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Costa MC, Angelini C, Franzese M, Iside C, Salvatore M, Laezza L, Napolitano F, Ceccarelli M. Identification of therapeutic targets in osteoarthritis by combining heterogeneous transcriptional datasets, drug-induced expression profiles, and known drug-target interactions. J Transl Med 2024; 22:281. [PMID: 38491514 PMCID: PMC10941480 DOI: 10.1186/s12967-024-05006-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/28/2023] [Accepted: 02/18/2024] [Indexed: 03/18/2024] Open
Abstract
BACKGROUND Osteoarthritis (OA) is a multifactorial, hypertrophic, and degenerative condition involving the whole joint and affecting a high percentage of middle-aged people. It is due to a combination of factors, although the pivotal mechanisms underlying the disease are still obscure. Moreover, current treatments are still poorly effective, and patients experience a painful and degenerative disease course. METHODS We used an integrative approach that led us to extract a consensus signature from a meta-analysis of three different OA cohorts. We performed a network-based drug prioritization to detect the most relevant drugs targeting these genes and validated in vitro the most promising candidates. We also proposed a risk score based on a minimal set of genes to predict the OA clinical stage from RNA-Seq data. RESULTS We derived a consensus signature of 44 genes that we validated on an independent dataset. Using network analysis, we identified Resveratrol, Tenoxicam, Benzbromarone, Pirinixic Acid, and Mesalazine as putative drugs of interest for therapeutics in OA for anti-inflammatory properties. We also derived a list of seven gene-targets validated with functional RT-qPCR assays, confirming the in silico predictions. Finally, we identified a predictive subset of genes composed of DNER, TNFSF11, THBS3, LOXL3, TSPAN2, DYSF, ASPN and HTRA1 to compute the patient's risk score. We validated this risk score on an independent dataset with a high AUC (0.875) and compared it with the same approach computed using the entire consensus signature (AUC 0.922). CONCLUSIONS The consensus signature highlights crucial mechanisms for disease progression. Moreover, these genes were associated with several candidate drugs that could represent potential innovative therapeutics. Furthermore, the patient's risk scores can be used in clinical settings.
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Affiliation(s)
- Maria Claudia Costa
- Biogem s.c.ar.l, Ariano Irpino, Italy
- Dipartimento di Ingegneria Elettrica e delle Tecnologie dell'Informazione, Università di Napoli Federico II, Napoli, Italy
| | - Claudia Angelini
- Istituto per le Applicazioni del Calcolo, Consiglio Nazionale delle Ricerche, Napoli, Italy
| | | | | | | | - Luigi Laezza
- Dipartimento di Ingegneria Elettrica e delle Tecnologie dell'Informazione, Università di Napoli Federico II, Napoli, Italy
- Department of Public Health Sciences, Miller School of Medicine, University of Miami, Miami, FL, USA
| | - Francesco Napolitano
- Dipartimento di Scienze e Tecnologie, Università degli Studi del Sannio, Benevento, Italy
| | - Michele Ceccarelli
- Dipartimento di Ingegneria Elettrica e delle Tecnologie dell'Informazione, Università di Napoli Federico II, Napoli, Italy.
- Department of Public Health Sciences, Miller School of Medicine, University of Miami, Miami, FL, USA.
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Tamman AJF, Koller D, Nagamatsu S, Cabrera-Mendoza B, Abdallah C, Krystal JH, Gelernter J, Montalvo-Ortiz JL, Polimanti R, Pietrzak RH. Psychosocial moderators of polygenic risk scores of inflammatory biomarkers in relation to GrimAge. Neuropsychopharmacology 2024; 49:699-708. [PMID: 37848731 PMCID: PMC10876568 DOI: 10.1038/s41386-023-01747-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/23/2023] [Revised: 08/25/2023] [Accepted: 09/25/2023] [Indexed: 10/19/2023]
Abstract
GrimAge acceleration has previously predicted age-related morbidities and mortality. In the current study, we sought to examine how GrimAge is associated with genetic predisposition for systemic inflammation and whether psychosocial factors moderate this association. Military veterans from the National Health and Resilience in Veterans study, which surveyed a nationally representative sample of European American male veterans, provided saliva samples for genotyping (N = 1135). We derived polygenic risk scores (PRS) from the UK Biobank as markers of genetic predisposition to inflammation. Results revealed that PRS for three inflammatory PRS markers-HDL (lower), apolipoprotein B (lower), and gamma-glutamyl transferase (higher)-were associated with accelerated GrimAge. Additionally, these PRS interacted with a range of potentially modifiable psychosocial variables, such as exercise and gratitude, previously identified as associated with accelerated GrimAge. Using gene enrichment, we identified anti-inflammatory and antihistamine drugs that perturbate pathways of genes highly represented in the inflammatory PRS, laying the groundwork for future work to evaluate the potential of these drugs in mitigating epigenetic aging.
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Affiliation(s)
- Amanda J F Tamman
- Department of Psychiatry, Baylor College of Medicine, Houston, TX, USA.
| | - Dora Koller
- Division of Human Genetics, Department of Psychiatry, Yale School of Medicine, New Haven, CT, USA
- Department of Psychiatry, Yale School of Medicine, New Haven, CT, USA
| | - Sheila Nagamatsu
- Division of Human Genetics, Department of Psychiatry, Yale School of Medicine, New Haven, CT, USA
- Department of Psychiatry, Yale School of Medicine, New Haven, CT, USA
| | - Brenda Cabrera-Mendoza
- Division of Human Genetics, Department of Psychiatry, Yale School of Medicine, New Haven, CT, USA
- Department of Psychiatry, Yale School of Medicine, New Haven, CT, USA
| | - Chadi Abdallah
- Department of Psychiatry, Baylor College of Medicine, Houston, TX, USA
- Department of Psychiatry, Yale School of Medicine, New Haven, CT, USA
| | - John H Krystal
- Department of Psychiatry, Yale School of Medicine, New Haven, CT, USA
- U.S. Department of Veterans Affairs National Center for Posttraumatic Stress Disorder, Clinical Neurosciences Division, VA Connecticut Healthcare System, West Haven, CT, USA
| | - Joel Gelernter
- Division of Human Genetics, Department of Psychiatry, Yale School of Medicine, New Haven, CT, USA
- Department of Psychiatry, Yale School of Medicine, New Haven, CT, USA
- U.S. Department of Veterans Affairs National Center for Posttraumatic Stress Disorder, Clinical Neurosciences Division, VA Connecticut Healthcare System, West Haven, CT, USA
| | - Janitza L Montalvo-Ortiz
- Division of Human Genetics, Department of Psychiatry, Yale School of Medicine, New Haven, CT, USA
- Department of Psychiatry, Yale School of Medicine, New Haven, CT, USA
- U.S. Department of Veterans Affairs National Center for Posttraumatic Stress Disorder, Clinical Neurosciences Division, VA Connecticut Healthcare System, West Haven, CT, USA
| | - Renato Polimanti
- Division of Human Genetics, Department of Psychiatry, Yale School of Medicine, New Haven, CT, USA
- Department of Psychiatry, Yale School of Medicine, New Haven, CT, USA
| | - Robert H Pietrzak
- Department of Psychiatry, Yale School of Medicine, New Haven, CT, USA
- U.S. Department of Veterans Affairs National Center for Posttraumatic Stress Disorder, Clinical Neurosciences Division, VA Connecticut Healthcare System, West Haven, CT, USA
- Department of Social and Behavioral Sciences, Yale School of Public Health, New Haven, CT, USA
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3
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Gupta P, Galimberti M, Liu Y, Beck S, Wingo A, Wingo T, Adhikari K, Stein MB, Gelernter J, Levey DF. A genome-wide investigation into the underlying genetic architecture of personality traits and overlap with psychopathology. medRxiv 2024:2024.01.17.24301428. [PMID: 38293137 PMCID: PMC10827244 DOI: 10.1101/2024.01.17.24301428] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/01/2024]
Abstract
Personality is influenced by both genetic and environmental factors and is associated with other psychiatric traits such as anxiety and depression. The "Big Five" personality traits, which include neuroticism, extraversion, agreeableness, conscientiousness, and openness, are a widely accepted and influential framework for understanding and describing human personality. Of the big five personality traits, neuroticism has most often been the focus of genetic studies and is linked to various mental illnesses including depression, anxiety, and schizophrenia. Our knowledge of the genetic architecture of the other four personality traits is more limited. Utilizing the Million Veteran Program (MVP) cohort we conducted a genome-wide association study (GWAS) in individuals of European and African ancestry. Adding other published data, we performed GWAS meta-analysis for each of the five personality traits with sample sizes ranging from 237,390 to 682,688. We identified 158, 14, 3, 2, and 7 independent genome-wide significant (GWS) loci associated with neuroticism, extraversion, agreeableness, conscientiousness, and openness, respectively. These findings represent 55 novel loci for neuroticism, as well as the first GWS loci discovered for extraversion and agreeableness. Gene-based association testing revealed 254 genes showing significant association with at least one of the five personality traits. Transcriptome-wide and proteome-wide analysis identified altered expression of genes and proteins such as CRHR1, SLC12A5, MAPT, and STX4. Pathway enrichment and drug perturbation analyses identified complex biology underlying human personality traits. We also studied the inter-relationship of personality traits with 1,437 other traits in a phenome-wide genetic correlation analysis, identifying new associations. Mendelian randomization showed positive bidirectional effects between neuroticism and depression and anxiety while a negative bidirectional effect was observed for agreeableness and these psychiatric traits. This study improves our comprehensive understanding of the genetic architecture underlying personality traits and their relationship to other complex human traits.
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Affiliation(s)
- Priya Gupta
- Division of Human Genetics, Department of Psychiatry, Yale University School of Medicine, New Haven, CT, USA
- Department of Psychiatry, Veterans Affairs Connecticut Healthcare Center, West Haven, CT, USA
| | - Marco Galimberti
- Division of Human Genetics, Department of Psychiatry, Yale University School of Medicine, New Haven, CT, USA
- Department of Psychiatry, Veterans Affairs Connecticut Healthcare Center, West Haven, CT, USA
| | - Yue Liu
- Department of Neurology and Human Genetics, Emory University School of Medicine, Atlanta, USA
| | - Sarah Beck
- Division of Human Genetics, Department of Psychiatry, Yale University School of Medicine, New Haven, CT, USA
- Department of Psychiatry, Veterans Affairs Connecticut Healthcare Center, West Haven, CT, USA
| | - Aliza Wingo
- Department of Psychiatry and Behavioral Sciences, Emory University School of Medicine, Atlanta, USA
- Atlanta Veterans Affairs Medical Center, USA
| | - Thomas Wingo
- Department of Neurology and Human Genetics, Emory University School of Medicine, Atlanta, USA
| | - Keyrun Adhikari
- Division of Human Genetics, Department of Psychiatry, Yale University School of Medicine, New Haven, CT, USA
- Department of Psychiatry, Veterans Affairs Connecticut Healthcare Center, West Haven, CT, USA
| | - Murray B Stein
- Psychiatry Service, VA San Diego Healthcare System, San Diego, CA
- Departments of Psychiatry, School of Medicine, and Herbert Wertheim School of Public Health, University of California San Diego, La Jolla, CA
| | - Joel Gelernter
- Division of Human Genetics, Department of Psychiatry, Yale University School of Medicine, New Haven, CT, USA
- Department of Psychiatry, Veterans Affairs Connecticut Healthcare Center, West Haven, CT, USA
| | - Daniel F Levey
- Division of Human Genetics, Department of Psychiatry, Yale University School of Medicine, New Haven, CT, USA
- Department of Psychiatry, Veterans Affairs Connecticut Healthcare Center, West Haven, CT, USA
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Otero-Carrasco B, Ugarte Carro E, Prieto-Santamaría L, Diaz Uzquiano M, Caraça-Valente Hernández JP, Rodríguez-González A. Identifying patterns to uncover the importance of biological pathways on known drug repurposing scenarios. BMC Genomics 2024; 25:43. [PMID: 38191292 PMCID: PMC10775474 DOI: 10.1186/s12864-023-09913-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/19/2023] [Accepted: 12/15/2023] [Indexed: 01/10/2024] Open
Abstract
BACKGROUND Drug repurposing plays a significant role in providing effective treatments for certain diseases faster and more cost-effectively. Successful repurposing cases are mostly supported by a classical paradigm that stems from de novo drug development. This paradigm is based on the "one-drug-one-target-one-disease" idea. It consists of designing drugs specifically for a single disease and its drug's gene target. In this article, we investigated the use of biological pathways as potential elements to achieve effective drug repurposing. METHODS Considering a total of 4214 successful cases of drug repurposing, we identified cases in which biological pathways serve as the underlying basis for successful repurposing, referred to as DREBIOP. Once the repurposing cases based on pathways were identified, we studied their inherent patterns by considering the different biological elements associated with this dataset, as well as the pathways involved in these cases. Furthermore, we obtained gene-disease association values to demonstrate the diminished significance of the drug's gene target in these repurposing cases. To achieve this, we compared the values obtained for the DREBIOP set with the overall association values found in DISNET, as well as with the drug's target gene (DREGE) based repurposing cases using the Mann-Whitney U Test. RESULTS A collection of drug repurposing cases, known as DREBIOP, was identified as a result. DREBIOP cases exhibit distinct characteristics compared with DREGE cases. Notably, DREBIOP cases are associated with a higher number of biological pathways, with Vitamin D Metabolism and ACE inhibitors being the most prominent pathways. Additionally, it was observed that the association values of GDAs in DREBIOP cases were significantly lower than those in DREGE cases (p-value < 0.05). CONCLUSIONS Biological pathways assume a pivotal role in drug repurposing cases. This investigation successfully revealed patterns that distinguish drug repurposing instances associated with biological pathways. These identified patterns can be applied to any known repurposing case, enabling the detection of pathway-based repurposing scenarios or the classical paradigm.
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Affiliation(s)
- Belén Otero-Carrasco
- Centro de Tecnología Biomédica, Universidad Politécnica de Madrid, Pozuelo de Alarcón, 28223, Spain
- ETS Ingenieros Informáticos, Universidad Politécnica de Madrid, Boadilla del Monte, 28660, Spain
| | - Esther Ugarte Carro
- Centro de Tecnología Biomédica, Universidad Politécnica de Madrid, Pozuelo de Alarcón, 28223, Spain
| | - Lucía Prieto-Santamaría
- Centro de Tecnología Biomédica, Universidad Politécnica de Madrid, Pozuelo de Alarcón, 28223, Spain
- ETS Ingenieros Informáticos, Universidad Politécnica de Madrid, Boadilla del Monte, 28660, Spain
| | - Marina Diaz Uzquiano
- Centro de Tecnología Biomédica, Universidad Politécnica de Madrid, Pozuelo de Alarcón, 28223, Spain
| | | | - Alejandro Rodríguez-González
- Centro de Tecnología Biomédica, Universidad Politécnica de Madrid, Pozuelo de Alarcón, 28223, Spain.
- ETS Ingenieros Informáticos, Universidad Politécnica de Madrid, Boadilla del Monte, 28660, Spain.
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5
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Wu J, Wang Y, An Y, Tian C, Wang L, Liu Z, Qi D. Identification of genes related to growth and amino acid metabolism from the transcriptome profile of the liver of growing laying hens. Poult Sci 2024; 103:103181. [PMID: 37939592 PMCID: PMC10656263 DOI: 10.1016/j.psj.2023.103181] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/14/2023] [Revised: 09/24/2023] [Accepted: 10/06/2023] [Indexed: 11/10/2023] Open
Abstract
The growing period is a critical period for the growth and development of hens and affects their production performance during the laying period. During the early stage of growing, bone and muscle growth is rapid, making it necessary to provide sufficient amino acids (AA) to support the growth and development of laying hens. In this experiment, RNA-Sequencing (RNA-Seq) was applied to compare the liver tissues from 6- to 12-wk-old growing laying hens to identify candidate genes related to growth and AA transport and metabolism. In the liver tissues, 596 differentially expressed genes (DEG) were identified, of which 424 genes were up-regulated and 172 were down-regulated. Through enrichment analysis and DEGs analysis, some DEGs and pathways related to AA transport and metabolism were identified. Additionally, there were significantly increased activities in the liver of glutamate dehydrogenase (GDH), glutamic oxaloacetic transaminase (GOT), and glutamate pyruvate transaminase (GPT). Meanwhile, the level of serum insulin-like growth factor binding protein-5 (IGFBP-5) significantly elevated, and insulin-like growth factor-1 (IGF-1) levels significantly reduced at 12 wk compared to 6 wk. The AA contents in the breast muscle were not significantly altered, while the levels of the free AA in the serum underwent significant changes. This study discovered that the transport and metabolism of AAs in growing laying hens at different ages changed, which influenced the growth and development of growing laying hens. This contributes to future research on the mechanisms of growth and AA metabolism during the growing period of laying hens.
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Affiliation(s)
- Jiayu Wu
- Department of Animal Nutrition and Feed Science, College of Animal Science and Technology, Huazhong Agricultural University, Wuhan 430070, China
| | - Yanan Wang
- Department of Animal Nutrition and Feed Science, College of Animal Science and Technology, Huazhong Agricultural University, Wuhan 430070, China
| | - Yu An
- Department of Animal Nutrition and Feed Science, College of Animal Science and Technology, Huazhong Agricultural University, Wuhan 430070, China
| | - Changyu Tian
- Department of Animal Nutrition and Feed Science, College of Animal Science and Technology, Huazhong Agricultural University, Wuhan 430070, China
| | - Lingfeng Wang
- Department of Animal Nutrition and Feed Science, College of Animal Science and Technology, Huazhong Agricultural University, Wuhan 430070, China
| | - Zuhong Liu
- Institute of Animal Husbandry and Veterinary Sciences, Wuhan Academy of Agricultural Sciences, Wuhan 430208, China
| | - Desheng Qi
- Department of Animal Nutrition and Feed Science, College of Animal Science and Technology, Huazhong Agricultural University, Wuhan 430070, China.
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6
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Makeeva VS, Dyrkheeva NS, Lavrik OI, Zakian SM, Malakhova AA. Mutant-Huntingtin Molecular Pathways Elucidate New Targets for Drug Repurposing. Int J Mol Sci 2023; 24:16798. [PMID: 38069121 PMCID: PMC10706709 DOI: 10.3390/ijms242316798] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/10/2023] [Revised: 11/18/2023] [Accepted: 11/24/2023] [Indexed: 12/18/2023] Open
Abstract
The spectrum of neurodegenerative diseases known today is quite extensive. The complexities of their research and treatment lie not only in their diversity. Even many years of struggle and narrowly focused research on common pathologies such as Alzheimer's, Parkinson's, and other brain diseases have not brought cures for these illnesses. What can be said about orphan diseases? In particular, Huntington's disease (HD), despite affecting a smaller part of the human population, still attracts many researchers. This disorder is known to result from a mutation in the HTT gene, but having this information still does not simplify the task of drug development and studying the mechanisms of disease progression. Nonetheless, the data accumulated over the years and their analysis provide a good basis for further research. Here, we review studies devoted to understanding the mechanisms of HD. We analyze genes and molecular pathways involved in HD pathogenesis to describe the action of repurposed drugs and try to find new therapeutic targets.
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Affiliation(s)
- Vladlena S. Makeeva
- Institute of Cytology and Genetics, Siberian Branch of Russian Academy of Sciences, 10 Akad. Lavrentiev Ave., 630090 Novosibirsk, Russia; (V.S.M.); (S.M.Z.); (A.A.M.)
| | - Nadezhda S. Dyrkheeva
- Institute of Chemical Biology and Fundamental Medicine, Siberian Branch of Russian Academy of Sciences, 8 Akad. Lavrentiev Ave., 630090 Novosibirsk, Russia;
| | - Olga I. Lavrik
- Institute of Chemical Biology and Fundamental Medicine, Siberian Branch of Russian Academy of Sciences, 8 Akad. Lavrentiev Ave., 630090 Novosibirsk, Russia;
| | - Suren M. Zakian
- Institute of Cytology and Genetics, Siberian Branch of Russian Academy of Sciences, 10 Akad. Lavrentiev Ave., 630090 Novosibirsk, Russia; (V.S.M.); (S.M.Z.); (A.A.M.)
| | - Anastasia A. Malakhova
- Institute of Cytology and Genetics, Siberian Branch of Russian Academy of Sciences, 10 Akad. Lavrentiev Ave., 630090 Novosibirsk, Russia; (V.S.M.); (S.M.Z.); (A.A.M.)
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Muniyappan S, Rayan AXA, Varrieth GT. EGeRepDR: An enhanced genetic-based representation learning for drug repurposing using multiple biomedical sources. J Biomed Inform 2023; 147:104528. [PMID: 37858852 DOI: 10.1016/j.jbi.2023.104528] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/13/2023] [Revised: 09/11/2023] [Accepted: 10/16/2023] [Indexed: 10/21/2023]
Abstract
MOTIVATION Drug repurposing (DR) is an imminent approach for identifying novel therapeutic indications for the available drugs and discovering novel drugs for previously untreatable diseases. Nowadays, DR has major attention in the pharmaceutical industry due to the high cost and time of launching new drugs to the market through traditional drug development. DR task majorly depends on genetic information since the drugs revert the modified Gene Expression (GE) of diseases to normal. Many of the existing studies have not considered the genetic importance of predicting the potential candidates. METHOD We proposed a novel multimodal framework that utilizes genetic aspects of drugs and diseases such as genes, pathways, gene signatures, or expression to enhance the performance of DR using various data sources. Firstly, the heterogeneous biological network (HBN) is constructed with three types of nodes namely drug, disease, and gene, and 4 types of edges similarities (drug, gene, and disease), drug-gene, gene-disease, and drug-disease. Next, a modified graph auto-encoder (GAE*) model is applied to learn the representation of drug and disease nodes using the topological structure and edge information. Secondly, the HBN is enhanced with the information extracted from biomedical literature and ontology using a novel semi-supervised pattern embedding-based bootstrapping model and novel DR perspective representation learning respectively to improve the prediction performance. Finally, our proposed system uses a neural network model to generate the probability score of drug-disease pairs. RESULTS We demonstrate the efficiency of the proposed model on various datasets and achieved outstanding performance in 5-fold cross-validation (AUC = 0.99, AUPR = 0.98). Further, we validated the top-ranked potential candidates using pathway analysis and proved that the known and predicted candidates share common genes in the pathways.
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Affiliation(s)
- Saranya Muniyappan
- Computer Science and Engineering, CEG Campus, Anna University, Chennai, Tamil Nadu, India.
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8
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Nichter B, Koller D, De Angelis F, Wang J, Girgenti MJ, Na PJ, Hill ML, Norman SB, Krystal JH, Gelernter J, Polimanti R, Pietrzak RH. Genetic liability to suicidal thoughts and behaviors and risk of suicide attempt in US military veterans: moderating effects of cumulative trauma burden. Psychol Med 2023; 53:6325-6333. [PMID: 36444557 DOI: 10.1017/s0033291722003646] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 11/30/2022]
Abstract
BACKGROUND Little is known about environmental factors that may influence associations between genetic liability to suicidality and suicidal behavior. METHODS This study examined whether a suicidality polygenic risk score (PRS) derived from a large genome-wide association study (N = 122,935) was associated with suicide attempts in a population-based sample of European-American US military veterans (N = 1664; 92.5% male), and whether cumulative lifetime trauma exposure moderated this association. RESULTS Eighty-five veterans (weighted 6.3%) reported a history of suicide attempt. After adjusting for sociodemographic and psychiatric characteristics, suicidality PRS was associated with lifetime suicide attempt (odds ratio 2.65; 95% CI 1.37-5.11). A significant suicidality PRS-by-trauma exposure interaction emerged, such that veterans with higher levels of suicidality PRS and greater trauma burden had the highest probability of lifetime suicide attempt (16.6%), whereas the probability of attempts was substantially lower among those with high suicidality PRS and low trauma exposure (1.4%). The PRS-by-trauma interaction effect was enriched for genes implicated in cellular and developmental processes, and nervous system development, with variants annotated to the DAB2 and SPNS2 genes, which are implicated in inflammatory processes. Drug repurposing analyses revealed upregulation of suicide gene-sets in the context of medrysone, a drug targeting chronic inflammation, and clofibrate, a triacylglyceride level lowering agent. CONCLUSION Results suggest that genetic liability to suicidality is associated with increased risk of suicide attempt among veterans, particularly in the presence of high levels of cumulative trauma exposure. Additional research is warranted to investigate whether incorporation of genomic information may improve suicide prediction models.
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Affiliation(s)
- Brandon Nichter
- Department of Psychiatry, Yale School of Medicine, New Haven, CT, USA
| | - Dora Koller
- Department of Psychiatry, Yale School of Medicine, New Haven, CT, USA
- VA Connecticut Healthcare System, West Haven, CT, USA
| | - Flavio De Angelis
- Department of Psychiatry, Yale School of Medicine, New Haven, CT, USA
| | - Jiawei Wang
- Computational Biology & Bioinformatics, Yale University, New Haven, CT, USA
| | - Matthew J Girgenti
- Department of Psychiatry, Yale School of Medicine, New Haven, CT, USA
- National Center for PTSD, VA Connecticut Healthcare System, West Haven, CT, USA
| | - Peter J Na
- Department of Psychiatry, Yale School of Medicine, New Haven, CT, USA
- National Center for PTSD, VA Connecticut Healthcare System, West Haven, CT, USA
| | - Melanie L Hill
- Department of Psychiatry, University of California San Diego, La Jolla, CA, USA
- VA San Diego Healthcare System, San Diego, CA, USA
| | - Sonya B Norman
- Department of Psychiatry, University of California San Diego, La Jolla, CA, USA
- VA San Diego Healthcare System, San Diego, CA, USA
- National Center for PTSD, White River Junction, VT, USA
| | - John H Krystal
- Department of Psychiatry, Yale School of Medicine, New Haven, CT, USA
- National Center for PTSD, VA Connecticut Healthcare System, West Haven, CT, USA
| | - Joel Gelernter
- Department of Psychiatry, Yale School of Medicine, New Haven, CT, USA
- National Center for PTSD, VA Connecticut Healthcare System, West Haven, CT, USA
| | - Renato Polimanti
- Department of Psychiatry, Yale School of Medicine, New Haven, CT, USA
- VA Connecticut Healthcare System, West Haven, CT, USA
| | - Robert H Pietrzak
- Department of Psychiatry, Yale School of Medicine, New Haven, CT, USA
- National Center for PTSD, VA Connecticut Healthcare System, West Haven, CT, USA
- Department of Social and Behavioral Sciences, Yale School of Public Health, New Haven, CT, USA
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9
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Dobbs Spendlove M, M. Gibson T, McCain S, Stone BC, Gill T, Pickett BE. Pathway2Targets: an open-source pathway-based approach to repurpose therapeutic drugs and prioritize human targets. PeerJ 2023; 11:e16088. [PMID: 37790614 PMCID: PMC10544355 DOI: 10.7717/peerj.16088] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/28/2023] [Accepted: 08/22/2023] [Indexed: 10/05/2023] Open
Abstract
Background Recent efforts to repurpose existing drugs to different indications have been accompanied by a number of computational methods, which incorporate protein-protein interaction networks and signaling pathways, to aid with prioritizing existing targets and/or drugs. However, many of these existing methods are focused on integrating additional data that are only available for a small subset of diseases or conditions. Methods We have designed and implemented a new R-based open-source target prioritization and repurposing method that integrates both canonical intracellular signaling information from five public pathway databases and target information from public sources including OpenTargets.org. The Pathway2Targets algorithm takes a list of significant pathways as input, then retrieves and integrates public data for all targets within those pathways for a given condition. It also incorporates a weighting scheme that is customizable by the user to support a variety of use cases including target prioritization, drug repurposing, and identifying novel targets that are biologically relevant for a different indication. Results As a proof of concept, we applied this algorithm to a public colorectal cancer RNA-sequencing dataset with 144 case and control samples. Our analysis identified 430 targets and ~700 unique drugs based on differential gene expression and signaling pathway enrichment. We found that our highest-ranked predicted targets were significantly enriched in targets with FDA-approved therapeutics for colorectal cancer (p-value < 0.025) that included EGFR, VEGFA, and PTGS2. Interestingly, there was no statistically significant enrichment of targets for other cancers in this same list suggesting high specificity of the results. We also adjusted the weighting scheme to prioritize more novel targets for CRC. This second analysis revealed epidermal growth factor receptor (EGFR), phosphoinositide-3-kinase (PI3K), and two mitogen-activated protein kinases (MAPK14 and MAPK3). These observations suggest that our open-source method with a customizable weighting scheme can accurately prioritize targets that are specific and relevant to the disease or condition of interest, as well as targets that are at earlier stages of development. We anticipate that this method will complement other approaches to repurpose drugs for a variety of indications, which can contribute to the improvement of the quality of life and overall health of such patients.
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Affiliation(s)
- Mauri Dobbs Spendlove
- Microbiology and Molecular Biology, Brigham Young University, Provo, UT, United States of America
| | - Trenton M. Gibson
- Microbiology and Molecular Biology, Brigham Young University, Provo, UT, United States of America
| | - Shaney McCain
- Microbiology and Molecular Biology, Brigham Young University, Provo, UT, United States of America
| | - Benjamin C. Stone
- Microbiology and Molecular Biology, Brigham Young University, Provo, UT, United States of America
| | | | - Brett E. Pickett
- Microbiology and Molecular Biology, Brigham Young University, Provo, UT, United States of America
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10
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Li X, Liao M, Wang B, Zan X, Huo Y, Liu Y, Bao Z, Xu P, Liu W. A drug repurposing method based on inhibition effect on gene regulatory network. Comput Struct Biotechnol J 2023; 21:4446-4455. [PMID: 37731599 PMCID: PMC10507583 DOI: 10.1016/j.csbj.2023.09.007] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [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: 03/22/2023] [Revised: 09/05/2023] [Accepted: 09/07/2023] [Indexed: 09/22/2023] Open
Abstract
Numerous computational drug repurposing methods have emerged as efficient alternatives to costly and time-consuming traditional drug discovery approaches. Some of these methods are based on the assumption that the candidate drug should have a reversal effect on disease-associated genes. However, such methods are not applicable in the case that there is limited overlap between disease-related genes and drug-perturbed genes. In this study, we proposed a novel Drug Repurposing method based on the Inhibition Effect on gene regulatory network (DRIE) to identify potential drugs for cancer treatment. DRIE integrated gene expression profile and gene regulatory network to calculate inhibition score by using the shortest path in the disease-specific network. The results on eleven datasets indicated the superior performance of DRIE when compared to other state-of-the-art methods. Case studies showed that our method effectively discovered novel drug-disease associations. Our findings demonstrated that the top-ranked drug candidates had been already validated by CTD database. Additionally, it clearly identified potential agents for three cancers (colorectal, breast, and lung cancer), which was beneficial when annotating drug-disease relationships in the CTD. This study proposed a novel framework for drug repurposing, which would be helpful for drug discovery and development.
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Affiliation(s)
- Xianbin Li
- Institute of Computational Science and Technology, Guangzhou University, Guangzhou, China
- School of Computer Science of Information Technology, Qiannan Normal University for Nationalities, Duyun, China
| | - Minzhen Liao
- Institute of Computational Science and Technology, Guangzhou University, Guangzhou, China
| | - Bing Wang
- School of Medicine, Southeast University, Nanjing, China
| | - Xiangzhen Zan
- Institute of Computational Science and Technology, Guangzhou University, Guangzhou, China
| | - Yanhao Huo
- Institute of Computational Science and Technology, Guangzhou University, Guangzhou, China
| | - Yue Liu
- Institute of Computational Science and Technology, Guangzhou University, Guangzhou, China
| | - Zhenshen Bao
- Institute of Computational Science and Technology, Guangzhou University, Guangzhou, China
- School of Computer Science of Information Technology, Qiannan Normal University for Nationalities, Duyun, China
| | - Peng Xu
- Institute of Computational Science and Technology, Guangzhou University, Guangzhou, China
- School of Computer Science of Information Technology, Qiannan Normal University for Nationalities, Duyun, China
| | - Wenbin Liu
- Institute of Computational Science and Technology, Guangzhou University, Guangzhou, China
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11
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Garana BB, Joly JH, Delfarah A, Hong H, Graham NA. Drug mechanism enrichment analysis improves prioritization of therapeutics for repurposing. BMC Bioinformatics 2023; 24:215. [PMID: 37226094 DOI: 10.1186/s12859-023-05343-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [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: 01/19/2023] [Accepted: 05/16/2023] [Indexed: 05/26/2023] Open
Abstract
BACKGROUND There is a pressing need for improved methods to identify effective therapeutics for diseases. Many computational approaches have been developed to repurpose existing drugs to meet this need. However, these tools often output long lists of candidate drugs that are difficult to interpret, and individual drug candidates may suffer from unknown off-target effects. We reasoned that an approach which aggregates information from multiple drugs that share a common mechanism of action (MOA) would increase on-target signal compared to evaluating drugs on an individual basis. In this study, we present drug mechanism enrichment analysis (DMEA), an adaptation of gene set enrichment analysis (GSEA), which groups drugs with shared MOAs to improve the prioritization of drug repurposing candidates. RESULTS First, we tested DMEA on simulated data and showed that it can sensitively and robustly identify an enriched drug MOA. Next, we used DMEA on three types of rank-ordered drug lists: (1) perturbagen signatures based on gene expression data, (2) drug sensitivity scores based on high-throughput cancer cell line screening, and (3) molecular classification scores of intrinsic and acquired drug resistance. In each case, DMEA detected the expected MOA as well as other relevant MOAs. Furthermore, the rankings of MOAs generated by DMEA were better than the original single-drug rankings in all tested data sets. Finally, in a drug discovery experiment, we identified potential senescence-inducing and senolytic drug MOAs for primary human mammary epithelial cells and then experimentally validated the senolytic effects of EGFR inhibitors. CONCLUSIONS DMEA is a versatile bioinformatic tool that can improve the prioritization of candidates for drug repurposing. By grouping drugs with a shared MOA, DMEA increases on-target signal and reduces off-target effects compared to analysis of individual drugs. DMEA is publicly available as both a web application and an R package at https://belindabgarana.github.io/DMEA .
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Affiliation(s)
- Belinda B Garana
- Mork Family Department of Chemical Engineering and Materials Science, University of Southern California, 3710 McClintock Ave., RTH 509, Los Angeles, CA, 90089, USA
| | - James H Joly
- Mork Family Department of Chemical Engineering and Materials Science, University of Southern California, 3710 McClintock Ave., RTH 509, Los Angeles, CA, 90089, USA
- Nautilus Biotechnology, San Carlos, CA, USA
| | - Alireza Delfarah
- Mork Family Department of Chemical Engineering and Materials Science, University of Southern California, 3710 McClintock Ave., RTH 509, Los Angeles, CA, 90089, USA
- Calico Life Sciences, South San Francisco, CA, USA
| | - Hyunjun Hong
- Department of Computer Science, Information Systems, and Applications, Los Angeles City College, Los Angeles, CA, USA
| | - Nicholas A Graham
- Mork Family Department of Chemical Engineering and Materials Science, University of Southern California, 3710 McClintock Ave., RTH 509, Los Angeles, CA, 90089, USA.
- Norris Comprehensive Cancer Center, University of Southern California, Los Angeles, CA, USA.
- Leonard Davis School of Gerontology, University of Southern California, Los Angeles, CA, USA.
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12
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Liang S, Liu D, Xiao Z, Greenbaum J, Shen H, Xiao H, Deng H. Repurposing Approved Drugs for Sarcopenia Based on Transcriptomics Data in Humans. Pharmaceuticals (Basel) 2023; 16:ph16040607. [PMID: 37111364 PMCID: PMC10145476 DOI: 10.3390/ph16040607] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/06/2023] [Revised: 03/31/2023] [Accepted: 04/10/2023] [Indexed: 04/29/2023] Open
Abstract
Sarcopenia, characterized by age-related loss of muscle mass, strength, and decreased physical performance, is a growing public health challenge amid the rapidly ageing population. As there are no approved drugs that target sarcopenia, it has become increasingly urgent to identify promising pharmacological interventions. In this study, we conducted an integrative drug repurposing analysis utilizing three distinct approaches. Firstly, we analyzed skeletal muscle transcriptomic sequencing data in humans and mice using gene differential expression analysis, weighted gene co-expression analysis, and gene set enrichment analysis. Subsequently, we employed gene expression profile similarity assessment, hub gene expression reversal, and disease-related pathway enrichment to identify and repurpose candidate drugs, followed by the integration of findings with rank aggregation algorithms. Vorinostat, the top-ranking drug, was also validated in an in vitro study, which demonstrated its efficacy in promoting muscle fiber formation. Although still requiring further validation in animal models and human clinical trials, these results suggest a promising drug repurposing prospect in the treatment and prevention of sarcopenia.
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Affiliation(s)
- Shuang Liang
- Center for System Biology, Data Sciences, and Reproductive Health, School of Basic Medical Science, Central South University, Changsha 410013, China
| | - Danyang Liu
- Laboratory of Molecular and Statistical Genetics, College of Life Sciences, Hunan Normal University, Changsha 410013, China
| | - Zhengwu Xiao
- Center for System Biology, Data Sciences, and Reproductive Health, School of Basic Medical Science, Central South University, Changsha 410013, China
| | - Jonathan Greenbaum
- Tulane Center of Biomedical Informatics and Genomics, Deming Department of Medicine, Tulane University School of Medicine, New Orleans, LA 999039, USA
| | - Hui Shen
- Tulane Center of Biomedical Informatics and Genomics, Deming Department of Medicine, Tulane University School of Medicine, New Orleans, LA 999039, USA
| | - Hongmei Xiao
- Center for System Biology, Data Sciences, and Reproductive Health, School of Basic Medical Science, Central South University, Changsha 410013, China
| | - Hongwen Deng
- Tulane Center of Biomedical Informatics and Genomics, Deming Department of Medicine, Tulane University School of Medicine, New Orleans, LA 999039, USA
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13
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He H, Duo H, Hao Y, Zhang X, Zhou X, Zeng Y, Li Y, Li B. Computational drug repurposing by exploiting large-scale gene expression data: Strategy, methods and applications. Comput Biol Med 2023; 155:106671. [PMID: 36805225 DOI: 10.1016/j.compbiomed.2023.106671] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/25/2022] [Revised: 02/05/2023] [Accepted: 02/10/2023] [Indexed: 02/18/2023]
Abstract
De novo drug development is an extremely complex, time-consuming and costly task. Urgent needs for therapies of various diseases have greatly accelerated searches for more effective drug development methods. Luckily, drug repurposing provides a new and effective perspective on disease treatment. Rapidly increased large-scale transcriptome data paints a detailed prospect of gene expression during disease onset and thus has received wide attention in the field of computational drug repurposing. However, how to efficiently mine transcriptome data and identify new indications for old drugs remains a critical challenge. This review discussed the irreplaceable role of transcriptome data in computational drug repurposing and summarized some representative databases, tools and strategies. More importantly, it proposed a practical guideline through establishing the correspondence between three gene expression data types and five strategies, which would facilitate researchers to adopt appropriate strategies to deeply mine large-scale transcriptome data and discover more effective therapies.
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Affiliation(s)
- Hao He
- College of Life Sciences, Chongqing Normal University, Chongqing, 400044, PR China; State Key Laboratory of Medical Neurobiology and MOE Frontiers Center for Brain Science, Institutes of Brain Science, Fudan University, Shanghai, 200032, PR China
| | - Hongrui Duo
- College of Life Sciences, Chongqing Normal University, Chongqing, 400044, PR China
| | - Youjin Hao
- College of Life Sciences, Chongqing Normal University, Chongqing, 400044, PR China
| | - Xiaoxi Zhang
- College of Life Sciences, Chongqing Normal University, Chongqing, 400044, PR China
| | - Xinyi Zhou
- College of Life Sciences, Chongqing Normal University, Chongqing, 400044, PR China
| | - Yujie Zeng
- College of Life Sciences, Chongqing Normal University, Chongqing, 400044, PR China
| | - Yinghong Li
- The Key Laboratory on Big Data for Bio Intelligence, Chongqing University of Posts and Telecommunications, Chongqing, 400065, PR China
| | - Bo Li
- College of Life Sciences, Chongqing Normal University, Chongqing, 400044, PR China.
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14
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Hartung M, Anastasi E, Mamdouh ZM, Nogales C, Schmidt HHHW, Baumbach J, Zolotareva O, List M. Cancer driver drug interaction explorer. Nucleic Acids Res 2022; 50:W138-W144. [PMID: 35580047 PMCID: PMC9252786 DOI: 10.1093/nar/gkac384] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [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: 03/02/2022] [Revised: 04/06/2022] [Accepted: 04/29/2022] [Indexed: 12/16/2022] Open
Abstract
Cancer is a heterogeneous disease characterized by unregulated cell growth and promoted by mutations in cancer driver genes some of which encode suitable drug targets. Since the distinct set of cancer driver genes can vary between and within cancer types, evidence-based selection of drugs is crucial for targeted therapy following the precision medicine paradigm. However, many putative cancer driver genes can not be targeted directly, suggesting an indirect approach that considers alternative functionally related targets in the gene interaction network. Once potential drug targets have been identified, it is essential to consider all available drugs. Since tools that offer support for systematic discovery of drug repurposing candidates in oncology are lacking, we developed CADDIE, a web application integrating six human gene-gene and four drug-gene interaction databases, information regarding cancer driver genes, cancer-type specific mutation frequencies, gene expression information, genetically related diseases, and anticancer drugs. CADDIE offers access to various network algorithms for identifying drug targets and drug repurposing candidates. It guides users from the selection of seed genes to the identification of therapeutic targets or drug candidates, making network medicine algorithms accessible for clinical research. CADDIE is available at https://exbio.wzw.tum.de/caddie/ and programmatically via a python package at https://pypi.org/project/caddiepy/.
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Affiliation(s)
- Michael Hartung
- Institute for Computational Systems Biology, University of Hamburg, 22607 Hamburg, Germany
| | - Elisa Anastasi
- School of Computing, Newcastle University, 2308 Newcastle upon Tyne, UK
| | - Zeinab M Mamdouh
- Department of Pharmacology and Personalised Medicine, Maastricht University, 6229 Maastricht, Netherlands.,Department of Pharmacology and Toxicology, Faculty of Pharmacy, Zagazig University, 44519 Zagazig, Egypt
| | - Cristian Nogales
- Department of Pharmacology and Personalised Medicine, Maastricht University, 6229 Maastricht, Netherlands
| | - Harald H H W Schmidt
- Department of Pharmacology and Personalised Medicine, Maastricht University, 6229 Maastricht, Netherlands
| | - Jan Baumbach
- Institute for Computational Systems Biology, University of Hamburg, 22607 Hamburg, Germany.,Computational Biomedicine Lab, Department of Mathematics and Computer Science, University of Southern Denmark, 5230 Odense, Denmark
| | - Olga Zolotareva
- Institute for Computational Systems Biology, University of Hamburg, 22607 Hamburg, Germany.,Chair of Experimental Bioinformatics, TUM School of Life Sciences, Technical University of Munich, 85354 Freising, Germany
| | - Markus List
- Chair of Experimental Bioinformatics, TUM School of Life Sciences, Technical University of Munich, 85354 Freising, Germany
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15
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Wendt FR, Pathak GA, Deak JD, De Angelis F, Koller D, Cabrera-Mendoza B, Lebovitch DS, Levey DF, Stein MB, Kranzler HR, Koenen KC, Gelernter J, Huckins LM, Polimanti R. Using phenotype risk scores to enhance gene discovery for generalized anxiety disorder and posttraumatic stress disorder. Mol Psychiatry 2022; 27:2206-2215. [PMID: 35181757 PMCID: PMC9133008 DOI: 10.1038/s41380-022-01469-y] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/26/2021] [Revised: 01/18/2022] [Accepted: 02/02/2022] [Indexed: 11/09/2022]
Abstract
UK Biobank (UKB) is a key contributor in mental health genome-wide association studies (GWAS) but only ~31% of participants completed the Mental Health Questionnaire ("MHQ responders"). We predicted generalized anxiety disorder (GAD), posttraumatic stress disorder (PTSD), and major depression symptoms using elastic net regression in the ~69% of UKB participants lacking MHQ data ("MHQ non-responders"; NTraining = 50%; NTest = 50%), maximizing the informative sample for these traits. MHQ responders were more likely to be female, from higher socioeconomic positions, and less anxious than non-responders. Genetic correlation of GAD and PTSD between MHQ responders and non-responders ranged from 0.636 to 1.08; both were predicted by polygenic scores generated from independent cohorts. In meta-analyses of GAD (N = 489,579) and PTSD (N = 497,803), we discovered many novel genomic risk loci (13 for GAD and 40 for PTSD). Transcriptomic analyses converged on altered regulation of prenatal dorsolateral prefrontal cortex in these disorders. Our results provide one roadmap by which sample size and statistical power may be improved for gene discovery of incompletely ascertained traits in the UKB and other biobanks with limited mental health assessment.
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Affiliation(s)
- Frank R Wendt
- Department of Psychiatry, Yale School of Medicine, New Haven, CT, USA. .,VA CT Healthcare System, West Haven, CT, USA.
| | - Gita A Pathak
- Department of Psychiatry, Yale School of Medicine, New Haven, CT, USA,VA CT Healthcare System, West Haven, CT, USA
| | - Joseph D Deak
- Department of Psychiatry, Yale School of Medicine, New Haven, CT, USA,VA CT Healthcare System, West Haven, CT, USA
| | - Flavio De Angelis
- Department of Psychiatry, Yale School of Medicine, New Haven, CT, USA,VA CT Healthcare System, West Haven, CT, USA
| | - Dora Koller
- Department of Psychiatry, Yale School of Medicine, New Haven, CT, USA,VA CT Healthcare System, West Haven, CT, USA
| | - Brenda Cabrera-Mendoza
- Department of Psychiatry, Yale School of Medicine, New Haven, CT, USA,VA CT Healthcare System, West Haven, CT, USA
| | - Dannielle S Lebovitch
- Pamela Sklar Division of Psychiatric Genomics, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA,Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA,Department of Genetics and Genomics, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA,Icahn Institute for Genomics and Multiscale Biology, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
| | - Daniel F Levey
- Department of Psychiatry, Yale School of Medicine, New Haven, CT, USA,VA CT Healthcare System, West Haven, CT, USA
| | - Murray B Stein
- VA San Diego Healthcare System, Psychiatry Service, San Diego, CA, USA,Department of Psychiatry, University of California San Diego, La Jolla, CA, USA,Herbert Wertheim School of Public Health and Human Longevity Science, University of California San Diego, La Jolla, CA, USA
| | - Henry R Kranzler
- University of Pennsylvania Perelman School of Medicine, Philadelphia, PA, 19104, USA,Mental Illness Research, Education, and Clinical Center, Crescenz Veterans Affairs Medical Center, Philadelphia, PA, 19104, USA
| | - Karestan C Koenen
- Broad Institute of MIT and Harvard, Stanley Center for Psychiatry Research, Cambridge, MA, USA,Massachusettes General Hospital, Psychiatry and Neurodevelopmental Genetics Unit (PNGU), Boston, MA, USA,Harvard School of Public Health, Department of Epidemiology, Boston, MA, USA
| | - Joel Gelernter
- Department of Psychiatry, Yale School of Medicine, New Haven, CT, USA,VA CT Healthcare System, West Haven, CT, USA,Department of Genetics, Yale School of Medicine, New Haven, CT, USA,Department of Neuroscience, Yale School of Medicine, New Haven, CT, USA
| | - Laura M Huckins
- Pamela Sklar Division of Psychiatric Genomics, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA,Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA,Department of Genetics and Genomics, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA,Icahn Institute for Genomics and Multiscale Biology, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA,Seaver Autism Center for Research and Treatment, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA,Mental Illness Research, Education and Clinical Center, James J. Peters Department of Veterans Affairs Medical Center, Bronx, NY 10468, USA
| | - Renato Polimanti
- Department of Psychiatry, Yale School of Medicine, New Haven, CT, USA. .,VA CT Healthcare System, West Haven, CT, USA.
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16
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Tamman AJF, Wendt FR, Pathak GA, Krystal JH, Southwick SM, Sippel LM, Gelernter J, Polimanti R, Pietrzak RH. Attachment Style Moderates Polygenic Risk for Incident Posttraumatic Stress in U.S. Military Veterans: A 7-Year, Nationally Representative, Prospective Cohort Study. Biol Psychiatry 2022; 91:637-646. [PMID: 34955171 DOI: 10.1016/j.biopsych.2021.09.025] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/07/2021] [Revised: 09/11/2021] [Accepted: 09/26/2021] [Indexed: 11/02/2022]
Abstract
BACKGROUND Posttraumatic stress disorder (PTSD) develops consequent to complex gene-by-environment interactions beyond the precipitating trauma. To date, however, no known study has used a prospective design to examine how polygenic risk scores (PRSs) interact with social-environmental factors such as attachment style to predict PTSD development. METHODS PRSs were derived from a genome-wide association study of PTSD symptoms (N = 186,689; Million Veteran Program cohort). We evaluated combined effects of PRS and attachment style in predicting incident PTSD in a 7-year, nationally representative cohort of trauma-exposed, European-American U.S. military veterans without PTSD (N = 1083). We also conducted multivariate gene-by-environment interaction and drug repositioning analyses to identify loci that interact with multiple environmental factors and potential pharmacotherapies that may be repurposed for this disorder. RESULTS Veterans with higher PTSD PRS were more likely to have an incident-positive screen for PTSD over 7 years. A gene-by-environment interaction was also observed, such that higher PRS only predicted incident PTSD in veterans with an insecure attachment style and not those with a secure attachment style. At an individual locus level, the strongest gene-by-environment interaction was observed for the rs4702 variant of the FURIN gene with cumulative lifetime trauma burden. Drug repositioning revealed that genes implicated in PRS are perturbated by the drug doxylamine. CONCLUSIONS Attachment style moderates polygenic risk for the development of PTSD in European-American veterans. These findings may inform PTSD prevention and treatment for veterans with high polygenic risk for PTSD and suggest a potential pharmacotherapeutic target for risk genes moderated by social-environmental factors.
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Affiliation(s)
- Amanda J F Tamman
- Department of Psychology, St John's University, Queens, New York; Mood and Anxiety Disorders Program, Baylor College of Medicine, Houston, Texas.
| | - Frank R Wendt
- Department of Psychiatry, Yale School of Medicine, New Haven, Connecticut; VA Connecticut Healthcare System, West Haven, Connecticut
| | - Gita A Pathak
- Department of Psychiatry, Yale School of Medicine, New Haven, Connecticut; VA Connecticut Healthcare System, West Haven, Connecticut
| | - John H Krystal
- Department of Psychiatry, Yale School of Medicine, New Haven, Connecticut; Clinical Neurosciences Division, National Center for PTSD, West Haven, Connecticut
| | - Steven M Southwick
- Department of Psychiatry, Yale School of Medicine, New Haven, Connecticut
| | - Lauren M Sippel
- Executive Division, National Center for PTSD, White River Junction, Vermont; Department of Psychiatry, Geisel School of Medicine at Dartmouth, Hanover, New Hampshire
| | - Joel Gelernter
- Department of Psychiatry, Yale School of Medicine, New Haven, Connecticut; VA Connecticut Healthcare System, West Haven, Connecticut
| | - Renato Polimanti
- Department of Psychiatry, Yale School of Medicine, New Haven, Connecticut; VA Connecticut Healthcare System, West Haven, Connecticut
| | - Robert H Pietrzak
- Department of Psychiatry, Yale School of Medicine, New Haven, Connecticut; Department of Social and Behavioral Sciences, Yale School of Public Health, New Haven, Connecticut; Clinical Neurosciences Division, National Center for PTSD, West Haven, Connecticut
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17
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Abstract
Rapamycin, also known as sirolimus, inhibits the mTOR pathway in complex diseases such as cancer, and its downstream targets are ribosomal S6 kinases (RPS6K). Sirolimus is involved in regulating cell growth and cell survival through roles such as the mediation of epidermal growth factor signaling. However, the systemic efficacy of sirolimus in pathway regulation is unclear. The purpose of this study is to determine systemic drug efficacy using computational methods and drug-induced datasets. We suggest a computational method using gene expression datasets induced by sirolimus and an inverse algorithm that simultaneously identifies parameters referring to gene–gene interactions. We downloaded two sirolimus-induced microarray gene expression datasets and used a computational method to obtain the most enriched pathway, then adopted an inverse algorithm to discover the gene–gene interactions of that pathway. In the results, RPS6KB1 was a target gene of sirolimus and was associated with genes in the pathway. The common gene interactions from two datasets were a hub gene, RPS6KB1, and 10 related genes (AKT3, CBLC, MAP2K7, NRG1/2, PAK3, PIK3CD/G, PRKCG, and SHC3) in the epidermal growth factor (ERBB) signaling pathway.
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18
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Lin WZ, Liu YC, Lee MC, Tang CT, Wu GJ, Chang YT, Chu CM, Shiau CY. From GWAS to drug screening: repurposing antipsychotics for glioblastoma. J Transl Med 2022; 20:70. [PMID: 35120529 PMCID: PMC8815269 DOI: 10.1186/s12967-021-03209-2] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [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: 10/17/2021] [Accepted: 12/19/2021] [Indexed: 02/06/2023] Open
Abstract
BACKGROUND Glioblastoma is currently an incurable cancer. Genome-wide association studies have demonstrated that 41 genetic variants are associated with glioblastoma and may provide an option for drug development. METHODS We investigated FDA-approved antipsychotics for their potential treatment of glioblastoma based on genome-wide association studies data using a 'pathway/gene-set analysis' approach. RESULTS The in-silico screening led to the discovery of 12 candidate drugs. DepMap portal revealed that 42 glioma cell lines show higher sensitivities to 12 candidate drugs than to Temozolomide, the current standard treatment for glioblastoma. CONCLUSION In particular, cell lines showed significantly higher sensitivities to Norcyclobenzaprine and Protriptyline which were predicted to bind targets to disrupt a certain molecular function such as DNA repair, response to hormones, or DNA-templated transcription, and may lead to an effect on survival-related pathways including cell cycle arrest, response to ER stress, glucose transport, and regulation of autophagy. However, it is recommended that their mechanism of action and efficacy are further determined.
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Affiliation(s)
- Wei-Zhi Lin
- Graduate Institute of Life Sciences, National Defense Medical Center, No.161, Sec. 6, Minquan E. Rd., Neihu Dist., Taipei City, 11490 Taiwan
| | - Yen-Chun Liu
- School of Medicine, National Defense Medical Center, No.161, Sec. 6, Minquan E. Rd., Neihu Dist., Taipei City, 11490 Taiwan
| | - Meng-Chang Lee
- School of Public Health, National Defense Medical Center, No.161, Sec. 6, Minquan E. Rd., Neihu Dist., Taipei City, 11490 Taiwan
| | - Chi-Tun Tang
- Graduate Institute of Medical Sciences, National Defense Medical Center, Taipei, Taiwan
- Department of Neurological Surgery, Tri-Service General Hospital, No. 325, Sec. 2, Chenggong Rd., Neihu District, Taipei, 11490 Taiwan
| | - Gwo-Jang Wu
- Graduate Institute of Medical Sciences, National Defense Medical Center, Taipei, Taiwan
- Department of Obstetrics and Gynecology, Tri-Service General Hospital, No. 325, Sec. 2, Chenggong Rd., Neihu District, Taipei, 11490 Taiwan
| | - Yu-Tien Chang
- School of Public Health, National Defense Medical Center, No.161, Sec. 6, Minquan E. Rd., Neihu Dist., Taipei City, 11490 Taiwan
| | - Chi-Ming Chu
- Graduate Institute of Life Sciences, National Defense Medical Center, No.161, Sec. 6, Minquan E. Rd., Neihu Dist., Taipei City, 11490 Taiwan
- School of Public Health, National Defense Medical Center, No.161, Sec. 6, Minquan E. Rd., Neihu Dist., Taipei City, 11490 Taiwan
| | - Chia-Yang Shiau
- Graduate Institute of Life Sciences, National Defense Medical Center, No.161, Sec. 6, Minquan E. Rd., Neihu Dist., Taipei City, 11490 Taiwan
- Fidelity Regulation Therapeutics Inc., 161, Sec. 6, Minquan E. Rd., Neihu Dist., Taipei City, 11490 Taiwan
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19
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Shao D, Dai Y, Li N, Cao X, Zhao W, Cheng L, Rong Z, Huang L, Wang Y, Zhao J. Artificial intelligence in clinical research of cancers. Brief Bioinform 2021; 23:6470966. [PMID: 34929741 PMCID: PMC8769909 DOI: 10.1093/bib/bbab523] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [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: 08/11/2021] [Revised: 11/06/2021] [Accepted: 11/13/2021] [Indexed: 12/16/2022] Open
Abstract
Several factors, including advances in computational algorithms, the availability of high-performance computing hardware, and the assembly of large community-based databases, have led to the extensive application of Artificial Intelligence (AI) in the biomedical domain for nearly 20 years. AI algorithms have attained expert-level performance in cancer research. However, only a few AI-based applications have been approved for use in the real world. Whether AI will eventually be capable of replacing medical experts has been a hot topic. In this article, we first summarize the cancer research status using AI in the past two decades, including the consensus on the procedure of AI based on an ideal paradigm and current efforts of the expertise and domain knowledge. Next, the available data of AI process in the biomedical domain are surveyed. Then, we review the methods and applications of AI in cancer clinical research categorized by the data types including radiographic imaging, cancer genome, medical records, drug information and biomedical literatures. At last, we discuss challenges in moving AI from theoretical research to real-world cancer research applications and the perspectives toward the future realization of AI participating cancer treatment.
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Affiliation(s)
- Dan Shao
- College of Computer Science and Technology, Key Laboratory of Human Health Status Identification and Function Enhancement of Jilin Province, Changchun University, Changchun 130022, China
| | - Yinfei Dai
- College of Computer Science and Technology, Key Laboratory of Human Health Status Identification and Function Enhancement of Jilin Province, Changchun University, Changchun 130022, China
| | - Nianfeng Li
- College of Computer Science and Technology, Key Laboratory of Human Health Status Identification and Function Enhancement of Jilin Province, Changchun University, Changchun 130022, China
| | - Xuqing Cao
- Department of Neurology, People's Hospital of Ningxia Hui Autonomous Region (The Affiliated people's Hospital of Ningxia Medical University and The First Affiliated Hospital of Northwest Minzu University), Yinchuan 750002, China
| | - Wei Zhao
- Department of Biochemistry and Molecular Biology, Ningxia Medical University, Yinchuan 750002, China
| | - Li Cheng
- Department of Electrical Diagnosis, Affiliated Hospital of Changchun University of Traditional Chinese Medicine, Changchun, 130021, China
| | - Zhuqing Rong
- School of Science, Key Laboratory of Human Health Status Identification and Function Enhancement of Jilin Province, Changchun University, Changchun 130022, China
| | - Lan Huang
- Key laboratory of Symbol Computation and Knowledge Engineering of Ministry of Education, College of Computer Science and Technology, Jilin University, Changchun 130012, China
| | - Yan Wang
- Key laboratory of Symbol Computation and Knowledge Engineering of Ministry of Education, College of Computer Science and Technology, Jilin University, Changchun 130012, China
| | - Jing Zhao
- Department of Biomedical Informatics, College of Medicine, The Ohio State University, Columbus, 43210, USA
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20
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Guo F, Jiang C, Xi Y, Wang D, Zhang Y, Xie N, Guan Y, Zhang F, Yang H. Investigation of pharmacological mechanism of natural product using pathway fingerprints similarity based on "drug-target-pathway" heterogenous network. J Cheminform 2021; 13:68. [PMID: 34544480 PMCID: PMC8454151 DOI: 10.1186/s13321-021-00549-5] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/24/2021] [Accepted: 09/03/2021] [Indexed: 02/05/2023] Open
Abstract
Natural products from traditional medicine inherit bioactivity from their source herbs. However, the pharmacological mechanism of natural products is often unclear and studied insufficiently. Pathway fingerprint similarity based on "drug-target-pathway" heterogeneous network provides new insight into Mechanism of Action (MoA) for natural products compared with reference drugs, which are selected approved drugs with similar bioactivity. Natural products with similar pathway fingerprints may have similar MoA to approved drugs. In our study, XYPI, an andrographolide derivative, had similar anti-inflammatory activity to Glucocorticoids (GCs) and non-steroidal anti-inflammatory drugs (NSAIDs), and GCs and NSAIDs have completely different MoA. Based on similarity evaluation, XYPI has similar pathway fingerprints as NSAIDs, but has similar target profile with GCs. The expression pattern of genes in LPS-activated macrophages after XYPI treatment is similar to that after NSAID but not GC treatment, and this experimental result is consistent with the computational prediction based on pathway fingerprints. These results imply that the pathway fingerprints of drugs have potential for drug similarity evaluation. This study used XYPI as an example to propose a new approach for investigating the pharmacological mechanism of natural products using pathway fingerprint similarity based on a "drug-target-pathway" heterogeneous network.
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Affiliation(s)
- Feifei Guo
- Institute of Chinese Materia Medica, China Academy of Chinese Medical Sciences, Beijing, China
| | - Chunhong Jiang
- Joint Institute of Virology (Shantou University and The University of Hong Kong), Shantou University Medical College, Shantou, China
| | - Yujie Xi
- Institute of Chinese Materia Medica, China Academy of Chinese Medical Sciences, Beijing, China
- Tianjin University of Traditional Chinese Medicine, Tianjin, China
| | - Dan Wang
- State Key Laboratory of Proteomics, Beijing Proteome Research Center, National Center for Protein Sciences-Beijing (PHOENIX Center), Beijing Institute of Lifeomics, Beijing, China
| | - Yi Zhang
- Institute of Chinese Materia Medica, China Academy of Chinese Medical Sciences, Beijing, China
| | - Ning Xie
- State Key Laboratory of Innovative Natural Medicine and TCM Injections, Ganzhou, China
| | - Yi Guan
- Joint Institute of Virology (Shantou University and The University of Hong Kong), Shantou University Medical College, Shantou, China
| | - Fangbo Zhang
- Institute of Chinese Materia Medica, China Academy of Chinese Medical Sciences, Beijing, China.
| | - Hongjun Yang
- Institute of Chinese Materia Medica, China Academy of Chinese Medical Sciences, Beijing, China.
- Beijing Key Laboratory of Traditional Chinese Medicine Basic Research on Prevention and Treatment for Major Diseases, Experimental Research Center, China Academy of Chinese Medical Sciences, Beijing, China.
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21
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Gupta D, Sharma G, Saraswat P, Ranjan R. Synthetic Biology in Plants, a Boon for Coming Decades. Mol Biotechnol 2021; 63:1138-1154. [PMID: 34420149 DOI: 10.1007/s12033-021-00386-9] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [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: 03/27/2021] [Accepted: 08/16/2021] [Indexed: 02/01/2023]
Abstract
Recently an enormous expansion of knowledge is seen in various disciplines of science. This surge of information has given rise to concept of interdisciplinary fields, which has resulted in emergence of newer research domains, one of them is 'Synthetic Biology' (SynBio). It captures basics from core biology and integrates it with concepts from the other areas of study such as chemical, electrical, and computational sciences. The essence of synthetic biology is to rewire, re-program, and re-create natural biological pathways, which are carried through genetic circuits. A genetic circuit is a functional assembly of basic biological entities (DNA, RNA, proteins), created using typical design, built, and test cycles. These circuits allow scientists to engineer nearly all biological systems for various useful purposes. The development of sophisticated molecular tools, techniques, genomic programs, and ease of nucleic acid synthesis have further fueled several innovative application of synthetic biology in areas like molecular medicines, pharmaceuticals, biofuels, drug discovery, metabolomics, developing plant biosensors, utilization of prokaryotic systems for metabolite production, and CRISPR/Cas9 in the crop improvement. These applications have largely been dominated by utilization of prokaryotic systems. However, newer researches have indicated positive growth of SynBio for the eukaryotic systems as well. This paper explores advances of synthetic biology in the plant field by elaborating on its core components and potential applications. Here, we have given a comprehensive idea of designing, development, and utilization of synthetic biology in the improvement of the present research state of plant system.
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Affiliation(s)
- Dipinte Gupta
- Plant Biotechnology Lab, Department of Botany, Faculty of Science, Dayalbagh Educational Institute (Deemed to be University), Dayalbagh, Agra, 282005, India
| | - Gauri Sharma
- Plant Biotechnology Lab, Department of Botany, Faculty of Science, Dayalbagh Educational Institute (Deemed to be University), Dayalbagh, Agra, 282005, India
| | - Pooja Saraswat
- Plant Biotechnology Lab, Department of Botany, Faculty of Science, Dayalbagh Educational Institute (Deemed to be University), Dayalbagh, Agra, 282005, India
| | - Rajiv Ranjan
- Plant Biotechnology Lab, Department of Botany, Faculty of Science, Dayalbagh Educational Institute (Deemed to be University), Dayalbagh, Agra, 282005, India.
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22
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Ensenyat-Mendez M, Íñiguez-Muñoz S, Sesé B, Marzese DM. iGlioSub: an integrative transcriptomic and epigenomic classifier for glioblastoma molecular subtypes. BioData Min 2021; 14:42. [PMID: 34425860 PMCID: PMC8381510 DOI: 10.1186/s13040-021-00273-8] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [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: 04/09/2021] [Accepted: 08/08/2021] [Indexed: 01/05/2023] Open
Abstract
BACKGROUND Glioblastoma (GBM) is the most aggressive and prevalent primary brain tumor, with a median survival of 15 months. Advancements in multi-omics profiling combined with computational algorithms have unraveled the existence of three GBM molecular subtypes (Classical, Mesenchymal, and Proneural) with clinical relevance. However, due to the costs of high-throughput profiling techniques, GBM molecular subtyping is not currently employed in clinical settings. METHODS Using Random Forest and Nearest Shrunken Centroid algorithms, we constructed transcriptomic, epigenomic, and integrative GBM subtype-specific classifiers. We included gene expression and DNA methylation (DNAm) profiles from 304 GBM patients profiled in the Cancer Genome Atlas (TCGA), the Human Glioblastoma Cell Culture resource (HGCC), and other publicly available databases. RESULTS The integrative Glioblastoma Subtype (iGlioSub) classifier shows better performance (mean AUC = 95.9%) stratifying patients than gene expression (mean AUC = 91.9%) and DNAm-based classifiers (AUC = 93.6%). Also, to expand the understanding of the molecular differences between the GBM subtypes, this study shows that each subtype presents unique DNAm patterns and gene pathway activation. CONCLUSIONS The iGlioSub classifier provides the basis to design cost-effective strategies to stratify GBM patients in routine pathology laboratories for clinical trials, which will significantly accelerate the discovery of more efficient GBM subtype-specific treatment approaches.
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Affiliation(s)
- Miquel Ensenyat-Mendez
- Cancer Epigenetics Laboratory at the Cancer Cell Biology Group, Institut d'Investigació Sanitària Illes Balears (IdISBa), Carretera de Valldemosa 79, -1F, 07120, Palma de Mallorca, Spain
| | - Sandra Íñiguez-Muñoz
- Cancer Epigenetics Laboratory at the Cancer Cell Biology Group, Institut d'Investigació Sanitària Illes Balears (IdISBa), Carretera de Valldemosa 79, -1F, 07120, Palma de Mallorca, Spain
| | - Borja Sesé
- Cancer Epigenetics Laboratory at the Cancer Cell Biology Group, Institut d'Investigació Sanitària Illes Balears (IdISBa), Carretera de Valldemosa 79, -1F, 07120, Palma de Mallorca, Spain
| | - Diego M Marzese
- Cancer Epigenetics Laboratory at the Cancer Cell Biology Group, Institut d'Investigació Sanitària Illes Balears (IdISBa), Carretera de Valldemosa 79, -1F, 07120, Palma de Mallorca, Spain.
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23
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Xu Y, Kong J, Hu P. Computational Drug Repurposing for Alzheimer's Disease Using Risk Genes From GWAS and Single-Cell RNA Sequencing Studies. Front Pharmacol 2021; 12:617537. [PMID: 34276354 PMCID: PMC8277916 DOI: 10.3389/fphar.2021.617537] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [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: 10/15/2020] [Accepted: 06/15/2021] [Indexed: 01/14/2023] Open
Abstract
Background: Traditional therapeutics targeting Alzheimer's disease (AD)-related subpathologies have so far proved ineffective. Drug repurposing, a more effective strategy that aims to find new indications for existing drugs against other diseases, offers benefits in AD drug development. In this study, we aim to identify potential anti-AD agents through enrichment analysis of drug-induced transcriptional profiles of pathways based on AD-associated risk genes identified from genome-wide association analyses (GWAS) and single-cell transcriptomic studies. Methods: We systematically constructed four gene lists (972 risk genes) from GWAS and single-cell transcriptomic studies and performed functional and genes overlap analyses in Enrichr tool. We then used a comprehensive drug repurposing tool Gene2Drug by combining drug-induced transcriptional responses with the associated pathways to compute candidate drugs from each gene list. Prioritized potential candidates (eight drugs) were further assessed with literature review. Results: The genomic-based gene lists contain late-onset AD associated genes (BIN1, ABCA7, APOE, CLU, and PICALM) and clinical AD drug targets (TREM2, CD33, CHRNA2, PRSS8, ACE, TKT, APP, and GABRA1). Our analysis identified eight AD candidate drugs (ellipticine, alsterpaullone, tomelukast, ginkgolide A, chrysin, ouabain, sulindac sulfide and lorglumide), four of which (alsterpaullone, ginkgolide A, chrysin and ouabain) have shown repurposing potential for AD validated by their preclinical evidence and moderate toxicity profiles from literature. These support the value of pathway-based prioritization based on the disease risk genes from GWAS and scRNA-seq data analysis. Conclusion: Our analysis strategy identified some potential drug candidates for AD. Although the drugs still need further experimental validation, the approach may be applied to repurpose drugs for other neurological disorders using their genomic information identified from large-scale genomic studies.
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Affiliation(s)
- Yun Xu
- Department of Biochemistry and Medical Genetics, University of Manitoba, Winnipeg, MB, Canada
| | - Jiming Kong
- Department of Human Anatomy and Cell Science, University of Manitoba, Winnipeg, MB, Canada
| | - Pingzhao Hu
- Department of Biochemistry and Medical Genetics, University of Manitoba, Winnipeg, MB, Canada
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24
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Napolitano F, Rapakoulia T, Annunziata P, Hasegawa A, Cardon M, Napolitano S, Vaccaro L, Iuliano A, Wanderlingh LG, Kasukawa T, Medina DL, Cacchiarelli D, Gao X, di Bernardo D, Arner E. Automatic identification of small molecules that promote cell conversion and reprogramming. Stem Cell Reports 2021; 16:1381-1390. [PMID: 33891873 PMCID: PMC8185468 DOI: 10.1016/j.stemcr.2021.03.028] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/13/2020] [Revised: 03/24/2021] [Accepted: 03/25/2021] [Indexed: 12/04/2022] Open
Abstract
Controlling cell fate has great potential for regenerative medicine, drug discovery, and basic research. Although transcription factors are able to promote cell reprogramming and transdifferentiation, methods based on their upregulation often show low efficiency. Small molecules that can facilitate conversion between cell types can ameliorate this problem working through safe, rapid, and reversible mechanisms. Here, we present DECCODE, an unbiased computational method for identification of such molecules based on transcriptional data. DECCODE matches a large collection of drug-induced profiles for drug treatments against a large dataset of primary cell transcriptional profiles to identify drugs that either alone or in combination enhance cell reprogramming and cell conversion. Extensive validation in the context of human induced pluripotent stem cells shows that DECCODE is able to prioritize drugs and drug combinations enhancing cell reprogramming. We also provide predictions for cell conversion with single drugs and drug combinations for 145 different cell types.
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Affiliation(s)
- Francesco Napolitano
- Telethon Institute of Genetics and Medicine (TIGEM), Pozzuoli (NA) 80078, Italy; Computational Bioscience Research Center, King Abdullah University of Science and Technology (KAUST), Thuwal 23955-6900, Saudi Arabia
| | - Trisevgeni Rapakoulia
- Computational Bioscience Research Center, King Abdullah University of Science and Technology (KAUST), Thuwal 23955-6900, Saudi Arabia; Max Planck Institute for Molecular Genetics, Ihnestrasse 63-73, 14195 Berlin, Germany
| | - Patrizia Annunziata
- Telethon Institute of Genetics and Medicine (TIGEM), Armenise/Harvard Laboratory of Integrative Genomics, Pozzuoli (NA) 80078, Italy
| | - Akira Hasegawa
- RIKEN Center for Integrative Medical Sciences, Yokohama, Kanagawa 230-0045 Japan
| | - Melissa Cardon
- RIKEN Center for Integrative Medical Sciences, Yokohama, Kanagawa 230-0045 Japan
| | - Sara Napolitano
- Telethon Institute of Genetics and Medicine (TIGEM), Pozzuoli (NA) 80078, Italy
| | - Lorenzo Vaccaro
- Telethon Institute of Genetics and Medicine (TIGEM), Armenise/Harvard Laboratory of Integrative Genomics, Pozzuoli (NA) 80078, Italy
| | - Antonella Iuliano
- Telethon Institute of Genetics and Medicine (TIGEM), Pozzuoli (NA) 80078, Italy
| | | | - Takeya Kasukawa
- RIKEN Center for Integrative Medical Sciences, Yokohama, Kanagawa 230-0045 Japan
| | - Diego L Medina
- Telethon Institute of Genetics and Medicine (TIGEM), Pozzuoli (NA) 80078, Italy; Department of Translational Medicine, University of Naples Federico II, Naples, Italy
| | - Davide Cacchiarelli
- Telethon Institute of Genetics and Medicine (TIGEM), Armenise/Harvard Laboratory of Integrative Genomics, Pozzuoli (NA) 80078, Italy; Department of Translational Medicine, University of Naples Federico II, Naples, Italy.
| | - Xin Gao
- Computational Bioscience Research Center, King Abdullah University of Science and Technology (KAUST), Thuwal 23955-6900, Saudi Arabia.
| | - Diego di Bernardo
- Telethon Institute of Genetics and Medicine (TIGEM), Pozzuoli (NA) 80078, Italy; Department of Chemical, Materials and Industrial Production Engineering, University of Naples Federico II, 80125 Naples, Italy.
| | - Erik Arner
- RIKEN Center for Integrative Medical Sciences, Yokohama, Kanagawa 230-0045 Japan; Graduate School of Integrated Sciences for Life, Hiroshima University, Kagamiyama, Higashi-Hiroshima, 739-8528 Japan.
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25
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Wang J, Wu Z, Peng Y, Li W, Liu G, Tang Y. Pathway-Based Drug Repurposing with DPNetinfer: A Method to Predict Drug-Pathway Associations via Network-Based Approaches. J Chem Inf Model 2021; 61:2475-2485. [PMID: 33900090 DOI: 10.1021/acs.jcim.1c00009] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/14/2022]
Abstract
Identification of drug-pathway associations plays an important role in pathway-based drug repurposing. However, it is time-consuming and costly to uncover new drug-pathway associations experimentally. The drug-induced transcriptomics data provide a global view of cellular pathways and tell how these pathways change under different treatments. These data enable computational approaches for large-scale prediction of drug-pathway associations. Here we introduced DPNetinfer, a novel computational method to predict potential drug-pathway associations based on substructure-drug-pathway networks via network-based approaches. The results demonstrated that DPNetinfer performed well in a pan-cancer network with an AUC (area under curve) = 0.9358. Meanwhile, DPNetinfer was shown to have a good capability of generalization on two external validation sets (AUC = 0.8519 and 0.7494, respectively). As a case study, DPNetinfer was used in pathway-based drug repurposing for cancer therapy. Unexpected anticancer activities of some nononcology drugs were then identified on the PI3K-Akt pathway. Considering tumor heterogeneity, seven primary site-based models were constructed by DPNetinfer in different drug-pathway networks. In a word, DPNetinfer provides a powerful tool for large-scale prediction of drug-pathway associations in pathway-based drug repurposing. A web tool for DPNetinfer is freely available at http://lmmd.ecust.edu.cn/netinfer/.
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Affiliation(s)
- Jiye Wang
- Shanghai Key Laboratory of New Drug Design, School of Pharmacy, East China University of Science and Technology, Shanghai 200237, China
| | - Zengrui Wu
- Shanghai Key Laboratory of New Drug Design, School of Pharmacy, East China University of Science and Technology, Shanghai 200237, China
| | - Yayuan Peng
- Shanghai Key Laboratory of New Drug Design, School of Pharmacy, East China University of Science and Technology, Shanghai 200237, China
| | - Weihua Li
- Shanghai Key Laboratory of New Drug Design, School of Pharmacy, East China University of Science and Technology, Shanghai 200237, China
| | - Guixia Liu
- Shanghai Key Laboratory of New Drug Design, School of Pharmacy, East China University of Science and Technology, Shanghai 200237, China
| | - Yun Tang
- Shanghai Key Laboratory of New Drug Design, School of Pharmacy, East China University of Science and Technology, Shanghai 200237, China
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26
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Fang M, Richardson B, Cameron CM, Dazard JE, Cameron MJ. Drug perturbation gene set enrichment analysis (dpGSEA): a new transcriptomic drug screening approach. BMC Bioinformatics 2021; 22:22. [PMID: 33435872 PMCID: PMC7805197 DOI: 10.1186/s12859-020-03929-0] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/01/2020] [Accepted: 12/09/2020] [Indexed: 11/24/2022] Open
Abstract
Background In this study, we demonstrate that our modified Gene Set Enrichment Analysis (GSEA) method, drug perturbation GSEA (dpGSEA), can detect phenotypically relevant drug targets through a unique transcriptomic enrichment that emphasizes biological directionality of drug-derived gene sets. Results We detail our dpGSEA method and show its effectiveness in detecting specific perturbation of drugs in independent public datasets by confirming fluvastatin, paclitaxel, and rosiglitazone perturbation in gastroenteropancreatic neuroendocrine tumor cells. In drug discovery experiments, we found that dpGSEA was able to detect phenotypically relevant drug targets in previously published differentially expressed genes of CD4+T regulatory cells from immune responders and non-responders to antiviral therapy in HIV-infected individuals, such as those involved with virion replication, cell cycle dysfunction, and mitochondrial dysfunction. dpGSEA is publicly available at https://github.com/sxf296/drug_targeting. Conclusions dpGSEA is an approach that uniquely enriches on drug-defined gene sets while considering directionality of gene modulation. We recommend dpGSEA as an exploratory tool to screen for possible drug targeting molecules.
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Affiliation(s)
- Mike Fang
- Department of Population and Quantitative Health Sciences, Case Western Reserve University School of Medicine, Wolstein Research Building, 2103 Cornell Road, Suite 1-314, Cleveland, OH, 44106-7295, USA
| | - Brian Richardson
- Department of Population and Quantitative Health Sciences, Case Western Reserve University School of Medicine, Wolstein Research Building, 2103 Cornell Road, Suite 1-314, Cleveland, OH, 44106-7295, USA.,Systems Biology and Bioinformatics Program, Case Western Reserve University, Cleveland, OH, USA
| | - Cheryl M Cameron
- Department of Nutrition, Case Western Reserve University, Cleveland, OH, USA.,Systems Biology and Bioinformatics Program, Case Western Reserve University, Cleveland, OH, USA
| | - Jean-Eudes Dazard
- Center for Proteomics and Bioinformatics, Case Western Reserve University, Cleveland, OH, USA. .,Systems Biology and Bioinformatics Program, Case Western Reserve University, Cleveland, OH, USA.
| | - Mark J Cameron
- Department of Population and Quantitative Health Sciences, Case Western Reserve University School of Medicine, Wolstein Research Building, 2103 Cornell Road, Suite 1-314, Cleveland, OH, 44106-7295, USA. .,Systems Biology and Bioinformatics Program, Case Western Reserve University, Cleveland, OH, USA.
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27
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Gao S, Han L, Luo D, Liu G, Xiao Z, Shan G, Zhang Y, Zhou W. Modeling drug mechanism of action with large scale gene-expression profiles using GPAR, an artificial intelligence platform. BMC Bioinformatics 2021; 22:17. [PMID: 33413089 PMCID: PMC7788535 DOI: 10.1186/s12859-020-03915-6] [Citation(s) in RCA: 11] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/02/2020] [Accepted: 11/30/2020] [Indexed: 01/03/2023] Open
Abstract
Background Querying drug-induced gene expression profiles with machine learning method is an effective way for revealing drug mechanism of actions (MOAs), which is strongly supported by the growth of large scale and high-throughput gene expression databases. However, due to the lack of code-free and user friendly applications, it is not easy for biologists and pharmacologists to model MOAs with state-of-art deep learning approach. Results In this work, a newly developed online collaborative tool, Genetic profile-activity relationship (GPAR) was built to help modeling and predicting MOAs easily via deep learning. The users can use GPAR to customize their training sets to train self-defined MOA prediction models, to evaluate the model performances and to make further predictions automatically. Cross-validation tests show GPAR outperforms Gene set enrichment analysis in predicting MOAs. Conclusion GPAR can serve as a better approach in MOAs prediction, which may facilitate researchers to generate more reliable MOA hypothesis.
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Affiliation(s)
- Shengqiao Gao
- Beijing Institute of Pharmacology and Toxicology, State Key Laboratory of Toxicology and Medical Countermeasures, Beijing, 100850, China
| | - Lu Han
- Beijing Institute of Pharmacology and Toxicology, State Key Laboratory of Toxicology and Medical Countermeasures, Beijing, 100850, China
| | - Dan Luo
- Beijing Institute of Pharmacology and Toxicology, State Key Laboratory of Toxicology and Medical Countermeasures, Beijing, 100850, China
| | - Gang Liu
- Beijing Institute of Pharmacology and Toxicology, State Key Laboratory of Toxicology and Medical Countermeasures, Beijing, 100850, China
| | - Zhiyong Xiao
- Beijing Institute of Pharmacology and Toxicology, State Key Laboratory of Toxicology and Medical Countermeasures, Beijing, 100850, China
| | - Guangcun Shan
- School of Instrumentation Science and Opto-Electronics Engineering and Beijing Advanced Innovation Center for Big Data-Based Precision Medicine, Beihang University, Beijing, 100083, China
| | - Yongxiang Zhang
- Beijing Institute of Pharmacology and Toxicology, State Key Laboratory of Toxicology and Medical Countermeasures, Beijing, 100850, China.
| | - Wenxia Zhou
- Beijing Institute of Pharmacology and Toxicology, State Key Laboratory of Toxicology and Medical Countermeasures, Beijing, 100850, China.
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28
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Kim S. A New Computational Approach to Evaluating Systemic Gene–Gene Interactions in a Pathway Affected by Drug LY294002. Processes (Basel) 2020; 8:1230. [DOI: 10.3390/pr8101230] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/11/2022] Open
Abstract
In this study, we investigate how drugs systemically affect genes via pathways by integrating information from interactions between chemical compounds and molecular expression datasets, and from pathway information such as gene sets using mathematical models. First, we adopt drug-induced gene expression datasets; then, employ gene set enrichment analysis tools for selecting candidate enrichment pathways; and lastly, implement the inverse algorithm package for identifying gene–gene regulatory networks in a pathway. We tested LY294002-induced datasets of the MCF7 breast cancer cell lines, and found a CELL CYCLE pathway with 101 genes, ERBB signaling pathway consisting of 82 genes, and MTOR pathway consisting of 45 genes. We consider two interactions: quantity strength depending on number of interactions, and quality strength depending on weight of interaction as positive (+) and negative (−) interactions. Our methods revealed ANAPC1-CDK6 (−0.412) and ORC2L- CHEK1(0.951) for the CELL CYCLE pathway; INS-RPS6 (−3.125) and PRKAA2-PRKAA2 (+1.319) for the MTOR pathway; and CBLB-RPS6KB1 (−0.141), RPS6KB1-CBLC (+0.238) for the ERBB signaling pathway to be top quality interactions. Top quantity interactions discovered include 12; the CDC (−,+) gene family for the CELL CYCLE pathway, 20; PIK3 (−), 23; PIK3CG (+) for the MTOR pathway, 11; PAK (−), 10; PIK3 (+) for the ERBB signaling pathway.
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Lau A, So HC. Turning genome-wide association study findings into opportunities for drug repositioning. Comput Struct Biotechnol J 2020; 18:1639-1650. [PMID: 32670504 PMCID: PMC7334463 DOI: 10.1016/j.csbj.2020.06.015] [Citation(s) in RCA: 15] [Impact Index Per Article: 3.8] [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: 11/10/2019] [Revised: 06/05/2020] [Accepted: 06/05/2020] [Indexed: 02/02/2023] Open
Abstract
Drug development is a very costly and lengthy process, while repositioned or repurposed drugs could be brought into clinical practice within a shorter time-frame and at a much reduced cost. Numerous computational approaches to drug repositioning have been developed, but methods utilizing genome-wide association studies (GWASs) data are less explored. The past decade has observed a massive growth in the amount of data from GWAS; the rich information contained in GWAS has great potential to guide drug repositioning or discovery. While multiple tools are available for finding the most relevant genes from GWAS hits, searching for top susceptibility genes is only one way to guide repositioning, which has its own limitations. Here we provide a comprehensive review of different computational approaches that employ GWAS data to guide drug repositioning. These methods include selecting top candidate genes from GWAS as drug targets, deducing drug candidates based on drug-drug and disease-disease similarities, searching for reversed expression profiles between drugs and diseases, pathway-based methods as well as approaches based on analysis of biological networks. Each method is illustrated with examples, and their respective strengths and limitations are discussed. We also discussed several areas for future research.
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Affiliation(s)
- Alexandria Lau
- School of Biomedical Sciences, Faculty of Medicine, The Chinese University of Hong Kong, Hong Kong SAR, China
| | - Hon-Cheong So
- School of Biomedical Sciences, Faculty of Medicine, The Chinese University of Hong Kong, Hong Kong SAR, China
- KIZ-CUHK Joint Laboratory of Bioresources and Molecular Research of Common Diseases, Kunming Zoology Institute of Zoology and The Chinese University of Hong Kong, Hong Kong SAR, China
- Department of Psychiatry, The Chinese University of Hong Kong, Hong Kong SAR, China
- Margaret K.L. Cheung Research Centre for Management of Parkinsonism, The Chinese University of Hong Kong, Hong Kong SAR, China
- Shenzhen Research Institute, The Chinese University of Hong Kong, Shenzhen, China
- Brain and Mind Institute, The Chinese University of Hong Kong, Hong Kong SAR, China
- Hong Kong Branch of the Chinese Academy of Sciences Center for Excellence in Animal Evolution and Genetics, The Chinese University of Hong Kong, Hong Kong SAR, China
- Corresponding author at: School of Biomedical Sciences, Faculty of Medicine, The Chinese University of Hong Kong, Hong Kong SAR, China.
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Martín-Hernández R, Reglero G, Ordovás JM, Dávalos A. NutriGenomeDB: a nutrigenomics exploratory and analytical platform. Database (Oxford) 2020; 2019:5607505. [PMID: 31665759 DOI: 10.1093/database/baz097] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/12/2019] [Revised: 06/03/2019] [Accepted: 07/01/2019] [Indexed: 12/21/2022]
Abstract
Habitual consumption of certain foods has shown beneficial and protective effects against multiple chronic diseases. However, it is not clear by which molecular mechanisms they may exert their beneficial effects. Multiple -omic experiments available in public databases have generated gene expression data following the treatment of human cells with different food nutrients and bioactive compounds. Exploration of such data in an integrative manner offers excellent possibilities for gaining insights into the molecular effects of food compounds and bioactive molecules at the cellular level. Here we present NutriGenomeDB, a web-based application that hosts manually curated gene sets defined from gene expression signatures, after differential expression analysis of nutrigenomics experiments performed on human cells available in the Gene Expression Omnibus (GEO) repository. Through its web interface, users can explore gene expression data with interactive visualizations. In addition, external gene signatures can be connected with nutrigenomics gene sets using a gene pattern-matching algorithm. We further demonstrate how the application can capture the primary molecular mechanisms of a drug used to treat hypertension and thus connect its mode of action with hosted food compounds.
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Affiliation(s)
- Roberto Martín-Hernández
- Bioinformatics and Biostatistics Unit, IMDEA Food Institute, CEI UAM+CSIC, Ctra. De Canto Blanco 8, Madrid 28049, Spain
| | - Guillermo Reglero
- Sección Departamental de Ciencias de la Alimentación, Facultad de Ciencias, Universidad Autónoma de Madrid, CEI UAM+CSIC, C/ Nicolas Cabrera 9, Madrid 28049, Spain.,Laboratory of Food Products for Precision Nutrition, IMDEA Food Institute, CEI UAM+CSIC, Ctra. De Canto Blanco 8, Madrid 28049, Spain
| | - José M Ordovás
- Nutrition and Genomics Laboratory, JM-USDA Human Nutrition Research Center on Aging, Tufts University, 711 Washington Street, Boston, MA 02111, USA.,Laboratory of Nutritional Genomics, IMDEA Food Institute, CEI UAM+CSIC, Ctra. De Canto Blanco 8, Madrid 280149, Spain
| | - Alberto Dávalos
- Laboratory of Epigenetics of Lipid Metabolism, IMDEA Food Institute, CEI UAM+CSIC, Ctra. De Canto Blanco 8, Madrid 28049, Spain
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Abstract
The bioengineering tools have significant advantages through less time-consuming and utilized as a promising stage for the production of pharmaceutical bioproducts under the single platform. This review highlighted the advantages and current improvement in the plant, animal and microbial bioengineering tools and outlines feasible approaches by biological and process’s bioengineering levels for advancing the economic feasibility of pharmaceutical’s production. The critical analysis results revealed that system biology and synthetic biology along with advanced bioengineering tools like transcriptome, proteome, metabolome and nano bioengineering tools have shown a promising impact on the development of pharmaceutical’s bioproducts. Tools to overcome and resolve the accompanying encounters of pharmaceutical’s production that include nano bioengineering tools are also discussed. As a summary and prospect, it also gives new insight into the challenges and possible breakthrough of the development of pharmaceutical’s bioproducts through bioengineering tools.
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Affiliation(s)
- Surendra Sarsaiya
- Key Laboratory of Basic Pharmacology and Joint International Research Laboratory of Ethnomedicine of Ministry of Education, Zunyi Medical University , Zunyi , China.,Bioresource Institute for Healthy Utilization, Zunyi Medical University , Zunyi , China
| | - Jingshan Shi
- Key Laboratory of Basic Pharmacology and Joint International Research Laboratory of Ethnomedicine of Ministry of Education, Zunyi Medical University , Zunyi , China
| | - Jishuang Chen
- Bioresource Institute for Healthy Utilization, Zunyi Medical University , Zunyi , China.,College of Biotechnology and Pharmaceutical Engineering, Nanjing Tech University , Nanjing , China
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Guillen-Guio B, Lorenzo-Salazar JM, Ma SF, Hou PC, Hernandez-Beeftink T, Corrales A, García-Laorden MI, Jou J, Espinosa E, Muriel A, Domínguez D, Lorente L, Martín MM, Rodríguez-Gallego C, Solé-Violán J, Ambrós A, Carriedo D, Blanco J, Añón JM, Reilly JP, Jones TK, Ittner CA, Feng R, Schöneweck F, Kiehntopf M, Noth I, Scholz M, Brunkhorst FM, Scherag A, Meyer NJ, Villar J, Flores C. Sepsis-associated acute respiratory distress syndrome in individuals of European ancestry: a genome-wide association study. Lancet Respir Med 2020; 8:258-266. [PMID: 31982041 DOI: 10.1016/s2213-2600(19)30368-6] [Citation(s) in RCA: 31] [Impact Index Per Article: 7.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/26/2019] [Revised: 07/25/2019] [Accepted: 08/07/2019] [Indexed: 12/30/2022]
Abstract
BACKGROUND Acute respiratory distress syndrome (ARDS) is a lung inflammatory process caused mainly by sepsis. Most previous studies that identified genetic risks for ARDS focused on candidates with biological relevance. We aimed to identify novel genetic variants associated with ARDS susceptibility and to provide complementary functional evidence of their effect in gene regulation. METHODS We did a case-control genome-wide association study (GWAS) of 1935 European individuals, using patients with sepsis-associated ARDS as cases and patients with sepsis without ARDS as controls. The discovery stage included 672 patients admitted into a network of Spanish intensive care units between January, 2002, and January, 2017. The replication stage comprised 1345 individuals from two independent datasets from the MESSI cohort study (Sep 22, 2008-Nov 30, 2017; USA) and the VISEP (April 1, 2003-June 30, 2005) and MAXSEP (Oct 1, 2007-March 31, 2010) trials of the SepNet study (Germany). Results from discovery and replication stages were meta-analysed to identify association signals. We then used RNA sequencing data from lung biopsies, in-silico analyses, and luciferase reporter assays to assess the functionallity of associated variants. FINDINGS We identified a novel genome-wide significant association with sepsis-associated ARDS susceptibility (rs9508032, odds ratio [OR] 0·61, 95% CI 0·41-0·91, p=5·18 × 10-8) located within the Fms-related tyrosine kinase 1 (FLT1) gene, which encodes vascular endothelial growth factor receptor 1 (VEGFR-1). The region containing the sentinel variant and its best proxies acted as a silencer for the FLT1 promoter, and alleles with protective effects in ARDS further reduced promoter activity (p=0·0047). A literature mining of all previously described ARDS genes validated the association of vascular endothelial growth factor A (VEGFA; OR 0·55, 95% CI 0·41-0·73; p=4·69 × 10-5). INTERPRETATION A common variant within the FLT1 gene is associated with sepsis-associated ARDS. Our findings support a role for the vascular endothelial growth factor signalling pathway in ARDS pathogenesis and identify VEGFR-1 as a potential therapeutic target. FUNDING Instituto de Salud Carlos III, European Regional Development Funds, Instituto Tecnológico y de Energías Renovables.
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Affiliation(s)
- Beatriz Guillen-Guio
- Research Unit, Hospital Universitario Nuestra Señora de Candelaria, Santa Cruz de Tenerife, Spain
| | - Jose M Lorenzo-Salazar
- Genomics Division, Instituto Tecnológico y de Energías Renovables, Santa Cruz de Tenerife, Spain
| | - Shwu-Fan Ma
- Division of Pulmonary & Critical Care Medicine, Department of Medicine, University of Virginia, Charlottesville, VA, USA
| | - Pei-Chi Hou
- Division of Pulmonary & Critical Care Medicine, Department of Medicine, University of Virginia, Charlottesville, VA, USA
| | - Tamara Hernandez-Beeftink
- Research Unit, Hospital Universitario Nuestra Señora de Candelaria, Santa Cruz de Tenerife, Spain; Research Unit, Hospital Universitario de Gran Canaria Dr Negrín, Las Palmas de Gran Canaria, Spain
| | - Almudena Corrales
- Research Unit, Hospital Universitario Nuestra Señora de Candelaria, Santa Cruz de Tenerife, Spain; CIBER de Enfermedades Respiratorias, Instituto de Salud Carlos III, Madrid, Spain
| | - M Isabel García-Laorden
- Research Unit, Hospital Universitario de Gran Canaria Dr Negrín, Las Palmas de Gran Canaria, Spain; CIBER de Enfermedades Respiratorias, Instituto de Salud Carlos III, Madrid, Spain
| | - Jonathan Jou
- University of Illinois College of Medicine at Peoria, Peoria, IL, USA
| | - Elena Espinosa
- Department of Anesthesiology, Hospital Universitario Nuestra Señora de Candelaria, Santa Cruz de Tenerife, Spain
| | - Arturo Muriel
- Intensive Care Unit, Hospital Universitario Rio Hortega, Valladolid, Spain
| | - David Domínguez
- Department of Anesthesiology, Hospital Universitario Nuestra Señora de Candelaria, Santa Cruz de Tenerife, Spain
| | - Leonardo Lorente
- Intensive Care Unit, Hospital Universitario de Canarias, La Laguna, Tenerife, Spain
| | - María M Martín
- Intensive Care Unit, Hospital Universitario Nuestra Señora de Candelaria, Santa Cruz de Tenerife, Spain
| | - Carlos Rodríguez-Gallego
- Department of Immunology, Hospital Universitario de Gran Canaria Dr Negrín, Las Palmas de Gran Canaria, Spain
| | - Jordi Solé-Violán
- Intensive Care Unit, Hospital Universitario de Gran Canaria Dr Negrín, Las Palmas de Gran Canaria, Spain; CIBER de Enfermedades Respiratorias, Instituto de Salud Carlos III, Madrid, Spain
| | - Alfonso Ambrós
- Intensive Care Unit, Hospital General de Ciudad Real, Ciudad Real, Spain
| | - Demetrio Carriedo
- Intensive Care Unit, Complejo Hospitalario Universitario de León, León, Spain
| | - Jesús Blanco
- CIBER de Enfermedades Respiratorias, Instituto de Salud Carlos III, Madrid, Spain; Intensive Care Unit, Hospital Universitario Rio Hortega, Valladolid, Spain
| | - José M Añón
- CIBER de Enfermedades Respiratorias, Instituto de Salud Carlos III, Madrid, Spain; Intensive Care Unit, Hospital Universitario La Paz, IdiPAZ, Madrid, Spain
| | - John P Reilly
- Division of Pulmonary, Allergy, and Critical Care Medicine, Perelman School of Medicine, University of Pennsylvania, Philadelphia PA, USA
| | - Tiffanie K Jones
- Division of Pulmonary, Allergy, and Critical Care Medicine, Perelman School of Medicine, University of Pennsylvania, Philadelphia PA, USA
| | - Caroline Ag Ittner
- Division of Pulmonary, Allergy, and Critical Care Medicine, Perelman School of Medicine, University of Pennsylvania, Philadelphia PA, USA
| | - Rui Feng
- Department of Biostatistics, Epidemiology, and Informatics, University of Pennsylvania, Philadelphia PA, USA
| | - Franziska Schöneweck
- Integrated Research and Treatment Center, Jena University Hospital, Jena, Germany
| | - Michael Kiehntopf
- Center for Sepsis Control and Care, Institute of Clinical Chemistry and Laboratory Diagnostics, Jena University Hospital, Jena, Germany; Integrated Biobank Jena, Jena University Hospital, Jena, Germany
| | - Imre Noth
- Division of Pulmonary & Critical Care Medicine, Department of Medicine, University of Virginia, Charlottesville, VA, USA
| | - Markus Scholz
- Institute for Medical Informatics, Statistics and Epidemiology, University of Leipzig, Leipzig, Germany
| | - Frank M Brunkhorst
- Center for Clinical Studies, Jena University Hospital, Jena, Germany; Paul-Martini-Clinical Sepsis Research Unit, Department of Anesthesiology and Intensive Care Medicine, Jena University Hospital, Jena, Germany
| | - André Scherag
- Integrated Research and Treatment Center, Jena University Hospital, Jena, Germany; Institute of Medical Statistics, Computer and Data Sciences, Jena University Hospital, Jena, Germany
| | - Nuala J Meyer
- Division of Pulmonary, Allergy, and Critical Care Medicine, Perelman School of Medicine, University of Pennsylvania, Philadelphia PA, USA
| | - Jesús Villar
- Research Unit, Hospital Universitario de Gran Canaria Dr Negrín, Las Palmas de Gran Canaria, Spain; CIBER de Enfermedades Respiratorias, Instituto de Salud Carlos III, Madrid, Spain; Keenan Research Center for Biomedical Sciences at the Li Ka Shing Knowledge Institute, St. Michael's Hospital, Toronto, ON, Canada
| | - Carlos Flores
- Research Unit, Hospital Universitario Nuestra Señora de Candelaria, Santa Cruz de Tenerife, Spain; Genomics Division, Instituto Tecnológico y de Energías Renovables, Santa Cruz de Tenerife, Spain; CIBER de Enfermedades Respiratorias, Instituto de Salud Carlos III, Madrid, Spain; Instituto de Tecnologías Biomédicas, Universidad de La Laguna, Santa Cruz de Tenerife, Spain.
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Ferguson LB, Patil S, Moskowitz BA, Ponomarev I, Harris RA, Mayfield RD, Messing RO. A Pathway-Based Genomic Approach to Identify Medications: Application to Alcohol Use Disorder. Brain Sci 2019; 9:brainsci9120381. [PMID: 31888299 PMCID: PMC6956180 DOI: 10.3390/brainsci9120381] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/04/2019] [Revised: 12/12/2019] [Accepted: 12/13/2019] [Indexed: 12/31/2022] Open
Abstract
Chronic, excessive alcohol use alters brain gene expression patterns, which could be important for initiating, maintaining, or progressing the addicted state. It has been proposed that pharmaceuticals with opposing effects on gene expression could treat alcohol use disorder (AUD). Computational strategies comparing gene expression signatures of disease to those of pharmaceuticals show promise for nominating novel treatments. We reasoned that it may be sufficient for a treatment to target the biological pathway rather than lists of individual genes perturbed by AUD. We analyzed published and unpublished transcriptomic data using gene set enrichment of Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways to identify biological pathways disrupted in AUD brain and by compounds in the Library of Network-based Cellular Signatures (LINCS L1000) and Connectivity Map (CMap) databases. Several pathways were consistently disrupted in AUD brain, including an up-regulation of genes within the Complement and Coagulation Cascade, Focal Adhesion, Systemic Lupus Erythematosus, and MAPK signaling, and a down-regulation of genes within the Oxidative Phosphorylation pathway, strengthening evidence for their importance in AUD. Over 200 compounds targeted genes within those pathways in an opposing manner, more than twenty of which have already been shown to affect alcohol consumption, providing confidence in our approach. We created a user-friendly web-interface that researchers can use to identify drugs that target pathways of interest or nominate mechanism of action for drugs. This study demonstrates a unique systems pharmacology approach that can nominate pharmaceuticals that target pathways disrupted in disease states such as AUD and identify compounds that could be repurposed for AUD if sufficient evidence is attained in preclinical studies.
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Affiliation(s)
- Laura B. Ferguson
- Waggoner Center for Alcohol and Addiction Research, The University of Texas at Austin, Austin, TX 78712, USA; (L.B.F.); (S.P.); (B.A.M.); (R.A.H.); (R.D.M.)
- Department of Neuroscience, The University of Texas at Austin, Austin, TX 78712, USA
- Department of Neurology, Dell Medical School, The University of Texas at Austin, Austin, TX 78712, USA
| | - Shruti Patil
- Waggoner Center for Alcohol and Addiction Research, The University of Texas at Austin, Austin, TX 78712, USA; (L.B.F.); (S.P.); (B.A.M.); (R.A.H.); (R.D.M.)
- Department of Neuroscience, The University of Texas at Austin, Austin, TX 78712, USA
| | - Bailey A. Moskowitz
- Waggoner Center for Alcohol and Addiction Research, The University of Texas at Austin, Austin, TX 78712, USA; (L.B.F.); (S.P.); (B.A.M.); (R.A.H.); (R.D.M.)
- Department of Neurology, Dell Medical School, The University of Texas at Austin, Austin, TX 78712, USA
| | - Igor Ponomarev
- Department of Pharmacology and Neuroscience, Texas Tech University Health Sciences Center, Lubbock, TX 79430, USA;
| | - Robert A. Harris
- Waggoner Center for Alcohol and Addiction Research, The University of Texas at Austin, Austin, TX 78712, USA; (L.B.F.); (S.P.); (B.A.M.); (R.A.H.); (R.D.M.)
- Department of Neuroscience, The University of Texas at Austin, Austin, TX 78712, USA
| | - Roy D. Mayfield
- Waggoner Center for Alcohol and Addiction Research, The University of Texas at Austin, Austin, TX 78712, USA; (L.B.F.); (S.P.); (B.A.M.); (R.A.H.); (R.D.M.)
- Department of Neuroscience, The University of Texas at Austin, Austin, TX 78712, USA
| | - Robert O. Messing
- Waggoner Center for Alcohol and Addiction Research, The University of Texas at Austin, Austin, TX 78712, USA; (L.B.F.); (S.P.); (B.A.M.); (R.A.H.); (R.D.M.)
- Department of Neuroscience, The University of Texas at Austin, Austin, TX 78712, USA
- Department of Neurology, Dell Medical School, The University of Texas at Austin, Austin, TX 78712, USA
- Correspondence: ; Tel.: +1-512-471-1735
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Napolitano F, Carrella D, Gao X, di Bernardo D. gep2pep: a Bioconductor package for the creation and analysis of pathway-based expression profiles. Bioinformatics 2019; 36:btz803. [PMID: 31647521 PMCID: PMC7703749 DOI: 10.1093/bioinformatics/btz803] [Citation(s) in RCA: 4] [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] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/14/2019] [Revised: 09/03/2019] [Accepted: 10/21/2019] [Indexed: 11/13/2022] Open
Abstract
SUMMARY Pathway-based expression profiles allow for high-level interpretation of transcriptomic data and systematic comparison of dysregulated cellular programs. We have previously demonstrated the efficacy of pathway-based approaches with two different applications: the Drug Set Enrichment Analysis and the Gene2drug analysis. Here we present a software tool that allows to easily convert gene-based profiles to pathway-based profiles and analyze them within the popular R framework. We also provide pre-computed profiles derived from the original Connectivity Map and its next generation release, i.e. the LINCS database. AVAILABILITY AND IMPLEMENTATION the tool is implemented as the R/Bioconductor package gep2pep and can be freely downloaded from https://bioconductor.org/packages/gep2pep. SUPPLEMENTARY INFORMATION Supplementary data are available at http://dsea.tigem.it/lincs.
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Affiliation(s)
- Farancesco Napolitano
- Telethon Institute of Genetics and Medicine (TIGEM), Pozzuoli, NA 80078, Italy
- Computational Bioscience Research Center (CBRC), Computer, Electrical and Mathematical Sciences and Engineering (CEMSE), King Abdullah University of Science and Technology (KAUST), Thuwal 23955-6900, Kingdom of Saudi Arabia
| | - Diego Carrella
- Telethon Institute of Genetics and Medicine (TIGEM), Pozzuoli, NA 80078, Italy
| | - Xin Gao
- Computational Bioscience Research Center (CBRC), Computer, Electrical and Mathematical Sciences and Engineering (CEMSE), King Abdullah University of Science and Technology (KAUST), Thuwal 23955-6900, Kingdom of Saudi Arabia
| | - Diego di Bernardo
- Telethon Institute of Genetics and Medicine (TIGEM), Pozzuoli, NA 80078, Italy
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Mottini C, Napolitano F, Li Z, Gao X, Cardone L. Computer-aided drug repurposing for cancer therapy: Approaches and opportunities to challenge anticancer targets. Semin Cancer Biol 2021; 68:59-74. [PMID: 31562957 DOI: 10.1016/j.semcancer.2019.09.023] [Citation(s) in RCA: 45] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/01/2019] [Revised: 09/24/2019] [Accepted: 09/24/2019] [Indexed: 12/14/2022]
Abstract
Despite huge efforts made in academic and pharmaceutical worldwide research, current anticancer therapies achieve effective treatment in a limited number of neoplasia cases only. Oncology terms such as big killers - to identify tumours with yet a high mortality rate - or undruggable cancer targets, and chemoresistance, represent the current therapeutic debacle of cancer treatments. In addition, metastases, tumour microenvironments, tumour heterogeneity, metabolic adaptations, and immunotherapy resistance are essential features controlling tumour response to therapies, but still, lack effective therapeutics or modulators. In this scenario, where the pharmaceutical productivity and drug efficacy in oncology seem to have reached a plateau, the so-called drug repurposing - i.e. the use of old drugs, already in clinical use, for a different therapeutic indication - is an appealing strategy to improve cancer therapy. Opportunities for drug repurposing are often based on occasional observations or on time-consuming pre-clinical drug screenings that are often not hypothesis-driven. In contrast, in-silico drug repurposing is an emerging, hypothesis-driven approach that takes advantage of the use of big-data. Indeed, the extensive use of -omics technologies, improved data storage, data meaning, machine learning algorithms, and computational modeling all offer unprecedented knowledge of the biological mechanisms of cancers and drugs' modes of action, providing extensive availability for both disease-related data and drugs-related data. This offers the opportunity to generate, with time and cost-effective approaches, computational drug networks to predict, in-silico, the efficacy of approved drugs against relevant cancer targets, as well as to select better responder patients or disease' biomarkers. Here, we will review selected disease-related data together with computational tools to be exploited for the in-silico repurposing of drugs against validated targets in cancer therapies, focusing on the oncogenic signaling pathways activation in cancer. We will discuss how in-silico drug repurposing has the promise to shortly improve our arsenal of anticancer drugs and, likely, overcome certain limitations of modern cancer therapies against old and new therapeutic targets in oncology.
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36
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DiNome ML, Orozco JIJ, Matsuba C, Manughian-Peter AO, Ensenyat-Mendez M, Chang SC, Jalas JR, Salomon MP, Marzese DM. Clinicopathological Features of Triple-Negative Breast Cancer Epigenetic Subtypes. Ann Surg Oncol 2019; 26:3344-3353. [PMID: 31342401 DOI: 10.1245/s10434-019-07565-8] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/22/2019] [Indexed: 12/31/2022]
Abstract
BACKGROUND/OBJECTIVE Triple-negative breast cancer (TNBC) is a heterogeneous collection of breast tumors with numerous differences including morphological characteristics, genetic makeup, immune-cell infiltration, and response to systemic therapy. DNA methylation profiling is a robust tool to accurately identify disease-specific subtypes. We aimed to generate an epigenetic subclassification of TNBC tumors (epitypes) with utility for clinical decision-making. METHODS Genome-wide DNA methylation profiles from TNBC patients generated in the Cancer Genome Atlas project were used to build machine learning-based epigenetic classifiers. Clinical and demographic variables, as well as gene expression and gene mutation data from the same cohort, were integrated to further refine the TNBC epitypes. RESULTS This analysis indicated the existence of four TNBC epitypes, named as Epi-CL-A, Epi-CL-B, Epi-CL-C, and Epi-CL-D. Patients with Epi-CL-B tumors showed significantly shorter disease-free survival and overall survival [log rank; P = 0.01; hazard ratio (HR) 3.89, 95% confidence interval (CI) 1.3-11.63 and P = 0.003; HR 5.29, 95% CI 1.55-18.18, respectively]. Significant gene expression and mutation differences among the TNBC epitypes suggested alternative pathway activation that could be used as ancillary therapeutic targets. These epigenetic subtypes showed complementarity with the recently described TNBC transcriptomic subtypes. CONCLUSIONS TNBC epigenetic subtypes exhibit significant clinical and molecular differences. The links between genetic make-up, gene expression programs, and epigenetic subtypes open new avenues in the development of laboratory tests to more efficiently stratify TNBC patients, helping optimize tailored treatment approaches.
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Affiliation(s)
- Maggie L DiNome
- Department of Surgery, David Geffen School of Medicine, University California Los Angeles (UCLA), Los Angeles, CA, USA
| | - Javier I J Orozco
- Cancer Epigenetics Laboratory, John Wayne Cancer Institute at Providence Saint John's Health Center, Santa Monica, CA, USA
| | - Chikako Matsuba
- Computational Biology Laboratory, John Wayne Cancer Institute at Providence St. John's Health Center, Santa Monica, CA, USA
| | - Ayla O Manughian-Peter
- Cancer Epigenetics Laboratory, John Wayne Cancer Institute at Providence Saint John's Health Center, Santa Monica, CA, USA
| | - Miquel Ensenyat-Mendez
- Cancer Cell Biology Group, Balearic Islands Health Research Institute (IdISBa), Palma, Islas Baleares, Spain
| | - Shu-Ching Chang
- Medical Data Research Center, Providence Saint Joseph Health, Portland, OR, USA
| | - John R Jalas
- Department of Pathology, Providence Saint John's Health Center, Santa Monica, CA, USA
| | - Matthew P Salomon
- Computational Biology Laboratory, John Wayne Cancer Institute at Providence St. John's Health Center, Santa Monica, CA, USA
| | - Diego M Marzese
- Cancer Epigenetics Laboratory, John Wayne Cancer Institute at Providence Saint John's Health Center, Santa Monica, CA, USA.
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Failli M, Paananen J, Fortino V. Prioritizing target-disease associations with novel safety and efficacy scoring methods. Sci Rep 2019; 9:9852. [PMID: 31285471 DOI: 10.1038/s41598-019-46293-7] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/21/2018] [Accepted: 06/25/2019] [Indexed: 01/24/2023] Open
Abstract
Biological target (commonly genes or proteins) identification is still largely a manual process, where experts manually try to collect and combine information from hundreds of data sources, ranging from scientific publications to omics databases. Targeting the wrong gene or protein will lead to failure of the drug development process, as well as incur delays and costs. To improve this process, different software platforms are being developed. These platforms rely strongly on efficacy estimates based on target-disease association scores created by computational methods for drug target prioritization. Here novel computational methods are presented to more accurately evaluate the efficacy and safety of potential drug targets. The proposed efficacy scores utilize existing gene expression data and tissue/disease specific networks to improve the inference of target-disease associations. Conversely, safety scores enable the identification of genes that are essential, potentially susceptible to adverse effects or carcinogenic. Benchmark results demonstrate that our transcriptome-based methods for drug target prioritization can increase the true positive rate of target-disease associations. Additionally, the proposed safety evaluation system enables accurate predictions of targets of withdrawn drugs and targets of drug trials prematurely discontinued.
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Abstract
Macro (Autophagy) is a catabolic process that relies on the cooperative function of two organelles: the lysosome and the autophagosome. The recent discovery of a transcriptional gene network that co-regulates the biogenesis and function of these two organelles, and the identification of transcription factors, miRNAs and epigenetic regulators of autophagy, demonstrated that this catabolic process is controlled by both transcriptional and post-transcriptional mechanisms. In this review article, we discuss the nuclear events that control autophagy, focusing particularly on the role of the MiT/TFE transcription factor family. In addition, we will discuss evidence suggesting that the transcriptional regulation of autophagy could be targeted for the treatment of human genetic diseases, such as lysosomal storage disorders (LSDs) and neurodegeneration.
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Affiliation(s)
- Chiara Di Malta
- Telethon Institute of Genetics and Medicine, Pozzuoli, Italy
- Department of Medical and Translational Sciences, University of Naples Federico II, Naples, Italy
| | - Laura Cinque
- Telethon Institute of Genetics and Medicine, Pozzuoli, Italy
| | - Carmine Settembre
- Telethon Institute of Genetics and Medicine, Pozzuoli, Italy
- Department of Medical and Translational Sciences, University of Naples Federico II, Naples, Italy
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Abstract
In this study, we identified enrichment pathway connections from MCF7 breast cancer epithelial cells that were treated with 87 drugs. We extracted drug-treated samples, where the sample size was greater than or equal to 5. The drugs included 17-allylamino-geldanamycin, LY294002, trichostatin A, valproic acid, sirolimus, and wortmannin, which had sample sizes of 11, 8, 7, 7, 7, and 5, respectively. We found meaningful pathways using gene set enrichment analysis and identified intradrug and interdrug pathway interactions, which implied the influence of drug combination. Among the top 20 enrichment pathways that were wortmannin induced, there were a total of 37 intradrug pathway interactions via common genes. Thirty-seven pathway interactions were induced by valproic acid, 11 induced by trichostatin A, 20 induced by LY294002, and 59 induced by sirolimus, all via common genes. The number of interdrug-induced pathway interactions ranged from one pair of pathways to 23. The pair of ERBB_SIGNALING and INSULIN_SIGNALING pathways showed the highest score from a pair of 2 individual drugs. The highest number of pathway interactions was observed between the drugs 17-allylamino-geldanamycin and LY294002.
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Affiliation(s)
- Shinuk Kim
- Department of Civil Engineering, Sangmyung University, Cheonan, Republic of Korea
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40
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Grenier L, Hu P. Computational drug repurposing for inflammatory bowel disease using genetic information. Comput Struct Biotechnol J 2019; 17:127-135. [PMID: 30728920 PMCID: PMC6352300 DOI: 10.1016/j.csbj.2019.01.001] [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] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/30/2018] [Revised: 01/01/2019] [Accepted: 01/02/2019] [Indexed: 12/22/2022] Open
Abstract
As knowledge of the genetics behind inflammatory bowel disease (IBD) has continually improved, there has been a demand for methods that can use this data in a clinically significant way. Genome-wide association analyses for IBD have identified 232 risk genetic loci for the disorder. While identification of these risk loci enriches our understanding of the underlying biology of the disorder, their identification does not serve a clinical purpose. A potential use of this genetic information is to look for potential IBD drugs that target these loci in a procedure known as drug repurposing. The demand for new drug treatments for IBD is high due to the side effects and high costs of current treatments. We hypothesize that IBD genetic variants obtained from GWAS and the candidate genes prioritized from the variants have a causal relationship with IBD drug targets. A computational drug repositioning study was done due to its efficiency and inexpensiveness compared to traditional in vitro or biochemical approaches. Our approach for drug repurposing was multi-layered; it not only focused on the interactions between drugs and risk IBD genes, but also the interactions between drugs and all of the biological pathways the risk genes are involved in. We prioritized IBD candidate genes using identified genetic variants and identified potential drug targets and drugs that can be potentially repositioned or developed for IBD using the identified candidate genes. Our analysis strategy can be applied to repurpose drugs for other complex diseases using their risk genes identified from genetic analysis.
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Affiliation(s)
- Liam Grenier
- Department of Biochemistry and Medical Genetics and The George and Fay Yee Centre for Healthcare Innovation, University of Manitoba, Winnipeg, MB, Canada
| | - Pingzhao Hu
- Department of Biochemistry and Medical Genetics and The George and Fay Yee Centre for Healthcare Innovation, University of Manitoba, Winnipeg, MB, Canada
- Department of Electrical and Computer Engineering, University of Manitoba, Winnipeg, MB, Canada
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41
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Abstract
Conventional drug discovery in general is costly and time-consuming with extremely low success and relatively high attrition rates. The disparity between high cost of drug discovery and vast unmet medical needs resulted in advent of an increasing number of computational approaches that can "connect" disease with a candidate therapeutic. This includes computational drug repurposing or repositioning wherein the goal is to discover a new indication for an approved drug. Computational drug discovery approaches that are commonly used are similarity-based wherein network analysis or machine learning-based methods are used. One such approach is matching gene expression signatures from disease to those from small molecules, commonly referred to as connectivity mapping. In this chapter, we will focus on how publicly available existing transcriptomic data from diseases can be reused to identify novel candidate therapeutics and drug repositioning candidates. To elucidate these, we will present two case studies: (1) using transcriptional signature similarity or positive correlation to identify novel small molecules that are similar to an approved drug and (2) identifying candidate therapeutics via reciprocal connectivity or negative correlation between transcriptional signatures from a disease and small molecule.
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Affiliation(s)
- Yunguan Wang
- Division of Biomedical Informatics, Cincinnati Children's Hospital Medical Center, Cincinnati, OH, USA
| | - Jaswanth Yella
- Division of Biomedical Informatics, Cincinnati Children's Hospital Medical Center, Cincinnati, OH, USA.,Department of Computer Science, University of Cincinnati College of Engineering, Cincinnati, OH, USA
| | - Anil G Jegga
- Division of Biomedical Informatics, Cincinnati Children's Hospital Medical Center, Cincinnati, OH, USA. .,Department of Pediatrics, University of Cincinnati College of Medicine, Cincinnati, OH, USA. .,Department of Computer Science, University of Cincinnati College of Engineering, Cincinnati, OH, USA.
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Iwata M, Hirose L, Kohara H, Liao J, Sawada R, Akiyoshi S, Tani K, Yamanishi Y. Pathway-Based Drug Repositioning for Cancers: Computational Prediction and Experimental Validation. J Med Chem 2018; 61:9583-9595. [PMID: 30371064 DOI: 10.1021/acs.jmedchem.8b01044] [Citation(s) in RCA: 16] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/14/2022]
Abstract
Developing drugs with anticancer activity and low toxic side-effects at low costs is a challenging issue for cancer chemotherapy. In this work, we propose to use molecular pathways as the therapeutic targets and develop a novel computational approach for drug repositioning for cancer treatment. We analyzed chemically induced gene expression data of 1112 drugs on 66 human cell lines and searched for drugs that inactivate pathways involved in the growth of cancer cells (cell cycle) and activate pathways that contribute to the death of cancer cells (e.g., apoptosis and p53 signaling). Finally, we performed a large-scale prediction of potential anticancer effects for all the drugs and experimentally validated the prediction results via three in vitro cellular assays that evaluate cell viability, cytotoxicity, and apoptosis induction. Using this strategy, we successfully identified several potential anticancer drugs. The proposed pathway-based method has great potential to improve drug repositioning research for cancer treatment.
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Affiliation(s)
- Michio Iwata
- Department of Bioscience and Bioinformatics, Faculty of Computer Science and Systems Engineering , Kyushu Institute of Technology , 680-4 Kawazu , Iizuka , Fukuoka 820-8502 , Japan
| | - Lisa Hirose
- Project Division of ALA Advanced Medical Research, The Institute of Medical Science , The University of Tokyo , 4-6-1 Shirokanedai , Minato-ku , Tokyo 108-8639 , Japan
| | - Hiroshi Kohara
- Project Division of ALA Advanced Medical Research, The Institute of Medical Science , The University of Tokyo , 4-6-1 Shirokanedai , Minato-ku , Tokyo 108-8639 , Japan.,Division of Molecular and Clinical Genetics, Department of Molecular Genetics, Medical Institute of Bioregulation , Kyushu University , 3-1-1 Maidashi , Higashi-ku , Fukuoka, Fukuoka 812-8582 , Japan
| | - Jiyuan Liao
- Project Division of ALA Advanced Medical Research, The Institute of Medical Science , The University of Tokyo , 4-6-1 Shirokanedai , Minato-ku , Tokyo 108-8639 , Japan.,Division of Molecular and Clinical Genetics, Department of Molecular Genetics, Medical Institute of Bioregulation , Kyushu University , 3-1-1 Maidashi , Higashi-ku , Fukuoka, Fukuoka 812-8582 , Japan
| | - Ryusuke Sawada
- Medical Institute of Bioregulation , Kyushu University , 3-1-1 Maidashi , Higashi-ku , Fukuoka, Fukuoka 812-8582 , Japan
| | - Sayaka Akiyoshi
- Medical Institute of Bioregulation , Kyushu University , 3-1-1 Maidashi , Higashi-ku , Fukuoka, Fukuoka 812-8582 , Japan
| | - Kenzaburo Tani
- Project Division of ALA Advanced Medical Research, The Institute of Medical Science , The University of Tokyo , 4-6-1 Shirokanedai , Minato-ku , Tokyo 108-8639 , Japan.,Division of Molecular Design, Research Center for Systems Immunology, Medical Institute of Bioregulation , Kyushu University , 3-1-1 Maidashi , Higashi-ku , Fukuoka, Fukuoka 812-8582 , Japan
| | - Yoshihiro Yamanishi
- Department of Bioscience and Bioinformatics, Faculty of Computer Science and Systems Engineering , Kyushu Institute of Technology , 680-4 Kawazu , Iizuka , Fukuoka 820-8502 , Japan.,PRESTO , Japan Science and Technology Agency , Kawaguchi , Saitama 332-0012 , Japan
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