Biology subjects

Lal, A.

Publications and source records attributed to Lal, A..

33 records · Page 2Linked to original sources

bioRxiv2021

Investigating the human host - ssRNA virus interaction landscape using the SMEAGOL toolbox

Viruses are intracellular parasites that need their host cell to reproduce. Consequently, they have evolved numerous mechanisms to exploit the molecular machinery of their host cells, including the broad spectrum of host RNA-binding proteins (RBPs). However, the RBP interactome of viral genomes and the consequences of these interactions for infection are still to be mapped for most RNA viruses. To facilitate these efforts we have developed SMEAGOL, a fast and user-friendly toolbox to analyze the enrichment or depletion of RBP binding motifs across RNA sequences (https://github.com/gruber-sciencelab/SMEAGOL). To shed light on the interaction landscape of RNA viruses with human host cell RBPs at a large scale, we applied SMEAGOL to 197 single-stranded RNA (ssRNA) viral genome sequences. We find that the majority of ssRNA virus genomes are significantly enriched or depleted in binding motifs for human RBPs, suggesting selection pressure on these interactions. Our analysis provides an overview of potential virus - RBP interactions, covering the majority of ssRNA viral genomes fully sequenced to date, and represents a rich resource for studying host interactions vital to the virulence of ssRNA viruses. Our resource and the SMEAGOL toolbox will support future studies of virus / host interactions, ultimately feeding into better treatments.

bioinformatics↗

bioRxiv2021

GEPSi: A Python Library to Simulate GWAS Phenotype Data

MotivationMany computational methods aim to identify genetic variants associated with diseases and complex traits. Due to the absence of ground truth data, simulated genotype and phenotype data is needed to benchmark these methods. However, phenotypes are frequently simulated as an additive function of randomly selected variants, neglecting biological complexity such as non-random occurrence of causal SNPs, epistatic effects, heritability and dominance. Including such features would improve benchmarking studies and accelerate the development of methods for genetic analysis. ResultsHere, we describe GEPSi (GWAS Epistatic Phenotype Simulator), a user-friendly python package to simulate phenotype data based on user-supplied genotype data for a population. GEPSi incorporates diverse biological parameters such as heritability, dominance, population stratification and epistatic interactions between SNPs. We demonstrate the use of this package to compare machine learning methods for GWAS analysis. Availability and ImplementationGEPSi is freely available under an Apache 2.0 license, and can be downloaded from https://github.com/clara-parabricks/GEPSi. Supplementary informationSupplementary data are available online.

genomics↗

bioRxiv2021

Improving long-read consensus sequencing accuracy with deep learning

The PacBio HiFi sequencing technology combines less accurate, multi-read passes from the same molecule (subreads) to yield consensus sequencing reads that are both long (averaging 10-25 kb) and highly accurate. However, these reads can retain residual sequencing error, predominantly insertions or deletions at homopolymeric regions. Here, we train deep learning models to polish HiFi reads by recognizing and correcting sequencing errors. We show that our models are effective at reducing these errors by 25-40% in HiFi reads from human as well as E. coli genomes.

genomics↗

bioRxiv2021

An Issue of Concern: Unique Truncated ORF8 Protein Variants of SARS-CoV-2

Open reading frame 8 (ORF8) protein is one of the most evolving accessory proteins in severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), the causative agent of coronavirus disease 2019 (COVID-19). It was previously reported that the ORF8 protein inhibits presentation of viral antigens by the major histocompatibility complex class I (MHC-I) and interacts with host factors involved in pulmonary inflammation. The ORF8 protein assists SARS-CoV-2 to evade immunity and replication. Among many contributing mutations, Q27STOP, a mutation in the ORF8 protein defines the B.1.1.7 lineage of SARS-CoV-2, which is engendering the second wave of COVID-19. In the present study, 47 unique truncated ORF8 proteins (T-ORF8) due to the Q27STOP mutations were identified among 49055 available B.1.1.7 SARS-CoV-2 sequences. The results show that only one of the 47 T-ORF8 variants spread to over 57 geo-locations in North America, and other continents which includes Africa, Asia, Europe and South America. Based on various quantitative features such as amino acid homology, polar/non-polar sequence homology, Shannon entropy conservation, and other physicochemical properties of all specific 47 T-ORF8 protein variants, a collection of nine possible T-ORF8 unique variants were defined. The question of whether T-ORF8 variants work similarly to ORF8 has yet to be investigated. A positive response to the question could exacerbate future COVID-19 waves, necessitating severe containment measures.

bioinformatics↗

bioRxiv2021

Implications Derived from S-Protein Variants of SARS-CoV-2 from Six Continents

Spike (S) proteins of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) are critical determinants of the infectivity and antigenicity of the virus. Several mutations in the spike protein of SARS-CoV-2 have already been detected, and their effect in immune system evasion and enhanced transmission as a cause of increased morbidity and mortality are being investigated. From pathogenic and epidemiological perspectives, spike proteins are of prime interest to researchers. This study focused on the unique variants of S proteins from six continents Asia, Africa, Europe, Oceania, South America, and North America. In comparison to the other five continents, Africa (29.065%) had the highest percentage of unique S proteins. Notably, only North America had 87% (14046) of the total (16143) specific S proteins available in the NCBI database(across all continents). Based on the amino acid frequency distributions in the S protein variants from all the continents, the phylogenetic relationship implies that unique S proteins from North America were significantly different from those of the other five continents. Overtime, the unique variants originating from North America are most likely to spread to the other geographic locations through international travel or naturally by emerging mutations. Hence it is suggested that restriction of international travel should be considered, and massive vaccination as an utmost measure to combat the spread of COVID-19 pandemic. It is also further suggested that the efficacy of existing vaccines and future vaccine development must be reviewed with careful scrutiny, and if needed, further re-engineered based on requirements dictated by new emerging S protein variants.

bioinformatics↗

bioRxiv2020

Variability of Accessory Proteins Rules the SARS-CoV-2 Pathogenicity

The coronavirus disease 2019 (COVID-19) is caused by the Severe Acute Respiratory Syndrome Coronavirus-2 (SARS-CoV-2) which is pandemic with an estimated fatality rate less than 1% is ongoing. SARS-CoV-2 accessory proteins ORF3a, ORF6, ORF7a, ORF7b, ORF8, and ORF10 with putative functions to manipulate host immune mechanisms such as interferons, immune signaling receptor NLRP3 (NOD-, LRR-, and pyrin domain-containing 3) inflammasome, inflammatory cytokines such as interleukin 1{beta} (IL-1{beta}) are critical in COVID-19 pathology. Outspread variations of each of the six accessory proteins of all complete proteomes (available as of October 26, 2020, in the National Center for Biotechnology Information depository) of SARS-CoV-2, were observed across six continents. Across all continents, the decreasing order of percentage of unique variations in the accessory proteins was found to be ORF3a>ORF8>ORF7a>ORF6>ORF10>ORF7b. The highest and lowest unique variations of ORF3a were observed in South America and Oceania, respectively. This finding suggests that the wide variations of accessory proteins seem to govern the pathogenicity of SARS-CoV-2, and consequently, certain propositions and recommendations can be made in the public interest.

bioinformatics↗

bioRxiv2020

Notable sequence homology of the ORF10 protein introspects the architecture of SARS-COV-2

The global public health is endangered due to COVID-19 pandemic, which is caused by Severe Acute Respiratory Syndrome Coronavirus-2 (SARS-CoV-2). Despite having similar pathology to MERS and SARS-CoV, the infection fatality rate of SARS-CoV-2 is likely lower than 1%. SARS-CoV-2 has been reported to be uniquely characterized by the accessory protein ORF10, which contains eleven cytotoxic T lymphocyte (CTL) epitopes of nine amino acids length each, across various human leukocyte antigen (HLA) subtypes. In this study, all missense mutations found in sequence databases were examined across twnety-two unique SARS-CoV-2 ORF10 variants that could possibly alter viral pathogenicity. Some of these mutations decrease the stability of ORF10, e.g. I4L and V6I were found in the MoRF region of ORF10 which may also possibly contribute to Intrinsic protein disorder. Furthermore, a physicochemical and structural comparative analysis was carried out on SARS-CoV-2 and Pangolin-CoV ORF10 proteins, which share 97.37% amino acid homology. The high degree of physicochemical and structural similarity of ORF10 proteins of SARS-CoV-2 and Pangolin-CoV open questions about the architecture of SARS-CoV-2 due to the disagreement of these two ORF10 proteins over their sub-structure (loop/coil region), solubility, antigenicity and change from the strand to coil at amino acid position 26, where tyrosine is present. Altogether, SARS-CoV-2 ORF10 is a promising pharmaceutical target and a protein which should be monitored for changes which correlate to change pathogenesis and clinical course of COVID-19 infection.

bioinformatics↗

bioRxiv2020

A unique view of SARS-CoV-2 through the lens of ORF8 protein

Immune evasion is one of the unique characteristics of COVID-19 attributed to the ORF8 protein of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). This protein is involved in modulating the host adaptive immunity through downregulating MHC (Major Histocompatibility Complex) molecules and innate immune responses by surpassing the interferon mediated antiviral response of the host. To understand the immune perspective of the host with respect to the ORF8 protein, a comprehensive study of the ORF8 protein as well as mutations possessed by it, is performed. Chemical and structural properties of ORF8 proteins from different hosts, that is human, bat and pangolin, suggests that the ORF8 of SARS-CoV-2 and Bat RaTG13-CoV are very much closer related than that of Pangolin-CoV. Eighty-seven mutations across unique variants of ORF8 (SARS-CoV-2) are grouped into four classes based on their predicted effects. Based on geolocations and timescale of collection, a possible flow of mutations was built. Furthermore, conclusive flows of amalgamation of mutations were endorsed upon sequence similarity and amino acid conservation phylogenies. Therefore, this study seeks to highlight the uniqueness of rapid evolving SARS-CoV-2 through the ORF8.

molecular biology↗

bioRxiv2020

Genome-wide bioinformatic analyses predict key host and viral factors in SARS-CoV-2 pathogenesis

The novel betacoronavirus named Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) caused a worldwide pandemic (COVID-19) after initially emerging in Wuhan, China. Here we applied a novel, comprehensive bioinformatic strategy to public RNA sequencing and viral genome sequencing data, to better understand how SARS-CoV-2 interacts with human cells. To our knowledge, this is the first meta-analysis to predict host factors that play a specific role in SARS-CoV-2 pathogenesis, distinct from other respiratory viruses. We identified differentially expressed genes, isoforms and transposable element families specifically altered in SARS-CoV-2 infected cells. Well-known immunoregulators including CSF2, IL-32, IL-6 and SERPINA3 were differentially expressed, while immunoregulatory transposable element families were overexpressed. We predicted conserved interactions between the SARS-CoV-2 genome and human RNA-binding proteins such as hnRNPA1, PABPC1 and eIF4b, which may play important roles in the viral life cycle. We also detected four viral sequence variants in the spike, polymerase, and nonstructural proteins that correlate with severity of COVID-19. The host factors we identified likely represent important mechanisms in the disease profile of this pathogen, and could be targeted by prophylactics and/or therapeutics against SARS-CoV-2. Graphical Abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=127 SRC="FIGDIR/small/225581v3_ufig1.gif" ALT="Figure 1"> View larger version (26K): org.highwire.dtl.DTLVardef@15ca5a6org.highwire.dtl.DTLVardef@17f455forg.highwire.dtl.DTLVardef@a39f50org.highwire.dtl.DTLVardef@306ec1_HPS_FORMAT_FIGEXP M_FIG C_FIG

bioinformatics↗

bioRxiv2020

FLAME: Macroscopic imaging with microscopic resolution. Optical biopsy of human skin

We introduce a compact, fast large area multiphoton exoscope (FLAME) system with enhanced molecular contrast for macroscopic imaging of human skin with microscopic resolution. A versatile imaging platform with multiple modes of operation for comprehensive analysis of live or resected thick human skin tissue, it produces 3D images that encompass sub-mm2 to cm2 scale areas of tissue within minutes. The FLAME imaging platform, which expands on a design recently introduced by our group, features deep learning, additional scanning hardware elements and time-resolved single photon counting detection to uniquely allow fast discrimination and 3D virtual staining of melanin. We demonstrate its performance and utility by fast ex vivo and in vivo imaging of human skin. With the ability to provide rapid access to depth resolved images of skin over cm2 area and to generate 3D distribution maps of key sub-cellular skin components such as melanocytic dendrites and melanin, FLAME represents a promising imaging tool for enhancing diagnosis accuracy, guiding therapy and understanding skin biology.

bioengineering↗

bioRxiv2019

AtacWorks: A deep convolutional neural network toolkit for epigenomics

We introduce AtacWorks (https://github.com/clara-genomics/AtacWorks), a method to denoise and identify accessible chromatin regions from low-coverage or low-quality ATAC-seq data. AtacWorks uses a deep neural network to learn a mapping between noisy ATAC-seq data and corresponding higher-coverage or higher-quality data. To demonstrate the utility of AtacWorks, we train a model on data from four human blood cell types and show that this model accurately denoises chromatin accessibility at base-pair resolution and identifies peaks from low-coverage bulk sequencing of unseen cell types and experimental conditions. We use the same framework to obtain high-quality results from as few as 50 aggregate single-cell ATAC-seq profiles, and also from data with a low signal-to-noise ratio. We further show that AtacWorks can be adapted for cross-modality prediction of transcription factor footprints and ChIP-seq peaks from low input ATAC-seq. Finally, we demonstrate the applications of our approach to single-cell genomics by using AtacWorks to identify regulatory regions that are differentially-accessible between rare lineage-primed subpopulations of hematopoietic stem cells.

genomics↗

bioRxiv2018

Comprehensive Genomic Characterization of Breast Tumors with BRCA1 and BRCA2 Mutations

BackgroundGermline mutations in the BRCA1 and BRCA2 genes predispose carriers to breast and ovarian cancer, and there remains a need to identify the specific genomic mechanisms by which cancer evolves in these patients. Here we present a systematic genomic analysis of breast tumors with BRCA1 and BRCA2 mutations.\n\nMethodsWe analyzed genomic data from breast tumors, with a focus on comparing tumors with BRCA1/BRCA2 gene mutations with common classes of sporadic breast tumors.\n\nResultsWe identify differences between BRCA-mutated and sporadic breast tumors in patterns of point mutation, DNA methylation and structural variation. We show that structural variation disproportionately affects tumor suppressor genes and identify specific driver gene candidates that are enriched for structural variation.\n\nConclusionsCompared to sporadic tumors, BRCA-mutated breast tumors show signals of reduced DNA methylation, more ancestral cell divisions, and elevated rates of structural variation that tend to disrupt highly expressed protein-coding genes and known tumor suppressors. Our analysis suggests that BRCA-mutated tumors are more aggressive than sporadic breast cancers because loss of the BRCA pathway causes multiple processes of mutagenesis and gene dysregulation.

cancer biology↗

bioRxiv2018

De Novo Mutational Signature Discovery in Tumor Genomes using SparseSignatures

Cancer is the result of mutagenic processes that can be inferred from tumor genomes by analyzing rate spectra of point mutations, or "mutational signatures". Here we present SparseSignatures, a novel framework to extract signatures from somatic point mutation data. Our approach incorporates a user-specified background signature, employs regularization to reduce noise in non-background signatures, uses cross-validation to identify the number of signatures, and is scalable to large datasets. We show that SparseSignatures outperforms current state-of-the-art methods on simulated data using a variety of standard metrics. We then apply SparseSignatures to whole genome sequences of pancreatic and breast tumors, discovering well-differentiated signatures that are linked to known mutagenic mechanisms and are strongly associated with patient clinical features. Authors SummaryCancer is a genetic disease, occurring as a result of mutagenic processes causing DNA somatic mutations in genes controlling cellular growth and division. These somatic mutations arise from processes such as defective DNA repair and environmental mutagens, which massively increase the rate of somatic variants. As a result, due to the specificity of molecular lesions caused by such processes, and the specific repair mechanisms deployed by the cell to mitigate the damage, mutagenic processes generate characteristic point mutation rate spectra which are called mutational signatures. These signatures can indicate which mutagenic processes are active in a tumor, reveal biological differences between cancer subtypes, and may be useful markers for therapeutic response. Here, we develop SparseSignatures, a novel framework for mutational signature discovery capable of both identifying the active signatures in a dataset of point mutations and calculating their exposure values, i.e., the number of mutations originating from each signature in each patient. We show that our approach outperforms current state-of-the-art methods on simulated data using a variety of standard metrics and then apply SparseSignatures to whole genome sequences of pancreatic and breast tumors, discovering well-differentiated signatures that are linked to known mutagenic mechanisms.

bioinformatics↗

bioRxiv2018

Multi-omic tumor data reveal diversity of molecular mechanisms underlying survival

Outcomes for cancer patients vary greatly even within the same tumor type, and characterization of molecular subtypes of cancer holds important promise for improving prognosis and personalized treatment. This promise has motivated recent efforts to produce large amounts of multidimensional genomic ( multi-omic) data, but current algorithms still face challenges in the integrated analysis of such data. Here we present Cancer Integration via Multikernel Learning (CIMLR), a new cancer subtyping method that integrates multi-omic data to reveal molecular subtypes of cancer. We apply CIMLR to multi-omic data from 36 cancer types and show significant improvements in both computational efficiency and ability to extract biologically meaningful cancer subtypes. The discovered subtypes exhibit significant differences in patient survival for 27 of 36 cancer types. Our analysis reveals integrated patterns of gene expression, methylation, point mutations and copy number changes in multiple cancers and highlights patterns specifically associated with poor patient outcomes.

cancer biology↗

bioRxiv2018

The proteome of the malaria plastid organelle, a key anti-parasitic target

Malaria parasites (Plasmodium spp.) and related apicomplexan pathogens contain a non-photosynthetic plastid called the apicoplast. Derived from an unusual secondary eukaryote-eukaryote endosymbiosis, the apicoplast is a fascinating organelle whose function and biogenesis rely on a complex amalgamation of bacterial and algal pathways. Because these pathways are distinct from the human host, the apicoplast is an excellent source of novel antimalarial targets. Despite its biomedical importance and evolutionary significance, the absence of a reliable apicoplast proteome has limited most studies to the handful of pathways identified by homology to bacteria or primary chloroplasts, precluding our ability to study the most novel apicoplast pathways. Here we combine proximity biotinylation-based proteomics (BioID) and a new machine learning algorithm to generate a high-confidence apicoplast proteome consisting of 346 proteins. Critically, the high accuracy of this proteome significantly outperforms previous prediction-based methods and extends beyond other BioID studies of unique parasite compartments. Half of identified proteins have unknown function, and 77% are predicted to be important for normal blood-stage growth. We validate the apicoplast localization of a subset of novel proteins and show that an ATP-binding cassette protein ABCF1 is essential for blood-stage survival and plays a previously unknown role in apicoplast biogenesis. These findings indicate critical organellar functions for newly discovered apicoplast proteins. The apicoplast proteome will be an important resource for elucidating unique pathways derived from secondary endosymbiosis and prioritizing antimalarial drug targets.

microbiology↗