Biology subjects

Schaap, P. J.

Publications and source records attributed to Schaap, P. J..

At least 19 recordsLinked to original sources

bioRxiv2026

Unraveling the metabolic interactions of a Dehalobacter-containinganaerobic mixed culture for bioremediation

Organohalide-respiring bacteria (OHRB), such as Dehalobacter, play key roles in the bioremediation of anoxic environments contaminated with chlorinated aromatic compounds. These obligate anaerobes rely on syntrophic interactions to obtain essential resources--hydrogen, acetate, and corrinoid cofactors--from acetogens and fermenters. However, the metabolic interactions enabling complete reductive dehalogenation of compounds like 1,2,4-trichlorobenzene (1,2,4-TCB) to benzene remain incompletely understood. In this study, we asked: (1) What are the key microbial taxa and their functional roles within a Dehalobacter-containing anaerobic microbial community detoxifying chlorinated benzenes? (2) How do syntrophic interactions enable complete dehalogenation of 1,2,4-TCB to benzene under anaerobic conditions? (3) Can genome-resolved metagenomics and genome-scale metabolic modeling elucidate the metabolic dependencies supporting organohalide respiration in complex consortia? To address these questions, we cultivated microbial communities in batch reactors using methanol as electron donor and either 1,2,4-TCB or monochlorobenzene (MCB) as electron acceptor. In active MCB-fed cultures, benzene increased from 0 to 62.3{micro}mol per bottle while MCB decreased from 88.3 to 22.0{micro}mol per bottle over 120 days, with this pattern repeating across multiple substrate additions. Using genome-resolved metagenomics to identify dominant taxa and select 12 high-quality metagenome-assembled genomes (MAGs) for modeling, we reconstructed genome-scale metabolic models (GEMs) to identify candidate metabolic interactions and predict syntrophic dependencies that may support organohalide respiration in these consortia. Community flux sampling predicted that methanol, H2, acetate, and CO2 formed the dominant exchange backbone of the modeled community, while also indicating competition for shared electron donors between the two Dehalobacter populations. Model-guided minimal-community analysis further identified a narrow dechlorinating core in which all feasible minimal consortia retained a Dehalobacter member together with Methanothrix. These results provide a modeling-informed framework for hypothesis generation and future experimental validation of anaerobic consortia relevant to bioremediation.

systems biology↗

bioRxiv2025

A metadata managed FAIR end-to-end workflow for microbial community Omics data analysis

BackgroundMolecular profiling using high-throughput omics technologies has tremendously increased our ability to interrogate complex microbial communities at the molecular level. In the context of data reuse, the FAIRification of these extensive datasets is frequently perceived as a secondary administrative task, addressed only after data analysis has been completed. However, this approach overlooks the potential benefits of early metadata integration as the procedures for processing and analyzing raw data are primarily dictated by the underlying research design and experimental conditions. Gathering interoperable research metadata at the earliest stages creates a standardized basis for managing, processing, and analyzing data enabling more efficient and reproducible FAIR workflows. ResultsThe single containment principle was used to develop modular containerized reproducible workflows that support the FAIR principles for research software by systematically capturing standardized metadata for each data processing step along with the resulting data products. Using defined mock metagenomic datasets as an example, we show that interoperable research metadata can be used to drive such computational workflows. By processing raw data accordingly, machine-actionable provenance chains are created that enhance the reproducibility and reusability of the resulting data products. ConclusionsA seamless integration of wet lab experiments with computational investigations is essential for a FAIR end-to-end research process. Meta-data-managed workflows prevent the need for unnecessary data manipulation. Workflow provenance registration explicates the complex multi-step methods employed for data processing and analysis. Combining FAIR principles with data provenance registration enhances the reusability of omics datasets by promoting transparency and reproducibility. O_TEXTBOXKey PointsO_LIWe present the first metadata-managed FAIR end-to-end workflow for microbial community Omics data analysis. C_LIO_LIThe framework links experiment metadata with CWL workflows, enabling seamless metadata-driven execution from experiment to data product. C_LIO_LIComprehensive provenance tracking using RDF standards creates machine-actionable chains connecting wet-lab protocols to computational outputs, enhancing reproducibility and supporting automated workflow validation. C_LIO_LIBenchmarking with defined mock communities demonstrates workflow reliability across assembly methods while generating FAIR-compliant research objects suitable for community reuse and comparative studies. C_LI C_TEXTBOX

bioinformatics↗

bioRxiv2025

MiGenPro: A linked data workflow for phenotype-genotype prediction of microbial traits using machine learning.

The availability of microbial genomic data and the development of machine learning methods have created a unique opportunity to establish associations between genetic information and phenotypes. Here, we introduce a computational workflow for Microbial Genome Prospecting (MiGenPro) that combines phenotypic and genomic information. MiGenPro serves as a workflow for the training of machine learning models that predict microbial traits from genomes that have been annotated. Microbial genomes have been consistently annotated and features were stored in a semantic framework that is easy to query using SPARQL. The data was used to train machine learning models and successfully predicted microbial traits such as motility, Gram stain, oxygen requirement, optimal temperature range, and sporulation capabilities. To ensure robustness, a hyperparameter halving grid search was used to determine optimal parameter settings followed by five randomised train-test split validations which demonstrated consistent model performance across iterations and without overfitting. Effectiveness was further validated through comparison with existing models, showing comparable accuracy, with modest variations attributed to differences in datasets rather than methodology. Classification can be further explored using feature importance characterisation to identify biologically relevant genomic features. MiGenPro provides an easy to use interoperable workflow to build and validate models to predict phenotypes from microbes based on their annotated genome. Key MessagesO_LIMiGenPro merges phenotypic and genomic data for microbial trait prediction using machine learning. C_LIO_LIMiGenPro mitigates phenotype prediction model constraints by leveraging linked data technologies. C_LIO_LIMiGenPros FAIR design enables adaptation for various phenotypes with available training data. C_LI

bioinformatics↗

bioRxiv2025

Metabolic modeling unveils potential probiotic roles of Flavonifractor plautii in reshaping the Western gut microbiota landscape

Flavonifractor plautii, a prevalent gut commensal, uniquely combines flavonoid degradation with the capacity to produce health-promoting short-chain fatty acids (SCFAs), notably butyrate and propionate. However, its metabolic pathways, ecological roles, and health impacts remain poorly characterized. To explore its probiotic potential and ecological functions, we developed a genome-scale metabolic model (GEM), iFP655, using automated reconstruction, deep-learning-based gap-filling, thermodynamic constraints, and transcriptomics. The iFP655 model substantially improved the predictions of growth rates and SCFA profiles compared to previous models. Simulations identified acetyl-CoA pathways as the preferred route for butyrate production, whereas the energetically costly lysine pathway remained inactive despite robust gene expression. Propionate synthesis occurred primarily via the methylmalonyl-CoA pathway. Community metabolic modeling with representative species of a Western minimal gut microbiota highlighted F. plautii s contributions to enhanced SCFA production, especially butyrate, amino acid metabolism, and syntrophic interactions driven by dietary substrates. Our findings indicate that diet-driven syntrophy significantly shapes microbial community structure and function, underscoring the ecological importance of F. plautii in gut microbial interactions and highlighting its potential as a probiotic candidate to beneficially modulate gut microbiota through dietary interventions.

systems biology↗

bioRxiv2024

Implementation of a Clostridium luticellarii genome-scale model for upgrading syngas fermentations

Syngas fermentation is a powerful platform for converting waste streams into sustainable carboxylic acid precursors for value-added biochemicals. Steel mills produce significant syngas, yet industrial microbial syngas valorization remains unrealized. The most promising syngas-converting biocat-alysts consist of Clostridia species, such as Clostridium kluyveri, Clostridium autoethanogenum, and Clostridium ljungdahlii. Clostridium luticellarii, a recently discovered species, shares close phylogenetic ties with these organisms. Preliminary metabolic studies suggest its potential for syngas acetogenesis as well as chain elongation. In this study, we create iSJ444, a constraint-based metabolic model of C. luticellarii using iHN637 of a close relative C. ljungdahlii as a starting point. Model predictions support hypothesized methanol and syngas pathways from the metabolic characterization studies; however, the use of propionate could not be accurately predicted. Thermodynamic Flux Analysis (TFA) reveals that C. luticellarii maintains stable energy dissipation across most reactions when exposed to varying pH, with significant increases observed in reactions associated with the Wood-Ljungdahl pathway (WLP), such as the HACD1 reaction, at higher pH (6.5), suggesting an adaptive role in energy management under neutral conditions. Flux sampling simulations exploring metabolic flux distributions show that C. luticellarii might fit into syngas fermenting platforms. In both cases, high hydrogen-to-carbon source ratios result in better production of (iso)butyrate and caproate. We present a minimal genome-scale metabolic model of C. luticellarii as a foundation for further exploration and optimization. Although our predictions of its metabolic behavior await experimental validation, they underscore the potential of C. luticellarii to enhance syngas fermentation platforms. HighlightsO_LIiSJ444 models C. luticellarii metabolism for syngas fermentation and chain elongation. C_LIO_LIThermodynamic flux analysis (TFA) reveals adaptive energy balancing in pathways. C_LIO_LISimulations highlight C. luticellarii as a producer of value-added biochemicals like butyrate, isobutyrate, and caproate C_LIO_LIMetabolic insights from iSJ444 suggest efficient syngas conversion using varied substrates for industrial use. C_LI

systems biology↗

bioRxiv2024

Deep sequencing of 16 Ixodes ricinus ticks unveils insights into their interactions with endosymbionts.

BackgroundIxodes ricinus ticks act as vectors for numerous pathogens that present substantial health threats. Additionally, they harbour vertically transmitted symbionts, some of which have been linked to diseases. The difficulty of isolating and cultivating these symbionts has hampered our understanding of their biological role, their potential to cause disease, and their modes of transmission. To expand our understanding on the tick symbiont Midichloria mitochondrii and on Rickettsia helvetica, which has been linked to disease in humans, we utilized deep sequencing on sixteen individual adult female ticks collected from coastal dune and forested areas in the Netherlands. ResultsBy employing a combination of second and third-generation sequencing techniques, we successfully reconstructed the complete genomes of M. mitochondrii from eleven individuals, R. helvetica from eight individuals and the mitochondrial genome from all ticks. Additionally, we visualised the location of R. helvetica in tick organs and constructed genome-scale metabolic models (GEMs) of both symbionts to study their environmental dependencies. Our analysis revealed a strong cophylogeny between M. mitochondrii and mitochondrial genomes, suggesting frequent maternal transmission. In contrast, the absence of cophylogeny between R. helvetica and the mitochondrial genomes, coupled with its presence in the receptaculum seminis of I. ricinus females, raises the possibility of paternal transmission of R. helvetica. Notably, the genetic diversity of R. helvetica was found to be very low, except for the rickA virulence gene, where the presence of up to thirteen insertions of a33nt-long repeat led to significant variability. However, this variation could not account for the differences in infection prevalence observed across eight distinct locations in the Netherlands. ConclusionsBy employing deep sequencing, it becomes feasible to extract complete genomes and genetic data of symbionts directly from their host organisms. This methodology serves as a robust means to gain fresh insights into their interactions. Our observations, which suggest paternal transmission of R. helvetica, a relatively unexplored mode of transmission in ticks, require validation through experimental investigations. The genetic variations identified in the rickA virulence gene of R. helvetica have the potential to influence the infectivity and transmission dynamics of R. helvetica

microbiology↗

bioRxiv2024

Metatranscriptomic analysis indicates prebiotic effect of Isomalto/malto-polysaccharides on human colonic microbiota in-vitro

Isomalto/malto-polysaccharides (IMMPs) are a novel type of soluble dietary fibres with a prebiotic potential promoting growth of beneficial microbes in the gut. However, the mode of action of IMMPs remains unknown. Previous studies on IMMPs showed an increase in total bacteria, especially lactobacilli, and higher production of short chain fatty acids (SCFA) when IMMPs were fed to rats or used during in vitro fermentation. Here we investigated with metatranscriptomics how IMMPs with different amounts of -(1[->]6) glycosidic linkages affected microbial function during incubation with human faecal inoculum. We showed that active microbial community dynamics during fermentation varied depending on the type of IMMP used and that the observed changes were reflected in the community gene expression profiles. Based on metatranscriptome analysis, members of Bacteroides, Lactobacillus and Bifidobacterium were the predominant degraders of IMMPs, and the increased gene expression in these bacteria correlated with high amounts of -(1[->]6) glycosidic linkages. We also noted an increase in relative abundance of these bacteria and an activation of pathways involved in SCFA synthesis. Our findings could provide a baseline for more targeted approaches in designing prebiotics for specific bacteria and to achieve more controlled modulation of microbial activity towards desired health outcomes.

microbiology↗

bioRxiv2023

A Structured Evaluation of Genome-Scale Constraint-Based Modeling Tools for Microbial Consortia

Harnessing the power of microbial consortia is integral to a diverse range of sectors, from healthcare to biotechnology to environmental remediation. To fully realize this potential, it is critical to understand the mechanisms behind the interactions that structure microbial consortia and determine their functions. Constraint-based reconstruction and analysis (COBRA) approaches, employing genome-scale metabolic models (GEMs), have emerged as the state-of-the-art tool to simulate the behavior of microbial communities from their constituent genomes. In the last decade, many tools have been developed that use COBRA approaches to simulate multi-species consortia, under either steady-state, dynamic, or spatiotemporally varying scenarios. Yet, these tools have not been systematically evaluated regarding their software quality, most suitable application, and predictive power. Hence, it is uncertain which tools users should apply to their system and what are the most urgent directions that developers should take in the future to improve existing capacities. This study conducted a systematic evaluation of COBRA-based tools for microbial communities using datasets from two-member communities as test cases. First, we performed a qualitative assessment in which we evaluated 24 published tools based on a list of FAIR (Findability, Accessibility, Interoperability, and Reusability) features essential for software quality. Next, we quantitatively tested the predictions in a subset of 14 of these tools against experimental data from three different case studies: a) syngas fermentation by C. autoethanogenum and C. kluyveri for the static tools, b) glucose/xylose fermentation with engineered E. coli and S. cerevisiae for the dynamic tools, and c) a Petri dish of E. coli and S. enterica for tools incorporating spatiotemporal variation. Our results show varying performance levels of the best qualitatively assessed tools when examining the different categories of tools. The differences in the mathematical formulation of the approaches and their relation to the results were also discussed. Ultimately, we provide recommendations for refining future GEM microbial modeling tools. Author summaryConstraint-based modeling employing genome-scale reconstructions of microbial species has become one of the most successful approaches for studying, analyzing, and engineering microbial consortia. Over the past decade, many constraint-based modeling tools have been published to examine an immense variety of microbial consortia spanning from the application areas of bioremediation to food and health biotechnology. However, new potential users lack an overview of the quality and performance of existing metabolic modeling tools that would guide their choice. To tackle this issue, we examined 24 tools for genome-scale metabolic modeling of microbial consortia. After an initial qualitative screening, we quantitatively evaluated 14 adequate tools against published experimental data that included different organisms and conditions. We conducted simulations and evaluated model features such as predictive accuracy, computational time, and tractability in capturing critical physiological properties. We found that, generally, more up-to-date, accessible, and documented tools were superior in many important aspects of model quality and performance. Although, in some cases, we observed tradeoffs in older, less elaborate tools that can be more accurate or flexible. This work has broad implications to help researchers navigate the most suitable tools, and suggests to developers opportunities for improvement of the currently existing capabilities for metabolic modeling of multi-species microbial consortia.

systems biology↗

bioRxiv2022

Model-driven approach for the production of butyrate from CO2/H2 by a novel co-culture of C. autoethanogenum and C. beijerinckii.

One-carbon (C1) compounds are promising feedstocks for sustainable production of commodity chemicals. CO2 is a particularly advantageous C1-feedstock since it is an unwanted industrial off-gas that can be converted to valuable products while reducing its atmospheric levels. Acetogens are known microorganisms that can grow on CO2/H2 and syngas converting these substrates into ethanol and acetate. Co-cultivation of acetogens with microbes that can further process such products can expand the variety of products to, for example, medium chain fatty acids (MCFA) and longer chain alcohols. Solventogens are microorganisms known to produce MCFA and alcohols via the acetone, butanol and ethanol (ABE) fermentation in which acetate is a key metabolite. Thus, co-cultivation of a solventogen and acetogen in a consortium provides a potential platform to produce valuable chemicals from CO2. In this study, metabolic modelling was implemented to design a new co-culture of an acetogen and a solventogen to produce butyrate from CO2/H2 mixtures. The model-driven approach suggested the ability of the studied solventogenic species to grow on lactate/glycerol with acetate as co-substrate. This ability was confirmed experimentally by cultivation of Clostridium beijerinckii on these substrates in serum bottles and subsequently in pH-controlled bioreactors. Community modelling also suggested that a novel microbial consortium consisting of the acetogen Clostridium autoethanogenum, and the solventogen C. beijerinckii would be feasible and stable. On the basis of this prediction, a co-culture was experimentally established. C. autoethanogenum grew on CO2/H2 producing acetate and traces of ethanol. Acetate was in turn, consumed by C. beijerinckii together with lactate, producing butyrate. These results show that community modelling of metabolism is a valuable tool to guide the design of microbial consortia for the tailored production of chemicals from renewable resources.

microbiology↗

bioRxiv2022

Exploring the adaptability and robustness of the central carbon metabolism of Mycoplasma pneumoniae

In this study we explored the adaptability and robustness of glycolysis and pyruvate metabolism of Mycoplasma pneumoniae (MPN). We used a dual approach, we analysed metabolomics data collected for a large number of OE and KO mutants and perturbation samples. Furthermore, we trained a dynamic model of central carbon metabolism and tested the models capacity to predict these mutants and perturbation samples as well as identify key controlling factors in central carbon metabolism. Our analysis of metabolite data as well as our model analysis indicate MPN metabolism is inherently robust against perturbations due to its network structure. Two key control hubs of central carbon metabolism were identified.

systems biology↗

bioRxiv2022

FAIR Data Station for Lightweight Metadata Management & Validation of Omics Studies

BackgroundThe Life sciences is an interdisciplinary field of research and one of the the biggest suppliers of scientific data. Reusing and connecting this data can uncover hidden insights and lead to new concepts, provided there is machine-actionable metadata available about the scientific experiments conducted with a degree of completeness that reflect the FAIR guiding principles. While stakeholders have embraced the FAIR principles, in practice there are a limited number of easy to adopt practical implementations available that fulfil the needs of data producers. FindingsWe developed the FAIR Data Station, a lightweight application written in Java, that aims to support researchers in managing research metadata according to the FAIR principles. It uses the ISA metadata framework and metadata standards to capture experimental metadata. The FAIR Data Station metadata registration workflow consists of three main modules. Based on the minimal information checklist(s) selected by the user, a web-based "form generation module" creates a standardized metadata template Excel workbook which is used as a familiar environment for offline sample metadata registration. A web-based "validation module" checks the format of the metadata recorded in the workbook. The "resource module" subsequently exports the validated set of recorded metadata into an RDF data file, enabling (cross-project) meta data searches. ConclusionsTurning FAIR into reality requires the availability of easy to adopt data FAIRification workflows that provide immediate beneficial incentives to the individual researcher. As such the FAIR Data Station provides in addition to the means to correctly FAIRify sequence data, the means to build searchable databases of (local) projects and can assists in ENA metadata submission of sequence data. The FAIR Data Station is available at http://fairbydesign.nl.

bioinformatics↗

bioRxiv2022

Comparative genome-scale constraint-based metabolic modeling reveals key lifestyle features of plant-associated Pseudomonas spp.

Plant Growth Promoting Rhizobacteria (PGPR) dwell in the rhizosphere, the area surrounding the root of plants, and enhance growth of the host through different mechanisms: they can protect plants against pathogens, assist in nutrient gathering, and in increasing stress tolerance. Hence, developing strategies to enhance their performance is important to increase crop productivity. Specific solutions are necessary to enhance the performance of the beneficials while simultaneously avoiding nurturing of pathogens. This requires insights into the mechanisms underlying these microbials interactions. Pseudomonas is one of the most studied genera and contains both beneficials and pathogenic species. Hence, we used comparative genome-scale constraint-based metabolic modeling to reveal key features of both classes of Pseudomonads and which can provide leads for the possible interventions regarding these solutions. Models of 75 plant-growth promoting rhizosphere and 33 epiphytic pathogenic Pseudomonas strains were automatically reconstructed and validated using phenotype microarray (Biolog) data. The models were used for compositional analysis and 12 representative strains, 6 of each group, were further selected for extensive simulation. The analyses reveal differences in the potential for metabolite uptake and transport between these two distinct classes that suggest their nutrient preferences and their differences in, among other, D-ornithine acquisition mechanisms. The models enable simulation of metabolic state of root exudates. Simulations highlighted and summarized the differences in pathway utilization and intracellular states between two groups. The insights obtained will be very valuable to broader such studies of rhizobiome and to possibly develop strategies to improve crop productivity by supporting the beneficial microbiome while reducing pathogen activities.

systems biology↗

bioRxiv2022

Genome-scale metabolic modelling enables deciphering ethanol metabolism via the acrylate pathway in the propionate-producer Anaerotignum neopropionicum

BackgroundMicrobial production of propionate from diluted streams of ethanol (e.g., deriving from syngas fermentation) is a sustainable alternative to the petrochemical production route. Yet, few ethanol-fermenting propionigenic bacteria are known, and understanding of their metabolism is limited. Anaerotignum neopropionicum is a propionate-producing bacterium that uses the acrylate pathway to ferment ethanol and CO2 to propionate and acetate. In this work, we used computational and experimental methods to study the metabolism of A. neopropionicum and, in particular, the pathway for conversion of ethanol into propionate. ResultsOur work describes iANEO_SB607, the first genome-scale metabolic model (GEM) of A. neopropionicum. The model was built combining the use of automatic tools with an extensive manual curation process, and it was validated with experimental data from this and published studies. The model predicted growth of A. neopropionicum on ethanol, lactate, sugars and amino acids, matching observed phenotypes. In addition, the model was used to implement a dynamic flux balance analysis (dFBA) approach that accurately predicted the fermentation profile of A. neopropionicum during batch growth on ethanol. A systematic analysis of the metabolism of A. neopropionicum combined with model simulations shed light into the mechanism of ethanol fermentation via the acrylate pathway, and revealed the presence of the electron-transferring complexes NADH-dependent reduced ferredoxin:NADP+ oxidoreductase (Nfn) and acryloyl-CoA reductase-EtfAB, identified for the first time in this bacterium. ConclusionsThe realisation of the GEM iANEO_SB607 is a stepping stone towards the understanding of the metabolism of the propionate-producer A. neopropionicum. With it, we have gained insight into the functioning of the acrylate pathway and energetic aspects of the cell, with focus on the fermentation of ethanol. Overall, this study provides a basis to further exploit the potential of propionigenic bacteria as microbial cell factories.

systems biology↗

bioRxiv2022

Identification of Aspergillus niger aquaporins involved in hydrogen peroxide signaling

Aspergillus niger is a robust microbial cell factory for organic acids production. However, the regulation of many industrially important pathways is still poorly understood. Recently the regulation of glucose oxidase (GOx) expression system involved in the biosynthesis of gluconic acid has been uncovered. Hydrogen peroxide, a by-product of the enzymatic conversion of glucose to gluconate, has a pivotal role as a signaling molecule in the induction of this system. In this study, facilitated diffusion of hydrogen peroxide via aquaporin water channels (AQPs) was studied. AQPs are transmembrane proteins of the major intrinsic proteins (MIPs) superfamily. In addition to water and glycerol, they may also transport small solutes such as hydrogen peroxide. Accordingly, the genome sequence of A. niger N402 was screened for putative AQPs. Seven were found which could be classified in three main groups. Their ability to facilitate diffusion of hydrogen peroxide was identified using yeast phenotypic growth assays and by studying AQP gene knock-outs in A. niger. Complementing from both yeast and A. niger experiments, the X-intrinsic protein AQPF plays apparent roles in facilitating hydrogen peroxide transport across cellular membrane. IMPORTANCEAquaporins (AQP) are integral membrane proteins that facilitate transfer of water, small solutes and hydrogen peroxide across the plasma membrane. As they have conserved structural features, it is not difficult to detect their presence in fungal genomes. Their specific roles in fungi are however less understood. Previously we have elucidated the role of the hydrogen peroxide as a second messenger in the induction and regulation of the expression of glucose oxidase involved in the extracellular conversion of glucose in gluconate (1). Here we identified the involvement of AQPF, a member of the X-intrinsic protein class in facilitating transport of the hydrogen peroxide signal across the cell membrane.

molecular biology↗

bioRxiv2021

Large scale function-based genome prospecting for industrial traits applied to 1,3-propanediol production.

Due to the success of next-generation sequencing, there has been a vast build-up of microbial genomes in the public repositories. FAIR genome prospecting of this huge genomic potential for biotechnological benefiting, require new efficient and flexible methods. In this study, Semantic Web technologies are applied to develop a function-based genome mining approach that follows a knowledge and discovery in database (KDD) protocol. Focusing on the industrial important trait of 1,3-propanediol (1,3-PD) production 187 new candidate species were identified. Furthermore, the genetic architecture of the particular trait was resolved, and persistent domains identified.

bioinformatics↗

bioRxiv2021

Classification of the plant-associated phenotype of Pseudomonas strains using genome properties and machine learning

The rhizosphere, the region of soil surrounding roots of plants, is colonized by a unique population of Plant Growth Promoting Rhizobacteria (PGPR). By enhancing nutrient uptake from the soil and through modulation of plant phytohormone status and metabolism, PGPR can increase the stress tolerance, growth and yield of crop plants. Many important PGPR as well as plant pathogens belong to the genus Pseudomonas. There is, however, uncertainty on the divide between phytobeneficial and phytopathogenic strains as previously thought to be signifying genomic features have limited power to separate these strains. Here the Genome properties (GP) common biological pathways annotation system was applied to establish the relationship between the genome wide GP composition and the plant-associated phenotype of 91 Pseudomonas strains representing both phenotypes. GP enrichment analysis, Random Forest model fitting and feature selection revealed 28 discriminating features. A validation dataset of 67 new strains confirmed the importance of the selected features for classification. A number of unexpected discriminating features were found, suggesting involvement of novel molecular mechanisms. The results suggest that GP annotations provide a promising computational tool to better classify the plant-associated phenotype. Author summaryWith a growing population the need to double the agricultural food production is specified. Simultaneously, there is an urgent need to implement sustainable and climate change resilient agricultural practices that preserve natural ecosystems. Cooperative microbiomes play important positive roles in plant growth development and fitness. Properly tuned, these microbiomes can significantly reduce the need for synthetic fertilizers and can replace chemicals in crop pest control. To select beneficial candidates, their traits need to be described and likewise, potential detrimental traits should be avoided. Here we applied GP-based comparative functional genomics, enrichment analysis and Random Forest model fitting to compare known phytobeneficial and phytopathogenic Pseudomonas strains. A number of unexpected discriminating features were found suggesting the involvement of novel molecular mechanisms.

genomics↗

bioRxiv2021

SALARECON connects the Atlantic salmon genome to growth and feed efficiency

Atlantic salmon (Salmo salar) is the most valuable farmed fish globally and there is much interest in optimizing its genetics and rearing conditions for growth and feed efficiency. Marine feed ingredients must be replaced to meet global demand, with challenges for fish health and sustainability. Metabolic models can address this by connecting genomes to metabolism, which converts nutrients in the feed to energy and biomass, but such models are currently not available for major aquaculture species such as salmon. We present SALARECON, a model focusing on energy, amino acid, and nucleotide metabolism that links the Atlantic salmon genome to metabolic fluxes and growth. It performs well in standardized tests and captures expected metabolic (in)capabilities. We show that it can explain observed hypoxic growth in terms of metabolic fluxes and apply it to aquaculture by simulating growth with commercial feed ingredients. Predicted limiting amino acids and feed efficiencies agree with data, and the model suggests that marine feed efficiency can be achieved by supplementing a few amino acids to plant- and insect-based feeds. SALARECON is a high-quality model that makes it possible to simulate Atlantic salmon metabolism and growth. It can be used to explain Atlantic salmon physiology and address key challenges in aquaculture such as development of sustainable feeds. Author summaryAtlantic salmon aquaculture generates billions of euros annually, but faces challenges of sustainability. Salmon are carnivores by nature, and fish oil and fish meal have become scarce resources in fish feed production. Novel, sustainable feedstuffs are being trialed hand in hand with studies of the genetics of growth and feed efficiency. This calls for a mathematical-biological framework to integrate data with understanding of the effects of novel feeds on salmon physiology and its interplay with genetics. We have developed the SALARECON model of the core salmon metabolic reaction network, linking its genome to metabolic fluxes and growth. Computational analyses show good agreement with observed growth, amino acid limitations, and feed efficiencies, illustrating the potential for in silico studies of potential feed mixtures. In particular, in silico screening of possible diets will enable more efficient animal experiments with improved knowledge gain. We have adopted best practices for test-driven development, virtual experiments to assay metabolic capabilities, revision control, and FAIR data and model management. This facilitates fast, collaborative, reliable development of the model for future applications in sustainable production biology.

systems biology↗

bioRxiv2020

Modeling a co-culture of Clostridium autoethanogenum and Clostridium kluyveri to increase syngas conversion to medium-chain fatty-acids

Microbial fermentation of synthesis gas (syngas) is becoming more attractive for sustainable production of commodity chemicals. To date, syngas fermentation focuses mainly on the use of Clostridium species for the production of small organic molecules such as ethanol and acetate. The cocultivation of syngas-fermenting microorganisms with chain-elongating bacteria can expand the range of possible products, allowing, for instance, the production of medium-chain fatty acids (MCFA) and alcohols from syngas. To explore these possibilities, we report herein a genome-scale, constraint-based metabolic model to describe growth of a co-culture of Clostridium autoethanogenum and Clostridium kluyveri on syngas for the production of valuable compounds. Community flux balance analysis was used to gain insight into the metabolism of the two strains and their interactions, and to reveal potential strategies enabling production of butyrate and hexanoate. The model suggests that addition of succinate is one strategy to optimize the production of medium-chain fatty-acids from syngas with this co-culture. According to the predictions, addition of succinate increases the pool of crotonyl-CoA and the ethanol/acetate uptake ratio in C. kluyveri, resulting in the flux of up to 60% of electrons into hexanoate. Other potential way to optimize butyrate and hexanoate is to increase ethanol production by C. autoethanogenum. Deletion of either formate transport, acetaldehyde dehydrogenase or formate dehydrogenase (ferredoxin) from the metabolic model of C. autoethanogenum leads to a (potential) increase in ethanol production up to 150%, which is clearly very attractive.Competing Interest StatementThe authors have declared no competing interest.View Full Text

synthetic biology↗