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Doostparast Torshizi A, Duan J, Wang K. A computational method for direct imputation of cell type-specific expression profiles and cellular compositions from bulk-tissue RNA-Seq in brain disorders. NAR Genom Bioinform 2021; 3:lqab056. [PMID: 34169279 PMCID: PMC8219045 DOI: 10.1093/nargab/lqab056] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/30/2020] [Revised: 05/24/2021] [Accepted: 06/21/2021] [Indexed: 02/06/2023] Open
Abstract
The importance of cell type-specific gene expression in disease-relevant tissues is increasingly recognized in genetic studies of complex diseases. However, most gene expression studies are conducted on bulk tissues, without examining cell type-specific expression profiles. Several computational methods are available for cell type deconvolution (i.e. inference of cellular composition) from bulk RNA-Seq data, but few of them impute cell type-specific expression profiles. We hypothesize that with external prior information such as single cell RNA-seq and population-wide expression profiles, it can be computationally tractable to estimate both cellular composition and cell type-specific expression from bulk RNA-Seq data. Here we introduce CellR, which addresses cross-individual gene expression variations to adjust the weights of cell-specific gene markers. It then transforms the deconvolution problem into a linear programming model while taking into account inter/intra cellular correlations and uses a multi-variate stochastic search algorithm to estimate the cell type-specific expression profiles. Analyses on several complex diseases such as schizophrenia, Alzheimer’s disease, Huntington’s disease and type 2 diabetes validated the efficiency of CellR, while revealing how specific cell types contribute to different diseases. In summary, CellR compares favorably against competing approaches, enabling cell type-specific re-analysis of gene expression data on bulk tissues in complex diseases.
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Affiliation(s)
- Abolfazl Doostparast Torshizi
- Raymond G. Perelman Center for Cellular and Molecular Therapeutics, Children's Hospital of Philadelphia, Philadelphia, PA 19104, USA
| | - Jubao Duan
- Center for Psychiatric Genetics, NorthShore University HealthSystem, Evanston, IL 60201, USA
| | - Kai Wang
- Raymond G. Perelman Center for Cellular and Molecular Therapeutics, Children's Hospital of Philadelphia, Philadelphia, PA 19104, USA
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Huang L, Fang L, Liu Q, Torshizi AD, Wang K. Integrated analysis on transcriptome and behaviors defines HTT repeat-dependent network modules in Huntington's disease. Genes Dis 2021; 9:479-493. [PMID: 35224162 PMCID: PMC8843892 DOI: 10.1016/j.gendis.2021.05.004] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/21/2021] [Revised: 04/13/2021] [Accepted: 05/12/2021] [Indexed: 01/02/2023] Open
Abstract
Huntington's disease (HD) is caused by a CAG repeat expansion in the huntingtin (HTT) gene. Knock-in mice carrying a CAG repeat-expanded Htt will develop HD phenotypes. Previous studies suggested dysregulated molecular networks in a CAG length genotype- and the age-dependent manner in brain tissues from knock-in mice carrying expanded Htt CAG repeats. Furthermore, a large-scale phenome analysis defined a behavioral signature for HD genotype in knock-in mice carrying expanded Htt CAG repeats. However, an integrated analysis correlating phenotype features with genotypes (CAG repeat expansions) was not conducted previously. In this study, we revealed the landscape of the behavioral features and gene expression correlations based on 445 mRNA samples and 445 microRNA samples, together with behavioral features (396 PhenoCube behaviors and 111 NeuroCube behaviors) in Htt CAG-knock-in mice. We identified 37 behavioral features that were significantly associated with CAG repeat length including the number of steps and hind limb stand duration. The behavioral features were associated with several gene coexpression groups involved in neuronal dysfunctions, which were also supported by the single-cell RNA sequencing data in the striatum and the spatial gene expression in the brain. We also identified 15 chemicals with significant responses for genes with enriched behavioral features, most of them are agonist or antagonist for dopamine receptors and serotonin receptors used for neurology/psychiatry. Our study provides further evidence that abnormal neuronal signal transduction in the striatum plays an important role in causing HD-related phenotypic behaviors and provided rich information for the further pharmacotherapeutic intervention possibility for HD.
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Affiliation(s)
- Lulin Huang
- The Key Laboratory for Human Disease Gene Study of Sichuan Province, Department of Clinical Laboratory, Sichuan Provincial People's Hospital, School of Medicine, University of Electronic Science and Technology of China, Chengdu, Sichuan 610054, PR China
- Raymond G. Perelman Center for Cellular and Molecular Therapeutics, Children's Hospital of Philadelphia, Philadelphia, PA 19104, USA
- Corresponding author. The Key Laboratory for Human Disease Gene Study of Sichuan Province, Department of Clinical Laboratory, Sichuan Provincial People's Hospital, School of Medicine, University of Electronic Science and Technology of China, Chengdu, Sichuan 610054, PR China.
| | - Li Fang
- Raymond G. Perelman Center for Cellular and Molecular Therapeutics, Children's Hospital of Philadelphia, Philadelphia, PA 19104, USA
| | - Qian Liu
- Raymond G. Perelman Center for Cellular and Molecular Therapeutics, Children's Hospital of Philadelphia, Philadelphia, PA 19104, USA
| | - Abolfazl Doostparast Torshizi
- Raymond G. Perelman Center for Cellular and Molecular Therapeutics, Children's Hospital of Philadelphia, Philadelphia, PA 19104, USA
| | - Kai Wang
- Raymond G. Perelman Center for Cellular and Molecular Therapeutics, Children's Hospital of Philadelphia, Philadelphia, PA 19104, USA
- Corresponding author.
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Doostparast Torshizi A, Ionita-Laza I, Wang K. Cell Type-Specific Annotation and Fine Mapping of Variants Associated With Brain Disorders. Front Genet 2020; 11:575928. [PMID: 33343624 PMCID: PMC7744805 DOI: 10.3389/fgene.2020.575928] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/24/2020] [Accepted: 11/05/2020] [Indexed: 12/19/2022] Open
Abstract
Common genetic variants confer susceptibility to a large number of complex brain disorders. Given that such variants predominantly localize in non-coding regions of the human genome, there is a significant challenge to predict and characterize their functional consequences. More importantly, most available computational methods, generally defined as context-free methods, output prediction scores regarding the functionality of genetic variants irrespective of the context, i.e., the tissue or cell-type affected by a disease, limiting the ability to predict the functional consequences of common variants on brain disorders. In this study, we introduce a comparative multi-step pipeline to investigate the relative effectiveness of context-specific and context-free approaches to prioritize disease causal variants. As an experimental case, we focused on schizophrenia (SCZ), a debilitating neuropsychiatric disease for which a large number of susceptibility variants is identified from genome-wide association studies. We tested over two dozen available methods and examined potential associations between the cell/tissue-specific mapping scores and open chromatin accessibility, and provided a prioritized map of SCZ risk loci for in vitro or in-vivo functional analysis. We found extensive differences between context-free and tissue-specific approaches and showed how they may play complementary roles. As a proof of concept, we found a few sets of genes, through a consensus mapping of both categories, including FURIN to be among the top hits. We showed that the genetic variants in this gene and related genes collectively dysregulate gene expression patterns in stem cell-derived neurons and characterize SCZ phenotypic manifestations, while genes which were not shared among highly prioritized candidates in both approaches did not demonstrate such characteristics. In conclusion, by combining context-free and tissue-specific predictions, our pipeline enables prioritization of the most likely disease-causal common variants in complex brain disorders.
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Affiliation(s)
- Abolfazl Doostparast Torshizi
- Raymond G. Perelman Center for Cellular and Molecular Therapeutics, Children's Hospital of Philadelphia, Philadelphia, PA, United States
| | - Iuliana Ionita-Laza
- Department of Biostatistics, Columbia University, New York, NY, United States
| | - Kai Wang
- Raymond G. Perelman Center for Cellular and Molecular Therapeutics, Children's Hospital of Philadelphia, Philadelphia, PA, United States.,Department of Pathology and Laboratory Medicine, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, United States
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Doostparast Torshizi A, Duan J, Wang K. Cell-Type-Specific Proteogenomic Signal Diffusion for Integrating Multi-Omics Data Predicts Novel Schizophrenia Risk Genes. Patterns (N Y) 2020; 1. [PMID: 32984858 PMCID: PMC7518509 DOI: 10.1016/j.patter.2020.100091] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 12/25/2022]
Abstract
Accumulation of diverse types of omics data on schizophrenia (SCZ) requires a systems approach to model the interplay between genome, transcriptome, and proteome. We introduce Markov affinity-based proteogenomic signal diffusion (MAPSD), a method to model intra-cellular protein trafficking paradigms and tissue-wise single-cell protein abundances. MAPSD integrates multi-omics data to amplify the signals at SCZ risk loci with small effect sizes, and reveal convergent disease-associated gene modules in the brain. We predicted a set of high-confidence SCZ risk loci followed by characterizing the subcellular localization of proteins encoded by candidate SCZ risk genes, and illustrated that most are enriched in neuronal cells in the cerebral cortex as well as Purkinje cells in the cerebellum. We demonstrated how the identified genes may be involved in neurodevelopment, how they may alter SCZ-related biological pathways, and how they facilitate drug repurposing. MAPSD is applicable in other polygenic diseases and can facilitate our understanding of disease mechanisms.
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Affiliation(s)
- Abolfazl Doostparast Torshizi
- Raymond G. Perelman Center for Cellular and Molecular Therapeutics, Children's Hospital of Philadelphia, Philadelphia, PA 19104, USA
| | - Jubao Duan
- Center for Psychiatric Genetics, North Shore University Health System, Evanston, IL 60201, USA
- Department of Psychiatry and Behavioral Neurosciences, University of Chicago, Chicago, IL 60637, USA
| | - Kai Wang
- Raymond G. Perelman Center for Cellular and Molecular Therapeutics, Children's Hospital of Philadelphia, Philadelphia, PA 19104, USA
- Department of Pathology and Laboratory Medicine, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA
- Corresponding author
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Borgmann-Winter KE, Wang K, Bandyopadhyay S, Torshizi AD, Hahn CG, Hahn CG. The proteome and its dynamics: A missing piece for integrative multi-omics in schizophrenia. Schizophr Res 2020; 217:148-161. [PMID: 31416743 PMCID: PMC7500806 DOI: 10.1016/j.schres.2019.07.025] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/09/2019] [Revised: 07/10/2019] [Accepted: 07/13/2019] [Indexed: 01/08/2023]
Abstract
The complex and heterogeneous pathophysiology of schizophrenia can be deconstructed by integration of large-scale datasets encompassing genes through behavioral phenotypes. Genome-wide datasets are now available for genetic, epigenetic and transcriptomic variations in schizophrenia, which are then analyzed by newly devised systems biology algorithms. A missing piece, however, is the inclusion of information on the proteome and its dynamics in schizophrenia. Proteomics has lagged behind omics of the genome, transcriptome and epigenome since analytic platforms were relatively less robust for proteins. There has been remarkable progress, however, in the instrumentation of liquid chromatography (LC) and mass spectrometry (MS) (LCMS), experimental paradigms and bioinformatics of the proteome. Here, we present a summary of methodological innovations of recent years in MS based proteomics and the power of new generation proteomics, review proteomics studies that have been conducted in schizophrenia to date, and propose how such data can be analyzed and integrated with other omics results. The function of a protein is determined by multiple molecular properties, i.e., subcellular localization, posttranslational modification (PTMs) and protein-protein interactions (PPIs). Incorporation of these properties poses additional challenges in proteomics and their integration with other omics; yet is a critical next step to close the loop of multi-omics integration. In sum, the recent advent of high-throughput proteome characterization technologies and novel mathematical approaches enable us to incorporate functional properties of the proteome to offer a comprehensive multi-omics based understanding of schizophrenia pathophysiology.
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Affiliation(s)
- Karin E. Borgmann-Winter
- Department of Psychiatry, University of Pennsylvania, Philadelphia, PA 19104-3403,Department of Child and Adolescent Psychiatry and Behavioral Sciences, Children’s Hospital of Philadelphia, Philadelphia, PA 19104
| | - Kai Wang
- Department of Pathology and Laboratory Medicine, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104,Raymond G. Perelman Center for Cellular and Molecular Therapeutics, Children’s Hospital of Philadelphia, Philadelphia, PA 19104
| | | | - Abolfazl Doostparast Torshizi
- Raymond G. Perelman Center for Cellular and Molecular Therapeutics, Children’s Hospital of Philadelphia, Philadelphia, PA 19104
| | - Chang-Gyu Hahn
- Department of Psychiatry, University of Pennsylvania, Philadelphia, PA 19104-3403, United States of America.
| | - Chang-Gyu Hahn
- Department of Psychiatry, University of Pennsylvania, Philadelphia, PA 19104-3403, United States of America.
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Doostparast Torshizi A, Armoskus C, Zhang H, Forrest MP, Zhang S, Souaiaia T, Evgrafov OV, Knowles JA, Duan J, Wang K. Deconvolution of transcriptional networks identifies TCF4 as a master regulator in schizophrenia. Sci Adv 2019; 5:eaau4139. [PMID: 31535015 PMCID: PMC6739105 DOI: 10.1126/sciadv.aau4139] [Citation(s) in RCA: 36] [Impact Index Per Article: 7.2] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 06/07/2018] [Accepted: 08/08/2019] [Indexed: 06/10/2023]
Abstract
Applying tissue-specific deconvolution of transcriptional networks to identify their master regulators (MRs) in neuropsychiatric disorders has been largely unexplored. Here, using two schizophrenia (SCZ) case-control RNA-seq datasets, one on postmortem dorsolateral prefrontal cortex (DLPFC) and another on cultured olfactory neuroepithelium, we deconvolved the transcriptional networks and identified TCF4 as a top candidate MR that may be dysregulated in SCZ. We validated TCF4 as a MR through enrichment analysis of TCF4-binding sites in induced pluripotent stem cell (hiPSC)-derived neurons and in neuroblastoma cells. We further validated the predicted TCF4 targets by knocking down TCF4 in hiPSC-derived neural progenitor cells (NPCs) and glutamatergic neurons (Glut_Ns). The perturbed TCF4 gene network in NPCs was more enriched for pathways involved in neuronal activity and SCZ-associated risk genes, compared to Glut_Ns. Our results suggest that TCF4 may serve as a MR of a gene network dysregulated in SCZ at early stages of neurodevelopment.
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Affiliation(s)
- Abolfazl Doostparast Torshizi
- Raymond G. Perelman Center for Cellular and Molecular Therapeutics, Children’s Hospital of Philadelphia, Philadelphia, PA 19104, USA
- Department of Pathology and Laboratory Medicine, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Chris Armoskus
- College of Medicine, SUNY Downstate Medical Center, Brooklyn, NY 11203, USA
- Zilkhe Neurogenetic Institute, University of Southern California, Los Angeles, CA 90089, USA
| | - Hanwen Zhang
- Center for Psychiatric Genetics, North Shore University Health System, Evanston, IL 60201, USA
| | - Marc P. Forrest
- Department of Physiology, Feinberg School of Medicine, Northwestern University, Chicago, IL 60611, USA
- Center for Autism and Neurodevelopment, Feinberg School of Medicine, Northwestern University, Chicago, IL 60611, USA
| | - Siwei Zhang
- Center for Psychiatric Genetics, North Shore University Health System, Evanston, IL 60201, USA
- Department of Psychiatry and Behavioral Neurosciences, University of Chicago, Chicago, IL 60015, USA
| | - Tade Souaiaia
- College of Medicine, SUNY Downstate Medical Center, Brooklyn, NY 11203, USA
- Zilkhe Neurogenetic Institute, University of Southern California, Los Angeles, CA 90089, USA
| | - Oleg V. Evgrafov
- College of Medicine, SUNY Downstate Medical Center, Brooklyn, NY 11203, USA
- Zilkhe Neurogenetic Institute, University of Southern California, Los Angeles, CA 90089, USA
| | - James A. Knowles
- College of Medicine, SUNY Downstate Medical Center, Brooklyn, NY 11203, USA
- Zilkhe Neurogenetic Institute, University of Southern California, Los Angeles, CA 90089, USA
| | - Jubao Duan
- Center for Psychiatric Genetics, North Shore University Health System, Evanston, IL 60201, USA
- Department of Psychiatry and Behavioral Neurosciences, University of Chicago, Chicago, IL 60015, USA
| | - Kai Wang
- Raymond G. Perelman Center for Cellular and Molecular Therapeutics, Children’s Hospital of Philadelphia, Philadelphia, PA 19104, USA
- Department of Pathology and Laboratory Medicine, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA
- Zilkhe Neurogenetic Institute, University of Southern California, Los Angeles, CA 90089, USA
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Huang G, Chen S, Chen X, Zheng J, Xu Z, Doostparast Torshizi A, Gong S, Chen Q, Ma X, Yu J, Zhou L, Qiu S, Wang K, Shi L. Uncovering the Functional Link Between SHANK3 Deletions and Deficiency in Neurodevelopment Using iPSC-Derived Human Neurons. Front Neuroanat 2019; 13:23. [PMID: 30918484 PMCID: PMC6424902 DOI: 10.3389/fnana.2019.00023] [Citation(s) in RCA: 27] [Impact Index Per Article: 5.4] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/01/2018] [Accepted: 02/07/2019] [Indexed: 11/13/2022] Open
Abstract
SHANK3 mutations, including de novo deletions, have been associated with autism spectrum disorders (ASD). However, the effects of SHANK3 loss of function on neurodevelopment remain poorly understood. Here we generated human induced pluripotent stem cells (iPSC) in vitro, followed by neuro-differentiation and lentivirus-mediated shRNA expression to evaluate how SHANK3 knockdown affects the in vitro neurodevelopmental process at multiple time points (up to 4 weeks). We found that SHANK3 knockdown impaired both early stage of neuronal development and mature neuronal function, as demonstrated by a reduction in neuronal soma size, growth cone area, neurite length and branch numbers. Notably, electrophysiology analyses showed defects in excitatory and inhibitory synaptic transmission. Furthermore, transcriptome analyses revealed that multiple biological pathways related to neuron projection, motility and regulation of neurogenesis were disrupted in cells with SHANK3 knockdown. In conclusion, utilizing a human iPSC-based neural induction model, this study presented combined morphological, electrophysiological and transcription evidence that support that SHANK3 as an intrinsic, cell autonomous factor that controls cellular function development in human neurons.
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Affiliation(s)
- Guanqun Huang
- Guangdong-Hongkong-Macau Institute of CNS Regeneration, Ministry of Education CNS Regeneration Collaborative Joint Laboratory, Jinan University, Guangzhou, China.,Department of Basic Medical Sciences, College of Medicine - Phoenix, The University of Arizona, Phoenix, AZ, United States
| | - Shuting Chen
- Guangdong-Hongkong-Macau Institute of CNS Regeneration, Ministry of Education CNS Regeneration Collaborative Joint Laboratory, Jinan University, Guangzhou, China
| | - Xiaoxia Chen
- Guangdong-Hongkong-Macau Institute of CNS Regeneration, Ministry of Education CNS Regeneration Collaborative Joint Laboratory, Jinan University, Guangzhou, China
| | - Jiajun Zheng
- Guangdong-Hongkong-Macau Institute of CNS Regeneration, Ministry of Education CNS Regeneration Collaborative Joint Laboratory, Jinan University, Guangzhou, China
| | - Zhuoran Xu
- Department of Biomedical Informatics, Columbia University, New York, NY, United States
| | - Abolfazl Doostparast Torshizi
- Department of Biomedical Informatics, Columbia University, New York, NY, United States.,Children's Hospital of Philadelphia, Philadelphia, PA, United States
| | - Siyi Gong
- Guangdong-Hongkong-Macau Institute of CNS Regeneration, Ministry of Education CNS Regeneration Collaborative Joint Laboratory, Jinan University, Guangzhou, China
| | - Qingpei Chen
- Guangdong-Hongkong-Macau Institute of CNS Regeneration, Ministry of Education CNS Regeneration Collaborative Joint Laboratory, Jinan University, Guangzhou, China
| | - Xiaokuang Ma
- Department of Basic Medical Sciences, College of Medicine - Phoenix, The University of Arizona, Phoenix, AZ, United States
| | - Jiandong Yu
- Guangdong-Hongkong-Macau Institute of CNS Regeneration, Ministry of Education CNS Regeneration Collaborative Joint Laboratory, Jinan University, Guangzhou, China
| | - Libing Zhou
- Guangdong-Hongkong-Macau Institute of CNS Regeneration, Ministry of Education CNS Regeneration Collaborative Joint Laboratory, Jinan University, Guangzhou, China
| | - Shenfeng Qiu
- Department of Basic Medical Sciences, College of Medicine - Phoenix, The University of Arizona, Phoenix, AZ, United States
| | - Kai Wang
- Department of Biomedical Informatics, Columbia University, New York, NY, United States.,Children's Hospital of Philadelphia, Philadelphia, PA, United States
| | - Lingling Shi
- Guangdong-Hongkong-Macau Institute of CNS Regeneration, Ministry of Education CNS Regeneration Collaborative Joint Laboratory, Jinan University, Guangzhou, China.,Co-innovation Center of Neuroregeneration, Nantong University, Nantong, China
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Doostparast Torshizi A, Petzold LR. Graph-based semi-supervised learning with genomic data integration using condition-responsive genes applied to phenotype classification. J Am Med Inform Assoc 2019; 25:99-108. [PMID: 28505320 DOI: 10.1093/jamia/ocx032] [Citation(s) in RCA: 16] [Impact Index Per Article: 3.2] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/16/2016] [Accepted: 03/14/2017] [Indexed: 11/14/2022] Open
Abstract
Objective Data integration methods that combine data from different molecular levels such as genome, epigenome, transcriptome, etc., have received a great deal of interest in the past few years. It has been demonstrated that the synergistic effects of different biological data types can boost learning capabilities and lead to a better understanding of the underlying interactions among molecular levels. Methods In this paper we present a graph-based semi-supervised classification algorithm that incorporates latent biological knowledge in the form of biological pathways with gene expression and DNA methylation data. The process of graph construction from biological pathways is based on detecting condition-responsive genes, where 3 sets of genes are finally extracted: all condition responsive genes, high-frequency condition-responsive genes, and P-value-filtered genes. Results The proposed approach is applied to ovarian cancer data downloaded from the Human Genome Atlas. Extensive numerical experiments demonstrate superior performance of the proposed approach compared to other state-of-the-art algorithms, including the latest graph-based classification techniques. Conclusions Simulation results demonstrate that integrating various data types enhances classification performance and leads to a better understanding of interrelations between diverse omics data types. The proposed approach outperforms many of the state-of-the-art data integration algorithms.
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Affiliation(s)
| | - Linda R Petzold
- Department of Computer Science, University of California, Santa Barbara, CA, USA
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Doostparast Torshizi A, Duan J, Wang K. Transcriptional network analysis on brains reveals a potential regulatory role of PPP1R3F in autism spectrum disorders. BMC Res Notes 2018; 11:489. [PMID: 30016992 PMCID: PMC6050725 DOI: 10.1186/s13104-018-3594-0] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/13/2018] [Accepted: 07/12/2018] [Indexed: 12/29/2022] Open
Abstract
OBJECTIVE This study aims at identifying master regulators of transcriptional networks in autism spectrum disorders (ASDs). RESULTS With two sets of independent RNA-Seq data generated on cerebellum from patients with ASDs and control subjects (N = 39 and 45 for set 1, N = 24 and 38 for set 2, respectively), we carried out a network deconvolution of transcriptomic data, followed by virtual protein activity analysis. We identified PPP1R3F (Protein Phosphatase 1 Regulatory Subunit 3F) as a candidate master regulator affecting a large body of downstream genes that are associated with the disease phenotype. Pathway analysis on the identified targets of PPP1R3F in both datasets indicated alteration of endocytosis pathway. Despite a limited sample size, our study represents one of the first applications of network deconvolution approach to brain transcriptomic data to generate hypotheses that may be further validated by large-scale studies.
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Affiliation(s)
- Abolfazl Doostparast Torshizi
- Raymond G. Perelman Center for Cellular and Molecular Therapeutics, Children’s Hospital of Philadelphia, Philadelphia, PA 19104 USA
| | - Jubao Duan
- Center for Psychiatric Genetics, North Shore University Health System, Evanston, IL 60201 USA
- Department of Psychiatry and Behavioral Neurosciences, The University of Chicago, Chicago, IL 60015 USA
| | - Kai Wang
- Raymond G. Perelman Center for Cellular and Molecular Therapeutics, Children’s Hospital of Philadelphia, Philadelphia, PA 19104 USA
- Department of Pathology and Laboratory Medicine, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104 USA
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Doostparast Torshizi A, Wang K. Next-generation sequencing in drug development: target identification and genetically stratified clinical trials. Drug Discov Today 2018; 23:1776-1783. [PMID: 29758342 DOI: 10.1016/j.drudis.2018.05.015] [Citation(s) in RCA: 28] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/20/2018] [Revised: 04/09/2018] [Accepted: 05/09/2018] [Indexed: 12/13/2022]
Abstract
Next-generation sequencing (NGS) enabled high-throughput analysis of genotype-phenotype relationships on human populations, ushering in a new era of genetics-informed drug development. The year 2017 was remarkable, with the first FDA-approved gene therapy for cancer (Kymriah™) and for inherited diseases (LUXTURNA™), the first multiplex NGS panel for companion diagnostics (MSK-IMPACT™) and the first drug targeting a genetic signature rather than a disease (Keytruda®). We envision that population-scale NGS with paired electronic health records (EHRs) will become a routine measure in the drug development process for the identification of novel drug targets, and that genetically stratified clinical trials could be widely adopted to improve power in precision-medicine-guided drug development.
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Affiliation(s)
- Abolfazl Doostparast Torshizi
- Raymond G. Perelman Center for Cellular and Molecular Therapeutics, Children's Hospital of Philadelphia, Philadelphia, PA 19104, USA
| | - Kai Wang
- Raymond G. Perelman Center for Cellular and Molecular Therapeutics, Children's Hospital of Philadelphia, Philadelphia, PA 19104, USA; Department of Biomedical and Health Informatics, Children's Hospital of Philadelphia, Philadelphia, PA 19104, USA; Department of Pathology and Laboratory Medicine, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA.
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Torshizi AD, Petzold L. Sparse Pathway-Induced Dynamic Network Biomarker Discovery for Early Warning Signal Detection in Complex Diseases. IEEE/ACM Trans Comput Biol Bioinform 2018; 15:1028-1034. [PMID: 28368826 DOI: 10.1109/tcbb.2017.2687925] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/07/2023]
Abstract
In many complex diseases, the transition process from the healthy stage to the catastrophic stage does not occur gradually. Recent studies indicate that the initiation and progression of such diseases are comprised of three steps including healthy stage, pre-disease stage, and disease stage. It has been demonstrated that a certain set of trajectories can be observed in the genetic signatures at the molecular level, which might be used to detect the pre-disease stage and to take necessary medical interventions. In this paper, we propose two optimization-based algorithms for extracting the dynamic network biomarkers responsible for catastrophic transition into the disease stage, and to open new horizons to reverse the disease progression at an early stage through pinpointing molecular signatures provided by high-throughput microarray data. The first algorithm relies on meta-heuristic intelligent search to characterize dynamic network biomarkers represented as a complete graph. The second algorithm induces sparsity on the adjacency matrix of the genes by taking into account the biological signaling and metabolic pathways, since not all the genes in the ineractome are biologically linked. Comprehensive numerical and meta-analytical experiments verify the effectiveness of the results of the proposed approaches in terms of network size, biological meaningfulness, and verifiability.
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Torshizi AD, Petzold L, Cohen M. Multivariate soft repulsive system identification for constructing rule-based classification systems: Application to trauma clinical data. Neurocomputing 2017. [DOI: 10.1016/j.neucom.2017.03.043] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/19/2022]
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Doostparast Torshizi A, Fazel Zarandi MH. Alpha-plane based automatic general type-2 fuzzy clustering based on simulated annealing meta-heuristic algorithm for analyzing gene expression data. Comput Biol Med 2014; 64:347-59. [PMID: 25035233 DOI: 10.1016/j.compbiomed.2014.06.017] [Citation(s) in RCA: 15] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/15/2014] [Revised: 06/17/2014] [Accepted: 06/21/2014] [Indexed: 10/25/2022]
Abstract
This paper considers microarray gene expression data clustering using a novel two stage meta-heuristic algorithm based on the concept of α-planes in general type-2 fuzzy sets. The main aim of this research is to present a powerful data clustering approach capable of dealing with highly uncertain environments. In this regard, first, a new objective function using α-planes for general type-2 fuzzy c-means clustering algorithm is represented. Then, based on the philosophy of the meta-heuristic optimization framework 'Simulated Annealing', a two stage optimization algorithm is proposed. The first stage of the proposed approach is devoted to the annealing process accompanied by its proposed perturbation mechanisms. After termination of the first stage, its output is inserted to the second stage where it is checked with other possible local optima through a heuristic algorithm. The output of this stage is then re-entered to the first stage until no better solution is obtained. The proposed approach has been evaluated using several synthesized datasets and three microarray gene expression datasets. Extensive experiments demonstrate the capabilities of the proposed approach compared with some of the state-of-the-art techniques in the literature.
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Doostparast Torshizi A, Fazel Zarandi MH. A new cluster validity measure based on general type-2 fuzzy sets: Application in gene expression data clustering. Knowl Based Syst 2014. [DOI: 10.1016/j.knosys.2014.03.023] [Citation(s) in RCA: 24] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/25/2022]
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Torshizi AD, Zarandi MHF, Torshizi GD, Eghbali K. A hybrid fuzzy-ontology based intelligent system to determine level of severity and treatment recommendation for Benign Prostatic Hyperplasia. Comput Methods Programs Biomed 2013; 113:301-313. [PMID: 24184111 DOI: 10.1016/j.cmpb.2013.09.021] [Citation(s) in RCA: 9] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/17/2013] [Revised: 09/06/2013] [Accepted: 09/30/2013] [Indexed: 06/02/2023]
Abstract
This paper deals with application of fuzzy intelligent systems in diagnosing severity level and recommending appropriate therapies for patients having Benign Prostatic Hyperplasia. Such an intelligent system can have remarkable impacts on correct diagnosis of the disease and reducing risk of mortality. This system captures various factors from the patients using two modules. The first module determines severity level of the Benign Prostatic Hyperplasia and the second module, which is a decision making unit, obtains output of the first module accompanied by some external knowledge and makes an appropriate treatment decision based on its ontology model and a fuzzy type-1 system. In order to validate efficiency and accuracy of the developed system, a case study is conducted by 44 participants. Then the results are compared with the recommendations of a panel of experts on the experimental data. Then precision and accuracy of the results were investigated based on a statistical analysis.
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Affiliation(s)
- Abolfazl Doostparast Torshizi
- Department of Industrial Engineering, Amirkabir University of Technology (Tehran Polytechnic), 15875-4413 Tehran, Iran
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