Biology subjects

Vatanen, T.

Publications and source records attributed to Vatanen, T..

7 recordsLinked to original sources

bioRxiv2026

Human Gut Bacteria Convert Endogenous Steroids into Host Cortisol Shuttle Inhibitors

11{beta}-hydroxysteroid dehydrogenase 2 (HSD11B2) protects the mineralocorticoid receptor from glucocorticoid overstimulation by inactivating cortisol. HSD11B2 inhibition can drive receptor overactivation and contribute to hypertension; however, endogenous inhibitors remain poorly defined. Glycyrrhetinic acid-like factors (GALFs) are steroid-like metabolites that inhibit HSD11B2. Given the capacity of gut bacteria to metabolize host steroids, we hypothesized that the gut microbiome could generate GALF-like inhibitors. Here, we identify two 11-oxygenated steroid metabolites that potently inhibit human HSD11B2 in colonic cells and organoids, enabling cortisol-dependent mineralocorticoid receptor activation. We identify gut bacteria and enzymes that produce these compounds and show that their levels are markedly reduced in antibiotic-treated humans. One metabolite is elevated during pregnancy, and both are higher in individuals with stage 2 hypertension-range blood pressure. These findings reveal a bacterial route to altered host cortisol signaling and suggest a potential link between microbiome-derived GALFs and blood pressure regulation.

microbiology↗

bioRxiv2026

LGTM: Gaussian Process Modulated Neural Topic Modeling for Longitudinal Microbiome

Longitudinal microbiome data are key to understanding the dynamics of microbial communities and their relationships with the host and environment. However, analysis of such data is challenging due to high dimensionality, compositionality, irregular sampling and temporal dependencies on external covariates. Existing analytical approaches typically address only subsets of these challenges, limiting their ability to yield biologically interpretable insights. We introduce LGTM, a probabilistic modeling framework that combines flexible non-linear longitudinal modeling with interpretable topic-based representations of the microbiome. LGTM simultaneously identifies microbial co-abundance patterns ("topics") and models how their proportions change over time and in relation to host and environmental covariates. Using multiple longitudinal human gut microbiome datasets, we demonstrate that LGTM identifies diverse microbial topics whose major patterns are reproducible across runs, while achieving competitive performance in imputation and forecasting tasks. A key strength of the framework is its interpretability: LGTM yields microbial topics with biologically interpretable taxonomic compositions and directly quantifies associations between covariates and microbial dynamics. LGTM is available at https://github.com/yuanx749/lgtm.

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↗

bioRxiv2020

Industrialization is associated with elevated rates of horizontal gene transfer in the human microbiome

Horizontal Gene Transfer, the process by which bacteria acquire new genes and functions from non-parental sources, is common in the human microbiome 1,2. If the timescale of HGT is rapid compared to the timescale of human colonization, then it could have the effect of personalizing bacterial genomes by providing incoming strains with the genes necessary to adapt to the diet or lifestyle of a new host. The extent to which HGT occurs on the timescale of human colonization, however, remains unclear. Here, we analyzed 6,188 newly isolated and sequenced gut bacteria from 34 individuals in 9 human populations, and show that HGT is more common among bacteria isolated from the same human host, indicating that the timescale of transfer is short compared to the timescale of human colonization. Comparing across 9 human populations reveals that high rates of transfer may be a recent development in human history linked to industrialization and urbanization. In addition, we find that the genes involved in transfer reflect the lifestyle of the human hosts, with elevated transfer of carbohydrate metabolism genes in hunter gatherer populations, and transfer of antibiotic resistance genes among pastoralists who live in close contact with livestock. These results suggest that host-associated bacterial genomes are not static within individuals, but continuously acquire new functionality based on host diet and lifestyle.

microbiology↗

bioRxiv2019

Structure-Based Function Prediction using Graph Convolutional Networks

The large number of available sequences and the diversity of protein functions challenge current experimental and computational approaches to determining and predicting protein function. We present a deep learning Graph Convolutional Network (GCN) for predicting protein functions and concurrently identifying functionally important residues. This model is initially trained using experimentally determined structures from the Protein Data Bank (PDB) but has significant de-noising capability, with only a minor drop in performance observed when structure predictions are used. We take advantage of this denoising property to train the model on > 200,000 protein structures, including many homology-predicted structures, greatly expanding the reach and applications of the method. Our model learns general structure-function relationships by robustly predicting functions of proteins with [≤] 40% sequence identity to the training set. We show that our GCN architecture predicts functions more accurately than Convolutional Neural Networks trained on sequence data alone and previous competing methods. Using class activation mapping, we automatically identify structural regions at the residue-level that lead to each function prediction for every confidently predicted protein, advancing site-specific function prediction. We use our method to annotate PDB and SWISS-MODEL proteins, making several new confident function predictions spanning both fold and function classifications.

bioinformatics↗

bioRxiv2018

LonGP: an additive Gaussian process regression model for longitudinal study designs

MotivationBiomedical research typically involves longitudinal study designs where samples from individuals are measured repeatedly over time and the goal is to identify risk factors (covariates) that are associated with an outcome value. General linear mixed effect models have become the standard workhorse for statistical analysis of data from longitudinal study designs. However, analysis of longitudinal data can be complicated for both practical and theoretical reasons, including difficulties in modelling, correlated outcome values, functional (time-varying) covariates, nonlinear effects, and model inference.\n\nResultsWe present LonGP, an additive Gaussian process regression model for analysis of experimental data from longitudinal study designs. LonGP implements a flexible, non-parametric modelling framework that solves commonly faced challenges in longitudinal data analysis. In addition to inheriting all standard features of Gaussian processes, LonGP can model time-varying random effects and non-stationary signals, incorporate multiple kernel learning, and provide interpretable results for the effects of individual covariates and their interactions. We develop an accurate Bayesian inference and model selection method, and implement an efficient model search algorithm for our additive Gaussian process model. We demonstrate LonGPs performance and accuracy by analysing various simulated and real longitudinal -omics datasets. Our work is accompanied by a versatile software implementation.\n\nAvailabilityLonGP software tool is available at http://research.cs.aalto.fi/csb/software/longp/.\n\nContactlu.cheng.ac@gmail.com, harri.lahdesmaki@aalto.fi

bioinformatics↗