Biology subjects

Grover, J.

Publications and source records attributed to Grover, J..

5 recordsLinked to original sources

bioRxiv2022

VE607 Stabilizes SARS-CoV-2 Spike In the "RBD-up" Conformation and Inhibits Viral Entry

SARS-CoV-2 infection of host cells starts by binding of the Spike glycoprotein (S) to the ACE2 receptor. The S-ACE2 interaction is a potential target for therapies against COVID-19 as demonstrated by the development of immunotherapies blocking this interaction. Here, we present the commercially available VE607, comprised of three stereoisomers, that was originally described as an inhibitor of SARS-CoV-1. We show that VE607 specifically inhibits infection of SARS-CoV-1 and SARS-CoV-2 S-expressing pseudoviral particles as well as authentic SARS-CoV-2. VE607 stabilizes the receptor binding domain (RBD) in its "up" conformation. In silico docking and mutational analysis map the VE607 binding site at the RBD-ACE2 interface. The IC50 values are in the low micromolar range for pseudoparticles derived from SARS-CoV-2 Wuhan/D614G as well as from variants of concern (Alpha, Beta, Gamma, Delta and Omicron), suggesting that VE607 has potential for the development of drugs against SARS-CoV-2 infections.

microbiology↗

bioRxiv2021

PDXNet Portal: Patient-Derived Xenograft model, data, workflow, and tool discovery

We created the PDX Network (PDXNet) Portal (https://portal.pdxnetwork.org/) to centralize access to the National Cancer Institute-funded PDXNet consortium resources (i.e., PDX models, sequencing data, treatment response data, and bioinformatics workflows), to facilitate collaboration among researchers, and to make resources easily available for research. The portal includes sections for resources, analysis results, metrics for PDXNet activities, data processing protocols, and training materials for processing PDX data. The initial portal release highlights PDXNet model and data resources, including 334 new models across 33 cancer types. Tissue samples of these models were deposited in the NCIs Patient-Derived Model Repository (PDMR) for public access. These models have 2,822 associated sequencing files from 873 samples across 307 patients, which are hosted on the Cancer Genomics Cloud powered by Seven Bridges and the NCI Cancer Data Service for long-term storage and access with dbGaP permissions. The portal also includes results from standardized analysis workflows on PDXNet sequencing files and PDMR data (2,594 samples from 463 patients across 78 disease types). These 15 analysis workflows for whole-exome and RNA-Seq data are freely available, robust, validated, and standardized. The model and data lists will grow substantially over the next two years and will be continuously updated as new data are available. PDXNet models support multi-agent treatment studies, determination of sensitivity and resistance mechanisms, and preclinical trials. The PDXNet portal is a centralized location for these data and resources, which we expect to be of significant utility for the cancer research community.

cancer biology↗

bioRxiv2020

Analysis of 46,046 SARS-CoV-2 whole-genomes leveraging principal component analysis (PCA)

Since the beginning of the global SARS-CoV-2 pandemic, there have been a number of efforts to understand the mutations and clusters of genetic lines of the SARS-CoV-2 virus. Until now, phylogenetic analysis methods have been used for this purpose. Here we show that Principal Component Analysis (PCA), which is widely used in population genetics, can not only help us to understand existing findings about the mutation processes of the virus, but can also provide even deeper insights into these processes while being less sensitive to sequencing gaps. Here we describe a comprehensive analysis of a 46,046 SARS-CoV-2 genome sequence dataset downloaded from the GISAID database in June of this year. SummaryPCA provides deep insights into the analysis of large data sets of SARS-CoV-2 genomes, revealing virus lineages that have thus far been unnoticed.

bioinformatics↗

bioRxiv2020

Amazonian soil metagenomes indicate different physiological strategies of microbial communities in response to land use change

Despite the global importance in ecological processes, the Amazon rainforest has been subjected to high rates of deforestation, mostly for pasturelands, over the last few decades. In this study, we used a combination of deep shotgun metagenomics and a machine learning approach to compare physiological strategies of microbial communities between contrasting forest and pasture soils. We showed that microbial communities (bacteria, archaea and viruses), and the composition of protein-coding genes are distinct in each ecosystem. The diversities of these metagenomic datasets are strongly correlated, indicating that the protein-coding genes found in any given sample of these soil types are predictable from their taxonomic lineages. Shifts in metagenome profiles reflected potential physiological differences caused by forest-to-pasture conversion with alterations in gene abundances related to carbohydrate and energy metabolisms. These variations in these gene contents are associated with several soil factors including C/N, temperature and H++Al3+ (exchangeable acidity). These data underscore that microbial community taxa and protein-coding genes co-vary. Differences in gene abundances for carbohydrate utilization, energy, amino acid, and xenobiotic metabolisms indicate alterations of physiological strategy with forest-to-pasture conversion, with potential consequences to C and N cycles. Our analysis also indicated that soil virome was altered and shifts in the viral community provide insights into increased health risks to human and animal populations.

microbiology↗

bioRxiv2017

Maternal RNA-directed DNA methylation is required for seed development in Brassica rapa

Some organisms deploy small RNAs from accessory cells to maintain genome integrity in the zygote, a mechanism that has been proposed but not demonstrated in plants. Here we show that maternal mutations in the Pol IV-dependent small RNA pathway cause abortion of developing seeds in Brassica rapa. Surprisingly, small RNA production is required in maternal somatic tissues, but not in maternal gametes or the developing zygote. We propose that parental influence over zygotic genomes is a common strategy in eukaryotes and that outbreeding species such as B. rapa are key to understanding the role of small RNAs during reproduction.

plant biology↗