43–45 Craig, 46,47 Weng 48–51 and Qiao. 52 When SP is incorporated into a polymer chain and elongated using single molecule force spectroscopy (SMFS) (tip velocity of 300 nm s −1 ), it turns into merocyanine (MC) at forces above 240 pN. 53 Although in our acrylate material the activation force may be a bit different, we did not observe any strain rate dependence of the activation (see Fig. S5 † ) and will consider that this is a reasonable approximation. In addition to the examples given above, the mechanosensitivity of SP and of MC has also been used to map the stress history of elastomers, 32 as these turn from colourless to blue upon loading and from blue to purple upon unloading. While the colorless-to-blue transition is clearly due to the formation of MC, 43,53,54 the molecular origin of the blue-to-purple transition upon unloading (blue in the loaded state and purple in the unloaded state) has been suggested to be the isomerization of MC because of its low activation energy 32,46,55 but direct spectroscopic proof has not been reported. In a previous report, 15 the luminescence of a dioxetane mechanophore used as a crosslinker in multiple network elastomers showed conclusively the occurrence of bond scission during uniaxial extension and crack propagation. This molecular evidence was instrumental in proving that the progressive increase in size of the region around the crack tip where bond scission (and hence energy dissipation) occurs was an effective mechanism to increase the fracture energy of elastomers. Yet the limited spatial resolution of the experiments precluded more advanced interpretations on how the load redistributes among the undamaged regions upon bond scission. The question of how the load redistributes upon damage of the first network was first experimentally addressed in analogous double network (DN) hydrogels by Gong and coworkers. 33 They observed that some DN hydrogels exhibited a stable necking process in uniaxial extension 33 and upon unloading and reloading, the necked region significantly softens and has an elastic modulus and tensile behavior almost identical to that of the second network alone. Based on this evidence, they proposed a mechanism of transfer of load from the progressively broken stiff filler network to the stretchable matrix. 33,35 Intrigued by this unusual behavior, they investigated the homogeneity of the structure in the neck by Small Angle Neutron Scattering (SANS) by deuterating the matrix network. 56 A pronounced scattering peak revealed the existence of matrix concentration inhomogeneities along the tensile direction spaced by a characteristic length scale around 1.5 μm. Yet the applied strain was only 50% and in a pure shear geometry making it difficult to ascertain that this SANS length scale was the same as that potentially formed in the necked region in uniaxial tension at strains of more than 500%. Nevertheless based on the mechanical macroscopic evidence, Gong and coworkers suggested a plausible mechanism of DN fracture where the stiff filler network breaks in separate islands that are physically connected by the second network as schematically illustrated in Fig. 1 . 33,35,57,58 Such hypothesis, also based on the very heterogeneous structure of the gel filler network of poly(2-acrylamido-2-methyl-1-propanesulfonic acid) as detected by light scattering, 59 implies that most of the filler network chains in the island should be unloaded after necking. Fig. 1 Scheme of the network structure of the DN gel before (a) and after (b) necking as seen by Gong. Above a critical stress, the filler network fractures into separate islands and the DN gel becomes much softer after the necking. Reprinted with permission from ref. 35 . Our own group discovered that some multiple network elastomers also undergo elastic necking in uniaxial tension 19 and addressed the question of bond scission, and indirectly load transfer. In contrast to the case of DN gels, we found that the necked region in the elastomers was softer than the original undamaged material but still stiffer than the matrix network alone. Additionally using the time resolved capability of the dioxetane mechanoluminescent crosslinkers we quantitatively compared the sacrificial bond scission events of the first network occurring up to the necking point, and during the propagation of the necking front. To our surprise as many sacrificial bonds per unit volume appeared to break before the necking point (in a regime where the material does not soften much) than during the propagation of the necking front (where the material softens and increases its extensibility dramatically). These observations both suggest that the picture of breaking into well-separated islands proposed by Gong for gels may not be general or be too simplistic. More recently, we labeled the filler network of multiple network elastomers with SP and quantified the stress ahead of the crack tip before propagation using the color change from (i) colorless to blue upon loading, 31 and (ii) blue to purple upon unloading. 32 We found that the shade of purple depended both on the concentration of MC formed upon loading and on the average force on the chains. Note that such a connection between the network architecture and macroscopic activation stress was also carried out in similar materials by Qiao et al. to demonstrate that an increasing prestretch of the filler network leads to an activation of SP into MC at a lower stretch level. 52 Here we use SP mechanophores to gain direct insight into the average force experienced by the crosslinkers of multiple network elastomers. The novelty of the present work is that we relate the force-induced molecular activation of SP to the load sustained by the crosslinkers of the filler and matrix networks in uniaxial extension and ahead of the crack tip prior to crack propagation. This information is unprecedented and instrumental to build a molecular model of stress transfer in tough elastomers."
} | 3,537 |
31187921 | null | s2 | 5,112 | {
"abstract": "Magnetotactic bacteria (MTB) are ubiquitous aquatic microorganisms that mineralize dissolved iron into intracellular magnetic crystals. After cell death, these crystals are trapped into sediments that remove iron from the soluble pool. MTB may significantly impact the iron biogeochemical cycle, especially in the ocean where dissolved iron limits nitrogen fixation and primary productivity. A thorough assessment of their impact has been hampered by a lack of methodology to measure the amount of, and variability in, their intracellular iron content. We quantified the iron mass contained in single MTB cells of Magnetospirillum magneticum strain AMB-1 using a time-resolved inductively coupled plasma-mass spectrometry methodology. Bacterial iron content depends on the external iron concentration, and reaches a maximum value of ~10"
} | 209 |
30930534 | null | s2 | 5,114 | {
"abstract": "Despite the common association of π-conjugated polymers with flexible and stretchable electronics, these materials can be rigid and brittle unless they are designed otherwise. For example, low modulus, high extensibility, and high toughness are treated as prerequisites for integration with soft and biological structures. One of the most successful and commercially available organic electronic materials is the conductive and brittle polyelectrolyte complex poly(3,4-ethylenedioxythiophene):poly(styrene sulfonate) (PEDOT:PSS). To make this material stretchable, additives such as ionic liquids must be used. These additives may render the composite incompatible with biological tissue. In this work, we describe the synthesis of an intrinsically stretchable variant of the conductive polymer PEDOT:PSS that is free of additives. The approach involves the synthesis of a block copolymer comprising soft segments of poly(polyethylene glycol methyl ether acrylate) (PPEGMEA) and hard segments of poly(styrene sulfonate) (PSS) using a reversible addition-fragmentation chain transfer (RAFT) polymerization. Subsequently, we used the newly synthesized ionic elastomer PSS-"
} | 292 |
31699148 | PMC6839119 | pmc | 5,116 | {
"abstract": "Background Rhizosphere microbial communities are key regulators of plant performance, yet few studies have assessed the impact of different management approaches on the rhizosphere microbiomes of major crops. Rhizosphere microbial communities are shaped by interactions between agricultural management and host selection processes, but studies often consider these factors individually rather than in combination. We tested the impacts of management (M) and rhizosphere effects (R) on microbial community structure and co-occurrence networks of maize roots collected from long-term conventionally and organically managed maize-tomato agroecosystems. We also explored the interaction between these factors (M × R) and how it impacts rhizosphere microbial diversity and composition, differential abundance, indicator taxa, co-occurrence network structure, and microbial nitrogen-cycling processes. Results Host selection processes moderate the influence of agricultural management on rhizosphere microbial communities, although bacteria and fungi respond differently to plant selection and agricultural management. We found that plants recruit management-system-specific taxa and shift N-cycling pathways in the rhizosphere, distinguishing this soil compartment from bulk soil. Rhizosphere microbiomes from conventional and organic systems were more similar in diversity and network structure than communities from their respective bulk soils, and community composition was affected by both M and R effects. In contrast, fungal community composition was affected only by management, and network structure only by plant selection. Quantification of six nitrogen-cycling genes ( nifH , amoA [bacterial and archaeal], nirK , nrfA , and nosZ ) revealed that only nosZ abundance was affected by management and was higher in the organic system. Conclusions Plant selection interacts with conventional and organic management practices to shape rhizosphere microbial community composition, co-occurrence patterns, and at least one nitrogen-cycling process. Reframing research priorities to better understand adaptive plant-microbe feedbacks and include roots as a significant moderating influence of management outcomes could help guide plant-oriented strategies to improve productivity and agroecosystem sustainability.",
"conclusion": "Conclusions Agricultural management and plant selection are known to be powerful influences on microbial community assembly, and our work shows that their interaction results in plant recruitment of management-system-specific taxa and shifts in microbial networks and at least one N-cycling pathway in the rhizosphere. Our finding that agricultural management practices impact rhizosphere microbial communities differently from the bulk soil should be used to guide research priorities and management decisions. The rhizosphere should be recognized as an integral component of sustainable agriculture research that behaves uniquely in comparison to bulk soil. On one hand, plant effects are often neglected in studies investigating how fertilization, tillage, crop rotations, or other management practices affect microbial communities, but valuable insight can be gained from analyzing both bulk and rhizosphere samples. Measuring responses of the bulk soil microbial community can help predict rates of biogeochemical processes at the field, landscape, or ecosystem scale [ 85 , 86 ]. When plant outcomes such as agricultural productivity are of interest, however, the rhizosphere microbes that are so tightly linked to the health of their host are of critical importance. On the other hand, plant-centric rhizosphere engineering and plant breeding efforts to capitalize on beneficial plant-rhizosphere microbe interactions should not overlook how agricultural management may modify their intended impacts. Understanding and optimizing the interactive effects of management and plant processes regulating rhizosphere assembly provides untapped opportunities to develop more sustainable and productive agroecosystems.",
"discussion": "Discussion We asked how agricultural management and plant roots act individually and in combination to shape microbial community composition, co-occurrence patterns, and N-cycling functions, and whether this interaction leads to system-specific adaptation. In accordance with known management and rhizosphere effects on microbial community structure and N dynamics in agroecosystems, we observed conventional/organic and bulk/rhizosphere differences in many of the parameters measured. Furthermore, many of our analyses supported the hypothesis that plant selective influence varies with management (an M × R interaction) to shape plant-associated microbial community composition and structure (Fig. 1 ). Management, rhizosphere, and M × R effects on microbial communities are likely mediated in large part by soil physicochemical properties, which differed between management systems and soil compartments (Additional file 2 : Table S1). Strong effects of management on soil physicochemical properties were visible in the higher NO 3 -N, P, K, Ca, Na, and SOM levels in the organic system and higher Mg and pH in the conventional system. Rhizosphere soil was depleted in NO 3 -N, P, and K in both management systems. M, R, and M × R effects on soil properties such as nutrient availability, pH, and organic matter likely contribute greatly to microbial community assembly in these treatments. Significant differences in the direction or magnitude of the rhizosphere effect were observed for bacterial diversity, community composition, and indicator species (Additional file 8 : Figure S1, Additional file 9 : Figure S2, Additional file 10 : Figure S3). Plant roots consistently imposed a strong selective filter, and similarity between rhizosphere communities (CR-OR) was greater than similarity between bulk soil communities (CB-OB). Nevertheless, rhizosphere communities still reflected the impacts of management on the contributing microbial pool, and rhizosphere communities were more similar to their corresponding bulk soil communities (CB-CR, OB-OR) than to one another (CR-OR). The direction of the rhizosphere effect varied with management for bacterial diversity, indicator species, and community structure. This M × R interaction resulted in rhizosphere bacterial communities that were more similar in diversity, composition, and structure than bulk soil bacterial communities. Rhizosphere bacterial/archaeal diversity was lower in the organic rhizosphere but higher in the conventional rhizosphere compared to bulk soil (Additional file 8 : Figure S1a). Although roots are often thought to impose a selective filter that decreases diversity, higher species richness in the rhizosphere as observed here in the conventional system has been reported elsewhere when plants select for enrichment of certain processes [ 49 ]. Here, however, whether functional enrichment is related to selection for increased diversity is unclear. Environmental filtering may account for the fact that bacterial rhizosphere networks were more similar than bulk soil networks. Although it has been hypothesized that niche sharing should lead to greater co-occurrence and thus more densely connected networks in the rhizosphere [ 50 ], this effect was seen only in the bacterial organic networks (Fig. 5 , Table 1 ). Viewed in combination with previous work showing smaller, less densely connected networks in rhizosphere soil [ 3 , 19 – 21 ], our results suggest that rhizosphere effects on co-occurrence networks, like other metrics of microbial community structure, may well be context- and system-dependent. The magnitude of plant effects on rhizosphere communities also differed between management systems. We generally found greater differences between bulk and rhizosphere community composition in conventional soils compared to organic (Figs. 2 , 3 , and 4 ). Hartman et al. attribute a similar M × R interaction observed in their study of wheat agroecosystems to the application of management practices immediately before root establishment [ 44 ]. This explanation may apply here as well, specifically with regard to the spatial scale of cover crop and fertilizer inputs. Inorganic fertilizer (conventional system) and composted poultry manure (organic system) were trenched in seed beds and therefore near crop roots, likely favoring divergence of bulk soil and rhizosphere microbial communities. Since cover crops were sown throughout the organic plots, cover-cropping-induced changes in microbial community composition were likely similar in the bulk soil and early root zone, whereas emerging roots in the conventional plots would likely have encountered a fertilizer-enriched zone already distinct from most of the bulk soil. We further hypothesized that rhizosphere communities would be enriched in system-specific beneficial taxa and functions of importance for plant adaptation to system-specific soil conditions. Although indicator species analysis revealed system-specific taxa, we cannot definitively conclude whether these taxa are beneficial based on amplicon sequencing data. Three members of the order Myxococcales (identified as the genera Phaselicystis , Archangium , and Myxococcus ) and two members of the order Burkholderiales (identified as the genera Rhizobacter and Achromobacter ) were indicators of organic environments, in line with previous studies showing these orders to be organic-system-specific [ 8 , 51 ] (Additional file 4 : Table S3). Two strains of the Anaerolineales, an order that displaces other fermenters under high-nitrate conditions [ 52 ], were indicators of the conventional system. Broad ecological information about soil fungi is limited in comparison to bacteria and archaea, despite extensive specialized literature on pathogens of humans and plants or AMF and other endophytes [ 53 ]. Many fungal indicators identified here belong to genera known to be pathogenic on other host species, and these were relatively evenly distributed among environments. The significance of pathogens as indicator species in these systems is unclear, especially for pathogens such as Boeremia exigua , which causes leaf spot on diverse host crops including tomato, the other crop in this rotation [ 54 ], but is not known to cause disease in maize. Fewer details of metabolism and ecology are available for non-pathogenic fungal indicators. Mortierella , the most common genus among fungal indicators in this study, are known to be a large genus of saprotrophs [ 55 ]. Exophiala equina and Didymella sp. have been reported elsewhere to be associated with plant roots [ 56 , 57 ]. Fungi are critical drivers of C/N cycling [ 58 , 59 ] and carbon sequestration [ 60 ] in agricultural systems, and linking specific taxa to roles beyond pathogenic interactions will be a valuable expansion of the existing literature. With regard to N-cycling functions, we quantified six genes involved in different steps of the nitrogen cycle, all of which were affected by plant selection and only two of which were differentially selected between systems (Fig. 6 ). The relative abundance of genes relative to one another was similar across treatments, suggesting that no system-specific bottlenecks in the N cycle were observed (Additional file 11 : Figure S4b). The abundances of the nifH , amoA (both archaeal and bacterial), nirK , nirS , and nosZ genes were higher in the bulk soil, in contrast to previous studies that found the maize rhizosphere was enriched in functional genes related to nitrogen fixation ( nifH ), nitrification ( amoA , hao ), and denitrification ( narG , nirS / nirK , norB , nosZ ) [ 35 – 37 ]. That effect was also observed with the addition of artificial maize root exudates [ 61 ], suggesting that exudates are the main mechanisms influencing microbial N cycling independently of other physicochemical characteristics of the rhizosphere. However, mechanisms other than exudates may be responsible for the discrepancy in the direction of the rhizosphere effect between the present study and the literature: while certain root exudates inhibit nitrification in wheat, sorghum, and rice, this effect has not been shown in maize [ 62 ]. Sampling in the present study occurred during the silking period of maize, when crop N uptake reaches a maximum. The rhizosphere may be N-depleted in comparison to bulk soil, and microbial N limitation may account for the decreased abundance of these N-cycling genes. Differences in soil organic matter or shifts in root exudation during development [ 63 ] leading to altered rhizosphere carbon availability may also account for the change in direction of the rhizosphere effect in the present study as compared to the literature. Increased sampling frequency over the course of the growing season paired with metabolomic analysis of root exudates would provide insight into the mechanisms linking root C release and N uptake dynamics to microbial N-cycling gene abundances. We hypothesized that differences in N-cycling gene abundance between conventional and organic systems would reflect adaptive shifts, increasing the abundance of gene pathways linking system-specific N inputs to plant-available species, but this hypothesis was not supported. Only two of six genes were affected by soil management history. The abundance of the nosZ and bacterial amoA genes, the only genes affected by the M × R interaction, was higher in the organic system (Fig. 6 ). The increase in abundance of the nosZ gene could potentially indicate greater conversion of N 2 O to N 2 and decreased greenhouse gas production [ 64 ], while increased abundance of the amoA gene may reflect increased conversion of ammonium to nitrite and subsequent nitrification products. Higher soil carbon as a result of long-term organic matter applications at this site [ 65 ] may contribute to higher abundances of the nosZ gene in bulk and rhizosphere soil in this system. Putz et al. found that higher soil organic carbon under a ley rotation increased expression of the nrfA and nosZ genes relative to the nirK gene as compared to a conventional cereal rotation, favoring higher rates of dissimilatory nitrate reduction to ammonium and lower rates of denitrification [ 66 ]. However, previous work in the treatments examined in the present study found that abundances of the amoA and nosZ genes were not correlated with gross rates of N transformation processes [ 29 ]. Prediction of cropping system impacts on microbial N cycling therefore requires a nuanced integration of gene abundances with parameters such as carbon availability, moisture content, and temperature within soil aggregate microenvironments over time. That few differences were observed late in the growing season between N-cycling genes in systems receiving organic or inorganic N inputs is consistent with the results of a meta-analysis by Geisseler and Scow [ 32 ], which found that N fertilizer impacts on microbial communities tend to fade over time. Sampling occurred at silking in the present study, long after the preplant fertilizer and compost applications that likely maximize differentiation between systems. Potential N limitation in the rhizosphere in both systems may also have outweighed management effects. Co-occurrence networks, which provide insight into ecological interactions among microbial taxa [ 10 ], were influenced by M, R, and M × R effects. Bulk and rhizosphere bacterial networks from the conventional system had the same number of nodes but were more densely connected than networks from the corresponding soil compartment in the organic system (Fig. 5 ). Other bulk soil comparisons of organic and conventional agroecosystems using networks constructed from OTU-level data have found conventional networks to have more nodes [ 13 ] or, alternatively, fewer nodes and edges than organic networks [ 14 , 15 ]. Clearly, predicting co-occurrence patterns of incredibly diverse microbial communities based on a conventional-versus-organic classification is too simplistic. Agricultural management is likely better represented as a continuum (or continua along multiple axes) than discrete categories, and causal relationships between specific practices and network topological properties have yet to be determined. An M × R interaction was also observed for network properties in which size, density, and centralization were lower in the rhizosphere network from the conventional system than from the organic system (Fig. 5 , Table 1 ). These network properties follow the same pattern as alpha diversity of bacterial communities, suggesting a shared yet perplexing cause: while the mechanism remains unclear, rhizosphere communities appear to be converging from very distinct bulk soils towards similar diversity and structural metrics. Conventional agriculture is hypothesized to disrupt the connections between bulk soil and rhizosphere networks, as tillage and mineral fertilization are proposed to disturb fungi and soil fauna that serve as a bridge between bulk soil and rhizosphere environments [ 50 ]. While tillage does not differ between the systems we measured, fertilization effects are likely partly responsible for the observed interaction. Regardless of the mechanisms involved, the system-specific direction of the rhizosphere effect on co-occurrence network properties suggests that management and plant influence interactively determine not only which taxa are present, but how they interact, with potential implications for agriculturally relevant functions and ecological resilience. Hub ASVs were identified in each network based on high values for normalized betweenness centrality, a metric often used to describe keystone taxa. Organic networks had lower normalized betweenness centrality values than conventional networks (Additional file 6 : Table S5). Lower betweenness centrality values for hub taxa may indicate that network structure depends less on individual species, potentially increasing resilience to environmental stresses that could destabilize networks overly dependent on hub taxa sensitive to those specific stresses. Different hub ASVs were identified in each rhizosphere environment, but information on the ecology of these taxa is generally absent from the literature. Although it would be misleading to state that these taxa are keystone species in their respective habitats without experimental validation [ 48 ], the fact that many of these taxa were also identified through indicator species analysis (Additional file 6 : Table S5, bold) suggests that they play important ecological roles. Future work could explore the genomes of these ASVs to discern why they are important in their respective agricultural systems and test the hypothesis that they serve as keystone species using synthetic communities. Concluding whether adaptive plant-microbe feedbacks result in an M × R interaction leading to shifts in other rhizosphere processes is complicated by the importance of poorly understood fungal communities and methodological limitations of this study. Numerous fungal taxa respond to the M × R interaction according to our differential abundance analysis (Fig. 4 ), yet knowledge of these taxa remains limited due in part to the constraints of culture-dependent methods prevalent in the past. Nonetheless, fungi influence inter-kingdom interactions and agriculturally relevant processes in the rhizosphere [ 67 ], and novel molecular biology tools could be used to improve our understanding of key fungal regulators identified in these analyses [ 68 ]. Metagenomics and -transcriptomics would facilitate a much more comprehensive analysis of potential functional shifts. A highly useful starting point would be to delve into dynamic variation in microbial genes involved in carbon metabolism and nitrogen cycling in the rhizosphere, in combination with root exudate metabolomics and measurements of root N uptake. Stable isotope labeling and in situ visualization methods [ 69 – 73 ] could further complement our understanding of how management, plant roots, and their interactive effects shape rhizosphere processes. The scope of this study was intentionally restricted to a single genotype of one crop in two management systems to limit the main sources of variation to the management and rhizosphere effects that were of primary interest, but the limits to inference of this small-scale study must be considered. Other studies in maize have found that strong legacy effects of soil management history are generally acted upon in a similar manner by two maize cultivars [ 74 ] and that rhizosphere bacterial community composition varies only slightly among hybrids from different decades of release [ 75 ]. Testing whether these limited effects of plant selection hold true for additional contrasting genotypes and genetic groups of maize would further complement this work. Furthermore, variation in root system architecture across crop genotypes might interact with tillage and soil properties responsive to management effects. Management practices such as the inclusion of forage or cover crops planted in stands rather than rows might affect the differentiation of bulk and rhizosphere soil uniquely from systems based on perennial crops, successive plantings of row crops in the same locations, and/or minimal tillage. Study designs incorporating more genotypes, management systems, and cultivation environments would therefore be highly useful to test how results of this study may be extrapolated to other settings. Future studies should also identify functional genes that are upregulated or downregulated in the rhizosphere under specific agricultural management practices. Whether such functional shifts are adaptive will provide insight into the relationship between agroecology and ecology. Positive eco-evolutionary feedbacks resulting in adaptive microbial communities have been described in unmanaged ecosystems, for example, habitat-adapted symbiosis in saline or arid environments [ 76 , 77 ]. If similar adaptive recruitment can occur with annual crops in the context of agroecosystems, maximizing this process should be added to the list of rhizosphere engineering strategies and targets for G × E breeding screens [ 78 , 79 ]. Finally, while our results provide evidence that management and plant influence interact to shape microbial communities at one sampling point, we highlight the need to reframe the M × R interaction as a dynamic process. Rhizosphere communities may be more different from one another than bulk soil communities because roots develop right after tillage and fertilization, when management systems are most distinct [ 44 ]. Plants are not static entities, but active participants in the ongoing process of rhizosphere recruitment. As an alternative to the “rhizosphere snapshot,” we propose a “rhizosphere symphony” model that acknowledges the active role of root exudates in orchestrating the composition and function of microbial communities. Altered root exudation during development [ 63 ] and in response to water [ 80 ] and nutrient limitation [ 81 ] can upregulate or downregulate microbial taxa and functions, as a conductor brings together different sections of instruments in turn during a symphony. Although it is unknown whether this plasticity in exudate composition occurs in response to agricultural management, observations of changed exudate quantity and quality in response to soil type [ 82 ] (perhaps mediated by the associated microbial communities [ 83 ]) and long-term N fertilization [ 84 ] suggest that it is possible. Differences in the timing of nutrient availability between management systems, such as delayed N release from cover crop mineralization compared to mineral fertilizer, could thus result in management-system-specific exudate dynamics and rhizosphere microbial communities, i.e., an M × R interaction. If true, this mechanism suggests that we may be able to manipulate the sound of the symphony by talking to the conductor: plant-driven strategies may be instrumental in maximizing beneficial rhizosphere interactions throughout the season."
} | 6,057 |
36882570 | PMC9992702 | pmc | 5,118 | {
"abstract": "The oscillating redox conditions that characterize coastal sandy sediments foster microbial communities capable of respiring oxygen and nitrate simultaneously, thereby increasing the potential for organic matter remineralization, nitrogen (N)-loss and emissions of the greenhouse gas nitrous oxide. It is unknown to what extent these conditions also lead to overlaps between dissimilatory nitrate and sulfate respiration. Here, we show that sulfate and nitrate respiration co-occur in the surface sediments of an intertidal sand flat. Furthermore, we found strong correlations between dissimilatory nitrite reduction to ammonium (DNRA) and sulfate reduction rates. Until now, the nitrogen and sulfur cycles were assumed to be mainly linked in marine sediments by the activity of nitrate-reducing sulfide oxidisers. However, transcriptomic analyses revealed that the functional marker gene for DNRA (nrfA) was more associated with microorganisms known to reduce sulfate rather than oxidise sulfide. Our results suggest that when nitrate is supplied to the sediment community upon tidal inundation, part of the sulfate reducing community may switch respiratory strategy to DNRA. Therefore increases in sulfate reduction rate in-situ may result in enhanced DNRA and reduced denitrification rates. Intriguingly, the shift from denitrification to DNRA did not influence the amount of N 2 O produced by the denitrifying community. Our results imply that microorganisms classically considered as sulfate reducers control the potential for DNRA within coastal sediments when redox conditions oscillate and therefore retain ammonium that would otherwise be removed by denitrification, exacerbating eutrophication.",
"introduction": "Introduction The permeable sandy sediments that fringe coastlines act as highly effective biocatalytic filters that remineralize organic carbon, and remove fixed nitrogen through denitrification [ 1 – 5 ]. The microbial communities that catalyze biogeochemical transformations in permeable sediments are subjected to frequent oscillations in electron acceptor supply, wherein the depth to which oxygen and nitrate penetrate the sediment can change in minutes [ 6 – 10 ]. These oscillations are due to changes in porewater advection resulting from changing tidal currents, waves, the shape of sandbed surfaces, and bio-turbation and bio-irrigation [ 4 , 11 , 12 ]. On longer time scales high currents and storm events mobilize sandy sediments, redistributing sand grains and their attached microorganisms between sediment layers [ 13 – 17 ]. Many of the microorganisms within permeable sediments appear to be adapted to the oscillating oxic and anoxic conditions [ 18 ]. Such adaptations include metabolic specialization of organisms involved in the process of denitrification, which leads to the removal of nitrate but also substantial nitrous oxide emissions [ 19 , 20 ]. Furthermore, rapid shifts in redox conditions and electron acceptor availability result in microorganisms simultaneously using terminal oxidases and N-reductases. This leads to the co-occurrence of denitrification and aerobic respiration processes, which are typically spatially or temporally separated in diffusion limited sediments [ 10 , 18 , 19 ]. Potentially, sulfate reduction and nitrate reduction may also occur simultaneously in surface sediments where nitrate is intermittently supplied [ 21 ], or even sulfate reduction and oxygen respiration. However, the potential interactions between simultaneous sulfate reduction and pathways of nitrate reduction in permeable sediments remain unexplored. Typically, microorganisms in marine sediments employ different electron acceptors over depth, largely in accordance with their decreasing energy yield, which often leads to an apparent spatial separation of sulfate reduction from nitrate reduction [ 22 – 24 ]. This separation is likely maintained by competitive exclusion, wherein N-reducers outcompete sulfate reducers because they conserve more energy per electron donated [ 24 – 27 ]. Furthermore, nitrite accumulation, which has been observed to occur due to metabolic specialization in sands [ 20 ], can also competitively inhibit sulfite reductase, an enzyme crucial for sulfate reduction [ 28 , 29 ]. Nevertheless, sulfate reduction and denitrification can be linked via microbial activity [ 22 ]. For example, microbes can bridge the distance between sulfidic and nitrate-rich sediment by either migration [ 30 ] or electrogenic pili and perform sulfide oxidation coupled to nitrate reduction [ 31 , 32 ]. When nitrate reduction is coupled to complete sulfide oxidation, sulfate reduction can therefore be underestimated [ 33 – 36 ]. Several lines of evidence suggest that sulfate reduction in intertidal permeable sediments should be tolerant of nitrate. Sulfate reducers are present and highly active in the upper layers of sediment, even though this area is frequently exposed to both nitrate and oxygen [ 6 , 37 – 40 ]. A recent study has shown that sulfate reducing bacteria have higher acetate assimilation rates in the uppermost sediment layer than in deeper sediment layers [ 41 ]. Furthermore, in chemostat enrichments of intertidal permeable sediments, sulfide produced from sulfate reducers fueled denitrifier and ammonifier populations [ 42 , 43 ]. Together, these studies suggest that sulfate reducers in permeable intertidal sediments can coexist with denitrifying microorganisms and could be adapted to, rather than inhibited by, frequent exposure to nitrate and even oxygen. The co-occurrence of nitrate and sulfate respiration has the potential to impact N-removal pathways. For example, the presence of sulfide has previously been predicted to lead to higher nitrous oxide emissions during denitrification [ 44 ], and might enhance emissions of this potent greenhouse gas from permeable sediments. The occurrence of sulfate reduction might also alter the balance between denitrification and dissimilatory reduction of nitrate/nitrite to ammonium (DNRA), a process which retains fixed N in coastal systems rather than removing it. For example, the oxidation of sulfide produced by sulfate reduction has recently been linked to the DNRA community rather than the denitrifying community in coastal salt marsh sediments [ 45 ]. The link between sulfate and nitrate respiration could also be more direct, as many organisms that are traditionally thought of as sulfate reducing bacteria also have the potential to reduce nitrite to ammonium. Of these, some have been shown to switch to canonical DNRA when nitrate becomes available and use the pathway to support growth [ 46 , 47 ], while others continue to preferentially reduce sulfate in the presence of oxidized N-compounds [ 48 – 50 ]. The reduction of nitrite to ammonia can be also catalyzed by sulphite reductase itself, although this conversion likely has no physiological benefit [ 28 , 29 ]. Organisms such as Desulfovibrio vulgaris can prevent this competitive inhibition of sulphite reductase via constitutive expression of periplasmic cytochrome c nitrite reductase (Nrf) to remove the nitrite, although there are contrasting reports as to whether this is also linked to energy generation [ 51 , 52 ]. In this study we hypothesised that the dynamic conditions typical for intertidal permeable sediments lead to simultaneous nitrate and sulfate respiration, analogous to previous observations of simultaneous aerobic and anaerobic respiration [ 18 ]. Furthermore, we investigated whether the co-occurrence of nitrate and sulfate respiration impacts the balance of denitrification, DNRA and N 2 O production and thereby the functioning of sands as biocatalytic filters. To test this, nitrate and sulfate reduction rates were determined simultaneously in freshly collected sediments from the upper two cm of the Janssand intertidal sand flat in the North Sea. Subsequently, flow-through cores comprised of the same sediments were used to gain mechanistic insights into how oscillations in NO 3 - availability typically caused by tidal currents or storm events impact the balance of denitrification, DNRA, N 2 O production and sulfate reduction. We found strong correlations between DNRA and sulfate reduction rates, indicating a close link between the two cycles. To gain further insights into the potential metabolism of the microorganisms responsible for this link, we examined the phylogenetic affiliations of transcripts associated with nrfA, the key marker gene for DNRA.",
"discussion": "Results and discussion Sediment conditioning Spatial and temporal overlaps between denitrification, DNRA, and sulfate reduction in permeable coastal sediments were investigated using both fresh surface sediment and surface sediment conditioned over five days to different electron acceptor supply. Sediment was conditioned immediately after collection in October 2018 using different nitrate regimes designed to mimic the variability that occurs in different sediment horizons in situ . On the tidal flat, oxygen and nitrate can penetrate to depths of 5–10 cm at high tide, but are quickly consumed when the tide recedes, whereupon they are only present in the upper mm [ 6 , 53 ]. On longer time scales, sediment redistribution can bury the microorganisms that are attached to sand grains deeper into the sand flat, or, bring sand grains from deeper, more stably anoxic depths to the surface (Fig. 1A ) [ 13 , 14 ]. To mimic this variability in electron acceptor availability, two flow-through sediment cores were supplied with nitrate for 6 h, followed by a period of 6 h with no nitrate, similar to the upper layer of the sand flat (Fig. 1A ). In one of these cores, flow was maintained constantly in order to remove metabolic products such as sulfide and Fe II (Variable Redox / Product Elimination), while in the other core, flow was stopped and metabolic products could accumulate (Variable Redox / Product Accumulation) (Fig. 1B ). To mimic the conditions in the uppermost and deeper layers of the sediment, a third core was constantly supplied with nitrate-rich seawater (Nitrate Replete), while a fourth was constantly supplied with nitrate free seawater (Nitrate Deplete). All cores were kept anoxic throughout the conditioning period to isolate the effect of nitrate variations from those caused by oxygen. Fig. 1 Exposure of the microbial community to variable NO 3 - conditions in intertidal sand flats. A Changes in electron availability in situ. Schematic of the changes that occur on hourly to daily time scales on intertidal sand flats. When the tide is in, advection can transport O 2 and NO 3 - to depths of up to 5–10 cm. When the tide goes out, both are rapidly consumed and are only present in the upper mm to cm. When bottom currents become strong enough, or when wave action is high, the rippled sediment structures start to migrate, redistributing sand and exposing deeper sediments which have been NO 3 - deplete for longer time periods. B Electron acceptor supply in the conditioned sediments: In addition to carrying out rate measurements on freshly collected sediments, sediments were exposed to different conditions over five days in flow-through reactors supplied with anoxic water. During the five day conditioning period, the nitrate provided to the cores was consumed, indicating that nitrate reduction was likely occurring, or alternatively had been stored by the sediment diatom community [ 54 ]. Free sulfide was not detected in porewater at the outlet of any of the four cores, however substantial concentrations of dissolved Fe II were measured (Supplementary Fig. 2 ). Fe II release combined with rapid formation of black spots and gray sediment (indicative of iron sulfide formation) in cores receiving no nitrate (Supplementary Fig. 1 ), suggested the occurrence of substantial sulfate reduction in the cores [ 55 ]. At the end of the conditioning period, sediment from the center of the cores was sub-sampled in an anaerobic hood and 2 cm 3 of sediment was placed into multiple 12 cm 3 glass vials which were filled to the top with anoxic, filtered seawater before capping to create slurries. Rates of both sulfate reduction and nitrate consumption were then determined in the slurries in incubations amended with 35 S sulfate tracer and no nitrate (Unamended incubations), or 35 S sulfate and 15 N-NO 3 - (NO 3 - Amended incubations). Nitrate reduction was determined in slurries receiving 15 N-NO 3 - but no 35 S tracer. Throughout the incubation period the slurries were gently mixed by placing the glass vials in a roller tank to avoid the formation of nitrate-depleted microniches. Ubiquitous sulfate reduction Sulfate reduction occurred in all of the freshly collected and conditioned sediments when they were incubated without NO 3 - (Unamended incubations) (Fig. 2 , Supplementary Tables 1 – 3 ). Sulfate reduction rates in conditioned sediments (0.9–11.5 nmol cm −3 sed hr −1 ) varied substantially between cores, but overlapped with the range observed in freshly collected sediments (1.6–3.0 nmol cm −3 sed hr −1 ) and those measured previously in the upper two cm of the sand flat (0.42–16 nmol cm −3 h −1 ) [ 37 , 56 ]. The rate of sulfate reduction in the sediment in the Variable Redox/ Product Elimination condition (1.8–3.7 nmol cm −3 sed hr −1 ) was most similar to that of the fresh sediment, indicating that this regime most closely simulated the surface sediments at the tidal flat. The occurrence of sulfate reduction in the surface layer of the sand flat, and in all of the conditioned sediments, including those that had been exposed to high NO 3 - concentrations for five days (250–150 µM NO 3 - in the variable cores) strongly indicates that sulfate reducers at the sandflat are acclimated to the recurring presence of NO 3 - and sulfate reduction is therefore ubiquitous in these sediments. This is consistent with the observation of high acetate uptake by sulfate reducers in the surface sediment [ 41 ], and with the continued transcription of sulfate reduction genes in chemostats containing Janssand sediments after 100 days of continuous but low NO 3 - exposure [ 42 ]. Fig. 2 Sulfate reduction rates: Sulfate reduction rates (SRR) from sediments amended with NO 3 - (black bars) or without NO 3 - (white bars) in nmol cm −3 sed. hr −1 . Sulfate reduction began immediately in all incubations, and rates are calculated from the period of time where rates were linear. See Supplementary Tables 1 + 2 for associated statistics. All rates had an adjusted R 2 of at least 0.85, except for the Variable Redox / Product Accumulation condition, which had an R 2 of 0.56 when amended with NO 3 - , and 0.75 without NO 3 - . All error bars represent standard error. The conditioned sediment incubations were carried out in October 2018. Nevertheless, the substantial differences in sulfate reduction rates between cores incubated with different NO 3 - availabilities indicate that nitrate exposure does have some control over net sulfate reduction. Rates were 5 times higher in the Nitrate Deplete core (which had received no NO 3 - for 5 days) than in the Nitrate Replete core (which had been constantly supplied with NO 3 - for 5 days) (Fig. 2 ). Compared to the in situ rates, sulfate reduction rates were lower in the Nitrate Replete core, and vice versa, were higher in the Nitrate Deplete core. In contrast, in the Variable Redox conditioned sediments, NO 3 - availability could not be clearly linked to the changes sulfate reduction rates. Although both Variable Redox sediments were exposed to similar nitrate regimes during the conditioning period, sulfate reduction rates were threefold higher in the flow-through core which had a stagnant period of 6 h (Variable Redox / Product Accumulation) in comparison to the reactor with constant flow (Variable Redox / Product Elimination). Co-occurrence of sulfate reduction with nitrate reduction In the slurry incubations amended with 50–60 µM NO 3 - , sulfate and NO 3 - reduction proceeded simultaneously in both the freshly collected and conditioned sediments (Fig. 3 , Supplementary Fig. 5 ). In combination with the persistent sulfate reduction in NO 3 - conditioned sediment (where no NO 3 - was added during the incubation itself), these results suggest that the dynamic conditions at the sand flat select for a background level of constitutive sulfate reduction in anoxic permeable Janssand sediments, even in the presence a more thermodynamically favorable electron acceptor (NO 3 - ). These results bear many similarities to the occurrence of denitrification in the presence of oxygen, previously observed in these sediments [ 10 , 18 ]. Fig. 3 Concurrent NO 3 - and sulfate reduction in freshly collected surface sediments: 15 NH 4 + (light gray circles) and 15 N-N 2 production (purples circles) and the total sulfate reduced (black squares) are plotted for parallel nitrate amended incubations of surface sediment collected in October 2018 ( A ) and May 2019 ( B ). Each point represents a measurement from a separate incubation. Note the different scale bars. Sulfate reduction always occurred in the presence of nitrate, albeit at ~20–60% of the rate observed in the unamended slurries (Fig. 2 ). There are at least three mechanisms that can explain this apparent decrease in sulfate reduction rates in the presence of NO 3 - ; (1) competitive inhibition of sulfite reductase by NO 2 - [ 29 ], (2) sulfate reducing bacteria switching their metabolism to DNRA [ 46 , 47 ] and (3) complete sulfide oxidation to sulfate coupled to NO 3 - reduction [ 57 , 58 ]. Reported thresholds at which NO 2 - completely inhibits sulfate reduction and/or growth by sulfate reducing bacteria vary greatly (0.04 mM – 10 mM), but are generally above the concentrations observed during our incubations, where NO 2 - concentrations peaked at 35 µM (Supplementary Figs. 3 , 4 ) [ 27 , 59 , 60 ]. While purified dissimilatory sulphite reductase has a high affinity (although low turnover) for NO 2 - (K m = 38 µM; k cat = 0.038 mol s −1 mol −1 haem) [ 29 ] there was no obvious link between an accumulation of NO 2 - and decreased sulfate reduction in the incubations (Supplementary Figs. 3 – 5 ). Therefore we suggest that the reoxidation of sulfide by sulfide oxidisers (sometimes referred to as cryptic sulfur cycling), or sulfate reducers switching their metabolism to DNRA are more likely explanations for the apparent decrease in sulfate reduction with NO 3 - addition. However, it should be noted that sulfate reduction samples were processed using the Cr-II reduction method (Roy et al., 2014), which captures both produced sulfide and sulfur intermediate oxidation state compounds (e.g., pyrite, elemental S, thiosulfate, sulfite). As such re-oxidation of sulfide to sulfur intermediates would be included in the sulfate reduction rate determinations, but not any 35 S-labeled sulfide that was rapidly and completely oxidized back to sulfate. Changing ratios of denitrification:DNRA in the presence of sulfate reduction During the course of the incubations 15 N-NO 3 - was reduced to both 15 N-N 2 and 15 N-NH 4 + , indicating that when nitrate was present there was the potential for both denitrification and DNRA to occur within the sediment (Fig. 4A ). However the ratio of N 2 : NH 4 + production differed substantially between sediments after they had been conditioned (Fig. 4B ). For example, in the Nitrate Replete condition, denitrification was the dominant process and 15 N- N 2 production was 12 times higher than 15 N- NH 4 + production (Fig. 4 ). This was much higher than the ratio observed in the freshly collected surface sediments, where, as is typical for these sediments, denitrification was around twice as high as DNRA [ 54 , 61 ]. Denitrification was also around twice as high as DNRA in the Variable Redox / Product Elimination sediment, while in the Variable Redox / Product Accumulation sediment, denitrification and DNRA rates were similar. Within the Nitrate Deplete core, DNRA was marginally higher than denitrification. Fig. 4 NO 3 - reduction: A Rates of N 2 (black) and NH 4 + production (white) in nmol 15 N cm −3 sed. in the four conditioned sediments and two freshly collected surface sediments. All error bars represent standard error of rates. All rates were fit to points where N 2 production was approximately linear, and all rates have an R 2 of at least 0.86 (see Supplementary Tables 1 , 2 for associated statistic). B Normalized data from panel A showing the 15 N-NO 3 - converted to NH 4 + or N 2 as a percentage of the total rate of 15 N-NO 3 - conversion to either N 2 or NH 4 + . Error bars represent propagated standard error. The conditioned sediment incubations were carried out in October 2018. Different factors seem to have driven the changes in ratio in the different conditions, for example, in the Nitrate Replete condition, denitrification rates were far higher than those normally measured in the freshly collected sediments, while DNRA rates showed little change. This suggests that constantly anoxic, nitrate replete conditions allow the denitrification community to thrive in permeable sands. More interestingly, the relative contributions of DNRA and denitrification consistently varied with respect to sulfate reduction rates in the incubations (Fig. 5A, B ), with the proportion of DNRA positively and strongly correlating with increased sulfate reduction rates (Fig. 5C ). This suggests that sulfate reduction might exert an important influence on N-respiration when the processes co-occur. Fig. 5 Correlations between sulfate reduction rates and nitrate reduction: A The rate of DNRA plotted against the sulfate reduction rate in the absence of nitrate. B The rate of denitrification (formation of 15 N-N 2 ) plotted against the sulfate reduction rate in the absence of nitrate. Vertical bars represent standard error and horizontal bars are the propagated standard errors of sulfate reduction rates. C The rate of 15 NH 4 + production (DNRA) as a percentage of the total production of reduced N (i.e. 15 N-N 2 + 15 NH 4 + ) plotted against the rate of sulfate reduction (SRR) that was determined in parallel incubations in the absence of nitrate. Horizontal bars represent standard error while vertical bars represent propagated standard error. These incubations were carried out in October 2018. Linking microorganisms capable of DNRA to sulfur cycling The correlation between denitrification to DNRA ratio and sulfate reduction was largely driven by increases in the DNRA rate (Fig. 5 ), rather than decreases in the denitrification rate (Fig. 5C ), the latter of which was similar in the Variable and the Nitrate deplete conditions (Fig. 4B ). In fact, DNRA rates in the Nitrate Deplete condition were more than double those measured in the freshly collected sediment, which suggests that the constantly anoxic, nitrate deplete conditions supported microorganisms capable of switching quickly to DNRA upon nitrate addition. Furthermore, the decrease in sulfate reduction rate that we observed upon addition of nitrate was also strongly correlated to the DNRA rate (Supplementary Fig. 6 ). These results suggest that DNRA could be linked to the re-oxidation of reduced compounds formed during sulfate reduction (i.e. Fe or H 2 S), or alternatively, that a portion of the sulfate reducing community may have switched to DNRA in the presence of nitrate. However, the complete re-oxidation of sulfide back to sulfate is notoriously hard to quantify experimentally in marine sediments [ 62 ], therefore we switched to an –omic approach to gain insights into the potential links between DNRA and sulfur cycling within these sediments. We examined the phylogenetic affiliations of nrfA transcripts (the key marker gene for DNRA) in three sediment layers at the sampling site (0–1 cm, 2–4 cm and 7–10 cm). On average, 90% of the identified nrfA transcripts could be taxonomically assigned to class level (Fig. 6 ). Transcript assignments were similar in all sediment layers, although relative levels of nrfA transcription were higher in the two deeper sediment layers (Supplementary Fig. 7 ). Around half of the transcripts were assigned to orders within the Desulfobacterota phylum (recently reclassified from the Deltaproteobacteria; see ref. [ 63 ]) which are associated with sulfate reduction; mainly Desulfobacterales, followed by Desulfuromonadales and Desulfovibrionales (Fig. 6B ). In contrast, there were very few nrfA transcripts assigned to classes containing sulfide oxidizers, such as the Chromatiales and Woeseiaceae, which are common in these sediments [ 64 , 65 ]. Most other nrfA transcripts were taxonomically assigned to a class that is rarely associated with dissimilatory sulfur metabolism; the Bacteroidetes and specifically, the families Bacteroidia and Flavobacteriia (Supplementary Fig. 7 and Supplementary Tables 4 , 5 ), which are generally facultative anaerobes and fermenters. Fig. 6 Transcription of nrfA, a key marker gene for DNRA in surface sediments at the sampling site A assignment of nrfA transcripts to phylum (bold), or order level. The colors indicate the potential of these classes to carry out sulfur metabolism as identified from literature surveys. Transcript abundance was normalized by gene length and against the total abundance of rpoB in the metatranscriptome. B Assignment of Desulfobacterota nrfA transcripts to order level, as a percentage of total nrfA transcripts assigned to Desulfobacterota. In both panels averages are shown from three individual metatranscriptomes and error bars are standard deviation. These samples were sequenced in 2015. The transcription of nrfA therefore suggests that DNRA in the sediment is largely carried out either by facultative anaerobes/fermenters and organisms that are classically considered to be sulfate reducers. Taken together our results indicate that the correlation between sulfate reduction and DNRA in the sediment is driven by sulfate reducing microorganisms switching between sulfate reduction and DNRA. This observation shows that the nitrogen and sulfur cycle in sediments can be linked by the direct activity of bacteria that switch electron acceptors, rather than, as is typically assumed, sulfide oxidation coupled to NO 3 - reduction. Nitrous oxide production remained similar regardless of N-reduction pathway The accumulation of sulfide during S-cycling has also been suggested to impact N-cycling via the inhibition of nitrous oxide reductase, thereby decreasing N 2 production and increasing N 2 O production [ 44 , 66 ]. In contrast, there was only a weak negative correlation between sulfate reduction rate and N 2 production rates in this study, and the correlation was mainly driven by the very high denitrification rate in the nitrate replete condition (Fig. 6 ). Changes in N 2 production rate were not compensated for by large increases in N 2 O production, which represented only a few percent of total gaseous N production (i.e. N 2 O + N 2 ) (Supplementary Table 6 ). Net N 2 O production occurred in all of the sediments in the first hours of the incubations, followed by net consumption as nitrate became limiting, as is typically observed in these sediments (Fig. 7 , Supplementary Figs. 3 , 4 , 8 , 9 ). Intriguingly, the net N 2 O production at the start of the incubations was similar regardless of the overall denitrification rate. This led to a substantial increase in the N 2 O:N 2 production ratio at the start of the incubations in which denitrification rates were low and DNRA and sulfate reduction rates were high. Furthermore, there was a slower net reduction of N 2 O when NO 3 - became limiting in these incubations. It is possible that the production of sulfide partially inhibited N 2 O reductase (although it should be noted that this would not have been a major driver of the denitrification:DNRA ratio). Alternatively, the production of Fe(II) in the incubations with higher sulfate reduction rates could have led to enhanced production of N 2 O by abiotic reactions [ 67 ]. Regardless of the mechanism, our results suggest that the release of the greenhouse gas N 2 O would not be reduced by a shift from denitrification to DNRA, despite the fact that DNRA itself does not release any N 2 O. Fig. 7 N 2 O and N 2 production The production of N 2 (black circles) and N 2 O (open circles, values multiplied by 10) in the Nitrate Replete core ( A ) and Nitrate Deplete core ( B ) over the entire incubation time in nmol 15 N cm −3 sediment. Lines connect the average value at each timepoint. Each point represents a measurement from a separate incubation. These incubations were carried out in October 2018. Environmental implications of overlaps between sulfate reduction, DNRA and denitrification Here we show that in coastal permeable sediments sulfate reduction occurs in nitrate replete sediments, where it overlaps with the processes of denitrification and DNRA, thereby increasing the volume of sediment in which sulfate reduction can occur. Nevertheless, sulfate reduction rates measured in freshly collected surface sediments were approximately 10–20% of the rate of N-reduction. This implies that while sulfate reducers seem to be tolerant to nitrate in the sediment, they only contribute to a minor proportion of total carbon turnover in the surface layer (0–2 cm), as has been noted for other permeable intertidal sediments [ 8 ]. Furthermore, we found that a substantial proportion of DNRA in the sediment appears to be performed by organisms considered to be classical sulfate reducers. The ability of these microorganisms to respire and even grow via nitrate reduction has long been recognized and interestingly has also been associated with a high tolerance to oxygen exposure [ 48 , 49 , 68 ]. However, the reduction of nitrate as respiration strategy by sulfate reducers in marine sediments has rarely been observed; likely because sulfate reduction is generally considered to occur only in stable, nitrate deplete, anoxic environments. In contrast, organisms that would typically be classed as sulfate reducers, appear to be key members of the microbial community in permeable sediments where there are rapid fluctuations between fully oxic and nitrate replete conditions and anoxic and nitrate deplete conditions (Fig. 8 ). As a consequence, sulfate and nitrate reduction do not only co-occur in the sediment, but are directly linked within the Desulforbacterota . This implies that the size and activity of the sulfate reducing community controls the potential for DNRA within these sediments (Fig. 8 ). This could also explain the enhanced DNRA activity and increased ammonium fluxes to the water column that have been observed in sediments underlying hypoxic water columns [ 69 ]. This contrasts with the common view that the ratio of electron donor to nitrate/nitrite is the major factor driving the balance between DNRA and denitrification [ 70 – 72 ]. As DNRA retains fixed nitrogen within ecosystems as ammonium, rather than removing it like denitrification, our results indicate that the presence of active sulfate reducing communities can influence eutrophication. Fig. 8 Influence of changing boundary conditions on process rates Schematic outlining the changes in microbial activity over a tidal cycle (left and middle panels) and in a case where sulfate reducing bacteria become more abundant (right panel). When the tide is out, only the upper surface of the sediment has nitrate, and nitrogen reduction is dominated by denitrification. When the tide is in, nitrate reaches deeper into the surface and correspondingly to more sulfate reducers, which switch their metabolism to DNRA. This results in a more even denitrification:DNRA ratio. In sediments with more sulfate reducers, it is expected that DNRA rates would also increase as some sulfate reducers perform DNRA. For simplicity, oxygen dynamics are neglected."
} | 8,034 |
30746438 | PMC6357865 | pmc | 5,121 | {
"abstract": "A microfluidic approach enables quick quantification of electrochemical activity in living bacteria.",
"introduction": "INTRODUCTION Extracellular electron transfer (EET) ( 1 , 2 ) is the capacity for microbes to transfer electrons between their interior and external electron donors or acceptors during anaerobic respiration. It empowers cell growth and/or maintenance of exoelectrogens and electrotrophs and makes them versatile for multiple applications including environmental remediation ( 2 ), microbial fuel cells (MFCs) ( 3 , 4 ), and microbial electrosynthesis ( 5 , 6 ). Microbial EET mechanisms have been explored using a number of dissimilatory metal-reducing bacteria (DMRB), among which Geobacter and Shewanella are the most studied. For example, Geobacter sulfurreducens uses a network of multiheme cytochromes ( 4 , 7 – 9 ) to transfer electrons, while Shewanella oneidensis uses different sets of proteins, forming a metal-reducing (Mtr) pathway ( 10 ), to route electrons across the cell envelope. Moreover, G. sulfurreducens can form extracellular conductive pili ( 11 ), and S. oneidensis can produce outer-membrane and periplasmic extensions ( 12 ) that may enable long-distance electron transport. Extensive genetic and biochemical analysis has substantially enhanced our understanding of the EET pathway in a few well-established model microorganisms and hastened the improvement of their related biotechnological applications. However, key knowledge gaps still remain, partially due to the fact that phenotyping techniques for EET investigations lag behind the development of genotyping methods. Although at least 111 putative c -type cytochromes have been reported for G. sulfurreducens by complete genome sequencing ( 13 ), only a few have been fully understood in their phenotype-genotype relationships and physiological functions ( 7 , 8 ). Conventional phenotyping techniques to evaluate microbial EET, including cell growth in various conditions ( 8 ), measurement of redox products [e.g., Fe(II) and Mn(III) concentrations] ( 8 , 9 , 14 , 15 ), and power output in MFCs ( 4 , 16 – 18 ), are time consuming and require large sample volumes, impeding the investigation of difficult-to-culture or slow-growing microorganisms. Rapid and precise phenotyping strategies for microbial EET are imperative to uncover the phenotype-genotype relationship and to select superior candidates for optimized production in microbial electrochemical systems. Recently, the electrical conductivity of individual G. sulfurreducens pili ( 19 ) and electrode-grown biofilms ( 20 , 21 ) have been measured, where G. sulfurreducens components/networks were treated as electronic materials. Compared to traditional biochemical analysis, these electrical phenotyping methods provide important parameters for G. sulfurreducens EET modeling and suggest the possibility to quantify EET using intrinsic physical properties of microbes. The advancement of microfluidic systems facilitates investigation of cellular electrical properties ( 22 – 24 ), opening a new dimension for understanding complex physiological cellular states. Microfluidic systems using dielectrophoresis (DEP) ( 25 ) induced by DC electric fields enable the study of cell surface properties exclusively (see section S1) ( 24 , 26 – 29 ), unlike cell impedance measurements ( 22 ) and electrorotation techniques ( 23 ) that use high-frequency electric fields to detect cell internal properties. Previous work has shown that three-dimensional insulator-based DEP (3DiDEP) provides a high-sensitivity approach to probe bacterial envelope phenotypes with subspecies-level resolution ( 27 , 28 ). In this work, we demonstrate that microbial EET (a cellular physiological property) is correlated with cell surface polarizability (an electrical property) that can be easily measured by microfluidic systems using 3DiDEP ( Fig. 1 , A to C). This work is the first to show the strong correlation between bacterial EET and cell surface polarizability. Polarizability represents the tendency to form electric dipoles in a material (not necessarily charged) subjected to externally applied electric fields. Cell surface polarizability represents the overall dielectric properties at the cell/medium interface. It should be noted that we consider polarizability as a physical property adopted from the area of electromagnetics, rather than the biological concept (e.g., cell polarity) defined as the ability to form asymmetric organization of cellular components and shape as in the case of cell division and cell migration. We show that c -type outer-membrane cytochromes known to be responsible for EET in the microbial cell envelope contribute to the cell surface polarizability. The compositional diversity of the cell envelope induced by the presence/abundance of c -type cytochromes significantly affects cell surface polarizability. Our analysis of wild-type (WT) G. sulfurreducens DL-1 and various cytochrome-deletion mutants shows that deficiency in expressing c -type outer-membrane cytochromes essential for EET measurably decreases cell surface polarizability. Similar correlations were found with S. oneidensis and Escherichia coli heterologously expressing S. oneidensis EET pathways. Moreover, we show that the decrease of S. oneidensis polarizability due to loss of EET pathways can be recovered by reintroducing the EET pathway. In addition, activation of the microbial EET pathway by switching electron acceptors from pure fumarate to an MFC anode (for G. sulfurreducens DL-1) or Fe(III) citrate (for S. oneidensis strains) enhances cell surface polarizability. Fig. 1 DEP phenotyping of G. sulfurreducens . ( A ) 3DiDEP microfluidic device with an array of multiple microchannels. A DC potential difference increasing linearly with time at 1 V/s was applied across the channel. Credit: Qianru Wang, MIT. ( B ) Magnified view of the microchannel highlighting the constricted area. ( C ) Schematic depicting the 3DiDEP trapping principle. Bacteria near the constriction are immobilized when the DEP force ( F ⇀ DEP ), which is proportional to ∇ E ⇀ 2 , is balanced by drag forces due to the background electroosmotic flow ( F ⇀ EOF ) and electrophoresis ( F ⇀ EP ). The magnitude distribution of the x component of ∇ E ⇀ 2 is illustrated in the background color scale (dark red indicates higher values). ( D ) Schematic showing that G. sulfurreducens \n c -type outer-membrane cytochromes mediate EET. ( E ) Measured trapping voltage [the threshold applied voltage at the onset of 3DiDEP trapping depicted in (C)] was plotted against the ratio of DEP mobility (μ DEP ) to the magnitude of linear electrokinetic mobility (μ EK ) of WT G. sulfurreducens DL-1, DL-1 inoculated in an MFC anode for 24 and 31 days, and various indicated cytochrome-deletion mutants. A significant difference ( P < 0.05) was found between data groups isolated by dashed circles using a Kruskal-Wallis test. The black line indicates the inverse relationship between the ratio |μ DEP /μ EK | and the applied voltage.\n\nIntroducing S. oneidensis EET conduits into E. coli enhances its cell surface polarizability We further investigated the cell polarizability of E. coli heterologously expressing an Mtr respiratory pathway from S. oneidensis . Heterologous expression of CymA and MtrABC (localization as depicted in Fig. 3A ) has been achieved by cotransforming the plasmid cymAmtrCAB with the cytochrome c maturation ( ccm ) plasmid into E. coli strain C43, enabling EET in E. coli ( 42 ). CymA is the c -type cytochrome anchored in the cytoplasmic membrane that donates electrons to a variety of respiratory pathways spanning the periplasm and outer membrane of S. oneidensis ( 10 , 42 ). Both the electrogenic E. coli strain expressing the MtrABC pathway (ccm + CymA/MtrABC) and the control strain (ccm) were grown with Fe(III) citrate and a small amount of fumarate. Previous studies have confirmed the expression and redox activity of CymA, MtrA, and MtrC in the electrogenic E. coli strain ( 42 ). Our dielectrophoretic screening shows that the electrogenic E. coli strain has a significantly stronger surface polarizability ( P < 0.0001) compared to the ccm strain ( Fig. 4A ). This result provides further evidence that the presence of the MtrABC pathway enhances cell surface polarizability, regardless of the species of the microbe. The corresponding data for the linear electrokinetic mobility and cell morphology can be found in fig. S4. The electrogenic E. coli strain reduces Fe(III) citrate ~3.5× faster than the ccm strain ( Fig. 4B ), consistent with the results of previous studies ( 42 ), suggesting a positive correlation between iron reduction and cell surface polarizability. Figure 4C plots the cell surface polarizability of the five S. oneidensis strains and two E. coli strains grown with Fe(III) citrate against their iron reduction rates. It suggests that S. oneidensis has a superior iron reduction activity and cell polarizability compared to the electrogenic E. coli . It also suggests that 3DiDEP can be used to distinguish microbes from different species based on their iron reduction activity (or other phenotypes related to redox activity), although species may differ in their baseline cell polarizability. Fig. 4 E. coli introduced with EET pathways from S. oneidensis gains strong polarizability. ( A ) Polarizability of the E. coli strain transformed with an empty cytochrome c maturation ( ccm ) plasmid (control) and the strain cotransformed with S. oneidensis MtrABC EET conduit grown with 15 mM Fe(III) citrate and 10 mM fumarate. The electrogenic E. coli strain obtains significantly enhanced polarizability ( P < 0.0001, two-tailed t test; n = 8) compared to the control. ( B ) Fe(III) citrate reduction over time measured for the control and the electrogenic E. coli strain. ( C ) Positive relationship between bacterial polarizability and iron reduction rate of the studied five S. oneidensis strains and two E. coli strains is indicated by a log fitting (dashed line) with a fitting goodness of R 2 = 0.91. The iron reduction rate was derived by taking the slope of the linear portion of the Fe(III) citrate reduction curves.",
"discussion": "DISCUSSION This work represents the first demonstration of the correlation between EET and cell surface polarizability. By comparing the Clausius-Mossotti factor (κ CM ) of G. sulfurreducens , S. oneidensis , and electrogenic E. coli strains in different growth conditions, we show that microbial EET can be distinguished by cell surface polarizability using 3DiDEP, and the correlation is generalizable to multiple species. The level of cell surface polarizability is contingent on the amount of crucial outer-membrane cytochromes and the integrity of EET pathways, for example, high polarizability was found in both WT G. sulfurreducens and S. oneidensis (versus their cytochrome-deletion mutants) and E. coli expressing an Mtr respiratory pathway (versus the nonelectrogenic E. coli strain). The necessity of this correlation is further evidenced by the fact that the decrease in polarizability of MtrABC-deficient S. oneidensis can be complemented by providing a complete EET pathway in trans . In addition to removing or replacing EET components, increasing their redox activity by switching the growth conditions [e.g., respiration on an MFC anode or Fe(III) citrate versus fumarate] boosts cell surface polarizability. Moreover, EET components bearing different physiological functions (e.g., OmcB versus OmcZ in G. sulfurreducens and MtrABC versus MtrDEF in S. oneidensis ) lead to diverse effects on cell surface polarizability. This study introduces surface polarizability as a novel physical property for assessing EET in several types of Gram-negative bacteria. We show that surface polarizability can be measured using 3DiDEP noninvasively with low sample volume (~100 μl), which suggests exciting potential for phenotypic-based screening of electrochemically active organisms using microfluidic DEP. In addition, our results prompt a new question on how cell surface polarizability maps to electrochemical activity of Gram-positive or other bacteria that may use different EET mechanisms. Recent studies have revealed EET mechanisms distinct from the heme-based EET system in Gram-positive bacteria—they employ membrane-anchored lipoproteins (e.g., PplA), which recruit environmental flavins to “shuttle” electrons to extracellular acceptors ( 43 ). Genes for these newly identified proteins are present in diverse bacterial species spanning the Firmicutes phylum, including species in human microbiota and bacteria used for food fermentation or probiotics ( 43 ). Further investigations on the coupling between cell surface polarizability and flavin-based EET found in Gram-positive bacteria may potentiate a broader application for this approach. Moreover, compared to conventional screening methods, such as fluorescence-activated cell sorting (requiring specific fluorophore targets) and proteomic analysis (invasive and time-consuming), dielectrophoretic screening of cell surface polarizability may unlock a vast repertoire of EET-related biochemical applications. Examples include sorting a library of genetically engineered microbes for optimized MFC performance or iron reduction in the iterative process of directed evolution. In addition to EET, other surface features such as the presence of LPS or ion channels may also correlate with cell envelope polarizability. The structure of LPS has a notable impact on bacterial antibiotic resistance, while ion channels are crucial for regulation of membrane potentials and cell electrical signaling. This method will be useful as guidance for further DEP-based phenotypic analysis of a diverse array of cells and organisms."
} | 3,482 |
33646814 | null | s2 | 5,122 | {
"abstract": "Directed evolution is a form of artificial selection that has been used for decades to find biomolecules and organisms with new or enhanced functional traits. Directed evolution can be conceptualized as a guided exploration of the genotype-phenotype map, where genetic variants with desirable phenotypes are first selected and then mutagenized to search the genotype space for an even better mutant. In recent years, the idea of applying artificial selection to microbial communities has gained momentum. In this article, we review the main limitations of artificial selection when applied to large and diverse collectives of asexually dividing microbes and discuss how the tools of directed evolution may be deployed to engineer communities from the top down. We conceptualize directed evolution of microbial communities as a guided exploration of an ecological structure-function landscape and propose practical guidelines for navigating these ecological landscapes."
} | 242 |
31337658 | PMC6650546 | pmc | 5,125 | {
"abstract": "Microbial methane oxidation plays a fundamental role in the biogeochemical cycle of Earth’s system. Recent reports have provided evidence for the acquisition of methane monooxygenases by horizontal gene transfer in methane-oxidizing bacteria from different environments, but how evolution has shaped the coding sequences to execute methanotrophy efficiently remains unexplored. In this work, we provide genomic evidence that among the different types of methanotrophs, type Ia methanotrophs possess a unique coding sequence of the pmoCAB operon that is under positive selection for optimal resource allocation and efficient synthesis of transcripts and proteins. This adaptive trait possibly enables type Ia methanotrophs to respond robustly to fluctuating methane availability and explains their global prevalence.",
"introduction": "INTRODUCTION Microbial methane oxidation plays a number of fundamental roles in the global ecosystem ( 1 ). Methane-oxidizing microorganisms can mitigate methane emissions by acting as methane sinks ( 2 , 3 ) and thereby reduce the contribution of methane to climate change ( 4 ). Microbial oxidation of methane also provides an entry point for methane into the global food web and can serve as a primary carbon source for large trophic systems ( 5 , 6 ). Methane oxidation is a globally distributed phenotype expressed in microorganisms from diverse taxonomic groups. Based on a range of phenotypic (e.g., metabolic pathways for carbon fixation, fatty acid compositions) and phylogenetic (e.g., Proteobacteria , Verrucomicrobia , NC10) features, methanotrophic bacteria can be categorized into seven major types ( 7 ): Ia, Ib, Ic, IIa, IIb, III, and the candidate division NC10. The distribution of marker gene sequences for the major methanotroph types suggests that they are differentially prevalent across environments ( 7 ). In bacteria, methane oxidation begins with two enzymatic steps where methane is first converted to methanol by a soluble or particulate methane monooxygenase (sMMO or pMMO, respectively), and then methanol is oxidized to formaldehyde by a pyrroloquinoline quinone-containing methanol dehydrogenase that can be calcium dependent (Mxa) or lanthanide dependent (Xox). The resulting formaldehyde can be directed to energy production or biomass synthesis ( 8 ). The functional sMMO is encoded in the six-gene operon mmoXYZBCD , while pMMO is encoded in the three-gene operon pmoCAB . Of the methanol dehydrogenases, the functional Mxa enzyme is encoded in the two-gene operon mxaFI and Xox is encoded by the gene xoxF . In evolutionary history, methane oxidation appeared at around the same time as oxygenic photosynthesis, nitrogen fixation, nitrification, and denitrification ( 9 ), and it is possible that the emergence of methanotrophy occurred soon after the last universal common ancestor ( 10 ). Hence, evolution has likely shaped methanotrophs, with many as-yet-undiscovered properties ( 10 ). One unexplored question of fundamental importance to our understanding of methanotrophy is how the genes that encode the methane oxidation metabolic module have been shaped by evolution to efficiently execute methanotrophy, especially after recent reports have suggested that MMOs were potentially acquired by horizontal gene transfer in some types of methanotrophs ( 11 – 14 ). In the three cellular domains and in viruses, evolution has selected gene sequences that perform cellular functions beyond just encoding the amino acid compositions of proteins. For example, gene sequences have nucleotide and codon variants that direct mRNA folding ( 15 , 16 ), transcript abundance ( 17 ), mRNA degradation ( 18 ), RNA toxicity ( 19 ), protein synthesis ( 20 ), and cotranslational protein folding ( 21 ), and they promote the interaction of peptides with the signal recognition particle ( 22 ). Gene sequences can also affect the cellular economy of protein synthesis ( 23 ), reduce the metabolic burden of nucleotide synthesis by incorporating less expensive nucleotides ( 24 ), and allocate resources required for transcription ( 25 ) and translation ( 26 ). In this study, we performed an in-depth genomic meta-analysis to investigate the coding sequences of the genes that encode the methane oxidation metabolic module (as defined in the KEGG metabolic module M00174 [conversion of methane to formaldehyde]) in bacteria. We found that evolution has shaped the pmoCAB operon of type Ia methanotrophs with a unique coding sequence that optimizes resource allocation by reducing the biosynthetic costs of transcription and translation while ensuring translation efficiency and accuracy. This study provides novel insights into the molecular biology and evolution of methanotrophic bacteria and extends our understanding of the mechanisms developed by nature to sustain metabolism and life on Earth.",
"discussion": "DISCUSSION This study provides genomic evidence that the pmoCAB coding sequences of type Ia methanotrophs possess a unique adaptive trait manifesting as a strong nucleotide bias ( Fig. 1 ) that fine-tunes codon usage ( Fig. 2 ) and can optimize methane oxidation through maximizing translation efficiency and accuracy ( Fig. 3 ), while minimizing protein ( Fig. 3 ) and transcript ( Fig. 4 ) synthesis costs. The discovery of this unique coding sequence was enabled by meta-analysis of a large number of isolate genomes and MAGs from diverse taxonomies and geographies ( Fig. S1 ). This finding illustrates a sophisticated adaptive linkage between molecular genotype and phenotype for methane oxidation. Given the high metabolic modularity of methanotrophy ( 10 , 42 ), the methods presented here can be applied to probe the molecular strategies encoded in other functional modules (e.g., copper uptake; mbnABCM , corAB , and copCD ) that are of particular relevance to our understanding of methanotrophs. From an environmental point of view, this adaptive trait enables type Ia methanotrophs to be competitive and efficient in oxidizing methane, which is a gas with limited solubility and mass transfer in liquid ( 43 ). Past reports on the molecular optimization of genetic sequences have shown that transcribed regions of genes can have fine-tuned nucleotide compositions that reduce their biosynthetic cost ( 24 ). For example, DNA coding sequences are found to reduce the number of nitrogen atoms per codon when nitrogen is scarce in the environment ( 44 ), and a large proportion of bacterial genomes present a dual-optimization strategy to reduce per-codon nitrogen demand while increasing translation efficiency ( 45 ). Extending these findings, we show that type Ia pmoCAB coding sequences possess an adaptation that minimizes not only nitrogen but also carbon and hydrogen per transcribed codon, while reducing the metabolic burden of erroneous biosynthesis ( 30 ) through optimal translation efficiency and accuracy. This optimization strategy may increase the fitness of type Ia methanotrophs in the competition for methane and might contribute to the prevalence of type Ia methanotrophs across diverse habitats with various environmental conditions ( 7 ). However, given that the number of sequenced genomes available for other types of methanotrophs is limited, we might find alternative optimization strategies at the molecular level with future sequencing projects. Nonetheless, the transcript optimization strategy found in type Ia pmoCAB coding sequences results in an increased demand for oxygen atoms per codon. The high demand for oxygen to synthesize pmoCAB transcripts may be part of a mechanism to modulate cellular metabolism based on oxygen availability in the environment and to tightly regulate the first enzymatic step of the methane oxidation metabolic module at the transcriptional level. This is supported by the presence of genes encoding the PAS domain proteins, potentially acting as oxygen sensors ( 41 ), flanking the pmoCAB coding sequences in the genomes of several type Ia methanotrophs ( Fig. S7 ). In the second enzymatic step of the methane oxidation metabolic module, methanol is oxidized to formaldehyde by the methanol dehydrogenase encoded by either the mxaFI or xoxF gene. We show here that the type Ia pmoCAB , mxaFI , and xoxF coding sequences have similar translation efficiencies ( Fig. 3a ), similar levels of access to tRNA isoacceptors ( Fig. 3b and c ), and similar levels of codon bias ( Fig. 2b ); however, their preferences for individual codons ( Fig. S2a ) and translation accuracy ( Fig. 3d ) differ. Considering only the genes for the second enzymatic step, the difference in codon usage between the mxaFI and xoxF coding sequences reduces the xoxF transcripts’ demand for carbon, nitrogen, and hydrogen atoms per codon ( Fig. 4 ). We also show that protein synthesis has been optimized through the frequent incorporation of prebiotic (inexpensive) amino acids in the pMMO and Xox protein sequences, though not in Mxa ( Fig. 3e ). Together, these results indicate that although the mxaFI and xoxF coding sequences have similar translation efficiencies, the xoxF coding sequence reduces transcript and protein synthesis costs ( Fig. 5 ), suggesting that Xox has evolved to be the predominantly expressed methanol dehydrogenase. Overall, the results in this study show that the pMMO-Xox configuration of the methane oxidation module should be the most efficient molecular strategy to catalyze the sequential oxidation of methane and methanol ( Fig. 5 ). This finding complements the results of recent investigations of isolates ( 46 ) and communities ( 47 ) by providing a molecular basis to explain the prevalent expression of pmoCAB and xoxF coding sequences. Future investigations focusing on the implications of the pmoCAB and xoxF coding sequences in the context of ecogenomics and ecophysiology will be required. FIG 5 Three dimensions of molecular optimization encoded in the coding sequences of the methane oxidation metabolic module of type Ia methanotrophs. Genes encoding pMMO have been scrutinized for nucleotide composition bias ( 48 , 49 ), and it has been proposed that they have evolved independently following an ancient speciation event ( 50 ). An earlier work indicated that methanotrophy in Alphaproteobacteria was a result of horizontal gene transfer of MMOs ( 11 ). In fact, recent evidence supports the horizontal transfer of MMO genes to methanotrophs in Proteobacteria ( 12 – 14 ). In the context of the literature, the most parsimonious explanation for our observations is that pmoCAB genes were acquired through an ancient horizontal gene transfer event and subsequently subjected to strong selective pressure that restricts gene amelioration ( 51 ) to the nucleotide composition of the type Ia genomes, which preserves a foreign codon usage bias that optimizes the transcriptional and translational machinery ( 52 ). Fundamentally, our results emphasize that the type Ia pmoCAB coding sequences have been shaped by natural selection and are not the result of random genetic drift even at the level of synonymous codons. The ecological role of methanotrophs as the primary producers in complex communities ( 5 , 6 ) can exert strong evolutionary pressures on pMMO. Efficient conversion of methane to methanol not only serves as the first enzymatic step for methanotrophs but also provides methanol as the carbon source for methylotrophic bacteria ( 37 ). The availability of methanol can also support methane oxidation in methanotrophs that have a low affinity for methane when methane concentration is at atmospheric levels ( 53 ). Consequently, the unique adaptations of the type Ia pmoCAB coding sequences are likely the result of molecular evolution at the organismal, community, and perhaps even planetary scale ( 54 )."
} | 2,955 |
34833251 | PMC8625594 | pmc | 5,126 | {
"abstract": "In this study, a novel idea was proposed to convert the polyethylene terephthalate (PET) waste drinking-water bottles into activated carbon (AC) to use for waste cooking oil (WCO) and palm fatty acid distillate (PFAD) feasibility to convert into esters. The acidic and basic char were prepared by using the waste PET bottles. The physiochemical properties were determined by employing various analytical techniques, such as field emission scanning electron microscopy (FESEM), thermogravimetric analysis (TGA), Fourier transform infrared (FTIR), Brunauer–Emmett–Teller (BET) and temperature-programmed desorption – ammonia/carbon dioxide (TPD-NH 3 /CO 2 ). The prepared PET H 3 PO 4 and PET KOH showed the higher surface area, thus illustrating that the surface of both materials has enough space for impregnation of foreign precursors. The TPD-NH 3 and TPD-CO 2 results depicted that PET H 3 PO 4 is found to have higher acidity, i.e., 18.17 mmolg −1 , due to the attachment of phosponyl groups to it during pretreatment, whereas, in the case of PET KOH, the basicity increases to 13.49 mmolg −1 . The conversion results show that prepared materials can be used as a support for an acidic and basic catalyst for the conversion of WCO and PFAD into green fuel.",
"conclusion": "4. Conclusions In this study, activated carbon derived from discarded plastic drinking bottles was converted into acidic and basic char and tested for the feasibility of conversion of the PFAD and WCO into esters. The porous structure and increase in acidity or basicity developed on the surface of PET Plain were produced after pretreatment of H 3 PO 4 and KOH. The maximum surface area was found in the case of PET H 3 PO 4 , having an acidity of 18.17 mmol g −1 . That one gives a conversion of 45%, whereas, in the case of PET KOH, the basicity of 13.49 mmol g −1 gives a conversion of 50%. Further improvement in the yield can be obtained by treating the PET Plain with various agents, such as metal oxides; metal nitrates; and sulfonating agents, e.g., sulphuric acid and chlorosulfonic acid, to convert it into an efficient catalyst. The PET-based char can be used for different purposes, such as carbon-based precursors, in adsorbents preparation and in the treatment of laundry water for removal of microplastics from it. Waste PET char has an interesting scope to solve the problem of environmental pollution due to non-biodegradable wastes which are dumped normally and change the focus from food and energy controversy.",
"introduction": "1. Introduction The activities carried out by humans are having a huge impact on the environment. There are always dire consequences of an activity on the natural resources and environment, as we tend to interfere in the natural mechanism of recycling. Polymers have been increasingly popular in recent years and have been used widely in almost every field of life. Around 300 million tons of plastic waste is generated globally each year. The number of benefits obtained from the usage of polymers is phenomenal, and that is why they replaced glass in 1970s, but this does not negate the fact that they cause pollution to our environment [ 1 ]. Over the passage of time, they have become an important material of our society because benefits associated with them, such as ease of adaptability in design, thermostability, transparency, durability as compared to glass and low cost. The consumption of PET has shown a fast rate of growth, due to the increase in growth of the plastic bottle market [ 2 ]. Plastic solid wastes are accumulating day by day in our surroundings, and this is posing a huge problem in their disposal without contaminating the environment. The amount of PET that is being recycled in a number of manners in the past is less, but its potential to be converted into activated carbon has been explored recently [ 3 , 4 ]. PET falls into the category of non-biodegradable plastic waste and is being dumped in the landfills and oceans, which poses a great danger to ecology, wild life and human health [ 5 ]. Sardon and Dove [ 6 ] anticipated that, by 2050, the mass of plastic waste will be more than that of fish, because plastic trash continues to rise, along with rapid disposal and weak recycling methods. All of the data suggest that plastic pollution has already become a severe concern, with far greater consequences than previously assumed. Malaysians are the biggest consumers of plastics in Asia and contribute the maximum in terms of dumping of PET waste in oceans. The amount of carbon emissions associated with plastic from various stages of production to burning amounted to 860 million tons in 2019, which is more than the annual emissions of Thailand, Vietnam and the Philippines combined [ 7 ]. Carbon materials or their precursors are a low-cost feedstock for preparation of carbon-based catalyst, thus improving its potential for use in biodiesel manufacturing processes [ 8 ]. PET char has been prepared for a long time and used for various purposes other than that of Biodiesel production. It has been used as a sieve for the cleaning of a gas mixtures containing O 2 , N 2 and CO 2 ; CH 4 and char can also use a tar filtering medium [ 9 ]. The activated carbon (AC) prepared showed good capacity for adsorption, and certain selectivity for O 2 /N 2 and CO 2 /CH 4 was prepared from granulated PET and Cork Oak with pore mouth-narrowing, using CVD from Benzene [ 10 ]. PET has been mixed with other synthetic polymers, such as Polyacrylonitrile (PAN), to obtain char with more yield and better textural properties. The production of highly porous AC utilizing a synthetic polymeric mixture with PET and PAN as a precursor for the removal of pesticides, such as diuron and 4-chloro-2-methylphenoxyacetic acid (MCPA), from the aqueous phase is an intriguing characteristic of this work. There are two advantages of PET char: one is the cost, and the second is the efficiency, which is very high [ 11 ]. PET char has been used for the removal of phenolic compounds from industrial-waste waters, which have proved to be a threat to the environment, as the effluents are directly drained into the rivers. The adsorption capacity is better for the char pretreated with KOH(s) rather than NaOH(s). Post modification with urea does not affect the adsorption capacity much, as compared to cloth and cork char [ 10 ]. PET char has been used for the removal of arsenic in wastewater. Among the many types of plastic chars tested, PET and PVC mixed char removed the most arsenic from the stock solution, ranging from 71.6 to 99.4 percent. For 99.4 percent Ar adsorption, the optimal equilibrium time of 20 min with acidic medium (pH 6.0) at 1.5 g was the most preferred dose [ 12 ]. The char produced by loading PET with 5% of Fe 2 O 3 and MgO has been found to be useful as a flame retardant, and this happens due to synergistic action of them [ 13 ]. PET char was employed to make improved epoxy resins, and the tensile strength, surface hardness and electrical characteristics of epoxy composite materials were determined [ 14 ]. Biodiesel (ester) is a mixture of fatty acid ester, which is produced from the oil/fats that come from any source. Ester has similar combustion properties to fossil diesel. It is eco-friendly, non-toxic, a greener fuel and sustainable. As per our knowledge, there have been few publications in this area where the method of conversion is complicated. The aim of this study is to develop a cheap and simple method for synthesis of activated carbon from waste plastic bottles and its applicability for green fuel production from waste feedstocks (i.e., PFAD and WCO).",
"discussion": "3. Results and Discussion 3.1. Field Emission Scanning Electron Microscopy (FESEM) Evaluation FESEM analysis was used to study the morphological structure and elemental composition of the PET Raw, PET Plain, PET H 3 PO 4 and PET KOH, as shown in Figure 2 a–d. Figure 2 a shows that external surface morphology of PET Raw, which appears as sheets with rough surface. The same behavior of rough surface was observed by Akinfalabi et al. [ 13 ]. Interestingly, PET Plain surface transformed after the calcination process and exhibited a nanotube structure with large irregularly shaped particles ( Figure 2 b), which is ascribed to the effect of sintering during high calcination temperature (450 °C). In contrast, Figure 2 c,d highlights the effect of the treatment process of PET Plain with H 3 PO 4 and KOH. Obviously, the chemical treatment led to the creation of pores, which are more pronounced in the case of KOH ( Figure 2 c). On the other hand, PET H 3 PO 4 had an agglomerated surface, which relates to the attachment of phosphonyl groups on the catalyst surface, giving it acidity. Malinas et al. [ 15 ] prepared the activated carbon and observed the rough surface and textural nature of the activated carbon. 3.2. N 2 Adsorption and Desorption Evaluation The adsorption isotherms of PET Raw have few values at the region of small relative pressure. Therefore, the sample can be presumed to have few micropores. The adsorption isotherms of after the heat treatments, such as PET Plain, PET H 3 PO 4 and PET KOH, indicated the gap at the right end of the graph ( Figure 3 ), that is, near 1 of relative pressure. The reason for the gap might be the absorption of the probe gas species. The adsorbed volume at small relative pressure of PET H 3 PO 4 was larger than that of PET KOH. Therefore, the H 3 PO 4 activation process should introduce more micropores than the KOH activation process. In the adsorption isotherm of PET KOH, the existence of mesopores could be presumed from the discontinuity at ca. 0.5 of relative pressure. The surface area, pore volume and diameter are presented in Table 1 ; the raw material and synthesis materials were determined by using BET. The surface area of the raw PET was 24 m 2 /g, which increased when the PET converted into AC after calcination to 65 m 2 /g. This shows that the calcination changes the nature of the raw PET, and when PET is exposed to a higher temperature, its surface changes and creates more pores, and this behavior confirms the previous studies [ 9 ]. Interestingly, the treated carbon PET H 3 PO 4 and PET KOH revealed a high surface area compared to raw material 140 and 261 m 2 /g, respectively. In the case of pore volume, the PET Raw displayed a minimum pore volume value of 0.06 cc/g. Conversely, the pore volume of PET Raw and treated PET H 3 PO 4 and PET KOH dramatically increased due to the formation of oxides by KOH during pyrolysis. The pore radius of PET Raw was 5.02 nm, wherein it was marginally enlarged to 5.92 nm in PET KOH, ascribed to the collapse of pore walls in the calcination and the chemical-treatment process. It was discovered that activating phosphoric acid might stimulate the creation of pores and enhance the specific surface area [ 16 ]. On the contrary, the pore radius PET H 3 PO 4 decreased as a result of the blocking of pores by phosphonyl groups of H 3 PO 4 . The pore diameter in all the samples was 18 nm, demonstrating the presence of mesopores. The BJH model was used to calculate mesopore volume at 0.1–1.0 p/p0, and it was found that all the chars had well-developed mesopore volume. The mesopore volume distribution confirmed in previous studies [ 17 ]. It can be seen from Table 1 that the pore formation is better in PET KOH as compared to others and basicity of the PET KOH is higher than the other impregnated AC. 3.3. Temperature-Programmed Desorption of Carbon Dioxide and Ammonia (TPD-CO 2 /NH 3 ) The acidity and basicity of the synthesized activated carbon catalysts were analyzed by using temperature programmed desorption, using ammonia and carbon dioxide as probe gases (TPD-NH 3 and TPD-CO 2 ), respectively. PET H 3 PO 4 was found to have higher acidity, i.e., 18.17 mmol g −1 , due to the attachment of phosponyl groups to it during pretreatment, whereas, in case of PET KOH, the basicity increases to 13.49 mmol g −1 ( Table 1 ), due to the formation of oxides by KOH during pyrolysis. H 3 PO 4 plays a dual role in the introduction of phosponyl groups into the structure and creation of pores, whereas KOH creates pores in the structure and imparts basicity to it by the formation of oxides of potassium. Similar results were reported in the previous studies [ 18 ]. 3.4. Material’s Thermal Stability Analysis Using Thermogravimetric Analysis (TGA) In this work, thermogravimetric analysis was carried out to analyze the thermal stability and decomposition rate of PET Raw, PET Plain, PET H 3 PO 4 and PET KOH. Figure 4 shows that the gradual weight loss of PET at different temperatures. A significant weight loss started at 360–490 °C, which is due to the degradation of the PET components (first degradation). The second degradation stage occurred at 500 °C with very slow change and completed at around 950 °C. The TGA of PET Plain shows that there is an initial increase in weight due to the absorption of moisture. Meanwhile, weight loss is seen at a temperature of 530 °C, which indicates that the sample is quite stable, and it continues to decompose till 950 °C, with a total weight loss of 92% of PET polymer, which is associated with the thermal decomposition of the carbon sheet’s structure [ 19 ]. On the other hand, the TGA of PET H 3 PO 4 exhibited marginal weight reduction attributed to the moisture loss at around 70 °C. At 90 °C, the sample starts to show an increase in weight loss that continues till 600 °C, being due to the breakdown of phosphoryl group. Notably, at 830 °C, the PET H 3 PO 4 starts further decomposition, and the weight loss is due to the breakdown of activated carbon. Remarkably, the TGA of the PET KOH shows an excellent stable characteristic from 50 to 800 °C. Beyond 800 °C, the catalyst starts to decompose with the mass loss being around 10%, which indicates that KOH play a vital role in stabilizing and decomposition rate of the PET KOH. 3.5. Crystallinity Evaluation via XRD Analysis The XRD spectra of PET Raw, PET Plain, PET H 3 PO 4 and PET KOH are presented in Figure 5 . Amorphous carbon peaks were obvious in the spectra from the PET Raw, PET Plain and PET H 3 PO 4 . After pretreatment with KOH and subsequent carbonization, K and K 2 O were observed in the char, giving an indication that KOH had reacted with the carbonaceous components in PET. Only amorphous carbon was detected in PET H 3 PO 4 , indicating that the changes due to H 3 PO 4 mainly affected the organic components of PET [ 20 ]. 3.6. Functional Groups Evaluation via FTIR The existence of functional groups in PET Raw, PET Plain, PET H 3 PO 4 and PET KOH was determined by using FTIR ( Figure 6 ). The observed spectra are fundamental to carbon materials. The infrared spectrum of PET Raw (-OCH 2 CH 2 OOCC 6 .H 4 CO-), has been studied from longtime. For the PET Raw, five main peaks are identified at wavenumbers 1715, 1245, 1100, 870 and 730 cm −1 , corresponding in ketones (C=O), ether aromatic (C–O), ether aliphatic (C–O), aromatic (C–H) and aromatic (C–H) bond [ 21 ]. FTIR spectra were obtained for the PET Raw sample and PET KOH. In the range of 3400–3500 cm −1 , a broad and powerful peak is found, implying stretching vibrations of –OH (hydroxyl) groups and adsorbed moisture. In activated samples, this band moves to a higher wavenumber (3445–3456 cm −1 ) due to the formation of hydrogen bonds after KOH pretreatment [ 19 ]. The stretching of methylene (–CH 2 ) and methyl (–CH 3 ) results in a tiny absorption band around 2900 cm −1 . Peaks about 1600 cm −1 are connected to the carboxylate and carbonyl groups. The existence of oxygen moieties is confirmed by a prominent peak at 1013 cm −1 for PET Raw and 3–700, which is connected to C–O–C stretching in aromatic compounds. The peak at 2300 cm −1 corresponds to C–H sp3 stretching, while the existence of peaks in the 690–762 cm −1 range is a benzene ring characteristic [ 22 ]. The absorption peaks due to stretching vibration of C–H on the benzene ring and C=O on PET H 3 PO 4 were at 725 and 1705 cm −1 , respectively. The weak absorption peak at 969 cm −1 of the PET H 3 PO 4 spectrum was attributed to P–O–C groups, which were formed by the reaction between polyester and phosphoric acid [ 20 ]. 3.7. Application of PET H 3 PO 4 and PET KOH for Green Fuel Production The prepared PET H 3 PO 4 and PET KOH were employed for conversion of PFAD and WCO, respectively, into esters. The PFAD gives 45% conversion when using PET H 3 PO 4 , whereas WCO gives 50% conversion yield when the reaction is performed by using the PET KOH, as shown in Table 1 . These results shows that both PET H 3 PO 4 and PET KOH have the potential to be used for the production of green fuel. By using these materials, the overall production cost can be reduced, and eventually, the final price of ester could be cheaper. There is still a need to optimize the green-fuel production process by using these materials."
} | 4,248 |
37538294 | PMC10394688 | pmc | 5,127 | {
"abstract": "Atmospheric water harvesting is a sustainable solution\nto global\nwater shortage, which requires high efficiency, high durability, low\ncost, and environmentally friendly water collectors. In this paper,\nwe report a novel water collector design based on a nature-inspired\nhybrid superhydrophilic/superhydrophobic aluminum surface. The surface\nis fabricated by combining laser and chemical treatments. We achieve\na 163° contrast in contact angles between the superhydrophilic\npattern and the superhydrophobic background. Such a unique superhydrophilic/superhydrophobic\ncombination presents a self-pumped mechanism, providing the hybrid\ncollector with highly efficient water harvesting performance. Based\non simulations and experimental measurements, the water harvesting\nrate of the repeating units of the pattern was optimized, and the\ncorresponding hybrid collector achieves a water harvesting rate of\n0.85 kg m –2 h –1 . Additionally,\nour hybrid collector also exhibits good stability, flexibility, as\nwell as thermal conductivity and hence shows great potential for practical\napplication.",
"conclusion": "3 Conclusions In summary, we introduce\na numerical design and fabricate a novel\nbio-inspired hybrid Al water collector for efficient AWH. The collector\nconsists of fs-laser fabricated periodic superhydrophilic patterns\non a chemically modified superhydrophobic background. Fs-laser structuring\nnot only works as a tool to fast superhydrophilize the surface, enlarge\nthe surface area, and direct the water flow but also introduces a\nself-organized microhole array at the bottom of the open microchannels\nthat allows enhanced capillary condensation. We adopted the hybrid\nstructure and established a significant contrast of wettability between\nthe superhydrophobic background and the superhydrophilic pattern of\nthe collector, a contact angle difference of ∼163°, which\nenables ultra-efficient droplet nucleation, coalescence, and removal\nphase. Concluding from the results of the experiment and simulation,\nwe demonstrated that the inverted teardrop might be the best shape\nfor the repeating unit of the superhydrophilic pattern, and the area\nand apex angle of the unit were also optimized to be 45 mm 2 and 35° for maximum water harvesting rate. Based on the results,\nwe have successfully optimized the hybrid collector for AWH and demonstrated\na water harvesting rate of 0.85 kg m –2 h –1 . Considering the stability, accessibility, flexibility, and thermal\nconductivity of Al, our superhydrophilic/superhydrophobic hybrid Al\ncollector shows a great potential in solving the global water crisis\nby AWH.",
"introduction": "1 Introduction The global water crisis\nis continuously increasing due to the combined\neffect of climate change, population increase, deforestation, and\nrapid industrialization and urbanization. Over 2 billion people are\ncurrently living in water-stressed regions, and the number is projected\nto increase to over 5 billion by 2050. 1 The atmosphere is a significant source of water, which can store\nup to 4% of water vapor at 30 °C. If harvested efficiently, atmospheric\nwater harvesting (AWH) can provide a decentralized solution to the\never-growing water crisis. Water can be generated at the locations\nof its end-users, and thus AWH can solve the issue of long-distance\ntransportation to deliver water in rural areas. In regions with low\nprecipitation, AWH can be used to harvest water for the daily life\nof the residents as well as for irrigation. 2 Additionally, AWH can reduce the content of water vapor, a greenhouse\nmedium, in the atmosphere to reverse global warming. 3 Owing to the importance of AWH, extensive research\nefforts have\nbeen devoted to developing water collectors based on different working\nprinciples. The early collectors were relatively simple—usually\nmeshes with a large surface area without any microstructures, but\nthey exhibited low water harvesting efficiency. 4 In nature, countless plants and animals inhabiting arid\nclimates have evolved to accommodate the harsh environment. Either\ntheir skin or the secretion they discharge possesses special microstructures\nthat help the surface to maximize the capillary effect, heterogeneous\nwettability, or Laplace pressure gradient to efficiently harvest water\nfrom the atmosphere. 5 Following the studies\nof biotic water condensation and transportation, many artificial bio-inspired\nmaterials for AWH have been developed, 6 including spindle-knots that imitate the spider silk of Uloborus walckenaerius , 7 non-parallel plates motivated by the feeding mechanism of Phalaropus , 8 conical wires\nwith gradient wettability 9 and coated nanoneedles 10 enlightened by the spines of Opuntia microdasys , surface with inclined arc pitted\nmicrochannels that imitate the peristome of Nepenthes\nalata , 11 three-dimensional\nhierarchical structure that shares the same feature with the leaves\nof Cotula fallax ( 12 ) or hair of Salsola crassa , 13 and hybrid hydrophobic/hydrophilic\npatterned surface that mimics the back of Stenocara\ngracilipes . 14 Among these\nmaterials, the hybrid surface that possesses self-pumped droplet-delivering\nability is suggested to be one of the most efficient designs. The most important characteristic of a collector for AWH is that\nit can condense water vapor from the atmosphere with high efficiency\nand release the condensed water droplets from its surface in a frequent\nmanner. Biomimetic hybrid collectors composed of a hydrophobic background\nand a hydrophilic pattern show promising performance in finishing\nboth tasks. The core of the hybrid collector is to continuously gather\nwater to its hydrophilic region so that water droplets can quickly\nreach their critical mass (the maximum mass of the droplet that can\nbe held on the hydrophilic area of the surface) to sustain a high\nrenewal rate of the surface. This function should be present in both\ngas and liquid phases. In the gas phase, water vapor will be repelled\nby the hydrophobic region of the hybrid collector, leading to a rise\nin the local vapor concentration around the hydrophilic region. The\nhigher vapor concentration over the hydrophilic region promotes filmwise\ncondensation. On the other hand, in the liquid phase, the water droplets\nformed in the hydrophobic region through dropwise condensation experience\nlower surface drag, and therefore, these droplets can be easily transported\nfrom the hydrophobic region to the hydrophilic region under the effect\nof gravity, airflow, or coalescence. 15 As\na result, rather than spreading through the whole surface of the collector,\nwater will be mostly converged to and constrained within the hydrophilic\nregion, which limits the area of the interface between water and air\nand reduces the re-evaporation from the surface. Unfortunately,\nthe currently available techniques of constructing\nhydrophilic/hydrophobic hybrid structure collectors present several\ndisadvantages—these designs are either not long-lasting enough\nor too complicated for large-scale fabrication. For instance, several\nproposed collectors were made of polymeric materials, 16 but these materials are poor thermal conductors and known\nfor aging, degrading, as well as creeping, 17 especially in high-temperature environments and under exposure to\nUV light. 18 Some collectors combined two\nor more different materials to obtain the hybrid architecture; 19 however the weak connection between the two\nmaterials deteriorates the structural stability of the collector.\nDuring the fabrication process of some other collectors, a mask was\nrequired to selectively modify the uncovered material and endow it\nwith different properties. 20 Consequently,\ndespite the huge existing value and potential in application, few\nAWH collectors are commercially accessible, 21 leaving a non-negligible gap between the need and supply. Recently, laser has been applied to fabricate composite AWH collectors\nby selectively removing one of the materials to generate a hybrid\nsurface, 22 drilling holes to connect the\ninside and outside of a bucket 23 and scanning\nmetal foams to form Janus membranes; 24 laser\ndeposition has also been used to transfer patterns from one material\nto another to create hybrid surfaces. 25 To address the as-mentioned issues, selective laser surface ablation\nof aluminum (Al), 26 copper (Cu), 27 and poly(methyl methacrylate) (PMMA) 28 has been applied to fabricate hybrid collectors.\nAlthough these designs tried to enhance the centralization of water\nand maximize the surface energy and Laplace pressure gradients, none\nof them focuses on minimizing the interfacial force between water\ndroplets and the collector. Herein, we report a novel water collector\nfor AWH based on a hybrid hydrophobic/hydrophilic Al surface, which\naimed at interfacial force reduction and unidirectional transportation\nby optimizing the pattern of the shape of the superhydrophilic unit,\nas illustrated in Figure 1 a. Al foil is chosen as a start because it is easily accessible,\nmachinable, resists to aging, and has good thermal conductivity. The\npreparation procedures are presented in Figure 1 b: the surface of Al is etched chemically\nand modified with stearic acid ( Figure S1a ) to form a superhydrophobic background and then selectively patterned\nwith microstructure using a femtosecond (fs)-laser ( Figure S1b ). It has been reported that highly stable superhydrophobic\nfunctionality can be achieved through a simple and low-cost method\nof coating the surface with nonpolar group-terminated surfactant molecules, 29 such as forming a stearic acid self-assembled\nmonolayer. 30 The stearic acid monolayer\nsignificantly reduces the water affinity of the Al surface. Its molecules\nconnect to the Al substrate through covalent bonds, which are much\nstronger than van der Waals force, and therefore the collector is\nmore robust and durable, poses no contamination threat, and can withstand\na harsh environment. 31 Fs-laser has an\nadvantage in introducing stable micro/nanostructures onto different\nmaterials. 32 A program-driven fs-laser\nscanner with a predesigned digital pattern is employed to create superhydrophilic\npatterns on the superhydrophobic background so that the whole scanning\nprocess can be done automatically, and no mask is needed. A comparison\nof our work and the related designs is shown in Table S1 . Figure 1 (a) Schematic diagram of the hybrid collector, including\nzoom-in\ncross-sectional views of the superhydrophobic background (top) and\nthe superhydrophilic pattern (bottom). (b) Schematic illustration\nof the fabrication sequence: (1) Al foil being cut into pieces as\nraw material. (2) Etching of the Al foil to obtain a surface with\nmicroroughness. (3) Stearic acid-coating of the etched Al to form\na superhydrophobic background. (4) Selective fs-laser-ablation to\ncreate a superhydrophilic pattern (zoom-in is the top view of the\nmicrochannels and self-organized microhole array introduced by fs-laser). The developed hybrid AWH collector with superhydrophilic\npatterns\nsurrounded by the superhydrophobic background (hybrid collector; hereafter)\nis bio-inspired by the surface of the Stenocara beetle’s elytra,\nwhich comprises a waxy background and randomly distributed smooth\nhydrophilic bumps with irregular shapes. 33 However, nature still leaves plenty of room for us to further exploit\nits design to enhance the AWH performance. It is desirable to improve\nthe design of the hybrid surface to assure that an even higher surface\nrenewal rate can be achieved. Here, the parameters for laser scanning\nand the shapes of the hydrophilic pattern are optimized by combining\nboth simulation and experimental methods to enhance its superwicking\nand superhydrophilic properties as well as reduce the critical mass\nof water that can be constrained within each repeating superhydrophilic\nunit of the pattern. Water harvesting rates were also measured to\ndemonstrate the more frequently regenerated surface and the improved\nAWH performance of our hybrid collector with the optimized pattern.",
"discussion": "2 Results and Discussion 2.1 Design and Optimization of the Superhydrophilic\nPattern The fast nucleation-coalescence-removal cycle reflects\nthe superiority of the hybrid surface to the others, and thus, developing\na collector with hybrid superhydrophilic and superhydrophobic characteristics\nis necessary. To reduce the difficulty in scaling up the collector,\nthe hybrid collector is designed to be a combination of a continuous\nsuperhydrophobic background and an array of identical superhydrophilic\nunits with the same size and shape. We combined both simulation and\nexperimental methods to study the effect of the size and shape of\neach repeating unit, starting from a circle, on the efficiency of\nAWH. To maximize the efficiency of the hybrid collector, we started\nby focusing on only one unit of the repeating pattern to simplify\nthe problem. The degree of retention that a droplet on the collector\nreceives can be described by the retention factor r ( 34 ) 1 where θ A and θ R are the critical advancing and receding contact angles (as\nillustrated in Figure S2a ), respectively,\nwhile γ and ρ are the surface tension and density of water,\nrespectively. Equation 1 shows that the droplet needs to have a larger difference between\nθ A and θ R to balance the retention\nand to start sliding on a hydrophilic surface. Based on this deduction,\nit is straightforward that a circle, being the only two-dimensional\nshape that owns infinite symmetry, corresponds to a droplet with an\nidentical contact angle along the contour of the superhydrophilic\nunit. We can also conclude that, by introducing asymmetry to the circular\nunit, a larger disparity between θ A and θ R can be obtained, and the retention of the droplet will therefore\ndecrease. The infinite symmetry of a circle can be broken by simply\nintroducing an apex to its contour. This will reduce the axis of symmetry\nof the shape to 1. We set the two sides of the apex as tangent to\nthe circle at the two intersections to ensure a smooth transition\nbetween the arc and the apex. Hereafter, this shape will be called\na teardrop, and the schematic illustration of which is shown in Figure S2b . We first studied the influence\nof the additional apex on the superhydrophilic\nunit with the area of the unit set as constant. This part of the simulation\nwas based on the assumption that the amount of water condensed on\nthe surface is proportional to the area of the superhydrophilic units,\nwhile the superhydrophobic background has no contribution to water\nharvesting. Thus, the volume of the droplet is constant throughout\nthe simulation. The simulation program HyDro100 was applied to study\nthe contact angle of static water droplets placed on the superhydrophilic\nunit. By performing an energy-minimization calculation, the stable\nstate of a water droplet that is constrained inside the pattern can\nbe simulated. All HyDro100 simulations were done with the collector\nplaced horizontally, which means gravity is perpendicular to its hybrid\nsurface, and thus no horizontal external force will affect the shape\nof the droplet on the collector. After adding an apex to the circle,\nthe contact angle of the droplet will no longer be equal around the\ncontour of the unit. Our simulation reveals that the contact angle\nhas a global minimum at the apex of the contour, and it reaches its\nglobal maxima at both shoulders of the apex ( Figure S3 ). As shown in Figure 2 a, when the area of the unit is kept constant, the contact\nangle maxima increase with the decrease in the apex angle. Decreasing\nthe apex angle results in an increase in the disparity between the\ncontact angles at the arc side and at the apex. Consequently, the\nteardrop shape is selected to be the initial shape of the repeating\nunit. Figure 2 Simulations of the shape of water droplets. (a) Superhydrophilic\nunits with the same area of 10 mm 2 but different apex angles\nof 15, 30, 45, 60, 90, and 180° and the maximum contact angle\nof a water droplet with the mass of 20 mg that is constrained inside\nthe corresponding unit. Apex angle versus maximum contact angle: disparity\nof contact angles on the arc and apex side of the unit of each droplet\nare shown in black and red with fitted curves (dashed lines). Side\nview of water droplets with mass of 50 mg constrained inside units\nwith the shape of (b) erected teardrop and (c) inverted teardrop with\nfixed apex angle of 60° and area of 50 mm 2 on a vertically\nplaced collector with and without vertical disturbance (shown in blue\nand gray, respectively). After designing the shape of the hydrophilic pattern,\ngravity is\ntaken into consideration. In this step, all investigations will be\nbased on the collector being placed vertically, which is the same\nway that the collector is placed in working conditions, and therefore\nthe influence of gravity on the motion of the droplet cannot be ignored.\nIn an ideal model represented by eq 2 , 35 for a static droplet\non a vertical surface, the gravitational force ( F g ) on the droplet numerically equals the interfacial force\nbetween the droplet and the surface (right side of the equation) while\nthey are opposite in direction 2 where m is the mass of the\ndroplet, g is the gravitational acceleration, and d is the maximum diameter of the contact area between the\ndroplet and the repeating unit. The simulation program Surface Evolver\nwas used to study how gravity shapes the surface of two droplets (denoted\ndroplet I and droplet II) that are constrained within two units on\nthe surface of two standing collectors. The area and apex angle of\nthe hydrophilic units and the volume of the droplet are set to be\n50 mm 2 , 60°, and 50 mg, respectively. The efficiency\nof the collectors was simulated in two opposite configurations, i.e.,\nwith the apex of the unit pointing upward (droplet I) and downward\n(droplet II) (as shown in the top-right insets of Figure 2 b,c, respectively) to judge\nwhich direction is better at promoting the movement of the droplet.\nThe parameter of gravitational acceleration was increased from 9.81\nm s –2 by 10% in the simulation to mimic a vertical\ndisturbance on the droplet. As shown in Figure 2 b,c, the term (cos θ R –\ncos θ A ) of droplet I and II is changed by 11.7 and\n61.5% after the disturbance was introduced, respectively. Hence, the\ndisturbance imposes a stronger impact on droplet II and more interfacial\nforce is needed to balance out the disturbance, otherwise, the droplet\nwill begin to slide. In addition, for a given area and apex angle,\nour experiment also revealed that the critical mass of the water droplet\nconstrained within the erected teardrop-shaped unit (droplet I) is\nmuch less than that of the droplet constrained inside an inverted\nteardrop-shaped (droplet II) unit. This result shows that the inverted\nteardrop has a lower retention than the erected one, which is beneficial\nto the regeneration of the surface. During the accumulation\nof water within the inverted teardrop-shaped\nunit, the center of gravity of the constrained droplet slowly moves\ntoward the apex of the unit. The two sides of the apex work as two\nblades of a scissor and exert shear force on both sides of the droplet\nto remove it from the surface. When the condensed water will slide\ndown from the unit, its contact area with the superhydrophilic unit\nwill decrease; however, the contact area with the hydrophobic background\nwill increase, which in turn will gradually reduce the interfacial\nforce between the collector surface and the water droplet. Since the\narc is at the upper part of the unit while the apex angle is at the\nlower part, the difference in contact angle will be even greater when\nthe collector is placed vertically in its working state, and the larger\nthe maximum contact angle is, the condensed water is more likely to\nroll off from the surface. The simulation results indicate that\nthe superhydrophilic pattern\nwith inverted teardrop-shaped units is the most suitable candidate\nfor further optimization. Since area and apex angle are the only two\nparameters that are needed to define a teardrop shape, we aim to find\nthe most compatible pair of these parameters through experimentation. 2.2 Fabrication and Characterizations The surface microstructure of different Al samples was characterized\nby confocal laser surface microscopy (CLSM) and scanning electron\nmicroscopy (SEM), and the results are shown in Figure 3 . The untreated Al foil possesses a smooth\nsurface. After chemical etching, the surface of Al becomes rough ( Figure 3 a) and uniform sponge-like\nvillous nanostructures can be seen ( Figure 3 b), as a result of the intense attack of\nacid during etching. Despite having the same apparent surface area,\nthe etched Al presents a much larger effective surface area owing\nto the nanostructures, which paves the way for tuning of the surface\nenergy of Al toward the extreme. Irradiated by the fs-laser, parallel\nstraight microchannels are ablated on the Al surface ( Figure 3 c). The morphology of the surface\naround the microchannels is constructed by microstructures covered\nwith nanoprotrusions and nanocavities formed by melting and refreezing\nof the metal. At the bottom of each microchannel, there is a string\nof self-organized microholes introduced by laser ablation-induced\nincubation ( Figure 3 d). 36 However, we reproduced the microhole\narray with only 2–3 scans ( Figure S4 ), much less than the minimum required scan number reported, which\nmight be due to the higher laser fluence used. Figure 3 (a) 3D CLSM map and corresponding\n(b) SEM image of the superhydrophobic\nregion of the hybrid collector. (c) CLSM map and corresponding (d)\nSEM image of the superhydrophilic region of the hybrid collector.\nDifferent colors in the elevation maps from blue to red only represent\nthe relative elevation from low to high. (e) Effects of the fs-laser\nscan numbers on the diameters and depths of the microholes along with\nthe volume of the condensed water in a microhole at equilibrium state. Formation of the self-organized microhole array\nis believed to\ntake place only under fs-laser irradiation due to the localized melting\nwithin the small heat-affected zone of fs-laser, accompanied by the\nMarangoni effect. These microholes are important in enabling capillary\ncondensation where vapor-phase water molecules get confined, resulting\nin an enhanced van der Waals interaction among them. The enhanced\ninteraction causes condensation of water vapor below the saturation\nvapor pressure. As soon as the water gets condensed into the microholes,\nit forms an extended meniscus at the air–water interface that\nsets an equilibrium below the saturation vapor pressure, as suggested\nby the Kelvin equation ( eq S5 ). 37 Since this phenomenon takes place easier in\nsmaller pores, the tenability of the microholes can be exploited to\nachieve better condensing property of the fs-laser-ablated Al. As\nshown in Figure S4 , with accumulating scan\nnumbers, the depth of the microholes increases and their tips become\nsharper, which is a favorable condition for capillary condensation.\nSince the self-organized microholes share a similar conical structure\nand the diameter of their opening is almost independent of the scan\nnumber, therefore for given experimental conditions, microholes with\na sharper apex angle will condense more water ( Figure 3 e; eq S6 ). The\ndepth and diameter of the pores increase with the scan number and\nstagnate at 5 scans, and thus this scan number is chosen for fs-laser\nfabricating of the superhydrophilic units. Chemical etching\nis used to create microroughness on the surface\nof the Al foil to increase the surface area in the fabrication of\nthe superhydrophobic background. After etching, not only the surface\nmorphology but also the surface chemistry has been changed. As shown\nin Figure S5 , the formation of hydroxyl\ngroups on the surface of Al during chemical etching is suggested by\nthe broad stretching band (νOH) centered at 3420 cm –1 , which increases the water affinity of the surface and provides\nactive sites for the carboxyl group of stearic acid to bond with. 38 After treating the etched Al with stearic acid,\na self-assembled stearic acid monolayer was engrafted on the surface\n(as illustrated in Figure 1 a). Several characteristic peaks emerged after the stearic\nacid modification, including peaks at 1459, 2852, and 2925 cm –1 , which are assigned to scissoring, symmetric, and\nasymmetric stretching of the methylene group in the alkyl chain of\nstearic acid (δ s CH 2 , ν s CH 2 , and ν as CH 2 ), respectively. 39 The peak at 1577 cm –1 is assigned\nto asymmetric stretching of the carboxylate group (ν as COO – ) while no sign of the carboxyl group can be\nfound, which is indicative of no free stearic acid being left on the\nsurface. 40 The formed insoluble aluminum\nstearate firmly anchors the stearic acid onto the Al surface through\na covalent ester bond, 41 and the nonpolar\nlong alkyl chains are forced to face outward ( Figure S5a ) and thus the surface energy of the sample is reduced.\nBecause of the ultralow surface energy of the superhydrophobic Al,\nwater on the surface tends to contract, forming a Cassie–Baxter\nstate droplet and leaving a minuscule interfacial area beneath it.\nThe air trapped between the micro-protrusions weakens the interfacial\nforce between the water droplet and the surface. These contributors\nprovide the Al surface with excellent superhydrophobicity. Fs-laser\ntreatment can selectively turn the surface superhydrophilic\nby modifying the selected area with the formation of microgrooves/microholes\nand the simultaneous removal of the steric acid monolayer. It is evidenced\nby identical FTIR spectra of the etched and the laser-ablated Al ( Figure S5b ). The band centered at 880 cm –1 is assigned to the stretching of the aluminum–oxygen\nbond (νAl–O) of Al 2 O 3 . 36 To evaluate the wettability of the untreated,\nchemically etched,\nstearic acid mono-layered, and fs-laser-treated Al samples, the contact\nangles of a water droplet on these surfaces were measured. It can\nbe found that the water on the untreated Al surface displays a contact\nangle of 95° ( Figure 4 a), representing that its surface is slightly hydrophobic.\nAlthough coating the material’s surface with nonpolar group-terminated\nmolecules is an effective way to lower the surface energy, it is known\nthat, even after reducing the surface energy to the theoretical minimum\nby coating only, the maximum contact angle of a water droplet on a\nsmooth flat surface that can be reached is merely ∼120°. 42 Therefore, chemical etching was applied to enlarge\nthe surface area, since changing surface area is the only way to modify\nthe wettability when the surface chemistry stays unchanged, as the\nCassie–Baxter equation predicts. 43 After etching Al, the contact angle shrinks to 0° ( Figure 4 b), indicating that\nthe enlarged surface area drastically raises the surface energy and\nthus will be able to compensate for the consumption of surface energy\nduring the spreading of water over the surface. After being coated\nwith stearic acid, the water-repelling property of the etched Al is\nalso enhanced consequentially, and a large contact angle of 163°\ncan be obtained ( Figure 4 c). Figure 4 Contact angles of a water droplet on (a) untreated, (b) chemically-etched,\nand (c) stearic acid-coated Al samples. (d) Frames taken from a high-speed\ncamera video of a water droplet at 0, 5, 10, 50, and 100 ms (from\nleft to right) after it touched the surface of a horizontally placed\nfs-laser-treated Al sample. (e) Time-series snapshots of positions\nof the waterfront on a vertically mounted fs-laser-ablated Al foil\nwith its microchannels parallel to the direction of gravitational\nforce and its lower end brought in contact with the water surface.\nThe time interval between two neighboring frames is 0.25 s (from left\nto right). The line spacing and the scan number are 0.15 and 5, respectively.\nThe red dashed lines are indication of the waterfront. Recently, we have reported the development of a\nsuperhydrophilic\nAl surface treated using almost unanimous fs-laser parameters that\nexhibits a superwicking property and water evaporation performance\nhigher than an ideal evaporator working at 100% efficiency. 44 Fs-laser treatment induces self-organized microholes,\nfurther enlarging the specific surface area to enhance the wettability\nof Al. Water spreads even faster within the rehydrophilized fs-laser-ablated\narea over the chemically etched and coated Al, which can be ascribed\nto the directional superwicking of the microchannels created by the\nfs-laser. This special wetting property of the fs-laser-ablated Al\nwas demonstrated by putting a droplet of water onto the sample. As\nshown in Figure 4 d,\nit takes less than only 0.1 s for the droplet to infiltrate into the\nmicrochannels and spread throughout the surface of a horizontally\nplaced fs-laser-treated Al sample. The strong wettability is attributed\nto the parallel array of microchannels ablated on the surface, enabling\na strong directional capillary effect. 45 This is supported by Figure 4 e, an observation of the anisotropic pervasion behavior of\na water droplet’s waterfront, which shows the fast spreading\nspeed of the waterfront along the direction of the microchannels.\nSuperwicking and directional transportation are important properties\nthat can be utilized by the hybrid collector so that the removal of\nthe condensed water on the collector can be promoted. The significant\ncontrast in wettability between the superhydrophilic and superhydrophobic\nregions established on the Al surface by combining chemical and fs-laser\ntreatments is the foundation of an efficient water collector and the\nhybrid superhydrophilic/superhydrophobic collector can be further\nimproved by combining the delicate optimization of the superhydrophilic\npattern. To ensure that the best wicking property of the superhydrophilic\npattern can be achieved, the parameters (period and depth) of the\nparallel straight microchannels were optimized by studying the water\nspreading speed on the surface of vertically mounted superhydrophilic\nAl samples. The superhydrophilic strips consisting of parallel microchannels\nwith line spacings of 0.1, 0.125, 0.15, 0.175, and 0.2 mm were fabricated\non superhydrophobic Al foils. As presented in Figure S6a , when the lower ends of the samples touched the\nwater surface, the water quickly climbed up through the capillary\neffect of the microchannels on every superhydrophilic strip. It is\nclear that the waterfronts elevate at different speeds and a faster\nelevation speed indicates a stronger wicking property of the strip.\nThe sample with a microchannel line spacing of 0.15 mm showed the\nhighest speed of the elevating waterfront. Furthermore, microchannels\nwith different depths were created by varying the scan numbers of\n1, 2, 3, 4, and 5 for laser ablation. As shown in Figure S6b , the wicking property of the superhydrophilic strips\nimproves with increasing microchannel depths while the increment gets\nsmaller after the scan number surpasses 3, which aligns with the prediction\nmade earlier by measuring the cross-sectional profile of the samples.\nTherefore, the line spacing of 0.15 mm and the scan number of 5 were\nchosen for the fabrication of the designed and optimized inverted\nteardrop-shaped superhydrophilic patterns on the superhydrophobic\nbackground sample in sample fabrication in the following experiments. 2.3 Nucleation, Growth, and Release of the Water\nDroplets To demonstrate the water harvesting superiority\nof the hybrid superhydrophilic/superhydrophobic collector over the\nother samples (untreated Al, completely superhydrophobic Al, and completely\nsuperhydrophilic Al), the nucleation, growth, and release dynamics\nof water droplets were recorded through a microscope with a setup\nshown in Figure S7 . The images in\nthe first column from the left ( Figure 5 a,e,i,m) are initial photographs of the clean samples\nbefore blowing vapor on them. The second, third, and fourth columns\n(from left to right) are the typical phases representing the nucleation,\ncoalescence, and removal processes of water droplets on each sample.\nAt the beginning of condensation, water nucleates and forms discrete\ndroplets on the untreated Al ( Figure 5 b) and the superhydrophobic Al ( Figure 5 j) samples. However, it forms a thin film\non the superhydrophilic Al surface ( Figure 5 f). The droplets on the untreated ( Figure 5 c) and superhydrophobic\nAl ( Figure 5 k) surfaces\ngrow larger, while the water layer on the superhydrophilic Al ( Figure 5 g) becomes thicker\nover time. Water condensing in the form of a film might be a disadvantage\nsince it extends the water–solid interface covering the entire\nsurface of the collector and resulting in three major consequences\nthat can be foreseen: (1) a larger interfacial force that prevents\nwater from falling and remarkably increases the critical mass; (2)\na higher evaporation rate of the condensed water that increases the\ntime needed to reach the critical mass; and (3) a larger thermal resistance\nfrom the water film blocks heat exchange between the water vapor and\nthe collector surface and thus affects the condensation performance\nof the surface. Figure 5 Microphotos recorded during the water condensation and\nregeneration\nprocess on (a–d) untreated, (e–h) completely superhydrophilic,\n(i–l) completely superhydrophobic, and (m–p) hybrid\nsuperhydrophilic/superhydrophobic Al. Schematic illustration of the\ntwo major water harvesting mechanisms of the hydrophobic/hydrophilic\nhybrid water collector take place in (q) gas phase and (r) liquid\nphase shown in cross section. Hydrophobic and hydrophilic regions\nof the collector are shown in yellow and green, respectively. Although the regular Al foil is less hydrophilic,\nits water adhesion\nis still considerably strong, leading to two major drawbacks: (1)\nthe critical mass of the water droplet on regular Al is larger than\nthat on the superhydrophobic surface. (2) A small amount of condensed\nwater will always be left in the path of a fallen droplet. Both of\nthese disadvantages are unfavorable to the regeneration of the collector\nsurface. However, in the case of the superhydrophobic surface, two\nadjacent cycles are highly overlapped: during the coalescence phase\nin the preceding cycle, the surface started regeneration and the nucleation\nof new droplets of the succeeding cycle can be seen in Figure 5 k. This functionality is absent\nin the untreated Al ( Figure 5 c). Faster release of the water droplets from the superhydrophobic\nAl surface and more frequent nucleation of new droplets are attributed\nto a significantly smaller liquid–solid interface. In contrast,\nwater droplets on both untreated Al and superhydrophobic Al have to\ncoalesce with the neighboring ones until their mass exceeds the critical\nvalue. The efficiency of water harvesting can be substantially\nenhanced\nif the discrepancy between the superhydrophilic and superhydrophobic\nregions is utilized. Opposite to the completely superhydrophobic and\ncompletely superhydrophilic samples, the hybrid sample builds up a\nconcentration gradient of water vapor between the superhydrophobic\nand superhydrophilic regions in the gaseous phase ( Figure 5 q). When a small disturbance\ntakes place, water droplets condensed in the peripheral regions can\nbe repelled from the superhydrophobic region and get collected by\nthe superhydrophilic region ( Figure 5 r). A water droplet growing through coalescence in\nthe superhydrophobic regions of the hybrid collector gets efficiently\nharvested by the superhydrophilic regions before its size reaches\nthe critical mass. Therefore, the maximum size of the water droplet\n(∼1 mm diameter) in the superhydrophobic region of the hybrid\ncollector ( Figure 5 p) is much smaller than the critical size (∼3 mm) of the droplet\non the completely superhydrophobic surface ( Figure 5 l ). This functionality accelerates water\naccumulation in the superhydrophilic region, so that the superhydrophobic\nregion achieves a fast regeneration ( Figure 5 p). Consequently, the surface of the hybrid\ncollector can be frequently refreshed, which outperforms other Al\nsurfaces. The water harvesting rate of a single repeating unit\n( R 0 ) of the superhydrophilic pattern can\nbe expressed as 3 where m c is the\ncritical mass of the water droplet that can be constrained within\nthe repeating unit, t c is the length of\na regeneration cycle (the time needed for the mass of the condensed\nwater in the droplet to reach m c ), and R e is the evaporation rate of water within the\nunit. Based on eq 3 ,\nto optimize the performance of a hybrid collector, the repeating unit\nneeds to have a higher m c to t c ratio and a lower R e . Additionally,\nsince t c and R e are both functions of temperature and relative humidity, R 0 indirectly depends on temperature and relative\nhumidity. From an energy conservation point of view, the collector\nconsumes the least amount of energy for cooling if it is designed\nto work just below the dew point. Therefore, the shape of the repeating\nunit was optimized for dew point operation at which condensation reaches\ndynamic equilibrium with evaporation, and thus R e in eq 3 can\nbe neglected. To determine R 0 , m c , and t c of the\nsuperhydrophilic\nunits, areas and apex angles of different superhydrophilic patterns\nwere measured. Thanks to galvanometer laser processing, teardrop-shaped\nsuperhydrophilic units with areas of 3.14, 7.07, 12.57, 19.63, 28.27,\n38.48, and 50.27 mm 2 (corresponding to the circular part\nof the teardrop shapes with diameters ranging from 1 to 4 mm with\nan interval of 0.5 mm) and apex angles of 15, 30, 45, 60, 90, and\n180° could be easily produced. The resulting m c and t c values were fitted\nwith polynomials to interpolate as well as extrapolate, and the squares\nof multiple-correlation coefficients of the fitted model were 0.985\nand 0.983. R 0 was calculated by dividing m c by t c . The mapped\nthree-dimensional graphs of m c , t c , and R 0 with the\narea and apex angle of the unit as independent variables are shown\nin Figure 6 . Figure 6 Dependency\nof (a) critical mass of water that can be constrained\nwithin one repeating unit of the pattern, (b) length of a regeneration\ncycle, and (c) water harvesting rate of one superhydrophilic unit\non the area and the apex angle of the repeating unit. Photo showing\nbefore (left) and after (right) putting the same amount of water onto\nthe teardrop-shaped superhydrophilic units with (d) apex angles of\n15, 30, 45, 60, 90, and 180° and a constant area of 45 mm 2 , and (e) area of 3.14, 7.07, 12.57, 19.63, 28.27, 38.48,\nand 50.27 mm 2 and a constant apex angle of 35°. It can be seen from Figure 6 a that the value of m c grows with\nthe area and apex angle of the superhydrophilic unit. The water affinity\nof the superhydrophilic unit favors the water condensation but also\nconstrains the condensed water within the unit. It has been proven\nthat a superhydrophilic pattern with an apex angle can create a surface\ntension gradient that drives the droplet toward the opposite side\nof the apex. 46 By reducing the apex angle\nof the unit, the weight distribution of the droplet becomes less even\nand the deformation of the droplet becomes more pronounced ( Figure 6 d), which explains\nthat smaller apex angles are more capable of tearing the droplet off\nfrom the surface. However, it might be counterintuitive that a smaller\nsuperhydrophilic unit is accountable for a longer t c , as suggested by Figure 6 b. This is because units with different areas correspond\nto different m c , a small area helps the\ndroplet to maintain a semi-spherical shape, while the gravitational\nforce has a stronger impact on the shape of the droplet constrained\nwithin a larger unit. The larger gravitational impact on the droplet\nshape brings a larger displacement of the center of gravity ( Figure 6 e). Additionally,\nbecause of the severer deformation, the upper part of the droplet\ncontributes less to the interfacial force. Furthermore, since the\ncondensation rate is positively correlated to the area of the interface\nbetween liquid and gas phases, i.e., the surface area of the droplet,\na more pronounced deformation of the droplet will result in a larger\nsurface area of the droplet, leading to a higher condensation rate.\nAll of the above reasons account for the shorter t c of the units with larger areas. Searching through\npossible combinations of the area and apex angle,\nonly a single maximum of R 0 of 3.08 mg\nmin –1 has been found ( Figure 6 c). By calculating the extreme of the curved\nsurface, we obtained the optimum area and apex angle, which are 45.11\nmm 2 and 35.06°, respectively. The approximated values,\n45 mm 2 and 35°, were used as the dimensional parameters\nin fabricating the superhydrophilic units of the designed hybrid collector.\nThe optimized superhydrophilic units were fs-laser written directly\non the superhydrophobic background, and the water harvesting performance\nof the hybrid collector was measured using the setup shown in Figure 7 a. The array of superhydrophilic\npatterns was designed to be vertically parallel strings of repeating\nunits ( Figure S2c ). The spatial period\nof the strings and the spatial period of the units within one string\nare 5.97 and 12.91 mm (the width and height of a unit), respectively.\nEvery other string was staggered 6.47 mm (half the height of a unit)\nto fully make use of the surface while reducing the interference between\nneighboring units. A photo of the fabricated hybrid collector is shown\nin Figure 7 b. Figure 7 (a) Schematic\nsetup for measuring water harvesting performances\nof different collectors. (b) Photo of a hybrid collector. (c) Water\nharvesting rates of untreated Al, complete superhydrophilic Al, complete\nsuperhydrophobic Al, and the hybrid collector. (d) Schematic illustration\nof the water harvesting process of the hybrid collector. (e) Start-up\ntime, (f) average time interval between two successive droplets, and\n(g) average mass of the collected droplets of different collectors. 2.4 AWH Performance of Different Collectors Water harvesting rates of untreated Al, complete superhydrophilic\nAl, complete superhydrophilic Al, and the hybrid collector were measured\nto provide a convenient comparison of the harvesting characteristics\nof different samples. The results can also be seen as the performance\nof the collectors in a foggy environment. After being cooled down\nto 7.5 °C (close to the dew point), all collectors experienced\na short period of starting-up, which is the time needed for the water\nvapor to condense and the condensed droplets to coalesce before reaching\ncritical mass. It can be found that the order of the starting-up times\nfollows the order of average hydrophobicity ( Figure 7 e). The superhydrophilic collector has the\nshortest starting up time of 6 min because its entire surface area\nis suitable for water condensation. The regular untreated Al has the\nsecond shortest (12 min) starting up time. Since over 41% of the surface\narea is superhydrophobic, the hybrid collector comes third with a\nstarting up time of 17 min. The superhydrophobic Al exhibits the slowest\nstarting up which takes 26 min. As presented in Figure 7 c, the masses of water collected by all collectors\nshow linear growth which is indicative of stable harvesting rates\nafter starting up. As predicted, the hybrid collector shows the best\nperformance of 0.85 kg m –2 h –1 . The harvesting rates of complete superhydrophilic, untreated, and\ncomplete superhydrophobic collectors are 0.77, 0.56, and 0.44 kg m –2 h –1 , respectively. The time interval\n(2.8 min) between two water droplets falling into the reservoir is\nsignificantly longer for the complete superhydrophobic collector ( Figure 7 f), which is clearly\ndue to the water-repelling nature of the surface that makes unfavorable\nconditions for the nucleation and growth of the water droplets. The\nsuperhydrophilic collector possesses characteristic filmwise condensation\nbehavior of seemingly identical droplet sizes, which is due to the\nfact that the mass of the droplet on the complete superhydrophilic\ncollector is affected by the area of the entire collector as described\nin Section 2.3 . The\nhybrid collector carries the advantage of fast condensing and frequent\nrefreshing inherited from the superhydrophilic collector as well as\nthe advantage of easier removal of water inherited from the superhydrophobic\ncollector and therefore presents a self-pumped mechanism ( Figure 7 d) and exhibits the\nshortest average time interval between two successive water droplets\n(115 s) as well as the highest average mass of the droplets (72 mg).\nTherefore, the hybrid collector achieves a 93% AWH harvesting rate\nenhancement over the regular untreated Al collector."
} | 11,237 |
38820146 | PMC11141615 | pmc | 5,129 | {
"abstract": "In recent years, there has been a growing interest in engineering dynamic and autonomous systems with robotic functionalities using biomolecules. Specifically, the ability of molecular motors to convert chemical energy to mechanical forces and the programmability of DNA are regarded as promising components for these systems. However, current systems rely on the manual addition of external stimuli, limiting the potential for autonomous molecular systems. Here, we show that DNA-based cascade reactions can act as a molecular controller that drives the autonomous assembly and disassembly of DNA-functionalized microtubules propelled by kinesins. The DNA controller is designed to produce two different DNA strands that program the interaction between the microtubules. The gliding microtubules integrated with the controller autonomously assemble to bundle-like structures and disassemble into discrete filaments without external stimuli, which is observable by fluorescence microscopy. We believe this approach to be a starting point toward more autonomous behavior of motor protein–based multicomponent systems with robotic functionalities.",
"introduction": "INTRODUCTION Living organisms are autonomous systems capable of sensing their environment, processing information, and executing the necessary actions ( 1 – 3 ). Inspired by this fascinating autonomy, researchers have been seeking to synthesize autonomous systems that do not require manual operations. Therefore, bioinspired robotics ( 4 ) has emerged as a field focusing on engineering from hard ( 5 ) and soft materials ( 6 ). The extreme miniaturization of bioinspired soft materials has led to molecular robotics ( 7 – 9 ), aiming to construct robots from molecular components. Biomolecules such as nucleic acids and proteins are promising building block candidates for molecular robots because of their programmability and high specificity ( 10 – 12 ). DNA is among the versatile materials for building molecular architectures ( 13 – 18 ) and designing chemical cascades ( 19 – 22 ) owing to its ability to arbitrarily tune the reactivity to other DNA by designing the sequences of its building units. Several attempts have been made to develop DNA-based molecular systems with dynamic robotic functionalities, such as assembly lines, autonomous molecular crawling, and reversible cumulative actuation ( 23 – 25 ). However, slow and small actuation abilities are major drawbacks of DNA-based dynamic molecular robots. Integrating various enzymes, such as DNA polymerase and exonuclease, into DNA strand displacement can accelerate its actuation ( 26 , 27 ). In contrast, combining relatively larger materials, such as polymers or microparticles, can magnify its motion ( 28 – 30 ). Yet, overcoming these drawbacks at the same time remains a challenge. Biomolecular motors have been identified as potential resources to address actuation limitations ( 31 ). For example, microtubules (MTs) propelled by kinesins ( 32 ) have been used as powerful molecular motors to drive macroscopic actuators, biosensors, and information processing systems ( 33 – 35 ). MTs used as molecular actuators are also compatible with DNA systems ( 36 ), where artificial muscles, spatiotemporal patterning, and active liquid crystals have been proposed ( 37 – 39 ), although these systems are used only once and are not reversible. To develop reversible molecular robots with fast and large actuation, DNA chemical reactions have been used to programmatically regulate the behavior of DNA-conjugated kinesins or MTs, including vesicle transformation, aster formation, and spatiotemporal pattern control ( 38 , 40 – 42 ). We have previously investigated the swarming behavior of MTs gliding on a kinesin-adsorbed glass substrate ( 43 ), reversible examples of which include step-by-step assembly/disassembly and cargo loading/unloading dynamics ( 44 – 46 ). Despite this important progress, the current generation of reversible MT-kinesin systems are limited in their ability to autonomously perform multiple tasks sequentially, as observed in living organisms, owing to the lack of autonomous control mechanisms suitable for molecular systems. To maximize the capability of an active molecular system, demonstrating spontaneous temporal behavior without planned external stimuli by combining chemical controller and molecular actuator is of importance. Toward the goal, this study introduces an active molecular system that exhibits autonomous swarming of self-propelled MTs. We used a cascade reaction system as a controller for the gliding MTs, which sequentially generates different DNA strands with a delay. The initially released DNA acts as a cross-linker, causing DNA-functionalized MTs to assemble into bundle-like swarms, and the subsequent DNA removes the cross-links that dissociate the bundles. During the process of the combination, fundamental trial and error was necessary to couple the DNA reactions with MT-kinesin system due to the differences in reaction conditions such as temperature and molecular concentration, which is one of the contributions of this work. After experimental optimization, sequential generation of the DNA strands by the designed reaction cascades was confirmed by gel electrophoresis under practical condition compatible to the MT-kinesin system. Using fluorescence microscopy, we finally demonstrated the autonomous association and dissociation of gliding MTs by the designed controller. This study paves the way for developing autonomous and self-regulated molecular robots.",
"discussion": "DISCUSSION This study demonstrated the autonomous association and dissociation of gliding MTs using a DNA-based molecular controller ( Fig. 1 ). The experimental results showed that a DNA chemical reaction system can sequentially generate two types of DNA strands with a sufficient delay, which can hybridize and dehybridize the two receptor DNAs ( Fig. 2 ). Last, by introducing the designed molecular controller into the DNA-conjugated MT system, we successfully controlled the assembly and disassembly of MTs gliding on a kinesin-coated surface without an external control ( Fig. 3 ). The integrated system clearly showed that the molecular controller made of DNA drove reversible state transitions in the behavior of the MT biomolecular motor system. The transitions were quantitatively measured by color and DDM analysis, indicating that the green and magenta MTs colocalized and formed large motile structures. The structures were observed from approximately 20 to 40 min and disappeared after that ( Fig. 3, C and D ). However, some limitations to the functionality of the proposed system exist. For example, this study did not address changing the delay between autonomous association and dissociation. Moreover, the autonomous association and dissociation of receptor DNAs can occur only once in our system. Installing DNA-based oscillators is one possible solution for repeating association and dissociation cycles ( 53 ). Although conventional DNA oscillators can control the behavior of stable materials such as polystyrene beads ( 29 ), slow oscillation is not suitable for fragile materials such as MTs with short lifetimes. The short lifetime of the MT-kinesin system (approximately 2 hours) is caused by the detachment of MTs from the kinesin-coated glass surface (fig. S8). We anticipate that the cause of detachment is the hindrance of the interaction between MTs and kinesins, which might result from high salt concentration and temperature. However, this limitation could be overcome by using thermostable kinesin extracted from thermophilic fungi ( 54 ) and further optimizing the buffer composition. Nevertheless, our rapid molecular controller has the potential to enable fragile MT systems to respond immediately to changes in the surrounding environment. As an issue for future research, the DNA circuit designed in this study has an output with a delay and signal amplification; however, the state of MT gliding as an actuator is not explicitly fed back. Molecular programming of the DNA circuits to respond according to the state of the actuated system, such as the thickness of the bundle, magnitude of the load, and difference in signal concentration, would provide smarter behavior of swarming molecular robots. By solving these issues, harnessing an autonomous molecular system for nano-micro technological applications ( 55 ) will be our next challenge. The proposed system offers a new concept of autonomously controlled materials, which we named “auto-matter” realized and driven by molecules and equipped with a smart controller that encodes the instructions of the system."
} | 2,164 |
24675756 | PMC3968457 | pmc | 5,130 | {
"abstract": "Cyanobacteria are oxygenic photosynthetic prokaryotes that play important roles in the global carbon cycle. Recently, engineered cyanobacteria capable of producing various small molecules from CO 2 have been developed. However, cyanobacteria are seldom considered as factories for producing proteins, mainly because of the lack of efficient strong promoters. Here, we report the discovery and verification of a super-strong promoter P cpc560 , which contains two predicted promoters and 14 predicted transcription factor binding sites (TFBSs). Using P cpc560 , functional proteins were produced at a level of up to 15% of total soluble protein in the cyanobacterium Synechocystis sp. 6803, a level comparable to that produced in Escherichia coli . We demonstrated that the presence of multiple TFBSs in P cpc560 is crucial for its promoter strength. Genetically transformable cyanobacteria neither have endotoxins nor form inclusion bodies; therefore, P cpc560 opens the possibility to use cyanobacteria as alternative hosts for producing heterogeneous proteins from CO 2 and inorganic nutrients.",
"discussion": "Discussion In this study, we discovered and verified a super-strong promoter P cpc560 for efficient and strong expression of heterologous genes in cyanobacteria. The newly discovered super-strong promoter P cpc560 consists of two predicted promoters from the cpcB gene and 14 predicted TFBSs. Using P cpc560 , two heterologous genes were expressed in the cyanobacterium S . 6803 to levels of up to 15% of total soluble protein. Despite the fact that only a single copy of the target gene was inserted into the chromosome of S . 6803, the expression level of the target gene driven by P cpc560 was comparable to that which can be achieved in an E. coli expression system using low- or medium-copy number plasmids. This demonstrates that P cpc560 has potential applications for efficient production of recombinant proteins in cyanobacteria. It is generally accepted that the genomic sequence 200–300 bp upstream of the initiation codon of a gene that is constitutively expressed at high levels can be used as a promoter sequence. Previous studies have shown that the thymine at 259 bp upstream of the initiation codon of cpcB is crucial for cpc promoter activity 24 . Recently, a genomic map of the transcriptional start sites (TSS) of S . 6803 showed that the cpcB gene is one of the genes with a long distance between its TSS and start codon 25 . Further examination revealed that most of the S . 6803 genes with a long distance between the TSS and start codon are responsive to environmental factors 25 . Since the cpcB gene is a light-and redox-responsive gene 26 27 , it is plausible that the length of the cpcB promoter is related to its responsiveness to environmental factors. P cpc560 is unusually long in that it contains not only two predicted promoters (at 135 bp and 374 bp), but also 14 predicted TFBSs located between 381 bp and 556 bp upstream of the initiation codon of cpcB . P cpc560 differs from P cpc374 in that it has an extra 186 bp DNA fragment containing the 14 predicted TFBSs. The large-scale difference in the expression levels of ter under the control of P cpc560 and P cpc374 demonstrated that the extra 186 bp DNA fragment in P cpc560 may contain positive TFBSs and that the presence of these multiple TFBSs is crucial for its promoter strength. Transcription factors are proteins that play roles in virtually every aspect of the transcription process 28 . In prokaryotes, RNA polymerase recognizes and binds to the promoter region and initiates transcription, and a sigma factor is required for RNA polymerase to bind to the promoter. In eukaryotes, three types of eukaryotic RNA polymerases all require transcription factors to bind to the promoter sequence before transcription can be initiated 28 . Therefore, compared with eukaryotic promoters such as the yeast promoter 29 , the promoter of E. coli is rather short (approximately 30–50 bp) and TFBSs are usually not required. For instance, when we scanned the sequences of E. coli strong promoters (including P trc , P lac , and T7) that have been used to drive gene expressions in cyanobacteria previously, we found only one or two TFBSs in each of the promoters. In this study, we found 14 predicted TFBSs located between 381 bp and 556 bp upstream of the initiation codon of cpcB , and verified that the extra 186 bp DNA fragment containing multiple predicted TFBSs is crucial for P cpc560 promoter strength in cyanobacteria. This novel discovery raises the possibility that the lack of TFBSs in E. coli strong promoters may explain why they perform poorly in driving gene expression in cyanobacteria. Thus, we propose that native positive TFBSs should be considered when designing promoters to drive gene expression in cyanobacteria. The newly discovered super-strong promoter P cpc560 will be useful for further research on the production of useful substances using transgenic cyanobacteria and the very cheap substrate CO 2 . The principle of considering TFBS in cyanobacterial promoter design will also contribute to designing strong and controllable promoters to drive the expressions of genes involved in new biosynthetic pathways from CO 2 in cyanobacteria."
} | 1,323 |
33746921 | PMC7973049 | pmc | 5,132 | {
"abstract": "The continuous cropping of plants can result in the disruption of the soil microbial community and caused significant declines in yields. However, there are few reports on the effects of continuous cropping of sugarcane on the microbial community structure and functional pathway. In the current study, we analyzed the structural and functional changes of microbial community structure in the rhizospheric soil of sugarcane in different continuous cropping years using Illumina Miseq high-throughput sequencing and metagenomics analysis. We collected rhizosphere soils from fields of no continuous cropping history (NCC), 10 years of continuous cropping (CC10), and 30 years of continuous cropping (CC30) periods in the Fujian province. The results demonstrated that continuous sugarcane cropping resulted in significant changes in the physicochemical properties of soil and the composition of soil bacterial and fungal communities. With the continuous cropping, the crop yield dramatically declined from NCC to CC30. Besides, the redundancy analysis (RDA) of the dominant bacterial and fungal phyla and soil physicochemical properties revealed that the structures of the bacterial and fungal communities were mainly driven by pH and TS. Analysis of potential functional pathways during the continuous cropping suggests that different KEGG pathways were enriched in different continuous cropping periods. The significant reduction of bacteria associated with rhizospheric soil nitrogen and sulfur cycling functions and enrichment of pathogenic bacteria may be responsible for the reduction of effective nitrogen and total sulfur content in rhizospheric soil of continuous sugarcane as well as the reduction of sugarcane yield and sugar content. Additionally, genes related to nitrogen and sulfur cycling were identified in our study, and the decreased abundance of nitrogen translocation genes and AprAB and DsrAB in the dissimilatory sulfate reduction pathway could be the cause of declined biomass. The findings of this study may provide a theoretical basis for uncovering the mechanism of obstacles in continuous sugarcane cropping and provide better guidance for sustainable development of the sugarcane.",
"conclusion": "Conclusion In this study, we have shown that continuous cropping in sugarcane cultivation led to significant declines in soil pH, OM, TN, TS, TK, and AN contents. The metagenomic sequencing and analysis confirmed our proposed hypothesis that continuous cropping reduces the diversity of bacterial and fungal communities and that soil microbial community structure and function were significantly affected by the continuous sugarcane cropping system. The dominant bacterial phyla were Proteobacteria, Actinobacteria, and Acidobacteria, and the dominant fungal phyla were Ascomycota, Basidiomycota, and Chytridiomycota. The reduction in diversity and abundance of beneficial soil microbes like Rhizobium and Sphingomonas , and an increase in harmful soil microbes like Mycobacterium , Fusarium , and Verticillium , could be the main reason for sugarcane poor growth and crop diseases. Variations in microbial community diversity and functional pathways were mainly caused by differences in pH, TN, and TS. In nitrogen metabolism, the dramatic decrease of nitrogen translocation from extracellular to intracellular and the accumulated nitrites and ammonia by the decreased denitrification could result in the decreased biomass. In sulfur cycling, the decreased abundance of AprAB and DsrAB in the energy-producing dissimilatory sulfate reduction pathway together with the increased abundance of most genes (except PAPSS and cysI ) in the energy-consuming assimilatory sulfate reduction pathway might be the contributors to the decreased biomass. Significant decreases in the abundance of bacterial genera associated with nitrogen and sulfur cycling, such as Nitrosomonas , Desulfobacter , Sulfuricella , and related functional genes may have contributed to the reduction of the energy cycle. All these changes might finally result in sugarcane crop yield and quality reduction during continuous cropping. This study provides a theoretical basis on the mechanism underlying obstacles in continuous cropping systems of sugarcane. However, the specific cause of continuous cropping obstacles was still uncertain. Further consideration must be given to the application of microbial methods such as exploring sustainable agricultural measures and special microbial fertilizers.",
"introduction": "Introduction Soil microorganisms are essential and play critical roles in soil organic matter decomposition, nutrient availability, and cycling, and in some instances, improve stress tolerance or suppress pathogens to regulate the soil-borne diseases ( Van Der Heijden et al., 2008 ; Sun et al., 2015 ). This complex plant-associated microbial community, is also referred to as the second genome of the plant ( Berendsen et al., 2012 ). Changes in microbial communities affect the absorption and transformation of soil nutrients via the root system of plants ( Beckers et al., 2017 ). In recent years, the important role of soil microbiome in regulating agricultural production and plant diseases has been elucidated in a large number of studies. For instance, a substantial number of bacterial strains of Bacillus isolated from rhizospheric soil samples of the sugarcane plants have N-fixation function and biocontrol property against two sugarcane pathogens ( Singh et al., 2020 ). In addition, several studies on Arabidopsis thaliana have shown that the diverse soil microbiomes applied to the roots of Arabidopsis thaliana were able to modulate plant growth and the leaf metabolome ( Badri et al., 2013 ). Moreover, soil microbiomes have the potential to help Arabidopsis thaliana plants deal with drought stress under in vivo conditions ( Zolla et al., 2013 ). Hence, the study on soil microorganisms has a great potential to provide essential new insights into the impact of microbial diversity on plant growth and soil ecosystem functioning. Continuous cropping refers to a system in which the same or similar crop is cultivated in the same soil year after year ( Shipton, 1977 ). There are three main factors associated with continuous cropping: imbalance of soil nutrients, autotoxicity of root exudates, and shifts in microbial community composition ( Zhu et al., 2018 ). However, long-term continuous cropping usually leads to soil-borne plant pathogen accumulation and crop yield reduction, which has been described as a continuous cropping obstacle ( Garbeva et al., 2004 ; Liu et al., 2018 ). Recently, increasing numbers of studies such as peanut ( Li et al., 2014 ), sweet potato ( Gao et al., 2019 ), cotton ( Xi et al., 2019 ), and soybean ( Tian et al., 2020 ) have reported that continuous cropping resulted in the disruption of soil microbial community and caused significant declines in yields. Whereas, continuous cropping is common with multiple agricultural systems due to the limited arable land and inappropriate farming strategies in China ( Lei et al., 2020 ). For these reasons, more attention should be paid to exploring the association and underlying mechanism of continuous monocropping and soil microbial community. Sugarcane is an important economic tropical crop widely cultivated all around the world, providing 80% of the world’s sugar production, and it is also a crucial source of biofuel to ethanol production ( Singh et al., 2020 ). Continuous cropping obstacles are common with sugarcane. In practice, the yield is severely hindered due to the continuous monocropping and thus becomes a bottleneck that hinders the sustainable development of the national sugarcane industry. Previous studies have focused on the effects of application of organic and inorganic amendments such as zinc and lime at different concentrations and postharvest straw burning on chemical properties, microbial diversity, microbial biomass, and functional genes in sugarcane-cultivated soils ( Souza et al., 2012 ; Rachid et al., 2013 ; Val-Moraes et al., 2016 ; Navarrete et al., 2017 ). However, there are few studies on continuous sugarcane cropping in association with soil microorganisms. In the last decade, most of the previous studies used 16S rRNA gene library construction and denaturing gradient gel electrophoresis (DGGE) methods to descript the soil microorganisms which are time and cost consuming and can only measure a small amount of dominant soil microbial groups ( Chen et al., 2012 ; Xi et al., 2019 ). Besides, studies on functional analyses of soil microbiome are still relatively scarce. Recently, the emergence of a variety of molecular tools and the rapid development of next-generation DNA sequencing (NGS) technology, such as the metagenomics analysis have provided unprecedented opportunities for us to advance knowledge of composition and function of soil microbial communities ( Navarrete et al., 2015 ). Therefore, in this study, we applied the Illumina Miseq high-throughput sequencing technology to analyze changes and the effects in microbial community structure in the rhizospheric soil of sugarcane varieties in different continuous cropping years. We believe that (a) soil properties and sugarcane agronomic characters are considerably affected by continuous cropping, leading to shifts in soil microbial structure and diversity and (b) these variations in soil microbial community composition would reflect shifts in soil microbial function. In addition, we hypothesized that (c) the changes caused by continuous cropping will differ in the diversity of bacterial and fungal communities and may reduce community diversity. The overall results of this study will provide us with useful information on the differences of the soil microbial communities in sugarcane grown under continuous cropping conditions.",
"discussion": "Discussion Continuous cropping obstacles are common phenomena in various plants, including medicinal plants ( Tang et al., 2015 ; Alami et al., 2020 ), fruits ( Hui-Yan and Yan-Song, 2009 ; Li and Liu, 2019 ), and especially in many crops ( Xi et al., 2019 ; Tian et al., 2020 ), which have adverse impacts on plant health resulting in great economic losses. However, continuous cropping obstacles are caused by complex factors, and the mechanisms remain unclear. Recently, an increasing number of studies have begun to focus on the roles of soil microbial communities in continuous cropping practices ( Xiong et al., 2015b ; Gao et al., 2019 ; Chen et al., 2020 ). Sugarcane, one of the important tropical crops in China, has been planted in monoculture in coastal areas of southern China due to the limited arable land area, which has resulted in soil sickness and significant losses to crop yields could be caused by changes in the microbial composition and soil characteristics ( Miura et al., 2016 ; Kelly et al., 2018 ). Thus, unveiling soil microbial community and function variations under continuous cropping system is extremely helpful in understanding the association between reduced crop yields and long-term monoculture. Previous studies have shown that continuous cropping affected the physicochemical properties of rhizosphere soil, thus reducing the nutrient absorption capacity of the plant’s root ( Alami et al., 2020 ). In this study, the decrease of pH, OM, TN, TS, and TK was observed in the continuous cropping of sugarcane fields, probably because of the application of organic fertilizer and the gradual decrease in nitrogen, sulfur, and potassium utilization capacity of the soil microbial community ( Chao et al., 2014 ). Additionally, the TP and AP showed an increasing trend with the increase of cultivation years, which may result from low Pi utilization, the misuse of inorganic fertilizers, and soil P sorption capacity increasing due to the low soil pH ( Hou et al., 2020 ; Zhang W. et al., 2020 ). The above changes in physicochemical properties of soils might limit the sugarcane growth and contribute to the decrease of sugarcane production after continuous cropping. Microbial community composition and diversity are associated with soil quality and plant health ( Berendsen et al., 2012 ). A large number of studies have shown that there are significant differences in the microbial biomass and composition of bacterial and fungal communities at different stages of monoculture ( Gao et al., 2019 ; Alami et al., 2020 ; Chen et al., 2020 ). Interestingly, the diversity of bacterial communities increased from NCC to CC10, and then decreased from CC10 to CC30 in this study. We found Proteobacteria and Actinobacteria were the dominant bacterial phyla in the bacterial communities at all stages of monoculture, which is consistent with numerous previous studies. The phylum Proteobacteria are dominant members of the rhizosphere microbial communities and enriched in the rhizosphere soil of plenty of plants ( Peiffer et al., 2013 ; Philippot et al., 2013 ). Besides, the phylum Actinobacteria belongs to Gram-positive bacteria, and it could produce antibiotics to inhibit plant pathogens in soil and playing a crucial role in the decomposition of organic matter ( Zhang H. et al., 2017 ). Previous studies have shown that the increase in Proteobacteria and Actinobacteria is associated with long periods of intense production which may be consistent with the increased abundance caused by continuous crop in our study ( Zhongmin et al., 2018 ). Our results also have shown the relative abundances of Actinobacteria, Bacteroidetes, and Cyanobacteria increased while that of Proteobacteria, Chloroflexi, Gemmatimonadetes, and Firmicutes decreased over time. In previous studies, the main reason for the increase of Actinobacteria abundance in rhizosphere soil was the decrease of soil pH caused by organic acid secreted by roots ( Haichar et al., 2014 ). Besides, Cyanobacteria were negatively correlated with soil N fractions in our study, which was inconsistent with the positive correlation between soil Cyanobacteria abundance and soil N content reported in previous studies ( Xu et al., 2017 ; Ren et al., 2020 ). The phylum Proteobacteria is one of the most diverse and fastest metabolism in bacteria which plays an important role in maintaining soil ecological stability by soil nitrogen supply ( Ren et al., 2020 ). It reported that Chloroflexi abundance showed a positive correlation with soil pH ( Khodadad et al., 2011 ) and could be decreased through regulating soil available N, available P, and available K, which is consistent with the results of our study. Moreover, previous studies have shown that the abundance of Gemmatimonadetes was particularly affected by environmental factors and soil types and positively related to soil moisture content ( Ren et al., 2020 ). This may mean that the changes after continuous cropping in this study may be related to the decreased water-holding capacity, which needs to be verified by further experiments. The phylum Firmicutes had a positive correlation with the stem length, stem diameter, and fiber yield of continuous ramie. Firmicutes was the dominant phylum in continuous ramie soils ( Zhu et al., 2018 ), but it accounted for only 0.81–1.26% in this study. These results suggested that each continuous cropping system had different mechanisms, especially in the composition of the bacterial community. At the genus level, Bradyrhizobium is symbiotic nitrogen-fixing bacteria which perhaps evolved from photosynthetic free-living bacteria by the acquisition of symbiotic functions ( Molouba et al., 1999 ). Streptomyces is the major Actinomycetes genus which can help to produce several antibiotics that are useful in medical practices as well as an important role in organic matter decomposition conducive to crop production ( Qin et al., 2011 ). Sphingomonas belongs to Gram-negative bacteria that are widely distributed in nature. Some Sphingomonas strains showed the ability of nitrogen fixation and denitrification in the nitrogen cycle and play a crucial role in reducing the toxic substances in soil ( Hu et al., 2007 ; Yang et al., 2016 ). Burkholderia is a kind of Gram-negative bacteria that belongs to the Proteobacteria. Most of Burkholderia are progrowth bacteria that can promote the growth and development of plants through nitrogen fixation, nodulation, and phosphorus solubilization ( Gomes et al., 2003 ). Our study showed that the abundance of Bradyrhizobium , Streptomyces , Sphingomonas , and Mycobacterium was relatively high in CC30 and that Arthrobacter , Pseudomonas , and Cupriavidus were more abundant in NCC. The Arthrobacter are Gram-positive, non-acid-resistant, aerobic, chemoheterotrophic bacteria that are the dominant bacteria in water and soil. It is also the plant growth-promoting bacteria (PGPR) capable of degrading nicotine and resisting insect attack ( Brown, 1974 ). In addition, a strain HS-G 8 of Arthrobacter with biological nitrogen fixation ability was isolated from the soil in Okinawa Prefecture, Japan ( Jiang et al., 2004 ). Pseudomonas can strengthen the cell wall of plant roots against pathogens by inducing changes in plant cell wall structure ( Benhamou, 1996 ). Cupriavidus is a Gram-negative bacterium of the order β-Amastigotes, which is widely found in the environment. It can tolerate and adsorb heavy metal ions and increase the pH of the soil microenvironment ( Zhaohui et al., 2014 ). Therefore, these genera may play a more important role in continuous sugarcane cropping. Fungi play a key role in decomposition, and the composition of soil fungi community is affected by management and soil nutrient status ( Sui et al., 2019 ). Among the fungi identified in our study, the diversity of fungal communities increased from NCC to CC30 and a similar result was also reported in continuous cropping of vanilla and peanut ( Xiong et al., 2015b ; Chen et al., 2020 ). The dominant phyla of fungi in continuous cropping of sugarcane were Ascomycota, Basidiomycota, and Chytridiomycota, which was consistent with previous studies in continuous cropping of sweet potato and Panax notoginseng ( Tan Y. et al., 2017 ; Gao et al., 2019 ). Interestingly, we observed the opposite situation for the increased proportion of Ascomycota during continuous cropping, which was decreased in other studies ( Feng et al., 2019 ; Gao et al., 2019 ). The relative abundance of Basidiomycota and Chytridiomycota decreases gradually. At the genus level, the relative abundance of Penicillium and Aspergillus increased during continuous cropping, indicating the increase of harmful microorganisms. Members of the genus Talaromyces are well known for their secondary metabolites, with some having antimicrobial activities ( Zhai et al., 2016 ). Fusarium is a soil-borne pathogen which can persist in the soil for a long time without any host. Numerous Fusarium species have been reported as the dominant pathogens of many crops ( Naeem et al., 2019 ). Eutypa belongs to the Ascomycetes; the secondary metabolites from Eutypa have antibacterial and immunosuppressive bioactivities ( Tan J.J. et al., 2017 ). Thus, the significant decrease of Eutypa may lead to a negative impact on disease resistance during continuous cropping. To analyze the overall distribution of microorganisms during the continuous sugarcane cropping, ternary plots were used for visualization in domain and phylum level, respectively ( Supplementary Figure 1 ). In domain level, we observed an enrichment of Archaea in NCC and a subset of Eukaryota and viruses were highly enriched in CC30. In phylum level, Gemmatimonadetes and Rokubacteria were enriched in NCC, while Bacteroidetes showed enrichment in CC10 and CC30. Interestingly, a subset of Cyanobacteria was highly enriched in CC30 compared with both NCC and CC10. Together, these results suggest that microbial composition may change with continuous cropping of sugarcane and that there are specific microbial species at different periods. All these specific microbial species may have complex functions and mechanisms that need to be intensively investigated in the future. We further used analysis of variance to identify genera that were significantly enriched or reduced during different years of continuous cropping which can explain how continuous sugarcane cultivation affects the rhizosphere soil microbial communities ( Figure 10 and Supplementary Figure 2 ). For the bacterial community, CC30 showed a significant increase in 187 genera and a significant decrease in 164 genera compared with NCC, with no significant change in the remaining 1,544 genera. The abundance of bacterial genera associated with nitrogen and sulfur cycling were significantly decreased in CC10 and CC30 compared with NCC, including Nitrosospira , Nitrosospina , and Nitrospira associated with nitrogen cycling ( Singh et al., 2020 ), and 12 Desulfobacter including Desulfuromonas associated with sulfur cycling. In contrast, the abundance of phytopathogenic bacteria Clavibacter , which can cause systemic plant diseases such as wilt, foliage, and bacterial canker disease ( Nandi et al., 2018 ); Mycobacterium , which comprises more than 177 species which is pathogenic to both animals and humans ( Sarbashis et al., 2018 ); Rhizobium and Sphingomonas , associated with nitrogen fixing ( Molouba et al., 1999 ; Hu et al., 2007 ); and Xanthomonas , a genus of pathogenic bacteria hazardous to agricultural production ( Bonas and Boch, 2010 ), increased significantly. Meanwhile, Streptosporangium , an Actinomycete known to produce antimicrobial compounds was also significantly increased ( Hardoim et al., 2015 ). For the fungal community, CC30 showed a significant increase in 38 genera and a significant decrease in three genera compared with NCC, with no significant changes in the remaining 241 genera. Penicillium is a toxin-producing genus that can cause fruit, vegetable, and meat rots, as well as citrus penicillium ( Chen et al., 2020 ); Aspergillus , which can produce toxic secondary metabolites ( Dagenais and Keller, 2009 ); Fusarium , which can cause plant rot, stem rot, flower rot, and spike rot ( Naeem et al., 2019 ); and Verticillium , which can cause plant Verticillium wilt ( Jian-Guo et al., 2009 ), were significantly enriched in CC30; and the first three genera increased with the years of continuous cropping in sugarcane. This suggests that long-term continuous cropping of sugarcane may lead to a decrease in the function of nitrogen and sulfur cycling in the rhizosphere soil of sugarcane, as well as the enrichment of pathogenic bacteria that are pathogenic to plants or animals. The above changes in microbial abundance may be closely related to the decrease in effective nitrogen and total sulfur content of sugarcane inter-root soil, sugarcane yield reduction, and sugar content decrease under long-term continuous cropping. FIGURE 10 Ternary plot depicting soil microorganisms with significant differences in abundance in three sugarcane soil samples. (A) Ternary plot of genus level in bacterial community with significant differences in abundance. (B) Ternary plot of genus-level in fungal community with significant differences in abundance. Vertex represents NCC, CC10, and CC30, respectively; each point corresponds to a species, and the size of the point represents the relative abundance of each species. Axes show reads accounted for by each species in each group of soil samples (NCC, CC10, and CC30), as a percentage of total (sum) reads observed for a given species across all three groups. Arrows indicate the corresponding axis directions for each point. “Enriched” representative compared with other two groups of abundance has significantly increased; “Depleted” representative compared with the other two groups have significantly lower abundance. Principal coordinate analyses is a non-constrained data dimensionality reduction analysis method that can be analyzed at different levels to observe the differences between individuals or groups ( Lozupone and Knight, 2005 ). ANOSIM analysis is a non-parametric test used to test whether the differences between groups (two or more groups) are significantly greater than the differences within groups to determine whether the groups are significant ( Tian et al., 2020 ). The result of the UniFrac-weighted PCoA and ANOSIM in this study demonstrated that the continuous cropping of sugarcane had strong effects upon the soil microbial community structure. This result was consistent with previous studies that soil microbial community structure significantly changed with the years of continuous cropping of black pepper, cucumber, and Coptis chinensis ( Zhou and Wu, 2012 ; Xiong et al., 2015a ; Alami et al., 2020 ). Therefore, we further supported the hypothesis that soil microbial communities could be affected by continuous cropping and may contribute to the poor sugarcane growth in continuous crop cultivation. Different metabolic pathways could lead to different physiological consequences. As shown in our study, carbon metabolism, biosynthesis of amino acids, ATP-binding cassette (ABC) transporters, quorum sensing, and two-component system were enriched in soil microorganisms regardless of the continuous cropping years. According to the KEGG database, carbon metabolism is the most basic aspect of life, contains carbon utilization pathways of glycolysis, pentose phosphate pathway, and citrate cycle, and six carbon fixation pathways, as well as some pathways of methane metabolism. Biosynthesis of amino acids is a modular architecture that includes the biosynthesis pathways of 20 amino acids. The ABC transporters form one of the largest known protein families and are widespread in bacteria, archaea, and eukaryotes. Quorum sensing (QS) is a regulatory system that allows bacteria to share information about cell density and adjust gene expression accordingly. Two-component signal transduction systems enable bacteria to sense, respond, and adapt to changes in their environment or their intracellular state ( Ogata et al., 1999 ). Moreover, NCC was primarily associated with glyoxylate and dicarboxylate metabolism, carbon fixation pathways in prokaryotes, nitrotoluene degradation, and nitrogen metabolism; these pathways mainly related to energy metabolism. Especially, carbon fixation is an important pathway for autotrophs living in various environments, and the biological process of nitrogen metabolism is a complex interaction of many microorganisms involved in the flow of energy through oxidation and reduction. However, CC10 and CC30 were associated with bacterial secretion system, flagellar assembly, and bacterial chemotaxis. Bacterial secretion system is related to a wide range of protein secretion including biogenesis of organelles, such as flagella, nutrient acquisition, virulence, and efflux of drugs and other toxins. The bacterial chemotaxis is the process by which cells sense chemical gradients in their environment and then move toward more favorable conditions. This interaction causes a change in behavior, such as in direction or speed of rotation of flagella. Some researches indicated that flagellar assembly is related to type III virulence secretion systems and can be used for the induction of maximum fluid secretion ( Cui et al., 2013 ). Virulence factors are molecules that play very important roles in enhancing the pathogen’s capability in causing diseases. Metabolism pathways, flagellar assembly, and chemotaxis, relating to cellular motility, may be of importance for virulence ( Berkelmann et al., 2020 ). In addition, increases in abundance of marker genes for flagellar assembly, chemotaxis, and types VI and IV secretion systems could indicate that an increment of motility and interaction in soil microbial communities accompanies sugarcane continuous cropping. Carbohydrate-active enzyme genes have the functions of degradation, modification, and generation of glycosidic bonds. CAZy genes were classified into six classes, including auxiliary activities (AA), carbohydrate-binding modules (CBM), carbohydrate esterases (CE), glycoside hydrolases (GH), glycosyl transferases (GT), and polysaccharide lyase (PL) ( Lombard et al., 2014 ). Further research on CAZy genes is of great significance for revealing the metabolic mechanism of microbial carbohydrates. The composition and abundance of CAZy genes during continuous cropping were characterized in our study. All these classes were found in three soil samples. GH is the class with the highest abundance which can hydrolyze the glycosidic bond through the addition of a water molecule and catalyze the hydrolysis of glycosidic linkages to generate smaller polysaccharides/monosaccharides ( Lombard et al., 2014 ). Nevertheless, the number of CAZy genes in CC30 was higher than in NCC and CC10. This result is unexpected and requires further study. Continuous cropping obstacles refer to crop yield reductions that occur in monocultures, which are often caused by the degradation of soil ecosystems such as declining pH, increased pathogenic bacteria, and decreased numbers of beneficial microorganisms. According to some reported studies, soil physicochemical properties have important roles in controlling microbial community structure ( Lauber et al., 2008 ; Val-Moraes et al., 2016 ). In addition, many bacterial communities are highly correlated with specific soil factors and can be used as indicators of soil condition ( Kuramae et al., 2015 ). Investigating the correlation between microbial community diversity and soil environmental factors can help us better understand the mechanism of continuous cropping obstacles. In our study, the RDA results suggest that many soil properties may have affected microbial community structures. We found that the structures of the bacterial and fungal communities were mainly driven by pH and TS. The Spearman’s correlation analysis shows that pH has no significant influence on the TS ( Supplementary Table 8 ). Previous research suggests that soil pH has strong effects on the soil microbial community composition and diversity ( Rousk et al., 2010 ). For instance, the abundance of fungi in soil is highly influenced by soil pH and fungal growth was increased 30-fold in acidic soils (pH = 4.5) ( Rousk et al., 2009 ). The main reason for continuous soybean cropping obstacle is the decrease in soil pH ( Tian et al., 2020 ). Besides, in the bacterial community, we found that the Arthrobacter abundance showed a positive correlation with pH and that Bradyrhizobium , Sphingomonas , Streptomyces , and Burkholderia showed a negative relationship with pH and TS. In the fungal community, the relative abundance of the Mycena , Penicillium , Talaromyces , and Aspergillus were all negatively correlated with pH and TS. The heatmap of the correlation between the top 20 genus and physicochemical characteristics showed that most genera were significantly correlated with TN and TS in the bacterial community and pH and TS in the fungal community, respectively. Previous studies also revealed the correlations between pH and fungal abundance. For instance, TN has been shown to affect soil microbial community structure and diversity ( Rousk et al., 2009 ). TS in the soil leads to an increase in microorganisms contained in the soil ( Klotz et al., 2011 ). Thus, the TN and TS content may be related to the nitrogen and sulfur cycling changes in microbial communities during continuous sugarcane cropping. Moreover, the relationships between KEGG level 2 pathway traits and environmental factors revealed that the abundance of replication and repair, glycan biosynthesis, and metabolism pathway are negatively correlated with pH in bacterial community, which was in coherence with a recent study showing that glycan and amino acid metabolism were pH sensitive ( Ricky et al., 2018 ). In the fungal community, most of the top 20 KEGG level 2 pathways were significantly negatively correlated with pH and TS which is consistent with the relationship between fungal composition and environmental factors. Soil microbial biomass is an essential indicator of soil quality and reflects the process of nutrient transfer and the energy cycle ( Powlson et al., 1987 ). In previous studies, soil enzyme activities and soil microbial biomass always have a strong positive correlation ( Fall et al., 2016 ; Zhang Y. et al., 2020 ). The N cycle consists of complex interplay pathways including assimilatory nitrate reduction, dissimilatory nitrate reduction, denitrification, nitrogen fixation, nitrification, and anammox. There are a variety of genes encoding enzymes that catalyze the important transformation reactions of various oxidation states ranging from + 5 in nitrate to −3 in ammonia ( Ren et al., 2017 ). N is a critical limiting factor for continuous sugarcane cropping yield, while excessive application of N fertilizer will lead to problems like soil acidification and high cost ( Yang et al., 2019 ). About nitrogen metabolism, we propose two possibilities to explain the relationship between the N cycle and biomass in our study. The first is that despite the increased abundance (no significant difference) of most genes in assimilatory nitrate reduction, the dramatic decrease (significant difference) of extracellular nitrogen translocation was a gradual restriction for substrates that participate in the intracellular N cycle which eventually decreased sugarcane biomass. Moreover, decreases of NRT and nrtABCD may result in a limitation of nitrogen, and induce chlorosis and inhibit chloroplast protein translation in the previous study ( Plumley and Schmidt, 1989 ). The second is that the decrease of denitrification leads to produce more ammonia within the cell. Then the accumulated nitrites and ammonia might act as toxins, resulting in the decreased biomass like a previous study ( Safdar et al., 2017 ). Sulfur is an essential element for life and occurs in various oxidation states ranging from + 6 in sulfate to −2 in sulfide (H 2 S). Sulfate reduction can occur in both an energy-consuming assimilatory pathway and an energy-producing dissimilatory pathway. The transformation of sulfur in the environment is critically dependent upon microbial activities ( Klotz et al., 2011 ). In our study, the abundance of most genes (except PAPSS and cysI ) in the energy-consuming assimilatory sulfate reduction pathway were increased, meanwhile, the abundance of genes ( AprAB and DsrAB ) in the energy-producing dissimilatory pathway were decreased during continuous cropping. This may be one of the contributors to decreased biomass. Thus, changes in relative abundances of functional genes in nitrogen and sulfur cycling indicated that shifts within soil microbes and functional pathways may have impacts on soil microbial biomass reflected in the growth of the plant eventually."
} | 8,751 |
22381679 | null | s2 | 5,134 | {
"abstract": "Predicting whether and how organisms will successfully cope with climate change presents critical questions for biologists and environmental scientists. Models require knowing how organisms interact with their abiotic environment, as well understanding biotic interactions that include a network of symbioses in which all species are embedded. Bacterial symbionts of insects offer valuable models to examine how microbes can facilitate and constrain adaptation to a changing environment. While some symbionts confer plasticity that accelerates adaptation, long-term bacterial mutualists of insects are characterized by tight lifestyle constraints, genome deterioration, and vulnerability to thermal stress. These essential bacterial partners are eliminated at high temperatures, analogous to the loss of zooanthellae during coral bleaching. Recent field-based studies suggest that thermal sensitivity of bacterial mutualists constrains insect responses. In this sense, highly dependent mutualisms may be the Achilles' heel of thermal responses in insects."
} | 263 |
20795657 | null | s2 | 5,135 | {
"abstract": "Arbuscular mycorrhizal fungi have been known to increase metal uptake in plants. In this study, mesquite (Prosopis juliflora-velutina) inoculated with Glomus deserticola or amended with EDTA were grown for 30 days in soil containing Cr(III) or Cr(VI) at 0, 40, 80, and 160 mg kg(-1). Total amylase activity (TAA) was monitored as a stress indicator. Element concentrations and distribution in tissue were determined using ICP-OES, electron scanning microprobe, and TEM. Inoculated Cr(VI) treated plants had 21% and 30% more Cr than uninoculated and EDTA treated roots, respectively, at 80 mg Cr kg(-1) treatment. In the case of Cr(III), EDTA produced the highest Cr accumulation in roots. TAA was higher in inoculated plants grown with Cr(III) at 80 and 160 mg kg(-1) and Cr(VI) at 40 and 160 mg kg(-1). The X-ray mapping showed higher metal concentrations in the vascular system of inoculated plants and the TEM micrographs demonstrated the presence of G. deserticola in roots."
} | 244 |
27590816 | PMC5086560 | pmc | 5,136 | {
"abstract": "ABSTRACT The interior of plants contains microorganisms (referred to as endophytes) that are distinct from those present at the root surface or in the surrounding soil. Herbaspirillum seropedicae strain SmR1, belonging to the betaproteobacteria, is an endophyte that colonizes crops, including rice, maize, sugarcane, and sorghum. Different approaches have revealed genes and pathways regulated during the interactions of H. seropedicae with its plant hosts. However, functional genomic analysis of transposon (Tn) mutants has been hampered by the lack of genetic tools. Here we successfully employed a combination of in vivo high-density mariner Tn mutagenesis and targeted Tn insertion site sequencing (Tn-seq) in H. seropedicae SmR1. The analysis of multiple gene-saturating Tn libraries revealed that 395 genes are essential for the growth of H. seropedicae SmR1 in tryptone-yeast extract medium. A comparative analysis with the Database of Essential Genes (DEG) showed that 25 genes are uniquely essential in H. seropedicae SmR1. The Tn mutagenesis protocol developed and the gene-saturating Tn libraries generated will facilitate elucidation of the genetic mechanisms of the H. seropedicae endophytic lifestyle. IMPORTANCE A focal point in the study of endophytes is the development of effective biofertilizers that could help to reduce the input of agrochemicals in croplands. Besides the ability to promote plant growth, a good biofertilizer should be successful in colonizing its host and competing against the native microbiota. By using a systematic Tn-based gene-inactivation strategy and massively parallel sequencing of Tn insertion sites (Tn-seq), it is possible to study the fitness of thousands of Tn mutants in a single experiment. We have applied the combination of these techniques to the plant-growth-promoting endophyte Herbaspirillum seropedicae SmR1. The Tn mutant libraries generated will enable studies into the genetic mechanisms of H. seropedicae -plant interactions. The approach that we have taken is applicable to other plant-interacting bacteria.",
"conclusion": "Conclusions. In this study, we have developed functional genomic techniques and resources for the model endophyte H. seropedicae that had not used previously in this species or in other bacterial endophytes. We have generated large comprehensive Tn libraries, and we have characterized the Tn insertion sites using next-generation sequencing (Tn-seq). These nearly saturated Tn libraries allowed us to perform robust essentiality analysis, and the results obtained are consistent with those reported for other bacteria. Our analysis of H. seropedicae Tn libraries from TY medium has enabled us to define the genes that are essential under those growth conditions. The results obtained enabled us to describe, at a functional level, the mechanisms of growth of H. seropedicae , including synthetic pathways, toxins, and regulatory mechanisms. Furthermore, these Tn libraries represent a valuable resource for the endophyte research community and will facilitate studies into the comprehensive assessment of the genetic mechanisms of the endophytic lifestyle of H. seropedicae , i.e., attachment to the root surface, internal colonization of the plant, and survival of the bacteria inside plants.",
"introduction": "INTRODUCTION Plants rely on beneficial interactions with their microbiota for nutrient availability, growth promotion, and suppression of disease. The plant interior, referred to as the endosphere, has been shown to contain a distinct microbiome that is less diverse than those from the rhizoplane (the root surface) and the rhizosphere (a narrow zone of soil subject to the influence of living roots) ( 1 ). Microorganisms that colonize the endosphere are referred to as endophytes ( 2 , 3 ); these include all microorganisms that for all or part of their lifetimes colonize internal plant tissues ( 4 ). The knowledge of plant-bacterial endophyte interactions at the genetic and molecular levels has increased due to the use of suitable (laboratory-controlled) biological models. A model endophyte is Herbaspirillum seropedicae , a member of the Betaproteobacteria subclass, which includes many plant-associated bacteria such as species of the genera Azoarcus , Burkholderia , and Ralstonia ( 5 ). Several characteristics make H. seropedicae a suitable model endophyte ( 6 ), i.e., (i) it provides fixed nitrogen for important agroeconomic cultivars, (ii) it is genetically tractable, (iii) it has mechanisms of plant growth promotion other than nitrogen fixation, (iv) it has a wide range of plant hosts, (v) culturable bacteria are not isolated from soil and are isolated only from inside plants ( 7 , 8 ), and (vi) there are publicly available genome sequences ( 8 ). Some isolates of H. seropedicae have been described as being pathogenic in plants, although this may be the result of the host being unable to control colonization, and there have also been reports that it can be an opportunistic pathogen in immunocompromised individuals ( 9 , 10 ). The most well-studied H. seropedicae strains, SmR1 and Z67, have been tested in different plant species without symptoms of disease ( 11 ). Recently, transcriptomic and proteomic approaches have identified genes and pathways that are regulated during the interactions of H. seropedicae with different plant hosts ( 12 – 14 ). In addition, comparative genomics and metagenomics studies have shown that certain functions, e.g., nutrient transport systems, type IV conjugal DNA-protein transfer secretion systems, plant growth promotion genes, and iron uptake systems, are overrepresented in the genomes of bacterial endophytes, compared to rhizospheric or soil bacteria ( 4 , 15 – 17 ). Gene inactivation/deletion studies have shown that lipopolysaccharide (LPS) production is essential for effective H. seropedicae attachment to maize roots ( 18 ), and high-affinity iron uptake mechanisms contribute to the competitive fitness of H. seropedicae inside host plants ( 19 ). Compared to gene expression and comparative genomics studies, high-throughput functional analyses of endophyte-plant interactions have lagged. In recent years, there has been much progress in the application of transposon (Tn)-based gene inactivation methods in combination with massively parallel sequencing of Tn insertion sites, e.g., Tn insertion site sequencing (Tn-seq) and related techniques ( 20 – 22 ), which have advanced, and continue to advance, the characterization of bacterium-host interactions. In this study, we successfully employed in vivo \n mariner Tn mutagenesis in H. seropedicae strain SmR1 and characterized the resulting Tn mutants by Tn-seq. The resulting data set was used to identify the genes that, upon inactivation, have detrimental effects on fitness during in vitro growth and survival, i.e., essential genes.",
"discussion": "RESULTS AND DISCUSSION Characterization of H. seropedicae SmR1 Tn mutant libraries. To identify genes critical for the growth of H. seropedicae, Tn mutant libraries were constructed under nutrient-rich conditions, i.e., in TY medium, using a biparental mating protocol. The in vivo Tn mutagenesis had an efficiency of ∼5 × 10 −6 Tn mutants per H. seropedicae recipient cell. A total of six Tn mutant libraries were constructed, with sizes ranging between 24,000 and 140,000 CFU ( Table 3 ). TABLE 3 Tn mutant libraries constructed in H. seropedicae SmR1 a Library (antibiotic marker) b Estimated Tn library size (CFU) No. of sequence reads No. (%) of aligned reads No. (%) of insertion site flanking sequences hit in library Average no. of reads/flanking sequence A (Km) 24,000 14,008,866 10,465,200 (74.7) 26,590 (15.5) 394 B (Km) 55,000 3,046,565 2,491,660 (81.8) 19,038 (11.1) 131 C (Km) 90,000 25,122,546 22,284,725 (88.7) 52,327 (30.5) 426 D (Km) 140,000 20,353,571 18,537,039 (91.1) 50,639 (29.5) 366 E (Tc) 70,000 12,165,012 10,998,288 (90.4) 30,984 (18.0) 355 F (Tc) 50,000 7,775,781 6,969,727 (89.6) 28,492 (16.6) 245 a The criteria for identification of unique Tn insertion sites are listed in Materials and Methods. b Km, kanamycin; Tc, tetracycline. Tn insertion site sequencing (Tn-seq) was performed using Illumina sequencing. Of the 88,320 potential TA dinucleotide mariner Tn insertion sites in the H. seropedicae SmR1 genome, 56,174 insertion sites (i.e., 63.6% of the total TA sites) were hit by a Tn insertion ( Table 3 ). A cumulative analysis of amalgamating libraries revealed that the number of new unique Tn insertion mutants leveled off at ∼55,000 mutants ( Fig. 1A ). This suggests that, although we achieved Tn insertions in only ∼64% of the potential TA dinucleotide mariner Tn insertion sites, the maximum empirical number of mutants was obtained with this approach (without the use of much larger libraries). In addition, rarefaction analysis showed that we reached saturation in terms of the number of genes in the H. seropedicae genome that could be mutated ( Fig. 1B ). Tn insertions were distributed evenly throughout the chromosome, without any apparent evidence of hot spots, with an average of one Tn insertion every 95 bp ( Fig. 1C ). FIG 1 Characterization of H. seropedicae SmR1 Tn mutant libraries. (A) Cumulative numbers of Tn insertions in the different constructed mutant libraries and the numbers of unique Tn insertion mutants obtained. (B) Rarefaction analysis of intragenic Tn insertion positions, indicating near saturation of the number of genes that can be inactivated with a Tn. (C) Circular genome visualization, indicating the genes required for growth and survival in H. seropedicae SmR1. It is widely assumed that genes with very few, or no, Tn insertions are essential for growth and survival or are underrepresented because their corresponding Tn insertion mutants have a growth defect ( 20 ) or they were not inactivated by a Tn element during Tn mutagenesis. To identify the genes required for growth under nutrient-rich conditions, a fold change was calculated between the actual number of sequence reads and the expected number of sequence reads ( Fig. 2A ); the latter takes into account the number of Tn mutants in the library, the length of the gene, and the number of possible Tn insertion positions (i.e., TA sites) for each gene ( 30 ). Of note, 43 genes lacked unique TA insertion site flanking sequences, and the essentiality of those genes could not be accurately addressed; the genes without unique TA flanking sequences are listed in Table S1 in the supplemental material. Analysis revealed that 136 genes had no reads at all and 296 genes showed log 2 fold change (actual/expected sequence reads) values below −6.86. Next, to reduce the number of genes falsely identified as essential, we applied a 0.95 probability (calculated with a derivative of Poisson's law) cutoff value that the gene, if possible, was inactivated by a Tn insertion (based on 56,176 unique Tn mutants). Application of this cutoff value excluded 37 genes from the analysis, yielding a total of 395 genes that were found to be essential for in vitro growth and survival of H. seropedicae SmR1 in TY medium (see Table S2 in the supplemental material). FIG 2 Identification and characterization of H. seropedicae SmR1 essential genes. (A) Density plot of log 2 fold changes (measured reads/expected reads per gene). Black dot, gene essentiality cutoff value. (B) Functional class enrichment analysis of essential genes based on COG categories. Bars, number of essential genes assigned to each COG category, with the number of essential genes over the total number of genes in the COG category displayed to the right of each bar. COG category enrichment was analyzed using Fisher's exact test, with correction for multiple testing using Q values, as a measure of significance representing the false discovery rate ( 31 ). *, Q = 0.1; ∗∗, Q = 0.01; ∗∗∗, Q = 0.001. Essential genes were distributed relatively uniformly across the genome. However, eight regions larger than 100,000 bp were found to be dispensable for growth and survival. The two largest dispensable regions were located between Hsero_2418 and Hsero-4580 ( trnL ) (202,525 bp) and between Hsero_4426 ( glmS ) and Hsero_4580 (194,479 bp). In-depth analysis of the genes required for in vitro growth and survival. Of the 395 genes identified as being required for growth and survival in TY medium, 22 corresponded to tRNA genes and 1 corresponded to a 23S rRNA gene (Hsero_4734 [ rrlC ]) (see Table S2 in the supplemental material). The other two 23S rRNA genes, i.e., rrlA (Hsero_0480) and rrlB (Hsero_3882), could not be evaluated for their essentiality as they had no unique TA insertion site flanking sequence ( rrlA ) or the probability of inactivation was only 0.632 ( rrlB ). Of the remaining 372 protein-coding genes required for in vitro growth, 346 were assigned a COG identifier. The COG categories significantly enriched among the genes identified as being essential in H. seropedicae are shown in Fig. 2B and included cell cycle control, cell division, and chromosome partitioning (category D); nucleotide transport and metabolism (category F); coenzyme transport and metabolism (category H); translation, ribosomal structure, and biogenesis (category J); replication, recombination, and repair (category L); and cell wall/membrane/envelope biogenesis (category M). The COG category of RNA processing and modification (category A) had only one representative, the product of the gene Hsero_1434, which is predicted to encode an oligoribonuclease. A total of 1,624 protein-coding genes containing transmembrane domains or signal peptides are present in the genome, and we identified 72 of those as being essential; 64 were assigned to one or more COG categories. As expected, the most represented COG category in this subset was cell wall/membrane/envelope biogenesis (category M). Essential metabolic pathways. In silico analysis has revealed that H. seropedicae cannot utilize l -histidine, l -arginine, or l -lysine as carbon sources ( 8 , 34 ). The l -histidine and l -lysine degradation pathways are incomplete, and no specific l -arginine transporter has been identified. In agreement with these findings, our Tn-seq data indicate that the genes involved in the biosynthesis pathways of these proteinogenic amino acids are essential. In addition, and to our knowledge not previously reported, both serine and glutamine synthesis seem to be essential for H. seropedicae growth in TY medium. In the case of glutamine, glnA (encoding glutamine synthetase) appears to be essential. Together with the glutamine oxoglutarate aminotransferase (GOGAT) enzyme, GlnA is the main route of assimilation of NH 4 + in bacteria ( 35 , 36 ) and, considering TY medium as a nitrogen-rich medium, we assume that GlnA activity should be low ( 36 ) and therefore nonessential under these conditions. GlnA activity and glnA expression were shown previously to be reduced but not absent when nitrogen levels were in excess of 20 mM NH 4 + ( 37 ). No reduction in the expression of this gene or the activity of the enzyme was observed in the presence of glutamate ( 37 ). It is possible that nitrogen, from amino acids and peptides, may be more abundant in TY medium; hence, glnA is probably expressed and GlnA is active. Another candidate essential gene related to nitrogen metabolism is ntrX (Hsero_0069), which encodes a two-component response regulator protein. Interestingly, a comparative genomics study reported that this gene is overrepresented in endophyte genomes, compared to the genomes of phytopathogens and rhizospheric bacteria ( 4 ). We determined several genes encoding proteins in the pentose phosphate and glycolysis pathways to be essential. Three enzymes of the citric acid (tricarboxylic acid [TCA]) cycle, i.e., aconitate hydratase ( acnA [Hsero_2979]), 2-oxoglutarate dehydrogenase components E1 ( sucA [Hsero_2969]) and E3 ( lpdA [Hsero_2967]), and two subunits of succinate dehydrogenase ( sdhB [Hsero_2972] and sdhC [Hsero_2974]), were essential. Hsero_2971 (annotated as hypothetical) was also found to be essential; this gene has homology to sdhE , the product of which assists in the covalent attachment of flavin adenine dinucleotide (FAD) to SdhA (the product of the gene Hsero_2973) ( 38 ), which was not identified as essential. Functional redundancy between genes precludes essentiality of central metabolic pathways; however, two homologous genes are not always redundant in their functions. In the case of the already mentioned acnA gene (Hsero_2979), which codes for the TCA cycle enzyme aconitate hydratase, H. seropedicae contains in its genome another gene annotated as acnA (Hsero_2283), with 41.89% identity. However, a mutant of that gene was identified in a Tn mutant library previously described for the closely related strain H. seropedicae Z67 ( 39 ); this suggests that acnA (Hsero_2283) does not participate in the TCA cycle. Homologs of iscA (Hsero_3845 and Hsero_3142), a gene involved in Fe-S cluster biogenesis, were identified as essential genes. The two genes belong to the same COG0316, pfam01521, and TIGR00049 families. Their essentiality indicates that they are not functionally redundant. This suggests the existence of different Fe-S biogenesis machineries for different proteins. The hfq gene (Hsero_2948), encoding an RNA chaperone, is also essential for H. seropedicae SmR1 under the conditions studied. Several attempts to construct a defined deletion mutant of this gene were unsuccessful (Emmanuel de Souza, personal communication). The Hsero_4268 gene encodes a plasmid maintenance system antidote protein that we identified as being essential in our analysis. Transcriptome sequencing (RNA-seq) expression analysis showed that this gene and its toxin counterpart gene, Hsero_4269, were actively expressed in minimal medium ( 13 ), which indicates that there is an active toxin-antitoxin system in H. seropedicae SmR1. Critical reflection on identified candidate essential genes. In this study, the Tn mutants were grown in pools. Consequently, Tn mutants with reduced fitness (i.e., slowly growing/dividing bacteria) would be present at lower abundance in the pools (reflected by lower read counts for Tn flanking sequences), and the corresponding genes could be tagged as essential in our analysis, i.e., the number of sequence reads per gene would fall below the essentiality cutoff value ( 21 ). As part of our preliminary studies of the Tn libraries, we performed Sanger sequencing to identify the Tn insertion site in eight randomly selected mutants. Through this, we identified a mutant in which the Tn was inserted in the dadX gene (Hsero_2150). The enzyme encoded by this gene is predicted to catalyze the conversion of l -alanine to d -alanine, which then is incorporated into the peptidoglycan biosynthesis pathway by the d -alanine– d -alanine ligase protein (encoded by the gene ddlB [Hsero_0338]). Interestingly, according to our Tn-seq data, dadX appears to be essential in H. seropedicae (see Table S2 in the supplemental material). We hypothesized that d -alanine may be synthesized via an alternative pathway at a lower rate, allowing recovery of the mutant as a single colony but not after growth in a Tn mutant pool, during which there is competition between Tn mutants. We hypothesized that the alternative pathway could rely on Hsero_4778, which is predicted to encode d -alanine transaminase (EC:2.6.1.21), which catalyzes the interconversion of pyruvate and d -glutamate to d -alanine and 2-oxoglutarate. Comparative analysis of candidate essential genes and genes in other bacteria. To identify orthologs of the 372 (including dadX ) protein-encoding candidate essential genes in H. seropedicae , a BLASTP search was performed ( E value cutoff of 1 × 10 −5 , with >30% sequence identity over >50% of the sequence length) against essential genes in 39 bacterial strains of 28 bacterial species present in the DEG (accessed in July 2016) ( 40 ). A total of 347 H. seropedicae SmR1 essential genes had at least one essential ortholog among the bacterial species present in the DEG. The 347 H. seropedicae SmR1 genes had 8,472 orthologs in the database (see Table S3 in the supplemental material). The high percentage of genes identified as essential in our study that were also described as being essential in other bacterial species reinforces the quality of our candidate essential gene set. A total of 25 genes were uniquely essential in H. seropedicae SmR1, i.e., no essential orthologs were found in the DEG (see Table S4 in the supplemental material). Of the 20 essential proteins annotated as hypothetical, 14 are essential only in H. seropedicae . Three are proteins related to secretion systems; Hsero_0751 and Hsero_0943 are related to the type VI secretion system and Hsero_0804 is related to the type III secretion system of H. seropedicae . Type VI secretion systems are important for bacterial competition through contact-dependent killing of competitors ( 41 ). RNA-seq analysis of H. seropedicae grown in minimal medium or attached to maize roots showed that genes encoding the type III secretion system were not expressed in either case ( 13 ). This might suggest that Hsero_0804 is essential conditionally, i.e., when the bacteria are grown in nutrient-rich media. Five of the genes uniquely essential in H. seropedicae code for transcriptional regulators, three of which belong to the transcription COG category. Hsero_1027 is homologous to the global regulator gene pecS from the phytopathogen Dickeya dadantii 33937, which is reported to repress the premature expression of virulence genes during the first stage of plant infection, when D. dadantii has to colonize the plant apoplast without provoking symptoms ( 42 ). A D. dadantii \n pecS mutant is hypervirulent ( 43 ). The expression of pecS is downregulated (fold change of −12.24; P = 9.39 × 10 −9 ) in H. seropedicae attached to maize roots, implying that the genes repressed by PecS are expressed and may be important under those conditions ( 13 ). However, the products of those genes may be toxic when expressed under nutrient-rich conditions. The genes Hsero_1086, Hsero_2104, and Hsero_2356 code for transcriptional regulators with lambda-repressor-like, DNA-binding domains. Hsero_2356 is part of a locus (Hsero_2351 to Hsero_2371) that has a lower GC content (56% GC) than the rest of the SmR1 genome (63% GC). Interestingly, RNA-seq expression profiling of bacteria grown in minimal medium as well as bacteria attached to maize roots showed that genes of this locus (Hsero_2351 to Hsero_2356) were highly expressed, while the genes downstream of this genomic locus were not ( 13 ). We hypothesize that the essentiality of these three regulators could be due to repression of genes that might be lethal under the growth conditions used in our study. The gene Hsero_4425 is annotated as a member of the AsnC family of transcription-regulating proteins. It is divergently transcribed from the essential gene glmS (Hsero_4426). Homologs of glmS have been described as essential for 25 other bacterial species, and the arrangement of these two genes is conserved in many proteobacteria (data not shown). It is possible that the essentiality of Hsero_4425 in H. seropedicae SmR1 is related to the expression of glmS . Finally, the essential hypothetical genes Hsero_2418 and Hsero_3074 are both adjacent to genes coding for homologs of the RNA polymerase sigma E factor protein RpoE (Hsero_2419 and Hsero_3073). Both genes have predicted transmembrane helices; in the case of Hsero_2418, it belongs to the pFAM PF13490 family, i.e., a putative zinc finger found in several anti-sigma factor proteins. Homologs of these two genes are always linked to RNA polymerase sigma factors in other bacteria. We hypothesize that the essentiality of these genes in TY medium could be due to regulation of genes activated by the cognate sigma factors. H. seropedicae candidate essential genes with described essential orthologs in only one or two of the strains in the DEG are indicated in Table S3 in the supplemental material. Interestingly, the gene Hsero_4295, which codes for an outer membrane porin, has essential orthologs only in the two betaproteobacteria Burkholderia thailandensis E264 and Burkholderia pseudomallei K96243 ( 44 , 45 ), for which the essential gene sets have been described. Further, the gene Hsero_4295 was reported to be upregulated when H. seropedicae was attached to wheat roots but downregulated when H. seropedicae was attached to maize roots ( 12 , 13 ), suggesting that this gene may be involved in host specificity. Six of the genes described in Table S3 were found to be essential only in H. seropedicae and in the soil inhabitant B. thailandensis . This subset of genes might indicate essential systems for Burkholderiales . Conclusions. In this study, we have developed functional genomic techniques and resources for the model endophyte H. seropedicae that had not used previously in this species or in other bacterial endophytes. We have generated large comprehensive Tn libraries, and we have characterized the Tn insertion sites using next-generation sequencing (Tn-seq). These nearly saturated Tn libraries allowed us to perform robust essentiality analysis, and the results obtained are consistent with those reported for other bacteria. Our analysis of H. seropedicae Tn libraries from TY medium has enabled us to define the genes that are essential under those growth conditions. The results obtained enabled us to describe, at a functional level, the mechanisms of growth of H. seropedicae , including synthetic pathways, toxins, and regulatory mechanisms. Furthermore, these Tn libraries represent a valuable resource for the endophyte research community and will facilitate studies into the comprehensive assessment of the genetic mechanisms of the endophytic lifestyle of H. seropedicae , i.e., attachment to the root surface, internal colonization of the plant, and survival of the bacteria inside plants."
} | 6,570 |
28690595 | PMC5481317 | pmc | 5,137 | {
"abstract": "Anaerobic digestion for biogas production is reliant on the tightly coupled synergistic activities of complex microbial consortia. Members of the uncultured A6 phylotype, within the phylum Chloroflexi, are among the most abundant genus-level-taxa of mesophilic anaerobic digester systems treating primary and surplus sludge from wastewater treatment plants, yet are known only by their 16S rRNA gene sequence. This study applied metagenomics to obtain a complete circular genome (2.57 Mbp) from a representative of the A6 taxon. Preliminary annotation of the genome indicates these organisms to be anaerobic chemoorganoheterotrophs with a fermentative metabolism. Given their observed abundance, they are likely important primary fermenters in digester systems. Application of fluorescence in situ hybridisation probes designed in this study revealed their morphology to be short filaments present within the flocs. The A6 were sometimes co-located with the filamentous Archaea Methanosaeta spp. suggesting potential undetermined synergistic relationships. Based on its genome sequence and morphology we propose the species name Brevefilum fermentans gen. nov. sp. nov.",
"introduction": "Introduction Anaerobic digestion (AD) involves the conversion of organics to valuable methane, which is facilitated by the tightly coupled synergistic activities of complex microbial communities. The process essentially consists of four sequential microbial-mediated processes: hydrolysis, fermentation (acidogenesis), acetogenesis (dehydrogenation) and methanogenesis (acetoclastic or hydrogenotrophic) ( Vanwonterghem et al., 2014 ). Members of the phylum Chloroflexi are widespread in full-scale ADs, constituting up to 50% of the bacterial community, and are largely confined to the family Anaerolineaceae ( Nelson et al., 2011 ; Kirkegaard et al., 2017 ). Surprisingly, although their abundance indicates they must play a considerable role in these systems, their physiology and ecology is largely unknown. Most species of the Anaerolineaceae were isolated from anaerobic digester systems and have a fermentative metabolism, utilizing carbohydrates and proteinaceous carbon sources under anaerobic conditions ( Sekiguchi et al., 2003 ; Yamada et al., 2006 , 2007 ; Sun et al., 2016 ). A role in fermentation in AD systems is additionally supported by the annotation of available genomes derived from metagenomes ( Xia et al., 2016 ) and with in situ evidence for the Chloroflexi phylum ( Ariesyady et al., 2007 ). An in-depth understanding of the ecology and function of the Chloroflexi in biogas systems requires the characterisation of the abundant genera of the phylum. A recent large scale amplicon sequencing survey of Danish full-scale AD communities revealed the A6 phylotype, a member of the Anaerolineaceae known only by their 16S rRNA gene sequence, to be among the most abundant genus-level-taxa in these systems; at times being present in excess of 20% of the amplicon reads ( Kirkegaard et al., 2017 ). Advances in sequencing and metagenomic analyses enable the attainment of full genomes from the uncultured majority of microorganisms ( Wrighton et al., 2012 ; Albertsen et al., 2013 ). In the absence of a pure culture, the aim of this study was to apply metagenomics to obtain a genome from a representative of the A6 phylotype, giving the first insight into their physiology.",
"discussion": "Results and Discussion Amplicon sequencing survey data of full-scale ADs at wastewater treatment plants in Denmark showed a high abundance of the A6 phylotype in many of the mesophilic anaerobic digester tanks, but not in the primary or secondary sludge fed into these systems, suggesting that they are growing and well-adapted to mesophilic digester environment ( Figure 1 ). In order to obtain genomes for the A6 taxon, metagenomes were generated for the Fredericia AD plant due to the observed high abundance of the target phylotype (representing up to 10% of the metagenome reads). A complete circular genome (CAMBI-1), classified to the novel MiDAS taxonomy defined A6 genus ( McIlroy et al., 2017 ) based on its 16S rRNA gene sequence, was successfully assembled from the metagenomes (see Table 1 for details). Phylogenetic analysis of the 16S rRNA gene revealed that CAMBI-1 clusters together with isolates of the Anaerolineaceae, sharing 85–90% 16S rRNA gene sequence identity ( Figure 2 ). Based on the recommendations of Yarza et al. (2014) , this indicates that CAMBI-1 should be considered to represent a novel genus within the family. FIGURE 1 Box plot 16S rRNA gene amplicon sequence analysis (V1–3 region) of the distribution of the CAMBI-1 phylotype in full-scale ADs treating surplus sludge. Mesophilic AD – 15 plants, 321 samples; mesophilic AD with thermal high pressure (THP) pre-treatment of sludge (Cambi TM ) – 2 plants, 47 samples; thermophilic – 5 plants, 102 samples; primary sludge – 14 plants, 121 samples; surplus sludge – 15 plants, 20 samples. Data is taken from the survey study of Kirkegaard et al. (2017) which the reader is referred to for further details. Table 1 Genome properties of the CAMBI-1 genome. Property Size 2.57 Mbp GC content 49.1% Protein coding density 88.9% CDS 2288 CDS assigned function ∗ 20.6% rRNA operons 1 Sequencing project accession no. PRJEB19949 CDS, Coding DNA sequence; ∗ MicroScope software prediction classes 1–3 . FIGURE 2 Maximum-likelihood (PhyML) 16S rRNA gene phylogenetic tree including CAMBI-1 and related described species within the phylum Chloroflexi. The tree was constructed using the ARB software with the SILVA SSU Ref NR99 v. 1.23 database ( Quast et al., 2013 ). Additional sequences were aligned with the online SINA aligner with default settings ( Pruesse et al., 2012 ) and imported into ARB. The alignment was trimmed and variable regions removed using a custom 40% base frequency filter giving 1372 aligned positions for tree calculation. Herpetosiphon aurantiacus was used to root the tree. Bootstrap values from 100 re-samplings are indicated for branches when >50%: white dots, >50%; gray, >70%; black, >90%. The scale bar represents substitutions per nucleotide base. Examination of the CAMBI-1 genome for PFAM proteins related to archetypic mono- and diderm cell envelopes, revealed a monoderm cell envelope architecture consistent with other Chloroflexi ( Figure 3 ). The genome annotation and specialized searches using the PilFind program ( Imam et al., 2011 ) did not reveal any genes associated with flagella, fimbriae or pili, suggesting a non-motile lifestyle. Putative genes associated with spore coat polysaccharide biosynthesis protein SpsC (CFX1CAM_0088; 1106) were annotated ( Cangiano et al., 2014 ), although definitive candidates for other spore related genes were not found and their ability to form spore like structures is unclear. FIGURE 3 Cell envelope classification of CAMBI-1. Analysis was based on a search of the genome for genes encoding PFAM proteins ( Finn et al., 2016 ) that are specific to archetypical mono- (M) or diderm bacteria with lipopolysaccharides (DL) or atypical diderm bacteria (DA) (as detailed previously by Albertsen et al., 2013 ). These include proteins involved in lipopolysaccharide synthesis (LPS), outer membrane associated proteins (OMP), and proteins associated with septum formation and sporulation. The percentage prevalence of each PFAM is given for each listed phylum. Phyla included are represented by all complete genomes (at least four each) in the IMG database (release 3.5) ( Chen et al., 2017 ). The numbers shown in the column for CAMBI-1 represent the number of hits for a given PFAM protein in the analyzed genome. The PFAM profile of CAMBI-1 is similar to those of archetypical monoderm bacteria, including other members of the Chloroflexi. The CAMBI-1 genome lacks a cytochrome oxidase, electron transport chain complexes and several key enzymes required for a complete TCA cycle, indicating a strict anaerobic metabolism. Annotation of a catalase (CFX1CAM_0578) and superoxide dismutase (CFX1CAM_2274) indicates some resistance to oxidative stress. Genes for the dissimilarity reduction of sulfate, nitrate or nitrite were also not annotated. Although an ability for denitrification was not annotated, the organism possesses a putative nitric oxide reductase ( norV ) (CFX1CAM_0414) and a putative hydroxylamine reductase ( hcp ) (CFX1CAM_0418), which both have suggested involvement in protection against nitrosative stress ( Vine and Cole, 2011 ). Key genes for the Wood-Ljungdahl pathway and the Calvin-Benson-Bassham cycle were not annotated, indicating an inability to fix carbon for autotrophy. Potential for the pentose phosphate and Embden-Meyerhof-Parnas glycolysis pathways were present. Several annotated genes suggest a fermentative physiology consistent with other members of the family Anaerolineaceae ( Figure 4 and Table 2 ). Pyruvate can be converted to acetyl-CoA by a pyruvate: ferredoxin oxidoreductase (CFX1CAM_0326), pyruvate dehydrogenase (CFX1CAM_1724-1726) or a pyruvate formate lyase (CFX1CAM_0333), with formate released from activity of the latter potentially oxidized to CO 2 by an annotated formate dehydrogenase (CFX1CAM_1212). Potential fermentation by-products from acetyl-CoA include acetate, mediated by an acetyl-CoA synthetase (CFX1CAM_0825; 1292), and ethanol, facilitated by acetaldehyde (CFX1CAM_1715) and alcohol dehydrogenases (CFX1CAM_0055). The annotation of putative genes associated with the methylmalonyl-CoA pathway (CFX1CAM_1019; 1020; 2064–2067) indicates that propionate could be produced as a metabolic by-product from the fermentation of amino acids. Annotated tungsten-containing aldehyde ferredoxin oxidoreductases (AORs) (CFX1CAM_1238; 2051) may function to oxidize aldehydes derived from amino acid oxidation ( Heider et al., 1995 ). Several described members of the Anaerolineaceae ( Table 2 ) produce hydrogen as a fermentation by-product. However, definitive evidence for a hydrogenase was not found in the CAMBI-1 genome. FIGURE 4 Selected catabolic pathways annotated in the A6 genome. Proteinaceous carbon substrates are given in purple, saccharide substrates in green and potential fermentation by-products in orange. PEP, phosphoenolpyruvate; THF, tetrahydrofolate. Table 2 Summary of phenotypic characteristics of members of the family Anaerolineaceae. Species Isolation source Temperature optimum Physiology Carbon sources/electron donors ∗ Fermentation by-products (from sugars) Reference CAMBI-1 Anaerobic digester Mesophile Strict anaerobe; chemoheterotroph; fermenter Carbohydrates; proteins ∗∗ Acetate; ethanol; formate; CO 2 ∗∗ This study Anaerolinea thermophila T Anaerobic digester Thermophile Strict anaerobe; chemoheterotroph; fermenter Carbohydrates; proteins w Acetate; H 2 ; lactate w ; succinate w ; formate w Sekiguchi et al., 2003 Anaerolinea thermolimosa T Anaerobic digester Thermophile Strict anaerobe; chemoheterotroph; fermenter Carbohydrates; proteins Acetate; lactate; H 2 Yamada et al., 2006 Levilinea saccharolytica T Anaerobic digester Mesophile Strict anaerobe; chemoheterotroph; fermenter Carbohydrates; proteins Acetate; formate; H 2 ; lactate w Yamada et al., 2006 Leptolinea tardivitalis T Anaerobic digester Mesophile Strict anaerobe; chemoheterotroph; fermenter Carbohydrates; proteins Acetate; lactate; pyruvate; H 2 ; succinate w ; formate w Yamada et al., 2006 Longilinea arvoryzae T Rice paddy soil Mesophile Strict anaerobe; chemoheterotroph; fermenter Carbohydrates; proteins Acetate; lactate; H 2 Yamada et al., 2007 Bellilinea caldifistulae T Anaerobic digester Thermophile Strict anaerobe; chemoheterotroph; fermenter Carbohydrates; proteins w Acetate; lactate; formate; H 2 ; propionate w ; pyruvate w Yamada et al., 2007 ‘Thermanaerothrix daxensis’ T Deep hot aquifier Thermophile Strict anaerobe; chemoheterotroph; fermenter Carbohydrates Lactate; acetate; CO 2 ; H 2 w Grégoire et al., 2011 Thermomarinilinea lacunofontalis T Hydrothermal vent Thermophile Strict anaerobe; chemoheterotroph; fermenter Proteins – Nunoura et al., 2013 Ornatilinea apprima T Hot water bath microbial mat Mesophile Strict anaerobe; chemoheterotroph; fermenter Carbohydrates; proteins Acetate; ethanol; H 2 ; lactate w ; formate w Podosokorskaya et al., 2013 Pelolinea submarina T Marine sediment Mesophile Strict anaerobe; chemoheterotroph; fermenter Carbohydrates Acetate; lactate; ethanol; H 2 ; pyruvate w ; propionate w Imachi et al., 2014 Flexilinea flocculi T Anaerobic digester Mesophile Strict anaerobe; chemoheterotroph; fermenter Carbohydrates Acetate; lactate; succinate; propionate; formate; H 2 Sun et al., 2016 All listed species have a filamentous morphology; ∗ Proteins = protein based substrates rich in amino acids and peptides; ∗∗ Not empirically demonstrated; w Relatively weak growth observed/trace amounts produced . T Type species . Fluorescence in situ hybridisation probes were designed to visualize the morphology of the A6 in situ ( Table 3 ). The CFX-A6-450 and CFX-A6-1278 probes were designed to cover the phylotype. These can be applied together with different fluorochromes, where the overlap gives a higher confidence in specificity, or with the same fluorochrome to give a higher coverage of the group and to increase the signal to background ratio that can be problematic with AD samples. Application of these probes to the Fredericia AD biomass, and several additional full-scale digesters, revealed that these organisms form short filaments that are typically approximately 0.3 μm thick and 5–10 μm long ( Figure 5C ), but were occasionally observed at lengths of >100 μm. Unlabelled helper probes were designed for the CFX-A6-1278 and CFX-A6-450 probes, but only CFX-A6-1278_H1 gave an increase in fluorescence and is recommended for use ( Table 3 ). Competitor probes were designed to cover un-validated single base mismatches in non-target sequences ( Table 3 ). Stringency of the CFX-A6-1278 probe was supported by its application to P. submarina MO-CFX1 T – a non-target isolate with a single mismatch to the probe – which gave no positive fluorescent signal. Unlike some prominent wastewater-related Chloroflexi ( Kragelund et al., 2007 , 2011 ; Speirs et al., 2009 ), the A6 are covered by the EUBmix FISH probe set routinely applied to cover most members of the domain bacteria ( Amann et al., 1990 ; Daims et al., 1999 ). Table 3 FISH probes designed in this study. Probe E. coli pos. Target group Coverage ∗ Sequence (5′-3′) [FA]% ∗∗ CFX-A6-1278 1278–1298 A6 clade 81% GAG GCC TGC TTT CAG GAT TG 45 CFX-A6-1278_C1 1278–1298 Competitor probe for CFX-A6-1278 N/A GAG GCC GGC TTT CAG GAT TG – CFX-A6-1278_C2 ∗∗∗ 1278–1298 Competitor probe for CFX-A6-1278 N/A GAG GCC TGC TTT DAG GAT TG – CFX-A6-1278_H1 1262–1277 Helper probe for CFX-A6-1278 N/A GCT CCG CCT YGC GRC T – CFX-A6-1278_H2 ∗∗∗∗ 1299–1322 Helper probe for CFX-A6-1278 N/A GRG TTG CAG ACT GCA ATC TGA ACT – CFX-A6-450 450–492 A6 clade 86% GGG AGT ACA GTC CTT CCT C 40 CFX-A6-450_C 450–492 Competitor probe for CFX-A6-450 N/A GGG AGT ACY GTC CTT CCT C – CFX-A6-450_H ∗∗∗∗ 494–519 Helper probe for CFX-A6-450 N/A GGC ACG TAG TTA GCC GAG ACT TAT TC – CFX-A6-mix N/A A6 clade 96% CFX-A6-1278 + CFX-A6-450 45 ∗ Coverage based on the MiDAS taxonomy version 2.1 ( McIlroy et al., 2017 ). There were no non-target hits for either probe. ∗∗ Optimal hybridisation formamide concentration % [v/v]. ∗∗∗ The CFX-A6-1278 probe did not give a positive signal when applied to Pelolinea submarina MO-CFX1 T (at the recommended formamide concentration), which represents the non-target single base mismatched sequences covered by this competitor probe – the CFX-A6-1278_C2 probe is therefore not required. ∗∗∗∗ Addition of these helper probes did not improve fluorescence when applied with their respective probes and are therefore not required . FIGURE 5 Fluorescence in situ hybridization (FISH) micrographs of the A6 in the Ejby Mølle AD, Denmark (sampled February, 2013). (A) DIC image of the biomass. (B) FISH image with MX825mix probe set (MX825 + MX825b + MX825c) targeting the genus Methanosaeta (green). (C) FISH image with the CFX-A6-mix probe set (CFX-A6-1278 + CFX-A6-450: red). (D) Overlay image of CFX-A6-mix and MX825mix images showing co-location of the A6 (red filaments) and Methanosaeta spp. (green filaments). All images are from the same field of view. Scale bar = 10 μm. The A6 were distributed throughout the flocs and were sometimes observed co-located with the filamentous Archaea Methanosaeta spp. ( Figure 5 ), indicating the potential for synergistic relationships. Parallel growth of these two filamentous genera was observed in multiple plants but was only frequent in the Ejby Mølle AD (23% of Methanosaeta filaments having associated A6; see Table 4 ). Unfortunately, the low FISH signal to background ratio for the thin A6 filaments did not permit qFISH studies or statistical co-location analyses ( Daims et al., 2006 ); noting that visual assessment often indicated a higher abundance of A6 relative to the Methanosaeta spp., with most of both genera growing independent of contact with the other. Amplicon sequencing surveys indicate that the Methanosaeta are the most abundant methanogenic archaeal genus in mesophilic ADs located at WWTPs in Denmark ( Kirkegaard et al., 2017 ), which was supported by qFISH in this study where they made up approximately half of the archaeal biovolume ( Table 4 ). As such, the potential synergistic relationship with the A6 filaments may have important implications for methane production in some full-scale AD systems (e.g., Ejby Mølle). Methanosaeta are widely known to be acetoclastic methanogens ( Whitman et al., 2014 ), which would therefore benefit from the use of any acetate theoretically produced by the fermentative A6. It has also been demonstrated that some Methanosaeta species in ADs can utilize electrons sourced through direct interspecies electron transfer (DIET), from ethanol oxidizing Geobacter spp., to reduce CO 2 to methane ( Rotaru et al., 2014 ). Thus, the A6 may transfer excess electrons from the oxidation of organic carbon to the Methanosaeta via DIET, facilitated by the observed close proximity of the two organisms. It is thought that cytochromes and electrically conductive pili structures facilitate electrons flow to the recipient organism ( Shrestha and Rotaru, 2014 ). A single membrane associated polyheme cytochrome c (CFX1CAM_1800) was annotated which had low homology to known DIET cytochromes (22% amino acid sequence similarity with OmcS from Geobacter sulfurreducens (Acc. No. AAR35877)), though no pili associated genes were identified in the CAMBI-1 genome. It may be that novel conductive structures are utilized by these organisms, or the CAMBI-1 genome may not represent the A6 species co-located with the Methanosaeta . Further work into the possibility for DIET and other potential interactions between the two genera is required. Table 4 FISH analyses of the abundance of the Methanosaeta spp. and their association with the A6. Measurement AD location Ejby Mølle Slagelse Randers Aalborg East Archaea % biovolume ∗ 4 ± 2 2 ± 1 5 ± 1 2 ± 1 Methanosaeta % biovolume ∗ 2 ± 1 1 ± 1 3 ± 1 1 ± 1 % Methanosaeta with associated A6 ∗∗ 23 ± 3 2 ± 1 1 ± 1 0 Values are averages ± standard deviation, calculated from ∗ 25 image sets or ∗∗ 3 manual counts of 100 randomly selected Methanosaeta filaments (>10 μm in length). Methanosaeta filaments were considered positive for association with A6 filaments if multiple of the latter appeared horizontally attached to the former. 12 other mesophilic ADs located in Denmark were screened but excluded from analyses due to high background fluorescence or the absence or low abundance of the A6 . This study provides the first insight into the ecology of the A6 phylotype. Genomic evidence, coupled with their high relative abundance, suggests that members of the phylotype are important fermenters in mesophilic AD systems. The annotation of the representative CAMBI-1 genome gives a basic understanding of their physiology, noting that most of the coding sequence was not associated with any function ( Table 1 ). A detailed understanding of the ecology of the A6 will be achieved with in situ and gene expression studies and by obtaining axenic cultures for representatives of the genus. The attainment of a genome and the optimisation of FISH probes in this study provides an important foundation for these approaches. Importantly, having complete genomes representing the abundant members of the community is an essential reference for metatranscriptomic and metaproteomic gene expression studies that will together allow organism-based metabolic networks to be developed for anaerobic digester systems – giving a comprehensive view of the ecology of these biotechnologically important systems ( Waldor et al., 2015 )."
} | 5,249 |
38289136 | PMC10880630 | pmc | 5,139 | {
"abstract": "ABSTRACT Engineering the plant microbiome with beneficial endophytic bacteria can improve the growth, health, and productivity of the holobiont. Here, we administered two beneficial bacterial strains, Kosakonia VR04 sp. and Rhizobium GR12 sp., to micropropagated grapevine cuttings obtained via somatic embryogenesis. While both strains colonized the plant endosphere, only Rhizobium GR12 sp. increased root biomass under nutritional-deficit conditions, as supported by the plant growth promotion traits detected in its genome. Phylogenetic and co-occurrence analyses revealed that the plant native bacterial community, originally dominated by Streptococcaceae and Micrococcaceae, dramatically changed depending on the inoculation treatments, as invading strains differently affected the relative abundance and the interactions of pre-existing taxa. After 30 days of plantlets’ growth, Pantoea became a predominant taxon, and considering untreated plantlets as references, Rhizobium sp. GR12 showed a minor impact on the endophytic bacterial community. On the other hand, Kosakonia sp. VR04 caused a major change in community composition, suggesting an opportunistic colonization pattern. Overall, the results corroborate the importance of preserving the native endophytic community structure and functions during plant microbiome engineering. IMPORTANCE A better comprehension of bacterial colonization processes and outcomes could benefit the use of plant probiotics in the field. In this study, we applied two different beneficial bacteria to grapevine micropropagated plantlets and described how the inoculation of these strains impacts endophytic microbiota assembly. We showed that under nutritional deficit conditions, the response of the receiving endophytic bacterial communities to the invasion of the beneficial strains related to the manifestation of plant growth promotion effects by the inoculated invading strains. Rhizobium sp. GR12 was able to preserve the native microbiome structure despite its effective colonization, highlighting the importance of the plant-endophyte associations for the holobiont performance. Moreover, our approach showed that the use of micropropagated plantlets could be a valuable strategy to study the interplay among the plant, its native microbiota, and the invader on a wider portfolio of species besides model plants, facilitating the application of new knowledge in agriculture.",
"conclusion": "Conclusions This study showed that in vitro micropropagated V. vinifera plantlets host a microbiota assembly mostly composed of bacterial taxa commonly detected in indoor and human-associated environments, sharply divergent from that commonly found in grapevine endosphere under field conditions. By introducing two potential beneficial bacterial strains in this simplified ecosystem, we revealed the different outcomes of the invasion process toward the native endophytic bacterial populations, describing a relationship between the differential impact on community structure and the plant growth promotion in conditions of nutritional depletion. Overall, our results confirm the importance of preserving the native endophytic community structure and functions when attempting to engineer the plant microbiome ( 59 ). Furthermore, the results generated by characterizing the cultured microbiota associated with grapevine cuttings and by the inoculation of Rhizobium sp. GR12 propose the possible exploitation of PGP bacteria for the biostimulation of in vitro plant cultures ( 60 ). This opens up a future research perspective for the reduction of chemical use and plant stress during the transplant phase.",
"introduction": "INTRODUCTION Endophytes are considered the most interesting among beneficial microbiome members, as they can interact intimately with plants, penetrating their internal tissues and moving in different compartments ( 1 ). These capabilities imply that endophytic strains can be highly efficient as plant probiotics since (i) they are less exposed to the variation of physicochemical parameters occurring in the outer plant environments (i.e., leaf surface, rhizoplane, and rhizosphere) and (ii) they suffer a minor competition in the endosphere compared to other microhabitats ( 2 , 3 ). The occurrence of a plant growth promotion (PGP) effect after the application of a beneficial bacterium, generally defined by measuring phenotypic traits, may depend upon several factors, including the bacteria’s permanence over time at a suitable density in the plant tissues ( 4 , 5 ). Plant probiotics can act directly by exerting PGP functions (i.e., biostimulation, biofertilization, and biocontrol) or their beneficial effect can be the result of interactions with the native endophytic community, whose composition can be modulated by the bacterial inoculum ( 6 ). The latter aspect is related to bacterial invasion, an ecological process that, in plant microbiology, has been mostly studied in terms of pathology ( 7 ). Nonetheless, a better understanding of beneficial bacteria establishment into the plant microbiome is a priority to overcome one of the major factors currently limiting the use of PGP strains in the field, namely their effective colonization of the holobiont ( 4 ). In this study, we took advantage of in vitro micropropagated plants, limiting the variability of other experimental systems, to disentangle the effects played by putative beneficial bacteria when invading the plant holobiont. Micropropagated plants could be a useful tool to address the need for a wider portfolio of species for studying plant-microbiome interactions, including non-model species of relevant interest in agriculture ( 8 ). Though in the past bacterial occurrence in micropropagated plants was considered detrimental, more recently it has been clarified that certain bacteria can have beneficial effects on the explants in culture and can improve the micropropagation of recalcitrant genotypes ( 9 ). The grapevine plantlets used in this study were generated by somatic embryogenesis, providing virus-free plants ( 10 ), whose endophytic bacterial microbiota was characterized here for the first time. This simplified system was inoculated by bacteria isolated from field-grown grapevine or lettuce and identified as putative PGP strains through in vitro tests. The response of the endophytic microbiota associated with micropropagated grapevine to bacterial invasion was assessed by high throughput 16S rRNA amplicon sequencing, diversity, and network analyses.",
"discussion": "DISCUSSION In this work, we characterized the endophytic microbiota of in vitro cultivated Vitis vinifera var. Chardonnay plantlets, using this simplified system to study the outcomes of the bacterial invasion played by two putative beneficial bacteria on the holobiont, under controlled conditions. The endophytic bacterial community of micropropagated grapevine cuttings was characterized both via high throughput 16S rRNA gene sequencing and cultivation approaches. The microbiota was dominated by taxa, such as Micrococcaceae, Streptococcaceae, Staphylococcaceae, Veillonellaceae, and Moraxellaceae, which are commonly detected and cultured from a broad range of environments including different plant tissues ( 40 – 42 ). However, this assembly showed to be radically different in terms of taxa’s relative abundance from those generally associated with Vitis vinifera cultivated in the field ( 43 ), even considering variations related to different rootstock-scion combinations ( 44 , 45 ). Instead, it was more similar to indoor and human-associated microbiota ( 46 , 47 ). The observed bacterial taxa, therefore, seem to be able to better adapt to the host plant under in vitro conditions and to replace, across generations of micropropagated plants, the dominating members of the native endophytic community, which were inherited from the parental plant tissue collected in the field. At the same time, plantlets obtained via somatic embryogenesis may recruit and maintain these taxa in the endosphere due to the establishment of a beneficial interaction. This was suggested by the detection of biostimulation activities, like ACC deaminase and the production of indole-acetic acid, in a high number of the strains isolated from cuttings. Even though the microbiota of in vitro cultured plants is poorly studied, evidence of stable association with PGP bacteria was found in other species such as strawberries ( 48 ) and papaya ( 49 ). The invasion experiment performed with Rhizobium sp. GR12-GFP and Kosakonia sp. VR04-mSc demonstrated that the bacteria successfully colonized the endosphere of micropropagated grapevines cultivated on both standard and highly diluted growth media. Despite the fact that the two bacteria displayed PGP potential according to previous in vitro screenings, a growth promotion effect was only observed with Rhizobium sp. under simulated nutritional depletion. Bacteria within the rhizobia group are well known to associate with and support the growth of different plant hosts including grapevine ( 50 ). The sequence of the genome of strain Rhizobium sp. GR12, isolated from the root endosphere of field-grown grapevine, showed that this bacterium is endowed with several genes potentially involved in biofertilization and biocontrol (i.e., siderophores), stress response, and related to plant hormone regulation. Therefore, as we observed in this study under controlled conditions, the activation of root growth promotion may be an adaptive response to nutritional stress and not be manifested under optimal growth conditions, as previously demonstrated for drought stress ( 51 ). The characterization of the bacterial microbiota of plantlets subjected to nutritional depletion revealed that the composition of the endophytic community was differently modulated by Rhizobium sp. GR12-GFP and Kosakonia sp. VR04-mSc, as specific taxa were enriched or depleted in response to the establishment of these bacteria, reflecting the different plant responses in terms of growth promotion. Considering the control plantlets as references, Rhizobium sp. GR12-GFP showed a minor impact on the plant bacterial community of recipient plantlets compared to Kosakonia sp. VR04-mSc. The different outcome is visible both considering the invader’s relative abundance over the total bacterial community and the variations of taxa composition. At the end of the experiment, we observed a drastic increase of ASV3, classified as Pantoea sp. within the family Erwinaceae, both in the controls and to a larger extent in plantlets inoculated with Rhizobium sp. GR12-GFP. This trend was not observed in the plantlets receiving the Kosakonia treatment, where the invader strain overcame all the other taxa in terms of relative abundance. Pantoea sp. is a well-known plant-associated bacterium with documented growth-promoting activities ( 52 ) and might have also played a role in sustaining micropropagated grapevine development in conditions of nutritional depletion. Kosakonia sp. VR04-mSc invasion caused a dramatic imbalance of the bacterial community structure as shown by co-occurrence network analysis. Such analysis showed the opposite outcome in terms of microbial interactions between the invader strain and the recipient endophytic community in Kosakonia sp. VR04-mSc or Rhizobium sp. GR12-GFP-inoculated plants. The highlighted negative or positive interactions among ASVs can also be explained by the indirect effects of the invader on the autochthonous microbial populations, as previously suggested ( 53 ). Indirect effects have been reported to be mediated by interactions with third species or by changes in any of the environmental factors, which drive the microbial community stability ( 54 , 55 ). In the present study, considering the in vitro controlled conditions, we can hypothesize that the observed effects in terms of co-presence/mutual exclusion are most probably mediated by direct and third-part indirect interactions among the bacterial ASVs. Moreover, the results of the growth-inhibition test support the hypothesis that the invading strains activated mainly indirect interactions with the recipient community rather than a direct antagonist effect. Modulation of the microbiota plays a key role in the capacity of the plant holobiont to balance nutrient acquisition. On the other hand, abiotic stress conditions such as starvation can make the plant more susceptible to opportunistic colonization ( 56 , 57 ). In the present study, invasion by Kosakonia sp. VR04-mSc was associated with a dysbiosis of the endophytic microbiota that likely hampered the improvement of the host’s fitness under non-optimal conditions ( 58 ). All in all, besides a plant-growth-promoting effect directly exerted by Rhizobium sp. GR12-GFP, the improved performance of plants treated with this strain and their response to nutritional stress may also be related to the preservation of the microbial community structure and the holobiont’s functional integrity. Conclusions This study showed that in vitro micropropagated V. vinifera plantlets host a microbiota assembly mostly composed of bacterial taxa commonly detected in indoor and human-associated environments, sharply divergent from that commonly found in grapevine endosphere under field conditions. By introducing two potential beneficial bacterial strains in this simplified ecosystem, we revealed the different outcomes of the invasion process toward the native endophytic bacterial populations, describing a relationship between the differential impact on community structure and the plant growth promotion in conditions of nutritional depletion. Overall, our results confirm the importance of preserving the native endophytic community structure and functions when attempting to engineer the plant microbiome ( 59 ). Furthermore, the results generated by characterizing the cultured microbiota associated with grapevine cuttings and by the inoculation of Rhizobium sp. GR12 propose the possible exploitation of PGP bacteria for the biostimulation of in vitro plant cultures ( 60 ). This opens up a future research perspective for the reduction of chemical use and plant stress during the transplant phase."
} | 3,569 |
39452789 | PMC11510107 | pmc | 5,140 | {
"abstract": "Mechanical signals from the extracellular matrix are crucial in guiding cellular behavior. Two-dimensional hydrogel substrates for cell cultures serve as exceptional tools for mechanobiology studies because they mimic the biomechanical and adhesive characteristics of natural environments. However, the interdisciplinary knowledge required to synthetize and manipulate these biomaterials typically restricts their widespread use in biological laboratories, which may not have the material science expertise or specialized instrumentation. To address this, we propose a scalable method that requires minimal setup to produce 2D hydrogel substrates with independent modulation of the rigidity and adhesiveness within the range typical of natural tissues. In this method, norbornene-terminated 8-arm polyethylene glycol is stoichiometrically functionalized with RGD peptides and crosslinked with a di-cysteine terminated peptide via a thiol–ene click reaction. Since the synthesis process significantly influences the final properties of the hydrogels, we provide a detailed description of the chemical procedure to ensure reproducibility and high throughput results. We demonstrate examples of cell mechanosignaling by monitoring the activation state of the mechanoeffector proteins YAP/TAZ. This method effectively dissects the influence of biophysical and adhesive cues on cell behavior. We believe that our procedure will be easily adopted by other cell biology laboratories, improving its accessibility and practical application.",
"conclusion": "6. Conclusions This protocol outlines a scalable method to produce 2D hydrogel substrates with independent modulation of rigidity and adhesiveness within the range typical of natural tissues. The process requires minimal setup and can be easily implemented in any biological laboratory to study the role of stiffness and adhesivity in cell mechanobiology [ 2 , 13 , 17 , 24 ]. Compared to other methods in the literature for synthesizing substrates for similar applications, our protocol offers precise control over both mechanical and cell adhesive properties, utilizing click-reaction polymerization rather than the more commonly used radical polymerization [ 16 , 17 , 25 , 26 , 27 ]. Additionally, this method enables the preparation of adhesive substrates with a broader range of elastic moduli, thanks to the optimization of the synthesis procedure, which surpasses other reported systems [ 28 , 29 ]. The impact of biophysical and adhesive cues on cell behavior has been demonstrated through immunofluorescence analysis, showing modulation of the activation state of the mechanoeffector proteins YAP/TAZ and cell spreading in response to modulations of both stiffness and adhesiveness.",
"introduction": "1. Introduction Longstanding oversights regarding the relationship between cell behavior and the mechanics of their biological environment have been largely due to the use of rigid, unphysiological culturing substrates made of solid plastic. On these substrates, cells display aberrant behavior characterized by distorted phenotypes with abnormal polarization, excessive proliferation, and unconstrained spreading, skewing natural cellular responses to their environment. This distortion in cellular perception of surrounding mechanics began to shift with advancements in biomaterials science. Notably, the development of hydrogels—water-swollen polymers that closely mimic the extracellular matrix (ECM)—has been transformative in cellular biology, particularly in mechanobiology [ 1 , 2 , 3 , 4 ]. This shift marks a significant paradigm change, aligning experimental conditions more closely with physiological realities. Hydrogels have emerged as pivotal tools in cell biology, with recent publications detailing their development and application [ 5 , 6 , 7 , 8 , 9 , 10 ]. Notably, 2D hydrogel substrates specifically engineered for cell culture [ 3 , 11 , 12 , 13 , 14 ] have demonstrated that substrate stiffness and adhesiveness crucially influence cell behavior and gene expression. However, studies aiming to dissect the separate impacts of these two parameters are scant and often yield incomparable results due to variations in substrate composition and synthesis techniques [ 3 , 5 , 10 , 15 , 16 , 17 , 18 , 19 , 20 , 21 ]. Despite these promising developments, the full potential of these advanced materials remains untapped, primarily because of the absence of standardized, easily reproducible hydrogel synthesis protocols that can be adopted by any biological laboratory. Tse and Engler [ 14 ] significantly advanced the field by introducing an excellent protocol to create polyacrylamide hydrogels. This protocol has become a fundamental guide for synthesizing mechanically tunable substrates essential for cell biology studies. Today, the vast majority of studies on substrate mechanics are limited to replicating the use of PAA-based substrates, with only minimal variations. Beyond polyacrylamide, polyethylene glycol (PEG) has also emerged as a versatile biomaterial due to its range in molecular weights, structures, functionalities, and chemistries, notably including “click” chemistry. This adaptability has spurred the development of synthesis methods based on a LEGO-like approach, where modular “building blocks” are assembled using biorthogonal and biocompatible chemistries to craft precise and reproducible hydrogel networks and functionalities. Despite these innovations, comprehensive and detailed protocols for controlled and standardized synthesis of PEG hydrogels remain scarce, which hampers their broader adoption in cell biology. Additionally, existing methods typically produce gels with limited stiffness ranges, either overly soft (<1 kPa) or excessively stiff (>4–5 kPa). To address this gap, we introduce a protocol for synthesizing PEG-based hydrogels, designed for straightforward implementation with equipment commonly available in biomaterials and biological laboratories. These hydrogels serve as versatile substrates for cell culture and mechanobiology studies, offering independent control over mechanical and adhesive properties. In particular, we use norbornene-terminated 8-arm polyethylene glycol, which is click-reacted with RGD cell adhesive motif and di-cysteine terminated peptides, via a norbornene–cystein thiol–ene reaction. This synthesis approach allows precise and independent modulation of gel substrate rigidity and adhesivity, by adjusting the crosslinking of the PEG macromer and the density of integrin-binding sites through stoichiometric control of adhesive peptides. Gel stiffness can be tuned from 0.3 to 14 kPa, as outlined in our previous publication [ 22 ]. These characteristics facilitate distinct analysis of how stiffness and adhesivity independently influence cell behavior."
} | 1,695 |
36507711 | PMC9871532 | pmc | 5,142 | {
"abstract": "Abstract Cattle manure has a low energy content and high fibre and water content, limiting its value for biogas production. Co‐digestion with a more energy‐dense material can improve the output, but the co‐substrate composition that gives the best results in terms of degree of degradation, gas production and digestate quality has not yet been identified. This study examined the effects of carbohydrate, protein and fat as co‐substrates for biogas production from cattle manure. Laboratory‐scale semi‐continuous mesophilic reactors were operated with manure in mono‐digestion or in co‐digestion with egg albumin, rapeseed oil, potato starch or a mixture of these, and chemical and microbiological parameters were analysed. The results showed increased gas yield for all co‐digestion reactors, but only the reactor supplemented with rapeseed oil showed synergistic effects on methane yield. The reactor receiving potato starch indicated improved fibre degradation, suggesting a priming effect by the easily accessible carbon. Both these reactors showed increased species richness and enrichment of key microbial species, such as fat‐degrading Syntrophomonadaceae and families known to include cellulolytic bacteria. The addition of albumin promoted enrichment of known ammonia‐tolerant syntrophic acetate‐ and potential propionate‐degrading bacteria, but still caused slight process inhibition and less efficient overall degradation of organic matter in general, and of cellulose in particular.",
"conclusion": "CONCLUSIONS This study clearly showed that manure‐based biogas production can be improved by addition of co‐substrates with different chemical composition, but with some differences in microbial community development/dynamics, gas production, digestate nutrient values and residual methane production. All investigated co‐substrates resulted in higher total gas production compared with manure alone and in the case of albumin also a higher ammonium‐nitrogen level in the digestate. The addition of rapeseed oil gave the overall highest gas yield, with values indicating synergistic effects, possibly due to the highly enriched population of Syntrophomonadaceae . The addition of albumin as co‐substrate caused some instability, with increasing VFA levels and less efficient degradation of cellulose, probably due to ammonia inhibition of cellulolytic activity and methanogenesis. In contrast, co‐digestion with potato starch suggested a priming effect, with slightly more efficient degradation of the fibre fraction than in mono‐digestion of manure, and enrichment of the known cellulolytic Acetivibrionales , Lachnospiraceae and Oscillospiraceae . However, the digestate from the reactor with potato starch showed higher RMP, suggesting continued degradation of fibre in the digestate. Interestingly the protein‐supplemented reactor showed the lowest RMP values, suggesting that ammonia inhibition could be a measure to reduce the risk of methane emissions during storage of digestate. Comparison between the FTHFS and 16S rRNA gene‐based microbial analysis illustrated that the former more clearly could identify a link between the applied co‐substrate and the related microbial community, such as dynamics of propionate‐degrading bacteria in the albumin‐supplemented reactors ( Ca . Cloacimonetes and Peptococcaceae ), In conclusion, several parameters should be considered when selecting a suitable co‐substrate for biogas production from manure. To optimise process outputs, it is important to target the desired outcome, that is, gas yield or digestate nutrient content, while also considering overall efficiency, risk of instability and methane emissions during storage. A mixture of proteins, fats and carbohydrates can be used as co‐substrate to increase resilience of the microbial community to process disturbance in a manure‐based biogas process. This will also increase digestion rates and the fertiliser quality of the digestate (higher nitrogen content), without any harmful effects on microbial community or on biogas quality and quantity. It is thus a more balanced approach than mono‐digestion of animal manure.",
"introduction": "INTRODUCTION Treatment of cattle manure (CM) by anaerobic digestion (AD) provides many benefits, such as production of renewable energy (biogas), recirculation of nutrients and reduction of GHG emissions from agricultural production (Holm‐Nielsen et al., 2009 ; Liebetrau et al., 2013 ; Petersen et al., 2013 ; Pucker et al., 2013 ; Zhang, Wang, Yin, & Dogot, 2021 ). The total amount of manure produced in Europe has been estimated to correspond to a biogas potential representing 4.5% of the consumption of nature gas, if collected entirely (Scarlat et al., 2018 ). Unfortunately, the development of manure‐based AD processes is hampered by CM having low levels of degradable organic matter, resulting in low methane production and efficiency and difficulties to achieve economic feasibility (Møller et al., 2004 ; Ruile et al., 2015 ; Triolo et al., 2011 ; Tufaner & Avşar, 2016 ). To achieve reasonable levels of degradation, the retention time in the reactor needs to be sufficiently long (Linke et al., 2013 ; Ruile et al., 2015 ). Unfortunately, CM has a low concentration of organic matter and a high content of water, making it difficult to achieve long retention time at reasonable organic loads (Ruile et al., 2015 ). Different strategies can be used to improve microbial degradation of manure, such as application of different reactor technologies and pre‐treatments or use of process additives and co‐digestion (Nasir et al., 2012 ). Co‐digestion is an interesting approach since, if applied for energy‐dense materials, it can allow a higher load without a marked decrease in retention time, resulting in improved volumetric and specific methane production (Esposito et al., 2012 ; Labatut et al., 2011 ; Li et al., 2021 ; Tufaner & Avşar, 2016 ). Moreover, co‐digestion can overcome any imbalances in nutrients and improve overall biodegradation (Ma et al., 2020 ; Mata‐Alvarez et al., 2014 ; Zhou et al., 2021 ). It has also been suggested to give a greater reduction in global warning impact than mono‐digestion of daily manure (Zhang, Wang, Yin, & Dogot, 2021 ). Many substrates with different chemical composition have been evaluated and shown to work as co‐substrates for AD of manure, such as straw, energy crops, food waste, slaughterhouse waste and residual fat (Ahlberg‐Eliasson et al., 2018 ; Mata‐Alvarez et al., 2014 ; Søndergaard et al., 2015 ). Most studies on co‐digestion report improved methane production compared with digesting CM alone, but in many cases, the increase in gas yield is attributable solely to the co‐substrate, and not to improved degradation of the CM per se (Li et al., 2021 ). However, some studies also suggest synergistic effects, with improved methane formation and/or degradation as a consequence of addition of co‐substrate (summarised in Li et al., 2021 ). Synergistic effects have, for example, been proposed for co‐digestion of CM with the organic fraction of municipal solid (Macias‐Corral et al., 2008 ), switchgrass (Zheng et al., 2015 ) and sheep manure (Li, Achinas, et al., 2020 ). Degradation of organic material in a biogas process is performed by an array of different microorganisms, working in a synchronised manner (Schnürer & Jarvis, 2017 ). The process involves four different microbial degradation steps (hydrolysis, acidogenesis, acetogenesis and methanogenesis), requiring the combined activity of several different groups of microorganisms. To create a stable, efficient biogas process, it is important to meet the growth requirements of all microorganisms involved. To provide favourable conditions for microbial growth, the substrate needs to supply growth factors, macronutrients and micronutrients and contain low levels of microbial inhibitors (Westerholm & Schnürer, 2019 ). By itself, CM can supply sufficient nutrients to maintain microbial growth during mono‐digestion, but the addition of a suitable co‐substrate can give a more balanced nutrient composition and thus result in synergistic effects, with improved degradation, higher methane yields and promotion of a more diverse microbial community (Mata‐Alvarez et al., 2014 ). However, the co‐digestion substrate needs to be chosen carefully, since instead of giving positive effects, some may result in antagonistic interactions, resulting in lower biogas productivity and biodegradability. For example, high ammonia levels have been shown to inhibit degradation of cellulose during co‐digestion of cow manure and protein‐rich material* (Li, Zhao, et al., 2020 ). Therefore, co‐digestion of animal manure and lignocellulosic feedstocks (crops) has been proposed as a solution to reduce the risk of ammonia inhibition and to bring the C/N ratio closer to the optimum value for microbial growth. The response of the microbial community to a co‐digestion substrate will depend on the character and composition of the co‐substrate and on operating conditions in the reactor (Westerholm & Schnürer, 2019 ). Many studies have investigated the response of microbial communities to co‐digestion, including in reactors operating with manure (Ahlberg‐Eliasson et al., 2018 ; Li, Achinas, et al., 2020 ; Song & Zhang, 2015 ; Wang et al., 2018 ; Wei et al., 2019 ; Xu et al., 2018 ; Zhang, Wang, Xing, et al., 2021 ), but only a few have assessed responses specifically related to enhanced biodegradability of the manure. One such study investigated co‐digestion of CM with sheep manure in continuously stirred tank reactors (CSTR) and observed improved degradation of lignocellulose compared with in mono‐digestion of CM (Li, Achinas, et al., 2020 ). Their analysis revealed enrichment of Firmicutes , genus Romboutisia and Turicibacter , and particularly Candidatus Cloacimonas and Methanoculleus , all showing a positive correlation with cellulose degradation (Li, Achinas, et al., 2020 ). Enrichment of Firmicutes has also been found to be linked to enhanced hydrolysis during co‐digestion of cattle manure and apple waste fructose (Lin et al., 2022 ). The general concept of co‐digestion of co‐substrate with cow manure is thus well known and investigated. However, most studies have focused mainly on methane productivity and only a few have included a deeper chemical and/or microbiological evaluation of potential synergistic effects on degradation of the cow manure. In addition, less attention has been devoted to examining effects of different categories of macromolecules, for example, fats, proteins or carbohydrates, in co‐digestion with manure or the effect of co‐digestion on residual methane potential (RMP) in the digestate. In theory, a well‐designed co‐digestion strategy would improve degradation efficiency and gas production, balance the nutrient content in the digestate and reduce RMP of the digestate. High RMP poses a risk of methane losses from storage and decreases the overall environmental benefits of biogas production from manure (Clemens et al., 2006 ; Liebetrau et al., 2013 ; Rodhe et al., 2012 ). The optimal co‐substrate to achieve the most efficient process in co‐digestion with manure is still not completely clear. The aim of this study was to assess the suitability of different co‐substrates for optimal biogas production from cattle manure. The specific objective was to identify links between co‐substrate composition and overall process efficiency and stability, levels of nutrients and RMP of the digestate. The co‐substrates selected for assessment were egg albumin (protein), rapeseed oil (fat) and potato starch (carbohydrates), alone and in combination, which were co‐digested with CM. Process performance was evaluated using different chemical parameters, such as gas production, degradation efficiency of different macromolecules and RMP. To capture changes in microbial community development caused by the different co‐substrates and assess the stability of the process, analyses of the microbial community were conducted. These analyses considered the overall microbial community, targeting the 16S rRNA gene, and also specifically the acetogenic/syntrophic community, targeting the FTHFS gene. Analysis of the FTHFS gene has recently been proposed as a useful method for detection of community changes before effects emerge in physico‐chemical profiles in biogas processes (Singh, 2021 ; Singh, Moestedt, et al., 2021 ).",
"discussion": "DISCUSSION Importance of co‐substrate for methane production in batch and continuous reactors When the substrates were evaluated individually in batch reactor tests, fat (rapeseed oil) showed the highest BMP, followed by protein (egg albumin) and carbohydrates (potato starch), which is in line with the theoretical values for these macromolecules (Angelidaki & Sanders, 2004 ). For CM, previously reported values typically range between 150 and 265 ml CH 4 g VS −1 (Møller et al., 2004 ; Ruile et al., 2015 ; Triolo et al., 2011 ). The CM used in this study had BMP of 195 ± 4 ml CH 4 g VS −1 , that is, within the reported range. On digesting manure with the co‐substrates no synergistic effects were seen, with the mixtures giving similar results as for additive values based on analysis of the single substrates. Previous studies investigating co‐digestion of CM in batch reactors have used more complex co‐substrates than in the present study, and thus direct comparison is difficult. However, a recent study identified synergistic effects during batch wise co‐digestion of CM with food waste (rich in protein and lipids), and maize straw (rich in carbohydrates) (Zhang, Wang, Xing, et al., 2021 ). In the study by Zhang, Wang, Xing, et al. ( 2021 ), the effect varied depending on the proportion of co‐substrate in the mix, which in all cases included a higher load of co‐substrate than in the present study. The synergistic effects observed in that study were attributed to high relative abundance of both hydrogenotrophic and acetotrophic methanogens. The CSTRs in the present study reached SMP values of 170–302 ml CH 4 g VS −1 (Table 2 ). These were in line with values reported in a meta‐analysis of different studies on methane yield during anaerobic co‐digestion of animal manure with other feedstocks, with mean methane yield in continuous reactors of 175.3 and 298.8 ml CH 4 g VS −1 for mono‐ and co‐digestion of cattle manure respectively (Ma et al., 2020 ). Methane production in our CSTRs R0‐R4 was in proportion to expected values based on batch trials, but with some indications of both synergistic and antagonistic effects during co‐digestion. Reactors R1, receiving protein, and R2, receiving fat, showed significantly ( p < 0.05) lower and higher methane production, respectively, than expected from the batch results. A recent meta‐analysis identified a recommended synergy interval of carbon‐nitrogen ratio of 20–27 for anaerobic co‐digestion of livestock manure, with a higher probability of synergy during co‐digestion when the fat/carbohydrate ratio exceeds 0.13 and the protein/carbohydrate ratio exceeds 0.26 (Zhou et al., 2021 ). In line with this suggestion, the reactor showing synergistic effects on methane production, R2, was the only reactor receiving substrate with ratio values in the recommended range (0.11 and 0.67 respectively) (Figure S5 ). In contrast, reactor R1 showed significantly lower methane production, as expected from theoretical values and from results in the batch trial. This was most likely a consequence of ammonia inhibition. In R1, degradation of protein originating from the added egg albumin resulted in high total ammonium‐nitrogen (TAN) concentration (up to 3.8 g L −1 ), resulting in a free ammonia concentration of 0.4 g NH 3 + L −1 (Table 2 ). In contrast, reactor R4, receiving a lower inclusion rate of albumin, reached more moderate levels (around 0.2 g NH 3 + L −1 ), with no apparent negative effect on process performance. Inhibition of biogas processes has been reported at various levels of free ammonia, depending on operating conditions, but is typically observed at around 0.15–0.5 g NH 3 + L −1 (Calli et al., 2005 ; Rajagopal et al., 2013 ; Westerholm et al., 2016 ). The inhibitory effect in R1 was illustrated by greater build‐up of fatty acids than in the other reactors (Figures S2 and S3 ). The reactors operating with co‐digestion showed a smaller increase (∆ 0.5 g VS L −1 day −1 ) in load than the reference reactor R0 (3.0 compared with 2.5 g VS L −1 day −1 ). However, this increase still resulted in much more efficient use of the reactor volume for all reactors, with an increase in volumetric methane production of between 27% and 100% in the co‐digestion reactors. The overall increase in yield brought about by co‐digestion depends on both composition and VS contribution of the co‐substrate. As all reactors in the present study were supplemented with the same amount of VS from the co‐substrate, the composition of the co‐substrate was the main influencing factor. Reactor R2, supplemented with fat, showed the highest gas production and highest methane content in the gas, resulting in the highest efficiency value (Table 2 , Table S2 ). This is a reasonable observation considering the high energy content and high gas potential of fat (Angelidaki et al., 2011 ; Schnürer, 2016 ; Schnürer et al., 2016 ). In this study, the added fat was well degraded (~85%, Figures S4 and S5 ) and the reactors showed no sign of VFA accumulation. However, co‐digestion with fat‐rich material can be challenging and, if fat is included at high rates, it can result in accumulation of fatty acids and inhibition of methanogenesis (Holohan et al., 2022 ). This was clearly illustrated in a study by Wang et al. ( 2021 ) where CM was co‐digested with glycerol trioleate (fat, oil, grease (FOG)) or glucose, applied in an increasing load from 3.2 to 5 g VS L −1 day −1 . The load was increased in two or four successive steps but, regardless of loading strategy, the reactors receiving lipids suffered from inhibition caused by accumulation of long‐chain fatty acids (LCFA). In contrast, no significant inhibition was seen on addition of glucose. The optimal load of FOG for co‐digestion with sludge has been found to be 0.5%–1.5% (v/v), giving 80%–90% degradation of the lipid, while above this inclusion level FOG causes VFA accumulation and low LCFA degradation (Usman et al., 2020 ). The final load of rapeseed oil in reactors R2 and R4 corresponded to 1.6% and 0.5% (v/v), respectively, that is, it was within the suggested optimal range. In a meta‐analysis/regression analysis by Ma et al. ( 2020 ), VS concentration and C/N ratio in the mixed substrate were identified as significant factors in improved methane yield compared with mono‐digestion of CM, with optimal VS content of 18.2 g L −1 and C/N ratio of 35. In the present study, inclusion of co‐substrate gave VS content close to this value (17.2 g L −1 , calculated based on the organic load and VS values from Table S1 ). Degradability and nutrient content of digestate In all cases, co‐digestion gave a greater VS reduction than mono‐digestion, with the highest values obtained for reactors R2 and R3, supplemented with fat and carbohydrates respectively (Table 2 , Figure S5 ). Reactor R3 also showed the greatest degradation of cellulose and hemicellulose, suggesting a small synergistic effect of co‐digestion. Insam and Markt ( 2016 ) suggested that co‐digestion with small amounts of easily accessible substrates can result in a priming effect , that is, a non‐additive interaction between decomposition of organic matter and the added substrate. In line with this suggestion, labile carbon (fructose) has been suggested to trigger a positive priming effect during co‐digestion of swine manure (Lin et al., 2022 ). The results in the present study indicate a priming effect, but this cannot be completely proven as the differences seen were not statistically significant. In contrast to R2 and R3, reactor R1 showed the lowest degree of degradation, likely caused by ammonia inhibition as discussed. However, even with residual protein left in the digestate, this reactor showed higher degradation of proteins compared with the other reactors. Instead, among the different macromolecules present cellulose showed the lowest degradability in this reactor. The effect of ammonia on methanogens has been widely investigated, but less is known about ammonia inhibition of cellulolytic bacteria. One study on solid‐state anaerobic digestion of maize stover revealed significant inhibition of the cellulose hydrolysis rate at a TAN concentration above 2.5 g L −1 (Wang et al., 2013 ) compared with 3.8 g L −1 in the present study. Inhibition of cellulose degradation by ammonia during digestion of cow manure and cellulose in batch reactors initiated with different inoculums has also been suggested (Li, Zhao, et al., 2020 ; Sun et al., 2016 ). In comparison, reactor R4, receiving a mixture of co‐substrates and with TAN of 2.5 g L −1 , did not show lower cellulose degradability than the other reactors. In addition to ammonia inhibition, the low degradability in R2 could have been partly caused by general inhibition of microbial activity through trace element limitation, since degradation of albumin in this reactor resulted in production of a significantly higher level of H 2 S in the raw biogas (Table 2 , Figure S2 ). Elevated levels of H 2 S decrease biogas quality and can also trap trace metals that are essential for microbial activity (Choong et al., 2016 ; Wang et al., 2016 ). As mentioned, the reactor receiving rapeseed oil (R2) had the highest efficiency value (Table 2 and Table S2 ). Residual methane production During optimisation of agricultural biogas processes, it is important to consider not only gas production and nutrient content in the digestate, but also residual methane production. An optimisation approach giving increased gas production can sometimes increase the risk of methane emissions during storage of digestate, decreasing the environmental benefits of biogas production from manure (Ahlberg‐Eliasson et al., 2021 ). In the present study, the RMP values were in the lower range (68–78 L CH 4 kg −1 ) as compared to previously reported values (range 20–240 L CH 4 kg −1 ) (Ahlberg‐Eliasson et al., 2017 , 2021 ; Ruile et al., 2015 ). Comparing all reactors, R1 had a lower degradation rate and less accumulation of VFA, representing potential for residual methane production. However, this digestate had a rather low RMP value, suggesting that high ammonia levels hamper methane production not only in the biogas process but also during storage of digestate. This is supported by previous findings that RMP is lowered at ammonium‐nitrogen levels above >2.7 g L −1 \n NH 4 + ‐N (as reviewed in Monlau et al., 2015 ). However, as illustrated in a recent study on biogas production from manure, RMP lowering at high ammonium‐nitrogen levels is not a general rule (Ahlberg‐Eliasson et al., 2021 ). Inhibition of methane production is thus most likely influenced also by other factors, such as temperature and pH, which affect the actual level of ammonia. The digestates from reactors R2 and R3 showed the highest RMP values, most likely represented by degradation of residual co‐substrate, for example, fat in R2. However, reactor R3 showed similar values despite high degradation of the added co‐substrate. Thus, the indicated synergistic effect in degradation of cellulose and hemicellulose probably persisted during RMP measurement (Table S2 ). Microbial community response Sampling for microbial analysis began on the third day after addition of the co‐substrate and analysis of these samples indicated an immediate effect that was most pronounced in the reactor (R3) receiving carbohydrates as co‐substrate (Figures 1 , 2 , 5 ). However, the reference reactor R0 also showed changes in community structure and dynamics over time, in line with previous findings in studies of manure digestion that stable, non‐altered anaerobic digesters can have a highly dynamic community structure (Fernández et al., 1999 ; St‐Pierre & Wright, 2014 ). The addition of different co‐substrates resulted in enrichment of candidate microbes specific to the co‐substrate added to the reactors, supporting previous findings that several sets of bacterial species are associated with specific substrate categories used in anaerobic reactors (Amani et al., 2010 ; Zhang, Wang, Xing, et al., 2021 ). We also identified key candidate taxa that were specific to the different macromolecules, which has not been done previously at this level of resolution in CM‐based reactors. In addition to the 16S rRNA gene amplicon sequencing results, FTHFS gene amplicon sequencing further helped in visualisation of microbial community profile. Acetogens and FTHFS‐harbouring bacterial communities are phylogenetically very diverse and metabolically dextrous components of the overall microbial community (Singh et al., 2019 ) and are associated with many degradation steps in biogas reactors. The FTHFS analyses revealed some specialist bacterial taxa with potential for metabolic tasks such as degradation of complex plant material (family Lachnospiraceae ) (Beaumont et al., 2021 ; Koeck et al., 2015 ; Lebuhn et al., 2014 ; Suksong et al., 2019 ), known syntrophic acetate/propionate/butyrate‐oxidising bacteria ( Tepidanaerobacter , Syntrophomonas , etc.) (Singh & Schnürer, 2022 ) and proposed acetate/propionate‐oxidising bacteria (phylum Cloacimonadota and family Clostridiaceae , Peptococcaceae ) (Ahlert et al., 2016 ; Singh & Schnürer, 2022 ; Singh, Schnürer, & Westerholm, 2021 ; Westerholm et al., 2022 ). Specific changes in community profile in the control reactor and reactors with co‐substrates are further discussed below. In all experimental reactors, the microbial community was dominated by two phyla, Firmicutes and Bacteroidota , which in turn were dominated by class Clostridia and Bacteroidia respectively. This is in line with previous findings for biogas processes operating with manure (Ahlberg‐Eliasson et al., 2021 ; Chen et al., 2016 ; Güllert et al., 2016 ; St‐Pierre & Wright, 2014 ; Sun et al., 2015 ). Firmicutes and Bacteroidota are primarily involved in initial decomposition of organic matter via hydrolysis and acidogenesis of complex polysaccharides, but can also be engaged in protein degradation (Vanwonterghem et al., 2016 ; Westerholm et al., 2018 ). Families belonging to phylum Firmicutes , class Clostridia , were observed to be present in higher and differential abundance in R3. Additionally, families (DTU013, DTU023, Dysgonomonadaceae ) that harbour known and potential cellulolytic bacteria (e.g. Clostridiaceae spp., Fermentimonas , Hungateiclostridiaceae spp., Ruminiclostridium spp. etc.), together with unknown family candidates (Acetivibrionales_NA, Bacteria_NA, Firmicutes_NA), showed higher differential abundance (high LFCa and LDa values) in R3 compared with the control or other reactors (Figures 2 , 3 , 4 ). FTHFS analyses of reactor R3 showed significant differential abundance also of family Oscillospiraceae , Lachnospiraceae , Eggerthellaceae and Peptoniphilaceae . Family Oscillospiraceae and Lachnospiraceae include members with saccharolytic capacity and with glycoside hydrolase enzymes responsible for the degradation of cellulose and hemicellulose (Beaumont et al., 2021 ; Laptev, 2021 ). Representatives of these families are common in rumen/gut environments, but have also been isolated from biogas environments (Flaiz et al., 2020 ; Rettenmaier et al., 2021 ). The availability of readily accessible carbohydrates in the form of soluble starch in R3 likely increased the relative abundance of the saccharolytic families, and potentially also increased their metabolic activity. Combined with the process data, this confirms that addition of starch has the potential to give a priming effect by boosting microbial abundance and their physiological activity and improving degradation of lignocellulose. Priming is a well‐known process in nature and has also been suggested to occur during co‐digestion of sewage sludge and whey (Aichinger et al., 2015 ). The overall increase in microbial community richness seen in reactor R3 could explain the greater degree of lignocellulose degradation than in the reference reactor (Figure S5 ). The enrichment of family Eggerthellaceae and family Peptoniphilaceae suggests that the addition of starch was also positive for protein degradation. Both families contain known protein‐ (peptone, polypeptide) and amino acid‐degrading species (Ezaki & Kawamura, 2015 ; Gupta, 2021 ; Johnson et al., 2014 ). Enhanced protein degradation at the end of operation of reactor R3 was also indicated by an increase in H 2 S levels (Figure 5 ). In an opposing trend to R3, reactor R1 showed decreased richness and evenness over time, likely explained by the increasing levels of ammonia (Lv et al., 2019 ). High ammonia levels result in inhibition of methanogens, giving overall less efficient degradation and VFA accumulation (Capson‐Tojo et al., 2020 ), as also seen in present study for reactor R1 (Figures S2 – S4 ). High ammonia levels typically result in a shift from acetoclastic methanogenesis to syntrophic acetate oxidation (SAO), enabling methane production at high ammonia levels (Liu et al., 2017 ; Sun et al., 2016 ; Westerholm et al., 2016 ). Such a shift was not detectable in the sequencing data, but qPCR analysis revealed the presence of two known syntrophic acetate oxidisers, Syntrophaceticus schinkii and Schnuerera ultunensis (Figure S12 ). Syntrophaceticus schinkii was present in all reactors and showed higher abundance at day 164 than at day 3 in both R0 and R2, while S. ultunensis was present in significantly higher abundance at day 164 compared with day 3 only in R2, suggesting higher SAO activity in this reactor. As discussed above, previous studies have reported an inhibitory effect of ammonia not only on methanogens, but also on cellulose degradation consortia, which would explain the higher levels of cellulose seen in the digestate from reactor R1. In line with this, lower differential abundance was indicated for several known cellulolytic families, such as Oscillospiraceae and Defluvitaleaceae , based on LFCa and LDa values for R1 compared with the control (Figures 3 and 4 ). In FTHFS‐based analysis, reduced relative and differential abundance of phylum Ca . Cloacimonetes was shown in R1. Members within this phylum are proposed to have the capacity to use both amino acids and carbohydrates and to perform propionate oxidation (Johnson & Hug, 2022 ; Westerholm et al., 2022 ). In previous studies on biogas processes, this phylum has been suggested as a biomarker for process disturbance (Klang et al., 2019 , 2020 ; Singh, 2021 ; Singh, Moestedt, et al., 2021 ; Singh, Müller, & Schnürer, 2021 ). In the present study, the reduced abundance of families belonging to phylum Ca . Cloacimonetes, under the influence of increased VFA, ammonia and reduced pH, can likely be seen as indicating an approaching disturbance. This confirms that functional FTHFS gene‐based analysis is a strong method for detecting process disturbance. The FTHFS‐based analysis also revealed that family Peptococcaceae is a more tolerant (to ammonia and VFA levels) and efficient propionate degrader than Ca . Cloacimonetes, and is thus a more sensitive indicator of process disturbance. A recent study on phylum Cloacimonadota showed that propionate oxidation is not a characteristic feature of this phylum (Johnson & Hug, 2022 ). Irrespective of the target gene used for microbial community analysis (FTHFS or 16S rRNA gene), family Syntrophomonadaceae increased in relative and differential abundance over time in reactor R2, as confirmed by the LCFa and LDa values (Figures 1 , 2 , 3 , 4 , 5 , 6 , 7 ). In family Syntrophomonadaceae , >13 species have been characterised as capable of LCFA degradation (Alves et al., 2009 ; McInerney et al., 2008 ; Sousa et al., 2009 ). This could explain the high degradability of fat in R2 (Figure S5 ). Degradation of fat results in glycerol and LCFA, with the latter being a known (microbial) inhibitor often seen accumulating and resulting in foaming (He et al., 2017 ; Rodríguez‐Méndez et al., 2017 ). A possible strategy to overcome this problem is to use pulse feeding instead of continuous feeding, which improves the conversion rate of the LCFA‐degrading community dominated by Syntrophomonadaceae (Ziels et al., 2018 ). In the present study, all fat was added at once, combined with the manure, and apparently this feeding approach was sufficient to allow the enriched population of Syntrophomonadaceae (genus JAAYJK01, also Syntrophomonas sp. according to older taxonomy) to efficiently degrade the fat, resulting in enhanced overall efficiency of the process (Figure S1 , Figures 2 , 3 , 4 , 5 , 6 , 7 ). The community pattern in R4 was similar to that in R0, which is likely explained by the balanced co‐substrate mixture (1:1:1 egg albumin: rapeseed oil: potato starch) and lower load of each substrate than in reactors R1‐R3. Thus, the microbial community was probably not exposed to high selection pressure from increased amount of any specific nutrient‐rich co‐substrate. Although the overall community structure in reactor R4 was similar to that in the control reactor, an interesting and very sensitive insight was obtained by the FTHFS analysis of the microbial community response to addition of a mix of co‐substrates. Initially, the easily digestible carbohydrates were likely degraded mainly by Lachnospiraceae and Eggerthellaceae (day 3) (Figure 5 , Figure S11 ), which probably caused the slight increase in VFA levels (day 29). With increasing VFA level, a decrease in the relative abundance of phylum Ca . Cloacimonetes (day 66) was observed and the increase in VFA, especially propionate, instead probably stimulated the propionate‐degrading Peptococcaceae , causing the reduction in propionate levels (day 66). In parallel, the higher microbial abundance and probable activity of Lachnospiraceae and Eggerthellaceae together with Peptoniphilaceae (Figure 5 , Figure S11 ) and continuous addition of proteins in the co‐substrate mix were associated with a gradual increase in H 2 S levels over time (Figure 5 ). As mentioned, Ca . Cloacimonetes has been proposed as a process biomarker, but the results for reactors R2 and R4 suggest that Ca . Cloacimonetes, together with Peptococcaceae , increases resilience of the process to disturbance (increased levels of VFA) and allows recovery to relative stability."
} | 8,746 |
37245065 | PMC10549214 | pmc | 5,143 | {
"abstract": "Abstract The successful design of economically viable bioprocesses can help to abate global dependence on petroleum, increase supply chain resilience, and add value to agriculture. Specifically, bioprocessing provides the opportunity to replace petrochemical production methods with biological methods and to develop novel bioproducts. Even though a vast range of chemicals can be biomanufactured, the constraints on economic viability, especially while competing with petrochemicals, are severe. There have been extensive gains in our ability to engineer microbes for improved production metrics and utilization of target carbon sources. The impact of growth medium composition on process cost and organism performance receives less attention in the literature than organism engineering efforts, with media optimization often being performed in proprietary settings. The widespread use of corn steep liquor as a nutrient source demonstrates the viability and importance of “waste” streams in biomanufacturing. There are other promising waste streams that can be used to increase the sustainability of biomanufacturing, such as the use of urea instead of fossil fuel-intensive ammonia and the use of struvite instead of contributing to the depletion of phosphate reserves. In this review, we discuss several process-specific optimizations of micronutrients that increased product titers by twofold or more. This practice of deliberate and thoughtful sourcing and adjustment of nutrients can substantially impact process metrics. Yet the mechanisms are rarely explored, making it difficult to generalize the results to other processes. In this review, we will discuss examples of nutrient sourcing and adjustment as a means of process improvement. One-Sentence Summary The potential impact of nutrient adjustments on bioprocess performance, economics, and waste valorization is undervalued and largely undercharacterized.",
"introduction": "Introduction On September 12, 2022, the President of the United States issued an executive order on advancing biotechnology and biomanufacturing innovations toward innovative solutions in health, climate change, energy, food security, agriculture, supply chain resilience, and national and economic security (The White House, 2022 ). Hence, biotechnology and biomanufacturing are recognized as critical tools for some of the most important needs of our society, such as climate and energy goals, improvement of food security, and a circular economy. Challenges discussed in this executive order include “advancing the science of scale-up production while reducing the obstacles for commercialization,” where one of these obstacles is the economic viability of bioprocesses. Bioprocesses are defined as the use of naturally existing or introduced capabilities and chemical reactions performed by enzymes to produce value-added products or replace existing upcycling reactions (Cossar, 2011 ). Products of a bioprocess include materials, fuels, and chemicals, among many others. The global chemical industry is dominated by petrochemical processes, which means that the current production technologies are dependent on non-renewable resources and are heavily expensive, both economically and environmentally (Ramamurthy et al., 2021 ). As a result of depleting valuable resources for the production of commodities, global issues such as the production of greenhouse gases and the endangerment of biodiversity are on the rise (Demeneix, 2020 ). However, the advancements of bioprocesses have introduced a new hope to replace existing technologies with those primarily dependent on sustainable resources (Zhang et al., 2015 ). Consequential to a long history of over-exploiting non-renewable resources, environmental pollutants are a critical issue for our world. Visibly detrimental to the environment and human health is the continuously increasing generation of plastic waste. When considered in context with recycling inefficiencies, only 9% of plastic is recycled and 75% is disposed of in landfills in the United States (Law et al., 2020 ). Biomanufacturing's promise of economic growth, increased sustainability and decreased waste production are well-known. With thoughtful nutrient sourcing, these processes can be used not only to produce a wide variety of important chemicals but also to remediate or reduce anthropogenic waste streams (Leong et al., 2021 ; National Research Council (U.S.), Committee on Bioprocess Engineering, 1992 ). The typical first step in bioprocess design is the selection of the product molecule, with the assumption that glucose will be used as the substrate (Fig. 1 ). A systematic evaluation of bioprocess designs for more than 200 high-production volume chemicals (at least 500 MT produced annually in the United States) from glucose determined that roughly 75% were unlikely to achieve economic viability (Wu et al., 2018 ). However, there is an increasing demand for bioprocesses that start with the desire to consume, remediate, or detoxify a complex waste stream, such as lignin (Kamimura et al., 2019 ), food waste (Isah & Ozbay, 2020 ), plastic waste (Jarboe et al., 2022 ; Skariyachan et al., 2022 ), industrial off-gas (Liew et al., 2016 ), and others (Saeed et al., 2022 ). To this end, a wide range of organisms have been studied for their ability to consume these wastes with the goal of capture, recycling, or valorization through the production of value-added chemicals (Gerotto et al., 2020 ; Liu & Hong, 2021 ; Verschoor et al., 2022 ). However, there are many obstacles in the design and development of an economically viable bioprocess. Fig. 1. Overview of bioprocess development. Every organism has generic (e.g., carbon, nitrogen, sulfur, phosphorus, etc.) and distinctive (vitamins, amino acids, metals, etc.) nutritional needs for robust performance. Knowledge of these needs can be obtained through elemental analysis and understanding of the needs of the metabolic pathway(s) of interest (Arigony et al., 2013 ; Heldal et al., 1985 ; Prescott et al., 2002 ; Rouf et al., 1964 ). While meeting these metabolic needs is essential, provisioning nutrients in excess can increase costs in the form of raw materials, waste treatment, and disposal, and in some cases may negatively impact organism performance. Thus, consideration of nutrient concentration, form, and source can improve process economics and footprint. Regardless of the process goal—production of a specific molecule or consumption of a waste stream—there are common strategies for process optimization. These considerations can be categorized into three groups (Fig. 1 ): (1) organism development; (2) feedstock (growth medium) optimization (nutrient sourcing); and (3) operational parameter and equipment design. Tools and methods for organism development and equipment design are invaluable for successful bioprocess development. However, they both are upfront costs, not operational costs. Operational costs, such as nutrient provisioning, handling, and storage, directly impact the economic viability of a biomanufacturing process (van Dien, 2013 ). The focus of this review is specifically on medium optimization of bioprocesses that use microorganisms as microbial cell factories, as opposed to abiotic processes that use biomass, such as crop residues, as substrate—and, when applicable, those aiming toward upcycling waste.",
"discussion": "Discussion Adjustment of nutrient form and concentration is a promising method for reducing bioprocess operating costs (Singh et al., 2016 ), but is often undervalued in the literature relative to organism development. Martinez set out to develop a simple mineral salt media for E. coli , arguably the most well-characterized microbial species (Martinez et al., 2007 ). Yet the optimum nutrient profile, particularly the metals, differed greatly from the initial expectations. We described multiple reports where increasing or decreasing the concentration of specific metals individually or in combination had a substantial impact on process performance, but often the reason for this effect is often unknown, making it difficult to generalize the knowledge to other processes. Instead, media optimization is often performed in industrial settings on a case-by-case basis, with minimal sharing of information. The relative absence of rational approaches for media optimization increases the burden on start-up companies in establishing process economic viability. Additional characterization, such as transcriptome analysis of nutrient uptake, can provide valuable insights (Vasylkivska et al., 2019 ). A great number of studies that consider media composition use molecular-scale information, such as pathway analysis, to determine a target for optimization, and the results from these studies might increase process costs. Few technoeconomic analyses consider specific nutrient costs, or recognize the potential of media optimization in process economics. A general economic analysis can help identify the media components that account for the most process costs, with subsequent investigation to design a more attractive process for scale-up efforts. The extensive use of CSL in biomanufacturing demonstrates the virtue of generically useful, low-cost, nutrient-rich waste streams (Zhou et al., 2022 ). However, there are concerns that CSL supply is not sufficient for the push for expanded biomanufacturing capability (Humbird et al., 2011 ). Expansion of economically viable biomanufacturing requires addressing the issue of nutrient sourcing, particularly for phosphorus. This search for reliable, low-cost nutrients provides the opportunity to upcycle waste streams that are rich in carbon, nitrogen, or other components. Thoughtful and resourceful nutrient sourcing, though possibly long and tedious, could possibly be the key to process economic viability, particularly for biological waste remediation."
} | 2,478 |
20811456 | PMC2936489 | pmc | 5,144 | {
"abstract": "Bacteria show remarkable adaptability in the face of antibiotic therapeutics. Resistance alleles in drug target-specific sites and general stress responses have been identified in individual endpoint isolates 1 – 7 . Less is known, however, about the population dynamics during the development of antibiotic-resistant strains. Here we follow a continuous culture of Escherichia coli facing increasing levels of antibiotic and show that the vast majority of isolates are less resistant than the population as a whole. We find that the few highly resistant mutants improve the survival of the population’s less resistant constituents, in part, by producing indole, a signaling molecule generated by actively growing, unstressed cells 8 . We show, through transcriptional profiling, that indole serves to turn on drug efflux pumps and oxidative stress protective mechanisms. The indole production comes at a fitness cost to the highly resistant isolates, and whole-genome sequencing reveals that this bacterial altruism is enabled by drug-resistance mutations unrelated to indole production. This work establishes a population-based resistance mechanism constituting a form of kin selection 9 whereby a small number of resistant mutants can, at some cost to themselves, provide protection to other more vulnerable cells, enhancing the survival capacity of the overall population in stressful environments."
} | 351 |
29026096 | PMC5638848 | pmc | 5,145 | {
"abstract": "Methane generated during enteric fermentation in ruminant livestock species is a major contributor to global anthropogenic greenhouse gas emissions. A period of moderate feed restriction followed by ad libitum access to feed is widely applied in cattle management to exploit the animal’s compensatory growth potential and reduce feed costs. In the present study, we utilised microbial RNA from rumen digesta samples to assess the phylogenetic diversity of transcriptionally active methanogens from feed-restricted and non-restricted animals. To determine the contribution of different rumen methanogens to methanogenesis during dietary restriction of cattle, we conducted high-throughput mcrA cDNA amplicon sequencing on an Illumina MiSeq and analysed both the abundance and phylogenetic origin of different mcrA cDNA sequences. When compared to their unrestricted contemporaries, in feed-restricted animals, the methanogenic activity, based on mcrA transcript abundance, of Methanobrevibacter gottschalkii clade increased while the methanogenic activity of the Methanobrevibacter ruminantium clade and members of the Methanomassiliicoccaceae family decreased. This study shows that the quantity of feed consumed can evoke large effects on the composition of methanogenically active species in the rumen of cattle. These data potentially have major implications for targeted CH 4 mitigation approaches such as anti-methanogen vaccines and/or tailored dietary management.",
"introduction": "Introduction The 2015 UNFCCC Paris agreement aims to pursue efforts to limit the increase in global warming to 1.5 °C 1 above temperatures prevailing during the pre-industrialisation era 1 . Globally, agriculture, forestry and other land use account for about 24% of annual greenhouse gas (GHG) emissions 2 . These include direct emissions from the cultivation of crops and livestock and also as a result of deforestation 3 . Enteric methane (CH 4 ) emissions from the livestock sector is the single largest contributor to global CH 4 emissions (40%) 3 . Most of the variation in the composition of the rumen microbiota is a direct result of feed composition and quantity consumed 4 – 6 . A period of feed-restriction followed by ad libitum feeding is widely used in livestock management for the beef industry to reduce feed costs 7 . Following a period of dietary restriction cattle typically undergo accelerated growth referred to as compensatory growth 8 , upon re-alimentation. Feed restricted animals have been previously shown to have slower passage rate of feed through the rumen 9 , 10 which has previously been correlated with increased CH 4 emissions per unit of feed from ruminants 9 , 11 , 12 . We previously found that feed-restricted animals had increased relative abundance of 16S DNA from the Methanobrevibacter gottschalkii clade 5 . This observation was positively correlated with a dramatic decrease (from 30% to less than 1%) in the relative abundance of a single bacterial 16S DNA sequence which could only be identified confidently as an uncultured Proteobacteria species and appeared to be related to the family Succinivibrionaceae 5 . As some Succinivibrionaceae species utilise hydrogen 13 we hypothesized that the aforementioned putative Succinivibrionaceae species was in competition for substrate, possibly hydrogen, with the Methanobrevibacter gottschalkii clade 5 in feed restricted animals. This was consistent with a decrease in ruminal propionate and attendant increased acetate:propionate (A:P) ratio, an indication of greater methanogenesis in these animals 5 . Following two months of re-alimentation, the volatile fatty acid ratios of ruminal digesta from previously feed restricted animals mirrored that of their non-restricted contemporaries, while a reversal in the relative abundance of 16S DNA from the Methanobrevibacter gottschalkii clade and the putative Succinivibrionaceae 16S DNA 5 was also observed. It is still not clear if analysis of 16S DNA accurately reflects the metabolic activity of rumen bacteria and archaea as metabolically inactive and dead bacteria retain large amounts of DNA 14 . We were therefore interested in determining which ruminal methanogens showed the highest methanogenic activity in cattle undergoing feed restriction followed by re-alimentation and compensatory growth. To do this we employed high-throughput amplicon sequencing of mcrA gene cDNA. Methyl co-enzyme reductase (MCR) is a multi-subunit enzyme of methanogenic archaea which catalyses the final step in CH 4 formation and the initial step in anaerobic CH 4 oxidation 15 . It reduces methyl-coenzyme M (CH3-S-CoM) by 7-mercaptoheptanoylthreonine phosphate (H-S-HTP) 16 . The MCR subunits are coded for by the mcr gene operon which usually comprises the genes mcrA, B, C, D and G . The mcrA gene codes for subunit A of MCR and can be used both as a phylogenetic marker for the identification of archaea and, as mcrA transcript abundance is positively correlated with methane production in peat soil 17 , is a quantitative marker for methane production. The objective of this study was to determine which methanogens were contributing to methanogenesis in the bovine rumen during periods of dietary restriction. To our knowledge this is the first time that high-throughput sequencing of mcrA cDNA amplicon libraries has been used to explore the diversity of metabolically active methanogens present in the rumen. This may be useful as a convenient and inexpensive method for use in large numbers of animals to answer the, as yet unanswered question, ‘which methanogens are making the greatest relative contribution to ruminal methane production’?",
"discussion": "Discussion We previously reported, using high-throughput 16S DNA amplicon sequencing, that a period of moderate feed restriction led to greater abundance of M . gottschalkii clade in the ruminal liquor of moderately feed restricted animals with an inverse relationship to a putative Succinivibrionaceae species 5 . Results from the study 5 show that both ruminal fractions, i.e. solid and liquid, exhibited similar community diversity, however, differences linked to feeding regimes were amplified in the rumen liquid. This informed the decision to go forward with detailed examination of the microbial RNA of the rumen liquid fraction in order to address the main research objective to identify which methanogens dominate methanogenesis during periods of bovine dietary restriction. Our approach utilised mcrA cDNA amplicon sequencing to determine the abundance of mcrA transcripts and their phylogenetic origin to identify the predominant methane generating species under different feed treatments. High-throughput mcrA amplicon sequencing, appears to have potential as a convenient and inexpensive tool for the identification of methane producing archaea and to assess their potential contribution to methane production in a large number of animals. There are two isoenzymes of MCR, denoted MCRI, which is encoded by operon mcrABCDG and MCR II, encoded by operon mrtABDG \n 16 . The different isoenyzme systems allow methanogens to be supported by different growth environments 18 . The mcrA gene is contained in both enzyme operons hence making it a useful molecular marker for methanogen activity in different environments. The RNA gene expression work described here partly supports the results of our 16S DNA amplicon sequencing. Methanobacteriaceae and Methanomassiliicoccaceae were found to be the dominant families in all treatment groups both at the RNA and DNA level. Methanobrevibacter was both the dominant methanogenic genus present in the 16S DNA study and produced the most mcrA transcripts relative to the other methanogen genera detected in the current study. The 16S amplicon sequencing analysis that we previously reported on these animals showed that relative abundance of the M . gottschalkii clade increased in bulls fed a restricted diet in comparison to the cattle fed an ad libitum diet 5 . The present study shows that this increase also occurred at the RNA level, indicating that feed restriction favours members of this clade. However, whereas our 16S DNA amplicon study on these animals showed that members of the M . ruminantium clade remained unchanged between treatments, our mcrA cDNA amplicon study shows a dramatic decrease in abundance in restricted animals. There are at least two possible explanations for this discrepancy. The first of these is that 16S DNA amplicon sequencing detects the presence or absence of bacteria and/or archaea, but does not give direct information on the activities and physiological states of microorganisms in samples 14 . The M . ruminantium clade detected in our 16S DNA amplicon study may have been present but not generating methane, while our mcrA cDNA amplicon data most likely provides a more accurate picture of which species are generating methane in the rumen. Secondly, our 16S DNA amplicon sequencing simultaneously targeted bacteria and archaea so it was possible to determine the abundance of archaea relative to bacteria. The mcrA primers, however, only show abundance of archaea relative to other archaea so the large increase in clusters in MCRA clusters in the M . gottschalkii clade would make it appear as though there was a decrease in other MCRA clusters. To resolve this, a further study is required in which an exogenous control RNA sequence is added at the start of RNA extraction to obtain an absolute rather than relative measure of mcrA transcript abundance. Alternatively, RNA-Seq analysis, for example, would permit the identification of mcrA transcripts relative to all other transcripts. Relative abundance of Methanomassiliicoccaceae did not significantly differ between treatment groups at either the DNA or RNA level. The phylogenetic assembly and relative abundance of mcrA cDNA found in this study are in agreement with the current literature on rumen composition derived from DNA based investigations 11 , 19 , 20 . This includes an international study 19 , which found that there is consistency in ruminant bacterial and archaeal communities regardless of species, diet and geographical location. Members of the M . gottschalkii and M . ruminantium clades were the two largest methanogenic groups in the rumen, accounting for approximately 74% of the total archaeal population 19 . Our mcrA cDNA results were consistent with this regardless of treatment. The two other dominant methanogen groups were Methanosphaera and Methanomassiliicoccaceae. In total, these four groups account for 89.2% of the total rumen archaeal population. Significantly, we have identified, using alpha diversity analysis, that feed restriction has a dramatic effect on species which generate methane in the rumen. Dietary restriction appears to provide a niche environment for specific methanogenic groups and removes the necessary environment required to sustain a more diverse archaeal community. High feed intake was previously associated with increased passage rate 9 and constant substrate supply to rumen microbes, ensuring diverse substrate to the rumen methanogen population. In our study, the microbiota of the restricted bulls probably resides in a different ecosystem to the ad libitum groups due to the smaller rumen size and reduced feed availability 8 . MCRA cluster 3, identified to be of the Methanobrevibacter genus, showed the second greatest methanogenic activity in all treatment groups. This indicates that dietary management inferred no bias toward the methanogenic activity of this Methanobrevibacter cluster. Feed-restriction did however, affect other clusters within the three major methanogenic groups active in the rumen. The relative abundance of MCRA clusters that were most closely related to the M . ruminantium clade and Methanomassiliicoccaceae were decreased in the rumen of feed restricted bulls, while the relative abundance of MRCA clusters of the Mbr . gottschalkii clade was increased. \n M . ruminantium M1 possesses mcrI \n 21 but does not possess the mcrII gene system. However, another known member of the M . ruminantium clade, M . olleyae possess mcrII so it is unclear if the lack of mcrI is typical of members of the M . ruminantium clade 22 . If it is assumed that the clusters 7094 and 883 of the M . ruminantium clade, identified in this current study, possess a similar mcrI system to M . ruminantium M1, then this could explain the reduction in this clade in the feed restricted animals. Expression of mcrI and mcrII are controlled preferentially on the levels of substrate available to methanogens present 18 . Low and high substrate concentrations for methanogens upregulate the expression of mcrI and mcrII respectively 18 . Reduced feed intake is correlated with a reduction in apparent digestibility despite an increase in particle retention time 10 . There was increased bacterial alpha diversity in the restricted animals and increased time for microbial fermentation, along with assumed decreased digestibility. Therefore, it was possible that there was a steady state of substrate available to methanogens present in R animals in comparison to animals fed ad libitum \n 9 , 10 . In animals fed ad libitum feed, there is typically an increase in digestibility leading to rapid substrate (H 2 ) availability to methanogens, initially 9 . This has a limiting effect on H 2 production from ruminal bacteria, as at a high H 2 concentration, H 2 producing pathways are less thermodynamically favourable and thus pathways that utilise H 2 are favoured, such as propionate production 9 . We hypothesise that due to the reduction in feed intake in restricted animals and the reduced degradability, rumen H 2 concentrations never reach the threshold concentration that was required for the negative feedback to be initiated. Steady substrate availability may have shifted expression from the mcrI isoenzyme in favour of mcrII , therefore decreasing the activity of members of the M . ruminantium clade. M . ruminantium clade members only possess mcrI therefore this increased the activity of members of the gottschalkii clade, where all members contain both versions of the isoenzyme. The methanogens active in the restricted group also were contained in a rumen with reduced volume in comparison to methanogens active in the ad libitum treatment groups 8 . This may contribute to the steady supply of H 2 present in the rumen and why it is sufficient to switch activity predominately to mcrII . In the rumen of animals fed ad libitum , the Mbr . ruminantium clade was active. This may be due to variation in the amount of H 2 present in the rumen at different times of the day, as members of this clade are active when supply of H 2 was low. The succinate producing bacterial family Succinivibrionaceae was significantly increased in animals fed an ad libitum diet and virtually absent in animals fed a restricted diet 5 . This bacterial family utilises H 2 to produce succinate which is rapidly converted to propionate in the rumen 23 . The ad libitum groups all had increased propionate in their rumens in comparison to the restricted group 5 . This provides further evidence that in ad libitum animals alternative pathways for H 2 utilisation are favoured 9 , while H 2 was below the threshold level for alternative pathways to be prioritised in restricted animals and therefore utilised for CH 4 production 24 by a less diverse group of methanogen with the majority made up of the M . gottschalkii clade. Four MCRA clusters identified to be from the Methanomassiliicoccaceae family were significantly less methanogenically active in the restricted treatment group in comparison to the ad libitum treatment groups. The Methanomassiliicoccaceae family are obligate H 2 -dependent methylotrophs, utilising methyl groups from methanol and methylamines (mono-, di-, and tri-methylamine) and methyl thiols for the production of methyl coenzyme 25 . It has previously been hypothesised that there is a positive association between Methanomassiliicoccaceae and Succinivibrionaceae 19 . Succinivibrionaceae degrade pectin 26 to produce methanol, which is a substrate required for the growth of Methanomassiliicoccaceae 27 . Therefore, dietary restriction may also have an influence on methylotrophic methanogens which utilise by-products of the microbial fermentation other than hydrogen, such as methylamines. This reduces the diversity of the active methanogen population. In conclusion we sequenced mcrA cDNA amplicon libraries to assess the abundance and diversity mcrA cDNA sequences present in the bovine rumen of animals undergoing dietary restriction and subsequent compensatory growth. This validated previous 16S DNA based data from our group and showed the most abundant methanogens were indeed the most methanogenically active. The study identifies a clear cohort of active methanogens in an environment where feed intake was restricted and methanogen substrate supply was limited but stable, when compared with when substrate was unrestricted. The majority of methanogens were hydrogentrophic and we hypothesise that H 2 concentration in the rumen was the main driver of methanogenic activity in the rumen. We found that under the condition of dietary restriction, where CH 4 production was predicted to be increased, relative abundance of the M . gottschalkii clade increased, while members of the M . ruminantium clade showed a dramatic decrease. Relative abundance of members of the Methanomassiliicoccaceae family, which are H 2 dependant methyltrophs, were also reduced in diet restricted bulls, as H 2 concentrations are regulated by passage rate. During the ad libitum feeding period we predicted that ruminal H 2 concentration was increased, directing H 2 utilisation away from CH 4 production in favour of other H 2 utilising pathways, such as succinate and propionate production pathways. In these pathways H 2 was used endogenously in the microbial cell rather than accumulating in the rumen. This contributes to a diverse methanogenic population that can survive at a variety of H 2 concentrations and also utilises by-products of the alternative H 2 utilising pathways such as methylamines. Restricted animals had a slower passage rate and a lower rate of digestibility within the rumen, providing a stable H 2 supply that is directed towards CH 4 production, giving rise to a low diversity population of active methanogens. This study therefore implies that a common livestock management system used to reduce feed costs may be contributing to increased ruminant CH 4 production. Research may be able to identify targeted CH 4 mitigation approaches such as anti-methanogen vaccines, dietary strategies and probiotics and therefore warrant further exploration."
} | 4,745 |
21596990 | null | s2 | 5,146 | {
"abstract": "Coevolution of mammals and their gut microbiota has profoundly affected their radiation into myriad habitats. We used shotgun sequencing of microbial community DNA and targeted sequencing of bacterial 16S ribosomal RNA genes to gain an understanding of how microbial communities adapt to extremes of diet. We sampled fecal DNA from 33 mammalian species and 18 humans who kept detailed diet records, and we found that the adaptation of the microbiota to diet is similar across different mammalian lineages. Functional repertoires of microbiome genes, such as those encoding carbohydrate-active enzymes and proteases, can be predicted from bacterial species assemblages. These results illustrate the value of characterizing vertebrate gut microbiomes to understand host evolutionary histories at a supraorganismal level."
} | 204 |
33418935 | PMC7825067 | pmc | 5,147 | {
"abstract": "Thermal management has become one of the crucial factors in designing electronic equipment and therefore creating composites with high thermal conductivity is necessary. In this work, a new insight on hybrid filler strategy is proposed to enhance the thermal conductivity in Thermoplastic polyurethanes (TPU). Firstly, spherical aluminium oxide/hexagonal boron nitride (ABN) functional hybrid fillers are synthesized by the spray drying process. Then, ABN/TPU thermally conductive composite material is produced by melt mixing and hot pressing. Then, ABN/TPU thermally conductive composite material is produced by melt mixing and hot pressing. Our results demonstrate that the incorporation of spherical hybrid ABN filler assists in the formation of a three-dimensional continuous heat conduction structure that enhances the thermal conductivity of the neat thermoplastic TPU matrix. Hence, we present a valuable method for preparing the thermal interface materials (TIMs) with high thermal conductivity, and this method can also be applied to large-scale manufacturing.",
"conclusion": "4. Conclusions In this work, a novel spherical hybrid filler (ABN) containing Al 2 O 3 NPs and h -BN was designed through simple mechanical mixing and spray drying processes. This filler was utilized to prepare the TPU composite matrix with the continuous three-dimensional (3D) thermal conduction network. The ABN/TPU composites prepared by melt mixing and hot compression was compared with h -BN/TPU composites in terms of their thermal conductivity wherein the ABN/TPU composite exhibited thermal conductivity of 1.39 Wm −1 K −1 with a filler loading of 30 wt.%, which is six times higher than that of pure TPU and three folds elevated than that of the h -BN/TPU composite. Enhancement in the thermal conductivity could be attributed to the three-dimensional (3D) thermal conduction network. As the filler, ABN particles provided a continuous heat conduction path while reducing the interface thermal resistance of the matrix. SEM images confirmed that Al 2 O 3 NPs were located between neighboring h -BN powders and served as bridges, which in turn assisted to build a continuous phonon transmission path. The above results can provide new insights into the construction of filler-contained composites with 3D isolation networks and demonstrated strong potential for the design of large-scale manufacturing of thermal interface materials.",
"introduction": "1. Introduction In this modern technological era, electronic gadgets and devices play a major role in every field. The electronics industry has been incessantly focusing on designing high performing, efficient, miniaturized, and cost-effective electronic products. Apart from the above criteria, thermal management also plays a critical part in designing electronic products. The excess irrelevant heat produced by the devices can get accumulated, affecting the operational efficiency and reliability of the devices [ 1 ]. Therefore, thermal interface materials (TIM) play a vital role in enhancing heat transfer between heat sources and heat sinks [ 2 ]. Conventional synthetic polymers have the advantages of excellent electrical insulation, good processability, and being lightweight, making them suitable for substrates of thermal interface materials. Among the available polymeric matrixes for TIMs, thermoplastic polyurethanes (TPU) are attractive due to their highly versatile and unique properties. TPU is a kind of multiphase block copolymers, the thermomechanical properties can be easily tailored by changing the molecular chain structure of the soft and hard segments, and the recyclability of thermoplastics gives it an added advantage [ 3 , 4 ]. Unfortunately, the thermal conductivity of most conventional polymeric substrates is very low, in the range of 0.1–0.5 W m −1 K −1 [ 5 ]. Ren et al. claimed that the thermal conductivity of laying graphite films/carbon fiber fabrics/TPU composite is up to 242 W m −1 K −1 at room temperature [ 6 ]. In addition, Dong et al. reported that the thermal conductivity and thermal stability were also found to be enhanced by introducing carbon black into TPU matrix [ 7 ]. However, most reports have only academic significance, and difficultly can be applied to electronic devices due to their extremely high electrical conductivity, leading device to malfunction because of electron leakage. Hence, the introduction of a thermally conductive but electrically non-conductive inorganic ceramic filler into the polymeric matrix would be one of the promising solutions [ 8 , 9 , 10 ]. Many studies have reported various methods to develop high thermally conductive and electrically insulating polymer-based composites which include the addition of various ceramic fillers such as boron nitride (BN), aluminum oxide (Al 2 O 3 ), aluminum nitride (AlN), silicon carbide (SiC), silicon dioxide (SiO 2 ), and zinc oxide (ZnO) into the polymeric matrix [ 11 , 12 , 13 , 14 , 15 , 16 , 17 , 18 ]. Among these ceramic fillers, hexagonal boron nitride ( h -BN) with a two-dimensional (2D) layered structure stands out owing to its excellent thermal conductivity (250–300 W m −1 K −1 ), low thermal expansion coefficient, stable crystal structure, low dielectric constant, high resistivity, and non-toxic properties [ 19 , 20 ]. Liu et al. reported that high thermal conductive h -BN filled TPU composites can be enhanced with 2-time by controlling the alignment level of h -BN through a fused deposition modeling 3D printing technique, but it is only limited along the printing direction [ 21 ]. Interfacial thermal resistance between the polymer matrix and filler is a key factor that influences the thermal conductivity of the composites as known from the literature [ 22 ]. The thermal energy in the system is mainly transmitted through the lattice vibrations (phonons) therefore, the discontinuous coupling between the polymer and filler causes phonon scattering, resulting in thermal resistance [ 5 ]. Several common solutions have been used to resolve the problem of interfacial thermal resistance. For example, surface functionalization or modification of the filler can create a bonding to improve the adhesion between the filler and polymer matrix that can reduce the intensity of phonon scattering [ 23 , 24 ]. However, some surface functionalization methods are challenging to perform and the effect of these methods in improving the thermal conductivity of composite materials is still limited [ 25 , 26 , 27 ]. Of late, many studies have reported various preparation methods for establishing a three-dimensional heat conduction network in the composites to deal with the issue mentioned above [ 27 , 28 , 29 , 30 ]. Through the compact network structure of continuous thermally conductive fillers, the effect of phonon scattering can be reduced which in turn reduces the interfacial thermal resistance thereby achieving higher thermal conductivity. On the other hand, it also provides continuous heat conduction paths in multiple dimensions and allows heat energy transfer throughout the network [ 31 ]. Indeed, these methods have their merits, however, some are time-consuming and complicated, limiting their use in large-scale industrial manufacturing and practical applications [ 32 ]. In general, increasing the filler content favors the establishment of a continuous thermal network structure in the composite materials. Nonetheless, adding in excess leads to problems such as the reduced processability of the composite and increased cost of the filler [ 33 , 34 ]. The introduction of a hybrid filler is one other promising strategy for enhancing the composites since it combines the advantages of different filler systems, such as their aspect ratios, geometric dimensions, etc. Fillers along with the polymer matrix forms a complex three-dimensional (3D) thermal network structure, consequently improving the performance and lowering the manufacturing cost of the composite materials [ 35 , 36 , 37 , 38 , 39 ]. During the preparation of thermally conductive polymer composites, the dispersion of fillers is one of the key factors that affects thermal conductivity [ 40 ]. Simple mixing strategies inevitably lead to an uncontrolled distribution of fillers, which might limit the synergistic enhancement between the fillers in the heat transfer network construction process [ 32 ]. Several methods have been reported in the literature that assist in resolving the uncontrolled distribution problem. Several methods have been reported in the literature that assist in resolving the uncontrolled distribution problem, such as solution compounding, roll mixing, and melt-compounding [ 41 , 42 , 43 ]. Among them, the melt-compounding is the method commonly utilized in the batch manufacturing of thermoplastic composites since it is a continuous process and involves simple operating methods wherein the fillers can be uniformly distributed in the continuously produced polymer matrix. However, when undergoing the screw extrusion process, the viscosity of composite influences the distribution as the high shear force generated during the mixing might damage and crack the thermally conductive fillers making it more challenging to manufacture on a large scale for industrial applications [ 44 ]. In this work, we propose a facile and effective method to prepare thermally conductive composite constituting compact and continuous fillers. Firstly, the spheroidized three-dimensional functional hybrid fillers, Al 2 O 3 / h -BN (ABN), were prepared by mechanical mixing and spray drying processes [ 45 , 46 ]. Secondly, the ABN functional hybrid fillers were uniformly mixed with the TPU matrix through the melt-compounding process to form the ABN/TPU thermally conductive composite which was then made into a pellet by hot pressing. The results indicate that the thermal conductivity of ABN/TPU composites is substantially improved from the synergistic association of Al 2 O 3 nanoparticles filled with h -BN. At 30 wt.% of filler content, the ABN/TPU thermally conductive composites can reach a high thermal conductivity of 1.39 Wm −1 K −1 and can considerably reduce the amounts of h -BN. It is confirmed that ABN functional hybrid thermal fillers can form a continuous three-dimensional (3D) thermal network structure in the TPU matrix and maintain the network framework during composite preparation. The method claimed in this study is facile, cost-effective and therefore offers new possibilities for the large-scale production of thermally conductive composite materials constituting a 3D thermal network of hybrid fillers with commercial applications in thermal interface materials.",
"discussion": "3. Results and Discussion The ABN functional hybrid filler was dispersed in the TPU matrix to form the thermally conductive composite. The FTIR was utilized in understanding the formation of the composite (30 wt.% of filler loading) with the preliminary identification of their chemical composition. Figure 2 a shows the FTIR spectrum of h -BN powders, Al 2 O 3 nanoparticles, and Al 2 O 3 / h -BN (ABN). The pure h -BN exhibits two sharp characteristic peaks at wavenumbers 1370 cm −1 and 810 cm −1 , indicating B-N in-plane stretching mode and B-N-B out-of-plane bending mode, respectively [ 20 ]. From the spectrum of Al 2 O 3 nanoparticles, the vast broadband at the wavenumber ranging from 400 cm −1 to 1000 cm −1 can be attributed to the Al-O-Al stretching vibration. In addition, two characteristic peaks are observed at wavenumbers 1600 cm −1 and 3400 cm −1 . Those two peaks respectively correspond to –OH groups bending mode and hydroxyl group (–OH) stretching mode of the absorbed water [ 48 ]. The spectrum of ABN is mainly dominated by the characteristic h -BN peak comparable to that of the h -BN spectrum. Correspondingly, the characteristic absorption broadband of alumina is seen at wavenumbers ranging from 400 cm −1 to 1000 cm −1 , verifying that the Al 2 O 3 nanoparticles and h -BN powders were successfully blended to produce ABN through the spray drying process. Figure 2 b displays the FTIR spectrum of pure TPU, h -BN/TPU (30 wt% filler loading), and ABN/TPU (30 wt.% filler loading) composites. In the pure TPU spectrum, the broadband observed at 3332 cm −1 corresponds to amine (-NH-) groups. Another broadband located at around 2900 cm −1 which is split into multiple peaks can be attributed to the –CH 2 -O stretching mode. Two sharp peaks at 1726 and 1702 cm −1 are correlated to the ester carbonyl group and the carbonyl stretching of the urethane groups. In addition, peaks at 1530 cm −1 and 1230 cm −1 correspond to urethane C-N stretching and N-H bending absorption, respectively [ 49 ]. The characteristic peaks of h -BN can be found in both the h -BN/TPU and ABN/TPU spectra, however, the intensity gets weaker due to the overlapping of the TPU and high-intensity additives peaks [ 27 ]. The overall FTIR results suggest that the inorganic fillers are not chemically linked but are physically attached to the TPU polymer and therefore there is no disappearance or appearance of new bonds in the h -BN/TPU and ABN/TPU composites [ 50 ]. The evolution of the structure of each sample can be realized from the XRD patterns shown in Figure 3 a. The main peaks of the pure h -BN can be observed at 2 θ = 27.11°, 41.95°, 50.50°, and 55.35° corresponding to the (002), (100), (102), and (004) planes, respectively that belong to the characteristic hexagonal crystal structure (JCPDS:85-1068) [ 34 , 51 ]. The crystallinity of Al 2 O 3 nanoparticles can also be reflected in the XRD pattern. The main peaks located at 2 θ = 19.43°, 31.99°, 37.70°, 45.88°, and 66.89°, can be assigned to the (111), (220), (311), (400), and (440) planes, respectively that matches well with the hexagonal phase of Al 2 O 3 belonging to the R-3c space group (JCPDS:00-010-0425) [ 52 ]. It is noticeable that the patterns correspond to the typical pure h -BN and Al 2 O 3 structures, and no other impurity phases or heterostructures can be detected according to the MDI Jade database. This demonstrates the h -BN and Al 2 O 3 samples prepared are of high purity. The XRD pattern of the ABN functional hybrid filler is composed of the characteristic peaks of h -BN and Al 2 O 3 , as seen in Figure 3 a with no other additional peaks inferring that the ABN functional filler can be effectively prepared by the spray drying process and is in accord with FTIR studies, as discussed in Figure 2 a. The XRD patterns of the TPU, h -BN/TPU, and thermally conductive ABN/TPU are shown in Figure 3 b. The broad diffraction peaks at around 2 θ of 15° and 30° designate the amorphous property of the TPU polymer. The presence of all the characteristic peaks of h -BN and ABN in the h -BN/TPU and ABN/TPU diffraction patterns denote, and with no obvious position shift, which suggested that the crystal structures of thermally conductive composites are not affected by the processing method. It is interesting to note that high-intensity h -BN peak dominates in both patterns. As verified by several research works, the degree of the orientation of h -BN sheets in the polymer matrix is one of the essential factors affecting the thermal conductivity and this feature can be examined by XRD analysis. The (002) and (004) planes are attributed to the horizontally oriented BN, while the (100) plane is due to vertically oriented h -BN [ 53 , 54 ]. As seen in Figure 3 b, the two strong peaks at 2 θ = 26.5° and 54.7° of h -BN/TPU composites representing (002) and (004) resulted from the horizontal orientation of the h -BN sheets that was induced by hot-pressing with a perpendicular pressure. On the other hand, in the XRD pattern of ABN/TPU composite, the (100) plane appeared while the intensity of (002) and (004) substantially reduced. This phenomenon indicates that the orientation of the h -BN sheets inside the ABN/TPU composites is random, contributing to a higher intensity of (100) plane, which are in accord with SEM analysis as discussed in the next section [ 29 , 55 ]. In addition, the intensity ratio of (002) and (100) characteristic peaks could be used to evaluate the orientation degree of h -BN filled in the polymer matrix. As verified by several research works [ 20 , 53 ], the more vertically arranged h -BN structures can be created when the value of I (002) /I (100) is lower. The I (002) /I (100) ratio of ABN/TPU composites and h -BN/TPU composites are 5.2 and 84.1, respectively. The I (002) /I (100) value of h -BN/TPU composite is 16 times higher than the ABN/TPU composite, indicating that a random structure of a h -BN-filled composite by a hot press process is complicated since h -BN sheets tend to distribute in the horizontal direction. Creating a random structure of a h -BN-filled composite by a hot press process is complicated since h -BN sheets tend to distribute in the horizontal direction. However, through the process presented in this work, more vertically arranged h -BN structures can be created, which is beneficial to the ABN filler in the composite material in order to form the effective continuous heat conduction chains to provide more heat conduction paths. The surface morphology of the pure h -BN powder, Al 2 O 3 nanoparticles, and ABN functional hybrid filler was studied by field emission scanning electron microscopy (FE-SEM). In Figure 4 a, it can be seen that the h -BNs displayed a hexagonal and plate-like shape with an average particle size of 3 μ m. A uniform spherical shape of Al 2 O 3 NPs with the particle size of the nanospheres ranging from 30 to 50 nm can be seen in Figure 4 b. The surface morphology of the ABN functional hybrid filler after spray drying, displayed in Figure 4 c, shows that most of the ABN particles form the spherical-like structures with their particle sizes ranging from 10 and 40 μ m. At a higher magnification, as demonstrated in the inset of Figure 4 c, the ABN particles have a rough surface, which is due to the decoration of Al 2 O 3 NPs on the surface. In conventional theory, if the components powders are approximately of the same size, they would tend to uniformly distribute in the composite particles. However, when the components powders are composed of two different particle sizes, the radial segregation of particles occurs. Through the Brownian motion, smaller particles with the higher mobility occlude larger particles and therefore, according to this mechanism, surrounding or a coating of one component by others can be created [ 46 , 56 , 57 ]. To further reveal that the h -BN powder surface was wrapped by compact Al 2 O 3 NPs layers, ABN particles were compressed to form a crack on the surface, and the results were confirmed by the elemental mapping images as shown in Figure 4 d. As expected, the abundant Al and O elements exhibited uniform and continuous distribution throughout the ABN particles demonstrating the Al 2 O 3 NPs coating on the h -BN powders. Besides, a considerable amount of Al 2 O 3 NPs is located between the neighboring h -BN particles that serve as bridges. This kind of unique structure is conducive to form a more efficient 3-D thermally conductive networks. Figure 5 a–c presents the cross-section images of the samples. All the samples underwent brittle fractures after being immersed in liquid nitrogen. The dispersion and structure distribution between fillers and polymer can be observed in these figures. Figure 5 a shows the cross-section image of pure TPU. Because of the brittle fracture, the surface of the pure TPU is clean and smooth. In contrast, as seen in Figure 5 b,c, incorporated hybrid functional filler composites exhibit a rough surface and crumpled fracture structure with many embedded particles, a result of the local polymer deformation that occurred due to cracking from the addition of the hybrid functional fillers [ 58 ]. Despite the rough surface, thermally conductive fillers show a uniform dispersion and homogeneity with no large clusters in the matrix. This is attributed to the state of particle dispersion according to the melt mixing method, as mentioned in the introduction. Most of the thermally conductive filler particles play an essential role in constructing the thermal conductive pathways, and they have higher thermal conductivity along the direction of the heat flow [ 53 ]. We further compared the cross-sectional morphologies of TPU composites filled with the different weight percentage of thermally conductive/functional fillers. Figure 5 b,c displays the images of h -BN/TPU and ABN/TPU composites with the filler loading of 20 wt.%. As seen in Figure 5 b, the h -BN sheets in h -BN/TPU composite film are almost horizontally oriented as rendered by hot-pressing with perpendicular pressure, signifying that the thermal conductivity could be quite different in various directions, thereby limiting its use in practical applications since heat dissipation between the devices and heat sinks usually occurs in the vertical direction [ 55 ]. In addition, from the figure, we observed that at a low weight percentage (20 wt.%) of h -BN loading, fillers are unable to create a continuous heat flow path with lower interfacial thermal resistance. Due to this inherent problem, a large amount of thermally conductive fillers is required to establish a thermally conductive pathway network structure, and hence increases the cost of fillers. On the other hand, as shown in Figure 5 c, the spherical ABN hybrid functional hybrid fillers, proposed in this work, are connected to form a continuous thermally conductive pathway network (marked in red), which plays a pivotal role in enhancing the thermal conductive of the composite. The most significant advantage of spherical ABN functional hybrid filler is that it has no specific orientation after being processed by hot pressing. The spherical structure provides continuous pathways in all dimensions and ensures most of the energy is being transferred through the filler networks [ 59 ]. Therefore, compared with the h -BN filler from our previous work, the spherical ABN functional hybrid filler possesses a more continuous structure in all dimensions and longer range, resulting in a significant improvement of composite thermal conductivity despite the low filler concentration. As the above mentioned, self-assembled 3-D network in TPU is successfully generated by the insertion of the Al 2 O 3 / h -BN hybrid through our designed method. Moreover, our designed method is not only facile, but also offers new chances for large-scale production. Thermal stability is crucial for polymer materials, which is the limiting factor in both processing and applications. In this work, the content of ABN in the composites and the thermal stability of the composites were verified by Thermogravimetric Analyzer (TGA) tested at a heating rate of 10 °C min −1 from 25 °C to 900 °C under the nitrogen atmosphere. Figure 6 demonstrates the TGA curves of the ABN filler, the pure polymer TPU, and ABN/TPU composites loaded with different ABN contents. The results show that the ABN functional hybrid fillers prepared by our method exhibited high thermal stability with no significant weight loss up to 900 °C. Compared to ABN filler, pristine TPU, and ABN/TPU composites demonstrate two main degradation stages as seen in the weight loss curve. The first stage is between 300 °C–350 °C, which is attributed to the cleavage of the urethane linkage to polyol and isocyanate in the TPU hard segment [ 60 ]. The second stage is between 350–480 °C, which is ascribed to the cleavage of the polyol and diisocyanate into smaller molecules in the TPU soft segment [ 61 ]. The residual weight of ABN/TPU composites is higher than that of pure TPU which completely degrades at around 900 °C. The content of ABN filler can be obtained from the residual weight at 900 °C and hence from calculating the differences in the residual weight, the addition of about 10 wt.%, 20 wt.%, and 30 wt.% ABN to the TPU substrate can be confirmed. Figure 7 shows the thermal conductivity of h -BN/TPU and ABN/TPU composites with different filler loadings ranging from 0 wt.% to 30 wt.%, respectively. Owing to its amorphous structure and phonon scattering, the thermal conductivity of the pristine TPU is extremely low, which is at around 0.2 W m −1 K −1 . For all composites, the thermal conductivity remarkably improved with the increasing content of the fillers. However, the enhancement in the thermal conductivity of the composites with different fillers showed a distinct difference. As clearly seen in Figure 7 , the ABN/TPU composite showed higher thermal conductivity than the h -BN/TPU with the same filler loading. For example, with 30 wt.% filler loading, the thermal conductivity of ABN/TPU composite is 1.39 W m −1 K −1 , while the h -BN/TPU composite showed a trifling thermal conductivity of 0.48 W m −1 K −1 which is three times lower, signifying that the spherical ABN functional filler obtained by spray-drying demonstrated a greater advantage in improving the thermal conductive properties of the composite. The trend observed in the thermal conductivity of ABN/TPU composites is nonlinear. With the 10 wt.% filler loading, the thermal conductivity is 0.34 W m −1 K −1 showing an improvement of around 45%. Beyond the threshold for percolation (between 10 wt.% and 20 wt.% of the filler loading), this value sharply increases [ 27 , 29 ]. When the content of spherical ABN particles is further increased to 30 wt.%, the thermal conductivity achieved is 1.39 W m −1 K −1 , equivalent to a dramatic upsurge of 488% compared to that of the pure matrix. The variation in the thermal conductivity acquired from the ABN/TPU composites can be explained by the network structure of the fillers and distribution state in the TPU matrix. The polymer matrix is interposed between the adjacent fillers under low filler loading, disrupting the contact between the particles. This results in the phonon scattering, further increasing the interface thermal resistance, which finally leads to reduced heat conduction. When the filler content is continually increased beyond the threshold for percolation (between 10 wt.% and 20 wt.%), the increasing number of spherical ABN fillers contact with the adjacent ones, forming a densely packed structure that facilitates phonon transfer in a continuous thermal network. To better illustrate the effect of different fillers on the formation of thermally conductive pathways, two models with the efficient network for heat flow in the composites were proposed as shown in Figure 8 . Usually, to obtain high thermal conductivity, a heat flow channel along the heat flow direction should be generated. However, the h -BN/TPU composites seen in Figure 8 a comprises of a few thermally conductive pathways, and many h -BN sheets are not involved in the construction of pathways. The horizontal orientation of h -BN sheets can be formed after vertical hot pressing, which results in the interruption of heat transfer along the vertical direction, but nonetheless, the thermal conductivity cannot be effectively improved. In contrast, the spherical ABN particles are linked to each other to form a continuous three-dimensional network structure for unhindered heat flow in the TPU polymer as shown in the schematic diagram in Figure 8 b. These spherical ABN particles can not only retain the structure after hot pressing but are also beneficial in achieving the dense packing of the particles to form thermally conductive paths with reduced interfacial thermal resistance, thereby enhancing the phonon transfer. The heat transfer always alternates between fillers and polymers when fillers randomly distributed in the composites, hence, the interface thermal resistance has a very bad effect on the improvement of thermal conductivity. The synergistic enhancement of the spherical ABN fillers on thermal conduction can also be clearly observed, in which the Al 2 O 3 NPs are connected to the neighboring h -BN platelets like bridges to construct the continuous phonon transmission pathways. Therefore, this demonstrates that the spherical ABN functional filler prepared in this work is promising in achieving a high thermal conductivity in polymer composites. The heat transfer capability of h -BN 30 wt.% loading and ABN 30 wt.% loading in the TPU composite materials were tested by heating on an electric hot plate for 40 s and analyzing the temperature response, recorded by the infrared thermography as shown in Figure 9 . Figure 9 a presents the photographs of the pure TPU, h -BN/TPU, and ABN/TPU composites. The pure TPU exhibits high transparency, while the h -BN/TPU and ABN/TPU composites are opaque and white. The heating curves of the samples are shown in Figure 9 b, and the images of infrared thermography at different stages of the three samples mentioned above are illustrated in Figure 9 c. The surface temperature changes with an increase in the heating time and the color of all the samples gradually change from blue to red. It can be seen that the rate of change in the surface temperature of all composites is faster than that of pure TPU. Figure 9 b,c shows the heat transfer trend of the three samples when heated from 25 °C to 65 °C on the hot plate. After heating for 40 s, it is apparent that the increase in the surface temperature of the ABN/TPU was the fastest, and the surface temperature of ABN/TPU was the highest. The addition of spherical ABN fillers for enhancing the thermal conductivity of composites is consistent with the order of their thermal conductivity values as mentioned above. From the IR results, it can be confirmed that through ABN/TPU composites prepared by the proposed method, the continuous thermal network can be formed and heat transfer occurs more effectively. To further realize the structure and interfacial properties between the ABN functional filler and TPU, the mechanical properties of ABN/TPU composites were investigated by using a tensile stress-strain test. The tensile stress-strain curves of pure TPU, and TPU composites with various ABN filler loading ranging from 10 wt.% to 30 wt.% are shown in Figure 10 . A typical tensile behavior of an elastomer can be observed in the pure TPU. The tensile strength and strain-at-failure of the pure TPU was 82 MPa and 570%. While adding the ABN functional filler, the tensile strength of TPU composites were firstly promoted, then decreased with the increase in the filler loading. The maximum tensile strength of 21 MPa can be reached when the filler loading is 20%, which is about three times that of the pure TPU matrix, and the elongation was only slightly decreased. This strengthened tensile stress is due to the intrinsic favorable mechanical property of ABN functional fillers and strongly interfacial interaction with the TPU matrix [ 62 , 63 ]. When subjected to stress, the TPU chains were initially stretched along the stress direction, then the external stresses applied to the composite can be shared efficiently by transferring to the filler via the interface interaction between the filler and TPU chain [ 51 ]. Furthermore, the filler has a higher tensile strength than those of the TPU matrix and can act as a skeleton to help the matrix to bear the load, resulting in the higher tensile stress of composites. However, the further increase of ABN functional filler content to 30 wt.%, results in both sharply decreased tensile strength and elongation at the break of the composites, and exhibited in obvious brittle fracture characteristics, a phenomena that can be attributed to the aggregation of the filler, which weakened the encapsulating and supporting role played by the TPU matrix, indicating that the presence of the ABN functional filler is unfavorable for maintaining the tensile ductility of the composite, especially at an extremely high content [ 29 , 60 ]. The above results demonstrated that the improvement of thermal conductivity in the TPU composites is due to the strong coupling in the interface of the ABN filler and TPU, which are beneficial in achieving and forming a densely continuous three-dimensional network for thermal conduction."
} | 8,083 |
40050947 | PMC11887087 | pmc | 5,149 | {
"abstract": "This study is the first to apply dilute acid pretreatment (DAP) under different severity conditions to poplar wood genetically modified for the cinnamyl alcohol dehydrogenase ( CAD1 ) gene, which is involved in the lignin biosynthesis pathway. The carefully selected pretreatment conditions resulted in glucose yields that were 15 points higher for the hpCAD poplar line than for the wild-type (WT) wood after 48 h of enzymatic hydrolysis. To explain this higher saccharification rate, the chemical, spectral and structural changes in WT and hpCAD wood were analyzed in relation to the severity of the pretreatment process. Although few differences were found at the chemical level, variations in autofluorescence and cell deformation were more significant: at high severity, the cells of hpCAD wood observed by nanotomography were more easily deformed, but their middle lamella was more resistant than those of WT wood. All these differences are possibly explained by changes in the molecular structure of lignin in hpCAD wood, leading to the formation of more hydrophobic shorter monomer chains with fewer lignin‒carbohydrate interactions. Graphical Abstract \n \n Supplementary Information The online version contains supplementary material available at 10.1186/s13068-025-02623-8.",
"conclusion": "Conclusion The saccharification yield of hpCAD wood treated with DAP was 15 points greater than that of WT wood at the lowest of the two severity factor values tested in this study (CSF 2.4 and 3.0). Despite these differences in saccharification, there was minimal variation in the holocellulose levels between hpCAD wood and WT wood, with most differences resulting from lignin. Indeed, lignin is known to be a key compound contributing to lignocellulose recalcitrance: the main hypothesis is that modifications occurring in hpCAD lead to shorter and less reactive monomeric chains with fewer carbohydrate interactions. This likely results in limited physical coverage of the lignin around the cellulose, especially when lignin is altered and hemicelluloses degrade at CSF 2.4, allowing better cellulose accessibility and enhancing saccharification. Considering the results reported in the literature for other CAD-downregulated biomasses, DAP appears to be a very promising pretreatment in comparison with alkaline pretreatment. The optimum method of hpCAD poplar saccharification might be studied by exploring CSF values between 1.5 and 2.5 and performing a detailed structural characterization of the polymer interactions to evaluate the economic viability.",
"introduction": "Introduction In 2023, the European pulp and paper industry represents 175,000 direct jobs in Europe, with an annual turnover of approximately 100 billion euros and contributes to 20 billion euros to the European Union GDP (Gross Domestic Product) [ 1 ]. Because of its economic importance, the process of making pulp from wood has been the subject of numerous studies since the 1990s, with the aim of reducing the consumption of chemicals, water and energy needed to separate lignin from cellulose fibers [ 2 , 3 ]. To further reduce the economic and ecological costs of paper manufacturing, research has focused on genetic engineering to help separate lignin from cellulose [ 3 ]. Lignin is a complex polymer composed of three hydroxycinnamyl alcohol subunits, S, G and H (syringyl, guaiacyl and p -hydroxyphenyl, respectively), also referred to as monolignols. Their proportions depend on the tree species and on genetic variability within tree species [ 4 , 5 ]. Depending on the subunits involved, different types of bonds are formed between these monolignols to create lignin, which directly impacts wood resistance to the delignification process. For example, ether β- O -4 bonds are the most common and labile, whereas the carbon‒carbon 5‒5 bonds formed between G subunits are much stronger [ 4 , 5 ]. One way to improve wood delignification is to genetically modify lignin to change the subunit composition and the type of bonds involved in the polymer, making the kraft pulping process simpler and less costly [ 3 ]. This strategy can involve downregulating the activity of any enzyme in the lignin biosynthetic pathway, leading to the development of modified wood genotypes [ 6 , 7 ]. Among these modifications, downregulation of the cinnamyl alcohol dehydrogenase ( CAD1 ) gene has shown interesting results, with easier delignification, a 6% reduction in the amount of chemicals used and a 2–3% increase in pulp yield [ 3 , 8 ]. The role of the enzyme encoded by this gene is to catalyze the final step in the biosynthesis of monolignols from their aldehyde form, called hydroxycinnamaldehyde, to their corresponding hydroxycinnamyl alcohol form [ 9 – 11 ]. This gene can be downregulated via a silencing approach with CAD1 hairpin RNAi ( hpCAD ), which reduces CAD activity to 15% of that of normal poplar [ 9 ]. Reducing the activity of the CAD enzyme results in high incorporation of hydroxycinnamaldehyde into lignin. Compared with the wild-type ( WT ) poplar line, the hpCAD poplar line has a 35-fold increase in the level of sinapaldehyde [ 10 , 11 ]. The S/G (Syringyl/Guaiacyl) ratio is also reduced, and the lignin content is slightly decreased by 8–10%, while red coloration of the xylem tissue is observed [ 10 ]. These modifications, initially intended for the paper industry at the research stage, are now being studied in biorefineries. Changing the properties of lignin could reduce the recalcitrance of lignocellulosic biomass (LB), thus increasing the yield of enzymatic hydrolysis [ 3 ]. The first results showed that the hpCAD poplar line did not naturally have a greater saccharification yield than WT poplar did: pretreatment remains necessary [ 10 ]. Alkaline pretreatment increased cellulose conversion by 12 to 27% in the hpCAD poplar line compared with the WT, depending on the amount of NaOH used (6.25 mM or 62.5 mM, respectively [ 10 ]). However, despite these good results, alkaline pretreatment is not currently the most economically viable option for wood pretreatment. Numerous studies have reported very good economic yields of steam explosion and dilute acid pretreatment (DAP) [ 12 – 14 ]. Although DAP has already been tested without success on the hpCAD poplar line [ 10 ], this study was limited to a single pretreatment condition at 90 °C, 180 min and 4% H 2 SO 4 [ 10 , 15 ]. However, these time and temperature treatment conditions, which are suitable for alkaline pretreatment [ 16 ], are very different from those of standard DAP (120 to 200 °C for 60 min [ 17 ]). Therefore, extending the DAP conditions to explore its effect on the saccharification yield of the hpCAD poplar line is relevant. The aim of this study was, therefore, to compare the saccharification rates of WT and hpCAD poplar lines subjected to a large set of DAP conditions and to relate these results to their chemical, spectral and structural properties.",
"discussion": "Discussion The hpCAD mutant improved saccharification after mild DAP To the best of our knowledge, this study is the first to show a significant 15% improvement in the total glucose yield at 48 h for the hpCAD poplar line compared with the WT under DAP, which was carried out at 150 °C for 20 min with 2%wt H 2 SO 4 (CSF 2.4). Few studies have focused on the pretreatment and saccharification of hpCAD mutants on poplar wood [ 9 , 10 , 24 , 25 ] or other types of biomass [ 26 – 30 ]. Among these studies, most focused on alkaline pretreatment, which results in good saccharification yields [ 10 ]. Only three studies used DAP [ 10 , 27 , 29 ], with two indicating higher cellulose conversion rates of 5–10% [ 27 ] and 15% [ 29 ] after 48–72 h of hydrolysis on switchgrass and rice, respectively. These results, although obtained for different biomasses, showed a yield increase in the same order for poplar, given that the DAP conditions used are also close enough: 121 °C, 20–40 min, and 1.8–2.7%wt H 2 SO 4 (CSF 1.5–2.0) [ 27 , 29 ]. The only study of DAP focused on the hpCAD poplar line [ 10 ] used a single low-severity condition of CSF 1.9, with very low-temperature conditions (90 °C, 180 min and 4% H 2 SO 4 ). This easily explains the low enzymatic yields obtained: the conditions used were not sufficient to modify the polymer biomass network, since they were far from the usual DAP conditions, which are more likely to be approximately 120 to 200 °C and under 60 min [ 17 ]. Specific DAP conditions are needed This improvement in yield for hpCAD wood is very significant for one of the hydrolyzed conditions at CSF 2.4 (20 min, 150 °C, 2%wt H 2 SO 4 ), but the CSF 0 and CSF 3.0 conditions showed no differences between WT and hpCAD . This means that there is a narrow range of conditions and severities of DAP that allows the hydrolysis potential of hpCAD wood to be exploited. At lower severities, cellulose is not sufficiently accessible, and at higher severities, the degradation of biomass is too advanced. Previous studies on other biomasses with good saccharification results have used CSFs ranging from 1.5 [ 29 ] to 2.0 [ 27 ]. It would, therefore, be possible that similar or even better yields could be obtained for even less severe conditions. An optimization study from CSF 1.5–2.5, similar to that used to determine the conditions employed here [ 20 ], would be necessary to explore this possibility. Cell wall composition does not explain saccharification differences Among the different biomass properties that might influence saccharification, the chemical composition was the first to be investigated. However, up to CSF 3, the global chemical composition is similar between WT and hpCAD , except for a 2-point difference in lignin content for native hpCAD wood. This difference, which is consistent with the literature [ 10 ], does not change between the pretreatment conditions and therefore cannot be attributed to a variation in the degree of cellulose conversion. However, the interactions between these compounds differ: the incorporation of sinapaldehyde into lignin involves a rearomatization mechanism that leads to a reduction in the number of hydroxyl groups in lignin, increasing its hydrophobicity and limiting noncovalent interactions with carbohydrates [ 10 ]. The same mechanism may also reduce the formation of recalcitrant lignin‒carbohydrate complexes [ 10 ], which may partly explain the improved hydrolysis results obtained. Autofluorescence highlights differences in lignin organization Significant changes in autofluorescence intensity were observed between WT and hpCAD , mainly at CSF 0 and CSF 1.2, where hpCAD fluorescence was notably lower. The massive incorporation of sinapaldehyde into hpCAD lignin to replace the syringyl (S) subunits, together with the slightly reduced amount of guaiacyl (G) [ 10 , 11 ], led to a decrease in the S/G ratio (from 1.84 to 1.5 [ 10 ]). It has been previously shown that a change in the S/G ratio can modify the excitation and emission wavelengths of lignin [ 31 ]. In particular, a positive correlation was found between the number of ether bonds (β- O -4) and lignin autofluorescence intensity [ 31 ]. Sinapaldehydes are more likely to form other ether bonds and carbon‒carbon (β‒β) bonds [ 10 ], so the decrease in β- O -4 bonds in hpCAD wood [ 11 ] may explain this loss in autofluorescence. However, these differences mainly explain the variations in fluorescence at low CSFs (0–1.2). The best hydrolysis rates for hpCAD wood were observed at CSF 2.4, where the fluorescence characteristics decreased due to advanced lignin degradation processes such as repolymerization [ 32 ]. At this level of severity, conclusions cannot be drawn about lignin interlinkages from its fluorescence and, therefore, about their influence on the saccharification yield of the biomass. Lignin influences cell wall mechanical properties Previous results revealed that the structural or mechanical properties of the raw hpCAD poplar line did not differ from those of WT wood [ 33 ]. However, depending on the severity of the pretreatment applied in this study, important structural differences were observed between the samples. First, structural analysis revealed that the cell wall thickness decreased in a similar manner between the WT and hpCAD samples up to CSF 2.4. This decrease was certainly due to the degradation of hemicelluloses, which occurred mainly at low severity levels in the same proportions for both WT and hpCAD wood. In terms of cell deformation or surface area occupied by vessels, major structural changes occurred mainly at CSF 3 and higher. This corresponded to the onset of cellulose degradation, and although this degradation appeared identical in WT and hpCAD wood considering glucose release, it left only lignin to provide the cell framework, which could explain the observed differences in collapse. Indeed, at CSF3, the cells begin to collapse together, artificially increasing the wall thickness, making accurate measurement of wall thickness above the CSF2.4 challenging. However, the collapse is significantly greater for hpCAD wood cells, suggesting that hpCAD wood may be less resistant to deformation than WT wood is. Finally, the reduction in the area occupied by vessels at CSF 3.5 and the disappearance of the cell structure at CSF 3.7 for WT wood indicate that the cells have been torn apart, whereas hpCAD wood has a constant vessel area and still has some identifiable structural features at these high CSFs. All these results seem to indicate that the cellular structures of hpCAD and WT wood display different characteristics after high-severity DAP: hpCAD wood appears to be less rigid but more resistant, with accentuated deformation properties but reduced tearing. Lignin contributes to the strength of the middle lamella and is the only major component that differs between WT and hpCAD , which may explain why cells remain more tightly bound together in the case of hpCAD . Indeed, one of the characteristics of the incorporation of sinapaldehyde into the lignin network is the low oxidizability of the terminal groups, which prevents polymer growth from these terminal units and leads to the formation of a disorganized lignin network with small lignin domains [ 11 ], which explains the better flexibility. In addition, hpCAD lignin appears to delay its conversion into humin at a very high CSF (Fig. 1 D), implying that it is less reactive and retains its structure more effectively than WT lignin. These likely characteristics of hpCAD lignin (shorter and less reactive polymers with potentially fewer interactions with polysaccharides [ 10 ]) are possibly responsible for the different structural behaviors of hpCAD wood but may also be responsible for its better saccharification yield at CSF 2.4 than that of WT wood. Indeed, they can lead to a naturally more limited physical coverage of the lignin around the cellulose in hpCAD wood, which intensifies with increasing severity and repolymerization of lignin. This could result in greater accessibility to enzymes once hemicelluloses have been degraded at CSF 2.4, whereas beyond this point, the wood compounds undergo too advanced degradation."
} | 3,814 |
33533590 | PMC7899176 | pmc | 5,150 | {
"abstract": "In this study, the possibilities of noise tailoring in filamentary resistive switching\nmemory devices are investigated. To this end, the resistance and frequency scaling of\nthe low-frequency 1/ f -type noise properties are studied in\nrepresentative mainstream material systems. It is shown that the overall noise floor is\ntailorable by the proper material choice, as demonstrated by the order-of-magnitude\nsmaller noise levels in Ta 2 O 5 and Nb 2 O 5 \ntransition-metal oxide memristors compared to Ag-based devices. Furthermore, the\nvariation of the resistance states allows orders-of-magnitude tuning of the relative\nnoise level in all of these material systems. This behavior is analyzed in the framework\nof a point-contact noise model highlighting the possibility for the disorder-induced\nsuppression of the noise contribution arising from remote fluctuators. These findings\npromote the design of multipurpose resistive switching units, which can simultaneously\nserve as analog-tunable memory elements and tunable noise sources in probabilistic\ncomputing machines.",
"conclusion": "Conclusions In summary, we have performed a comparative study on the resistance scaling of the\nlow-frequency noise in mainstream RRAM systems. We demonstrated that the noise\ncharacteristics of Ta 2 O 5 and Nb 2 O 5 memristors\nare well described by our point-contact model recently developed for the noise analysis of\nAg-based filamentary resistive switches. In the diffusive transport regime, the relative\nnoise levels of all of these systems exhibit a universal and sufficiently steep resistance\nscaling power for efficient noise tailoring. However, we found markedly different overall\nnoise levels in the silver-based and the transition-metal-oxide-based systems. Our analysis\nyields a counterintuitive explanation for this difference highlighting the role of\ndisorder-induced noise suppression in Ta 2 O 5 and\nNb 2 O 5 memristors. This phenomenon is also reflected by the markedly\ndifferent frequency scaling tendencies: in Ag-based filaments, the 1/ f -type\nspectrum is dominated by an ensemble of remote fluctuators, whereas in the\nTa 2 O 5 - and Nb 2 O 5 -based systems, the frequency\ndependencies exhibit significant Lorentzian contributions due to the suppressed effect of\nremote TLSs and the dominance of single nearby fluctuators. These findings underpin the\ngreat potential of resistive switching memory technologies in novel probabilistic computing\napplications, demonstrating that besides the hardware implementation of analog-tunable\nneural weights, RRAM devices are also ideal candidates as tailorable noise sources. An\noptional application may follow the scheme in ref ( 51 ), where a memristor crossbar array accelerates the operation of a Hopfield neural network\nby performing the vector-matrix multiplications in single time-steps. An additional\nmemristor row in the crossbar array is proposed as a tunable noise source. To find the\nglobal minimum in the energy landscape of the targeted computational problem, first, a\nlarger noise can be applied, and then the noise is gradually decreased according to the\nsimulated annealing protocol.",
"introduction": "Introduction In traditional electrical engineering, noise is considered as an issue, which is to be\nsuppressed to the lowest possible level. 1 , 2 Accordingly, the introduction of new components is usually\npreceded by lengthy material optimization steps to decrease the low-frequency,\n1/ f -type noise generated by material imperfections. 3 , 4 The emergence of novel neuromorphic\ncomputing architectures, 5 , 6 however, brings a paradigm change in noise engineering, demonstrating\nthat tailored noise can be harvested as a useful computing resource in probabilistic\ncomputing schemes. As specific examples, stochastic magnetic tunnel junctions were utilized\nto solve integer factorization in a probabilistic bit computing architecture, 7 whereas a Hopfield neural network of resistive switching memory (RRAM) units\nwas applied to solve nondeterministic polynomial-time (NP)-hard max-cut problems. 8 Noise tuning served as a key ingredient in the operation in both\napproaches. Resistive switching memories, or memristors, 9 , 10 have already demonstrated their pioneering role in the\ndevelopment of information technologies, including energy-efficient, fast, and compact\napplications in mass data storage, 11 in-memory computing, 12 or the hardware implementation of artificial neural networks\n(ANNs). 13 − 16 In the latter case, the nonvolatile and analog-tunable RRAM\nresistance states serve as the neural weights of ANNs and dense crossbar RRAM architectures\nperform massively parallel operations, such as single time-step vector-matrix\nmultiplication. Similar crossbar architectures served as the hardware accelerator in the\nabove RRAM-based combinatorial optimization machines, 8 where the\nsimulated annealing protocol was realized by the amplification/suppression of the intrinsic\nRRAM noise using an external hysteretic threshold circuitry. To support further potential applications in noise engineering, here, we analyze how the\nintrinsic noise of RRAM devices can be tailored. More specifically, (i) we study the\ninfluence of material choice on the base noise level; (ii) we investigate how the relative\nnoise level scales with the analog-tunable resistance states of RRAMs; and (iii) we deliver\na fundamental understanding of the resistance scaling of the noise by model considerations.\nTo this end, we analyze the resistance and frequency dependence of the intrinsic noise in\nTa 2 O 5 - and Nb 2 O 5 -based resistive switching\ndevices and compare these results to the markedly different noise levels observed in\nAg-based resistive switching filaments. The above transition-metal oxide memristors\nrepresent well-established resistive switching systems with robust switching\ncharacteristics, 17 , 18 \nmultilevel programming, 19 , 20 and ultrafast switching. 20 , 21 Ta 2 O 5 is an especially widely studied\ncompound with great potential for near-future neuromorphic computing\napplications. 6 , 22 \nControversially, the noise analysis of these systems is very limited, 23 − 26 and the detailed\ninvestigation and understanding of the noise’s resistance scaling is lacking. Our\nreference systems, the Ag-based filamentary devices, serve as another fundamentally\nimportant platform in the RRAM technology. 27 − 33 Our related noise analysis 34 highlighted\na universal resistance scaling behavior, revealing that the resistance fluctuations are\ndominated by the internal fluctuations of the Ag nanowires, whereas the embedding\nenvironment does not have an important influence on the noise. The 1/f-type noise in nanoscale devices may originate from either atomic fluctuations or\ncharge trapping/detrapping effects. In our previous study, we have demonstrated that our\nNb 2 O 5 scanning tunneling microscope (STM) point-contact devices\npreserve the metallic conduction through electronically transparent, unbroken filaments down\nto the level of single-atom diameters. 35 In this unbroken filamentary\nregime, charge-trap states in the embedding insulating matrix are efficiently screened by\nthe metallic filament; therefore, we rather consider atomic fluctuations inside or at the\nsurface of the filament as the dominant noise source. In the transition-metal oxide systems,\nwe consider oxygen vacancies as the major fluctuators. In our further analysis, we solely\ntreat this metallic unbroken filamentary regime, which is related to the resistance regime\nbelow the inverse conductance quantum, G 0 –1 ≈ 12.9 kΩ. This regime is favorable in RRAM\ncrossbars utilized to implement vector-matrix multiplication\noperations. 6 , 14 , 15 Previous noise studies have indicated that the noise level may depend of the resistance\nstate in selected RRAM systems. 34 , 36 − 41 Prior studies\nmostly described the noise’s resistance scaling in the metallic regime by a simple\ngeometrical model relying on a cylinder (or prism) geometry with a single two-state\nfluctuator at the filament surface. 36 , 39 − 41 Here, we analyze the noise data in terms of our recently proposed model,\nwhich considers the scattering on dynamical defects (or two-level systems, TLSs) in a\nmetallic point-contact geometry, also taking into account the crossover between the\ndiffusive and ballistic transport regimes. 34 This model is able to\ndescribe an ensemble of fluctuators, which also accounts for the dominance of the\nfluctuators located nearby the narrowest part of the filament, and the suppressed\ncontribution of more remote fluctuators. The fitting of the noise data with this model\nuncovers the material specificity of the two key parameters, the TLS density and the\nelectron mean free path. Our analysis brings a counterintuitive conclusion demonstrating\nthat the order-of-magnitude noise suppression of the transition-metal oxide resistive\nswitching units compared to the Ag-based systems is primarily related to the enhanced level\nof disorder in the former material systems. This disorder enhancement yields an increased\nsuppression of those “remote” fluctuators’ noise contribution, which\nare located outside the narrowest region of the filaments.",
"discussion": "Results and Discussion To study the noise characteristics of Ta 2 O 5 and\nNb 2 O 5 memristive systems, we have applied two approaches: (i) we\nhave established resistive switching junctions in both material systems by touching\nthin-film structures at various lateral positions with the PtIr tip of a custom-designed STM\n(see the insets in Figure 1 a,b). With this\napproach, we could collect noise data on a statistical ensemble of independent junctions\nwith various resistances. (ii) We have investigated the variation of the noise with the\ndevice resistance in Al/Nb 2 O 5 /Pt crosspoint RRAM structures (see the\ninset in Figure 1 c), where the resistance states\nwere tuned by voltage pulses. The growth of the oxide layer was performed by either anodic\noxidation (STM devices) or reactive sputtering (crosspoint devices). Both oxide growth\nprotocols were optimized to achieve a pentoxide stoichiometry, which was confirmed by X-ray\nphotoelectron spectroscopy (XPS) analysis. The peaks in the XPS spectra characteristic of\nthe Ta 2 O 5 and Nb 2 O 5 compositions are\ndemonstrated in Figure 1 d,e, respectively. More\ndetails on the preparation and electroforming of the devices are provided in the Experimental Section . Figure 1 a–c exemplifies the resistive switching current–voltage\n( I ( V )) characteristics of the\nTa 2 O 5 (dark brown) and Nb 2 O 5 (light brown) STM\npoint-contact devices and Nb 2 O 5 crosspoint devices (gray). We observed\nstable switching cycles, which are illustrated by the reproducibility of ≈100\nconsecutive I ( V ) curves on each panel. Figure 1 Representative resistive switching I ( V ) curves of a\nTa/Ta 2 O 5 /PtIr (a) and Nb/Nb 2 O 5 /PtIr (b)\nSTM point-contact device and an Al/Nb 2 O 5 /Pt crosspoint structure\n(c). The sample architectures are illustrated in the insets (see the Experimental Section for more details). It is noted that\nNb 2 O 5 resistive switching devices often exhibit highly nonlinear\n I ( V ) characteristics (see panel (b)). This is\nattributed to the interplay of resistive switching in the core conducting filament and\nhighly nonlinear Frenkel–Poole conduction 42 , 43 in the surrounding volume. This happens in\ndevices where the switching threshold precisely coincides with the onset of the\nnonlinearity. 20 , 44 , 45 (d, e) XPS spectra measured on the surface of\nTa 2 O 5 layers established by anodic oxidation and\nNb 2 O 5 layers grown by reactive sputtering. The\nTa 2 O 5 composition was identified on the basis of the specific\nbinding energies of 26.4 and 28.4 eV corresponding to the Ta 5+ \n4f 7/2 and 4f 5/2 states, whereas the binding energies of 210.4\nand 207.7 eV identify the Nb 5+ 3d 3/2 and 3d 5/2 states\nof Nb 2 O 5 . The C1s of the natural carbon surface contamination was\nshifted to 284.8 eV for charging compensation. A similar XPS spectrum of our\nNb 2 O 5 layers grown by anodic oxidation is available in ref\n( 20 ). A reliable noise study builds on the detailed understanding and careful separation of the\nsimultaneous noise contributions present in the measurement setup and a corresponding data\nprocessing. Therefore, we first describe the scheme of our analysis shown in Figure 2 . The measurement circuit ( Figure 2 c) includes a driving unit, the memristor device, a\ncurrent amplifier, and an R S serial resistor. The latter\nterminates the switching at the low-resistance state once the R M \nmemristor resistance becomes comparable to R S . The\n V bias voltage drop on the memristor junction is calculated as\n V bias = V drive –\n I × R S . More details on the measurement\ninstrumentation are provided in the Experimental Section . Figure 2 (a) Illustration of the sequence of higher driving voltage-level\n I ( V ) measurements and intermediate-level noise\nmeasurements. (b) The first (brown) and second (green) resistive switching\n I ( V ) curves of a Nb/Nb 2 O 5 /PtIr\nSTM point-contact junction together with the corresponding low-bias\n I ( V ) branches evaluated from the noise measurement\ndata acquired in the HRS (dark red) and LRS (dark blue). (c) Schematics of the\nmeasurement circuit (see text). (d, e) Bias voltage dependence of the current noise PSD\nmeasured along the HRS (red shades) and LRS (blue shades)\n I ( V ) branches highlighted in (b). The black spectra\ncorrespond to the S 0 zero-bias background noise. The\nvertical dotted lines indicate the frequency interval, where the noise is integrated to\ncalculate Δ I / I . (f) Voltage dependence of\nΔ I / I as evaluated from data shown in (d) and\n(e). (g) Example noise spectrum of a memristive Nb/Nb 2 O 5 /PtIr\njunction demonstrating the added contribution of a dominating fluctuator located close\nto the filamentary region (orange) and remote fluctuators (green) (see text). Figure 2 a illustrates the basic sequence of the\nmeasurement, which is illustrated by the I ( V ) and noise\ndata acquired in a particular Nb 2 O 5 STM junction device ( Figure 2 b,d–f). First, a full-scale\n I ( V ) curve is measured with a triangular\n V drive signal exceeding the switching threshold, as\nexemplified by the light brown trace in Figure 2 b.\nThe voltage cycle leaves the junction in its high-resistance state (HRS). Next, a\nlower-amplitude voltage staircase is applied, where we evaluate the mean current (red curve\nin Figure 2 b) and the power spectral density (PSD)\nof the current noise ( Figure 2 d) for each voltage\nstep (see the Experimental Section for more details of the PSD\ncalculation). Then, another higher-amplitude I ( V )\nmeasurement is performed (green curves in Figure 2 a,b), preparing the junction in the low-resistance state (LRS) for the subsequent\nnoise measurement (blue curves in Figure 2 a,b,e).\nThe sequence is completed by performing a final I ( V )\nmeasurement. To grant the mechanical stability of the STM point-contact devices, only those\ndata are accepted for later analysis, where these three\n I ( V ) curves are reproducible also agreeing with the\nlow-bias I ( V ) branches calculated from the voltage\nstaircase data. In the case of the crosspoint devices, such a validation of each resistance\nstate is not necessary. Instead, we measure the I ( V ) curve\nat the beginning, and later positive and negative voltage pulses are used for the fine\nanalog tuning of the resistance states. The noise is measured in each state with the same\nprotocol using identical voltage staircase signals. We wish to study the resistance scaling of the steady state\n S G ( f ) = (Δ G ) f 2 /Δ f conductance\nnoise PSD of the memristor junctions, where the (Δ G ) 2 \nmean-square conductance deviation is evaluated within a small Δ f \nbandwidth around the central frequency f . 1 According to\nOhm’s law, this conductance noise PSD converts to current noise PSD as\n S I ( f ) =\n(Δ I ) f 2 /Δ f \n= V 2 × S G ( f ),\ni.e., the V 2 voltage scaling of the current noise is a benchmark\nthat the steady-state conductance noise is studied, and voltage-induced fluctuations and/or\nnonlinear features are excluded from the analysis. To evaluate the\nΔ I / I relative current fluctuation, we remove the\n S 0 zero-bias noise floor and integrate the current noise PSD\nin the frequency interval between 100 Hz and 50 kHz as . Note that in the case of steady-state\nnoise measured in the linear part of the I ( V ) curve,\nΔ I / I =\nΔ G / G =\nΔ R / R holds, and these relative\ncurrent/conductance/resistance fluctuations are independent of the driving amplitude. Figure 2 f demonstrates that the\nΔ I / I relative current fluctuations calculated from\nthe spectra in Figure 2 d,e are indeed\nvoltage-independent within the scattering of the data, confirming that the steady-state\nconductance noise is measured in both states. For further analysis, we have set two criteria\non the data: (i) the integrated noise PSD should be at least 1 order of magnitude larger\nthan the S 0 noise floor integrated for the same band and (ii)\nnonlinear features should be excluded, i.e., we use a voltage interval, where the variance\nof the R M = V / I resistance and\nthe Δ I / I relative current fluctuation are,\nrespectively, less than 5 and 20% compared to the mean value for every device. Accordingly,\nwe have used the 75–115 mV voltage interval for further analysis as a safe margin\n(see the gray interval in Figure 2 f), where the\nnegligibility of the background noise and nonlinear features is satisfied for all\ndevices. We have found that the frequency dependence of the noise spectra of\nNb 2 O 5 - and Ta 2 O 5 -based memristors is often\nqualitatively different from the typical spectra acquired in Ag-based memristors. 34 In such cases, the\nlog S I vs\nlog f spectrum cannot be fitted with a single line, rather a bump\nis superimposed on an ∼1/ f γ background, as\nexemplified in Figure 2 g. This behavior can be\nmodeled by a spatial distribution of the fluctuators, as illustrated in the inset of Figure 2 g: a single (orange) fluctuator with a\nwell-defined τ characteristic time is positioned very close to the narrowest part of\nthe metallic filament, whereas the rest of the relevant fluctuators exhibiting various\ncharacteristic time scales (green) are more remote. In this case, the nearby fluctuator\ninduces a conductance noise with a temporal correlation function described by a single time\nconstant, C G (Δ t ) =\n⟨Δ G ( t ) ×\nΔ G ( t + Δ t )⟩ =\n⟨Δ G 2 ⟩ ×\nexp(−Δ t /τ), yielding a Lorentzian noise spectrum in\nthe frequency space: S G ( f ) =\n(4⟨Δ G 2 ⟩ × τ)/(1 +\n(2π f ) 2 τ 2 ) denoted by the orange line in\n Figure 2 g. In the case of the remote fluctuators,\nthe superposition of such Lorentzians characterized by different time constants yield a\n1/ f -type envelope. The latter is well described by the\n∼1/ f γ spectrum, where γ is usually close to\nunity, as represented by the green line in Figure 2 g. Relying on this observation, we have evaluated all PSD spectra following two\nprocedures: (i) we have calculated the numerical integral of the noise yielding the actual\n(Δ I / I ) numint relative current\nfluctuation of the junction irrespective of the spectrum shape; (ii) we have fitted each\nspectrum with the superposition of a Lorentzian spectrum and a\n1/ f γ spectrum (see the yellow fitting curve in Figure 2 g). Based on the latter method, the relative\ncurrent fluctuations arising from the nearby fluctuator,\n(Δ I / I ) Lor , and the remote fluctuators,\n(Δ I / I ) 1/ f , can be\ndecomposed by separately integrating the individual Lorentzian fitting function and the\n∼1/ f γ term within the same 100 Hz to 50 kHz\nfrequency band. In Figure 3 , we analyze the resistance scaling of\nthe noise including its specificity to (i) the nature of the state (HRS or LRS), (ii) the\npreparation method (STM devices relying on anodic oxidation or crosspoint devices fabricated\nby reactive sputtering), and (iii) the material system (Nb 2 O 5 ,\nTa 2 O 5 , Ag 2 S). Furthermore, we investigate the relative\ncontribution of the nearby fluctuators as well as the γ frequency scaling exponent of\nthe remote fluctuators. Finally, to extract the relevant parameters for practical noise\nengineering, we employ our recently proposed point-contact noise model. 34 Figure 3 (a) Resistance scaling of\n(Δ I / I ) numint for the HRSs (dark\nred) and LRSs (dark blue) of several independent Nb 2 O 5 STM\npoint-contact devices as well as for the voltage-tuned resistance states of a\nNb 2 O 5 crosspoint device (gray). As a reference, the model\nfitting function used in (c) is reprinted by the red/blue dotted lines. (b) Illustration\nof ballistic (diffusive) point-contact geometries. (c–e) The black (yellow)\nsquares represent the resistance scaling of\n(Δ I / I ) numint \n((Δ I / I ) Lor ). For each material\nsystem, the data rely on >20 independent junctions and 5 repeated\n I ( V ) and noise measurement cycles on each junction,\nand from each noise measurement cycle, four different bias steps are used for the\nanalysis (see the gray interval in Figure 2 f).\nThe best-fitting theoretical curves describe the diffusive (red) to ballistic (blue)\ncrossover (see text). The fitting curves are calculated with the numerical values of\n I Ω = 767.29 34 and the\n k F = 1.2 × 10 10 \nm –1 coincident Fermi wavenumbers of Ag and Nb. 46 \nRelying on the similar band structures of Nb and Ta, we use the same value for Ta as\nwell. The green curves and the corresponding green axis on the right show the relative\nnoise contribution of the nearby fluctuator as deduced from the Lorentzian fitting\ncomponent (see text). (f–h) Frequency scaling exponents as deduced from the\n1/ f γ fitting component. Figure 3 a demonstrates the clear resistance\nscaling of (Δ I / I ) numint evaluated in the\nHRSs (dark red) and LRSs (dark blue) of several Nb 2 O 5 STM devices as\nwell as of a Nb 2 O 5 crosspoint device (gray). In the former case (STM\ndevices), the data represent several (>20) independent point-contact junctions with\nvarious LRS and HRS resistances, whereas in the latter case, the multilevel programming of a\nparticular crosspoint device was achieved by voltage pulses with typical amplitudes of\n±(2–3) V (see the Experimental Section \nfor the pulsing scheme). This analysis demonstrates that all of the blue, red, and gray data\npoints follow the same resistance scaling tendency, implying that the noise is not specific\nto the preparation method, neither to the HRS or LRS nature of the state, but it solely\ndepends on the device resistance in a certain material system. This conclusion agrees with\nour previous noise measurements on Ag-based filaments, where Ag 2 S and AgI\nembedded as well as stand-alone Ag nanowires exhibited the same universal resistance\nscaling. The black data points in Figure 3 c–e show\nthe resistance scaling of (Δ I / I ) numint \nfor Nb 2 O 5 , Ta 2 O 5 , and Ag 2 S STM\npoint-contact devices, respectively. To better resolve the average resistance dependencies,\na statistical ensemble of noise data acquired on independent devices is grouped into\nresistance bins, which are equally spaced along the logarithmic resistance axis. The data\npoints and error bars represent the mean values and standard deviations for the various\nnoise measurements corresponding to a resistance bin. Note that the Ag 2 S noise\ndata represent our previous measurements acquired by a similar STM point-contact\narrangement. 34 Here, these data are reevaluated according to the\nprotocol used for the analysis of the Nb 2 O 5 and\nTa 2 O 5 noise data in panels (c) and (d), i.e., we have evaluated\n(Δ I / I ) numint in the same frequency\nband, applied the resistance binning, and performed the decomposition of the\n(Δ I / I ) Lor and\n(Δ I / I ) 1/ f \ncontributions. This comparison shows that the noise increases with increasing resistance for\nall material systems; however, the overall noise level is markedly lower in the\ntransition-metal oxide memristors than in the Ag 2 S devices. The (Δ I / I ) Lor noise contribution of the\nnearby fluctuators is shown by the yellow dots in Figure 3 c–e. This analysis also highlights a clear difference between the\ntransition-metal oxide systems and Ag 2 S: in the former case, the noise of the\nnearby fluctuators is comparable to the total noise, whereas in the latter,\n(Δ I / I ) Lor is more than an order of\nmagnitude smaller than (Δ I / I ) numint . This\nis also demonstrated by the green lines showing the relative noise contribution of the\nnearby fluctuators compared to the total noise,\n(Δ I Lor ) 2 /((Δ I Lor ) 2 \n+ (Δ I 1/ f ) 2 ), demonstrating an\n≈30% noise contribution of the nearby fluctuators for Nb 2 O 5 and\nTa 2 O 5 , and a significantly smaller value for Ag 2 S. We\nnote that the statistically underpinned significance of nearby fluctuators in\ntransition-metal oxide memristors agrees with previously reported\n1/ f 2 -type noise spectra of Ta 2 O 5 RRAM\ndevices 25 representing the high-frequency limit of a dominant\nLorentzian spectrum. The γ frequency scaling exponents are close to unity for all of the three systems, as\nshown in Figure 3 f–h. Note that in the\nAg 2 S system, the noise spectrum is highly dominated by a\n1/ f -type dependency, i.e., the fitting provides more precise values for\nγ. In contrast, the significant Lorentzian contribution in the\nTa 2 O 5 and Nb 2 O 5 noise spectra also yields a\nconsequently higher error in the fitted γ values. In our previous work, we have quantitatively analyzed the resistance scaling of the noise\nin terms of our model taking electron scattering on dynamical defects (TLSs) into\naccount. 34 In particular, the model considers the conducting filament\nas a point-contact device with a realistic geometry, where the Δ G \nconductance noise contribution of a dynamical defect scales with the probability that an\nelectron returns to the junction after scattering on the TLS. 47 This\nreturn probability strongly depends on the relation of the d diameter of\nthe junction and the l mean free path, i.e., the average distance at which\nthe electrons loose their momentum due to scattering on lattice defects, impurities, or at\nthe filament surface. 46 In the ballistic regime, where the\n l electron mean free path is larger than the d diameter\n(see Figure 3 b (top), where the dark gray/white\ncircles represent a TLS and the arrows illustrate possible electron trajectories), this\nreturn probability scales with the square of the solid angle at which the junction is seen\nfrom the TLS position. However, if d becomes larger than l \n(see Figure 3 b (bottom)), the diffusive motion of\nthe electrons reduces the return probability by a factor of\n(2 l / d ) 2 compared to the ballistic\nregime. 47 Relying on these geometrical coefficients and assuming a\nconstant ρ TLS TLS density as well as the validity of the Maxwell (Sharvin)\nconductance formulas in the diffusive (ballistic) regimes, we estimate the resistance\nscaling of the relative current fluctuations as 34 1 2 where\n G 0 = 2 e 2 / h is the\nuniversal conductance quantum, k F is the Fermi\nwavenumber, I Ω is a constant resulting from a solid angle\nintegral, C = Δ G / G 0 sets\nthe average amplitude of the conductance noise resulting from a TLS located at the center of\nthe point contact, and l TLS = ρ TLS –1/3 is the average spacing of neighbor TLSs.\nIn the diffusive (ballistic) limit, Δ I / I scales with\nthe 3/2 (1/4) power of the memristor resistance, respectively. Note that this model is\nderived for an orifice-like geometry, but it also well approximates a more realistic (e.g.,\nhyperboloid) point-contact geometry. 47 This model has two fitting\nparameters, l and C / l TLS 3/2 . Using a C = 0.5\nestimate, 34 , 48 we can\nextract the two relevant length scales, l and\n l TLS , from the fitting. Note that the fitting is performed on\nthe binned data in panels (c–e); however, the raw data (see the red and blue dots for\nthe Nb 2 O 5 STM point-contact junctions in panel (a)) and the binned\ndata provide practically the same fitting results. This model fits well to our\nAg 2 S memristor noise data, 34 yielding\n l Ag 2 S = 1.02 ± 0.02 nm and\n l TLS,Ag 2 S = 1.93 ± 0.03 nm. The best-fitting\ncurve composed of the diffusive and ballistic branches according to eqs\n 1 and 2 is shown by the red and blue dashed lines\nin Figure 3 e. In Figure 3 c,d, we fit the Nb 2 O 5 and\nTa 2 O 5 noise data with the same model (red and blue dotted lines),\nyielding l Nb 2 O 5 = 0.484 ± 0.036 nm,\n l TLS,Nb 2 O 5 = 3.27 ± 0.33 nm,\n l Ta 2 O 5 = 0.380 ± 0.021 nm, and\n l TLS,Ta 2 O 5 = 3.71 ± 0.29 nm. As a\nreference, the fitting curve of the Ag 2 S noise data is reproduced on both panels\nas a red/blue dashed line. The reduced noise level in the transition-metal oxide memristive\nsystems compared to the Ag 2 S data is the most pronounced in the diffusive regime,\nwhere Nb 2 O 5 (Ta 2 O 5 ) memristors exhibit a factor\nof ≈14 (≈31) reduction of Δ I / I with\nrespect to Ag 2 S devices, according to the offset of the diffusive fitting lines.\nThis significant noise reduction has a twofold origin: the decrease of the electron mean\nfree path and the decrease of the TLS density. The above analysis allows us to draw clear conclusions on the aspects of noise engineering\nin the metallic regime of filamentary resistive switching memories: (i) The diffusive regime is ideal for noise tailoring, as\nthe Δ I / I ∼ R M 3/2 resistance\nscaling is steep enough to customize the noise level within a sufficiently wide range\nby the analog tuning of the resistance states. As the ballistic regime is reached, a\nless steep resistance scaling is achieved, which hinders further noise tailoring. It\nis also noted that the mean free path is mainly extracted from the resistance\nthreshold, where the noise evolution deviates from the diffusive scaling (≈1.5\nkΩ, ≈2.5 kΩ, and ≈400 Ω for\nNb 2 O 5 , Ta 2 O 5 , and Ag 2 S,\nrespectively). In the transition-metal oxide devices, this boundary is close to the\ninverse conductance quantum, G 0 –1 ≈ 12.9 kΩ, i.e., the ballistic regime\nspans less than an order of magnitude along the resistance axis. The even larger\nresistances are clearly outside the validity range of our point-contact model;\nhowever, other studies considering fluctuations in broken filaments reported saturated\nnoise in the R M > G 0 –1 \nregime. 36 , 38 − 41 Based on all of these, we propose the\n≲2 kΩ (≲400 Ω) resistance range for the\ntransition-metal-oxide (Ag 2 S) systems as an optimal working range for noise\ntuning. At larger resistances (i.e., in the ballistic regime and in the regime of\nbroken filaments), the saturated character of the relative noise levels is detrimental\nfor efficient noise tailoring. Furthermore, in close vicinity to the inverse\nconductance quantum, the truly atomic-scale diameter of the filament introduces an\nextreme sensitivity to the precise atomic position of a nearby fluctuator. 25 (ii) It is evident that the\ndevice noise can be reduced by decreasing the density of dynamical defects generating\nthe noise, which was also demonstrated by a recent study relying on training-induced\nnoise reduction. 49 Our analysis, however, highlights a further,\nless obvious noise reduction strategy via the suppression of the remote\nfluctuators’ noise contribution by decreasing the electron mean free path. If\nthe degree of static disorder can be increased without introducing further nearby\nfluctuators, the suppression of the remote fluctuators’ noise delivers a\nsignificant overall noise reduction. Moreover, as the noise scales with a higher (5/2)\npower of l than l TLS (3/2 power), a\nfactor of 2 reduction in l yields twice as large reduction in\nΔ I / I , as a factor of 2 increase in\n l TLS considering a constant memristor resistance.\nAlternatively, one can study the scaling of the relative current fluctuation as a\nfunction of the filament size by converting the memristor resistances to the\n d filament diameter according to the Maxwell (Sharvin) formula in\nthe diffusive (ballistic) regime. 34 This conversion yields\n(Δ I / I ) diff ∼\n l × d –3/2 ×\n l TLS –3/2 × k F –2 , and\n(Δ I / I ) ball ∼\n d –1/2 × l TLS –3/2 ×\n k F –2 , again demonstrating that the enhancement of static disorder yields\nnoise reduction in the diffusive\nregime. (iii) In the transition-metal oxide\nmemristors, the noise contribution of remote fluctuators is so much suppressed that a\nsingle nearby fluctuator gives a major contribution to the total noise, which is a\ngreat advantage if noise reduction is targeted. However, the noise contribution of a\nsingle fluctuator is sensitive to the actual atomic position of the fluctuator within\nthe narrowest filament region, which may change upon the resistive switching cycles.\nIf noise tailoring is considered, it may be beneficial to eliminate this sensitivity\nby choosing material systems (like Ag-based memristors) with a higher overall noise\nlevel and a smaller relative contribution of the nearby\nfluctuators. (iv) Finally, we consider the\nmaterial aspects behind the significant noise suppression in transition-metal oxide\nfilaments compared to Ag filaments. In the former case, the host metal is highly\ndecorated with oxygen impurities. Furthermore, electron transport is dominated by\nd-orbitals, making the conductance sensitive to the details of the actual bond\nstructure. Both aspects significantly decrease the mean free path of the electrons\ncompared to Ag filaments, which are considered as pure metallic wires, where the\nhighly delocalized s electrons are less sensitive to the actual\natomic landscape. While the mean free path relies on all scattering processes\nincluding the static disorder of the filament, the noise solely originates from\ndynamical defects, where atoms are fluctuating between metastable positions. We\nconsider unstable oxygen vacancies (Ag atoms) driven by temperature-activated Langevin\ndynamics 50 as the major noise sources in these\ntransition-metal-oxide (silver) filaments. Interestingly, our analysis highlights that\nthe enhanced level of static disorder in the Nb 2 O 5 and\nTa 2 O 5 systems is not accompanied by an enhanced dynamical\ndefect density. On the contrary, l TLS is further increased\ncompared to the Ag 2 S system, i.e., these transition-metal oxide filaments\nare more stable against internal fluctuations than the Ag\nfilaments."
} | 8,627 |
30951668 | null | s2 | 5,151 | {
"abstract": "Long-range (>10 μm) transport of electrons along networks of Geobacter sulfurreducens protein filaments, known as microbial nanowires, has been invoked to explain a wide range of globally important redox phenomena. These nanowires were previously thought to be type IV pili composed of PilA protein. Here, we report a 3.7 Å resolution cryoelectron microscopy structure, which surprisingly reveals that, rather than PilA, G. sulfurreducens nanowires are assembled by micrometer-long polymerization of the hexaheme cytochrome OmcS, with hemes packed within ∼3.5-6 Å of each other. The inter-subunit interfaces show unique structural elements such as inter-subunit parallel-stacked hemes and axial coordination of heme by histidines from neighboring subunits. Wild-type OmcS filaments show 100-fold greater conductivity than other filaments from a ΔomcS strain, highlighting the importance of OmcS to conductivity in these nanowires. This structure explains the remarkable capacity of soil bacteria to transport electrons to remote electron acceptors for respiration and energy sharing."
} | 270 |
37577445 | PMC10416242 | pmc | 5,152 | {
"abstract": "Microbes continually shape Earth’s biochemical and physical landscapes by inhabiting diverse metabolic niches. Despite the important role microbes play in ecosystem functioning, most microbial species remain unknown highlighting a gap in our understanding of structured complex ecosystems. To elucidate the relevance of these unknown taxa, often referred to as “microbial dark matter,” the integration of multiple high throughput sequencing technologies was used to evaluate the co-occurrence and connectivity of all microbes within the community. Since there are no standard methodologies for multi-omics integration of microbiome data, we evaluated the abundance of “microbial dark matter” in microbialite-forming communities using different types meta-omic datasets: amplicon, metagenomic, and metatranscriptomic sequencing previously generated for this ecosystem. Our goal was to compare the community structure and abundances of unknown taxa within the different data types rather than to perform a functional characterization of the data. Metagenomic and metatranscriptomic data were input into SortMeRNA to extract 16S rRNA gene reads. The output, as well as amplicon sequences, were processed through QIIME2 for taxonomy analysis. The R package mdmnets was utilized to build co-occurrence networks. Most hubs presented unknown classifications, even at the phyla level. Comparisons of the highest scoring hubs of each data type using sequence similarity networks allowed the identification of the most relevant hubs within the microbialite-forming communities. This work highlights the importance of unknown taxa in community structure and proposes that ecosystem network construction can be used on several types of data to identify keystone taxa and their potential function within microbial ecosystems.",
"conclusion": "5. Conclusion “Microbial dark matter” is highly prevalent and active in microbialite-forming communities. Using different sequencing methodologies, we were able to apply a co-occurrence network approach to understand community structure and find keystone taxa. Furthermore, the use of different methodologies allowed us to measure different aspects of the biological system, such as taxa presence or absence and the transcriptionally active portion. However, methods present different biases that may impact analysis results. While our study illustrates the gap of knowledge in the composition of microbialite-forming communities, this work provides a methodology to identify and prioritize taxa for downstream analyses.",
"introduction": "1. Introduction Often regarded as one of the planet’s first ecosystems, microbialites are organo-sedimentary structures formed as a result of trapping and binding activities of benthic microbial mat communities ( Reid et al., 2000 ; Dupraz et al., 2009 ; Suarez-Gonzalez et al., 2019 ). Microbialites represent an important interface between the biosphere and geosphere, and mat communities that form these structures have been known to play an important role in planetary evolution by regulating global cycles of major elements, such as carbon, oxygen, nitrogen, and sulfur ( Grotzinger and Knoll, 1999 ). There are several key functional groups of microbes within microbialite communities including oxygenic and anoxygenic phototrophs, aerobic heterotrophs, sulfate reducers, methanogens, and fermenters ( Dupraz and Visscher, 2005 ; Baumgartner et al., 2009 ; Dupraz et al., 2009 ; Myshrall et al., 2010 ; Babilonia et al., 2018 ). However, studies on microbialite-forming microbial communities have shown that ~30% of the taxa recovered from high-throughput sequencing efforts are unclassified at the phyla level and more than 60% of the recovered genes and transcripts are of unknown function ( Louyakis et al., 2018 ) demonstrating the need for improved approaches to explore these unknown taxa. Colloquially named “microbial dark matter,” these unknown elements of microbial life drastically limit our understanding of microbial life and diversity, as well as the metabolic inner workings of microbial-dominated ecosystems, such as microbialite communities ( Marcy et al., 2007 ; Rinke et al., 2013 ; Lok, 2015 ). Since much of our microbial knowledge is derived from relatively few cultivable taxa, this has resulted in a biased and limited view of the genetic and metabolic capabilities of microbial life. However, advances in sequencing technologies have been instrumental in studying these unknown and uncultured microbes, allowing us to sequence microorganisms eluding cultivation. The most common methods to study microbial communities include amplicon, metagenomic and metatranscriptomic sequencing. Each methodology is chosen according to the scientific questions and the needs of each individual study leading to the discovery of new genes, metabolic pathways, and taxa ( Schulz et al., 2017 ; Bernard et al., 2018 ; Jiao et al., 2021 ). For instance, metagenomic sequencing has yielded evidence of dozens of new phyla as well as thousands of new taxa ( Brown et al., 2015 ; Hug et al., 2016 ). Despite this progress, it is unclear how these methods can be integrated to better understand the role of unknown taxa, as meta-omic datasets are large, noisy, and not easy to mine or interpret ( Marx, 2013 ; Parks et al., 2017 ). To analyze ecosystem structure, microbial communities can be modeled as networks, where taxa are represented by nodes and their relationships are represented as edges, respectively. Networks effectively capture the general structure of the ecosystem, and provide information of the importance of different taxa within the community ( Proulx et al., 2005 ). The intricate relationship between microorganisms within a microbial community can be analyzed using different network metrics that represent co-occurrence (degree centrality), connectivity (betweenness centrality) and centrality (closeness centrality), thus providing a measurement of how taxa within the ecosystem operate ( Proulx et al., 2005 ; Ma'ayan, 2011 ; Ma et al., 2016 ). Keystone species that have high levels of all three of these metrics are “hubs” that contribute to maintain the network structure. Since their removal impacts the network connectivity, they are believed to hold ecological relevance within the ecosystem ( Berry and Widder, 2014 ). In a previous study we developed MDMnets, a methodology that utilizes network theory to model microbial communities from amplicon data leading to the identification of unknown hub species that play a central role in the microbial communities ( Zamkovaya et al., 2021 ). In this study, we tested the pipeline MDMnets on three different types of data (amplicon, metagenome and metatranscriptome) from diverse microbialite-forming communities across the globe to investigate the contribution of “microbial dark matter” within these communities. Networks were analyzed for every combination of dataset and taxonomic classification level, both including and excluding the microbial dark matter component. For every dataset, a hub score was calculated for every node. Top scoring hubs were evaluated based on taxonomy and compared across the different methods. By identifying those currently unknown taxa that are forming potentially synergistic connections within the communities, we can prioritize important taxa for characterization, allowing us to improve our understanding of complex microbial ecosystems.",
"discussion": "4. Discussion In this study, we applied a network approach to model microbial interactions in microbialite-forming communities utilizing data from amplicon, metagenomic and metatranscriptomic data sets to understand the role of unknown taxa within these communities and extend the network methodology of MDMnets to other types of data. We analyzed how these different sequencing approaches impacted the detection of the unknown components in the microbialite-forming communities. The results of this study suggest that: (1) microbial dark matter is abundant within microbialite-forming communities and they play an important role in maintaining community structure; (2) unknown taxa occupy keystone positions within the microbialite community; and (3) different types of sequencing data can used for network analysis and provide different perspectives and insight into microbial communities. Amplicon sequencing is often the preferred method to investigate the composition of a microbial ecosystem and studies have shown that utilizing amplicon data to model microbial communities using co-occurrence networks can lead to the identification of unknown keystone taxa ( Zamkovaya et al., 2021 ). Network approaches can lead to a better understanding of which organisms to prioritize for subsequent sequencing and characterization. In this study, we adapted the previously described methodology to other types of sequencing data to understand to which extent the type of sequencing profiling biases the assessment of unknown taxa relevance. Our strategy for adaptation of the MDMnets approach to metagenomic and metatranscriptomic data was to extract SSU reads from these datasets to subsequently process in a similar way as amplicon data. An expansion of this approach to include both metagenomic and metatranscriptomic data can provide valuable insight into active members of the community. Furthermore, the adaptation of this methodology to metagenomics and metatranscriptomics data allows the mining of these data in cases where no other data type is available. Analysis of the networks generated for all three datasets showed that uncultured and unsequenced microbes were highly abundant in microbialite-forming communities, regardless of origin, type and method used. To make sure the obtained SSU sequences from metagenomics and metatranscriptomics had enough information for taxonomic classification, we applied a filter based on percentage of identity to the sequences on the SILVA database, which resulted in a significant reduction of the number sequences considered for network analysis. While this filtering approach guarantees the removal of low-quality sequences from our analysis, it may also exclude unknown taxa with divergent SSU genes present in our ecosystem, reflecting a limitation of current databases. Still, many these high information content sequences could not be taxonomically assigned at levels such as phylum, class or order, reflecting the widespread prevalence of insufficiently characterized microorganisms within the microbialite environments. Our study found that the exclusion of unknown taxa from the network caused an alteration in network metrics for all datasets, showing that “microbial dark matter” was not only abundant in microbialites, but they also comprised an important component of the metabolically active fraction. Typically, different network metrics normally indicate various characteristics of the microbial environment ( Proulx et al., 2005 ). Degree centrality represents nodes that interact with most members in the network, whereas betweenness centrality refers to those nodes that have a high influence on the flow of information in a graph and closeness centrality describes nodes that are central to a network. Analyzing these metrics provided insight into the importance of different nodes within the community. For example, nodes that exhibited both high degree and high betweenness centrality were identified as hubs and may represent the most important members within the microbialite community as they were involved in many interactions and connections. Thus, their removal altered the structure of the network and resulted in the fragmentation. A deeper look into each individual data type showed differences in taxonomic identification and unknown taxa prevalence between amplicon, metagenomic and metatranscriptomic. However, a core of 10 phyla were identified by all three methods (i.e., Proteobacteria, Bacteroidota, Myxococcota, Cyanobacteria, Planctomycetes, Chloroflexi, Desulfobacterota, NB1-j, Acidobacteriota and Bdellovibrionota) out of 23 different identified phyla classifications, revealing the presence a consistent microbial identity for the environment regardless the genomics assay applied, and the utilization of public data obtained from different sources and locations. Observed differences across datasets may reflect the sampling and sequencing strategy used in each case. Amplicon sequencing consists of the targeted sequencing of hypervariable regions within the highly conserved 16S rRNA marker gene. These regions are then utilized for taxonomy profiling, however, the adequate classification of taxa through amplicon sequencing is affected by a variety of biases, such as the choice of hypervariable region to utilize ( Chakravorty et al., 2007 ; Haas et al., 2011 ) or even PCR or sequencing errors generated during library preparation. These biases impact both community profiling and diversity estimation in datasets ( Smith et al., 2012 ; Nelson et al., 2014 ; Salipante et al., 2014 ; Brooks et al., 2015 ; Onywera and Meiring, 2020 ). Unlike the amplicon dataset, metagenomic sequencing is a shotgun approach that aims to sample the collective genome of the microbial ecosystem in any chosen environment. Previous studies have shown that when comparing 16S rRNA gene sequencing with whole metagenome, the resulting microbial profiles are often different. Specifically, shotgun sequencing methods have a difficulty capturing lowly abundant species ( Zhang et al., 2021 ; Jin et al., 2022 ) or lowly expressed genes in metatranscriptomic datasets ( Kuske et al., 2015 ). Our results indicate that the lack of amplification bias in metagenomic and metatranscriptomic sequencing could be the reason for the higher abundance of unknown taxa in these datasets when compared to amplicon data. Although amplicon sequencing introduces biases by including an amplification step that may exclude taxa that cannot be amplified with the normally used primers, this approach can help reduce the number of queried sequences, thus better capturing lowly-expressed or lowly-abundant sequences. Despite having a similar approach and biases, metagenomic and metatranscriptomic approaches represent different aspects of a microbial community. While the metagenomic networks reflected the abundance of unknown taxa in microbialite-forming communities without the amplification biases of amplicon sequencing, metatranscriptomic networks revealed their high transcriptional activity. In addition, the extraction of SSU reads from metatranscriptomics results in the identification of more taxa than in metagenomics, as SSU genes are highly expressed, whereas in metagenomics the SSU genes represent a small portion of the genome, which results in less identified taxa. Here, we were able to show that the network approach can be used with different types of meta-omics data. Additionally, the adaptation of this methodology to different types of data revealed unknown keystone taxa that are abundant and occupy active metabolic roles within the microbialite-forming communities. This insight can be used to prioritize taxa for downstream characterization. Finally, our results show that the capacity for detecting “microbial dark matter” depends on the adopted-omics technology, and their different strengths and biases must be considered."
} | 3,833 |
35197806 | PMC8857939 | pmc | 5,153 | {
"abstract": "Abstract Studies of self-organizing groups like schools of fish or flocks of birds have sought to uncover the behavioral rules individuals use (local-level interactions) to coordinate their motion (global-level patterns). However, empirical studies tend to focus on short-term or one-off observations where coordination has already been established or describe transitions between different coordinated states. As a result, we have a poor understanding of how behavioral rules develop and are maintained in groups. Here, we study the emergence and repeatability of coordinated motion in shoals of stickleback fish ( Gasterosteus aculeatus ). Shoals were introduced to a simple environment, where their spatio-temporal position was deduced via video analysis. Using directional correlation between fish velocities and wavelet analysis of fish positions, we demonstrate how shoals that are initially uncoordinated in their motion quickly transition to a coordinated state with defined individual leader-follower roles. The identities of leaders and followers were repeatable across two trials, and coordination was reached more quickly during the second trial and by groups of fish with higher activity levels (tested before trials). The rapid emergence of coordinated motion and repeatability of social roles in stickleback fish shoals may act to reduce uncertainty of social interactions in the wild, where individuals live in a system with high fission-fusion dynamics and non-random patterns of association.",
"introduction": "INTRODUCTION Studies of self-organizing groups like schools of fish or flocks of birds have sought to uncover the behavioral rules individuals use (local-level interactions) to coordinate their motion (global-level patterns) ( Couzin et al. 2002 ; Herbert-Read 2016 ). For example, experimental work and theoretical models have shown that individuals monitor and respond to nearby neighbors resulting in the emergence of coordinated motion in groups of insects (e.g., Kelley and Ouellette 2013 ; Attanasi et al. 2014 ), fish (e.g., Herbert-Read et al. 2011 ; Katz et al. 2011 ), birds (e.g., Cavagna et al. 2010 ; Bialek et al. 2012 ; Pettit et al. 2013 ; Ling et al. 2019 ), and ungulates (e.g. King et al. 2012 ; Torney et al. 2018 ). However, studies tend to examine “snap-shots” of collective behavior where coordination has already been established, or describe transitions between different coordinated states ( Buhl et al. 2006 ; Tunstrøm et al. 2013 ). In doing so, only few works to date study the emergence or repeatability of coordination in biological systems ( Buhl et al. 2006 ; Dyson et al. 2015 ; Murakami et al. 2017 ). Individuals that live in groups often start (or re-start) interacting with one another from random or disorganized positions (see Biro et al. 2016 for a review). For example, a sudden predator attack upon a group of prey can spread individuals to new locations or environments, resulting in a completely different pattern of association ( Herbert-Read et al. 2017 ). Interaction networks can also be interrupted when individuals have conflicting information ( Merkle et al. 2015 ), and in social systems that exhibit high fission-fusion dynamics, individuals joining and leaving groups creates uncertainty in their social environment ( Kelley et al. 2011 ; Ramos-Fernandez et al. 2018 ). In each of these contexts, coordination of individuals’ behavior in space and over time is disrupted or halted completely. To understand how coordination is achieved in such contexts, it requires repeated observations of animal groups when groups are formed, or during periods of disorder, that occur prior to the onset of coordination ( Biro et al. 2016 ). Here, we study the emergence and repeatability of coordinated motion in three-spined stickleback fish ( Gasterosteus aculeatus ). Three-spined sticklebacks are small gregarious fish that have become key models for our understanding of collective animal behavior (e.g., Harcourt et al. 2009 ; Jolles et al. 2018 ; Ward et al. 2018 ; Fürtbauer et al. 2020 ). Previous work has shown three-spined sticklebacks have defined leader-follower roles enabling coordinated motion among individuals (e.g. Harcourt et al. 2009 ; Nakayama et al. 2012 , 2016 ; Hansen et al. 2016 ; Bevan et al. 2018 ; Jolles et al. 2020 ). We, therefore, expected leader-follower dynamics within shoals (prediction 1) affording coordinated motion (prediction 2). To identify leader-follower roles and coordinated motion, we tracked the motion of fish via video analysis and used the correlation among fish’s velocities through time ( Nagy et al. 2010 ; Strandburg-Peshkin et al 2018 ; Fürtbauer et al. 2020 ) in combination with Wavelet analysis ( Daubechies 1990 ; Torrence and Compo 1995 ; Gaucherel 2011 ; Aguiar-Conraria and Soares 2014 ). We did not anticipate fish leader-follower roles and group coordination to be instantaneous, but instead expected to see a transition from a disordered (uncoordinated, non-shoaling) to an ordered (coordinated, shoaling) state (prediction 3) since fish would need to (re-) establish social interactions/roles ( Kelley et al. 2011 ; Borner et al. 2015 ; Merkle et al. 2015 ; Nadler et al. 2016 ). We, therefore, focused our analyses at the start of trials when fish shoals were introduced to a simple environment. We also expected any leader-follower roles identified to be repeatable (prediction 4), since in a variety of shoaling fish species individuals show consistency in their tendency to act as leaders and followers (e.g., guppies, Poecilia reticulate: Ioannou et al. 2017 ; mosquitofish, Gambusia holbrooki : Burns et al. 2012 ) and three-spined stickleback fish show repeatable individual differences in behavior ( Bell 2005 ; Dingemanse et al. 2007 ; King et al. 2013 ) that modulate leadership in shoals ( Bevan et al. 2018 ). We, therefore, observed groups of fish across two trials, allowing us to investigate the influence of specific individuals on shoal motion over time. Finally, leadership and followership roles can be related to a particular phenotype ( Johnstone and Manica 2011 ; Jolles et al. 2020 ), and more active/exploratory three-spined stickleback fish are seen to adopt leader roles, whereas less active/exploratory fish adopt follower roles ( Harcourt et al. 2009 ; Nakayama et al. 2012 , 2016 ). We, therefore, tested whether inter-individual variation in fish motion when in a “start-box” before trials predicted fish leadership during trials (prediction 5). Furthermore, if leader-follower roles do exist and are linked to group coordination (see above) then we expected that coordination would be achieved quicker in the second trial (prediction 6) indicating a learning effect as the fish habituate to their environment and each other ( Biro et al. 2016 ).",
"discussion": "DISCUSSION We show that shoals of stickleback fish introduced to a simple environment are initially uncoordinated in their motion, but quickly transition to a coordinated state with defined individual leader-follower roles. The identities of leaders and followers were repeatable across two trials, and coordination was reached more quickly during the second trial, and by groups of fish with higher mean levels of motion recorded before free-swimming trials commenced. Defined leader-follower roles were repeatable across two observations. The adoption of specific leader-follower roles within stickleback fish shoals is in keeping with previous work (e.g. Hansen et al. 2016 ; Bevan et al. 2018 ; Jolles et al. 2020 ). Indeed, experiments with pairs of stickleback fish ( Harcourt et al. 2009 ; Nakayama et al. 2012 , 2016 ) have shown that individuals that are more likely to leave cover and explore their environment when tested alone (“bolder” individuals) are more likely to lead their partners, whereas fish that are less likely to leave cover when alone (“shyer” individuals) follow their partners motion and elicit greater leadership tendencies in their bold partners. Other work has shown the likelihood of individuals to approach conspecifics is negatively correlated with an individual’s tendency to leading stickleback dyads and shoals ( Jolles et al. 2014 , 2017 ). In this study, we tested whether fish motion in small start boxes prior to the start of trials predicted leadership. Although we found repeatable individual differences in the level of motion observed, this did not predict frequency of leadership. The lack of a link between motion in the start box and leadership could indicate that motion captured prior to free-swimming does not reflect “activity” as measured in other behavioral studies ( Carter et al. 2013 ). However, this finding is similar to other work with stickleback fish shoals (of the same shoal size) that did not find links between exploratory tendency and leadership ( Jolles et al. 2017 ), and a recent study of our fish population has found fine-scale motion and broad-scale behavioral parameters are broadly equivalent, suggesting value in this approach ( Bailey et al. 2021 ). Future work should now focus on between-individual variation in how fish balance goal-oriented movement and socially oriented behaviors ( Conradt et al. 2009 ) and attempt to measure this trade-off (sticking with others versus moving away from them) in-situ during controlled experiments, and in different contexts (e.g. responses to unpredictable food resources: MacGregor et al. 2020 ). We show a rapid emergence of coordinated motion in the stickleback fish groups. At the start of trials, the correlation in velocity among fish was low, no consistent leader-follower dynamics were observed, and wavelet analysis showed fish were slow-moving without a consistent oscillatory pattern. Then, relatively quickly the mean correlation in velocity among fish increased, fish moved together tending to cycle around the edges of the arena, and consistent leader-follower dynamics were present (see Supplementary Movies S1–S5 for examples). Combining directional correlation in fish velocities with wavelet analysis offers promise for future work. For example, wavelet analysis can be used to decompose a signal (e.g., positional data) into its frequency characteristics in a time localized manner thus providing information on the major types of collective behavior displayed by individuals or groups. Wavelet analysis may thus provide a way of characterizing a group’s collective state that is not determined by one aspect of behavior (e.g. group polarization: Tunstrøm et al. 2013 ). Examination of local dynamics (e.g., directional correlation in velocity) for different collective states as identified by wavelet analyses can then be used to test if local interaction rules are flexible or robust with respect to change ( King et al. 2018 ). For example, in the laboratory, this could be changes in physical boundaries ( Pinter-Wollman 2015 ), or in the wild moving from a closed habitat to an open habitat ( King et al. 2009 ). If interaction rules are flexible, then individuals in animal collectives should adaptively change their behavior when faced with a change in environment. If interaction rules are robust, then individual behavior should persist (but perhaps be sub-optimal) when experiencing change. As yet, these sorts of questions are relatively unexplored, but are critical for understanding the impact of environmental features and changes in the environment upon the behaviors of individuals, groups, and species ( Flood and Wong 2017 ; Snijders et al. 2017 ; King et al. 2018 ). Work investigating the role of the built environment in shaping collective outcomes in social insects offers ideas here, since methods and theory in this area are relatively well developed (e.g. Pinter-Wollman et al. 2017 , 2018 ). The onset of coordination was quicker during the second trial. It has been proposed that there should be a feedback loop between leadership, learning, and competence with the potential to affect improvements in collective performance over time ( Biro et al. 2016 ). Our finding that coordination occurs more quickly in trial two indicates previously established leader-follower interactions might be reinforced at the start of the second trial. However, the fact that we also found that groups containing fish with higher levels of motion in their start boxes also achieved coordination faster, it may be that familiarity with the test arena during trial two resulted in overall quicker coordination in groups because fish began moving and interacting more quickly. To confirm the presence of such (collective) learning would require further repeat tests, involving changing environments so that fish are only learning about one another (and not their environment). For instance, a study of newly formed monk parakeet ( Myiopsitta monachus ) groups ( N = 21 and 19) showed a feedback between behavior and knowledge (as inferred by model fitting and comparisons) that allowed groups to rapidly transition to large-scale order in aggressive interactions ( Hobson and DeDeo 2015 ). This structuring happened in a manner that could not be accounted for by individual characteristics, or by the spatial position of individuals. Therefore, we suggest that further work on re-establishing directed interactions (e.g. leader-follower) as studied here, will allow us to determine if roles emerge as a consequence of differences in individual characteristics (e.g. size, speed) ( Jolles et al. 2020 ), or by recognizing and monitoring the behaviors of those around them ( King et al. 2011 ). The latter tends to be assumed for studies of collective behaviors in, for example, primates ( King and Sueur 2011 ), whilst the former interpretation applies to studies of fish shoals or bird flocks ( Killen et al. 2017 ). Our finding that coordination is reached more quickly in a second trial suggests a role for learning/memory ( Biro et al. 2016 ) in the stickleback system – whether it be for their ecological and/or social environments. In the simple and stable environment studied here, the dynamics on the collective behavior appear to stabilize quickly, but we do not know if the emergence of coordinated motion we see for our study fish is “fast” (though it intuitively seems to be). We, therefore, propose that the speed with which the fish achieve coordination may be useful in the wild where individuals form large groups exhibiting fission–fusion dynamics ( Peuhkuri 1998 ; Couzin 2006 ) and may act to reduce uncertainty of social interactions ( Sueur et al. 2011 ; Ramos-Fernandez et al. 2018 ). What now needs to be determined is whether the consistency of social roles (leader-follower dynamics) we see in our study is also repeatable when individuals find themselves in different social settings. For instance, if we create groups composed of all top-ranked and bottom-ranked leaders in our study groups, will individuals similarly order themselves with respect to leadership and quickly achieve coordination, or fail to effectively coordinate? Given that our small sample of randomly composed groups all showed similar local interaction rules and collective behaviors, it unlikely social roles are innate; instead they are likely to emerge through repeated interactions ( King et al. 2018 ). Understanding how these roles emerge and change over time will be imperative to understanding individual- and group-level behavioral evolution ( Bengston and Jandt 2014 )."
} | 3,868 |
37661811 | PMC10478748 | pmc | 5,154 | {
"abstract": "ABSTRACT Algae-based biofuel developed over the past decade has become a viable substitute for petroleum-based energy sources. Due to their high lipid accumulation rates and low carbon dioxide emissions, microalgal species are considered highly valuable feedstock for biofuel generation. This review article presented the importance of biofuel and the flaws that need to be overcome to ensure algae-based biofuels are effective for future-ready bioenergy sources. Besides, several issues related to the optimization and engineering strategies to be implemented for microalgae-based biofuel derivatives and their production were evaluated. In addition, the fundamental studies on the microalgae technology, experimental cultivation, and engineering processes involved in the development are all measures that are commendably used in the pre-treatment processes. The review article also provides a comprehensive overview of the latest findings about various algae species cultivation and biomass production. It concludes with the most recent data on environmental consequences, their relevance to global efforts to create microalgae-based biomass as effective biofuels, and the most significant threats and future possibilities.",
"conclusion": "7. Conclusion In addition to waste from agricultural practices, waste from industrial activities is the primary source of water contamination in the United States. Along with the depletion of fossil fuel supplies, there is a continuously rising demand for other energy sources. Compared to other physical-chemical therapy techniques, microalgae are the biological agents best suited to treating both WW and the energy crisis. Before algae can be used to produce commercial biofuels, three factors must be carefully considered. These are the species of microalgae that will be utilized, the habitats in which they will thrive, and the extraction methods that will be utilized. Finding reliable renewable energy sources is invariably the most important challenge associated with developing environmentally friendly technologies. Utilizing microalgae is a cutting-edge biotechnology approach that cleanses wastewater while lowering the amount of contamination caused by heavy metals. This method is beneficial to the environment. The most recent statistics on the status of research initiatives aimed at simplifying the process of extracting biofuels from microalgae were seriously considered in this particular study. As was pointed out in this specific study, a growing body of work is being done in the research field about the relationship between microalgae and environmentally friendly technology. This study examined the benefits that can be obtained as well as the challenges that are currently associated with the production of a wide range of biofuels. These biofuels include biodiesel, bioethanol, biohydrogen, and biomethane, all of which can be made by employing microalgae as a source of biomass. Because wastewater is used as a nutrient in microalgae culture, these fuels can significantly reduce environmental pollution, which is one of the most significant advantages of these fuels, along with their capacity to contribute to sustainable development. However, it may also be advantageous to find and alter microalgae that are resilient enough to tolerate a variety of conditions and stresses within the framework of the water treatment infrastructure that is already in place. Additionally, using the current water treatment method in commercial manufacturing may result in cost reductions; nevertheless, this technology requires significant research before it can be utilized to its full potential. Additionally, researchers in this discipline can study other bioenergy sources and value-added commodities that can profit from microalgae.",
"introduction": "1. Introduction Biomass materials are organic substances converted into biofuel from different sources, such as plant matter, algae, or animal by-products and their processed forms. Unlike nonrenewable energy sources like petroleum, natural gas, and coal are the commercial feedstock material used to produce synthetic fuels [ 1 ]. The rising petroleum prices and concerns about fossil fuels role contribution to global warming have triggered alternative resolutions in the search for biofuel due to their environmentally friendly benefits toward the ecological aspects [ 2 ]. Based on both financial cost and environmental perspective, the process involving petroleum-based products provides expansion of wider opportunities, such as the production of high-dense fuel, over alternative biofuels. Which requires large tracts of arable land for the production of food crops and other biomass. Industrialization and human population expansion increased the global demand for energy on a global scale by around 4 to 5% [ 3 ]. Biofuels are renewable resources that produce fewer harmful emission when burned compared to fossil fuels and do not emit carbon dioxide (CO 2 ) into the atmosphere, moreover the released carbon are absorbed by plant during their growth. Somehow most of the released compounds, such as carbon, hydrogen, and oxygen atoms, are absorbed by the organisms producing the biomass. The processes that produce items based on microalgae are said to result in the emission of considerable volumes of carbon dioxide, as determined by evaluations of ecological footprints. The plan for the zero-carbon effort that was made public includes reductions based on the construction of a climate action project that has three trends that can earn carbon credits. These trends are carbon capture, mitigation of wastewater treatment, and renewable or clean electricity. To satisfy the requirements for energy and triumph over these obstacles, eco-friendly approaches are necessary. Much interest has focused on researching and developing sustainable, clean, and renewable energy sources, including biofuels [ 4 ]. One such example is the continued consumption of fossil fuels, causing increasingly severe problems despite their widespread use. Anthropogenic activities have resulted in an excessive buildup of a wide variety of hazardous elements and pollutants, which are believed to be destructive to life on Earth and the many ecosystems found worldwide. It appears that in the not-too-distant future, the application of microalgal biotechnology will be vital to the process of lowering the number of dangerous compounds in the environment. Microalgae have a smaller footprint than first-generation energy crops, where microalgae biomass can produce the similar amount of oil and biomass due to their faster growth rate, higher lipid content, and higher biomass yield. Microalgae can thrive in harsh environments, metabolize various nutrients, and survive on water that would normally be unusable by humans. It is anticipated that microalgae will play an essential role in this mitigation, and it will be explored how the production of renewable energy from microalgae can help to the reduction of a variety of environmental challenges in an integrated manner. The use of biodiesel as a biofuel has recently gained significant interest. There has been discussion about using microalgae as the feedstock for the third and fourth generations of biodiesel production. Several optimistic predictions have made that microalgae have exponential potential as a source of bioenergy [ 5 ]. Microalgae not only serve as a natural carbon sink that contributes to the reduction of global warming but also create a wide range of commercially significant and valuable products. Due to the fact that microalgae can flourish in nutrient-rich wastewater, microalgae can be utilized to treat effluent in a manner that is both non-harmful to the environment and sustainable. Since it does not compete with food crops for arable land, it can grow in various environments, including seawater, and it removes CO 2 and phosphorous while growing, making it useful for treating wastewater. Therefore, algal oil production does not harm traditional agriculture [ 6 ]. Commercializing microalgae-based biofuels involves choosing the optimum species for outdoor growing, optimizing photosynthetic efficiency, and lowering production costs. Increased research on biomass residues, gas, liquid fractions, and extraction of lipids extraction would also enhance the productivity of biofuel [ 7 ]. Heated power plant generators with waste heat and used to create electricity. Some biomass-burning power plants have been running for some time [ 8 ]. Similarly, wastewater contamination can be reduced by the process of bioremediation, which makes use of microalgae [ 9 ]. Numerous types of research have provided evidence for this strategic approach. Making biofuels from microalgae while cleaning wastewater has significant economic and environmental benefits [ 10 ]. Microalgae with a short life cycle, a rapid growth rate, and a high CO 2 utilization efficiency could be one strategy to produce biomass from waste water nutrients while also using renewable resources. Microalgae are not only more productive, but they may also thrive in murky water on unarable ground. Therefore, much effort has been put toward providing sustainable microalgae-based biofuels [ 11 ]. Bioenergy can also be generated by various methods such as thermochemical processes, acid/base transesterification, supercritical solvents, and microorganism-derived fuel cells. Although the techniques mentioned above are simple to execute, they are not yet widely employed in business due to their high price tag and the requirement for technological advancements [ 12 ]. This necessitates using the most efficient downstream process available to maintain competitively low production costs. A novel integration approach is currently being applied into the circular bioeconomy concept of zero-carbon or low-carbon emission [ 13 ]. This strategy places primary emphasis on the utilization of reducing, recycling, and reusing solutions, which ultimately results in a small carbon footprint. This initiative seeks to reduce humans reliance on fossil fuels by shifting toward alternative sources of energy and value-based products as part of its implementation. This review paper covers the basics and preliminary extraction methods from both traditional and cutting-edge modern techniques on microalgal fuel production that uses extraction and pre-treatment process. A few alternative methods were also investigated elaborate the basic engineering concepts involved and utilized for the biofuel production. Recent breakthroughs in microalgal biofuel generation from a different source will be critically addressed, along with several microalgae growing and cultivation methods and the numerous species of microalgae that are utilized for harvesting biomass. Further procedures use acid and base-mediated transesterification to convert biomass into fuel from algae. This study also examined ethanol, biobutanol, and biodiesel, as well as prospective energy sources of the future, such as hydrogen and microbial fuel cells, which are investigated 1.1. Significance of different biofuels from biological sources Liquid biofuels have garnered much attention recently due to the extensive infrastructure to use them, particularly in the transportation sector [ 14 ]. Bioethanol, which can be produced by fermenting either starch or sugar into ethyl alcohol, has become the most widely produced liquid biofuel. Second-generation biofuel, or cellulosic ethanol, is made from low-value biomass composed of cellulose instead of edible food crops [ 15 ]. Some examples of cellulose-based biomass include lignocellulosic crop residues, wood chips, and municipal waste. The production of cellulosic ethanol typically involves using a wide variety of grasses that can be grown on low-quality land such as sugarcane bagasse, a by-product of the sugar processing industry [ 16 ]. Since cellulose biomass does not convert as quickly as biofuels from the first generation, cellulosic ethanol is typically utilized as a gasoline component rather than a standalone fuel [ 17 ]. Figure 1 shows the different generations of biofuel production mechanisms, their respective by-product conversation in each stage, and elaborates on the sequential process between the recent advancement in biofuel production from biological sources.\n Figure 1. Schematic diagram of the first and second generation of biofuel from biological resources (a) biodiesel and (b) bioethanol production."
} | 3,118 |
35520454 | PMC9054217 | pmc | 5,157 | {
"abstract": "A synthetic route to amphiphilic conetwork (APCN) gels was developed and involved (1) a ring-opening polymerization (ROP) synthesis of the macromonomer HEMA-PLLA/PDLA, and (2) a radical polymerization of a stereocomplex of the synthesized macromonomers with P(MEO 2 MA- co -OEGMA) to form the APCN gels. The structure of the gel was successfully verified using X-ray diffraction. Thermal analysis and differential scanning calorimetry data showed that the thermal behaviors of the gels were greatly improved compared with that of polylactic acid (PLA). The mechanical properties of the gels were measured by using a dynamic viscometer, and the results indicated a greater mechanical strength before swelling than afterwards, and an increasing strength of the gels with increasing amount of PLA stereocomplex. Gels placed in different aqueous phases at different temperatures showed different swelling ratio (SR) values. Specifically, the SR gradually decreased as the temperature was increased, indicating a temperature sensitivity of the gels. In addition, the gels placed in the aqueous and organic phases presented as hydrogels and hydrophobic gels, respectively, and their SR values were relatively low. These results indicated the amphiphilic nature of the gel, and indicated great application prospects for the gel in biomedicine.",
"conclusion": "4. Conclusions APCNs were successfully synthesized by carrying out free radical copolymerization of PLLA and PDLA stereocomplex with MEO 2 MA and OEGMA comonomers. A series of APCN gels with different compositions were obtained by tuning the ratio of the amount of hydrophobic macromonomer to that of the hydrophilic monomer. The appearance of characteristic XRD and DSC peaks indicated that the gel stereocomplexations were successful. The gels swelled in both the organic and aqueous phases, and the swelling behaviors of the gels were different in aqueous phases at different temperatures, indicative of the relatively high amphiphilicity and temperature sensitivity levels of the gels. DMA and thermal analysis system measurements showed that PLA was greatly improved by MEO 2 MA and OEGMA, and indicated the higher mechanical strengths and better thermal performances of the amphiphilic co-network gels. These APCNs and gels may, due to their unique structures and properties, find potential biomedical applications such as in encapsulating and delivering hydrophobic drug molecules.",
"introduction": "1. Introduction Polylactic acid (PLA) is an important degradable polymer material. Its synthetic raw materials are obtained from renewable resources. After being used, it can be degraded into H 2 O and CO 2 , and hence introduce no pollution to the environment. 1–3 At present, polylactic has been widely used in controlled drug release, non-removable surgical sutures and microcapsules for injections, landfills, as support materials, and as repair materials in tissue engineering. 4 PLA forms two enantiomers: poly ( l -lactide) (PLLA) and poly ( d -lactide) (PDLA), which can form a racemate by hydrogen bonding, i.e. , the so-called stereocomplex. 5–7 Compared with the individual enantiomers, PLA stereocomplexes exhibit better physical properties, such as higher melting points, higher mechanical strength levels, and improved thermal and hydrolytic stability levels. 8–12 These unique characteristics are beneficial to the applications of PLA in biomedicine. Most importantly, using the PLA stereocomplex as a cross-linker has several advantages over other cross-linking methods, such as reaction under mild conditions and avoiding the use of catalysts, auxiliary crosslinking agents, and other active molecules. 13 Poly( N -isopropylacrylamide) (PNIPAM) has been by far the most-studied thermoresponsive polymer in materials science. Indeed, this synthetic polymer exhibits a low critical solution temperature (LCST) of about 32 °C in aqueous medium and is, therefore, very useful for preparing smart materials for biological applications. However, in recent years, polymer chemists reported very interesting alternatives to PNIPAM. 14,15 Temperature-sensitive, water soluble biocompatible copolymers of 2-(2-methoxyethoxy) ethyl methacrylate (MEO 2 MA) and oligo(ethylene glycol) methacrylate (OEGMA) have been widely studied. LCST values of 26 °C and 90 °C have been measured for PMEO 2 MA and POEGMA, respectively—with the copolymers exhibiting LCSTs between these temperatures, and which can be precisely adjusted by varying the ratio of the amount of one co-monomer to that of the other. 16–18 P(MEO 2 MA- co -OEGMA) has been extensively studied in drug delivery systems due to its excellent temperature response, and excellent hydrophilicity, biocompatibility and non-toxicity. Amphiphilic conetwork (APCN) gels as a rapidly emerging new material, consist not only of hydrophilic polymer components, but also hydrophobic polymer components, which are covalently bonded together in a cross-linked macromolecular assembly. 19–22 APCN gels are more mechanically stable than are their pure homopolymeric analogues. Importantly, APCN gels have some extraordinary properties, such as swelling independent of solvent polarity, formation of a nanophase structure, excellent mechanical strength, and good biocompatibility, which make them promising for applications in a wide variety of areas. 23–30 In this study, we successfully synthesized biocompatible APCN gels, namely the poly[2-(2-methoxyethoxy)ethylmethacrylate- co -oligo(ethylene glycol)methacrylate]- l -stereocomplex of poly( l -lactide) and poly( d -lactide) (P[MEO 2 MA- co -OEGMA]- l-S -(PLLA-PDLA)). First, physical cross-linking was accomplished via stereocomplexation of the macromonomers HEMA-PLLA and HEMA-PDLA, and then the hydrophilic monomers MEO 2 MA and OEGMA were added to form the APCN gels. Hydrogels formed by physical crosslinking display better mechanical strengths and higher melting points than do hydrogels formed by chemical crosslinking. 31–34 Importantly, these materials were found to be biocompatible and degradable, which can promote their application in the field of biomedicine.",
"discussion": "3. Results and discussion 3.1. Characterization of polymers and gels 3.1.1. Syntheses of the polymers and gels In this study, macromolecular monomers and gels were synthesized by performing ring-opening polymerization (ROP) and free radical polymerization. First, l -lactide (or d -lactide) ring opening was reacted with 2-hydroxyethyl methacrylate by using the catalyst DBU to obtain the product HEMA-PLLA as shown in Scheme 1 . The obtained macromonomers HEMA-PLLA and HEMA-PDLA were thoroughly stirred under the action of ultrasonication, and they were physically cross-linked via PLA stereocomplexes. After the addition of the hydrophilic substances MEO 2 MA and OEGMA, the synthesis of the gel was carried out under the action of the initiator AIBN. The synthesis route is shown in Scheme 2 . The composition of the gel was controlled by controlling the feed ratio of the PLLA/PDLA blend (see Table 1 ). Scheme 1 Synthesis routes to macromonomers HEMA-PLLA/HEMA-PDLA. Scheme 2 Synthesis route to the amphiphilic conetwork gel. Synthesis data for the copolymers Samples HEMA/PLLA(PDLA) ( n : n ) OEGMA/MEO 2 MA ( n : n ) HEMA-PLLA/HEMA-PDLA ( W : W ) \n W \n a : W b (%) P1 1 : 9 5 : 95 1 : 1 1 : 5 P2 1 : 13 5 : 95 1 : 1 1 : 5 P3 1 : 16 5 : 95 1 : 1 1 : 5 P4 1 : 20 5 : 95 1 : 1 1 : 5 a 1 : 1 mixtures of macromonomers HENA-PLLA and HEMA-PDLA. b 5 : 95 mixtures of OEGMA and MEO 2 MA. 3.1.2. Structure of the macromonomer HEMA-PLLA The acquired 1 H NMR spectrum of HEMA-PLLA is shown in Fig. 1(a) . The methyl proton peaks at 1.35–1.54 ppm can be assigned to –(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)–CH–(CH 3 ). The methyl proton peak at 2.01 ppm can be assigned to CH 3 –. The active hydrogen peak at 3.3–3.4 ppm can be assigned to –CH–(CH 3 )–OH. The methylene proton peak at 4.3 ppm can be assigned to –O–CH 2 –. The methine proton peak at 5.1 ppm can be assigned to –(C O)–CH–(CH 3 ). The peaks at 5.5 ppm and 6.2 ppm are the absorption peaks of the –(CH 3 )C CH 2 methylene proton. The molecular weight of HEMA-PLLA (HEMA-PDLA) was calculated from the 1 H NMR spectra by using the formula 13 where A a+b and A d stand for the integral areas of peaks (a+b) and (d), respectively. The values of 72 and 130 are the molecular weights of the repeat units of PLLA and the macroinitiator HEMA, respectively. Fig. 1 (a) The acquired 1 H NMR and (b) IR spectra of HEMA-PLLA. As shown in Fig. 1(b) , the infrared absorption spectrum of HEMA-PLLA showed various absorption peaks: at 1195 cm −1 , characteristic of –COC–; at 1600 cm −1 , characteristic of –C C–; a characteristic peak of C O at 1758 cm −1 ; characteristic peaks of –CH 2 , –CH 3 at 3001 cm −1 and 2949 cm −1 ; and a characteristic peak of –OH at 3649 cm −1 . The nuclear magnetic resonance and infrared spectra of HEMA-PDLA were observed to be essentially identical to those of HEMA-PLLA. 3.1.3. Structures of the gels X-ray diffraction (XRD) was used to verify the existence of PLA stereocomplexes in the polymer co-network. 35–39 Fig. 2 shows the acquired XRD patterns of the macromolecular monomer HEMA-PLLA, the polymer without complete stereocomplexing of PLA, and the stereocomplexed PLA gel. As shown in curve (a) of the figure, HEMA-PLLA yielded diffraction peaks at 2 θ = 15.8°, 18.2°, and 19.8°, which corresponded to PLLA, and the peak shapes were sharp and narrow, indicating the high crystallinity of HEMA-PLLA. (PDLA showed similar results.) As shown in curve (b) of the figure, the polymer without complete stereocomplexing of PLA no longer showed the characteristic diffraction peak of the macromolecular monomer, and the diffraction peaks that were observed were significantly wider than those for the monomer. This result was attributed to the addition of the hydrophilic substance modifying the hydrophobic PLLA, and the polymer developing a relatively amorphous structure. As shown in curve (c) of the figure, diffraction peaks again appeared at 2 θ = 12° and 21° in the pattern produced by the stereocomplexed PLA gel, and the peak shapes were sharp and narrow, confirming the presence of PLA stereocomposite crystals. Fig. 2 . The acquired XRD patterns of the macromolecular monomer HEMA-PLLA (a), the polymer without complete stereocomplexing of PLA (b), and the stereocomplexed PLA gel (c). 3.2. Properties of the gels 3.2.1. Mechanical strength levels of the gels The curves obtained using dynamic mechanical analysis (DMA) of the different gels after 4 days of swelling are shown in Fig. 3(a) . These curves reflected the mechanical properties of the gels. The four curves showed the same trend, with the storage modulus increasing with frequency. Also as clearly seen in the figure, the storage modulus values of the hydrogels increased with PLA polymerization degree. When the frequency was 10 Hz, the storage modulus values of the gels were 49.34 kPa, 48.07 kPa, 52.92 kPa and 62.63 kPa, respectively. This result was due to higher amounts of stereocomplexes of PLA being associated with more points of action for forming hydrogen bonds between PLLA and PDLA, and hence gels with smaller pores and less likely to collapse. DMA curves of the P3 gel swelling in deionized water at different times are shown in Fig. 3(b) . For a frequency of 10 Hz, these curves indicated a storage modulus of 154 kPa for the dry gel, and modulus decreasing continuously from this level with increasing swelling time, specifically to 85 kPa at 2 days and 67 kPa at 4 days. This experiment provided strong evidence for the high mechanical strength of the gels and of the great impact of swelling time on their strength and elasticity levels. The longer the swelling time, the larger the pores of the gel, and the easier it apparently was to break the gel, resulting in the relatively poor gel strength and elasticity. (The experiment was repeated three times to ensure the accuracy of the data.) Fig. 3 (a) Dynamic mechanical analysis (DMA) curves of the P1, P2, P3, P4 gels after swelling for 4 days. (b) The P3 DMA curves after swelling for 0 d, 2 d and 4 d in distilled water. At 25 °C, P3 was subjected to constant stresses of 6 kPa, 8 kPa and 10 kPa. Inspection of the corresponding creep curves (displayed in Fig. 4 ) showed the gel strain changing with time, reflecting the extensibility of the gels. When the applied stress was 10 kPa, the gels showed a great strain of 23%; and when the applied stresses were 8 kPa and 6 kPa, the strains were sequentially reduced to 19% and 16%, respectively. After the stress was removed, the deformation the gel was observed to diminish. Obviously, the greater the stress, the smaller was the recovered strain. The experimental results showed a certain level of elasticity displayed by the hydrogel, and as the stress was increased, the elasticity of the gel gradually decreased. (The experiment was repeated three times to ensure the accuracy of the data ( Fig. 5 ).) Fig. 4 Curves of strain versus time for P3 gels subjected to constant stresses of 6 kPa, 8 kPa and 10 kPa. Fig. 5 Photographs of unstretched (top) and stretched (bottom) P3 gels. The photographs were taken with a digital camera. 3.2.2. Thermal properties of the gel The thermal stability of the gel was evaluated by performing thermogravimetric analysis. The thermal degradation temperature refers to the temperature of the first weight loss. Fig. 6 shows the thermogravimetric curves for different gels. These curves showed that all of the gels began to lose weight at 220–280 °C, indicative of their relatively high thermal stability levels. Clearly, as described above, the greater the applied stressed, the smaller was the strain recovered. And the thermogravimetric analysis showed higher phase change temperatures for gels with higher degrees of PLA polymerization, specifically 220 °C for P1 and 280 °C for P4. The higher observed decomposition temperatures of the gels with greater degrees of PLA polymerization were attributed to these gels having more hydrogen bonding interactions between PLLA and PDLA molecules. Fig. 6 The acquired thermogravimetric curves of various gels. Further evidence for the relatively high thermal stability levels of our gels was provided by the results of DSC analysis. As shown in Fig. 7 , the DSC results of the gels indicated two typical physical transitions: a glass transition at a temperature ( T g ) close to 180 °C and crystal melting at a temperature ( T m ) roughly in the vicinity of 280 °C. The crystal melting peak was due to the successful stereocomplexation of PLLA and PDLA and the presence of SC crystallites. Moreover, with the increase of the degree of PLA polymerization, the T m value gradually increased, completely consistent with the results of the thermogravimetric analysis. The glass transition temperature ( T g ) did not much vary from 180 °C as the PLA polymerization degree was increased, indicating little effect of the stereocomplex on the movement of the segments in the blend. Fig. 7 The acquired DSC curves of the PLLA/PDLA blends with different contents. 3.2.3. Swelling studies One of the most interesting properties of our amphiphilic conetworks was their abilities to swell in both polar and nonpolar solvents. Some dry gel samples were transferred into deionized water and others into THF, and allowed to swell until they reached equilibrium. The masses of the swollen gels were measured and used to calculate the SR values. The swelling kinetics curves of the P1, P2, P3 and P4 gels placed in deionized water with a pH of 7 at 25 °C are shown in Fig. 8(a) . These curves showed similar trends for P1, P2, P3 and P4, with SR gradually increasing with time, but gradually decreasing with the increasing degree of PLA polymerization. The swelling kinetics curves of the P1, P2, P3 and P4 gels in THF are shown in Fig. 8(b) . These curves also showed overall trends, i.e. , increasing SR with time, similar to each other and to those of the gels in the aqueous phase—but interestingly, in THF, the gel SR gradually increased with the increasing degree of PLA polymerization. This result was attributed to the gels in the organic phase being related to the degree of PLA polymerization: the greater the amount of the hydrophobic substance, the more organic points of action, and the greater was the SR. We also examined gels in different solvents as shown in Table 2 . The gels swelled in both polar and different nonpolar solvents. That is, these new materials were shown to display amphiphilic characters. Thus, depending on their outer environment, these conetworks could behave either as hydrogels (in the presence of water) or hydrophobic gels (in case of hydrophobic, nonpolar solvents). Besides, it can be clearly seen from the figure that whether the gels swelled in the aqueous phase or the organic phase, the SR values of the gels were relatively small. Placement of these gels in the body would thus not be expected to cause local edema, and the use of such gels has led to great progress in the field of drug delivery. 23 (The experiment was repeated three times to ensure the accuracy of the data.) Fig. 8 Swelling ratios as a function of time for the P1, P2, P3 and P4 gels placed in (a) deionized water at 25 °C and pH 7, and (b) THF. Results of swelling of gels in different solvents Swelling degree (%) Sample H 2 O CH 3 OH THF DCM P1 692 ± 12 212 ± 28 593 ± 25 928 ± 22 P2 436 ± 15 253 ± 23 715 ± 36 1312 ± 24 P3 357 ± 8 283 ± 20 778 ± 21 1307 ± 18 P4 328 ± 10 318 ± 37 1144 ± 29 1533 ± 32 \n Fig. 9 shows pictures of the gel taken with a digital camera. Panel (A) shows a picture of the untreated gel; panel (B) shows a picture of the gel after it was completely swollen in a deionized water bath at pH 7, 25 °C; and panel (C) shows a picture of the gel after it was swollen in THF. Inspection of these photographs also showed that the gels swelled in both the aqueous phase and the organic phase, completely consistent with the results in Fig. 8(a) and (b) , thus further illustrating the amphiphilic natures of the gels. Fig. 9 Photographs taken with a digital camera of (A) an untreated gel, (B) gel completely swollen in a deionized water bath at pH 7, 25 °C and (C) gel swollen in THF. 3.2.4. Temperature sensitivities of the gels In order to determine the low solution critical temperature (LCST) values of the gels, their SR values were determined at various temperatures from 20–42 °C, and at 30 minutes when they presumably reached swelling equilibrium. As shown in Fig. 10 , the gels showed relatively constant and high degrees of swelling at temperatures lower than 35 °C. But when the temperature was increased from just below 35 °C to just above this temperature, the swelling degree markedly decreased, to as low as about 20%. As the temperature was further increased, the swelling degrees remained relatively constant at this low level. Therefore, the mutation point temperature of the gels was 35 °C. (The experiment was repeated three times to ensure the accuracy of the data.) Fig. 10 Swelling ratios of the four gels at various temperatures. 3.2.5. Swelling and de-swelling dynamics of the gels Swelling kinetics curves of the P1, P2, P3, and P4 gels in deionized water at pH = 7, 25 °C are shown in Fig. 11(a) . These curves were observed to be similar to each other, with the swelling degree increasing rapidly with time during the first 15 h and gradually so after 15 h. At swelling equilibrium, the swelling degrees of the P1, P2, P3 and P4 gels were 692%, 435.5%, 356.6% and 328.77%, respectively. Due to the formation of the gel mainly depending on hydrogen bonding between PLLA and PDLA, the lower the PLA polymerization degree of the gel, the fewer hydrogen bonding points present between PLLA and PDLA, and the larger were the pores of the gel network structure. Therefore, the degree of swelling of P1 was the greatest. At 25 °C, hydrogels that had reached swelling equilibrium were immersed in a pH 7 water bath at 40 °C ( T > LCST) to study their de-swelling kinetics. As shown in Fig. 11(b) , the gels tended to shrink and dehydrate at 40 °C, and the water retention was obviously reduced within 10 min. Thus, the gels were found to be temperature sensitive. When the temperature of the gels was higher than the LCST, the hydrogen bonds between the hydrophilic chains P(MEO 2 MA- co -OEGMA) and water molecules became broken, and the P(MEO 2 MA- co -OEGMA) chains agglomerated into a spherical shape. So the gels rapidly lost water and violently shrank, and the amount of water retained was reduced. (The experiment was repeated three times to ensure the accuracy of the data.) Fig. 11 (a) Swellings of the P1, P2, P3, and P4 gels in deionized water at 25 °C and pH = 7 and (b) de-swellings of the gels at 40 °C. 3.2.6. Reversible swelling of the gels In order to study the reversibility of the gel swelling, the P3 gel was subjected to swelling, de-swelling experiments at certain temperatures. After the gel was completely swollen, its swelling degree reached 100%. Then it was swollen, de-swollen and swollen at 25 °C and 42 °C, and a change of the curve was observed. As shown in Fig. 12 , the P3 gel displayed good reversible swelling. (The experiment was repeated three times to ensure the accuracy of the data.) Fig. 12 The swelling and de-swelling of the P3 gel in deionized water at 25 °C and 42 °C (see text). \n Fig. 13 shows photographs of this P3 gel taken with a digital camera. (A′) shows a photograph of the untreated gel; (B′) shows a picture of the gel completely swelled in a deionized water bath at pH = 7, 25 °C; and (C′) shows a photograph after the gel was placed in a 40 °C deionized water bath. These photographs showed that after the gel was completely swollen, it shrank and whitened with increasing temperature, completely consistent with the results of 3.6.2 (a) and (b). These results illustrated the temperature sensitive nature of the gels. Fig. 13 Photographs taken with a digital camera of (A′) an untreated gel, (B′) gel completely swelled in a water bath at pH 7, 25 °C, and (C′) gel stabilized in a 40 °C water bath. 3.2.7. DSEM images of cross-sections of the gels The morphologies of the APCN gels were further studied using a desktop scanning electron microscope (DSEM). Fig. 14(A)–(D) show images of cross-sections of the gels with different PLA stereocomplex contents. Inspection of these images showed larger pores for the gels with lower extents of PLA polymerization. This result was attributed to the gels having been formed by the physical crosslinking of PLLA and PDLA. As the amount of the stereocomplex of PLA was increased, the quantity of interaction points of hydrogen bonding between PLLA and PDLA increased, and the pore size gradually decreased, completely consistent with the conclusions drawn from the gel swelling curve in water. Fig. 14 DSEM images of cross-sections of completely swollen (A) P1, (B) P2, (C) P3 and (D) P4 gels at room temperature."
} | 5,886 |
35798840 | PMC7613230 | pmc | 5,158 | {
"abstract": "Phosphorus (P) acquisition is key for plant growth. Arbuscular mycorrhizal fungi (AMF) help plants acquire P from soil. Understanding which factors drive AMF-supported nutrient uptake is essential to develop more sustainable agroecosystems. Here, we collected soils from 150 cereal fields and 60 non-cropped grassland sites across a 3,000 km trans-European gradient. In a greenhouse experiment, we tested the ability of AMF in these soils to forage for the radioisotope 33 P from a hyphal compartment. AMF communities in grassland soils were much more efficient in acquiring 33 P and transferred 64% more 33 P to plants compared to AMF in cropland soils. Fungicide application best explained hyphal 33 P transfer in cropland soils. The use of fungicides and subsequent decline in AMF richness in croplands reduced 33 P uptake by 43%. Our results suggest that land-use intensity and fungicide use are major deterrents to the functioning and natural nutrient uptake capacity of AMF in agroecosystems.",
"introduction": "Introduction With the global population growing, we need to find ways to promote crop production while minimizing environmental degradation 1 , 2 . Understanding and harnessing the natural functions provided by species above- and below-ground in the agricultural landscape is a promising approach to address both goals, thus paving the way to an ecological intensification of agroecosystems 3 , 4 . Arbuscular mycorrhizal fungi (AMF) inhabit the soils of virtually all terrestrial ecosystems and form symbiotic associations with most plants, including agricultural crops 5 , 6 . Plants deliver reduced carbon in the form of sugars and lipids to AMF in return for nutrients, especially phosphorus (P) and nitrogen, which AMF acquire through their extensive soil hyphal networks. AMF may supply up to 90% of the host plant’s P requirements, especially in nutrient-poor and undisturbed vegetation 5 , 7 . Promoting the natural potential of AMF for crop P nutrition could therefore circumvent the adverse environmental effects of high P fertilization 8 , 9 , reduce associated economic and environmental production costs, increase P availability in cropping systems with low fertilizer access, while contributing to other services provided by AMF such as soil aggregation 10 . The AMF-symbiosis is thus one key asset to improving P-use efficiency and to the design of sustainable agroecosystems 11 – 13 . However, whether current cropping systems support AMF functioning remains unclear, fueling the debate about the relevance of AMF for agricultural production 14 – 17 . The contribution of AMF to plant nutrition are context-dependent, given that AMF communities are shaped by local environmental conditions 18 and that the benefits of AMF to plant growth are cultivar dependent 19 – 21 . Some modern crop cultivars are less efficiently colonized by AMF and have been shown to only benefit from AMF under severe P limitation 22 , 23 likely since crop breeding and selection have not prioritized these symbiotic associations 24 . However, other studies indicate that AMF support crop yield and are a key factor to make agroecosystem more sustainable 3 , 10 , 13 , 17 . Apart from this, the abundance and diversity of AMF communities are negatively affected by intensive management practices 25 – 27 , especially tillage, inorganic fertilization, and pesticide use 28 . Several studies indicate that AMF richness promotes plant productivity and plant P uptake 29 , 30 . Thus, a reduction in AMF richness due to intensive management may reduce the natural nutrient uptake capacity of agricultural soils. Whether management-induced changes in AMF communities subsequently impact their ability to acquire P for growing plants is still poorly understood. Until now, broad scale assessments of AMF functioning across different environmental conditions and land use types have not been performed, contributing to our lack of understanding of the drivers of hyphal P acquisition in intensively and extensively managed plant-soil systems. To address these knowledge gaps, we collected 210 soils originating from cropland fields and neighboring non-cropped grassland sites across a 3,000 km European north-to-south gradient ( ED Fig. 1 ). Plantago lanceolata , well known for its associations with a wide range of AMF and hence ideal for a broad screening of the potential AMF activity across various soil conditions 31 , was then grown on these soils in a greenhouse experiment in which we measured the capacity of the associated AMF to mediate the uptake of the radioisotope 33 P from a labelled hyphal compartment ( Fig. 1 ). This allowed us to 1) estimate the capacity of native AMF communities in different soils to provide plants with P, and 2) assess the drivers of hyphal P transfer by including the climatic background, biotic and abiotic soil properties, and legacy effects of land use and crop management practices in the analysis. We hypothesized that i) hyphal P transfer is strongly driven by the large gradient in climate and soil characteristics, ii) hyphal P transfer is lower in cropland compared to non-cropped grassland soils, and iii) intensive agricultural practices (pesticide use, tillage, and fertilization) and a reduction in AMF richness negatively impact hyphal P transfer. Ultimately, our research contributes to a better understanding of the main drivers of and constraints to AMF functioning in agroecosystems.",
"discussion": "Discussion Lower hyphal 33 P transfer in cropland vs. grassland soils AMF in extensively managed or undisturbed soils are usually more abundant and diverse compared to intensively managed cropland soils that receive substantial amounts of fertilizers and pesticides 26 , 27 , 32 – 36 . However, the functional implications of such differences have not been investigated previously. This study demonstrated that AMF communities from non-cropped grassland sites are generally more active and transfer higher amounts of 33 P to host plants compared to AMF communities from cropland soils. Our observation, stemming from a vast diversity of different soil and climatic characteristics at a broad spatial scale, suggests that current cropping practices impair AMF functioning and that the capacity of AMF to support plant nutrition thus remains underexploited in European croplands. Various mechanisms could explain the observed differences in AMF functioning between cropland and grassland soils. First, a range of studies showed that management practices associated with intensive land use, including soil tillage, pesticide and fertilizer use, reduce AMF abundance, spore abundance, AMF diversity and alter AMF community composition 37 – 39 . Our study confirmed that both microbial biomass as well as AMF richness were reduced in intensively managed croplands, and this was linked to a reduced hyphal 33 P transfer ( Table 1 ). This suggests that hyphal P transfer is affected by intensive land-use to a similar extent as soil microbes, which have been shown to be sensitive indicators for land-use change 40 – 42 . AMF are estimated to contribute to 20-30% of the total soil microbial biomass 24 and it is likely that the positive link between 33 P recovery and microbial biomass is connected to AMF, especially because various studies showed that mycorrhizal hyphal density can correlate strongly with hyphal P transfer 43 – 45 . Interestingly, hyphal 33 P transfer was positively correlated with AMF richness in cropland soils, but not in grassland soils, perhaps indicating that functional diversity of AMF is more important under disturbed conditions like in cropland soils. Further studies are required to investigate this in more detail. It also should be noted that although earlier studies demonstrated that AMF are the main actors in the transfer of P from hyphal compartments to plants, the possibility that other microorganisms ( e.g. , non-mycorrhizal hyphae) might have contributed to the transfer of P to the root zone cannot be excluded. Bacteria can also facilitate or suppress AMF activity in the soil 44 . Recent studies indicate that AMF fungal hyphae are colonized by specific bacterial communities 46 , which in turn influence nutrient uptake, particularly from organic sources 47 . Finally, P transfer via hyphal networks depends on the distinct functional traits, activity, and foraging strategies of individual strains of AMF 48 – 51 and such aspects need more attention in future work. While we could not make these connections in this present study, further studies should investigate whether specific microbial groups that are affected by intensive management, also influence the ability of AMF to acquire P. AMF compensate for lower P availability in grassland soils In addition to direct effects, management can be indirectly linked with AMF-symbiosis through alterations of soil properties, such as available soil P 27 . High levels of available soil P, often accompanied by low soil N:P ratios, were shown to inhibit AMF root colonization and decrease the AMF’s relative contribution to plant P nutrition 52 – 54 . In accordance with these previous findings, we observed that AMF hyphal 33 P transfer was negatively associated with available soil P levels ( Table 1 , ED Fig. 2 c ), which were on average 60% higher in the cropland soils. In turn, regarding implications for plant nutrition and growth, our findings indicate that AMF mediated P uptake compensated for the lower available P levels in the grassland soils, allowing for a similar average total P uptake and shoot biomass. This assumption is further supported by a negative correlation between the hyphal 33 P transfer and the soil available P in grassland soils, and a positive correlation with the plant N:P ratio, respectively ( ED Fig. 3 a ). This implies that when plant P demand in grassland is high and plant productivity is limited by P ( i.e. , plants with N:P ratios above 16 55 ), AMF supply additional P to the plant. However, this trend could not be confirmed in the cropland soils where neither a link between available soil P, nor the plant N:P ratio, and hyphal P transfer was found, possibly indicating a dysfunctional symbiosis in croplands. Verbruggen et al . (2015) argued that the selective loss of AMF communities connected to soils with high N:P ratios might leave cropland fields with AMF of reduced symbiotic quality 34 . While we agree that this could be the case for a multitude of fields with high soil N:P ratios in this study, the relatively large range of soil N:P ratios in both land use systems suggests that this is not the only mechanisms at play. The observed inability of putatively P-limited plants to acquire P through AMF hyphal activity in cropland soils provides further evidence that AMF are heavily affected by crop management (e.g., fungicide application), inhibiting their potential contribution to plant P nutrition in current cropping systems. Soil pH and P drive hyphal 33 P transfer in grassland soils Using a multi-model inference approach, we were able to identify the main drivers of AMF hyphal 33 P transfer to growing plants. In grassland soils, we found that much of the observed variation in hyphal 33 P transfer could be explained by soil pH, available P, SOC content and climatic factors ( e.g ., aridity). These results are in line with other studies demonstrating that these factors influence AMF abundance, colonization, and activity 44 , 52 , 56 – 58 . In addition to the above discussed influence of available soil P, our results suggest a suppression of hyphal 33 P transfer with decreasing pH, emphasizing the crucial role of soil pH for AMF activity, abundance, community structure as well as the occurrence of AMF host plants 32 , 44 , 59 – 62 . Van Aarle et al. (2002) 56 found reduced growth and activity of the AMF extraradical mycelium in low pH substrates, arguing that AMF are directly stressed by acidic environments. However, it is possible that indirect effects of pH on the activity of the AMF extraradical mycelium through interactions with other microbiota might be an even more important mechanism behind the suppressiveness of low pH soils 44 . Apart from the major importance of soil abiotic factors, our results indicate that increasing aridity decreased hyphal 33 P transfer in grassland systems. Although we did not manipulate water availability directly and observed only the legacy effects of aridity, our observation parallels various studies that determined direct negative effects of drought on AMF abundance and extraradical hyphae 57 , 58 . Additionally, plant communities, C inputs and SOC might be influenced by aridity, resulting in indirect effects on AMF, possibly due to reduced net photosynthesis and energy supply to AMF under water-limited conditions 63 , corroborating the positive relationship between 33 P transfer and SOC observed in this present study. Ultimately, these results suggest that the goals of promoting C sequestration and improving plant P nutrition through AMF go hand in hand, at least in non-cropped systems where SOC is generally higher ( Table 1 ). Fungicide application reduces the ability of AMF to acquire P While a considerable amount of variation in hyphal 33 P transfer in grassland soils was explained by soil pH and P availability, we found that the number of fungicide application events was the most important predictor of hyphal 33 P transfer in cropland soil ( Fig. 3 ). Interestingly, this reduced hyphal 33 P transfer in cropland soils was paralleled and partially mediated by a reduced AMF richness ( ED Fig. 4 d ) indicating that fungicides indirectly reduce hyphal mediated P uptake by reducing AMF richness. Prior work suggests that AMF richness can promote P uptake 29 , providing further evidence that a reduction of AMF richness by fungicides may have implications for plant P nutrition. A recent study found that the abundance of AMF is negatively linked to the amount of pesticide residues in agricultural soils, and certain pesticide residues could be detected even decades after their last application 64 . This indicates that adverse effects of fungicides on AMF might be long-lasting, corresponding to observations by Pánková et al. (2018) 65 , who showed that AMF infection rates of plants were reduced up to five years after the application of fungicides in a grassland site. These earlier studies focused on AMF abundance, whereas our results add a functional component to this debate and indicate that fungicide use in real agricultural contexts suppress both AMF diversity and functioning. Given the great variety of amounts and compounds of the applied fungicide products ( Supplementary Table 5 ), it is remarkable that the number of fungicide application events used as rough indicator in this study could capture the adverse effects posed by fungicide use on AMF. Although fungicides are applied to combat fungal diseases such as mildew and rusts, they often have non-target effects on other fungi, including beneficial AMF. Detrimental effects of various fungicides have repeatedly been reported for AMF biomass, spore density, root colonization, and alkaline phosphatase activity in internal and external hyphae 66 – 70 . The negative effects of some fungicides on AMF have also been previously used to assess the importance of AMF for plant community structure and diversity in grasslands ( e.g. , 71 , 72 ). However, the close links between fungicide application rate and AMF functioning in a wide range of cropland soils observed here have not been reported before. It further indicates that the common use of fungicides hampers the natural ability of soil organisms to provide crops with nutrients and supports the findings of Sallach et al . (2021) who showed that also fungicides applied unintentionally through wastewater or biosolids can decrease the ability of AMF to transfer P to plants 39 . Future studies under controlled conditions in the field now need to specifically test to which extent fungicides suppress the ability of AMF to support crop growth and test whether such effects are persistent. Our results also call for reconsidering the design of agricultural systems to be able to make full use of the potential of AMF-symbiosis for plant nutrition. For example, applying agroecological techniques, such as crop diversification, can be a promising way to reduce disease pressure 4 and hence the need to use pesticides, while at the same time promoting AMF richness 73 which could indirectly support plant P uptake ( Fig 5 B ) as well as to other benefits provided by AMF. In addition to the establishment of such AMF-promoting practices, the breeding and use of AMF responsive crops, an aspect which hasn’t been directly investigated in this study, is a way to promote AMF-supported crop production that requires further consideration in future research. In conclusion, despite the wide range of different environmental conditions along the surveyed European gradient, the results show that the capacity of AMF to support plant P nutrition is impaired in croplands compared to non-cropped grasslands, particularly by the use of fungicides. Thus, we emphasize that there is a need to reconsider the design of agricultural systems to fully exploit the natural potential of AMF-symbiosis for a sustainable crop production."
} | 4,349 |
37210404 | PMC10199937 | pmc | 5,159 | {
"abstract": "Bathymodioline mussels rely on thiotrophic and/or methanotrophic chemosynthetic symbionts for nutrition, yet, secondary heterotrophic symbionts are often present and play an unknown role in the fitness of the organism. The bathymodioline Idas mussels that thrive in gas seeps and on sunken wood in the Mediterranean Sea and the Atlantic Ocean, host at least six symbiont lineages that often co-occur. These lineages include the primary symbionts chemosynthetic methane- and sulfur-oxidizing gammaproteobacteria, and the secondary symbionts, Methylophagaceae, Nitrincolaceae and Flavobacteriaceae, whose physiology and metabolism are obscure. Little is known about if and how these symbionts interact or exchange metabolites. Here we curated metagenome-assembled genomes of Idas modiolaeformis symbionts and used genome-centered metatranscriptomics and metaproteomics to assess key symbiont functions. The Methylophagaceae symbiont is a methylotrophic autotroph, as it encoded and expressed the ribulose monophosphate and Calvin-Benson-Bassham cycle enzymes, particularly RuBisCO. The Nitrincolaceae ASP10-02a symbiont likely fuels its metabolism with nitrogen-rich macromolecules and may provide the holobiont with vitamin B12. The Urechidicola (Flavobacteriaceae) symbionts likely degrade glycans and may remove NO. Our findings indicate that these flexible associations allow for expanding the range of substrates and environmental niches, via new metabolic functions and handoffs.",
"conclusion": "Conclusions The I. modialiformis symbiosis has a higher number of symbiont species than the typical chemosynthetic associations in most other hosts. The metabolic flexibility in these symbioses expands the range of catabolized substrates and likely allows for the colonization of not only chemosynthetic environments but also organic substrates, such as wood. This nutritional plasticity may have played a role in the adaptation and evolutionary transition of these mussels from organic substrates to the deep sea [ 107 ]. Yet, most large bathymodioline mussels, such as Gigantidas and most Bathymodiolus , host only a few species of chemosynthetic bacteria, highlighting the advantage of limited symbiont diversity. Expanding the symbiont diversity and substrate range may lead to energetic costs, resulting in lower growth rates or reduced reproductive output [ 20 ]. These costs may be reduced as only the symbiont that can use the locally abundant substrate grows up to high abundances. For example, at seeps and brine pools, where the key substrate is methane, the methanotrophic symbiont dominates in terms of biomass. We hypothesize that even in the absence of relevant amounts of external organic substrates the secondary symbionts might provide additional benefits, which include the recycling and efficient use of resources such as NO, methanol and formate, as well as sharing goods such as vitamin B12 (Fig. 6 ). Some of these positive interactions have equivalents in free-living communities, suggesting that symbiotic interactions may have evolved based on the existing interactions. Fig. 6 Schematic representation of hypothetical key metabolic handoffs in the Idas symbioses. C1 routes (yellow), nitrogen (green) and macromolecule (black) are highlighted by arrows. For nitrate, the routes comprise both assimilatory and dissimilatory ones. A mitochondrion is included (mt). DIC is dissolved inorganic carbon.",
"introduction": "Introduction Chemosynthetic symbioses allow animals and some protists to colonize extreme environments including hydrothermal vents and cold seeps in the deep sea, as well as key productive habitats in coastal areas, such as the seagrass meadows [ 1 , 2 ]. Chemosynthesis, that is, the assimilation of single-carbon molecules, such as carbon dioxide and methane, using the energy stored in reduced compounds, primarily reduced sulfur (sulfide, thiosulfate, elemental sulfur) and methane, fuels these symbioses. Chemosynthetic symbioses are usually characterized by low diversity and high fidelity of key nutritional symbionts at the species level [ 3 ]. They exhibit a broad range of transmission strategies, including vertical, horizontal and mixed modes [ 4 ], that may determine the fidelity of these associations, as well as the strength of environmental selection for the fittest symbionts and genetic diversity within the symbiont populations [ 3 , 5 – 7 ]. As in free-living bacterial populations, genetic diversity broadens the functions of symbionts, allowing the holobiont to acquire new metabolic capabilities and environmental niches [ 8 , 9 ]. Symbiont taxonomic and functional diversity varies markedly between lineages of bathymodioline mussels, which include the chemosynthetic genera Bathymodiolus, Gigantidas and Idas among others [ 10 ]. The key symbionts of bathymodioline mussels include the sulfur-oxidizing Thioglobaceae (gammaproteobacterial order PS1, also known as the SUP05 clade), and the Methyloprofundus (gammaproteobacterial order Methylococcales) methane-oxidizing bacteria. Some bathymodioline mussels, such as Bathymodiolus earlougheri , B. billschneideri and B. nancyschneideri host single-species populations of thiotrophic symbionts [ 11 ], whereas Gigantidas childressi and G. platifrons host mainly the methane-oxidizing bacteria [ 12 – 14 ]. Others host both [ 15 ]. There are also extreme cases of symbiont diversity loss and gain in bathymodioline symbioses. For example, a small deep-sea bathymodioline mussel Idas argenteus appears to lack symbionts, which may have been gained in the past and recently lost [ 16 ]. Others, such as B. heckerae , from the deep Gulf of Mexico, host several taxa of symbionts with distinct functions: apart from two species of methane-oxidizing symbionts and two species of sulfur-oxidizing symbionts, B. heckerae has additional bacterial symbionts, including the Methylophagaceae sp. methylotrophs, as well as Cycloclasticus that catabolize short-chain alkanes [ 17 – 19 ]. Here we focus on another extreme case of multi-species symbioses in bathymodioline mussels, namely Idas mussels from the Mediterranean Sea. Several previous studies noted the co-occurrence of at least six gill symbiont phylotypes in an Idas species from the east Atlantic and the Mediterranean Sea; this Idas species was first identified as Idas sp. MED, and is now called Idas modiolaeformis [ 20 – 23 ]. These small mussels thrive in seeps and wood falls and the diversity of their symbionts appears to be linked to the availability and composition of reduced fluids [ 20 ]. Early studies identified the following six key phylotypes: the Methyloprofundus sedimenti- related type I methanotrophs (M1), two phylotypes of Thioglobaceae sulfur-oxidizing symbionts (S1 and S2), Methylophagaceae methylotrophs (M2), as well as two phylotypes that lacked the potential for being chemosynthetic – Bacteroidetes (CFB) and a gammaproteobacterial lineage (G) [ 21 ]. Hereafter we refer to the methanotrophs (M1) and thiotrophs (S1 and S2) as the primary symbionts, and the others as secondary symbionts (M2, CFB, and G). These phylotypes were found to be associated with bacteriocytes in the symbiont-bearing gill tissue using fluorescence in situ hybridization (FISH), confirming the symbiotic nature of their association with I. modiolaeformis [ 21 , 22 ]; however, these observations did not provide conclusive evidence for the intracellular localization of all the symbionts [ 22 ]. Other variants of the gammaproteobacterial secondary symbionts, were discovered later [ 20 , 23 ]. While the functions of the primary symbionts and the methylotroph M2 were estimated based on the detection of marker genes, the functionality of the symbiotic methylotrophs has not been studied in detail using omics, and little is known about the role of other symbiont lineages. We aimed to characterize the metabolism and physiology of the Idas symbionts, using genome-centric metagenomics, metatranscriptomics and metaproteomics. We collected 14 Idas individuals from hydrocarbon seeps and brine pools off the shore of Israel [ 24 , 25 ]. We hypothesized that the secondary symbionts may contribute to holobiont fitness by providing important functions via metabolic handoffs. Alternatively, these potential heterotrophs, which are most abundant in mussels that colonize plant debris, such as sunken wood, may extract nutrients from organic substrates [ 20 , 23 ]. We thus investigated the metabolic potential of the Idas symbionts, providing a snapshot of host-symbiont-symbiont interactions in this specific multi-member symbiosis.",
"discussion": "Results and discussion Idas modiolaeformis from the eastern Mediterranean hosts at least six symbiont genotypes The mussels were identified as Idas modiolaeformis , based on the analysis of mitochondrial cytochrome c oxidase I (MT-CO1) sequences from the six metagenomes, using the Barcode of Life Data System identifier. These sequences were 98.4–98.6% similar to those of the I. modiolaeformis in the database (for example, the eastern Atlantic individuals, KT216487 in NCBI). The best hits, 98.7–98.9% similarity, were to the sequences of Idas individuals recovered from the Nile Deep Sea Fan mud volcano at the depth of 1693 meters (FM212787). Hereafter we refer to our samples as I. modiolaeformis , as among other known Idas species, the next closest hit was Idas macdonaldi with a lower MT-CO1 similarity of 95%. Yet, given that the individuals from the Plamahim Disturbance and the Nile Deep Sea Fan are not genetically identical, it is plausible that genetic exchange between these populations is low. We observed some variation of MT-CO1 and full mitochondrial sequences among the Palmahim individuals corresponding to the occurrence of different haplotypes. Genetic distance may increase with the geographical distance or time, as the individual that we collected in 2011 from a distinct seep located several hundreds of meters from the brine pool site where other individuals were obtained, was the most diverged (Supplementary Fig. S2 ). Yet, as we discuss below, the host phylotypes were not linked to the symbiont diversity, in agreement with previous findings [ 20 ]. We generated metagenome-assembled genomes (MAGs) for the six key I. modiolaeformis symbiont phylotypes. We note that besides these six lineages, metagenomic binning resulted in the discovery of MAGs for two additional taxa – a Rhizobiales alphaproteobacterium, as well as a Bdellovibrionaceae species. Given that the nature of the association between these species and I. modiolaeformis is unclear, as they have not been previously described by amplicon sequence, we hereafter focus only on the six previously detected lineages. For these, good or high-quality genomes were assembled and binned (Table 1 ). Table 1 Taxonomy (GTDB) and completeness (CheckM2) statistics for the Idas modiolaeformis symbiont metagenome-assembled genomes. NCBI BioSample Class Order Family Genus Completeness (%) Contamination (%) N50 (Mbp) Size (Mbp) SAMN33052357 Gammaproteobacteria Methylococcales Methylomonadaceae Methyloprofundus 99.99 0.00 0.030 2.50 SAMN33052358 Gammaproteobacteria PS1 Thioglobaceae Thioglobus_ A 90.88 0.18 0.010 1.49 SAMN33052359 Gammaproteobacteria PS1 Thioglobaceae Thiodubilierella 98.12 0.50 0.014 1.30 SAMN33052360 Gammaproteobacteria Nitrosococcales Methylophagaceae GCA-002733105 99.96 0.34 0.026 2.17 SAMN33052361 Gammaproteobacteria Pseudomonadales Nitrincolaceae ASP10-02a 98.18 0.01 0.037 2.10 SAMN33052362 Bacteroidia Flavobacteriales Flavobacteriaceae Urechidicola 95.79 0.30 0.020 3.36 SAMN33052363 Alphaproteobacteria Rhizobiales 99.76 0.78 0.034 1.33 NA* Bdellovibrionia Bdellovibrionales Bdellovibrionaceae 73.48 1.76 0.003 1.42 *Not submitted to NCBI due to low completeness. In the previous amplicon-based sequencing studies, the symbionts, particularly the secondary ones, were only classified to class and phylum taxonomic levels. We used our MAGs to obtain improved taxonomic assignments through the Genome Taxonomy Database (GTDB) inference tool and validated their taxonomic placement using phylogenies based on the reference collections of closely related genomes from GenBank. We confirmed that the methane-oxidizing symbionts belonged to the Methyloprofundus clade, also known as marine methylotrophic group 1 or MMG1, and verified their placement using a PmoA protein phylogeny, as genomes of most Methyloprofundus symbionts are missing from the databases (Supplementary Fig. S3 ). The sulfur oxidizers belonged to two distinct clades, Candidatus Thioglobus A and Candidatus Thiodubilierella (GTDB inference, hereafter Thioglobus and Thiodubilierella ). The Thiodubilierella genus name has been suggested for the symbionts of B. septemdierum [ 53 ], and was integrated into the GTDB taxonomy. Yet, Thiomultimodus has been proposed as an alternative name for both the Thioglobus and Thiodubilierella clades, with two SUP05 A and B subclades [ 54 ]. Whereas Thioglobus (SUP05 clade A) is often represented by free-living species and clam symbionts, Thiodubilierella (SUP05 clade B) has been described only in mussel symbioses [ 54 ]. Hereafter we refer only to Thioglobus and Thiodubilierella nomenclature for the two thiotrophic symbiont clades. The co-occurrence of these clades in an individual has only been documented in B. heckerae [ 17 – 19 ] and I. modiolaeformis [ 22 ]. The new MAG-based taxonomic assignments greatly improved the classification of the secondary symbionts and allowed us to make predictions about their physiology based on previously characterized relatives. The methylotrophic symbionts were assigned to the Methylophagaceae clade GCA-002733105. Phylogenetic placement based on a tree using single-copy genes indicated that this clade lacks a cultivated representative, and includes lineages commonly found at cold seeps (e.g., the genome of the most closely related lineage, Methylophaga GLR1851, was curated from a sample at the Hikurangi Margin gas hydrate deposits), as well as the only other Methylophagaceae symbionts that occur in B. heckerae (Supplementary Fig. S4 ). Phylotype G belonged to the Nitrincolaceae family ASP10-02a (Table 1 , Supplementary Fig. S5 ). Nitrincolaceae are rarely found in symbioses; however, the Rs1 and Rs2 symbionts of the bone-eating worm Osedax belong to the Nitrincolaceae family (unnamed genus Rs1) [ 55 – 57 ]. Nitrincolaceae are prominent degraders of nitrogen-rich compounds such as amino acids [ 55 , 58 , 59 ], and the ASP10-02a clade is prominent in the water column, often exhibiting high abundance and activity during algal blooms [ 60 , 61 ]. The symbionts Idas and Osedax belong to distinct clades within Nitrincolaceae, their 16 S rRNA gene sequences are only ~90% similar and their average nucleotide identity (ANI) is <70%. Given that the hosts thrive on different substrates, and that the symbionts occupy different tissues, we hypothesize that the functionalities of these symbionts may differ. The Bacteroidota (CFB) phylotype belonged to the genus Urechidicola (Flavobacteriaceae) (Table 1 , Supplementary Fig. S6 ). The first cultivated Urechidicola genus representative, U. process , was isolated from the intestine of a marine spoonworm, Urechis unicinctus [ 62 ]. Similar to other Flavobacteriaceae, such as the closely related Lutibacter , Urechidicola can degrade multiple organic compounds, including DNA and starch [ 62 , 63 ]. The closest relative of the Urechidicola symbiont in Idas was found in a bone-degrading biofilm, and it is represented by GenBank assembly accession GCA_016744415.1. Taxonomic affiliation suggests that both of these secondary symbionts are likely copiotrophs that can catabolize both difficult-to-degrade and protein-rich organic matter, such as polysaccharides, nucleic acids and peptides, in the marine environment. The metagenomic read abundances of the primary symbionts varied among the six Idas individuals that were analyzed with metagenomics (Fig. 1 ). A single individual ( Idas 16) lacked the methane-oxidizing and methylotrophic symbionts. In this individual, the Nitrincolaceae symbiont was the most abundant, and Urechidicola was found (metatranscriptomics and metaproteomics were, unfortunately, not performed for this individual). We performed metatranscriptomics and metaproteomics on four individuals, which were distinct from the ones used for metagenomics. In these four individuals, the relative abundances of symbionts were consistent between metatranscriptomics and metaproteomics measurements, showing that the sulfur oxidizers were most abundant in sample 3 (5% and 2% biomass for Thioglobus and Thiodubilierella , respectively, Fig. 1B, C ). It is important to note that the relative abundances obtained from metagenomics cannot be compared directly with the relative abundances obtained with metaproteomics; metagenomics-derived abundances are a good estimator of cell numbers, whereas metaproteomics-derived abundances are a good estimator of species biomass in a microbial population [ 31 ]. The methane-oxidizing bacteria were the most abundant in the four samples analyzed with metatranscriptomics (~70–80%) and metaproteomics (~89–98% estimated biomass). The biomasses of the other symbionts were much lower, in the range of 1.6 to 7.2% for both thiotrophs, with only very few peptides detected for Urechidicola . In agreement with the previous study [ 20 ], our data suggest that the symbiont proportions differ between individuals, likely based on local environmental conditions experienced by individuals. Methylophagaceae symbionts may require methane-oxidizers to be present to supply their substrate methanol. Nitrincolaceae are consistently found among the samples with meta-omics, and are metabolically active, as their genes and proteins were expressed. Methylophagaceae, Nitrincolaceae and Urechidicola symbionts appear to co-occur with the primary symbionts in the gill tissue where the primary symbionts were present (Fig. 2 ), in agreement with previous data [ 22 ]. Yet, it is still not clear if the secondary symbionts are extra- or intra-cellular. Fig. 1 The relative abundances and biomass distributions of the six key symbiont genotypes in individual Idas modiolaeformis specimens. A Read abundances in metagenomes (Idas12-16 are samples from the brine pool site, Idas2011 was collected in 2011 from carbonates at a nearby seep site). B Read abundances in metatranscriptomes. C Biomass estimates based on proteomics following Kleiner et al. 2017. Metatranscriptomics and metaproteomics were performed on the same four individuals (tr1-4 are equivalent to pr1-4). Due to the low confidence of detection, Urichidicola proteins were not counted in panel C. Fig. 2 Fluorescent in situ hybridization (FISH) shows the presence of the secondary symbionts in the Idas gill tissue. A All the symbiotic bacteria are detected with the EUB338 probe (red), and large methanotroph morphotypes can be observed. B Methylophagaceae symbionts (BhecM2-822 probe, yellow) appear to often aggregate along the gill tissue. C Nitrincolaceae symbionts (ImedaG-193 probe, yellow) were found sporadically throughout the tissue. D Flavobacteriales ( Urechidicola ) symbionts in the gill tissues (CF319, yellow). DAPI staining was used to visualize all DNA. The scale bar is 20 µm. Oxidation of methane and its products fuel the Idas symbiosis Methane oxidizers are the key nutritional symbionts of I. modiolaeformis , as suggested by their abundance in the metatranscriptomes and metaproteomes. The particulate methane monooxygenase PmoCAB complex was most well-expressed at both mRNA and protein levels (Supplementary Fig. S7 , here and hereafter see Supplementary Table S1 for the full list of genes and proteins expressed). The lanthanide-containing XoxF type methanol dehydrogenase was the predominant one; the calcium-dependent enzyme MxaFI was also found but expressed at lower levels. Carbon assimilation and energy conservation from formaldehyde oxidation function via the ribulose monophosphate (RuMP) pathway, using only the Entner–Doudoroff (ED) bypass, but not the Embden–Meyerhof–Parnas (EMP) variant [ 64 ], as the pfk gene encoding the pyrophosphate-dependent phosphofructokinase was not found in the genomes of the Methyloprofundus symbiont of I. modiolaeformis (Supplementary Fig. S7 ). This is similar to Bathymodiolus japonicus symbionts and in contrast to those of Bathymodiolus platifrons , which have both the ED and EMP variants [ 65 ]. The serine cycle is incomplete, as hydroxypyruvate reductase was missing; however, some central proteins of this pathway, such as the serine-glyoxylate aminotransferase and malyl-CoA lyase, were substantially expressed at both the RNA and amino acid levels, indicating that the partial serine pathway is functional (Supplementary Fig. S7 ) [ 15 ]. All the genes in the tricarboxylic acid (TCA, Krebs) cycle were found and expressed, therefore energy can be conserved through the oxidation of compounds derived from methane assimilation, such as pyruvate (Supplementary Fig. S7 ). Formaldehyde detoxification and oxidation to CO 2 via the dissimilatory tetrahydromethanopterin route is likely prominent, due to the high expression of this pathway, in particular, that of the 5,6,7,8-tetrahydromethanopterin hydro-lyase ( fae gene, Supplementary Fig. S7 ). Both oxygen and nitrate respiration is feasible, given the presence of the respiratory NarGHIJ and NirK, yet the respiratory complex IV was expressed at much higher levels, indicating that oxygen is the key electron acceptor for energy conservation (Supplementary Fig. S7 ). The metabolism of the Methylophagaceae symbiont is a rare case of methylotrophic autotrophy in chemosynthetic symbioses. Their co-occurrence with the methane oxidizers suggests that they might use methanol, formaldehyde and other metabolites produced by the methane oxidizers. Methane oxidizers excrete metabolites such as methanol, formaldehyde, formate, acetate, and succinate [ 66 – 68 ], which can be used by co-occurring organisms, in particular, methylotrophs [ 69 ]. Partnerships between methane oxidizers and methylotrophs are widespread in free-living communities [ 69 , 70 ], and our results suggest that this association plays a role in Idas symbiosis. The Methylophagaceae symbionts lack methane monooxygenases, yet highly express the pyrroloquinoline quinone (PQQ)-dependent methanol dehydrogenase, as well as the key enzymes of the RuMP pathway for formaldehyde assimilation (Fig. 3 ). As opposed to the primary methane-oxidizing symbionts, the methylotrophs encoded and expressed both the ATP-dependent EMP and ED variants of the RuMP pathway, which have distinct energetic demands [ 64 ]. Formate oxidation is also more flexible in the methylotrophs, as we identified the occurrence and expression of not only the two-subunit tungsten-containing formate dehydrogenase FdhAB, but also the respiratory molybdoenzyme dehydrogenase-O (FdoGHI) that catalyzes the oxidation of formate to carbon dioxide, donating electrons to the membrane soluble quinone pool [ 71 – 74 ]. This hints that the oxidation of formate alone can fuel the metabolism of the Methylophagaceae symbiont. In addition to originating from methane oxidation, the used formate could also come from mitochondrial metabolism [ 75 ]. In terms of terminal electron acceptors (TEA) for methanol and formate oxidation, we only found genes for the use of oxygen as TEA and no genes for other TEAs such as nitrate. Fig. 3 Central carbon metabolism in the Methylophagaceae symbiont of Idas modiolaeformis . Both the Embden–Meyerhof–Parnas (EMP) and Entner–Doudoroff (ED) variants of the ribulose monophosphate (RuMP) pathway are feasible. This bacterium can fix inorganic carbon via the Calvin–Benson–Bassham (CBB) cycle. The tricarboxylic acid cycle is incomplete, as the 2-oxoglutarate dehydrogenase (OGDH) was not found. The genes are as follows: methanol dehydrogenase mxaF ; 3-hexulose-6-phosphate synthase hps / rmpA ; 3-hexulose-6-phosphate isomerase hpi / rmpB ; transketolase tkt ; ribose-5-phosphate isomerase rpiA ; phosphoribulokinase prk ; ribulose-phosphate 3-epimerase rpe ; transaldolase talB ; ATP-dependent 6-phosphofructokinase pfk ; fructose-1,6-bisphosphate aldolase/phosphatase fbp : glucose-6-phosphate isomerase gpi ; glucose-6-phosphate 1-dehydrogenase zwf ; 6-phosphogluconolactonase pgl ; phosphogluconate dehydratase edd ; 2-dehydro-3-deoxy-phosphogluconate/2-dehydro-3-deoxy-6-phosphogalactonate aldolase eda ; phosphoglucomutase pgm ; glucose-1-phosphate adenylyltransferase glgC ; fuctose-bisphosphate aldolase fba ; triosephosphate isomerase tpi ; formaldehyde activating enzyme fae ; methylene tetrahydromethanopterin dehydrogenase mtdB ; methenyltetrahydromethanopterin cyclohydrolase mch ; formyltransferase/hydrolase complex fhcABCD ; NAD(P)-dependent methylenetetrahydromethanopterin dehydrogenase mtdA ; bifunctional 0methylenetetrahydrofolate dehydrogenase/methenyltetrahydrofolate cyclohydrolase fold ; formate–tetrahydrofolate ligase fhs ; formate dehydrogenase fdhAB ; formate dehydrogenase, nitrate-inducible fdnGHI ; glyceraldehyde-3-phosphate dehydrogenase gapdh ; phosphoglycerate kinase pgk ; phosphoglucomutase pgm ; enolase eno ; phosphoenolpyruvate synthase ppsA ; phosphoenolpyruvate carboxykinase pepck ; oxaloacetate decarboxylase Na(+) pump oadABC ; pyruvate dehydrogenase aceEF - lpdA ; citrate synthase gltA ; aconitase acnA ; isocitrate dehydrogenase idh ; 2-oxoglutarate dehydrogenase complex OGDH ; succinate–CoA ligase sucCD ; succinate dehydrogenase sdhABC ; fumarate hydratase class I, aerobic fumA ; malate:quinone oxidoreductase mqo . Metabolites: OA, oxaloacetate; PEP, phosphoenolpyruvate; 2-phosphoglycerate, 2PG; 3-phosphoglycerate, 3PG; 1,3-bisphosphoglycerate 1,3BPG; 3-phosphoglyceraldehyde, G3P; dihydroxyacetone phosphate, DHAP; fructose 1,6-bisphosphate, F1,6BP; fructose 6-phosphate, F6P; hexulose 6-phosphate, H6P; ribulose 5-phosphate, Ru5P; ribulose-1,5-bisphosphate, Ru1,5BP; ribose 5-phosphate, R5P; glucose 6-phosphate, G6P; 6-phosphogluconolactonase, 6PGL; 2-Dehydro-3-deoxy-D-gluconate 6-phosphate, 6PGC; 2-keto-3-deoxy-6-phosphogluconate, KPDG; glucose 1-Phosphate, G1P; ADP-glucose, ADP-G; tetrahydrofolate, H 4 F; tetrahydromethanopterin, H 4 MPT. Average expression values from 4 individuals are shown. Alongside the RuMP pathway, Methylophagaceae symbionts have the genes for all steps of the Calvin–Benson–Bassham (CBB) cycle and highly express the key enzyme, form II ribulose-1,5-bisphosphate carboxylase/oxygenase (RuBisCO, Fig. 3 ). We note that although form I RuBisCo appeared to be widespread in Methylophagaceae, form II RuBisCo was rare in this clade (Supplementary Fig. S8 ). These closely related RuBisCo forms differ in their affinity to oxygen and CO 2 , and form II enzymes have a low specificity factor, that is, function better under high CO 2 and low O 2 conditions [ 76 – 78 ]. Such conditions may exist in the host’s bacteriocytes, giving an advantage to bacteria with form II RuBisCO [ 79 ]. Similar to obligate autotrophs [ 80 , 81 ], the TCA cycle appears to be incomplete in most cultivated Methylophagaceae, which lack the 2-oxoglutarate dehydrogenase activity [ 82 – 84 ]. We did not find the 2-oxoglutarate dehydrogenase-encoding genes in the high-quality MAG of the symbiotic Methylophagaceae, indicating that the TCA cycle was incomplete. The incomplete TCA allows for the production of intermediates from one-carbon (C1) compounds and prevents futile cycling, that is, the destruction of larger organic compounds via catabolism. This is typical of obligate methylotrophs, such as Methylobacillus flagellates [ 85 ]. In these bacteria, energy and reducing equivalents may largely result from the oxidation of formaldehyde to CO 2 [ 85 ] (Fig. 3 ). Variability in terminal electron acceptors, metabolite uptake and vitamin usage may lead to niche differentiation among the thiotrophic symbionts The two sulfur-oxidizing symbionts, Thioglobus and Thiodubilierella species, appear to be obligate autotrophs as the key enzymes of the TCA cycle, 2-oxoglutarate dehydrogenase and malate dehydrogenase, were not found in either MAG (Supplementary Fig. S9 ). This is in agreement with previous observations of symbiotic and free-living organisms from this clade [ 54 , 86 , 87 ]. Both sulfur-oxidizing symbionts have the genes to gain energy from sulfur compounds using the sulfide: quinone oxidoreductase (Sqr), the Sox sulfur oxidation system (SoxYZXAB) and the reverse dissimilatory sulfite reduction (rDSR) system, which catalyzes the oxidation of sulfide to sulfate via the adenosine-5’-phosphosulfate, using the adenylylsulfate reductase and sulfate adenylyltransferase for the two final steps of the pathway. The genes encoding these functions were among the most abundantly transcribed ones in the metatranscriptomes and were often detected at the protein level (Supplementary Fig. S9 ). Both lineages highly expressed the Calvin-Benson-Bassham cycle enzymes, in particular the two subunits of the type I RuBisCo (in particular, the rbclLS genes and respective proteins). These obligate autotrophs likely can import some organic compounds similar to the symbionts of Bathymodiolus azoricus [ 15 ], as TRAP-type dicarboxylate transport systems were encoded and expressed (C4-dicarboxylate and unknown substrate 6 in Thioglobus , only the latter in Thiodubilierella ). Several differences between Thioglobus and Thiodubilierella were observed in their terminal electron acceptor use. Only Thiodubilierella carried the periplasmic NapABGH for the use of nitrate as a terminal electron acceptor, confirming the modularity of nitrogen metabolism in the Thioglobaceae clade [ 54 ]. We also found some discrepancies in oxygen respiration were found: Thioglobus encoded and highly expressed only the ccoNOP genes encoding the cbb 3 -type cytochrome c oxidase, whereas Thiodubilierella encoded three variants (cbb 3 , bo 3 , ba 3 ). These terminal oxidases differ in their affinity to O 2 and H 2 S [ 88 ], likely highlighting adaptation to distinct redox conditions. Secondary symbionts are heterotrophs that can use nitrogen-rich and difficult-to-degrade metabolites Data from all three meta-omics approaches suggested that the Nitrincolaceae symbiont is a heterotroph that is capable of using numerous substrates, given the presence and substantial expression of the complete TCA cycle, as well as that of multiple transport systems for organic compounds, such as peptides, amino acids and nucleosides, in line with the metabolic reconstruction of Osedax Nitrincolaceae symbionts [ 55 , 59 ]. Relevant examples include: (1) most enzymes for the degradation of branched-chain amino acids co-occurred in the genomes and were transcribed (Fig. 4 ); (2) a cluster 11 RfuABCD riboflavin/purine/nucleoside transporter, which clustered with nucleoside degradation enzymes, such as deaminases and phosphorylases (Cda, Ada, DeoABC); (3) the putrescine transport (PotABCD) and degradation system (PuuABCDE) (although the puuC gene was not found, likely due to an assembly issue, as these genes occurred at the edge of a contig). An additional gene cluster encoded the complete PaaA-K and HpaA-I machinery for the catabolism of phenylacetate and hydroxyphenylacetate[ 89 ]. This pathway allows for the degradation of recalcitrant, plant-derived organic aromatics, especially in marine bacteria that thrive under fluctuating oxygen conditions in the Mediterranean Sea [ 90 , 91 ]. Some catabolic reactions may be carried out anaerobically, particularly given the fermentative potential of this bacterium, suggested by the presence of lactate dehydrogenases (some expressed at the mRNA level, Fig. 4 ). We also identified the genes for nitrate respiration to nitric oxide including the genes for the respiratory nitrate reductase NarGHIJ and copper-dependant nitrite reductase NirK. The nirK gene was among the most highly expressed genes at the mRNA level in this symbiont (Fig. 4 ). However, oxygen is likely the key electron acceptor for these symbionts as the cbb 3 - and caa 3 complex IV-encoding genes were highly expressed at the mRNA level (Fig. 4 ). Fig. 4 The Nitrincolaceae symbiont can import and catabolize multiple metabolites, including peptides, amino acids (in particular, branched-chain amino acids), nucleosides and some aromatics, such as phenylacetate. The tricarboxylic acid cycle is complete. The symbiont can likely ferment pyruvate to lactate, as the cytochrome c-dependent D-lactate and L-lactate dehydrogenases were found and expressed. A near-complete pathway of adenosylcobalamin (B12) is encoded and partially transcribed. * Among the presented proteins, only the glutamine synthetase ( glt gene) was expressed at the protein level. Outlines of the same color indicate gene clusters. The following genes are shown: adenosine deaminase ada , polyribonucleotide nucleotidyltransferase pnp ; cytidine deaminase cda ; thymidine phosphorylase deoA ; phosphopentomutase deoB ; deoxyribose-phosphate aldolase deoC ; aldehyde-alcohol dehydrogenase adhE ; acetyl-CoA carboxylase accEF-lpdE ; L-lactate dehydrogenase ykgEFG ; L-lactate dehydrogenase ldh ; citrate synthase gltA ; aconitase acnA ; isocitrate dehydrogenase idh ; 2-oxoglutarate dehydrogenase complex sucAB-lpdE ; succinate–CoA ligase sucCD ; succinate dehydrogenase sdhABC ; fumarate hydratase class I, aerobic fumA ; malate dehydrogenase mdh ; pyruvate dehydrogenase aceEF - lpdA ; phenylacetate catabolon paaA-K ; 4-hidroxyphenylacetate catabolon hpaB-I ; succinate-semialdehyde dehydrogenase [NADP( + )] gabD ; putrescine catabolon puuA-D ; acetolactate synthase ilvB ; isovaleryl-CoA dehydrogenase ivd ; short/branched chain specific acyl-CoA dehydrogenase acadsb ; enoyl-CoA hydratase ech ; methylcrotonoyl-CoA carboxylase mcc1 , 2 ; methylglutaconyl-CoA hydratase auh ; 3-hydroxyisobutyryl-CoA hydrolase hibch ; hydroxyacyl-coenzyme A dehydrogenase hadH ; hydroxymethylglutaryl-CoA lyase hmgcl ; 3-hydroxyisobutyrate dehydrogenase hibadh ; acetyl-CoA acyltransferase hadhb ; glutamine synthetase glt ; glutamate dehydrogenase gdhA ; glutamine amidotransferase glxABC ; cbb 3 -type cytochrome c oxidase ccoNOP ; caa 3 -type cytochrome c oxidase ccaABC ; respiratory nitrate reductase narGHIJ , copper-containing nitrite reductase nirK ; nitrate/nitrite antiporter narK ;. The putative transporter substrates are mentioned in the figure. Glyceraldehyde 3-phosphate, GAP; oxaloacetate, OA; 2-oxoglutarate, 2-OG. Average expression values from 4 individuals are shown. A key feature of the metabolism encoded in the Urechidicola symbiont genome (Flavobacteriaceae, Bacteroidota) is its potential to degrade glycans (polysaccharides). Bacteroidota, Flavobacteriaceae in particular, are ubiquitous degraders of complex glycans and are found in many environments from human gut mucus to algal blooms in the ocean [ 92 – 95 ]. The diversity of glycans is very large and glycan degradation is carried out by an array of carbohydrate-active enzymes (CAZymes) that are often organized in polysaccharide utilization loci (PULs), which are typical of Bacteroidota [ 92 ]. The key features of PULs are the presence of SusCD transporters: the cell surface glycan-binding lipoprotein SusD and the outer membrane TonB-dependent transporter SusC, which can be found in multiple copies in Bacteroidota genomes [ 96 , 97 ]. We identified four SusCD pairs in the Urechidicola symbiont, two of which were among the top 50 most abundantly transcribed genes in the metatranscriptomes, and one SusC protein was detected in the metaproteomes despite the low overall protein identification rate for this symbiont. We note that genomic coding sequence coverage was very low for Urechidicola in the metatranscriptome (17%) and metaproteome (5%, mostly with low confidence), and thus only the most active features were detected by these analyses. Given the considerable fragmentation of this genome (300 scaffolds), we couldn’t identify large PULs, yet multiple CAZymes were found and often clustered (Fig. 5 ). For example, a single 30,996 bp contig contained two SusCD pairs, as well as multiple CAZymes, such as DD-carboxypeptidase EC 3.4.16.4; L-Ala-D/L-Glu epimerase EC 5.1.1.20; glucosamine-6-phosphate deaminase EC 3.5.99.6; N-acetylmuramic acid 6-phosphate etherase EC 4.2.1.126; as well as an AmpG family muropeptide MFS transporter; and glycosyl hydrolases of family 18, family 10, and family 3 (Fig. 5 ) . Thus, genomic and transcriptomic data provided evidence for the glycan degrading activity of Urechidicola , yet whether the substrates are derived from the gill (e.g., mucus), or the environment remains unknown. To date, FISH analyses do not show clearly if these bacteria are endo- or ectosymbionts of Idas gills. One piece of evidence suggesting that they might be ectosymbionts is that cell adhesion appears to play an important role for Urechidicola , as a sequence encoding the FAS1 (fascilicin) domain protein was among the top 20 most abundantly transcribed genes [ 98 , 99 ]. One hypothesis that remains to be explored is whether there are metabolic handoffs between Urechidicola and Nitrincolaceae symbionts, for example, via the exchange of aromatic derivatives of polymer degradation, such as phenylacetate and hydroxyphenylacetate [ 100 ]. Fig. 5 Polysaccharide utilization loci (PULs) in Urechidicola symbionts of Idas modiolaeformis . A Phylogeny and mRNA-level expression of the SusD proteins (MEGA11, 576 amino acid positions, LG + G + I model). The expression values are based only on a library from a single Idas individual, in which expression of these rare symbionts had detectable coverage. B The largest contiguous PUL (~27,000 bp) in Urechidicola symbiont comprised two susCD pairs, as well as genes encoding the following: transcription regulator, deoR ; sodium/iodide co-transporter, nis ; N-acetylmuramic acid 6-phosphate etherase, murQ , glucosamine-6-phosphate deaminase, nagB ; MFS permease, ampG ; aminotransferase, bioF ; L-Ala-D/L-Glu epimerase, ycgG ; N-acetylmuramoyl-L-alanine amidase, ampD ; D-Ala-D-Ala carboxypeptidase, dacA ; Na + /substrate antiporter, nhaC ; as well as glycoside hydrolases 3,18,171,10. Most of these genes are involved in murein metabolism. * Indicates this gene was identified by an NCBI domain search, but not by dbCAN. The full list of CAZymes is available in Supplementary Table S2 . The tree scale represents the number of substitutions per site. Expression values from a single individual (TR4, detectible Urechidicola coverage) are shown. Nitrincolaceae symbionts may contribute to holobiont fitness by producing vitamin B12 The Nitrincolaceae symbiont can produce adenosylcobalamin (vitamin B12), given the presence of almost all the genes needed for its synthesis (Fig. 4 ), similar to the Nintrincolaceae Osedax symbionts [ 55 , 59 ]. The cobG gene was the only gene, out of 25 genes for vitamin B12 biosynthesis, that was undetected, likely due to the incompleteness of the MAG. Many of the vitamin B12 biosynthesis genes were transcribed and detected in the metatranscriptomes. We also found the genes for vitamin B12 biosynthesis in the methane-oxidizing symbiont, which is a likely key producer of adenosylcobalamin in I. modiolaeformis , given its dominant biomass in most individuals (Fig. 1 ). In hosts that lack the methane-oxidizing symbionts, B12 production by the alternative Nitrincolaceae symbionts may be crucial for the holobiont. Nitrincolaceae, specifically the ASP10-02a lineage to which the symbionts belong, are (i) dominant species in Earth’s cold oceans, (ii) are often associated with algal bloom degradation, (iii) appear to be the most prominent B12 producers in these habitats and (iv) supply B12 specifically to Methylophagaceae, suggesting that B12-based ASP10-02a-methylotroph/autotroph associations can be advantageous under some conditions [ 61 , 101 ]. The B12-based dynamics play a key role in the water column [ 102 ], but also human gut [ 103 ] and insect symbioses [ 104 ], and thus also may be crucial for chemosynthetic symbioses. The outer membrane receptor BtuB of the vitamin B12 transporter was found in the genomes Methyloprofundus , Thioglobus, Urechidicola and Methylophagaceae symbionts. Whereas methane-oxidizing symbionts are likely prototrophs for cobalamin, but still can take it up from the environment, the other symbionts are auxotrophs and may depend on its uptake. In bacteria, the key reaction that may depend on cobalamin is methionine synthesis, as cobalamin is required by methionine synthase; thus, the lack of this vitamin may hinder DNA synthesis, methionine regeneration and lead to homocysteine accumulation [ 105 ] . Thiodubilierella appears to lack the btuB gene needed for B12 import; however, unlike the Thioglobus symbiont, which encodes only the cobalamin-dependent methionine synthase (MetH; EC 2.1.1.13), Thiodubilierella has both the MetH, and the cobalamin-independent methionine synthase (MetE; 5-methyltetrahydropteroyltriglutamate–homocysteine methyltransferase; EC 2.1.1.14), and therefore may not depend on cobalamin for production of the essential amino acid methionine and thus growth [ 104 ]. We thus hypothesize, that similar to insect symbioses [ 104 ], the interplay between cobalamin requirement, synthesis and uptake may contribute to determining the complexity of Idas symbiosis. In our metagenomic dataset, the relative read abundances of Nitrincolaceae and Thiodubilierella , but not Nitrincolaceae and Thioglobus symbionts, appear to positively correlate (R 2 = 0.79 and R 2 = 0.03, respectively, for linear regression of centered-log transformed data). Although compositional data correlation should be treated with caution, our results hint at the interdependence of these two symbiont populations. Urechidicola symbionts may denitrify to N 2 , removing NO Urechidicola appears to be the only bacterium capable of complete denitrification to N 2 . Nitrate and nitrite reduction can be catalyzed by the periplasmic enzymes heterodimeric nitrate reductase (NapAB, no expression found) and the NrfAB cytochrome c552 nitrite reductase (both subunits were expressed at the mRNA level). The norBC genes that encode the respiratory nitric oxide (NO) reductase were highly transcribed. Most importantly, the nosZ gene encoding the periplasmic sec-dependant nitrous oxide reductase was the 11th most abundantly transcribed gene in this bacterium. Both the methane-oxidizing and methylotrophic symbionts can oxidize nitrite to nitric oxide but lack the genes needed to complete the remaining steps of denitrification. Symbiont nitrate respiration can contribute to holobiont fitness, as it fuels energy conservation during hypoxia induced by natural perturbations or when the shell valves are closed and reduce competition between host and symbiont for oxygen [ 15 ]. Removal of the toxic nitrite respiration product NO could benefit the symbionts [ 106 ]. Whereas the Urechidicola symbiont is only present in low abundance, complete denitrification is likely beneficial mainly for their population, which can take advantage of excess NO produced by the host or the other symbionts. We speculate that in some cases the Urechidicola symbiont may also contribute to NO removal at the holobiont level."
} | 10,958 |
20217779 | null | s2 | 5,160 | {
"abstract": "No abstract available"
} | 5 |
40075558 | PMC11903912 | pmc | 5,162 | {
"abstract": "ABSTRACT Warming seawater temperatures and low dissolved inorganic nitrogen (DIN) levels are environmental stressors that affect the health and abundance of marine macroalgae and their microbiomes. Nereocystis luetkeana , a canopy‐forming species of brown algae that forms critical habitat along the Pacific coast, has declined in regions impacted by these synergistic stressors. Little is known about how these environmental factors affect the microbiome of N. luetkeana , which could affect nutrient availability, vitamin production, and stress response for the host. We experimentally tested the interactive effects of three seawater temperatures (13°C, 16°C, 21°C) crossed with abundant and replete DIN levels on the diversity and composition of blade‐associated microbiomes from two spatially separated kelp host populations. We hypothesised that kelp microbiomes exposed to high temperatures and low DIN would experience the lowest diversity. Contrary to our hypothesis, the highest temperature treatment resulted in the largest increase in microbial diversity, and microbiomes in all temperature treatments experienced a decrease in previously dominant taxa. Temperature had a larger effect than DIN on the kelp microbiome in all cases. The disruption to the kelp microbiome across all temperatures, especially at the highest temperature, suggests that the effects of warming on N. luetkeana extend to the microbiome.",
"conclusion": "5 Conclusions Elevated seawater temperatures and decreased nitrogen availability can influence both the algal host and its microbiome (Minich et al. 2018 ; Vadillo Gonzalez et al. 2024 ; Florez et al. 2019 ), often with negative consequences for fitness and population persistence. Kelp populations are likely to face these environmental stressors in the future under climate change conditions in the Salish Sea (Khangaonkar et al. 2019 ) and elsewhere worldwide (Werner et al. 2016 ). Through experimental manipulation of seawater temperature and DIN, we found that elevated temperatures greatly altered the microbiome associated with N. luetkeana and that elevated temperature had stronger effects than manipulations of DIN, like the results on kelp host performance reported in Fales et al. ( 2023 ). At the highest temperature (21°C), we observed increased microbiome diversity driven by declines in the relative abundance of taxa thought to be beneficial to the host, coupled with the growth of many previously low‐abundance bacterial taxa, which is a potential sign of microbiome dysbiosis. How the change in the kelp blade microbial community affects its function is unclear and requires more research. While seawater DIN depletion contributed less to compositional changes in the microbiome than elevated temperatures, we found significant effects of DIN on taxa with nitrogen‐transforming metabolisms. We found evidence to support the importance of population‐level variation in determining microbial community structure, even after environmental stress is applied. Overall, we show that high temperatures can alter kelp‐associated microbiomes, underscoring the continued vulnerability of canopy kelp species to warming coastal waters.",
"introduction": "1 Introduction Kelp are an important foundational species in nearshore marine environments. Kelp forests create critical biodiversity hotspots (Steneck et al. 2002 ), are loci for coastal nutrient cycling (Pfister et al. 2019 ), and are a major contributor to carbon fixation (Wheeler and Druehl 1986 ; Krause‐Jensen et al. 2018 ; Wilmers et al. 2012 ; Weigel and Pfister 2021 ). Their health and abundance are influenced by environmental variables associated with global climate change, such as water temperature and changing nitrogen concentrations in marine environments (Krumhansl et al. 2016 ; Fales et al. 2023 ). These environmental variables further affect the diverse microbiome hosted on kelp tissue (Qiu et al. 2019 ). Microbial taxa found on kelp may be selected for their ability to metabolise dissolved organic carbon kelp exuded by their host (Egan et al. 2013 ; Selvarajan et al. 2019 ; Weigel and Pfister 2019 ). This kelp microbiome is thought to be functionally important to the host (Weigel et al. 2022 ; Miranda et al. 2022 ; King, Moore et al. 2023a ; Davis et al. 2023 ; Burgunter‐Delamare et al. 2023 ), providing several benefits, including the provisioning of nutrients and vitamins (Croft et al. 2005 ; Hochroth and Pfister 2024 ) and protection from pathogenic bacteria and infections (Li et al. 2022 ). \n Nereocystis luetkeana , also known as bull kelp, is a canopy‐forming kelp that grows from the Aleutian Islands in Alaska to central California. Highly abundant bacterial genera—such as Mariniblastus or Granulosicoccus— of the N. luetkeana blade microbiome possess genes necessary for nitrogen cycling and vitamin B12 synthesis (Weigel et al. 2022 ; Younker et al. 2024 ), activities that may benefit the kelp host. While bull kelp populations have historically been relatively stable and persistent at coastal sites in Washington state (Pfister et al. 2018 ), some populations have rapidly declined in recent years within the Salish Sea, including in the Puget Sound and the Strait of Georgia (Berry et al. 2021 ; Starko et al. 2022 ). These inland waters of the Salish Sea experience higher temperatures and lower dissolved inorganic nitrogen (DIN) concentrations than waters on the outer coast of Washington State (Berry et al. 2021 ; Hochroth and Pfister 2024 ). High temperatures have led to rapid declines in many kelp populations worldwide (Smale 2020 ), including N. luetkeana (Berry et al. 2021 ; Supratya et al. 2020 ), and abundant nitrogen is important for sustaining kelp growth, especially at high temperatures (Fernández et al. 2020 ). Kelp blade‐associated microbes display significant variation across the geographical range of Washington State (Weigel and Pfister 2019 ), with evidence that microbiome abundance and diversity were reduced at the southernmost bull kelp population in the Salish Sea (Ramírez‐Puebla et al. 2022 ). While the mechanisms driving these differences in community composition across geographic areas are yet unclear, salinity, temperature, and the health of the host kelp population are thought to play a role (Weigel and Pfister 2019 ; Florez et al. 2019 ; Ramírez‐Puebla et al. 2022 ). Increased seawater temperatures have significant impacts on marine microbiomes. High seawater temperatures are often correlated with coral bleaching, disease outbreaks, and functional change in coral microbiomes (Lima et al. 2020 ; Voolstra et al. 2024 ), and changes in the availability of nitrogen alter microbial community composition in the water column and on the host tissues of Sargassum species (Meyer‐Reil and Köster 2000 ; Li et al. 2022 ). Many marine microbial taxa cannot grow at high water temperatures (MacLeod 1965 ; Huete‐Stauffer et al. 2015 ), and slow‐growing taxa can outcompete fast‐growing taxa at high temperatures (Abreu et al. 2023 ). Increased temperature can drive dysbiosis, which is associated with reduced kelp growth in \n Macrocystis pyrifera \n (Minich et al. 2018 ) as well as an increase in the abundance of pathogenic taxa on Ecklonia radiata (Vadillo Vadillo Gonzalez et al. 2024 ). However, how microbial community structure responds to temperature can vary among individuals, such as on the rockweed \n Fucus vesiculosus \n , though the cause of inter‐individual variability remains unknown (Stratil et al. 2013 ). Nitrogen availability can impact algal microbiome composition, with particularly strong effects on nitrogen‐fixing taxa. In \n M. pyrifera \n populations, a lack of available nitrogen can cause an increase in the abundance of ammonifying bacteria in the kelp‐associated microbiomes (Florez et al. 2019 , 2021 ). In one instance, nitrogen nutrient stress had a dominant effect over temperature on microbial community composition (Mancuso et al. 2023 ), while the combination of nutrient and temperature stress was relatively similar and not synergistic for the overall bacterial diversity or the resilience of the bacterial epibiota (Morrissey et al. 2021 ). Given increasing evidence of the microbiome's importance to its host's overall fitness, understanding how microbes respond to climate change‐driven environmental stressors in the natural context of the host is a key aspect of understanding the resilience of a host species to our changing climate. We experimentally tested how the N. luetkeana blade‐associated microbiome responded to the interactive effects of seawater temperature and DIN availability. Using a controlled seawater mesocosm system, we crossed three seawater temperatures (13°C, 16°C, 21°C) with two DIN concentrations (high, 80 μM vs. low, < 3 μM) to achieve 6 treatment conditions. Kelp blades were collected from two spatially separated populations—a population that experiences cold seawater and high flow conditions with higher nitrate concentrations (Turn Rock, WA, USA) and a contrasting population that experiences warmer seawater conditions and lower nitrate concentrations (Cherry Point, WA, USA) (Gierke et al. 2023 ). We quantified microbial community and diversity shifts on kelp blades across all six treatments. We hypothesised that microbiome diversity would be negatively correlated with temperature and positively correlated with DIN, following patterns seen for kelp blade microbiomes in the wild (Ramírez‐Puebla et al. 2022 ). Further, we hypothesised that blade‐associated microbiomes from the warm water population (Cherry Point) and the cold water population (Turn Rock) would be initially different in composition, and that microbiomes from Cherry Point would be more resistant to changes in diversity and composition at higher seawater temperatures.",
"discussion": "4 Discussion After 10 days of experimental manipulation, the composition and diversity of microbes associated with Nereocystis luetkeana responded to elevated temperatures and decreased nitrogen concentrations. Alpha diversity increased significantly at the highest temperature of 21°C, compared with more typical temperatures of 13°C, and community composition was significantly affected by site, DIN, and temperature. Of these three variables, temperature explained the most variation (around 20%) between microbial communities at the end of the experiment. An environmental variable explaining only 20% of compositional variation is relatively low but not novel to seaweeds (Stratil et al. 2013 ). The bacterial classes Verrucomicrobiae (mainly Luteolibacter ) and Gammaproteobacteria (mainly Granulosicoccus ) were dominant before the experiment, while the classes Alphaproteobacteria (mainly Litorimonas and Octadecabacter ), Planctomycetes (mainly Mariniblastus ), and Verrucomicrobiae (mainly Rubritalea and Persicirhabdus ) were most abundant at the end of the experiment. Kelp blade microbiomes were distinct in composition from and shared few ASVs with seawater microbial communities, further lending evidence to the role of the kelp host as a strong filter of microbial taxa (Weigel and Pfister 2019 ; Liu et al. 2022 ; Michelou et al. 2013 ). FIGURE 2 Principal Coordinates Analysis (PCoA) of kelp blade microbial communities following temperature and nutrient manipulation. Each point on the PCoA represents the microbiome of a specific kelp blade. (A) Bacterial communities on kelp blades grouped by temperature and nitrogen treatments and (B) grouped by site only. (C) The relative abundance of bacterial classes in the kelp blade microbiome following experimental temperature and nitrogen manipulations. 4.1 Elevated Temperature Resulted in Increased Diversity and Shifts in Microbial Community Composition Elevated seawater temperatures resulted in increases in microbial alpha diversity. The increased alpha diversity at high temperature contrasts with a previous study of wild N. luetkeana populations in the Salish Sea, where lower microbial abundance and diversity characterised a population exposed to high temperatures and low DIN (Ramírez‐Puebla et al. 2022 ). However, temperature‐induced increases in alpha diversity have been detected in corals (Maher et al. 2019 ) and in other kelp species in controlled incubation experiments (Minich et al. 2018 ; Vadillo Gonzalez et al. 2024 ). Our experimental conditions might have favoured fast‐growing or generalist microbes such as the Alphaproteobacteria Litorimonas , one of the most abundant genera after incubation. Alphaproteobacteria are common in algal microbiomes (Goecke et al. 2013 ) and have been characterised as fast‐growing opportunistic generalists (Bengtsson et al. 2011 ). We acknowledge that changes to bacterial alpha diversity can result when using experimental mesocosms, either through stress‐related bacterial species loss or through the colonisation of atypical species when enclosed with a host, and may differ from bacterial community changes in intact kelp in the marine environment. FIGURE 3 Genus‐level heatmap of the most abundant taxa at the beginning and end of the nitrogen and temperature manipulations. Abundances are centre log‐ratio transformed. Shifts in the abundance of different genera are shown in Table S5 . Several initially low‐abundance taxa increased in relative abundance in all temperature treatments throughout the experiment, alongside relative decreases in the most abundant taxa at the start of the experiment (Figure 3 ). The decreased dominance of a few genera and the increase in the abundance of many taxa, especially at high temperatures, could lead to an increased number of pathogenic microbes, as seen in other studies (Case et al. 2011 ; Vadillo Gonzalez et al. 2024 ). Macroalgae‐associated microbiomes become vulnerable to invasion by pathogenic bacteria, demonstrated among diverse macroalgae such as \n Cystoseira compressa \n (Mancuso et al. 2023 ), Delisea pulchra (Case et al. 2011 ), and \n Macrocystis pyrifera \n (Minich et al. 2018 ). However, the genera that were found to be highly abundant after incubation have diverse functions and may not be strictly pathogenic. For example, the genus Mariniblastus became the most abundant across all temperatures, consistent with observations on the kelp Ecklonia radiata (Vadillo Gonzalez et al. 2024 ). Mariniblastus is thought to play a role in maintaining macroalgal biofilms and degrading macroalgal‐produced polysaccharides (Faria et al. 2018 ). Litorimonas , which increased at 13°C, is thought to be an opportunistic pathogen on Saccharina japonica (Li et al. 2020 ) but is also present in similar abundances on healthy \n S. japonica \n individuals (Zhang et al. 2020 ) and in many other species of macroalgae (Park et al. 2022 ), including \n Macrocystis pyrifera \n (James et al. 2020 ). Other studies report that Litorimonas potentially plays a role in photosynthesis and is reported as a core taxon across many seaweed microbiomes (King, Uribe et al. 2023b ). The increase in specific bacterial taxa post‐incubation, such as Octadecabacter and Granulosicoccus, could have positive effects on the kelp host if these colonists provide benefits such as B vitamins (Croft et al. 2005 ; Younker et al. 2024 ). Octadecabacter and Granulosicoccus both may produce vitamin B12 (Dogs et al. 2017 ; Weigel et al. 2022 ), and increases in Octadecabacter at 21°C could compensate for the loss of Granulosicoccus following host exposure to high temperatures. Finally, the Verrucomicrobiae Luteolibacter was one of the most abundant bacteria before incubation but declined after incubation in all treatments, especially at 21°C. Members of this class have antimicrobial properties on kelp tissues (Vollmers et al. 2017 ), and their decrease may have allowed previously suppressed taxa to increase. In this light, the relative decrease of the dominant microbial taxa throughout the course of the experiment may better explain the increase in diversity seen. Regardless of the pathogenic or beneficial functions of the taxa that increased after incubation, the overall change in microbial community structure could be a sign of dysbiosis associated with environmental or host stressors. In the context of the experiments here, we define dysbiosis as a shift in the microbiome that occurs with host stress. We assessed if post‐incubation microbiomes were consistent with the Anna Karenina Principle, which states that dysbiotic microbiomes vary more in composition than healthy microbiomes (Zaneveld et al. 2017 ). This principle has been observed in coral microbiomes (McDevitt‐Irwin et al. 2019 ) and macroalgal microbiomes (Bonthond et al. 2023 ). In our experiment, the increase in abundance of previously low‐abundance genera at the highest temperature (21°C) was associated with increased kelp stress, including elevated respiration rates and extremely low growth rates (Fales et al. 2023 ). However, kelp blade microbiomes did not become significantly more dispersed in composition after incubation in our experiment, so they did not reveal dysbiosis as characterised by the Anna Karenina Principle (Table S6 ). An increase in alpha diversity as community composition becomes dissimilar could also be an indicator of dysbiosis (McDevitt‐Irwin et al. 2019 ; Maher et al. 2019 ). Our experiment partially agrees with this, as Observed ASV richness did increase overall—most significantly at the highest temperature—but 21°C microbiomes did not diverge in composition significantly more than microbiomes at 13°C or 16°C (Table S6 ). While we can conclude that the structure of the microbiomes was significantly altered post‐incubation, we cannot yet address whether there are functional consequences to the host, as there is little functional information on this kelp's microbiome. We do demonstrate, however, that an association between host and environmental stress changes the associated microbial community and note that continued feedback among the microbial community and host health is likely. The effects of elevated temperatures were seen in both host performance traits and microbial community composition. The response of the host kelp to high temperatures (Fales et al. 2023 ) likely plays a role in the changes that we observed in the microbiome. Regardless of nitrogen levels, kelp blades in the highest temperature (21°C) treatments experienced noticeable stress with blade tissue decay, decreased growth rates, and increased respiration rates (Fales et al. 2023 ). When temperatures increase, algal host defences may be compromised (Wright et al. 2000 ; Egan et al. 2013 ), as is the case with furanone compound‐based chemical defence production in macroalgae (Zozaya‐Valdés et al. 2016 ). Further, decreases in secondary metabolite and chemical defence production are correlated with bacterial pathogen‐induced kelp bleaching (Campbell et al. 2011 ). High temperatures were also correlated with increases in alpha and beta diversity in the microbiome of Gracilaria vermiculophylla , hypothesised to be the result of higher metabolic rates of the microbes or reduced host control of microbial community structure, including a decline in the secondary metabolites of the host (Bonthond et al. 2023 ). If these defences are reduced due to host stress at high temperatures, it could create an opportunity for taxa usually held at low abundance to increase in abundance, an outcome consistent with our results. 4.2 \n DIN Had Limited Effects on Microbiome Composition and Alpha Diversity Although temperature and concentrations of nitrogen often negatively covary in coastal marine ecosystems influenced by upwelling (Palacios et al. 2013 ), altered DIN availability was not as influential as temperature on N. luetkeana and its microbiome. Instead, Fales et al. ( 2023 ) found strong responses in the kelp host to high temperature stress with minimal effects of low nitrogen levels, patterns which were mirrored here in the kelp microbiome. Responses to DIN could be taxon‐specific, limiting its impact to bacterial taxa that primarily metabolise DIN compounds (Florez et al. 2019 ), and only if there are no further micronutrient limitations to bacterial metabolism (Kirchman et al. 2003 ). Nitrogen metabolisms are diverse in marine bacteria, and kelp‐associated bacteria are also capable of metabolising dissolved organic nitrogen (Hochroth and Pfister 2024 ; Younker et al. 2024 ). Given the diversity of microbial nitrogen metabolisms and the feedback between the host kelp and its microbiome, it is likely that the kelp microbiome responds in species composition or in metabolic functions to nitrogen limitation, as has been demonstrated in other manipulations of nitrogen in mesocosm experiments (Florez et al. 2021 ). In contrast, temperature may have more immediate effects on both the host and associated microbes, with the effects of nitrogen availability taking longer to manifest than the timeframe of our experiment. Nutrient and nutrient‐temperature interactions remained a significant determinant of microbial community composition, but these patterns did not hold across all temperatures. Nutrient availability likely directly influenced the abundance of genera with nitrogen metabolisms, including Mariniblastus , Granulosicoccus , and Octadecabacter (Morrow et al. 2018 ; Weigel et al. 2022 ) which were more abundant in the high DIN treatments in our study (Figure 4 , Table S5 ). However, the effect of nitrogen on abundance remained masked at specific temperatures. Mariniblastus decreased in abundance at 21°C regardless of DIN treatments, a trend that has been observed in studies of Ecklonia radiata (Vadillo Gonzalez et al. 2024 ). Octadecabacter only showed differential abundance with DIN treatment at 21°C, a temperature that is within the recorded optimal growth temperature for the genus (Park and Yoon 2014 ; Jin et al. 2023 ). Temperature‐inhibited growth could mask the benefits that specific taxa gain from abundant environmental DIN. FIGURE 4 (A) ASV richness and (B) Shannon Diversity Index of kelp‐associated bacteria before ( T \n 0 ) and after ( T \n f ) the experimental manipulation of temperature and nitrogen. Each point is the ASV richness of an individual kelp blade microbiome. 4.3 Source Population Remained a Significant Determinant of Microbiome Composition but Does Not Predict Response to Perturbations Kelp blade microbiome composition differed between the two source populations (Cherry Point vs. Turn Rock, Figure 1B ), and these site‐based differences remained even after co‐incubation of kelp blades from the two populations together in treatment tanks (Figure 2B ). Before incubation, Cherry Point microbiomes were dominated by Verrucomicrobiae, and Turn Rock microbiomes were dominated by Gammaproteobacteria. Differentiation across sites by different bacterial classes was also found in a larger‐scale geographic survey of N. luetkeana populations in Washington State, with abundant Verrucomicrobia found on kelp in warmer seawater locations in the inland Puget Sound and abundant Gammaproteobacteria found on kelp populations in cooler coastal seawater (Weigel and Pfister 2019 ). Site specificity in seaweed‐associated microbiomes has been recorded elsewhere (Egan et al. 2013 ; Lachnit et al. 2009 ), and site effects were persistent following co‐incubation in previous studies with N. luetkeana (Chen and Parfrey 2018 ). Furthermore, studies of giant kelp \n M. pyrifera \n populations in Chile found that microbiomes of kelp sampled from different populations responded differently to temperature (Florez et al. 2019 ). While we did not correlate host performance to microbiome characteristics in this experiment, there were differences in the performance of the kelp from each population: N. luetkeana from Turn Rock grew significantly more in biomass, while N. luetkeana from Cherry Point grew significantly more in total blade area (Fales et al. 2023 ). These physiological differences between populations could play a role in how the microbiome responds to the environmental variables tested, but this is outside the scope of this paper. While the source population remains an important factor in determining differences in microbial community structure, we did not find that Cherry Point microbiomes were more resistant to changes in diversity due to temperature and nitrogen manipulations. Cherry Point microbiomes experienced a greater overall increase in observed ASV richness and Shannon diversity (Table S1 ) and an increase in dispersion over the course of the experiment (Table S6 ), yet none of these changes were significantly different from Turn Rock (Table S6 ). In fact, Turn Rock microbiomes were significantly less dispersed at the end of the experiment (Table S6 ), unlike Cherry Point. However, due to a lack of significant results, we did not find evidence that Cherry Point microbiomes were more resilient to changing temperature and nitrogen conditions than Turn Rock microbiomes."
} | 6,288 |
30616349 | null | s2 | 5,164 | {
"abstract": "Biomacromolecules often possess information to self-assemble through low energy competing interactions which can make self-assembly responsive to environmental cues and can also confer dynamic properties. Here, we coupled self-assembling systems to create biofunctional multilayer films that can be cued to disassemble through either molecular or electrical signals. To create functional multilayers, we: (i) electrodeposited the pH-responsive self-assembling aminopolysaccharide chitosan, (ii) allowed the lectin Concanavalin A (ConA) to bind to the chitosan-coated electrode (presumably through electrostatic interactions), (iii) performed layer-by-layer self-assembly by sequential contacting with glycogen and ConA, and (iv) conferred biological (i.e., enzymatic) function by assembling glycoprotein (i.e., enzymes) to the ConA-terminated multilayer. Because the ConA tetramer dissociates at low pH, this multilayer can be triggered to disassemble by acidification. We demonstrate two approaches to induce acidification: (i) glucose oxidase can induce multilayer disassembly in response to molecular cues, and (ii) anodic reactions can induce multilayer disassembly in response to electrical cues."
} | 300 |
32649019 | PMC7769238 | pmc | 5,165 | {
"abstract": "Summary High‐temperature bioconversion of lignocellulose into fermentable sugars has drawn attention for efficient production of renewable chemicals and biofuels, because competing microbial activities are inhibited at elevated temperatures and thermostable cell wall degrading enzymes are superior to mesophilic enzymes. Here, we report on the development of a platform to produce four different thermostable cell wall degrading enzymes in the chloroplast of Chlamydomonas reinhardtii . The enzyme blend was composed of the cellobiohydrolase CBM3GH5 from C . saccharolyticus , the β‐glucosidase celB from P . furiosus , the endoglucanase B and the endoxylanase XynA from T . neapolitana . In addition, transplastomic microalgae were engineered for the expression of phosphite dehydrogenase D from Pseudomonas stutzeri, allowing for growth in non‐axenic media by selective phosphite nutrition. The cellulolytic blend composed of the glycoside hydrolase (GH) domain GH12/GH5/GH1 allowed the conversion of alkaline‐treated lignocellulose into glucose with efficiencies ranging from 14% to 17% upon 48h of reaction and an enzyme loading of 0.05% (w/w). Hydrolysates from treated cellulosic materials with extracts of transgenic microalgae boosted both the biogas production by methanogenic bacteria and the mixotrophic growth of the oleaginous microalga Chlorella vulgaris . Notably, microalgal treatment suppressed the detrimental effect of inhibitory by‐products released from the alkaline treatment of biomass, thus allowing for efficient assimilation of lignocellulose‐derived sugars by C . vulgaris under mixotrophic growth.",
"introduction": "Introduction Lignocellulose, the most abundant organic carbon source on Earth, has great potential for conversion into renewable fuels. However, the lack of methods for its efficient hydrolysis to easily fermentable sugars limits such potential (Saini et al ., 2015 ; Sanderson, 2011 ). Different methods including physical, chemical and biological pretreatments have been employed for enhancing lignocellulose degradation (Badiei et al ., 2014 ; Harmsen et al ., 2010 ; Kumar and Sharma, 2017 ). Physical pretreatments include thermal, microwave and ultrasounds treatments (Battista et al ., 2015 ; Ren et al ., 2018 ; Savoo and Mudhoo 2018 ), promoting substrate disaggregation and breaking of large molecules into smaller oligomers for digestion by microorganisms. Chemical pretreatments are harmful for the environment and negatively impact the rationale of using lignocellulose to produce cleaner forms of fuels. Furthermore, such treatments generate by‐products that inhibit the microbial fermentation of lignocellulose‐derived sugars, thus reducing the conversion yield into biofuel‐related compounds (Jönsson and Martín 2016 ). Biological treatments include the use of microbial cell wall degrading enzymes (CWDEs), which are currently obtained by culturing mesophilic fungi and bacteria with lignocellulolytic activities (Sánchez 2009 ). In general, such organisms secrete a wide array of CWDEs in low amounts, as they are strictly required for their own livelihood. Competitive industrial production should combine high productivity (understood as high quantity of enzymes expressed per day) at a low production cost. A strain that fits these traits would be a valuable candidate for large‐scale expression of CWDEs. From this perspective, transgenic microalgae have the potential of becoming biofactories on an industrial scale due to their relatively fast growth on low‐cost media, including wastewaters and agro‐industrial waste (Benedetti et al . 2018a ; Brasil et al., \n 2017 ). However, genetic engineering of microalgae still lags behind other microorganisms and it presents several constraints, including poor technological development of microalgae as a heterologous expression system, which strongly limits their application as bioreactors. Among the factors that negatively impact nuclear expression of proteins in microalgae, gene silencing can play a prominent role (Schroda 2006 ). However, the nuclear expression of recombinant proteins was further improved in last years, allowing to reach higher yields than those previously reported (Lauersen et al . 2013 ; Ramos‐Martinez et al . 2017 ), including the expression of fungal xylanases (Rasala et al . 2012 ). The chloroplast of C . reinhardtii was also used as a biofactory for the production of thermophilic endoglucanases (Faè et al . 2017 ; Richter et al . 2018 ). Chloroplast expression has a number of advantages for recombinant protein production compared with nuclear transformation, including precise transgene integration, absence of gene silencing and high transgene copy number (Mayfield et al . 2007 ). Moreover, since CWDEs of bacterial origin do not require post‐translational modification for proper functioning, the algal transplastomic system appears ideal for their expression. Arrays of CWDEs need to be produced by cellulolytic fungi and bacteria to achieve efficient degradation of cellulose (Horn et al . 2012 ). CWDEs constitute a highly heterogeneous family, divided into many subcategories and classes (Choi et al . 2013 ; Kubicek et al . 2014 ). Degradation of cellulose by mesophilic organisms involves glycosyl‐hydrolases and oxidoreductases (Dimarogona et al., \n 2012 ), which synergistically act to efficiently degrade amorphous and crystalline regions of cellulose, respectively. Glycosyl‐hydrolases include endo‐, exo‐glucanases and β‐glucosidases. The endoglucanases cleave cellulose through a multi‐chain attack mode, generating fragments with different degrees of polymerization. Concomitantly, the exo‐acting glycosyl‐hydrolases, such as cellobiohydrolases, depolymerize such fragments into cellobiose units, which, in turn, are converted into glucose by the β‐glucosidases (Singhania et al., \n 2013 ). The crystalline region of cellulose is the main target of cellulolytic oxidases. Among them, lytic polysaccharide mono‐oxygenase (LPMO) disrupts cellulose fibres by oxidative cleavages, thus enhancing the action of cellulolytic hydrolases (Laurent et al . 2019 ; Villares et al . 2017 ). When compared to the use of their mesophilic counterparts, hyperthermophilic CWDEs (HCWDEs) have several advantages for degradation of plant biomass, which are mainly dependent on the high temperature at which HCWDEs exert the activity (Anitori 2012 ; Peng et al . 2015 ). High temperature promotes partial detachment of lignin from the hemicellulose–cellulose assembly favouring the hydrolysing activity of HCWDEs. High temperature also prevents contamination by mesophilic microbes (Sarmiento et al . 2015 ). Moreover, CWDE‐inhibiting proteins, which are widely distributed in the plant cell wall as a defence mechanism (Benedetti et al., \n 2018b ; Juge 2006 ; Kalunke et al., \n 2015 ; Locci et al., \n 2019 ; York et al., \n 2004 ), are also inactivated at high temperatures (Liu et al., \n 2017 ; Locci et al., \n 2019 ), thus do not interfere with enzymatic cellulose degradation. Furthermore, the structural stability of HCWDEs sustains their activity even in the presence of chemicals, surfactants and extreme pH (de Miguel Bouzas et al . 2006 ; Souza et al . 2016 ). Such harsh reaction conditions may be exploited in industrial applications to promote detachment of different lignocellulose components, thus further increasing the efficiency of HCWDE enzymatic hydrolysis (Li et al . 2016 ; Ooshima et al . 1986 ). Algal productivity in both closed and open growth systems is affected by the competition of undesirable microorganisms. This problem has been tackled by metabolic engineering transforming the target organism with the gene encoding phosphite dehydrogenase D (PTXD) from Pseudomonas stutzeri WM88 (Loera‐Quezada, 2016 ; López‐Arredondo, 2012 ). As PTXD oxidizes PO 3 \n 3‐ (phosphite) into PO 4 \n 3‐ (phosphate), its expression confers the ability of metabolizing phosphite as the sole phosphorous source, allowing growth of the target organism in a phosphate‐depleted/phosphite‐repleted medium (Costas et al . 2001 ; Loera‐Quezada et al . 2016 ). Here, we report the design and use of a transplastomic/transgenic C . reinhardtii expression system to produce a hyperthermostable cellulolytic blend, capable of breaking down polysaccharides of plant cell walls into simple sugars for fermentation. The hydrolysates from microalgal‐treated lignocellulosic materials were successfully used both to sustain mixotrophic growth of the microalga C . vulgaris , and to promote anaerobic digestion by methanogenic bacteria, allowing the establishment of a proof of concept for energy recovery from waste biomass (i.e. corn bran, corn cob). Moreover, the double‐transgenic microalgae (referred to as HC‐PTXD strain) were cultured in non‐sterile conditions using phosphite as the sole phosphorus source without loss in productivity. Notably, the microalgal extract showed a detoxifying effect towards the inhibitory by‐products released from the alkaline treatment of lignocellulosic biomass, opening the way to novel processing procedures for biofuel industry.",
"discussion": "Discussion The development of an enzymatic preparation for the biological deconstruction of lignocellulosic biomass is still a major challenge for biofuel production. To achieve an efficient conversion of lignocellulosic residues into fermentable sugars, current enzyme‐based products require pretreatment of lignocellulosic material by harsh physical and chemical methods including steam explosion (Agbor et al . 2011 ; Mosier et al . 2005 ), wet oxidation (Varga et al . 2003 ), the use of ionic liquids (Prado et al . 2012 ), alkaline (Xu et al . 2016 ) or dilute acid treatments (Kumar and Sharma 2017 ). Therefore, there is urgent need for replacing polluting methods with environmentally friendly enzyme‐based strategies to make feasible the production of biofuels from lignocellulosic residues. To date, enzymatic hydrolysis of lignocellulosic agro‐industrial scraps is characterized by low efficiency and high operating costs, essentially due to (i) the incomplete knowledge of the enzymatic activities required by the process (Alessi et al . 2017 ), (ii) the limited efficiency of microbes to degrade raw lignocellulosic materials and (iii) the low expression levels of CWDEs which impact the costs of the microbial‐derived products with (thermolabile) cellulolytic activities (Herrero‐Garcia et al . 2019 ). Plant expression of CWDEs is an interesting alternative to microbial‐based biofactories because of their high productivity/production cost ratio and carbon‐neutral process. However, expression of CWDEs in plants could have side effects as CWDEs from lignocellulolytic fungi and bacteria are well‐known pathogenic factors that could have detrimental effects for plant growth (Benedetti et al . 2019b ; Choi and Klessig, 2016 ; Ma et al . 2015 ; Poinssot et al . 2003 ). To prevent potential side effects of the expression of CWDEs in plants, different strategies have been proposed such as compartmentalized expression/accumulation (Park et al . 2016 ), inducible gene expression (Tomassetti et al . 2015 ) and expression of CWDEs with inducible activity, for example hyperthermophilic enzymes (Mir et al . 2017 ). Compartmentalized expression of CWDEs in the chloroplast may enhance the yield of recombinant protein since chloroplast expression is not prone to gene silencing (Li et al . 2019 ); nevertheless, the use of endogenous promoters and other cis‐acting elements to drive chloroplast expression of transgenes may be subjected to the host regulation, requiring the optimization of specific growth conditions in order to enhance recombinant protein yield (Fields et al . 2018 , Figure 2 ). Transplastomic tobacco plants that accumulate high level of GH5, GH6 and GH9 endoglucanases and pectin lyases have been reported (Faè et al . 2017 ; Schmidt et al . 2019 ; Verma et al . 2010 ). Moreover, transplastomic tobacco plants expressing the GH3 β‐glucosidase Bgl1 from Trichoderma reesei had an increased biomass yield and an improved resistance towards aphids than wild‐type plants (Jin et al . 2011 ). Expression of GH10 xylanase from Alicyclobacillus acidocaldarius (Xyn) (Castiglia et al . 2016 ) produced both healthy plants and high enzyme activity ( Table 1 ), whereas plant treatment with GH11 xylanases from different fungal pathogens induced an immune reaction in plants, independently of their enzymatic activity, thus pointing to the CWDE–plant interaction as strictly dependent on the specific CWDE (Frias et al . 2019 ). Nevertheless, according to recent results, the yield of chloroplast‐expressed cellulases is higher in transplastomic tobacco plants than in microalgae ( Table 1 ). Here, we report on a novel strategy to use C . reinhardtii as an efficient biofactory to produce CWDEs in a more cost‐effective manner that could make it an alternative to plant‐based systems. C . reinhardtii can grow at a much faster rate than plants, have an immune system less prone to react to plant CWDEs and reach higher productivities on cheap substrates (Specht et al . 2010 ). Moreover, culturing microalgae in non‐axenic conditions, by phosphite selective nutrition, may significantly reduce the current cost of algal biomass production. The combined use of (i) PTXD together with (ii) a cheaper growth medium (i.e.T10APhi medium) and (iii) a low‐light demand for optimal CWDE‐expression (50–100 µE, Figure 2b‐c ) can reduce production cost of PBR‐grown microalgae to 3.2–3.8 € kg/DW as argued by Tredici et al . ( 2016 ) and Slade and Bauen ( 2013 ), a value close to that reported for field‐grown tobacco plants (2 € kg/DW; Maksymowicz, and Palmer 1997 ; Schmidt et al . 2019 ). Similarly, transgenic tobacco production is low cost in open field and under optimal conditions, which unfortunately could only be achieved in countries with no restriction on GMO cultivation, while production cost is significantly higher for greenhouse‐grown plants (6 € kg/DW; Faè et al . 2017 ). Moreover, microalgae are still more productive than an optimal tobacco production system with 3 growth cycles per year (60 t DW/ha/y vs 8.1 t DW/ha/y) (Giovannoni et al . 2020 ). However, two important aspects still need to be addressed to enhance potential for production of HCWDEs: (i) HC‐PTXD microalgae expressed HCWDEs at variable levels, ranging from 0.003% to 0.1% (w/w), pointing to the need of genetic strategies to optimize transgene expression, and (ii) the highly diverse level of expression we obtained for T‐EG, C‐CBH, P‐BG and T‐XY, pointed to the importance of ensuring sufficient level of enzyme catalysing the rate‐limiting step for the overall process in the blend. The high enzyme cost from C . reinhardtii ‐based biofactory suggests that the microalgal system might be economically competitive over N . tabacum when producing those categories of CWDEs whose expression might be challenging in tobacco plants ( Table 1 ). The use of HCWDEs has several advantages, mainly deriving from the high temperature at which these enzymes are active (Unsworth et al . 2007 ). High temperature loosens plant cell wall structures compensating the low functional heterogeneity of the HC‐PTXD mix and prevents most microbial contaminations, thus increasing the yield of saccharification. Moreover, HCWDEs are characterized by robust structures that confer marked enzymatic stability: the HC‐PTXD mix can be stored in the form of dried powder at RT for a long time without significant loss of activity (Figure 3c ), while some of the blend components (i.e. T‐EG and C‐CBH) proved to be resistant to 2% SDS (Figure 1a ), thus pointing to a possible exploitation under severe reaction conditions. Moreover, the robustness of HCWDEs also allowed the possibility of recycling the HC‐PTXD mix for three consecutive 24h‐reaction cycles (Figure 3b ), thus reducing the enzyme loading in the process. In our instance, the use of a thermostable (GH12/GH5/GH1)‐based blend allowed the conversion of alkaline‐treated lignocellulose into glucose with efficiencies ranging from 14% to 17% upon 48 h of reaction and an enzyme loading of 0.05% (w/w) (Figure 5a ). Notably, the HC‐PTXD mix is further valorized by some intrinsic characteristics of the algal extract: that is the biogas stimulating property (Figure 4b , Figure S7 ) and the detoxifying action towards the inhibitory by‐products released from lignocellulosic biomass upon alkaline pretreatment (Figure 5b, d ), a well‐recognized problem in cellulose degradation (Jönsson et al . 2013 ; Jönsson and Martín 2016 ), that may synergistically act with the above listed enzymatic features. At present, the commercially available enzymatic blends (including last‐generation enzyme‐based products, e.g. Cellic CTec3 for hydrolysis of lignocellulosic materials, Novozymes, Denmark) still require biomass pretreated by physico‐chemical methods for an efficient hydrolysis. Chloroplast expression in N . tabacum offers the advantage of bio‐containing transplastomic plants in situ since the plastome is maternally inherited, and therefore cannot be dispersed through the pollen by vertical gene transfer (Daniell 2007 ). However, in the case of C . reinhardtii as a biofactory to produce CWDEs, biocontainment strategies would be necessary to avoid its accidental dispersion to the environment. Some technologies that could be combined with the CWDE‐expressing Chlamydomonas is the CRISPR‐Cas9 technology, developed by Baek et al . ( 2016 ), that can be used to generate nitrate reductase‐deficient C . reinhardtii strains, capable of only surviving in NO 3 - depleted/ NO 2 - replete conditions, to ensure confinement in PBRs. Moreover, horizontal gene transfer of chloroplast transgenes to other microbes can be avoided by a codon reassignment‐based strategy in C . reinhardtii (Young and Purton, 2016 ). Considering that both enzyme and biomass yield can be further enhanced by genetic tools and optimized cultivation strategies (Dall’Osto et al . 2019 ; Fields et al . 2018 ; Manuell et al . 2007 ), a microalgal‐based biofactory can produce the desired amount of the CWDE of interest in shorter time and/or irrespective from seasonal constraints and/or in countries with restrictions for the release of GMO into the environment. In the future, the HC‐PTXD mix will be tested for increasing the bio‐ethanol production by yeasts (Özçimen and İnan, et al., \n 2015 ) as well as further CWDEs will be added to the HC‐PTXD mix in order to enlarge the spectrum of hydrolysable agricultural wastes by concomitantly reducing the severity of chemical pretreatments. Additional candidates will include thermostable laccases (Miyazaki 2005 ), hemicellulases (Benedetti et al., \n 2019a ) and pectinases (Kluskens et al . 2005 )."
} | 4,743 |
25319678 | null | s2 | 5,166 | {
"abstract": "Industrial biotechnology and microbial metabolic engineering are poised to help meet the growing demand for sustainable, low-cost commodity chemicals and natural products, yet the fraction of biochemicals amenable to commercial production remains limited. Common problems afflicting the current state-of-the-art include low volumetric productivities, build-up of toxic intermediates or products, and byproduct losses via competing pathways. To overcome these limitations, cell-free metabolic engineering (CFME) is expanding the scope of the traditional bioengineering model by using in vitro ensembles of catalytic proteins prepared from purified enzymes or crude lysates of cells for the production of target products. In recent years, the unprecedented level of control and freedom of design, relative to in vivo systems, has inspired the development of engineering foundations for cell-free systems. These efforts have led to activation of long enzymatic pathways (>8 enzymes), near theoretical conversion yields, productivities greater than 100 mg L(-1) h(-1) , reaction scales of >100 L, and new directions in protein purification, spatial organization, and enzyme stability. In the coming years, CFME will offer exciting opportunities to: (i) debug and optimize biosynthetic pathways; (ii) carry out design-build-test iterations without re-engineering organisms; and (iii) perform molecular transformations when bioconversion yields, productivities, or cellular toxicity limit commercial feasibility."
} | 376 |
34576705 | PMC8466333 | pmc | 5,167 | {
"abstract": "A new biorefinery concept is proposed that integrates the novel LX-Pretreatment with the fermentative production of L-(+)-lactic acid. Lignocellulose was chosen as a substrate that does not compete with the provision of food or feed. Furthermore, it contains lignin, a promising new chemical building material which is the largest renewable source for aromatic compounds. Two substrates were investigated: rye straw (RS) as a residue from agriculture, as well as the fibrous digestate of an anaerobic biogas plant operated with energy corn (DCS). Besides the prior production of biogas from energy corn, chemically exploitable LX-Lignin was produced from both sources, creating a product with a low carbohydrate and ash content (90.3% and 88.2% of acid insoluble lignin). Regarding the cellulose fraction of the biomass, enzymatic hydrolysis and fermentation experiments were conducted, comparing a separate (SHF), simultaneous (SSF) and prehydrolyzed simultaneous saccharification and fermentation (PSSF) approach. For this purpose, thermophilic B. coagulans 14-300 was utilized, reaching 38.0 g L −1 LA in 32 h SSF from pretreated RS and 18.3 g L −1 LA in 30 h PSSF from pretreated DCS with optical purities of 99%.",
"introduction": "1. Introduction Biorefineries are evolving, since industry and consumer demand for materials made from renewable resources is continually on the rise. Coupled with sufficient political incentives, the bioeconomy is expected to be thriving in the next few decades [ 1 , 2 ]. Similar to the vast expansion of the petrochemical product range, biorefineries are anticipated to diversify their production lines and establish sustainable platform technologies. To meet this objective, the need for stronger connections between feedstock, conversion technologies and end usage of the obtained products has been emphasized [ 1 ]. Whilst starch or sucrose-containing feedstocks (e.g., corn, sugar cane) are the main sources for industrial biotechnology at present, alternative feedstocks are increasingly coming into focus of research and innovative enterprises [ 3 , 4 , 5 ]. Lignocellulose is one example of such a novel substrate: It is the part of the plant that provides a structure as well as resilience against several natural stresses (e.g., mechanical, chemical or microbial stresses). To provide these abilities, lignocellulose comprises of the three chemical structures cellulose, hemicellulose and lignin, whose proportions and composition differ for each plant, as well as the plant’s part, age, cultivation conditions or time of harvest [ 6 , 7 ]. Cellulose essentially consists of crystalline fibers from glucose monomers and is encompassed by a heteropolymer hemicellulose network that contains varying pentose and hexose sugars (e.g., xylose, arabinose, galactose) [ 8 ]. Lignin is a very complex polymeric structure of three phenolic compounds that build covalent bonds with the hemicelluloses [ 9 , 10 ]. When separating the structures, sugars of the cellulose and hemicellulose can serve as substrates for fermentation processes, while lignin itself is expected to become a groundbreaking chemical building material. It is anticipated that various novel chemical routes and products can be established from chemically valuable lignin [ 9 , 10 , 11 ]. Due to its innate robustness, biomass fractionation is one of the major challenges when lignocellulose is processed [ 12 , 13 ]. While lignin utilization in industrial applications is currently often limited to heat and power production, recent studies on pretreatment strategies and biorefineries focused on complete biomass utilization and aimed to achieve the selective fractionation of lignin [ 14 , 15 ]. About a decade ago, cellulose solvent- and organic solvent-based lignocellulosic fractionation processes (COSLIF) emerged and were addressed as forms of “feedstock-independent biomass pretreatment” [ 16 , 17 ]. Recently, the LXP Group GmbH (LXP) optimized the involved precipitation process and reduced the cellulose related operating materials, thereby facilitating their recovery (LX-Pretreatment) [ 18 ]. Furthermore, a lignin recovery process was suggested which replaces the burning of the lignin and allows its extraction as a valuable side stream with a low carbohydrate and mineral content [ 19 , 20 ]. The options for biorefinery concepts from lignocellulose are vast, considering the wide range of biotechnological and chemical production routes that have been pointed out in several reviews [ 4 , 9 , 11 ]. Most importantly, concepts that aim to develop valuable products from lignin alongside the production of bioethanol have been proposed [ 21 , 22 , 23 , 24 , 25 ]. In this study, the cascade utilization of lignocellulosic biomass for the production of valuable lignin and L-(+)-lactic acid is presented, to our knowledge, for the first time. Considered to be one of the main chemical building blocks for establishing a sustainable bio-based economy [ 26 ], lactic acid (LA) has many applications ranging from the food, pharmaceutical and, more recently, to the polymer industry. Its stereoisomeric forms L-(+)- and D-(−)-LA are used for the production of polymer lactic acid (PLA), an alternative, non-fossil based compound for the production of plastic goods [ 27 , 28 ]. The study presented here was financed by the European Regional Development Fund, which is especially designed to support small or medium-sized enterprises (SMEs) and to connect research to innovations that promote a low-carbon economy. The scope of the project was the production of two valuable products from lignocellulosic residues: Lignin and optical pure L-(+)-LA as sustainable platform chemicals. Two different feedstocks from established processes were tested for this application: rye straw (RS; residue from agriculture) on the one hand and the digestate of energy corn silage (DCS; residue of an anaerobic biogas plant) on the other hand. Both substrates are grown regionally, which is logistically advantageous when planning a biorefinery in Germany. Within Europe, about 92% of the rye produced worldwide is cultivated. Germany is the leading producer with 3528 kt per year on an area of 627,000 hectares (projection for 2021) [ 29 ]. Regarding the cultivation of energy crops for biogas production, 9.7% of German agricultural land was used in 2020. The main share of 64% is energy corn with a cultivated area of 989,000 hectares [ 30 ]. The LXP Group GmbH focused on the production of LX-Lignin, conducting their innovative, patented LX-Pretreatment. The generated process residue, designated as LX-Cellulose, was handed to the research facility ATB for the biotechnological production of L-(+)-LA, employing the bacterial strain Bacillus coagulans . The thermophilic nature of B. coagulans enables the testing of simultaneous enzymatic hydrolysis and fermentation approaches, since the optimum of the applied enzymes usually lies at elevated temperatures. In addition, an increased temperature lessens the risk of contamination by mesophilic microorganisms, thereby allowing processes to be conducted with a reduced necessity of sterilization [ 31 ]. Furthermore, B. coagulans is able to consume both hexoses and pentoses as well as tolerate inhibitor compounds derived from pretreated lignocellulose [ 32 , 33 ], while nutrition requirements remain simple [ 34 , 35 ]. Considering all this, B. coagulans qualifies as an excellent candidate for the fermentation of lignocellulosic substrates. For this study, three different fermentation approaches were compared to find the optimal experimental set-up for further investigations: Separate hydrolysis and fermentation (SHF), simultaneous saccharification and fermentation (SSF) as well as prehydrolyzed simultaneous saccharification and fermentation (PSSF). The exploitation of the lignocellulosic feedstock is proposed in a biorefinery concept: A value-adding cascade from renewable feedstock was established, achieving up to three valuable products from one source. Additional to the rye harvested for food and feed and the biogas produced from energy corn, process residues could successfully transformed into LX-Lignin and L-(+)-LA by the novel LX-Process combined with microbial fermentation of B. coagulans .",
"discussion": "4. Discussion In this study, a biorefinery concept is proposed, which aims at a complete lignocellulose biomass utilization. The integrated LX-Pretreatment allowed the selective fractionation of lignin, while also producing the LX-Cellulose side stream, which was tested for fermentation for the first time in this publication. Tolerance of a low pH-regime as well as the thermophilic nature of B. coagulans allowed the testing of simultaneous fermentation and saccharification approaches, working at the optimum process conditions of the employed enzyme solution (50 °C and pH 5). At first, experiments for the enzymatic hydrolysis of LX-Cellulose were carried out. For utilizing DCS-LX, 11.2 TS% with the addition of 0.3 mL CCT2 g −1 cellulose were chosen, as well as 10 TS% and 0.4 mL g −1 cellulose for the work with RS-LX. Subsequently, SHF, SSF and PSSF processes were conducted and compared for the two lignocellulosic substrates. Depending on the substrate, these tests led to different results: DCS-LX showed reduced values for LA titer and productivity in SSF compared to the separate process. However, allowing a prehydrolysis of 12 h before adding the preculture improved the results of the SSF in terms of the LA concentration, reaching 18.3 g L −1 in 30 h of overall process time with an optical purity of >99.9%. Here, residual xylose of 1.7 g L −1 remained in the medium. Full consumption of the monosaccharides released from DCS-LX was noted in SHF, reaching 20.6 g L −1 LA in 38 h overall process time with 99.5% optical purity. For substrate RS-LX, the best results in terms of LA concentration and overall process duration were indeed achieved by conducting SSF. With this fermentation approach, 38.0 g L −1 L-LA were produced in 32 h and a maximum titer of 39.3 g L −1 L-LA was measured after 48 h of the process (99% optical purity). Again, residual xylose remained in the medium, resulting in 2.3 g L −1 sugars at the maximum LA titer. Complete sugar consumption was only achieved by performing SHF, reaching 31.1 g L −1 LA in 44 h of overall process time. Comparing the obtained results with recent publications, it primarily must be noted that titers of over 100 g L −1 LA were aimed for [ 47 ]. However, this is often only achieved when time- or energy-consuming strategies are used. For example, Wei et al. (2018) achieved 130.3 g L −1 L-LA when fermenting pretreated corn stover with the genetically modified Pediococcus acidilactici ZY271 after 6 h of prehydrolysis and 72 h of fermentation. Although the fermentation is rapid and it results in this high titer, the procedure is time consuming, since the process is preceded by three days of biodetoxification. Hereby, inhibitor compounds are reduced that would otherwise hinder the growth of the organism [ 48 , 49 ]. Another example for a high-titer fermentation from pretreated corn stover is the work from Ma et al. (2016). Within 33 h of fermentation, 98.3 g L −1 L-LA could be achieved by employing B. coagulans NBRC 12714. However, this high titer could only be obtained by separating the hydrolysate in advance by means of membrane filtration and concentrating it subsequently by vacuum evaporation [ 50 ]. In the study of Hu et al. (2015), no lengthy or energy-intensive procedures were required and therefore their work is most comparable to our results. Corn stover was pretreated with NaOH and afterwards was washed to remove inhibitors. With this material, SSF with 5% solids loading was performed using B. coagulans LA204 and Cellic ® CTec2 (30 FPU g −1 stover). After 60 h, this process resulted in 29.9 g L −1 LA with an optical purity of 93.7% and 60% yield (g LA/ g of total pretreated biomass used). The process exhibited 0.50 g L −1 h −1 average productivity and 4.5 g L −1 acetic acid were measured [ 51 ]. In our study, regarding the substrate RS-LX, a higher LA titer was achieved in less process time with an average productivity of 1.2 g L −1 h −1 in 32 h, and 0.83 g L −1 h −1 in 48 h. Furthermore, no acetic acid was measured and the produced LA had higher optical purity. The overall process yield of 56% (based on the theoretical quantity of monosaccharides present in the RS-LX substrate) was lower than that reported by Hu et al. (2015). Furthermore, a higher enzyme charge of 67 FPU g −1 dry matter was used. However, since twice as many solids (10 TS%) were employed in our process with RS-LX, the water input of the fermentation process was most likely reduced. When considering the PSSF fermentation with DCS-LX, the lactic acid concentration of 18.3 g L −1 was considerably lower than the value obtained by Hu et al. (2015). However, the process took only half the amount of time with an average productivity of 1.02 g L −1 h −1 (12 h prehydrolysis followed by 18 h of fermentation). Also, our result was positively enhanced by the fact that biogas and LX-Lignin have already been successfully obtained from the substrate prior to its utilization for LA-fermentation. Thus, while the cascade use of this biomass has already generated two valuable products, 61% of the lactic acid titer reported by Hu et al. (2015) can still be obtained by performing PSSF, and 71% by performing SHF. To further increase the LA titer, Hu et al. (2015) performed fedbatch SSF and produced 97.6 g L −1 LA in 60 h with an optical purity of 98.1%. The solid content was increased to 14.4% over the course of the fermentation [ 51 ]. By performing fedbatch SHF and employing B. coagulans P38, Peng et al. (2013) achieved 180 g L −1 L-LA in 75 h, which is, to our knowledge, the highest LA concentration from corn stover currently found in the literature [ 52 ]. However, even in this case, the corn stover pretreated with acid was concentrated in advance. Nevertheless, fedbatch fermentation is a promising process route to increase the LA titer while reducing the water-to-solid ratio. The SSF and PSSF results of the LX-Substrates presented in this report provide an excellent basis for planning future fedbatch experiments. In conclusion, a new biorefinery concept was presented in this study which follows a value-adding cascade approach to gain several products from the renewable resources rye or energy corn. After harvest of the first and biogas generation from the latter, the lignocellulosic residues were further utilized for the novel LX-Pretreatment. While gaining chemically usable LX-Lignin, LX-Cellulose was also created which contains negligible inhibitors and an increased amount of glucan, making it an ideal candidate for fermentation studies. By employing thermophilic B. coagulans 14-300, LA of a high optical purity could be successfully obtained from LX-Cellulose of both substrates via simultaneous fermentation and saccharification. Results of the fermentation experiments with RS-LX from rye straw are promising and exceeded data from comparable research. To further increase the LA concentration, future studies will focus on a fedbatch SSF approach. Furthermore, scale-up experiments will be conducted to perform studies on the downstream of the fermentation broth to obtain chemically pure L-LA."
} | 3,856 |
37753963 | PMC10538451 | pmc | 5,168 | {
"abstract": "ABSTRACT Methanogens, reductive acetogens and sulfate-reducing bacteria play an important role in disposing of hydrogen in gut ecosystems. However, how they interact with each other remains largely unknown. This in vitro study cocultured Blautia hydrogenotrophica (reductive acetogen), Desulfovibrio piger (sulfate reducer) and Methanobrevibacter smithii (methanogen). Results revealed that these three species coexisted and did not compete for hydrogen in the early phase of incubations. Sulfate reduction was not affected by B. hydrogenotrophica and M. smithii . D. piger inhibited the growth of B. hydrogenotrophica and M. smithii after 10 h incubations, and the inhibition on M. smithii was associated with increased sulfide concentration. Remarkably, M. smithii growth lag phase was shortened by coculturing with B. hydrogenotrophica and D. piger . Formate was rapidly used by M. smithii under high acetate concentration. Overall, these findings indicated that the interactions of the hydrogenotrophic microbes are condition-dependent, suggesting their interactions may vary in gut ecosystems.",
"introduction": "Introduction The gut microbiota consists of a wide variety of microbial species with the ability to ferment dietary fibers and other complex substrates that escape digestion and absorption, resulting in the production of short chain fatty acids as well as carbon dioxide and hydrogen. 1–3 The accumulation of hydrogen thermodynamically restricts further microbial fermentation and growth. 4–6 Hydrogenotrophic microbes using hydrogen as the electron donor for their anaerobic respiration play an important role in maintaining the hydrogen balance in gut ecosystems. 4 , 7 Moreover, hydrogenotrophic microbes have been suggested to play an important role in human health. 5 , 6 Methanogens are considered beneficial or harmful for human health, and the associations of methanogens with obesity, anorexia, colorectal cancer, inflammatory bowel disease, irritable bowel syndrome, diverticulosis, atherosclerosis and periodontitis were described by Chaudhary and colleagues. 8 Hydrogen sulfide has been implicated in the development of colorectal cancer. 9 , 10 Higher sulfate-reducing bacteria (SRB) abundance or hydrogen sulfide concentrations have been reported in ulcerative colitis patients compared to healthy individuals. 11 , 12 Hydrogenotrophic microbes in humans consist of three major functional groups, namely methanogens, reductive acetogens and SRB. 6 Methanogens reduce carbon dioxide to methane using hydrogen as electron donor (4 H 2 + CO 2 → CH 4 + 2 H 2 O). 13 Reductive acetogens use hydrogen and carbon dioxide producing acetate via the Wood-Ljungdahl metabolic pathway (4 H 2 + 2 CO 2 → CH 3 COOH + 2 H 2 O). 13 SRB reduce sulfate to hydrogen sulfide using hydrogen as electron donor (4 H 2 + SO 4 2 − + 2 H + → H 2 S + 4 H 2 O). 13 The prevalence of gut methanogens varies between populations with estimates of ~ 30% prevalence in the Western world and ~ 80% in Africa with Methanobrevibacter smithii as the dominant methanogenic archaeal species in the human gut. 5 , 14 SRB that colonize the guts of ∼50% of humans show greater taxonomic diversity than methanogens with Desulfovibrio piger described as the most common species. 15 Reductive acetogens are phylogenetically diverse, and Blautia hydrogenotrophica is the most well-known and studied reductive acetogenic species. 5 It has been estimated that one-third or one-fourth of acetate in the gut is produced via reductive acetogenesis. 16 Competition between the three hydrogenotrophic functional groups has been considered because all of them can use hydrogen as an energy source. 4 , 5 Thermodynamically, sulfate reduction with hydrogen is more favorable with a Gibbs free energy change under standard conditions of −152.2 kJ mol −1 , compared to methanogenesis and acetogenesis with Gibbs free energy changes of −131 kJ mol −1 and −95 kJ mol −1 , respectively. 17 It has been reported that SRB and methanogens are mutually exclusive. 18 SRB were rarely detected in the gut microbiota of so-called methane excretors that are subjects with an above average methanogen abundance, while the gut microbiota of non-methane excretors harbors a higher abundance of SRB. 18 In addition, lower acetogenesis has been found in the presence of methanogens, and inhibition of methanogens concomitantly led to higher acetate production in fecal cultures. 19 However, mutual exclusivity is not always found, and several studies have reported that no significant relationship was observed between methanogens and SRB. 20 , 21 A recent study by Wang et al (2022) found that methanogens, reductive acetogens and SRB abundances did not show a negative correlation with each other indicating their coexistence in adult fecal samples. 22 Although these previous studies indicate that the three hydrogenotrophic functional groups may impact each other considering all of them use hydrogen as energy source, detailed insights into the interactions between them remains unknown. Therefore, we cocultured the hydrogenotrophic microbial species B. hydrogenotrophica , D. piger and M. smithii with each other in vitro under hydrogenotrophic conditions, aiming to understand how these three species affect each other’s growth and metabolic activity in vitro . Moreover, the impact of sulfide concentrations on these species was investigated as hydrogen sulfide produced by sulfate reduction is highly reactive and toxic to microbes. 23 , 24 Furthermore, we described the impact of formate and acetate concentrations produced during incubations on the growth of M. smithii as they could serve as substrates for methanogens. 25 , 26",
"discussion": "Discussion In this study, B. hydrogenotrophica , D. piger and M. smithii were cocultured to investigate their interactions under hydrogenotrophic conditions. The main findings are summarized in Figure 4 and indicated that the three hydrogenotrophic species coexisted and did not compete for hydrogen in the early phase of incubations. Coculturing with B. hydrogenotrophica and D. piger shortened the lag phase of M. smithii , concomitantly resulting in faster methane production. However, the presence of D. piger inhibited the growth of B. hydrogenotrophica and M. smithii and their metabolite production in the late phase of incubations. In addition, we found that high sulfide concentrations inhibited methanogenesis. A higher acetate concentration stimulated the usage of formate by M. smithii .\n Figure 4. Summary of the interactions between B. \n hydrogenotrophica , D. \n piger and M. \n smithii under hydrogenotrophic conditions. It has been considered that the three hydrogenotrophic functional groups may compete because all of them use hydrogen as an energy source in the human gut. 4 , 5 However, our results by coculturing the three hydrogenotrophic species clearly showed that the interactions between the three hydrogenotrophic functional groups are condition dependent. Accordingly, considering the complexity of the human gut ecosystem including its nutrient supply, the variable environmental conditions throughout the gut, as well as the metabolic flexibility of some hydrogenotrophic microbes, the interactions between hydrogenotrophic microbes in the gut are potentially complex and certainly environment-dependent, 5 , 28 , 29 which might explain the inconsistent findings between studies. SRB have the greatest affinity for hydrogen and dissimilatory sulfate reduction is thermodynamically more favorable than methanogenesis and reductive acetogenesis. 17 , 30 It has been suggested this advantage is negated when sulfate is depleted in the gut. Consistently, in our study, we observed that 20 mM of sulfate was quickly depleted by D. piger within 24 h, which limits the growth of D. piger under hydrogenotrophic conditions. Hydrogen sulfide is highly reactive and toxic to microbes since it can diffuse across the cell membrane and is involved in protein denaturation and enzyme inhibition. 23 , 24 Moreover, this toxicity has been suggested to be associated with hydrogen sulfide concentrations. 24 We indeed confirmed that 3 mM sulfide inhibited the growth of M. smithii . However, the growth of D. piger and B. hydrogenotrophica was not affected by high sulfide concentrations, indicating that the toxicity of sulfide is species dependent. Sulfide concentrations vary between individuals. It has been reported that the mean total sulfide content in wet feces was 0.66 mmol/kg. 31 However, a study in which individuals consumed a high-meat diet (600 g/day) rich in sulfur-containing amino acids, showed a much higher fecal sulfide content reaching levels of 3.38 mmol/kg. 32 This suggests that interactions between SRB and M. smithii may vary and are partially determined by the daily diet consumed as multiple sources of sulfur are present in the gut, including organic components from consumed plant-based diets as well as host-secreted components, such as mucus. SRB and methanogens have been reported to be coexisted or mutually exclusive 18 , 20 , 21 and these inconsistent observations could be associated with the luminal sulfide concentrations, which should be further confirmed to illustrate the importance of hydrogen sulfide in the hydrogen metabolism in gut ecosystems. Synergistic metabolic relationships have been proposed between M. smithii and hydrogen-producers. 29 , 33 \n Bacteroides thetaiotaomicron is a well-known saccharolytic bacterium, and its fermentation resulting in production of hydrogen, formate and acetate supports the growth of M.smithii . 29 , 34 In turn, the presence of M. smithii increases the metabolic efficiency of Bacteroides thetaiotaomicron . 29 Interestingly, cocolonization of M. smithii and Bacteroides thetaiotaomicron in a humanized gnotobiotic mouse model showed an increased population size of both in cecum and distal colon. 35 In addition, coculturing M. smithii and the species of the bacterial family Christensenellaceae indicated a syntrophic relationship via interspecies hydrogen transfer, resulting in higher acetate but lower butyrate production compared to monocultures of Christensenellaceae strains. 33 Although B. hydrogenotrophica and D. piger are hydrogenotrophic microbes, they are not obligatorily dependent on hydrogen and could even potentially have similar syntrophic interactions as described above to B. thetaiotaomicron and strains of Christensenellaceae . B. hydrogenotrophica can use glucose and fructose to grow and produce acetate and formate. 36 , 37 Besides hydrogen consumption D. piger is also able to perform fermentation of pyruvate resulting in the production of hydrogen, formate and acetate. 38 Both formate and acetate can favor the growth of M. smithii as indicated in a study published recently, 26 suggesting possible syntrophic relationships between the M. smithii and the B. hydrogenotrophica and D. piger . Interestingly, we found that the lag phase of M. smithii was consistently shortened by both B. hydrogenotrophica and D. piger . However, this was not due to the increased formate and acetate concentrations. The exact mechanism of this stimulation effect by B. hydrogenotrophica and D. piger remains unknown and needs further studies. Moreover, we found that formate was rapidly converted to methane only under high acetate concentration (20 mM). Therefore using formate as an energy source with high acetate concentration can be an alternative of hydrogen and carbon dioxide for growing M. smithii . 26 In this study, we cocultured the three hydrogenotrophic functional groups to give insights into their interactions, which could improve our understanding in their interactions in the human gut. However, our study, like any in vitro study, cannot completely mimic the complexity of the gut environment in vivo and further research is needed to evaluate our findings in vivo . Unfortunately, given the multiple comparative cultivations in our study, we could only obtain duplicate samples that are not sufficient to support sound statistical assessment of potential differences between groups. Nevertheless, although having at least triplicate measurements would be necessary to allow for statistical analyses, our data generally showed that the duplicate measurements followed the expected trend with acceptable hydrogen balances (Supplementary Table S1). In conclusion, this in vitro study gives a detailed overview of interactions between the three hydrogenotrophic species under hydrogenotrophic conditions. Results revealed that the interactions and metabolisms of these hydrogenotrophic functional groups are condition-dependent in vitro . Their relationships are complex and may vary throughout the gut considering the variable environmental conditions in vivo , and thus not easy to extrapolate to the in vivo situations in the gut. Our study thus may explain why inconsistent observations are reported about the coexistence of hydrogenotrophs in the gut."
} | 3,285 |
31857604 | PMC6923392 | pmc | 5,169 | {
"abstract": "The emergence of memristor technologies brings new prospects for modern electronics via enabling novel in-memory computing solutions and energy-efficient and scalable reconfigurable hardware implementations. Several competing memristor technologies have been presented with each bearing distinct performance metrics across multi-bit memory capacity, low-power operation, endurance, retention and stability. Application needs however are constantly driving the push towards higher performance, which necessitates the introduction of a standard benchmarking procedure for fair evaluation across distinct key metrics. Here we present an electrical characterisation methodology that amalgamates several testing protocols in an appropriate sequence adapted for memristors benchmarking needs, in a technology-agnostic manner. Our approach is designed to extract information on all aspects of device behaviour, ranging from deciphering underlying physical mechanisms to assessing different aspects of electrical performance and even generating data-driven device-specific models. Importantly, it relies solely on standard electrical characterisation instrumentation that is accessible in most electronics laboratories and can thus serve as an independent tool for understanding and designing new memristive device technologies.",
"introduction": "Introduction Emerging memory-resistive devices, also known as memristors 1 , have exhibited an unmatched potential for a broad range of applications ranging from non-volatile memories 2 to neuromorphic computing 3 , 4 and reconfigurable circuits 5 , 6 . As the scope of these resistive memories expands, there is a growing interest in identifying all appropriate techniques for evaluating the different attributes of electrical performance 7 and the physical aspects 8 of Resistive Random Access Memory (RRAM) devices. While these techniques do offer valuable insights into the operation and underpinning physical aspects of devices they are limited to individual performance metrics. In order to establish a workflow to evaluate devices in a consistent manner that can be transferred across different laboratories a more unified testing framework is needed. Our methodology presents a characterisation suite that allows to fully evaluate RRAM devices in a consistent and repeatable manner. Due to its all-electrical nature, it does not require using expensive equipment and its modular structure allows accessing insights on the underlying mechanisms without resourcing to complex and highly specialised equipment. Having multiple steps for testing a device in sequence empowers the user to cross-validate experimental observations as they occur through the complementarity of the individual modules. We endeavour not only to cover benchmarking performance aspects of the device but also capture signatures related to the underpinning switching mechanism, providing useful insights on device operation without the need for bespoke and expensive physicochemical validation tools. Our overarching aim is for this methodology to serve as an independent tool for pushing the development frontiers of novel memristive device technologies and their translation into emerging applications. Having a standardised methodology will also help with validating published data through repeating, without any ambiguity, memristors’ testing procedures. The characterisation protocol, the overview of which can be seen in Fig. 1 , consists of a series of consecutive modules each geared towards a specific performance target. Initially we deal with the functionality of the device itself, if any. In effect we query the capacity of any two-terminal device to act as a tuneable resistive element and determine its switching threshold and polarity dependency, forming a module we herein call Switching Dynamics . Next, we evaluate the stability of the Device Under Test (DUT) in its given resistive state thus evaluating the existence of volatile (metastable) dynamics. This is accomplished through a series of pulse stimuli, with voltage amplitudes below the switching threshold (sub-threshold) determined from the previous step. Our functionality testing is concluded with temperature dependent voltage cycling that can provide insights into the conduction mechanisms governing the switching in the DUT. This can be performed by considering the switching (supra-threshold) and the non-switching (sub-threshold) regimes of operation 9 . Figure 1 An overview of the proposed characterisation procedure introduced in this paper. Testing is split to four modules depending on their particular scope. Functionality testing establishes the switching capacity of the memristive cell as well as dominant conduction mechanisms and Benchmarking evaluates the actual performance of the device under test. Although Functionality and Benchmarking are to be understood as a sequence the operating range of the device can be tuned using the Electroforming procedure. Modelling is finally used to extract a behavioural model out of fully characterised devices. Once a tuneable resistive functionality has been established, a series of benchmarking routines can be used to examine the actual performance metrics of the DUT. Given that a key feature of such elements is their history-dependence, we first evaluate the DUT characteristics with protocols that are the least possible to lead to an irreversible change in the device and then progressively increasing the applied stimuli. Initially, we employ a bespoke programming protocol to determine the ultimate memory capacity of the device in terms of number of non-volatile resistive states as a means to determine the potential of the device to operate in an analogue fashion, a key aspect of reconfigurable electronics. After the resistive states of the device have been identified, several retention steps across a series of these states can inform us of their stability as well as the DUT’s ability to retain the observed memory window that defines the dynamic range of switching. Moreover, another key metric is the number of cycles that a DUT can undergo before failing. Endurance testing can be performed in a bespoke manner, based on the intended use of operation, for example either between consecutive memory states or the extremes defining the DUT OFF/ON ratio. We note that all of the above are dependent on the DUT preconditioning, often described as electroforming , a process that allows setting devices in distinct operating resistive “bands”. As electroforming fundamentally affects the physical characteristics of the DUT, both on a structural and interfacial level, this evaluation process should be repeated post-electroforming, or directly (without electroforming) for technologies that are electroforming-free. Finally, to properly integrate a device into a circuit design workflow it is also important to have accurate behaviour models. The proposed memristor testing methodology thus culminates in the production of a phenomenological model that is driven by data and can closely match the response of the DUT for specific stimulus within the operating range that has been established throughout testing. Overall, the introduced methodology offers a holistic, yet versatile, characterisation routine that: ( i) incorporates traditional techniques in standard use, ( ii) introduces advanced techniques to capture finer effects and ( iii) refines specialist techniques oriented towards understanding the underlying physical mechanisms."
} | 1,880 |
37786262 | PMC10724387 | pmc | 5,170 | {
"abstract": "Abstract The performance limitations of traditional computer architectures have led to the rise of brain‐inspired hardware, with optical solutions gaining popularity due to the energy efficiency, high speed, and scalability of linear operations. However, the use of optics to emulate the synaptic activity of neurons has remained a challenge since the integration of nonlinear nodes is power‐hungry and, thus, hard to scale. Neuromorphic wave computing offers a new paradigm for energy‐efficient information processing, building upon transient and passively nonlinear interactions between optical modes in a waveguide. Here, an implementation of this concept is presented using broadband frequency conversion by coherent higher‐order soliton fission in a single‐mode fiber. It is shown that phase encoding on femtosecond pulses at the input, alongside frequency selection and weighting at the system output, makes transient spectro‐temporal system states interpretable and allows for the energy‐efficient emulation of various digital neural networks. The experiments in a compact, fully fiber‐integrated setup substantiate an anticipated enhancement in computational performance with increasing system nonlinearity. The findings suggest that broadband frequency generation, accessible on‐chip and in‐fiber with off‐the‐shelf components, may challenge the traditional approach to node‐based brain‐inspired hardware design, ultimately leading to energy‐efficient, scalable, and dependable computing with minimal optical hardware requirements.",
"conclusion": "4 Conclusion Broadband frequency conversion in optical fibers can perform various tasks commonly undertaken by artificial neural networks at just a fraction of their training complexity and energy consumption per inference. The results support the use of transient system dynamics, [ \n \n 21 \n \n ] particularly optical nonlinear dynamics, to emulate the functionality of multiple neural network topologies. To ensure trainability and reproducibility, coherence (i.e., pulse‐to‐pulse spectral stability) must be maintained at all times. In anomalous dispersive fibers, this can only be achieved by strictly adhering to the outlined guidelines [ \n \n 26 \n \n ] which includes keeping the soliton number N below 10 and pulse durations well below 200 fs. The latter requires advanced dispersion compensation techniques in fiber‐integrated systems and low‐phase altitudes for data encoding. Our accessible implementation uses exclusively off‐the‐shelf telecom fiber components and is, in principle, transferable to on‐chip nanophotonic devices [ \n \n 45 \n , \n 46 \n \n ] and novel material fiber systems. [ \n \n 33 \n , \n 47 \n \n ] In particular, photonic chip technologies offer full system integration, higher energy efficiency, and potentially picosecond inference latencies for cm‐scale waveguides. Also, on‐chip solutions offer highly reproducible waveguide properties and might allow for the direct transfer of trained weights from one chip to another. Yet, novel fiber systems are widely accessible and can open new research grounds to further explore uncommon propagation dynamics for neuromorphic wave computing. For example, specially designed dispersion with more than one or varying zero‐dispersion wavelengths might enrich the cascade of nonlinear effects by multiple, locally distributed dispersive emissions and four‐wave mixing events. [ \n \n 31 \n \n ] In addition, current developments of highly nonlinear non‐silica fibers [ \n \n 47 \n , \n 48 \n \n ] or dispersion‐engineered fibers [ \n \n 49 \n \n ] might allow for a further reduction in the energy consumption to sub‐pJ per computation. In general, each nonlinear optical process sensitive to phase and amplitude modulations can be used for such information processing. Yet, the achievable performance may differ with the dynamic range of the respective system output (e.g., bandwidth and spectral sensitivity to the input field may vary). Overall, frequency‐domain approaches involving second‐order [ \n \n 13 \n , \n 14 \n , \n 50 \n \n ] or third‐order nonlinearity (this work) seem to be a particularly promising degree of freedom regarding sub‐pJ energy consumption per inference. Nonlinear systems that operate on the Kerr effect (e.g., soliton fission, four‐wave mixing) would come with the additional benefit of frequency windows that are widely customizable for a wide range of optical amplifiers and cascaded operations. Our experiments have shown evidence for improving computational performance with increasing system nonlinearity, confirming earlier theoretical predictions in non‐dissipative multi‐soliton systems. [ \n \n 18 \n \n ] In summary, these findings indicate a new approach to designing neuromorphic hardware. Instead of building neural hardware one node at a time, the system's inherent dynamics can be used to scale and enhance its inference capabilities. Yet, in all anomalous dispersive systems, the coherence conditions (i.e., N < 10) impose an upper bound to the scalability of the system's computational performance. Moreover, by closely tracking our system accuracy with equally performing neural network primitives, we find that our fiber‐based processor can emulate a variety of neural networks, including multi‐layer networks (see Abalone results), with a single system setting. Considering single pulse inference and waveguides with tailored dispersion, it is possible to attain energies as low as a few pico‐joules per inference, regardless of the task at hand. This outperforms current GPU performance by two to three orders of magnitude (see Supporting Information, Section G), which contradicts some recent assumptions that nonlinear optics lacks energy efficiency for computing. [ \n \n 51 \n \n ] However, while pulse‐wise spectral measurements exist (e.g., time‐stretch techniques [ \n \n 52 \n \n ] ), new approaches to scalable ultrafast electronic‐to‐optical interfaces are required to enable information encoding at such bandwidths and rates. Overall, computing with transient nonlinear dynamics may open opportunities in developing a new generation of versatile, cost‐ and energy‐effective neuromorphic hardware for future sustainable photonic computing and machine learning applications.",
"introduction": "1 Introduction The ongoing transformative success of artificial intelligence comes at the price of a significant environmental footprint. [ \n \n 1 \n , \n 2 \n \n ] The use of brain‐inspired algorithms on our current von Neumann computing architectures, which separate data processing and storage, requires a significant amount of extra energy to maintain continuous information exchange between units. A practical solution to this energy inefficiency is optical computing. [ \n \n 3 \n , \n 4 \n , \n 5 \n \n ] Computing with light utilizes complex‐valued electromagnetic fields instead of electric currents to transport and process multi‐dimensional data. This enables parallel processing in various optical degrees of freedom [ \n \n 6 \n \n ] at femtojoule energy levels per operation. [ \n \n 7 \n \n ] Recent approaches aim to replicate mathematical core operations used in artificial neural networks (ANNs) – the current backbone of artificial intelligence – in ultrafast light‐driven hardware. Operations such as arbitrary matrix multiplications, [ \n \n 8 \n \n ] convolutions, [ \n \n 9 \n \n ] or nonlinear activation functions [ \n \n 10 \n , \n 11 \n , \n 12 \n \n ] were implemented in multi‐component hardware to realize single optical neural network layers. Their inference capabilities are comparable to their digital counterparts in low‐level benchmark tasks, such as time series prediction or audio and image recognition. Yet, further scaling of these design‐ and equipment‐heavy approaches toward deep neural architectures comes with many challenges. For instance, emulating the firing of a neuron necessitates programmable nonlinear optical interconnects which enact synaptic activation functions. [ \n \n 13 \n , \n 14 \n , \n 15 \n \n ] These interconnects are power‐hungry and difficult to scale, [ \n \n 16 \n \n ] making the sequential arrangement of optical neural nodes, unlike biological neurons, potentially impractical. Neuromorphic wave computing (and related concepts [ \n \n 17 \n , \n 18 \n , \n 19 \n \n ] ) may offer a solution to go beyond the limitations of conventional node‐by‐node hardware design. The analog computing principle relies on the natural wave dynamics of a physical system to perform computations rather than using complex heterogeneous hardware architectures with tailored information trajectories. Information is encoded in wave modes and processed in the complex‐number space via transient (temporary) wave phenomena, such as diffraction, interference, and nonlinear (i.e., intensity‐dependent) wave mixing. By training the timing of these phenomena, propagating waves may be able to resample transient nonlinear graphs comparable to nonequilibrium neural networks. [ \n \n 20 \n , \n 21 \n \n ] The concept is currently finding its way into hydrodynamics, [ \n \n 22 \n \n ] acoustics, [ \n \n 23 \n \n ] and optics. [ \n \n 18 \n , \n 24 \n , \n 25 \n \n ] \n In the latter, wave computing comes with unique advantages. It presents an intrinsic, energy‐efficient approach to leveraging the ultrafast nonlinearity inherent to optical media. This not only promises to surpass the technological limitations in power consumption, data latency, and bandwidth of electro‐optical signal conversion – a major challenge in enabling deep (i.e., multi‐layer) all‐optical neuromorphic computing [ \n \n 19 \n , \n 25 \n \n ] – but may also allow for scaling the computational performance with the nonlinearity in the system. [ \n \n 18 \n \n ] \n Yet, questions remain about the computational merit of using transient nonlinearities in various optical degrees of freedom, as well as which types of wave dynamics are sufficiently complex to perform scalable computations. Further, new metrics are needed to quantify the scalability of such analog systems in terms of neural performance and energy efficiency. Here, we demonstrate the broadening of optical pulses in time and frequency within a single waveguide as a scalable resource for fast and powerful neuromorphic computing. Modes (i.e., elementary waves) in time and frequency provide an energy‐efficient vehicle to explore such an approach, as they are well‐understood and controllable in optical media, particularly in the context of broadband light generation. [ \n \n 26 \n , \n 27 \n \n ] We first revisit the analogy between neural networks and wave‐based neuromorphic computing. We then introduce coherent broadband frequency mixing mediated by the fission of higher‐order solitons (i.e., self‐regulating pulses) as a specific realization of such computing. The outstanding phase sensitivity of this complex phenomenon is ideal for nonlinearly transforming information encoded on a femtosecond data carrier into new frequency bands. Following this, we show how such frequency bands can be used for effortless data separation or prediction solely by training a linear mapping of the spectral intensities to a prediction label or value. Finally, we demonstrate this concept experimentally in an “off‐the‐shelf” fiber system and evaluate the system performance with various neural network benchmarks, including COVID‐19 diagnosis. We introduce network primitives, i.e., software‐based ANNs of minimal size with similar properties, as a new method to compare the performance of analog and digital platforms. Our results complement spatial approaches in nonlinear wave computing [ \n \n 24 \n \n ] and earlier attempts to interpret narrowband frequency mixing in fibers as a computational kernel. [ \n \n 28 \n \n ]"
} | 2,917 |
37083527 | PMC10121165 | pmc | 5,174 | {
"abstract": "Nanowire networks (NWNs) mimic the brain’s neurosynaptic connectivity and emergent dynamics. Consequently, NWNs may also emulate the synaptic processes that enable higher-order cognitive functions such as learning and memory. A quintessential cognitive task used to measure human working memory is the n -back task. In this study, task variations inspired by the n -back task are implemented in a NWN device, and external feedback is applied to emulate brain-like supervised and reinforcement learning. NWNs are found to retain information in working memory to at least n = 7 steps back, remarkably similar to the originally proposed “seven plus or minus two” rule for human subjects. Simulations elucidate how synapse-like NWN junction plasticity depends on previous synaptic modifications, analogous to “synaptic metaplasticity” in the brain, and how memory is consolidated via strengthening and pruning of synaptic conductance pathways.",
"introduction": "INTRODUCTION The brain’s powerful information processing capacity can be largely attributed to neuronal microcircuits established by synaptic connectivity patterns ( 1 , 2 ). Precisely how neurosynaptic connectivity gives rise to higher-order cognitive functions such as learning and memory remains elusive ( 3 ). However, an important clue is that neural connectivity is spatiotemporally sparse and dynamic ( 4 , 5 ). Here, learning and memory are demonstrated in a unique physical substrate with these properties. Nanowire networks (NWNs) emulate the physical nature of neurons and synapses in the brain ( 6 ). They are “neuromorphic” by virtue of not only their efficient integration of processing and memory in nanowire-nanowire cross-point junctions ( 7 , 8 ) but also their ability to mimic both threshold-driven spike-like neuronal dynamics and conductance-based synapses ( 9 , 10 ). Nanowire junctions exhibit resistive memory (“memristive”) switching between high and low resistance states ( 11 ). Because of NWN self-assembly, these memristive junctions are interconnected in a heterogeneous circuitry with recurrent feedback loops ( 12 ). Thus, NWN devices operate in a fundamentally different way from top-down fabricated memristor devices in a cross-bar architecture ( 8 ). In particular, NWNs exhibit emergent nonlinear dynamics as a result of the interplay between their memristive junctions and heterogeneous, recurrent network connectivity ( 13 – 15 ). Previous studies have demonstrated how nonlinear dynamics can be harnessed for learning by treating the NWN as a physical “reservoir” in a reservoir computing paradigm [e.g., ( 9 , 13 , 15 – 23 )]. This paradigm exploits the network’s ability to nonlinearly transform dynamical input signals into a higher-dimensional feature space, such that the outputs are linearly separable ( 24 – 26 ). NWN device readouts can then be used in a highly computationally efficient linear output layer, where only linear weights need to be trained to complete a desired machine learning task ( 27 ). In contrast, learning in the brain is thought to occur via three main mechanisms ( 28 ): supervised learning, typically linked to the cerebellum ( 29 – 32 ); reinforcement learning ( 33 ), typically linked to the basal ganglia ( 33 – 37 ); and unsupervised learning, typically linked to the cerebral cortex ( 28 ). In our recent study ( 38 ), we demonstrated Hebbian-like unsupervised learning via signal transduction pathways in NWNs. We reshaped these conductance pathways by altering the spatial location of input and output electrodes, as well as the order in which they were activated. Such “dynamic pathway tuning” revealed that NWNs preserve information from previously established pathways when forming new pathways through the network, analogous to how synaptic plasticity in the brain depends on previous synaptic modifications ( 39 ). Here, we investigate the other two mechanisms of learning, which are more context dependent. Supervised learning encapsulates an iterative process whereby the system’s response to a given input is evaluated against a desired outcome, and deviations from that outcome are used to adjust adaptive elements within the system ( 40 ). In reinforcement learning, synaptic weights are modified in response to information related to positive (or negative) feedback ( 33 ). Here, these brain-inspired learning mechanisms are physically implemented in NWNs, extending previous studies ( 38 , 41 ) by explicitly applying context-dependent external feedback. In addition to demonstrating brain-like learning in NWNs, we also demonstrate working memory (WM) by implementing sequence memory tasks inspired by the well-known cognitive task, the n -back task ( 42 – 45 ). In experiments with human subjects, the n -back WM task requires participants to identify whether each stimulus (e.g., visual pattern) in a sequence matches a stimulus that was presented n -steps back ( 43 ). As n increases, reaction times tend to increase, and accuracy tends to decrease due to processing load ( 44 , 46 ). Furthermore, regions of the brain related to verbal WM processes tend to show increasing magnitudes of activation during large n values ( 46 ). WM is thought to pertain to short-term memory and involve information manipulation ( 47 , 48 ). The ability to temporarily hold and manipulate information requires adaptive processing of multiple incoming dynamical inputs while retaining information about previously encoded input. This means that synaptic connections that form memories must be protected from being overwritten when storing new information ( 49 , 50 ). Through sequence memory tasks inspired by the n -back WM task, we demonstrate the ability of NWNs to recall previous information while continually processing new information. In addition, we show how information initially in short-term WM may be consolidated into long-term memory through physical reinforcement learning (PRL), which manipulates topological reconfiguration of NWNs via pathway strengthening and pruning.",
"discussion": "DISCUSSION This study is the first to demonstrate a nontrivial cognitive task—inspired by the WM n -back task—in a physical non–CMOS (complementary metal-oxide semiconductor) substrate with native neuromorphic properties (i.e., not requiring implementation of neuromorphic algorithms). In a previous study, Neftci and colleagues ( 53 ) demonstrated a simple cognitive task by emulating spiking neurons in a CMOS system. Their method used an intermediate computational layer in which silicon neurons are configured as soft winner-take-all (WTA) networks ( 54 ). The WTA mechanism has been reported in previous NWN studies ( 14 , 15 , 38 , 41 , 55 , 56 ). Functional connectivity maps in the current study, generated by the simulations, indicate that the network uses more than one key pathway, in contrast to previous findings. This is because of the low voltages used in this study, which are well below the threshold needed to activate the WTA path. This is to ensure that the network is maintained in an intermediate conductance state, enabling control of conductance paths via the electrodes. We previously visualized conductance pathway formation in a similar multielectrode NWN device using lock-in thermography ( 38 ). Despite the poor spatial resolution, we were able to demonstrate the principle of reshaping conductance paths in the network by dynamically changing the spatiotemporal patterns of input signals delivered by the electrodes. In that study, we used Ag@TiO 2 nanowires as Ag–polyvinyl pyrrolidone (PVP) nanowires, used in this study, are difficult to image using this technique due to their much lower resistance, making them more susceptible to damage by Joule heating. The training methods introduced here for learning a cognitive task have strong links to two unique neuroscientific learning theories. The first method, in which “nudging” was used, is similar to supervised learning in the brain ( 29 ). This method is also similar to the gradient nudging described in Æqprop ( 57 ) or other in materio gradient descent methods such as described by Boon and colleagues ( 58 ). Diaz-Alvarez et al. ( 41 ) previously demonstrated associative routing in an Ag-PVP NWN using the same multielectrode device configuration as used in this study. They effectively trained pathways by opening and closing selected electrodes to prompt the network to use specific pathways and associate them with specific spatiotemporal patterns delivered by the electrodes. However, they found that this technique was unable to maintain reliable pathway selectivity, particularly as more paths became established, which limited the ability to train multiple different patterns. By implementing selective feedback (PRL), our study demonstrates how the strengthening and pruning mechanism underlying PRL can control specific unique pathways to enable training of multiple distinct patterns and long-term memory of a target pattern. When supervised learning is implemented in NWNs without any reinforcement, drain electrode voltages are altered and nudged closer to the target. However, because of the finite decay rate of NWN junctions ( 59 ), coupled with a fixed current threshold (θ), the conductance pathways are only remembered temporarily, reflecting the network’s WM capacity. In humans, WM is an example of information retention and consistent manipulation via synaptic modifications until the information is no longer needed ( 48 ), at which point it decays in seconds up to minutes or is encoded ( 60 ). The cognitive task used in this study, a sequence memory task inspired by the n -back task, is extensively used in cognitive psychology for testing WM in humans ( 42 – 44 , 61 ). Sequence memory and n -back memory tasks have also been applied to recurrent neural networks with bio-inspired topology ( 62 ). Well-known studies in humans originally suggested a capacity to store 7 ± 2 items in WM ( 63 ), although subsequent studies estimate it at closer to three to five “chunks” of memory ( 64 , 65 ). Here, the n -back task was adapted into subtasks that could be implemented in NWNs. Task 3, the most similar to the original n -back task ( 61 ), showed that NWNs can store up to seven items in memory (and potentially more) at substantially higher than chance levels without reinforcement training and near-perfect accuracy with reinforcement training. One theory of WM at the synaptic level describes how an item is maintained in WM via increased residual calcium levels at presynaptic terminals of the neurons that code for that item ( 66 ). Since removal of residual calcium is a relatively slow process (around 1 s in humans), memories can be held over this time without the need for further spiking ( 67 ). The depletion of residual calcium is conceptually similar to atomic filament decay in NWNs ( 6 ). The second method of learning implemented in this study, PRL, is similar to reinforcement in the brain, which is thought to occur, at least in part, via strengthening of synaptic dopamine channels through Hebbian plasticity, in response to a positive (or negative) outcome ( 35 , 68 ). Contrastingly, and particularly during early development, when a synaptic pathway is unused, unwanted, or punished, it is pruned ( 69 ). Pruning occurs via a weight-dependent synaptic modification process called neuronal regulation ( 70 ). This study showed both reinforcement of desired pathways via PRL, as well as pruning of penalized pathways in NWNs. A clear distinction must be made between non-PRL results and PRL results in the n -back task. Without PRL, task performance reflects the network’s WM capacity, i.e., its ability to temporarily recall information pathways while establishing new ones. When PRL is introduced, however, memory is consolidated. Memory consolidation in the brain involves the process of encoding information in a long-term manner via strengthening of synaptic pathways and brain regions that activate in response to that information ( 1 , 71 ). These long-term modifications can last from hours up to an entire lifetime ( 1 , 60 ). In NWNs, PRL allows for strengthening specific pathways over time (and weakening of others), based on a desired output. Once pathways are consistently and repeatedly activated, they take notably longer to decay. In experiment, 3-hour rest was allocated between trials for the physical network. However, this was likely not long enough for conductance pathways to fully decay, particularly once PRL was introduced. NWNs have previously been shown to retain information even 24 hours after dynamic pathway tuning ( 38 ). Consequently, after reinforcement or repeated prolonged activation of specific pathways, NWNs’ memory for those pathways is also lengthened and consolidated. In contrast, memristive junctions and pathways in simulated NWNs were completely reset between each trial and displayed a lower WM capacity. These results are consistent with findings by Benna and Fusi ( 39 ), which suggest that synaptic plasticity depends on the history of synaptic modifications, referred to as synaptic metaplasticity. In physical NWNs, memory of previous junction modifications is carried on between epochs and trials more effectively than in simulation, increasing the WM capacity of the network. Similar behavior was previously reported in Ag-PVP NWNs by Milano et al. ( 72 ), who found that the structural topology of NWNs evolves depending on synaptic history. In that study, however, rerouting of conductance pathways was demonstrated by applying sufficiently high current densities to rupture physical connections between wires. In contrast, the present study uses much lower voltages, which maintains persistent activity in the network. This is identified with WM ( 1 ). Synaptic metaplasticity as described here is a result of external feedback signals into the network rather than physical restructuring. In task 3, the network was charged with only retaining pathway information for one target pattern. While the NWNs still had to contend with six interference patterns and therefore provided a comprehensive insight into the WM capabilities of the networks, the capacity for multiple classes to be held in memory and recalled was not measured. Consequently, NWNs demonstrate stimulus-specific manipulation, while WM in humans also involves domain-specific manipulation ( 73 ). The latter of these would require memory across multiple classes of stimuli, not just a single target pattern. To properly mimic large-scale parallel information manipulation in the brain, future studies into the network’s capacity to remember and recall multiple pathways associated with different input patterns are warranted. However, it may be that multiple, highly modular NWNs will be required to be linked up in parallel to demonstrate such information processing abilities ( 21 ). While NWNs are highly scalable as they are straightforwardly synthesized by bottom-up self-assembly, device scalability is limited by fabrication of the multielectrode system. Previously, other NWN devices have been fabricated in CMOS multielectrode arrays (MEAs) for implementing reservoir computing. These devices have not shown marked performance improvements when scaling from a 16-electrode MEA ( 10 , 18 , 23 ) to a 64-electrode MEA ( 74 ). The present study implements cognitive tasks rather than reservoir computing, and therefore an increased number of electrodes would allow demonstration of the n -back WM task with more complex patterns. Neuromorphic systems that can learn, remember, and adapt to external time-varying stimuli would represent a breakthrough platform for neuro-inspired computing ( 75 ). The present study demonstrates the potential for NWNs to achieve this. The ability to process dynamically changing information is key in many real world applications, such as robotics and sensor edge devices, where there is a need to make on-the-fly decisions in a nondeterministic environment ( 76 ) . In conclusion, by applying supervised and reinforcement learning strategies similar to those operating in the brain, we have demonstrated WM and memory consolidation in NWNs. These higher-order cognitive functions were achieved by implementing a nontrivial cognitive task routinely applied to human subjects. Results reveal that neuromorphic learning paradigms implemented in NWNs leverage similar mechanisms to the brain, namely, synaptic metaplasticity and synaptic strengthening and pruning, to optimize WM and memory consolidation."
} | 4,138 |
37602224 | PMC10435966 | pmc | 5,175 | {
"abstract": "Summary Spiking neural networks (SNNs) serve as a promising computational framework for integrating insights from the brain into artificial intelligence (AI). Existing software infrastructures based on SNNs exclusively support brain simulation or brain-inspired AI, but not both simultaneously. To decode the nature of biological intelligence and create AI, we present the brain-inspired cognitive intelligence engine (BrainCog). This SNN-based platform provides essential infrastructure support for developing brain-inspired AI and brain simulation. BrainCog integrates different biological neurons, encoding strategies, learning rules, brain areas, and hardware-software co-design as essential components. Leveraging these user-friendly components, BrainCog incorporates various cognitive functions, including perception and learning, decision-making, knowledge representation and reasoning, motor control, social cognition, and brain structure and function simulations across multiple scales. BORN is an AI engine developed by BrainCog, showcasing seamless integration of BrainCog’s components and cognitive functions to build advanced AI models and applications.",
"introduction": "Introduction The human brain can self-organize and coordinate different cognitive functions to flexibly adapt to changing environments. A major challenge for artificial intelligence (AI) and computational neuroscience is integrating multi-scale biological principles to build brain-inspired intelligent models. As the third generation of neural networks, 1 spiking neural networks (SNNs) are more biologically plausible at multiple scales, including membrane potential, neuronal firing, synaptic transmission, synaptic plasticity, and coordination of multiple brain areas. More importantly, SNNs are more biologically interpretable, more energy efficient, and naturally more suitable for modeling various cognitive functions of the brain and creating brain-inspired AI. Existing neural simulators attempt to simulate elaborate biological neuron models, implement large-scale neural network simulations, and build neural dynamics models and deep SNN models. Neuron 2 focuses on simulating elaborate biological neuron models. Neural simulation tool ( NEST) 3 implements large-scale neural network simulations. Brian/Brian2 4 , 5 provides an efficient and convenient tool for modeling SNNs. Shallow SNNs implemented by Brian2 can realize unsupervised visual classification. 6 Further, BindsNET 7 builds SNNs by coordinating various neurons and connections and incorporates multiple biological learning rules for training SNNs. SNNs implemented by these frameworks can realize machine learning tasks, including supervised, unsupervised, and reinforcement learning. However, supporting more complex tasks remains a challenge for current SNN frameworks, and there is a performance gap compared with traditional deep neural networks (DNNs). Deep SNNs trained by surrogate gradient or converted from well-trained DNNs have achieved remarkable progress in the fields of speech recognition, 8 computer vision, 9 and reinforcement learning. 10 Motivated by this, the SNN conversion toolbox (SNN-TB) 11 provides an artificial neural network (ANN)-to-SNN framework that can transform DNN models built from different deep learning libraries (such as Keras, TensorFlow, and PyTorch) into SNN models and can provide interfaces with simulation platforms (such as PyNN 12 and Brian2) as well as deployment to hardware (SpiNNaker 13 and Loihi 14 ). SINABS 15 implements spiking convolutional neural networks (SCNNs) based on PyTorch. It integrates different types of neurons and various SCNN training algorithms (such as ANN-to-SNN conversion, training by backpropagation through time [BPTT]) and supports deploying models to neuromorphic hardware. SpikingJelly (SJ) 16 develops a deep learning SNN framework (trained by surrogate gradient or converting well-trained DNNs to SNNs). It provides convenient basic components for deep supervised learning and reinforcement learning. These platforms are relatively more inspired by deep learning and focus on improving the performance of different tasks. They currently lack in-depth inspiration from brain information processing mechanisms and hence short at simulating large-scale functional brains. BrainPy 17 excels at modeling, simulating, and analyzing the dynamics of brain-inspired neural networks from multiple perspectives, including neurons, synapses, and networks. While it focuses on computational neuroscience research, it fails to consider the learning and optimization of deep SNNs or the implementation of brain-inspired functions. Semantic pointer architecture unified network (SPAUN) 18 is a large-scale brain function model consisting of 2.5 million simulated neurons and is implemented by Nengo. 19 It integrates multiple brain areas and can perform various brain cognitive functions, including image recognition, working memory, question answering, reinforcement learning, and fluid reasoning. However, SPAUN is not suitable for solving challenging and complex AI tasks that deep learning models can handle. In summary, the infrastructures for brain simulation and brain-inspired intelligence do not seem to have the same goal. Thus, the platforms for brain simulation and brain-inspired intelligence have been developed separately in the past. However, with a design that organizes biological plausibility and computational complexity at different levels, the two can be integrated and unified at the infrastructure level, eliminating the need for separate development. This integration is beneficial from the perspective of revealing the computational nature of intelligence and developing intelligent applications. Considering the various limitations of existing frameworks mentioned above, in this paper, we present the brain-inspired cognitive intelligence engine (BrainCog), an SNN-based open-source platform for brain-inspired AI and brain simulation at multiple scales. As shown in Figure 1 , BrainCog provides basic components such as different types of neuron models, learning rules, encoding strategies, etc., as building blocks to construct various brain areas and neural circuits to implement brain-inspired cognitive functions. Based on these essential components, BrainCog can perform a wide variety of brain-inspired AI modeling and simulate brain cognitive functions and structures, showing considerable scalability and flexibility. BrainCog also supports hardware-software co-design to facilitate the deployment of different SNN-based computational models. The platform includes several brain-inspired cognitive SNN models divided into five categories of cognitive functions: perception and learning, decision-making, motor control, knowledge representation and reasoning, and social cognition. For brain simulation, BrainCog provides simulations of brain structures and functions at different scales, from microcircuits and cortical columns to whole-brain structure simulations (covering the mouse brain, macaque brain, and human brain). We compare BrainCog with other platforms in terms of brain structure, learning mechanisms, and cognitive functions in Table 1 . Figure 1 The architecture of the brain-inspired cognitive intelligence engine (BrainCog) Table 1 Comparison of the brain-inspired SNN and brain simulation platform Framework SNN-TB (Rueckauer et al. 11 ) BindsNet (Hazan et al. 7 ) SINABS (SynSense SNN Library 15 ) SJ (Fang et al. 16 ) BrainPy (Wang et al. 17 ) SPAIC (Hong et al. 20 ) BrainCog Brain structure neuron connection brain area ✓ ✓ × ✓ ✓ × ✓ ✓ × ✓ ✓ × ✓ ✓ × ✓ ✓ × ✓ ✓ ✓ Learning mechanisms biologically conversion BP RL × ✓ × × ✓ ✓ × ✓ × × ✓ × × ✓ ✓ ✓ × × ✓ × ✓ × ✓ × ✓ ✓ ✓ ✓ Functions brain-inspired AI brain simulation types ✓ × little ✓ × little ✓ × little ✓ × little × ✓ much ✓ ✓ much ✓ ✓ rich BrainCog is developed based on the deep learning framework (currently, it is based on PyTorch, but it is easy to migrate to other frameworks, such as PaddlePaddle, TensorFlow, etc.). The online repository of BrainCog can be accessed at http://www.brain-cog.network . With comprehensive, easy-to-use essential components and a considerable number of use cases (covering brain-inspired AI models, brain function, and structure simulation), BrainCog enables researchers to learn the platform quickly and implement their algorithms. In summary, BrainCog provides a powerful infrastructure for developing AI and computational neuroscience research based on SNNs.",
"discussion": "Discussion BORN: An SNN-driven AI engine based on BrainCog BrainCog is an open-source platform to enable the community to build SNN-based, brain-inspired AI models and brain simulators. Here we discuss future research and potential applications of the BrainCog platform. Based on the essential components developed for BrainCog, one can develop domain-specific or general-purpose AI engines. To further demonstrate how BrainCog can support the development of a brain-inspired AI engine, we introduce BORN, an ongoing SNN-driven, brain-inspired AI engine that leverages SNNs to build a general-purpose living AI system. As shown in Figure 10 , the high-level architecture of BORN integrates spatial and temporal plasticities to implement various brain cognitive functions, such as perception and learning, decision-making, motor control, working memory, long-term memory, attention and consciousness, emotion, knowledge representation and reasoning, and social cognition. Spatial plasticity incorporates multi-scale neuroplasticity principles at micro, meso, and macro scales. Temporal plasticity considers learning and developmental and evolutionary plasticity at different timescales. How the human brain selects and coordinates various learning methods to solve complex tasks is crucial for understanding human intelligence and inspiring future AI. BORN is dedicated to addressing critical research issues like this. The learning framework of BORN consists of multi-task continual learning, few-shot learning, multi-modal concept learning, online learning, lifelong learning, teaching learning, transfer learning, etc. To demonstrate the ability and principles of BORN, we provide a relatively complex application of emotion-dependent robotic music composition and playing. This application involves a humanoid robot that can compose and play music based on visual emotion recognition. This application of BORN covers the whole process, from perception and learning to knowledge representation and reasoning and motor control. It consists of three modules built by BrainCog: the visual (emotion) recognition module, the emotion-dependent music composition module, and the robot music-playing module. As shown in Figure 11 , the visual emotion recognition module enables robots to recognize the emotions (such as joy or sadness) expressed in images captured by the humanoid robot’s eyes. The emotion-dependent music composition module generates music pieces that correspond to the emotions in the image. Finally, with the help of the robot music-playing module, the robot controls its arms and fingers to perform the music on the piano. We introduce some details of these modules as follows. (1) Visual emotion recognition. For emotion recognition, inspired by the ventral visual pathway, we construct a deep convolutional SNN with the LIF neuron model and surrogate gradient provided by BrainCog. The structure of the network is 32C3-32C3-MP-32C3-32C3-300-7, where 32C3 means the output channels of the convolution layer are 32, the kernel size is 3, and MP means max pooling. We train and test our model on the Emotion6 dataset, 72 which contains 6 emotions: anger, disgust, fear, joy, sadness, and surprise. Each emotion consists of 330 samples. On this basis, we extend the original Emotion6 dataset with exciting emotion, which we collect online. We use 80% of the images as the training set and the remaining 20% as the test set. (2) Emotion-dependent music composition. We construct an SNN that contains multiple subnetworks that collaborate to simulate different brain areas involved in representing, learning, and generating music melodies with different emotions. The model uses LIF neurons provided by BrainCog and the STDP learning rule to update the synaptic connections. We train the model on a dataset of 331 MIDI files of classical piano works. 47 As shown in Figure 11 , the amygdala network receives the outputs of visual emotion recognition as the input. The PFC and primary auditory cortex (PAC) networks then generate musical melodies that match the emotional categories. More details of the model are given in Supplemental experimental procedures S10. (3) Robot music-playing. We build a multi-brain area coordinated robot motor control SNN model based on the brain motor control circuit. The SNN model uses LIF neurons and incorporates SMA, PMC, BG, and cerebellum functions. The music notes are first processed by SMA, PMC, and BG networks to generate high-level target movement directions, and the output of the PMC is encoded by population neurons to target movement directions. The population coding of movement directions is then processed by the cerebellum model for low-level motor control. A humanoid robot, iCub, is used to validate the abilities of robotic music composition and playing, depending on the result of visual emotion recognition. The cerebellum SNN module implements the three-level residual architecture to process motor intentions and generate joint control outputs for the robot arms. The robot plays the music by moving its hand according to the generated sequence of music notes and pressing the keys with corresponding fingers. BrainCog aims to provide a community-based, open-source platform for developing SNN-based AI models and cognitive brain simulators. It integrates multi-scale biological plausible computational units and plasticity principles. Unlike existing platforms, BrainCog provides task-ready SNN models for AI and supports brain function and structure simulations at multiple scales. With the basic and functional components provided in the current version of BrainCog, we have shown how a variety of models and applications can be implemented for brain-inspired AI and brain simulations. Based on BrainCog, we are also committed to building BORN into a powerful SNN-based AI engine that incorporates multi-scale plasticity principles to realize human-level brain-inspired cognitive functions. Powered by 9 years of developing BrainCog modules, components, and applications, and inspired by biological mechanisms and natural evolution, continuous efforts on BORN will enable it to be a general-purpose AI engine. We have already started efforts to extend BrainCog and BORN to support high-level cognition, such as theory of mind, 49 consciousness, 48 and morality, 49 which are essential for building true and general-purpose AI for human and ecological good. We invite you to join us on this exploration to create a future for a human-AI symbiotic society. Figure 10 The functional framework and vision of BORN Figure 11 The procedure of multi-cognitive function coordinated emotion-dependent music composition and playing by a humanoid robot based on BORN Limitations of study This paper introduces BrainCog, a brain-inspired cognitive intelligence engine that supports brain-inspired AI and brain simulation research. This integrated design enables researchers from different domains to collaborate more effectively on a common platform. However, we still face some challenges in achieving deep coordination between them. Although we strive to integrate the precise simulation of brain functions with the computational efficiency of deep learning, the current brain-inspired AI module has not been able to fully simulate the functions and structures of the real brain. Moreover, even though our brain simulation tools have demonstrated commendable performance on various tasks, they face difficulties when dealing with higher-complexity tasks that are inherent to deep learning. These challenges may affect the performance of our platform in some scenarios that require precise brain simulation. In the future, we will continue to improve the BrainCog platform to promote deep coordination between brain simulation and brain-inspired AI and further enhance its applications in neuroscience and AI research. BrainCog will play a key role in interdisciplinary collaboration and research, and we will also actively address its current limitations."
} | 4,139 |
31428070 | PMC6688125 | pmc | 5,176 | {
"abstract": "Biofilms provide cells favorable growth conditions, which have been exploited in industrial biotechnological processes. However, industrial application of the biofilm has not yet been reported in Escherichia coli , one of the most important platform strains, though the biofilm has been extensively studied for pathogenic reasons. Here, we engineered E. coli by overexpressing the fimH gene, which successfully enhanced its biofilm formation under industrial aerobic cultivation conditions. Subsequently, a biofilm-based immobilized fermentation strategy was developed. L -threonine production was increased from 10.5 to 14.1 g/L during batch fermentations and further to 17.5 g/L during continuous (repeated-batch) fermentations with enhanced productivities. Molecular basis for the enhanced biofilm formation and L -threonine biosynthesis was also studied by transcriptome analysis. This study goes beyond the conventional research focusing on pathogenic aspects of E. coli biofilm and represents a successful application case of engineered E. coli biofilm to industrial processes.",
"conclusion": "Conclusion An immobilized fermentation system for L -threonine production by E. coli was developed by taking advantages of biofilm formation. The engineered strain overexpressing fimH successfully enhanced biofilm formation under industrial cultivation conditions, which could also apply to continuous (repeated-batch) immobilized fermentation. L -threonine production was increased from 10.5 to 14.1 g/L using E. coli W1688-fimH * during batch fermentations and was further improved to 17.5 g/L during continuous (repeated-batch) fermentations, with a productivity of 0.63 g/L/h. Transcriptome profiles indicated that the biofilm formation was enhanced by regulation of biofilm-related genes. Meanwhile, L -threonine biosynthesis was also enhanced by up- or down-regulating related genes in L -threonine metabolic pathway. The engineered E. coli W1688-fimH * would be of great value for immobilized fermentation of L -threonine. This study will also provide a reference for developing more biochemical-producing processes based on E. coli biofilm.",
"introduction": "Introduction L -threonine is one of the most essential amino acids in human body, and its demand is sharply increasing due to its wide application in food, chemical, and pharmaceutical industries ( Leuchtenberger et al., 2005 ). Currently, microbial fermentation is widely employed for industrial L -threonine production with Escherichia coli as the best candidate strain ( Lee et al., 2006 ; Dong et al., 2011 ). However, L -threonine fermentation has been operated in a free-cell batch fermentation mode, wherein cells cannot be reused after fermentation ( Rajkumar et al., 2013 ; Boelee et al., 2014 ). This batch fermentation and single-use of cells would increase the cost of operation and reduce productivities. Meanwhile, the free cells dispersed in fermentation media are often challenged by stress conditions such as shear forces during aerobic fermentation, resulting in decreased cell viability over the fermentation process. These problems need to be solved urgently to improve the fermentation efficiency. Alternatively, biofilm-based immobilized fermentation has been proposed as an alternative to free-cell fermentation owing to its advantages such as protection by biofilm matrix, enhanced metabolic activities, and repeated use of cells compared with free-cell fermentation processes ( Zhao et al., 2015 ). The biofilms of some microorganisms such as Clostridium acetobutylicum , Corynebacterium glutamicum , Aspergillus niger , and Saccharomyces cerevisiae have been applied to immobilized batch or continuous (repeated-batch) fermentation effectively ( Liu et al., 2013 ; Shi et al., 2014 ; Yang et al., 2018 ; Yu et al., 2018 ). However, for E. coli , one of the most important platform strains, industrial application of the biofilm has not yet been reported, though the biofilm has been extensively studied for pathogenic reasons. Biofilms are complex cell communities living in close association with biological or abiotic surfaces ( Sauer, 2003 ). For pathogenic bacteria, formation of biofilms is one of the most important factors leading to medical infection which is difficult to be removed ( Stoodley et al., 2002 ). Type I fimbriae is one of the most important factors for biofilm formation in Gram-negative bacteria such as E. coli ( Tripathi et al., 2013 ). In E. coli , a fimH -encoded protein that is secreted and located at the top of type I fimbriae plays a key function to generate biofilm structures by serving as an adhesin ( Nishiyama et al., 2008 ; Le Trong et al., 2010 ). Cells could use these structures to obtain nutrients and withstand shear forces. It was found that E. coli cells covered by biofilms could tolerate stricter conditions such as high osmotic pressure, oxygen limitation, and high cell density, which is a desired characteristic during fermentation ( Prigent-Combaret et al., 1999 ; Weissman et al., 2006 ). In this study, E. coli was first metabolically engineered with overexpression of fimH gene to enhance biofilm formation ( Figure 1 ). A biofilm-based fermentation system was constructed using a carrier to support the biofilm. Cells adhered to the surface of the carrier and formed a large amount of biofilm so that it could withstand high-speed shaking. Moreover, the biofilm cells that attached to the carrier surface could be renewed when the fermentation broth was replaced with fresh medium ( Huang et al., 2002 ; Kim et al., 2014 ). Due to high cell activities and repeated use of cells in the biofilm-immobilization fermentation, no seed culture was needed and cellular lag phase and fermentation period were reduced substantially. Overall, this study represents a successful case of development of biofilm-based immobilized fermentation under aerobic industrial conditions for efficient biochemical production. FIGURE 1 A schematic illustration of constructions for the overexpression or knockout of fimH in Escherichia coli W1688 and the effects on the biofilm formation.",
"discussion": "Results and Discussion Characterization of Biofilm Formation in Engineered Strains PCR and sequencing results confirmed that recombinant strains, in which fimH gene was overexpressed ( E. coli W1688-fimH * ) or knocked out ( E. coli W1688-ΔfimH) were constructed successfully. The 96-well plates experiment showed that the biofilm formation abilities of these strains were different. The optical density from crystal violet staining (which was an indicator of biofilm amount) for E. coli W1688-fimH * in LB medium increased greatly by 75.9% compared with that of the original strain (1.34 vs. 2.35), which could be attributed to the overexpression of fimH gene ( Figure 2A ). On the contrary, the optical density of E. coli W1688-ΔfimH was decreased by 38.8% due to the deletion of fimH gene (1.34 vs. 0.82). Similar results were also observed in fermentation medium. Furthermore, SEM and fluorescence microscope images showed that biofilm formation and cell adhesion were more obvious in E. coli W1688-fimH * compared with the original strain ( Figures 2B,C ). In E. coli W1688-ΔfimH, biofilm formation was apparently reduced and a sparse bacterial distribution was observed. Taken together, these results indicated that overexpression of fimH gene facilitated cell adhesion to abiotic surfaces and contributed to the clustering effects of E. coli and resulted in the biofilm formation. Whereas, deletion of fimH gene had a negative effect on the biofilm formation. So, the fimH gene had a significant regulatory effect on E. coli biofilm formation. FIGURE 2 Quantitative analysis for biofilm formation in the three strains with different characterization methods. (A) Crystal violet staining for LB medium and fermentation medium; (B) Scanning electron microscope (SEM) images of biofilm formation and cell adhesion under different magnification levels; and (C) Fluorescence microscope images of biofilm and cell adhesion under different magnification levels. I: E. coli W1688, II: E. coli W1688-fimH * , and III: E. coli W1688-ΔfimH. Biofilm-Based Fermentation for Enhanced L -Threonine Production The recombinant and original strains with different capabilities for the biofilm formation were investigated in batch fermentations. As seen in Figure 3 , L -threonine production was increased by 42.9% in E. coli W1688-fimH * compared with that in the original strain (14.1 g/L vs. 10.5 g/L). Besides, the fermentation period was shortened from 36 h to 32 h. In contrast, L -threonine production in E. coli W1688-ΔfimH showed a decrease compared with the original strain (8.7 g/L vs. 10.5 g/L) as well as a delay in glucose consumption at the initial phase of fermentation. Also, the final cell density showed a reduction of 21% compared with the fimH overexpression strain. Since some enzymes involved in L -threonine biosynthesis, the different level of expression in three strains might affect cell growth. All these observations suggested that L -threonine production and productivity were enhanced in strain E. coli W1688-fimH * . FIGURE 3 L -threonine production and glucose consumption in batch fermentation by E. coli W1688, E. coli W1688-fimH * , and E. coli W1688-ΔfimH. To further improve the fermentation efficiency, a biofilm-based immobilized fermentation strategy was developed. The polyurethane carrier, which could be beneficial to cell aggregation owing to its high strength and toughness was used to support the biofilm ( Zhao et al., 2015 ). The pore size of the carrier was also important for biofilm immobilization ( Yu et al., 2018 ). Here, immobilized fermentations by above-mentioned three strains were carried out with 10 mm × 10 mm × 10 mm polyurethane sponge pieces. In the immobilized continuous (repeated-batch) fermentation, L -threonine production in the first four batches was improved gradually in strain E. coli W1688-fimH * ( Figure 4A ), while L -threonine production did not show obvious improvement in strain E. coli W1688 (around 10.4 g/L) and E. coli W1688-ΔfimH (around 9.5 g/L; Data not shown). After the 4th batch, L -threonine production was maintained at an average of 17.5 g/L during a fermentation period decreased from 30 to 28 h. L -threonine productivity was kept at about 0.63 g/L/h from fourth batch, which was much higher compared with that from free-cell fermentation by the original strain (0.63 g/L/h vs. 0.35 g/L/h) ( Figure 4B ). Near 1-fold improved productivity contributed to a less time in a batch fermentation and we could achieve more products in the same fermentation time. These indicated that the continuous (repeated-batch) immobilized fermentation strategy taking advantage of biofilm formation in fimH overexpression strain could enhance L -threonine titer and productivity. FIGURE 4 (A) \n L -threonine production by E. coli W1688-fimH * in continuous immobilized fermentation. (B) Comparison of L -threonine productivities in continuous (repeated batch) immobilized fermentation with those in free-cell fermentation. To further confirm biofilm formation by strain E. coli W1688-fimH * under industrial fermentation conditions, scanning electron microscope experiments were performed. The images of carriers during the immobilized fermentation with different strains are shown in Figure 5 . Biofilm formation could be observed obviously when using E. coli W1688-fimH * . It could be concluded that the carrier could fix bacterial cells on the surface and provided good conditions for oxygen- and mass-transfer during the cell growth process ( Lan et al., 2013 ). Furthermore, it was shown that the carrier could provide surfaces for cell adhesion and facilitated biofilm formation during the fermentation process. Hence, seed culture was avoided before each batch of fermentation owing to the existence of cells in the biofilm. In contrast, strains E. coli W1688 and E. coli W1688-ΔfimH did not show noticeable adhesion and biofilm formation on the surface ( Figures 5II,IV ). As a result, the immobilized fermentation by E. coli W1688-fimH * biofilm could be continually operated to produce L -threonine. In such a fermentation mode, cell degeneration and cell growth were supposed to be in an equilibrium, suggesting that an ideal state of balance was achieved ( De Ory et al., 2004 ). This combination of biofilm and immobilized fermentation generates a new idea, which is also applicable to other industrial fermentation processes. FIGURE 5 Scanning electron microscopy images of carrier in the immobilized fermentation by three different strains. I: fresh carrier, II: E. coli W1688, III: E. coli W1688-fimH * , IV: E. coli W1688-ΔfimH. Transcriptome Analysis for Enhanced Biofilm Formation To investigate the mechanism of enhanced biofilm formation, transcriptome analysis was performed for wild-type, E. coli W1688-fimH * and E. coli W1688-ΔfimH. A total of over 22.7, 23.0, and 21.3 million raw reads were obtained, respectively. The expression ratios of genes involved in biofilm biosynthesis which showed significant differences among these strains were calculated in Figure 6A , where the regulated genes could be classified into six distinct clusters. The fimH (encoding type I fimbriae adhesin) and flu genes guide the secretion of adhesins responsible for cell adhesion to surfaces ( Reisner et al., 2003 ; Schembri et al., 2003 ). FlhD and FlhC are transcriptional activators involved in flagellar assembly and regulon, which is related to cell motility and biofilm formation ( Beloin et al., 2004 ). CsgD is in charge of curli assembly, transport and structural components biosynthesis for biofilm formation together with csgA , csgB , and csgC ( Reisner et al., 2003 ). The glgA , glgC , and glgP control glycogen biosynthesis ( Beloin et al., 2004 ). The luxS , metK , speD , and lsrR are involved in biosynthesis of quorum sensing (QS) signal molecule AI-2 (autoinducer-2), which can activate transcription factors to promote formation of biofilm when the cell density reaches to a threshold ( Vendeville et al., 2005 ). Actually, gene luxS concerning catalyzing the reaction to AI-2 was widely spread in Gram-negative and -positive bacterium, which shows a high homologous conservation. All these genes were up-regulated in varying degrees in the fimH -overexpressed strain E. coli W1688-fimH * , while they were down-regulated in the fimH -deleted strain compared with the wild-type. Concentration of extracellular AI-2 could also be decreased rapidly by lsr ’s ABC (ATP-binding cassette) transporter, which can transport AI-2 into the cell. The transcriptome data showed that some genes in lsr operon were down-regulated, which might further lead to accumulation of extracellular AI-2 and promote the expression of biofilm-related genes ( Schauder et al., 2001 ; Barrios et al., 2006 ). The genes showed various degrees of upregulation in fimH overexpression strain, which was validated by qRT-PCR analysis ( Figure 6B ). Notably, the transcription level by fimH was 6.2-fold higher than that in E. coli W1688-fimH * compared to the wild type, which was close to 0-fold in fimH deletion strain, indicating that the deletion of fimH was successful in E. coli W1688-ΔfimH. Besides, flu gene expression showed more than 2-fold in E. coli W1688-fimH * by qRT-PCR analysis. CyaA , csgD , luxS , and lsrR involved in flagellar assembly and signal secretion and reception of quorum sensing were up-regulated with more than 1-fold with original strain, while these genes showed down-regulated with varying degrees in E. coli W1688-ΔfimH. Taken together, the overexpression of fimH gene triggered modulation of related genes to enhance biofilm formation in E. coli W1688-fimH * . FIGURE 6 (A) Transcriptome analysis of genes involved in biofilm biosynthesis pathway in E. coli W1688, E. coli W1688-fimH * , and E. coli W1688-ΔfimH. Red and blue indicate up- and down-regulated genes, respectively. (B) qRT-PCR verification of the genes related to biofilm biosynthesis. Values and error bars represent the mean and the s.d. ( n = 3). ∗∗∗ p < 0.001, ∗∗ p < 0.01, * p < 0.05 as determined by two-tailed t -test. Transcriptome Analysis for Overexpressing FimH Gene to Increase L -Threonine Production in E. coli The overexpression of fimH resulted in oversecretion of adhesion protein, which was beneficial for the gather of cells to biofilm production. Moreover, the immobilized fermentation system based on biofilm formation for L -threonine production was applied in increasing L -threonine production. To figure out whether the increase in L -threonine production was also associated with regulation of biosynthetic pathway genes and elaborate the molecular mechanism linking biofilm formation to L -threonine biosynthesis, further transcriptome analysis was performed. Fortunately, it was found that overexpression of fimH gene resulted in not only the accumulation of biofilm, but also regulation of genes in the L -threonine biosynthetic pathway ( Figure 7A ). The key genes such as thrA , lysC , metL , and asd that dominate the pathway from L-aspartate to L-homoserine, and thrB and thrC that dominate the pathway from L-homoserine to L -threonine ( Lee et al., 2003 ; Livshits et al., 2003 ) were all up-regulated by an average of 11-fold in E. coli W1688-fimH * compared with those in wild-type strain and E. coli W1688-ΔfimH. The L -threonine transporter-encoding genes rhtA, rhtB , and rhtC were also up-regulated, which would facilitate the extracellular accumulation of L -threonine ( Kruse et al., 2002 ). These results indicated that the enhanced biofilm formation affected the enzymes expression of L -threonine pathway and facilitated the central carbon flux. On the other hand, the tdcB gene encoding threonine dehydratase was down-regulated, which would benefit the accumulation of target products. In addition, down-regulated genes were lysA and metA , which catalyzed the last step in L-lysine biosynthesis and generated the L-methionine, respectively. More precursor substances involved in central carbon metabolism diverted to L -threonine formation. Obviously, down-regulation of these genes could beneficially facilitate the accumulation of L -threonine. Furthermore, expression of genes in competing branch pathways such as aspA , mediating the pathway from L-aspartate to fumarate were all decreased apparently in E. coli W1688-fimH * . Indeed, aspA encoding aspartase to synthesize target chemicals in TCA cycle always brings a competitive effect on carbon flux. Therefore, the overexpression of FimH adhesin protein could facilitate the redistribution of carbon flux via down-regulation of aspA . The gene expression levels of acs , pta , and ackA ( Lee et al., 2006 ; Nahku et al., 2010 ) were down-regulated notably by 3.7, 5.9, and 6.1-fold, respectively. Since acetate accumulation has a detrimental effect on biofilm formation, cell growth and production, the transcriptional level in whole module of acetate pathway was down-regulated which could be beneficial for L -threonine production. As a result, it could be conducive to keeping the pH of broth relatively stable due to decreased acetate flux decreased. This would create a favorable condition for L -threonine biosynthesis and glucose consumption ( De Mey et al., 2007 ). The genes for branched metabolic pathway showed various degrees of downregulation, while the genes related to central carbon metabolism and L -threonine transportation were significantly upregulated in E. coli W1688-fimH * , which were quantified by qRT-PCR ( Figure 7B ). The relative expressions of ackA , aspA , lysA , and tdcB were decreased at least 23% in E. coli W1688-fimH * , which these genes related to biosynthesis of acetate, the degradation of L -threonine and branch by-products like fumarate, L-lysine and L-methionine. The genes involved in L -threonine direct synthesis ( thrA/B/C ) showed more than 2.3-fold expressions than that in original strain. Significantly, the relative expression of rhtA was 10.7-fold higher in E. coli W1688-fimH * than that in wild-type strain. It meant that more carbon flux contributed to L -threonine biosynthesis and the target product could be transported out of cell membrane easier. Meanwhile, the four genes didn’t show any obvious differences in E. coli W1688-ΔfimH compared with original strain. Overall, these results showed that the overexpression of fimH gene also enhanced the metabolic flux toward L -threonine by up- or down-regulating related pathway genes in E. coli W1688-fimH * . FIGURE 7 (A) Transcriptome analysis of L -threonine biosynthesis pathway genes in three different strains and the expression levels of these genes in E. coli W1688-fimH * . Red and blue indicate up- and down-regulated genes, respectively. (B) qRT-PCR verification of the genes related to L -threonine biosynthesis and transportation. Values and error bars represent the mean and the s.d. ( n = 3). ∗∗∗ p < 0.001, ∗∗ p < 0.01, * p < 0.05 as determined by two-tailed t -test. Comparison of L -Threonine Production by E. coli W1688-fimH * With Other Studies Currently, production of L -threonine in E. coli can be enhanced by metabolic engineering such as inactivation in TCA cycle, increasing glycolysis pathway flux and facilitating L -threonine central carbon metabolism. Besides these established metabolic pathway approaches and strategies, there are still several limiting factors hampering further improvement of L -threonine productivity such as fermentation strategy of optimization. ThrB and thrC are clustered with thrA in the thrABC operon, which is mainly responsible for central carbon metabolism to L -threonine ( Lee et al., 2003 ). Increased rhtA , rhtB , and rhtC expression will hence L -threonine exported from intracellular to extracellular through transmembrane protein ( Yuzbashev et al., 2013 ). Recently, fermentation optimization has been proved in improving L -threonine production effectively such as two-stage feeding strategy and fed-batch fermentation mode ( Table 4 ). Compared to the original strain E. coli W1688, the cell growth and biofilm formation of E. coli W1688-fimH * in the medium containing 30 g/L glucose was improving. Moreover, glucose was completely consumed in 28 h after 4th batch in biofilm-based immobilized repeated-fed batch fermentation. The highest yield (0.59 g/g) of L -threonine was achieved owing to the high expression of genes in the central carbon metabolism and decrease of by-products, which was a 60% increase compared to the original strain E. coli W1688. Since the biofilm formation performance can be varied depending on many genes, we further performed to screen and excavate potential genes to facilitate biofilm formation and increase the L -threonine production. TABLE 4 Comparison of L -threonine production in engineered E. coli strains. Strains Carbon source Time (h) L -threonine (g/L) Productivity (g/L/h) Yield (g/g) Fermentation mode References E. coli βIM4 (pBR322-thrA r ) Glucose 72 13.4 0.186 0.45 Batch Miwa et al., 1983 E. coli TWF006/pFW01- thrA * BC - asd Glucose 36 15.9 0.44 0.53 Batch Zhao et al., 2018 E. coli TH28C (pBRThrABCR3) Glucose 50 82.4 1.648 0.46 Fed-batch Lee et al., 2007 E. coli MT201 Glucose 28 102 3.643 0.38 Fed-batch Lee et al., 2018 E. coli THPE5 Glucose 40 70.8 1.77 0.404 Fed-batch Liu et al., 2019 E. coli W1688 Glucose 36 10.5 0.292 0.367 Batch This study E. coli W1688-fimH * Glucose 28 17.5 0.63 0.59 Repeated batch This study"
} | 5,972 |
38127977 | PMC10756307 | pmc | 5,177 | {
"abstract": "Significance Life on earth relies on sunlight for energy, but this energy can only be exploited through the collective recycling of matter by communities of microbes, plants, and animals. Yet we lack a framework for understanding how ecosystems can organize themselves to collectively capture the sun’s energy by running cycles of matter subject to thermodynamic constraints. We advance a conceptual model to study the collective properties of nutrient-cycling ecosystems. Surprisingly, even though species “greedily” extract energy from the environment, sufficiently diverse communities of species almost always manage to sustain themselves by extracting enough energy. Further, the amount of energy extracted by these communities is close to the maximum possible and much greater (100 × ) than extracted by random collections of species.",
"discussion": "Discussion Here, we proposed a theoretical framework to study self-organized energy extraction by ecosystems, which incorporates an essential aspect of metabolism thus far missing from most ecological models: Organisms acquire energy through redox transformations of matter, not matter itself. Modeling resources as transformations is not only biologically accurate but also provides a modeling framework to address fundamental questions about thermodynamic constraints on the self-organization of ecosystems. Using this model, we studied the impact of closure to matter and redox metabolism on nutrient cycling in ecosystems. By sampling large random ensembles that satisfy these constraints, we found that ecosystems converge in similar thermodynamic features, such as nutrient cycling fluxes and the total energy extracted. We also found that ecosystems converge to a lesser degree when the extent of detailed balance breaking increases, i.e., when the available potential energy from light increases. We also find that the collective energy extraction is remarkably high, given that dynamics through which ecosystems assemble in our model involve local “selfish” growth rules with no awareness of the global collective energy extracted. Specifically, the energy extracted by our model ecosystems was 100-fold closer to the theoretical maximum when compared with ecosystems with random abundances. While the increase in communal energy extraction from random initial conditions toward a steady state can be explained in part by the model’s dynamics—species grow exponentially as they acquire more energy—a complete theoretical understanding of the origin of this surprising result remains difficult. This is because the dynamics and feedback in the model are more complicated—species that grow exponentially at first might start to die if enough others do not recycle the nutrients they need fast enough. Moreover, analytical progress is challenging because of the complexity of the model, requiring an extremely large number of simulations. We believe that simpler toy models—of self-organized non-equilibrium systems with greedy replicators—will better capture the essence of these systems. Such future work might be able to delineate the necessary and sufficient conditions for atypical energy extraction by closed ecosystems. While there is a growing body of work documenting functional convergence in ecosystems ( 38 , 45 – 47 ), our work expands the domain of functional convergence to explicitly thermodynamic features and the impact of the environmental driving potential. Our basic result is that functional convergence is strongest for weakly driven near-equilibrium ecosystems; strong external driving potentials decrease convergence. This result suggests a deep theoretical connection between different manifestations of functional convergence as representing the multiplicity of ways in which communities break detailed balance. To calculate the energy extracted, we made the simplifying assumption that organisms extracted energy equal to the entire energy gap between their respective donors and acceptors. Realistically, a fraction of the energy gap is extracted and stored as chemical energy (e.g., ATP) while the rest is dissipated ( 43 , 56 ). This can be incorporated in our framework, by including suitable “ATP coupling” parameters for every donor-acceptor pair. We expect that such an extension of our model doing will generally increase niche competition between species coupling the same donor-acceptor pairs, and thus might decrease total energy extraction. While aspects such as biodiversity, cross-feeding, and emergent ecological interactions are relevant to our work, our study did not focus on them since they have been considered extensively in previous work on open ecosystems ( 27 , 29 , 31 , 34 ). Instead, our manuscript focused on communal energy extraction—its convergence and dependence on detailed balance breaking by the physical environment—which are emergent thermodynamic properties that could only be simultaneously studied in a model like ours. Moreover, while the stability of complex ecosystems has been studied for more than half a century ( 32 , 33 , 35 ), the stability of emergent nutrient cycles in our work was due to a thermodynamic feedback mechanism distinct from previous work. While this manuscript focused on materially closed, energy-limited ecosystems, our theoretical framework can be extended in a variety of ways. Examples include extending the framework to account for the dual role of resources, as sources of both energy and biomass, where organismal growth would depend on which of the two—energy extraction or biomass generation—is limiting. Another is to extend the model to be spatially explicit, in order to study spatiotemporal pattern formation such as self-organized stratification in microbial mats and Winogradsky columns see related work ( 6 , 57 ). Finally, our work can help identify likely signatures of life on redox towers in astrobiological contexts, e.g., by studying the self-organized adjusted potentials like in Fig. 3 A and SI Appendix , Fig. S9 . In all these questions, redox constraints are essential to the underlying phenomena. Thus, our work opens up lines of inquiry in redox ecology. In addition to ecology, our framework could be used to understand the role of energy extracted in driving the evolution of living systems. Ecologists have widely argued that energy might serve as a natural fitness function during the evolution of biological communities ( 58 ). However, natural selection acts on individuals and not on directly on community function. Extensions of our work can provide a framework to understand the tension between “selfish” evolution of individuals and collective energy extracted by an ecosystem. Such a framework could be useful in guiding the engineering of evolutionarily stable photosynthetic communities. Another feature of our model is emergent detailed balance breaking. Unlike in other models of non-equilibrium systems ( 36 )—where the extent of detailed balance breaking is a fixed external quantity—in our work, we set a fixed external driving potential (e.g., that of light) in the redox tower but the amount of detailed balance breaking is determined by self-organization of the ecosystem (e.g., through species abundances and material abundances that change chemical potentials through product inhibition). As a consequence, e.g., there is a minimal non-zero external drive below which there is no detailed balance breaking ( Fig. 3 B , gray region). In this way, our work suggests an ecology-inspired framework for studying the emergence of spontaneously self-organized non-equilibrium steady states (NESS), adding to prior work on the origin of dissipative structures inspired by Rayleigh–Benard convection cells and other physico-chemical systems ( 16 , 59 – 65 )."
} | 1,937 |
35540083 | PMC9076254 | pmc | 5,179 | {
"abstract": "The influence of elastic deformation and elastic modulus on the release of adhered bacteria was investigated in this paper. Four silicone elastomers (SE) with different elastic moduli and one rigid polystyrene sheet were prepared to verify the antifouling effect of elastic deformation. The SE film has an elastic deformation effect under the stimulus of fluid medium, which makes the surface unstable. That could reduce the adhesion of fouling organisms and provide a foul-release basis. Distinct anti-adhesion properties were observed in our study in that cells more easily adhered to the rigid surface than the elastic surfaces under hydrodynamic conditions. However, the bacterial attachment test showed a similar antifouling performance of SE and the rigid surface under static conditions. To investigate the anti-adhesion ability of the elastic surface and rigid surface, the bacterial adhesive kinetics were studied by Discrete Element Method (DEM)–Computational Fluid Dynamics (CFD) coupling simulation. Results indicated the number of bacteria adhering on the elastic wall was significantly lower than on the rigid wall. And as the elastic modulus increased, the bacterial adhesion increased accordingly within a certain range. This work should not only enhance understanding of elastomer-based antifouling materials, but also facilitate the design and construction of other types non-toxic foul-release materials.",
"conclusion": "Conclusion In summary, we have demonstrated the anti-adhesion activity of silicone elastomers for investigating anti-fouling mechanism based on elastic deformation. The surface of GSE had elastic deformation under the stimulus of fluid medium, so it could reduce the adhesion of biofouling and provide foul release basis. Under simulated marine environment, the GSE coatings showed excellent anti-adhesion properties than rigid surface. Moreover, to investigate the anti-adhesion mechanism of the elastic materials, we described a novel method to explore the bacterial adhesive kinetics by DEM–CFD coupling simulation. Results indicated that the number of bacteria adhering on elastic wall was significantly lower than that of the rigid wall. As the elastic modulus increased, the adhesion of bacterial particles was increased accordingly at the same time. We hope that this work has not only provided new insights into deciphering elastic material based antifouling coatings, but also will facilitate the design and construction of other types of elastic antifouling materials.",
"introduction": "Introduction Antifouling coatings are widely used on the hulls of ships to prevent the adhesion of marine organisms. Marine biofouling is a widespread problem in the maritime industry, and it has serious impacts, such as increased navigation resistance, higher fuel consumption, and decreased navigation speed. 1–4 In the recent past, several environmentally benign strategies have been proposed increasingly to control biofouling, such as foul-release coatings. 5–7 Foul-release coatings provide a very smooth, low-friction surface and have an adjusted elasticity, which reduces the strength of adhesion of fouling. 8–10 The most promising coatings are generally based on silicone elastomers (SE), which have been considered environmentally benign. 11,12 SE has a number of properties, such as low surface energy, low microroughness and low modulus, which are necessary to reduce chemical and mechanical locking of fouling organisms. 13,14 Although all these properties are benefit to foul-release, but the surface instability, which induced by the elastic deformation under the stimulus of a fluid medium, could inhibit the incipient bacterial adhesion. Understanding the antifouling mechanisms is conducive to the development of environmentally friendly fouling-resistant technologies. Biofouling occurs via the formation of biofilm, which triggered by the adhesion of primary colonizing bacteria. 15 In the early stages of biofilm formation, physical interactions are the first forces for bacteria adhesion, which frequently influenced by the physical properties of interaction surface. 16 As previous reports, the micron-scale deformations are presented on elastic surface, and it is benefit to antifouling. 17 However, the relationship between bacterial adhesion and the elastic deformation was not completely clear. This work describes an easy and novel method to investigate the influence of elastic deformation on antifouling effects. In this paper, four SE films with adjusted elasticity was prepared by varying the graphene concentration. This surface of graphene-SE (GSE) had elastic deformation effect under the stimulus of fluid medium ( Scheme 1 ), which could inhibit the adhesion of biofouling and provide foul release basis. Traditional ideas describing the mechanisms of foul-release coatings is that mechanical factors and surface chemistry are major determinants of adhesion strength, whereas settled cells of macro-fouling species appear to be more sensitive to surface chemistry. 13,18 However, a different relationship was observed in our study that cells were more easily adhere to the polystyrene (PS) sheet (rigid material) than the elastic surfaces ( Scheme 1 ). Scheme 1 Schematic illustration showing the different antifouling response of elastic film and rigid film under marine environment. A novel method was explored to investigate the bacterial deposition corresponding to flowing particles being captured by the elastic and rigid membrane. The bacterial adhesive kinetics, including bacterial motions and collisions, were simulated by coupling the Discrete Element Method (DEM) and Computational Fluid Dynamics (CFD) methods for the first time. 15,19 The simulations of particulates as described in this work can predict initial deposition patterns of both elastic membrane and rigid membrane. It evaluates the role of elastic deformation, which could influence the bacteria adhesion. Simulations considered 1 μm diameter as the bacterial particle size, which is about the same size as common marine microorganisms, such as Paracoccus pantotrophus ( P. pantotrophus ). Results indicated that there is a great difference of bacterial adhesion between elastic wall and rigid wall. The number of bacteria adhering on elastic wall is significantly lower than that of the rigid wall, indicating that the elastic wall is more effective to inhibit bacterial adhesion under the effect of water flow. The bacteria morphology will influence the adhesion rate for elastic wall, but for rigid surface, the influence of bacteria morphology is ignorable. The purpose of this study was to investigate the initial attachment kinetics of bacteria on elastic surface and rigid surface, which should provide insight into the functions of elastic deformation. Moreover, this study demonstrated the usefulness of DEM–CFD technique for the investigation of particulate fouling, and that could enhance the understanding of the adhesion behavior of bacteria on different surfaces, facilitating the construction of eco-friendly coatings.",
"discussion": "Results and discussion Prepared and characterizations of elastic films and PS sheet To revealed the influence of elastic deformation on antifouling effects, four elastic surfaces of GSE film with different graphene concentration were prepared, and the content of graphene was 0 wt%, 0.18 wt%, 0.36 wt% and 0.72 wt%, respectively (Fig. S1 † ). Meanwhile, PS sheet (rigid surface) was prepared as a control. The surface morphology was characterized by SEM, as revealed in Fig. 1 . The pristine surface of SE was smooth ( Fig. 1a ), and the surface of GSE became crimpling after graphene was mixed in this film ( Fig. 1b and c ). However, the graphene was aggregation in the film when the content of graphene was 0.72 wt%. That might be induced by the high concentration of graphene nanosheets which made the compatibility decreasing between graphene and silicone rubber ( Fig. 1d ). The wrinkles in these surfaces are benefit to elastic deformation, and it should make the surface unstable and drive bacteria departing from these surfaces. Meanwhile, the elastic modulus measurement was conducted and the elastic modulus curves for these anti-fouling films were shown in Fig. 2a . We could conclude that the addition of graphene (GSE) decreased the elastic modulus compared to the pristine SE films (0 wt%), and the elastic modulus of 0.36 wt% GSE was the lowest than other GSE films. As previous reports, a low modulus is favorable for foul-release coatings. 23,24 When the concentration of graphene was 0.72 wt%, the elastic modulus was higher than 0.36 wt% GSE, that is corresponding with the result of SEM images. Therefore, either the property of elastic modulus or the ability of elastic deformation of 0.36 wt% GSE was more proper for fouling-release than others. The presence of graphene in the composite film was further confirmed by Raman spectroscopy. The Raman spectrum of GSE film displays two prominent peaks ( Fig. 2b ). One is G-band at 1588 cm −1 and another is relatively broad 2D-band of GSE film at around 2680 cm −1 . 25 Surface energy is one of the important factors which could affect bacterial adhesion and biofilm formation. So, the contact angle of elastic surface (GSE film) and rigid surface (PS sheet) were measured, and the surface energy of these materials were determined by previous reported method. 20 As revealed in Fig. S2, † it is observed that the mean surface energy values for the PS sheet and GSE films (0–0.72 wt%) were 28.9, 19.6, 19.2, 20.6 and 19.1 mN m −1 , respectively. According to Baier curve, the surface energy of PS sheet and GSE film are all close to foul-release zone. 26,27 Fig. 1 The elastic film of (a) pristine SE film (0 wt%), and GSE film with different graphene content, the content of graphene was (b) 0.18 wt%, (c) 0.36 wt% and (d) 0.72 wt%, respectively. Fig. 2 (a) The elastic modulus of pristine SE film and GSE film with different graphene content. (b) Raman spectra of the pristine SE and GSE films. (c) The surviving bacteria of P. pantotrophus incubated on rigid surface and antifouling surfaces. Representative digital images showed the rigid surface (outside the green border) and elastic surface (inside the green border) after incubated with P. pantotrophus for (d) 0 h, (e) 60 h and (f) 120 h in simulated marine environment. The graphene concentration of elastic surface is 0 wt%, 0.18 wt%, 0.36 wt% and 0.72 wt%, respectively. Inhibitory effects of elastic film and rigid film on bacteria adhesion and biofilm formation Bacterial biofilms are important initiators for the successful settlement of marine organism. Therefore, we compared the anti-adhesion ability between the rigid surface and GSE surface under different conditions, including static condition and simulated marine environment. Firstly, the PS sheet and elastic films were incubated with bacteria for 24 h in quasi-static condition, and the results were determined by optical density (OD) measurements. As Fig. S3 † revealed, the quantities of bacteria adhering to rigid surface and elastic surface were almost same. That might be attributed to the similar surface energy of GSE films and PS sheet. Secondly, we designed an instrument to simulate the marine environment that boat sail in fluid medium, as described in Fig. S4. † These coatings were settled at the bottom of test area in this instrument, and the flowing speed was set as about 0.2–0.5 m s −1 . We systematically tested the adhesion efficiency of P. pantotrophus , which was chosen as a model to evaluate the anti-adhesion performance and mechanism. Fig. 2d–f showed the biofilm formation of P. pantotrophus on these surfaces, respectively. Compared with the rigid surface, a remarkable difference was observed in the elastic surface of GSE after 60 h and 120 h incubation. Clear biofilm bands were observed on the rigid surface after 60 h and 120 h incubation. In contrast, no evident biofilms were observed on the surface of 0.36 wt% GSE, indicating that most bacteria were expelled from the surface under the fluid medium. However, some of the biofilm removed from the rigid surface after 120 h incubation, that might because the life cycle and aging of biofilm, which resulted in the release of planktonic cells and biofilm dispersal. 28,29 Further tests of the biofilm formation were conducted by observing the number of colony-forming units on agar plate (Fig. S5 † ), and the numbers of bacteria were quantified ( Fig. 2c ). The results demonstrated that after 120 h culture, elastic GSE exhibited stronger anti-adhesion activity than rigid surface, and the anti-adhesion ability of 0.36 wt% GSE was the best. Meanwhile, the biofilm mass was quantified by the crystal violet staining method as well. 19 The results revealed that the 0.36 wt% GSE film were effective for biofilm inhibition (Fig. S6 † ), and it confirmed that 0.36 wt% GSE film exhibited the best inhibition ability toward bacteria adhesion and biofilm formation. Exploring the influence and mechanism of elastic deformation on antifouling performance by DEM–CFD coupling simulation The non-bactericidal film of GSE can expel bacteria through elastic deformation and make the surface unstable. The pressure pulsations of turbulent flow give rise to various fluid/solid interactions. The elastic modulus is defined as: E = F stress / L strain . According to this equation, under the same F (pressure pulsations), elastic materials, which have a lower elastic modulus than rigid materials, will achieve a larger deformation. In the ocean, the intensity of turbulence covers a wide-range, so that materials with a low elastic modulus are helpful for interacting with a wider range of flow. To further explore the anti-adhesion mechanism of GSE and study the influence of elastic modulus, DEM–CFD coupling simulation was used in this paper. DEM Mechanistic principles are implemented via the EDEM 2.3 software, which allows the consideration of particle contact through elastic Hertz models, and enables coupling with CFD data. 25 The coupling method used in this paper is one-way DEM–CFD coupling. It permits the fluid to influence particles only, while particles loading has no influence on the fluid. DEMs have been used to simulate the flow of granular materials in various applications. 30,31 In the process of DEM calculation, the contact model between particles is defined as soft-particle contact models, and a single particle is regarded as a calculation unit. From this simulation, we can calculate the relative displacement and the interaction force between the particles. The commercial software FLUENT 12.1 was used to solve the steady-state Navier–Stokes (N–S) equations under laminar flow hydrodynamics, 32,33 using the SIMPLEC (SIMPLE-consistent) algorithm, this expressed as: 1 where u g is speed, t is time, ρ g is density of liquids, ε g is void fraction of liquids; 2 where g is gravity, u g is dynamic viscosity of liquids, S is momentum sink and P is liquid pressure. \n S represent the summation of the drag force, and it is achieved through the calculation of the drag force produced by the relative motion between two phases. The momentum sink S is calculated by 3 where F D is the drag force in mesh cell, V is the volume of the CFD mesh cell. The interphase force between seawater and bacterial particles is mainly drag force, so we choose Ergun and Wen & Yu (Gidaspow) as drag models in the EDEM–FLUENT coupling modules, 34,35 which expressed as: 4 5 6 7 where ε s is the volume fraction of particles, ε g is the volume fraction of liquid, F d is the drag force of single particle, C D is the drag coefficient, d p is particle diameter, ρ g is the density of liquid, μ g is the viscosity coefficient of liquid, ν g is the liquid velocity, and ν s is the particle velocity. To establish the bacterial particles model, the morphology of P. pantotrophus was studied by SEM. The SEM images indicated these bacteria are spherical or ellipsoidal, and the diameter is 1–2 μm. In order to make the model similar to the real bacterial morphology, three types of bacterial model were established in EDEM particle modeling module, as described in Fig. 3 . The simulation parameters common to all tested configurations are presented in Table S1. † Fig. 3 SEM images of P. pantotrophus (a and b) and three types of bacterial models, including coccoid bacteria (c), dividing bacteria (d), and combined spheroidal (e) models. Consider the viscous contact between bacterial particles, the Hertz–Mindlin with JKR Cohesion was chosen as particle contact models. The contact force for bacterial particles was represented by the Hertz–Mindlin elastic contact model. 36,37 Except for the contact repulsive force, the cohesive nature of bacterial particles leads to an attractive force. Herein, we used the JKR cohesion model, 38 which was originally implemented to incorporate the van der Waals forces in the contact domain: 8 9 where R * is equivalent radius, and E * is equivalent Young's modulus. In this model, the cohesion force is mainly determined by the contact overlap ( δ ), interaction parameter, and the surface energy ( γ ). For this simulation experiment, the reasonable parameter setting can improve the accuracy of the results. Before the simulation calculation, it is necessary to determine the properties of each material and the relevant parameters. In order to better distinguish the properties of elastic surface and rigid surface, silicone rubber and stainless steel were selected as the model of elastic surface and rigid surface. And the corresponding parameters were presented in Table 1 . In this model, these simulation parameters are used common to all tested configurations. Parameters of materials Material parameters Elastic surface Rigid surface Poisson's ratio 0.48 0.30 Density (kg m −3 ) 1.03 × 10 3 8.03 × 10 11 Shear modulus (Pa) 1.5 × 10 5 1.9 × 10 11 Elastic modulus (Pa) 4.6 × 10 5 5.6 × 10 11 Parameter setting of bacterial particle factory Bacterial particles were randomly created one by one following a uniform law over the control surface ( Fig. 4 ), meaning that the probability of particles creation was equal and uniform for each point of the inlet window at each time step. In order to allow a high enough concentration of particles in the computational volume and favour particle-to-particle collisions and cluster formation, the bacterial particle creation rate was set to 100 000 particles per s and the generation method was spray form. The initial orientation is 60° from the particle formation surface and the initial average velocity of particles is set to 0.001 m s −1 . Fig. 4 Schematic diagram of EDEM–FLUENT coupling calculation model. The specific size of the calculation domain is length, width, height is 200 mm × 30 mm × 60 mm. The simulation parameters are usually set as empirical values, and the corresponding specific parameters of this paper are shown in Table S2. † The calculation time-step is set in accordance with EDEM setting principle, which is 18% of Rayleigh time-step. Grid size is 3 R min and the total simulation time is 0.075 s. After the above operation completed, the CFD coupling program can be started by opening the EDEM coupling connection. In the whole EDEM–FLUENT coupling simulation, the calculation of EDEM is completely controlled by FLUENT. FLUENT simulation First of all, the structured hexahedron mesh is generated by the Hypermesh software, which is shown in Fig. S7. † Boundary conditions for the model include inlet-velocity and pressure-outlet, as shown in Fig. 4 . The relationship between inlet-velocity and flow-time no-slip was described in Fig. S8. † The remaining walls were treated as wall boundary conditions. Before solving this model, the flow field is set as incompressible liquid, and the flow state is set as turbulent. Meanwhile, boundary condition of the inlet fluid velocity is set by user-defined function, and the velocity equation is expressed as: 10 where T is cycle, L is channel length, t is run time, X is the coordinate in the entry surface. The entry speed was changed over time, as revealed in Fig. S8. † Pressure-based solver and second-order upwind discretization is used to reduce the numerical diffusion. The outlet is set as atmospheric pressure (1.01 × 10 5 Pa). The convergence criteria are set as RMS = 1 e −005 , and the relaxation factor is reduced to make the calculation more stable. The domain used in this coupling simulation is shown in Fig. 4 , with size of 200 mm × 30 mm × 60 mm. The length of the domain was chosen to allow any inlet effects to dissipate and to ensure a developed flow towards the outlet end of the fetch. Results and analysis of EDEM–FLUENT coupling calculation The adhesion of bacterial particles on elastic wall and rigid wall at different periods was processed by DEM–CFD coupling methods, as shown in Fig. S9 † and 5 . In order to distinguish the adhesion state and free state, particles were dyed according to their speed, such as red, green and blue particles. When the velocity of particles is lower than 6.6 × 10 −10 m s −1 , they are all dyed blue, including static particles (Fig. S9 † ). When t = 0.01 s, bacterial particles float in the whole flow field and some of them adhere to the bottom walls under the effect of drag and gravity, but the adhesion amount on the elastic wall or rigid wall is not obvious. And the number of green and blue bacterial particles were basically same, indicating that the particles were in a state of low-speed movement. However, when t = 0.03 s, the particles adhesion to rigid wall is much higher than that of elastic wall. This might be related to the different physical properties of rigid wall and elastic wall, including Poisson's ratio, density, shear modulus and elastic modulus. When t = 0.075 s, the bacterial adhesion on the elastic wall is distinct from the rigid wall. Meanwhile, the particles speed decreased over time and the number of blue particles were increasing gradually, even most of them adhered to the wall. And the number of bacteria adhering on rigid wall was much higher than that of the elastic wall. However, a large number of bacterial particles were observed on the boundary. That might be induced by the adhesion phenomenon between bacterial particles, which made the floating bacteria aggregated on the adjacent elastic wall. The number of bacterial particles adhering to the elastic wall increases with time, but the number of bacteria occasionally decreases at the next point. That might be due to the bacterial particles not attached to the elastic wall firmly, and separating from the elastic wall under the effect of water flow. Fig. 5 The number of deposited bacteria in elastic surface (a) and rigid surface (b). (c) Bacterial adhesion on elastic wall and rigid wall at t = 0.075 s. The influence of bacterial morphology and elastic modulus on bacterial adhesion. In fact, bacterial morphology is not only spherical, some of them are irregular ellipsoidal, rod-shaped, and agglomerated spheroidal, etc. Therefore, it is necessary to simulate the adhesion of bacteria on the elastic wall with different bacterial morphology. In this study, three kinds of typical bacterial models are established, which represent coccoid bacteria, dividing bacteria and agglomerated bacteria (as shown in Fig. 3 ), respectively. The adhesion of these bacterial morphology on the elastic wall and rigid wall was shown in Fig. 6 . Results revealed that, for the elastic wall, the anti-adhesion effect for spherical particles was more effective than that of ellipsoidal and agglomerated spherical particles. But for the rigid wall, the adhesion of these bacterial particles was similar, and it was much larger than that of elastic wall, indicating that the elastic wall has the property to inhibit bacterial adhesion under the effect of water flow. Fig. 6 The evolution of deposited particles on elastic wall (a) and rigid wall (b) with different bacterial morphology. Based on the above results, we could conclude that the elastic deformation was benefit to anti-adhesion. To further verify the effect of elastic modulus on bacterial adhesion, three models of elastic wall with different shear modulus and elastic modulus were designed, and the parameters were presented in Table 2 . The deposited bacterial particles were counted and the results indicate that the adhesion of bacterial particles increased with the increase of elastic modulus at the same simulation time ( Fig. 7 ). So, we could conclude that the smaller of the elastic modulus is, the less of bacterial particles adhering to the elastic wall. This confirmed that the elastic transform could influence the adhesion of bacteria. Parameters settings of elastic walls for different models Models Model 1 Model 2 Model 3 Shear modulus (Pa) 1.50 × 10 6 1.50 × 10 5 1.50 × 10 4 Elastic modulus (Pa) 4.46 × 10 6 4.46 × 10 5 4.46 × 10 4 Fig. 7 Evolution with time of deposited particles under different elastic modulus."
} | 6,288 |
33924411 | PMC8069042 | pmc | 5,182 | {
"abstract": "Microbiological tools, biofertilizers, and biocontrol agents, which are bacteria and fungi capable of providing beneficial outcomes in crop plant growth and health, have been developed for several decades. Currently we have a selection of strains available as products for agriculture, predominantly based on plant-growth-promoting rhizobacteria (PGPR), soil, epiphytic, and mycorrhizal fungi, each having specific challenges in their production and use, with the main one being inconsistency of field performance. With the growing global concern about pollution, greenhouse gas accumulation, and increased need for plant-based foods, the demand for biofertilizers and biocontrol agents is expected to grow. What are the prospects of finding solutions to the challenges on existing tools? The inconsistent field performance could be overcome by using combinations of several different types of microbial strains, consisting various members of the full plant microbiome. However, a thorough understanding of each microbiological tool, microbial communities, and their mechanisms of action must precede the product development. In this review, we offer a brief overview of the available tools and consider various techniques and approaches that can produce information on new beneficial traits in biofertilizer and biocontrol strains. We also discuss innovative ideas on how and where to identify efficient new members for the biofertilizer and biocontrol strain family.",
"conclusion": "7. Conclusions and Future Prospects The earth is currently suffering from chemical pollution on all fronts. To ensure that the next generations have a healthy living environment, effective solutions must be found and executed promptly. Further development and increased use of biofertilizers and biocontrol agents is necessary for sustainable food production in the future. Currently, we have ~50 functional strains that are applied in agriculture ( Figure 1 and Supplementary Table S1 ). The demand for both biofertilizers and biocontrol agents is expected to rise, which provides a good basis for further development. The major problem regarding the current tools is the inconsistency of the products in field conditions. Using combinations of microbial strains that rely on different biocontrol or biofertilizer properties could alleviate the problem of inconsistency; this approach is already is in use to some extent. However, while the current tools are all based on the few well-known mechanisms of action, plant–microbe and microbe–microbe interactions are more multifaceted than that and need a thorough investigation. Detailed understanding of each microbial strain, their mechanisms of action, and components of a healthy plant microbiome will likely provide a firm basis for developing reliable tools to enhance plant health and growth in varying field conditions. To achieve this, the following are needed: (1) A continuous search for new efficient microorganisms, enabling healthy plant cultivation in various field conditions, is pertinent. The potential new microbiological tools are likely to be found in plants adapted to extreme, in nutrient-poor growth conditions, in resistant plants under pathogen exposure, and in environments with high biodiversity, such as forests. (2) Studies on microbial traits beneficial for plants should be extended beyond mechanisms already known. The research for new microbial traits should cover not only plant–microbe interactions, but also microbe–microbe interactions, including phage effects and transmission. (3) The focus should be turned from the rhizosphere towards the plant holobiont, including strains occupying all plant organs, both bacteria and fungi. A high number of microbial inoculants should be combined in the biocontrol and biofertilizer products, with the aim of plant microbiome engineering. (4) For enabling the production of such complex biological products, the bottleneck of formulation needs to be solved. Therefore, more efforts should be placed on the development of technologies for microbial culture and live preservation. With the recent enormous advances in molecular and microbiological techniques, a technological jump enabling microbial live preservation in various conditions should be more than feasible. (5) A thorough analysis of the capacity of the strains to enhance plant health and growth under several growth conditions should become a routine in biofertilizer and biocontrol agent discovery. There are frequent reports in the scientific literature on new potent strains identified, but usually they are only tested in the laboratory conditions for few of the well-known microbial traits, and in one or two plant-growth conditions [ 144 , 145 ]. The effects of inoculants need to be tested under stressful conditions, along with an analysis of changes in the microbiome of the holobiont. To conclude, a full array of well-studied essential microbiome components in a formulation easily applied on crops could provide reliable, consistent results of crop production in any field condition of future agriculture.",
"introduction": "1. Introduction The UN Food and Agriculture Organization (FAO) estimates that farmers will have to produce 70% more food by 2050 to meet the global needs of the predicted population of 9-billion people on earth ( www.fao.org ). A recent development is the popularity of plant-based food over meat and dairy production to balance the global greenhouse gas budget [ 1 ]. However, there is a simultaneous increased global concern on pollution by inorganic residuals of fertilizers, as well as effects of synthetic plant protection compounds on human and animal health [ 2 , 3 , 4 ]. Chemical fertilizers play an important role in fulfilling the continuously increasing food demand of the world population. The three major types of commercial fertilizers, nitrogen (N), phosphate (P), and potassium (K), are used to achieve maximum yields in crop production [ 5 ]. However, increased agricultural use of chemical fertilizers causes harmful impacts on ecosystems. Due to insufficient uptake of chemical fertilizers by plants, they eventually enter the water bodies through leaching, where they cause eutrophication. Furthermore, they can have various harmful effects on soils, resulting in depletion of water holding capacity and unbalanced soil fertility [ 6 ]. Besides being costly, they increase greenhouse gas emissions due to fossil fuel use in their production. For a long time, there has been a need to develop alternative, low cost, effective, and ecofriendly fertilizers, which work without disturbing the nature [ 7 ]. Another serious problem is the extensive use of pesticides, many of which are harmful not only for humans, but also for animals, such as pollinators [ 8 ]. Furthermore, pesticides may alter the composition of plant-associated microbial communities in the soil [ 9 ]. There have been attempts to reduce the use of toxic pesticides; for example, the European Union (EU) promotes use of less harmful chemicals in agriculture through the European Pesticide Regulation (EC) No. 1107/2009. Whereas the European Pesticide Regulation is the strictest one drafted among the four major agriculture producers, European Union, United States, China, and Brazil [ 10 ], even this document contains loopholes, which result in health hazards posed by pesticides for humans within the EU [ 11 ]. The reason for a high tolerance of toxic pesticides worldwide might be that a tight regulation of agrochemicals creates challenges in plant production and can result in crop-yield reductions. Furthermore, climatic change poses a threat of unpredictable yields due to increased abiotic and biotic stresses on crop plants [ 12 , 13 ]. Therefore, the search for environmentally friendly alternatives is becoming imperative. Microbiological tools, namely biofertilizers and biocontrol agents, which are bacteria and fungi capable of providing beneficial outcomes in plant growth and health, respectively, have been developed for several decades. Biofertilizers have a great potential to improve crop yields through environmentally friendly mechanisms [ 7 ]. A biofertilizer is defined as a product which contains living microorganisms that, when applied to soil, seeds, or surfaces of plant, colonize the rhizosphere or the plant internal tissues and induce plant growth. Biofertilizers are typically bacteria or fungi capable of nitrogen fixation, phosphate solubilization, sulfur oxidization, plant hormone production, or decomposition of organic compounds [ 14 ]. For example, Pseudomonas fluorescens K-34 produces organic acids, potentially responsible for phosphate release for the plant. P. fluorescens K–34, P. fluorescens 1773/K, P. trivalis BIHB 745, and Bacillus circulans are also capable of producing the plant hormone indole acetic acid (IAA) [ 15 ]. Overall, biofertilizers carry out nutrient cycling and ensure optimal growth and development of crops [ 16 ]. The microbial inoculants potentially replacing harmful pesticides are called biocontrol agents. Biological control, by definition, provides a non-chemical method for management of plant diseases by using other living entities, such as microorganisms. The biocontrol capacity of a microbe can result from production of antibiotic compounds, or enzymes capable of fungal cell wall lysis, depletion of iron from the rhizosphere, induced systemic resistance, and competition for niches with pathogens within the rhizosphere [ 15 ]. Production of one or more antibiotics is a mechanism most commonly associated with biocontrol ability. A number of biocontrol strains can also produce antifungal enzymes, for example chitinases, β 1,3-glucanases, proteases, or lipases, with the capacity to lyse fungal cells. Synthesis of low-molecular mass siderophores that chelate iron in the soil near roots can inhibit the proliferation of fungal pathogens [ 17 ]. For example, Pseudomonas trivalis strain BIHB 745 can produce siderophores [ 15 ], and the siderophores pyochelin and pyoverdine have been identified in P. fluorescens [ 18 ]. Many biocontrol strains can protect the host plant by out-competing phytopathogens for nutrients. They help the plant also by colonizing niches in the rhizosphere and preventing pathogens from infecting the plant [ 19 ]. In general, microbial inoculants are promising tools for sustainable agriculture, because, optimally, they can both support the health of the plant along with promoting plant growth and enhancing nutrient availability and uptake [ 20 ]. In this paper, we briefly review the microbiological tools that have been developed and consider their formulation and current status for application in crop production. We then discuss approaches potentially revealing new traits of biofertilizers and biocontrol agents, and how to screen for new strains usable in modern environmentally friendly agriculture."
} | 2,726 |
39397304 | PMC11471926 | pmc | 5,184 | {
"abstract": "Bacterial wilt caused by Ralstonia solanacearum is a destructive disease that affects potato production, leading to severe yield losses. Currently, little is known about the changes in the assembly and functional adaptation of potato rhizosphere microbial communities during different stages of R. solanacearum infection. In this study, using amplicon and metagenomic sequencing approaches, we analyzed the changes in the composition and functions of bacterial and fungal communities in the potato rhizosphere across four stages of R. solanacearum infection. The results showed that R. solanacearum infection led to significant changes in the composition and functions of bacterial and fungal communities in the potato rhizosphere, with various microbial properties (including α,β-diversity, species composition, and community ecological functions) all being driven by R. solanacearum infection. The relative abundance of some beneficial microorganisms in the potato rhizosphere, including Firmicutes , Bacillus , Pseudomonas , and Mortierella , decreased as the duration of infection increased. Moreover, the related microbial communities played a significant role in basic metabolism and signal transduction; however, the functions involved in soil C, N, and P transformation weakened. This study provides new insights into the dynamic changes in the composition and functions of potato rhizosphere microbial communities at different stages of R. solanacearum infection to adapt to the growth promotion or disease suppression strategies of host plants, which may provide guidance for formulating future strategies to regulate microbial communities for the integrated control of soil-borne plant diseases.",
"discussion": "Discussion Interactions between plants and associated microbial communities play a crucial role in promoting the productivity and health of plants in natural environments ( Mendes et al., 2011 ; Vandenkoornhuyse et al., 2015 ). The health of the host is largely affected by complex dynamic interactions among the host, microbes, and environment. Elucidation of the assembly and ecological functions of host plant-associated microbial communities under pathogen stress is essential for the application of related microbial communities in the sustainable future improvement of agricultural productivity ( Sessitsch et al., 2019 ). In this study, we first analyzed the effects of four stages of R. solanacearum infection on the assembly of the potato rhizosphere microbiome. We found that R. solanacearum infection and the duration of infection explained the significant variations in the potato rhizosphere microbial communities. Based on PCoA using the Bray-Curtis distance algorithm, we found the separation of R. solanacearum infection duration along the second principal component. In particular, the samples taken after 15 days of R. solanacearum infection showed distinct separation from the other three treatments, implying significant differences in the structure of the potato rhizosphere microbial communities across different stages of R. solanacearum infection. In addition, we observed a gradual decrease in the Shannon index of bacterial communities from T1QK to T3QK to T7QK, but the Shannon index of T15QK was significantly higher than that of the other three treatments. With increasing duration of R. solanacearum infection, the Chao1 index of the fungal communities gradually increased from T1QK to T15QK. These findings are consistent with previous studies, indicating the dynamic changes in the bacterial and fungal communities of the potato rhizosphere in response to stress during different stages of R. solanacearum infection ( Ahmed et al., 2022b ; Gao et al., 2021 ; Yang et al., 2023 ). Further analysis of the composition of the bacterial and fungal communities in the potato rhizosphere at different infection stages revealed that 2,665 and 458 core bacterial and fungal OTUs coexisted in potato rhizosphere soil samples at different infection stages. We observed significant differences in some dominant bacterial and fungal communities during different infection stages. For example, the relative abundances of Firmicutes and Chloroflexi were not significantly different between T1QK and T3QK, while T7QK showed significantly higher relative abundance of Firmicutes and significantly lower relative abundance of Chloroflexi than did the former two groups. We observed the lowest relative abundance of Firmicutes and the highest relative abundance of Chloroflexi in the T15QK treatment group. Studies have shown that Firmicutes is associated with C source utilization and can degrade cellulose, lignin, and wood fibers by secreting hydrolytic enzymes, while Chloroflexi is associated with the utilization of organic halides ( Wang et al., 2020 ). Metagenomic sequencing revealed that functional genes involved in C metabolism were significantly reduced in potato rhizosphere microbes at the most severely infected stage (T15QK). Additionally, it has been previously reported that the specific destruction of protective Actinobacteria and Firmicutes communities in the tomato rhizosphere increases the incidence of bacterial wilt ( Lee et al., 2021 ). At the genus level, many strains of Bacillus and Pseudomonas have been reported to produce broad-spectrum antibacterial compounds to inhibit the growth of pathogens, colonize host plants, and induce systemic disease resistance in plants; they are thus widely used as biological control agents against soil-borne diseases ( Ahmed et al., 2022b ). Here, we found that the relative abundance of Bacillus in the T3QK and T15QK groups was significantly lower than that in T1QK, while the relative abundance in the T7QK group was significantly higher than that in T1QK. Moreover, the relative abundance of Pseudomonas was higher in T3QK than in T1QK, while that in T7QK and T15QK were lower than that in T1QK. Certain strains of Sphingomonas are closely related to N fixation and can enhance plant survival under environmental stress by improving the soil environment and degrading toxic substances ( Xie and Yokota, 2006 ). Interestingly, in this study, the relative abundance of Sphingomonas gradually increased as the infection time increased, with the relative abundance being the highest in the most severely infected T15QK group. In terms of fungal communities, Ascomycota contains a large number of pathogens that are not pathogenic to crops and usually aggregate in plant roots, causing damage to the root surface and creating conditions for infection by certain pathogenic bacteria ( Yang et al., 2023 ). Here, we found that the relative abundance of Ascomycota in T3QK and T15QK was higher than that in T1QK and T7QK. Species of Mortierella have been reported to be associated with the suppression of soil-borne diseases and to participate in N transformation in the soil ( Wang et al., 2022 ). In this study, we revealed that the relative abundance of Mortierella was lower in T3QK and T15QK than in T1QK. These findings further support that the infection of R. solanacearum can result in significant alterations in the composition of the potato rhizosphere microbial community. This may be an indirect result of changes in potato root exudates following pathogen invasion. Previous studies have also highlighted that pathogen-induced changes in root exudation profiles may serve to control pathogens both by direct inhibition and by indirectly shifting the composition of the rhizosphere microbiome ( Gu et al., 2016 ). In the future, we will integrate additional experiments to delve deeper into the mechanisms of rhizosphere microbiota restructuring in potatoes under pathogen invasion. In addition to community composition, the ecological functions of potato rhizosphere microbial communities were found in this study to change during different infection stages. Similar to the microbial community composition, the KO, COG, and CAZy functional compositions of the T15QK samples were distinctly separated from those of the other three treatments on the NMDS2 axis, indicating substantial changes in the microbial community functions of the T15QK treatment group. The T3QK, T7QK, and T15QK treatment groups exhibited greater COG and CAZy functional diversity than did T1QK. However, T15QK had lower KO, COG, and CAZy functional diversity than did T3QK and T7QK. We observed that as the duration of infection increased (from T3QK to T15QK), the relative abundance of genes involved in signal transduction mechanisms, energy production and conversion, replication, recombination and repair, and the biosynthesis, transport, and catabolism of secondary metabolites in the potato rhizosphere significantly increased compared to that in T1QK. Among the four treatments, the T15QK treatment group had the lowest relative abundance of genes associated with cell wall/membrane/envelope biogenesis and the highest relative abundance of genes related to defense mechanism modules. These results indicated that the duration of R. solanacearum infection significantly affected the basic functions of the potato rhizosphere soil microbial communities and the information transfer process with the surrounding environment, thereby influencing the ecological effects of microbial communities in the soil environment ( Yang et al., 2023 ). C, N, and P in the soil are three key nutrient elements for soil fertility and plant growth and development. Soil microbes often participate in the transformation of C, N, and P through their metabolic activities ( Wu et al., 2016 ; Xiong et al., 2021 ). A recent study has shown that the rice rhizosphere bacterial microbiota can profoundly affect the N use efficiency of host plants ( Zhang et al., 2019 ). Here, we found that compared with that in T1QK, the relative abundance of N cycle-related enzyme genes (such as β-glucosidase and α-glucosidase) and N metabolism-related genes (such as nitrate reductase/nitrite oxidoreductase, NADH large subunit, and nitrate reductase/nitrite oxidoreductase) in the rhizosphere microbial communities in the T3QK, T7QK, and T15QK treatment groups decreased with increasing duration of infection. In particular, in the most severely infected T15QK treatment group, the relative abundance of C, N, and P cycle-related genes, such as phosphogluconate dehydratase, nitrate/nitrite transporter, NADH small subunit, and nitrite reductase (NO-forming)/hydroxylamine reductase, was the lowest among the four treatments. Previous studies have shown that the occurrence of soil-borne diseases is closely related to the imbalance of the plant rhizosphere microecology. On one hand, the imbalance of soil nutrients (such as C, N, and P) can lead to nutrient deficiencies or toxicity, weakening plant defenses and making plants more susceptible to diseases ( Huang et al., 2020 ; Liu et al., 2016 ). On the other hand, the metabolism of C, N, and P by soil microorganisms also affects the composition and activity of the soil microbial community. Changes in the metabolism of carbon, nitrogen, and phosphorus in the soil can lead to nutrient competition among different microbial groups, affecting the balance between beneficial and pathogenic microorganisms, and thus leading to disease outbreaks ( Cai et al., 2021 ; Wang et al., 2020 ). Overall, as the duration of potato infection by R. solanacearum increased, the rhizosphere microbial communities played a significant role in basic metabolism and signal transduction, but the functions involved in soil C, N and P transformation weakened, suggesting an adverse shift in the potato rhizosphere microecological environment. Through the investigation of the temporal dynamics of the potato rhizosphere bacterial and fungal communities during the four stages of R. solanacearum infection, we have gained a deeper understanding of the assembly and functional adaptability of the potato rhizosphere microbiome under pathogen stress. Our results showed that R. solanacearum infection led to significant shifts in the composition and functions of the bacterial and fungal communities in the potato rhizosphere. The relative abundance of some beneficial microorganisms in the potato rhizosphere, including Firmicutes , Bacillus , Pseudomonas , and Mortierella , decreased as the duration of infection increased. Moreover, the related microbial communities played a significant role in basic metabolism and signal transduction; however, the functions involved in soil C, N, and P transformation weakened. These results further support the dynamic changes in the composition and functions of potato rhizosphere microbial communities across different stages of R. solanacearum infection to adapt to the growth promotion or disease suppression strategies of the host plant. These findings further deepen our understanding of the reassembly strategies of host plant rhizosphere microbial communities under pathogen stress and contribute toward efforts to explore and utilize functional microbial resources."
} | 3,264 |
28924453 | PMC5598073 | pmc | 5,186 | {
"abstract": "Background The selective lignin-degrading white-rot fungi are regarded to be the best lignin degraders and have been widely used for reducing the saccharification recalcitrance of lignocellulose. However, the biological delignification and conversion of lignocellulose in biorefinery is still limited. It is necessary to develop novel and more efficient bio-delignification systems. Results \n Physisporinus vitreus relies on a new versatile peroxidase (VP)-based delignification strategy to remove enzymatic recalcitrance of corn stover efficiently, so that saccharification of corn stover was significantly enhanced to 349.1 mg/g biomass (yield of glucose) and 91.5% (hydrolysis yield of cellulose) at 28 days, as high as levels reached by thermochemical treatment. Analysis of the lignin structure using pyrolysis–gas chromatography–mass spectrometry (Py–GC/MS) showed that the total abundance of lignin-derived compounds decreased by 54.0% and revealed a notable demethylation during lignin degradation by P. vitreus . Monomeric and dimeric lignin model compounds were used to confirm the ligninolytic capabilities of extracellular ligninases secreted by P. vitreus . The laccase (Lac) from P. vitreus could not oxidize nonphenolic lignin compounds and polymerized β- O -4 and 5-5′ dimers to precipitate which had a negative effect on the enzymatic hydrolysis of corn stover in vitro. However, the VP from P. vitreus could oxidize both phenolic and nonphenolic lignin model compounds as well as break the β- O -4 and 5-5′ dimers into monomeric compounds, which were measured by high-performance liquid chromatography–electrospray ionization–mass spectrometry (LC–ESI–MS). Moreover, we showed that addition of purified VP in vitro improved the enzymatic hydrolysis of corn stover by 14.1%. Conclusions From the highly efficient system of enzymatic recalcitrance removal by new white-rot fungus, we identified a new delignification strategy based on VP which could oxidize both phenolic and nonphenolic lignin units and break different linkages in lignin. In addition, this is the first evidence that VP could break 5-5′ linkage efficiently in vitro. Moreover, VP improved the enzymatic hydrolysis of corn stover in vitro. The remarkable lignin-degradative potential makes VP attractive for biotechnological applications. Electronic supplementary material The online version of this article (doi:10.1186/s13068-017-0906-x) contains supplementary material, which is available to authorized users.",
"conclusion": "Conclusions In the study, firstly, a new fungal isolate, Physisporinus vitreus was found to show a strong potential to enhance the enzymatic hydrolysis of lignocellulosic biomass. After pretreatment with P. vitreus , the saccharification of corn stover was significantly enhanced to 349.1 mg g −1 biomass (yield of glucose) and 91.5% (hydrolysis yield of cellulose), as high as levels reached by thermochemical treatment [ 28 , 29 ] and reaches levels similar to that of biomass pretreated using thermochemical processes [ 5 , 6 , 30 ]. Analysis of the lignin structure after pretreatment with P. vitreus using Py–GC/MS, revealed significant demethoxylation and lignin degradation by P. vitreus , suggesting that P. vitreus is an efficient lignin-degrading fungus. Then study of extracellular ligninases from this highly efficient pretreatment system, we found a novel delignification strategy was based on VP, which could oxidize both phenolic and nonphenolic lignin units and break different linkages in lignin. Moreover, VP improved the enzymatic hydrolysis of corn stover in vitro reached to the level of biological treatment. On this basis, a new and efficient VP treatment system was established, and the role of VP in biological pretreatment also was verified. Moreover, we firstly indicated that VP can improve the enzymatic hydrolysis of corn stover in vitro. Overall, the VP from P. vitreus in this study shows a potential use in lignin depolymerization, which makes it a really interesting enzyme for biotechnological applications, such as production of biofuel and other high value-added bio-based materials.",
"discussion": "Results and discussion Enzymatic hydrolysis, component analysis, and enzyme production during pretreatment of corn stover with P. vitreus As shown in Fig. 1 a, corn stover without pretreatment were much more resistant to enzymatic hydrolysis and the yield of glucose only was 119.8 mg/g corn stover after 72-h hydrolysis. The conversion ratio of cellulose in raw corn stover was only 26.1%. Higher glucose yields and cellulose conversion ratio were achieved when the corn stover pretreated with P. vitreus . There was a rapid increase in the glucose yield and cellulose conversion ratio with increasing pretreatment time before 28-day pretreatment. At the same time, it can be easily seen that the weight loss of corn stover increased with an increasing pretreatment time. Thus, the glucose yield and cellulose conversion ratio after enzymatic hydrolysis were used to evaluate the effect of pretreatment by taking the weight loss into consideration. The maximum glucose yield and cellulose conversion ratio reached up to 349.1 mg/g corn stover and 91.5% after 28 days of pretreatment, increasing by 2.9 and 3.5 times, respectively, compared with untreated corn stover. The conversion of cellulose to glucose was higher than the fungal pretreatments reported previously [ 28 , 29 ], and similar to that of biomass pretreated with thermochemical processes [ 5 , 6 , 30 ]. The hydrolysis yields of various substrates by different fungal strains reported previously are usually ranging from 30 to 83% [ 29 ]. The hydrolysis yields of straw pretreated with thermochemical processes are usually about 80–100% [ 5 ]. Compared to thermochemical pretreatment, biological delignification has been considered as an advantageous alternative, because it is low-cost, environmental friendly, and does not produce inhibitors to fermentation [ 7 ]. The efficiency of pretreatment depended on fungus species due to versatile delignification strategies of white-rot fungi, and the delignification abilities of different fungi species vary greatly [ 31 ]. To date, only several fungi strains can improve the saccharification efficiency significantly as high as levels reached by thermochemical treatment [ 28 , 29 ]. Thus, development of novel and more efficient fungi strains like P. vitreus is necessary and meaningful. Fig. 1 \n a The glucose yield and hydrolysis yield of cellulose after enzymatic hydrolysis of corn stover pretreated with P. vitreus for different times. b The component analysis of corn stover after P. vitreus pretreatment for different times. c Enzyme production during P. vitreus pretreatment of corn stover \n Moreover, as shown in Fig. 1 a, after increasing pretreatment time from 28 to 35 days, a decrease in hydrolysis yield is observed. This result can be explained by the fact that digestibility of cellulose is affected by both lignin removal and lignin modification [ 32 ]. Removal of lignin from lignocellulose increased substrate hydrophilicity and enlarged the volume of accessible pores. This allowed more cellulase to infiltrate into the lignocellulosic matrix to access cellulose [ 33 , 34 ], thus improving saccharification of corn stover from 7 to 28 days of biological pretreatment. However, changes on the lignin surface take place during the pretreatment process that might increase the nonproductive absorption of cellulase could negatively influenced hydrolysis yields from 28 to 35 days of biological pretreatment [ 35 , 36 ]. This effect will be further studied in detail in a future work. As shown in Fig. 1 b, fungal pretreatment preserved most of the cellulose and removed 63.6 and 32.6% of the lignin and hemicellulose, respectively. A major drawback of biological pretreatment processes is the potential loss in sugar content. P. vitreus selectively degrade lignin and consume only little sugar while preserving most of the cellulose content. Although some sugar was consumed by P. vitreus , the final yield of glucose was improved compared to the untreated corn stover. In lignocellulosic biomass, cellulose and hemicellulose are densely packed by lignin layers, which protect them against enzymatic hydrolysis [ 37 ]. The improvement of glucose production might be mainly attributed to the oxidation of lignin by P. vitreus [ 3 , 38 ]. On one hand, fungal pretreatment can reduce saccharification recalcitrance of lignocellulosic biomass by lignin degradation [ 39 ]. On the other hand, biological pretreatment has also been shown to promote lignin modifications, such as changes in lignin hydrophobicity, which then decreased the unproductive adsorption of cellulase onto lignin [ 32 ]. In addition to the lignin degradation, there was a loss in hemicellulose content. Hemicellulose serves as a connection between lignin and cellulose fibrils and its degradation may contribute to reducing the natural recalcitrance of lignocellulosic substrates [ 40 ]. The decrease in hemicellulose content is likely a consequence of lignin degradation, which enhances the accessibility of hemicellulose for enzyme hydrolysis by xylanase [ 41 ]. Indeed, we detected xylanase activity (5 U/g corn stover) during the pretreatment with P. vitreus . Thus, the degradation of lignin promotes the degradation of hemicellulose, which also contributes to saccharification. The nonglucose sugar from hemicellulose might was a potential carbon and energy source for fungal growth. In general, fungal lignin oxidation plays an important role in improving saccharification yields of lignocellulosic substrates. The ability of white-rot fungi to degrade lignin is mainly attributed to the release of a battery of ligninolytic enzymes [ 12 , 13 ]. In order to analyze the high efficiency of this strain in enzymatic recalcitrance removal and degradation of lignin, we studied its extracellular ligninases (Fig. 1 c). The extracellular extracts of pretreatment cultures at different periods were assayed to determine the different ligninolytic enzymes. Two main ligninolytic enzymes were detected during pretreatment of corn stover with P. vitreus . One was identified as Lac; the other was a special MnP, which could oxidize Mn 2+ but was also able to oxidize veratryl alcohol (VA) and reactive black 5 (RB5) in the absence of Mn 2+ . This MnP was identified as a VP since it shares typical features of both MnP and LiP. For example, both VP and LiP oxidize VA, but only VP oxidizes Mn 2+ , RB5, and other dyes. To date, VPs had only been found in Pleurotus sp. [ 42 , 43 ] and Bjerkandera sp. [ 44 , 45 ]. This is the first report of a novel VP from white-rot fungus Physisporinus sp. As shown in Fig. 1 d, VP displayed high activity from the 2nd week on and reached a maximum activity of 18.9 IU/g corn stover at 14 days of pretreatment. In contrast, Lac reached its maximum activity of 15.8 IU/g corn stover at later period, after 35 days of pretreatment. Interestingly, major lignin degradation occurred by day 21 of pretreatment (Fig. 1 b), which implies that VP might play a key role in the degradation of lignin from P. vitreus . Py–GC–MS analysis of corn stover To obtain a more detailed insight into the chemical modifications of the lignin structure after fungal pretreatment, the untreated and 28-day treated corn stovers were analyzed by Py–GC/MS. Additional file 1 shows the pyrograms of the untreated and 28-day treated samples, which were dominated by peaks of phenolic compounds derived from the lignin moiety. The identities and relative abundances of the lignin-derived compounds released are listed in Table 1 . In both samples, syringyl- (S-) and guaiacyl (G-)-type phenols were released, with a predominance of the latter and similar distribution patterns, together with minor amounts of p -hydroxyphenyl (H-)-type phenols, which is in agreement with previous reports [ 46 , 47 ]. After fungal pretreatment, the total abundance of lignin-derived compounds decreased by 54.0%, which means that fungal treatment led to a considerable degradation of lignin. This is consistent with the component analysis of lignocellulosic materials after fungal pretreatment. In addition, the relative contents of most S-, G-, and H-type lignin derivatives decreased and even disappeared. For example, guaiacol (peak 4) derived from G-type lignin decreased by 49.5% and syringol (peak 14) derived from S-type lignin decreased by 31.2% after fungal treatment, while 4-methylguaiacol (peak 7), 4-ethylguaiacol (peak 9) derived from G-type lignin and 3,5-dimethoxyacetophenone (peak 23), 3,5-dimethoxy-4-hydroxycinnamaldehyde (peak 30) derived from S-type lignin disappeared. After fungal delignification, the total peak area of H-, G- and S-type lignin derivatives was reduced by 44.0, 52.5, and 60.1% respectively, indicating that P. vitreus preferentially degrades S-type units, followed by G-type units, and is least efficient at degrading H-type units. Thus, the presence of more methoxy groups in the lignin correlated with a higher degradation rate by P. vitreus , which agrees with the finding of our previous study [ 48 ]. Compared with the raw sample, the treated sample contained less S-type lignin derivatives, which means that fungal delignification could have led to a substantial demethoxylation of lignin, and part of the G and H units in the treated sample might be derived from the fungal demethoxylation of S units [ 49 ]. Table 1 Relative peak areas (%) of lignin-derived compounds identified by analytical pyrolysis Label Compound Origin Peak area (%) Corn stover Fungal-treated corn stover 1 Phenol H 0.7 ± 0.0 1.1 ± 0.2 2 2-Methylphenol H 0.6 ± 0.1 ND 3 4-Methylphenol H 0.6 ± 0.0 0.3 ± 0.1 4 Guaiacol G 4.1 ± 0.2 2.1 ± 0.1 5 2,4-Dimethylphenol H 0.1 ± 0.0 ND 6 4-Ethylphenol H 0.6 ± 0.1 0.6 ± 0.0 7 4-Methylguaiacol G 1.0 ± 0.1 ND 8 Benzofuran, 2,3-dihydro- G 3.6 ± 0.1 2.5 ± 0.1 9 4-Ethylguaiacol G 1.6 ± 0.1 ND 10 4-Methylguaiacol G 0.6 ± 0.0 0.4 ± 0.0 11 4-Vinylguaiacol G 4.9 ± 0.1 1.8 ± 0.1 12 4-Hydroxybenzaldehyde H 0.7 ± 0.1 ND 13 4-(2-Propenyl)phenol H 0.4 ± 0.0 ND 14 Syringol S 4.4 ± 0.1 2.8 ± 0.1 15 4-Hydroxy-3-methoxybenzyl alcohol G 0.7 ± 0.0 ND 16 2-Methoxy-5-propenyl-Phenol G 0.2 ± 0.0 0.4 ± 0.0 17 Vanillin G 0.5 ± 0.0 0.8 ± 0.1 18 4-Allylguaiacol G 0.4 ± 0.0 0.2 ± 0.0 19 4-Hydroxy-3-methoxy-Benzoic acid G 1.6 ± 0.0 1.0 ± 0.1 20 (4-Hydroxy-3-methoxyphenyl)acetone G 0.3 ± 0.0 0.3 ± 0.0 21 1,2,3-Trimethoxy-5-methylbenzene S 1.8 ± 0.1 ND 22 Homovanillyl alcohol G 0.7 ± 0.0 0.2 ± 0.0 23 3,5-Dimethoxyacetophenone S 1.9 ± 0.1 ND 24 4-Allylsyringol S 0.3 ± 0.0 0.1 ± 0.0 25 4-Allylsyringol S 0.3 ± 0.1 0.1 ± 0.0 26 Syringaldehyde S 0.3 ± 0.0 0.3 ± 0.0 27 4-Allylsyringol S 1.0 ± 0.1 0.2 ± 0.1 28 Acetosyringone S 0.6 ± 0.0 0.5 ± 0.0 29 3,5-Dimethoxy-4-hydroxyphenylacetic acid S 0.4 ± 0.0 0.4 ± 0.0 30 3,5-Dimethoxy-4-hydroxycinnamaldehyde S 0.1 ± 0.0 ND Total peak areas of H 3.6 ± 0.1 2.0 ± 0.1 Total peak areas of G 20.2 ± 1.5 9.6 ± 0.9 Total peak areas of S 11.0 ± 0.3 4.4 ± 0.2 \n S syringyl type lignin derivatives, G guaiacyl type lignin derivatives, H p -hydroxy phenylpropane \n The basic analysis of lignin structure using Py–GC/MS revealed that the selective white-rot fungus P. vitreus possesses strong demethoxylation properties and is a powerful tool for delignification. It may provide an efficient system for exploring new and effective delignification strategies. Purification and characterization of extracellular VP and Lac To verify the effect of ligninolytic enzymes in lignin depolymerization by P. vitreus , we purified and characterized the two ligninolytic enzymes we identified. As summarized in Additional file 2 , Lac and VP produced by P. vitreus were successfully purified by the use of hydrophobic and ion-exchange chromatography after fractionation by ammonium sulfate precipitation. The specific activity of Lac and VP increased from 3.2 and 3.7 U/mg protein to 147.7 and 126.4 U/mg protein, respectively. The final yields of Lac and VP were 22.2 and 20.4% with a purification factor of 46.2- and 34.2-fold, respectively. After completion of the purification steps, both Lac and VP extracts showed a single band on both SDS-PAGE and native-PAGE (Additional file 3 ). The apparent molecular mass of Lac and VP in P. vitreus were estimated to be around 54.8 and 50.5 kDa, respectively, based on SDS-PAGE. For native-PAGE, the 21-day culture crude extract only showed two bands: one was Lac and the other was VP, which was consistent with assaying the enzyme activity in the crude extract. This implied that Lac and VP were the main ligninolytic enzymes present during pretreatment of corn stover by P. vitreus . The pH optima of Lac and VP were pH 3.5 and 4.5, respectively (Fig. 2 a). Stability studies showed that VP was stable at a pH range of 3.0–7.5, and more than 50% of activity remained after 24 h, while Lac was stable at a pH of 4.0–8.0, and more than 50% of activity remained after 24 h (Fig. 2 b). At acidic and neutral pH, VP retained much more residual activity than Lac. In addition, VP was highly stable at its optimum pH, indicating a great potential for biotechnological applications. The optimal temperature of both Lac and VP was 60 °C (Fig. 2 c), which is similar to most ligninolytic enzymes [ 48 ]. Thermal stability studies showed that Lac retained 70% of its activity after incubation at 60 °C for 1 h and was stable at and below 50 °C after incubation for 2 h. VP was stable at and below 40 °C, and retained 60% of its activity after incubation at 50 °C for 1 h; however, VP activity decreased drastically at 60 °C (Fig. 2 d). Thermal stability at higher temperature in enzyme’s industrial application is advantageous, and surely the type of application would demand longer incubation times than 2 h with an in vitro enzyme preparation. Therefore, we further investigated that both Lac and VP retained more than 40 and 95% enzymatic activity at 40 °C and room temperature, respectively, after incubation for 2 days. Fig. 2 Effects of pH and temperature on the activity and stability of the purified Lac and VP. a Optimum pH; b pH stability after incubation for 24 h; c optimum temperature; d thermal stability at 40, 50, and 60 °C. The pH and temperature at which the enzyme retained the maximum residual activity was taken as 100%. Error bars shown are standard deviations of triplicate samples \n The kinetic parameters for oxidation of substrates including 2,2′-azino-bis (3-ethylbenzothiazoline-6-sulfonic acid) (ABTS), 2,6-dimethylphenol (2,6-DMP), guaiacol, MnSO 4 , H 2 O 2 , RB5, and VA, by purified VP and Lac are included in Table 2 . Lac from P. vitreus oxidized the typical laccase substrates ABTS, 2,6-DMP, and guaiacol, and the highest affinity was observed in ABTS, as reported for many other fungal laccases [ 19 ]. VP showed the lowest K \n m value (14.2 μM) for RB5, indicating that it had the highest affinity towards RB5. Moreover, P. vitreus VP showed a higher affinity towards VA ( K \n m = 213 μM) than the VPs found in P . eryngii ( K \n m = 3000 μM) [ 15 ] and B. adusta ( K \n m 4000 μM) [ 50 ]. A typical lignin polymer is 10–15% phenolic in composition, while the remaining 85–90% consist of nonphenolic polymer, which possesses a high redox potential [ 16 ]. RB5 and VA represent the high redox potential compounds and nonphenolic lignin model compounds. The high reactivity of VP with high oxidation–reduction potential compounds such as RB5 and VA indicates that VP has a strong ability for delignification. Table 2 Substrate specificities of Lac and VP purified from P. vitreus \n Substrate Wavelength (nm) Molar extinction coefficient ε (L mol cm −1 ) \n K \n m (mol L −1 ) \n V \n max (μmol L −1 min −1 ) \n K \n cat (s −1 ) \n K \n cat / K \n m (s −1 mM −1 ) Lac VP Lac VP Lac VP Lac VP ABTS 420 36,000 1.3 × 10 −5 \n 3.5 × 10 −5 \n 16.8 32.3 22.8 17.2 1791.9 487.5 2,6-DMP 470 49,600 2.8 × 10 −4 \n 4.4 × 10 −5 \n 5.2 66.7 7.1 35.5 25.1 804.2 Guaiacol 465 12,100 9.0 × 10 −4 \n 1.3 × 10 −4 \n 1.7 85.5 2.2 45.5 2.5 347.1 MnSO 4 \n 270 11,590 – 5.4 × 10 −5 \n – 18.2 – 9.7 – 179.1 H 2 O 2 \n 270 11,590 – 4.4 × 10 −5 \n – 39.7 – 21.1 – 475.4 RB5 598 59,800 – 1.4 × 10 −5 \n – 14.5 – 7.7 – 543.2 VA 310 4700 – 2.1 × 10 −4 \n – 23.1 – 12.3 – 57.7 \n The redox catalytic potentials of VP and Lac were determined by cyclic voltammetry and the CV data were shown in the Additional file 4 . The redox potential of Lac in this study is 0.578 eV, as reported for many other fungal laccases (0.48–0.78 eV) [ 15 ]. The redox potential of VP in this study is 1.131 eV, similar to many other reported fungal VPs (>1.0 eV) [ 51 ]. Compared to Lac, VP is a high redox potential enzyme with oxidative activity on a wide variety of substrates, which contain low and high redox potential compounds, phenolic and nonphenolic lignin units [ 23 , 24 ]. These properties of VP make this enzyme more suitable than Lac for delignification in biorefinery processes. Oxidation of lignin model compounds by purified VP and Lac from P . vitreus Six monomeric lignin model compounds and two dimeric lignin model compounds (Fig. 3 ) were used to study the reactions of Lac and VP with lignin: cinnamic acid (1), 3-methoxycinnamic acid (2), 3,5-dimethoxycinnamic acid (3), p-coumaric acid (4), ferulic acid (5), sinapic acid (6), guaiacylglycerol β-guaiacyl ether (7), and dehydrodivanillic alcohol (8). These monomeric and dimeric lignin model compounds represent substructures and linkages similar to those found in native lignin, respectively. Fig. 3 Lignin model compounds used in this study: (1) Cinnamic acid (H), (2) 3-methoxy cinnamic acid (G), (3) 3,5-dimethoxy cinnamic acid (S), (4) p -coumaric acid (H), (5) ferulic acid (G), (6) sinapic acid (S), (7) guaiacylglycerol β-guaiacyl ether (β- O -4 dimer), and (8) dehydrodivanillic alcohol (5-5′ dimer) \n The six monomeric lignin model compounds (Fig. 3 1–6), which represented three types of units in the lignin structure, are referred to as H, G, and S [ 52 ]. These compounds were divided into phenolic and nonphenolic structures and differ in their methoxylation levels. Both Lac and VP could oxidize the three types of phenolic monomeric lignin model compounds (4–6) completely. In addition, VP was able to oxidize nonphenolic monomeric lignin model compounds partially, and was able to degrade 20.6 ± 1.8% of the H- (1), 27.4 ± 1.1% of the G- (2), and 39.3 ± 1.2% of the S-type compound (3) after 48 h (Table 3 ). It appears that the more methoxy groups the lignin model compound contained, the higher the degradation ratio was [ 53 ]. This is consistent with the observation that analysis of lignin structure by Py–GC/MS as aforementioned, which revealed that fungal pretreatment predominantly led to the degradation of S-type lignins, followed by G- and H-type ones. Table 3 Monomeric lignin model compounds degradation by Lac and VP of P. vitreus \n Phenolic lignin model compounds Lignin structure types Nonphenolic lignin model compounds Substrate a \n Different enzyme addition Degradation ratio (%) Substrate a \n Different enzyme addition Degradation ratio (%) \n p -coumaric acid (4) VP 97.4 ± 1.4 H Cinnamic acid (1) VP 20.6 ± 1.8 Lac 98.7 ± 2.1 Lac 0.0 ± 0.0 Ferulic acid (5) VP 98.2 ± 1.0 G 3-Methoxy cinnamic acid (2) VP 27.4 ± 1.1 Lac 98.2 ± 0.9 Lac 0.0 ± 0.0 Sinapic acid (6) VP 99.3 ± 0.2 S 3,5-Dimethoxy cinnamic acid (3) VP 39.3 ± 1.2 Lac 99.2 ± 1.97 Lac 0.0 ± 0.0 \n a Numbers corresponding to compounds in Fig. 3 are included in brackets \nLac had no effect on nonphenolic monomeric lignin model compounds; however, VP could oxidize both phenolic and nonphenolic lignin units. Differences in substitution were expected to cause differences in the redox potentials of the lignin model compounds, which might have been reflected in the reactivity with the two different ligninolytic enzymes [ 53 ]. Laccases have a low redox potential, and it does not have sufficient energy to extract electrons from the nonphenolic aromatic substrates [ 54 ]. VP combined the catalytic properties of both LiP and MnP with high redox potential [ 20 ], which broadens the substrate specificity of VP and enabled it to attack both phenolic and nonphenolic compounds, which comprise 80–90% of the lignin [ 55 ]. To investigate the ability of VP and Lac to break linkages in lignin, two model dimers, β- O -4 (7) and 5-5′ (8), were used, which represent the most abundant (more than 50% of all interunit linkages) and the most resistant (C–C linkage) interunit linkages in native lignin, respectively [ 13 , 19 ]. Additional file 5a shows the HPLC chromatograms for the reaction of β- O -4 dimer with VP; the amount of substrate decreased dramatically upon treatment with VP, and two new major peaks were observed. Analysis of these two new peaks at 4.5 min and 5.8 min by LC–ESI–MS gave m/z values of 637 [M − H] − and 183 [M + H] + , respectively (Table 4 ), which indicates that the oxidization of β- O -4 dimer by VP may occur via depolymerization to a monomer and polymerization to a tetramer simultaneously. Sale and Kenneth [ 56 ] recently reported that VP could convert β- O -4 lignin dimer to monomeric products, but this reaction was competing with repolymerization. By optimizing the reaction conditions, such as VP loading, H 2 O 2 concentration, and pH, the equilibrium between depolymerization and polymerization could be controlled to a certain extent [ 56 ]. Additional file 5 b illustrates the HPLC chromatograms for the reaction progress of 5-5′ dimer with VP; the amount of 5-5′ dimer decreased dramatically upon treatment with VP and a series of new peaks was observed. Analysis of the new major peaks at 4.5, 5.2, 5.7, 6.3, and 8.4 min by LC–MS gave m/z values of 153 [M − H] − , 167 [M − H] − , 303 [M − H] − , 151 [M − H] − , and 301 [M + H] + , respectively (Table 4 ). This indicates that the degradation of 5-5′ dimer by VP occurs not only via the oxidation of side chains but also via cleavage of the 5-5′ linkage, which generated monomeric products, such as vanillic alcohol, vanillic aldehyde, and vanillic acid. To be representative of more condensed lignin structures, many researchers have studied 5-5′ model compounds, which resulted in bond cleavage through oxidative side-chain reactions, but no cleavage of the actual 5-5′ bond [ 19 , 57 , 58 ]. Lignin with relatively high numbers of C–C linkages compared to ether linkages are often referred to as condensed lignin [ 59 ]. Condensed lignin is frequently more rigid and less prone to degradation. In this study, we first found VP could cleave the actual 5-5′ linkage in vitro. In contrast, treatment of both β- O -4 and 5-5′ dimer with Lac led to the formation of precipitates, and then the reaction of supernatants with Lac was analyzed by HPLC which did not detect any compounds. These results indicated that Lac could not depolymerize the dimer to monomers. The formation of precipitates was due to the fact that Lac leads to polymerization of β- O -4 and 5-5′ dimer, which were consistent with previous reports. Ramalingam [ 18 ] treated the β- O -4 dimer with two types of Lac, and both treatments led to polymerization and the formation of precipitate; Lahtinen [ 60 ] also reported that polymerization of oligomers is a well-known reaction in the Lac oxidation of β- O -4 guaiacylic lignin model compounds. Lac caused polymerization via phenoxy radical coupling [ 61 , 62 ], and the most likely structure formed by C–C coupling of the free phenolic coupling would be the biphenyl structure [ 18 , 60 , 61 ]. Phenolic 5-5′ dimer can also further polymerize via phenoxy radical coupling to precipitate [ 57 , 60 ]. Compared with the polymerization of β- O -4 and 5-5′ dimer by Lac, VP could break both β- O -4 and 5-5′ linkages, which are the predominant linkage type and the most difficult-to-degrade linkage type in native lignin, respectively. In summary, these remarkable ability of VP to degrade lignin might be a key factor that confers P. vitreus its high efficiency (hydrolysis yield of cellulose was 91.5% after P. vitreus pretreatment) as a pretreatment system. Table 4 Molecular ions detected by LC–ESI–MS for the main products of dimeric lignin model compounds in VP-catalyzed reactions Substrate Retention time (min) Product peak a \n \n m/z \n Proposed products 7 (β- O -4 dimer) 4.5 A 637 [M − H] − \n β- O -4 tetramer 5.8 B 183 [M + H] + \n Dihydroconiferyl alcohol 8 (5-5′ dimer) 4.5 C 153 [M − H] − \n Vanillic alcohol 5.2 D 167 [M − H] − \n Vanillic acid 5.7 E 303 [M − H] − \n 2′,6-Dihydroxy-5′-(hydroxymethyl)-3′,5-dimethoxy-[1,1′-biphenyl]-3-carbaldehyde 6.3 F 151 [M − H] − \n Vanillic aldehyde 8.4 G 301 [M − H] − \n Dehydrodivanillic aldehyde \n a Product peaks marked on HPLC chromatograms in Fig. 4 \n \n Enhancement of the enzymatic hydrolysis of corn stover by VP from P . vitreus We next investigated the effectiveness of VP and Lac in corn stover bioconversion by including these ligninolytic enzymes in the enzymatic hydrolysis step. As shown in Fig. 4 , the hydrolysis of the lignocellulosic material using VP as a supplement to the commercial cellulase significantly improved the yield of glucose by 14.1% compared to cellulase alone ( P < 0.05), but Lac has no effect on the hydrolysis ( P > 0.05). This is consistent with the observation that Lac and VP have different ability in degradation of lignin model compounds investigated above. The VP used in this study could oxidize nonphenolic lignin units and break main linkages in lignin, which possibly improves the accessibility of cellulase to cellulose within the biomass [ 2 , 3 , 63 ]. In contrast, Lac could not oxidize nonphenolic lignin units and break main linkages in lignin, which limited delignification of Lac to reduce the enzymatic recalcitrance of biomass. Moreover, when commercial cellulase was supplemented with both Lac and VP together, the yield of glucose was similar to that with VP supplementation alone ( P > 0.05). VP improved and Lac had no effect on the enzymatic hydrolysis of corn stover, which further indicated that VP is a key enzyme in the degradation of lignin from P. vitreus . Fig. 4 Glucose yields of corn stover during simultaneous ligninolytic enzyme treatments and hydrolysis. The following enzyme combinations were used: commercial cellulase only (CK), commercial cellulase supplemented with purified Lac from P. vitreus (Lac), commercial cellulase supplemented with purified VP from P. vitreus (VP), and commercial cellulase supplemented with both Lac and VP (VP + Lac) (* P < 0.05; n.s . not significant) \n In recent years, many studies have demonstrated that white-rot fungal pretreatment can enhance the enzymatic hydrolysis of lignocellulose, which are due to versatile delignification strategies of white-rot fungi [ 31 ]. Its efficiency in these processes has been mainly attributed to the release of different ligninolytic enzymes [ 12 , 13 ]. However, it remains unclear which fungal delignification strategy based on ligninolytic enzyme plays a key role in unlocking the recalcitrant structure of lignocellulase and improves the enzymatic hydrolysis greatly. A detailed understanding of fungal delignification strategy will contribute to the development of lignocellulose biorefinery strategies based on the ligninolytic enzymes from white-rot fungi. Fungal pretreatment has some advantages, it is low-cost, environmentally friendly, and does not produce inhibitors to fermentation, while the potential loss in sugar content and long incubation times hinder its industrial applications [ 64 , 65 ]. Therefore, the treatment with the effective ligninolytic enzymes from fungi in vitro was imperative. Bio-treatment with the VP in vitro enhances the glucose yield after hydrolysis similar to fungal pretreatment but without the consumption of sugar during the pretreatment process. We are the first to report that VP can improve the enzymatic hydrolysis of corn stover in vitro. This opens the way to seek more efficient VPs for the enhancement of enzymatic hydrolysis."
} | 8,051 |
36850143 | PMC9961285 | pmc | 5,187 | {
"abstract": "Plastics are engineering marvels that have found widespread use in all aspects of modern life. However, poor waste management practices and inefficient recycling technologies, along with their extremely high durability, have caused one of the major environmental problems facing humankind: waste plastic pollution. The upcycling of waste plastics to chemical feedstock to produce virgin plastics has emerged as a viable option to mitigate the adverse effects of plastic pollution and close the gap in the circular economy of plastics. Pyrolysis is considered a chemical recycling technology to upcycle waste plastics. Yet, whether pyrolysis as a stand-alone technology can achieve true circularity or not requires further investigation. In this study, we analyzed and critically evaluated whether oil obtained from the non-catalytic pyrolysis of virgin polypropylene (PP) can be used as a feedstock for naphtha crackers to produce olefins, and subsequently polyolefins, without undermining the circular economy and resource efficiency. Two different pyrolysis oils were obtained from a pyrolysis plant and compared with light and heavy naphtha by a combination of physical and chromatographic methods, in accordance with established standards. The results demonstrate that pyrolysis oil consists of mostly cyclic olefins with a bromine number of 85 to 304, whereas light naphtha consists of mostly paraffinic hydrocarbons with a very low olefinic content and a bromine number around 1. Owing to the compositional differences, pyrolysis oil studied herein is completely different than naphtha in terms of hydrocarbon composition and cannot be used as a feedstock for commercial naphtha crackers to produce olefins. The findings are of particular importance to evaluating different chemical recycling opportunities with respect to true circularity and may serve as a benchmark to determine whether liquids obtained from different polyolefin recycling technologies are compatible with existing industrial steam crackers’ feedstock.",
"conclusion": "4. Conclusions This study focuses on comparing two different pyrolysis oils to light and heavy naphtha, and critically evaluates the suitability of using pyrolysis oil as a feedstock for producing polyolefins to close the gap in the circularity of plastic waste. Liquid samples obtained from the noncatalytic pyrolysis of PP were analyzed by several standardized tests. Analyses were also performed for two different naphtha samples for comparison purposes. The results show that although some of the physical properties such as density, initial boiling point, and sulfur content of the pyrolysis oils and naphtha are similar, pyrolysis oils examined in this study exhibit two major differences than the naphtha samples: (i) pyrolysis oils have a wider carbon distribution than naphtha. This difference, however, can be alleviated by integrating a condenser into the reactor or distilling the pyrolysis oil to obtain the desired carbon range. (ii) Cyclic olefins make up the majority of the pyrolysis oils, whereas the majority of naphtha samples are paraffinic hydrocarbons. If the studied pyrolysis oils are used as feedstock for steam crackers, excessive carbon formation may occur inside the naphtha cracker and operational issues may arise because of reactive unsaturated hydrocarbons present in the pyrolysis oil. The results demonstrate that the compositional differences prevent pyrolysis oil being used as a substitute for naphtha and a feedstock for steam crackers. Even after distillation, less than 10% of the pyrolysis oil in our study exhibited naphtha-like properties. This, however, does not mean that all pyrolysis oils fall under this conclusion. Pyrolysis oil properties are heavily dependent on system parameters, operating conditions, and catalyst attributes. It is also possible to combine pyrolysis with other chemical technologies to upgrade the properties of pyrolysis oil. Therefore, the findings of our study should not be generalized to mean that pyrolysis oil could never be used as a feedstock for steam crackers to close the gap in the circular economy. However, if pyrolysis oil is used as a feedstock for existing steam crackers and refinery infrastructure, it should exhibit naphtha-like properties. For making such an assessment, this study can serve as a benchmark for evaluating the naphtha-like feedstock properties of steam crackers.",
"introduction": "1. Introduction Plastics are ubiquitous and versatile materials, and they are used in all aspects of modern civilization at tremendous quantities. Four hundred million tons (400 Mt) of plastics are produced each year. It is estimated that the production of plastics will only increase and exceed one million tons by the end of 2050 [ 1 , 2 , 3 , 4 , 5 ]. After being used for their intended purposes, plastics complete their useful lifecycles and are discarded. Some plastics such as straws and utensils are produced for single use with a useful lifetime between seconds to minutes, whereas some plastics such as shampoo bottles or garbage bins can be used for longer durations with a useful lifetime between weeks to years. Regardless of the time scale at which plastics are discarded, their waste management becomes crucial because they degrade slowly. For instance, it takes up to 200 years for a plastic straw to degrade naturally [ 6 ]. Although a simple comparison between useful lifetimes and natural degradation durations points out the importance of waste management of plastics to prevent their haphazard accumulations in the environment, existing infrastructures cannot cope with the waste plastics, causing one of the biggest environmental challenges facing humankind: plastic pollution [ 1 , 2 , 3 , 5 ]. Global mass production analysis [ 7 ] of plastics shows that out of eight billion metric tons of plastics ever produced by 2017, 70% of them ended up in landfills or in aquatic life polluting our planet. Another 14% of them were incinerated to produce energy. However, this option causes the emission of greenhouses gases such as CO 2 at large volumes. The remaining 16% was recycled to obtain lower-value materials with a low efficacy [ 8 ]. These methods are not environmentally friendly and cause continuous consumption of natural resources (from crude oil or natural gas to plastics to waste plastics), favoring a linear economy. Plastics are, however, engineering marvels with high energy and chemical content. Producing 400 Mt of plastics approximately requires a consumption of 7% of crude oil and natural gas produced [ 3 ]. Considering the expensive and scarce fossil fuel resources used in plastic production, it is unfortunate to waste these resources for creating waste, polluting the environment, or losing their value to low-quality recycled products, which oftentimes cannot be recycled after several life cycles [ 9 , 10 , 11 ]. This process can be considered as an example of a linear economy because resources are linearly converted to plastics and eventually to waste. Although the linear system creates a massive economic value by producing and selling plastics, the result is the global waste plastic problem and the loss of value and resources. New and innovative approaches are needed to replace the linear economy of plastics by the circular economy. There are currently many efforts to break the linear system and repurpose or upcycle waste plastic to value-added products. One of the most recent examples is the chemical conversion of single-use PE to lubricants by a catalytic upcycling process using platinum nanoparticles supported on perovskites [ 8 , 12 ]. Producing lubricants from PE is of particular importance because they can be successfully recycled with infinite turns, creating a circular carbon economy [ 13 , 14 ]. The study also demonstrates that lubricants perform as well as their commercial counterparts and the conversion of PE to lubricants is economically feasible. Another viable option that can truly close the gap in the circular economy of polyolefins is the chemical conversion of waste to feedstock that are used to produce virgin polyolefins. Polyolefin precursors are produced from steam cracking of naphtha [ 15 ]. Naphtha is a hydrocarbon fraction, which usually constitutes 15–30% by weight of crude oil and has a boiling point range between 30 °C and 200 °C. It contains hydrocarbon molecules with 5–12 carbon atoms, mostly including saturated hydrocarbons such as paraffins and naphthenes with minor compounds including olefins and aromatics [ 16 ]. There are two types of naphtha blends produced from the distillation of crude oil in the refineries: (i) heavy naphtha which consists of mainly alkanes and cycloalkanes with a boiling point of 70 to 200 °C and is used to produce aromatics [ 17 ], and (ii) light naphtha (also known as low-boiling naphtha) which consists of mostly pentane and hexane derivatives and is fed to the steam cracker unit to produce polyolefin precursors [ 18 ]. If the objective is to obtain virgin polyolefins from the waste to achieve circularity, waste polyolefins should be converted to a compound that resembles naphtha and fed to the cracker unit. This is only possible by chemical recycling, a process that breaks down longer polymeric chains into smaller units which can be recycled into a range of useful materials. Various chemical recycling methods, such as pyrolysis, gasification, and hydrothermal processing, can be used to convert plastic wastes into gases, fuels, and other compounds. Yet, pyrolysis is a more viable choice if the intended product is a liquid that can be fed to the steam crackers [ 19 , 20 ]. Pyrolysis is the thermal degradation of hydrocarbon-based feedstock materials by heating in an oxygen-free environment at high temperatures (300–700 °C). Because of heating, large-chain polymers decompose into smaller hydrocarbons. The pressure is typically atmospheric although it can also be performed under vacuum. The pyrolysis performance also depends on the properties of the feedstock such as molecular structure including chain irregularities, branching, initiators, chain lengths, and crystallinity, etc. When a pyrolysis-like technology is applied, the carbon number of the cleaved polymers decrease, eventually reaching a point where it exists as liquid in pseudo-equilibrium with its vapor in the pyrolysis reactor. The properties of the liquid obtained by pyrolysis can be very similar to conventional fuels (in terms of energy content, octane and cetane number, and other physical properties such as density, viscosity, flash-point, etc.). What in turn determines the properties of these liquids is the chemical composition of the liquid (including aromatic content, distillation range, paraffinic content, etc.) [ 21 , 22 , 23 , 24 , 25 ]. Herein, the suitability of pyrolysis as a stand-alone chemical recycling technique for producing the precursors for virgin polyolefins is examined. Liquid products obtained from the noncatalytic pyrolysis of polypropylene at two different temperatures were analyzed to obtain physical properties and chemical composition by several chromatography techniques, in comparison to two different naphtha mixtures, namely light naphtha and heavy naphtha. Liquid samples were then distilled under vacuum and fractionated to examine if a portion of the pyrolysis oil can be used as naphtha. The results demonstrate that, although there is a very small fraction of pyrolysis oil consisting of saturated alkanes and naphthenes, pyrolysis oil obtained from PP exhibits distinct compositional differences than naphtha and cannot be used as a substitute for it.",
"discussion": "3. Results and Discussion Two different pyrolysis oils, shown in Figure 1 , were studied in comparison with light and heavy naphtha to examine the suitability of using pyrolysis oil for producing polyolefins to close the gap in the circularity of plastics. The first observed difference is the color of the samples. Py oil-1 is yellow, Py oil-2 is orange, and light and heavy naphtha are colorless. All four liquids, two different pyrolysis oils and naphtha mixtures, have similar density values ( Table 1 ) at 15 °C, as determined by the ASTM D4052 method. Pyrolysis oils have a density of 0.78-0.79 g/cm 3 , whereas the naphtha mixtures are slightly lighter having density in the range of 0.67 to 0.72 g/cm 3 for light naphtha and a minimum density of 0.73 g/cm3 for heavy naphtha. In addition to density, total sulfur amount is determined by ASTM D5453. The total sulfur content was determined to be higher in the Py oil-1 sample collected at lower temperatures compared with the naphtha samples, and within the desired specification values in the Py oil-2 sample collected at higher temperatures. Yet, it should be noted that the purity of pyrolysis oils depends on the purity of the waste plastics. If the waste plastic is contaminated with sulfur-containing compounds, pyrolysis mixtures will contain higher amount of sulfur. The boiling point fractionation was determined with vacuum distillation by the ASTM D1160 method and tabulated in Table 1 . Vacuum distillation results of light naphtha and heavy naphtha are similar, albeit a slightly lower initial boiling point (IBP) for light naphtha. Vacuum distillation results of pyrolysis oils, however, show major differences than naphtha blends. IBPs of pyrolysis oils are slightly higher than naphtha mixtures. The required temperature for 90% fractionation of both pyrolysis oils is around 380–385 °C, significantly higher than that of naphtha mixtures, 90% of which fractionate at a minimum of 170 °C. The fractionation of pyrolysis oils and naphtha liquids was also performed with simulated distillation (SIMDIST) to determine the volumetric fraction of pyrolysis oil that is similar to naphtha, based on where the final boiling point (FBP) of light and heavy naphtha are located in the distillation results of pyrolysis oils. This information is then used for determining the volumetric percentage of naphtha-like liquid in pyrolysis oils. The SIMDIST results in Table 2 show that light naphtha-like composition is 10–15% in Py oil-1 and 5–10% in Py-oil 2, whereas heavy naphtha-like composition is 30–40% in both Py oil-1 and Py oil-2. In Figure 2 , the alkane profile of the samples based on the carbon numbers are illustrated. The C8 fraction had the highest value in the pyrolysis oil samples. These values were determined as 28.7% for Py oil-1 and 25.6% for Py oil-2. The carbon number distribution of the pyrolysis oils varied in the range of C5–C44. The distillation studies and alkane distribution analysis point out that naphtha mixtures have a significantly lower number of carbons than pyrolysis oils. This is a major difference between naphtha and pyrolysis oil that may limit using the latter as a substitute for the former. Existing pyrolysis technologies are, however, equipped with a condenser that helps with decreasing the carbon number to the desired range. For instance, the patent disclosed by BlueAlp Innovations B.V. technology for chemical recycling of plastics describes the use of a partial condenser, which controls the composition of the pyrolyzed gas by the condenser temperature. In addition, pyrolysis oil can be distillated to obtain the desired fraction for downstream operations [ 26 ]. The carbon range of pyrolysis oil could be brought to the similar range with naphtha. What remains as a huge challenge is the compositional differences. To examine the compositional differences, the bromine number of each liquid was measured and given in Table 3 . The number of grams of bromine that will react with 100 g of the specimen under the conditions of the test is defined as the bromine number. The bromine number quantifies and indicates the aliphatic unsaturated fraction of the petroleum products. By using this method, an estimation of the percentage of olefins in petroleum distillates boiling up to approximately 315 °C can be obtained, albeit at a lower precision above the bromine number of 185 [ 27 ]. Py oil-2 has a higher bromine number (304) than Py oil-1 (85), and hence, higher olefinic content. Mangest et al. reported that straight-chain olefins, branched-chain olefins, cyclic olefins, and diolefins have bromine numbers between 63–235, 58–235, 134–237, and 185–352, respectively [ 28 ]. Based on these, it can be stated that Py oil-1 is likely to be composed of straight and branched-chain olefins, while the majority of Py oil-2 contains diolefins. Note that bromine numbers for light and heavy naphtha are significantly smaller than pyrolysis oils, indicating the major compositional differences in terms of olefinic content. Py oil-1 and Py oil-2 were distilled up to 210 °C to obtain naphtha-like fractionations so that their exact compositions can be obtained using PIONA. Fractionated pyrolysis oil samples are more suitable samples for PIONA analysis than their unfractionated counterparts because PIONA analysis is limited to hydrocarbons that have boiling points lower than 210 °C and a carbon number around C 11 [ 29 ]. The distillates were labelled as Py oil-1-F210 and Py oil-2-F210. Bromine numbers of the fraction of pyrolysis oils up to 210 °C are given in Table 3 . The distillation did not change the bromine number of Py oil-1, whereas it decreased that of Py oil-2 from 304 to 216, indicating high olefinic content. It should be noted that these numbers are still much higher than the bromine numbers of the naphtha samples. The PIONA analysis results are given in Table 4 . Saturated components (naphthenes, and paraffins) of light naphtha and heavy naphtha are approximately 98% and 91%, respectively, with remaining minor components being olefins and aromatics. The ratio of the saturated components in Py oil-1-F210 and Py-oil-2-F210 are approximately 36% and 35%, respectively, significantly lower than that of the naphtha blends. The majority of components in fractionated pyrolysis oils were found to be cyclic olefins (~44%). A high concentration of olefinic substances is typically obtained when the pyrolysis oil is obtained from PP, as reported in an earlier study by Kusenberg et al. [ 30 ]. A high fraction of i-paraffins in pyrolysis oils is attributed to using PP as a feedstock for pyrolysis. If PE without significant branching was used as a feedstock, a higher fraction of n-paraffins would be anticipated since PE is likely to decompose into linear hydrocarbons. Aromatics in pyrolysis oils formed during pyrolysis and their formation cannot be linked to other polymer resins such as polystyrene in the feedstock since virgin PP is used for pyrolysis to produce oils [ 24 , 31 , 32 , 33 , 34 , 35 ]. When pyrolysis is applied to waste PP, it produced a complex mixture of hydrocarbons with low selectivity to desired products (saturated hydrocarbons) and high selectivity to highly reactive olefins that may form deposits on the cracker walls and aromatics that are precursors for coke formation [ 35 ]. Kopinke et al. studied the relative rate of coke formation from a variety of 14 C-labeled hydrocarbons using tracer experiments under the experimental conditions of steam cracking of naphtha at 810 °C [ 32 , 33 ]. They found that there is a strong correlation between the structure of hydrocarbons and tendency for coke formation. Coking potential of hydrocarbons were found to decrease with increasing degree of saturation. In other words, coking tendency of hydrocarbons increase in the order of alkanes < olefins < acetylenes. In addition, coking tendency of naphthenes, an important component of naphtha, was found to be similar to or slightly higher than paraffins, and lower than olefins. Polycycling aromatic hydrocarbons such as acenaphthylene, 9-methylanthracene, and chrysene were found to have very high coking tendency than monocyclic aromatic compounds such as benzene and toluene. Fractionated pyrolysis oil mixtures composition of which are shown in Table 4 cannot be fed to steam crackers without decreasing olefin levels and aromatic levels to acceptable levels [ 32 , 33 ]. Kopinke et al. recommended acceptable levels of olefins in naphtha to be lower than 5% [ 32 ]. However, real light naphtha and heavy naphtha samples taken from the feedstock of ethylene plant and aromatics plants, respectively, of PETKIM contains low amounts (0.1%-0.3%) of olefins. A higher fraction of aromatics can be tolerated in the heavy naphtha since it will be used for aromatics production. For ethylene production, however, aromatic levels should be decreased to prevent coke formation in the steam crackers. The evaluation of pyrolysis oil compositions in terms of tendency for coke formation shows that significant operational issues would arise if these fractionated pyrolysis oils were to be fed to the steam crackers directly without any upgrading. Thus, as a stand-alone technology, pyrolysis oil can neither replace nor be blended with naphtha and is not a viable option for closing the circularity of waste plastics."
} | 5,257 |
26872039 | PMC5029171 | pmc | 5,189 | {
"abstract": "The chemolithoautotrophic microbial community of the rocky subseafloor potentially provides a large amount of organic carbon to the deep ocean, yet our understanding of the activity and metabolic complexity of subseafloor organisms remains poorly described. A combination of metagenomic, metatranscriptomic, and RNA stable isotope probing (RNA-SIP) analyses were used to identify the metabolic potential, expression patterns, and active autotrophic bacteria and archaea and their pathways present in low-temperature hydrothermal fluids from Axial Seamount, an active submarine volcano. Metagenomic and metatranscriptomic results showed the presence of genes and transcripts for sulfur, hydrogen, and ammonium oxidation, oxygen respiration, denitrification, and methanogenesis, as well as multiple carbon fixation pathways. In RNA-SIP experiments across a range of temperatures under reducing conditions, the enriched 13 C fractions showed differences in taxonomic and functional diversity. At 30 °C and 55 °C, Epsilonproteobacteria were dominant, oxidizing hydrogen and primarily reducing nitrate. Methanogenic archaea were also present at 55 °C, and were the only autotrophs present at 80 °C. Correspondingly, the predominant CO 2 fixation pathways changed from the reductive tricarboxylic acid (rTCA) cycle to the reductive acetyl-CoA pathway with increasing temperature. By coupling RNA-SIP with meta-omics, this study demonstrates the presence and activity of distinct chemolithoautotrophic communities across a thermal gradient of a deep-sea hydrothermal vent.",
"introduction": "Introduction Venting fluids at deep-sea hydrothermal systems export members of the extensive microbial communities of the rocky subseafloor. Within the fluids circulating through the seafloor, chemolithoautotrophs convert CO 2 to biomass using the geochemical energy created from chemical disequilibria when hydrothermal fluid mixes with seawater ( Amend et al. , 2011 ). Previous studies found high rates of carbon fixation and concentrations of organic carbon and ATP in venting fluids and plume waters ( Karl et al. , 1980 ; Jannasch and Wirsen, 1981 ; Jannasch et al. , 1989 ; Wirsen et al. , 1993 ). Energetic modeling suggests that subseafloor mixing zones are likely to be the most productive of vent habitats with respect to microbial growth, potentially providing an important source of carbon to the deep ocean ( McCollom, 2005 ). 16S ribosomal RNA (rRNA) gene surveys and culture-dependent approaches have revealed the extensive taxonomic diversity and metabolic plasticity of chemolithoautotrophs at deep-sea hydrothermal vents ( Reysenbach and Shock, 2002 ; Sievert and Vetriani, 2012 ). Further studies of diffuse hydrothermal fluids have shown that the population structure of the subseafloor community can vary with time ( Huber et al. , 2002 , 2003 ; Perner et al. , 2009 ), space ( Opatkiewicz et al. , 2009 ; Huber et al. , 2010 ), and geochemical gradients ( Huber et al. , 2007 ; Perner et al. , 2010 ; Akerman et al. , 2013 ). In addition, the genomes of key autotrophs from hydrothermal vents have revealed the broad array of CO 2 fixation pathways and energy acquisition strategies used by these organisms ( Takai et al. , 2005 ; Campbell et al. , 2009 ; Yamamoto and Takai, 2011 ; Meyer and Huber, 2014 ). Recent metagenomic and metatranscriptomic studies from hydrothermal plumes and diffuse hydrothermal fluids have provided additional insights into the metabolic potential and gene expression patterns of the microbial communities in hydrothermal systems (for example, Lesniewski et al. , 2012 ; Anantharaman et al. , 2013 ; Baker et al. , 2013 ; Anderson et al. , 2014 ; Urich et al. , 2014 ). Studies of hydrothermal vent plumes, which are a mixture of deep-water and hydrothermal vent microbes ( Dick et al. , 2013 ), demonstrated the metabolic versatility of plume microbial communities, with genes for ammonium, sulfur, and methane oxidation highly expressed ( Lesniewski et al. , 2012 ; Anantharaman et al. , 2013 ) and presence of both chemolithoautotrophic populations such as SUP05 and heterotrophic archaea ( Dick et al. , 2013 ; Li et al. , 2015 ). Targeting the subseafloor chemolithoautotrophic community is especially important for determining their role in the primary production of biomass by dark fixation. However, as these subsurface communities are difficult to sample, studies of their microbiology have relied on sampling vent fluids, which represent a mixture of subseafloor hydrothermal fluids and deep seawater consisting of 75–99% seawater and 1–25% hot hydrothermal end-member. This results in an extremely diverse microbial community ( Huber et al. , 2007 ) where the subseafloor microbial signal can be dampened by the overwhelming signature of seawater mixing. Stable isotope probing (SIP) is one powerful tool for focusing on specific active communities through incorporation of isotopically labeled substrates under near natural conditions ( Dumont and Murrell, 2005 ). During a SIP experiment, the active community will incorporate the labeled substrate and increase in biomass relative to the non-active fraction of the community. The active community can then be identified using various molecular techniques ( Dumont and Murrell, 2005 ; Neufeld et al. , 2007 ). Recently, SIP studies have expanded from purely taxonomic identification (for example, Lueders et al. , 2004 ; Bernard et al. , 2007 ; Glaubitz et al. , 2010 ) to using DNA-SIP to construct metagenomic libraries of the labeled populations ( Dumont et al. , 2006 ; Neufeld et al. , 2008 ). SIP studies have also coupled RNA-SIP with metatranscriptomics to link actively transcribed genes to specific microbial populations ( Huang et al. , 2009 ; Dumont et al. , 2013 ). The first study to attempt mRNA-SIP used reverse transcriptase-PCR and primers specific to naphthalene degrading genes to understand the specific bacterial species that were degrading this compound in groundwater ( Huang et al. , 2009 ). A more recent study was able to construct a metatranscriptome from a SIP experiment of lake sediments looking at aerobic methanotrophs and showed the presence of transcripts for methane oxidation, carbon assimilation and nitrogen metabolism ( Dumont et al. , 2013 ). Although SIP presents an excellent way to link community structure and function, there are limitations that must be considered. The active microbial community must be able to take up the labeled isotope and incorporate it into nucleic acids within a given incubation time under particular experimental conditions. Too short of an incubation may bias against microbes with slow growth rates and lead to incomplete labeling of the community, while too long of a time may lead to nonspecific labeling of the community (that is, cross-feeding) ( Radajewski et al. , 2003 ). It is also important to differentiate between assimilation and metabolism of a labeled isotope. For example, the general uptake of 13 C-labeled bicarbonate into anaplerotic pathways of the TCA cycle may result in nonspecific labeling of the community ( Yakimov et al. , 2014 ). Thus, it is important to take these limitations into account when analyzing SIP results. In this study, traditional metagenomics and metatranscriptomic analyses of diffuse vent fluids were coupled with RNA-SIP metatranscriptomics to determine which autotrophs were active under relevant subseafloor conditions, what metabolisms and carbon fixation pathways were used, and how these organisms function across different temperatures representing subseafloor thermal gradients. Experiments were carried out at Axial Seamount, a submarine volcano located on the Juan de Fuca Ridge and a site of long term geological, chemical, and biological study ( Chadwick et al. , 2010 ). 16S rRNA and functional gene surveys have shown that diverse Epsilonproteobacteria dominate across diffuse vents at Axial and their community structure can often be linked to the chemistry of the vent sampled ( Huber et al. , 2007 ; Opatkiewicz et al. , 2009 ; Akerman et al. , 2013 ; Meyer et al. , 2013 ). Epsilonproteobacteria were first isolated from hydrothermal vents in 2001 ( Campbell et al. , 2001 ) and all isolates to date are chemolithoautotrophic and either moderately thermophilic hydrogen oxidizers or mesophilic hydrogen and sulfur oxidizers ( Campbell et al. , 2006 ). Recent studies of hydrothermal vent Epsilonproteobacteria also show that many are capable of nitrate reduction and denitrification ( Campbell et al. , 2006 ; Vetriani et al. , 2014 ), as well as nitrogen fixation ( Meyer and Huber, 2014 ). In addition to Epsilonproteobacteria, Gammaproteobacteria, specifically SUP05 populations, were also shown to be abundant and active within both diffuse fluids and plumes at Axial ( Bourbonnais et al. , 2012 ; Akerman et al. , 2013 ; Anderson et al. , 2013 ). Although SUP05 remains uncultivated from the deep ocean, genomic evidence indicates these bacteria possess genes to carry out both sulfide and hydrogen oxidation, similar to many Epsilonproteobacteria ( Takai et al. , 2005 ; Campbell et al. , 2006 ; Anantharaman et al. , 2013 ; Meyer and Huber, 2014 ). Hydrogen oxidation is a more energetically favorable reaction compared with sulfur oxidation and thus may be an alternate energy pathway for these chemolithoautotrophs in venting fluids ( Amend et al. 2011 ). In addition to the dominance of sulfur and hydrogen-oxidizing microbes, Axial Seamount has also been a study site of subseafloor methanogenic archaea, with the detection, quantification, and isolation of many mesophilic, thermophilic, and hyperthermophilic strains from diffuse fluids ( Huber et al. , 2002 ; Ver Eecke et al. , 2012 ; Meyer et al. , 2013 ; Ver Eecke et al. , 2013 ), including isolation of the first nitrogen-fixing hyperthermophilic methanogen ( Mehta and Baross, 2006 ). Growth experiments with methanogenic archaea isolated from diffuse fluids at Axial indicate they require concentrations of hydrogen 10 times lower than previous estimates and have the potential for syntrophic relationships with hydrogen producing heterotrophs ( Ver Eecke et al. , 2012 , 2013 ). Building upon previous studies, we used metagenomics and metatranscriptomics to first establish the metabolic potential and gene expression patterns of the microbial community from a singular vent at Axial Seamount, Marker 113. We then used RNA-SIP combined with metatranscriptomics to separate out the chemolithoautotrophic signature in the mixed fluids in order to determine the key taxa and metabolisms representative of the warm, reducing subseafloor habitat. Results showed the metabolic complexity of the vent microbial community and highlight the diverse metabolic pathways of the potential members of the subseafloor chemolithoautotrophic community across different temperatures.",
"discussion": "Discussion The microbial communities in low-temperature diffuse hydrothermal fluids are metabolically and taxonomically complex because of the mixing of deep seawater and subseafloor vent communities. This mixing can make it difficult to tease apart the vent, seafloor, and subseafloor signatures from those of deep-water microorganisms growing in background seawater near the seafloor ( Akerman et al. , 2013 ). In this work, we set out to characterize the hydrogenotrophic chemolithoautotrophic community present in vent fluids using both meta-omics and RNA-SIP. Through metagenomics and metatranscriptomics, we first determined the taxonomic diversity and range of metabolic processes in un-manipulated diffuse fluids. We then used RNA-SIP metatranscriptomics in hydrogen-enriched incubations to specifically target the subseafloor chemolithoautotrophic community and to identify the key taxa and metabolisms occurring across different temperatures representative of the warm, reducing subseafloor habitat. Analysis of un-manipulated fluids showed a phylogenetically and metabolically diverse group of bacteria and archaea from both background seawater and vent environments including members of the subseafloor community. However, under experimental conditions at elevated temperatures, hydrogen amendment, and reducing conditions, diversity was greatly reduced. Still, distinct genera and metabolisms dominated the subseafloor autotrophic community when comparing different temperature regimes. In the shipboard experimental treatment of vent fluids, there were a number factors that needed to be addressed, including the introduction of oxygen, outgassing of methane and hydrogen, pH changes, limited access to samples, and timing of incubations. Hydrogen concentrations at Marker 113 are low, yet previous work has shown that subseafloor hyperthermophilic methanogenic archaea are abundant there and require a minimum hydrogen concentration of ~17–23 μ M for growth ( Ver Eecke et al. 2012 ). Given these known constraints, hydrogen was added to the SIP experiments at this level in order to mimic likely energy-rich, reducing, and warm subseafloor conditions. When hydrogen was not added, there was no label uptake at any temperature (30 °C, 55 °C and 80 °C) and little RNA was recovered (data not shown). The length of incubations is also a challenge to SIP experiments, as it is important to minimize cross-feeding, the uptake of the labeled isotope by a non-target community. The incubation time of the experiments was estimated from previous work with cultured representatives from mesophilic, thermophilic, and hyperthermophilic subseafloor communities ( Mehta and Baross, 2006 ; Ver Eecke et al. , 2012 ; Meyer and Huber, 2014 ). In our 80 °C experiments, the 36-h incubations resulted in almost all transcripts assigned to heterotrophic bacteria, indicating the consumption of labeled organic matter and an incubation time that was too long to capture the autotrophic community ( Supplementary Figure S1 ). Cross-feeding also appeared to occur, but to a lesser extent, in the 80 °C 18-h incubation, with about 30% of annotated transcripts assigned to the heterotrophic phylum Thermococci, and specifically to the genera Thermococcus , Palaeococcus , and Pyrococcus . A possible syntrophic relationship between hyperthermophilic heterotrophs like Thermococcus and methanogens has been previously observed ( Ver Eecke et al. , 2012 ) and may explain the labeling of this heterotrophic community even in our shorter 18-h incubation. The length of our experiments plus amendment with hydrogen resulted in RNA being concentrated in only a few density fractions ( Figure 1 ) and thus we were unable to taxonomically analyze all fractions to verify separation of 12 C and 13 C-labeled RNA. Our RNA-SIP experiments instead resemble experimental enrichments with a labeled substrate, in this case 13 C-labeled bicarbonate. In density fractions with low RNA concentrations, mainly in the 12 C-heavy and 13 C-light controls, we found a high abundance of transcripts for a common laboratory contaminant. Betaproteobacteria comprised a large percentage of the annotated transcripts from many of the 12 C-heavy and 13 C-light metranscriptomes, regardless of temperature. This group has recently been shown to be present as a common lab contaminant in extraction kit reagents and is abundant in sequence data of samples with low microbial biomass ( Salter et al. , 2014 ), consistent with our findings. Given the challenges of carrying out RNA-SIP under simulated subseafloor conditions, it was important to compare our experimental treatments to the Marker 113 metagenome and metatranscriptome of un-manipulated fluids, which represent the potential and active metabolic processes occurring within mixed seawater-vent fluids as they exit the seafloor. The Marker 113 metagenome was comprised of genes for a diverse array of aerobic and anaerobic metabolic processes including sulfur oxidation and reduction, hydrogen oxidation, denitrification, and methanogenesis ( Figure 6 ). Transcripts for all of these processes were also observed in the Marker 113 metatranscriptome. In addition, genes for many different carbon fixation pathways were observed and expressed, again showing the metabolic flexibility of vent communities ( Figure 4 ). Epsilonproteobacteria comprised over one-quarter of the 16S rRNA gene sequences in the metagenome and 8% of annotated transcripts in the metatranscriptome. Epsilonproteobacteria are an abundant and metabolically versatile group in many habitats at deep-sea hydrothermal vents, including diffuse vent fluids ( Huber et al. , 2003 ; Takai et al. , 2003 ; Campbell et al. , 2006 ). The metabolic flexibility of the Epsilonproteobacteria was observed in both the Marker 113 metagenome and metatranscriptome, with genes and transcripts for both hydrogen and sulfur oxidation present. The measured oxygen at Marker 113 was just below 7 μ M , indicating a low oxygen environment at the point of venting and the potential for use of oxygen or nitrate as terminal electron acceptors. Epsilonproteobacteria appeared to take advantage of these conditions, as both cytochrome c oxidase and denitrification genes that classified as Epsilonproteobacteria were present and expressed. In fact, in the Marker 113 metatranscriptome, all transcripts for the cbb 3 -type cytochrome c oxidase, along with 85% of all denitrification gene transcripts, were classified as Epsilonproteobacteria, demonstrating the genomic plasticity of this key phylum and the ability of these bacteria to flourish across redox gradients at hydrothermal vents and within the underlying seafloor. Methanogenic archaea are also important autotrophs at Marker 113. Long-term chemistry measurements at Marker 113 have shown anomalously high methane concentrations in the diffuse vent fluids, which suggest a biologically derived source and prominence of methanogenesis at this site ( Ver Eecke et al. , 2012 ). Previous work using quantitative PCR and sequencing of the mcrA gene for methanogenesis have shown the presence of different methanogen genera at a wide range of temperatures at Marker 113 ( Ver Eecke et al. , 2012 ). In this study, we observed the expression of genes associated with mesophilic, thermophilic, and hyperthermophilic methanogens including Methanococcus , Methanothermococcus , and Methanocaldococcus genera, respectively. In the Marker 113 metatranscriptome, 66% of archaeal transcripts and nearly half the transcripts for the mcr gene complex ( mcrABG) for methanogenesis were classified as the mesophilic group Methanococcus . Methanogens are thus present and active across a wide temperature range and are important contributors to deep-sea primary production at this site. In response to the hydrogen enrichment of diffuse vent fluids, the main autotrophic metabolism in the 30 °C and 55 °C SIP experiments was hydrogen oxidation coupled with use of oxygen or nitrate as a terminal electron acceptor. Molecular hydrogen is a key energy source for hydrothermal plume, diffuse fluid, subseafloor, and vent symbiont communities ( Petersen et al. , 2011 ; Wankel et al. , 2011 ; Anantharaman et al. , 2013 ). Within the 55 °C experiment, hydrogenase gene transcripts were classified as Caminibacter and Nautilia , both known thermophilic, hydrogen oxidizing, nitrate (or sulfur) reducing Epsilonproteobacteria genera ( Meyer and Huber, 2014 ). At 30 °C, hydrogenase gene transcripts were classified mostly as Sulfurimonas and Sulfuricurvum , specifically as Sulfurimonas sp. GD1 , Sulfurimonas denitrificans and Sulfuricurvum kujiense , all of which possess the capability to oxidize hydrogen (or sulfide) using either nitrate or oxygen. Although there are many mesophilic Epsilonproteobacteria that use both oxygen and nitrate, there is currently only one vent-associated cultured representative from the Sulfurimonas genus that has been shown to both oxidize hydrogen and reduce nitrate, Sulfurimonas paralvinellae , isolated from a vent at the Mid-Okinawa Trough ( Takai et al. , 2006 ). Sulfurimonas paralvinellae is a facultative anaerobe with optimal growth observed with nitrate as the sole electron acceptor ( Takai et al. , 2006 ). The results seen in the 30 °C SIP metatranscriptome suggest a similar metabolism for subseafloor Sulfurimonas species at Axial Seamount. Likely, the little oxygen present in the fluids was consumed during the 36-h enrichment with hydrogen in all incubations. Accordingly, the transition between aerobic and anaerobic metabolisms was seen in the 30 °C, and to a lesser extent in the 55 °C SIP experiments. In both the 30 °C and 55 °C, the cbb 3 -type cytochrome c oxidase gene was the cytochrome oxidase gene most highly expressed. The cbb 3 -type cytochrome c oxidase is present in bacteria only and has been shown to be a high-affinity cytochrome c oxidase, with the highest activity under low oxygen conditions ( Ekici et al. , 2012 ). The depletion of oxygen probably induced the expression of genes for nitrate reduction at 30 °C and 55 °C by Epsilonproteobacteria. Transcripts for nitrate reduction via the napA gene comprised about 2% of the total annotated transcripts in the 30 °C experiment and 0.5% in the 55 °C experiment. Nitrate reduction via the nap operon has been shown to be highly conserved and widespread throughout the Epsilonproteobacteria ( Vetriani et al. , 2014 ). NapA has been shown to be a high-affinity nitrate reductase and only expressed during low nitrate conditions ( Potter et al. , 1999 ), which may explain the prevalence of this gene within the Epsilonproteobacteria, as hydrothermal fluid is depleted in nitrate compared with deep seawater ( Vetriani et al. , 2014 ). At 30 °C, other mesophilic autotrophic groups, specifically SUP05, were not detected. However, we did not add sulfide to the enrichments, which may have been limiting further growth of sulfide-oxidizing autotrophs. SUP05 is a key sulfur and hydrogen oxidizer in the deep sea, oxygen minimum zones, and hydrothermal plumes ( Walsh et al. , 2009 ; Stewart et al. , 2012 ; Anantharaman et al. , 2013 ) and is present and active at Axial Seamount and specifically at Marker 113 ( Akerman et al. , 2013 ; Anderson et al. , 2013 ). Sequences identified to the SUP05 group were present in low concentrations in both the metagenome and metatranscriptome but were absent in the 30 °C SIP metatranscriptome. Given their ubiquity in the deep ocean and hydrothermal plumes where temperatures rarely exceed 5 °C, SUP05 populations likely have a lower growth optima temperature than 30 °C and thus are not expected to be present in our SIP experiments. In addition, another explanation could be the possible lack of nitrate reduction genes in SUP05 populations. A recent genomic study of two SUP05 isolates from a hydrothermal plume revealed they did not possess genes for nitrate reduction ( Anantharaman et al. , 2013 ). In our SIP experiment, the main metabolic process occurring at 30 °C was hydrogen oxidation using oxygen or nitrate. This lack of nitrate reduction genes could also explain why SUP05 was not present in our mesophilic SIP experiment. In the Marker 113 metatranscriptome, most transcripts identified as methanogenic archaea were classified as the putatively mesophilic Methanococcus genus, but in our SIP experiments, methanogenesis at higher temperatures was observed. Methanogenesis was the only autotrophic metabolism observed at 80 °C, with nearly 20% of transcripts annotated to methanogenesis genes and all were classified as the hyperthermophilic genus, Methanocaldococcus . At 55 °C, along with the thermophilic Epsilonproteobacteria, Methanothermococcus , a thermophilic methanogen, was present, making up 16% of the community. Although carbon fixation gene transcripts for the reductive acetyl-CoA pathway were observed at 55 °C and 80 °C, transcripts for Rubisco genes were also present and were all classified as Methanothermococcus and Methanocaldococcus . These methanogens are not using Rubisco for carbon fixation as they lack other important enzymes in the CBB cycle, namely phosphoribulokinase, but they have been shown to actively synthesize the enzyme ( Finn and Tabita, 2004 ). The function of Rubsico in methanogens is not fully understood, but it could have a role in purine recycling ( Finn and Tabita, 2004 ). These experiments show that at Marker 113, thermophilic and hyperthemophilic subseafloor methanogens are active in subseafloor carbon cycling. In conclusion, metagenomic and metatranscriptomic analyses revealed the high taxonomic diversity and extensive metabolic potential of microbial communities found in venting fluids at hydrothermal vents. RNA-SIP experiments under simulated subseafloor conditions revealed that different phylogenetic groups and metabolisms dominated at different temperature regimes. Future experiments at geochemically diverse sites will help to further determine the extent of taxonomic and metabolic diversity across hydrothermal vents and the key autotrophic metabolisms that occur under different redox and geothermal conditions."
} | 6,304 |
37180017 | PMC10168023 | pmc | 5,190 | {
"abstract": "The upcycling of poly(ethylene terephthalate) (PET) waste can simultaneously produce value-added chemicals and reduce the growing environmental impact of plastic waste. In this study, we designed a chemobiological system to convert terephthalic acid (TPA), an aromatic monomer of PET, to β-ketoadipic acid (βKA), a C6 keto-diacid that functions as a building block for nylon-6,6 analogs. Using microwave-assisted hydrolysis in a neutral aqueous system, PET was converted to TPA with Amberlyst-15, a conventional catalyst with high conversion efficiency and reusability. The bioconversion process of TPA into βKA used a recombinant Escherichia coli βKA expressing two conversion modules for TPA degradation ( tphAabc and tphB ) and βKA synthesis ( aroY , catABC , and pcaD ). To improve bioconversion, the formation of acetic acid, a deleterious factor for TPA conversion in flask cultivation, was efficiently regulated by deleting the poxB gene along with operating the bioreactor to supply oxygen. By applying two-stage fermentation consisting of the growth phase in pH 7 followed by the production phase in pH 5.5, a total of 13.61 mM βKA was successfully produced with 96% conversion efficiency. This efficient chemobiological PET upcycling system provides a promising approach for the circular economy to acquire various chemicals from PET waste.",
"conclusion": "Conclusions In this study, we report a chemobiological upcycling strategy to produce βKA from PET waste. PET depolymerization into the TPA substrate was achieved by microwave-assisted hydrolysis with the heterogeneous acid catalyst Amberlyst-15. The sulfonate group of Amberlyst-15 was easily recovered by sulfuric acid treatment and showed 97% catalytic efficiency for TPA conversion. The fed-batch bioconversion of TPA into βKA was successfully developed by regulating acetic acid accumulation and applying two-stage bioconversion. The engineered E. coli ( E. coli βKA (Δ poxB )) developed in this study enabled the production of 13.61 mM βKA with a 96% conversion rate. Despite the successful development of the bioconversion system, several issues should be considered. First, the development of the bioconversion system may require operating high concentration TPA by chassis engineering, such as adaptive laboratory evolution. Next, introducing an efficient TPA transportation system, such as the TPA transporter system of TPA-degrading bacteria Comamonas sp. E6 may be also necessary. The proposed chemobiological system for the valorization of PET waste provides an innovative strategy to reduce the overall environmental impact of plastic-based industries by energy-efficient bioconversion.",
"introduction": "Introduction Increasing awareness of environmental issues has inspired an intensified search for novel solutions to counteract the accumulation of plastic waste in landfills and oceans. 1,2 One contributor to plastic pollution is polyethylene terephthalate (PET), which is produced at a rate of 70 million tons annually and widely used in single-use packaging. In line with carbon-neutral policies and a circular economy approach, strategies for the upcycling of PET waste have gained increasing attention for their potential in reducing pollution and minimizing carbon dioxide emissions. When estimating the cost of recycled terephthalic acid (rTPA) sourced from conventional chemical recycling process of PET waste, the potential production costs of rTPA would be $1.93 per kg after production of rTPA with 69–83% less greenhouse gas (GHG) emissions but at a higher cost. Hence it is needed for further development in cost reduction. Suggesting further development in cost reduction. 3,4 To address the problem, several upcycling methods have been suggested, including chemical, biological, and chemo-biological upcycling. While enzymatic hydrolysis offers a promising biotechnological approach to PET degradation under mild conditions, but it has serious limitations, such as the prerequisite for amorphous or low-crystallinity PET necessary for the proper enzyme activity. 5 Recently, a combination of chemical depolymerization of PET and biological upcycling of TPA have been developed to produce value-added chemicals such as protocatechuic acid (PCA), gallic acid, pyrogallol, catechol, muconic acid, and vanillic acid. These chemicals have various industrial applications and can be produced through whole-cell bioconversion. 4–8 Chemical depolymerization processes of PET, such as glycolysis, methanolysis, aminolysis, and hydrolysis have all been extensively studied and vary by the type of reagent used. 9 Particularly, the hydrolysis process can directly produce TPA by cleavage of ester bonds in the PET chain in acidic or basic aqueous conditions without an organic solvent, thereby increasing the energy economy and allowing its direct utilization as the substrate for bioconversion. Microwave radiation can be further employed in hydrolysis to reduce the thermodynamic kinetic energies in PET depolymerization under mild conditions. 4 For being energy effective, the hydrolysis reaction should incorporate a heterogenous catalyst with an active surface group that functions as Lewis/Brønsted acids and bases along with an easily recoverable rigid framework. Accordingly, Amberlyst-15, a sulfonic acid-based styrene-divinylbenzene copolymer, is used to catalyze a variety of acidic reactions such as esterification, phenol alkylation, and condensation, and can also be used for hydrolysis. 10 Furthermore, Amberlyst-15's rigid non-corrosive macroporous structure provides physical and chemical stability allowing its recovery from the reaction solution via filtration and centrifugation. 11 The biological upcycling process of monomers hydrolyzed from PET can be accomplished using a whole-cell microbial catalyst suitably constructed. Because of its ease of manipulation, culturing, and scalability, Escherichia coli ( E. coli ) is the most well-established production host strain in the cell factory fields. 12 To initiate bioconversion, the substrates should not be toxic to microbial cells and should be able to pass through the cell membrane into the cytoplasmic space in the culture media, under suitable pH conditions. However, the bioconversion of TPA is limited by its potential toxicity which affects biocatalyst growth and metabolism capacity, and by its absolute concentration in reaction media resulting from the low solubility. In a previous study, TPA inhibited bioconversion and reduced the yield of a biocatalytic reaction by over 1 g L −1 . 5 Therefore, overcoming the TPA-induced inhibition is crucial to develop an efficient biological upcycling process with sustainability. In this study, we present a chemobiological approach for producing β-ketoadipic acid (βKA) from PET waste. To validate our proposed system for βKA production, we utilized an actual disposable plastic coffee cup to establish proof-of-concept, as experiments conducted solely with reagents would not be sufficient to demonstrate real-world applicability. Our approach involves hydrolysis of PET using Amberlyst-15 (as shown in Fig. 1 ) under microwave conditions, followed by bioconversion with E. coli strains that overexpress enzymes involved in the βKA synthesis pathway. We chose to use the commercially available and cost-effective acidity catalyst, Amberlyst-15, to showcase the practicality of our system in industrial settings. To develop an efficient biological upcycling process and a TPA fed-batch conversion system, we profiled TPA conversion to identify deleterious factors under flask culturing conditions. Acetic acid accumulation, the critical deleterious factor for conversion, was controlled by deleting the acetate-forming gene ( poxB ) from the E. coli strain and modulating the dissolved oxygen (DO) level in the bioreactor. Finally, after determining the optimal pH for TPA transportation into the cytoplasm to improve substrate availability and enhance the bioconversion rate, a two-stage fed-batch bioconversion system was successfully developed. The proposed sequential waste PET upcycling composed of industrial catalyst-based PET depolymerization and followed by easily scalable whole-cell catalyst, E. coli strain-based βKA production, would be enabling a plastic circular economy. Fig. 1 Schematic illustration of the preparation of TPA from PET using the acidic catalyst, Amberlyst-15.",
"discussion": "Results and discussion Production of TPA by microwave-assisted PET hydrolysis with Amberlyst-15 We chose the PET waste model as a clear plastic disposable coffee cup to verify the ability of our proposed chemobiological PET upcycling system. The coffee cups were cut into small squares with dimensions of 1 × 1 and then grounded using a SPEX 6857D Freezer/Mill to obtain a powdered form for efficient hydrolysis (Fig. S1 † ). To hydrolyse the grounded PET powder in DI water without any additives under microwave irradiation, we employed the commercial heterogeneous catalyst Amberlyst-15, which has an active surface sulfonate group that promotes acidity (as shown in Fig. 2a ). The use of microwave heating during hydrolysis facilitated the transfer of heat to specific regions of PET, thereby accelerating degradation and potentially reducing the required reaction kinetic energy. The yield of TPA from PET was calculated to confirm the feasibility of the method and optimize the reaction conditions using Amberlyst-15. With increasing amounts of catalyst, the corresponding Brønsted acidic sites in the sulfonate group increased the level of hydroxonium ions, which in turn induced the nucleophilic attack of water on PET for depolymerization into TPA ( Fig. 2b ). 4 During the initial 10 min of the reaction, the efficiency of PET hydrolysis was constant; however, after 40 min, it increased linearly in time until it reached saturation at about 100% conversion efficiency. The initial pause in the reaction might be due to the random chain scission of PET, while the participation of depolymerized TPA accelerated the rate of the reaction ( Fig. 2c ). 16 In this study, we investigated the proposed chemobiological upcycling of hydrolysate TPA into high purity βKA. To achieve this, we filtered the solution for ethylene glycol (EG) that had also degraded from the PET granules. NMR spectroscopy confirmed the production of TPA by Amberlyst-15-mediated PET hydrolysis. The hydrolysate showed two main peaks representing aromatic single protons and hydroxyl protons at 8.03 and 13.31 ppm, respectively (Fig. S2 † ). We found that Amberlyst-15 could be easily recovered from the reaction solution by simple filtration and used up to the 4th cycle with a conversion efficiency of over 80% ( Fig. 2d ). We compared the FT-IR spectra of Amberlyst-15 at the first and 4th reaction to determine the state of the surface functional group ( Fig. 2e ). The characteristic peaks of the catalyst after the 4th round of reaction was comparable to those of the pure catalyst, except in the region 3500–3300 cm −1 around the peak at 3425 cm −1 corresponding to the O–H region in the sulfonate group. 17 Microwave radiation only affects the energy transfer during PET depolymerization, but has a negligible effect on the functional groups present on the surface of the Amberlyst-15 catalyst until its fourth reusability. To regenerate the sulfonate groups on the outer surface of the catalyst, we treated it with sulfuric acid, which unlocked the sulfonate groups and restored the catalytic efficiency of Amberlyst-15 to its initial state, as shown in Fig. 2e . The recoverable catalytic efficiency of Amberlyst-15 under the microwave irradiation following this simple treatment with sulfuric acid can increase energy efficiency. Fig. 2 (a) Chemical structure of Amberlyst-15 (b) and(c) TPA yield over catalyst amount and reaction time, respectively (d) recyclability and efficiency of the catalyst after regeneration by immersion in sulfuric acid (e) FT-IR spectra of Amberlyst-15 before (red) and after (blue) hydrolysis of PET. Development of a biological upcycling process for βKA from TPA Building on our previous efforts to produce value-added aromatic monomers from PCA as a precursor, we designed artificial pathways composed of a TPA degradation module (pKM212 vector) for PCA production and a βKA synthesis module (pKA312 and pKE112 vector), a three-vector system overexpressing the enzymes needed for an effective PCA conversion ( Fig. 3 ). 18 E. coli XL1-Blue expressing these vectors ( E. coli βKA hereafter) was used to produce βKA from the substrate hydrolysate TPA in the presence of carbon sources such as glucose for initial cell growth and glycerol to supply redox potential for TPA conversion. The main carbon source used in this study was glycerol, which is a major byproduct of the biodiesel industry. The surplus amount of crude glycerol produced can cause environmental issues, and thus, there is a demand to utilize it efficiently. Flask cultivations of E. coli βKA were repeatedly conducted to identify potentially deleterious factors hindering the biological upcycling process. We found that all enzymes encoded in E. coli βKA were expressed, enabling the successful conversion of TPA into βKA with a 98% molar yield, as shown in Fig. 4 . Furthermore, the rate of TPA conversion was boosted in parallel with the activation of glycerol metabolism, indicating a correlation between glycerol consumption and βKA bioconversion. However, glycerol was slowly metabolized and was still present at the end of the conversion reaction. Because glycerol enters E. coli cells passively via the GlpF glycerol facilitator protein, where it undergoes anaerobic metabolism to produce the glycolytic intermediate dihydroxyacetone phosphate, we postulated that the delayed glycerol consumption in the flask cultures of recombinant E. coli could be attributed to the limited oxygen level. From these observations, we hypothesized that the TPA conversion rate could be enhanced by improving glycerol metabolism. Furthermore, the steady accumulation of acetic acid as a byproduct was also observed, its concentration reaching 0.9 mM at the end of the reaction. Acetic acid is a potentially toxic compound, having deleterious effects on cell fitness by decreasing intracellular pH and leading to metabolic perturbation. Thus, its accumulation results in low bioconversion efficiency and should be appropriately regulated. 19 Taken together, bioconversion profiling of TPA in E. coli βKA indicated that improving glycerol metabolism as well as properly regulating acetic acid accumulation could be critical in developing a fed-batch conversion system to produce βKA. Fig. 3 Schematic illustrations of (a) the bioconversion pathway of TPA to βKA in the E. coli βKA strain and (b) expression vectors to construct βKA biosynthetic pathway from TPA. Fig. 4 Flask cultivation profiling by E. coli βKA in a minimal medium containing TPA as the substrate and glucose and glycerol as carbon sources for βKA production containing TPA as the substrate and glucose and glycerol as carbon sources for βKA production. All cultivations were done in duplicates. Improved biological upcycling process of βKA from TPA by controlling deleterious factors To support oxidative glycerol respiration, bioconversion of TPA into βKA was performed using E. coli βKA in a bioreactor with supplied oxygen (dissolved oxygen, DO level of 30%). As a result, glycerol metabolism was improved, and a total of 36 g L −1 glycerol was consumed, which is 3 times higher than that in the flask cultivation (12 g L −1 ). However, 4 g L −1 acetic acid was synthesized, which is 5 times higher than that in shaking flask cultivation ( Fig. 5a ). Additionally, 0.5 mM TPA was not entirely converted into βKA until the end of the bioconversion reaction, even though the initial TPA conversion rate was improved compared to that in the shaking flask cultivation. Under flask cultivation conditions, glycerol metabolism can easily switch from oxidative respiration to fermentative metabolism, resulting in an excess of nicotinamide adenine dinucleotide hydrogen (NADH). Therefore, fermentative glycerol metabolism generates enough NADH pools to support the initial TPA conversion. However, because the bioconversion of TPA into PCA occurs in a self-compensated manner, fermentative metabolism may trigger an intrinsic redox imbalance, leading to the formation of byproducts such as acetic acid to replenish NAD + content. Consequently, exposure to the toxic organic acid could deteriorate cell fitness and hinder bioconversion. We hypothesized that improved glycerol metabolism in the bioreactor induced the excessive formation of pyruvate or acetyl-CoA, which are precursors of acetic acid, leading to increased acetic acid formation. The accumulated acetic acid negatively affected the bioconversion of TPA into βKA. Therefore, they deleted the acetic acid-forming gene to regulate the acetic acid formation and improve TPA bioconversion into βKA. E. coli strains have three independent acetate-producing pathways: phosphotransacetylase ( Pta ), acetate kinase ( AckA ), acetyl-CoA synthetase ( Acs ), and pyruvate oxidase ( PoxB ). In the E. coli strain, PoxB only catalyzes the irreversible conversion of pyruvate into acetic acid, unlike the other acetic acid-forming genes. Thus, the poxB gene was deleted to regulate acetic acid production and improve the efficacy of βKA production, and E. coli βKA (Δ poxB ) was further constructed. In the presence of E. coli βKA (Δ poxB ) at a DO level of 30%, TPA bioconversion led to a dramatically reduced acetic acid accumulation–below 0.94 g L −1 , which was 4.2-fold lower than that measured when using E. coli βKA ( Fig. 5b ). In addition, E. coli βKA (Δ poxB ) improved bioconversion, as 3.26 mM βKA was synthesized from TPA with a 100% molar conversion yield. Thus, deletion of poxB gene efficiently regulated the acetic acid metabolism and reduced its accumulation enhancing bioconversion of TPA into βKA. The steady increase in cell growth and rates of glycerol consumption during bioconversion can be regarded as positive parameters indicating that E. coli βKA (Δ poxB ) could be applied for fed-batch bioconversion to maximize the production of βKA. However, the slow TPA conversion rate can be regarded as a negative parameter of the fed-batch bioconversion of TPA into βKA, indicating that TPA was not participating in the TPA conversion owing to poor transportation into the cytoplasmic space. Fig. 5 Bioconversion of TPA into βKA in (a) E. coli βKA and (b) E. coli βKA (Δ poxB ) at a 30% DO level with stepwise glycerol feeding. The red and blue arrows indicate the accumulation of acetic acid, respectively. All fermentations were done in duplicates. βKA production enhancement by two-stage fed-batch bioconversion In neutral pH (≈7), optimal for favourable bacterial growth, charge repulsion might occur between the aromatic carboxylic groups in the substrate and phosphate groups in the outer membrane of Gram-negative bacteria. 21,24 TPA, one of the aromatic carboxylates, exists as a negatively charged ion; thus, the bacterial membrane may prevent its diffusion through the surface of E. coli . 22,23 Instead, TPA uptake is known to occur in a moderate acid condition (<6) because carboxylic groups protonate allowing the passive diffusion of TPA through the bacterial membrane. 20 Therefore, considering the final pH of 5.5 at the end of the flask cultivation shown in Fig. 4 is consistent with the conditions required for maintaining E. coli strain viability, we designed a two-stage bioconversion process. Initially, appropriate E. coli βKA (Δ poxB ) was grown at pH 7 to express the relevant enzyme for the bioconversion of TPA into βKA. Then, pH was shifted from 7 to 5.5 to increase protonated TPA in reaction media and efficiently convert it to βKA. 20 At pH 5.5, the initial 3.2 mM TPA conversion was terminated within 24 h, which was 2.1-fold faster than the sole conversion stage occurring at pH 7 ( Fig. 6 ). Based on the increased reaction rate, TPA stepwise feeding was conducted using E. coli βKA (Δ poxB ). As a result, from a total TPA amount of 14.13 mM with stepwise feeding of TPA, we produced 13.61 mM βKA; this corresponds to a 96% conversion rate at the end of fermentation. As a result of stepwise feeding of TPA, a total amount of 14.13 mM TPA was used to produce 13.61 mM βKA, which corresponds to a 96% conversion rate at the end of fermentation. In addition, the two-stage fed-batch bioconversion system presented in this study exhibited comparable cell density (OD600 = 51) to that used at pH 7 (OD600 = 56). Collectively, our findings indicate that we have successfully constructed a chemobiological system for hydrolysing PET waste using Amberlyst-15 and biologically converting TPA into βKA. The synergetic effect of polar sulfonate groups producing hydrogen ions and the non-corrosive matrix of Amberlyst-15 was clearly demonstrated in the high catalytic activity and reusability for producing the virgin monomeric component of actual plastic cups, TPA. Subsequently, TPA was used as a sequential substrate to produce βKA with high conversion efficiency using a recombinant E. coli strain with deletion of the related byproduct-producing gene under a two-stage fed-batch bioconversion process. However, the inherent issue of insufficient absolute glycerol utilization suggests that further research on glycerol utilization pathways in E. coli is required to enhance βKA production titers to levels that are suitable for industrial applications. Fig. 6 Two-stage fed-batch bioconversion of TPA into βKA in E. coli βKA (Δ poxB ) under optimized conditions (pH 5.5 and 30% DO level) with stepwise TPA and glycerol feeding. Fed-batch fermentation was carried out once."
} | 5,472 |
34440597 | PMC8401924 | pmc | 5,191 | {
"abstract": "The large production of non-degradable petrol-based plastics has become a major global issue due to its environmental pollution. Biopolymers produced by microorganisms such as polyhydroxyalkanoates (PHAs) are gaining potential as a sustainable alternative, but the high cost associated with their industrial production has been a limiting factor. Post-transcriptional regulation is a key step to control gene expression in changing environments and has been reported to play a major role in numerous cellular processes. However, limited reports are available concerning the regulation of PHA accumulation in bacteria, and many essential regulatory factors still need to be identified. Here, we review studies where the synthesis of PHA has been reported to be regulated at the post-transcriptional level, and we analyze the RNA-mediated networks involved. Finally, we discuss the forthcoming research on riboregulation, synthetic, and metabolic engineering which could lead to improved strategies for PHAs synthesis in industrial production, thereby reducing the costs currently associated with this procedure.",
"conclusion": "3. Conclusions and Perspectives PHAs are polyesters synthesized and biodegraded by microorganisms, which are produced from large accessible renewable resources and have potential use for numerous applications. However, detailed understanding and subsequent optimization of their production and purification are still mandatory to reduce their production costs [ 10 , 35 , 41 , 44 ]. 3.1. Role of Post-Transcriptional Regulation during the Native Synthesis of PHAs Free-living bacteria often need to develop flexible and versatile metabolic and regulatory networks to adapt to fast fluctuations in nutrient availability. Therefore, the destiny of C aims to maximize bacterial fitness and safety [ 151 ]. Phylogenetic analysis of the ability of bacteria and archaea to synthesize PHAs has revealed extensive horizontal gene transfer events of the genes and corresponding transcriptional regulators involved in the accumulation of these polymers [ 64 , 155 ]. However, in the vast majority of cases, their post-transcriptional regulation still remains unknown. Riboregulation has a major role in the fine-tuning of multiple bacterial processes and is important to rapidly adjust cell growth in response to environmental changes [ 69 ]. sRNAs are non-translated small RNA molecules that are very important in the control of gene expression that usually silence their targets [ 68 ]. Ribonucleases are the enzymes that process and degrade all types of RNA and it is known that the RNA chaperone Hfq can protect RNA from the action of ribonucleases [ 53 , 81 ]. As shown in this review, the PHAs synthesis is also adjusted, directly or indirectly, through post-transcriptional regulation exerted by different kinds of RNAs molecules [ 61 , 62 , 63 , 64 , 128 ]. Although nothing has been published about the implication of RNases in the control of PHAs synthesis, they are expected to play an important role based on their marked importance in controlling other regulators and processes [ 54 , 72 , 156 ]. 3.2. Controlling PHAs Production in Bacteria via Synthetic Small Non-Coding RNAs Synthetic biology is a compelling and expanding interdisciplinary research field which intends to provide a systematic framework for the design and construction of biological systems. It relies on the application of logical engineering principles to program or reprogram cellular functions at a genetic and metabolic level ( Figure 4 ) [ 157 ]. One of the most important endeavors in contemporary synthetic biology is the search for optimal genomic chassis for industrial applications [ 158 , 159 ]. With this idea in mind, there has been a great effort to develop customizable regulators using genetic tools such as the CRISPR/Cas system, TALEs, and sRNAs [ 160 ], which would enable the precise control of gene expression, aiming to attain the desired functional outputs. Driven by the widespread role of post-transcriptional regulation in natural systems, the attention paid to RNA regulators is increasing [ 161 ]. Recent advances in nucleic acid engineering encourage the design of RNA components as building blocks in the construction of synthetic biological systems, mainly due to the plasticity of these molecules to interact with a myriad of proteins, metabolites, and other nucleic acids [ 162 ]. Synthetic RNA regulators display a wide range of programmable functions, offering important advantages over other protein-based mechanisms [ 156 ]. Among them, synthetic small non-coding RNAs (synthetic sRNAs) emerge as promising components to fine-tune gene expression. These customizable RNA regulators can be rationally designed to target different mRNAs, modulating their expression by altering their target-binding sequences ( Figure 4 ) [ 12 , 156 , 163 , 164 , 165 ]. Improving the quality and reducing the costs in industrial production of PHAs is a matter of pressing importance. In the future, synthetic sRNAs could be used to domesticate bacteria throughout the modulation of their genetic expression, in particular on the enzymes involved in the PHAs synthesis. The construction of these customized sRNA systems could be used for this purpose, in combination with the use and further development of plasmid genetic tools, such as the SEVA plasmids, for the modulation of genes-of-interest [ 12 , 156 , 163 , 164 , 165 , 166 , 167 ]. Figure 4 exemplifies this process. The MicC scaffold can be used to design tailor-made sRNAs that target the genes of interest, with the help of the Hfq protein. More details about this synthetic sRNA system can be found in [ 156 , 163 , 164 , 165 ]. It is important to continue investing the biotechnological domestication of microorganisms using synthetic biology and metabolic engineering to implement the portfolio of PHAs and improve strategies to lower the costs in industrial production [ 12 , 168 ]. In this review, we have indicated many examples of how post-transcriptional control can be an instrumental tool for the regulation of polyhydroxyalkanoates synthesis.",
"introduction": "1. Introduction 1.1. The Age of Plastics Petroleum-based plastics are pervasively used and appear as cheap and easy to make but at the cost of the environmental toll [ 1 , 2 ]. Eight million tons of plastic end up in the oceans every year, where they break down into micro-and nanoplastics [ 3 , 4 ]. Plastics have also been found falling out of the air in several mountain locations. This discovery suggests that, after the evaporation of the water, microplastics are carried around the planet in atmospheric winds, becoming part of the breathable air [ 5 ]. The impact of deposition of waste plastics in the land is also extremely relevant. Animals eat plastic and can get wrapped up, trapped, or asphyxiated by them [ 6 ]. In addition, plastics can easily enter the food chain and have adverse consequences for humans. During their processing and consumption, they release toxic additives that were used to shape them, harden them, or make them flexible, and these additives can enter into the food chain and water supply. For instance, bisphenol A (BPA), a common precursor of widely used plastics, was found in the urine of approx. 93% of the 2517 individuals tested in a study [ 7 ]. In addition, these molecules could interfere with our endocrine system since they are thought to adopt hormonal functions in the human body [ 8 ]. Therefore, to break the plastic wave, bio-based and biodegradable alternatives to synthetic plastics should be considered [ 9 , 10 ], especially with the drastic increase in plastic pollution due to the current COVID-19 pandemic [ 11 , 12 ]. However, the elevated cost of industrial procedures and lack of significant large-scale production [ 13 , 14 , 15 , 16 ], together with the availability of appropriated carbon sources, have limited faster progress in these processes and consequently greater market penetration [ 17 , 18 ]. 1.2. Polyhydroxyalkanoates: Bio-Based Biodegradable Plastics The word “bioplastics” has commonly been used to make a distinction from petrochemical polymers, which is partially misleading, since not all types of bioplastics are bio-based and biodegradable [ 16 , 19 ] ( Figure 1 A). Some bioplastics are biodegradable but fully fossil-based. Their chemical structure can be degraded in a slow process catalyzed by enzymes of some aerobic and anaerobic microorganisms that are widely distributed in various ecosystems. However, they are not biodegradable in animal bodies and sometimes they remain in marine waters [ 16 , 20 ] ( Figure 1 A, bottom-right). Others are bio-based but chemically identical to their fossil counterparts, so they are not biodegradable [ 16 , 21 ] ( Figure 1 A, upper-left). Only bio-based and biodegradable bioplastics are more ecologically friendly and serve as the best substitute for conventional plastics ( Figure 1 A, upper-right). Among them, one of the most promising class of bioplastics are the bacterial polyesters polyhydroxyalkanoates (PHAs), which are produced through industrial bacterial fermentation of sugar or lipids by numerous Gram-positive and Gram-negative bacteria [ 16 , 20 ]. Inside the cells, PHAs molecules aggregate to form water-insoluble granules, the carbonosomes, which are intracellular reserves of energy during starvation [ 22 , 23 ] ( Figure 1 B). In carbonosomes there is a constant cycle of synthesis and degradation, and this bidirectional process is a great advantage in the adaptation to rapid changes in the environment [ 24 , 25 ]. During the last few years, PHAs are being proclaimed as the best alternative to fossil-based plastic due to their good balance between biodegradability rate, material properties that range from thermoplastics to elastomers, and the possibility to be processed into different final products [ 9 , 10 , 26 ]. However, production costs of PHAs are still too high when compared to the synthetic plastics [ 13 , 14 ]. Although they have not yet reached industrial scale, in the last decade a more cost-effective processes for the production of PHA have been developed based on the use of wastes, industrial products and less energy-demanding approaches [ 27 , 28 ]. Once the process scale constraints are overcome, PHA will become more competitive and replace the synthetic plastics in many applications. Figure 1 Material coordinate system of plastics. ( A ) Type of plastics. Division of plastics into four groups, according to their biodegradability and biological origin. Upper-right, PHA: polyhydroxyalkanoates-are biodegradable polymers naturally produced by numerous microorganisms (Modified after [ 16 , 19 ]). (B ) PHAs: bio-based biodegradable plastics. When a carbon substrate is present in excess, in parallel to depletion of other nutrients essential for biomass formation, PHAs are stored in the form of cytoplasmic spherical inclusions. These PHA granules are multi-complexes usually called “carbonosomes”. They contain a hydrophobic core surrounded by PHA granule-associated proteins, such as PHA synthase, PHA depolymerases, regulatory and structural proteins (Modified after [ 24 , 29 ]). 1.3. Types and Chemical Structure of PHAs Polymers PHA generally consists of (R)-hydroxy fatty acid monomer units, which contain an alkyl side chain R group that varies in carbon length from methyl (C1) [ 30 , 31 ] ( Figure 2 ). These polymers are usually divided into three different types, according to the number of carbons in the monomeric subunits [ 31 ]. Short-chain-length PHA (scl-PHA) polymers are composed of monomers containing 3 to 5 carbon atoms, whereas medium-chain-length PHA (mcl-PHA) polymers are composed of monomers containing 6 to 14 carbon atoms. The third type are the long-chain-length PHAs (lcl-PHA), with a minimum 15 carbons [ 30 , 31 ] ( Figure 2 B). Their chemical properties are different and depend on the bacterial host and the fermentation conditions used for their production, making them suitable for different purposes. Scl-PHAs are highly crystalline, which makes them relatively stiff and brittle [ 30 , 32 ]. However, polymers with a greater number of carbons are more flexible and elastic, resulting in increased research interests [ 33 ]. PHAs are classified into homopolyesters, with only one variety of monomer, and heteropolyesters, which can be subdivided into copolyesters (monomers differing in either backbones or side chains) and terpolyesters (different side chains and backbones) [ 29 , 34 ]. The so-called polyhydroxybutyrate (PHB) is one of the most common homopolymer PHA and best studied scl-PHA, containing the shortest possible side chain with only one methyl group [ 35 , 36 ] ( Figure 2 B). The mechanical properties of PHB are comparable to conventional fossil-based plastics such as polypropylene or polyethylene [ 30 , 32 ], and are reaching new interest for applications in medicine, where chemical composition and product purity are crucial [ 37 ]. Other uses are food packaging and containers, utensils, biofuel, bottles, and disposable personal hygiene [ 30 , 32 , 38 ]. mcl and lcl-PHAs can be produced from many different substrates and have been studied in numerous bacterial species, particularly in pseudomonads [ 27 , 39 ]. The versatility in the physical properties of mcl-PHAs makes those materials appropriate for a wide range of applications, including daily use and medical purposes [ 10 , 28 ]. As described in [ 26 , 38 ], the uses comprise tissue engineering, orthopedic, urological and cardiovascular devices, wound management, and drug delivery, among others. 1.4. Natural PHA Producers and Engineering of Non-PHA Producers Although the list of natural PHA producers is large and includes extremophile bacteria, mainly Gram-negative species have been explored for their capacity to synthesize PHAs. Among this list, the most known are: Cupriavidus necator (previously Ralstonia eutropha ), Azotobacter vinelandii , and Burkholderia spp., as scl-PHA producers [ 31 , 40 , 41 ]; Pseudomonas strains (especially Pseudomonas putida ), as mainly mcl-PHA accumulators, while some strains are able to produce scl-mcl-PHA co-polymers [ 15 , 33 , 42 ]. Natural PHA-producing bacteria usually harbor the enzymatic repertoire for polymer degradation and are often difficult to lyse, which makes the recovery of PHA laborious and expensive. For this reason, engineered bacteria are currently being utilized as an alternative in the industrial PHA production, carrying pha biosynthetic genes, with Escherichia coli as one of the most used hosts [ 15 , 43 , 44 ]. In the last few years, new knowledge was gained about biosynthetic pathways (largely confined to Acetyl-CoA precursors) and the enzymes involved in PHAs accumulation [ 35 , 45 ]. Nevertheless, several aspects still remain elusive, and it is quite important to be able to regulate and improve the process. 1.5. PHA Composition and Preferred Carbon Source To produce PHAs, bacteria can use different carbon sources as substrate such as saccharides, fatty acids, alcohols, or gases [ 31 , 46 ]. Generally, different bacteria have preference for one of them depending on the metabolic pathways they harbor, so the metabolic routes in which those substrates are integrated are different, as well as the final product composition [ 31 , 35 , 47 ]. The metabolic flux from the intermediary acetyl-CoA to different PHA compositions is greatly dependent on nutrient conditions and the supplied carbon source [ 31 , 47 ]. Under carbon-rich conditions, the level of cellular coenzyme A increases substantially, causing the oxidation of acetyl-CoA into the Krebs cycle for energy production and cell growth. However, in the presence of unbalanced C/N conditions, acetyl-CoA can be used for the PHA synthetic pathways [ 31 , 47 , 48 ]. The genes ( pha ) that regulate the synthesis and degradation of PHA at the transcriptional level are widely known among the prokaryotes. In the extensively studied P. putida, the genetic organization of the pha genes integrates a very conserved pha cluster composed by two synthases ( phaC1 and phaC2 ) responsible for the PHA synthesis; a depolymerase ( phaZ ) encoding for the PHA mobilization; the transcriptional regulator ( phaD) ; and the regulatory and functional phasins ( phaF and phaI ) [ 12 , 25 , 49 ]. In the last few years, new knowledge has been deciphered about the PHAs synthesis and degradation in pseudomonads and other organisms [ 33 , 50 , 51 , 52 ]. However, the molecular regulation at the post-transcriptional level of PHA synthesis is still unclear and needs further investigation. 1.6. RNA World Post-transcriptional control of gene expression involves important enzymes such as ribonucleases (RNases), and bacterial small non-coding RNAs (sRNAs) [ 53 , 54 , 55 , 56 , 57 ]. In recent decades, RNA regulators were shown to be a key step in the control of many cellular processes. sRNAs are not translated into proteins and have the ability to post-transcriptionally modulate and regulate gene expression, in response to specific environmental or physiological signals, facilitating adaptation to diverse environmental stresses [ 58 , 59 , 60 ]. As reported for many other cellular processes, riboregulation has also been involved in the production of PHAs in different bacterial organisms. Herein, we describe the published work on different bacteria, where post-transcriptional control is the protagonist during the bioplastics synthesis; either shown to be involved in the control of important genes or used as a tool to control them [ 61 , 62 , 63 , 64 ]. This review enables the reader to acquire better knowledge on the molecular mechanisms underlying the bacterial accumulation of biopolyesters, emphasizing the post-transcriptional control, a neglected cellular regulation mechanism, as indicated by the reduced bibliography that is available. Furthermore, we provide new insights for the future domestication of microorganisms, which, in our view, have the potential to improve quality and reduce costs in industrial production of PHAs."
} | 4,535 |
28211468 | PMC5314357 | pmc | 5,192 | {
"abstract": "The use of lubricants (solid or liquid) is a well-known and suitable approach to reduce friction and wear of moving machine components. Another possibility to influence the tribological behaviour is the formation of well-defined surface topographies such as dimples, bumps or lattice-like pattern geometries by laser surface texturing. However, both methods are limited in their effect: surface textures may be gradually destroyed by plastic deformation and lubricants may be removed from the contact area, therefore no longer properly protecting the contacting surfaces. The present study focuses on the combination of both methods as an integral solution, overcoming individual limitations of each method. Multiwall carbon nanotubes (MWCNT), a known solid lubricant, are deposited onto laser surface textured samples by electrophoretic deposition. The frictional behaviour is recorded by a tribometer and resulting wear tracks are analysed by scanning electron microscopy and Raman spectroscopy in order to reveal the acting tribological mechanisms. The combined approach shows an extended, minimum fivefold longevity of the lubrication and a significantly reduced degradation of the laser textures. Raman spectroscopy proves decelerated MWCNT degradation and oxide formation in the contact. Finally, a lubricant entrapping model based on surface texturing is proposed and demonstrated.",
"conclusion": "Conclusions In the present work, CNT coating and laser texturing of steel surfaces are combined in order to act synergetically in terms of reducing friction and wear. The laser textures are perfectly copied by the CNT coating using EPD as coating technique. A tribological comparison to laser-textured/uncoated surfaces, untextured/CNT-coated surfaces and untextured/uncoated references is conducted. The following statements refer to a comparison with an untextured/uncoated reference: ≤1000 sliding cycles: Frictional reduction in the case of a textured/uncoated sample , which is accompanied by a gradual degradation of the surface texture. ≤2200 sliding cycles: Frictional reduction by a factor of four in the case of untextured/coated sample until the CNTs are dragged out of the contact zone and the COF increases. ≥10000 sliding cycles: Stable frictional reduction by a factor of three in case of textured/coated sample . The slightly less pronounced frictional reduction of point 3 compared to point 2 can be explained with a higher roughness of the textured/coated sample (rms of 300 nm compared to 30 nm), thus hindering the CNT rolling movement and reducing the dimension of the lubricating effect. Furthermore, a direct correlation of the CNT degradation in a tribological contact with an already known three-stage phenomenological model (transition from graphitic-like to amorphous-like structures) is established using Raman spectroscopy. It is found, that the combination of laser texturing and CNT-coating slows down the degradation process of the CNTs within a tribological contact and also the formation of oxidic wear particles. Finally, the significant extension of the lubricant longevity in point 3 is related to a synergistic effect of CNTs and laser textures and can be exemplified by a lubricant entrapping model, which is proposed and demonstrated.",
"discussion": "Results and Discussion EPD coating After laser-texturing ( Fig. 1c ), the samples were coated with CNTs by EPD. As expected from the negative surface charge of the functionalised CNTs, the deposition occurs at the anode and without any detection of hydrogen evolution during the deposition. Both untextured and textured samples could be homogenously coated ( Fig. 1a,b ). The deposited thickness is 1–2 μm, measured at different spots of either sample type by FIB-cross sections. Furthermore, the cross-sections (insets of Fig. 1a,b ) show that the CNT film follows the surface’s profile. Finally, small CNT agglomerates are found within the coatings, possibly a consequence of the stated van der Waals interactions. Nonetheless, the entire surface is covered with disaggregated CNTs, providing a consistent surface coating. Frictional behaviour The different sample sets were compared in terms of the temporal evolution of the coefficient of friction (COF), which can be seen in Fig. 2 for a maximum of 500 sliding cycles. The COF of the reference increases during the first 200 sliding cycles from 0.25 to roughly 0.7. After 200 sliding cycles, the COF remains stable at 0.7, thus reaching steady state conditions. The described behaviour is typical for pure metals and has already been extensively investigated elsewhere 42 . In the beginning of the experiment, only few single asperities of the ball and the substrate are in contact with each other thus generating a small contact area and consequently, a small COF. The increasing COF can be explained by the increasing real contact area, which is generated by wearing off said asperities and also by increasing the indentation depth of the ball into the substrate. However, the development of the COF appears to be very unstable for the reference compared to the other samples. The formation and disintegration of wear particle agglomerates might be responsible for this. The nature of those wear particles and the possible formation of an oxidic layer will be examined later in this work by SEM and Raman spectroscopy. In general, the steady state COF of the used material pairing for the reference correlates well with the literature 43 . Regarding the textured sample, a continuous increase in the COF from 0.2 to 0.4 during the entire experiment can be noticed. It has already been shown, that the observed friction reduction is mainly attributed to a reduced real contact area 2 3 . This is reasonable, as the ball only gets in contact with the maximum position of the laser texture. The continuous increase of the COF during the experiment could be related to the steady increase in the real contact area due to a gradual removal of the surface texture. After 450 cycles, the COF fluctuates similarly to the reference, which might also be indicative of the destruction of the surface texture, therefore allowing the direct contact of the ball with wear particle agglomerates that are formed and broken up in a statistical manner. When regarding the COF of the ref + coated sample, a gradual decrease during 500 cycles from almost 0.4 to 0.2 is noted. Additionally, it is worth mentioning that the COF of the ref + coated sample is higher than the reference for the first 10 cycles. This may be explained by large amounts of entangled CNTs being shifted to the sides and ends of the wear track. The shifting and stacking of entangled CNTs requires a higher transversal force thus resulting in an even higher COF than the reference. Subsequently, a continuous supply of small amounts of CNTs seems to set in, acting as solid lubricant and reducing the COF 11 15 17 19 20 . Also, CNTs might be transformed to a lubricating carbon film 11 16 . Finally, the COF of the textured + coated sample appears constant and stable throughout the experiment at 0.2. This means that it shows influences of both friction reducing methods: laser-texturing at the beginning of the measurement and lubricant coating at the end of the measurement. Thus, this indicates a composite type behaviour of texture and solid lubrication. As for the other samples, a detailed wear track investigation and an investigation of the CNT lubrication activity might give a hint on the underlying friction mechanism, which will be discussed in detail later in the present study. As the COF of the ref + coated- and the textured + coated samples is nearly identical after the first 150 cycles, it is reasonable to assume that this behaviour is mainly induced by CNTs present in the contact zones. This being stated, it is of interest to examine the long-term behaviour of these samples, as the textured + coated samples might provide CNTs to the contact area for a longer period of time. The temporal evolution of the COF at 10000 cycles provides us with information about the lifespan behaviour ( Fig. 3 ). As described for the 500 cycles measurement, the reference clearly shows steady state behaviour after the first 200 sliding cycles for the already discussed reasons, approaching a COF of roughly 0.65. The textured sample reveals an evolution of the COF very similar to that of the reference, yet only reaching steady state conditions after 1000 cycles. As already mentioned for the 500 cycles measurement, this might be a consequence of the ongoing destruction of the laser-texture, finally leading to the same contact conditions as the reference. Looking at the ref + coated sample, the COF drops within the first 750 cycles from 0.35 to 0.15, remaining at this value for the next 1500 cycles. Subsequently, the COF sharply increases to 0.4 within 100 cycles, followed by a gradual approach to the reference value during the next 3500 cycles. This sharp increase might be a consequence of the degradation or disappearance of CNTs in the contact area, as described in the wear and Raman sections (3.3 and 3.4, respectively). It is noteworthy that the COF of the ref + coated sample shows a very high standard deviation after the first 2200 sliding cycles. This is due to individual measurements that have shown the observed sharp increase of the COF up to the reference value after different amounts of sliding cycles. All individual measurements have shown this sharp increase between 2200 and 4000 sliding cycles. Finally, it should be pointed out that the textured + coated sample features the same low COF (0.2) for the entire 10000 sliding cycles. However, between 200 and 2200 cycles, the COF is higher than that of the ref + coated sample, which could be explained by the ability of CNTs to roll only on top of a flat, polished surface as already reported by Dickrell et al . 26 . This ability might be reduced in the case of the textured + coated sample, as the textured surface shows a much higher roughness of 300 nm compared to 30 nm of the reference (which is within the same order of magnitude as the CNT mean diameter), therefore hindering a rolling movement of the CNTs to a certain degree. However, the ongoing lubrication effect of the textured + coated sample for 10000 cycles is significant. A possible explanation for this would be the entrapping of solid lubricant (CNTs) by the laser-texture. Wear track In general, a better understanding of the frictional behaviour of a tribological contact pair can be achieved by analysing the dominating wear mechanisms. In this context, it should be noted that different wear mechanisms usually act simultaneously during a tribological experiment. Therefore, only the dominating mechanisms are named and discussed in the following section. The wear tracks of all samples after 500 sliding cycles are depicted in Fig. 4 to allow for further discussion. For the reference wear track, the dominating wear mechanism is ploughing, as can be seen in Fig. 4a . This is evident as the hardness of the counter body (Al 2 O 3 ball) greatly exceeds that of the steel substrate. Wear particles are observed within and around the wear track, which might add an abrasive component in this respect. Also, the formation of a steady-state oxide layer might occur and contribute to the stabilisation of the COF 44 . Regarding the textured sample after 500 cycles in Fig. 4b , the continuous increase of the COF during the experiment can be related to the steady increase in the real contact area due to a gradual removal of the surface texture. As in the reference wear track, the main acting wear mechanism is ploughing. However, it can be seen that the surface texture is not yet completely removed after 500 cycles and less severe wear is observed, compared to the reference. In general, less wear particles are found that are spread over the wear track. This might be a consequence of the reduced direct contact of the ball with wear particles as the minimum positions can trap wear debris, thus reducing the abrasive component 2 3 . As the laser-texture is still partially intact, the real contact area is still reduced compared to the reference and the COF is lower. The friction reducing effect within the first 500 cycles, induced by laser-textured steel surfaces has been published in previous studies 2 3 . Regarding the corresponding wear track of the ref + coated sample ( Fig. 4c ), a clear shift of the CNT coating towards the end of the wear track can be observed. This supports the assumption that the high initial COF could be explained by shifting large amounts of entangled CNTs to the sides and ends of the wear track within the first sliding cycles. Subsequently, the wear track can be continuously supplied with small amounts of CNTs that are transferred from the ends of the wear track back to the contact area (grey area in the middle of the wear track), acting as solid lubricant and therefore reducing the COF again 11 15 17 19 20 . The CNTs, responsible for this lubrication effect, can be observed at both ends of the grey area and will be analysed in more detail within the chemical analysis. Despite that, no severe wear or wear particles can be noticed within the wear track compared to those of the reference and the textured sample. Finally, the wear track of the textured + coated sample shows a very different appearance. After 500 cycles, the maximum positions are still present, even though they are a flattened, and the minimum positions are filled with CNTs as can be seen in Fig. 4d . This observation strongly supports the hypothesis of a solid lubricant entrapping caused by the laser-texture. The wear tracks of the long-term measurements (10000 cycles) must be considered to understand the observed sharp increase of the COF after 2200 cycles in the case of the ref + coated sample, as opposed to the stability observed in the textured + coated sample. Therefore, SEM micrographs of the wear tracks after 10000 sliding cycles are depicted in Fig. 5 . For the reference, the behaviour of the COF as well as the dominating wear mechanism are already well explained by the previously described 500 cycle measurements. The only difference is the occurrence of much more severe wear as can be seen in Fig. 5a . Regarding the wear track of the textured sample ( Fig. 5b ), it can be stated that the laser-texture has been completely removed within the contact area by ploughing, throughout exhibiting severe wear. Hence, a reduction of the real contact area can no longer be achieved and the COF behaves very similarly to the reference after the first 1000 cycles. Comparing the wear track of the ref + coated sample after 10000 cycles in Fig. 5c , to that after 500 cycles in Fig. 4c , the additional appearance of severe wear is clearly noticeable. A reasonable explanation for this observation is the disappearance of the lubricating CNTs within the contact zone. The continuous removal of CNTs by the ball out of the contact zone as well as their absence at the ends of the severe wear area might be the reason for that. Hence, a direct contact of ball and substrate material is very likely to happen after the first 2200 sliding cycles, when a sharp increase of the COF can be observed. The slow increase of the COF for the next 3500 cycles (until it reaches the reference value) can be explained with an increasing real contact area between ball and steel substrate as well as individual measurements that show the described sharp increase of the COF at a later or earlier stage of the measurement. When analysing the wear track of the textured + coated sample ( Fig. 5d ), different regions can be distinguished. In the middle of the wear track width, severe wear is observed with ploughing being the dominant wear mechanism. This region is very similar to the reference wear track after 10000 cycles. However, regarding the flanks of the wear track, an almost unworn laser-texture can still be noticed, storing CNTs in the minimum positions. It is therefore reasonable, that even after 10000 sliding cycles, the wear track is still supplied with CNTs that lubricate the system and keep the COF at a low value of 0.2. In comparison to the ref + coated sample, the CNTs are stored within the contact zone and don’t have to be applied from the surrounding area. Thus, the contacting surfaces are provided with CNTs for a considerably prolonged period of time. A differentiated discussion about oxide formation and structural state of the CNTs within the wear tracks is done in the following part of this study. Structural and chemical analysis A deeper discussion of the COF evolution and the solid lubrication activity of the CNTs requires a detailed analysis of the tribochemistry as well as the structural integrity of the CNTs. In this study, the analysis is focused on the interpretation of the most relevant structural indicators obtained by Raman spectroscopy, namely: defect index (I D /I G ), purity index (I G′ /I D ), and the G-band centre position (X CG ). Figure 6 shows the electron micrographs and respective Raman spectra of the wear tracks after 500 and 10000 cycles for the reference and the laser-textured samples. In all the cases, regions of interest were defined based on their relevance to the study. For the reference wear track after 500 cycles ( Fig. 6a ), the appearance of iron and chromium oxides (II) indicates that a tribologically-induced oxidation took place, due to the direct contact of the ball with the steel substrate under high local pressure 2 45 . The peak at 225 cm −1 corresponds to the α-Fe 2 O 3 band, whereas the peak at 270 cm −1 is composed by a convolution of α-Fe 2 O 3 and Cr 2 O 3 peaks. The peaks at 380 cm −1 and 480 cm −1 are related to the occurrence of (Fe, Cr)O 3 and γ- Fe 2 O 3 , respectively 45 46 47 . Finally, the peak with its maximum intensity at 660 cm −1 and a large full width at half maximum (Γ) is a convolution of FeO, Fe 3 O 4 and Fe 2 O 3 vibrational modes 45 . This observation consists of two different phenomena, the formation of a layer of oxidic nature and the development of oxidic wear particles during the experiment, which add an abrasive component. The latter is strongly related to the former, since the high local contact pressure applied breaks the oxide layer, generating wear particles that are included in the contact. These particles are subsequently embedded within the wear track (composed of the softer base metal) during the sliding motion, inducing a composite-type effect and contributing to the stabilisation of the COF. In the case of the reference wear track after 10000 cycles ( Fig. 6b ), iron oxide is predominantly observed. This is reasonable as the COF of the reference is already in a steady-state condition after the first 200 cycles. Therefore, a change in the tribochemistry is rather unexpected. However, in comparison to the wear track after 500 cycles, the observed compositional changes in the oxide formation are probably due to a lack of chromium oxide detection based on its low volume fraction (compared to iron). In the case of the textured sample after 500 sliding cycles ( Fig. 6c ), metal oxides are also found in the maximum (II) and minimum (I) positions outside of the severe wear track. This is explained by the thermally induced melting process during laser texturing and the consequent formation of an oxide layer 34 . Within the area of severe wear (III), the oxide peaks are considerably more pronounced, accordingly indicating a similar tribo-oxidation effect as observed on the reference material. Finally, for the wear track of the textured sample after 10000 cycles ( Fig. 6d ), no significant change in the oxidation behaviour can be observed compared to the short-term measurement. This leads to the conclusion, that the change in the COF (at almost 1000 cycles) in the specific case of the textured sample is exclusively generated by the increment in the real contact area due to wear occurring at the maximum positions. Interestingly, certain areas (I) can be found with pronounced oxide formation, whereas other regions (II) show less oxide formation. This might be a consequence of the on going ploughing mechanism which reorders oxides and bare metal in a stochastic manner. For the analysis of the CNT lubrication activity, electron micrographs and respective Raman spectra of the coated sample wear tracks after 500 and 10000 cycles are depicted in Fig. 7 . In addition to the Raman analysis of the centre and the outside of the wear track, it is also important to analyse the shifted CNTs that pile-up at the end of the wear track since it may also play a role during the tribological experiment. The EPD coated sample shows no formation of oxides after 500 cycles ( Fig. 7a ). This supports prior observations on similar systems, where the CNT coatings have been proposed as oxidation protective coatings for low-cycle friction conditions 28 . In this context, CNTs are able to efficiently separate the steel surface from the ball, acting as a roller bearing and thus reducing friction and wear 11 48 . However, in Fig. 7b , intense oxide peaks can be found within the centre (I) and the end of the wear track (II) after 10000 cycles. Their appearance can be straightforwardly correlated to the coating failing after around 2200 sliding cycles, where a sharp increase in the COF is noticeable. Specifically, as no surface separation is possible anymore, the oxidation protection induced by the interstitial CNTs ceases. This leads to the formation of an oxide scale that, due to the large contact pressure develops into oxidic wear particles that act as an abrasive third body component participating in the transition from mild to severe wear. Raman spectra of the wear track of the textured + coated sample ( Fig. 7c ) show a weak oxide peak on the texture maximum positions (II) after 500 cycles. As already mentioned, this is an unavoidable feature of the laser texturing. On the other hand, as observed in the ref + coated sample, the structure within the wear track (I and III) does not show remarkable oxide peaks, likely due to the oxidation damping provided by the CNTs. When analysing the wear track after 10000 cycles ( Fig. 7d ), it can be observed that, the intensity of the oxide peaks correlates to the extent of the effective contact area between counterpart and sample, being the minimum the position with the lowest amount (I), follow by the surface maximum (II), the end of the severe wear track (III) and finally within the severe wear track (IV); where the effective contact area between counterpart and sample is the highest. Interestingly, the strongest oxide peaks are detected at the very end of the wear track (V). This might be due to the fact that the generated oxidic wear particles of the severe wear track are shifted to the very end and there stored in the minima. In the case of the ref + coated and the textured + coated samples, aside from the discussion of their oxidation behaviour, the focus of the analysis is also placed on the structural state of the CNTs. Table 1 shows the most relevant structural indicators to allow for a direct comparison. The shifted CNTs (a III) have been transported out of the contact zone in the beginning of the measurement, showing almost identical defect and purity indexes as the reference state. Thus, they can be excluded from any tribological activity. Since these CNTs are out of the contact zone they do not exhibit any damage and retain their original structural integrity even after 10000 cycles (b III). CNTs found at the centre (a II) and end of the wear track (a I) show an increase in the I D /I G ratio and a decrease in the purity index, indicative of CNT degradation. Comparing both regions, degradation seems to be significantly more pronounced in the centre (a I). This is due to the fact that this zone is situated where the maximum relative velocity is reached between the sample and the counterpart and the most severe mechanical and thermal stresses are expected. Specifically, the centre (a II) is located within the direct tribological contact zone, whereas the end (a I) would act as supplementary CNT storage. Although the shifted CNTs (a III) and the CNTs at the end of the wear track (a I) maintain the position of their G band, those present at the centre (a II) show an up-shifting to roughly 1600 1/cm. This up-shifting has been thoroughly discussed in the literature by Ferrari and co-workers 49 and corresponds to a transient state of the CNTs (clustering of the affected graphitic structure) towards nano-crystalline graphite. This is enclosed in a three-stage phenomenological model proposed by them, which analyses the transition from a graphitic-like to an amorphous-like structure (predominantly sp 3 -hybridisation) as function of the G-band centre position (X CG ). When observing the descriptive indexes for the ref + coated sample after 10000 cycles, an up-shift in the G band towards 1600 1 /cm is observed for CNTs within the centre (b I) and the end of the wear track (b II). The transient state is reached at the end of the wear track (b II) as well, inferring an accumulated CNT degradation towards a disordered state. Regarding the textured + coated sample after 500 cycles, all measured regions show a degradation of the CNTs compared to the reference state CNTs. Particularly, those regions within the wear track (c I and II) have a defect index higher than at the end of the wear track (c III). However, the degradation in the interior regions is not as pronounced as in the centre (a II) of the ref + coated sample. This might be a consequence of the ongoing transfer of CNTs between maxima and minima during the experiment, thus exchanging the tribo-active CNTs more frequently. It should be kept in mind that the measured Raman spectra present a mean value of all the CNTs measured (for example degraded CNTs might be stacked upon intact CNTs in the minima). Interestingly, the G band position of the CNTs stored within the minima (c I) lies within the first stage of the amorphisation trajectory described in the Ferrari model. Then, it is reasonable to assume that those CNTs would sequentially be driven towards the contact zone as the experiment carries on. Thus, it becomes evident that the transition towards amorphous nanocarbons seems to be detrimental for the solid lubrication effect. However, the exchange between minima and maxima might lead to a general prolonged lifetime of the involved CNTs, as always just a small part of the involved CNTs is in a direct tribological contact. After the long term tests (d I–d V), all the analysed regions show similar Raman indices, each lying at the end of the first stage of amorphisation. Mechanism of entrapping solid lubricant As both the wear track and Raman analyses correlate perfectly with the idea of a solid lubricant entrapping by the laser texture, the following part is focused mainly on this trapping mechanism and provides a schematic model to understand the frictional behaviour of the system. In Fig. 8 , a high magnification SEM micrograph of a FIB cross-section within the wear track of the textured + coated sample after 500 sliding cycles (a) is depicted and correlated to a schematic draft of the mechanisms acting during the sliding motion (b). In b1, the intact, coated laser texture is depicted. As soon as the ball gets into contact with the coating, CNTs are partially transferred to the alumina ball by adhesion (b2). This leads to the shifting of the CNTs towards the minima. At the same time, pressure is applied to the maxima of the laser-texture throughout the coating, resulting in a plastic deformation and a shearing-off of metallic material (b3). Due to adhesion effects, some CNTs are dragged towards the contact area (b4), preventing the direct contact of the alumina-ball with the steel substrate and therefore reducing the COF. Furthermore, the laser-texture prevents the full transfer of CNTs by storing most of the particles within the minima (b4-b5). In addition to the CNT storage capabilities of the minima, also metallic or oxidic wear particles could be partially stored (b5), therefore reducing abrasive wear. Finally, a slight lift of the stored CNTs mainly on one side of the maxima can be noticed (a). This is due to the mentioned adhesion between the alumina ball and the stored CNT particles during the sliding motion (b6), and elastic recovery of the compressed CNT volume after the stroke (b7). In the FIB cross-section (a), the last sliding cycle direction of the experiment was from left to right. Due to this observation and the ongoing lubrication, it can be expected that the protruding CNTs in the minima (b7) will be redragged into the direct contact zone of the maxima as soon as the sliding direction is inverted. These mechanisms can be well correlated to the lower COF compared to the ref + coated sample within the first 150 sliding cycles, as no additional transversal force for the CNT shifting is generated. The CNTs of the textured + coated sample (that were stacked and transferred out of the wear track in the ref + coated sample) are now stored in the minima, thus acting as a lubricant reservoir. In addition to a reduction of the real contact area by laser-texturing, the maxima are lubricated by the CNTs derived from the minima. Apart from milder wear than the textured sample, a reduced oxide formation was also noticed by Raman spectroscopy. Summarizing, the combination of both methods, laser texturing and EPD coating, is found to overcome individual limitations by complementing one another. While the laser-texture provides the system with a certain load carrying capacity and lubrication storage, the CNT coating prevents its degradation, oxidation and acts as a long lasting solid lubricant."
} | 7,479 |
35434604 | PMC9006728 | pmc | 5,196 | {
"abstract": "Cyanobacteria are potent microorganisms for sustainable photo-biotechnological production processes, as they are depending mainly on water, light, and carbon dioxide. Persisting challenges preventing their application include low biomass, as well as insufficient process stability and productivity. Here, we evaluate different cyanobacteria to be applied in a novel capillary biofilm reactor. Cultivated as biofilms, the organisms self-immobilize to the reactor walls, reach high biomass and enable long and robust production processes. As ‘best performer’ Tolypothrix sp. PCC 7712 emerged from this study. It reached the highest biomass in the reactors with 62.6 ± 6.34 g BDW L −1 , produced 0.14 μmole H 2 mg Chl a −1 h −1 under N 2 -fixing conditions, showed optimal surface coverage of the available growth surface, and only minor detachment in contrast to other tested species, highlighting its potential for photobiotechnology in the near future.",
"conclusion": "5 Conclusion This study provides a comprehensive assessment of biofilm formation of a number of cyanobacteria as defined-consortia in a CBR. Although the current study was limited to six species, it was evidenced, that there are huge differences between the tested strains, and that a couple of those performed superior compared to the current, broadly established workhorse Synechocystis sp. PCC 6803. Our findings indicate that Tolypothrix sp. PCC 7712 is a species with great potential in photo-biotechnology. This is reflected in its high biomass production up to 62.6 ± 6.34 and 57.5 ± 1.08 g BDW L −1 in the nitrate-enriched and nitrate-deficient conditions, respectively. Furthermore, it needs 10 times less of NaHCO 3 compared to a previous study conducted by Hoschek et al. [ 6 ]. The continuous production rate of 0.14 μmole H 2 mg Chl a −1 h −1 of the wild type organism is a promising preliminary result for its future application for white H 2 production. These findings highlight the potential usefulness of other cyanobacteria besides the well-established species Synechocystis sp. PCC 6803.",
"introduction": "1 Introduction Cyanobacteria are prokaryotes performing oxygenic photosynthesis. In addition, there are many species known for being able of fixing molecular nitrogen. Due to these features cyanobacteria are a highly interesting class of organisms for whole-cell based biocatalysis since they can grow on atmospheric CO 2 (and N 2 ) as the only source of carbon (and nitrogen), while using water and light for electron and energy supply, respectively. Applied as solar cell factories, they could significantly contribute to future bioeconomy, as they represent potentially sustainable biocatalysts that are independent of organic carbon (and reduced nitrogen compounds) and are able to grow on non-arable land (primacy of food security). In the last decade, several successful proof-of-principle studies have been published, showing that cyanobacteria can be engineered to produce a wide range of diverse products [ [1] , [2] , [3] ]. Nevertheless, application of cyanobacteria on a productive scale remains an exception. Examples are restricted to biomass based processes, like the production of various pigments or applications in the area of biofuel production [ 4 ]. Only a handful of species of this very diverse group have been investigated in more detail, including Synechocystis sp . , Synechococcus sp. and Nostoc sp . . Persisting challenges like low biomass, low reaction rates, low product titers, and intermittent non-continuous synthesis, prevent their successful industrial scale application [ 5 ]. One interesting, novel approach utilizing cyanobacterial biofilms to reach high cell densities has been recently published [ 6 ]. Biofilms are surface associated microbial communities embedded in self-produced extracellular polymeric substances (EPS), in which the organisms live in a coordinate fashion whereby they benefit from ecological (micro)niches. The properties of these microbial societies are partially governed by community structure, diffusion of nutrients and extracellular metabolites throughout the biofilm, and the physiological activity of the community [ 7 ]. Biofilms are resilient to a wide variety of environmental stresses and are in general less affected by toxic substrates and/or products [ 8 ]. Since they are composed of living cells permanently regenerating themselves, biofilms can be regarded as biocatalysts with, in principal, an infinite turn-over number and, therefore, with the potential of establishing continuous bioprocesses. Furthermore, biofilms feature remarkably high cell densities; up to 60 g CDW L −1 can be achieved, compared to 4–8 g CDW L −1 for suspended cell cultures employing cyanobacteria [ 5 , 6 ]. Despite the fact that cyanobacteria are the primary producers in microbial mats in nature [ 9 ], and thus ubiquitous biofilm growing organisms, only little is known about their biofilm forming abilities, especially in technical systems. Studies mostly focus on their role in food webs in an ecological context, investigating natural systems. Recent studies reported that biofilms found in distinct niches often comprehend diverse cyanobacterial species, belonging to the genera Leptolyngbya , Nostoc , Synechocystis and Tolypothrix [ 10 , 11 ]. Among these genera, it has been shown that Nostoc sp. secretes high amounts of capsular/bound polysaccharides and released polysaccharides [ 12 ], both being important for EPS structure and thus attachment behavior. Here, we evaluate different cyanobacterial strains including those described by Bharti et al. as being abundant in natural mats for their ability to form biofilms in capillary biofilm reactors (hereafter CBRs). A couple of unicellular growing cyanobacteria ( Synechocystis sp. , Synechococcus sp . , Cyanothece sp.) were compared to filamentous ones ( Tolypothrix sp. , Nostoc sp., and Leptolyngbya sp.). Furthermore, biofilm formation in the CBR was investigated under nitrogen fixing conditions versus addition of nitrate to the medium. The CBR concept was introduced by Hoschek et al. as a means to cultivate cyanobacterial whole-cell biocatalysts at high cell-densities in a continuous mode, using the well-established cyanobacterial lab strain Synechocystis sp. PCC 6803 as model organism [ 6 ]. Furthermore, CBRs profit from an excellent surface to volume ratio of 1333–4000 m 2 m −3 and low light penetration depth preventing light limitation in the system and increasing the light to chemical energy conversion efficiency [ 5 , 6 , 13 , 14 ]. Here, we wanted to extend the CBR concept to other cyanobacterial species, as a first step towards establishing them as potential photo-biotech workhorses. As performance indicators biomass formation, surface coverage and biofilm stability have been monitored.",
"discussion": "4 Discussion In this study we wanted to extent the application of the CBR, which was so far only operated with Synechocystis sp. PCC 6803, to other cyanobacteria. The CBR technology was shown to enable high cell densities and continuous bioprocessing, making it a promising system for productive whole-cell catalysis. However, the system is restricted to approaches, where cells are functioning as solar cell factories, meaning that they constantly excrete a product of choice which can easily be extracted from the broth. It is not suited for biomass based products. Scaling is possible via numbering up, as shown for other microscale production systems [ 13 ]. Despite their large presence in microbial mats, growing cyanobacteria in a technical environment may turn out to be sophisticated, depending on the bioreactor applied. A crucial parameter is the O 2 concentration in the system. Due to oxygenic photosynthesis by cyanobacteria, O 2 may accumulate in the bioreactor, reaching high concentration that promote the formation of radical oxygen species (ROS), which finally lead to cell toxification [ 26 ]. Running the CBR in a segmented flow fashion (with air segments) facilitates oxygen removal from the system, keeping it at around 250 μmol L −1 [ 6 ]. Furthermore, the co-cultivation with an O 2 respiring organism like P. taiwanensis VLB 120 is beneficial in this aspect. The EPS, as an essential part of a biofilm, may be a good indicator if the organisms are prone to attach tightly to a surface. Cyanobacteria are well known to produce EPS, also, if they are growing planktonic. Cyanobacterial EPS can stay fixed to the cell surface, either as capsule, slime form, or sheath, or be secreted into the proximate environment. Furthermore, EPS exhibit extreme structural complexity as polymers, which makes them a very promising product in biotechnological applications [ 23 ]. Moreover, various cyanobacteria are known to produce EPS which favors the binding of metal ions [ 27 ]. Sometimes, the excretion of EPS is part of the cellular stress response system, like described for Synechocystis sp. [ 28 ]. In a biotechnologically employed system, the EPS is a double-edged sword. On the one side, it is needed for cell attachment and protection. On the other side it represents a substantial mass transfer barrier, hampering the transfer of compounds in and out of the cells. Furthermore, resources in terms of carbon, nitrogen and electrons are lost if they end up in the EPS. In an ideal biocatalytic system, the major portion of available electrons and carbon is channeled into the product of choice, while only a minor amount is utilized for biomass generation and maintenance. In reality, microbial systems use a large fraction of the available resources for biomass, soluble microbial products (SMPs), and EPS production [ 29 ]. For Synechocystis sp. PCC 6803 growing in batch cultures it was shown that up to 84% of the available resources are used for biomass formation, whereas up to 25% end up in SMPs. A strain modified to produce lauric acid channeled up to 30% into SMP production [ 30 ]. For biofilms, such investigations are missing so far. Our data show that also in attached growth modes, significant amounts of SMPs are produced and are constantly flushed out of the system, together with detached cells and EPS compounds. In addition, cyanobacterial biofilms are prone to encounter ‘catastrophic sloughing events’ during which large parts of the biofilm detach, like observed for Leptolyngbia sp. in our study. These events are not well understood. Presumably, it is a mechanism to support biofilm regeneration and thus overall system robustness. However, from an economic perspective, it means that large amounts of the biocatalyst are lost, together with a significant portion of the nutrients, carbon and electrons. Together with the SMPs, sloughed cells may account for 50% of the total carbon and energy fixed during photosynthesis. To establish an economic process based on biofilms, it will be important to minimize such losses and think about measures to reuse these waste products. In our study Tolypothrix sp. and N. punctiforme both showed very low carbon loss during nitrogen fixation. Their filamentous morphology might promote the attachment efficiency by creating a very effective 3-D structure. Since they are both heterocyst-forming filamentous cyanobacteria, the low detachment values could also be attributed to heterocyst formation, which may cause a stronger attachment on the capillary surface. It is known that the envelope of heterocysts is a thick structure consisting of specialized glycolipids and polysaccharides that shields the nitrogenase from oxygen [ 31 , 32 ]. These additional EPS layers might be the reason behind less detachment occurring in nitrogen fixing conditions for Tolypothrix sp. and N. punctiforme . This study showed, that we are only about to tap the huge potential of cyanobacterial workhorses and that there are many more highly interesting strains waiting to be discovered. Tolypothrix sp. PCC 7712 is self-sustaining in terms of carbon and nitrogen supply, which both can be utilized directly from the atmosphere. Thus, this strain is widely independent from expensive media components that have a significant carbon footprint in terms of nitrate supply. The results of this study suggest that Tolypothrix sp. PCC 7712 had a better performance compared to the current workhorse Synechocystis sp. PCC 6803, which cannot fix atmospheric nitrogen and, showed higher cell detachment and carbon loss, and less biofilm biomass formation in the CBR. Tolypothrix sp. has been already used for outdoor biofilm cultivation and valuable food-grade phycocyanin production [ 19 ]. Velu et al. [ 19 ] observed considerably high phycocyanin and biomass yields and very high metal removal rates while high CO 2 was supplied (15%) under meso-scale outdoor cultivation and nitrogen-fixing conditions. Additionally, hydrogen may be produced as a side product of the nitrogenase activity [ 33 ]. In our study, the hydrogen evolution rates are rather low. However, the CBR was not yet optimized for H 2 production and probably a significant fraction of the product was escaping from the reactor. Further, we could identify the uptake hydrogenase subunits HupS and HupL encoded on the genome of Tolypothrix sp. PCC 7712 (Unpublished results), which may also contribute to the low H 2 production [ 34 ]. Perspectively, deletion of these genes may improve the H 2 evolution, like shown for Nostoc sp. PCC 7422. In this strain it was possible to increase its H 2 production 3 fold by deleting the hupL gene [ 35 ]."
} | 3,388 |
31420965 | PMC7384014 | pmc | 5,197 | {
"abstract": "Summary \n Pectobacterium atrosepticum SCRI1043 is a phytopathogenic Gram‐negative enterobacterium. Genomic analysis has identified that genes required for both respiration and fermentation are expressed under anaerobic conditions. One set of anaerobically expressed genes is predicted to encode an important but poorly understood membrane‐bound enzyme termed formate hydrogenlyase‐2 (FHL‐2), which has fascinating evolutionary links to the mitochondrial NADH dehydrogenase (Complex I). In this work, molecular genetic and biochemical approaches were taken to establish that FHL‐2 is fully functional in P. atrosepticum and is the major source of molecular hydrogen gas generated by this bacterium. The FHL‐2 complex was shown to comprise a rare example of an active [NiFe]‐hydrogenase‐4 (Hyd‐4) isoenzyme, itself linked to an unusual selenium‐free formate dehydrogenase in the final complex. In addition, further genetic dissection of the genes encoding the predicted membrane arm of FHL‐2 established surprisingly that the majority of genes encoding this domain are not required for physiological hydrogen production activity. Overall, this study presents P. atrosepticum as a new model bacterial system for understanding anaerobic formate and hydrogen metabolism in general, and FHL‐2 function and structure in particular.",
"conclusion": "Concluding remarks In this work, P. atrosepticum SCRI1043 has been established as a tractable new model organism for studying hydrogen metabolism in general and FHL function in particular. The organism is a rare example of a bacterium with an active hydrogenase‐4‐containing FHL‐2 complex, however, in the course of this work, hydrogenase‐4 activity was reported in T. guamensis , another γ‐proteobacterium (Lindenstrauß and Pinske, 2019 ). Interesting, the T. gaumensis Hyd‐4 was found to be active in vivo but very poorly reactive in vitro in standard enzymatic assays with redox‐active dyes (Lindenstrauß and Pinske, 2019 ). This again highlights the need for development of new approaches to characterise FHL‐2 and its component parts. In P. atrosepticum , the active hydrogenase‐4 enzyme operates in tandem with an unusual selenium‐free formate dehydrogenase, which may be more amenable to biotechnological engineering than selenium‐dependent isoenzymes. In evolutionary terms, the FHL‐2 complex has been discussed as a key intermediate in the evolution of the NADH dehydrogenase (Complex I) from a structurally simpler membrane‐bound hydrogenase (Friedrich and Scheide, 2000 ; Marreiros et al. , 2013 ; Schut et al. , 2016 ). The most obvious difference in the predicted quaternary structures inferred from the genetics is the large membrane arm present in FHL‐2 compared to FHL‐1, and data presented here points to the extra membrane proteins being not essential for formate‐dependent hydrogen evolution in vivo . The role of the FHL membrane arm in generating a transmembrane ion gradient remains to be fully explored in enteric bacteria.",
"introduction": "Introduction Many members of the γ‐proteobacteria are facultative anaerobes with the ability to switch their metabolisms to exploit the prevailing environmental conditions. Aerobic or anaerobic respiration is generally preferred, depending on the availability of respiratory electron acceptors. In this phylum, and specifically under anaerobic conditions, the three‐carbon product of glycolysis, pyruvate, is often further metabolised by the oxygen‐sensitive pyruvate formatelyase enzyme to generate acetyl CoA and the one‐carbon compound formic acid (Pinske and Sawers, 2016 ). Studies of the model bacterium Escherichia coli have established that endogenously produced formate is initially excreted directly from the cell using a dedicated channel (Suppmann and Sawers, 1994 ; Hunger et al. , 2014 ; Mukherjee et al. , 2017 ). Under respiratory conditions this formate would be used as an electron donor through the activity of periplasmic enzymes, but under fermentative conditions the formate accumulates in the extracellular milieu until its rising concentration begins to cause a drop in extracellular pH. This is thought to trigger formate re‐uptake, which in turn induces synthesis of formate hydrogenlyase (FHL) activity in the cell (Rossmann et al. , 1991 ; McDowall et al. , 2014 ; Sargent, 2016 ). FHL activity then proceeds to detoxify the formic acid by disproportionation to carbon dioxide and molecular hydrogen (H 2 ). While FHL activity has been characterised in E. coli (Sargent, 2016 ), it is not confined to enteric bacteria and has been reported across the prokaryotic domains, including in hyperthermophilic archaea where it is not only involved in pH homoeostasis but also in generating transmembrane ion gradients (Kim et al. , 2010 ; Lim et al. , 2014 ; Bae et al. , 2015 ). The ion‐pumping activity stems from an evolutionary link between FHL and the respiratory NADH dehydrogenase Complex I (Bohm et al. , 1990 ; Friedrich and Scheide, 2000 ; Batista et al. , 2013 ; Marreiros et al. , 2013 ; Schut et al. , 2016 ). Like Complex I, FHL comprises a cytoplasm‐facing catalytic domain (termed the peripheral arm in Complex I terminology) linked to an integral membrane arm. In FHL, the peripheral arm contains a [NiFe]‐hydrogenase of the ‘Group 4’ type, which is primarily dedicated to H 2 production (Greening et al. , 2015 ), and is linked by [Fe‐S]‐cluster‐containing proteins to a molybdenum‐dependent formate dehydrogenase (Maia et al. , 2015 ). The FHL membrane arm is predicted to take two different forms allowing the enzyme to be further sub‐classified as either ‘FHL‐1’ or ‘FHL‐2’ (Friedrich and Scheide, 2000 ; Marreiros et al. , 2013 ; Sargent, 2016 ; Finney and Sargent, 2019 ). The FHL‐1 is the predominant archetypal FHL activity of E. coli (McDowall et al. , 2014 ) and comprises [NiFe]‐hydrogenase‐3 (Hyd‐3), formate dehydrogenase‐H (FdhF), and a relatively small membrane arm compared to Complex I that contains only two proteins (Fig. 1 A). Genes for the much less well‐understood FHL‐2 enzyme are also found in E. coli (Andrews et al. , 1997 ). This isoenzyme is predicted to comprise a [NiFe]‐hydrogenase‐4 (Hyd‐4), an as‐yet undefined formate dehydrogenase, and a much larger membrane arm than FHL‐1, containing at least five individual integral membrane subunits and more closely resembling the Complex I structure (Fig. 1 B). Understanding the structure, function and physiological role of E. coli Hyd‐4 and FHL‐2 has been hindered by poor native expression levels (Skibinski et al. , 2002 ; Self et al. , 2004 ); a missing important accessory gene from the E. coli hyf cluster (Sargent, 2016 ); and a lack of consensus on the appropriate experimental conditions to test (Bagramyan et al. , 2001 ; Mnatsakanyan et al. , 2004 ). Thus, in order to bring fresh impetus to understanding the physiology and biochemistry of the FHL‐2 complex, it was considered important that an appropriate alternative biological model system was established. In this work, Pectobacterium atrosepticum SCRI1043 was chosen (Bell et al. , 2004 ; Babujee et al. , 2012 ). However, very recently an operon encoding a Hyd‐4 isoenzyme was cloned from Trabulsiella guamensis , which is a bacterium previously mistaken for a subspecies of Salmonella (McWhorter et al. , 1991 ), and found to be functional in E. coli (Lindenstrauß and Pinske, 2019 ). Figure 1 Biochemistry and genetics of formate hydrogenlyase. Structural models of (A) formate hydrogenlyase‐1 (FHL‐1) from Escherichia coli and (B) formate hydrogenlyase‐2 (FHL‐2) from Pectobacterium atrosepticum . Subunits related at the primary and tertiary levels are coloured similarly. Structural modelling of the formate hydrogenlyases complexes was performed using Phyre 2 predictions of respective subunits (Kelley and Sternberg, 2009 ). Using Chimera (Pettersen et al., 2004 ) and the Cryo‐EM structure of the Pyrococcus furiosus Membrane Bound Hydrogenase, MBH (PDB: 6CFW), individual FHL‐2 subunits were manually assembled. FdhF, which is not present in Pyrococcus furiosus MBH, was positioned principally to align its [4Fe–4S] cluster with that of the surface‐exposed [4Fe–4S] cluster from HyfA. C. The genetic organisation of the hydrogen metabolism gene cluster of P. atrosepticum (ECA1225–ECA1252). Predicted gene product functions are indicated and the operon for Hyd‐4 is colour coded to match the structure model in panel (B). \n Pectobacterium atrosepticum SCRI1043 is a phytopathogenic γ‐proteobacterium that can grow under anaerobic conditions (Babujee et al. , 2012 ). A global transcriptomic study identified a chromosomal locus (Fig. 1 C) that was transcribed under anaerobic conditions in this organism (Bell et al. , 2004 ; Babujee et al. , 2012 ). This locus neatly collects together almost all of the known genes for hydrogen metabolism (Fig. 1 C), including genes for a bidirectional Hyd‐2‐type [NiFe]‐hydrogenase; genes for specialist metallo‐cofactor biosynthesis; a putative formate‐responsive transcriptional regulator; a predicted formate dehydrogenase gene; and an 11‐cistron operon apparently encoding a Hyd‐4 isoenzyme and its associated accessory proteins (Babujee et al. , 2012 ). In this work, a molecular genetic approach was taken to characterise the hydrogen metabolism locus of P. atrosepticum . A bank of un‐marked and in‐frame gene deletion mutants was constructed and used to demonstrate unequivocally that the unusual FHL‐2 identified in the genome is functional in P. atrosepticum and responsible for the majority of H 2 production under anaerobic conditions. The complex was shown to contain an active Hyd‐4 and, unusually, a version of formate dehydrogenase that does not rely on selenocysteine. Surprisingly, it was shown that many of the genes encoding the large membrane arm of FHL‐2 can be removed without adversely affecting H 2 production activity. This has potential implications for the molecular architecture of the membrane arm. Overall, this work introduces P. atrosepticum as a tractable model system and presents important genetic, biochemical and physiological characterisation of FHL‐2 and [NiFe]‐hydrogenase‐4.",
"discussion": "Discussion Key differences between FHL‐2 and FHL‐1 Formate hydrogenlyases can be classified into two structural classes, FHL‐1 and FHL‐2 (Finney and Sargent, 2019 ). The most obvious structural difference between an FHL‐1, such as the best‐characterised E. coli enzyme (McDowall et al. , 2014 ; Pinske and Sargent, 2016 ), and an FHL‐2, such as the P. atrosepticum enzyme characterised here, is the predicted size and composition of the membrane arm (Fig. 1 B). Indeed, this large membrane arm is thought to be the ancient progenitor to the ion‐pumping membrane arm of respiratory Complex I (Batista et al. , 2013 ; Marreiros et al. , 2013 ; Yu et al. , 2018 ). Although eukaryotic Complex I, prokaryotic Complex I, and Group 4 hydrogenases such as FHL‐1, FHL‐2, Ech and MBH are clearly evolutionarily related, the gene and protein names for each type of enzyme are different. Some review articles contain useful tables to highlight the relatedness of the individual subunits (Friedrich and Scheide, 2000 ; Marreiros et al. , 2013 ; Schut et al. , 2016 ). FHL‐1 includes only two membrane proteins, which are a single HycD/HyfC‐type protein together with a single HycC/HyfB. This is sufficient to anchor the peripheral arm close to the membrane and, in the case of Thermococcus onnurineus FHL‐1 (Lim et al. , 2014 ) and the related Ech hydrogenase from Methanosarcina mazei (Welte et al. , 2010 ), will also allow initial generation of a proton gradient (Yu et al. , 2018 ). Operons encoding FHL‐2 complexes encode at least three further integral membrane proteins. In P. atrosepticum these are HyfD and HyfF, which are extra versions of the HycC/HyfB putative ion channels, and the HyfE protein, which is more closely related to a region of NuoK in Complex I. Interestingly, if FHL‐2 is modelled based on the Complex I structure (Fig. S2 ), the extra HyfDEF proteins would be placed between HyfBC, thus separating them and pushing HyfB to the most distal point in the peripheral arm (Marreiros et al. , 2013 ). Alternatively, if FHL‐2 is modelled based on the Hyd‐4‐like MBH structure from Pyrococcus furiosus (Yu et al. , 2018 ), then HyfBC remain in contact with each other and HyfDEF form the distal region of the membrane arm (Figs 1 and S2). The experimental evidence presented in this work suggests P. atrosepticum FHL‐2 adopts a membrane arm architecture similar to the Pyrococcus furiosus MBH hydrogenase (Fig. 1 ). This is because removal of all of the extra HyfDEF membrane proteins from FHL‐2 had no discernible effect on the physiological activity of the P. atrosepticum system (Fig. 5 A), suggesting an active FHL‐1‐like core enzyme remains. Clearly if HyfB was normally separated from HyfC by the extra proteins they would be unlikely to come together to form a complex when placed in a Δ hyfDEF background, and E. coli FHL‐1, for instance, is completely inactive when lacking its HyfB homolog HycC (Pinske and Sargent, 2016 ). This highlights the principle of modularity in metalloenzyme evolution, since it is clear that the HyfDEF module may be added or removed depending on both selective pressure and also the, as yet undefined in terms of hydrogenases, biochemical function of these membrane proteins (Friedrich and Scheide, 2000 ). Indeed, it is notable that distal components of the Pyrococcus furiosus MBH membrane arm (MbhABC) could also be genetically removed with only minor effects on cellular hydrogenase activity (Yu et al. , 2018 ). Taken together, this perhaps points to Hyd‐3 from FHL‐1 as the minimal module of a Group 4 hydrogenase. Western immunoblotting pointed towards either stabilisation or upregulation of the catalytic subunit HyfG in the absence of hyfDEF or hyfBCDEF (Fig. 5 B). This is unlikely to be caused by an accumulation of formate in the cells, perhaps leading to maximal transcription, because the Δ hyfDEF strain retained normal levels of formate hydrogenlyase activity (Fig. 5 A). It is more likely that the removal of genes encoding large membrane proteins from immediately upstream of hyfG relaxes some restrictions on the rates of transcription and translation. In bacteria, transcription, translation and membrane insertion of the nascent chain are thought to be coupled together in a process called transertion (Roggiani and Goulian, 2015 ), and removal of some or all of the elaborate membrane integration step could have an effect on translation of downstream genes. At native levels, the HyfG His protein can be detected as a single species migrating at 56.4 kDa in SDS‐PAGE (Fig. 5 B), and occasionally a slower migrating form is detectable migrating at 62.6 kDa (Fig. 5 C). These two forms of HyfG His become prominent when the membrane arm of FHL‐2 is genetically modified (Fig. 5 B). It is known that almost all [NiFe]‐hydrogenases are proteolytically processed at their C‐termini following successful insertion of the Ni–Fe–CO–2CN ‐ cofactor (Bock et al. , 2006 ). In P. atrosepticum HyfG, processing is expected to occur at Arg‐546 and would remove 32 amino acids. Thus, in theory, HyfG should be processed from a 67.6 kDa inactive precursor to a 63.8 kDa active mature form. In practice, the motility of HyfG in SDS‐PAGE does not match precisely the theoretical values (Fig. 5 B and C); however, only the mature form of HyfG could contribute to physiological formate hydrogenlyase activity. The P. atrosepticum HyfG catalytic subunit from the Hydrogenase‐4 component of FHL‐2 shares 74% overall sequence identity (85% similarity) with the E. coli HycE protein from Hydrogenase‐3/FHL‐1. The sequence variation between these two Group 4A hydrogenases is therefore small with only subtle notable differences. For instance, each protein is known or predicted to undergo cleavage during cofactor insertion and maturation leaving a C‐terminal arginine residue in the mature form of the proteins. The cleavage sites themselves are slightly differently conserved in an FHL‐1‐type enzyme compared to an FHL‐2, for example, …R*MTVV… for HycE‐like proteins compared to …R*VTLV… for HyfG. This may reflect the need for a different maturation protease for each type of hydrogenase, however, this remains to be tested experimentally. In addition, it is notable that both E. coli and P. atrosepticum hyfG initiate translation with a GUG start codon, which may have a role in controlling cellular levels of the enzyme (Belinky et al. , 2017 ). Phylogenetic analysis of the Group 4A [NiFe]‐Hydrogenase subunits, including HycE and HyfG, shows that the enzymes associated with FHL‐1 separate into a clearly distinct evolutionary clade from those associated with FHL‐2, which form their own distinct clade (Fig. S3 ). Examples of species that encode both FHL‐1 and FHL‐2 are rare (Fig. S3 ). A selenium‐free formate dehydrogenase Arguably one of the best‐studied FdhF enzymes is the E. coli version, which contains selenocysteine at its active site (Axley et al. , 1991 ; Gladyshev et al. , 1994 ; Boyington et al. , 1997 ). Selenocysteine is incorporated co‐translationally at a special UGA ‘nonsense’ codon within the coding sequence (Zinoni et al. , 1987 ), and replacement of selenocysteine with cysteine in the E. coli enzyme resulted in a dramatically reduced turnover number (Axley et al. , 1991 ). One surprising aspect of P. atrosepticum SCRI1043 is that it contains none of the biosynthetic machinery to synthesise selenocysteine (Babujee et al. , 2012 ) and the fdhF gene studied in this work contains a cysteine codon where selenocysteine would be encoded in the E. coli enzyme (Fig. S1 ). Certainly, the discovery of an active FHL‐2 with no need for selenocysteine would benefit scientists interested in engineering this activity into other biological systems. Indeed, an in‐frame deletion in the fdhF gene located in the FHL‐2 gene cluster (Fig. 1 C) resulted in a ~ 500 times reduction in H 2 production (Fig. 4 ), indicating the majority of H 2 production from P. atrosepticum is dependent on this formate dehydrogenase engaging with Hyd‐4 to form an FHL‐2 complex. However, the Δ hybC Δ fdhF double mutant still produced low, but quantifiable, levels of H 2 (Fig. 4 ). Compare that with the behaviours of the Δ hybC Δ fdhF strain (Fig. 3 B) and the Δ hypF mutant (Fig. 5 C), neither of which produced any detectable H 2 gas. This genetic approach points to the residual H 2 emitting from Hyd‐4, perhaps through the activity of alternative electron donors. Certainly for the E. coli FHL, it is known that FdhF is only loosely attached (Boyington et al. , 1997 ) and this may be because the enzyme is ‘moonlighting’ in other biochemical pathways (Iwadate and Kato, 2019 ). It raises the possibility that other FdhF‐like enzymes in particular could ‘plug in’ to Hyd‐4 and pass excess reducing electrons on to protons. In this work, ECA1507 was found to partially rescue the phenotype of a Δ fdhF strain (Fig. 4 D) suggesting it could be an alternative redox partner: note well, however, that the potential substrates and kinetics of ECA1507 cannot be reliably predicted and should be determined empirically. The FdhF formate dehydrogenase from P. atrosepticum shares 65% overall sequence identity (and 85% similarity) with the well‐known E. coli enzyme (Fig. S1 ). Interestingly, phylogenetic analysis suggests that > 50% of bacterial species that contain FHL genes utilise a cysteine‐dependent, rather than selenocysteine‐dependent, formate dehydrogenase (Fig. S4 ). P. atrosepticum ECA1507 and ECA1964 were identified here as two FdhF‐like proteins that could potentially interact with Hydrogenase‐4 to generate novel FHL‐like complexes. Sequence analysis revealed ECA1507 and ECA1964 share 65% and 22% overall sequence identity with FdhF, respectively, and phylogenetic analysis determined that ECA1964 is more similar to E. coli YdeP than any other predicted molybdenum dependent oxidoreductases in P. atrosepticum (Fig. S5 ). YdeP has a putative role in acid resistance in E. coli (Masuda and Church, 2003 ). A role for formate metabolism in a plant pathogen In the potato pathogen P. atrosepticum , FHL‐2 activity was found to be expressed at lower growth temperatures (Fig. 2 ). This suggests that FHL‐2 may be produced in planta during the infection or colonisation event. Formate is produced endogenously by enteric bacteria under fermentative conditions, but plants and tubers have multiple metabolic pathways that generate and consume formate. Potato tubers produce a NAD + ‐dependent formate dehydrogenase (FDH), and the levels of this enzyme are boosted under stress conditions (Hourton‐Cabassa et al. , 1998 ). Indeed, proteomic experiments have identified FDH as a differentially produced protein during wound healing in potato tuber slices, with order of magnitude level changes in protein during this process (Chaves et al. , 2009 ). It could be hypothesised that the expression of FDH in the potato tuber could be coordinated with the initial secretion of formate by a fermenting pathogen. Potentially, this would generate NADH from formate in stressed or damaged plant tissues. Recently, it was shown that FDH co‐ordinates cell death and defence responses to phytopathogens in Capsicum annum (Bell pepper) (Choi et al. , 2014 ). There is also indication that formate and other molecules that lead to the generation of formate, such as methanol and formaldehyde, induce the production of the NAD + ‐dependent FDH, perhaps suggesting there is a signalling response to these C1 compounds in plants (Hourton‐Cabassa et al. , 1998 ). Concluding remarks In this work, P. atrosepticum SCRI1043 has been established as a tractable new model organism for studying hydrogen metabolism in general and FHL function in particular. The organism is a rare example of a bacterium with an active hydrogenase‐4‐containing FHL‐2 complex, however, in the course of this work, hydrogenase‐4 activity was reported in T. guamensis , another γ‐proteobacterium (Lindenstrauß and Pinske, 2019 ). Interesting, the T. gaumensis Hyd‐4 was found to be active in vivo but very poorly reactive in vitro in standard enzymatic assays with redox‐active dyes (Lindenstrauß and Pinske, 2019 ). This again highlights the need for development of new approaches to characterise FHL‐2 and its component parts. In P. atrosepticum , the active hydrogenase‐4 enzyme operates in tandem with an unusual selenium‐free formate dehydrogenase, which may be more amenable to biotechnological engineering than selenium‐dependent isoenzymes. In evolutionary terms, the FHL‐2 complex has been discussed as a key intermediate in the evolution of the NADH dehydrogenase (Complex I) from a structurally simpler membrane‐bound hydrogenase (Friedrich and Scheide, 2000 ; Marreiros et al. , 2013 ; Schut et al. , 2016 ). The most obvious difference in the predicted quaternary structures inferred from the genetics is the large membrane arm present in FHL‐2 compared to FHL‐1, and data presented here points to the extra membrane proteins being not essential for formate‐dependent hydrogen evolution in vivo . The role of the FHL membrane arm in generating a transmembrane ion gradient remains to be fully explored in enteric bacteria."
} | 5,897 |
25868062 | null | s2 | 5,198 | {
"abstract": "Metabolic engineering strategies have enabled improvements in yield and titer for a variety of valuable small molecules produced naturally in microorganisms, as well as those produced via heterologous pathways. Typically, the approaches have been focused on up- and downregulation of genes to redistribute steady-state pathway fluxes, but more recently a number of groups have developed strategies for dynamic regulation, which allows rebalancing of fluxes according to changing conditions in the cell or the fermentation medium. This review highlights some of the recently published work related to dynamic metabolic engineering strategies and explores how advances in high-throughput screening and synthetic biology can support development of new dynamic systems. Dynamic gene expression profiles allow trade-offs between growth and production to be better managed and can help avoid build-up of undesired intermediates. The implementation is more complex relative to static control, but advances in screening techniques and DNA synthesis will continue to drive innovation in this field."
} | 272 |
39741173 | PMC11688422 | pmc | 5,202 | {
"abstract": "Despite a lot of efforts devoted to construct efficient microbiomes, there are still major obstacles to moving from the lab to industrial applications due to the inapplicability of existing technologies or limited understanding of microbiome variation regularity. Here we show a domestication strategy to cultivate an effciient and resilient functional microbiome for addressing phenolic wastewater challenges, which involves directional domestication in shaker, laboratory water test in small-scale, gas test in pilot scale, water test in pilot scale, and engineering application in industrial scale. The domestication process includes the transition from water to gas, which provided complex transient environment for screening of a more adaptable and robust microbiome, thereby mitigating the performance disparities encountered when transitioning from laboratory experimentation to industrial engineering applications. Within the domestication and application processes for treating phenolic resin wastewater, a powerful functional microbiome was built by self-assembly. This leads to an augmented biodiversity and the development of more intricate phenol and formaldehyde metabolic pathways. The incorporation of increased stochastic processes and random network characteristics further suggested the stability of the microbial community during the application phase. This study elucidates the self-assembly process of microbial communities during the artificial construction process, showcasing their adaptive evolution under different adverse conditions. It serves as a noteworthy case study for the artificial construction of a microbiome for the engineering application of treating industrial wastewater.",
"introduction": "Introduction Microbial domestication is an ancient and traditional technique, particularly critical in the field of wastewater treatment, where it plays a key role in the initiation and operation of treatment processes 1 . Numerous studies have demonstrated that microbial domestication aligns with the ecological principles of “selection and adaptation” 2 – 4 . Consequently, the source of microbial strains, wastewater quality, and treatment processes are the main factors influencing the success of domestication 5 – 7 . The diversity of microbial communities is fundamental to domestication, with the process involving the selection of microbial populations under domestication conditions 8 , 9 . Microorganisms that adapt to these conditions survive and grow, while those that do not are inhibited or eliminated. During the domestication process, microbial community succession occurs; dominant populations under low concentration conditions are replaced by other populations at higher concentrations 10 , 11 . This is due to the functional redundancy of microorganisms, where multiple microorganisms possess the same metabolic function but differ physiologically, allowing them to adapt to different domestication conditions, leading to the replacement of dominant populations 12 . Therefore, changes in domestication conditions are significant for obtaining highly efficient and stable microbial communities. The domestication of microorganisms for wastewater treatment typically involves gradually increasing the proportion of wastewater and progressively intensifying conditions until the inoculated microorganisms are fully adapted to the wastewater quality 13 . However, in practical industrial applications, wastewater quality conditions are highly complex. Phenolic resin industrial wastewater, for example, in actual industrial production, the intermittent discharge of wastewater results in unstable water quantities, accompanied by significant fluctuations in contaminant concentrations 14 , 15 . The pronounced inhibitory and toxic effects of high concentrations of phenols and formaldehyde on microbes contribute to the poor biodegradability of phenolic resin wastewater, posing substantial processing challenges 16 . The microbial seed employed for phenolic wastewater treatment must possess the capability to address two critical situations: 1) The presence of multiple contaminated substrates, primarily comprising phenol and formaldehyde, alongside methanol and other organic pollutants 15 , necessitating the community to exhibit the ability to metabolize diverse substrates; 2) Fluctuations in working conditions, giving rise to emergent properties in biological systems, encompassing physical and chemical stressors, as well as substrate and nutrient heterogeneity 15 , 17 . This demands that the community maintains stable functionality and can adapt to environmental perturbations within engineered ecosystems. Significantly, in applications such as the treatment of phenol or formaldehyde, leveraging diverse biological control methods has demonstrated exceptional efficacy in eliminating contaminants 16 , 18 , 19 . While most of the reported studies were conducted based on the synthetic wastewater with clear pollutant composition (Supplementary Table 1 ), which was not fit for the application in actual industrial phenolic wastewater, and this is an important barrier for translation to industrial biotreatment systems. In addition, the reported study based on actual wastewater from industrial processes necessitate multistage processing in conjunction with chemical-physical methods which would result in high cost (Supplementary Table 1 ). Consequently, efficient phenolic resin wastewater - degrading microbial consortia capable of withstanding complex operational conditions must be developed. This would address the barriers impeding the transition from laboratory-scale research to practical applications, thereby reducing costs and enhancing efficiency. In response to these challenges, we present an innovative paradigm for industrial microbiome domestication, akin to a self-assembly approach. This novel strategy integrates water-gas domestication, water pilot domestication, field domestication, and a field application mode in a sequential combination of domestication methods. The goal is to obtain microbiome capable of swift adaptation to field environments, showcasing elevated degradation efficiency, particularly in addressing the complex challenge posed by phenol-formaldehyde resin wastewater. Furthermore, we delved into the assembly and adaptation process of this community, elucidating how a stable and efficient community emerges during the self-assembly progression. These findings provide valuable insights and serve as a robust reference for the development of functional microflora intended for application in engineering contexts.",
"discussion": "Discussion In this study, a domestication approach from lab to industry application was proved to facilitate the acquisition of an efficient and stable functional microbiome. Initiated with microbes adapted to phenol as the sole carbon source, the microbial community’s performance, initially suboptimal in actual coking wastewater treatment, undergoes a remarkable surge in phenol degradation efficiency and formaldehyde removal during the MUB stage. Microbial diversity experiences a significant upswing in the MUB stage compared to the BF stage. Genes encoding enzymes involved in the different metabolic pathways of formaldehyde have all been found in MUB stage, although the taxonomic diversity of some genes is limited (supplementary Table 7 , Fig. 6 ). Subsequent stages witness the augmentation of taxonomic diversity for each key enzyme involved in phenol and formaldehyde metabolism. From this vantage point, the MUB stage emerges as a pivotal step in microbial domestication. During the gas test stage, utilizing real production waste gas with substantial concentration fluctuations, the phenolic gas removal rate displays initial variability in the first 18 days of domestication (April 2 to April 20) before stabilizing and steadily increasing (Supplementary Fig. 2 ). We postulate that during the gas domestication process, the microbial community undergoes a “catastrophe” induced by the unique characteristics of gaseous pollutants. According to Fig. 2b , there was a dramatic increase of α-diversity in MUB stage compared with BF stage. As shown in Fig. 7 , in MUB stage, there developed the species (MUB bin.7 Thauera humireducens ) with both complete phenol and formaldehyde metabolic pathways, species (MUB bin.12 and MUB bin.31) with complete phenol degradation pathway. While, the most prevalent species in MUB stage was Acinetobacter kookii , which did not reveal complete phenol-formaldehyde metabolic pathways. However, Acinetobacter species are reported to have biofilm formation abilities 29 and are commonly detected in municipal and hospital wastewater effluents 30 . Moreover, in the MUB stage, some MAGs, such as MUB_bin.21 (Xanthomonadaceae) and MUB_bin.35 ( Stenotrophomonas acidaminiphila ), accounted for 10.91% and 5.86% of their respective samples, lacking phenol-formaldehyde metabolism genes, exhibited more complete polyhydroxybutyrate (PHB) synthesis-related genes compared to other MAGs (indicated in sheet3 of Supplementary Data 2 ). PHB, synthesized by many microorganisms (primarily prokaryotes) under non-steady-state conditions (such as complex water quality and transient environmental conditions), serves as a carbon and energy source for cells when extracellular nutrients are scarce 31 , 32 . The rapid variation in inlet gas concentration creates an unstable environment, shaping environmental disturbance for the habitat of microbes. This environment swiftly eliminates microbes unable to adapt, leaving only the more resilient ones. Unlike conventional domestication, where pollutant concentrations increase gradually, this method subject microbes to abrupt changes, screening for species that can resist drastic fluctuations. In this domestication process, the developed community had the ability of stress tolerance which may be acted by the “PHB synthetist” (such as Acinetobacter kookii ), and microbes (such as Thauera humireducens ) which could proliferated using the phenol or formaldehyde pollutes as substrate in high-disturbance habitat may could be considered as “ruderals” 33 . With these traits, the microbial community could better adapt for the living circumstances. The mechanism behind this MUB domestication method warrants further investigation. Nevertheless, our findings indicate that this method holds promise for obtaining a functional community with suitable for industrial applications. During the engineering stage, a remarkable surge in biodiversity, concurrent with elevated efficiency and unwavering stability in water treatment performance was achieved. While theoretical frameworks posit a positive correlation between biodiversity and functional stability 34 , 35 , the tangible variations in community diversity within real-world industrial wastewater treatment settings have been limited. Notably, our study navigated the challenges of characterizing microbial community processes in an open, complex, and large-scale ecosystem. Conventionally, wastewater treatment processes targeting specific pollutants have been associated with a decline in biodiversity due to the dominance of key functional species adept at colonizing unique ecological niches during substrate degradation or transformation 36 – 38 . However, these observations have primarily emanated from experiments conducted in anaerobic or anoxic environments within closed or semi-closed systems. Distinctively, our study maintained an open and aerobic environment to streamline equipment investment and operational management. The BCO method was utilized, which presents advantages over the anaerobic method for treating high-concentration phenol-formaldehyde wastewater (Supplementary text 1 ). Microbial community functions during the process is of core importance for a steady treatment effect. Throughout the domestication process, we observed a uniform distribution of microbial communities in the Phase3 stage, characterized by a significant enrichment of taxa with phenol or formaldehyde metabolic pathways, also species with no complete phenol or formaldehyde metabolic pathways detected may also play a support role for the community to cope with the complex environments. Such as the Hyphomicrobium capable of formaldehyde metabolism were the most prevalent in CH stage, which also showed potential of PHB synthesis (sheet3 in Supplementary Data 2 ). Members of Hyphomicrobium were recognized for its methylotrophic traits and formaldehyde assimilation capabilities, to a spectrum of environments, especially in wastewater treatment processes 24 , 39 – 42 . Thauera humireducens were detected with complete formaldehyde and phenol metabolism pathway was dominant in Eng stage (Fig. 7 ), and the Thauera sp029982755 with formaldehyde metabolism pathway were detected the most abundant in Eng and HMAJ stages. These two members of Thauera genus both owned the potential of PHB synthesis (sheet3 in Supplementary Data 2 ). In addition, members of Thauera are noted for their ability to metabolize various aromatic compounds under both aerobic and anaerobic (denitrifying) conditions 43 , produce exopolysaccharide (EPS) 44 , suggesting their potential in water pollutant remediation 43 . Other relatively abundant species such as Eng_bin.12 (Burkholderiaceae), Eng_bin.18 (Alphaproteobacteria), Eng_bin.20 ( Hydrogenophaga ), Eng_bin.28 ( Sphingobium ), and CH_bin.14 ( Stappia sp900185725) did not possess phenol and formaldehyde metabolism genes but did show a high proportion of PHB synthesis genes. It is hypothesized that these microorganisms, despite lacking phenol-formaldehyde metabolism capabilities, may play a supportive role in the microbial community’s response to complex and transient environments. These evidences suggested that to adapt to environmental changes, the microbial community has developed a comprehensive strategy for substrate degradation and self-support under adverse conditions. Additionally, the prevalence of methylotrophic prokaryotes like Methanolobus , Methanomethylovorans , Methylococcaceae , in addition to a emergence of methylotrophic archaeon in the third phase (Supplementary Fig. 23 ) signifies their importance in adapting to anoxic conditions (Some hypoxic areas may be formed during BCO treatment) and their resilience to high concentrations of Na + and formaldehyde 45 , 46 . Based on the metagenome analysis, multiple pathways targeted at phenol and formaldehyde metabolism were developed for processing of pollutes substrate during the domestication process. For formaldehyde metabolism, in BF stage only RuMP was consistently found, while in MUB stage the RuMP, serine pathway, GSH, H 4 F and H 4 MPT were all found, but with low gene diversity. While, the community developed the most abundant and redundant genes participated in formaldehyde metabolism in the application stage (Supplementary Table 7 ). The relationship between the presence of multiple pathways towards a specific target (such as processing of substrate) and functional stability has been inferred to promote functional stability, usually resulting from greater functional redundancy and functional niche complementation 12 . This further emphasizes that ecosystem stability is the outcome not of population diversity per se, but of functional redundancy, which is ensured by the presence of a reservoir of species able to perform the same ecological function. As in this study, higher population diversity and functional redundancy both appeared in the third phase, which demonstrates the reliability of this domestication method for obtaining an efficiently steady phenolic wastewater treatment community. During the domestication process the ecosystem captured more species, likely from the open air or in the origin influent, which developed a comprehensive seed library encompassing multiple relevant functional groups to ensure the processing stability. For the RuMP pathway, which was formerly considered to be restricted to the fixation of formaldehyde into cellular constituents in methylotropic prokaryotes 47 , and now recognized as a widespread prokaryotic pathway involved in formaldehyde fixation and detoxification 47 . The abundant methylotrophic prokaryotes developed in the third phase mainly contributed the HPS and PHI enzymes in the RuMP pathway (Supplementary Fig. 16 ; Supplementary Fig. 23 ). The physiological functions of HPS and PHI in methylotrophic as well as nonmethylotrophic bacteria had been elucidated through biochemical and genetic approaches 47 . Such as the hps - phi gene of Pyrococcus spp. 48 and the faeB - hpsB gene of Methanosarcina spp. 49 ). These fused gene products exhibit functions corresponding to the individual enzyme activities, and are more efficient than equivalent systems made up of discrete enzymes. In this study, the Methanosarcina was detected in CH and HMAJ stage samples (Supplementary Fig. 23 ), the homologous protein genes of K13812 ( fae - hps ) and K13831( hps-phi ) mostly contributed by Methanolobus and Methanomethylovorans in the application stage samples were detected, which suggested important role for the existence of these archaea to deal with the extreme environmental conditions containing high concentration formaldehyde 50 . Of course, the most abundant species such as Eng_bin.1 ( Thauera sp029982755), CH_bin.7 ( Hyphomicrobium _C), HMAJ_bin.21 ( Thauera sp029982755) in the corresponding application stages also contained the genetic capacity to contribute to formaldehyde fixation and detoxification, with the metabolic pathway for formaldehyde detected in these abundant species being the H4MPt pathway (sheet2 in Supplementary Data 2 ). In the developmental phases of our investigation, intricate modules of H 4 F, H 4 MPT, and the serine pathway underwent significant evolution. Methylobacterium spp. (especially the Methylobacterium extorquens ) have long been studied for their metabolism of simple C1 compounds such as methanol, formate, methylamine, and halogenated methanes 51 . Formaldehyde is a central intermediate in the metabolism of many of these substrates, raising the possibility that, if these methylotrophs were capable of utilizing these C1 compounds, they could also use the formaldehyde as a growth substrate. As reported, exploration into the formaldehyde metabolism of Methylobacterium extorquens AM1 unveiled an interconnected and dynamic interplay among the H 4 F, H 4 MPT, and serine modules 52 . This intricate metabolic loop emerged as a robust formaldehyde flux buffer, adept at accommodating transitions driven by substrate variations. This strategic design affords the cell the time to respond to the introduction of a methylotrophic substrate, thereby harnessing energy benefits while averting the accumulation of toxic intermediates, particularly formaldehyde 52 . As the serine cycle’s activity escalates, a heightened capacity for safely diverting formaldehyde to assimilatory metabolism via a direct, ATP-independent route has been observed 52 . This significant discovery reveals a paradigm for managing high-throughput toxic intermediates, highlighting the need for seemingly redundant but functionally vital modules to co-exist in our system. In the context of our industrial phenolic wastewater treatment, wherein phenol and formaldehyde were predominant pollutants, methanol emerged as a substantial contaminant closely intertwined with formaldehyde metabolism. Based on Methylobacterium spp. having the above-mentioned ability to balance formaldehyde metabolism, the presence of Methylobacterium spp. in the application samples was of particular interest, and members of this genus did indeed increase in abundance during the application stage (Supplementary Fig. 23 ). The resilient response of Methylobacterium spp. to changing methylotrophic substrates may potentially extend to other methylotrophic microorganisms, which were also abundantly enriched during the application stage (Supplementary Fig. 23 ). Throughout the phenol degradation process, the microbial community exhibited a comprehensive degradation enzyme system from the initial stages to the application stages based on the metagenome analysis results. The prevalence of multiple isozyme-encoding genes presumably confers a selective advantage, enabling the strains to thrive in diverse environmental conditions compared to those with only a single copy 53 . This collective functional repertoire within the bioreactor system, particularly the presence of multiple catechol dioxygenase genes, likely rendered the community capable of metabolizing a broader range of aromatic compounds 54 , 55 . The catE gene (K07104), encoding catechol 2,3-dioxygense and representing a pivotal enzyme in aromatic compound degradation, exhibited an increased abundance primarily attributed to Hyphomicrobium (Supplementary Fig. 14 ). The specific characteristics of catechol 2,3-dioxygenase in Hyphomicrobium warrant further in-depth investigation, as it is common to find genes encoding catechol 2,3-dioxygenase in the genomes of Hyphomicrobium species 56 , 57 . Our metagenomic analysis revealed that Hyphomicrobium was one of the main contributors of this enzyme, and the metagenome-assembled genomes (MAGs) annotated as Hyphomicrobium all contain genes encoding catechol 2,3-dioxygenase (sheet2 in Supplementary Data 2 ). Previous literature has reported that Hyphomicrobium often plays a dominant role in phenol-containing wastewater and is considered to be responsible for the degradation of aromatic compounds such as benzene and phenol 58 , 59 . Studies have shown that catechol can enhance the activity of catechol 2,3-dioxygenase in microbes, altering the metabolic pathway of phenol and thereby changing the accumulation characteristics of intermediate products, which in turn increases the efficiency of phenol mineralization in microbial cells 60 . Additionally, the acylated homoserine lactone (AHL) mediated quorum sensing (QS) system has been reported to regulate the catechol meta-cleavage pathway, and potentially enhance aromatics biodegradation 61 . It was found that deletion of the rhl QS system (an AHL-mediated QS system) on Pseudomonas resulted in a significant decrease in aromatics biodegradation as well as the activity of catechol 2,3-dioxygenase 61 . Notably, our study revealed a higher abundance of the rhl QS-related genes ( rhlA , rhlB , rhlC ) in MUB, Water, Eng, CH, and HMAJ compared to BF (Supplementary Fig. 24 ), suggesting these more abundant signalling enzymes (RhlA, RhlB, and RhlC) may be produced in the system to enhance the catechol 2,3-dioxygenase activity. Moreover, the rhl QS-related genes ( rhlI , rhlR , rhlA , rhlB , rhlC ) were responsible for rhamnolipid biosynthesis, which is a kind of biosurfactant mainly used in the petroleum industry and for bioremediation of different pollutants 62 . The biosurfactant’s role in facilitating the bioavailability of hydrophobic pollutants, such as polycyclic aromatic hydrocarbons and n-alkanes, holds promising implications for enhanced remediation of toxic compounds. Previous reports have also indicated that elevated substrate concentrations could induce the meta-pathway for benzoate degradation 63 . The co-existence of carbon sources, such as methanol, has previously been found to promote meta-cleavage as the primary ring-opening pathway, concurrently fostering cell growth and expediting phenol degradation 64 . These findings collectively suggest that meta-cleavage plays a pivotal role in managing high concentrations of phenol, and complements ortho-cleavage for comprehensive phenol mineralization. Consistent with this existing literature, catE (K07104) was observed here to increase in abundance in the third phase, with Hyphomicrobium spp. likely being the primary organisms responsible for this increase (Fig. 2d ). Additionally, genes associated with surfactant production and the rhl quorum sensing (QS) system also showed elevated levels (Supplementary Fig. 24 ), and may have facilitated the activity of catechol 2,3-dioxygenase 61 . Finally, various other carbon substrates, such as methanol, are also present in the phenolic wastewater and thus were introduced to the system during the application phase. These substrates may also further enhance phenol degradation by stimulating the meta-cleavage pathway 64 . Overall, these findings suggest that the microbial community developed characteristics that were more adaptive to high concentrations and complex pollutants during the application phase. We conducted a comprehensive examination of the co-occurrence network properties in samples from both the domestication and application stages to unravel the community’s organizational strategies in adapting to complex wastewater treatment environments. Extensive evidence suggests that ecological network properties, serving as proxies for interactions among coexisting organisms, can play an important role in explaining community responses to environmental changes 65 . Our study revealed a higher prevalence of competitive relationships and increased modularity during the engineering application stages (Table 1 ). Communities exhibiting a substantial proportion of members connected through positive links are considered unstable, as these members may synchronously respond to environmental fluctuations, leading to positive feedback and co-oscillation 66 . Conversely, the presence of negative links tends to stabilize co-oscillation and enhance network stability 66 . Furthermore, the lower R 2 of the power-law in Phase3 compared to Phase1-2 indicated a shift towards non-scale-free network characteristics in Phase3, which are known for their robustness compared to scale-free networks 67 . Keystone taxa identified in both Phase3 and Phase1-2 networks were predominantly found in shared edges, keystone species, characterized by their low biomass and pivotal roles in food webs, are often associated with the formation and functionality of microbial communities, displaying substantial connectivity within or between modules in co-occurrence networks 68 . Approximately 37.6% of interactions persisted from Phase1-2 to Phase3, with most keystone species contributing to these shared interactions, underscoring their crucial role in maintaining system robustness. The microbial world contains keystone guilds (groups of keystone taxa with similar functions) 69 . Keystone guilds are generated based on many factors, such as complementary resource acquisition strategies, resource sharing, and niche differentiation 70 . Research has indicated that in heterogeneous environments, microbial networks typically exhibit minimal number of shared edges (eg. only 6) 71 . In our study, a notably higher number of shared edges (922) was observed in the networks from the first two phases to the third phase. Furthermore, the majority of keystone species were identified as nodes within these shared edges. This suggests that these keystone species possess extensive applicability in the treatment of complex pollutant-laden wastewater under varying conditions. As list in Supplementary Table 8 , the characteristics of these keystone species for treatment included nitrogen and phosphorus metabolism (Rhodocyclaceae, Candidatus Xiphinematobacter , Ammoniphilus ), survival under stressful conditions such as anoxia (Pasteurellales), helping to form biofilms (Neisseriaceae), as well as organic pollutant degradation and detoxification ( Lactobacillus , Anaerolinea ), which indicated that these organisms thus appear to belong to keystone guilds composed of multiple keystone taxa influencing a broad range of processes. On the community level, assembly processes between prokaryotic communities during Phase1 to Phase2, Phase2 to Phase3 stages were predominantly influenced by stochastic processes (Fig. 4a ). However, deterministic processes significantly contributed to the transitions from BF to MUB, MUB to Water, Water to Eng, compared to Eng to CH and Eng to HMAJ. This aligns with the observation that six bins were significantly affected by heterogeneous selection during Phase1 to Phase2 and Phase2 to Phase3. The characteristics of the representative taxa in these bins were list in Supplementary Table 9 . These taxa demonstrated degradability to a wide variety of pollutants and tolerance to water treatment environments. Notably, these taxa exhibited higher abundance in the engineering application stage samples than in the domestication stage (sheet6 in Supplementary Data 1 ), indicating the development of a comprehensive and robust functional genetic library during the domestication to application stage. Overall, during the application stage (phase3), a higher proportion of stochastic processes dominated the community assembly processes, contributing to the stability of the system through an abundance of taxa contributing redundant functions. Most notably, members of Hyphomicrobium and methylotrophic microorganisms ( Methanomethylovorans , Methanolobus ) were abundant in the phase3 stage, and all are putative formaldehyde metabolisers. In addition, one MAG representing a Thauera sp. contained the genetic potential for both formaldehyde and phenol metabolism, and was also more abundant in phase3 samples (Eng and HMAJ) (Fig. 7 ). In summary, the water to gas test in tandem with industrial phenolic wastewater domestication (pilot test, engineering test), a robust phenol and formaldehyde microbial seed has been achieved. It’s crucial to note that this approach is tailored for phenolic resin wastewater; however, we envision its potential applicability to other types of industrial wastewater, especially in field sites characterized by unstable conditions. This groundbreaking research not only expands the horizons of microbial ecology but also presents a transformative strategy for harnessing the potential of microbiomes in complex industrial settings. In terms of specifically guaranteeing the stability and efficiency of this functional group of microorganisms, the results of this study indicate that this functional microbiome was primarily influenced by two factors. First, the microbial community’s adaptation to environments dominated by phenol-formaldehyde wastewater: when using the microbiome for wastewater treatment, consistent pollutant profiles are important. Second, the preservation of effective microbial seeds for the applied field is also essential, and these microbial seeds should be reactivated using the relevant wastewater as a substrate prior to industrial application. Due to the complexity and variability of the environmental microbiome, the potential functional species analyzed in this study were mainly based on sequencing results, with the aim of revealing the effects of domestication methods on the microbiome. Functional characterization for the dominant or core species have certain limitations, and much endeavors (such as single cell screening, strain culture and functional verification) will be needed to reveal their true characters in the community."
} | 7,809 |
32991583 | PMC7546477 | pmc | 5,203 | {
"abstract": "Dynamic flux balance analysis uses a quasi-steady state assumption to calculate an organism’s metabolic activity at each time-step of a dynamic simulation, using the well-known technique of flux balance analysis. For microbial communities, this calculation is especially costly and involves solving a linear constrained optimization problem for each member of the community at each time step. However, this is unnecessary and inefficient, as prior solutions can be used to inform future time steps. Here, we show that a basis for the space of internal fluxes can be chosen for each microbe in a community and this basis can be used to simulate forward by solving a relatively inexpensive system of linear equations at most time steps. We can use this solution as long as the resulting metabolic activity remains within the optimization problem’s constraints (i.e. the solution to the linear system of equations remains a feasible to the linear program). As the solution becomes infeasible, it first becomes a feasible but degenerate solution to the optimization problem, and we can solve a different but related optimization problem to choose an appropriate basis to continue forward simulation. We demonstrate the efficiency and robustness of our method by comparing with currently used methods on a four species community, and show that our method requires at least 91% fewer optimizations to be solved. For reproducibility, we prototyped the method using Python. Source code is available at https://github.com/jdbrunner/surfin_fba .",
"conclusion": "Conclusion Understanding, predicting, and manipulating the make-up of microbial communities requires understanding a complex dynamic process. Genome-scale metabolic models provide an approximation to this process through the quasi-steady state assumption which leads to dynamic flux balance analysis. However, this system is large and hard to simulate numerically, let alone analyze for qualitative behaviors. As a first step towards a thorough analysis of community of organisms modeled with dynamic FBA, an efficient method of numerical simulation would provide an essential tool. However, modern tools for simulating dynamic FBA rely on repeatedly solving an optimization problem at every time step [ 24 , 31 , 35 – 38 ]. Dynamic FBA simulation can be improved by considering the structure of these linear programs so that many fewer optimizations are required. As of now, the algorithm of Höffner et al. [ 40 ] is the only published method which takes advantage of this observation. However, that method does not account for the degeneracy of solutions to the relevant linear programs, meaning that it can choose a solution that cannot be carried forward in time. We present a method that chooses a basis for forward simulation. In contrast to the method of Höffner et al., we choose this basis in such a way that increases the likelihood that this forward simulation is actually possible. Efficient dynamic FBA will allow better parameter fitting to time-longitudinal data. Furthermore, it allows for a search of parameter space which can help predict likely model outcomes or learn maps from parameter values to model outcomes.",
"introduction": "Introduction Microbial communities and human health The makeup of microbial communities is often complex, dynamic, and hard to predict. However, microbial community structure has a profound effect on human health and disease [ 1 – 7 ]. These two facts have led to significant interest in mathematical models which can predict relative abundances among microbes in a community. Various dynamical models have been proposed to explain and predict microbial community population dynamics [ 8 – 12 ]. Among these are models which propose that interactions between species are mediated by the metabolites that each species produces and consumes [ 13 , 14 ], and there is significant evidence that these models perform better than models which depend on direct interaction between species [ 15 , 16 ]. Recently, advances in genetic sequencing have allowed the creation of genome-scale models (GEMs) that reflect the internal network of cellular metabolism, and can therefore be used to predict metabolite use and production [ 17 – 19 ]. This technique can be extended to microbial community modeling by combining GEMs of different species. There has been significant interest in using GEMs to predict relative populations of stable microbial communities [ 20 – 26 ]. Community metabolic modeling can not only predict relative populations, but also holds the potential to predict and explain the community metabolite yield, which can have a profound effect on health [ 4 ]. Furthermore, model repositories such as the online bacterial bioinformatics resource PATRIC [ 27 ] or the BiGG model database [ 28 ] make it possible to build community models using information from individual species investigations. GEMs can be used to predict microbial growth rates as well as metabolite consumption and production rates using a process called flux balance analysis (FBA). Because these predictions appear in the form of rates of change, they can be used to define a metabolite mediated dynamical model simply by taking as a vector field the rates of change predicted by FBA. We can therefore combine the techniques of metabolite mediated dynamic modeling and community metabolic modeling to produce dynamic predictions of microbial community population size and metabolite yield. This strategy is called dynamic FBA [ 29 – 31 ], and has recently been used to model microbial communities [ 32 – 34 ]. Dynamic FBA, when implemented naïvely, requires a linear optimization problem to be repeatedly solved, and carries a high computational cost for even small communities. Furthermore, in silico experiments may need to be repeated many times over various environmental conditions or using various parameter choices in order to make robust conclusions or to accurately fit model parameters. As a result, implementations of dynamic FBA which depend on optimization at every time-step carry a prohibitively high computational cost when used to simulate larger microbial communities. The implementation of dynamic FBA in the popular COBRA toolbox software package [ 17 ] is done in this way, and essentially all more efficient available tools for simulating dynamic FBA fundamentally use an ODE solver approach with optimization at each time-step [ 24 , 31 , 35 – 38 ]. Dynamic FBA can be improved by taking advantage of the linear structure of the optimization problem which provides a choice of basis for an optimal solution that may be reused at future time-steps [ 39 , 40 ]. However, the optimizations that are required by this strategy involve solutions with non-unique bases. This means that a basis chosen at random may not provide an optimal solution to the linear program at future time-steps because it provides a solution that is non-optimal or infeasible. In order to implement dynamic FBA without optimizing at each time step, we use an optimal basic set for the FBA linear optimization problem to create a system of linear equations whose solutions at future time-steps coincide with the solutions to the FBA optimization problem. To solve the problem of non-uniqueness among bases, we prove that there exists a choice of basis that allows forward simulation for a given optimal flux solution and provide a method to choose this basis. Note that this method does not choose among a set of non-unique optimal flux solutions, but instead chooses a basis for a single given optimum. To choose among multiple optimal flux solutions, biological, rather than mathematical, considerations should be used. In this manuscript, we detail how dynamic FBA can be simulated forward without re-optimization for some time interval, and give a method for doing so. We propose conditions on an optimal basic set for the FBA linear optimization problem which allows for forward simulation, and we prove that such a choice exists. We then detail how to choose this basis set, and finally give examples of simulations which demonstrate the power of our method. For reproducibility, we make a prototype implementation of our method in the Python language available at https://github.com/jdbrunner/surfin_fba ."
} | 2,066 |
35492552 | PMC9047333 | pmc | 5,204 | {
"abstract": "This paper presents a green method for fabricating dual temperature- and pH-responsive electrospun fibrous mats from an aqueous-based blend poly( N -isopropylacrylamide- co -acrylic acid) (P(NIPAAm- co -AAc)) and regenerated silk fibroin (RSF) by employing electrospinning technique. P(NIPAAm- co -AAc) was synthesized by free radical solution polymerization and its low critical solution temperature (LCST) was in the physiological range (38.8 °C). The P(NIPAAm- co -AAc)/RSF fibers were prepared by electrospinning technology in the presence of the crosslinking agents (EDC·HCl and NHS) with water as solvent. After in situ crosslinking and water-annealing process, the water-stable composite fibrous mats were obtained. Scanning electron microscopy (SEM) and Fourier transform infrared spectroscopy (FTIR) were used to analyze the crosslinking process. Temperature and pH dual stimuli-responsive swelling-shrinking behavior of the fibrous mats were observed when the temperature was below and above the LCST of the copolymer at different pHs. In addition, rhodamine B-loaded the fibrous mats also showed dual temperature and pH controlled release behavior, demonstrating the potential use of the fibrous mats for “smart” controlled drug delivery applications.",
"conclusion": "Conclusions In this work, P(NIPAAm- co -AAc) copolymers were successfully synthesized and blended with RSF to create dual temperature- and pH-responsive of fibrous mats via electrospinning technique. The LCST of the copolymer increased with the increase of AAc unit content in the copolymer and could be adjusted close to human body temperature. Beadless, uniform as-spun fibers could be successfully produced by electrospinning from aqueous-based the copolymer and RSF blends in the presence of the crosslinking agents (EDC·HCl and NHS). After in situ crosslinking and water-annealing process, the water-stable fibrous mats were obtained. SEM images, FTIR and DSC were used to analyze the crosslinking process. Temperature and pH dual stimuli-responsive swelling-shrinking behavior of the fibrous mats were observed by SEM images and contact angle measurement when the temperature of PB solution was below and above the LCST of the copolymer at different pHs. Finally, rhodamine B-loaded the fibrous mats also showed dual temperature and pH controlled release behavior. Based on the above characteristics and the biocompatibility and biodegradation of RSF, we believe that P(NIPAAm- co -AAc)/RSF fibrous mats will show promising applications in controlled drug delivery, tissue engineering and biosensors.",
"introduction": "Introduction “Smart” or stimuli-responsive polymers respond to small changes in their environment, such as temperature, pH, ionic strength, or light etc. , by markedly changing their chemical and physical properties. These polymers have been intensively studied due to their promising applications in drug delivery, tissue engineering, catalyst carrier and biosensors. 1–6 Among the “Smart” polymers, temperature-responsive polymers respond to changes in the temperature and undergo a phase transition at the lower critical solution temperature (LCST). At temperatures below the LCST, these macromolecules are hydrophilic, while at temperatures above LCST, they become hydrophobic and collapse. 7 Poly( N -isopropylacrylamide) (PNIPAAm) is one of the most popular temperature-sensitive polymers, 8,9 and its LCST is around 32 °C. In order to raise the LCST of PNIPAAm to make it closer to the physiological temperature of the human body, NIPAAm can be copolymerized with hydrophilic monomers. This can be attributed to the formation of intrachain hydrogen bonds between the two groups, hydrophilic monomer and NIPAAm. 10,11 Acrylic acid (AAc) is a kind of hydrophilic monomer which can be copolymerized with NIPAAm to obtain poly( N -isopropylacrylamide- co -acrylic acid) P(NIPAAm- co -AAc). 12 Due to its molecular structure contains temperature-sensitive amide group and pH-sensitive carboxyl group, the LCST of P(NIPAAm- co -AAc) can be adjusted depending to the change of composition, meanwhile, the copolymer has the characteristic of dual response of temperature and pH, which has a good application prospect in the field of drug carriers. 13,14 Responsive drug carriers come in various forms, such as hydrogels, 15,16 microgels, 17 films 18 and macro-nanofibers. 19–21 The form of macro-nanofibers have uniquely advantage because the features provide extremely large surface area and porosity, which enhance the sensitivity to external stimuli. 22 Electrospinning is the most facile, convenient and effective method to produce micro and nano-fibrous mats. 23,24 A variety of nanostructured fibers can be prepared by several kinds of electrospinning processes, such as 2-fluid coaxial, 25 side-by-side, 26 and other complex electrospinning processes. 27 In recent years, the related work of the electrospinning PNIPAAm as temperature responsive fiber carriers have been published, 28–32 but there are a limited number of studies concerning temperature and pH dual-stimuli responsive systems of P(NIPAAm- co -AAc), moreover, most of these systems were processed into hydrogels and nanoparticles. 33–35 This is because preparing water-stable P(NIPAAm- co -AAc) nanofibers is a quite challenging task when the temperature of aqueous media is lower than LCST. Generally, the method of preparing water-insoluble fibers is post-treatment crosslinking, which can be physical or chemical crosslinking. For example, NIPAAm had to be copolymerized with crosslinkable monomers (NMA) to achieve water-insoluble electrospun fibers by a heat induced chemical crosslinking. 36 For P(NIPAAm- co -AAc) copolymer, because it contains a small number of carboxyl groups in the macromolecules structure, chemical crosslinking can be achieved by bioconjugation reaction between carboxyl groups and amino groups with the assistance of crosslinking agent. Commonly crosslinking agents are glutaraldehyde, genipin, 1-ethyl-3-(3-dimethylaminopropyl) carbodiimide (EDC), etc. , among them, the EDC is higher the crosslinking efficiency when it combined with n -hydroxysuccinimide (NHS). Adamsen and co-workers had successfully fabricated in situ crosslinked PNIPAM/gelatin nanofibers by electrospining PNIPAM–NHS and gelatin in the presence of the crosslinking agents EDC and NHS. 37 Lin and co-workers 38 obtained a stimuli-responsive electrospun composite nanofibers from P(NIPAAm- co -AAc) and polyurethane (PU) blends. However, due to the use of DMF organic solvent in the preparation process, it would raise safety concerns when it applied to drug carrier and tissue engineering. Regenerated silk fibroin (RSF), extracted from Bombyx mori cocoons, is a protein polymer which has good biocompatibility and biodegradation and has been successfully used as drug delivery systems in various forms, such as nanospheres, films and nanofibers. 39–43 Mixing RSF with P(NIPAAm- co -AAc) is expected to improve the biocompatibility and biodegradability of P(NIPAAm- co -AAc)-based materials, which is accordant with the economic friendly and green development strategy. The aim of the present study was to developed a green method for fabricating temperature and pH dual-stimuli responsive fibrous mats by using electrospinning technique from P(NIPAAm- co -AAc) and RSF blends. In brief, firstly, P(NIPAAm- co -AAc) copolymer was synthesized by free radical solution polymerization, and then the composite fibers of P(NIPAAm- co -AAc)/RSF were prepared by electrospinning technology combined with in situ crosslinking and with water as solvent. Moreover, after water-annealing process, the insolubility of the composite fibrous mats was further improved, which was due to the physical crosslinking of RSF in composite fiber. Finally, rhodamine B was selected as a model drug and encapsulated in composite fibers and the drug release behavior of the fibrous mats at different temperatures and pHs were investigated.",
"discussion": "Results and discussion Synthesis and characterization of P(NIPAAm- co -AAc) copolymers P(NIPAAm- co -AAc) copolymers of different composition named P-0, P-1, P-2, P-3 shown in Table 2 were synthesized by free radical solution polymerization in ethanol with AIBN as initiator. The prepared random copolymers were identified by 1 H NMR and FTIR. In the 1 H NMR spectrum of P(NIPAAm- co -AAc) copolymer, the proton signals of the NIPAAm moiety are observed at peaks 1.0 (–CH 3 ), 3.81 (–CH) and 7.16 (–NH) ppm, respectively, while the peak related to AAc moiety is at 11.97 (–COOH) ppm. In addition, the copolymer main chain proton signals are shown at 1.41–1.94 ppm. The NIPAAm/AAc composition of copolymers estimated from the ratio on the area integration of the peak at 7.16 (–NH) with at 11.97 (–COOH) and the results are shown in Table 2 . In the FTIR spectrum of copolymer P-2, the peaks at 1650 and 1550 cm −1 are assigned to 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 bending and N–H stretching, respectively. The broad absorption band at around 3300–3400 cm −1 is assigned to the N–H stretching, the peaks at 1368 cm −1 and 1386 cm −1 are arisen from the stretching of the –CH(CH 3 ) 2 groups in NIPAAm. The peaks at 1715 cm −1 is assigned to the C O stretching of carboxylic group of AAc units. The details of 1 H NMR and FTIR spectrum are shown in Fig. S1 and S2 in ESI Appendix. † Thus, the P(NIPAAm- co -AAc) copolymer have been successfully synthesized via the radical copolymerization. Polymerization conditions, molecular weight, and LCSTs of P(NIPAAm- co -AAc) copolymers Sample Feeding ratio NIPAAm : AAc (mol mol −1 ) Composition ratio NIPAAm : AAc a (mol mol −1 ) \n M \n w \n b (g mol −1 ) PDI b LCST (°C) pH 5.7 pH 6.6 P-0 100 : 0 100 : 0 32.9 32.9 P-1 100 : 8 100 : 11 36.5 45.4 \n P-2 \n \n 100 : 12 \n 100 : 16 \n 6.6 × 10 \n \n 4 \n \n \n 1.57 \n \n 38.8 \n \n 54.6 \n P-3 100 : 20 100 : 24 50.8 a Determined by 1 H NMR. b Determined by GPC. The weight average molecular weight ( M w ), and PDI ( M w / M n ) of copolymer P-2 calculated from GPC is 6.6 × 10 4 and 1.57 (the GPC curve is displayed in Fig. S3 in ESI Appendix † ). The phase-transition behaviors and LCSTs of the P(NIPAAm- co -AAc) copolymers in aqueous solutions were characterized by monitoring the change in light transmittance. The temperature at 50% of the initial transmittance was defined as the LCST. Fig. 1 shows variation in the transmittance at 500 nm of 1 wt% P(NIPAAm- co -AAc) in PB aqueous solution with temperature. The LCST of the copolymer (P-1, P-2 and P-3) increases from 36.5 to 50.8 °C with the increase of the content of AAc in the copolymer at pH 5.7 ( Fig. 1a or Fig. S4 in ESI Appendix † ), which are slightly higher than that of PNIPAAm with 32 °C, due to the hydrophilic characteristic of AAc units. Meanwhile, the copolymer P-2 exhibits pH-sensitive behaviors, the LCST increases with the increase of the pH value ( Fig. 1b ), due to the increase in the hydrophilicity of the AAc units caused by the hydrolysis increases of the copolymer with the increase of pH value. 7 Fig. 1 Variation in the transmittance at 500 nm of 1 wt% P(NIPAAm- co -AAc) in PB aqueous solution with temperature: (a) different AAc content at pH 5.7, (b) copolymer P-2 at different pHs. In particular, the LCST of copolymer P-2 at pH 5.7 is 38.8 °C, close to the physiological temperature of human body. Therefore, copolymer P-2 was selected for following studies. Morphology of P(NIPAAm- co -AAc)/RSF fibers The electrospinning process is influenced by many factors, resulting in different fiber morphology. The literatures reported that the solution concentration and surface tension have important effects on the morphology and diameter of the fibers. 45,46 In this work, spinning solutions with different mass ratio of copolymer and RSF named solution a–f were prepared according to the composition in Table 1 and the corresponding fibrous mats named sample a–f. Fig. 2 shows the morphology and diameter distribution of electrospun fibers fabricated from different ratios of P(NIPAAm- co -AAc) and RSF. At low initial concentrations of the copolymer (20 wt%), the fibers exhibit a “beads-on-a-string” morphology, with increase in the initial ratio of P(NIPAAm- co -AAc) in the P(NIPAAm- co -AAc)/RSF blended solution from 22 to 26 wt%, the beadless, uniform and stable flat ribbons fibers can be obtained ( Fig. 2a–d ). This is because the surface tension of the solution reduces ( Table 1 ), which improves the stability of the jet of polymer solution, and favors formation of fibers without beads. 45 Therefore, 22 wt% is the lower boundary concentration of the P(NIPAAm- co -AAc) in the P(NIPAAm- co -AAc)/RSF blended solution in the case of the spinning parameters constant. Fig. 2 SEM images of electrospun P(NIPAAm- co -AAc)/RSF fibers: (a)–(d), the fibers fabricated from P(NIPAAm- co -AAc)/RSF blended solutions with the mass ratio of copolymer and RSF is 1 : 1, but the initial concentrations of P(NIPAAm- co -AAc) is 20, 22, 24 and 26 wt%, respectively. (e–g) The fibers fabricated from the different mass ratio of copolymer and RSF solutions with the content of P(NIPAAm- co -AAc) from 15.7, 13.3 to 10.9 wt%, respectively. (h and i) Element mapping images of P(NIPAAm- co -AAc)/RSF with the content of P(NIPAAm- co -AAc) 13.3 wt%, and P(NIPAAm- co -AAc), respectively. \n Fig. 2e–g show the fibers fabricated from the different mass ratios of P(NIPAAm- co -AAc) and RSF solutions with the content of P(NIPAAm- co -AAc) from 15.7, 13.3 to 10.9 wt%, respectively. With decrease the content of P(NIPAAm- co -AAc) in the P(NIPAAm- co -AAc)/RSF blends, the morphology of the fibers is changed from flat ribbons into approximate cylindrical and the average diameter of the fibers is decreased from 2.142 ± 0.543 to 1.165 ± 0.214 μm. This phenomenon can be attributed to the different volatilization of solvents in the spinning solution. The solvent in the spinning solution is water, and there are strong hydrogen bonds between the copolymer and water which results in the low volatility of residual solvent in the spinning jet and the production of ribbon fibers with larger diameter. When the content of copolymer in spinning solution decrease, the water is easy to volatilize and the morphology of the fibers is changed from flat ribbons into approximate cylindrical, meanwhile, the diameters of the fibers decreased. This finding is similar to that of Rockwood et al. 46 \n Fig. 2h and i show the elemental mapping images of P(NIPAAm- co -AAc)/RSF fibers and P(NIPAAm- co -AAc) fibers. The contents of C, O and N of P(NIPAAm- co -AAc)/RSF fibers are 61.7%, 22.2% and 16.1%, respectively ( Fig. 2h ). Compared with pure P(NIPAAm- co -AAc) fibers ( Fig. 2i ), the content of C decreases and the content of O and N increases in P(NIPAAm- co -AAc)/RSF fibers. This is due to the high proportion of amino acids in the composition of RSF, which indicates that RSF has been evenly mixed with P(NIPAAm- co -AAc) in the fibers. Crosslinking of P(NIPAAm- co -AAc)/RSF as-spun fibers The as-spun fibers of P(NIPAAm- co -AAc)/RSF were soluble in water when the temperature was lower than LCST. In order to realize stable of the as-spun fibers, this work adopted two steps of crosslinking: in situ crosslinking and water-annealing process. In situ crosslinked P(NIPAAm- co -AAc)/RSF fibers were produced by chemical crosslinking between P(NIPAAm- co -AAc) and RSF with the help of EDC·HCl and NHS when the as-spun fibers had experienced a longer storage time, and water-annealing process could induce physical crosslinking through the conformational transformation of RSF. Fig. 3 shows FTIR spectra of P(NIPAAm- co -AAc)/RSF fibers at different crosslinking stages. In the spectrum of as-spun fibers, the peak of 1714 cm −1 is assigned to the C O stretching of carboxylic group of AAc ( Fig. 3a ). As the in situ crosslinking occurred, the peak of 1714 cm −1 disappears and the peak of 1642 cm −1 corresponding to amide I (C O) is shifted to 1630 cm −1 , indicating the occurrence of crosslinking. In addition, the DSC curve of P(NIPAAm- co -AAc)/RSF fibrous mats (Fig. S5 in ESI Appendix † ) shows the thermal decomposition temperature of the fibrous mats increases after crosslinking process. Fig. 3 FTIR spectra of P(NIPAAm- co -AAc)/RSF fibers: (a) as-spun fibers (b) in situ crosslinking fibers (c) after water-annealing of in situ crosslinking fibers. The reaction of in situ crosslinking could be explained in Fig. 5B : P(NIPAAm- co -AAc) (a) or RSF (f) are activated by EDC·HCl (b) to get the intermediate (c), however, intermediate (c) is unstable which is easy to hydrolyze into (a) or (f), so the introduction of the NHS (d) makes the unstable intermediates into stable intermediates (e), (e) with (f′) are aminated to realize the crosslinking between the copolymer and RSF (g) or RSF and RSF (h). 47 The water-solubility of the fibers after in situ crosslinking was reduced ( Fig. 4a1 ), however, when the fibrous mats were immersed into PB buffer solution at temperature lower than LCST, it still had a large volume reduction and the morphology of single fiber almost lost ( Fig. 4a and a1 ), indicating that the fibrous mats was insufficient crosslinking. After water-annealing of in situ crosslinking fibers, the stability of the fibrous mats were improved, and the fiber basically maintained its morphology ( Fig. 4b and b1 ). By comparing the FTIR spectra of before and after water-annealing of in situ crosslinking fibers ( Fig. 3b and c ), it can be found that the peak of 1547 cm −1 corresponding to amide II (α-helix and random coil conformation of RSF) is slight shifted to 1517 cm −1 (β-sheet conformation of RSF), meanwhile, the peak of 1630 cm −1 corresponding to amide I (β-sheet conformation of RSF) is also existence further indicating that water-annealing process can induced the conformation transformation of RSF from α-helix and random coil conformation to β-sheet conformation. 48 The crosslinking mechanisms of P(NIPAAm- co -AAc)/RSF fibers are shown in Fig. 5 . Fig. 4 SEM images and digital photos of in situ crosslinked P(NIPAAm- co -AAc)/RSF fibrous mats before and after water-annealing: (a) dried fibrous mats before water-annealing, (a1) wetted fibrous mats before water-annealing immersed in PB solution with a pH of 5.7 at 25 °C, and (b) dried fibrous mats after water-annealing, (b1) wetted fibrous mats after water-annealing immersed in PB solution with a pH of 5.7 at 25 °C. Fig. 5 Schematic mechanisms of in situ crosslinking and water-annealing of P(NIPAAm- co -AAc)/RSF fibers: (A) as-spun fibers, (B) in situ crosslinking fibers, (C) after water-annealing of in situ crosslinking fibers. Temperature- and pH-dual stimuli-responsive behaviors of the fibrous mats Stimuli-response behaviors of the fibers at different temperature and pH are the swelling and shrinkage, and resulting in changes in fiber diameter. These changes can be reflected by the morphology and contact angle values of fibrous mats. Fig. 6 shows the SEM images and contact angle photos of P(NIPAAm- co -AAc)/RSF fibrous mats at different temperatures and pH. Comparing Fig. 6a and a1 or Fig. 6b and b1 , the composite fibers exhibit swelling and shrinkage behaviors when the temperature of PB solution increases from 25 to 40 °C. At room temperature (25 °C), the composite fibrous mats are hydrophilic corresponding with contact angle 44.4 or 21.7° respectively. At higher temperature (40 °C > LCST), the composite fibrous mats turn relatively hydrophobic exhibiting contact angles with 100.3° or 70.0°, which reflects temperature-responsive behavior of the composite fibers. Comparing Fig. 6a and b or Fig. 6a1 and b1 , the composite fibers also exhibits swelling behaviors when the pH of PB solution increases from 5.7 to 6.6. At pH 5.7, the contact angle of the composite fibrous mats is 44.4 or 100.3°, at pH 6.6, the contact angle of the composite fibrous mats is 21.7 or 70.0°, the hydrophilic of the composite fibrous mats increases with the pH of PB solution from 5.7 to 6.6, which reflects pH-responsive behavior of the composite fibers. Based on the above data, P(NIPAAm- co -AAc)/RSF fibers have temperature- and pH-dual stimuli-responsive behaviors. Fig. 6 SEM images and contact angle photos of P(NIPAAm- co -AAc)/RSF fibers at different temperatures and pHs: (a) the fibrous mats in PB solution with a pH of 5.7 at 25 °C, (a1) the fibrous mats in PB solution with a pH of 5.7 at 40 °C; (b) the fibrous mats in PB solution with a pH of 6.6 at 25 °C, (b1) the fibrous mats in PB solution with a pH of 6.6 at 40 °C. \n In vitro drug release behavior of fibers Rhodamine B was chosen as model drug to be encapsulated during the one-step electrospinning process. Fig. 7 shows phase-contrast and fluorescent images of drug-loaded P(NIPAAm- co -AAc)/RSF fibers. The red fluoresce from rhodamine B ( Fig. 7b ) suggests rhodamine B exist in the fibers. Fig. 7 Phase-contrast and fluorescent images of rhodamine B-loaded P(NIPAAm- co -AAc)/RSF fibers. (a) Phase-contrast image, (b) fluorescent image. To investigate the temperature sensitivity of the fibrous mats, the temperature of in vitro drug release study were chosen 25 °C (<LCST), 40 °C (>LCST, close to the body temperature), and 60 °C (>LCST). Fig. 8 shows the release profiles of drug-release P(NIPAAm- co -AAc)/RSF fibrous mats at different temperature, pH and the content of P(NIPAAm- co -AAc) in fibrous mats. Fig. 8a presents rhodamine B release behaviors of P(NIPAAm- co -AAc)/RSF fibrous mats (sample d) in PB solution with a pH of 5.7 at different temperature. It can be seen that the cumulative release amount of rhodamine B increases from 56%, 68% to 74% with the increase of temperature from 25, 40 to 60 °C after the release process of 110 h, which shows temperature-dependent responsive release behavior. Due to the LCST of P(NIPAAm- co -AAc)/RSF fibrous mats (copolymer P-2) is 38.8 °C, a small amount of rhodamine B are released into release medium at 25 °C. When the temperature rises to 40 °C (>LCST), the hydrogen bonds between P(NIPAAm- co -AAc) chains (–COOH with –COOH, –COOH with –NHCO, –NHCO with –NHCO) increase which lead to hydrophobic, 13 the fibers would shrink ( Fig. 6a1 ), and rhodamine B is squeezed then released into PB solution, resulting the increase of accumulative release. When the temperature rises to 60 °C, the number of intrachain hydrogen bonds increases, resulting in a higher accumulative release. Fig. 8 The release profiles of rhodamine B-loaded P(NIPAAm- co -AAc)/RSF fibrous mats: (a) the fibrous mats (sample d) in PB solution with a pH of 5.7 at different temperature, (b) the fibrous mats (sample d) in PB solution with different pH at 40 °C, (c) The fibrous mats (sample c, d and e) fabricated from P(NIPAAm- co -AAc)/RSF solutions with the content of P(NIPAAm- co -AAc) from 15.7, 13.3 to 10.9 wt% respectively, in PB solution with a pH of 5.7 at 40 °C. \n Fig. 8b presents rhodamine B release behaviors of the fibrous mats (sample d) in PB solution with different pH at 40 °C. It shows that the release amount of rhodamine B decreases from 68% to 53% with the increase of pH from 5.7 to 6.6 after the release process of 110 h, indicating a pH-dependent responsive release behavior. The is because the carboxyl group (–COOH) in AAc unit of P(NIPAAm- co -AAc) chains is more easily ionized in the media environment of pH 6.6 than pH 5.7, it forms relatively more hydrogen bonds with water, resulting its hydrophilicity and swelling degree increase. Therefore, compared with the release amount of the fibrous mats at pH 5.7, which driven mainly by the shrinkage of the fibers, the that of fibrous mats decreases somewhat at pH 6.6. In addition, Fig. 8c presents rhodamine B release behaviors of different content of P(NIPAAm- co -AAc) in the fibrous mats (sample c, d and e) immersed it in PB solution with a pH of 5.7 at 40 °C. It can be seen that the cumulative release amount of rhodamine B decreases from 86%, 68% to 62%, with the content of P(NIPAAm- co -AAc) decreases from 15.7, 13.3 to 10.9 wt% respectively, after the release process of 110 h, which shows P(NIPAAm- co -AAc) content-dependent responsive release behavior. Because the lower content P(NIPAAm- co -AAc) in the fibers, the lower sensitive the fibers is to the change of external environment, that is, the lower intermolecular hydrogen bonding of the P(NIPAAm- co -AAc) chains, which lead to the weaker shrinkage of the composite fibers, and the lower amount of rhodamine B are released. Schematic diagram of the controlled release of the rhodamine B-loaded fibrous mats at different temperature and pH is shown in Fig. 9 . Fig. 9 Schematic diagram of the controlled release of rhodamine B-loaded P(NIPAAm- co -AAc)/RSF fibers at different temperature and pH. To investigate the stability of P(NIPAAm- co -AAc)/RSF fibrous mats during drug release, we took out the fibrous mats at different times and weighed them after drying for 24 hours. Fig. 10 shows that the fibrous mats have good stability, and there is a slight increase in mass due to water absorption and swelling. Meanwhile, SEM images ( Fig. 10a and b ) shows that the surface of the fibers is smooth and no holes before and after drug release, which also proved the water-stability of the fibrous mats. Fig. 10 The mass changes of P(NIPAAm- co -AAc)/RSF fibrous mats over time during drug release at variable temperature and pH conditions. And SEM images of fibrous mats: (a) before drug release; (b) after release 110 h."
} | 6,526 |
22099187 | null | s2 | 5,205 | {
"abstract": "N-Acyl-L-homoserine lactones (AHLs) are a major class of quorum-sensing signals used by Gram-negative bacteria to regulate gene expression in a population-dependent manner, thereby enabling group behavior. Enzymes capable of generating and catabolizing AHL signals are of significant interest for the study of microbial ecology and quorum-sensing pathways, for understanding the systems that bacteria have evolved to interact with small-molecule signals, and for their possible use in therapeutic and industrial applications. The recent structural and functional studies reviewed here provide a detailed insight into the chemistry and enzymology of bacterial communication."
} | 168 |
32839450 | PMC7445162 | pmc | 5,206 | {
"abstract": "Intercellular signaling is indispensable for single cells to form complex biological structures, such as biofilms, tissues and organs. The genetic tools available for engineering intercellular signaling, however, are quite limited. Here we exploit the chemical diversity of biological small molecules to de novo design a genetic toolbox for high-performance, multi-channel cell–cell communications and biological computations. By biosynthetic pathway design for signal molecules, rational engineering of sensing promoters and directed evolution of sensing transcription factors, we obtain six cell–cell signaling channels in bacteria with orthogonality far exceeding the conventional quorum sensing systems and successfully transfer some of them into yeast and human cells. For demonstration, they are applied in cell consortia to generate bacterial colony-patterns using up to four signaling channels simultaneously and to implement distributed bio-computation containing seven different strains as basic units. This intercellular signaling toolbox paves the way for engineering complex multicellularity including artificial ecosystems and smart tissues.",
"introduction": "Introduction Intercellular signaling is essential for single cells to acquire multicellular behaviors by facilitating division of labor, coordinating population physiological activities, and organizing tissue development and differentiation 1 . The natural gene pool contains a plethora of intercellular communication systems 2 , 3 . One well-studied case is the bacterial quorum sensing (QS) systems that govern the physiological transition of bacterial populations to form biofilms 4 , as well as to express bioluminescence and virulence factors 5 . In multicellular organisms, short- (autocrine), medium- (paracrine), and long- (endocrine) range intercellular signaling is key for the control of spatial and temporal development, generation of immune responses, and maintenance of physiological homeostasis 6 . In analogy to electronic wires that coordinate the large number of computational units in a computer, intercellular signaling systems are chemical wires for a multicellular body to achieve organism-level performance. Current efforts of engineering complex biological computations in living cells have met with much frustration. This is largely due to our very limited ability to program large-scale genetic circuits that are often resource-taxing and error-prone in a single cell. Taking a divide-and-conquer strategy by packaging computation modules into different cells and wiring them together may break the bottleneck by achieving stability, programmability, and ultimately computational complexity at the cell consortium level 7 . Proof-of-principle studies have included engineered biological spatial patterns 8 , 9 , synthetic microbial ecosystems 10 , synchronized genetic oscillators 11 , mammalian bio-computers with complexity up to full adder logics 12 , therapeutic circuits for antibiotic-free pathogen control 13 , 14 , and autonomous induction systems for metabolic production 15 – 19 . In most of these studies, communications between different computing units were channeled by the abovementioned QS systems 10 , 20 , in which the signal molecules, acyl-homoserine lactones (AHLs), are synthesized from S -adenosyl-methionine and acyl-Acyl Carrier Proteins (ACPs), and secreted by sender cells, before they are sensed by the corresponding allosteric transcription factors (aTFs) in receiver cells 21 . Beyond AHLs, yeast peptide-pheromones, human histamine and dopamine hormones, and other endogenous signal molecules were also used for synthetic cell–cell communications 19 , 22 – 24 . Although natural intercellular signaling systems constitute a huge repertoire of genetic materials for the engineering of multicellular bio-computation, two aspects limit their applicability. Universality: ideal cell–cell communications should work in a modular manner applicable to a wide range of cell types, especially for scenarios requiring cross-kingdom communications such as microbiome therapy. However, intercellular signaling systems used in previous studies either required the addition of exogenous precursors to synthesize signal molecules or were mechanistically incapable of being transferred from one species to another 12 , 23 , 25 , 26 . Orthogonality: ideal cell–cell communications rely on an array of well-insulated channels for correct signaling. In electronics, insulation of different channels is usually achieved by spatial segregation, whereas in biological systems the most feasible way to achieve insulation is through chemical orthogonality. Recent studies have quantitatively revealed the extensive cross-talk among the conventionally used QS systems 20 , 27 – 29 , which can be largely attributed to the structural similarity among AHLs and among the corresponding aTFs. To eliminate cross-talk, a number of strategies have been attempted, including rational engineering of the signal-sensing promoters 20 , directed evolution of signal-sensing aTFs 30 , and large-scale screening of kinase–substrate pairs 31 . We aim to design a truly modular intercellular signaling toolbox for multi-channel cell–cell communications and biological computations by targeting precisely these two key properties. Specifically, universality is achieved by choosing universal cellular metabolites as precursors for synthesizing the selected small molecules as the signal molecules and designing minimal biosynthetic pathways from the common precursors, and orthogonality is achieved by taking advantage of the chemical diversity of biologically synthesized small molecules and the abundant resource of small molecule-sensing aTFs. Taking a de novo approach combining biosynthetic pathway design, genetic circuit engineering, and directed evolution, we have designed ten novel intercellular signaling systems as cell–cell communication channels, of which six are successfully obtained and quantitatively characterized in Escherichia coli . Subsequently, two of them are transferred to yeast Saccharomyces cerevisiae and one to human HEK-293T cells for cross-kingdom communication. To demonstrate the advantage of the intercellular signaling toolbox, genetic circuits operating multi-channel (two-, three-, and four-channel) communications are constructed to form biological spatial patterns and to implement an AND–XOR function by coordinating seven NOR/Buffer gate cells. We believe this intercellular signaling toolbox would significantly expand the capability of synthetic biology in multicellular organism engineering and present one of the cornerstones for large-scale biological computations in living cells.",
"discussion": "Discussion Cell–cell communications are ubiquitous in nature 2 , 53 , 54 . From an engineering perspective, these widespread communication systems provide a vast reserve of potential synthetic communication parts including signal molecules, highly specific receptors and aTFs 55 . However, naturally evolved parts are not perfect for synthetic gene circuit construction. In this study, we proposed a de novo design route for synthetic intercellular communication channels. By rational design and directed evolution approaches, we established a toolbox of biochemical channels that can be used for multi-channel communications in applications involving pattern formation and distributed cellular bio-computation. Most natural intercellular communication systems are species- or kingdom-specific 23 . For example, previous efforts were made to transfer the bacterial AHL systems into mammalian cells in order to acquire orthogonal intercellular signaling systems for artificial tissue and organ engineering. Unfortunately, the essential precursors (acyl-ACP) in mammalian cells are locked in by the Type II fatty acid synthesis multi-domain enzymes and not available for the biosynthesis of the AHL molecules 56 . In our toolbox, however, the pC and IV molecules were synthesized from canonical amino acid (i.e., l -tyrosine and l -leucine) sources despite being structurally similar to AHLs. The successful transfer of the pC channel to human HEK-293T cells highlights the design rationale of diverting common cellular metabolic pathways for synthetic circuits, and we expect the de novo designed channels, including IV, DAPG, MMF, and NG, to apply to mammalian systems as well. We also optimized the receiver modules by directed evolution for better dynamic ranges and sensitivities, to reduce the metabolic burden to the sender cells (Supplementary Figs. 22 and 23 ), as well as to improve channel compatibility for eukaryotic receiver cells. These dedicated channels with microbial and plant origins would be especially suited for mammalian systems, because they would not interfere with endogenous signaling systems as those based on dopamine and histamine 12 would. On the other hand, although recent work indicates that natural QS signaling in bacterial pathogens is tap-wired by the host AhR signaling pathway in various vertebrates for immunomodulation 57 , our designer signal molecules may not cause unwanted host responses because they are structurally different from natural QS molecules and may thus evade host surveillance. Inspired by electric circuits and telecommunications, where channels are spatially insulated or functions in different wave-bands, we took advantage of the enormous chemical space of biologically derived molecules for channel insulation, which was further enhanced by optimizing the specificity of the receiver modules 5 , 23 . The success of our construction underscores a general principle that naturally occurring biochemical machineries have merely exploited all possible solutions, leaving almost boundless design space for synthetic biological construction. A recent study on engineered kinases has also supported the view 31 . Universality and orthogonality together constitute the essence of modular design for complex synthetic biological functions. In two examples, we showed the use of these modular channels to spatially and logically organize different computing units. We successfully implemented up to four channels in an engineered cell consortium, which to our knowledge is the largest in multicellular computing studies 28 , 52 . Notably, in the second example, channels were serially connected to form computing cascades. Currently, there are maximally two-channel modules implanted in a single cell. The fact that our individual modules imposed minimal metabolic burden to the host cell could enable engineered communication hubs with possibly more than two channels intersecting in a single cell. The exact limit of this number remains an open question, but it ultimately defines the information processing complexity of cell consortium computation and its real-world application potential. Intercellular communication plays a pivotal role in expanding the engineered functions from single cellular behaviors to multicellular artificial tissues, microbiome therapy such as in the human gastrointestinal tract or in tumors 58 and general biocomputing systems 22 , 25 , 59 , 60 . Our study has demonstrated the possibility of engineering natural secondary metabolites and signaling modules into dedicated intercellular communication channels. With an expanded toolbox of modular channels, more sophisticated circuits could be designed in mammalian cell lines to implement stable multi-input, multi-output, and structurally organized computing systems for in vivo therapeutic applications."
} | 2,887 |
40343886 | PMC12063886 | pmc | 5,208 | {
"abstract": "The structure and function of plant-associated fungal communities (i.e. mycobiome) is shaped by biotic and abiotic factors, and can impact plant community dynamics. We evaluated the effects of different environmental factors in structuring the communities of seedling-associated fungi in temperate tree species, considering both the Janzen-Connell hypothesis as well as the impacts of climate warming. We tested the hypothesis that fungal host-specialization is observed at both the individual fungus and fungal community levels and is modulated by environmental conditions. The seedling fungal communities were characterized from tree species grown in two forests, under experimental manipulation of light, warming, and distance to and density of conspecifics. Fungal communities were analyzed using generalized joint attribute models. While warming, light, and forest site played a role in structuring seedling fungal communities, host, distance to, and density of conspecifics were stronger contributors. Furthermore, we could identify which fungal taxa responded to which predictors. This work supports the concept of fungal host-specialization at the community level, and points to particular fungal taxa which may play roles in density- and distance-dependent regulation of plant species diversity in the studied forests.",
"introduction": "Introduction That pathogens play a key role in regulating plant diversity via conspecific negative density dependence (CNDD) is now well established [ 1 , 2 ]. Determining which pathogens are involved in this mechanism has become more of a challenge than initially anticipated. Early studies documenting distance- and density-dependent seedling survival assumed that these patterns were driven by host-specific natural enemies [ 3 , 4 ]. But, as understanding of seedling fungal communities has deepened, this idea of strict host specificity has been recognized as an oversimplification, with many root-associated fungi colonizing multiple hosts and demonstrating different impacts on seedling survival based on host identity [ 5 ] or environmental context [ 6 ]. Further, seedling root mycobiomes are now recognized as quite complex, with many co-occurring fungal species [ 7 ], such that assessing the role of each individual fungal species is challenging. Finally, all of these interactions take place in the context of a warming climate; warming can increase the relative abundance of soil pathogens [ 8 , 9 ] and the strength of CNDD [ 9 ]. Here, we demonstrate an analytical approach that can identify specific fungal taxa responding to different predictors, including conspecific host density and distance as well as experimental warming, that may be critical in understanding distance and density-dependent responses in light of changing environmental context. Patterns of plant abundance and demographic rates consistent with regulation by natural enemies [e.g. 10 , 11 ] have typically been discussed in the framework of the Janzen-Connell (JC) hypothesis [ 3 , 4 ]. Under this framework, plant diversity is promoted by the host-specific attack from plant pests and pathogens. Seedling recruitment is limited by host-specific enemies in areas where the host is locally abundant, thereby increasing diversity. Demographic evidence shows that high host density and/or short distance from conspecific adults decreases seedling survival and recruitment rates [ 11 – 13 ]. Diversity is maintained because ostensibly host-specific pathogens attack one species and not others, and are thereby able to act as a biotic filter that limits recruitment of individuals of the same host. However, a growing body of evidence does not find strict host specificity in individual fungal pathogens, but instead effective specialization [ 14 ]. The requirement for host-specific pathogens [ 15 ] conflicts with observations that many of the individual fungal taxa associated with seedlings roots and stems are typically found in multiple hosts [ 6 , 16 – 19 ], but see also [ 20 ], though overall fungal community composition may differ between host species [ 21 , 22 ]. Negative interactions between pathogens and their tree hosts could be modulated by additional interactions with beneficial mycorrhizal fungi, especially ectomycorrhizae [ 23 – 25 ]. Further, environmental characteristics such as light [ 26 , 27 ] and water availability [ 17 , 28 , 29 ] could alter the nature of plant-pathogen interactions by altering fungal community structure, function, or both. These same environmental variables that alter fungal communities also directly affect plant health and plant community composition. For instance, seedling survival is differentially affected by abiotic conditions, such as light availability [ 30 , 31 ] and soil moisture [ 6 ], potentially contributing to niche differentiation in plants [ 30 ]. Under the warmer temperatures predicted under climate change, forests may experience a greater relative abundance of soil pathogens [ 8 , 9 ], higher foliar disease severity [ 32 , 33 ] or intensification of negative feedbacks [ 9 , 32 ]. However, other studies have found weaker feedbacks [ 34 , 35 ] and decreased pathogen abundance [ 34 ] under warmer temperatures. As climate warming progresses, an understanding of plant-soil feedbacks under warming becomes more critical, yet studies exploring these feedbacks under experimental warming treatments are sparse [ 36 ]. Benítez et al. [ 14 ] hypothesized that JC effects could result from effective specialization, where pathogen specialization to a host results from the interaction of the host and pathogen with particular local conditions. If considering multiple fungal species infecting one host, effective specialization will occur as a result of the combined effect of different pathogen infections in a host. These pathogen effects need not to be additive, and may each in turn be modulated by the environmental conditions (see also [ 6 ]). Therefore, different combinations of fungal infections will affect host species in different ways in a context-dependent manner. Resolving the apparent contradictions between demographic studies that support JC and the lack of observed pathogen specificity, which does not support JC, requires analysis of plant-associated fungal communities and their joint relationships with host and environment. In this study, we test the hypothesis that fungal host specialization to tree species is observed for both individual fungal taxa and for communities of fungi, and that host specialization is modulated by environmental conditions. Seedling-associated fungal communities were intensively surveyed from experimental sites at two temperate forests, with an experimental design that incorporates treatments of light availability and elevated temperature as well as distance to and density of conspecifics. The seedling fungal communities (mycobiome) were characterized using high-throughput sequencing approaches. Generalized joint attribute modeling [GJAM; 37 ] was used to jointly predict both host health status and the host-associated mycobiome based on common predictors. Specifically, we tested a set of a priori hypotheses focused on biotic and abiotic factors which could influence seedling health status and the mycobiome of a given seedling and evaluated them using a model selection approach. We centered on distance from conspecific adults and conspecific seedling density as predictors relevant to the JC hypothesis, as well as environmental factors (light, site) which could modulate JC interactions. A subset of seedlings were exposed to an experimental warming treatment that allowed for prediction of changing fungal responses to elevated temperature.",
"discussion": "Discussion In this study, we tested the hypothesis that fungal communities in tree seedlings are modulated by host identity, conspecific seedling density, distance to conspecific adults, and several abiotic covariates, most notably experimental warming. We found that each tree species harbors a distinct community of fungi, but the structure of the fungal community is modulated by characteristics in ways that differ between host plants. Interactions between host identity and density of conspecifics, or host identity and distance to conspecific adults, exerted stronger influence over fungal communities than environmental variables that included warming, light availability, and forest site, and even host identity alone. Although fungal taxa are certainly generalists as gauged by the traditional view of numbers of hosts they infect, we found that the host by conspecific seedling density interaction and host by distance to conspecific adult interaction is fungal-taxon specific. This suggests that fungal host specialization occurred both at the individual taxon (OTU) level and the fungal community as a whole. Unique sets of fungal taxa responded to each biotic (i.e. distance to conspecific adults and density of conspecifics) and environmental (i.e. warming, light, site) variable. However, the responses of fungal OTUs to hosts depended on the presence and location of conspecific individuals. This result may explain previous observations of either distance or density dependent responses (but not both) in different forests [ 12 , 53 , 54 ]. Distance and density responses are not mutually exclusive, but instead both can lead to Janzen-Connell-like responses from different members of the fungal community [ 55 ]. For instance, most taxa responded to a limited number of predictors, but a small subset responded to more than ten. Taxon-specific responses to host, site and environment (e.g. warming, light) were consistent with previous studies showing that seedling survival was differentially affected by combinations of generalist fungi, depending on soil moisture [ 6 ]. Host-associated microbial communities that are modulated by environment have been described in other plant species. For instance, host genotype effects of the herbaceous plant Boechera stricta on the leaf bacterial microbiome was modulated by site. Host genotype by site interactions could then be used to predict the abundance of bacterial taxa in Boechera stricta [ 56 ]. The set of fungal taxa that responded to different combinations of hosts and predictors set the stage for subsequent experimental studies that verify the role of different fungal taxa in seedling survival. Notably for future studies in these ecosystems, this work highlighted a subset of fungal taxa that responded, positively and negatively, to elevated temperatures consistent with a warming world. The majority of the species who responded to temperature (18/22, 81%) also had some kind of host-specific differential response to distance and/or density, indicating a potential interaction between the impacts of conspecific negative density dependence and warming that could be explored further. Large-scale experimental studies exploring the interactions between individual fungi and hosts [ 5 ] can be challenging to design given the many potential combinations; modeling approaches like this prune the list of taxa-host combinations that sets the stage for future experimental work. This is particularly important in studies of warming effects on plant-soil feedbacks, which are currently quite limited. Communities of fungi did not respond equally to all predictors. Analysis of the structure of responses of taxa to predictors show clusters of fungi that differ in the strength to which they respond to the analyzed predictors. However, based on this analysis, it is not evident to which specific predictors these clusters are responding, or alternatively, the clustering is influenced by non-measured variables. In previous research [ 57 – 59 ], fungal communities are often treated uniformly, based on richness or evenness estimates or ordination axes as a dimension reduction strategy; or experimentally, through comparisons of comparing fungicide vs. non-fungicide treatment effect. Alternatively, specific host-pathogen interactions tend to be studied as a single interaction at a time. Instead, different members (or group of members) of a large and diverse community are responding differently to host and environmental factors collectively. The application of GJAM models to the fungal sequencing survey presented in this work allowed an exhaustive analysis of the fungal communities to identify specialization of individual taxa and community. Individual taxa do not necessarily follow patterns of predicted functionality based on fungal taxonomy assignment and prior knowledge of fungal lifestyle, with the caveat that limited information on functionality is available given the taxonomy resolution used in this work. Changes in fungal community composition in response to conspecific density and distance to conspecific adults is observed for plant pathogens, beneficial symbionts and saprotrophs. While CNDD interactions have been reported previously for mycorrhizal host associations, we note that in this study fungal-host interactions were evaluated for seedlings > 1 year old, and that to our knowledge mycorrhizal CNDD interactions have not been noted for tree seedlings less than 1 year old [ 24 , 60 ]. For this reason, we did not evaluate CNDD within the context of host mycorrhizal status for this study. Further, the unequal germination between AM and EM tree species in this study confounded potential comparisons. However, hypotheses on shared functions could be developed for clusters of taxa with similar responses to predictors. For instance, subsets of the analyzed fungal OTUs share responses to the model predictors; and contrasting responses to predictors are observed by individual taxa. For example, though both Colletotrichum1 and Diaporthacea1 were recovered from several hosts and respond to multiple host and density interactions, Colletotrichum1 was preferentially recovered from distances near to a conspecific host; whereas Diaporthacea1 from further distances, as well as under low density of conspecifics. Determining the functions of the recovered fungal taxa, however, remains dependent on better taxonomic resolution, along with additional functional data from methods such as enzyme assays, genomics, and pathogenicity analysis. Previous studies that had identified pathogen drivers of plant community dynamics focused on a combination of culture-based surveys [e.g., 6 , 16 , 61 , 62 ], amplicon based surveys [ 63 – 68 ] or fungicide disturbance treatments [ 69 ]. Even though cultured isolates provide an advantage when testing hypotheses of function, extensive surveys and factorial interactions combine to make it prohibitive to carry out these tests using diverse hosts and environmental conditions. Studies using a molecular approach have linked increased putative pathogen frequency to conspecific distance or density [ 19 , 21 , 67 , 70 ], but have not considered variation due to environmental factors. By employing GJAM, this study was able to directly identify the responses of the fungal community and individual fungal OTUs to biotic and environmental variables, as well as interactions relevant to negative conspecific density dependence. It also highlights groups of fungi that share similar responses to predictors, and potentially functionality. The fact that different collections of fungi are associated with each tree, but that these fungi are responding to different suites of predictors, also suggests that effective specialization is mediated by interactions between biotic and abiotic factors. These differences in fungal community composition and individual taxa responses to host and environment interactions can explain niche partitioning effects on seedling survival [ 71 ] from the perspective of the fungal communities. Fungal taxa responding specifically to elevated temperature, both positive and negative, should be explored further to better understand the potential changes in plant-fungal interactions under climate change. The data presented here supports the hypothesis of mechanisms through which effective specialization allow generalist pathogens to mediate plant community dynamics [ 14 ]."
} | 4,039 |
24862580 | PMC4033924 | pmc | 5,209 | {
"abstract": "Living organisms have to adjust to their surrounding in order to survive in stressful conditions. We study this mechanism in one of most primitive creatures – photosynthetic green sulfur bacteria. These bacteria absorb photons very efficiently using the chlorosome antenna complexes and perform photosynthesis in extreme low-light environments. How the chlorosomes in green sulfur bacteria are acclimated to the stressful light conditions, for instance, if the spectrum of light is not optimal for absorption, is unknown. Studying Chlorobaculum \n tepidum cultures with far-red to near-infrared light-emitting diodes, we found that these bacteria react to changes in energy flow by regulating the amount of light-absorbing pigments and the size of the chlorosomes. Surprisingly, our results indicate that the bacteria can survive in near-infrared lights capturing low-frequency photons by the intermediate units of the light-harvesting complex. The latter strategy may be used by the species recently found near hydrothermal vents in the Pacific Ocean.",
"conclusion": "Conclusions In this study, we analyzed the response of Chlorobaculum [ Cba. ] tepidum species of green sulfur bacteria to light with constrained spectral properties. Our results suggest that Cba. tepidum tune up the light absorption properties mostly by: (a) increasing number of chlorosomes per unit of mass, and (b) changing the size of the chlorosomes. We find that the red shift of the chlorosome absorption peak, previously reported as a response to low-light conditions, may result in reduced absorption efficiency. All the reported changes occur on the timescale comparable to the bacteria doubling time and can be considered as acclimation. Finally, we observe that the cultures can grow with 850 nm light, which has a negligible overlap with the chlorosome Q y -band and is, possibly, absorbed by Fenna-Matthews-Olson protein complexes and reaction centers. This result supports the recent finding that green sulfur bacteria can live on the bottom of the ocean where only geothermal lights are available.",
"discussion": "Discussion In order to characterize the obtained growth rates and compare them with the data available from literature we applied the following model. We assumed that the cuvettes with the bacterial cultures are irradiated homogeneously. Then, we estimated the growth rate as a function of absorbed light energy using the relation introduced previously by Baly 26 and Tamiya 27 , For low light intensities, I , Eq. (2) provides a linear dependence of the growth rate with a slope α . The rate saturates to the value r s at high intensities. While various models were introduced for photosynthetic organisms 28 all of them show a qualitatively similar rate dependence. To account for the spectral profiles of LEDs we weighted the photon flux densities P ( ω ), see Fig. 4 , with the normalized absorption spectra of the cultures A ( ω ) as This weighted intensity characterizes the amount of light energy that can be absorbed by LHCs. The resulted growth rates together with the data from Refs. 13 14 15 24 are shown in Fig. 6 . For the high and intermediate light intensities, our results are comparable with the data from the other studies. In contrast, the estimated low-light intensity growth rate is about 5 times larger than the previously reported growth rates. This discrepancy cannot be described by accounting for the spatially inhomogeneous irradiation of the culture (the 850 nm bacterial cultures where irradiated by single LEDs which provided inhomogeneous light intensity on the culture) in our model. The dependence of the growth rate on the light intensity is either linear or sub-linear. Thus, our estimates should provide a lower bound for the growth rate. One of the possibilities which we consider is that in white or fluorescent lights used in previously reported experiments, the fraction of the spectrum overlapping with the Q y -band of LHC may be smaller as compared to that of a flat emission spectrum. Those spectra are, usually, shifted towards shorter wavelengths. While for high intensities close to saturation of the growth rate the cultures are less sensitive to the variation in energy flux, the effect is more important for low intensities. Another interesting finding is that the weighted photon flux for 700 nm cultures was about I ≈ 1.5 μmol/m 2 /sec, which is twice as large as the weighted flux for the cultures illuminated by 850 nm LEDs when employing a filter. Nevertheless, the former cultures were not able to grow well. This suggests that a model with a 100% energy transfer efficiency of LHCs may be oversimplified for a quantitative characterization of these experiments. For example, in 700 nm cultures some amount of absorbed energy can be lost during the transfer through the LHC due to the exciton trapping and recombination. However, to our knowledge neither experimental pump-probe studies of energy transfer 29 30 31 in chlorosomes nor microscopic computational models 32 33 support this hypothesis. We believe that more extensive studies are required in order to verify this observation. We observed the red-shift of the chlorosome Q y -band for the most of low-intensity experiments. In fact, the largest shift is obtained for 700 nm cultures. In this case the chlorosome band is shifted away from the spectrum of available light, which reduces the amount of absorbed energy (energy absorption is controlled by the overlap of these two spectra). This suggests that green sulfur bacteria do not maximize the absorption cross section of LHC by tuning its frequency. Instead, the red-shift of the chlorosome band results in a larger spectral overlap between the chlorosome band and the absorption bands of the baseplate and FMO, thus increasing the energy transfer efficiency between the structural subunits of LHC. The grown bacterial cultures can be classified in two groups: the group of high energy flux (750 nm, 780 nm and 800 nm) and that of a low energy flux (850 nm with and without filter). These groups differ in: (a) the chlorosome peak position, (b) ratio of BChl a and BChl c peaks, and (c) amount of pigment per unit of mass, see Fig. 5 . The differentiation in LHCs occurs on a timescale shorter than 12 hrs, as discussed in the SI . Thus, provided that the mutation rate of bacteria is rather slow 34 the modifications of the LHC complexes can be assigned to bacterial acclimation rather than adaptation, which should take much more time. The observed timescale is actually shorter that the acclimation timescale of cyanobacteria 35 . Finally, we would like to draw attention to the recent report where green sulfur bacteria were found near thermal vents on the bottom of the Pacific Ocean 2 . The radiative emission from thermal vents can be described by a blackbody radiation model with a corresponding temperature of several hundred degrees Celsius 36 . Our simple estimates give that the photon flux density at the FMO absorption peak, 810 nm, should be about one order of magnitude stronger than the flux at 750 nm, where BChl c pigments absorb, which almost compensates the difference in the absorption cross sections of these units, Fig. 5(c) . Thus, one can hypothesize that the low-frequency light absorption may be used by green sulfur bacteria in order to survive in natural habitats."
} | 1,839 |
36639807 | PMC9840269 | pmc | 5,212 | {
"abstract": "Background The microbiome of the Sinai Desert farming system plays an important role in the adaptive strategy of growing crops in a harsh, poly-extreme, desert environment. However, the diversity and function of microbial communities under this unfavorable moisture and nutritional conditions have not yet been investigated. Based on culturomic and metagenomic methods, we analyzed the microbial diversity and function of a total of fourteen rhizosphere soil samples (collected from twelve plants in four farms of the Sinai desert), which may provide a valuable and meaningful guidance for the design of microbial inoculants. Results The results revealed a wide range of microbial taxa, including a high proportion of novel undescribed lineages. The composition of the rhizosphere microbial communities differed according to the sampling sites, despite similarities or differences in floristics. Whereas, the functional features of rhizosphere microbiomes were significantly similar in different sampling sites, although the microbial communities and the plant hosts themselves were different. Importantly, microorganisms involved in ecosystem functions are different between the sampling sites, for example nitrogen fixation was prevalent in all sample sites while microorganisms responsible for this process were different. Conclusion Here, we provide the first characterization of microbial communities and functions of rhizosphere soil from the Sinai desert farming systems and highlight its unexpectedly high diversity. This study provides evidence that the key microorganisms involved in ecosystem functions are different between sampling sites with different environment conditions, emphasizing the importance of the functional microbiomes of rhizosphere microbial communities. Furthermore, we suggest that microbial inoculants to be used in future agricultural production should select microorganisms that can be involved in plant-microorganism interactions and are already adapted to a similar environmental setting. Supplementary Information The online version contains supplementary material available at 10.1186/s40793-023-00463-3.",
"conclusion": "Conclusions A deep understanding of microbial diversity and function of rhizosphere microbiomes could further provide the comprehensive and directional insights required for manipulating these microbial communities. Oriented by this idea, our study provides the first comprehensive insights into microbial diversity and function of rhizosphere microbiomes in the Sinai desert farming systems. Our results revealed that variations in rhizosphere microbiome composition were associated with changes in geographic location our sampling, rather than with the type of crop. These rhizosphere microbial communities included a diverse composition but stable metabolic function. Importantly, key microorganisms involved in ecosystem functions which are different between the sampling sites, indicating the \"soil-bacteria veins\". Based on the results of this study, we suggest that microbial inoculants to be used in future agricultural production should selecting microorganisms that can be involved in plant-microorganism interactions and are already adapted to a similar environmental setting. This principle should be validated and used to guide the targeted synthesis of improved microbial inoculants to assist in future agricultural production processes in desert farms and beyond. Our preliminary results will support further work required to confirm the generality of these results in field experiments and other farming systems and on its potential for implementation.",
"discussion": "Discussion The diversity of soil microorganisms is an important indicator of soil quality [ 44 ] and plant health [ 45 ], which is also essential for the maintenance of soil ecosystem functions. In this study, we integrated culture-dependent and -independent methods to investigate rhizosphere microbial diversity at our target environment, leading to the isolation of 528 pure strains and assembly of 837 MAGs, covering a wide diversity of microbial taxa. Based on our results, both Proteobacteria and Actinobacteria showed high abundance, which is consistent with prior studies in other arid and semi-arid environments [ 46 , 47 ]. Interestingly, members of the phylum Thaumarchaeota (GTDB: c_Nitrososphaeria) -the dominant group within the archaeal community- were present in relatively high proportions. This might be related to the mesophilic and thermophilic adaptability of Thaumarchaeota , or their ability to oxidize ammonia aerobically [ 48 ]. In addition to the aforementioned three phyla, other phyla are found to be enriched in the samples, including Acidobacteria , Chloroflexi , Bacteroidetes , Firmicutes and Gemmatimonadetes . Fittingly, previous studies suggested that these phyla might be indispensable for maintaining ecosystem functions and nutrient cycling in desert environment, namely in carbon and nitrogen fixation [ 49 ]. The isolated strains and MAGs contained a high proportion of undescribed taxa, many of which were also involved in important soil functions, indicating a vast richness of untapped microbial resources in the desert farming system. Previous studies revealed the dynamics of soil microbial community structure response to the state of the soil environment, such as soil drought [ 7 , 9 ], acidification [ 50 ], or diseases [ 51 ]. In this study, the microbial community structure of rhizosphere samples within each sampling site were more similar to each other, even when their plant hosts were quite different, indicating that soil properties dominated the assembly of the rhizosphere microbial community. Although some previous studies had shown that soil microbial community structure is usually related to plant type and agricultural management [ 52 ], this seems not to be the case here. Indeed, the possible dominant factors at play in the current study are linked with soil properties rather than plant type. This has also been previously proposed by other authors in a variety of environments, including farmland [ 53 , 54 ], desert [ 55 ], tropical seagrass beds [ 56 ], etc. In the current study, we found similar functional composition and abundance in different sampling sites, despite significant differences in the taxonomic composition of their rhizosphere microbial communities. The biogeography of soil microorganisms, particularly those carrying out soil metabolic processes is linked with the ecological function traits of soils [ 57 ]. Furthermore, by mapping the soil microorganisms to the metabolic pathways associated with plant-rhizosphere microbiome interactions, we observed that the abundances of key genes related to specific metabolic processes were similar across sampling sites, but the microorganisms involved in these metabolic processes were obviously different. In summary, our results emphasize the importance of the functional microbiomes of rhizosphere microbial communities. Based on these results, we propose a new approach in the design of microbial inoculants which takes into account that different soil properties generate different microbiomes (diversity and function etc.). According to our preliminary results, the rhizosphere shapes the same ecological functions with different microbial communities, emphasizing the importance of the functional microbiome rather than the taxonomic microbiome. In the design of microbial inoculants, one usually considers the metabolic function and interactions of microorganisms in these communities at the individual level [ 13 ]. Only rarely are the functional microbiomes of rhizosphere microbial communities under real soil conditions systematically considered. This results in inconsistent field efficacy of microbial inoculants, varying with the specific conditions of the different environments where they are being applied [ 45 ]. To address this critical problem, the functional microbiomes of rhizosphere microbial communities under different soil conditions must be taken into consideration. This would allow for selection of microorganisms that carry out key metabolic functions and are also already adapted to the local soil environments. As shown in Fig. 5 , in the rhizosphere environment, the composition of rhizosphere microbial community is different under different soil conditions. Based on our results, one could target the isolation of these key functional microbial taxa to construct soil-associated inoculants and then use them to inoculate local farm soils with similar soil properties. Using this approach, the soil-associated inoculants can more easily adapt to the local soil environment and play a more effective role in diverse plant growth regulation and increased crop yields. Fig. 5 Proposed strategy for the design of microbial inoculants. The composition of rhizosphere microbial community is different among the different soil conditions of rhizosphere environments. The functional microbiome of rhizosphere microbial communities under different soil conditions must be taken into consideration, selecting the soil microorganisms that carry out metabolic functions and adapted to local soil environments"
} | 2,298 |
23637740 | PMC3634786 | pmc | 5,213 | {
"abstract": "It is thought that the science of ecology has experienced conceptual shifts in recent decades, chiefly from viewing nature as static and balanced to a conception of constantly changing, unpredictable, complex ecosystems. Here, we ask if these changes are reflected in actual ecological research over the last 30 years. We surveyed 750 articles from the entire pool of ecological literature and 750 articles from eight leading journals. Each article was characterized according to its type, ecological domain, and applicability, and major topics. We found that, in contrast to its common image, ecology is still mostly a study of single species (70% of the studies); while ecosystem and community studies together comprise only a quarter of ecological research. Ecological science is somewhat conservative in its topics of research (about a third of all topics changed significantly through time), as well as in its basic methodologies and approaches. However, the growing proportion of problem-solving studies (from 9% in the 1980s to 20% in the 2000 s) may represent a major transition in ecological science in the long run.",
"introduction": "Introduction Ecologists often describe ecological science as dynamic. ‘Ecology is a science in transition’ [1] . This transition is characterized by several significant shifts in emphasis and perspective [2] . During most of the 20 th century, the majority of ecologists conceptualized ecological systems as balanced and stable, typically at equilibrium, or as returning to such equilibrium deterministically following rare disturbances [3] . In recent decades, there has been a shift towards an understanding of ecological systems as nonlinear, constantly changing, and unpredictable in time and space [4] , [5] . The concept of equilibrium was replaced by other concepts, for example, the concept of non-equilibrium change, in which the system is often described as rotating between alternative states [6] . Ecologists are split on the question of whether the changes in ecological science represent a Kuhnian ‘paradigm shift’ [5] , [7] , [8] , [9] , or, alternatively, a gradual accumulation of modifications, better characterized as ‘evolution’ rather than ‘revolution’ [2] , [10] . In contrast, other ecologists maintained that progress in ecology is lacking [11] or limited [12] . Here, we ask if the topics and methodologies of ecological research as reflected in the literature of the last 30 years provide evidence to support notions of dramatic shifts, or of gradual change. We characterize various aspects of ecological research, using an extensive survey of ecological literature. In particular, we ask three questions regarding general aspects of ecology, and look for possible changes in these aspects over the last 30 years: \n Domains of ecological research : What proportion of research is devoted to the various domains in ecology (population, species, community, and ecosystem)? What are the major topics of ecological study? Has there been a change in the frequency of investigation of any of these topics and, if so, which ones? \n Types of research : Is ecology an experimental science, or a science of observation and measurement? How often are models used in ecological research? To what degree do ecologists use meta-analysis of data from previous studies (vs. collecting new data in each research)? \n Basic science or problem-solving oriented discipline : Is ecology becoming a problem-solving science? In other words, how often does ecology relate to actual, specific environmental problems, in an attempt to provide solutions (or at least new insights on how to make progress towards solutions)? Preliminary expectations A. Domains of ecological research The concepts of ecosystem and community have become increasingly dominant in ecological thinking. In a survey conducted among members of the British Ecological Society, ecosystem was identified as the single most important concept in ecology [13] . More recently, the Ecological Visions Committee of the Ecological Society of America issued a report that listed eight critical environmental issues for prioritizing ecological research [14] . Only two of those topics related to populations and species, while five topics were clearly within the domains of ecosystems and communities . We expected an increase in research conducted at the ecosystem level, and at the community level, accompanied by a proportional decrease in studies of single species. We also expected specific topics to become more frequent subjects of ecological study (such as biodiversity, climate change, biogeochemistry, and scale). B. Types of research Observations and experiments are known to be the two dominant tools of ecological research . In this research, we expected to identify an increase in the frequency of models, for two reasons: (1) the ecosystem has increasingly been described as ‘complex’, and models are often the only tools available for the study of complex systems, and (2) due to the substantial increase in the availability of modelling tools during the last three decades. We also expected an increase in the proportion of meta-analysis studies, for two major reasons: (1) a growing awareness of the incapacity of single studies of specific systems, conducted under narrow ranges of conditions, to provide insights on broader ecological issues [15] , and (2) the increased access to information and data in the age of the Internet. C. Is ecology a problem-solving science? In the past, ecologists have been reluctant to engage in applied research [16] . Applied science was considered inferior to basic, ‘pure’ science [17] . Some applied ecological issues, such as conservation, are emotionally charged [2] , and perceived by some ecologists as ‘advocacy’ [18] . More recently, ecologists have become increasingly concerned about the implications of their work to society's problems [15] , [17] , while environmental agencies have expressed an increased demand for ecological solutions to environmental problems [19] . For these reasons, we expected to find an increase in the proportion of applied studies over the last three decades. In order to attempt to answer these questions, a quantitative survey of ecological research is required. Surprisingly, few attempts have been made to systematically quantify trends in ecological research. Typically, these studies have used an automated count of words in titles and abstracts to assess trends in ecology [20] , [21] , [22] , [23] . Shorrocks [24] used an alternative method to survey trends in ‘the Journal of animal ecology’ –he actually sampled 13 volumes of the journal between 1932 and 1992. Here, we followed that method: we inspected a large sample of the ecological literature, classifying it according to its content. This process is time-consuming, but the resulting analysis is probably more reliable than an automated word count.",
"discussion": "Discussion Few systematic surveys of ecological literature have been conducted to date, and most have been restricted to a single theme or a narrow branch of ecological science [20] , [21] , [22] , [23] . For example, [22] evaluated relations between the size of the organism and its relative representation in ecological research. Swihart [12] quantified the rates of appearance of new ecological terms and disappearance of old terms. Shorrocks [24] was perhaps the only investigator to quantify various trends in ecological science, using articles published in The Journal of Animal Ecology between 1932 and 1992. To the best of our knowledge, the present study is the first attempt to systematically survey the entire breadth of ecological literature, in order to quantify various characteristics of the science of ecology, as well as their temporal trends. The results suggest that ecology may be substantially less dynamic than is generally acknowledged. Domains of ecological research Ecology is mostly a study of single species. Most of the ecological research focused on the demography, physiology and distribution of single species. The proportion of single-species studies has slightly decreased in the past three decades, but still consists of more than 60% of the studies. In comparison, community and ecosystem studies represented a minor fraction of ecological research. This surprising finding seems at odds with the strong emphasis on the community and the ecosystem as major concepts in ecology [27] , [28] . Also surprising was the scarcity of a few topics which are thought to be central in ecology. Two notable examples are evolution, and food-web, each of which appeared as a research topic in 2–4% of the articles. Most of the increase in community studies occurred in the 2000s, probably reflecting the renewed interest in this field, after the neutral theory challenged the prevalence of the niche concept. The analysis of changes in the frequency of research topics over time provided inconclusive results. Only two topics, climate change and biodiversity, showed a significant change in both surveys. The increase in both topics probably relates to the fact that both were non-issues at the beginning of the period under study. Four other topics changed significantly, and seven other topics changed nearly significantly, in only one of the surveys. Overall, there does not seem to be a drastic transformation in the relative importance of domains and topics in the field of ecology, but the apparent change in topics and research types signifies that ecological science is not entirely stagnant. The frequency of more than half of the topics and domains was very similar in both surveys, but nearly a third of the topics differed significantly between the surveys. Interestingly, the topics that were significantly more frequent in the ‘all journals’ survey related to the basic and static aspects of a species (genetics and physiology), and the ecosystem (biomass and productivity). In contrast, the topics that were significantly more frequent in the ‘core journals’ related to dynamic processes (demography, vegetation dynamics, and grazing). Type of research Observation and experiment were by far the predominant tools of ecological study, together accounting for 80% of the research; these proportions did not change over time. Interestingly, modelling (∼12% of all studies), is no more common today than it was thirty years ago, despite a drastic increase in the availability of modelling tools during this period. Data-analysis became a more common research tool. Many of the studies in this category were, in fact, meta-analyses (analyses of data from several sources). The major increase in data-analysis studies was in the mid-90s, suggesting that the increased availability of information in the age of the Internet had an important role in this trend. Comparing the two surveys in terms of type of research revealed a fundamental difference: the ratio of experiments to observations in the ‘all journals’ survey was 1:2, while in the ‘core journals’ survey it was 7:9. The prevalent consensus that ecology has changed during the 20 th century, from an observational to an experimental science, may be somewhat overstated; nevertheless, such a change appeared more prominently in the ‘core journals’ survey. Is ecology a problem-solving science? Ecological research is mostly a basic science, with only a small proportion of ‘problem solving’ studies. Yet, in both surveys we found a significant and consistent increase in the number of ‘problem solving’ articles published during the survey period. If this trend continues in future decades, it may prove to be a major shift in the orientation of ecology. Is ecology a dynamic science? Prominent ecologists have claimed that ecology has undergone transitions [29] , and even paradigm shifts [5] in recent decades, and is now a mature and competent science [30] . Our survey reveals that these claims perhaps overstate the case. The science of ecology appears to be changing slowly, in the sense that major research subjects and principal methodologies have not changed dramatically for at least 30 years. In particular, the popular image of ecology as a science in transition [7] , dealing chiefly with ecosystems and communities [1] seems at odds with the major proportion of single species studies reported here. A contrasting view, put forward by O'Connor [11] , claimed that ecology lags after other life sciences, and makes very little progress. O'Conner's study ignited a debate, wherein various arguments were employed to disprove this claim [31] \n , \n [23] , or put it in a balanced perspective [12] . This debate is still ongoing, and is probably driven by emotions no less than by objective evaluations. The current study does not substantiate O'Connor's claim, and it was not meant to evaluate progress. However, it is safe to assume that a major advance in ecology would be accompanied by a major change in the frequency of domains, topics, and types of research; yet, as shown here, these have changed only moderately in the course of three decades. A major aspect of progress in science is the rate at which basic questions in ecology are being answered [12] , which we have not evaluated, and is very difficult to evaluate quantitatively. Also, we could not detect conceptual shifts, such as network thinking, that do not connect to particular terms or topics. Swihart et al. [12] provide an interesting attempt to quantify progress based on ‘birth rate’ and ‘death rate’ of ecological terms, and claim to show viable progress in ecology. In contrast, the list of 100 fundamental questions in ecology [32] reports profound knowledge gaps regarding the central mechanisms driving ecosystems, communities, and even population dynamics. Our approach could not, and was not meant to detect changes in particular methods and technologies applied within each research domain or topic. The availability of advanced molecular and genetic tools and the increase in computing power have allowed analyses to become more complex and sophisticated. However, the use of these new technologies and processing power does not imply enhanced knowledge or understanding. Also, such surveys may not detect conceptual shifts, such as network thinking, which do not connect to particular terms or topics. Perhaps the single and most important change in the study of ecology is the growing proportion of ecological research directed towards problem solving. This trend by itself, if continued, may represent a major transition in ecology in the long run. Our results may be disturbing to some researchers, insofar as they portray an ecological discipline which is considerably less dynamic than ecologists would like to believe. The value of this research is precisely in reviving the debate and presenting an opportunity for self-assessment to those who strive to advance the discipline, all of which can serve to stimulate the investigation of new and groundbreaking tools, paradigms and perspectives. Only through meta-scale monitoring of the scope of research can we understand, and hope to influence, the trajectory of ecological research in the years to come."
} | 3,802 |
28317065 | PMC5486607 | pmc | 5,215 | {
"abstract": "The impact of fungal endophytes and the modulating role of arbuscular mycorrhizal fungi (AMF) on the vitality of Verbascum lychnitis , grown in the laboratory in a substratum from a post-mining waste dump was investigated. We report that inoculation with a single endophyte negatively affected the survival rate and biomass production of most of the plant-endophyte consortia examined. The introduction of arbuscular mycorrhiza fungi into this setup (dual inoculation) had a beneficial effect on both biomass yield and survivability. V. lychnitis co-inoculated with AMF and Cochliobolus sativus , Diaporthe sp., and Phoma exigua var. exigua yielded the highest biomass, exceeding the growth rate of both non-inoculated and AMF plants. AMF significantly improved the photosynthesis rates of the plant-endophyte consortia, which were negatively affected by inoculation with single endophytes. The abundance of PsbC, a photosystem II core protein previously shown to be upregulated in plants colonized by Epichloe typhina , exhibited a significant increase when the negative effect of the fungal endophyte was attenuated by AMF. Electronic supplementary material The online version of this article (doi:10.1007/s00572-017-0768-x) contains supplementary material, which is available to authorized users.",
"conclusion": "Conclusions Studies investigating interactions between fungal endophytes, AMF, and non-grass hosts have received little attention until recently. The experiments conducted here showed that V. lychnitis did not benefit from single inoculation with endophytes in a substrate containing toxic metals. Inoculation with both AMF and endophytes significantly improved plant growth parameters. Particularly, V. lychnitis co-inoculated with AMF and C . sativus , Diaporthe sp., and P. exigua var. exigua yielded the highest biomass, exceeding the growth rate of both non-inoculated and AMF plants. These results, hopefully, can be utilized in phytoremediation strategies. Inoculation with the fungi tested did not affect photosynthesis efficiency. Interestingly, however, in the beneficial setups that included fungal endophytes, the abundance of PsbC was increased. This indicates that upon colonization with endophytic fungi, changes in the composition of proteins involved in light harvesting do take place. Previously, this has been found to be associated with an improvement of photosynthesis efficiency.",
"introduction": "Introduction Plants, in their natural environments, are a refuge for numerous organisms ranging from small mammals, through invertebrates and fungi, to unicellular bacteria (Beckett et al. 1992 ; Lindow and Brandl 2003 ). From the functional standpoint, a single plant can be referred to as a specific “microcosmos,” where structure and functions are organized by a dense net of multidimensional interactions between the plant and its inhabitants, as well as by interactions between the organisms colonizing the plant (Van der Putten et al. 2001 ; Singh et al. 2004 ; Felton and Tumlinson 2008 ; Aly et al. 2011 ). The symbiosis of plants has been the subject of a multitude of studies. Microorganisms, fungi, and bacteria take central positions in this field of symbiosis due to their impacts on the host’s biology and potential application. Microorganisms inhabiting the plant differ in their trophic strategy ranging from pathogens and parasites to mutualists (Redman et al. 2001 ). Additionally, the character of the relationship of a specific microorganism and its host plant is variable and may change over time. In many cases, abiotic and biotic constraints such as resource availability, herbivores, factors limiting carbon assimilation, and stage of ontogeny among others may elicit a switch from mutualism to parasitism and vice-versa (Kiers et al. 2010 ). In plant symbiotic interactions, the microorganism functions as an additional sink for carbohyrates. In instances where the costs of maintaining a fungal partner do not exceed benefits, the plant acts as the most effective provider to the fungus. However, under environmental challenges, a trade-off in resource allocation may limit carbon flow from the plant to its symbiotic partner and disturb the mutualistic equilibrum (Ahlholm et al. 2002 ; Bever et al. 2009 ). This phenomenon, described for symbiotic fungi, is referred to as the endophyte-parasite continuum (Schulz and Boyle 2005 ; Mandyam et al. 2013 ; Reininger and Schlegel 2016 ). Additionally, the presence of fungi and bacteria with pest control capabilities may influence the lifestyle of co-inhabitants. This ability of symbiotic microorganisms may be exploited as an environmentally friendly alternative for traditional pest control in agriculture (Doan et al. 2008 ; Jäschke et al. 2010 ). The growing deposition of toxic metals in the environment severely restricts plant growth. This has serious economic and social implications, due to yield losses in agriculture, contamination of water reservoirs, and increased incidence of diseases. A promising method for restoring areas polluted by industrial activities is phytoremediation. Phytoremediation utilizes the ability of plants to remove and/or immobilize a wide range of organic and inorganic pollutants, including toxic metals deposited in the substratum (Turnau et al. 2006 ). The remedial capacity of plants can be greatly improved by microorganisms such as bacteria and fungi. There are two major classes of ubiquitous fungal symbionts associated with plants in terrestrial ecosystems: arbuscular mycorrhizal fungi (AMF) and fungal endophytes. The roots of more than 80% of plants on land are colonized by AMF (Smith and Read 2008 ). The benefits related to mycorrhization include increased root absorption area and hence increased uptake of nutrients (Smith and Read 1997a , 1997b ), a better tolerance to water stress (Porcel and Ruiz-Lozano 2004 ), high salinity (Daei et al. 2009 ), pathogens (Cordier et al. 1998 ), and toxic metals (Turnau et al. 2006 ). Endophytes are commonly isolated from roots and shoots of plants (Stone et al. 2000 ; Zhang et al. 2006 ; Wężowicz et al. 2014 ). Their presence within host tissues may promote plant growth, nutrient acquisition, and confer tolerance against abiotic and biotic stresses (Redman et al. 2002 ; Lewis 2004 ; Schulz and Boyle 2005 ; Waller et al. 2005 ; Hiruma et al. 2016 ), including toxic metal tolerance (Deng et al. 2011 ; Li et al. 2011a ; Li et al. 2011b ; Li et al. 2012 ). Fungal endophytes have been shown to possess a remarkable ability to accumulate toxic metals: the DSE (dark septate endophyte) Exophiala pisciphila (H93) from the roots of Urundinella bengalensis accumulated over 5% Cd in its dry mass (Zhang et al. 2008 ). Interestingly, DSEs are ubiquitous and colonize roots of plants growing in extremely unfavorable, post-industrial environments. Furthermore, their colonization increases with the growing quantity of toxic metals in the substratum (Regvar et al. 2010 ; Shen et al. 2015 ; Zhao et al. 2015 ). Excess of toxic metals alters photosynthesis functions at various levels of organization. For instance, disturbances in chlorophyll biosynthesis and thylakoid electron transport are induced in the presence of Pb, Zn, and Cd (Tripathy and Mohanty 1980 ; Sheoran et al. 1990 ; Stefanov et al. 1995 ; Vodnik et al. 1999 ; Villiers et al. 2011 ). As a result, plant growth and development is severely impaired. Symbiosis with fungi was reported to attenuate the effects of toxic metals stress. Inoculation with fungal endophytes increased photosynthetic pigment abundance and enhanced water use efficiency, promoting photosynthesis and rice growth in Pb stress conditions (Li et al. 2012 ). Mycorrhizal rice showed higher chlorophyll content and photosynthetic efficiency in the presence of As, in comparison to non-mycorrhizal plants (de Andrade Lopez et al. 2015 ). AMF were shown to upregulate detoxification mechanisms including the antioxidant system. Efficient ROS (reactive oxygen species) scavenging is crucial to maintaining proper function of electron transport and subsequent ATP and NADPH production in the photosynthetic electron transport chain. Improved photosynthetic efficiency was attributed not only to the protective role of the fungi, but also, according to Rozpądek et al. ( 2015 ), to symbiont-related alterations in the composition, i.e. the abundance of specific photosystem proteins, including PsbC and Lhcb3 that may positively impact the function of the photosynthesis apparatus. So far, AMF-plant or endophyte-plant interactions were studied in single plant-fungus models. The few co-inoculation studies, where plants were inoculated by more than one group of fungi, were performed using grasses as the host plants. The results of these experiments are not ambiguous, showing that the microorganisms inhabiting plant hosts positively influenced plant growth (Mack and Rudgers 2008 ; Scervino et al. 2009 ; Larimer et al. 2012 ). AMF have been described as important factors in remediation strategies (Leyval et al. 1997 ; Turnau et al. 2005 ). The role of endophytes in this process also has been well demonstrated (Weyens et al. 2009 and publications cited there; Soleimani et al. 2010 ; Li et al. 2012 and publications cited there). Nevertheless, the majority of research related to the use of endophytes in phytoremediation comes from studies of bacterial endophytes. Endophytic fungi are yet to receive the appropriate attention from the scientific community. In a previously published article, the diversity of fungal endophytes from Verbascum lychnitis , a biennial herb from the Scrophulariaceae family, inhabiting post-industrial wastelands in South Poland was shown (Wężowicz et al. 2014 ). The aim of this study was to evaluate the growth response, photosynthesis performance, and possible alterations in the composition of selected photosynthetically active pigments and structural proteins of V. lychnitis inoculated with selected endophytic fungi and co-inoculated with endophytes and AMF. The plants were cultivated in the substratum of the mine dump “Chrzanów” under laboratory conditions. This permitted selection of fungal endophytes with the highest potential for utilization in phytoremediation. We predicted that the isolated fungal endophytes would affect their host differently and their interaction would be modulated by AMF.",
"discussion": "Discussion The role of fungal endophytes in plant physiology and their possible utilization as growth promotion and stress protection agents recently has been a popular subject of research. Here, we report that, under laboratory conditions, fungal endophytes did not promote V. lychnitis growth in substrate from a post-mining waste dump. In single inoculation experiments, plant fitness, in terms of biomass production and leaf area (data not shown), was negatively affected by 5 out of the 8 endophytes tested. The remaining three fungi had no effect on plant growth. It seems that reduced growth was the cost of maintaining the endophytes within plant tissues. AMF in this setup remarkably improved the growth parameters of the endophyte-inoculated plants. In all plant-fungi consortia examined, biomass production and survivability (excluding P. exigua var. exigua ) was improved compared to the NM control, suggesting that AMF positively modulates the plant-fungal endophyte interaction. V. lychnitis yielded the highest biomass when co-inoculated with AMF and C. sativus , Diaporthe sp., and P. exigua var. exigua. The survival rate of M plants co-inoculated with endophytes was notably higher than the NM control, but not as high as the AMF control (with no endophyte colonization). This probably can be attributed to the methodology used in the experiment. Inoculation with the endophyte took place under in vitro conditions, 4 weeks before inoculation with AMF to ensure endophyte colonization. It seems as though that the attenuating effect of AMF was not sufficient enough to allow all the plants inoculated with endophytes 4 weeks earlier to survive. In order to gain a comprehensive understanding of the endophyte-induced growth dynamics, a different experimental setup (simultaneous inoculation with AMF and fungal endophytes) is necessary. V. lychnitis is a biennial herb, which grows up to 2 m in height in its second year. To describe the role of symbiotic fungi in V. lychnitis fitness, more thorough analysis of plant growth, seed production, etc. after two growth seasons is required. Another issue that needs to be raised is that V. lychnitis in natural conditions is almost exclusively found associated with mycorrhiza (unpublished data). This suggests that any depletion of the mycorrhizal partner may have negatively affected the plant’s physiology, which, in turn, may have affected its interaction with the endophyte. Overall, V. lychnitis could only benefit from the endophytic fungi in its mycorrhizal state. The mechanisms by which mycorrhizas improve plant growth and fitness under metal toxicity have been reviewed recently (Ruytinx et al. 2016 ). The majority of data describing the beneficial role of AMF under metal toxicity comes from experiments conducted under controlled conditions, or that did not recognize the potential role of other symbiotic organisms in mycorrhiza-dependent toxic metal stress alleviation, even though in natural environments plants are inhabited by a multitude of microorganisms. In this study, we examined the growth response of V. lychnitis in both single inoculation experiments and in co-inoculation experiments with both AMF and fungal endophytes. We found that co-inoculation with certain species may facilitate plant growth in a toxic metal-enriched environment. The models used in this study do not exhaust all the possible interactions of V. lychnitis in nature, but are a step forward in understanding the role of different microorganisms in plant biology. To elucidate the mechanisms of this tripartite interaction, further research is necessary. According to the available literature, the fungal endophytes tested in this study usually establish beneficial or neutral associations with their hosts. Being one of the most dominant endophytes, Diaporthe ( Phomopsis ) species are commonly isolated as endophytes from different dicot plant hosts (Suryanarayanan et al. 2002 ; Sun et al. 2011 ). Some species of Diaporthe , however, show either pathogenic or mutualistic interactions with plants depending on the host and its health; for example, D. phaseolorum is pathogenic to soybean (Santos et al. 2011 ), but endophytic in mangroves (Sebastianes et al. 2011 ). Diaporthe spp. have been reported to cause a wide range of plant diseases (Scheper et al. 2000 ; Heller and Gierth 2001 ). Xylaria species occur on a broad diversity of plant hosts; however, they are frequently isolated from wood tissues (Frohlich et al. 2000 ; Wang et al. 2014 ). Fungi from this genus present fungal activity against plant pathogens (Park et al. 2005 ; Fukasawa et al. 2009 ) but also show strong capacity to degrade cellulose and lignin (Osono and Takeda 2002 ). This strategy may be adapted for a saprotrophic lifestyle. Species from the Phoma genus are asymptomatically present in numerous plants: Junker et al. ( 2012 ) isolated this fungus along with other fungi from wild Arabidopsis thaliana in which it did not cause any disease symptoms. In a single inoculation in vitro experiment, however, Phoma sp. parasitized Arabidopsis , suggesting that the A. thaliana - Phoma sp. interaction is modulated by the plant microbiota (in vitro conditions may had also affected the interaction). A similar relationship was reported by Larimer et al. ( 2010 ) who showed that fungal foliar endophytes decreased plant performance; however, co-inoculation with AMF attenuated the negative effect of endophytes. The growth promoting role of fungal endophytes, under limited resource availability, is controversial. Studies performed on Festuca grass species and the Neothyphodium/Epichloe endophytes give conflicting results (Ahlholm et al. 2002 ). In Ahlholm et al.’s ( 2002 ) experiment, fungus species previously described as neutral and mutualistic endophytes severely affected the growth of their host. Endophytic fungi acquire all necessary growth and maintenance resources from their host plant, thereby affecting resource transport from source to sinks and allocation within the plant (Ahlholm et al. 2002 ). Vegetation in conditions of limited resource availability, such as a post-mining waste dump, severely hinder plant performance. An additional sink probably exceeded the host ability to maintain its own vegetative functions. The potential benefits coming from the presence of the endophyte did not compensate the costs of the symbiosis leading to decreased plant biomass production. In contrast to fungal endophytes, AMF hyphae extend beyond the rhizosphere. This allows AMF to aquire resources independent of plant growth. Mineral nutrients acquired by the fungi may be translocated to the plant. We speculate that the endophyte confined to plant tissues was not efficient in provisioning its host, which resulted in growth retardation. The presence of the AMF in the setup remarkably improved plant growth, suggesting that the benefits (nutrient acquisition, stress protection) provided by the AMF were sufficient to allow the plant to provide nutrients to all its symbiotic partners. Additionally, a role in modulating endophytic fungi growth and lifestyle by AMF cannot be excluded, but this requires further research. Toxic metals significantly affect the performance of the photosynthetic apparatus, limiting the efficiency of carbon assimilation, thus affecting the overall nutritional status of the plant. Limiting the production of the “source” forces the “sinks” to adapt. An additional energy sink, the endophyte, requires the plant to share its carbon resources with its symbiotic partner. Under optimal conditions, plant production may exceed its needs, allowing it to allocate a share of its photoassimilates to the fungi. In photosynthesis limiting conditions, however, the plant suffers from carbon deficiencies (with a consequent slower growth rate, diminished ability to counteract stress-induced damage, etc.) hindering its ability to invest in additional sinks for resources. Aloui et al. ( 2011 ) showed that Glomus irregulare alleviated the negative effects of Cd on photosynthesis in Medicago truncatula by increasing the plant’s ability to utilize light energy and by facilitating electron transport. According to Rozpądek et al. ( 2015 ), colonization of orchard grass by Epichloe typhina led to changes in the host’s photosynthetic apparatus, e.g., increased electron density flux, increased stomatal conductance, and an abundance of Chl b and PSI and PSII proteins. As a result, PSII photochemistry efficiency, carbon assimilation, and light harvesting capacity in plants were improved, allowing orchard grass to cope with endophyte energy demands while also sustaining orchard grass growth. In single inoculation experiments with Diaporthe sp., V. lychnitis exhibited a significant decline in photosynthesis performance. The efficiency of energy production in relation to absorption, manifested as electron flow in PSII (PI ABS ), decreased by close to 50%. The photosynthesis index PI ABS combines three variables indicative of different processes associated with PSII photochemistry: the density of reaction centers, the quantum yield of primary photochemistry, and the ability to transfer electrons from PSII to PSI (Corrêa et al. 2006 ). In single inoculation with Diaporthe sp., the photochemistry of PSII was decreased due to a significant decline in the efficiency of primary photochemistry (φEo, φPo). Even though, the light-harvesting capacity was improved (ABS/RC), the photosynthetic apparatus was unable to transform incident light into energy (lower electron transport efficiencies) necessary for carbon assimilation. These results suggest that even though solar energy was absorbed, it was not transformed into fixed energy, but instead was dissipated as heat (DIo/RC). Interestingly, inoculation with mycorrhiza (co-inoculation with AMF) restored to control levels, or even above control levels, all the photosynthesis indicies that were negatively affected by single inoculation with Diaporthe sp. The restoration was correlated with a significant growth improvement. In the case of Xylaria sp.-inoculated plants (single inoculation) which did not respond to the endophyte growth-wise, a significant improvement in φEo and Ψ0 was found, but this did not translate into elevated overall photosynthesis efficiency (PI ABS ). According to previous reports, inoculation with mycorrhiza improves plant growth in toxic metal-enriched habitats. Higher biomass yield often is correlated with improved photosynthesis performance (Rozpądek et al. 2014 , reviewed in Ruytinx et al. 2016 ). In this study, however, mycorrhizas did not affect the photosynthesis efficiency of V. lychnitis , suggesting that accelerating the carbon assimilation rate was not necessary for improved growth. It also was not required for provisioning its symbiotic partners, indicating that the energy demands of the plant-microbe consortium were met by means other than improved photosynthesis efficiency. The role of toxic metals in chlorophyll (Chl) biosynthesis and stability has been comprehensively described. Toxic metals were reported to inhibit chlorophyll biosynthesis (Ouzounidou 1995 ). Pb was reported to inhibit chlorophyll biosynthesis by causing impaired uptake of essential elements such as Mg and Fe by plants (Burzyński and Grabowski 1984 ). Chl b is more vulnerable to Pb toxicity than Chl a (Vodnik et al. 1999 ). Pb changes the lipid composition of thylakoid membranes (Stefanov et al. 1995 ) and damages the photosynthetic apparatus due to its affinity for protein N- and S-ligands (Ahmed and Tajmir-Riahi 1993 ). Cd is known to decrease the total Chl a/b ratio in plants (Sheoran et al. 1990 ) and to suppress chloroplast development (Malik et al. 1992a , b ). Here, inoculation with endophytes Xylaria sp. and Diaporthe sp. significantly increased the abundance of Chl b . Diaporthe sp. also increased energy absorption and flow through a single reaction center (although it did not translate into energy production). Chl b plays an important role in the assembly of light-harvesting complexes and in the structure of reaction centers. In a previous study, fungal endophytes were found to alter the composition of photosynthetic pigments and light harvesting center proteins (LHC), including the relative abundance of Chl b , PsbC, and other proteins involved in light harvesting and processing. This was accompanied by improved photosynthesis efficiency (Rozpądek et al. 2015 ). Higher energy fluxes potentially accelerate energy production in PSII. In this experiment, however, probably due to plant growth conditions, structural changes did not translate into enhanced performance. In order to map endophyte-induced changes in the composition of photosynthesis functional proteins, we tested those shown previously to be affected by Dactylis glomerata inhabited by E. typhina and others. Unfortunately, due to methodology issues (the antibodies available did not react with epitopes from V. lychnitis proteins or did not allow us to perform a semi-quantitative analysis), we were not able to quantify the abundance of other photosynthesis-related proteins. Those that we quantified did not exhibit significant changes upon inoculation. Our results indicated that only the abundance of the LHC structural PsbC protein increased in M plants co-inoculated with endophytes, but not in single inoculation experiments with endophytic fungi or AMF. Because inoculation with the AMF alone did not yield such results, we propose that increasing the abundance of PsbC is an endophyte-specific feature, but in the case of V. lychnitis , only plants co-inoculated with AMF ( V. lychnitis is mycorrhizal in nature) exhibit such an alteration. The abundance of the remaining proteins (Rubisco LSU and Lhcb3) evaluated in this study did not change in any of the experimental setups. This suggest that even though the presence of the endophytes did affect the composition of photosynthetically active pigments and proteins in V. lychnitis , these changes were insufficient to improve photosynthesis performance."
} | 6,161 |
27102357 | PMC4840358 | pmc | 5,216 | {
"abstract": "Understanding the diversity and community structure of arbuscular mycorrhizal fungi (AMF) is important for potentially optimizing their role in mining phosphorus (P) in agricultural ecosystems. Here, we conduct a comprehensive study to investigate the vertical distribution of AMF in a calcareous field and their temporal structure in maize-roots with fertilizer P application over a three-year period. The results showed that soil available-P response to P fertilization but maize yields did not. Phosphorus fertilization had no-significant effect on richness of AMF except at greater soil-depths. High P-supply reduced root colonization while optimum-P tended to increase colonization and fungal richness on all sampling occasions. Crop phenology might override P-supply in determining the community composition of active root inhabiting fungi. Significant differences in the community structure of soil AMF were observed between the controls and P treatments in surface soil and the community shift was attributable mainly to available-P, N/P and pH. Vertical distribution was related mainly to soil electrical conductivity and Na content. Our results indicate that the structure of AMF community assemblages is correlated with P fertilization, soil depth and crop phenology. Importantly, phosphorus management must be integrated with other agricultural-practices to ensure the sustainability of agricultural production in salinized soils.",
"conclusion": "Conclusions The soil studied had relatively high residual P levels and soil available P showed some response to P supply but maize yields showed no response after three years of fertilizer P application. The responses of AMF communities in maize roots and soil to fertilizer P application are complex. Here, we found that P fertilization significantly affected root colonization but not the diversity or community structure of AMF in maize roots. The temporal changes in the AMF community in maize roots indicate that crop phenology might override fertilizer P in determining the community composition of active root inhabiting fungi. By contrast, a shift in AMF community seen in the surface soil is mainly attributable to soil available P and pH, and optimum P tends to increase the diversity of AMF. The vertical distribution of AMF in the soil is related to soil EC and Na content. Hence, P management should be integrated with cropping design and other agricultural practices to ensure the sustainable agricultural production in these salinized soils.",
"discussion": "Discussion Maize yields did not show any response to fertilizer P application over the three consecutive years because the soil in the present study contained relatively high levels of residual P due to previous intensive cultivation. Yet we found that mycorrhizal colonization and AMF communities were altered by P application. The fertilizer P effect started to show in the second year. Our sampling time was the tipping point in terms of soil residual P shifting from moderate P supply to P deficiency (P0). Hence, our study of the status of AMF communities at this time point is of particular interest. Considerable evidence shows that AMF are strongly controlled by host P status and soil P availability 9 . In general, root colonization by AMF is inversely related to soil available P and plant P nutrition 25 . As expected, compared to P0, at P100 %RLC, %AC and %HC in maize roots decreased significantly, while the effect of P25 was variable and colonization sometimes increased ( Table 1 ). At P25 soil available P at 0–20 cm depth fell within the critical P values reported for maize production (3.9 to 17.3 mg P · kg −1 ) 26 , while P100 may lead to a P-leaching risk as the value was close to 40 mg P kg −1 27 . Our results indicate that high P supply leads to low root infection by AMF while optimum P may possibly stimulate the potential activity of indigenous AMF in the soil. Root colonization and AM-specific Pi transporter genes are significantly up-regulated when soil Olsen-P is below a critical level (10 mg kg −1 ) 28 . The inconsistent results at P25 may be related to the heterogeneity of P distribution in the soil because fertilizer P was applied only at 0–20 cm depth but the roots were sampled to below 20 cm depth. The positive effect of P25 and the negative effect of P100 on %RLC, %AC and %HC tended to be more pronounced at R4 ( Table 1 ), indicating that crop phenology is important in determining root colonization. High P demand and C allocation to roots at R4 in maize plants potentially affected the dependency of maize on mycorrhizal fungi to acquire P 29 . Similarly, Liu et al . 24 also found changes in AMF colonization across the maize growing season and colonization increases over the growth period. In contrast to AM colonization, spore density and hyphal length density were significantly affected neither by P fertilization nor by growth stage ( Table 1 ). These results are inconsistent with changes in spore density 30 , or the amount of AM determined by the fatty acid biomarker C16:1w5 29 over the plant growth period. One explanation is that AM colonization is more sensitive to short-term P fertilization and crop phenology than are the growth and spore production of AMF, as the colonization structure is more closely associated with the host plant. The influence of soil P on the diversity of AM fungi remains controversial, and the outcome is related to the forms of P fertilizer, P rates 31 , sampling times 32 and host plant species 16 . Here, fertilizer P application did not have any overall significant effect on T-RF richness in the maize roots or in the soil ( Fig. 1 ) except for a decrease in T-RF richness at 40–60 cm depth. Similarly, Beauregard et al . 32 also found that AMF diversity was not affected by P level. By contrast, Lin et al . 23 found that long-term P fertilization decreased AMF diversity and richness in an arable soil in north China. The AMF community composition in the soil was differentiated by the fertilizer P treatment and soil depth. The separation of the AMF community among P treatments occurred at the surface soil layer and was mainly attributed to available P, N/P and pH ( Fig. 2 ). The vertical distribution of AMF was significantly correlated with EC and Na content ( Fig. 2 ). The impact of fertilizer P is in agreement with previous results based on agricultural soils 23 and other ecosystems 33 . Soil pH and/or pH-driven changes in soil chemistry are important in shaping AMF communities in both natural and agricultural ecosystems 34 . The change in soil pH due to fertilizer P application might be related to plant N uptake. Phosphorus fertilization has been shown to affect N uptake and N use efficiency 35 . In addition, we found that the variation in the AMF community structure was associated with soil N/P. According to the functional equilibrium model 36 , a pronounced increase in N/P with soil depth implies that AMF deeper in the soil may enhance mutualistic benefits. However, whether this may offset the negative impact of high P on AMF in the surface soil needs further investigation. Similarly, significant effects of combined N and P fertilization on soil AMF species composition 37 and richness 38 have been reported. Thus, the response of the AMF community to P fertilization should also consider N levels in order to provide a more predictive picture of AM structure and function in agricultural ecosystems. The high number of T-RFs at P25 in the surface soil (0–20 cm) is particularly interesting. Whether or not this indicates that low rates of fertilizer P increase the diversity of AMF in the soil requires further study. Of the soil physio-chemical properties determined, we found that soil EC and Na content had a significant impact on the vertical distribution of AMF in the soil. This is in accordance with our previous N fertilization study at the same site 24 and a recent study in a semi-arid prairie ecosytem 34 . Soil salinity can impact AMF. Our study area has been substantially affected historically by high salinity and was desalinized in three stages in 1973, 1978 and 1982 39 . It is unusual that few T-RFs were detected at P100 at 40–60 cm. As the missing T-RFs (97 and 258 bp) were detected frequently in the maize roots ( Table S2 ), this effect may be due to the spatial heterogeneity of the soil. Soil spatial heterogeneity can influence AMF communities. Alternatively, high EC and Na contents at P100 indeed act as a strong filter for specific fungal taxa. In addition to EC and Na content, it is possible that other soil properties and plant attributes also affect the vertical distribution of the AMF communities, and this requires further study. Application of P did not have a significant influence on the community composition of AMF in the roots ( Table S2 ). Previous studies show that some addition of fertilizer P can increase the diversity of AMF but high application rates can substantially reduce AMF diversity and change species composition 25 . By contrast, the AMF community of the three host plant species maize, viola and soybean changed significantly only at higher P concentrations (>46–70 mg l −1 ). Similarly, AMF communities associated with alfalfa were not affected by P level 32 . A large scale study in Swiss agricultural soils also reported that soil available P levels had no effect on the structure of the AMF community 40 . In the present study the soil used had a relatively high background P due to high application rates of fertilizer P to preceding crops prior to the start of the experiment. It is possible that the indigenous AMF community has been selected towards specific taxa or strains that are strong competitors and less sensitive to fertilizer P. This is supported by the six dominant representative T-RFs (97, 116, 141, 189, 258 and 259 bp) in maize roots across all P treatments and sampling times ( Table S2 ). Hence, the residual effect of P fertilization may override the current P management practices with respect to its impacts on the AMF community, although differences in soil Olsen-P and maize P uptake were observed among the fertilizer P treatments. Nevertheless, we found that two relatively rare T-RFs (157 and 168 bp) were present only at P0 and P25 but not at P100, indicating that high application rates of fertilizer P have the potential to eliminate or reduce AMF taxa. Whether these AMF species ( Glomus indicum and an uncultured Glomus ) are sensitive to P fertilization requires further investigation. Evidence from cloning data shows that the relative abundance of Glo12 (affiliated with Glomus indicum ) was substantially reduced by P fertilization (data not shown). Seasonal variation in root AMF communities is correlated with compounding factors such as host plant species and P flux 34 , climatic conditions 41 , crop phenology 24 and the life history traits of the fungi 42 . We found that the AMF community in maize roots shifted at certain growth stages of the maize crop. For example, R4 in the control, V13 in P25, and V6 in both P25 and P100 were separated from the remaining P treatments and growth stages. This may be due to the alteration of carbon investment in belowground parts or signaling of plants to the environment over the maize growth period. The presence of certain rare taxa (e.g. 107, 142 and 157 bp) may also affect community composition because rare species may be important in affecting AMF community structure in response to nutrient applications 38 . In the present study P fertilization did not affect the AMF community profiles in the maize roots and the AMF community was generally clustered across the different P treatments ( Table S2 ). Likewise, the AMF community structure in the soil was altered by long-term fertilization in a wheat-rice crop rotation 43 . The frequency of occurrence of T-RFs did not differ significantly between fertilizer P treatments vs. the control (data not shown). This is consistent with findings from a long-term P fertilization (~40 years) field trial in pasture where the addition of fertilizer P to the soil was more important than the quantity applied in affecting AMF communities 33 . In the present study, N fertilizer was applied at a rate for optimal maize growth. Nitrogen fertilization at low P availability might increase the C supply belowground 36 and enhance the colonization and diversity of AMF in maize roots in low P treatment. In addition, the common P fertilization practice in this region may have selected certain AMF taxa which show weak responses to P inputs. Future studies should include analysis of the abundance of AMF taxa in the roots to elucidate the potential mechanisms of fungal competition in the construction of AMF assemblages. The complex linkages among climatic variation, crop phenology, soil conditions and AMF dynamics need to be clarified in long-term studies. AMF genetic diversity was found to be higher in soils than in roots in grassland ecosystems 44 . Our results are in accordance with these previous studies. We found 27 sequence types in the present study, similar to the 26 sequence types reported in maize roots in a long-term nitrogen fertilization experiment conducted in southeast Nebraska 45 and 22 sequence types in north China 24 . Most of the sequence types were affiliated with the identified VTX reported in the Maarj AM database ( Table S4 ), indicating that the VTX are ubiquitous. Six fungal T-RFs (97, 116, 141, 189, 258 and 259 bp) were detected in both maize roots and soil ( Table S2 ). Sequence types of these OTUs belonging to the genera Glomus, Funneliformis, Rhizophagus, Diversispora, Acaulospora, Septoglomus and Sclerocystis have been frequently detected in north China 23 . The T-RFs 258 bp ( Glomus viscosum and uncultured Glomus ) and 97 bp ( Acaulospora mellea , uncultured Diversispora, Rhizophagus intraradices, Septoglomus constrictum and Glomus viscosum ) occurred in almost all root samples but were less frequently detected in the soil. The T-RFs 190 bp ( Glomus sp. and uncultured Glomus ) and 169 bp (uncultured Diversispora ) showed the opposite trend. Glomaceae and Acaulosporaceae groups have been shown to be commonly associated with maize 43 . The enriched T-RFs indicate that maize roots showed some preference for these fungal species and selection towards certain fungal taxa. This is partly supported by the higher turnover of T-RFs in the soil than in the plant roots. Consideration of both roots and soil together provides a more comprehensive picture of AMF diversity and its potential function in agricultural ecosystems."
} | 3,659 |
35562544 | PMC9106740 | pmc | 5,219 | {
"abstract": "Microbial lifestyles may reveal niche-specific signatures and can contribute to detecting the effects of abiotic fluctuations on biogeochemical cycles. Microorganisms make a tradeoff between optimizing nutrient uptake, improving biomass yield, and overcoming environmental changes according to environmental hostility. Soda lakes are natural environments rich in carbonate and bicarbonate water, resulting in elevated pH and salinities that frequently approach saturation. We hypothesized that during the dry period (elevated pH and salinity), microorganisms try to overcome this harshness by allocating energy to the cellular maintenance process. As these environmental conditions improve during the wet period, microorganisms will begin to invest in nutrient uptake. To test this hypothesis, we evaluated four soda lakes in two different seasons by applying metagenomics combined with flow cytometry (estimate heterotrophic bacterial biomass). The natural occurrence of cyanobacterial blooms in some lakes is the main driver of carbon. These primary producers provide organic carbon that supports heterotrophic bacterial growth and, consequently, a high biomass yield. Under harsh conditions (dry season), cyanobacteria invest in nutrient uptake mechanisms, whereas heterotrophic bacteria allocate energy to survive at the expense of biomass yield. Lakes without cyanobacteria blooms invest in nutrient uptake independent of environmental hostility. This study clarifies the microbial tradeoffs in hostile environments and the impact of this choice on carbon and energy flux in tropical alkaline lakes.",
"conclusion": "Conclusion Therefore, cyanobacterial blooms mediate carbon and flux energy in tropical alkaline lakes. During the dry season, cyanobacteria can adapt to harsh environmental conditions (e.g., high UV irradiation) through CO 2 uptake mediated by the CCM mechanism. As a consequence, these alternative processes for CO 2 fixation could promote alkalization of the water, driving heterotrophic bacteria to adopt a strategy focused on maintaining cellular functioning over the biomass yield. When environmental conditions become more favorable during the wet period, cyanobacteria support bacterial growth. This “cyanobacteria factor” is evident in the CVO lake where cyanobacteria are absent. Independent of the sampling period, the heterotrophic bacterial community inhabiting the CVO lake took up nutrients to support cellular functioning, which compromised biomass yields.",
"introduction": "Introduction Knowledge of the microbial metabolic pathways may be critical for improving the prognosis of the effects of abiotic fluctuations on biogeochemical cycles. Under ever-changing environmental conditions in time and space, microorganisms must constantly adapt their functionality to guarantee continuity 1 , 2 . Life strategies represent sets of traits that tend to correlate physiological or evolutionary tradeoffs with strategies that are favored under different environmental conditions 3 , 4 . Three main microbial life-history strategies were recently described by Malik and coworkers 4 ; a high yield life strategy (Y), a resource acquisition life strategy (A), and a stress tolerance life strategy (S). The high-yield strategist maximizes resource uptake and allocates it to biosynthetic processes, whereas the resource acquisition strategist is based on competition for nutrient acquisition. Stress tolerance involves the production and secretion of components or cellular structures associated with overcoming stress conditions 4 . The Brazilian Pantanal biome (specifically in the Nhecolândia sub-region) contains hundreds of soda lakes with high pH values (ranging from 9.5 to 11) and salinities approaching saturation. In contrast to other Pantanal sub-regions, Nhecolândia soda lakes are mainly influenced by the evaporation ratio (high water evaporation relative to precipitation, with no input from rivers or flooding) 5 – 7 . During the dry season, alkaline lakes remain isolated from the other lakes due to the low permeability of soil horizons (especially the silica layer), promoting solute concentration 8 . However, intensive rainfall (during the wet season) causes a rise in the water level on the subsurface that could connect these lakes, bypassing the silica layer 6 , 8 . Furthermore, these lakes are separated from the regional drainage system by “cordilheiras” (narrow and elongated sand hills covered by savanna vegetation) that are 2–3 m higher than adjacent plains. This barrier maintains alkaline waters and shows a high amount of organic matter that is more dependent on local cycles than terrestrial inputs by annual flooding 9 , 10 . Despite harsh conditions (high pH and salinity), alkaline lakes are remarkably productive because of elevated temperatures and luminosity 11 . These lakes host diverse microbial communities and frequently experience seasonal or permanent cyanobacterial blooms 12 , 13 . These cyanobacterial blooms generate a pulse of organic carbon that affects the carbon turnover 14 . Approximately 20% of photosynthetic carbon is released in aquatic systems, and this organic matter supports a substantial portion of the heterotrophic community 15 – 17 . Photosynthetic primary production is the driving force behind nutrient recycling in marine and freshwater environments 16 , 18 . However, the carbon and energy fluxes in alkaline lakes have not been explored. In a hostile environment, it has been suggested that bacterial growth efficiency (BGE) is reduced, and energy is allocated to non-growth reactions that help cells maintain their molecular, cellular, and functional integrity, an equivalent of the S-strategy 4 , 18 . Based on this, we hypothesized that microorganisms try to overcome this harshness during the dry season to allocate energy to the cellular maintenance process. The improvement in these environmental conditions during the wet season allows the microorganism to invest in nutrient uptake. To test this hypothesis, we evaluated four shallow, alkaline lakes on the Pantanal during two seasons, wet and dry, by applying metagenomics and flow cytometry.",
"discussion": "Discussion The associated microbiome of alkaline lakes has been described in several ecosystems worldwide. In general, the major phyla associated with alkaline lakes were Actinobacteria, Bacteriodetes, Chloroflexi, Cyanobacteria, Firmicutes, Planctomycetes, Proteobacteria, Spirochaetes, Tenericutes, and Verrucomicrobia, a similar bacterial community composition observed on this study 12 , 19 . This similar frequency of bacterial community composition could be associated with niche conservatism, whereby species present traits that allow them to cope with certain environmental conditions 20 . However, this bacterial composition pattern was not static. It is possible to detect fluctuations in bacterial abundance and composition over time and space. This shift is associated with the selection of populations that are more suitable for the given abiotic conditions 1 . During the dry season, it was possible to observe an increase in nutrient and ion concentrations owing to the water evaporation rate. This increase in nutrient concentrations allows organisms to grow in environments where resources are abundant 21 . Usually, these organisms have high growth and metabolic rates, which can be disadvantageous in stable, nutrient-poor environments 21 . During the dry season, a differential abundance of Firmicutes, Nitrospirae, and Tenericutes was observed. Specifically, Nitrospirae was abundant in Lake OT, where water showed a high concentration of particulate material resulting in a black color (low irradiance). During this season, this lake had a high concentration of nitrite and nitrate, which are important elements associated with the physiology of this bacterial group 13 . Organisms belonging to the phylum Tenericutes are frequently described as obligate symbionts because of their small genomes. However, they are resistant to osmotic lysis and show an enrichment in DNA repair mechanisms on their genomes, a feature associated with stress tolerance 22 . Interestingly, Tenericutes was positively correlated with chlorophyll-a, indicating a possible association with phytoplankton (mainly cyanobacteria). However, the increase in rainfall during the wet season results in the dilution of nutrients in alkaline lakes, promoting a shift in bacterial abundance and composition. The reduction in nutritional status selects for organisms well-adapted to nutrient-poor environments, where resource uptake is prioritized in relation to biomass growth 21 . Chlorobi, Planctomycetes, and Verrucomicrobia were enriched during this period. Although the nutritional status was reduced during the wet season, the pH increased slightly. This increase in pH promotes an increase in organic carbon availability, which could stimulate Planctomycetes metabolism 23 , 24 . Planctomycetes have slow growth taxa that compensate for this through the efficient use of organic matter 24 . This increase in pH could be associated with the carbon concentration mechanism (CCM). Some autotrophic organisms, especially Cyanobacteria, enhance carbon fixation during photosynthesis through the uptake of inorganic carbon (CO 2 , HCO − 3 , and CO 2 –3 ) 25 , 26 . As a result, the environmental pH increases owing to the excretion of OH- and the generation of pericellular CaCO 3 precipitation 27 , 28 . Cyanobacteria are described as environmental engineers because they have strong effects on higher trophic levels and ecosystems functioning as critical drivers of bacterial assembly. Microorganisms exhibit versatile metabolism, and this variability modulates the organization and functioning of communities. The trait-based approach, which analyses trait variation, is widely being adopted in ecology because it can clarify the microbial adaptations that permit the colonization of a specific niche and how these organisms will respond to environmental change 29 , 30 . Some criteria have been suggested to organize and classify the traits in the function of different microbial lifestyle strategies. However, this is not a consensus, and it is continuously updating 4 , 31 . Resource utilization and competition for nutrients are important factors that shape phytoplankton communities 32 , 33 . Resource availability in the aquatic environment is directly associated with spatiotemporal variations and is dependent on the quantity and quality of these resources. During the dry season, the bacterial community preferentially adopted an A-strategy [except for ET(08SR) lake], wherein the bacterial groups enhance nutrient acquisition at the expense of growth yield 4 . Although a high nutrient concentration was promoted by the high evaporation ratio, the quality and availability of these nutrients could be low, enhancing the necessity to efficiently uptake nutrients rather than microbial biomass production. Notably, the tradeoff between nutrient uptake and biomass production was modified if the target was exclusively the heterotrophic bacterial community. Heterotrophic bacteria preferentially adopt an S-strategy when subjected to hostile environments. This tradeoff results in a direct energy flux for cell maintenance at the expense of bacterial growth efficiency (BGE). This mechanism is well known in freshwater and marine environments 18 . Therefore, the adoption of the A-strategy by the whole bacterial community during the dry season is predominantly associated with cyanobacteria. The CCM mechanism described above is an important process of CO 2 uptake and an important strategy for adapting to the major changes in CO 2 availability that can be encountered during cyanobacteria blooms 34 . However, these microbial tradeoffs change during the wet season, and this change represents niche differentiation among species, which emerges from individual-level constraints within an environmental context 35 . During the wet season, microbial communities adopt the Y-strategy, especially those inhabiting lakes with blooms. Primary productivity drives the energy flux through food webs, supporting the respiration and yield of heterotrophic bacteria 36 . Lakes with cyanobacterial blooms showed eutrophic characteristics, such as a high concentration of carbon, nitrogen, and phosphorus nutrients, which stimulate the microbial community to grow and consequently increase biomass production 37 ."
} | 3,105 |
35981004 | PMC9426924 | pmc | 5,220 | {
"abstract": "A general method to infer both positive and purifying selection during the real-time evolution of hypermutator pathogens would be broadly useful. To this end, we introduce a Simple Test to Infer Mode of Selection (STIMS) from metagenomic time series of evolving microbial populations. We test STIMS on metagenomic data generated by simulations of bacterial evolution, and on metagenomic data spanning 62,750 generations of Lenski’s long-term evolution experiment with Escherichia coli (LTEE). This benchmarking shows that STIMS detects positive selection in both nonmutator and hypermutator populations, and purifying selection in hypermutator populations. Using STIMS, we find strong evidence of ongoing positive selection on key regulators of the E . coli gene regulatory network, even in some hypermutator populations. STIMS also detects positive selection on regulatory genes in hypermutator populations of Pseudomonas aeruginosa that adapted to subinhibitory concentrations of colistin–an antibiotic of last resort–for just twenty-six days of laboratory evolution. Our results show that the fine-tuning of gene regulatory networks is a general mechanism for rapid and ongoing adaptation. The simplicity of STIMS, together with its intuitive visual interpretation, make it a useful test for positive and purifying selection in metagenomic data sets that track microbial evolution in real-time.",
"introduction": "Introduction Organisms often evolve defects in DNA repair and recombination pathways that cause very high mutation rates. Hypermutability is readily observed in cancer [ 1 – 3 ] and in opportunistic pathogens attacking immunocompromised individuals [ 4 ]. Hypermutability is also associated with the evolution of antibiotic resistance, including multi-drug-resistant tuberculosis [ 5 ]. Hypermutability can increase the rate of beneficial mutations, but it can also obscure the genomic basis of adaptation [ 6 ], because vast numbers of nearly-neutral mutations hitchhike to high frequencies with the beneficial mutations that drive the selection dynamics. In this regime, called “emergent neutrality” [ 7 ], it is challenging to identify selection on particular genes, unless those genes are under very strong selection [ 8 , 9 ]. As such, there is a need for methods that can resolve positive and purifying selection in hypermutator populations, due to their importance in basic research, genetic engineering, and medicine. To address this research gap, we present a Simple Test to Infer Mode of Selection (STIMS) from metagenomic time series of large asexual hypermutator populations, and use this method to study positive and purifying selection in bacterial populations that evolved hypermutability during long-term experimental evolution. We developed STIMS in the context of Lenski’s long-term evolution experiment with Escherichia coli (LTEE) [ 10 , 11 ]. The LTEE has become an important test bed for many fundamental questions in evolutionary biology, due to its simplicity (daily serial transfer of twelve populations) and comprehensive record of frozen bacterial samples. Previous studies have used the LTEE as a model system to study the tempo and mode of both genomic [ 12 – 16 ] and phenotypic evolution [ 10 , 17 – 22 ]. Many of those previous studies focused on the evolutionary dynamics underlying adaptive evolution. The evolution of hypermutability in six of the LTEE populations has obscured the genomic signatures of adaptation [ 6 , 12 , 15 , 16 , 23 ], again due to emergent neutrality. STIMS gains statistical power to infer selection despite emergent neutrality, by aggregating mutations within a focal set of genes, counting their occurrence, then comparing the counts to a background distribution of the number of mutations per gene set. Researchers can use STIMS to test genes that cluster by co-expression, interaction, or function [ 24 – 28 ] for aggregate signatures of selection. This work expands on previous observations suggesting the action of purifying selection on hypermutator populations of the LTEE [ 21 ]. In that work, we found that aerobic-specific genes were depleted in mutations compared to randomized sets of genes in three hypermutator populations: Ara−2, Ara+3, and Ara+6. That finding suggests that aerobic-specific genes may have transitioned from positive selection to purifying selection in those LTEE populations [ 21 ]. Our work also complements recent findings of purifying selection affecting protein evolution [ 29 ] and protein-protein-interaction network evolution in the LTEE [ 30 ]. Here, we demonstrate that STIMS recovers signals of positive and purifying selection in evolutionary simulations of haploid populations under a Wright-Fisher model, and show that STIMS recovers signals of positive and purifying selection on gold-standard sets of genes with a priori evidence of those selection pressures in the LTEE. We then use STIMS to examine the tempo of molecular evolution in modules of co-regulated genes [ 24 – 26 ], and to generate testable hypotheses about the mode (i.e. purifying or positive selection) of evolution on those gene modules in the LTEE. Finally, we use STIMS to test for positive selection on regulatory genes in a 26-day evolution experiment involving adaptation of hypermutator Pseudomonas aeruginosa to subinhibitory concentrations of colistin, an antibiotic of last resort. These results indicate that STIMS is a useful and general test for selection, even over relatively short timescales of molecular evolution.",
"discussion": "Discussion We show that signals of both positive and purifying selection can be detected in metagenomic time series of large asexual haploid populations, including Escherichia coli from Lenski’s long-term evolution experiment (LTEE) and a clinically relevant pathogen, Pseudomonas aeruginosa , in a short-term evolution experiment under antibiotic selection. In contrast to previous studies, which examined global patterns in the tempo and mode of evolution in the LTEE [ 13 , 15 , 16 ], our analysis focuses on functional modules encoded by the E . coli genome. We find that the tempo of evolution in particular molecular subsystems gives us insight into the mode of evolution acting on those modules (i.e., relaxed, purifying, or positive selection). We ran simulations to demonstrate that STIMS works in an idealized system in which selection pressures and the genomic DFE can be pre-specified a priori , and we ran computational positive control experiments to confirm that STIMS works on genes with prior evidence of relaxed ( Fig 5 ), purifying ( Fig 6 ), and positive selection ( Fig 7 ). Our findings indicate that the accelerated pace of genomic evolution in the hypermutators, combined with the detailed record of molecular evolution provided by metagenomic time series, may open new opportunities for understanding the genomic basis of adaptive evolution, even though the signal of selection is obscured in hypermutator genomes due to genomic draft [ 6 ]. First, the vast number of nearly neutral hitchhikers that are observed in hypermutator populations provides insight into how mutations in modifier alleles affect genome-wide and local mutation rates and biases [ 33 ]. Second, regions of the genome that are highly depleted in mutations in the hypermutator populations are strong candidates for purifying selection. Third and most importantly—metagenomic sequencing gives deep sampling of genetic variation off the (eventual) line of descent. We found compelling evidence of purifying selection in the hypermutator populations of the LTEE. Many of the strongest candidate genes for purifying selection are deeply conserved over evolutionary time, such as those encoding ribosomal subunits. The action of purifying selection on the hypermutator populations is also consistent with the observation that antimutator alleles have fixed in several populations of the LTEE [ 33 , 38 ], and with the experimental finding that overexpressing RNA chaperones in some hypermutator LTEE strains reduces the mutation load in those strains [ 39 ]. Together, these observations suggest that hypermutator lineages are accumulating some deleterious passenger mutations, even as their fitness continues to increase. In any case, our analysis shows that the depletion of mutations in particular genes is not due to the evolution of antimutator alleles: by bootstrapping a null distribution of background rates, STIMS controls for the genome- and population-wide effects of antimutator alleles over time. The resampling approach used by STIMS can also take the effects of local mutation biases into account [ 40 ]. For instance, one can control for local mutation biases by constructing null distributions that directly model chromosomal variation in mutation rates and biases. This can be somewhat complicated, given that mutational biases [ 6 ] and regional mutation rates over the chromosome have evolved idiosyncratically across the replicate LTEE populations [ 33 ]. Our implementation of STIMS samples gene sets uniformly over the E . coli genome: we also implemented a sampling procedure that take the wave pattern of mutation rate variation over the E . coli chromosome into account [ 33 , 41 , 42 ], but this more complicated procedure produced the same results as sampling genes uniformly over the chromosome ( Materials and Methods ). When we applied STIMS to different module decompositions of the E . coli genome, we found compelling evidence of strong positive selection on key global regulators of the E . coli gene regulatory network, especially in comparison to the genes that they regulate. Furthermore, we found an excess of mutations in the cis -regulatory regions of those regulators in comparison to the genes that they regulate. One explanation could be that mutations that affect the cis -regulation and structure of global regulators at higher levels of the GRN cause a cascade of effects on downstream targets, and so are more effective targets for fine-tuning the E . coli GRN. Strikingly, we found evidence of continued strong positive selection on key regulatory genes in the two populations with the most mutations in the LTEE: Ara+3 and Ara+6 ( Fig 8 ). By contrast, two nonmutator populations, Ara−5 and Ara−6, showed no mutations at all in I-modulon regulators in the last 20,000 generations of the time course. This suggests that the GRN may initially evolve close to some local fitness maximum (subject to pleiotropic constraints), but then evolve further to compensate for the effects of mutations elsewhere in the genome. Further work will be needed to test the hypothesis that ongoing evolution of I-modulon regulators is related to compensatory evolution. It is possible that the hypermutator populations are evolving to compensate for an increasing mutation load of deleterious hitchhiker mutations [ 8 ]. The appearance of multiple antimutator alleles in the LTEE suggests that that hypermutability has a hidden cost [ 15 , 33 , 38 ], but the magnitude of any mutation load in the LTEE populations remains unknown. Ongoing positive selection on the E . coli GRN could also be due to compensatory evolution that is not specifically for hitchhiking deleterious mutations. For instance, it is possible that early beneficial mutations become deleterious due to further mutations [ 43 , 44 ]. Furthermore, natural selection may greedily favor mutationally accessible but suboptimal trajectories [ 45 , 46 ] that then open new, idiosyncratic paths for further refinement [ 47 ]. Our work has some limitations. By using STIMS to examine the tempo of GRN evolution in the LTEE using the same dataset, we find a number of patterns with an unknown rate of false positives. Therefore, future work is needed to test the specific hypotheses that we have generated, either by using additional time-course data from the LTEE, by analyzing related evolution experiments, or by experimental validation. An important caveat is that deletions that fix in the LTEE can lead to spurious inferences of purifying selection, if they are not taken into account, since deleted genes cannot accumulate mutations. By that same token, gene duplications, amplifications, or other forms of copy number variation could lead to spurious inferences of positive selection, or elevated mutation rates [ 48 , 49 ]. Those types of mutations appear to be rare in these data, in comparison to the point mutations, indels, and transposon insertions that we count. The complications induced by copy number variation may need to be considered (or safely ignored), depending on the context in which STIMS is used. Another limitation of STIMS is that any given set of genes may be composed of subsets of genes evolving under very different selection pressures. For instance, a query set of 200 genes may be composed of 100 genes evolving under positive selection, and 100 genes evolving under purifying selection. Depending on the relative strength of selection on the underlying sets of genes within the query, STIMS might show no signal, positive selection, or negative selection on aggregate. Statistically rigorous approaches to solving this problem are needed; this is one direction for future research. Finally, STIMS is computationally intensive due to its use of bootstrapping. Often equivalent statistical results can be derived much faster, using exact tests like the binomial test or Fisher’s exact test [ 8 ], or by using tests based on the Poisson distribution [ 36 , 50 ]. The advantage of STIMS is that it provides greater biological insight, by visualizing how statistical signatures of positive and purifying selection change in an evolving population over time. In addition, we caution researchers using STIMS that with great statistical power comes great statistical responsibility. We recommend that researchers use STIMS to test pre-specified hypotheses, with query gene sets that are chosen a priori based on independent considerations. For instance, we hypothesized that I-modulon regulators would show different patterns of evolution than genes regulated within I-modulons, and then used STIMS to test this hypothesis. Researchers can also use STIMS to find patterns in data, because new observations are the foundation for new hypotheses. However, we emphasize that any patterns discovered with STIMS should be validated using additional, independent datasets, before claiming biological significance. For instance, by using STIMS to examine the evolutionary tempo of each E. coli I-modulon in the LTEE, we find a number of patterns with an unknown rate of false positives ( S3 Table and S1 File ). Additional work is needed to test these specific hypotheses, either by using additional time-course data from the LTEE, by analyzing related evolution experiments, or by experimental validation. It is also important to keep in mind that if one uses STIMS as a data mining tool by testing many gene sets, then multiple testing is a potentially serious issue, especially if those gene sets overlap with each other. Overall, our work provides greater insight into the mechanistic connection between genotypic and phenotypic evolution in evolution experiments like the LTEE. The genetic architecture underlying fitness improvements in the LTEE likely involves a relatively small number of loci that control global aspects of cellular physiology [ 15 , 16 , 23 , 51 ]. These factors may control many downstream pathways, which are being modulated in response to selection in the LTEE. Hence, we hypothesize that the regulation of these pathways is being rewired during the LTEE, often without significantly changing their downstream effectors. This would explain why the modules that show the strongest changes in expression, and best predict growth rate and fitness in response to environmental conditions [ 24 – 26 ] show little evidence of adaptive evolution in the LTEE. Overall, the genotype-phenotype map of E . coli , at least in the context of the LTEE, resembles the hierarchical “supervisor-worker” gene architecture proposed by Chen and colleagues [ 52 ] to explain the genetic architecture of quantitative traits in Saccharomyces cerevisiae . As the costs of protein production exerts a strong contrast on cellular energetics, the proper allocation of proteome resources in order to maximize growth, rather the evolution of particular genetic modules, may be the cellular phenotype under strongest selection in the LTEE [ 53 – 56 ]. Future work could test this hypothesis by trying to mimic evolutionary changes in the LTEE by directly perturbing the balance of global physiological and metabolic state variables (chromosome conformation, ppGpp, cAMP levels and redox potential) to maximize growth in the ancestral REL606 strain. Coevolution between the function of those proteome resources, pleiotropic constraints on optimal allocation, as well as feedback with the environment could all play a part in causing the open-ended increases in fitness seen in the LTEE [ 20 , 37 ]. Finally, we expect our methodology will be broadly useful, especially for workers in the experimental evolution field. To demonstrate the generality of STIMS, we reanalyzed data from a second evolution experiment in which two replicate hypermutator populations of P . aeruginosa adapted to subinhibitory concentrations of colistin [ 8 ]. Our finding of positive selection on regulatory genes in this experiment validates STIMS as a general method and indicates that the rapid evolution of bacterial gene regulatory networks may be a general mechanism for adaptation during experimental evolution, and perhaps during adaptation to novel environments in general. We expect that our basic idea could be more rigorously justified by deeper theoretical work, and further extended to study evolving populations and communities in both the laboratory and in the wild. Purifying selection is of particular interest in the study of viruses and cancers, for the sake of finding conserved and effective drug targets [ 57 – 60 ]. In particular, we anticipate that STIMS could be applied to clinical time series, such as genomic sampling from cystic fibrosis patients [ 61 , 62 ], in order to discover gene modules that are under selection in pathogens as they adapt to their host [ 63 , 64 ]."
} | 4,585 |
27025898 | PMC4820995 | pmc | 5,221 | {
"abstract": "Being the only sustainable source of organic carbon, biomass is playing an ever-increasingly important role in our energy landscape. The conversion of renewable lignocellulosic biomass into liquid fuels is particularly attractive but extremely challenging due to the inertness and complexity of lignocellulose. Here we describe the direct hydrodeoxygenation of raw woods into liquid alkanes with mass yields up to 28.1 wt% over a multifunctional Pt/NbOPO 4 catalyst in cyclohexane. The superior performance of this catalyst allows simultaneous conversion of cellulose, hemicellulose and, more significantly, lignin fractions in the wood sawdust into hexane, pentane and alkylcyclohexanes, respectively. Investigation on the molecular mechanism reveals that a synergistic effect between Pt, NbO x species and acidic sites promotes this highly efficient hydrodeoxygenation of bulk lignocellulose. No chemical pretreatment of the raw woody biomass or separation is required for this one-pot process, which opens a general and energy-efficient route for converting raw lignocellulose into valuable alkanes.",
"discussion": "Discussion We have presented a one-pot catalytic process for the direct production of liquid alkanes from a wide variety of raw woody biomass over Pt/NbOPO 4 catalyst with excellent mass and carbon yields in a cyclohexane medium. This one-pot approach avoids the separation of raw biomass into isolated components and the use of an alkane solvent further simplifies downstream separation, as the alkane products can be used as solvents for the next run. The exceptional activity of the Pt/NbOPO 4 catalyst enabled direct conversion of raw woody biomass into liquid alkanes under mild conditions (190 °C) over a single multifunctional catalyst. The superior efficiency of this catalyst for direct hydrodeoxygenation of lignocellulose is found to originate from the synergistic effect between Pt, NbO x species and acidic sites. This brand new one-pot route requires no chemical pretreatment or separation of the raw woody biomass and thus tremendous energy savings can be potentially gained in comparison to the existing thermochemical and hydrolysis-based approaches for production of liquid fuels and chemical feedstocks from lignocellulose."
} | 562 |
37389550 | PMC10485807 | pmc | 5,222 | {
"abstract": "Contact electrification\nis an interfacial process in which two\nsurfaces exchange electrical charges when they are in contact with\none another. Consequently, the surfaces may gain opposite polarity,\ninducing an electrostatic attraction. Therefore, this principle can\nbe exploited to generate electricity, which has been precisely done\nin triboelectric nanogenerators (TENGs) over the last decades. The\ndetails of the underlying mechanisms are still ill-understood, especially\nthe influence of relative humidity (RH). Using the colloidal probe\ntechnique, we convincingly show that water plays an important role\nin the charge exchange process when two distinct insulators with different\nwettability are contacted and separated in <1 s at ambient conditions.\nThe charging process is faster, and more charge is acquired with increasing\nrelative humidity, also beyond RH = 40% (at which TENGs have their\nmaximum power generation), due to the geometrical asymmetry (curved\ncolloid surface vs planar substrate) introduced in the system. In\naddition, the charging time constant is determined, which is found\nto decrease with increasing relative humidity. Altogether, the current\nstudy adds to our understanding of how humidity levels affect the\ncharging process between two solid surfaces, which is even enhanced\nup to RH = 90% as long as the curved surface is hydrophilic, paving\nthe way for designing novel and more efficient TENGs, eco-energy harvesting\ndevices which utilize water and solid charge interaction mechanism,\nself-powered sensors, and tribotronics.",
"conclusion": "6 Conclusions We have shown that distinct\ninteractions\nexist between different\ncombinations of colloidal probes and substrates, namely, (i) hydrophilic–hydrophilic,\n(ii) hydrophobic–hydrophilic, (iii) hydrophilic–hydrophobic,\nand (iv) hydrophobic–hydrophobic, as a function of relative\nhumidity. As expected, the capillary force dominates the adhesion\nbetween a hydrophilic silica probe and a hydrophilic substrate, whereas\nthe adhesion remains approximately constant as a function of the relative\nhumidity as soon as a hydrophobic material is involved. As the RH\nincreases, in three of the four cases (situations i, ii, and iii)\na clear increasing dependence is observed in the contact electrification\nvoltage. These findings confirm the influence of humidity, and especially\nthe presence of anions and cations in water, on the charging process\nbetween two materials, specifically nonionic insulators. Our results\nconfirm that electron transfer is the primary mechanism for contact\nelectrification for the hydrophobic–hydrophobic combination,\nas no change in contact electrification voltage is measured with varying\nhumidity levels. The colloidal probe configuration enables examining\nthe contact\nelectrification in <1 s at a higher relative humidity (RH >\n40%)\nbecause of the controlled retraction mechanism, which reduces the\ninfluence of capillary forces. This allows studying the charging process\nitself, revealing that the charging time constant is strongly decreasing\nwith increasing relative humidity, similar to the charge relaxation\ntime constant. In addition, we show for the first time that the curved\nsurface (of the colloidal probe) enhances the charging process between\nthe surfaces because wet and dry patches are present on curved surfaces\n(even at high relative humidity), which drives the ion exchange. We\nenvision that the colloidal probe technique can serve as a promising\nplatform in studying the charging process and the concomitant development\nof more efficient miniaturized energy harvesters, e.g., various adaptations\nof TENGs, needed in our collective effort to transition towards a\ngreen industry.",
"introduction": "1 Introduction Contact\nelectrification (CE) or triboelectric charging is the process\nof exchanging electrostatic charges when two surfaces are in contact.\nHowever, the exact mechanism at the heart of this phenomenon is still\nunder debate. For insulators, even the nature of the charge carrier\nassociated with contact charging has not been settled. 4 , 48 Basically, three kinds of charge transfer mechanisms are proposed:\n(i) electron transfer, (ii) ion transfer, 1 and (iii) transfer of material. 2 The\nreason that a unifying mechanism explaining the tribocharging is missing\ncan be ascribed to the fact that the electrostatic interactions between\nsurfaces are highly complex as they hinge on material, 3 , 4 size, 5 electrical properties, surface\nproperties, 6 and relative humidity (RH)\nas well. 7 , 8 Understanding the contact electrification\nmechanism at the micro-\nand nanoscale is pivotal, as it is currently leveraged in many energy\napplications, e.g., in triboelectric nanogenerators (TENGs), introduced\nby the Wang group in 2012, 9 , 10 which received great\nattention as a new energy harvesting application, such as mechanical\nenergy harvesting, self-powered sensing, and tribotronics. Since TENG-based\nportable and wearable electronic devices will usually operate in varying\nenvironmental conditions across the globe, relative humidity is one\nof the most studied factors that affect tribocharging. 11 − 14 The power output is enhanced with increasing RH until reaching a\nmaterial-dependent optimum that typically lies around 40% RH. 8 , 13 , 15 Above this optimum, the electric\noutput decreases with increasing RH, which can be ascribed to water\npresent at the interface. As the RH changes, water molecules adsorb\non the surface, transforming the contact configuration from a single\nsolid–solid interface to a double solid–liquid interface. 1 , 13 , 16 − 22 Consequently, the charge transfer mechanism also changes; from electron\ntransfer (at solid–solid interactions) to a combination of\nboth electron and ion transfer (at solid–liquid interfaces). 10 , 23 Given that water has the ability to charge solid surfaces upon contact,\nit is thriving as a promising strategy for the massive development\nof solid–liquid TENGs, droplet-based TENGs, moisture-enabled\nelectric nanogenerators, 24 and generation\nof hydrogen peroxide, 25 to harvest green\nand renewable electricity from the abundantly present water on Earth. A key obstacle when studying solid–liquid–solid interacting\nconfigurations is that surfaces tend to stick to one another when\nonly hydrophilic surfaces are involved at high humidity levels. Consequently,\nsurfaces can not be released, and the electrostatic charging process\nat high humidity levels is challenging, if not impossible. To overcome\nthis limitation, we utilize the colloidal probe technique for the\nfirst time to investigate the electrostatic interaction induced by\nCE by immediately measuring (<1 s) the contact electrification\nvoltage between a hydrophilic silica or hydrophobic polystyrene colloidal\nprobe and various hydrophilic uncoated or hydrophobic fluorocarbon-coated\nsubstrates as a function of the relative humidity up to 90%. In most\nmaterial combinations, a clear increasing dependence is observed between\nthe contact electrification voltage and increasing RH. In contrast\nto previous studies 7 , 8 , 13 , 16 , 26 also above\nthe typical optimum of 40–50% RH, an increase in contact charge\nis observed in this study when the spherical probe is hydrophilic\nas opposed to when a flat surface in the form of a plateau tip is\nused. From a fundamental perspective, this is a valuable result as\nit is indicative of a mechanism in which patches on surfaces contribute\nto contact electrification, 4 , 27 , 28 as plausibly wet and dry patches are present on the curved colloidal\nprobe leading to an enhancement of the electrostatic interaction between\nhydrophilic colloidal probes and flat substrates. On the application\nside, our results are obtained in a similar fashion as contact-separation\n(CS) operating TENG devices, CS-TENGs, are in agreement with other\nstudies that show that the performance of TENG devices can be enhanced\nin high humid conditions (RH = 90%) when hydrophobic sliding friction\nlayers in DC TENGs are used, 29 or cellulose-based\nsurfaces. 30 In addition, the influence\nof the contact time on the charging behavior between the two materials\nis investigated, showing that not only the charge relaxation time\nconstant is dropping with RH but also the time constant of charging.\nIn contrast to room-temperature experiments, measurements performed\nat elevated temperatures close to the water’s boiling point\nshowed that the charging is constant and lower than that at room temperature.\nThe work presented here explores the contact electrification of a\ndynamically changing solid–liquid–solid interface and\naddresses the pressing matter of the influence of surface water on\nthe charging process of DC TENGs, very recently posed by Lyu and Ciampi, 31 and other energy harvesting applications exploiting\nthe water–solid electrification mechanism."
} | 2,198 |
36547355 | PMC9778368 | pmc | 5,225 | {
"abstract": "Generated by a hierarchical and multiscale self-assembling phenomenon, peptide-based hydrogels (HGs) are soft materials useful for a variety of applications. Short and ultra-short peptides are intriguing building blocks for hydrogel fabrication. These matrices can also be obtained by mixing low-molecular-weight peptides with other chemical entities (e.g., polymers, other peptides). The combination of two or more constituents opens the door to the development of hybrid systems with tunable mechanical properties and unexpected biofunctionalities or morphologies. For this scope, the formulation, the multiscale analysis, and the supramolecular characterization of novel hybrid peptide-polymer hydrogels are herein described. The proposed matrices contain the Fmoc-FF (N α -fluorenylmethyloxycarbonyl diphenylalanine) hydrogelator at a concentration of 0.5 wt% (5.0 mg/mL) and a diacrylate α-/ω-substituted polyethylene-glycol derivative (PEGDA). Two PEGDA derivatives, PEGDA 1 and PEGDA2 (mean molecular weights of 575 and 250 Da, respectively), are mixed with Fmoc-FF at different ratios (Fmoc-FF/PEGDA at 1/1, 1/2, 1/5, 1/10 mol/mol). All the multicomponent hybrid peptide-polymer hydrogels are scrutinized with a large panel of analytical techniques (including proton relaxometry, FTIR, WAXS, rheometry, and scanning electronic microscopy). The matrices were found to be able to generate mechanical responses in the 2–8 kPa range, producing a panel of tunable materials with the same chemical composition. The release of a model drug (Naphthol Yellow S) is reported too. The tunable features, the different topologies, and the versatility of the proposed materials open the door to the development of tools for different applicative areas, including diagnostics, liquid biopsies and responsive materials. The incorporation of a diacrylate function also suggests the possible development of interpenetrating networks upon cross-linking reactions. All the collected data allow a mutual comparison between the different matrices, thus confirming the significance of the hybrid peptide/polymer-based methodology as a strategy for the design of innovative materials.",
"conclusion": "3. Conclusions Short and ultrashort peptides have been identified as manageable, tunable, and versatile building blocks for the fabrication of supramolecular systems. The features of peptides allow the combination, in the same system, of two or more molecular constituents, which can also differ in chemical characteristics. In the case of the study herein reported, for the first time, the development of mixed peptide-polymer matrices using Fmoc-FF and two different polydisperse diacrylate-capped PEGs (PEGDA, specifically PEGDA 575, PEGDA1, and PEGDA 250, PEGDA2) was evaluated. A solvent switch methodology was used for the hydrogel formulation, and different molar ratios of peptide/polymer (1/1, 1/2, 1/5, and 1/10 mol/mol) were evaluated. Both the polymer molecular weight and abundance were found to be able to modify the features of the hybrid materials in terms of water content, surface topology, stability, and model drug release (Napthol Yellow S) profiles. The supramolecular organization of the hydrogels is dictated by Fmoc-FF self-assembling, as supported by CD, FTIR, and WAXS analysis. Additionally, by simply modifying the quantity of the inserted polymer, a range of mechanically multivalent materials can be formulated, and each of them is easily adaptable to the desired application scope. The rheological analysis pointed out the versatility of the proposed matrices that, even if formed from the same chemical constituents, present different mechanical response. This evidence suggests the potential use of these materials in different application areas. Collectively, the data reported make possible a mutual comparison between the hybrid systems and the pure components, thus confirming the peptide-based approach as an easy, modulable, and accessible strategy for the design of novel nanostructured materials. The capability of these hydrogels to encapsulate NYS, as a very preliminary model drug, demonstrates the proof of concept that they can potentially serve as drug reservoirs for drug delivery applications. However, other important issues such as the hydrogel biocompatibility (at their different molar ratios) and the choice of the drug (in terms of net charge, lipophilicity, and molecular weight) have to be investigated before an in vivo application. Finally, the presence of diacrylate functionalities in the supramolecular Fmoc-FF hydrogel network could be employed to promote a photo-activated cross-linking reaction, thus providing the generation of interpenetrating networks (IPNs) with a higher organization level in the system.",
"introduction": "1. Introduction Hydrogels (HGs) are hydrophilic materials structurally characterized by a three-dimensional (3D) network originated by the macroscopic organization of polymer chains or of supramolecular elements [ 1 , 2 , 3 ]. The ability of these matrices to retain a large amount of aqueous fluid (~95/99%) confers on them a non-Newtonian flow behavior associated with a self-supporting tendency. The physicochemical properties of hydrogels (e.g., durability, reproducibility, and bio-restorability) and their resemblance to the human body tissue microenvironment were found to be determinant features for their application in both the biotechnological and industrial fields, including drug delivery [ 4 ], optoelectronics [ 5 ], water purification [ 6 ], and tissue engineering [ 7 ]. Peptide-based low-molecular-weight gelators (LMWGs) are a typical class of molecular entities that can be used for the generation of self-supporting three-dimensional gel networks [ 8 , 9 , 10 ]. The interest in this class of LMWGs is related to the advantages they offer with respect to natural or synthetic polymers. For instance, peptides can easily be synthetized and modified; moreover, they exhibit good biocompatibility profiles and moderately inexpensive manufacturing procedures. Additionally, in comparison to covalently-based polymeric hydrogels, the peptide gelation phenomenon is a multiscale process, which allows one to avoid crosslinking agents. Specifically, peptide-based LMWGs are designed as self-assembling sequences able to produce fibrillary aggregates [ 11 , 12 , 13 ]. These latter, above a critical gelation concentration (CGC), undergo a mutual physical cross-link. The final non-covalent entanglement leads to further association in a space-spanning network, giving rise to the macroscopic self-supporting gels. Fmoc-FF (N α -fluorenylmethoxycarbonyl-diphenylalanine, Figure 1 A) represents a typical case of peptide-based LMWGs [ 14 , 15 , 16 , 17 , 18 , 19 ]. HGs of Fmoc-FF can be obtained under physiological conditions (pH, temperature, and ionic strength) without the use of cross-linking agents by using different methodologies [ 20 ]. Since its identification in 2006 [ 14 , 15 ] as a suitable building block for the preparation of hydrogels, Fmoc-FF has been deeply studied for many biomedical applications [ 21 , 22 , 23 , 24 ]. The addition of other components (like natural or synthetic polymers [ 25 ], peptides [ 26 , 27 ], polysaccharides [ 28 ], or dyes [ 29 ]) to Fmoc-FF has been found to improve and tune the physiochemical features of the final material. At the state of the art, co-assembly of two different chemical entities can sponsor the formation of novel functional materials with improved mechanical or biocompatibility profiles [ 30 , 31 , 32 ]. By way of examples, Fmoc-FF/pentafluorinate Fmoc-F [ 33 ] and Fmoc-FF/Phe-Tyr-containing peptides [ 34 ] systems have been recently formulated and proposed as innovative all-peptidic scaffolds for both tissue regeneration and drug delivery applications. The incorporation of different kinds of polymers was also estimated by studying hybrid Fmoc-FF-based matrices containing chitosan, polyethylene-glycol (PEG) [ 35 ], or polyaniline (PAni) [ 36 ]. Specifically, PEG and its α- and/or ω-substituted derivatives are particularly interesting due to their biocompatibility and pharmaceutical and biomedical advantages, including the increase in solubility and the in vivo protection of bioactive macromolecules, antibodies, and oligonucleotides [ 37 , 38 , 39 , 40 ]. After FDA approval, PEGylation has indeed become the method of choice for the delivery of biopharmaceuticals. For this, here is described the formulation and the supramolecular architecture of multicomponent hydrogels containing some derivatives of polyethylene glycol, the PEG diacrylates (PEGDA, Figure 1 A), into the gelled Fmoc-FF matrix to expand the class of hydrogels as biotechnological tools. The advantage of using PEGDA derivatives is the presence in their formula of a diacrylate moiety that, according to opportune synthetic protocols, can undergo a controlled polymerization reaction. For this study, two PEGDAs with different mean molecular weights (PEGDA 575 and PEGDA 250, named PEGDA1 and PEGDA2, respectively) were selected. Additionally, different molar ratios between Fmoc-FF and PEGDA derivatives (1/1, 1/2, 1/5, and 1/10 mol/mol) were studied with the aim of analyzing the effect of both molecular weight and relative polymer abundance on the organization and on the properties of the final hybrid matrix. The gelation process was prompted using the DMSO/H 2 O solvent switch methodology, incorporating PEGDA in the hydrogels during the organic solvent rehydration step. The water behavior in all the matrices was scrutinized, reporting the swelling ratio, the Langmuir frequency, the dehydration curves, and the relaxometric properties. Secondary structuration analysis was conducted recurring to fluorescence, circular dichroism (CD), FTIR spectroscopies, and wide-angle X-ray scattering (WAXS). Moreover, the mechanical properties of hybrid matrices were studied in rheological experiments, and their capability to encapsulate and release a drug was also preliminary evaluated using a fluorescent dye (Naphthol Yellow S) as a model.",
"discussion": "2. Results and Discussion 2.1. Fmoc-FF/PEGDA Matrices Formulation The Fmoc-N α -protected variant of diphenylalanine (FF), Fmoc-FF, represents one of the most studied ultra-short peptide sequences able to efficiently self-organize into β-sheet nanostructured fibrous hydrogels in physiological conditions [ 14 , 15 ]. Due to their easy preparation and long shelf-stability, Fmoc-FF matrices have been proposed as tools for the development of hybrid systems too, thus allowing enlargement of the available biomaterials for nanotechnology applications. Fmoc-FF HGs can be produced using different approaches: solvent switch, pH switch, and the enzymatic method [ 20 ]. Due to the hydrophilic nature of the PEGDA components, the formulation of hybrid Fmoc-FF/PEGDA HGs at 0.5 wt% (5.0 mg/mL) was achieved using the solvent switch procedure. The Fmoc-FF concentration chosen for the present study was 0.5 wt% because it is above the critical gelation concentration (CGC) and is one of the most studied concentrations in literature [ 14 , 15 , 34 ]. According to the solvent-switch method, Fmoc-FF was primarily dissolved in DMSO as the organic phase at a concentration of 100 mg/mL. The resulting solution was then diluted in water (an antisolvent) to the desired final gel concentration. The rehydration triggers gel formation, and a vertexing step promotes efficient mixing of the solvent/antisolvent phases, ensuring sample homogeneity. For mixed Fmoc-FF/PEGDA HGs, the solvent switch procedure was modified to use PEGDA solution instead of water to trigger the hydrogel formation during the DMSO hydration phase. This methodology allows the Fmoc-FF matrix enrichment of polymer with different molar ratios with respect to the peptide component (9.53·10 − 6 mol) by preparing different PEGDA solutions. The Fmoc-FF/PEGDA ratios selected for the study are 1/1, 1/2, 1/5, and 1/10 mol/mol. In all the tested ratios, we can produce self-supporting materials, as testified by the inverted tube test in Figure 1 B,C. The maximum hydrogel ratio formation for PEGDA 1 was found for a 1/50 ratio (see Figure S1 ). On the contrary, no hydrogel formation was observed for PEGDA2 above a ratio of 1/10, indicating the impossibility of forming stable and reproducible PEGDA2 solutions. Fmoc-FF represents a key structural element for the formation of the proposed mixed matrices. Indeed, at the tested concentrations, both PEGDA derivatives are not able to gel alone. Fmoc-FF dipeptide, driving the initial aggregation process, probably allows the formation of PEGDA hydrogels at a concentration value lower than their CGC. As previously reported for pure or multicomponent HGs based on Fmoc-FF, the formation of the self-supporting matrix is associated with an opaque-to-limpid macroscopic transition. This change in optical transparency is attributed to the progressive fibril growth from spherulitic architectures acting as nucleation points [ 41 ]. The same macroscopic behavior was observed for all the tested Fmoc-FF/PEGDA samples. Moreover, no significant variations were detected in the gelation kinetics of multicomponent hydrogels of both PEGDA derivatives (at 1/1, 1/2 and 1/5 ratio) with respect to pure Fmoc-FF (~3 min). A slight increase in the gelation time (~1 min plus) is only detected for 1/10 PEGDAs ratio. As clearly shown in Figure 1 , samples differ in turbidity. Whilst relatively translucent matrices are formed for 1/1 and 1/2 ratios, both PEGDA-containing HGs at 1/5 and 1/10 ratios are opaque. The macroscopic evidence of turbidity is quantified by UV-Vis measurements, looking at the absorbance values at 600 nm. At this wavelength, the light absorption from peptide chromophores is absent, and absorbance values can be ascribed to matrix turbidity, arising from scattering phenomena [ 42 ]. The absorbance values for Fmoc-FF/PEGDA 1 increase from 0.183 to 0.270 a.u. by passing from the ratio of 1/1 to 1/10 one. A similar behavior was also observed for PEGDA 2 containing HGs, but with a slightly lower increase in turbidity (from 0.166 to 0.224 a.u. from 1/1 and 1/10 ratios). The aging of HGs was evaluated over 6 months, keeping the material inverted. All the samples showed very good shelf stability without any macroscopic change in terms of homogeneity, visual appearance, and self-supporting behavior. Only a modest syneresis event (weight lost ~5%) was found for 1/10 ratio samples. 2.2. Water Behavior in Multicomponent HGs Water represents the main component in gelled matrices, and it can be classified as strongly bound, weekly bound, and free (non-bound) water [ 43 ]. The analysis of the water behavior can provide valuable information about the supramolecular organization, permeation properties, pore architecture, and solute diffusion. The water behavior in the proposed multicomponent matrices was characterized using several macroscopic assays (swelling ratio, stability test longitudinal relaxation rate (R1 = 1/T1) as a function of the applied magnetic field. The swelling ratio q at room temperature of each mixed HG was established as the percentage difference between the weight before and after overnight gel incubation in water. Values of q, reported in the Table 1 , increase with the amount of PEGDA in the hybrid materials and with respect to pure Fmoc-FF ( q = 29.7%). Moreover, q values for PEGDA1 were found to be higher than those of the corresponding PEGDA 2 containing matrices. This difference can probably be attributed to the more hydrophilic nature of PEGDA1 polymer respect to PEGDA2. Indeed, the higher number of ethoxy repetitions in PEGDA1 (around 3-fold higher) with respect to PEGDA2 could cause an increase in the number of H-bonding acceptor groups, thus expanding the interactions with water and consequently the swelling properties. Analogously, the stability ratios (Δ W ) were found depending on the PEGDA incorporation amount (see Table 1 ). The matrices’ stability was tested using a Ringer’s solution, which mimics the physiological ionic strength. As clearly shown by Δ W values, all the Fmoc-FF/PEGDA hydrogels are more stable than the pure Fmoc-FF matrix, which has a Δ W = 27.4% in the same experimental conditions. The stability of hybrid hydrogels seems to improve with the increasing percentage of polymer in the gel, thus indicating that the presence of PEG diacrylate in the formulation preserves the materials from degradation. This major stability could also be attributed to the higher rigidity of mixed hydrogels with respect to the pure Fmoc-FF one (vide intra, rheological section). PEGDA incorporation additionally confers on matrices a functional property of water retention, which is evaluated by studying the dehydration phenomenon of both PEGDA at 1/1 and 1/10 mol/mol. As clearly visible in Figure S2 , PEGDA-containing HGs were found to be able to hold back elevated percentages (>92%) of entrapped water with respect to pure Fmoc-FF matrices (<9%). This feature was found to be substantially independent from both PEGDA molecular weight and amount. The unexpected behavior related to water retention can be imputed to the hydrophilic nature of PEGDA, which is able to avoid the evaporation of water by keeping it strongly anchored via non-covalent interactions. Finally, water dynamics in the hydrogel matrix have been investigated using a relaxometric approach. The analysis and quantification of the dynamic parameters related to water motion have often been investigated through the measurement of the applied magnetic field dependence of the relaxation rate (nuclear magnetic resonance dispersion (NMRD) profiles) of different water-containing materials [ 44 , 45 , 46 , 47 , 48 , 49 ]. Fitting of the experimental data in the proton frequency range between 0.01 MHz and 10 MHz allows one to distinguish between slower and faster motions, respectively, associated with water molecules constrained in the hydrogel scaffold or more freely diffusing water. In Figure 2 A,B, the NMRD profiles acquired for Fmoc-FF/PEGDA HGs are reported. In the case of PEGDA1 containing HGs, two additional samples with higher polymer contents (1/20 and 1/30) were investigated. The data were fitted as previously reported, and the parameters extracted from fitting are collected in Table 2 . In general, the here obtained τ1 (faster motion) and τ2 (slower motion) correlation times are rather similar to those previously observed for analogous Fmoc-FF-containing hybrid HGs [ 27 ]. Likewise, the percentage of slow motion (% slow) and the average correlation time (τ C aν ), which can be calculated as previously achieved [ 27 ], are close to the previous values. For both PEGDA1 and PEGDA2, the values of % slow and τ C aν ( Table 2 and Figure 2 C) increase with the amount of PEGDA in the hybrid materials but, quite surprisingly, are considerably lower with respect to pure Fmoc-FF. Moreover, the parameters associated with water mobility were found to be independent from PEGDA molecular weight. 2.3. Secondary Structure Characterization It is well known from the literature that the Fmoc-FF peptide self-assembles into hydrogels with a β-sheet amyloid-like organization of the peptide moiety [ 14 , 15 ]. This structural motif is commonly investigated in peptide nanostructures using a combination of spectroscopic techniques (e.g., circular dichroism (CD) and Fourier transform infrared spectroscopy (FTIR)) and qualitative staining assays with Thioflavin T (ThT) and Congo Red (CR). The effect of PEGDA incorporation in the structural arrangement of the Fmoc-FF network was evaluated by comparing pure and hybrid peptide hydrogels. CD spectra ( Figure 3 A) were acquired for Fmoc-FF/PEGDA hydrogels at 1/1 and 1/10 ratios between 350 and 190 nm and reported as optical density (mdeg/O.D.). The CD signature, deriving from the supramolecular packing of monomers, is related to the configurational alignment of the formed fibers, leading to higher order architectures. Independently from PEGDA molecular weight or their relative amounts, all the spectra show a similar signature, thus suggesting no significant differences in the gel organization and topography. Fmoc-FF/PEGDA systems present some differences in the dichroic signature with respect to the pure Fmoc-FF hydrogel alone [ 34 ], which exhibits a minimum at 220 nm and a broad maximum around 259 nm. Indeed, the spectra of all the PEGDA-doped hydrogels showed four main dichroic signals: two negative peaks (around 209 and 240 nm) and two positive ones (at 231 and at 267 nm). These differences can be attributed to the presence of the PEG moiety since they were observed for other hybrid hydrogels containing Fmoc-FF in combination with PEG-peptide derivatives [ 34 ]. The signal located at 231 nm is generally indicative of β-sheet structuration of the peptide building block in supramolecular architectures [ 50 , 51 , 52 ]. This signal undergoes an hypsochromic effect (228 nm) in 1/10 matrices, imputable to the decreasing of the materials’ ability to absorb light because of the increased turbidity. Instead, the positive and broad band at 267 nm, detectable for all the mixed PEGDA hydrogels, can be attributed to the fluorenyl group on the peptide. The significant bathochromic effect at the typical wavelength (259 nm) can be explained considering the difference in the dielectric constant induced by the incorporation of PEGDA into the hybrid peptide-polymer matrices. The comparison with the CD profile of nude Fmoc-FF suggests a substantial maintenance of gel matrix topology without alteration of the β-sheet organization, with an antiparallel left-twisted structuration [ 53 ]. An additional investigation about the secondary structuration in the final material was carried out by FTIR spectroscopy. The IR spectra of peptides and proteins are described as containing nine different amide bands (I to VII, A and B). These IR signals are generated from both the vibrational contributions of the backbone and of amino acid side chains [ 54 ]. The IR spectra for all the matrices at 0.5 wt% in water are collected in Figure 3 B,C. All the spectra share a common tendency, dominated by only two different bands: (i) an intense signal in the amide A region (~3300 cm −1 ), occurring as a consequence of water exposure of the aggregate with asymmetric and symmetric O-H and N-H stretching and indicative of intermolecular amide-amide bond interactions; (ii) a band in the amide I region (centered at 1636 cm −1 ) related to the presence of β-rich assemblies. This spectral region (ranging between 1700 and 1600 cm −1 associated with the C=O stretching motus) is of relevance for the secondary structuration analysis [ 55 ]. According to this consideration, an absorbance deconvolution was acquired ( Figure 3 D,E). For all the samples, the spectral deconvolution is dominated by a main peak around 1650 cm −1 , conducive to C=O stretching and suggesting the presence of β-sheets secondary structures. Moreover, the additional band at ~1690 cm −1 is typically observed for the antiparallel orientation of the β-strands in assemblies [ 56 , 57 , 58 ]. The common IR signature for all the matrices suggests that the fundamental β-secondary structure organization is not significantly altered by both the PEGDA length and the percentage of polymer incorporation, as supported by CD measurements. The formation of β-sheets rich matrices was further confirmed on xerogels by the thioflavin T (ThT) assay. Thioflavin T (ThT, alternatively named methylene yellow or Basic Yellow 1) is a benzothiazole fluorescent dye regularly used for the quantification of amyloid and amyloid-like fibrils. Binding amyloid-like structures, ThT changes its fluorescent behavior, with a strong fluorescence signal located approximately at 482 nm (λ exc = 450 nm) [ 59 , 60 ]. The fluorescence activation of ThT is imputable to the rotational immobilization of the central C–C bond connecting the benzothiazole and aniline rings. As visible in Figure 4 , the ThT dye can stain the PEGDA-containing HGs, giving rise to a fluorescent emission in the GFP spectral window. This evidence highlights the presence of an amyloid-like structure as a supramolecular element of the hydrogels. The presence of an amyloid-like structure was further confirmed using the Congo Red (sodium salt of benzidinediazo-bis-1-naphthylamine-4-sulfonic acid) assay, both on samples in solution and in the solid state ( Figure 5 ). The UV-Vis spectra of mixed hydrogels prepared in a CR solution at the final dye concentration of 10 µM are reported in Figure 5 A,B. The spectrum of the dye alone in water is also reported for comparison. From the inspection of the figure, a red shift of the absorbance peak can be observed from 480 to 540 nm. This bathochromic shift of the maximum is typically associated with the detection of β-sheet secondary structures in the sample. Analogously, amyloid-like structures are also confirmed by optical microscopy images, under bright field and cross-polarized light, of mixed xerogels stained with CR ( Figure 5 C). Images showed that, independently from the composition and the PEGDA molecular weight, all the xerogels exhibit birefringence. 2.4. Solid State Characterization To collect information about the morphology of mixed xerogels, scanning electron microscopy (SEM) images were acquired. A set of representative microphotos for samples containing increasing amounts of diacrylate are shown in Figure 6 . A substantial difference in the surface topography of systems can be detected with respect to the sponge-like structure (generally reported for PEGDA-based matrices) [ 61 , 62 ] and the entangled fiber network (noticed for HGs of Fmoc-FF) [ 14 , 15 , 34 ]. This primary evidence is indicative of a modification of the aggregation properties of both chemical building blocks, reinforcing the evidence that co-aggregation enlarges the plethora of supramolecular behaviors of molecules. More specifically, the PEGDA1 and PEGDA2 matrices mutually differ. For PEGDA1 xerogels, a quasi-fractal drapery surface is detected. The geometric distribution, the fineness, and the grade of detail increase with the amount of PEGDA polymer, letting us postulate that the change in surface morphology is attributed to its intermolecular interaction. On the contrary, PEGDA2 hydrogels show fiber-like architectures, which manifest more in matrices with higher polymer ratios. The evident discrepancies between PEGDA1 and the two samples indicate a rule of polymer molecular weight in the topological arrangements of the matrices, probably imputable to a different network of H-bonds. This specific physicochemical parameter also changed the peptide-polymer interaction networking, in turn producing very different superficial morphologies in hybrid matrices. Further structural characterization of the peptide’s supramolecular architecture was achieved by a wide-angle X-ray scattering (WAXS) study. Measurements were collected on the macroscopic fibers of samples prepared according to the stretch-frame method [ 63 ]. In all the cases, the WAXS data present the typical fiber diffraction pattern with two crossed main axes: the meridional along the fiber direction and the equatorial perpendicular to it (depicted by white arrows in 2D WAXS patterns). The 1D profiles, reported in Figure 7 and Figure 8 E–H, have been obtained from the integration along both meridional and equatorial axes of 2D data, and the principal peaks along the axis are schematically summarized in Table S1 . In all the cases, the WAXS data present the typical fiber diffraction pattern with two crossed main axes: the meridional along the fiber direction and the equatorial perpendicular to it (depicted by white arrows in 2D WAXS patterns). As reported in Figure 7 and Figure 8 E–H, 1D profiles have been obtained from the integration along both meridional and equatorial axes of 2D data, and the principal peaks along the axis are schematically summarized in Table S1 . According to data previously collected on the Fmoc-FF hydrogel, hybrid gels containing PEGDA1 or PEGDA2 polymers exhibit the well-known diffraction pattern of cross-β amyloid-like structures, and the two axes (meridional and equatorial) correspond to the axis along the fiber and perpendicular to it, respectively [ 64 ]. The main diffraction peak at q = 1.29 Å −1 (d = 4.9 Å) gives information on the distance existing between adjacent peptide backbones organized into β-strands along the fiber axis. On the other hand, the peak at q ∼ 0.5 Å −1 (d = 12.5 Å) can be associated with the distance between two distinct β-sheets. The only exception is the absence of cross-β amyloid-like structures for the sample Fmoc-FF/PEGDA2 (1/10) due to the difficulty of realizing the solid fiber with the described method. The WAXS profiles of mixed hydrogels are very similar to those previously measured for pure Fmoc-FF, whose 2D and 1D WAXS results are reported in Figure S3 . The relevant difference we detect in mixed Fmoc-FF/PEGDA1 and Fmoc-FF/PEGDA2 hydrogels (at different ratios) with respect to pure Fmoc-FF is the presence of several additional equatorial and meridional reflections (see Figures S4 and S5 and Table S1 for positions and corresponding distances). This finding suggests a significant increase in the hierarchical order along the fiber induced by PEGDA1 and PEGDA2 (especially at the higher concentrations of the polymeric component). 2.5. Rheological Characterization The mechanical response of matrices was evaluated via rheological analysis, describing the viscoelastic behavior of each Fmoc-FF/PEGDA hydrogel (at 0.5 wt%) in terms of G’ (storage modulus) and G’’ (loss modulus). The analysis was conducted by performing time-sweep oscillatory measurements (for 15 min, with 1.0 Hz frequency and 0.1% strain), supported by a preliminary identification of the optimal measurement conditions. Specifically, dynamic oscillation strain sweep (at a frequency of 1.0 Hz) and dynamic frequency sweep (at 0.1% strain) were acquired for Fmoc-FF/PEGDA at the two ratios of 1/1 and 1/10 ( Figures S6 and S7 ). Collectively, the linear viscoelastic region (LVE region) was found in the 0.02–3.0% stain range. The G’ and G’’ time sweep values, collected in the histograms of Figure 9 and Table 3 , analytically demonstrate the gel state of all the tested matrices due to the values of G’ higher than G’’ and tan δ > 1 (G’/G’’> 1). All these values are higher than the ones for pure Fmoc-FF at the same concentration (G’~950 Pa), indicating that the multicomponent systems are characterized by enhanced mechanical properties. This evidence has already been reported for other hybrid peptide/polymer or peptide/peptide matrices, suggesting the co-assembly and co-aggregation strategies as suitable methodologies to modify the mechanical performance of matrices [ 33 , 34 ]. In detail, it can be noted that PEGDA1-containing matrices result in greater rigidity than PEGDA2 ones at the same polymer ratio. This evidence is also pointed out by observing comparable strain break points (5 and 6% for PEGDA1 and PEGDA2, respectively) for systems at 1/10 ratios. In contrast, significant differences were found for the strain break points of PEGDA 1 and PEGDA2 at 1/1 ratios (61 and 12%, respectively). All these data suggest a positive impact of the higher number of non-covalent interactions (H-bound) related to the number of PEG repetitions. According to thermodynamic principles, it is also suggested that there is a linear correlation between the modulus of rigidity and both the molecular weight and concentration of the polymer for pure PEG hydrogels. In the analysis of the Fmoc-FF/PEGDA series, a different trend can be detected for the two polymers. In the PEGDA1 series, G’ value is four times higher when passing from 1/1 to 1/2 ratios (2123 Pa to 8099 Pa). However, a further increase in the polymer does not cause an additional increase of the gel rigidity (7695 Pa for a 1/5 ratio). Finally, it can be observed a decrease in G’ for the gel at a 1/10 ratio (4323 Pa). This trend is also visible by comparing the tan δ values. On the contrary, a gradual and constant reduction of the G’ value is associated with the PEGDA2 series. Analogously, this decrease is also detectable in tan δ ratios. The general rheological behavior of Fmoc-FF/PEGDA hydrogels is not the expected one. Indeed, it was previously observed that an increase in the PEG concentration can allow an improvement in the mechanical modification of supramolecular entanglement. This evidence is indicative of a multifactorial correlation between the final G’ and the total intermolecular network interactions and physical parameters (including water mobility, the total hydrophilicity of the system, a progressive increase in the hydrophilicity for a higher PEGDA ratio, and predictable matrix rigidity). The modulable mechanical responses of these matrices suggest good applicative versatility. The viscoelastic nature of the HGs recalls their potential employment as scaffold elements for tissue engineering or as tridimensional supporting materials for cell attachments. According to the G’ values, the proposed hydrogels are also candidates as reservoirs for the delivery of active pharmaceutical ingredients (APIs). The G’ values suggest suitable physical entrapment of host molecules and tunability in shape, thickness, and resistance. All these features are compatible with implant development. To further scrutinize this latter purpose, the capability of the resulting matrices to encapsulate and retain a drug was tested. 2.6. Release of Naphthol Yellow S HGs are often proposed as matrices able to serve as drug reservoirs. Prolonged and modified release of active pharmaceutical ingredients, including small drugs or diagnostics, can be modulated via encapsulation in hydrogel matrices. According to this evidence, we attempted to load the hydrogels with Naphthol Yellow S (NYS), which is a water-soluble disodium salt of 5,7-dinitro-8-hydroxynaphthalene-2-sulfonic acid used as a histological dye and here employed as a drug model ( Figure 10 A). The hydrophilic nature of NYS allows for its incorporation it into the hydrogel during the rehydration step of the Fmoc-FF stock solution prepared in DMSO. The macroscopic observation of gels clearly indicates that the loading of the dye into the matrices does not affect the gelation process at the tested concentration (6.02 mmol·L −1 ). The amount of NYS encapsulated in the gel was considered to be 100% of the loaded dye. Release profiles of NYS from PEGDA-based HGs over time (up to 144 h) are reported in Figure 10 in terms of NYS released percentage. From the inspection of Figure 10 , it appears that all the mixed hydrogels exhibit a slower release with respect to pure Fmoc-FF HG (red line). This result can be explained as function of the higher swelling ratio q of hybrid hydrogels with respect to Fmoc-FF ones (see Table 1 ). Indeed, it is reasonable to expect the existence of a relationship between the passive water permeation of the hydrogel and the swelling ratio q, which is indicative of the mobile water in a completely swollen state. Moreover, the released percentage decreases from 88 to 73% and from 90 to 80% for PEGDA1 and PEGDA2, respectively, within the series by moving from a 1/1 to a 1/10 molar ratio. This trend suggests that the increase of PEGDA in the mix allows for higher retention of the NYS into the aqueous hydrogel matrix. This result is not surprising considering the ability of PEGDA to establish non-covalent interactions such as hydrogen bonds with the NYS. According to this consideration, it is reasonable to expect that the percentage of the drug release decreases with the increase in the PEGDA amount and the PEGDA molecular weight."
} | 8,976 |
28961252 | PMC5621664 | pmc | 5,226 | {
"abstract": "This paper investigates a well-known complex combinatorial problem known as the vehicle routing problem with time windows (VRPTW). Unlike the standard vehicle routing problem, each customer in the VRPTW is served within a given time constraint. This paper solves the VRPTW using an improved artificial bee colony (IABC) algorithm. The performance of this algorithm is improved by a local optimization based on a crossover operation and a scanning strategy. Finally, the effectiveness of the IABC is evaluated on some well-known benchmarks. The results demonstrate the power of IABC algorithm in solving the VRPTW.",
"conclusion": "Conclusion This paper develops an improved ABC algorithm for solving VRPTW, a complicated combinatorial optimization problem. ABC algorithms, which randomly search for the optimal solution, have been gradually adopted in combinatorial optimization, artificial intelligence and other fields. Other uses of ABCs are robots that search for optimal routes, scheduling and services that seek to transport fresh food in the best order of delivery. The positive feedback and coordination of the ABC algorithm are effective for distribution systems. This paper improves the performance of standard ABC by a crossover operation borrowed from the genetic algorithm, and a scanning strategy. The Comparison with best-known solution in classic VRPTW experiment verifies the capability of the IABC algorithm. The comparison of the results of ABC-C, ABC-S, and IABC shows that the incorporation of crossover operation and scanning strategy can improve the performance of ABC for VRPTW. However, the crossover operation may increase the computation time for the convergence value with the reason that it requires time to search the crossover nodes. In addition, good performance of IABC for small instances cannot ensure the validity in large instances. In future work, we will improve the convergence speed and demonstrate the effectiveness of the algorithm in large instances and practical case studies. And the performance of the IABC in artificial intelligence and other fields are also expected. Besides, information and communication technology advances have encouraged the development of advanced traveler information systems (ATIS) [ 51 ]. Applying ATIS to this paper should be studied in the future.",
"introduction": "Introduction The Vehicle Routing Problem (VRP) was first described by Dantzig and Ramser in 1959 [ 1 ]. The problem involves determining the delivery routes, which start and end at the depot and also serve all the customers. Each customer is visited once and the total demand of all customers in a specific route can’t exceed the capability of the vehicle. The goal of the VRP is to minimize the total cost, where cost can be defined as distance or time [ 2 ]. In 1981, Lenstra and Rinnooy Kan [ 3 ] proved that the VRP is NP-hard. Its main drawback is the well-known curse of dimensionality. Constraint programming is considered as an efficient method for giving the optimal solution [ 4 ] but the time to find the optimal is prohibitive in large problems. Some studies improved constraint programming to solve the VRP: Backer et al. [ 5 ] developed a method using iterative improvement techniques within a Constraint Programming framework to get a good solution under a relatively short computing time; Ozfirat and Ozkarahan [ 6 ] introduced an algorithm which decomposing the problem into smaller scale ones firstly and then solve it by constraint programming; Guimarans et al. [ 7 ] proposed a method of combining Constraint Programming, Probabilistic Algorithms and Lagrangian Relaxation. The vehicle routing problem with time windows (VRPTW) is a variant of the VRP that has an additional time window constraint. In the VRPTW, a fleet of vehicles set off from a depot to serve a number of customers, which have various demands and specific time windows. The objective of the VRPTW includes maximizing the sum of the on-time delivery probabilities to customers, minimizing the expected total cost, and some others [ 8 ]. The VRPTW, as an extension of the VRP, has also been proved to be an NP-hard problem [ 9 ]. The time window require the time constraint and the traffic speed prediction is very important in the delivery routes in VRPTW [ 10 – 11 ]. In addition, Heuristic algorithms were considered effective methods to solve this combinational problem [ 12 – 16 ]. Thangiah et al . [ 9 ] and Wainwright [ 14 ] developed genetic algorithms to solve the VRPTW. The former's objective was to minimize the distance of vehicle routes, while the latter study had two objectives: use the least vehicles, and minimize the total costs under the condition of the first objective. Zhang et al. [ 17 ] improved tabu search heuristic algorithm by incorporating a route reduction mechanism to reduce the number of required vehicles. Yao et al. [ 18 ] proposed an improved particle swarm optimization to solve carton heterogeneous vehicle routing problem with a collection depot. The results suggested that the proposed algorithm was an effective approach for the problem. When a bee colony looks for nectar, they often divide into three initial groups: leaders, scouts and onlookers. By communicating information among the three roles, the colony gathers nectar quickly and efficiently. The Artificial Bee Colony (ABC) algorithm is inspired by the behavior of bees as they search for nectar and some studies have proposed improvements to the algorithm. Seely [ 19 ] proposed a self-organization simulation model for the colony, where the entire colony collaborates to complete a complicated problem, such as building the hive or harvesting pollen. Karaboga [ 20 ] successfully applied the bee colony algorithm to the numerical optimization of functions and proposed a systematic artificial bee colony algorithm. In 2007, ABC theory was further applied to solve restrictive numerical optimization problems by Basturk and Karaboga [ 21 ], and the authors presented promising results. As a novel heuristic algorithm, the ABC algorithm has been successful in solving complex combinational optimization problems. Özbakir et al . [ 22 ] presented an ABC algorithm to solve generalized assignment problems with an ejection chain neighborhood mechanism. Koudil et al . [ 23 ] attempted to use an ABC algorithm to solve an integrated partitioning/scheduling problem in codesign. Karaboga et al . [ 24 ] proposed a modified ABC algorithm to determine the parameters of a Schottky barrier diode. Cuevas et al . [ 25 ] developed an ABC algorithm to solve an image segmentation problem by computing threshold selection. Zhang et al . [ 26 ] addressed problems using three enhanced versions of the original ABC algorithm, based on multi-species co-evolution. Huo et al . [ 27 ] proposed a Discrete Gbest-guided ABC algorithm for cloud service composition. AlMuhaideb et al . [ 28 ] presented a two-phase strategy that combined ant colony optimization and the ABC algorithm. Other researches involving ABC algorithms can be found in the literatures [ 20 , 29 ]. These successful applications motivated us to apply the ABC in this paper to generate and optimize a variety of potential routes to solve the VRPTW problem. The ABC algorithm is a popular optimization algorithm, but it is often easily trapped in local optima before it finds the global optimum. Besides, if the current search settles on obviously bad information and inputs that information into the next search stage, search accuracy will decrease. Thus, it is necessary to expand the solution space of the algorithm or, equivalently, to expand the diversity of its search information. Crossover operation of the genetic algorithm is an effective way to expand the range of the solution search. Vaira et al. [ 30 ] investigated the crossover operators for a vehicle routing problem and proved its superiority. Other researches using crossovers to expand solution space include Jih et al. [ 31 ], Misevičius et al. [ 32 ]; Kumar et al. [ 33 ]. Scanning strategy judges the relationship between two paths from geometric knowledge and makes adjustments if the new solution satisfies the requested intersection and time window constraints. It can prevent bad information—e.g. unnecessary intersections between two paths—from entering the following search iteration. The main contribution of this paper is combining the ABC algorithm with crossover and scanning strategy, which accelerate the convergence speed and improve the solution to VRPs compared with conventional ABC algorithms. The remainder of this paper is organized as follows. In Section 2, we construct a mathematical model for the VRPTW. The ABC algorithm and the proposed strategies outlined above are detailed in Section 3. Section 4 discusses the computational results and Section 5 concludes the paper. Finally, a list of the notation used in the proposed algorithm is presented in the S1 Table ."
} | 2,229 |
37210399 | PMC10199942 | pmc | 5,227 | {
"abstract": "The motional consensus of self-propelled particles is studied in both noise-free cases and cases with noise by the standard Vicsek model. In the absence of noise, we propose a simple method, using grid-based technique and defining the normalized variance of the ratio of the number of particles locally to globally, to quantitatively study the movement pattern of the system by the spatial distribution of the particles and the degree of aggregation of particles. It is found that the weaker correlation of velocity leads to larger degree of aggregation of the particles. In the cases with noise, we quantify the competition between velocity alignment and noise by considering the difference of the variety of order parameter result from the velocity alignment and noise. The variation of the effect of noise on motional consensus is non-monotonic for the change of the probability distribution of noise from uniform to non-uniform. Our results may be useful and encourage further efforts in exploring the basic principles of collective motion.",
"conclusion": "Conclusion In conclusion, we have studied the motional consensus of self-propelled particles in both the noise-free cases and the cases with noise by standard Vicsek model. For the noise-free cases, we have proposed a method to quantitatively describe the spatial distribution of the particles by divided the two-dimensional space into some grid with equal size and count the normalized variance of the ratio of \\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$${N_{i}}$$\\end{document} N i to N . It is found that the smaller r or larger N builds weaker correlation of the velocity among particles, which leads to larger degree of aggregation of the particles when the system reaches motional consensus. For the cases with noise, we have quantitatively analyzed the competition between the effects of velocity alignment and noise on the degree of motional consensus. The results show that the non-monotonic variation of the effect of noise on motional consensus result from the non-uniform probability distribution of the noise. Collective behaviors of active systems present various patterns. Pattern formation of active systems may be studied by generalizing the Smoluchowski aggregation theory which focus on the growth and distribution of clusters for passive systems 47 . Bridging the Vicsek model(particles-based model) and the theroy proposed by Tu and Toner based on hydrodynamics 48 is also an interesting perspective of study about collective behavior of active systems. As for the further studies concerning the collective behavior of active systems, our study may be useful for exploring the basic principle of collective motion.",
"introduction": "Introduction Collective behavior extensively exists in both the macroscopic systems such as the crowds of human 1 , the herds of mammal 2 , the flocks of bird 3 – 6 , the schools of fish 7 and the swarms of insect 8 and the microscopic systems including bacterial colonies, cells etc 9 – 12 . Studying the motional consensus of collective behavior is of great importance in discovering the basic principle of collective motion, which enables us to better understand the mechanism of escaping from predators, foraging etc 13 , 14 . In addition, the study of motional consensus inspires the control of multibody systems such as a number of robotic machines 15 , which is beneficial to exploring efficient motion strategies for escaping from fire etc 16 . The Vicsek model 17 , proposed in 1995, is one of the classical models for studying collective motion of active systems. It considers the velocity alignment and noise for updating the velocity of all of the particles. Flocking phenomena is observed when the noise is small, which means the motion of all the agents almost reach global alignment and the degree of motional consensus is high. Spontaneously local 18 and global velocity alignment 19 also be explored. Because the Vicsek model is simple but catches the main factors of collective motion, many researchers have paid attention to it in theory as well as simulation 20 – 24 . Series of interesting phenomena are observed including traveling bands 25 , moving crystals 26 , Swirlonic state 27 , phase transition 28 – 30 and circular pattern 31 , 32 . Various factors, including hybrid noise 33 , aggregation interaction 34 , low density and low speed 35 , auditory sensing 36 , view angle 37 , 38 , chirality 39 and complex noise environment 40 , are considered for exploring the diverse phenomenon of collective behavior as well. Some model, modifying based on Vicsek model, is proposed to improve the speed of motional consensus by adjusting the rules of velocity alignment such as remote neighbors strategy 41 , updating the direction with exponential weights depending on the neighbor numbers 42 , 43 or according to the direction of the neighbors 42 , 44 . Besides the progress that has been made in exploring collective behavior, the quantitative description of the movement pattern of the particles and the mechanism for the formation of different movement patterns of standard Vicsek model remain unclear. And how the competition between velocity alignment and noise affects motional consensus is not clear enough. Studying them is important to improving our understanding of the mechanism of collective motion. Therefore, in noise-free cases, we study the movement pattern of motional consensus by the spatial distribution of the particles and the degree of aggregation of the particles. And we proposed a method to quantitatively describe the spatial distribution of the particles. The effect of different parameters on the movement pattern of motional consensus is analyzed and the reason of different movement patterns is explored. As for the effect of velocity alignment and noise on motional consensus, we quantify the competition between them and find the non-monotonic variation of the effect of noise on motional consensus.",
"discussion": "Result and discussion The noise-free cases In noise-free cases, \\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}$${\\eta = 0}$$\\end{document} η = 0 . When the number of simulated time steps is large enough, the order parameter of the system can reach 1. This means that the system arrives in a flocking state (strict motional consensus). In order to catch the main feature of motional consensus without sacrificing long simulation times, we take \\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$${\\phi _{m} = 0.979}$$\\end{document} ϕ m = 0.979 to be the standard for reaching motional consensus. \\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$${\\phi _{m} = 0.979}$$\\end{document} ϕ m = 0.979 , which means the system almost reaches flocking state, is large enough to ensure the validity of analysis of motional consensus below will not change. In simulation, we set \\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$${L = 10}$$\\end{document} L = 10 . The total time steps for simulation are 1000, which is long enough for the system to reach motional consensus. Considering the rules of velocity alignment in the standard Vicsek model, the update of the direction of velocity will be directly influenced by their neighbors. Particles with common neighbors will build correlation of their velocity, while there will be no correlation of the velocity among the particles without common neighbor. To quantify simply, here, we only consider the correlation of each pair of particles consisting of two particles. When the system reaches motional consensus, they are found to have different movement patterns which show different spatial distribution and different degree of aggregation of all of the particles. As shown in Fig. 1 , with the increasing of the interaction radius r , the spatial distribution of the particles is more uniform and the particles aggregate less closer. As the total number of particles increases, the particles are distributed more evenly and clustered closer together. The increase in velocity makes the spatial distribution of particles more uneven and the particles aggregate more closer. Figure 1 The movement pattern of the system reaching motional consensus for different interaction radius r , different number of particles N and different velocity v . The blue points denote the particles and the red arrows denote the direction of the velocity of the particles. \\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}$${v = 0.04}$$\\end{document} v = 0.04 for ( a )-(i). \\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$${r = 0.8}$$\\end{document} r = 0.8 for ( a ) \\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$${N = 100}$$\\end{document} N = 100 , ( b ) \\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$${N = 300}$$\\end{document} N = 300 and ( c ) \\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$${N = 500}$$\\end{document} N = 500 . \\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}$${r = 1.2}$$\\end{document} r = 1.2 for ( d ) \\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$${N = 100}$$\\end{document} N = 100 , ( e ) \\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$${N = 300}$$\\end{document} N = 300 and ( f ) \\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$${N = 500}$$\\end{document} N = 500 . \\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}$${r = 5.0}$$\\end{document} r = 5.0 for ( g ) \\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$${N = 100}$$\\end{document} N = 100 , ( h ) \\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$${N = 300}$$\\end{document} N = 300 and ( i ) \\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$${N = 500}$$\\end{document} N = 500 . \\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}$${r = 1.2}$$\\end{document} r = 1.2 for ( j ) \\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}$${v = 0.01}$$\\end{document} v = 0.01 , ( k ) \\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$${v = 0.16}$$\\end{document} v = 0.16 and ( l ) \\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}$${v = 0.20}$$\\end{document} v = 0.20 . The value of other parameters for simulation are \\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}$${v = 0.04, L = 10}$$\\end{document} v = 0.04 , L = 10 in ( a )-(i) and \\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$${N = 200, L =10}$$\\end{document} N = 200 , L = 10 in ( j )–( l ). To quantitatively study the movement pattern of the systems reaching motional consensus, we proposed a method with grid-based technique. Grid-based technique is widely used in many numerical approaches including Fast Multipole Method(FMM) 45 and Multi-Particles Collision(MPC) etc 46 . FMM divides the space into different number of cells depending on the level of division. By analyzing the spatial relationship between the target cells and other cells, the interaction among particles in the target cell and the interaction of that with other cells can be obtained. Then the total interaction among the particles of the system can be evaluated. MPC introduces randomly shifted cells in the simulation of each time step. Evaluating the streaming and collision of the particles in each cell to obtain the position and velocity of the mass of the center of each cell. Then analyzing the dynamics of the system by considering the interaction among all of the cells. Both of the method mentioned above can improve the efficient of the simulation. But they are more suitable to solve the interaction among the particles. Here, we aim to catch the feature of the movement pattern of the system and our method with grid-based technique is simple and efficient. As shown in Fig. 2 a, we divide the two-dimensional \\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$${L \\times L}$$\\end{document} L × L space into G grids, where \\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$${G = 25}$$\\end{document} G = 25 here. Then we investigate the normalized variance of the ratio of the number of particles in each grid \\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$${N_{i}}$$\\end{document} N i to total number of the particles N . Figure 2 ( a ) Scheme of dividing the two-dimensional \\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$${L \\times L}$$\\end{document} L × L space into 25 square grids with equal size. The normalized variance of the ratio of the number of particles in each grid to total number of the particles \\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}$${\\chi _{ratio}}$$\\end{document} χ ratio , showing the spatial distribution of the particles, as a function of ( b ) interaction radius r for \\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}$${v = 0.04, L = 10}$$\\end{document} v = 0.04 , L = 10 , ( c ) total number of particles N for \\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}$${v = 0.04, L = 10}$$\\end{document} v = 0.04 , L = 10 and ( d ) the velocity of the particle v for \\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}$${r = 1.2, L = 10}$$\\end{document} r = 1.2 , L = 10 . The value of each data point in ( b ), ( c ) and ( d ) is the average of 200 different realizations. The rationality of current grid selection is discussed in the Supplementary Information. The ratio of the number of particles in each grid \\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$${N_{i}}$$\\end{document} N i to total number of the particles N is obtained as follows 7 \\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$$\\begin{aligned} R_{i} = \\frac{N_{i}}{N} \\end{aligned}$$\\end{document} R i = N i N The normalized variance of the ratio of \\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$${N_{i}}$$\\end{document} N i to N is 8 \\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$$\\begin{aligned} \\chi _{ratio} = \\frac{\\sigma }{\\sigma _{max}} \\end{aligned}$$\\end{document} χ ratio = σ σ max and \\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$${\\sigma }$$\\end{document} σ is the variance of the ratio of \\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$${N_{i}}$$\\end{document} N i to N 9 \\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$$\\begin{aligned} \\sigma = \\frac{ \\left( \\sum _{i} (R_{i} - {\\bar{R}})^{2} \\right) }{G} \\end{aligned}$$\\end{document} σ = ∑ i ( R i - R ¯ ) 2 G where \\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$${{\\bar{R}}} =\\left(\\sum _{i} R_{i}\\right)/G = 1/G$$\\end{document} R ¯ = ∑ i R i / G = 1 / G is the average of \\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$${R_{i}}$$\\end{document} R i . \\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$${\\sigma _{max}}$$\\end{document} σ max denotes the maximum of the variance of the ratio of \\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$${N_{i}}$$\\end{document} N i to N when all of the particles aggreagate in the same grid, which is 10 \\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$$\\begin{aligned} \\sigma _{max} = \\frac{1}{G}\\left( 1-\\frac{1}{G} \\right) ^{2} + \\left( \\frac{1}{G} \\right) ^{2G-1} \\end{aligned}$$\\end{document} σ max = 1 G 1 - 1 G 2 + 1 G 2 G - 1 The normalized variance \\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}$${\\chi _{ratio}}$$\\end{document} χ ratio will be 1 when all of the particles aggregate in the same grid, while \\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}$${\\chi _{ratio} = 0}$$\\end{document} χ ratio = 0 when all of the particles are uniformly distributed in G grid. As Fig. 2 b–d shown, the increase of r or N and the decrease of v leads to smaller value \\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}$${\\chi _{ratio}}$$\\end{document} χ ratio which means the more uniform spatial distribution of the particles (Supplementary Information 1 ). To quantify the degree of aggregation of the particles, we investigate the average number of particles for the grid that occupied by particles \\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}$${\\langle N_{grid} \\rangle }$$\\end{document} ⟨ N grid ⟩ . As Fig. 3 a shown, with the increasing of r , the value of \\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$${\\langle N_{grid} \\rangle }$$\\end{document} ⟨ N grid ⟩ becomes smaller, which means the aggregation of the particles is less close. As shown in Fig. 3 b,c, \\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}$${\\langle N_{grid} \\rangle }$$\\end{document} ⟨ N grid ⟩ increases as N or v increase. The increase of N or v makes the particles aggregate more closer. Figure 3 The average number of particles for the grid that occupied by particles \\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}$${\\langle N_{grid} \\rangle }$$\\end{document} ⟨ N grid ⟩ , showing the degree of aggregation of the particles, as a function of ( a ) the interaction radius r for \\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}$${v = 0.04, L = 10}$$\\end{document} v = 0.04 , L = 10 , ( b ) total number of particles N for \\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}$${v = 0.04, L = 10}$$\\end{document} v = 0.04 , L = 10 and ( c ) the velocity of the particles v for \\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}$${r = 1.2, L = 10}$$\\end{document} r = 1.2 , L = 10 . The value of each data point is the average of 200 different realizations. In order to understand the variation of the degree of aggregation of particles and the spatial distribution of the particles with different r and N , we study the common neighbors of particles. The common neighbors of particles are significant for the movement pattern of the particles by affecting the update of the velocity and position of particles. For noise-free cases, the movement pattern of particles when reaching motional consensus depends only on the initial state of all of the particles. Given the initial state, the state of the system is fixed after updating in each time step, because the update in each time step is not affected by noise. For the initial state, we first pay attention to the average ratio of the number of common neighbor \\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$${n_{com}}$$\\end{document} n com to the number of particles within the field of vision of pairs of particles \\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$${n_{pair}} = n_{A} + n_{B} - n_{com}$$\\end{document} n pair = n A + n B - n com , where \\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$${n_{A}}$$\\end{document} n A and \\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$${n_{B}}$$\\end{document} n B are the number of neighbors of particle A and B, which are any two of the N particles. \\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}$${\\langle n_{com} / n_{pair} \\rangle }$$\\end{document} ⟨ n com / n pair ⟩ reveals the average strength of the correlation of velocity for each pair of particles. As Fig. 4 a,b shown, the decrease of r or the increase of N leads to weaker correlation of velocity for each pair of particles. Figure 4 The average ratio of the number of common neighbors to the number of particles within the field of vision of pairs of particles \\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}$${\\langle n_{com}/n_{pair} \\rangle }$$\\end{document} ⟨ n com / n pair ⟩ , showing the average strength of the correlation of velocity for each pair of particles, as a function of ( a ) r and ( b ) N . The average ratio of the number of common neighbors to the total number of the particles N in initial state \\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}$${\\langle n_{com}/N \\rangle }$$\\end{document} ⟨ n com / N ⟩ , showing the average strength of the correlation of velocity between pairs of particles and all of the particles, as a function of ( c ) r and ( d ) N . In ( a )–( d ), the value of other parameters for simulation are \\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}$${v = 0.04, L =10}$$\\end{document} v = 0.04 , L = 10 . The value of each data point is the average of 200 different realizations. Because the motional consensus of the system is not only affected by the pairwise correlations of the velocity, but also related to all of the particles, we also investigate the average ratio of the number of common neighbor \\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$${n_{com}}$$\\end{document} n com to the total number of the particles N in initial state. As shown in Fig. 4 c,d, with the decreasing of r or the increasing of N there is weaker correlation of velocity between pairwise particles and all of the particles. What have been analyzed about Fig. 4 shows that increasing N and decreasing r will weaken correlation of velocity among particles. Weaker correlation of velocity among particles makes it more difficult for particles to reach motional consensus. The particles will keep moving in their direction respectively until the correlation between their velocity is large enough to enable them to move in the almost same direction. In order to build stronger correlation of their velocity, particles will move more closer, resulting in larger degree of aggregation when the system reaches motional consensus. There is a different mechanism for the effect of velocity to the degree of aggregation of particles. For small velocity, particles move slowly to close in order to reach motional consensus by building stronger correlation of their velocity. Because particles move slowly, they are more sensitive to the boundary that whether they can reach motional consensus or not. With the increasing velocity, particles move fast and are insensitive to the boundary that whether they can reach motional consensus or not, which leads to larger aggregation of the particles. Cases with noise The rules of velocity alignment make the velocity of all of the particles unified, while the noise disturbs the motional consensus. For the motion of the particles, the restriction of velocity alignment will be weakened by the effect of noise, which makes the motional consensus more difficult or even impossible to reach. The temporal evolution of the order parameter for various strengths of noise are shown in Fig. 5 a. Figure 5 Order parameter \\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$${\\phi }$$\\end{document} ϕ as a function of time steps for different values of \\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$${\\eta }$$\\end{document} η ( a ) With the effect of both velocity alignment and noise. ( b ) Just with the effect of noise. In both ( a ) and ( b ), the value of other parameters for simulation are \\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}$${v = 0.04, L = 10, N = 400, r = 1.0}$$\\end{document} v = 0.04 , L = 10 , N = 400 , r = 1.0 . There is perturbation of order parameter when the state of the system can be thought to be steady. In order to quantify the value of order parameter when the system is nearly steady, we take the average of order parameter from 500 steps to 1000 steps as the order parameter of the system in the nearly steady state. With the increasing of \\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$${\\eta }$$\\end{document} η , the steady value of the order parameter is smaller, which means the less unified of the motion of all of the particles. Figure 5 b shows the order parameter for different values of \\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$${\\eta }$$\\end{document} η in the absence of velocity alignment. It is impossible to reach motional consensus even in the case of small \\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}$${\\eta }$$\\end{document} η . In order to quantify the effect of noise on motional consensus, we investigate the difference of order parameter \\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$${\\Delta \\phi }$$\\end{document} Δ ϕ between two cases as follows 11 \\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$$\\begin{aligned} \\Delta \\phi = \\phi _{va} - \\phi _{vn} \\end{aligned}$$\\end{document} Δ ϕ = ϕ va - ϕ vn where \\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$${\\phi _{va}}$$\\end{document} ϕ va denotes the steady value of order parameter in the cases that the motion of particles is only restricted by velocity alignment and \\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$${\\phi _{vn}}$$\\end{document} ϕ vn denotes the steady value of order parameter in the cases that the motion is affected by both velocity alignment and noise. As shown in Fig. 6 a, the difference of order parameter \\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$${\\Delta \\phi }$$\\end{document} Δ ϕ increases with the increasing of the value of \\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$${\\eta }$$\\end{document} η for different interaction radius r , which shows the larger effect of noise on motional consensus. Figure 6 ( a ) The difference of order parameter between the cases that the update of the motion of particles is affected by both velocity alignment and noise and the cases that the motion is just affected by noise \\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}$${\\Delta \\phi }$$\\end{document} Δ ϕ , showing the effect of noise on motional consensus, as a function of \\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$${\\eta }$$\\end{document} η for different interaction radius r . Inset: \\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}$${\\Delta \\phi }$$\\end{document} Δ ϕ in the range of [0.7, 1] for clearly showing the variation of it as the \\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$${\\eta }$$\\end{document} η increases. ( b ) The competition between velocity alignment and noise \\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}$${\\kappa }$$\\end{document} κ as a function of \\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$${\\eta }$$\\end{document} η for different interaction radius r . The value of each data point is the average of 200 different realizations. In both ( a ) and ( b ), the value of other parameters for simulation are \\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}$${v = 0.04, N = 400, L =10}$$\\end{document} v = 0.04 , N = 400 , L = 10 . To compare the influence of velocity alignment and noise on reaching motional consensus and quantify the competition between the effects of velocity alignment and noise, we defined the difference of the difference of the order parameter \\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$${\\kappa }$$\\end{document} κ which is shown as follows 12 \\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$$\\begin{aligned} \\kappa = \\Delta \\varphi - \\Delta \\phi \\end{aligned}$$\\end{document} κ = Δ φ - Δ ϕ where \\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$${\\Delta \\varphi = \\phi _{va} - \\phi _{n}}$$\\end{document} Δ φ = ϕ va - ϕ n denotes the difference of order parameter between the cases that the update of the motion of particles is affected by both velocity alignment and noise and the cases that the motion is just affected by noise. As shown in Fig. 6 b, the value of \\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$${\\kappa }$$\\end{document} κ will change from positive to negative as \\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$${\\eta }$$\\end{document} η increases, which means the increasing of \\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$${\\eta }$$\\end{document} η improves the effect of the noise to the motion of the particles and the effect of velocity alignment becomes more and more weak comparing that with the noise. When \\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}$${\\kappa = 0}$$\\end{document} κ = 0 , velocity alignment and noise affect motional consensus equally. We also observed that the variation of \\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$${\\Delta \\phi }$$\\end{document} Δ ϕ is not monotonous when the value of \\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$${\\eta }$$\\end{document} η is larger than 6. When \\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}$${\\eta = 6}$$\\end{document} η = 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}$${\\Delta \\theta \\in [-3.0, 3.0]}$$\\end{document} Δ θ ∈ [ - 3.0 , 3.0 ] , which is close to \\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}$${[-\\pi , \\pi ]}$$\\end{document} [ - π , π ] , the probability of all the direction of velocity effected by noise is almost equal as shown in Fig. 7 a. Figure 7 Scheme of the probability distribution of the direction of all of the particles. ( a ) The grey area denotes \\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}$${\\Delta \\theta }$$\\end{document} Δ θ is in the range of \\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$${[-\\pi , \\pi ]}$$\\end{document} [ - π , π ] . ( b ) The probability distribution of \\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$${\\Delta \\theta }$$\\end{document} Δ θ when \\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}$${\\Delta \\theta \\in [-1.5\\pi , 1.5\\pi ]}$$\\end{document} Δ θ ∈ [ - 1.5 π , 1.5 π ] . The green area denotes the probability in the case of \\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$${\\Delta \\theta \\in [-1.5\\pi , \\pi ]}$$\\end{document} Δ θ ∈ [ - 1.5 π , π ] and the red area denotes the probability when \\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}$${\\Delta \\theta \\in [\\pi , 1.5\\pi ]}$$\\end{document} Δ θ ∈ [ π , 1.5 π ] . ( c ) The probability distribution of \\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$${\\Delta \\theta }$$\\end{document} Δ θ when unifing all of the value from \\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$${[-1.5\\pi , 1.5\\pi ]}$$\\end{document} [ - 1.5 π , 1.5 π ] to \\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}$${[-\\pi , \\pi ]}$$\\end{document} [ - π , π ] . ( d ) The probability distribution of \\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$${\\Delta \\theta }$$\\end{document} Δ θ when unifing all of the value from \\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$${[-1.2\\pi , 1.2\\pi ]}$$\\end{document} [ - 1.2 π , 1.2 π ] to \\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}$${[-\\pi , \\pi ]}$$\\end{document} [ - π , π ] . The red area and the green area denotes the probability when \\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}$${\\Delta \\theta \\in [-1.2\\pi ,\\pi ]}$$\\end{document} Δ θ ∈ [ - 1.2 π , π ] and \\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$${\\Delta \\theta \\in [\\pi , 1.2\\pi ]}$$\\end{document} Δ θ ∈ [ π , 1.2 π ] respectively before unifying and they denote \\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}$${\\Delta \\theta \\in [-\\pi , -0.8\\pi ]}$$\\end{document} Δ θ ∈ [ - π , - 0.8 π ] and \\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$${\\Delta \\theta \\in [0.8\\pi , \\pi ]}$$\\end{document} Δ θ ∈ [ 0.8 π , π ] after unifying. Because of the periodicity of the angle denoting the direction of velocity, the probability distribution of the noise will be changed when \\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}$${\\eta > 6}$$\\end{document} η > 6 . For example, as shown in Fig. 7 b,c, when \\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}$${\\eta = 3\\pi }$$\\end{document} η = 3 π which means \\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}$${\\Delta \\theta \\in [-1.5\\pi , 1.5\\pi ]}$$\\end{document} Δ θ ∈ [ - 1.5 π , 1.5 π ] , the probability of \\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$${\\Delta \\theta \\in [-1.5\\pi , -\\pi ]}$$\\end{document} Δ θ ∈ [ - 1.5 π , - π ] and \\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$${\\Delta \\theta \\in [\\pi , 1.5\\pi ]}$$\\end{document} Δ θ ∈ [ π , 1.5 π ] will be unified in the probability of \\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$${\\Delta \\theta \\in [-0.5\\pi , -\\pi ]}$$\\end{document} Δ θ ∈ [ - 0.5 π , - π ] and \\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$${\\Delta \\theta \\in [0.5\\pi , \\pi ]}$$\\end{document} Δ θ ∈ [ 0.5 π , π ] respectively, which changing the probability distribution of \\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$${\\Delta \\theta }$$\\end{document} Δ θ . As shown in Fig. 7 c,d, in the cases of \\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$${\\eta \\in [2\\pi , 3\\pi ]}$$\\end{document} η ∈ [ 2 π , 3 π ] , the more the \\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$${\\eta }$$\\end{document} η larger than \\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$${2\\pi }$$\\end{document} 2 π , the less the \\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$${\\Delta \\phi }$$\\end{document} Δ ϕ will be. When \\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}$${\\eta \\in [3\\pi , 4\\pi ]}$$\\end{document} η ∈ [ 3 π , 4 π ] , the probability unified in \\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$${[-\\pi , \\pi ]}$$\\end{document} [ - π , π ] makes the probability distribution of \\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$${\\Delta \\theta }$$\\end{document} Δ θ more uniform, which leads to the increment of \\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$${\\Delta \\phi }$$\\end{document} Δ ϕ . In order to confirm our analysis of the reason of the non-monotonic variation of \\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$${\\Delta \\phi }$$\\end{document} Δ ϕ when \\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}$${\\eta > 6}$$\\end{document} η > 6 , we investigate the probability distribution of noise with different value of \\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$${\\eta }$$\\end{document} η . As shown in Fig. 8 , different values of \\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$${\\eta }$$\\end{document} η lead to different probability distribution of \\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$${\\Delta \\theta }$$\\end{document} Δ θ , which affect the value of \\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$${\\Delta \\phi }$$\\end{document} Δ ϕ . Figure 8 Probability distribution of noise \\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}$${P_{\\Delta \\theta }}$$\\end{document} P Δ θ when ( a ) \\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}$${\\eta = 3.0}$$\\end{document} η = 3.0 , ( b ) \\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}$${\\eta = 6.0}$$\\end{document} η = 6.0 , ( c ) \\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}$${\\eta = 7.0}$$\\end{document} η = 7.0 , ( d ) \\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$${\\eta = 10.0}$$\\end{document} η = 10.0 , ( e ) \\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}$${\\eta = 12.0}$$\\end{document} η = 12.0 , ( f ) \\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}$${\\eta = 15.0}$$\\end{document} η = 15.0 . This is consistent with the above analysis of noise. As shown in the inset of Fig. 7 a, the degree of the non-monotonic variation about the difference of order parameter is larger with the increasing of the interaction radius r . This is because the larger interaction radius improves the effect of velocity alignment on motional consensus, which makes the competition between velocity alignment and noise more intense. This makes the difference of order parameter \\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$${\\Delta \\phi }$$\\end{document} Δ ϕ more sensitive to the change of the distribution of noise."
} | 16,289 |
27606598 | PMC5015988 | pmc | 5,228 | {
"abstract": "Coral reefs are increasingly threatened by global and local anthropogenic stressors such as rising seawater temperature, nutrient enrichment, sedimentation, and overfishing. Although many studies have investigated the impacts of local and global stressors on coral reefs, we still do not fully understand how these stressors influence coral community structure, particularly across environmental gradients on a reef system. Here, we investigate coral community composition across three different temperature and productivity regimes along a nearshore-offshore gradient on lagoonal reefs of the Belize Mesoamerican Barrier Reef System (MBRS). A novel metric was developed using ultra-high-resolution satellite-derived estimates of sea surface temperatures (SST) to classify reefs as exposed to low (low TP ), moderate (mod TP ), or high (high TP ) temperature parameters over 10 years (2003 to 2012). Coral species richness, abundance, diversity, density, and percent cover were lower at high TP sites relative to low TP and mod TP sites, but these coral community traits did not differ significantly between low TP and mod TP sites. Analysis of coral life history strategies revealed that high TP sites were dominated by hardy stress-tolerant and fast-growing weedy coral species, while low TP and mod TP sites consisted of competitive, generalist, weedy, and stress-tolerant coral species. Satellite-derived estimates of Chlorophyll-a ( chl-a ) were obtained for 13-years (2003–2015) as a proxy for primary production. Chl-a concentrations were highest at high TP sites, medial at mod TP sites, and lowest at low TP sites. Notably, thermal parameters correlated better with coral community traits between site types than productivity, suggesting that temperature (specifically number of days above the thermal bleaching threshold) played a greater role in defining coral community structure than productivity on the MBRS. Dominance of weedy and stress-tolerant genera at high TP sites suggests that corals utilizing these two life history strategies may be better suited to cope with warmer oceans and thus may warrant protective status under climate change.",
"conclusion": "Conclusions High TP reefs exhibit lower coral diversity, abundance, species richness, and cover than do low TP and mod TP reefs in Belize. These high TP reefs are exposed to higher annual temperatures, greater temperature variability, more time above the regional bleaching threshold, elevated chl-a concentrations, and likely increased sedimentation rates and lower flow than low TP and mod TP reefs. Temperature parameters, most notably time spent above the bleaching threshold, correlate best with differences in coral community structure. In addition, stress-tolerant and weedy coral life history strategies dominate at high TP reefs. Due to exposure to generally more stressful environmental conditions, high TP reefs may offer a snapshot into the projected future of coral reefs as they become increasingly exposed to local (pollution, runoff, land-use change, and overpopulation) and global (warming and acidification) stressors. Previously, such reefs have been suggested as possible refugia against climate change [ 84 ]. Globally, this would mean a shift towards dominance of stress-tolerant and weedy corals [ 53 ]. Such a shift would dramatically impact the structure and function of reefs, essentially creating novel ecosystems [ 85 ]. High TP reefs should be protected in addition to more pristine reefs in order to improve conservation success [ 35 ]. More pristine reefs should be protected as they contain more diversity and provide more ecosystem services than do high TP reefs [ 86 ]. However, high TP reefs host coral holobionts that may be best suited to survive in future ocean conditions. To ensure survival and future success of reefs while maintain current diversity both heavily impacted and pristine ecosystems must be protected. The results of the current study highlight the need to better protect and understand impacted nearshore reef systems, including investigations into what conditions allow more sensitive species (e.g., competitive and generalist) to survive and persist on nearshore reefs.",
"introduction": "Introduction Coral reefs are threatened locally and globally by anthropogenic stressors such as warming induced by increasing greenhouse gas emissions, excessive nutrients from runoff and sewage effluent, overfishing, and habitat destruction [ 1 – 3 ]. Of particular concern are increasing greenhouse gas emissions that continue to cause warming of the global oceans [ 1 , 4 ]. This warming trend is especially troubling in the Caribbean Sea, where rates of warming are higher than in many other tropical basins [ 5 ], and where coral cover has declined by up to 80% in recent decades [ 6 ]. Elevated sea surface temperature (SST) is the major cause of the breakdown of the essential coral-algal symbiosis, which if widespread results in mass coral bleaching [ 7 , 8 ]. In Belize, the 1998 El Niño bleaching event was the most significant bleaching induced mass coral mortality event on lagoonal reefs over the last 3000 years [ 9 ]. These large-scale coral bleaching events are projected to increase in frequency and severity as the climate continues to warm [ 4 , 10 ]. In fact, if ocean warming persists, corals in the Caribbean Sea are predicted to bleach biannually within the next 20–30 years [ 11 ], with annual bleaching events occurring as soon as 2040 [ 12 ]. Caribbean-wide and global-scale bleaching events are predicted to continue unless corals can increase their thermal tolerance at a rate of 0.2–1.0°C per decade [ 4 ]. Annual and daily thermal variability have recently been identified as important factors influencing coral thermal tolerance [ 13 – 15 ]. Indeed, previous exposure to thermally variable environments increases a coral’s tolerance to future temperature stress [ 14 , 16 – 18 ], and research suggests that Pacific and Red Sea corals living in areas with high summer maximum SST are less susceptible to bleaching [ 19 , 20 ]. Along the Belize Mesoamerican Barrier Reef System (MBRS) and on Pacific Atolls, corals historically exposed to less thermal variability exhibited slower growth rates and/or greater susceptibility to bleaching in response to SST increases [ 17 , 18 ]. In the Florida Keys, coral growth rates and coral cover were higher in nearshore environments exposed to more variable seawater temperatures than on deeper reefs experiencing more stable temperatures [ 21 ]. In contrast, while many studies suggest that high temperature variability leads to higher coral resilience [ 14 – 16 ], there is also evidence that corals experiencing moderate long term temperature variability (either annual or daily variation) are better able to cope with stress [ 13 ]. Collectively, these studies emphasize the importance of thermal variability on the response of corals to environmental stress, and highlight its capacity to shape coral community composition across a reef system. Multi-species coral assemblages have recently been proposed to comprise four major life history guilds: competitive (large, fast growing, broadcast spawning, e.g., Caribbean Acropora spp .), weedy (small, opportunistic colonizers of recently disturbed habitat, e.g., Caribbean Porites spp .), stress-tolerant (massive, slow growing, broadcast spawning, e.g., Siderastrea siderea ), and generalist (share traits characteristic of all three other groups, e.g., Orbicella spp .) [ 22 ]. Grouping species by life history strategy allows for prediction of responses to disturbance (e.g., temperature stress) as life history strategies are trait based [ 23 ]. Additionally, each guild is expected to be differentially impacted by stressors and life histories predict coral community response to multiple stressors [ 24 ]. Therefore, life history strategies offer a more elegant and predictive alternative to traditional genus or species level analysis. Competitive corals are by definition not very stress tolerant [ 22 ]. As such, region-wide decline of these species would be expected as the impact of anthropogenic stressors increase (including coral disease). This decline has already occurred in the Caribbean [ 6 ]. Generalist corals became dominant on Caribbean reefs in the late 1970s following mass die off of competitive corals. Generalists are more stress tolerant than competitive species but bleaching and other stressors have led to high mortality of Orbicella spp . in the Caribbean [ 25 ] and continued decline is expected as temperature stress increases [ 6 , 26 , 27 ], leading to a decline in reef complexity [ 28 ] Weedy and stress tolerant corals have been shown to be more resilient than competitive and generalist species [ 22 , 24 ], and are hypothesized to dominate warmer and more impacted reefs (e.g., reefs closer to the shore). A shift from dominance of competitive and generalist species to weedy and stress tolerant species occurred on Okinawan reefs following the 1998 El Niño bleaching event [ 29 , 30 ] and an overall decline in coral cover and abundance currently occurring in the Caribbean has been coupled with an increase in abundance of weedy species [ 27 , 31 ]. Interestingly, fossil assemblages from excavated pits on reefs in Panama reveal that mortality and changes in reef communities caused by anthropogenic impact (such as land clearing and overfishing) predate mass bleaching events, indicating that other sub-lethal stressors can impact coral community structure [ 32 – 34 ]. Collectively, evidence suggests that differential responses between coral species to increasing anthropogenic stressors may lead to community scale shifts in reef composition from dominance of competitive and generalist species to dominance of stress tolerant and weedy species. The purpose of the current study was to investigate the impact of thermal regimes on present day coral community composition (coral abundance, species richness, diversity, percent cover, density, and life history strategies) of lagoonal reefs (i.e., region extending from the barrier reef’s crest to the mainland) across the Belize MBRS. A novel GIS-based metric was developed to characterize lagoonal reefs across this reef system into three thermally distinct regimes. Within these three regimes, thirteen reef sites were identified and benthic surveys were conducted to quantify coral community composition. These thermal regimes exist along a nearshore-offshore productivity gradient, which may also influence coral community structure. Quantifying coral community differences among these thermally distinct reefs will help us better predict how coral community structure may be impacted by climate change. Identifying which areas and species are best able to cope with environmental stress (and which are least able) may allow for more targeted management strategies, as it is important to protect both high-risk and low-risk reef sites to improve our chances of conservation success [ 35 ].",
"discussion": "Discussion Coral community composition Coral species richness, abundance, diversity, density, and percent cover were all lower at high TP sites compared to low TP and mod TP sites ( Fig 2 ). Differences in coral community composition between high TP sites and low TP /mod TP sites are historically explained by more stressful conditions nearshore and less stressful conditions offshore [ 46 , 47 ]. These nearshore stressors include, but are not limited to temperature, eutrophication, sedimentation, and wave energy [ 46 , 47 ]. Our findings suggest that lower coral species richness, diversity, abundance, percent cover, and density at high TP sites may be driven by high thermal variability, elevated maximum temperatures, and prolonged duration of exposure to temperatures above the bleaching threshold; three variables that have been shown to cause coral community decline [ 13 , 29 , 48 – 50 ]. These temperature parameters were more strongly correlated with changes in coral community composition between site types than with chl-a ( S3 Fig ), indicating that they likely play a greater role in determining coral community composition than productivity. High weekly thermal variability has also been shown to correlate with low coral cover on nearshore reefs in the Florida Keys [ 13 ]. Therefore, differences in thermal variability observed across site types may have influenced coral community composition in Belize. Our findings are contrary to the results of Soto et al . (2011) [ 13 ], which showed that reef sites with moderate temperature variability (equivalent to mod TP sites in the current study) in Florida had higher coral cover than sites exposed to low (offshore deep reefs) or high temperature variability. Soto et al . (2011) [ 13 ] suggests that corals exposed to moderate weekly thermal variation are able acclimatize to a wide range of environmental conditions, making them more resilient than corals that experience less variation. At the same time, corals exposed to extremely high thermal variation generally do not survive[ 13 ]. Our results may contrast with that of Soto et al. (2011) because fore reef locations were not included in the present study (i.e., low TP sites are located in the back reef). Our high TP sites follow the same pattern seen in Soto et al . (2011) [ 13 ] as they have lower coral cover than mod TP sites ( Fig 2 ). Our results also contrast those of Lirman and Fong (2007) [ 21 ], which showed that nearshore reefs (equivalent to our high TP sites) exhibited higher coral cover and growth rates than offshore reefs (equivalent to our low TP sites) in the Florida Keys. Interestingly, these nearshore Florida reefs also experienced lower water quality than the offshore reefs [ 21 ]. The authors hypothesized that higher coral cover and growth rates on nearshore reefs were due to the ability of some corals to switch trophic mode under adverse conditions [ 21 ], a pattern that has been observed in previous studies, but was not quantified in the current study [ 51 , 52 ]. Differences in coral community composition between the Florida Reef tract and the Belize MBRS may explain our contrasting results in coral cover as nearshore patch reefs in Florida appear to have relatively high numbers of Orbicella spp . [ 21 ], whereas high TP sites in Belize were almost void of this species. Life history strategies In the current study, high TP sites contained no competitive species, few generalists, and were dominated by stress-tolerant and weedy genera, while both low TP sites and mod TP sites contained all 4 life history types ( Fig 4 ). Low TP sites contained all four life history strategies in roughly equal proportions. Mod TP sites were similar but with fewer competitive species than low TP sites, and high TP sites had comparatively fewer of all four life histories, but were dominated by weedy and stress tolerant genera. Shifts toward weedy and stress tolerant genera under climate change conditions were predicted by Darling et al . (2012) [ 22 ], and have been recorded in many areas of the world [ 29 , 53 ], including the Caribbean [ 25 , 31 , 54 ]. Even in the face of region-wide decline in coral cover and decrease in abundance of competitively dominant species [ 6 ], some weedy species, such as Porites astreoides , are actually increasing in prevalence within the Caribbean [ 31 ]. This weedy coral species is likely able to succeed in high stress environments due to its ability to brood and mature quickly, which allows it to rapidly colonize a recently disturbed area [ 22 , 31 ]. In contrast, a stress-tolerant species such as S . siderea is likely able to survive in high TP environments due to its massive size and long life span, which allows it to sustain a population in the absence of successful recruitment. This can increase the long-term survival potential of this species in harsh conditions [ 55 ]. These two contrasting strategies seem most effective in high TP environments ( Fig 4 ), and are likely to be most effective in future conditions as the oceans continue to warm. This prediction is corroborated by Loya et al . (2001) [ 29 ], who showed that mounding (e.g., S . siderea ) and encrusting (e.g., P . astreoides ) species survived a mass bleaching event in 1997–1998 better than corals of other morphologies (e.g., branching). Ten years after the bleaching event these same types of coral continued to dominate. However, some branching species recovered and increased in abundance [ 56 ]. In the current study, branching species were almost non-existent in high TP sites, which indicates that these sites have experienced a recent thermal stress event or are exposed to chronic stress (e.g., temperature, eutrophication) that prevents such species from succeeding in these environments. It is also possible that high TP sites are more frequently disturbed than both low TP and mod TP sites. Disturbances such as bleaching events, eutrophication, sedimentation, and overfishing are known to cause declines in coral cover, species richness, and diversity [ 29 , 30 ]. These more disturbed or impacted reefs can then become dominated by stress-tolerant corals and corals that quickly colonize areas after a perturbation (i.e., weedy corals) [ 13 , 29 , 30 , 57 ], as observed in the current study ( Fig 4 ). Historical and/or geological investigation of reef assemblages (i.e., through pit excavating or coring of reef framework [ 9 , 32 , 34 ]) would be a useful next step, as it would allow insight into how reef communities within the three thermal regimes have changed after disturbances and over long periods of time. Influence of primary productivity on coral community composition Cross-reef chl-a concentrations follow the same patterns as temperature (elevated nearshore and decreasing with increasing distance from the Belize coast) ( Fig 1 , S2 Fig ). This means that reefs with higher chl-a concentrations have lower coral species richness, abundance, diversity, density, and percent cover. This supports a previous finding that shows a strong negative relationship between chl-a and coral cover, species richness, and abundance at nearshore reefs on the Great Barrier Reef (GBR) [ 58 ]. However, our results reveal that chl-a concentrations are not strongly correlated (R 2 = 0.040) with changes in coral community structure (e.g., percent cover, abundance, diversity, species richness, and density) across site types ( S3J Fig ), suggesting that chl-a concentrations may not best explain differences in community composition between site types in Belize. This may be due to spatial scale (e.g., we focused on nearshore, patch reef, and back reef sites as opposed to exclusively nearshore sites) [ 58 ], or the coarse scale of the chl-a dataset (4 km x 4 km grid; each survey site is <1 km). Focusing on variation within nearshore (high TP ) sites, we do see a correlation between chl-a and changes in coral community structure ( S3E Fig ), which supports results from previous work [ 58 , 59 ]. Other potential factors influencing coral community structure across reef types Eutrophication Eutrophication has led to local degradation of reefs [ 60 – 62 ]. However, larger scale (regional) reef degradation due to nutrients alone has not been quantitatively shown [ 63 ]. Wooldridge (2009) [ 64 ] demonstrates that lower water quality (e.g., higher nutrient concentrations) are linked to lower bleaching thresholds on nearshore reefs in Australia. If bleaching thresholds are depressed at high TP sites for some species, it may help explain lower diversity measured at these sites, as they experience warmer temperatures and spend more time above the regional bleaching threshold than do mod TP and low TP sites ( S2 Fig ). While chl-a does not correlate well with changes in coral community structure in this study ( S3 Fig ), it should be noted that chl-a is an estimate of nutrient delivery and primary productivity, not a measurement of the concentration of any one nutrient pool. Due to this limitation, manipulative field experiments such as Vega-Thurber et al. (2014)[ 65 ] and Zaneveld et al. (2016)[ 66 ] are needed to understand the influence of nutrients on coral community structure and bleaching thresholds at local scales. Sedimentation Coastal (nearshore) reefs throughout Belize are influenced by runoff from smaller local rivers, and reefs in southern Belize experience additional runoff and river plumes originating from larger watersheds in Honduras and Guatemala [ 67 , 68 ]. It has been previously shown that Orbicella faveolata corals on reefs with higher sedimentation rates exhibited suppressed skeletal extension rates for a longer duration than corals on reefs with lower sedimentation rates following the 1998 bleaching event in Belize [ 69 ]. In contrast, increased sedimentation did not affect skeletal extension of S . siderea or P . astreoides corals in Puerto Rico [ 70 ]. The results of these two studies suggest that there may be species-specific responses to increased sedimentation rates. In Barbados, reefs with high sedimentation rates were dominated by coral species with high recruitment and high natural mortality (e.g., P . astreoides ) and reefs with lower sedimentation rates were dominated by coral species with lower recruitment and low natural mortality (e.g., boulder corals) [ 71 ]. As sedimentation rate was not quantified in this study, the impacts of sedimentation on coral community structure are not clear. Circulation and wave energy The Belize MBRS lies west of the Honduras Gyre, a hydraulic feature that recirculates water inside the Cayman basin [ 72 ]. The coastal waters of northern Belize are influenced by the Cayman and Yucatan currents, which move water northwest up the coastline toward Mexico [ 72 – 75 ]. In central and southern Belize, current velocities are lower and dominant circulation patterns are less consistent throughout the year [ 74 ]. However, currents appear to bring water and potentially pollution, nutrients, or sediment plumes from coastal Honduras and Guatemala west to southern Belize where they recirculate before slowly moving northward [ 67 , 68 , 74 – 78 ]. These circulation patterns have the potential to influence the stress tolerance of corals across sites and latitude in the current study. Our results reveal no spatial autocorrelation between sites for any of our measured variables with the exception of chl-a suggesting that the influence of these currents may be minimal. Additionally, wave energy may play a role in shaping coral communities. Wave energy may be elevated at low TP sites as they are located near channels in the fore reef and may not be as sheltered by the reef crest as other mod TP . Similarly, wave energy may be elevated at high TP sites due to the large fetch between the reef crest and nearshore reefs and the prevailing wind direction from offshore to inshore. Light Irradiance (light intensity) has been shown to decrease along an offshore-nearshore gradient on the GBR as chl a concentrations increase [ 79 ]. Chlorophyll-a concentrations increase with proximity to shore in Belize ( Fig 1 ), so this pattern of decreasing light intensity towards the nearshore likely holds for Belize as well. However, in southern Belize offshore reefs (and nearshore reefs) are subject to seasonal sedimentation and runoff from larger rivers in Honduras and Guatemala [ 77 , 78 ]. Irradiance is a known stressor, proven to cause coral bleaching alone or in consort with elevated temperatures [ 80 ]. Although depth was held constant in the present study, it is possible that differing light levels both between site types and between individual sites may influence coral community composition across the site types investigated in the current study. Proximity to human populations Declining health of coral reefs worldwide has been linked to land-based stressors including nutrients and human use and exploitation (e.g., overfishing) [ 60 , 80 , 81 ] as well as proximity to sources of these stressors (e.g., major human population centers) [ 82 ]. However, not all reefs that are near to or influenced by land-based stressors are unhealthy [ 21 , 83 ]. Some of the study sites were within close proximity to a major human population center, particularly the high TP sites (populations of major towns and cities in Belize can be seen in S4 Table ). Analysis of spatial autocorrelation revealed no significant differences between high TP sites or between high TP sites and sites that were further offshore, suggesting that proximity to human population centers did not have a major impact on coral community composition."
} | 6,199 |
35530790 | PMC9072994 | pmc | 5,229 | {
"abstract": "A tannin-based hybrid coating was coated on the PVDF membrane surface through a simple one-step co-deposition of tannin and KH550. A micro/nano hierarchical structure was formed on the PVDF membrane surface through hydrolysis/condensation of KH550 and Michael addition reaction between oxidized tannin and an amino group revealed by the field-emission scanning electron microscopy, atomic force microscopy and Fourier transform infrared spectroscopy, which established a harsh surface. Abundant hydrophilic groups and high surface roughness endowed the modified membranes with high hydrophilicity and underwater superoleophobicity. The modified PVDF membranes possess excellent oil/water separation and antifouling performance due to the underwater superoleophobicity. Moreover, the modified membrane exhibited outstanding stability."
} | 208 |
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