Biology subjects

Heintz-Buschart, A.

Publications and source records attributed to Heintz-Buschart, A..

At least 19 recordsLinked to original sources

bioRxiv2026

Supervised restricted data fusion with common, local & distinct components

In multi-block data, the dominant sources of variation are not always most relevant to a response of interest, meaning that purely exploratory decompositions may fail to recover subtle but important response-associated structure. We introduce PESCAR, a supervised extension of Penalised Exponential Simultaneous Component Analysis (PESCA) that incorporates response information directly into the estimation of common, local, and distinct (CLD) structure across multiple data blocks. This allows simultaneous multiblock decomposition and response variable influenced recovery of latent structure. Through simulation studies, we show that PESCAR can detect weak response-related components across a range of settings, including different noise levels and model-rank mis-specification. Applied to a real multi-omics dataset, PESCAR recovers biologically meaningful response-associated patterns and retains interpretable block structure. We further demonstrate that sparsity in the fitted loading matrices admits a hypergraph-based interpretability layer, summarising overlapping support patterns across components and blocks. These results show that direct incorporation of response information into multiblock decomposition can improve detection of subtle relevant signal and facilitate interpretation in complex systems.

systems biology↗

bioRxiv2026

The induction of systemic resistance to barley powdery mildew by rhizosphere bacterial communities does not disrupt the structure or function of native microbial communities

1.Synthetic microbial communities (SynComs) could help plants withstand biotic stress and reduce the need for pesticides. With this in mind, we created two SynComs, comprising bacterial strains isolated from the rhizospheres of barley and wheat. We then studied their potential to trigger induced systemic resistance against the barley pathogen Blumeria graminis f. sp. hordei (Bgh). To investigate the plant-microbial interactions from the perspective of both plants and microbes, we performed DAF staining to quantify Bgh propagation in plant leaves, analysed leaf transcriptomes and conducted rhizosp here 16S rRNA gene metabarcoding and rhizosphere metatranscriptome analysis. Our results demonstrate that the SynComs elicit defence responses in barley against Bgh in a manner similar to that of the positive control strain Pseudomonas simiae WCS417r. The SynComs act without triggering a strong gene response prior to inoculation with the plant pathogen or affecting plant-associated prokaryote communities; they only mildly influence bacterial gene expression in the rhizosphere. Instead, they act as priming agents, preparing the plant for further pathogen attack. These findings suggest that protective SynComs can be applied in the field without causing signficant disruption to native microbial communities.

microbiology↗

bioRxiv2025

ACMTF-R: supervised multi-omics data integration uncovering shared and distinct outcome-associated variation

The rapid growth of high-dimensional biological data has necessitated advanced data fusion techniques to integrate and interpret complex multi-omics and longitudinal datasets. Shared and unshared structure across such datasets can be identified in an unsupervised manner with Advanced Coupled Matrix and Tensor Factorization (ACMTF), but this cannot be related to an outcome. Conversely, N-way Partial Least Squares (NPLS) is supervised and captures outcome-associated variation but cannot identify shared and unshared structure. To bridge the gap between data exploration and prediction, we introduce ACMTF-Regression (ACMTF-R), an extension of ACMTF that incorporates a regression step, allowing for the simultaneous decomposition of multi-way data while explicitly capturing variation associated with a dependent variable. We present a detailed mathematical formulation of ACMTF-R, including its optimisation algorithm and implementation. Through extensive simulations, we systematically evaluate its ability to recover a small y-related component shared between multiple blocks, its robustness to noise, and the impact of the tuning parameter ({pi}) which controls the balance between data exploration and outcome prediction. Our results demonstrate that ACMTF-R can robustly identify the y-related component, correctly identifying outcome-associated shared and distinct variation, distinguishing it from existing approaches such as NPLS and ACMTF. The development of ACMTF-R was motivated by a real-world dataset investigating how maternal pre-pregnancy BMI affects the human milk microbiome, human milk metabolome, and infant faecal microbiome. Emerging evidence suggests that inter-generational transfer of maternal obesity may affect multiple omics layers, highlighting the need to identify outcome-associated variation. The applicability of ACMTF-R is therefore validated by applying it to this multi-omics dataset. ACMTF-R successfully identifies novel mother-infant relationships associated with maternal pre-pregnancy BMI, underscoring its utility in multi-omics research. Our findings establish ACMTF-R as a versatile tool for multi-way data fusion, offering new insights into complex biological systems by integrating common, local, and distinct variation in the context of a dependent variable. Author SummaryIn recent years, biological research has been transformed by the rise of high-throughput technologies, allowing us to simultaneously measure multiple different data (genes, microbes, and metabolites) within the same subject. While these datasets hold great promise, analysing them in an integrated way remains challenging. Existing tools either focus on uncovering patterns in the data or on predicting outcomes, but rarely both. In this study, we present a new method called ACMTF-Regression (ACMTF-R), which combines these aspects. ACMTF-R helps researchers identify shared and distinct biological patterns across different datasets while also relating these patterns to specific outcomes. Using simulated data, we show that ACMTF-R can detect subtle signals that would otherwise go unnoticed. We also apply it to a real-world study of mothers and their infants, revealing how maternal obesity influences breast milk and gut microbes in the baby. Our approach provides a powerful new tool for studying complex biological systems and can be especially valuable in fields like microbiome research, metabolomics, and personalized medicine.

systems biology↗

bioRxiv2025

Plant diversity shapes plant volatile emission differently at the species and community level

Studies have investigated the interactions between plants through competition and resource sharing to understand the mechanisms behind the positive effects of plant diversity on productivity. Volatile organic compounds (VOCs) are important info-chemicals in plant-plant interactions, but they have so far rarely been considered in this context. Here, we measured VOC emissions at both community and species levels (Plantago lanceolata) in experimental plant communities of varying diversity (The Jena Experiment) to understand the role of VOCs in driving biodiversity-ecosystem functioning relationships. We show that plant diversity determines the release of plant VOCs at both levels. At the community level, plant species richness directly enhanced VOC emission and increased VOC richness both directly and indirectly by altering herbivore damage and LAI. At the species level, plant diversity did not directly affect the VOC emissions of P. lanceolata but indirectly affected it by influencing the VOC emissions from the surrounding community. Plantago lanceolata individuals in communities with high concentrations of green leaf volatiles decreased their VOC emission, while those in communities with high concentrations of terpenoids increased their VOC diversity. Our results provide first evidence that plant diversity shapes community-level plant VOC emission and thus influences focal plant VOC emission inside the community.

ecology↗

bioRxiv2024

Integrated multi-omics highlights alterations of gut microbiome functions in prodromal and idiopathic Parkinson's disease

Parkinsons disease (PD) is associated with gut microbiome shifts, but the functional consequences remain unclear. Here, we use an integrated multi-omics approach to compare the gut microbiomes of individuals with PD and prodromal PD as well as healthy individuals. After analyzing each omics, meta-metabolomic was selected to inform the analysis as it represents the most discriminatory and robust ome. We identified 11 metabolites that were differentially abundant between the groups, amongst which {beta}-glutamate was increased in PD and prodromal PD, and correlated with the transcriptional activities of Methanobrevibacter smithii and Clostridium spp. We identified decreases in transcripts, but not in gene abundances, related to glutamate metabolism, bile acids, chemotaxis and flagellar assembly in PD, particularly in keystone genera such as Roseburia, Agathobacter and Blautia. Our findings, integrated into the Expobiome map, reveal multifactorial microbiome alterations which converge with PD pathways. Our study highlights the importance of investigating the gut microbiomes functional dimensions to better resolve microbiome-host interactions in health and disease.

systems biology↗

bioRxiv2024

Pseudomonas intra-genus competition determines protective function of SynComs in Arabidopsis thaliana

The plant root microbiota is crucial for nutrient acquisition, development, and disease suppression. Although commensal bacteria display host preference, their beneficial impact on their cognate host and mechanisms of species selection by the plant are still unclear. We use bacterial culture collections derived from the two model species Arabidopsis thaliana (At) and Lotus japonicus (Lj) to design synthetic communities (SynComs) and test their protective function upon exposure of At Col-0 to the detrimental root-colonizing Pseudomonas isolate R401. Lj-derived SynComs were fully protective, whereas At-derived SynComs displayed full protective activity only towards a R401 mutant impaired in the production of inhibitory exometabolites. The protective phenotypes were associated with a reduced titer of the R401 opportunistic pathogen. In vitro antagonist assays, in planta and in vitro bacterial community profiling, as well as strain-swapping and strain-dropout experiments revealed that competition among commensal Pseudomonas strains and R401 determines the success of the opportunist, independent of the original host or the phylogeny of the commensals. Furthermore, we determine the carbon utilization potential of these isolates, which may explain the competition with the detrimental strain and the role of host-secreted compounds. Our results provide evidence that intra-genus interactions within SynComs modulate plant health and disease, and that an individual beneficial strain can be sufficient to outcompete an opportunistic relative. This has implications for the successful development of beneficial microbial consortia for agriculture.

plant biology↗

bioRxiv2024

parafac4microbiome: Exploratory analysis of longitudinal microbiome data using Parallel Factor Analysis

Studies investigating microbial temporal dynamics are increasingly common, leveraging longitudinal designs that collect microbial abundance data across multiple time points from the same subjects. Traditional exploratory approaches like Principal Component Analysis (PCA) fail to fully utilize this structure. By organizing data as a three-way array--subjects as rows, microbial abundances as columns, and time points as the third dimension--multi-way methods such as Parallel Factor Analysis (PARAFAC) can better capture temporal and structural patterns. This study demonstrates Parallel Factor Analysis (PARAFAC) as a method to explore longitudinal microbiome data using three exemplary studies. In the first example, a long time series of in vitro microbiomes, PARAFAC identifies primary time-resolved variations. The second example, a longitudinal infant gut microbiome study, shows that PARAFAC can distinguish subject groups and enhance comparative analysis, even with moderate missing data. In the third example, a gingivitis intervention study of the oral microbiome, PARAFAC enables the identification of microbial subcommunities of interest through post-hoc clustering. These examples highlight PARAFACs broad applicability for analysing longitudinal microbiome data across diverse environments. The approach is implemented in the R package parafac4microbiome, available on CRAN, providing researchers with accessible tools for similar analyses. ImportanceUnderstanding how microbiomes change over time can give us valuable insights into their role in health and disease. Many traditional methods like Principal Component Analysis (PCA) miss important patterns in data collected over time, but Parallel Factor Analysis (PARAFAC) helps uncover these trends in a much clearer way. Using this approach, we were able to identify key changes in microbiomes across different settings, like lab experiments, the infant gut, and the mouth. PARAFAC also works well even when some data is missing, which is a common issue. To make this tool accessible, we have included it in a user-friendly R package, enabling other researchers to analyse microbiome dynamics in their own studies and explore how these changes might influence health and treatments.

microbiology↗

bioRxiv2024

Multi-way modelling of oral microbial dynamics and host-microbiome interactions during induced gingivitis

Gingivitis - the inflammation of the gums - is a reversible stage of periodontal disease. It is caused by dental plaque formation due to poor oral hygiene. However, gingivitis susceptibility involves a complex set of interactions between the oral microbiome, oral metabolome and the host. In this study, we investigated the dynamics of the oral microbiome and its interactions with the salivary metabolome during experimental gingivitis in a cohort of 41 systemically healthy participants. We use Parallel Factor Analysis (PARAFAC), which is a multi-way generalization of Principal Component Analysis (PCA) that can model the variability in the response due to subjects, variables and time. Using the modelled responses, we identified microbial subcommunities with similar dynamics that connect to the magnitude of the gingivitis response. By performing high level integration of the predicted metabolic functions of the microbiome and salivary metabolome, we identified pathways of interest that describe the changing proportions of Gram-positive and Gram-negative microbiota, variation in anaerobic bacteria, biofilm formation and virulence.

systems biology↗

bioRxiv2024

Common soil history is more important than plant history for arbuscular mycorrhizal community assembly in an experimental grassland diversity gradient

The relationship between biodiversity and ecosystem functioning strengthens with ecosystem age. However, the interplay between the plant diversity - ecosystem functioning relationship and Glomeromycotinian arbuscular mycorrhizal fungi (AMF) community assembly has not yet been scrutinized in this context, despite AMFs role in plant survival and niche exploration. We study the development of AMF communities by disentangling soil- and plant-driven effects from year effects. Within a long-term grassland biodiversity experiment, the pre-existing plant communities of varying plant diversity were re-established as split plots with combinations of common plant and soil histories: split plots with neither common plant nor soil history, with only soil but no plant history, and with both common plant and soil history. We found that bulk soil AMF communities were primarily shaped by common soil history and additional common plant history had little effect. Further, the steepness of AMF diversity and plant diversity relationship did not strengthen over time, but AMF community evenness increased with common history. Specialisation of AMF towards plant species was low throughout giving no indication of AMF communities specialising or diversifying over time. The potential of bulk soil AMF as mediators of variation in plant and microbial biomass over time and hence as drivers of BEF relationships was low. Our results suggest that soil processes may be key for the build-up of plant community-specific mycorrhizal communities with likely feedback effects on ecosystem productivity, but the plant-available mycorrhizal pool in bulk soil itself does not explain the strengthening of BEF relationships over time.

microbiology↗

bioRxiv2024

MASCARA: coexpression analysis in data from designed experiments

Experiments in plant transcriptomics are usually designed to induce variation in a pathway of interest. Harsh experimental conditions can cause widespread transcriptional changes between groups. Discovering coexpression within a pathway of interest (here the strigolactone pathway) in this context is hampered by the dominant variance induced by the design. Minor changes in experimental conditions not controlled for may affect the plants, leading to small coordinated differences in genes within pathways of interest and related pathways between replicate plants in the same controlled experimental condition. These systematic differences are usually averaged out, but we argue here that they can be used to improve the detection of genes that co-express. We introduce a novel framework "MASCARA" which combines ANOVA simultaneous component analysis and partial least squares to remove the experimentally induced variance and investigate multivariate relationships in the non-designed variance. MASCARA is tested against a selection of competitors on simulated data, created to mimic a designed transcriptome study, where its benefit is demonstrated. In a coexpression analysis of a real dataset MASCARA detects several uncharacterised but relevant transcripts. Our results indicate that there is sufficient structure left in a typical dataset after correcting for experimental variance and that this residual information is useful to investigate coexpression. Author SummaryExperiments in the life sciences usually purposefully induce significant variance between different treatments, in order to activate or repress certain mechanisms of interest. Whilst this is necessary it can make it challenging to detect meaningful relationships within pathways of interest, particularly when the experimental conditions are drastically different. Instead of focusing on the drastic changes in response due to the different treatment, MASCARA uses the systematic synchronous variances between replicates to find related features within the pathway of interest. Through simulation studies and application to a real dataset, we demonstrate the effectiveness of MASCARA in detecting relevant transcripts and extracting coexpression patterns from gene expression data.

bioinformatics↗

bioRxiv2023

Bioenergetic mapping of 'healthy microbiomes' via compound processing potential imprinted in gut and soil metagenomes

Despite mounting evidence of their importance in human health and ecosystem functioning, the definition and measurement of healthy microbiomes remain unclear. More advanced knowledge exists on health associations for compounds used or produced by microbes. Environmental microbiome exposures (especially via soils) also help shape, and may supplement, the functional capacity of human microbiomes. Given the synchronous interaction between microbes, their feedstocks, and micro-environments, with functional genes facilitating chemical transformations, there exists an intriguing opportunity to examine microbiomes in terms of their capacity to process compounds relevant to human health. Here we integrate functional genomics and biochemistry frameworks to derive new quantitative measures of the potential for human gut and environmental soil metagenomes to process major compound classes (e.g., lipids, carbohydrates) and selected biomolecules (e.g., vitamins, short-chain fatty acids) linked to human health. Metagenome functional potential profile data were translated into a universal compound mapping landscape based on bioenergetic van Krevelen mapping of function-level meta-compounds and corresponding functional relative abundances, reflecting imprinted genetic capacity of microbiomes to metabolize an array of different compounds. We show that measures of compound processing potential associated with human health and disease (examining atherosclerotic cardiovascular disease, colorectal cancer, type 2 diabetes and anxious-depressive behavior case studies), and displayed seemingly predictable shifts along gradients of ecological disturbance in plant-soil ecosystems (three case studies). Ecosystem quality explained 60-92% of variation in soil metagenome compound processing potential measures in a post-mining restoration case study dataset. With growing knowledge of the varying proficiency of environmental microbiota to process human health associated compounds, we might design environmental interventions or nature prescriptions to modulate our exposures, thereby advancing microbiota-oriented approaches to human health. Compound processing potential offers a simplified, integrative approach for applying metagenomics in ongoing efforts to understand and quantify the role and linkages of microbiota in environmental- and human-health.

bioinformatics↗

bioRxiv2023

Effects of recurrent summer droughts on arbuscular mycorrhizal and total fungal communities in experimental grasslands differing in plant diversity and community composition

Biodiversity loss and climate change have been determined as major global drivers affecting ecosystems and their functioning. In this context, drought was shown to have negative effects on ecosystems by disrupting ecological processes, which could be buffered in more biodiverse systems. Many studies, however, focus on effects on aboveground communities of single drought events, while dynamics of soil-borne communities are still widely unclear, despite their important roles in ecosystem functioning. To elucidate the effect of recurrent summer drought periods on fungal communities in a long-term grassland biodiversity experiment, roof shelters were installed on grassland plots ranging in plant species richness from 1 to 16 species and plant functional group richness (1-4 groups) and composition. After 9 years of summer droughts, bulk soil was sampled and used for Illumina sequencing of the ITS2 and SSU genes to characterize the total fungal and arbuscular mycorrhizal fungal (AMF) communities, respectively. We found shifts of AMF and total fungi community structures caused by recurrent drought and plant species richness, but no buffering of drought effects by plant diversity. Alpha-diversity (VT or ASV richness) of both AMF and total fungi increased with plant species richness but was not significantly affected by drought. Even though drought overall had minimal long-lasting effects, we found Diversispora and Paraglomus among the AMF and Penicillium among total fungal communities to be more abundant after the drought treatment. AMF communities were affected by the presence of individual plant functional groups, reacting stronger to presence of legumes under drought, while total fungal interaction with plant communities were similar under drought as control. AMF -diversity differed between plant functional groups in control conditions but was independent of plant community composition under drought. In contrast, total fungi -diversity was increased by presence of herbs and legumes only under drought. From our results, we conclude that recurring moderate summer droughts do not strongly affect soil fungal communities. All shifts can be explained by indirect effects through the plant community and its top-down effect on soils altered by drought. Further, AMF are not less affected than total fungal communities, but rather respond differently by interacting more strongly with legumes in response to drought. Consequently, not plant species richness, but plant functional composition, dominates in shaping fungal communities under recurrent droughts.

ecology↗

bioRxiv2022

Alterations of oral microbiota and impact on the gut microbiome in type 1 diabetes mellitus revealed by multi-omic analysis

BackgroundAlterations of the gut microbiome have been linked to multiple chronic diseases. However, the drivers of such changes remain largely unknown. The oral cavity acts as a major route of exposure to exogenous factors including pathogens, and processes therein may affect the communities in the subsequent compartments of the gastrointestinal tract. Here, we perform strain-resolved, integrated multi-omic analyses of saliva and stool samples collected from eight families with multiple cases of type 1 diabetes mellitus (T1DM). ResultsWe identified distinct oral microbiota mostly reflecting competition between streptococcal species. More specifically, we found a decreased abundance of the commensal Streptococcus salivarius in the oral cavity of T1DM individuals, which is linked to its apparent competition with the pathobiont Streptococcus mutans. The decrease in S. salivarius in the oral cavity was also associated with its decrease in the gut as well as higher abundances in facultative anaerobes including Enterobacteria. In addition, we found evidence of gut inflammation in T1DM as reflected in the expression profiles of the Enterobacteria as well as in the human gut proteome. Finally, we were able to follow transmitted strain-variants from the oral cavity to the gut at the metagenomic, metatranscriptomic and metaproteomic levels, highlighting not only the transfer, but also the activity of the transmitted taxa along the gastrointestinal tract. ConclusionsAlterations of the oral microbiome in the context of T1DM impact the microbial communities in the lower gut, in particular through the reduction of "oral-to-gut" transfer of Streptococcus salivarius. Our results indicate that the observed oral-cavity-driven gut microbiome changes may contribute towards the inflammatory processes involved in T1DM. Through the integration of multi-omic analyses, we resolve strain-variant "mouth-to-gut" transfer in a disease context.

microbiology↗

bioRxiv2021

binny: an automated binning algorithm to recover high-quality genomes from complex metagenomic datasets

The reconstruction of genomes is a critical step in genome-resolved metagenomics and for multi-omic data integration from microbial communities. Here, we present binny, a binning tool that produces complete and pure metagenome-assembled genomes (MAG) from both contiguous and highly fragmented genomes. Based on established metrics, binny outperforms or is highly competitive with commonly-used and state- of-the-art binning methods and finds unique genomes that could not be detected by other methods. binny uses k-mer-composition and coverage by metagenomic reads for iterative, non-linear dimension reduction of genomic signatures, as well as subsequent automated contig clustering with cluster assessment using lineage-specific marker gene sets. When compared to seven widely used binning algorithms, binny provides substantial amounts of uniquely identified MAGs and almost always recovers the most near-complete (>95% pure, >90% complete) and high-quality (>90% pure, >70% complete) genomes from simulated data sets from the Critical Assessment of Metagenome Interpretation (CAMI) initiative, as well as substantially more high-quality draft genomes, as defined by the Minimum Information about a Metagenome-Assembled Genome (MIMAG) standard, from a real-world benchmark comprised of metagenomes from various environments than any other tested method.

bioinformatics↗

bioRxiv2021

Distinct effects of host and neighbour tree identity on arbuscular and ectomycorrhizal fungi along a tree diversity gradient

Plant diversity and plant-related ecosystem functions have been in focus in biodiversity-ecosystem functioning studies. However, in this context, biotic interactions with mycorrhizal fungi have been understudied although they are crucial for plant-resource acquisition. We investigated the effects of tree species richness, tree mycorrhizal type on arbuscular (AMF) and ectomycorrhizal fungal (EMF) communities. We aimed to understand how dissimilarities in taxa composition and beta-diversity are related to target trees and neighbours of the same/different mycorrhizal type. We sampled a tree experiment with saplings ([~]7 years old), where tree species richness (monocultures, 2-species, and 4-species mixtures) and mycorrhizal type were manipulated. AMF and EMF richness significantly increased with increasing tree species richness. AMF richness of mixture plots resembled that of the sum of the respective monocultures, whereas EMF richness of mixture plots was lower compared to the sum of the respective monocultures. Specialisation scores revealed significantly more specialised AMF than EMF suggesting that, in contrast to previous studies, AMF were more specialised, whereas EMF were not. We further found that AMF communities were little driven by the surrounding trees, whereas EMF communities were. Our study revealed the drivers of mycorrhizal fungal communities and further highlights the distinct strategies of AMF and EMF.

ecology↗

bioRxiv2020

Efficiently processing amplicon sequencing data for microbial ecology with dadasnake, a DADA2 implementation in Snakemake

BackgroundAmplicon sequencing of phylogenetic marker genes, e.g. 16S, 18S or ITS rRNA sequences, is still the most commonly used method to determine the composition of microbial communities. Microbial ecologists often have expert knowledge on their biological question and data analysis in general, and most research institutes have computational infrastructures to employ the bioinformatics command line tools and workflows for amplicon sequencing analysis, but requirements of bioinformatics skills often limit the efficient and up-to-date use of computational resources. Resultsdadasnake wraps pre-processing of sequencing reads, delineation of exact sequence variants using the favorably benchmarked, widely-used the DADA2 algorithm, taxonomic classification and post-processing of the resultant tables, and hand-off in standard formats, into a user-friendly, one-command Snakemake pipeline. The suitability of the provided default configurations is demonstrated using mock-community data from bacteria and archaea, as well as fungi. ConclusionsBy use of Snakemake, dadasnake makes efficient use of high-performance computing infrastructures. Easy user configuration guarantees flexibility of all steps, including the processing of data from multiple sequencing platforms. dadasnake facilitates easy installation via conda environments. dadasnake is available at https://github.com/a-h-b/dadasnake.

microbiology↗

bioRxiv2020

The archives are half-empty: a field-wide assessment of the availability of microbial community sequencing data

The sequencing revolution has resulted in the explosive growth of public genetic repositories. These repositories now hold invaluable collections of 16S rRNA gene amplicon sequences, but the extent to which the currently archived data is findable, accessible, and reusable has not been evaluated. We conducted a field-wide assessment of the availability and state of publicly archived 16S rRNA gene amplicon sequencing data. Using custom-built pattern-based text extraction algorithms, we searched 26,927 publications in 17 microbiology or microbial ecology journals, and identified 2,015 studies which performed 16S rRNA gene amplicon sequencing. We found, for example, that 7.2% of these had not been made public at the time of analysis, a trend which increased over time. Of the 635 studies targeting the V3-V4 region of the 16S rRNA gene, 40.3% contained data which was not available or not reusable, and for 25.5% of the studies, faults in data formatting or data labelling were likely to create obstacles in data reuse. Taken together, only 34% of these datasets had potentially reusable data. Our study reveals significant gaps in the availability of currently deposited community sequencing data, identifies major contributors to data loss, and offers suggestions for improving data archiving practices in the future.

ecology↗

bioRxiv2020

PathoFact: A pipeline for the prediction of virulence factors and antimicrobial resistance genes in metagenomic data

BackgroundPathogenic microorganisms cause disease by invading, colonizing and damaging their host. Virulence factors including bacterial toxins contribute to their pathogenicity. Additionally, antimicrobial resistance genes allow pathogens to evade otherwise curative treatments. To understand causal relationships between microbiome compositions, functioning, and disease, it is therefore essential to identify virulence factors and antimicrobial resistance genes in metagenomic datasets. At present, there is a clear lack of computational approaches to simultaneously identifying these factors. Here we present PathoFact, a tool for the contextualized prediction of virulence factors and antimicrobial resistance genes in metagenomic data. ResultsPathoFact predicts virulence factors, bacterial toxins and antimicrobial resistance genes with high accuracy (0.92, 0.83 and 0.99) and specificity (0.96, 0.99 and 0.98), respectively. The performance of PathoFact was furthermore demonstrated on three publicly available case-control metagenomic datasets representing an actual infection as well as chronic diseases in which either pathogenic potential or bacterial toxins were predicted to play a role. With PathoFact, we identified virulence factors (including toxins) and antimicrobial resistance genes, and identified signature genes which differentiated between the disease and control groups. ConclusionPathoFact is an easy-to-use, modular, and reproducible pipeline for the identification of virulence factors, toxins and antimicrobial resistance genes in metagenomic data. Additionally, PathoFact combines the prediction of these pathogenicity factors with the identification of mobile genetic elements. This provides further depth to the analysis by considering the genomic context of the pertinent genes. Furthermore, each module (virulence factors, toxin and antimicrobial resistance genes) of PathoFact is also a standalone component, making it a flexible and versatile tool. PathoFact is freely available online at https://git-r3lab.uni.lu/laura.denies/PathoFact.

microbiology↗