Biology subjects

Koehorst, J. J.

Publications and source records attributed to Koehorst, J. J..

14 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↗

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↗

bioRxiv2022

An ultra-fast, proteome-wide response to the plant hormone auxin

The plant signaling molecule auxin controls growth and development through a simple nuclear pathway that regulates gene expresion. There are however several cellular and physiological responses to auxin that occur within seconds, far too rapid to be mediated by transcriptional changes, for which no molecular mechanism has yet been identified. Using a phosphoproteomic strategy in Arabidopsis thaliana roots, we identify an ultra-rapid auxin response system that targets over 1700 proteins, many within 30 seconds. Auxin response is chemically specific, requires known auxin-binding proteins, and targets various pathways. Through exploring its temporal dynamics, we infer auxin-triggered kinase-substrate networks and identify apoplastic pH changes as a target of signaling and as part of a relay mechanism. By generating a variety of phosphoproteomic datasets, integrated with structural information in a web-app, and by demonstrating analysis and inference strategies, we offer a resource to explore rapid and dynamic signaling in plants.

plant 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↗

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

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

A chromosome-level assembly of the black tiger shrimp (Penaeus monodon) genome facilitates the identification of novel growth-associated genes

The black tiger shrimp (Penaeus monodon) is one of the most prominent farmed crustacean species with an average annual global production of 0.5 million tons in the last decade. To ensure sustainable and profitable production through genetic selective breeding programs, several research groups have attempted to generate a reference genome using short-read sequencing technology. However, the currently available assemblies lack the contiguity and completeness required for accurate genome annotation due to the highly repetitive nature of the genome and technical difficulty in extracting high-quality, high-molecular weight DNA in this species. Here, we report the first chromosome-level whole-genome assembly of P. monodon. The combination of long-read Pacific Biosciences (PacBio) and long-range Chicago and Hi-C technologies enabled a successful assembly of this first high-quality genome sequence. The final assembly covered 2.39 Gb (92.3% of the estimated genome size) and contained 44 pseudomolecules, corresponding to the haploid chromosome number. Repetitive elements occupied a substantial portion of the assembly (62.5%), highest of the figures reported among crustacean species. The availability of this high-quality genome assembly enabled the identification of novel genes associated with rapid growth in the black tiger shrimp through the comparison of hepatopancreas transcriptome of slow-growing and fast-growing shrimps. The results highlighted several gene groups involved in nutrient metabolism pathways and revealed 67 newly identified growth-associated genes. Our high-quality genome assembly provides an invaluable resource for accelerating the development of improved shrimp strain in breeding programs and future studies on gene regulations and comparative genomics.

genomics↗

bioRxiv2020

A protocol for adding knowledge to Wikidata, a case report

Pandemics, even more than other medical problems, require swift integration of knowledge. When caused by a new virus, understanding the underlying biology may help finding solutions. In a setting where there are a large number of loosely related projects and initiatives, we need common ground, also known as a "commons". Wikidata, a public knowledge graph aligned with Wikipedia, is such a commons and uses unique identifiers to link knowledge in other knowledge bases However, Wikidata may not always have the right schema for the urgent questions. In this paper, we address this problem by showing how a data schema required for the integration can be modelled with entity schemas represented by Shape Expressions. As a telling example, we describe the process of aligning resources on the genomes and proteomes of the SARS-CoV-2 virus and related viruses as well as how Shape Expressions can be defined for Wikidata to model the knowledge, helping others studying the SARS-CoV-2 pandemic. How this model can be used to make data between various resources interoperable, is demonstrated by integrating data from NCBI Taxonomy, NCBI Genes, UniProt, and WikiPathways. Based on that model, a set of automated applications or bots were written for regular updates of these sources in Wikidata and added to a platform for automatically running these updates. Although this workflow is developed and applied in the context of the COVID-19 pandemic, to demonstrate its broader applicability it was also applied to other human coronaviruses (MERS, SARS, Human Coronavirus NL63, Human coronavirus 229E, Human coronavirus HKU1, Human coronavirus OC4).

bioinformatics↗

bioRxiv2019

Organohalide-respiring Desulfoluna species isolated from marine environments

The genus Desulfoluna comprises two anaerobic sulfate-reducing strains, D. spongiiphila AA1{top} and D. butyratoxydans MSL71{top} of which only the former was shown to perform organohalide respiration (OHR). Here we isolated a third member of this genus from marine intertidal sediment, designed D. spongiiphila strain DBB. All three Desulfoluna strains harbour three reductive dehalogenase gene clusters (rdhABC) and corrinoid biosynthesis genes in their genomes. Brominated but not chlorinated aromatic compounds were dehalogenated by all three strains. The Desulfoluna strains maintained OHR in the presence of 20 mM sulfate or 20 mM sulfide, which often negatively affect OHR. Strain DBB sustained OHR with 2% oxygen in the gas phase, in line with its genetic potential for reactive oxygen species detoxification. Reverse transcription-quantitative PCR (RT-qPCR) revealed differential induction of rdhA genes in strain DBB in response to 1,4-dibromobenzene or 2,6-dibromophenol. Proteomic analysis confirmed differential expression of rdhA1 with 1,4-dibromobenzene, and revealed a possible electron transport chain from lactate dehydrogenases and pyruvate oxidoreductase to RdhA1 via menaquinones and either RdhC, or Fix complex (electron transfer flavoproteins), or Qrc complex (Type-1 cytochrome c3:menaquinone oxidoreductase).

microbiology↗

bioRxiv2018

Expected and observed genotype complexity in prokaryotes: correlation between 16S-rRNA phylogeny and protein domain content

BackgroundThe omnipresent 16S ribosomal RNA gene (16S-rRNA) is commonly used to identify and classify bacteria though it does not take into account the distinctive functional characteristics of taxa. We explored functional domain landscapes of over 5700 complete bacterial genomes, representing a wide coverage of the bacterial tree of life, and investigated to what extent the observed protein domain diversity correlates with the expected evolutionary diversity, using 16S-rRNA as metric for evolutionary distance.\n\nResultsAnalysis of protein domains showed that 83% of the bacterial genes code for at least one of the 9722 domain classes identified. By comparing clade specific and global persistence scores, candidate horizontal gene transfer and signifying domains could be identified. 16S-rRNA and functional domain content distances were used to evaluate and compare species divergence and overall a sigmoid curve is observed. Already at close 16S-rRNA evolutionary distances, high levels of functional diversity can be observed. At a larger 16S-rRNA distance, functional differences accumulate at a relatively lower pace.\n\nConclusionsAnalysis of 16S-rRNA sequences in the same taxa suggests that, in many cases, additional means of classification are required to obtain reliable phylogenetic relationships. Whole genome protein domain class phylogenies correlate with, and complement 16S-rRNA sequence-based phylogenies. Moreover, domain-based phylogenies can be constructed over large evolutionary distances and provide an in-depth insight of the functional diversity within and among species and enables large scale functional comparisons. The increased granularity obtained paves way for new applications to better predict the relationships between genotype, physiology and ecology.

genomics↗

bioRxiv2018

Memote: A community-driven effort towards a standardized genome-scale metabolic model test suite

Several studies have shown that neither the formal representation nor the functional requirements of genome-scale metabolic models (GEMs) are precisely defined. Without a consistent standard, comparability, reproducibility, and interoperability of models across groups and software tools cannot be guaranteed.\n\nHere, we present memote (https://github.com/opencobra/memote) an open-source software containing a community-maintained, standardized set of metabolic model tests. The tests cover a range of aspects from annotations to conceptual integrity and can be extended to include experimental datasets for automatic model validation. In addition to testing a model once, memote can be configured to do so automatically, i.e., while building a GEM. A comprehensive report displays the models performance parameters, which supports informed model development and facilitates error detection.\n\nMemote provides a measure for model quality that is consistent across reconstruction platforms and analysis software and simplifies collaboration within the community by establishing workflows for publicly hosted and version controlled models.

systems biology↗