Biology subjects

Srivastava, A.

Publications and source records attributed to Srivastava, A..

At least 91 records · Page 5Linked to original sources

bioRxiv2023

GenomeMUSter mouse genetic variation service enables multi-trait, multi-population data integration and analyses

Hundreds of inbred laboratory mouse strains and intercross populations have been used to functionalize genetic variants that contribute to disease. Thousands of disease relevant traits have been characterized in mice and made publicly available. New strains and populations including the Collaborative Cross, expanded BXD and inbred wild-derived strains add to set of complex disease mouse models, genetic mapping resources and sensitized backgrounds against which to evaluate engineered mutations. The genome sequences of many inbred strains, along with dense genotypes from others could allow integrated analysis of trait - variant associations across populations, but these analyses are not feasible due to the sparsity of genotypes available. Moreover, the data are not readily interoperable with other resources. To address these limitations, we created a uniformly dense data resource by harmonizing multiple variant datasets. Missing genotypes were imputed using the Viterbi algorithm with a data-driven technique that incorporates local phylogenetic information, an approach that is extensible to other model organism species. The result is a web- and programmatically-accessible data service called GenomeMUSter (https://muster.jax.org), comprising allelic data covering 657 strains at 106.8M segregating sites. Interoperation with phenotype databases, analytic tools and other resources enable a wealth of applications including multi-trait, multi-population meta-analysis. We demonstrate this in a cross-species comparison of the meta-analysis of Type 2 Diabetes and of substance use disorders, resulting in the more specific characterization of the role of human variant effects in light of mouse phenotype data. Other applications include refinement of mapped loci and prioritization of strain backgrounds for disease modeling to further unlock extant mouse diversity for genetic and genomic studies in health and disease.

genetics↗

bioRxiv2023

Nextflow Pipeline for Visium and H&E Data from Patient-Derived Xenograft Samples

HighlightsWe have developed an automated data processing pipeline to quantify mouse and human data from patient-derived xenograft samples assayed by Visium spatial transcriptomics with matched hematoxylin and eosin (H&E) stained image. We enable deconvolution of reads with Xenome, quantification of spatial gene expression from host and graft species with Space Ranger, extraction of B-allele frequencies, and splicing quantification with Velocyto. In the H&E image processing sub-workflow, we generate morphometric and deep learning-derived feature quantifications complementary to the Visium spots, enabling multi-modal H&E/expression comparisons. We have wrapped the pipeline into Nextflow DSL2 in a scalable, portable, and easy-to-use framework. SummaryWe designed a Nextflow DSL2-based pipeline, Spatial Transcriptomics Quantification (STQ), for simultaneous processing of 10x Genomics Visium spatial transcriptomics data and a matched hematoxylin and eosin (H&E)-stained whole slide image (WSI), optimized for Patient-Derived Xenograft (PDX) cancer specimens. Our pipeline enables the classification of sequenced transcripts for deconvolving the mouse and human species and mapping the transcripts to reference transcriptomes. We align the H&E WSI with the spatial layout of the Visium slide and generate imaging and quantitative morphology features for each Visium spot. The pipeline design enables multiple analysis workflows, including single or dual reference genomes input and stand-alone image analysis. We showed the utility of our pipeline on a dataset from Visium profiling of four melanoma PDX samples. The clustering of Visium spots and clustering of imaging features of H&E data reveal similar patterns arising from the two data modalities.

bioinformatics↗

bioRxiv2023

Exploring the Role of MMP9 and its Interaction with TGFβ Signalling in Patients with Mesial Temporal Lobe Epilepsy-Hippocampal Sclerosis

Inflammation and blood brain barrier (BBB) damage are associated with epileptogenesis in Mesial Temporal lobe epilepsy with Hippocampal sclerosis (MTLE-HS). Animal studies have predicted the role of Matrix metalloproteinase 9 (MMP9) in extracellular matrix (ECM) modulation, BBB leakage and neuro-inflammation, while Transforming growth factor beta (TGF{beta}) signalling in astrocytes potentiates hyper-excitability leading to seizure generation. We hypothesize whether changes in activity and expression of MMP9, and the ratio of MMP9 and its inhibitor, Tissue inhibitor of metalloproteinase 1 (TIMP1), have a role in epileptogenesis in the patients with MTLE-HS through zona occludens 1 (ZO1) modulation. We also proposed the role of astrocytic TGF{beta} signalling in these patients. mRNA expression of MMP9 and TIMP1 was significantly up-regulated. The ratio of MMP9 to its inhibitor TIMP1 was greater than one, suggesting activation of MMP9, further confirmed by gelatin zymography. MMP9 activity as well as immunoreactivity was higher in patients with MTLE-HS as compared to non-seizure controls, whereas the immunoreactivity of ZO1 was significantly lower in the patients. The downstream TGF{beta} signalling effector molecules, SMAD3 and pSMAD3 immunoreactivity were also significantly higher in MTLE-HS patients and both molecules showed co-localisation with astrocytes in the hippocampal region. Further, we showed preliminary data about interaction of MMP9 and TGF{beta}1 in these patients as evidenced by a co-immunoprecipitation assay. This study highlighted the MMP9 and astrocytic TGF{beta} signalling mediated potential mechanism of epileptogenesis in MTLE-HS patients.

neuroscience↗

bioRxiv2023

Prediction of Alzheimer's Disease from Single Cell Transcriptomics Using Deep Learning

Alzheimers disease (AD) is a progressive neurological disorder characterized by brain cell death, brain atrophy, and cognitive decline. Early diagnosis of AD remains a significant challenge in effectively managing this debilitating disease. In this study, we aimed to harness the potential of single-cell transcriptomics data from 12 Alzheimers patients and 9 normal controls (NC) to develop a predictive model for identifying AD patients. The dataset comprised gene expression profiles of 33,538 genes across 169,469 cells, with 90,713 cells belonging to AD patients and 78,783 cells belonging to NC individuals. Employing machine learning and deep learning techniques, we developed prediction models. Initially, we performed data processing to identify genes expressed in most cells. These genes were then ranked based on their ability to classify AD and NC groups. Subsequently, two sets of genes, consisting of 35 and 100 genes, respectively, were used to develop machine learning-based models. Although these models demonstrated high performance on the training dataset, their performance on the validation/independent dataset was notably poor, indicating potential overoptimization. To address this challenge, we developed a deep learning method utilizing dropout regularization technique. Our deep learning approach achieved an AUC of 0.75 and 0.84 on the validation dataset using the sets of 35 and 100 genes, respectively. Furthermore, we conducted gene ontology enrichment analysis on the selected genes to elucidate their biological roles and gain insights into the underlying mechanisms of Alzheimers disease. While this study presents a prototype method for predicting AD using single-cell genomics data, it is important to note that the limited size of the dataset represents a major limitation. To facilitate the scientific community, we have created a website to provide with code and service. It is freely available at https://webs.iiitd.edu.in/raghava/alzscpred. Key PointsO_LIPredictive Model for Alzheimers Disease Using Single Cell Transcriptomics Data C_LIO_LIOveroptimization of models trained on single-cell genomics data. C_LIO_LIApplication of dropout regularization technique of ANN for reducing overoptimization C_LIO_LIRanking of genes based on their ability to predict patients Alzheimers Disease C_LIO_LIStandalone software package for predicting Alzheimers Disease C_LI Authors BiographyO_LIAman Srivastava is pursuing M. Tech. in Computational Biology from Department of Computational Biology, Indraprastha Institute of Information Technology, New Delhi, India. C_LIO_LIAnjali Dhall is currently working as Ph.D. in Computational Biology from Department of Computational Biology, Indraprastha Institute of Information Technology, New Delhi, India. C_LIO_LISumeet Patiyal is currently working as Ph.D. in Computational Biology from Department of Computational Biology, Indraprastha Institute of Information Technology, New Delhi, India. C_LIO_LIAkanksha Arora is currently working as Ph.D. in Computational Biology from Department of Computational Biology, Indraprastha Institute of Information Technology, New Delhi, India. C_LIO_LIAkanksha Jarwal is pursuing M. Tech. in Computational Biology from Department of Computational Biology, Indraprastha Institute of Information Technology, New Delhi, India. C_LIO_LIGajendra P. S. Raghava is currently working as Professor and Head of Department of Computational Biology, Indraprastha Institute of Information Technology, New Delhi, India. C_LI

bioinformatics↗

bioRxiv2023

sAMP-VGG16: Drude polarizable force-field assisted image-based deep neural network prediction model for short antimicrobial peptides

During the last three decades, antimicrobial peptides (AMPs) have emerged as a promising therapeutic alternative to antibiotics. The approaches for designing AMPs span from experimental trial-and-error methods to synthetic hybrid peptide libraries. To overcome the exceedingly expensive and time-consuming process of designing effective AMPs, many computational and machine-learning tools for AMP prediction have been recently developed. In general, to encode the peptide sequences, featurization in these rely on approaches based on (a) amino acid composition, (b) physicochemical properties, (c) sequence similarity, and (d) structural properties. In this work, we present an image-based deep neural network model to predict AMPs, where we are using feature encoding based on Drude polarizable force-field atom types, which can capture the peptide properties more efficiently compared to conventional feature vectors. The proposed prediction model identifies AMPs with promising accuracy and efficiency and can be used as a next-generation screening method for predicting new AMPs. The source code is publicly available at the Figshare server sAMP-VGG16.

biophysics↗

bioRxiv2023

Functional whole genome screen of nutrient-starved Mycobacterium tuberculosis identifies genes involved in antibiotic tolerance

Mycobacterium tuberculosis (Mtb), the causative agent of tuberculosis (TB), poses a global health challenge and is responsible for over a million deaths each year. Current treatment is lengthy and complex, and new, abbreviated regimens are urgently needed. Mtb adapts to nutrient starvation, a condition experienced during host infection, by shifting its metabolism and becoming tolerant to the killing activity of bactericidal antibiotics. An improved understanding of the mechanisms mediating antibiotic tolerance in Mtb can serve as the basis for developing more effective therapies. We performed a forward genetic screen to identify candidate Mtb genes involved in tolerance to the two key first-line antibiotics, rifampin and isoniazid, under nutrient-rich and nutrient-starved conditions. In nutrient-rich conditions, we found 220 mutants with differential antibiotic susceptibility (218 in the rifampin screen and 2 in the isoniazid screen). Following Mtb adaptation to nutrient starvation, 82 mutants showed differential antibiotic susceptibility (80 in the rifampin screen and 2 in the isoniazid screen). Using targeted mutagenesis, we validated the rifampin-hypersusceptible phenotype under nutrient starvation in Mtb mutants lacking the following genes: ercc3, moeA1, rv0049, and rv2179c. These findings shed light on potential therapeutic targets, which could help shorten the duration and complexity of antitubercular regimens. ImportanceTreatment of Mtb infection requires a long course of combination antibiotics, likely due to subpopulations of tolerant bacteria exhibiting decreased susceptibility to antibiotics. Identifying and characterizing the genetic pathways involved in antibiotic tolerance is expected to yield therapeutic targets for the development of novel TB treatment-shortening regimens.

microbiology↗

bioRxiv2023

HSP70 binds to specific non-coding RNA and regulates human RNA Polymerase III

Molecular chaperones are critical for protein homeostasis and are implicated in several human pathologies such as neurodegeneration and cancer. While the binding of chaperones to nascent and misfolded proteins has been studied in great detail, the direct interaction between chaperones and RNA has not been systematically investigated. Here we provide the evidence for widespread interaction between chaperones and RNA in human cells. We show that the major chaperone Heat-Shock Protein 70 (HSP70) binds to non-coding RNA transcribed by RNA Polymerase III (Pol III) such as tRNA and 5S rRNA. Global chromatin profiling revealed that HSP70 binds genomic sites of transcription by Pol III. Detailed biochemical analyses showed that HSP70 facilitates transcription of its target non-coding RNA by binding to Pol III. Thus our study uncovers an unexpected role of HSP70-RNA interaction in the biogenesis of a specific class of non-coding RNA with wider implications in neurodegeneration and cancer.

biochemistry↗

bioRxiv2023

Magnipore: Prediction of differential single nucleotide changes in the Oxford Nanopore Technologies sequencing signal of SARS-CoV-2 samples

Oxford Nanopore Technologies (ONT) allows direct sequencing of ribonucleic acids (RNA) and, in addition, detection of possible RNA modifications due to deviations from the expected ONT signal. The software available so far for this purpose can only detect a small number of modifications. Alternatively, two samples can be compared for different RNA modifications. We present Magnipore, a novel tool to search for significant signal shifts between samples of Oxford Nanopore data from similar or related species. Magnipore classifies them into mutations and potential modifications. We use Magnipore to compare SARS-CoV-2 samples. Included were representatives of the early 2020s Pango lineages (n=6), samples from Pango lineages B.1.1.7 (n=2, Alpha), B.1.617.2 (n=1, Delta), and B.1.529 (n=7, Omicron). Magnipore utilizes position-wise Gaussian distribution models and a comprehensible significance threshold to find differential signals. In the case of Alpha and Delta, Magnipore identifies 55 detected mutations and 15 sites that hint at differential modifications. We predicted potential virus-variant and variant-group-specific differential modifications. Magnipore contributes to advancing RNA modification analysis in the context of viruses and virus variants.

bioinformatics↗

bioRxiv2023

Glycans modulate lipid binding in Lili-Mip lipocalin protein

The unique viviparous Pacific Beetle cockroaches provide nutrition to their embryo by secreting milk proteins Lili-Mip, which is a lipid-binding glycoprotein that crystallizes in vivo. The resolved in vivo crystal structure of variably glycosylated Lili-Mip shows a classical Lipocalin fold with an eight-stranded antiparallel beta-barrel enclosing a fatty acid. The availability of physiologically unaltered glycoprotein structure makes Lili-Mip a very attractive model system to investigate the role of glycans on protein structure, dynamics, and function. Towards that end, we have employed all-atom molecular dynamics simulations on various glycosylated stages of a bound and free Lili-Mip protein and characterized the impact of glycans and the bound lipid on the dynamics of this glycoconjugate. Our work provides important molecular-level mechanistic insights into the role of glycans in the nutrient storage function of the Lili-Mip protein. Our analyses show that the glycans locally stabilize spatially proximal residues and regulate the low amplitude opening motions of the residues at the entrance of the binding pocket. Glycans, which are located at the portal end of the barrel, also restrict the distal barrel depth and allosterically modulate the lipid dynamics in the barrel. A simple but effective distance-based network analysis of the protein also reveals the role of glycans in the subtle rewiring of residues crucial for determining the barrel depth and lipid orientation.

biophysics↗

bioRxiv2023

Identification and characterization of metabolic subtypes of endometrial cancer using systems-level approach

BackgroundEndometrial cancer (EC) is the most common gynaecological cancer worldwide. Understanding the metabolic adaptation and its heterogeneity in tumor tissues may provide new insights and help in cancer diagnosis, prognosis, and treatment. In this study, we investigated metabolic alterations of EC to understand the variations in the metabolism within tumor samples. MethodsWe integrated the TCGA transcriptomics data of EC (RNA-Seq) with the human genome-scale metabolic model (HMR2.0) and performed unsupervised learning to identify the metabolic subtypes of EC and uncover the underlying dysregulated metabolic pathways and reporter metabolites in each subtype. The relationship between metabolic subtypes and clinical variables was explored. Further, we characterized each subtype at the molecular level and correlated the subtype-specific metabolic changes occurring at the transcriptome level with the genomic alterations. ResultsEC patients are stratified into two robust metabolic subtypes (cluster-1 and cluster-2) that significantly correlate to patient survival, tumor stages, mutation, and copy number variations. We observed coactivation of pentose phosphate pathway and one-carbon metabolism along with genes involved in controlling estrogen levels in cluster-2, which is linked to poor survival. PNMT and ERBB2 are also upregulated in cluster-2 samples and present in the same chromosome locus 17q12, which is amplified. PTEN and TP53 mutations show mutually exclusive behavior between subtypes and display a difference in survival. ConclusionThis work identifies metabolic subtypes with distinct characteristics at the transcriptome and genome levels, highlighting the metabolic heterogeneity within EC.

systems biology↗

bioRxiv2022

Clustering conformational ensembles of intrinsically disordered proteins with t-distributed stochastic neighbor embedding

Intrinsically disordered proteins (IDPs) populate a range of conformations that are best described by a heterogeneous ensemble. Grouping an IDP ensemble into "structurally similar" clusters for visualization, interpretation, and analysis purposes is a much-desired but formidable task as the conformational space of IDPs is inherently high-dimensional and reduction techniques often result in ambiguous classifications. Here, we employ the t-distributed stochastic neighbor embedding (t-SNE) technique to generate homogeneous clusters of IDP conformations from the full heterogeneous ensemble. We illustrate the utility of t-SNE by clustering conformations of two disordered proteins, A{beta}42, and a C-terminal fragment of -synuclein, in their APO states and when bound to small molecule ligands. Our results shed light on ordered sub-states within disordered ensembles and provide structural and mechanistic insights into binding modes that confer specificity and affinity in IDP ligand binding. t-SNE projections preserve the local neighborhood information and provide interpretable visualizations of the conformational heterogeneity within each ensemble and enable the quantification of cluster populations and their relative shifts upon ligand binding. Our approach provides a new framework for detailed investigations of the thermodynamics and kinetics of IDP ligand binding and will aid rational drug design for IDPs. SignificanceGrouping heterogeneous conformations of IDPs into "structurally similar" clusters facilitates a clearer understanding of the properties of IDP conformational ensembles and provides insights into "structural ensemble: function" relationships. In this work, we provide a unique approach for clustering IDP ensembles efficiently using a non-linear dimensionality reduction method, t-distributed stochastic neighbor embedding (t-SNE), to create clusters with structurally similar IDP conformations. We show how this can be used for meaningful biophysical analyses such as understanding the binding mechanisms of IDPs such as -synuclein and Amyloid {beta}42 with small drug molecules. Graphical Abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=136 SRC="FIGDIR/small/516231v2_ufig1.gif" ALT="Figure 1"> View larger version (52K): org.highwire.dtl.DTLVardef@8ef906org.highwire.dtl.DTLVardef@7f14e5org.highwire.dtl.DTLVardef@134f9eeorg.highwire.dtl.DTLVardef@104a2ef_HPS_FORMAT_FIGEXP M_FIG C_FIG

biophysics↗

bioRxiv2022

Selectivity and ranking of tight-binding JAK-STAT inhibitors using Markovian milestoning with Voronoi tessellations

Janus kinases (JAK) are a group of proteins in the non-receptor tyrosine kinase (NRTKs) family that play a crucial role in growth, survival, and angiogenesis. They are activated by cytokines through the Janus kinase - signal transducer and activator of transcription (JAK-STAT) signaling pathway. JAK-STAT signaling pathways have significant roles in the regulation of cell division, apoptosis, and immunity. Identification of the V617F mutation in the Janus homology 2 (JH2) domain of JAK2 leading to myeloproliferative disorders has stimulated great interest in the drug discovery community to develop JAK2-specific inhibitors. However, such inhibitors should be selective towards JAK2 over other JAKs and display an extended residence time. Recently, novel JAK2/STAT5 axis inhibitors (N-(1H-pyrazol-3-yl)pyrimidin-2-amino derivatives) have displayed extended residence times (hours or longer) on target and adequate selectivity excluding JAK3. To facilitate a deeper understanding of the kinase-inhibitor interactions and advance the development of such inhibitors, we utilize a multiscale Markovian milestoning with Voronoi tessellations (MMVT) approach within the Simulation-Enabled Estimation of Kinetic Rates v.2 (SEEKR2) program to rank-order these inhibitors based on their kinetic properties and further explain the selectivity of JAK2 inhibitors over JAK3. Our approach investigates the kinetic and thermodynamic properties of JAK-inhibitor complexes in a user-friendly, fast, efficient, and accurate manner compared to other brute force and hybrid enhanced sampling approaches.

biophysics↗

bioRxiv2022

Interplay of the folded domain and disordered low-complexity domains along with RNA sequence mediate efficient binding of FUS with RNA

RNA binding ability of Fused in Sarcoma (FUS) protein is crucial to its cellular function. Our molecular simulation study on FUS-RNA complex provides atomic resolution insights into the observations from biochemical studies and also illuminate our understanding of molecular driving forces that mediate the structure, stability, and interaction of RRM and RGG domains of FUS with a stem-loop junction RNA. We observe a clear cooperativity and division of labour among the ordered (RRM) and disordered domains (RGG1 and RGG2 domain) of FUS that leads to an organized and tighter RNA binding. Irrespective of the length of RGG2, the RGG2-RNA interaction is confined to the stem-loop junction and the proximal stem regions. On the other hand, the RGG1-RNA interactions are primarily with the longer RNA stem. We find that the C-terminus of RRM, which make up the "boundary residues" that connect the folded RRM with the long disordered RGG2 stretch of the protein, plays a critical role in RNA binding with the RRM domain. Our study provides high-resolution molecular insights into the FUS-RNA interactions and forms the basis for understanding the molecular origins of full-length FUS interaction with RNA.

biophysics↗

bioRxiv2022

Nascent-Chain Interaction Networks and Their Effect on the Bacterial Ribosome

In order to become bioactive, proteins need to be biosynthesized and protected from aggregation during translation. The ribosome and molecular chaperones contribute to both of these tasks. While it is known that some ribosomal proteins (r-proteins) interact with ribosome-bound nascent chains (RNCs), specific interaction networks and their role within the ribosomal machinery remain poorly characterized and understood. Here, we find that RNCs of variable sequence and length (beyond the 1st C-terminal reside) do not modify the apparent stability of the peptidyl-transferase center (PTC) and r-proteins. Thus, RNC/r-protein interaction networks close to the PTC have no effect on the apparent stability of ribosome-RNC complexes. Further, fluorescence anisotropy decay, chemical-crosslinking and Western blots show that RNCs of the foldable protein apoHmp1-140 have an N-terminal compact region (63-94 residues) and interact specifically with r-protein L23 but not with L24 or L29, at the ribosomal-tunnel exit. Longer RNCs bear a similar compact region and interact either with L23 alone or with L23 and another unidentified r-protein, or with molecular chaperones. The apparent strength of RNC/r-protein interactions does not depend on RNC sequence. Taken together, our findings show that RNCs encoding foldable protein sequences establish an expanding specific interaction network as they get longer, including L23, another r-protein and chaperones. Interestingly, the ribosome alone (i.e., in the absence of chaperones) provides indiscriminate support to RNCs bearing up to ca. 190 residues, regardless of nascent-chain sequence and foldability. In all, this study highlights the unbiased features of the ribosome as a powerful nascent-protein interactor. Significance StatementThe presence of interactions between nascent chains bearing a foldable amino-acid sequence (with no signal or arrest tags) and specific ribosomal proteins has never been experimentally demonstrated, up to now. Here, we identify the ribosomal protein L23 as a specific nascent-chain interacting partner. We show that L23 establishes noncovalent contacts with nascent chains of the multi-domain foldable model protein apoHmp, which lacks signal/arrest sequences. Interactions with another ribosomal protein and with the trigger-factor and Hsp70 chaperones were also detected. Interestingly, ribosomal-protein/nascent-chain complexes have similar apparent stability, in the case of nascent chains of variable sequence and degree of foldability. These findings are significant because they advance our knowledge on ribosome-mediated nascent-protein interaction networks and suggest avenues to prevent undesirable aggregation.

biophysics↗

bioRxiv2022

A multi-omics analysis of glioma chemoresistance using a hybrid microphysiological model of glioblastoma

Chemoresistance is a major clinical challenge in the management of glioblastoma (GBM) Temozolomide (TMZ) is the chemotherapeutic drug of choice for GBM; however, the therapeutic effect of TMZ is limited due to the development of resistance. Recapitulating GBM chemoresistance in a controlled environment is thus essential in understanding the mechanism of chemoresistance. Herein, we present a hybrid microphysiological model of chemoresistant GBM-on-a-chip (HGoC) by directly co-culturing TMZ-resistant GBM spheroids with healthy neurons to mimic the microenvironment of both the tumor and the surrounding healthy tissue. We characterized the model with proteomics, lipidomics, and secretome assays. The results showed that our artificial model recapitulated the molecular signatures of recurrent GBM in humans. Both showed alterations in vesicular transport and cholesterol pathways, mitotic quiescence, and a switch in metabolism to oxidative phosphorylation associated with a transition from mesenchymal to amoeboid. This is the first report to unravel the interplay of all these molecular changes as a mechanism of chemoresistance in glioblastoma. Moreover, we have shown that the acquisition of resistance increases invasiveness and the presence of neurons decreases this property.

bioengineering↗

bioRxiv2022

Development and application of genetic ancestry reconstruction methods to study diversity of patient-derived models in the NCI PDXNet Consortium

Personalized medicine holds great promise for improving cancer outcomes, yet there is a large inequity in the demographics of patients from whom genomic data and models, including patient derived xenografts (PDX), are developed and for whom treatments are optimized. In this study we develop a genetic ancestry pipeline for the Cancer Genomics Cloud, which we use to assess the diversity of models currently available in the National Cancer Institute (NCI) supported PDX Development and Trial Centers Research Network (PDXNet). We show that there is an over-representation of models derived from patients of European ancestry, which is consistent with other cancer model resources. We discuss these findings in the context of disparities in cancer incidence and outcomes among demographic groups in the US. For example, for the top cancer health disparities affecting African Americans and Latinos, there is a significant lack of ethnic/race appropriate models needed to advance pre-clinical research and personalized clinical treatment. For stomach and liver tumors, which represent disparities in these two minority populations, there are only three available models derived from patients from such backgrounds. Fortunately, ongoing NCI-funded efforts in minority focused PDXNet centers are actively addressing these gaps. We further discuss these results in the context of power analyses to highlight the immediate need for the development of models from minority populations to address cancer health equity in personalized medicine.

cancer biology↗

bioRxiv2022

ACDA: Implementation of an Augmented Drug Synergy Prediction Algorithm

MotivationDrug synergy prediction is a complex problem typically approached with machine learning techniques using molecular data, pharmacological data, and knowledge of biological-interaction networks. The recently published Cancer Drug Atlas (CDA) uses a logistic regression model to predict a binary synergy outcome in cell-line models by utilizing drug target information, knowledge of genes mutated in each model, and the models monotherapy drug sensitivity. However, we observed low performance, 0.33, of the CDA measured by Pearson correlation of predicted versus measured sensitivity when we evaluated datasets from six studies that were not considered during the development of the CDA. Here we describe improvements to the CDA algorithm, the Augmented CDA, that improved performance by 71% and robustness to dataset variations in drug response values. ResultsWe augmented the drug-synergy prediction-modeling approach CDA described in Narayan et al. by applying a random forest regression and optimization via cross-validation hyper-parameter tuning. We benchmarked the performance of our Augmented CDA (ACDA) compared to the original CDA algorithm using datasets from DrugComb, an open-access drug-combination screening data resource. The ACDAs performance is 71% higher than that of the CDA when trained and validated on the same dataset spanning ten tissues. The ACDA performs marginally better (6% increase) than the CDA when trained on one dataset and validated on another dataset in 22 cases that cover seven tissues. We also compared the performance of ACDA to one of the winners of the DREAM Drug Combination Prediction Challenge (Mikhail Zaslavskiys algorithm which we denoted as EN). The performance of EN was smaller than that of the ACDA in 15 out of 19 cases. In addition to data from cell lines, we also trained the ACDA algorithm on Novartis Institutes for BioMedical Research PDX encyclopedia (NIBR PDXE) data and generated sensitivity predictions for the cases where drug-combination tumor-volume measurements were unavailable. Finally, we developed an approach to visualize synergy-prediction data using dendrograms and heatmaps instead of the Voronoi diagrams used in the CDA. The latter has a complex algorithmic realization and no publicly available implementation, whereas the ACDA visualization approach is more transparent and has open access. We implemented and wrapped the ACDA algorithm in an easy-to-use python package available from PyPI. AvailabilityThe source code is available at https://github.com/TheJacksonLaboratory/drug-synergy, and the software package can be installed directly from PyPI using pip. ContactAnuj.Srivastava@jax.org, Carol.Bult@jax.org

bioinformatics↗

bioRxiv2022

Correlating the differences in the receptor binding domain of SARS-CoV-2 spike variants on their interactions with human ACE2 receptor

Spike protein of SARS-CoV-2 variants play critical role in the infection and transmission through its interaction with hACE2 receptor. Prior findings using molecular docking and biomolecular studies reported varied findings on the difference in the interactions among the spike variants with hACE2 receptor. Hence, it is a prerequisite to understand these interactions in a more precise manner. To this end, firstly, we performed ELISA with trimeric spike proteins of Wild (Wuhan Hu-1), Delta, C.1.2 and Omicron variants. Further, to study the interactions in a more specific manner by mimicking the natural infection, we developed hACE2 receptor expressing HEK-293T cell line and evaluated binding efficiencies of the variants and competitive binding of spike variants with D614G spike pseudotyped virus. In lines with the existing findings, we observed that Omicron had higher binding efficiency compared to Delta in both ELISA and Cellular models. Intriguingly, we found that cellular models could differentiate the subtle differences between the closely related C.1.2 and Delta in their binding to hACE2. From the analysis in receptor binding domain (RBD) revealed that a single common modification, N501Y, present in both Omicron and C.1.2 is driving the enhanced spike binding to the receptor and showed two-fold superior competitive binding than Delta. Our study using cellular model provides a precise method to evaluate the binding interactions between spike sub-lineages to hACE2 receptors and signifies the role of single common modification N501Y in RBD towards imparting superior binding efficiencies. Our approach would be instrumental in understanding the disease progression and developing therapeutics. Author SummarySpike proteins of evolving SARS-CoV2 variants demonstrated their signature binding to hACE2 receptor, in turn contributed to driving the infection and transmission. Prior studies to scale the binding efficiencies between the spike variant and the receptor had consensus in distinct variants, but discrepancies in the closely related ones. To this end, we compared spike variants-receptor interactions with ELISA, from cells expressing hACE2 receptor. Intriguingly, we found that cellular models could differentiate the subtle differences between the closely related C.1.2 and Delta in their binding to hACE2. More importantly, competitive binding studies in presence of pseudovirus, demonstrated that a single common modification, N501Y, present in both Omicron and C.1.2 showed two fold superior competitive binding than Delta. Collectively, our study suggests a precise approach to evaluate the binding interactions between spike sub-lineages to hACE2 receptor. This would be instrumental in understanding the disease progression and developing therapeutics.

pharmacology and toxicology↗