Biology subjects

Aasmets, O.

Publications and source records attributed to Aasmets, O..

6 recordsLinked to original sources

bioRxiv2024

Metagenome-assembled genomes of Estonian Microbiome cohort reveal novel species and their links with prevalent diseases

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. IMPORTANCEMicrobiome 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.

bioinformatics↗

bioRxiv2024

Fecal microbial load is a major determinant of gut microbiome variation and a confounder for disease associations

The microbiota in individual habitats differ both in relative composition and absolute abundance. While sequencing approaches determine only the relative abundances of taxa and genes, experimental techniques for absolute abundance determination are rarely applied to large-scale microbiome studies. Here, we developed a machine learning approach to predict fecal microbial loads (microbial cells per gram) solely from relative abundance data. Applied to large-scale datasets (n = 34,539), we demonstrate that microbial load is the major determinant of gut microbiome variation and associated with numerous host factors. We found that for several diseases, the altered microbial load, not the disease itself, was the main driver of the gut microbiome changes. Adjusting for this effect substantially reduced the significance of more than half of the disease-associated species. Our analysis reveals that the fecal microbial load is a major confounder in microbiome studies, highlighting its importance for understanding microbiome variation in health and disease.

microbiology↗

bioRxiv2024

A history of repeated antibiotic usage leads to microbiota-dependent mucus defects

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.

microbiology↗

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↗

bioRxiv2021

Using fecal immunochemical tubes for the analysis of gut microbiome has potential to improve colorectal cancer screening

BackgroundColorectal cancer (CRC) is an important and challenging public health problem which successful treatment depends on the early detection of the disease. Recently, colorectal cancer specific microbiome signatures have been proposed as an additional marker for CRC detection. A desirable aim would be the possibility to analyze microbiome from the fecal samples collected during CRC screening programs into FIT tubes for fecal occult blood testing. MethodsWe investigated the impact of the Fecal Immunohistochemical Test (FIT) and stabilization buffer on the microbial community structure in stool samples from 30 volunteers and compared their communities to fresh-frozen samples highlighting also the previously published cancer-specific communities. Altogether 214 samples were analyzed including positive and negative controls using 16S rRNA gene sequencing. ResultsThe variation between individuals is greater than the differences introduced by collection strategy. The vast majority of the genera are stable for up to 7 days. None of the changes observed between fresh frozen samples and FIT tubes are related to previously shown colorectal-cancer specific bacteria. ConclusionsOverall, our results show that FIT tubes can be used for profiling the gut microbiota in colorectal cancer screening programs as the community is similar to fresh frozen samples and stable at least for 7 days. ImpactSample material from FIT tubes could be used in addition to fecal immunochemical tests for future investigations into the role of gut microbiota in colorectal cancer screening programs circumventing the need to collect additional samples and possibly improving the sensitivity of FIT.

cancer biology↗

bioRxiv2020

Machine learning reveals time-varying microbial predictors with complex effects on glucose regulation

The incidence of type 2 diabetes (T2D) has been increasing globally and a growing body of evidence links type 2 diabetes with altered microbiota composition. Type 2 diabetes is preceded by a long pre-diabetic state characterized by changes in various metabolic parameters. We tested whether the gut microbiome could have predictive potential for T2D development during the healthy and pre-diabetic disease stages. We used prospective data of 608 well-phenotyped Finnish men collected from the population-based Metabolic Syndrome In Men (METSIM) study to build machine learning models for predicting continuous glucose and insulin measures in a shorter (1.5 year) and longer (4.5 year) period. Our results show that the inclusion of gut microbiome improves prediction accuracy for modelling T2D associated parameters such as glycosylated hemoglobin and insulin measures. We identified novel microbial biomarkers and described their effects on the predictions using interpretable machine learning techniques, which revealed complex linear and non-linear associations. Additionally, the modelling strategy carried out allowed us to compare the stability of model performances and biomarker selection, also revealing differences in short-term and long-term predictions. The identified microbiome biomarkers provide a predictive measure for various metabolic traits related to T2D, thus providing an additional parameter for personal risk assessment. Our work also highlights the need for robust modelling strategies and the value of interpretable machine learning. ImportanceRecent studies have shown a clear link between gut microbiota and type 2 diabetes. However, current results are based on cross-sectional studies that aim to determine the microbial dysbiosis when the disease is already prevalent. In order to consider microbiome as a factor in disease risk assessment, prospective studies are needed. Our study is the first study that assesses the gut microbiome as a predictive measure for several type 2 diabetes associated parameters in a longitudinal study setting. Our results revealed a number of novel microbial biomarkers that can improve the prediction accuracy for continuous insulin measures and glycosylated hemoglobin levels. These results make the prospect of using microbiome in personalized medicine promising.

microbiology↗