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Behera S, Reddy RR, Taunk K, Rapole S, Pharande RR, Suryawanshi AR. Delineation of altered brain proteins associated with furious rabies virus infection in dogs by quantitative proteomics. J Proteomics 2021; 253:104463. [PMID: 34954397 DOI: 10.1016/j.jprot.2021.104463] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/17/2021] [Revised: 12/02/2021] [Accepted: 12/19/2021] [Indexed: 11/24/2022]
Abstract
Rabies is a fatal zoonotic disease caused by rabies virus (RABV). Despite the existence of control measures, dog-transmitted human rabies accounts for ˃95% reported cases due to unavailability of sensitive diagnostic methods, inadequate understanding of disease progression and absence of therapeutics. In addition, host factors and their role in RABV infection are poorly understood. In this study, we used 8-plex iTRAQ coupled with HRMS approach to identify differentially abundant proteins (DAPs) of dog brain associated with furious rabies virus infection. Total 40 DAPs including 26 down-regulated and 14 up-regulated proteins were statistically significant in infected samples. GO annotation and IPA showed that calcium signaling and calcium transport, efficient neuronal function, metabolic pathway associated proteins were mostly altered during this infection. Total 34 proteins including 10 down-regulated proteins pertaining to calcium signaling and calcium transport pathways were successfully verified by qRT-PCR and two proteins were verified by western blot, thereby suggesting these pathways may play an important role in this infection. This study provides the map of altered brain proteins and some insights into the molecular pathophysiology associated with furious rabies virus infection. However, further investigations are required to understand their role in disease mechanism. SIGNIFICANCE: Transmission of rabies by dogs poses the greatest hazard world-wide and the rare survival of post-symptomatic patients as well as severe neurological and immunological problems pose a question to understand the molecular mechanism involved in rabies pathogenesis. However, information regarding host factors and their function in RABV infection is still inadequate. Our study has used an advanced quantitative proteomics approach i.e. 8-plex iTRAQ coupled with HRMS and identified 40 DAPs in furious rabies infected dog brain tissues compared to the controls. Further analysis showed that calcium signaling and transport pathway, efficient neuronal functions and metabolic pathway associated brain proteins were most altered during furious rabies virus infection. This data provides a map of altered brain proteins which may have role in furious rabies virus infection. Hence, this will improve our understanding of the molecular pathogenesis of RABV infection.
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Affiliation(s)
- Suchismita Behera
- Clinical Proteomics, Institute of Life Sciences, Bhubaneswar, Odisha, India; Regional Centre for Biotechnology, Faridabad, India
| | - R Rajendra Reddy
- Clinical Proteomics, Institute of Life Sciences, Bhubaneswar, Odisha, India
| | - Khushman Taunk
- Proteomics Lab, National Centre for Cell Science, Pune, India
| | - Srikanth Rapole
- Proteomics Lab, National Centre for Cell Science, Pune, India
| | | | - Amol Ratnakar Suryawanshi
- Clinical Proteomics, Institute of Life Sciences, Bhubaneswar, Odisha, India; Regional Centre for Biotechnology, Faridabad, India.
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Shameer K, Naika MB, Shafi KM, Sowdhamini R. Decoding systems biology of plant stress for sustainable agriculture development and optimized food production. PROGRESS IN BIOPHYSICS AND MOLECULAR BIOLOGY 2019; 145:19-39. [DOI: 10.1016/j.pbiomolbio.2018.12.002] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/01/2017] [Revised: 10/23/2018] [Accepted: 12/06/2018] [Indexed: 12/13/2022]
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Shameer K, Nayarisseri A, Romero Duran FX, Gonzalez-Diaz H. Editorial: Improving Neuropharmacology using Big Data, Machine Learning and Computational Algorithms. Curr Neuropharmacol 2018; 15:1058-1061. [PMID: 29199918 PMCID: PMC5725537 DOI: 10.2174/1570159x1508171114113425] [Citation(s) in RCA: 19] [Impact Index Per Article: 3.2] [Reference Citation Analysis] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/22/2022] Open
Affiliation(s)
- Khader Shameer
- Institute of Next Generation Healthcare (INGH), Icahn Institute of Genomics and Multiscale Biology, Department of Genetics and Genomic Sciences, Mount Sinai Health System, Manhattan, NY, USA
| | - Anuraj Nayarisseri
- Bioinformatics Research Laboratory, Eminent Biosciences, Vijaynagar, Indore-452010, Madhya Pradesh, India.,In silico Research Laboratory, Legene Biosciences, Vijaynagar, Indore-452010, Madhya Pradesh, India
| | | | - Humberto Gonzalez-Diaz
- Department of Organic Chemistry II, University of Basque Country UPV/EHU, 48940, Leioa, Biscay, Spain.,IKERBASQUE, Basque Foundation for Science, 48011, Bilbao, Biscay, Spain
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Shameer K, Glicksberg BS, Hodos R, Johnson KW, Badgeley MA, Readhead B, Tomlinson MS, O’Connor T, Miotto R, Kidd BA, Chen R, Ma’ayan A, Dudley JT. Systematic analyses of drugs and disease indications in RepurposeDB reveal pharmacological, biological and epidemiological factors influencing drug repositioning. Brief Bioinform 2018; 19:656-678. [PMID: 28200013 PMCID: PMC6192146 DOI: 10.1093/bib/bbw136] [Citation(s) in RCA: 56] [Impact Index Per Article: 9.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/19/2016] [Revised: 11/29/2016] [Indexed: 12/22/2022] Open
Abstract
Increase in global population and growing disease burden due to the emergence of infectious diseases (Zika virus), multidrug-resistant pathogens, drug-resistant cancers (cisplatin-resistant ovarian cancer) and chronic diseases (arterial hypertension) necessitate effective therapies to improve health outcomes. However, the rapid increase in drug development cost demands innovative and sustainable drug discovery approaches. Drug repositioning, the discovery of new or improved therapies by reevaluation of approved or investigational compounds, solves a significant gap in the public health setting and improves the productivity of drug development. As the number of drug repurposing investigations increases, a new opportunity has emerged to understand factors driving drug repositioning through systematic analyses of drugs, drug targets and associated disease indications. However, such analyses have so far been hampered by the lack of a centralized knowledgebase, benchmarking data sets and reporting standards. To address these knowledge and clinical needs, here, we present RepurposeDB, a collection of repurposed drugs, drug targets and diseases, which was assembled, indexed and annotated from public data. RepurposeDB combines information on 253 drugs [small molecules (74.30%) and protein drugs (25.29%)] and 1125 diseases. Using RepurposeDB data, we identified pharmacological (chemical descriptors, physicochemical features and absorption, distribution, metabolism, excretion and toxicity properties), biological (protein domains, functional process, molecular mechanisms and pathway cross talks) and epidemiological (shared genetic architectures, disease comorbidities and clinical phenotype similarities) factors mediating drug repositioning. Collectively, RepurposeDB is developed as the reference database for drug repositioning investigations. The pharmacological, biological and epidemiological principles of drug repositioning identified from the meta-analyses could augment therapeutic development.
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Affiliation(s)
- Khader Shameer
- Institute of Next Generation Healthcare, Mount Sinai Health System, New York,
NY, USA
| | - Benjamin S Glicksberg
- Icahn School of Medicine at Mount Sinai, Mount Sinai Health System, New York,
NY, USA
| | - Rachel Hodos
- Icahn School of Medicine at Mount Sinai, Mount Sinai Health System, New York,
NY, USA
- New York University, New York, NY, USA
| | - Kipp W Johnson
- Icahn School of Medicine at Mount Sinai, Mount Sinai Health System, New York,
NY, USA
| | - Marcus A Badgeley
- Icahn School of Medicine at Mount Sinai, Mount Sinai Health System, New York,
NY, USA
| | - Ben Readhead
- Institute of Next Generation Healthcare, Mount Sinai Health System, New York,
NY, USA
| | - Max S Tomlinson
- Institute of Next Generation Healthcare, Mount Sinai Health System, New York,
NY, USA
| | | | - Riccardo Miotto
- Institute of Next Generation Healthcare, Mount Sinai Health System, New York,
NY, USA
| | - Brian A Kidd
- Institute of Next Generation Healthcare, Mount Sinai Health System, New York,
NY, USA
| | - Rong Chen
- Clinical Genome Informatics, Icahn Institute of Genetics and Multiscale
Biology, Mount Sinai Health System, New York, NY
| | - Avi Ma’ayan
- Mount Sinai Center for Bioinformatics, Mount Sinai Health System, New York,
NY
| | - Joel T Dudley
- Institute of Next Generation Healthcare, Mount Sinai Health System, New York,
NY, USA
- Department of Genetics and Genomic Sciences, Mount Sinai Health System, New
York, NY, USA
- Department of Population Health Science and Policy, Mount Sinai Health System,
New York, NY, USA
- Director of Biomedical Informatics, Icahn School of Medicine at Mount Sinai,
Mount Sinai Health System, New York, NY
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Baiesi M, Orlandini E, Trovato A, Seno F. Linking in domain-swapped protein dimers. Sci Rep 2016; 6:33872. [PMID: 27659606 PMCID: PMC5034241 DOI: 10.1038/srep33872] [Citation(s) in RCA: 27] [Impact Index Per Article: 3.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/15/2016] [Accepted: 09/05/2016] [Indexed: 11/24/2022] Open
Abstract
The presence of knots has been observed in a small fraction of single-domain proteins and related to their thermodynamic and kinetic properties. The exchanging of identical structural elements, typical of domain-swapped proteins, makes such dimers suitable candidates to validate the possibility that mutual entanglement between chains may play a similar role for protein complexes. We suggest that such entanglement is captured by the linking number. This represents, for two closed curves, the number of times that each curve winds around the other. We show that closing the curves is not necessary, as a novel parameter G', termed Gaussian entanglement, is strongly correlated with the linking number. Based on 110 non redundant domain-swapped dimers, our analysis evidences a high fraction of chains with a significant intertwining, that is with |G'| > 1. We report that Nature promotes configurations with negative mutual entanglement and surprisingly, it seems to suppress intertwining in long protein dimers. Supported by numerical simulations of dimer dissociation, our results provide a novel topology-based classification of protein-swapped dimers together with some preliminary evidence of its impact on their physical and biological properties.
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Affiliation(s)
- Marco Baiesi
- Department of Physics and Astronomy, University of Padova, Padova, Italy
- INFN-Sezione di Padova, Padova, Italy
| | - Enzo Orlandini
- Department of Physics and Astronomy, University of Padova, Padova, Italy
- INFN-Sezione di Padova, Padova, Italy
| | - Antonio Trovato
- Department of Physics and Astronomy, University of Padova, Padova, Italy
- CNISM-Unità di Padova, Padova, Italy
| | - Flavio Seno
- Department of Physics and Astronomy, University of Padova, Padova, Italy
- CNISM-Unità di Padova, Padova, Italy
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Upadhyay AK, Sowdhamini R. Genome-Wide Prediction and Analysis of 3D-Domain Swapped Proteins in the Human Genome from Sequence Information. PLoS One 2016; 11:e0159627. [PMID: 27467780 PMCID: PMC4965083 DOI: 10.1371/journal.pone.0159627] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/02/2015] [Accepted: 07/06/2016] [Indexed: 11/19/2022] Open
Abstract
3D-domain swapping is one of the mechanisms of protein oligomerization and the proteins exhibiting this phenomenon have many biological functions. These proteins, which undergo domain swapping, have acquired much attention owing to their involvement in human diseases, such as conformational diseases, amyloidosis, serpinopathies, proteionopathies etc. Early realisation of proteins in the whole human genome that retain tendency to domain swap will enable many aspects of disease control management. Predictive models were developed by using machine learning approaches with an average accuracy of 78% (85.6% of sensitivity, 87.5% of specificity and an MCC value of 0.72) to predict putative domain swapping in protein sequences. These models were applied to many complete genomes with special emphasis on the human genome. Nearly 44% of the protein sequences in the human genome were predicted positive for domain swapping. Enrichment analysis was performed on the positively predicted sequences from human genome for their domain distribution, disease association and functional importance based on Gene Ontology (GO). Enrichment analysis was also performed to infer a better understanding of the functional importance of these sequences. Finally, we developed hinge region prediction, in the given putative domain swapped sequence, by using important physicochemical properties of amino acids.
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Affiliation(s)
- Atul Kumar Upadhyay
- National Centre for Biological Sciences (TIFR), GKVK Campus, Bellary Road, Bangalore 560 065, India
| | - Ramanathan Sowdhamini
- National Centre for Biological Sciences (TIFR), GKVK Campus, Bellary Road, Bangalore 560 065, India
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Shameer K, Tripathi LP, Kalari KR, Dudley JT, Sowdhamini R. Interpreting functional effects of coding variants: challenges in proteome-scale prediction, annotation and assessment. Brief Bioinform 2015; 17:841-62. [PMID: 26494363 DOI: 10.1093/bib/bbv084] [Citation(s) in RCA: 17] [Impact Index Per Article: 1.9] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/14/2015] [Indexed: 12/20/2022] Open
Abstract
Accurate assessment of genetic variation in human DNA sequencing studies remains a nontrivial challenge in clinical genomics and genome informatics. Ascribing functional roles and/or clinical significances to single nucleotide variants identified from a next-generation sequencing study is an important step in genome interpretation. Experimental characterization of all the observed functional variants is yet impractical; thus, the prediction of functional and/or regulatory impacts of the various mutations using in silico approaches is an important step toward the identification of functionally significant or clinically actionable variants. The relationships between genotypes and the expressed phenotypes are multilayered and biologically complex; such relationships present numerous challenges and at the same time offer various opportunities for the design of in silico variant assessment strategies. Over the past decade, many bioinformatics algorithms have been developed to predict functional consequences of single nucleotide variants in the protein coding regions. In this review, we provide an overview of the bioinformatics resources for the prediction, annotation and visualization of coding single nucleotide variants. We discuss the currently available approaches and major challenges from the perspective of protein sequence, structure, function and interactions that require consideration when interpreting the impact of putatively functional variants. We also discuss the relevance of incorporating integrated workflows for predicting the biomedical impact of the functionally important variations encoded in a genome, exome or transcriptome. Finally, we propose a framework to classify variant assessment approaches and strategies for incorporation of variant assessment within electronic health records.
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Glicksberg BS, Li L, Cheng WY, Shameer K, Hakenberg J, Castellanos R, Ma M, Shi L, Shah H, Dudley JT, Chen R. An integrative pipeline for multi-modal discovery of disease relationships. PACIFIC SYMPOSIUM ON BIOCOMPUTING. PACIFIC SYMPOSIUM ON BIOCOMPUTING 2015; 20:407-18. [PMID: 25592600 PMCID: PMC4345399] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Subscribe] [Scholar Register] [Indexed: 06/04/2023]
Abstract
In the past decade there has been an explosion in genetic research that has resulted in the generation of enormous quantities of disease-related data. In the current study, we have compiled disease risk gene variant information and Electronic Medical Record (EMR) classification codes from various repositories for 305 diseases. Using such data, we developed a pipeline to test for clinical prevalence, gene-variant overlap, and literature presence for all 46,360 unique diseases pairs. To determine whether disease pairs were enriched we systematically employed both Fishers' Exact (medical and literature) and Term Frequency-Inverse Document Frequency (genetics) methodologies to test for enrichment, defining statistical significance at a Bonferonni adjusted threshold of (p < 1 × 10(-6)) and weighted q < 0.05 accordingly. We hypothesize that disease pairs that are statistically enriched in medical and genetic spheres, but not so in the literature have the potential to reveal non-obvious connections between clinically disparate phenotypes. Using this pipeline, we identified 2,316 disease pairs that were significantly enriched within an EMR and 213 enriched genetically. Of these, 65 disease pairs were statistically enriched in both, 19 of which are believed to be novel. These identified non-obvious relationships between disease pairs are suggestive of a shared underlying etiology with clinical presentation. Further investigation of uncovered disease-pair relationships has the potential to provide insights into the architecture of complex diseases, and update existing knowledge of risk factors.
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Affiliation(s)
- Benjamin S. Glicksberg
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, 1 Gustave L. Levy Pl. New York City, NY 10029, USA
- Department of Neuroscience, Icahn School of Medicine at Mount Sinai, 1 Gustave L. Levy Pl. New York City, NY 10029, USA
| | - Li Li
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, 1 Gustave L. Levy Pl. New York City, NY 10029, USA
| | - Wei-Yi Cheng
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, 1 Gustave L. Levy Pl. New York City, NY 10029, USA
| | - Khader Shameer
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, 1 Gustave L. Levy Pl. New York City, NY 10029, USA
| | - Jörg Hakenberg
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, 1 Gustave L. Levy Pl. New York City, NY 10029, USA
| | - Rafael Castellanos
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, 1 Gustave L. Levy Pl. New York City, NY 10029, USA
| | - Meng Ma
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, 1 Gustave L. Levy Pl. New York City, NY 10029, USA
| | - Lisong Shi
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, 1 Gustave L. Levy Pl. New York City, NY 10029, USA
| | - Hardik Shah
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, 1 Gustave L. Levy Pl. New York City, NY 10029, USA
| | - Joel T. Dudley
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, 1 Gustave L. Levy Pl. New York City, NY 10029, USA
- Department of Neuroscience, Icahn School of Medicine at Mount Sinai, 1 Gustave L. Levy Pl. New York City, NY 10029, USA
| | - Rong Chen
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, 1 Gustave L. Levy Pl. New York City, NY 10029, USA
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Shameer K, Naika MB, Mathew OK, Sowdhamini R. POEAS: Automated Plant Phenomic Analysis Using Plant Ontology. Bioinform Biol Insights 2014; 8:209-14. [PMID: 25574136 PMCID: PMC4274039 DOI: 10.4137/bbi.s19057] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/30/2014] [Revised: 10/03/2014] [Accepted: 10/07/2014] [Indexed: 11/05/2022] Open
Abstract
Biological enrichment analysis using gene ontology (GO) provides a global overview of the functional role of genes or proteins identified from large-scale genomic or proteomic experiments. Phenomic enrichment analysis of gene lists can provide an important layer of information as well as cellular components, molecular functions, and biological processes associated with gene lists. Plant phenomic enrichment analysis will be useful for performing new experiments to better understand plant systems and for the interpretation of gene or proteins identified from high-throughput experiments. Plant ontology (PO) is a compendium of terms to define the diverse phenotypic characteristics of plant species, including plant anatomy, morphology, and development stages. Adoption of this highly useful ontology is limited, when compared to GO, because of the lack of user-friendly tools that enable the use of PO for statistical enrichment analysis. To address this challenge, we introduce Plant Ontology Enrichment Analysis Server (POEAS) in the public domain. POEAS uses a simple list of genes as input data and performs enrichment analysis using Ontologizer 2.0 to provide results in two levels, enrichment results and visualization utilities, to generate ontological graphs that are of publication quality. POEAS also offers interactive options to identify user-defined background population sets, various multiple-testing correction methods, different enrichment calculation methods, and resampling tests to improve statistical significance. The availability of such a tool to perform phenomic enrichment analyses using plant genes as a complementary resource will permit the adoption of PO-based phenomic analysis as part of analytical workflows. POEAS can be accessed using the URL http://caps.ncbs.res.in/poeas.
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Affiliation(s)
- Khader Shameer
- National Centre for Biological Sciences (TIFR), GKVK Campus, Bangalore, India
| | - Mahantesha Bn Naika
- National Centre for Biological Sciences (TIFR), GKVK Campus, Bangalore, India. ; Department of Plant Biotechnology, University of Agricultural Sciences, GKVK Campus, Bangalore, India
| | - Oommen K Mathew
- National Centre for Biological Sciences (TIFR), GKVK Campus, Bangalore, India
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