Tomasz Kosciolek

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. IMPORTANCE: Our findings demonstrate robust taxonomic comparability between MGI and NovaSeq platforms, while revealing systematic functional differences that should be carefully considered in cross-platform metagenomic studies.

Next-generation sequencing has revolutionized microbiome research, yet the transition from taxonomic to functional profiling remains a major technical challenge. While marker gene sequencing provides a widely accessible ecological view, it often lacks the resolution for actionable insights. This perspective argues that shifting to whole metagenomic sequencing is essential for mapping functional potential, such as antimicrobial resistance, and metabolic pathways. However, we identify a critical bottleneck: excessive multiplexing. High multiplexing ratios reduce the number of unique molecules per sample, leading to high duplication rates and the stochastic dropout of low-abundance genes. We demonstrate that functional profiles are far more sensitive to these library complexity issues than taxonomic ones. We recommend prioritizing total sequencing depth and reducing multiplexing to ensure sufficient unique coverage. Additionally, adopting long-read or hybrid architectures is vital for providing the genomic context necessary for strain-level resolution. These optimizations are prerequisites for robust global microbiome synthesis and translational science.

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; pathways typically affected by host-microbiome interactions. We also find a greater relative abundance of bacterial functional amyloids in patients diagnosed with Parkinson’s disease in two out of three analyzed datasets. Our results generate hypotheses on a tentative link between neurodegeneration and gut bacterial functional amyloids, which require further experimental validation.

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.

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.

Recent breakthroughs in protein structure prediction have led to a surge in high-quality 3D models, highlighting the need for efficient computational solutions. In our work, we examine the structural clusters 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. We show that, while each database occupies distinct regions, 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 representation of protein structure space, 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 biological questions to be asked about taxonomic assignments, environmental factors, or functional specificity. This approach is generalizable, thus enabling further discovery beyond findings presented here.

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. Importance: Our 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.

This study examined how high-altitude exposure and expedition-specific dietary changes influence gut microbiome composition, functional pathways, and their relationships with performance and health markers in alpinists. Seventeen male mountaineers (age 30.29±5.8 years) participating in multi-week expeditions (>3,000 MASL) were assessed before and after their climbs. Assessments included dietary intake analysis, blood and urine biomarkers, aerobic and anaerobic performance tests, and metagenomic sequencing of the gut microbiome. Bioinformatic and statistical analyses evaluated changes in microbiome composition and function and their correlations with physiological and dietary parameters. High-altitude exposure was associated with significant shifts in gut microbial composition and functional capacity. While the total number of bacterial species and functions remained stable, the glucose degradation pathway increased post-expedition. Participants with greater microbiome shifts showed improved performance and had richer baseline microbiomes. Pre- expedition, certain microbial functions were associated with vitamin B ₆ and C intake, while post-expedition correlations involved specific macronutrients and micronutrients. Additionally, some microbiome changes correlated with blood markers, indicating links to nutrient metabolism and electrolyte balance. The gut microbiome of alpinists adapts to extreme environmental stress and dietary changes, influencing metabolic, immune, and performance- related processes. Optimizing dietary strategies to support a beneficial microbiome profile may enhance resilience and performance in challenging high-altitude environments.

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”.