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Biology subjects

Varma, S.

Publications and source records attributed to Varma, S..

26 records · Page 2Linked to original sources

Molecular basis for SARS-CoV-2 spike affinity for human ACE2 receptor

Severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) has caused substantially more infections, deaths, and economic disruptions than the 2002-2003 SARS-CoV. The key to understanding SARS-CoV-2s higher infectivity may lie in its host receptor recognition mechanism. This is because experiments show that the human ACE2 protein, which serves as the primary receptor for both CoVs, binds to CoV-2s spike protein 5-20 fold stronger than SARS-CoVs spike protein. The molecular basis for this difference in binding affinity, however, remains unexplained and, in fact, a comparison of X-ray structures leads to an opposite proposition. To gain insight, we use all-atom molecular dynamics simulations. Free energy calculations indicate that CoV-2s higher affinity is due primarily to differences in specific spike residues that are local to the spike-ACE2 interface, although there are allosteric effects in binding. Comparative analysis of equilibrium simulations reveals that while both CoV and CoV-2 spike-ACE2 complexes have similar interfacial topologies, CoV-2s spike protein engages in greater numbers, combinatorics and probabilities of hydrogen bonds and salt bridges with ACE2. We attribute CoV-2s higher affinity to these differences in polar contacts, and these findings also highlight the importance of thermal structural fluctuations in spike-ACE2 complexation. We anticipate that these findings will also inform the design of spike-ACE2 peptide blockers that, like in the cases of HIV and Influenza, can serve as antivirals.

biophysics↗

Unraveling Endothelial Cell Phenotypic Regulation By Spatial Hemodynamic Flows With Microfluidics

Human endothelial cells (hECs) experience complex spatiotemporal hemodynamic flows and that directly regulate hEC function and susceptibility to cardiovascular disease. Recent medical imaging studies reveal that helical flows strongly correlate with lowered disease susceptibility, as contrasted to multidirectional disturbed flows. However, a lack of platforms to replicate these spatial profiles of flow (SPF) has prevented biological studies to investigate the role hECs play in tuning the observed SPF-correlated disease susceptibility. Here, we utilize microfluidic devices to apply varying SPF upon hECs for the first time, and discover that these flows can differentially impact hEC morphology, transcription, and polarization. Collectively, our platform and studies significantly advance our ability to delineate flow-regulated hEC function and disease susceptibility. Significance StatementIn vivo, hECs experience complex hemodynamic flows, including those that are spatially helical or disturbed, which is in stark contrast to the unidirectional flows typically used to study hECs in vitro. Understanding the impact of SPF on hEC function informs our understanding of the pathophysiology of hEC dysfunction and can lead to interventional solutions that specifically perturb SPF to lower disease risk. Here, we leverage microfluidics to apply and discover the specific impact of SPF on hECs for the first time. Broadly, our platform bridges the mutual interests of the vascular biology and interventional cardiology communities to collectively understand how cardiovascular health is tied to the way blood flows upon the endothelium.

bioengineering↗

SCLC_CellMiner: Integrated Genomics and Therapeutics Predictors of Small Cell Lung Cancer Cell Lines based on their genomic signatures

Model systems are necessary to understand the biology of SCLC and develop new therapies against this recalcitrant disease. Here we provide the first online resource, CellMiner-SCLC (https://discover.nci.nih.gov/SclcCellMinerCDB) incorporating 118 individual SCLC cell lines and extensive omics and drug sensitivity datasets, including high resolution methylome performed for the purpose of the current study. We demonstrate the reproducibility of the cell lines and genomic data across the CCLE, GDSC, CTRP, NCI and UTSW datasets. We validate the SCLC classification based on four master transcription factors: NEUROD1, ASCL1, POU2F3 and YAP1 (NAPY classification) and show transcription networks connecting each them with their downstream and upstream regulators as well as with the NOTCH and HIPPO pathways and the MYC genes (MYC, MYCL1 and MYCN). We find that each of the 4 subsets express specific surface markers for antibody-targeted therapies. The SCLC-Y cell lines differ from the other subsets by expressing the NOTCH pathway and the antigen-presenting machinery (APM), and responding to mTOR and AKT inhibitors. Our analyses suggest the potential value of NOTCH activators, YAP1 inhibitors and immune checkpoint inhibitors in SCLC-Y tumors that can now be independently validated. Graphical Abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=151 SRC="FIGDIR/small/980623v2_ufig1.gif" ALT="Figure 1"> View larger version (65K): org.highwire.dtl.DTLVardef@10930e7org.highwire.dtl.DTLVardef@699d9corg.highwire.dtl.DTLVardef@1ea773corg.highwire.dtl.DTLVardef@3a1589_HPS_FORMAT_FIGEXP M_FIG C_FIG HighlightsO_LISCLC-CellMiner provides the most extensive SCLC resource in terms of number of cell lines (118 cell lines), extensive omics data (exome, microarray, RNA-seq, copy number, methylomes and microRNA) and drug sensitivity testing. C_LIO_LIWe find evidence of distinct epigenetic profile of SCLC cell lines (global hypomethylation and histone gene methylation), which is consistent with their plasticity. C_LIO_LITranscriptome analyses demonstrate the coherent transcriptional networks associated with the 4 main genomic subgroups (NEUROD1, ASCL1, POU2F3 & YAP1 = NAPY classification) and their connection with the NOTCH and HIPPO signaling pathways. C_LIO_LISCLC-CellMiner provides a conceptual framework for the selection of therapies for SCLC in a personalized fashion allowing putative biomarkers according molecular classifications and molecular characteristics. C_LIO_LISCLC-Y cell lines differ from the other cancer cell lines; their transcriptome resemble NSCLC cell lines. YAP1 cell lines while being the most resistant to standard of care treatments (etoposide, cisplatin and topotecan) respond to mTOR and AKT inhibitors and present native immune predisposition suggesting sensitivity to immune checkpoint inhibitors. C_LI

cancer biology↗

Clinically-relevant cell type cross-talk identified from a human lung tumor microenvironment interactome

Tumors comprise a complex microenvironment of interacting malignant and stromal cell types. Much of our understanding of the tumor microenvironment comes from in vitro studies isolating the interactions between malignant cells and a single stromal cell type, often along a single pathway. To develop a deeper understanding of the interactions between cells within human lung tumors we performed RNA-seq profiling of flow-sorted malignant cells, endothelial cells, immune cells, fibroblasts, and bulk cells from freshly resected human primary non-small-cell lung tumors. We mapped the cell-specific differential expression of prognostically-associated secreted factors and cell surface genes, and computationally reconstructed cross-talk between these cell types to generate a novel resource we call the Lung Tumor Microenvironment Interactome (LTMI). Using this resource, we identified and validated a prognostically unfavorable influence of Gremlin-1 production by fibroblasts on proliferation of malignant lung adenocarcinoma cells. We also found a prognostically favorable association between infiltration of mast cells and less aggressive tumor cell behavior. These results illustrate the utility of the LTMI as a resource for generating hypotheses concerning tumor-microenvironment interactions that may have prognostic and therapeutic relevance.\n\nSummaryRNA-seq profiling of sorted populations from primary lung cancer samples identifies prognostically relevant cross-talk between cell types in the tumor microenvironment.

cancer biology↗

YAP-independent mechanotransduction drives breast cancer progression

Increased tissue stiffness is a driver of breast cancer progression. The transcriptional regulator YAP is considered a universal mechanotransducer, based largely on 2D culture studies. However, the role of YAP during in vivo breast cancer remains unclear. Here, we find that mechanotransduction occurs independently of YAP in breast cancer patient samples and mechanically tunable 3D cultures. Mechanistically, the lack of YAP activity in 3D culture and in vivo is associated with the absence of stress fibers and an order of magnitude decrease in nuclear cross-sectional area relative to 2D culture. This work highlights the context-dependent role of YAP in mechanotransduction, and establishes that YAP does not mediate mechanotransduction in breast cancer.\n\nOne Sentence SummaryBreast cancer mechanotransduction occurs independently of YAP, due to compact nuclear morphologies in vivo.

cancer biology↗

Integrative analysis of pharmacogenomics in major cancer cell line databases using CellMinerCDB

As precision medicine demands molecular determinants of drug response, CellMinerCDB provides (https://discover.nci.nih.gov/cellminercdb/) a web-based portal for multiple forms of pharmacological, molecular, and genomic analyses, unifying the richest cancer cell line datasets (NCI-60, NCI-SCLC, Sanger/MGH GDSC, and Broad CCLE/CTRP). CellMinerCDB enables genomic and pharmacological data queries for identifying pharmacogenomic determinants, drug signatures, and gene regulatory networks for researchers without requiring specialized bioinformatics support. It leverages overlaps of cell lines and tested drugs to allow assessment of data reproducibility. It builds on the complementarity and strength of each dataset. A panel of 41 drugs evaluated in parallel in the NCI-60 and GDSC is reported, supporting drug reproducibility across databases, repositioning of bisacodyl and acetalax for triple negative breast cancer, and identifying novel drug response determinants and genomic signatures for topoisomerase inhibitors and schweinfurthins in development. CellMinerCDB also allowed the identification of LIX1L as a novel mesenchymal gene regulating cellular migration and invasiveness.

bioinformatics↗

Rapid proteotyping reveals cancer biology and drug response determinants in the NCI-60 cells

We describe the rapid and reproducible acquisition of quantitative proteome maps for the NCI-60 cancer cell lines and their use to reveal cancer biology and drug response determinants. Proteome datasets for the 60 cell lines were acquired in duplicate within 30 working days using pressure cycling technology and SWATH mass spectrometry. We consistently quantified 3,171 proteotypic proteins annotated in the SwissProt database across all cell lines, generating a data matrix with 0.1% missing values, allowing analyses of protein complexes and pathway activities across all the cancer cells. Systematic and integrative analysis of the genetic variation, mRNA expression and proteomic data of the NCI-60 cancer cell lines uncovered complementarity between different types of molecular data in the prediction of the response to 240 drugs. We additionally identified novel proteomic drug response determinants for clinically relevant chemotherapeutic and targeted therapies. We anticipate that this study represents a significant advance toward the translational application of proteotypes, which reveal biological insights that are easily missed in the absence of proteomic data.

systems biology↗

Blind estimation and correction of microarray batch effect

Microarray batch effect (BE) has been the primary bottleneck for large-scale integration of data from multiple experiments. Current BE correction methods either need known batch identities (ComBat)or have the potential to overcorrect, by removing true but unknown biological differences (SVA).Even though the effects of technical differences on measured expression have been published, there are no BE correction algorithms that take the approach of predicting technical effects from parameters computed from a fixed reference sample set. We show that a set of signatures, each of which is a vector the length of the number of probes, calculated on a Reference set of microarray samples can predict much of the batch effect in other Validation sets. We present a rationale of selecting a Reference set of samples designed to estimate technical differences without removing biological differences. Putting both together, we introduce the Batch Effect Signature Correction (BESC) algorithm that uses the BES calculated on the Reference set to efficiently predict and remove BE. Using two independent Validation sets, we show that BESC is capable of removing batch effect without removing unknown but true biological differences. Much of the variations due to batch effect is shared between different microarray datasets. That shared information can be used to predict signatures (i.e. directions of perturbation) due to batch effect in new datasets. The correction is blind (without needing to re-compute the parameters on new samples to be corrected), single sample, (each sample is corrected independently of each other) and conservative (only those perturbations known to be likely to be due to technical differences are removed ensuring that unknown but important biological differences are maintained). Those three characteristics make it ideal for high-throughput correction of samples for a microarray data repository. An R Package besc implementing the algorithm is available from http://explainbio.com.

bioinformatics↗