Biology subjects

Srivastava, H.

Publications and source records attributed to Srivastava, H..

4 recordsLinked to original sources

bioRxiv2024

Comparative genomics of the sexually transmitted parasite Trichomonas vaginalis reveals relaxed and convergent evolution and genes involved in spillover from birds to humans

Trichomonas vaginalis is the causative agent of the venereal disease trichomoniasis which infects men and women globally and is associated with serious outcomes during pregnancy and cancers of the human reproductive tract. Trichomonads parasitize a range of hosts in addition to humans including birds, livestock, and domesticated animals. Recent genetic analysis of trichomonads recovered from columbid birds has provided evidence that these parasite species undergo frequent host-switching, and that a current epoch spillover event from columbids likely gave rise to T. vaginalis in humans. We undertook a comparative evolutionary genomics study of seven trichomonad species, generating chromosome-scale reference genomes for T. vaginalis and its avian sister species Trichomonas stableri, and assemblies of five other species that infect birds and mammals. Human-infecting trichomonad lineages have undergone recent and convergent genome size expansions compared to their avian sister species, and the major contributor to their increased genome size is increased repeat expansions, especially multicopy gene families and transposable elements, with genetic drift likely a driver due to relaxed selection. Trichomonads have independently host-switched twice from birds to humans, and genes implicated in the transition to the human host include those associated with host tissue adherence and phagocytosis, extracellular vesicles, and CAZyme virulence factors.

microbiology↗

bioRxiv2023

ATOMDANCE: interpretable kernel-based learning for denoising functional and evolutionary comparisons of protein dynamics

Comparative methods in molecular biology and molecular evolution rely exclusively upon the analysis of DNA sequence and protein structure, both static forms of information. However, it is widely accepted that protein function results from changes in dynamic machine-like motions induced by molecular interactions, a type of data for which comparative methods of analysis are challenged by the large fraction of protein motion created by random thermal noise in the surrounding solvent. Here, we introduce ATOMDANCE, a suite of statistical and kernel-based machine learning tools designed for comparing and denoising functional states of protein motion captured in time-series from molecular dynamics simulations. ATOMDANCE employs interpretable Gaussian kernel functions to compute site-wise maximum mean discrepancy (MMD) between learned features of motion representing two functional, malfunctional or evolutionary protein states (e.g. bound vs. unbound, wild-type vs. mutant). ATOMDANCE derives empirical p-values identifying functional similarity/difference in dynamics at each amino acid site on the protein. ATOMDANCE also employs MMD to contextually analyze potential random amino-acid replacements thus allowing for a site-wise test of neutral vs. non-neutral evolution in the divergence of dynamic function in protein homologs. Lastly, ATOMDANCE also employs mixed-model ANOVA combined with graph network community detection to identify functional shifts in protein regions that exhibit time-coordinated dynamics or resonance of motion across sites. Here, we demonstrate the utility of the software for identifying key sites involved in dynamic responses during functional binding interactions involving DNA, small molecule drugs, and virus-host recognition. We also demonstrate its utility in understanding dynamic resonance changes occurring during the allosteric activation of a pathogenic protease. ATOMDANCE offers a user-friendly interface and only requires an input structure, topology and trajectory files for each of the two proteins being compared (i.e .pdb, .prmtop, and .nc). A separate interface for generating molecular dynamics simulations via open-source tools is also offered. Lay Audience Summary - ATOMDANCE is a suite of software pipelines controlled by a single user interface and designed to comprehensively simulate, calculate and compare protein motions between two functional or evolutionary states while controlling for random noise. It is useful for finding amino acid sites on a given protein that are important in binding other proteins, DNA, or drugs/toxins. It can also be used to assess the effect of genetic mutation on protein motion, and identifies regions of sites on proteins that tend to move together or resonate as a whole unit or community.

biophysics↗

bioRxiv2022

Interpreting the contributions of transcript features to protein abundance prediction models

Protein and mRNA levels correlate only moderately. The availability of proteogenomics data sets with protein and transcript measurements from matching samples is providing new opportunities to assess the degree to which protein levels in a system can be predicted from mRNA information. Here we examined the contributions of input features in protein abundance prediction models. Using large proteogenomics data from 8 cancer types within the Clinical Proteomic Tumor Analysis Consortium (CPTAC) data set, we trained models to predict the abundance of over 13,000 proteins using matching transcriptome data from up to 958 tumor or normal adjacent tissue samples each, and compared predictive performances across algorithms, data set sizes, and input features. Over one-third of proteins (4,648) showed relatively poor predictability (elastic net r [≤] 0.3) from their cognate transcripts. Moreover, we found widespread occurrences where the abundance of a protein is considerably less well explained by its own cognate transcript level than that of one or more trans locus transcripts. The incorporation of additional trans-locus transcript abundance data as input features increasingly improved the ability to predict sample protein abundance. Transcripts that contribute to non-cognate protein abundance primarily involve those encoding known or predicted interaction partners of the protein of interest, including not only large multi-protein complexes as previously shown, but also small stable complexes in the proteome with only one or few stable interacting partners. Network analysis further shows a complex proteome-wide interdependency of protein abundance on the transcript levels of multiple interacting partners. The predictive model analysis here therefore supports that protein-protein interaction including in small protein complexes exert post-transcriptional influence on proteome compositions more broadly than previously recognized. Moreover, the results suggest mRNA and protein co-expression analysis may have utility for finding gene interactions and predicting expression changes in biological systems.

bioinformatics↗

bioRxiv2020

Chromosome scale reference genome of Cluster bean (Cyamopsis tetragonoloba (L.) Taub.)

Clusterbean (Cyamopsis tetragonoloba (L.) Taub.), also known as Guar is a widely cultivated dryland legume of Western India and parts of Africa. Apart from being a vegetable crop, it is also an abundant source of a natural hetero-polysaccharide called guar gum or galactomannan which is widely used in cosmetics, pharmaceuticals, food processing, shale gas drilling etc. Here, for the first time we are reporting a chromosome-scale reference genome assembly of clusterbean, from a high galactomannan containing popular guar cultivar, RGC-936, by combining sequenced reads from Illumina, 10x Chromium and Oxford Nanopore technologies. The initial assembly of 1580 scaffolds with an N50 value of 7.12 Mbp was generated. Then, the final genome assembly was obtained by anchoring these scaffolds to a high density SNP map. Finally, a genome assembly of 550.31 Mbp was obtained in 7 pseudomolecules corresponding to 7 chromosomes with a very high N50 of 78.27 Mbp. We finally predicted 34,680 protein-coding genes in the guar genome. The high-quality chromosome-scale cluster bean genome assembly will facilitate understanding of the molecular basis of galactomannan biosynthesis and aid in genomics-assisted breeding of superior cultivars.

genomics↗