Biology subjects

Soyer, O. S.

Publications and source records attributed to Soyer, O. S..

8 recordsLinked to original sources

bioRxiv2024

Ammonia leakage can underpin nitrogen-sharing among soil microbes

AbstractSoil microbial communities host a large number of microbial species that support important ecological functions such as biogeochemical cycling and plant nutrition. The extent and stability of these functions are affected by inter-species interactions among soil microbes, yet the different mechanisms underpinning microbial interactions in the soil are not fully understood. Here, we study the extent of nutrient-based interactions among two model, plant-supporting soil microbes, the fungi Serendipita indica and the bacteria Bacillus subtilis. We find that S. indica is unable to grow with nitrate - a common nitrogen source in the soil - but this inability can be rescued, and growth restored in the presence of B. subtilis. We demonstrate that this effect is due to B. subtilis utilising nitrate and releasing ammonia, which can be used by S. indica. We refer to this type of mechanism as ammonia mediated nitrogen sharing (N-sharing). Using a mathematical model, we demonstrate that the pH dependent equilibrium between ammonia (NH3) and ammonium (NH4+) results in an inherent cellular leakiness, and that reduced amonnium uptake or assimilation rates can result in higher levels of leaked ammonia. In line with this model, a mutant B. subtilis - devoid of ammonia uptake - shows higher S. indica growth support in nitrate media. These findings highlight that ammonia based N-sharing can be a previously under-appreciated mechanism underpinning interaction among soil microbes and could be influenced by microbial or abiotic alteration of pH in microenvironments. Significance statementSoil microbial communities are an important factor in environmental nutrient cycling and sub-sequently plant health. S. indica is a well-studied plant growth promoting soil fungus but its inability to use nitrate, a major component of both agricultural/natural soils and crop fertilisers, may have important implications for agriculture and microbial ecology. We have demonstrated that S. indica is dependant on external sources of nitrogen in nitrate-only environments and these can be produced by B. subtilis, another common soil microbe. We then demonstrate that this nitrogen sharing interaction is likely mediated by leaked ammonia and that ammonia leakage is influenced by environmental pH. Ammonia leakage and sharing represent currently unexplored and potentially vital components of nutrient interactions between microbes in soil communities, with profound implications for microbiome community structure and subsequent consequences for soil biogeochemical cycling and crop health.

microbiology↗

bioRxiv2024

Cellular (de)coordination in gliding motility and plectoneme formation

2Cyanobacteria are key contributors to biogeochemical cycles through photosynthesis and carbon fixation. In filamentous, multicellular cyanobacteria these functions can be influenced through gliding motility, which enables filaments to localise in response to light and also form aggregates. Here, we use the aggregate forming species Fluctiforma draycotensis to study gliding motility dynamics in detail. We find that filaments move in curved and straight trajectories interspersed with re-orientation or reversal of direction. Most reversals take few seconds but some take substantially longer, resulting in a long-tailed distribution of stoppage times. Mean filament speeds range around a micron per second with a relatively uniform distribution against filament length, implying that all or fixed proportion of cells in a filament contribute to movement. We implement a biophysical model that can recapitulate these findings. Model simulations show that for filaments to reverse quickly, cells in a filament must achieve high coordination of the direction of the forces that they generate. To seek experimental support of this prediction, we track individual cells in a filament. This reveals that cells translational movement is fully coupled with their rotation along the long-axis of the filament, and that cellular movement remains coordinated throughout a reversal. For some filaments, especially longer ones, however, we also find that cellular coordination can be lost, and filaments can form buckles that can twist around themselves, resulting in plectonemes. The experimental findings and the biophysical model presented here will inform future studies of individual and collective filament movement. 1 Significance StatementCyanobacteria contribute to global oxygen production and carbon capture. Some cyanobacteria exist as multicellular filaments and display gliding motility that allows them to respond to light and to form aggregates, which influences their biological functions. Here, we study the dynamics of gliding motility. We find that filaments movement is interspersed with re-orientation or reversal of direction and that mean filament speed is mostly independent of filament length. We implement a biophysical model that predicts these features to relate to cells in a filament having high coordination of the direction of the forces that they generate. We find experimental support for this predicted cellular coordination, but also discover instances of longer filaments loosing coordination, resulting in buckling and entangling with other filaments.

biophysics↗

bioRxiv2022

Co-substrate pools can constrain and regulate pathway fluxes in cell metabolism

Cycling of co-substrates, whereby a metabolite is converted among alternate forms via different reactions, is ubiquitous in metabolism. Several cycled co-substrates are well known as energy and electron carriers (e.g. ATP and NAD(P)H), but there are also other metabolites that act as cycled co-substrates in different parts of central metabolism. Here, we develop a mathematical framework to analyse the effect of co-substrate cycling on metabolic flux. In the cases of a single reaction and linear pathways, we find that co-substrate cycling imposes an additional flux limit on a reaction, distinct to the limit imposed by the kinetics of the primary enzyme catalysing that reaction. Using analytical methods, we show that this additional limit is a function of the total pool size and turnover rate of the cycled co-substrate. Expanding from this insight and using simulations, we show that regulation of co-substrate pool size can allow regulation of flux dynamics in branched and coupled pathways. To support theses theoretical insights, we analysed existing flux measurements and enzyme levels from the central carbon metabolism and identified several reactions that could be limited by co-substrate cycling. We discuss how the limitations imposed by co-substrate cycling provide experimentally testable hypotheses on specific metabolic phenotypes. We conclude that measuring and controlling co-substrate pools is crucial for understanding and engineering the dynamics of metabolism.

systems biology↗

bioRxiv2021

Novel microbial syntrophies identified by longitudinal metagenomics

Identifying species interactions in a microbial community and how this relates to community function is a key challenge. Towards addressing this challenge, we present here an extensive genome-resolved, longitudinal dataset and associated metadata. We collected weekly samples of microbial communities and recorded operating conditions from industrial methane producing anaerobic digestion reactors for a year. This allowed us to recover 2240 dereplicated metagenome assembled genomes (dMAGs), together with their coverage dynamics and functional annotations from which functional traits were inferred. Of these dMAGs, 1910 were novel species, with 22 representing novel orders and classes. Methanogenic communities are expected to be strongly structured by syntrophic and other associations between the methanogens and syntrophs that produce their substrates. We identified 450 potential syntrophic dMAGs by searching for pairs of methanogenic and non-methanogenic dMAGs that had highly correlated time-series. Genomes of potential syntrophs were enriched for oxidoreductases and sugar transport genes and there was a strong taxonomic signal in their associations with methanogens. Of particular note, we found that Bathyarchaeiea associated specifically with methanogens from the Thermoplasmata, and Thermococci classes. Same syntrophic associations were only rarely observed across multiple reactors, suggesting that syntrophies might be facultative, with particular strains within a species forming syntrophic associations only sometimes and not necessarily always with the same methanogenic partner. The presented results show that longitudinal metagenomics is a highly valuable approach for identifying species and their interactions in microbial communities. One Sentence SummaryLongitudinal study of microbial communities identifies novel species and predicts their interactions and role in community function.

microbiology↗

bioRxiv2020

Metagenomics Strain Resolution on Assembly Graphs

We introduce a novel bioinformatics pipeline, STrain Resolution ON assembly Graphs (STRONG), which identifies strains de novo, when multiple metagenome samples from the same community are available. STRONG performs coassembly, followed by binning into metagenome assembled genomes (MAGs), but uniquely it stores the coassembly graph prior to simplification of variants. This enables the subgraphs for individual single-copy core genes (SCGs) in each MAG to be extracted. It can then thread back reads from the samples to compute per sample coverages for the unitigs in these graphs. These graphs and their unitig coverages are then used in a Bayesian algorithm, BayesPaths, that determines the number of strains present, their sequences or haplotypes on the SCGs and their abundances in each of the samples. Our approach both avoids the ambiguities of read mapping and allows more of the information on co-occurrence of variants in reads to be utilised than if variants were treated independently, whilst at the same time exploiting the correlation of variants across samples that occurs when they are linked in the same strain. We compare STRONG to the current state of the art on synthetic communities and demonstrate that we can recover more strains, more accurately, and with a realistic estimate of uncertainty deriving from the variational Bayesian algorithm employed for the strain resolution. On a real anaerobic digestor time series we obtained strain-resolved SCGs for over 300 MAGs that for abundant community members match those observed from long Nanopore reads.

bioinformatics↗

bioRxiv2019

Thermodynamic modelling of synthetic communities predicts minimum free energy requirements for sulfate reduction and methanogenesis.

Microbial communities are complex dynamical systems harbouring many species interacting together to implement higher-level functions. Among these higher-level functions, conversion of organic matter into simpler building blocks by microbial communities underpins biogeochemical cycles and animal and plant nutrition, and is exploited in biotechnology. A prerequisite to predicting the dynamics and stability of community-mediated metabolic conversions, is the development and calibration of appropriate mathematical models. Here, we present a generic, extendable thermodynamic model for community dynamics accounting explicitly for metabolic activities of composing microbes, system pH, and chemical exchanges. We calibrate a key parameter of this thermodynamic model, the minimum energy requirement associated with growth-supporting metabolic pathways, using experimental population dynamics data from synthetic communities composed of a sulfate reducer and two methanogens. Our findings show that accounting for thermodynamics is necessary in capturing experimental population dynamics of these synthetic communities that feature relevant species utilising low-energy growth pathways. Furthermore, they provide the first estimates for minimum energy requirements of methanogenesis and elaborates on previous estimates of lactate fermentation by sulfate reducers. The open-source nature of the developed model and demonstration of its use for estimating a key thermodynamic parameter should facilitate further thermodynamic modelling of microbial communities.

microbiology↗

bioRxiv2018

Machine learning based prediction of functional capabilities in metagenomically assembled microbial genomes

The increasing popularity of genome resolved meta genomics - the binning of genomes of potentially uncultured organisms direct from the environmental DNA - has resulted in a deluge of draft genomes. There is a pressing need to develop methods to interpret this data. Here, we used machine learning to predict functional and metabolic traits of microbes from their genomes. We collated an extensive database of 84 phenotypic traits associated with 9407 prokaryotic genomes and trained different machine learning models on this data. We found that a lasso logistic regression based on the frequency of gene orthologs had the best combination of functional prediction performance and interpretability. This model was able to classify 65 phenotypic traits with greater than 90

microbiology↗

bioRxiv2017

The most efficient microbial community dominates during community coalescence

Microbial communities commonly coalesce in nature, but the consequences for resultant community structure and function is unclear. Consistent with recent theory, we demonstrate using methanogenic communities that the most productive communities in isolation dominated when communities were mixed. As a corollary of this dynamic, total methane production increased with the number of inoculated communities. The cohesion and dominance of single communities was explained by more \"niche-packed\" communities being both more efficient at exploiting resources and resistant to invasion, rather than a function of the average performance of component species. These results are likely to be relevant to the ecological dynamics of natural microbial communities, as well as demonstrating a simple method to predictably enhance microbial community function in biotechnology, health and agriculture.

microbiology↗