Biology subjects

Kosciolek, T.

Publications and source records attributed to Kosciolek, T..

At least 19 recordsLinked to original sources

bioRxiv2026

A universal taxonomic and functional human gut microbiome model for disease classification and phenotype discovery

The human gut microbiome is a powerful indicator of host health, yet its compositional nature, high sparsity, and inter-individual variability complicate downstream analysis. Here, we introduce two complementary approaches to characterize gut microbiome structure at population scale. First, we define eight functional signatures of the human gut microbiome using Non-negative Matrix Factorization, revealing coordinated metabolic patterns that partially decouple from taxonomic composition. Second, we present GUT-FORMer, a transformer-based autoencoder that jointly models taxonomic and functional metagenomic profiles from close to 21,000 publicly available samples. The learned latent representations capture biologically meaningful structure, reflect geographic and disease-associated variation, and enable accurate classification of 25 diseases in both binary and multiclass settings, as well as regression of host age and BMI. GUT-FORMer outperforms existing microbiome indices and deep learning methods across all tasks, establishing a generalizable framework for microbiome-based precision medicine.

bioinformatics↗

bioRxiv2026

Scaling and democratising structure-based protein function prediction with metagenomic-deepFRI

High-quality protein structure models have become widely available, offering insights into protein function, yet they remain underutilized. Here, we introduce metagenomic-deepFRI, a framework incorporating structural templates into functional annotation pipelines at speeds comparable to sequence-alignment methods. Notably, structural features improved GO term prediction confidence and Information Content by up to 50%. Applied to metagenomic datasets, the framework achieves nearly 90% annotation coverage, enabling protein function inference without explicit orthology-based transfer.

bioinformatics↗

bioRxiv2025

A large-scale comparative metagenomic analysis of short-read sequencing platforms indicates high taxonomic concordance and functional analysis challenges

Driven by the increasing scale of microbiome studies and the rise of large, continuously expanding population cohorts, the volume of sequencing data is growing rapidly. As such, ensuring the comparability of data generated across different sequencing platforms has become a pressing concern in efforts to uncover robust links between the microbiome and human health. In this study, we conducted a comprehensive comparison of taxonomic and functional profiles from 1,351 matched human gut microbiome sample pairs, sequenced using both the MGISEQ-2000 (MGI) and NovaSeq 6000 (Illumina NovaSeq) platforms. Taxonomic profiles showed high concordance within and between platforms: 96.44 {+/-} 5.96% of species were shared between MGI-MGI pairs, and 92.07 {+/-} 5.20% were shared between MGI and NovaSeq pairs. The proportion of platform-specific species was low, at 3.42% for MGI-MGI comparisons and 5.89% for MGI-NovaSeq comparisons. No significant differences in Shannon diversity were observed for either within-platform or between-platform comparisons. However, functional profiles revealed notable discrepancies between platforms, which were attributed to differences in pre-sequencing protocols. ImportanceOur findings demonstrate robust taxonomic comparability between MGI and NovaSeq platforms, while revealing systematic functional differences that should be carefully considered in cross-platform and even cross-cohort metagenomic studies.

bioinformatics↗

bioRxiv2025

Novel insights into the genetic architecture and mechanisms of host/microbiome interactions from a multi-cohort analysis of outbred laboratory rats

The intestinal microbiome influences health and disease. Its composition is affected by host genetics and environmental exposures. Understanding host genetic effects is critical but challenging in humans, due to the difficulty of detecting, mapping and interpreting them. To address this, we analysed host genetic effects in four cohorts of outbred laboratory rats exposed to distinct but controlled environments. We found that polygenic host genetic effects were consistent across environments. We identified three replicated microbiome-associated loci, one of which involved a sialyltransferase gene and Paraprevotella. We found a similar association in a human cohort, between ST6GAL1 and Paraprevotella, both of which have been linked with immune and infectious diseases. Moreover, we found evidence of indirect genetic effects on microbiome phenotypes, which substantially increased their total genetic variance. Finally, we identified a novel mechanism whereby indirect genetic effects can contribute to "missing heritability".

genetics↗

bioRxiv2024

Aggregating gut: on the link between neurodegeneration and bacterial functional amyloids

Amyloids are insoluble protein aggregates with a cross-beta structure, which are traditionally associated with neurodegeneration. Similar structures, named functional amyloids, expressed mostly by microorganisms, play important physiological roles, e.g. bacterial biofilm stabilization. Using a bioinformatics approach, we identify gut microbiome functional amyloids and analyze their potential impact on human health via the gut-brain axis. The results point to taxonomically diverse sources of functional amyloids and their frequent presence in the extracellular space. The retrieved interactions between gut microbiome functional amyloids and human proteins indicate their potential to trigger inflammation, affect transport and signaling processes. We also find a greater relative abundance of bacterial functional amyloids in patients diagnosed with Parkinsons disease and specifically a higher content of the curli amyloid protein, CsgA, in Alzheimers disease patients than in healthy controls. Our results provide a rationale for the tentative link between neurodegeneration and gut bacterial functional amyloids.

bioinformatics↗

bioRxiv2024

Integrative Metagenomic and Blood Biomarker Analysis Reveals Distinct Metabolic Responses to Aerobic and Anaerobic Interventions in Athletic and Non-Athletic Individuals

BackgroundThe gut microbiome influences physiological responses to exercise through interactions with inflammatory markers and metabolite production. Athletes often exhibit a more diverse gut microbiome that is associated with improved performance, but the mechanisms, particularly in relation to different training modalities, are not fully understood. This study aims to integrate blood serum markers with metagenomic data to explore the interplay between exercise type, gut microbiome, and biochemical responses in athletic and non-athletic individuals. MethodsFifty-two male participants (endurance athletes, strength athletes, and controls) underwent two maximal exercise tests: the anaerobic Wingate test and the aerobic Bruce treadmill test. Blood and stool samples were collected at multiple time points for biochemical analysis and metagenomic sequencing. Blood markers were correlated with shifts in microbiome composition and function. ResultsWhile most biochemical parameters showed similar trends across all groups post-exercise, SPARC and adiponectin levels showed distinct responses depending on the exercise modality. The strength group showed unique microbiome associations with blood markers after the Wingate test. Baseline enrichment of specific bacteria (Clostridium phoceensis and Catenibacterium spp.) was associated with an inhibited response to the Bruce test in strength athletes. ConclusionsThe integration of metagenomic and blood serum analyses reveals that exercise modality and training background elicit complex physiological and biochemical responses mediated by the gut microbiome. These findings suggest that specific microbial species may play significant roles in recovery and adaptation processes following acute exercise. Further research with larger cohorts is needed to validate these findings and explore microbiome-targeted interventions to improve performance and recovery.

microbiology↗

bioRxiv2024

Large protein databases reveal structural complementarity and functional locality

Recent breakthroughs in protein structure prediction have led to an unprecedented surge in high-quality 3D models, highlighting the need for efficient computational solutions to manage and analyze this wealth of structural data. In our work, we comprehensively examine the structural clusters obtained from the AlphaFold Protein Structure Database (AFDB), a high-quality subset of ESMAtlas, and the Microbiome Immunity Project (MIP). We create a single cohesive low-dimensional representation of the resulting protein space. Our results show that, while each database occupies distinct regions within the protein structure space, they collectively exhibit significant overlap in their functional profiles. High-level biological functions tend to cluster in particular regions, revealing a shared functional landscape despite the diverse sources of data. By creating a single, cohesive low-dimensional representation of protein structure space integrating data from diverse sources, localizing functional annotations within this space, and providing an open-access web-server for exploration, this work offers insights for future research concerning protein sequence-structure-function relationships, enabling various biological questions to be asked about taxonomic assignments, environmental factors, or functional specificity. This approach is generalizable to other or future datasets, enabling further discovery beyond findings presented here.

bioinformatics↗

bioRxiv2024

Expanding the cultivated human archaeome by targeted isolation of novel Methanobrevibacter strains from fecal samples

Archaea are integral components of the human microbiome but persist as understudied entities within the gastrointestinal tract (GIT), primarily due to the lack of cultured representatives for comprehensive mechanistic investigations. With only four Methanobrevibacter smithii isolates from humans available according to the Global Catalogue of Microorganisms (GCM), the existing cultures fail to adequately represent the observed diversity, as underscored by recent findings. This study introduces a targeted cultivation method for enriching methanogenic archaea from human fecal samples. Applied to 16 stool samples from healthy and diseased donors, the method aimed to genomically characterize the archaeal cultures and establish correlations with gastrointestinal disorders. The procedure combines methane breath testing, in silico metabolic modelling, media optimization, FACS, dilution series, and genomic sequencing through Nanopore technology. Additional analyses include co-cultured bacteriome, comparative genomics of archaeal genomes, functional comparisons, and structure-based protein function prediction of unknown differential traits. Successful establishment of stable archaeal cultures from 14 out of 16 fecal samples yielded nine previously uncultivated strains, eight of which were absent from a recent archaeome genome catalog. Comparative genomic and functional assessments of Methanobrevibacter smithii and Candidatus Methanobrevibacter intestini strains from diverse participant cohorts revealed features potentially associated with gastrointestinal diseases. This work substantially broadens the scope of available archaeal representatives for functional and mechanistic studies in the human GIT. The established protocol facilitates the cultivation of methanogenic archaea from nearly every human fecal sample, offering insights into the adaptability of Candidatus Methanobrevibacter intestini genomes in critical microbiome situations.

microbiology↗

bioRxiv2023

Healthy microbiome - moving towards functional interpretation

Microbiome-based disease prediction has significant potential as an early, non-invasive marker of multiple health conditions linked to dysbiosis of the human gut microbiota, thanks in part to decreasing sequencing and analysis costs. Microbiome health indices and other computational tools currently proposed in the field often are based on a microbiomes species richness and are completely reliant on taxonomic classification. A resurgent interest in a metabolism-centric, ecological approach has led to an increased understanding of microbiome metabolic and phenotypic complexity revealing substantial restrictions of taxonomy-reliant approaches. In this study, we introduce a new metagenomic health index developed as an answer to recent developments in microbiome definitions, in an effort to distinguish between healthy and unhealthy microbiomes, here in focus, inflammatory bowel disease (IBD). The novelty of our approach is a shift from a traditional Linnean phylogenetic classification towards a more holistic consideration of the metabolic functional potential underlining ecological interactions between species. Based on well-explored data cohorts, we compare our method and its performance with the most comprehensive indices to date, the taxonomy-based Gut Microbiome Health Index (GMHI), and the high dimensional principal component analysis (hiPCA)methods, as well as to the standard taxon-, and function-based Shannon entropy scoring. After demonstrating better performance on the initially targeted IBD cohorts, in comparison with other methods, we retrain our index on an additional 27 datasets obtained from different clinical conditions and validate our indexs ability to distinguish between healthy and disease states using a variety of complementary benchmarking approaches. Finally, we demonstrate its superiority over the GMHI and the hiPCA on a longitudinal COVID-19 cohort and highlight the distinct robustness of our method to sequencing depth. Overall, we emphasize the potential of this metagenomic approach and advocate a shift towards functional approaches in order to better understand and assess microbiome health as well as provide directions for future index enhancements. Our method, q2-predict-dysbiosis (Q2PD), is freely available (https://github.com/Kizielins/q2-predict-dysbiosis).

bioinformatics↗

bioRxiv2023

Gut Microbiome Dynamics and Predictive Value in Hospitalized COVID-19 Patients: A Comparative Analysis of Shallow and Deep Shotgun Sequencing

The COVID-19 pandemic caused by SARS-CoV-2 has led to a wide range of clinical presentations, with respiratory symptoms being common. However, emerging evidence suggests that the gastrointestinal (GI) tract is also affected, with angiotensin-converting enzyme 2, a key receptor for SARS-CoV-2, abundantly expressed in the ileum and colon. The virus has been detected in GI tissues and fecal samples, even in cases with negative respiratory results. GI symptoms have been associated with an increased risk of ICU admission and mortality. The gut microbiome, a complex ecosystem of around 40 trillion bacteria, plays a crucial role in immunological and metabolic pathways. Dysbiosis of the gut microbiota, characterized by a loss of beneficial microbes and decreased microbial diversity, has been observed in COVID-19 patients, potentially contributing to disease severity. We conducted a comprehensive gut microbiome study in 204 hospitalized COVID-19 patients using both shallow and deep shotgun sequencing methods. We aimed to track microbiota composition changes induced by hospitalization, link these alterations to clinical procedures (antibiotics administration) and outcomes (ICU referral, survival), and assess the predictive potential of the gut microbiome for COVID-19 prognosis. Shallow shotgun sequencing was evaluated as a cost-effective diagnostic alternative for clinical settings.

bioinformatics↗

bioRxiv2023

Adaptation to space conditions of novel bacterial species isolated from the International Space Station revealed by functional gene annotations and comparative genome analysis

BackgroundThe extreme environment of the International Space Station (ISS) puts selective pressure on microorganisms unintentionally introduced during its 20+ years of service as a low-orbit science platform and human habitat. Such pressure leads to the development of new features not found in the Earth-bound relatives, which enable them to adapt to unfavorable conditions. ResultsIn this study, we generated the functional annotation of the genomes of five newly identified species of Gram-positive bacteria, four of which are non-spore-forming and one spore-forming, all isolated from the ISS. Using a deep-learning based tool - deepFRI - we were able to functionally annotate close to 100% of protein-coding genes in all studied species, overcoming other annotation tools. Our comparative genomic analysis highlights common characteristics across all five species and specific genetic traits that appear unique to these ISS microorganisms. Proteome analysis mirrored these genomic patterns, revealing similar traits. The collective annotations suggest adaptations to life in space, including the management of hypoosmotic stress related to microgravity via mechanosensitive channel proteins, increased DNA repair activity to counteract heightened radiation exposure, and the presence of mobile genetic elements enhancing metabolism. In addition, our findings suggest the evolution of certain genetic traits indicative of potential pathogenic capabilities, such as small molecule and peptide synthesis and ATP-dependent transporters. These traits, exclusive to the ISS microorganisms, further substantiate previous reports explaining why microbes exposed to space conditions demonstrate enhanced antibiotic resistance and pathogenicity. ConclusionOur findings indicate that the microorganisms isolated from ISS we studied have adapted to life in space. Evidence such as mechanosensitive channel proteins, increased DNA repair activity, as well as metallopeptidases and novel S-layer oxidoreductases suggest a convergent adaptation among these diverse microorganisms, potentially complementing one another within the context of the microbiome. The common genes that facilitate adaptation to the ISS environment may enable bioproduction of essential biomolecules need during future space missions, or serve as potential drug targets, if these microorganisms pose health risks.

bioinformatics↗

bioRxiv2023

Effects of Data Transformation and Model Selection on Feature Importance in Microbiome Classification Data

Accurate classification of host phenotypes from microbiome data is essential for future therapies in microbiome-based medicine and machine learning approaches have proved to be an effective solution for the task. The complex nature of the gut microbiome, data sparsity, compositionality and population-specificity however remain challenging, which highlights the critical need for standardized methodologies to improve the accuracy and reproducibility of the results. Microbiome data transformations can alleviate some of the aforementioned challenges, but their usage in machine learning tasks has largely been unexplored. Our aim was to assess the impact of various data transformations on the accuracy, generalizability and feature selection by analysis using more than 8,500 samples from 24 shotgun metagenomic datasets. Our findings demonstrate the feasibility of distinguishing between healthy and diseased individuals using microbiome data with minimal dependence on the algorithm and transformation selection. Remarkably, presence-absence transformation performed comparably well to abundance-based transformations, and only a small subset of predictors is crucial for accurate classification. However, while different transformations resulted in comparable classification performance, the most important features varied significantly, which highlight the need to reevaluate machine-learning based biomarker detection. Our research provides valuable guidance for applying machine learning on microbiome data, offering novel insights and highlighting important areas for future research.

bioinformatics↗

bioRxiv2023

Microbiome Features Associated with Performance Measures in Non-Athletic and Athletic Individuals

The influence of human gut microbiota on health and disease is now well established. Therefore, it is not surprising that microbiome research has found applications within the sports community, hoping to improve health and optimize performance. Every comparison study found new species or pathways that were more enriched in elites than in sedentary controls. In addition, sport-specific and performance-level-specific microbiome features have been identified. However, the results remain inconclusive and indicate the need for further assessment. In our study, we conducted two interventions, anaerobic (Wingate Anaerobic Tests (WAnTs)) and aerobic (Bruce Treadmill Test), on fit non-athletic but physically active controls and athletic strength (training professional sports which increase anaerobic efforts such as weightlifting, powerlifting, and bodybuilding) and endurance (training professional sports which increase aerobic efforts such as race walking, long-distance running, or ski running) individuals to compare sport-specific and training-specific responses. While we did not identify any differences in alpha and beta diversity or significant differential abundance of microbiome components at baseline, we noted that one-third of the species identified were unique to either group. Longitudinal analysis of samples (pre- and post-intervention) revealed an abundance of Alistipes communis in the strength group during the WAnT intervention and 88 species with notable differences between groups during the Bruce Test intervention. SparCC recognized Bifidobacterium longum and Bifidobacterium adolescentis, short-chain fatty acid producers with probiotic properties, as species strongly associated with VO2max. Ultimately, we identified several taxa with different baseline abundances and longitudinal changes when comparing individuals based on their VO2max, average power, and maximal power parameters. Our results confirmed the health status of the individuals involved in the study, based on previous assumptions about microbiome health. Furthermore, our findings indicate that microbiome features are associated with better performance measures previously identified in elite athletes.

microbiology↗

bioRxiv2023

Microbiome time series data reveal predictable patterns of change

BackgroundThe gut microbiome is crucial for human health and disease. Longitudinal studies are gaining importance in understanding its dynamics over time, compared to cross-sectional approaches. Investigating the temporal dynamics of the microbiome, including individual bacterial species and clusters, is essential for comprehending its functionality and impact on health. This knowledge has implications for targeted therapeutic strategies, such as personalized diets and probiotic therapy. ResultsHere, by adopting a rigorous statistical approach, we aim to shed light on the temporal changes in the gut microbiome and unravel its intricate behavior over time. We leveraged four long and dense time series of the gut microbiome in generally healthy individuals examining how its composition evolves as a community and how individual bacterial species behave over time. We also explore whether specific clusters of bacteria exhibit similar fluctuations, which could provide insights into potential functional relationships and interactions within the microbiome Our study reveals that despite its high volatility, the human gut microbiome is stable in time and can be predicted based solely on its previous states. We characterize the unique temporal behavior of individual bacterial species and identify distinct longitudinal regimes in which bacteria exhibit specific patterns of behavior. Finally, through cluster analysis, we identify groups of bacteria that exhibit coordinated fluctuations over time. ConclusionsOur findings contribute to our understanding of the dynamic nature of the gut microbiome and its potential implications for human health. The provided guidelines support scientists studying gut microbiome complex dynamics, promoting further research and advancements in microbiome analysis.

bioinformatics↗

bioRxiv2023

Reply to: Caution Regarding the Specificities of Pan-Cancer Microbial Structure

The cancer microbiome field tremendously accelerated following the release of our manuscript nearly three years ago1, including direct validation of our cancer type-specific conclusions in independent, international cohorts2,3 and the tumor microbiomes adoption into the hallmarks of cancer4. Disentangling contamination signals from biological signals is an important consideration for this research field. Therefore, despite numerous, high-impact, peer-reviewed research papers that either validated our conclusions or extended them using data we released2,5-13, we carefully considered criticism raised by Gihawi et al. about potential mishandling of contaminants, batch effects, and machine learning approaches--all of which were central topics in our manuscript. Nonetheless, a close examination of each concern alongside the original manuscript and re-analyses of our published data strongly demonstrates the robustness of the original findings. To remove all doubt, however, we have reproduced all key conclusions from the original manuscript using only overlapping bacterial genera identified in a highly decontaminated, multi-cancer, international cohort (Weizmann Institute of Science, WIS)2, with or without batch correction, and with multiclass machine learning analyses to mitigate class imbalances. Our published pan-cancer mycobiome manuscript3 also affirms these findings using updated, state-of-the-art methods. We also note that every analysis shown here was possible using public data and code that we had already provided.

bioinformatics↗

bioRxiv2022

Spatial variability of the microbiome in chronic rhinosinusitis is associated with the patients' clinical characteristics but not with sinus ostia occlusion

BackgroundThe sinus microbiome in patients with chronic rhinosinusitis (CRS) is considered homogenous across the sinonasal cavity. The middle nasal meatus is the recommended sampling site for 16S rRNA sequencing. However, individuals with unusually high between-site variability between the middle meatus and the sinuses were identified in previous studies. This study aimed to identify which factors determine increased microbial heterogeneity between sampling sites in the sinuses. MethodologyIn this cross-sectional study samples for 16S rRNA sequencing were obtained from the middle meatus, the maxillary and the frontal sinus in 50 patients with CRS. The microbiome diversity between sampling sites was analysed in relation to the size of the sinus ostia and clinical metadata. ResultsIn approximately 15% of study participants, the differences between sampling sites within one patient were greater than between the patient and other individuals. Contrary to a popular hypothesis, obstruction of the sinus ostium resulted in decreased dissimilarity between the sinus and the middle meatus. The dissimilarity between the sampling sites was patient-specific: greater between-sinus differences were associated with greater meatus-sinus differences, regardless of the drainage pathway patency. Decreased spatial variability was observed in patients with nasal polyps and extensive mucosal changes in the sinuses. ConclusionsSampling from the middle meatus is not universally representative of the sinus microbiome. The differences between sites cannot be predicted from the patency of communication pathways between them.

microbiology↗

bioRxiv2022

Comprehensive function annotation of metagenomes and microbial genomes using a deep learning-based method

Comprehensive protein function annotation is essential for understanding microbiome-related disease mechanisms in the host organisms. Still, a large portion of human gut microbial proteins lack functional annotation. Here, we have developed a new metagenome analysis workflow integrating de novo genome reconstruction, taxonomic profiling and deep learning-based functional annotations from DeepFRI. We validate DeepFRI functional annotations by comparing them to orthology-based annotations from eggNOG on a set of 1,070 infant metagenome samples from the DIABIMMUNE cohort. Using the workflow, we have generated a sequence catalogue of 1.9 million non-redundant microbial genes. The functional annotations revealed 70% concordance between GO annotations predicted by DeepFRI and eggNOG. However, DeepFRI improved the annotation coverage, with 99% of the gene catalogue obtaining GO molecular function annotations, albeit less specific compared to eggNOG. Additionally, we construct pan-genomes in a reference-free manner using high-quality metagenome assembled genomes (MAGs) and analyse the associated annotations. eggNOG annotated more genes on well-studied organisms such as Escherichia coli while DeepFRI was less sensitive to taxa. This workflow will contribute to novel understanding of the functional signature of the human gut microbiome in health and disease as well as guide future metagenomics studies.

bioinformatics↗

bioRxiv2022

Sequence-structure-function relationships in the microbial protein universe

Abstract / SummaryFor the past half-century, structural biologists relied on the notion that similar protein sequences give rise to similar structures and functions. While this assumption has driven research to explore certain parts of the protein universe, it disregards spaces that dont rely on this assumption. Here we explore areas of the protein universe where similar protein functions can be achieved by different sequences and different structures. We predict [~]200,000 structures for diverse protein sequences from 1,003 representative genomes1 across the microbial tree of life, and annotate them functionally on a per-residue basis. Structure prediction is accomplished using the World Community Grid, a large-scale citizen science initiative. The resulting database of structural models is complementary to the AlphaFold database, with regards to domains of life as well as sequence diversity and sequence length. We identify 148 novel folds and describe examples where we map specific functions to structural motifs. We also show that the structural space is continuous and largely saturated, highlighting the need for shifting the focus from obtaining structures to putting them into context, to transform all branches of biology, including a shift from sequence-based to sequence-structure-function based meta-omics analyses.

bioinformatics↗