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Bhattacharya, R.

Publications and source records attributed to Bhattacharya, R..

32 records · Page 2Linked to original sources
bioRxiv2022

Targeting IRS-1/2 in uveal melanoma inhibits in vitro cell growth, survival and migration, and in vivo tumor growth.

Uveal melanoma (UM) originating in the eye and metastasizing to the liver is associated with poor prognosis and has only one approved therapeutic option. We hypothesized that liver-borne growth factors may contribute to UM growth. Therefore, we investigated the role of insulin-like growth factor -1 and its receptor (IGF-1/IGF-1R) signaling in UM. We found that the insulin receptor substrate -1 (IRS-1) is overexpressed in UM cells and tumors. Since we previously observed that IGF-1R antibody therapy was not clinically effective in UM, we investigated the potential of NT157, a small molecule inhibitor of IRS-1/2 in blocking this pathway in UM. NT157 treatment in UM cells resulted in reduced cell survival and migration, and increased apoptosis. This treatment also significantly inhibited UM tumor growth in vivo, in the chicken egg chorioallantoic membrane (CAM) and subcutaneous mouse models, validating the in vitro effect. Mechanistically, through reverse phase protein array (RPPA), we identified significant proteomic changes in the PI3K/AKT pathway, a downstream mediator of IGF-1 signaling, with NT157 treatment. Together, these results suggest that NT157 inhibits cell survival, migration in vitro and tumor growth in vivo via inhibiting IGF-1 signaling in UM.

cell biology↗
bioRxiv2022

Activation of ERK by altered RNA splicing in cancer

Many cancers carry change-of-function mutations affecting RNA splicing factors, however, less is known about the functional consequences of upregulated RNA splicing factors in cancer. Here, we demonstrate that SMNDC1, a poorly studied splicing factor, which we found to be upregulated in multiple carcinomas and associated with poor patient prognosis, promotes cell proliferation, clonal expansion, and tumor growth by promoting the retention of G-rich exons, which otherwise would be excluded or retained at a lower rate after RNA splicing in normal cells. Inclusion of exon 4 (E4) of MAPK3 (ERK1), which encodes both kinase phosphorylation sites (Thr202/Tyr204), was among the promoted exons by SMNDC1. Forced exclusion of MAPK3-E4 using anti-sense oligos inhibited the ERK1 phosphorylation, expression of target genes and decreased tumor cell growth. These data support that cancer cells exploit a "splicing switch" to promote ERK kinase activity and offer a druggable alternative to block oncogenic signaling and altered RNA splicing in cancer cells SIGNIFICANCEERK signaling promotes tumor growth and survival. Exon 4 of MAPK3 (ERK1) encodes the activation phosphorylation sites of ERK1 kinase. Aberrant RNA splicing induced by SMNDC1 in cancer cells increases the retention of exon 4 during mRNA splicing, unleashes the kinase activity. SMNDC1 potentializes as a cancer therapeutic target.

cancer biology↗
bioRxiv2021

BSG/CD147 and ACE2 receptors facilitate SARS-CoV-2 infection of human iPS cell-derived kidney podocytes

BackgroundSevere acute respiratory syndrome coronavirus 2 (SARS-CoV-2) causes the Coronavirus disease 2019 (COVID-19), which was declared a pandemic by the World Health Organization (WHO) in March 2020. The disease has caused more than 5.1 million deaths worldwide. While cells in the respiratory system are frequently the initial target for SARS-CoV-2, clinical studies suggest that COVID-19 can become a multi-organ disease in the most severe cases. Still, the direct affinity of SARS-CoV-2 for cells in other organs such as the kidneys, which are often affected in severe COVID-19, remains poorly understood. MethodIn this study, we employed a human induced pluripotent stem (iPS) cell-derived model to investigate the affinity of SARS-CoV-2 for kidney glomerular podocytes. We studied uptake of the live SARS-CoV-2 virus as well as pseudotyped viral particles by human iPS cell derived podocytes using qPCR, western blot, and immunofluorescence. Global gene expression and qPCR analyses revealed that human iPS cell-derived podocytes express many host factor genes (including ACE2, BSG/CD147, PLS3, ACTR3, DOCK7, TMPRSS2, CTSL CD209, and CD33) associated with SARS-CoV-2 binding and viral processing. ResultInfection of podocytes with live SARS-CoV-2 or spike-pseudotyped lentiviral particles revealed viral uptake by the cells at low Multiplicity of Infection (MOI of 0.01) as confirmed by RNA quantification and immunofluorescence studies. Our results also indicate that direct infection of human iPS cell-derived podocytes by SARS-CoV-2 virus can cause cell death and podocyte foot process retraction, a hallmark of podocytopathies and progressive glomerular diseases including collapsing glomerulopathy observed in patients with severe COVID-19 disease. Additionally, antibody blocking experiments identified BSG/CD147 and ACE2 receptors as key mediators of spike binding activity in human iPS cell-derived podocytes. ConclusionThese results show that SARS-CoV-2 can infect kidney glomerular podocytes in vitro. These results also show that the uptake of SARS-CoV-2 by kidney podocytes occurs via multiple binding interactions and partners, which may underlie the high affinity of SARS-CoV-2 for kidney tissues. This stem cell-derived model is potentially useful for kidney-specific antiviral drug screening and mechanistic studies of COVID-19 organotropism. Significant statementMany patients with COVID19 disease exhibit multiorgan complications, suggesting that SARS-CoV-2 infection can extend beyond the respiratory system. Acute kidney injury is a common COVID-19 complication contributing to increased morbidity and mortality. Still, SARS-Cov-2 affinity for specialized kidney cells remain less clear. By leveraging our protocol for stem cell differentiation, we show that SARS-CoV-2 can directly infect kidney glomerular podocytes by using multiple Spike-binding proteins including ACE2 and BSG/CD147. Our results also indicate that infection by SARS-CoV-2 virus can cause podocyte cell death and foot process effacement, a hallmark of podocytopathies including collapsing glomerulopathy observed in patients with severe COVID-19 disease. This stem cell-derived model is potentially useful for kidney-specific antiviral drug screening and mechanistic studies of COVID-19 organotropism.

bioengineering↗
bioRxiv2021

Homology Modelling, In Silico Prediction And Characterization Of Cytochrome c oxidase In Cyprinus carpio And Tubifex tubifex And Molecular Docking Studies Between The Modelled Protein And Three Commonly Used Surfactants Sodium Dodecyl Sulphate, Cetylpyridinium Chloride And Sodium Laureth Sulphate

The purpose of this work is to evaluate the homology modeling, in silico prediction, and characterisation of Cytochrome c oxidase from Cyprinus carpio and Tubifex tubifex, as well as molecular docking experiments between the modelled protein and three frequently used surfactants. Using the template crystal structure of bovine heart Cytochrome c oxidase, homology modeling of Cytochrome c oxidase (Subunit 2) of Cyprinus carpio (Accession # P24985) and Cytochrome c oxidase (Subunit 1) of Tubifex tubifex (Accession # Q7YAA6) was conducted. The model structure was improved further with 3Drefine, and the final 3D structure was verified with PROCHEK and ERRATA. The physiochemical, as well as the stereochemical parameters of the modelled protein, were evaluated using various tools like ExPASys ProtParam, Hydropathy Analysis and EMBOSS pepwheel. The projected model was then docked with toxic ligands, Sodium dodecyl sulfate (SDS), Cetylpyridinium chloride (CPC), and Sodium laureth sulfate (SLES), whose 3D structures were obtained from the Uniprot database. CPC interacted best with Cytochrome c oxidase subunit 2 of Cyprinus carpio and Cytochrome c oxidase subunit 1 of Tubifex tubifex, according to our findings. Furthermore, in the case of all surfactants, hydrophobic interactions with the active site amino acid residues of the modelled protein were observed to be more common than hydrogen bonds and salt bridges. Molecular simulation studies exhibited that the surfactants alter the structural flexibility of the predicted proteins. Hence it may be inferred that the surfactants might alter the structure and dynamics of Cytochrome c oxidase of both worm and fish.

bioinformatics↗
bioRxiv2021

Liver Endothelium Microenvironment Promotes HER3-mediated Cell Survival in KRAS Wild-type and Mutant Colorectal Cancer

We previously showed that liver endothelial cells (ECs) secreted soluble factors in a paracrine fashion and activated human epidermal growth factor receptor 3 (HER3, also known as ERBB3) for promoting colorectal cancer (CRC) growth and chemoresistance. However, RAS proteins play a critical role in receptor tyrosine kinase signaling pathways, and KRAS mutations mediate CRC resistance to therapies targeting EGFR, another HER protein. Therefore, the role of KRAS mutation status in EC-induced HER3 activation and CRC survival was investigated as it has therapeutic implications. We used CRC cell lines and patient-derived xenografts harboring KRAS wild-type or mutant genes and demonstrated that liver EC-secreted factors promoted HER3-mediated CRC cell growth independent of KRAS mutation status. Also, blocking HER3 in CRC cells by siRNAs or a HER3 antibody seribantumab blocked EC-induced CRC survival. Our findings highlight the potential of utilizing HER3 targeted therapies for treating patients with mCRC independent of RAS mutational status.

cancer biology↗
bioRxiv2020

Understanding the Evolutionary Games in NSCLC Microenvironment

While initially highly successful, targeted therapies eventually fail as populations of tumor cells evolve mechanisms of resistance, leading to resumption of tumor growth. Historically, cell-intrinsic mutational changes have been the major focus of experimental and clinical studies to decipher origins of therapy resistance. While the importance of these mutational changes is undeniable, a growing body of evidence suggests that non-cell autonomous interactions between sub-populations of tumor cells, as well as with non-tumor cells within tumor microenvironment, might have a profound impact on both short term sensitivity of cancer cells to therapies, as well as on the evolutionary dynamics of emergent resistance. In contrast to well established tools to interrogate the functional impact of cell-intrinsic mutational changes, methodologies to understand non-cell autonomous interactions are largely lacking. Evolutionary Game Theory (EGT) is one of the main frameworks to understand the dynamics that drive frequency changes in interacting competing populations with different phenotypic strategies. However, despite a few notable exceptions, the use of EGT to understand evolutionary dynamics in the context of evolving tumors has been largely confined to theoretical studies. In order to apply EGT towards advancing our understanding of evolving tumor populations, we decided to focus on the context of the emergence of resistance to targeted therapies, directed against EML4-ALK fusion gene in lung cancers, as clinical responses to ALK inhibitors represent a poster child of limitations, posed by evolving resistance. To this end, we have examined competitive dynamics between differentially labelled therapy-naive tumor cells, cells with cell-intrinsic resistance mechanisms, and cells with cell-extrinsic resistance, mediated by paracrine action of hepatocyte growth factor (HGF), within in vitro game assays in the presence or absence of front-line ALK inhibitor alectinib. We found that producers of HGF were the fittest in every pairwise game, while also supporting the proliferation of therapy-naive cells. Both selective advantage of these producer cells and their impact on total population growth was a linearly increasing function of the initial frequency of producers until eventually reaching a plateau. Resistant cells did not significantly interact with the other two phenotypes. These results provide insights on reconciling selection driven emergence of subpopulations with cell non-cell autonomous resistance mechanisms, with lack of evidence of clonal dominance of these subpopulations. Further, our studies elucidate mechanisms for co-existence of multiple resistance strategies within evolving tumors. This manuscript serves as a technical report and will be followed up with a research paper in a different journal.

cancer biology↗
bioRxiv2020

Degenerating Drosophila Larval Epidermal Cells Drive Thorax Closure

Adult thorax formation in Drosophila begins during pre-pupal development by fusion of its two contralateral progenitor halves, the heminotal epithelia (HE). HEs migrate and replace an underlying cell layer of thoracic larval epidermal cells (LECs) during a morphogenetic process called thorax closure. The LEC layer has so far been proposed to be a passive substrate over which HEs migrate before their zipping. By contrast, here we show that the pull forces generated within the LEC layer drive HE migration. During thorax closure, the LECs display actomyosin-mediated contraction, via enrichment of non-muscle myosin-II and actin, besides squamous-to-pseudostratified columnar epithelial transition and tissue shrinkage. This shrinkage of the LEC layer is further accompanied by cell extrusion and death, that prevent overcrowding of LECs, thereby promoting further shrinkage. The pull forces thus generated by the shrinking LEC layer are then relayed to the HEs by their mutual adhesions via {beta}PS1 (Mys) and PS3 (Scb) integrins. Suppression of cell death in the LEC layer by a gain of p35 leads to cell overcrowding, which impedes HE migration and zipping. Further, knockdown of sqh, the light chain of non-muscle myosin II, in LECs or integrins (mys or scb) in either the LEC layer or in the HEs, or both abrogate thorax closure. Mathematical modeling also reveals the biophysical underpinnings of the forces that drive this tissue closure process wherein a degenerating LEC layer mediates its succession by the future adult primodia. These essential principles of thorax closure appear ancient in origin and recur in multiple morphogenetic contexts and tissue repair.

developmental biology↗
bioRxiv2020

A conserved neuropeptide system links head and body motor circuits to enable adaptive behavior

Neuromodulators promote adaptive behaviors that are often complex and involve concerted activity changes across circuits that are often not physically connected. It is not well understood how neuromodulatory systems accomplish these tasks. Here we show that the C. elegans NLP-12 neuropeptide system shapes responses to food availability by modulating the activity of head and body wall motor neurons through alternate G-protein coupled receptor (GPCR) targets, CKR-1 and CKR-2. We show ckr-2 deletion reduces body bend depth during movement under basal conditions. We demonstrate CKR-1 is a functional NLP-12 receptor and define its expression in the nervous system. In contrast to basal locomotion, biased CKR-1 GPCR stimulation of head motor neurons promotes turning during local searching. Deletion of ckr-1 reduces head neuron activity and diminishes turning while specific ckr-1 overexpression or head neuron activation promote turning. Thus, our studies suggest locomotor responses to changing food availability are regulated through conditional NLP-12 stimulation of head or body wall motor circuits. Impact statementInvestigation of neuromodulatory control of ethologically conserved area-restricted food search behavior shows that NLP-12 stimulation of the head motor circuit promotes food searching through the previously uncharacterized CKR-1 GPCR.

neuroscience↗
bioRxiv2020

Lipid Sponge Droplets as Programmable Synthetic Organelles

Living cells segregate molecules and reactions in various subcellular compartments known as organelles. Spatial organization is likely essential for expanding the biochemical functions of synthetic reaction systems, including artificial cells. Many studies have attempted to mimic organelle functions using lamellar membrane-bound vesicles. However, vesicles typically suffer from highly limited transport across the membranes and an inability to mimic the dense membrane networks typically found in organelles such as the endoplasmic reticulum. Here we describe programmable synthetic organelles based on highly stable nonlamellar sponge phase droplets that spontaneously assemble from a single-chain galactolipid and non-ionic detergents. Due to their nanoporous structure, lipid sponge droplets readily exchange materials with the surrounding environment. In addition, the sponge phase contains a dense network of lipid bilayers and nanometric aqueous channels, which allows different classes of molecules to partition based on their size, polarity, and specific binding motifs. The sequestration of biologically relevant macromolecules can be programmed by the addition of suitably functionalized amphiphiles to the droplets. We demonstrate that droplets can harbor functional soluble and transmembrane proteins, allowing for the co-localization and concentration of enzymes and substrates to enhance reaction rates. Droplets protect bound proteins from proteases, and these interactions can be engineered to be reversible and optically controlled. Our results show that lipid sponge droplets permit the facile integration of membrane-rich environments and self-assembling spatial organization with biochemical reaction systems. Significance statementOrganelles spatially and temporally orchestrate biochemical reactions in a cell to a degree of precision that is still unattainable in synthetic reaction systems. Additionally, organelles such as the endoplasmic reticulum (ER) contain highly interconnected and dense membrane networks that provide large reaction spaces for both transmembrane and soluble enzymes. We present lipid sponge droplets to emulate the functions of organelles such as the ER. We demonstrate that lipid sponge droplets can be programmed to internally concentrate specific proteins, host and accelerate biochemical transformations, and to rapidly and reversibly sequester and release proteins to control enzymatic reactions. The self-assembled and programmable nature of lipid sponge droplets will facilitate the integration of complex functions for bottom up synthetic biology.

synthetic biology↗
bioRxiv2019

High-throughput prediction of MHC Class I and Class II neoantigens with MHCnuggets

Computational prediction of binding between neoantigen peptides and major histocompatibility complex (MHC) proteins is an emerging biomarker for predicting patient response to cancer immunotherapy. Current neoantigen predictors focus on in silico estimation of MHC binding affinity and are limited by low positive predictive value for actual peptide presentation, inadequate support for rare MHC alleles and poor scalability to high-throughput data sets. To address these limitations, we developed MHCnuggets, a deep neural network method to predict peptide-MHC binding. MHCnuggets is the only method to handle binding prediction for common or rare alleles of MHC Class I or II, with a single neural network architecture. Using a long short-term memory network (LSTM), MHCnuggets accepts peptides of variable length and is capable of faster performance than other methods. When compared to methods that integrate binding affinity and HLAp data from mass spectrometry, MHCnuggets yields a fourfold increase in positive predictive value on independent MHC-bound peptide (HLAp) data. We applied MHCnuggets to 26 cancer types in TCGA, processing 26.3 million allele-peptide comparisons in under 2.3 hours, yielding 101,326 unique candidate immunogenic missense mutations (IMMs). Predicted-IMM hotspots occurred in 38 genes, including 24 driver genes. Predicted-IMM load was significantly associated with increased immune cell infiltration (p<2e-16) including CD8+ T cells. Notably, only 0.16% of predicted immunogenic missense mutations were observed in >2 patients, with 61.7% of these derived from driver mutations. Our results provide a new method for neoantigen prediction with high performance characteristics and demonstrate its utility in large data sets across human cancers.\n\nSynopsisWe developed a new in silico predictor of Major Histocompatibility Complex (MHC) ligand binding and demonstrated its utility to assess potential neoantigens and immunogenic missense mutations (IMMs) in 6613 TCGA patients.

immunology↗
bioRxiv2018

Y chromosomal noncoding RNA regulates autosomal gene expression via piRNAs in mouse testis

Majority of the genes expressed during spermatogenesis are autosomal. Mice with different deletions of Yq show sub-fertility, sterility and sperm abnormalities. The connection between Yq deletion and autosomal gene regulation is not well understood. We describe a novel mouse Yq-derived long noncoding RNA, Pirmy, which shows unprecedented number of splice variants in testis. Further, Pirmy transcript variants act as templates for several piRNAs. We identified ten differentially expressed autosome-encoded sperm proteins in mutant mice. Pirmy transcript variants have homology to 5/3UTRs of these deregulated autosomal genes. Thus, subfertility in Y-deleted mice appears to be a polygenic phenomenon that is partially regulated epistatically by the Y-chromosome. Our study provides novel insights into possible role of MSY-derived ncRNAs in male fertility and reproduction. Finally, sperm phenotypes from the Y-deleted mice seem to be similar to that reported in inter-specific male-sterile hybrids. Taken together, this study provides novel insights into possible role of Y-derived ncRNAs in male sterility and speciation.

genomics↗
bioRxiv2017

CRAVAT 4: Cancer-Related Analysis of Variants Toolkit

Cancer sequencing studies are increasingly comprehensive and well-powered, returning long lists of somatic mutations that can be difficult to sort and interpret. Diligent analysis and quality control can require multiple computational tools of distinct utility and producing disparate output, creating additional challenges for the investigator. The Cancer-Related Analysis of Variants Toolkit (CRAVAT) is an evolving suite of informatics tools for mutation interpretation that includes mutation projecting and quality control, impact prediction and extensive annotation, gene- and mutation-level interpretation including joint prioritization of all nonsilent consequence types, and structural and mechanistic visualization. Results from CRAVAT submissions are explored in an interactive, user-friendly web-environment with dynamic filtering and sorting designed to highlight the most informative mutation, even in the context of very large studies. CRAVAT can be run on a public web-portal, in the cloud, or downloaded for local use, and is easily integrated with other methods for cancer omics analysis.\n\nConflict of interestAll authors declare no potential conflict of interest.

bioinformatics↗
bioRxiv2017

Prediction of peptide binding to MHC Class I proteins in the age of deep learning

Binding of peptides to Major Histocompatibility Complex (MHC) proteins is a critical step in immune response. Peptides bound to MHCs are recognized by CD8+ (MHC Class I) and CD4+ (MHC Class II) T-cells. Successful prediction of which peptides will bind to specific MHC alleles would benefit many cancer immunotherapy appications. Currently, supervised machine learning is the leading computational approach to predict peptide-MHC binding, and a number of methods, trained using results of binding assays, have been published. Many clinical researchers are dissatisfied with the sensitivity and specificity of currently available methods and the limited number of alleles for which they can be applied. We evaluated several recent methods to predict peptide-MHC Class I binding affinities and a new method of our own design (MHCnuggets). We used a high-quality benchmark set of 51 alleles, which has been applied previously. The neural network methods NetMHC, NetMHCpan, MHCflurry, and MHCnuggets achieved similar best-in-class prediction performance in our testing, and of these methods MHCnuggets was significantly faster. MHCnuggets is a gated recurrent neural network, and the only method to our knowledge which can handle peptides of any length, without artificial lengthening and shortening. Seventeen alleles were problematic for all tested methods. Prediction difficulties could be explained by deficiencies in the training and testing examples in the benchmark, suggesting that biological differences in allele-specific binding properties are not as important as previously claimed. Advances in accuracy and speed of computational methods to predict peptide-MHC affinity are urgently needed. These methods will be at the core of pipelines to identify patients who will benefit from immunotherapy, based on tumor-derived somatic mutations. Machine learning methods, such as MHCnuggets, which efficiently handle peptides of any length will be increasingly important for the challenges of predicting immunogenic response for MHC Class II alleles.\n\nAuthor SummaryMachine learning methods are a popular approach for predicting whether a peptide will bind to Major Histocompatibility Complex (MHC) proteins, a critical step in activation of cytotoxic T-cells. The input to these methods is a peptide sequence and an MHC allele of interest, and the output is the predicted binding affinity. MHC Class I and II proteins bind peptides of 8-11 amino acids and 16-26 amino acids respectively. This has been an obstacle for machine learning, because the methods used to date can only handle fixed-length inputs. We show that a recently developed technique known as gated recurrent neural networks can handle peptides of variable length and predict peptide-MHC binding as well or better than existing methods, at substantially faster speeds. Our results have implications for the hundreds of MHC alleles that cannot be predicted with current methods.

bioinformatics↗