Kateryna Pantiukh

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.

Metagenomic profiling has advanced the understanding of microbe-host interactions. However, widely used read-based approaches are limited by incomplete reference databases and the inability to resolve strain-level variation. Here, we present a scalable, genome-resolved framework that integrates population-specific metagenome-assembled genomes (MAGs) to discover novel species, within-species diversity, and disease associations. From 1,878 deeply sequenced samples in the Estonian Microbiome Cohort (EstMB-deep), we reconstructed 84,762 MAGs representing 2,257 species, including 353 (15.6%) previously uncharacterized species reaching up to 30% relative abundances in some individuals. We integrated these MAGs with the Unified Human Gastrointestinal Genome collection to create an expanded reference (GUTrep), enabling profiling of 2,509 EstMB individuals and testing associations with 33 prevalent diseases. Of the 25 diseases with significant associations, 8 involved newly identified species, underscoring the value of population-specific MAGs. To quantify within-species diversity, we developed the genome unit number (GUN), a novel MAG-based metric that informed within-species analyses. Based on normalized GUN, we prioritized Odoribacter splanchnicus, a prevalent species with the lowest within-species heterogeneity, yielding sufficient power for a within-species association study. We identified two dominant genome units, GU-N1 and GU-N2, with distinct gene repertoires and divergent disease associations. Notably, GU-N1 was negatively associated with gastritis, duodenitis, and hypertensive heart disease, associations undetected at the species level. Our study expands the human gut reference landscape, demonstrates the importance of population-specific MAGs for uncovering novel microbial diversity, and reveals new disease associations at the within-species level obscured at higher taxonomic levels, highlighting the need for genome-resolved approaches in microbiome research. IMPORTANCE: Microbiome studies increasingly recognize that species-level profiles can mask critical within-species differences relevant to health and disease. However, our work shows that within-species diversity varies drastically across gut microbes, with some species exhibiting almost as many distinct within-species clusters as recovered genomes, making association studies at the within-species level essentially intractable. To address this, we introduce the genome unit number (GUN), a scalable metric for quantifying within-species structure. Using GUN, we demonstrate that only species with limited within-species diversity, such as Odoribacter splanchnicus , currently allow for robust within-species association testing. These findings emphasize the need to systematically evaluate species structure across the gut microbiome and call for the development of new computational and statistical approaches to enable meaningful within-species analyses in highly diverse species.

While microbiota plays a crucial role in maintaining overall health, archaea, a component of microbiota, remain relatively unexplored. Here, we present a newly assembled set of archaeal metagenome-assembled genomes (MAGs) from 1,878 fecal microbiome samples. These MAGs were reconstructed from metagenomic reads of the Estonian Microbiome Deep (EstMB-deep) cohort, which were reused here specifically for archaeal MAG reconstruction. We identified 273 archaeal MAGs, representing 21 species and 144 strains which we curated into the “EstMB MAGdb Archaea-273” MAGs collection.

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.

The gut microbiome is crucial for human health. Its disruption has been linked to several chronic diseases, including joint disorders. The gut–joint axis has been implicated in the pathogenesis of osteoarthritis (OA), but conflicting findings and study limitations have led to uncertainty regarding the role of microbiota. We conducted a multi-cohort gut microbiota analysis in 1,395 OA patients from four European cohorts (Lifelines, EstMB, FINRISK 2002, TwinsUK), using stringent exclusion criteria and matched controls. When assessing microbial diversity, taxa, functional gene profiles, and gut permeability biomarkers, no significant differences were found between OA and controls. Although this does not exclude a causal contribution of the microbiota in the gut-joint-axis, its dysbiosis does not seem to be linked with OA disease progression. Instead, age and BMI appeared as the main contributing factors to microbiome changes. Microbiome studies in complex diseases often face challenges such as small sample sizes, batch effects, and limited ability to match appropriate controls, particularly in single-cohort designs. By combining data from multiple large cohorts, we were able to mitigate these limitations and provide a more robust assessment of microbiome association with OA. Our findings emphasize the need for rigorous study design in microbiome research and challenge the OA-gut dysbiosis hypothesis.

While microbiota plays a crucial role in maintaining overall health, archaea, a component of microbiota, remain relatively unexplored. Here, we present a newly assembled set of archaeal metagenome-assembled genomes (MAGs) from 1,887 fecal microbiome samples. These archaeal MAGs were recovered for the first time from the Estonian population, specifically from the Estonian Microbiome Deep (EstMB-deep) cohort. In total, we identified 273 archaeal MAGs, representing 21 species and 144 strains (“EstMB MAGdb Archaea-273” MAGs collection). Of these 21 species, 12 species belonged to the order Methanobacteriales and Methanomassilicicoccales , other 9 species from Methanomassiliicoccales were novel. Notably, 7 of the 9 new species belonged to the UBA71 genus. Given that the latest version of the Unified Human Gastrointestinal Genome (UHGG v2.0.2) database includes 27 archaeal species, we expanded the known archaeal diversity at the species level by 30%.

Recent evidence indicates that repeated antibiotic usage lowers microbial diversity and ultimately changes the gut microbiota community. However, the physiological effects of repeated - but not recent - antibiotic usage on microbiota-mediated mucosal barrier function are largely unknown. By selecting human individuals from the deeply phenotyped Estonian Microbiome Cohort (EstMB), we here utilized human-to-mouse fecal microbiota transplantation to explore long-term impacts of repeated antibiotic use on intestinal mucus function. While a healthy mucus layer protects the intestinal epithelium against infection and inflammation, using ex vivo mucus function analyses of viable colonic tissue explants, we show that microbiota from humans with a history of repeated antibiotic use causes reduced mucus growth rate and increased mucus penetrability compared to healthy controls in the transplanted mice. Moreover, shotgun metagenomic sequencing identified a significantly altered microbiota composition in the antibiotic-shaped microbial community, with known mucus-utilizing bacteria, including Akkermansia muciniphila and Bacteroides fragilis, dominating in the gut. The altered microbiota composition was further characterized by a distinct metabolite profile, which may be caused by differential mucus degradation capacity. Consequently, our proof-of-concept study suggests that long-term antibiotic use in humans can result in an altered microbial community that has reduced capacity to maintain proper mucus function in the gut.

Metagenomic profiling has advanced understanding of microbe-host interactions. However, widely used read-based approaches are limited by incomplete reference databases and the inability to resolve strain-level variation. Here, we present a scalable, genome-resolved framework that integrates population-specific metagenome assembled genomes (MAGs) to discover novel species, sub-species diversity, and disease associations. From 1,878 deeply sequenced samples in the Estonian microbiome cohort (EstMB-deep), we reconstructed 84,762 MAGs representing 2,257 species, including 353 (15.6%) previously uncharacterized species reaching up to 30% relative abundances in some individuals. We integrated these MAGs with the Unified Human Gastrointestinal Genome (UHGG) collection to create an expanded reference (GUTrep), enabling profiling of 2,509 EstMB individuals and testing associations with 33 prevalent diseases. Of 25 diseases with significant associations, 8 involved newly identified species, underscoring the value of population-specific MAGs. To quantify within-species diversity, we developed the Genome Unit Number (GUN), a novel MAG-based metric that informed sub-species analyses. Based on normalized GUN (nGUN), we prioritized Odoribacter splanchnicus, a prevalent species with the lowest sub-species heterogeneity, yielding sufficient power for sub-species association study. We identified two dominant genome units, GU-N1 and GU-N2, with distinct gene repertoires and divergent disease associations. Notably, GU-N1 was negatively associated with gastritis and duodenitis and hypertensive heart disease, associations undetected at the species level. Our study expands the human gut reference landscape, demonstrates the importance of population-specific MAGs for uncovering novel microbial diversity, and reveals new disease associations on sub-species level obscured at higher taxonomic levels, highlighting the need for genome-resolved approaches in microbiome research. IMPORTANCE: Microbiome studies increasingly recognize that species-level profiles can mask critical sub-species differences relevant to health and disease. However, our work shows that within-species diversity varies drastically across gut microbes, with some species exhibiting almost as many distinct sub-species clusters as recovered genomes, making association studies at the sub-species level essentially intractable. To address this, we introduce the Genome Unit Number (GUN), a scalable metric for quantifying sub-species structure. Using GUN, we demonstrate that only species with limited within-species diversity, such as Odoribacter splanchnicus , currently allow for robust sub-species association testing. These findings emphasize the need to systematically evaluate species structure across the gut microbiome and call for the development of new computational and statistical approaches to enable meaningful sub-species analyses in highly diverse species.

Recent evidence indicates that repeated antibiotic usage lowers microbial diversity and lastingly changes the gut microbiota community. However, the physiological effects of repeated – but not recent – antibiotic usage on microbiota-mediated mucosal barrier function are largely unknown. By selecting human individuals from the deeply-phenotyped Estonian Microbiome Cohort (EstMB) we here utilised human-to-mouse faecal microbiota transplantation to explore long-term impacts of repeated antibiotic use on intestinal mucus function. While a healthy mucus layer protects the intestinal epithelium against infection and inflammation, using ex-vivo mucus function analyses of viable colonic tissue explants, we show that microbiota from humans with a history of repeated antibiotic use causes reduced mucus growth rate and increased mucus penetrability compared to healthy controls in the transplanted mice. Moreover, shotgun metagenomic sequencing identified a significantly altered microbiota composition in the antibiotic-shaped microbial community, with known mucus-utilising bacteria, including Akkermansia muciniphila and Bacteroides fragilis , dominating in the gut. The altered microbiota composition was further characterised by a distinct metabolite profile, which may be caused by differential mucus degradation capacity. Consequently, our findings suggest that long-term antibiotic use in humans results in an altered microbial community that has reduced capacity to maintain proper mucus function in the gut.