Biology subjects

Krutkin, D. D.

Publications and source records attributed to Krutkin, D. D..

4 recordsLinked to original sources

bioRxiv2026

ARGus: A Co-assembly workflow for MAG generation, ARG detection, and virulence analysis

The emergence of antibiotic resistance among pathogenic bacteria is a significant global health challenge with multidrug resistance becoming increasingly common. Moreover, since antibiotic resistance genes (ARGs) can be transferred horizontally more bacteria are rapidly evolving resistance. In addition, emerging bacterial pathogens continue to arise from a combination of urbanization, animal agriculture, global movements of people, and inadequate sewage infrastructure. Researchers have begun applying deep sequencing and shotgun metagenomics to detect known and unknown pathogenic organisms and ARGs directly from environmental samples. Here, we describe a bioinformatics workflow that uses a co-assembly approach to assemble contigs across metagenomes and bin them into high coverage metagenomic assembled genomes (MAGs), while segregating out unbinned contigs that includes mobile elements (e.g., plasmids). The workflow includes annotation of coding sequences and differential determination of ARGs and virulence factors (VF) within the sets of both MAG genome bins and unbinned contigs and allows quantification of MAG, ARG and VF abundances for ecological (alpha and beta diversity) and network analyses. Workflow analysis of metagenomic samples collected from the heavily polluted Tijuana River identified hundreds of MAGs, including many high-quality bins and many novel potential pathogens, and found the vast majority of ARG sequence matches in the unbinned contigs. A combined network analysis found strong correlations (r > 0.90) between ARGs and specific MAGs, indicating which bacterial species is likely to contain the ARG. This workflow provides a powerful approach for public health metagenomics studies of emerging pathogens and ARGs.

bioinformatics↗

bioRxiv2025

Moisture and material shape microbial communities in the built environment through disturbance-productivity relationships

The built environment houses diverse microbial communities whose diversity and composition differ among building materials and environmental conditions. Ecological theory makes predictions about how productivity and diversity shape communities, and experiments in the built environment provide an opportunity to test these. We manipulated moisture (constant or repeated wet-dry cycling) on three common building materials to test predictions about alpha and beta diversity. The most productive material (oriented strand board) supported the highest bacterial alpha and beta diversity, and these diversity levels were reduced by repeated drying disturbances. Diversity patterns for fungi were more variable, with the highest alpha diversity on low-moderate productivity material (gypsum wallboard). Fungal beta diversity was reduced by disturbance on high-productivity material, but increased on the other materials. These patterns were driven largely by members of Bacillaceae, Sphingomonadaceae, and Aspergillaceae that reached high abundances in some treatments. Differences between bacteria and fungi may be due to the scale-dependence of productivity-diversity relationships. Together, these results indicate that disturbances can interact with building materials, in some cases leading to variation in community composition that makes it difficult to predict the conditions under which microorganisms with potential importance to health and safety will occur. ImportanceThe built environment--the homes, workplaces, vehicles, and other spaces where people spend most of their time--contains an enormous diversity of microorganisms with significance for human wellbeing, yet we know little about the factors shaping these microbial communities. We found that patterns of wetting and drying that mimic indoor leaks on common building materials affects the diversity of bacteria and fungi growing on the materials. But the identity of these microorganisms differed from one building material to another, and this was especially variable with wetting and drying. This means that common disturbances that lead to microbial growth in homes and offices can make it difficult to predict which microbes, including those that represent health threats to people, will occur in the built environment.

microbiology↗

bioRxiv2024

To impute or not to impute in untargeted metabolomics - that is the compositional question

Untargeted metabolomics often produce large datasets with missing values, arising from biological or technical factors, which can undermine statistical analyses and lead to biased biological interpretations. Imputation methods, such as k-Nearest Neighbors (kNN) and Random Forest (RF) regression are commonly used but their effects vary depending on the type of missing data e.g. Missing Completely At Random (MCAR) and Missing Not At Random (MNAR). Here, we determined the impacts of degree and type of missing data on the accuracy of kNN and RF imputation using two datasets: a targeted metabolomic dataset with spiked-in standards and an untargeted metabolomic dataset. We also assessed the effect of compositional data approaches (CoDA), such as the centered log-ratio (CLR) transform, on data interpretation, since these methods are increasingly being used in metabolomics. Overall, we found that kNN and RF performed more accurately when the proportion of missing data across samples for a metabolic feature was low. However, these imputations could not handle MNAR data and generated wildly inflated values or imputed values where none should exist. Furthermore, we show that the proportion of missing values had a strong impact on the accuracy of imputation which affected the interpretation of the results. Our results suggest extreme caution should be used with imputation even with modestly levels of missing data or when the type of missingness is unknown.

bioinformatics↗

bioRxiv2024

Genetic hypogonadal (Gnrh1 hpg) mouse model uncovers influence of reproductive axis on maturation of the gut microbiome during puberty

The gut microbiome plays a key role in human health and gut dysbiosis is linked to many sex-specific diseases including autoimmune, metabolic, and neurological disorders. Activation of the hypothalamic-pituitary-gonadal (HPG) axis during puberty leads to sexual maturation and development of sex differences through the action of gonadal sex steroids. While the gut microbiome also undergoes sex differentiation, the mechanisms involved remain poorly understood. Using a genetic hypogonadal (hpg) mouse model, we sampled the fecal microbiome of male and female wild-type and hpg mutant mice before and after puberty to determine how microbial taxonomy and function are influenced by age, sex, and the HPG axis. We showed that HPG axis activation during puberty is required for sexual maturation of the gut microbiota composition, community structure, and metabolic functions. We also demonstrated that some sex differences in taxonomic composition and amine metabolism developed independently of the HPG axis, indicating that sex chromosomes are sufficient for certain sex differences in the gut microbiome. In addition, we showed that age, independent of HPG axis activation, led to some aspects of pubertal maturation of the gut microbiota community composition and putative functions. These results have implications for microbiome-based treatments, indicating that sex, hormonal status, and age should be considered when designing microbiome-based therapeutics.

microbiology↗