Biology subjects

Szczerbiak, P.

Publications and source records attributed to Szczerbiak, P..

3 recordsLinked to original sources

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↗

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↗