Biology subjects

Shakya, M.

Publications and source records attributed to Shakya, M..

8 recordsLinked to original sources

bioRxiv2026

Global untreated wastewater hosts a vast reservoir of previously uncharacterized microbial lineages

Untreated wastewater are reservoirs of microbial communities originating from different sources within an urbanized human-populated area and is now regularly used for tracking and assessing levels of certain human pathogens like SARS-CoV-2, Salmonella, and Poliomyelitis. Wastewater microbial communities can exhibit high diversity, comprised of mostly non-pathogens alongside a smaller population of pathogens. Much of the research on wastewater has focused on either pathogens or microbiomes in the treatment plants, but not on the microbiome of untreated wastewater, which is perhaps more reflective of community health. Moreover, as wastewater usage expands to more known and unknown pathogen detection, a deeper understanding of both wastewaters overall genetic diversity and diversity over time is pivotal. Towards that goal, we characterized the observed microbial diversity of global untreated wastewater by analyzing 1,344 publicly available metagenomes, representing 6 continents, and created a global wastewater database hosted at https://doi.org/10.5281/zenodo.17794790. We elucidated the full spectrum of prokaryotic diversity by recovering and characterizing high quality bins, contigs, and reads from all the data. We provide the first comprehensive, publicly available database describing global wastewater by uncovering previously uncharacterized microbes, identifying geographically specific microbial populations, and creating a high-quality dataset to advance future microbiome, epidemiological, and biosurveillance research.

genomics↗

bioRxiv2022

Towards increased accuracy and reproducibility in SARS-CoV-2 next generation sequence analysis for public health surveillance

During the COVID-19 pandemic, SARS-CoV-2 surveillance efforts integrated genome sequencing of clinical samples to identify emergent viral variants and to support rapid experimental examination of genome-informed vaccine and therapeutic designs. Given the broad range of methods applied to generate new viral genomes, it is critical that consensus and variant calling tools yield consistent results across disparate pipelines. Here we examine the impact of sequencing technologies (Illumina and Oxford Nanopore) and 7 different downstream bioinformatic protocols on SARS-CoV-2 variant calling as part of the NIH Accelerating COVID-19 Therapeutic Interventions and Vaccines (ACTIV) Tracking Resistance and Coronavirus Evolution (TRACE) initiative, a public-private partnership established to address the COVID-19 outbreak. Our results indicate that bioinformatic workflows can yield consensus genomes with different single nucleotide polymorphisms, insertions, and/or deletions even when using the same raw sequence input datasets. We introduce the use of a specific suite of parameters and protocols that greatly improves the agreement among pipelines developed by diverse organizations. Such consistency among bioinformatic pipelines is fundamental to SARS-CoV-2 and future pathogen surveillance efforts. The application of analysis standards is necessary to more accurately document phylogenomic trends and support data-driven public health responses.

bioinformatics↗

bioRxiv2022

Direct inference and control of genetic population structure from RNA sequencing data

RNAseq data can be used to infer genetic variants, yet its use for estimating genetic population structure remains underexplored. Here, we construct a freely available computational tool (RGStraP) to estimate RNAseq-based genetic principal components (RG-PCs) and assess whether RG-PCs can be used to control for population structure in gene expression analyses. Using whole blood samples from understudied Nepalese populations and the Geuvadis study, we show that RG-PCs had comparable results to paired array-based genotypes, with high genotype concordance and high correlations of genetic principal components, capturing subpopulations within the dataset. In differential gene expression analysis, we found that inclusion of RG-PCs as covariates reduced test statistic inflation. Our paper demonstrates that genetic population structure can be directly inferred and controlled for using RNAseq data, thus facilitating improved retrospective and future analyses of transcriptomic data.

bioinformatics↗

bioRxiv2022

Intrahost SARS-CoV-2 k-mer identification method (iSKIM) for rapid detection of mutations of concern reveals emergence of global mutation patterns

Despite unprecedented global sequencing and surveillance of SARS-CoV-2, timely identification of the emergence and spread of novel variants of concern (VoCs) remains a challenge. Several million raw genome sequencing runs are now publicly available. We sought to survey these datasets for intrahost variation to study emerging mutations of concern. We developed iSKIM ("intrahost SARS-CoV-2 k-mer identification method") to relatively quickly and efficiently screen the many SARS-CoV-2 datasets to identify intrahost mutations belonging to lineages of concern. Certain mutations surged in frequency as intrahost minor variants just prior to, or while lineages of concern arose. The Spike N501Y change common to several VoCs was found as a minor variant in 834 samples as early as October 2020. This coincides with the timing of the first detected samples with this mutation in the Alpha/B.1.1.7 and Beta/B.1.351 lineages. Using iSKIM, we also found that Spike L452R was detected as an intrahost minor variant as early as September 2020, prior to the observed rise of the Epsilon/B.1.429/B.1.427 lineages in late 2020. iSKIM rapidly screens for mutations of interest in raw data, prior to genome assembly, and can be used to detect increases in intrahost variants, potentially providing an early indication of novel variant spread.

genomics↗

bioRxiv2021

Experimental evidence for the impact of soil viruses on carbon cycling during surface plant litter decomposition

To date, the potential impact of viral communities on biogeochemical cycles in soil has largely been inferred from indirect evidence, such as virus-driven changes in microbial abundances, viral auxiliary metabolic genes, and correlations with soil physiochemical properties. To more directly test the impact of soil viruses on carbon cycling during plant litter decomposition, we added concentrated viral community suspensions to complex litter decomposer communities in 40-day microcosm experiments. Microbial communities from two New Mexico alpine soils, Pajarito (PJ) and Santa Fe (SF), were inoculated onto grass litter on sand, and three treatments were applied in triplicate to each set of microcosms: addition of buffer (no added virus), addition of live virus (+virus), or killed virus (+killed-virus) fractions extracted from the same soil. Significant differences in respiration were observed between the +virus and +killed-virus treatments in the PJ, but not the SF microcosms. Bacterial and fungal community composition differed significantly by treatment in both PJ and SF microcosms. Combining data across both soils, viral addition altered links between bacterial and fungal diversity, dissolved organic carbon and total nitrogen. Overall, we demonstrate that increasing viral pressure in complex microbial communities can impact terrestrial biogeochemical cycling but is context-dependent.

ecology↗

bioRxiv2019

Machine-learning classification suggests that many alphaproteobacterial prophages may instead be gene transfer agents

Many of the sequenced bacterial and archaeal genomes encode regions of viral provenance. Yet, not all of these regions encode bona fide viruses. Gene transfer agents (GTAs) are thought to be former viruses that are now maintained in genomes of some bacteria and archaea and are hypothesized to enable exchange of DNA within bacterial populations. In Alphaproteobacteria, genes homologous to the head-tail gene cluster that encodes structural components of the Rhodobacter capsulatus GTA (RcGTA) are found in many taxa, even if they are only distantly related to Rhodobacter capsulatus. Yet, in most genomes available in GenBank RcGTA-like genes have annotations of typical viral proteins, and therefore are not easily distinguished from their viral homologs without additional analyses. Here, we report a support vector machine classifier that quickly and accurately distinguishes RcGTA-like genes from their viral homologs by capturing the differences in the amino acid composition of the encoded proteins. Our open-source classifier is implemented in Python and can be used to scan homologs of the RcGTA genes in newly sequenced genomes. The classifier can also be trained to identify other types of GTAs, or even to detect other elements of viral ancestry. Using the classifier trained on a manually curated set of homologous viruses and GTAs, we detected RcGTA-like head-tail gene clusters in 57.5% of the 1,423 examined alphaproteobacterial genomes. We also demonstrated that more than half of the in silico prophage predictions are instead likely to be GTAs, suggesting that in many alphaproteobacterial genomes the RcGTA-like elements remain unrecognized.\n\nData depositionSequence alignments and phylogenetic trees are available in a FigShare repository at DOI 10.6084/m9.figshare.8796419. The Python source code of the described classifier and additional scripts used in the analyses are available via a GitHub repository at https://github.com/ecg-lab/GTA-Hunter-v1

genomics↗

bioRxiv2017

Hi-C deconvolution of a human gut microbiome yields high-quality draft genomes and reveals plasmid-genome interactions.

The assembly of high-quality genomes from mixed microbial samples is a long-standing challenge in genomics and metagenomics. Here, we describe the application of ProxiMeta, a Hi-C-based metagenomic deconvolution method, to deconvolve a human fecal metagenome. This method uses the intra-cellular proximity signal captured by Hi-C as a direct indicator of which sequences originated in the same cell, enabling culture-free de novo deconvolution of mixed genomes without any reliance on a priori information. We show that ProxiMeta deconvolution provides results of markedly high accuracy and sensitivity, yielding 50 near-complete microbial genomes (many of which are novel) from a single fecal sample, out of 252 total genome clusters. ProxiMeta outperforms traditional contig binning at high-quality genome reconstruction. ProxiMeta shows particularly good performance in constructing high-quality genomes for diverse but poorly-characterized members of the human gut. We further use ProxiMeta to reconstruct genome plasmid content and sharing of plasmids among genomes--tasks that traditional binning methods usually fail to accomplish. Our findings suggest that Hi-C-based deconvolution can be useful to a variety of applications in genomics and metagenomics.

genomics↗

bioRxiv2017

Insights into Origin and Evolution of α-proteobacterial Gene Transfer Agents

Several bacterial and archaeal lineages produce nanostructures that morphologically resemble small tailed viruses, but, unlike most viruses, contain apparently random pieces of the host genome. Since these elements can deliver the packaged DNA to other cells, they were dubbed Gene Transfer Agents (GTAs). Because many genes involved in GTA production have viral homologs, it has been hypothesized that the GTA ancestor was a virus. Whether GTAs represent an atypical virus, a defective virus, or a virus co-opted by the prokaryotes for some function, remains to be elucidated. To evaluate these possibilities, we examined the distribution and evolutionary histories of genes that encode a GTA in the -proteobacterium Rhodobacter capsulatus (RcGTA). We report that although homologs of many individual RcGTA genes are abundant across bacteria and their viruses, RcGTA-like genomes are mainly found in one subclade of -proteobacteria. When compared to the viral homologs, genes of the RcGTA-like genomes evolve significantly slower, and do not have higher %A+T nucleotides than their host chromosomes. Moreover, they appear to reside in stable regions of the bacterial chromosomes that are generally conserved across taxonomic orders. These findings argue against RcGTA being an atypical or a defective virus. Our phylogenetic analyses suggest that RcGTA ancestor likely originated in the lineage that gave rise to contemporary -proteobacterial orders Rhizobiales, Rhodobacterales, Caulobacterales, Parvularculales, and Sphingomonadales, and since that time the RcGTA-like element has co-evolved with its host chromosomes. Such evolutionary history is compatible with maintenance of these elements by bacteria due to some selective advantage. As for many other prokaryotic traits, horizontal gene transfer played a substantial role in the evolution of RcGTA-like elements, not only in shaping its genome components within the orders, but also in occasional dissemination of RcGTA-like regions across the orders and even to different bacterial phyla.

evolutionary biology↗