Biology subjects

Anand, P.

Publications and source records attributed to Anand, P..

At least 19 recordsLinked to original sources

bioRxiv2026

Genome-wide LD scores enable robust heritability and genetic correlation estimation from summary statistics across genetic ancestries

Our knowledge of human complex trait architecture across diverse populations remains limited. Estimating SNP heritability and genetic correlation from GWAS summary statistics - successful in revealing genetic architecture in homogeneous cohorts - is challenging in structured and admixed populations due, in part, to the difficulty of representing long-range LD. We present SUMMIT, a statistical framework that combines genome-wide LD scores, efficiently estimated using random projections, with GWAS summary statistics to estimate genome-wide and annotation-specific heritability and genetic correlation. In simulations that explore diverse aspects of genetic architecture across European (EUR), South Asian (SAS), and admixed African-European cohorts (AFR) in the UK Biobank (UKB), SUMMIT had increased accuracy and reduced bias compared to prior methods, with the largest gains in non-European ancestries reflecting improved modeling of long-range LD. Across 40 UKB traits, SUMMIT produced estimates of genome-wide common-SNP heritability (h_g^2) and heritability enrichments in genomic annotations that were broadly concordant across ancestries with the SUMMIT h_g^2 estimates more closely matching recent whole-genome sequencing based estimates. SUMMIT substantially improved genetic correlation analysis recovering more significant findings across ancestries (23 SAS and 3 AFR at FDR < 0.05 compared with none and two for cov-LDSC) with the non-European discoveries showing concordant effects in Europeans suggesting that, like heritability, global patterns of pleiotropy are largely shared across ancestries. By combining accurate genome-wide LD and efficient moment estimation, SUMMIT enables investigation into how heritability and genetic correlation vary across populations and genomic annotations, including in structured and admixed cohorts, at the genome-wide, annotation-specific, and cross-trait levels.

genomics↗

bioRxiv2025

Comprehensive gene heritability estimation reveals the genetic architecture of rare coding variants underlying complex traits

Whole-exome sequencing (WES) enables high-resolution interrogation of the contribution of rare coding variants to complex trait variation. However, existing methods for heritability estimation attributed to rare-coding variants are often limited by the effects of linkage disequilibrium (LD) and by the sparse nature of rare variant data. We introduce FLEX (Fast, LD-aware Estimation of eXome-wide and gene-level heritability), a scalable and flexible framework for estimating and partitioning heritability across genes or sets of genes using WES data. FLEX integrates all coding variants- from common to ultra-rare - within a unifled model and corrects for LD-induced effects to improve the accuracy of heritability estimates. In addition, FLEX supports both individual-level and summary statistic data and is computationally efflcient for biobank-scale datasets. Through extensive simulations, we show that FLEX is well-calibrated while providing accurate heritability estimates. We applied FLEX to WES data across N = 153, 351 unrelated European ancestry individuals and 20 quantitative traits in the UK Biobank. We identifled 64 gene-trait pairs with signiflcant gene-level heritability (p < 0.05/18, 624 accounting for the number of protein-coding genes tested), among which rare coding variants explained 38% of gene-level heritability, on average. Compared to heritability estimates from genome-wide imputed SNPs, incorporation of rare and ultra-rare coding variants led to a 24.8% increase in heritability on average, while effect sizes at rare and ultra-rare variants are substantially larger ({approx} 18x on average). Partitioning across variant effect annotations, we flnd that predicted loss-of-function variants had stronger individual effects than missense variants (24% on average) while missense variants accounted for a greater share of rare coding heritability. Together, FLEX provides an adaptable and accurate approach for quantifying gene-level heritability, advancing our understanding of the genetic architecture of complex traits, and facilitating the discovery of trait-relevant genes.

genetics↗

bioRxiv2025

Loss of the Y chromosome drives cancer metabolic reprogramming

Metabolic reprogramming promotes cancer aggressiveness and an immune-suppressive tumor microenvironment. Loss of the Y chromosome (LOY) drives both phenotypes in bladder cancer (BC). We investigated the hypothesis that LOY leads to metabolic reprogramming using untargeted metabolomic profiling of human BC cells and analysis of pan-cancer transcriptomic datasets. This revealed that aerobic glycolysis is activated in LOY BC cells. Since prior work showed that expression of collagen receptor DDR2 drives BC progression and DDR2 is a regulator of tumor metabolism, we investigated if DDR2 is implicated in metabolic reprogramming of LOY-tumors. Analysis of scRNAseq data from 251 patients with 12 tumor types found that LOY and DDR2 expression promote aerobic glycolysis, and this was confirmed by metabolomics. Deletion of DDR2 in LOY BC cells reduced glycolytic flux, inhibited cell proliferation, reduced EMT and stemness features, and promoted apoptosis. Our data provide a rationale for using LOY as a tumor selection biomarker for DDR2 targeted therapeutics. TeaserLoss of the Y chromosome augments glycolytic metabolism in bladder cancer cells, and this is in part dependent on the collagen receptor DDR2.

cancer biology↗

bioRxiv2025

Human iPSC-derived prostate organoids with germline BRCA2 mutation undergo tumorigenic transformations

The lack of physiologically relevant in vitro prostate models has impeded studies of organ development and prostate tumorigenesis. We reprogrammed peripheral blood mononuclear cells (PBMCs) from individuals with and without pathogenic-germline BRCA2 mutation (MUT_BRCA2, CON_BRCA2) into induced pluripotent stem cells (iPSCs), which showed no differences in morphology, proliferation, or pluripotency markers. Differentiation of MUT_BRCA2 iPSCs into prostate organoids (iPROS) using defined growth factors and signaling molecules resulted in disrupted morphology, impaired polarity, increased proliferation, and elevated prostate-specific antigen (PSA) secretion compared to CON_BRCA2 iPROS. Transcriptomic profiling revealed early prostate cancer (PCa) signatures. Upon exposure to dietary carcinogens, MUT_BRCA2 iPROS showed further PSA elevation, enhanced proliferation, AMACR upregulation, p63 reducetion are markers of aggressive PCa. In vivo, MUT_BRCA2 iPROS formed tumors in immunodeficient mice. This patient-derived iPROS-platform recapitulates human-prostate mopphology and function, models early tumorigenesis events, and provides a valuable tool for studying PCa biology and enabling personalized drug discovery. IN BRIEFIn this study, we developed patients iPSC-derived prostate organoids (iPROS) with or without a pathogenic BRCA2 germline mutation that display human-prostate like morphology and function. MUT_BRCA2 iPROS displayed disrupted morphology, early tumorigenic changes, and formed tumors in mice. Upon carcinogen exposure, they showed markers of aggressive prostate cancer. This platform models early prostate tumorigenesis and enables personalized studies of cancer initiation and therapeutic response.

cancer biology↗

bioRxiv2024

Metapipeline-DNA: A Comprehensive Germline & Somatic Genomics Nextflow Pipeline

SummaryThe price, quality and throughout of DNA sequencing continue to improve. Algorithmic innovations have allowed inference of a growing range of features from DNA sequencing data, quantifying nuclear, mitochondrial and evolutionary aspects of both germline and somatic genomes. To automate analyses of the full range of genomic characteristics, we created an extensible Nextflow meta-pipeline called metapipeline-DNA. Metapipeline-DNA analyzes targeted and whole-genome sequencing data from raw reads through pre-processing, feature detection by multiple algorithms, quality-control and data- visualization. Each step can be run independently and is supported robust software engineering including automated failure-recovery, robust testing and consistent verifications of inputs, outputs and parameters. Metapipeline-DNA is cloud-compatible and highly configurable, with options to subset and optimize each analysis. Metapipeline-DNA facilitates high-scale, comprehensive analysis of DNA sequencing data. AvailabilityMetapipeline-DNA is an open-source Nextflow pipeline under the GPLv2 license and is available at https://github.com/uclahs-cds/metapipeline-DNA.

bioinformatics↗

bioRxiv2024

A Scalable Adaptive Quadratic Kernel Method for Interpretable Epistasis Analysis in Complex Traits

Our knowledge of the contribution of genetic interactions (epistasis) to variation in human complex traits remains limited, partly due to the lack of efficient, powerful, and interpretable algorithms to detect interactions. Recently proposed approaches for set-based association tests show promise in improving power to detect epistasis by examining the aggregated effects of multiple variants. Nevertheless, these methods either do not scale to large numbers of individuals available in Biobank datasets or do not provide interpretable results. We, therefore, propose QuadKAST, a scalable algorithm focused on testing pairwise interaction effects (also termed as quadratic effects) of a set of genetic variants on a trait and quantifying the proportion of phenotypic variance explained by these effects. We performed comprehensive simulations and demonstrated that QuadKAST is well-calibrated. Additionally, QuadKAST is highly sensitive in detecting loci with epistatic signal and accurate in its estimation of quadratic effects. We applied QuadKAST to 53 quantitative phenotypes measured in {approx} 300, 000 unrelated white British individuals in the UK Biobank to test for quadratic effects within each of 9, 515 protein-coding genes (after accounting for linear additive effects). We detected 32 trait-gene pairs across 17 traits that demonstrate statistically significant signals of quadratic effects ([Formula] accounting for the number of genes and traits tested). Our method enables the detailed investigation of epistasis on a large scale, offering new insights into its role and importance.

genetics↗

bioRxiv2023

A biobank-scale test of marginal epistasis reveals genome-wide signals of polygenic epistasis

The contribution of epistasis (interactions among genes or genetic variants) to human complex trait variation remains poorly understood. Methods that aim to explicitly identify pairs of genetic variants, usually single nucleotide polymorphisms (SNPs), associated with a trait suffer from low power due to the large number of hypotheses tested while also having to deal with the computational problem of searching over a potentially large number of candidate pairs. An alternate approach involves testing whether a single SNP modulates variation in a trait against a polygenic background. While overcoming the limitation of low power, such tests of polygenic or marginal epistasis (ME) are infeasible on Biobank-scale data where hundreds of thousands of individuals are genotyped over millions of SNPs. We present a method to test for ME of a SNP on a trait that is applicable to biobank-scale data. We performed extensive simulations to show that our method provides calibrated tests of ME. We applied our method to test for ME at SNPs that are associated with 53 quantitative traits across {approx} 300 K unrelated white British individuals in the UK Biobank (UKBB). Testing 15, 601 trait-loci associations that were significant in GWAS, we identified 16 trait-loci pairs across 12 traits that demonstrate strong evidence of ME signals (p-value [Formula]). We further partitioned the significant ME signals across the genome to identify 6 trait-loci pairs with evidence of local (within-chromosome) ME while 15 show evidence of distal (cross-chromosome) ME. Across the 16 trait-loci pairs, we document that the proportion of trait variance explained by ME is about 12x as large as that explained by the GWAS effects on average (range: 0.59 to 43.89). Our results show, for the first time, evidence of interaction effects between individual genetic variants and overall polygenic background modulating complex trait variation.

genetics↗

bioRxiv2021

Genetic alteration of human MYH6 is mimicked by SARS-CoV-2 polyprotein: mapping viral variants of cardiac interest

Acute cardiac injury has been observed in a subset of COVID-19 patients, but the molecular basis for this clinical phenotype is unknown. It has been hypothesized that molecular mimicry may play a role in triggering an autoimmune inflammatory reaction in some individuals after SARS-CoV-2 infection. Here we investigate if linear peptides contained in proteins that are primarily expressed in the heart also occur in the SARS-CoV-2 proteome. Specifically, we compared the library of 136,704 8-mer peptides from 144 human proteins (including splicing variants) to 9,926 8-mers from all 17 viral proteins in the reference SARS-CoV-2 proteome. No 8-mers were exactly identical between the reference human proteome and the reference SARS-CoV-2 proteome. However, there were 45 8-mers that differed by only one amino acid when compared to the reference SARS-CoV-2 proteome. Interestingly, analysis of protein-coding mutations from 141,456 individuals showed that one of these 8-mers from the SARS-CoV-2 Replicase polyprotein 1a/1ab (KIALKGGK) is identical to a MYH6 peptide encoded by the c.5410C>A (Q1804K) genetic variation, which has been observed at low prevalence in Africans/African Americans (0.08%), East Asians (0.3%), South Asians (0.06%) and Latino/Admixed Americans (0.003%). Furthermore, analysis of 4.85 million SARS-CoV-2 genomes from over 200 countries shows that viral evolution has already resulted in 20 additional 8-mer peptides that are identical to human heart-enriched proteins encoded by reference sequences or genetic variants. Whether such mimicry contributes to cardiac inflammation during or after COVID-19 illness warrants further experimental evaluation. We suggest that SARS-CoV-2 variants harboring peptides identical to human cardiac proteins should be investigated as viral variants of cardiac interest.

genomics↗

bioRxiv2021

High diversity in Delta variant across countries revealed via genome-wide analysis of SARS-CoV-2 beyond the Spike protein

The highly contagious Delta variant of SARS-CoV-2 has emerged as the new dominant global strain, and reports of reduced effectiveness of COVID-19 vaccines against the Delta variant are highly concerning. While there has been extensive focus on understanding the amino acid mutations in the Delta variant s Spike protein, the mutational landscape of the rest of the SARS-CoV-2 proteome (25 proteins) remains poorly understood. To this end, we performed a systematic analysis of mutations in all the SARS-CoV-2 proteins from nearly 2 million SARS-CoV-2 genomes from 176 countries/territories. Six highly-prevalent missense mutations in the viral life cycle-associated Membrane (I82T), Nucleocapsid (R203M, D377Y), NS3 (S26L), and NS7a (V82A, T120I) proteins are almost exclusive to the Delta variant compared to other variants of concern (mean prevalence across genomes: Delta = 99.74%, Alpha = 0.06%, Beta = 0.09%, Gamma = 0.22%). Furthermore, we find that the Delta variant harbors a more diverse repertoire of mutations across countries compared to the previously dominant Alpha variant (cosine similarity: meanAlpha = 0.94, S.D.Alpha = 0.05; meanDelta = 0.86, S.D.Delta = 0.1; Cohen s dAlpha-Delta = 1.17, p-value < 0.001). Overall, our study underscores the high diversity of the Delta variant between countries and identifies a list of targetable amino acid mutations in the Delta variant s proteome for probing the mechanistic basis of pathogenic features such as high viral loads, high transmissibility, and reduced susceptibility against neutralization by vaccines.

genomics↗

bioRxiv2020

Transcriptome profiling-based evidences reiterate the intrinsic role of MAPKAPK2 in facilitating molecular crosstalk during HNSCC pathogenesis

BackgroundTranscriptome profiling has been pivotal in better comprehending the convoluted biology of tumors including head and neck squamous cell carcinoma (HNSCC). Recently, growing evidence has implicated the role of mitogen-activated protein kinase-activated protein kinase-2 (MAPKAPK2 or MK2) in many human diseases including tumors. MK2 has been recently reported as a critical regulator of HNSCC that functions via modulating the transcript turnover of crucial genes involved in its pathogenesis. Comprehensive MK2-centric transcriptomic analyses could help the scientific community to delve deeper into MK2-pathway driven mechanisms of tumor progression, but such studies have not yet been reported. Consequently, to delineate the biological relevance of MK2 and its intricate crosstalk in the tumor milieu, an extensive transcriptome analysis of HNSCC was conceptualized and effectuated with MK2 at the nexus. MethodsIn the current study, comprehensive next-generation sequencing-based transcriptome profiling was accomplished to ascertain global patterns of mRNA expression profiles in both in vitro and in vivo models of the HNSCC microenvironment. The findings of the RNA-sequencing analysis were cross-validated via robust validation using nCounter gene expression assays, immunohistochemistry, and real-time quantitative polymerase chain reaction (RT-qPCR). ResultsTranscriptomic characterization followed by annotation and differential gene expression analyses identified certain MK2-regulated candidate genes constitutively involved in regulating HNSCC pathogenesis, and the biological significance of these genes was established by pathway enrichment analysis. Additionally, advanced gene expression assays through the nCounter system in conjunction with immunohistochemical analysis validated the transcriptome profiling outcomes quite robustly. Furthermore, the results obtained from immunohistochemistry and transcript stability analysis indicated the crucial role of MK2 in the modulation of the expression pattern of these genes in HNSCC tumors and cells. ConclusionsConclusively, the findings have paved the way toward the identification of new effective tumor markers and potential molecular targets for HNSCC management. The results have accentuated the importance of certain differentially expressed MK2-regulated genes that are constitutively involved in HNSCC pathogenesis to potentially serve as putative candidates for future endeavors pertaining to diagnosis and therapeutic interventions for HNSCC.

cancer biology↗

bioRxiv2020

Global Importance Analysis: A Method to QuantifyImportance of Genomic Features in Deep NeuralNetworks

Deep neural networks have demonstrated improved performance at predicting the sequence specificities of DNA- and RNA-binding proteins compared to previous methods that rely on k-mers and position weight matrices. To gain insights into why a DNN makes a given prediction, model interpretability methods, such as attribution methods, can be employed to identify motif-like representations along a given sequence. Because explanations are given on an individual sequence basis and can vary substantially across sequences, deducing generalizable trends across the dataset and quantifying their effect size remains a challenge. Here we introduce global importance analysis (GIA), a model interpretability method that quantifies the population-level effect size that putative patterns have on model predictions. GIA provides an avenue to quantitatively test hypotheses of putative patterns and their interactions with other patterns, as well as map out specific functions the network has learned. As a case study, we demonstrate the utility of GIA on the computational task of predicting RNA-protein interactions from sequence. We first introduce a convolutional network, we call ResidualBind, and benchmark its performance against previous methods on RNAcompete data. Using GIA, we then demonstrate that in addition to sequence motifs, ResidualBind learns a model that considers the number of motifs, their spacing, and sequence context, such as RNA secondary structure and GC-bias.

genomics↗

bioRxiv2020

Induced Disassembly of a Virus-Like Particle under Physiological Conditions for Venom Peptide Delivery

Virus-like nanoparticles (VLPs) show considerable promise for the in vivo delivery of therapeutic compounds such as bioactive venom peptides. While loading and targeting protocols have been developed for numerous VLP prototypes, induced disassembly under physiological conditions of neutral pH, moderate temperature, and aqueous medium, remain a challenge. Here, we implement and evaluate a ring-opening metathesis polymerization (ROMP) general mechanism for controllable VLP disassembly that is independent of cell-specific factors or the manipulation environmental conditions such as pH and temperature that cannot be readily controlled in vivo. The ROMP substrate norbornene is covalently conjugated to surface-exposed lysine residues of a P22 bacteriophage-derived VLP, and ROMP is induced by treatment of water-soluble ruthenium catalyst AquaMet. Disruption of the P22 shell and release of a GFP reporter is confirmed via native agarose electrophoresis and quantitative microscopy and light scattering analyses. Our ROMP disassembly strategy does not depend on the particular structure or morphology of the P22 nanocontainer and is adaptable to other VLP prototypes for the potential delivery of venom peptides for pharmacological applications. O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=90 SRC="FIGDIR/small/278598v1_ufig1.gif" ALT="Figure 1"> View larger version (19K): org.highwire.dtl.DTLVardef@1d24020org.highwire.dtl.DTLVardef@702e3aorg.highwire.dtl.DTLVardef@eb0d25org.highwire.dtl.DTLVardef@1ac00d0_HPS_FORMAT_FIGEXP M_FIG C_FIG

bioengineering↗

bioRxiv2020

Various RNA-binding proteins and their conditional networks explain miRNA biogenesis and help to reveal the potential SARS-CoV-2 host miRNAome system

Formation of mature miRNAs and their expression is a highly controlled process. It is very much dependent upon the post-transcriptional regulatory events. Recent findings suggest that several RNA binding proteins beyond Drosha/Dicer are involved in the processing of miRNAs. Deciphering of conditional networks for these RBP-miRNA interactions may help to reason the spatio-temporal nature of miRNAs which can also be used to predict miRNA profiles. In this direction, >25TB of data from different platforms were studied (CLIP-seq/RNA-seq/miRNA-seq) to develop Bayesian causal networks capable of reasoning miRNA biogenesis. The networks ably explained the miRNA formation when tested across a large number of conditions and experimentally validated data. The networks were modeled into an XGBoost machine learning system where expression information of the network components was found capable to quantitatively explain the miRNAs formation levels and their profiles. The models were developed for 1,204 human miRNAs whose accurate expression level could be detected directly from the RNA-seq data alone without any need of doing separate miRNA profiling experiments like miRNA-seq or arrays. A first of its kind, miRbiom performed consistently well with high average accuracy (91%) when tested across a large number of experimentally established data from several conditions. It has been implemented as an interactive open access web-server where besides finding the profiles of miRNAs, their downstream functional analysis can also be done. miRbiom will help to get an accurate prediction of human miRNAs profiles in the absence of profiling experiments and will be an asset for regulatory research areas. The study also shows the importance of having RBP interaction information in better understanding the miRNAs and their functional projectiles where it also lays the foundation of such studies and software in future.

bioinformatics↗

bioRxiv2020

Multi-pronged human protein mimicry by SARS-CoV-2 reveals bifurcating potential for MHC detection and immune evasion

The hand of molecular mimicry in shaping SARS-CoV-2 evolution and immune evasion remains to be deciphered. We identify 33 distinct 8-mer/9-mer peptides that are identical between SARS-CoV-2 and human proteomes, along similar extents of viral mimicry observed in other viruses. Interestingly, 20 novel peptides have not been observed in any previous human coronavirus (HCoV) strains. Four of the total mimicked 8-mers/9-mers map onto HLA-B*40:01, HLA-B*40:02, and HLA-B*35:01 binding peptides from human PAM, ANXA7, PGD, and ALOX5AP proteins. This mimicry of multiple human proteins by SARS-CoV-2 is made salient by the targeted genes being focally expressed in arteries, lungs, esophagus, pancreas, and macrophages. Further, HLA-A*03 restricted 8-mer peptides are shared broadly by human and coronaviridae helicases with primary expression of the mimicked human proteins in the neurons and immune cells. This study presents the first comprehensive scan of peptide mimicry by SARS-CoV-2 of the human proteome and motivates follow-up research into its immunological consequences.

evolutionary biology↗

bioRxiv2020

SARS-CoV-2 selectively mimics a cleavable peptide of human ENaC in a strategic hijack of host proteolytic machinery

Molecular mimicry of host proteins is an evolutionary strategy adopted by viruses to evade immune surveillance and exploit host cell systems. We report that SARS-CoV-2 has evolved a unique S1/S2 cleavage site (RRARSVAS), absent in any previous coronavirus sequenced, that results in mimicry of an identical FURIN-cleavable peptide on the human epithelial sodium channel -subunit (ENaC-). Genetic truncation at this ENaC- cleavage site causes aldosterone dysregulation in patients, highlighting the functional importance of the mimicked SARS-CoV-2 peptide. Single cell RNA-seq from 65 studies shows significant overlap between the expression of ENaC- and ACE2, the putative receptor for the virus, in cell types linked to the cardiovascular-renal-pulmonary pathophysiology of COVID-19. Triangulating this cellular fingerprint with amino acid cleavage signatures of 178 human proteases shows the potential for tissue-specific proteolytic degeneracy wired into the SARS-CoV-2 lifecycle. We extrapolate that the evolution of SARS-CoV-2 into a global coronavirus pandemic may be in part due to its targeted mimicry of human ENaC and hijack of the associated host proteolytic network.

bioinformatics↗

bioRxiv2019

Deficiency in the endocytic adaptor protein PHETA1/2 impairs renal and craniofacial development

A critical barrier in the treatment of endosomal and lysosomal diseases is the lack of understanding of the in vivo functions of the putative causative genes. We addressed this by investigating a key pair of endocytic adaptor proteins, PH domain containing endocytic trafficking adaptor 1 and 2 (PHETA1/2, also known as FAM109A/B, Ses1/2, IPIP27A/B), which interact with the protein product of OCRL, the causative gene for Lowe syndrome. Here we conducted the first study of PHETA1/2 in vivo, utilizing the zebrafish system. We found that impairment of both zebrafish orthologs, pheta1 and pheta2, disrupted endocytosis and ciliogenesis. In addition, pheta1/2 mutant animals exhibited reduced jaw size and delayed chondrocyte maturation, indicating a role in craniofacial development. Deficiency of pheta1/2 resulted in dysregulation of cathepsin K, which led to an increased abundance of type II collagen in craniofacial cartilages. The abnormal renal and craniofacial phenotypes in the pheta1/2 mutant animals were consistent with the clinical presentations of a patient with a de novo arginine (R) to cysteine (C) variant (R6C) of PHETA1. Expressing the patient-specific variant in zebrafish exacerbated craniofacial deficits, suggesting that the R6C allele acts in a dominant-negative manner. Together, these results provide insights into the in vivo roles of PHETA1/2 and suggest that the R6C variant is contributory to the pathogenesis of disease in the patient.

developmental biology↗

bioRxiv2019

Antitumor effects of Tv1 venom peptide in liver cancer

A strategy for treating the most common type of liver cancer, hepatocellular carcinoma (HCC) applies a targeted therapy using venom peptides that are selective for ion channels and transporters overexpressed in tumor cells. Here, we report selective anti-HCC properties of Tv1, a venom peptide from the predatory marine terebrid snail, Terebra variegata. Tv1 was applied in vitro to liver cancer cells and administered in vivo to allograft tumor mouse models. Tv1 inhibited the proliferation of murine HCC cells via calcium dependent apoptosis resulting from down-regulation of the cyclooxygenase-2 (COX-2) pathway. Additionally, tumor sizes were significantly reduced in Tv1-treated syngeneic tumor-bearing mice. Tv1s mechanism of action involves binding to specific transient receptor potential (TRP) cation channels that are overexpressed in HCC cell models. Our findings demonstrate the unique potential of venom peptides to function as tumor specific ligands in the quest for targeted cancer therapies.

cancer biology↗

bioRxiv2018

Inferring Sequence-Structure Preferences of RNA-Binding Proteins with Convolutional Residual Networks

To infer the sequence and RNA structure specificities of RNA-binding proteins (RBPs) from experiments that enrich for bound sequences, we introduce a convolutional residual network which we call ResidualBind. ResidualBind significantly outperforms previous methods on experimental data from many RBP families. We interrogate ResidualBind to identify what features it has learned from high-affinity sequences with saliency analysis along with 1st-order and 2nd-order in silico mutagenesis. We show that in addition to sequence motifs, ResidualBind learns a model that includes the number of motifs, their spacing, and both positive and negative effects of RNA structure context. Strikingly, ResidualBind learns RNA structure context, including detailed base-pairing relationships, directly from sequence data, which we confirm on synthetic data. ResidualBind is a powerful, flexible, and interpretable model that can uncover cis-recognition preferences across a broad spectrum of RBPs.

genomics↗