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Shang B, Yang R, Lian K, Dong L, Liu H, Wang T, Yang G, Xi K, Xu X, Cheng Y. Family-based genetic analysis in schizophrenia by whole-exome sequence to identify rare pathogenic variants. Am J Med Genet B Neuropsychiatr Genet 2024; 195:e32968. [PMID: 38293813 DOI: 10.1002/ajmg.b.32968] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/18/2023] [Revised: 12/29/2023] [Accepted: 01/07/2024] [Indexed: 02/01/2024]
Abstract
Schizophrenia (SCZ) is influenced by a combination of genetic and environmental factors. Although several studies have been conducted to identify the causative loci and genes, few of these loci or genes can be repeated due to the high phenotypic and genetic heterogeneity of disease, and their mechanisms are not fully understood. There may be some "missing heritability" that has not yet been found. In order to investigate the deleterious heritable mutations, whole-exome sequencing (WES) in pedigrees with SCZ was used in the current work. Two unrelated pedigrees with SCZ were recruited to perform WES. Genetic analysis was next performed to find potential variants in accordance with the prioritized strategy. Followed by genetic analysis to detect candidate variants according to the prioritized strategy. Next, a series of algorithms was used to predict the pathogenicity of variants. Sanger sequencing was finally conducted to verify the co-segregation. Recessive mutations in six genes (TFEB, SNAI2, TFAP2B, PRKDC, ST18 in Pedigree 1 and PKHD1L1 in Pedigree 2) that co-segregated with SCZ in two families were discovered through genetic analysis by WES. Sanger sequencing verified that all of the mutations in the affected siblings were homozygous. These results corroborated the hypothesis that SCZ exhibits strong heterogeneity and complex inheritance patterns. The newly discovered homozygous variations deepen our understanding of the mutation spectrum and offer more proof for the involvement of TFEB, SNAI2, TFAP2B, PRKDC, ST18, and PKHD1L1 in the development of SCZ.
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Affiliation(s)
- Binli Shang
- Department of Psychiatry, First Affiliated Hospital of Kunming Medical University, Kunming, China
| | - Runxu Yang
- Department of Psychiatry, First Affiliated Hospital of Kunming Medical University, Kunming, China
- Yunnan medical Centre for Mental Health, Kunming, China
| | - Kun Lian
- Department of Psychiatry, First Affiliated Hospital of Kunming Medical University, Kunming, China
| | - Lei Dong
- Department of Psychiatry, First Affiliated Hospital of Kunming Medical University, Kunming, China
| | | | | | | | - Kang Xi
- Department of Psychiatry, First Affiliated Hospital of Kunming Medical University, Kunming, China
| | - Xiufeng Xu
- Department of Psychiatry, First Affiliated Hospital of Kunming Medical University, Kunming, China
- Yunnan medical Centre for Mental Health, Kunming, China
| | - Yuqi Cheng
- Department of Psychiatry, First Affiliated Hospital of Kunming Medical University, Kunming, China
- Yunnan medical Centre for Mental Health, Kunming, China
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Lin YL, Yao T, Wang YW, Zhou ZX, Hong ZC, Shen Y, Yan Y, Li YC, Lin JF. Potential drug targets for gastroesophageal reflux disease and Barrett's esophagus identified through Mendelian randomization analysis. J Hum Genet 2024; 69:245-253. [PMID: 38429412 DOI: 10.1038/s10038-024-01234-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/07/2023] [Revised: 02/07/2024] [Accepted: 02/18/2024] [Indexed: 03/03/2024]
Abstract
Gastroesophageal reflux disease (GERD) is a prevalent chronic ailment, and present therapeutic approaches are not always effective. This study aimed to find new drug targets for GERD and Barrett's esophagus (BE). We obtained genetic instruments for GERD, BE, and 2004 plasma proteins from recently published genome-wide association studies (GWAS), and Mendelian randomization (MR) was employed to explore potential drug targets. We further winnowed down MR-prioritized proteins through replication, reverse causality testing, colocalization analysis, phenotype scanning, and Phenome-wide MR. Furthermore, we constructed a protein-protein interaction network, unveiling potential associations among candidate proteins. Simultaneously, we acquired mRNA expression quantitative trait loci (eQTL) data from another GWAS encompassing four different tissues to identify additional drug targets. Meanwhile, we searched drug databases to evaluate these targets. Under Bonferroni correction (P < 4.8 × 10-5), we identified 11 plasma proteins significantly associated with GERD. Among these, 7 are protective proteins (MSP, GPX1, ERBB3, BT3A3, ANTR2, CCM2, and DECR2), while 4 are detrimental proteins (TMEM106B, DUSP13, C1-INH, and LINGO1). Ultimately, C1-INH and DECR2 successfully passed the screening process and exhibited similar directional causal effects on BE. Further analysis of eQTLs highlighted 4 potential drug targets, including EDEM3, PBX3, MEIS1-AS3, and NME7. The search of drug databases further supported our conclusions. Our study indicated that the plasma proteins C1-INH and DECR2, along with 4 genes (EDEM3, PBX3, MEIS1-AS3, and NME7), may represent potential drug targets for GERD and BE, warranting further investigation.
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Affiliation(s)
- Yun-Lu Lin
- The Second Affiliated Hospital and Yuying Children's Hospital of Wenzhou Medical University, Wenzhou, 325000, Zhejiang, China
| | - Tao Yao
- The Second Affiliated Hospital and Yuying Children's Hospital of Wenzhou Medical University, Wenzhou, 325000, Zhejiang, China
| | - Ying-Wei Wang
- The Second Affiliated Hospital and Yuying Children's Hospital of Wenzhou Medical University, Wenzhou, 325000, Zhejiang, China
| | - Zhi-Xiang Zhou
- The Second Affiliated Hospital and Yuying Children's Hospital of Wenzhou Medical University, Wenzhou, 325000, Zhejiang, China
| | - Ze-Chao Hong
- The Second Affiliated Hospital and Yuying Children's Hospital of Wenzhou Medical University, Wenzhou, 325000, Zhejiang, China
| | - Yu Shen
- The Second Affiliated Hospital and Yuying Children's Hospital of Wenzhou Medical University, Wenzhou, 325000, Zhejiang, China
| | - Yu Yan
- The Second Affiliated Hospital and Yuying Children's Hospital of Wenzhou Medical University, Wenzhou, 325000, Zhejiang, China
| | - Yue-Chun Li
- The Second Affiliated Hospital and Yuying Children's Hospital of Wenzhou Medical University, Wenzhou, 325000, Zhejiang, China.
| | - Jia-Feng Lin
- The Second Affiliated Hospital and Yuying Children's Hospital of Wenzhou Medical University, Wenzhou, 325000, Zhejiang, China.
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Luo J, Tu L, Zhou C, Li G, Shi L, Hu S. SGLT2 inhibition, circulating proteins, and insomnia: A mendelian randomization study. Sleep Med 2024; 119:480-487. [PMID: 38795402 DOI: 10.1016/j.sleep.2024.05.036] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/30/2024] [Revised: 04/11/2024] [Accepted: 05/16/2024] [Indexed: 05/27/2024]
Abstract
BACKGROUND Sodium-glucose cotransporter 2 inhibitors (SGLT2i) initially emerged as oral antidiabetic medication but were subsequently discovered to exhibit pleiotropic actions. Insomnia is a prevalent and debilitating sleep disorder. To date, the causality between SGLT2 inhibitors and insomnia remains unclear. This study aims to evaluate the causality between SGLT2 inhibitors and insomnia and identify potential plasma protein mediators. METHODS Using a two-sample Mendelian Randomization (MR) analysis, we estimated the causality of SGLT2 inhibition on insomnia and sleep duration. Additionally, employing a two-step and proteome-wide MR analysis, we evaluated the causal link of SGLT2 inhibition on 4907 circulating proteins and the causality of SGLT2 inhibition-driven plasma proteins on insomnia. We applied a false discovery rate (FDR) correction for multiple comparisons. Furthermore, mediation analyses were used to identify plasma proteins that mediate the effects of SGLT2 inhibition on insomnia. RESULTS SGLT2 inhibition was negatively correlated with insomnia (odds ratio [OR] = 0.791, 95 % confidence interval [CI] [0.715, 0.876], P = 5.579*10^-6) and positively correlated with sleep duration (β = 0.186, 95 % CI [0.059, 0.314], P = 0.004). Among the 4907 circulating proteins, diadenosine tetraphosphatase (Ap4A) was identified as being linked to both SGLT2 inhibition and insomnia. Mediation analysis indicated that the effect of SGLT2 inhibition on insomnia partially operates through Ap4A (β = -0.018, 95 % CI [-0.036, -0.005], P = 0.023), with a mediation proportion of 7.7 %. CONCLUSION The study indicated a causality between SGLT2 inhibition and insomnia, with plasma Ap4A potentially serving as a mediator.
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Affiliation(s)
- Jinlan Luo
- Department of Geriatric Medicine, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, 430030, China; Hubei Key Laboratory of Genetics and Molecular Mechanisms of Cardiological Disorders, Wuhan, 430030, China
| | - Ling Tu
- Department of Geriatric Medicine, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, 430030, China; Hubei Key Laboratory of Genetics and Molecular Mechanisms of Cardiological Disorders, Wuhan, 430030, China
| | - Chenchen Zhou
- Department of Emergency Medicine, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, 430030, China
| | - Gen Li
- Department of Cardiothoracic and Vascular Surgery, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, 430030, China
| | - Lili Shi
- Department of Geriatric Medicine, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, 430030, China.
| | - Shuiqing Hu
- Division of Cardiology, Department of Internal Medicine and Hubei Key Laboratory of Genetics and Molecular Mechanism of Cardiologic Disorders, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, 430000, China.
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Qi T, Song L, Guo Y, Chen C, Yang J. From genetic associations to genes: methods, applications, and challenges. Trends Genet 2024:S0168-9525(24)00095-7. [PMID: 38734482 DOI: 10.1016/j.tig.2024.04.008] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/08/2023] [Revised: 04/15/2024] [Accepted: 04/16/2024] [Indexed: 05/13/2024]
Abstract
Genome-wide association studies (GWASs) have identified numerous genetic loci associated with human traits and diseases. However, pinpointing the causal genes remains a challenge, which impedes the translation of GWAS findings into biological insights and medical applications. In this review, we provide an in-depth overview of the methods and technologies used for prioritizing genes from GWAS loci, including gene-based association tests, integrative analysis of GWAS and molecular quantitative trait loci (xQTL) data, linking GWAS variants to target genes through enhancer-gene connection maps, and network-based prioritization. We also outline strategies for generating context-dependent xQTL data and their applications in gene prioritization. We further highlight the potential of gene prioritization in drug repurposing. Lastly, we discuss future challenges and opportunities in this field.
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Affiliation(s)
- Ting Qi
- Westlake Laboratory of Life Sciences and Biomedicine, Hangzhou 310024, China; School of Life Sciences, Westlake University, Hangzhou 310024, China.
| | - Liyang Song
- Westlake Laboratory of Life Sciences and Biomedicine, Hangzhou 310024, China; School of Life Sciences, Westlake University, Hangzhou 310024, China
| | - Yazhou Guo
- Westlake Laboratory of Life Sciences and Biomedicine, Hangzhou 310024, China; School of Life Sciences, Westlake University, Hangzhou 310024, China
| | - Chang Chen
- Westlake Laboratory of Life Sciences and Biomedicine, Hangzhou 310024, China; School of Life Sciences, Westlake University, Hangzhou 310024, China
| | - Jian Yang
- Westlake Laboratory of Life Sciences and Biomedicine, Hangzhou 310024, China; School of Life Sciences, Westlake University, Hangzhou 310024, China.
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Wen J, Antoniades M, Yang Z, Hwang G, Skampardoni I, Wang R, Davatzikos C. Dimensional Neuroimaging Endophenotypes: Neurobiological Representations of Disease Heterogeneity Through Machine Learning. Biol Psychiatry 2024:S0006-3223(24)01286-1. [PMID: 38718880 DOI: 10.1016/j.biopsych.2024.04.017] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/17/2024] [Revised: 03/29/2024] [Accepted: 04/22/2024] [Indexed: 05/21/2024]
Abstract
Machine learning has been increasingly used to obtain individualized neuroimaging signatures for disease diagnosis, prognosis, and response to treatment in neuropsychiatric and neurodegenerative disorders. Therefore, it has contributed to a better understanding of disease heterogeneity by identifying disease subtypes with different brain phenotypic measures. In this Review, we first present a systematic literature overview of studies using machine learning and multimodal MRI to unravel disease heterogeneity in various neuropsychiatric and neurodegenerative disorders, including Alzheimer's disease, schizophrenia, major depressive disorder, autism spectrum disorder, multiple sclerosis, as well as their potential in a transdiagnostic framework, where neuroanatomical and neurobiological commonalities were assessed across diagnostic boundaries. Subsequently, we summarize relevant machine learning methodologies and their clinical interpretability. We discuss the potential clinical implications of the current findings and envision future research avenues. Finally, we discuss an emerging paradigm called dimensional neuroimaging endophenotypes (DNE). DNE dissects the neurobiological heterogeneity of neuropsychiatric and neurodegenerative disorders into low-dimensional yet informative, quantitative brain phenotypic representations, serving as robust intermediate phenotypes (i.e., endophenotypes) presumably reflecting the interplay of underlying genetic, lifestyle, and environmental processes associated with the disease etiology.
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Affiliation(s)
- Junhao Wen
- Laboratory of AI and Biomedical Science (LABS), Stevens Neuroimaging and Informatics Institute, Keck School of Medicine of USC, University of Southern California, Los Angeles, CA, USA.
| | - Mathilde Antoniades
- Artificial Intelligence in Biomedical Imaging Laboratory (AIBIL), Center for AI and Data Science for Integrated Diagnostics (AI(2)D), Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Zhijian Yang
- Artificial Intelligence in Biomedical Imaging Laboratory (AIBIL), Center for AI and Data Science for Integrated Diagnostics (AI(2)D), Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Gyujoon Hwang
- Psychiatry and Behavioral Medicine, Medical College of Wisconsin, Watertown Plank Rd, Milwaukee, WI, USA
| | - Ioanna Skampardoni
- Artificial Intelligence in Biomedical Imaging Laboratory (AIBIL), Center for AI and Data Science for Integrated Diagnostics (AI(2)D), Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Rongguang Wang
- Artificial Intelligence in Biomedical Imaging Laboratory (AIBIL), Center for AI and Data Science for Integrated Diagnostics (AI(2)D), Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Christos Davatzikos
- Artificial Intelligence in Biomedical Imaging Laboratory (AIBIL), Center for AI and Data Science for Integrated Diagnostics (AI(2)D), Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA.
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Minikel EV, Painter JL, Dong CC, Nelson MR. Refining the impact of genetic evidence on clinical success. Nature 2024; 629:624-629. [PMID: 38632401 PMCID: PMC11096124 DOI: 10.1038/s41586-024-07316-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/05/2023] [Accepted: 03/14/2024] [Indexed: 04/19/2024]
Abstract
The cost of drug discovery and development is driven primarily by failure1, with only about 10% of clinical programmes eventually receiving approval2-4. We previously estimated that human genetic evidence doubles the success rate from clinical development to approval5. In this study we leverage the growth in genetic evidence over the past decade to better understand the characteristics that distinguish clinical success and failure. We estimate the probability of success for drug mechanisms with genetic support is 2.6 times greater than those without. This relative success varies among therapy areas and development phases, and improves with increasing confidence in the causal gene, but is largely unaffected by genetic effect size, minor allele frequency or year of discovery. These results indicate we are far from reaching peak genetic insights to aid the discovery of targets for more effective drugs.
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Affiliation(s)
| | - Jeffery L Painter
- JiveCast, Raleigh, NC, USA
- GlaxoSmithKline, Research Triangle Park, NC, USA
| | | | - Matthew R Nelson
- Deerfield Management Company LP, New York, NY, USA.
- Genscience LLC, New York, NY, USA.
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Valančienė J, Melaika K, Šliachtenko A, Šiaurytė-Jurgelėnė K, Ekkert A, Jatužis D. Stroke genetics and how it Informs novel drug discovery. Expert Opin Drug Discov 2024; 19:553-564. [PMID: 38494780 DOI: 10.1080/17460441.2024.2324916] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/05/2023] [Accepted: 02/26/2024] [Indexed: 03/19/2024]
Abstract
INTRODUCTION Stroke is one of the main causes of death and disability worldwide. Nevertheless, despite the global burden of this disease, our understanding is limited and there is still a lack of highly efficient etiopathology-based treatment. It is partly due to the complexity and heterogenicity of the disease. It is estimated that around one-third of ischemic stroke is heritable, emphasizing the importance of genetic factors identification and targeting for therapeutic purposes. AREAS COVERED In this review, the authors provide an overview of the current knowledge of stroke genetics and its value in diagnostics, personalized treatment, and prognostication. EXPERT OPINION As the scale of genetic testing increases and the cost decreases, integration of genetic data into clinical practice is inevitable, enabling assessing individual risk, providing personalized prognostic models and identifying new therapeutic targets and biomarkers. Although expanding stroke genetics data provides different diagnostics and treatment perspectives, there are some limitations and challenges to face. One of them is the threat of health disparities as non-European populations are underrepresented in genetic datasets. Finally, a deeper understanding of underlying mechanisms of potential targets is still lacking, delaying the application of novel therapies into routine clinical practice.
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Affiliation(s)
| | | | | | - Kamilė Šiaurytė-Jurgelėnė
- Department of Human and Medical Genetics, Institute of Biomedical Sciences, Faculty of Medicine, Vilnius University, Vilnius, Lithuania
| | | | - Dalius Jatužis
- Center of Neurology, Vilnius University, Vilnius, Lithuania
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Yang N, Shi L, Xu P, Ren F, Lv S, Li C, Qi X. Identification of potential drug targets for insomnia by Mendelian randomization analysis based on plasma proteomics. Front Neurol 2024; 15:1380321. [PMID: 38725646 PMCID: PMC11079244 DOI: 10.3389/fneur.2024.1380321] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/01/2024] [Accepted: 04/12/2024] [Indexed: 05/12/2024] Open
Abstract
Introduction Insomnia, a common clinical disorder, significantly impacts the physical and mental well-being of patients. Currently, available hypnotic medications are unsatisfactory due to adverse reactions and dependency, necessitating the identification of new drug targets for the treatment of insomnia. Methods In this study, we utilized 734 plasma proteins as genetic instruments obtained from genome-wide association studies to conduct a Mendelian randomization analysis, with insomnia as the outcome variable, to identify potential drug targets for insomnia. Additionally, we validated our results externally using other datasets. Sensitivity analyses entailed reverse Mendelian randomization analysis, Bayesian co-localization analysis, and phenotype scanning. Furthermore, we constructed a protein-protein interaction network to elucidate potential correlations between the identified proteins and existing targets. Results Mendelian randomization analysis indicated that elevated levels of TGFBI (OR = 1.01; 95% CI, 1.01-1.02) and PAM ((OR = 1.01; 95% CI, 1.01-1.02) in plasma are associated with an increased risk of insomnia, with external validation supporting these findings. Moreover, there was no evidence of reverse causality for these two proteins. Co-localization analysis confirmed that PAM (coloc.abf-PPH4 = 0.823) shared the same variant with insomnia, further substantiating its potential role as a therapeutic target. There are interactive relationships between the potential proteins and existing targets of insomnia. Conclusion Overall, our findings suggested that elevated plasma levels of TGFBI and PAM are connected with an increased risk of insomnia and might be promising therapeutic targets, particularly PAM. However, further exploration is necessary to fully understand the underlying mechanisms involved.
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Affiliation(s)
- Ni Yang
- Department of First Clinical Medical College, Shandong University of Traditional Chinese Medicine, Jinan, China
| | - Liangyuan Shi
- Qingdao Traditional Chinese Medicine Hospital (Qingdao Hiser Hospital) Qingdao Hiser Hospital Affiliated of Qingdao University, Qingdao, China
| | - Pengfei Xu
- Qingdao Traditional Chinese Medicine Hospital (Qingdao Hiser Hospital) Qingdao Hiser Hospital Affiliated of Qingdao University, Qingdao, China
| | - Fang Ren
- Department of Laboratory, Jimo District Qingdao Hospital of Traditional Chinese Medicine, Qingdao, China
| | - Shimeng Lv
- Department of First Clinical Medical College, Shandong University of Traditional Chinese Medicine, Jinan, China
| | - Chunlin Li
- Affiliated Hospital of Shandong University of Traditional Chinese Medicine, Jinan, China
| | - Xianghua Qi
- Affiliated Hospital of Shandong University of Traditional Chinese Medicine, Jinan, China
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Jin X, Mei Y, Yang P, Huang R, Zhang H, Wu Y, Wang M, He X, Jiang Z, Zhu W, Wang L. Prioritization of therapeutic targets for cancers using integrative multi-omics analysis. Hum Genomics 2024; 18:42. [PMID: 38659038 PMCID: PMC11040978 DOI: 10.1186/s40246-024-00571-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/08/2023] [Accepted: 01/17/2024] [Indexed: 04/26/2024] Open
Abstract
BACKGROUND The integration of transcriptomic, proteomic, druggable genetic and metabolomic association studies facilitated a comprehensive investigation of molecular features and shared pathways for cancers' development and progression. METHODS Comprehensive approaches consisting of transcriptome-wide association studies (TWAS), proteome-wide association studies (PWAS), summary-data-based Mendelian randomization (SMR) and MR were performed to identify genes significantly associated with cancers. The results identified in above analyzes were subsequently involved in phenotype scanning and enrichment analyzes to explore the possible health effects and shared pathways. Additionally, we also conducted MR analysis to investigate metabolic pathways related to cancers. RESULTS Totally 24 genes (18 transcriptomic, 1 proteomic and 5 druggable genetic) showed significant associations with cancers risk. All genes identified in multiple methods were mainly enriched in nuclear factor erythroid 2-related factor 2 (NRF2) pathway. Additionally, biosynthesis of ubiquinol and urate were found to play an important role in gastrointestinal tumors. CONCLUSIONS A set of putatively causal genes and pathways relevant to cancers were identified in this study, shedding light on the shared biological processes for tumorigenesis and providing compelling genetic evidence to prioritize anti-cancer drugs development.
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Affiliation(s)
- Xin Jin
- Department of Hepatic Surgery, Fudan University Shanghai Cancer Center, Shanghai Medical College, Fudan University, Shanghai, 200032, People's Republic of China
- Department of Oncology, Shanghai Medical College, Fudan University, Shanghai, 200032, People's Republic of China
| | - Yunyun Mei
- Department of Neurosurgery, Fudan University Shanghai Cancer Center, Shanghai Medical College, Fudan University, Shanghai, 200032, People's Republic of China
| | - Puyu Yang
- Department of Epidemiology, School of Public Health, Medical College of Soochow University, Suzhou, People's Republic of China
| | - Runze Huang
- Department of Hepatic Surgery, Fudan University Shanghai Cancer Center, Shanghai Medical College, Fudan University, Shanghai, 200032, People's Republic of China
- Department of Oncology, Shanghai Medical College, Fudan University, Shanghai, 200032, People's Republic of China
| | - Haifeng Zhang
- Department of Epidemiology, School of Public Health, Medical College of Soochow University, Suzhou, People's Republic of China
| | - Yibin Wu
- Department of Hepatic Surgery, Fudan University Shanghai Cancer Center, Shanghai Medical College, Fudan University, Shanghai, 200032, People's Republic of China
- Department of Oncology, Shanghai Medical College, Fudan University, Shanghai, 200032, People's Republic of China
| | - Miao Wang
- Department of Hepatic Surgery, Fudan University Shanghai Cancer Center, Shanghai Medical College, Fudan University, Shanghai, 200032, People's Republic of China
- Department of Oncology, Shanghai Medical College, Fudan University, Shanghai, 200032, People's Republic of China
| | - Xigan He
- Department of Hepatic Surgery, Fudan University Shanghai Cancer Center, Shanghai Medical College, Fudan University, Shanghai, 200032, People's Republic of China
- Department of Oncology, Shanghai Medical College, Fudan University, Shanghai, 200032, People's Republic of China
| | - Ziting Jiang
- Department of Oncology, Shanghai Medical College, Fudan University, Shanghai, 200032, People's Republic of China.
- Department of Endoscopy, Fudan University Shanghai Cancer Center, Shanghai Medical College, Fudan University, Shanghai, 200032, People's Republic of China.
| | - Weiping Zhu
- Department of Hepatic Surgery, Fudan University Shanghai Cancer Center, Shanghai Medical College, Fudan University, Shanghai, 200032, People's Republic of China.
- Department of Oncology, Shanghai Medical College, Fudan University, Shanghai, 200032, People's Republic of China.
| | - Lu Wang
- Department of Hepatic Surgery, Fudan University Shanghai Cancer Center, Shanghai Medical College, Fudan University, Shanghai, 200032, People's Republic of China.
- Department of Oncology, Shanghai Medical College, Fudan University, Shanghai, 200032, People's Republic of China.
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10
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Bao C, Tan T, Wang S, Gao C, Lu C, Yang S, Diao Y, Jiang L, Jing D, Chen L, Lv H, Fang H. A cross-disease, pleiotropy-driven approach for therapeutic target prioritization and evaluation. CELL REPORTS METHODS 2024; 4:100757. [PMID: 38631345 PMCID: PMC11046034 DOI: 10.1016/j.crmeth.2024.100757] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/27/2023] [Revised: 01/08/2024] [Accepted: 03/22/2024] [Indexed: 04/19/2024]
Abstract
Cross-disease genome-wide association studies (GWASs) unveil pleiotropic loci, mostly situated within the non-coding genome, each of which exerts pleiotropic effects across multiple diseases. However, the challenge "W-H-W" (namely, whether, how, and in which specific diseases pleiotropy can inform clinical therapeutics) calls for effective and integrative approaches and tools. We here introduce a pleiotropy-driven approach specifically designed for therapeutic target prioritization and evaluation from cross-disease GWAS summary data, with its validity demonstrated through applications to two systems of disorders (neuropsychiatric and inflammatory). We illustrate its improved performance in recovering clinical proof-of-concept therapeutic targets. Importantly, it identifies specific diseases where pleiotropy informs clinical therapeutics. Furthermore, we illustrate its versatility in accomplishing advanced tasks, including pathway crosstalk identification and downstream crosstalk-based analyses. To conclude, our integrated solution helps bridge the gap between pleiotropy studies and therapeutics discovery.
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Affiliation(s)
- Chaohui Bao
- Shanghai Institute of Hematology, State Key Laboratory of Medical Genomics, National Research Center for Translational Medicine at Shanghai, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai 200025, China
| | - Tingting Tan
- Shanghai Institute of Hematology, State Key Laboratory of Medical Genomics, National Research Center for Translational Medicine at Shanghai, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai 200025, China
| | - Shan Wang
- Shanghai Institute of Hematology, State Key Laboratory of Medical Genomics, National Research Center for Translational Medicine at Shanghai, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai 200025, China
| | - Chenxu Gao
- Shanghai Institute of Hematology, State Key Laboratory of Medical Genomics, National Research Center for Translational Medicine at Shanghai, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai 200025, China
| | - Chang Lu
- MRC London Institute of Medical Sciences, Imperial College London, W12 0HS London, UK
| | - Siyue Yang
- Shanghai Institute of Hematology, State Key Laboratory of Medical Genomics, National Research Center for Translational Medicine at Shanghai, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai 200025, China; Faculty of Medical Laboratory Science, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai 200025, China
| | - Yizhu Diao
- College of Finance and Statistics, Hunan University, Changsha, Hunan 410079, China
| | - Lulu Jiang
- Translational Health Sciences, University of Bristol, BS1 3NY Bristol, UK
| | - Duohui Jing
- Shanghai Institute of Hematology, State Key Laboratory of Medical Genomics, National Research Center for Translational Medicine at Shanghai, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai 200025, China
| | - Liye Chen
- Nuffield Department of Orthopaedics, Rheumatology and Musculoskeletal Sciences, University of Oxford, OX3 7LD Oxford, UK.
| | - Haitao Lv
- Shanghai Institute of Hematology, State Key Laboratory of Medical Genomics, National Research Center for Translational Medicine at Shanghai, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai 200025, China; School of Chinese Medicine, State Key Laboratory of Environmental and Biological Analysis, Hong Kong Chinese Medicine Phenome Research Center, Hong Kong Baptist University, Hong Kong 999077, China.
| | - Hai Fang
- Shanghai Institute of Hematology, State Key Laboratory of Medical Genomics, National Research Center for Translational Medicine at Shanghai, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai 200025, China.
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Su Z, Wan Q. Potential therapeutic targets for membranous nephropathy: proteome-wide Mendelian randomization and colocalization analysis. Front Immunol 2024; 15:1342912. [PMID: 38707900 PMCID: PMC11069303 DOI: 10.3389/fimmu.2024.1342912] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/22/2023] [Accepted: 02/21/2024] [Indexed: 05/07/2024] Open
Abstract
Background The currently available medications for treating membranous nephropathy (MN) still have unsatisfactory efficacy in inhibiting disease recurrence, slowing down its progression, and even halting the development of end-stage renal disease. There is still a need to develop novel drugs targeting MN. Methods We utilized summary statistics of MN from the Kiryluk Lab and obtained plasma protein data from Zheng et al. We performed a Bidirectional Mendelian randomization analysis, HEIDI test, mediation analysis, Bayesian colocalization, phenotype scanning, drug bank analysis, and protein-protein interaction network. Results The Mendelian randomization analysis uncovered 8 distinct proteins associated with MN after multiple false discovery rate corrections. Proteins related to an increased risk of MN in plasma include ABO [(Histo-Blood Group Abo System Transferase) (WR OR = 1.12, 95%CI:1.05-1.19, FDR=0.09, PPH4 = 0.79)], VWF [(Von Willebrand Factor) (WR OR = 1.41, 95%CI:1.16-1.72, FDR=0.02, PPH4 = 0.81)] and CD209 [(Cd209 Antigen) (WR OR = 1.19, 95%CI:1.07-1.31, FDR=0.09, PPH4 = 0.78)], and proteins that have a protective effect on MN: HRG [(Histidine-Rich Glycoprotein) (WR OR = 0.84, 95%CI:0.76-0.93, FDR=0.02, PPH4 = 0.80)], CD27 [(Cd27 Antigen) (WR OR = 0.78, 95%CI:0.68-0.90, FDR=0.02, PPH4 = 0.80)], LRPPRC [(Leucine-Rich Ppr Motif-Containing Protein, Mitochondrial) (WR OR = 0.79, 95%CI:0.69-0.91, FDR=0.09, PPH4 = 0.80)], TIMP4 [(Metalloproteinase Inhibitor 4) (WR OR = 0.67, 95%CI:0.53-0.84, FDR=0.09, PPH4 = 0.79)] and MAP2K4 [(Dual Specificity Mitogen-Activated Protein Kinase Kinase 4) (WR OR = 0.82, 95%CI:0.72-0.92, FDR=0.09, PPH4 = 0.80)]. ABO, HRG, and TIMP4 successfully passed the HEIDI test. None of these proteins exhibited a reverse causal relationship. Bayesian colocalization analysis provided evidence that all of them share variants with MN. We identified type 1 diabetes, trunk fat, and asthma as having intermediate effects in these pathways. Conclusions Our comprehensive analysis indicates a causal effect of ABO, CD27, VWF, HRG, CD209, LRPPRC, MAP2K4, and TIMP4 at the genetically determined circulating levels on the risk of MN. These proteins can potentially be a promising therapeutic target for the treatment of MN.
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Affiliation(s)
| | - Qijun Wan
- Department of Nephrology, Shenzhen Second People’s Hospital, The First Affiliated Hospital of Shenzhen University, Shenzhen, China
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12
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Lalagkas PN, Melamed RD. Shared etiology of Mendelian and complex disease supports drug discovery. RESEARCH SQUARE 2024:rs.3.rs-4250176. [PMID: 38699347 PMCID: PMC11065072 DOI: 10.21203/rs.3.rs-4250176/v1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/05/2024]
Abstract
Background Drugs targeting disease causal genes are more likely to succeed for that disease. However, complex disease causal genes are not always clear. In contrast, Mendelian disease causal genes are well-known and druggable. Here, we seek an approach to exploit the well characterized biology of Mendelian diseases for complex disease drug discovery, by exploiting evidence of pathogenic processes shared between monogenic and complex disease. One way to find shared disease etiology is clinical association: some Mendelian diseases are known to predispose patients to specific complex diseases (comorbidity). Previous studies link this comorbidity to pleiotropic effects of the Mendelian disease causal genes on the complex disease. Methods In previous work studying incidence of 90 Mendelian and 65 complex diseases, we found 2,908 pairs of clinically associated (comorbid) diseases. Using this clinical signal, we can match each complex disease to a set of Mendelian disease causal genes. We hypothesize that the drugs targeting these genes are potential candidate drugs for the complex disease. We evaluate our candidate drugs using information of current drug indications or investigations. Results Our analysis shows that the candidate drugs are enriched among currently investigated or indicated drugs for the relevant complex diseases (odds ratio = 1.84, p = 5.98e-22). Additionally, the candidate drugs are more likely to be in advanced stages of the drug development pipeline. We also present an approach to prioritize Mendelian diseases with particular promise for drug repurposing. Finally, we find that the combination of comorbidity and genetic similarity for a Mendelian disease and cancer pair leads to recommendation of candidate drugs that are enriched for those investigated or indicated. Conclusions Our findings suggest a novel way to take advantage of the rich knowledge about Mendelian disease biology to improve treatment of complex diseases.
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Malla R, Viswanathan S, Makena S, Kapoor S, Verma D, Raju AA, Dunna M, Muniraj N. Revitalizing Cancer Treatment: Exploring the Role of Drug Repurposing. Cancers (Basel) 2024; 16:1463. [PMID: 38672545 PMCID: PMC11048531 DOI: 10.3390/cancers16081463] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/02/2024] [Revised: 04/04/2024] [Accepted: 04/09/2024] [Indexed: 04/28/2024] Open
Abstract
Cancer persists as a global challenge necessitating continual innovation in treatment strategies. Despite significant advancements in comprehending the disease, cancer remains a leading cause of mortality worldwide, exerting substantial economic burdens on healthcare systems and societies. The emergence of drug resistance further complicates therapeutic efficacy, underscoring the urgent need for alternative approaches. Drug repurposing, characterized by the utilization of existing drugs for novel clinical applications, emerges as a promising avenue for addressing these challenges. Repurposed drugs, comprising FDA-approved (in other disease indications), generic, off-patent, and failed medications, offer distinct advantages including established safety profiles, cost-effectiveness, and expedited development timelines compared to novel drug discovery processes. Various methodologies, such as knowledge-based analyses, drug-centric strategies, and computational approaches, play pivotal roles in identifying potential candidates for repurposing. However, despite the promise of repurposed drugs, drug repositioning confronts formidable obstacles. Patenting issues, financial constraints associated with conducting extensive clinical trials, and the necessity for combination therapies to overcome the limitations of monotherapy pose significant challenges. This review provides an in-depth exploration of drug repurposing, covering a diverse array of approaches including experimental, re-engineering protein, nanotechnology, and computational methods. Each of these avenues presents distinct opportunities and obstacles in the pursuit of identifying novel clinical uses for established drugs. By examining the multifaceted landscape of drug repurposing, this review aims to offer comprehensive insights into its potential to transform cancer therapeutics.
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Affiliation(s)
- RamaRao Malla
- Cancer Biology Laboratory, Department of Biochemistry and Bioinformatics, GITAM School of Science, GITAM (Deemed to be University), Visakhapatnam 530045, Andhra Pradesh, India
| | - Sathiyapriya Viswanathan
- Department of Biochemistry, ACS Medical College and Hospital, Chennai 600007, Tamil Nadu, India;
| | - Sree Makena
- Maharajah’s Institute of Medical Sciences and Hospital, Vizianagaram 535217, Andhra Pradesh, India
| | - Shruti Kapoor
- Department of Genetics, University of Alabama, Birmingham, AL 35233, USA
| | - Deepak Verma
- Department of Oncology, Johns Hopkins University School of Medicine, Baltimore, MD 21231, USA
| | | | - Manikantha Dunna
- Center for Biotechnology, Jawaharlal Nehru Technological University, Hyderabad 500085, Telangana, India
| | - Nethaji Muniraj
- Center for Cancer and Immunology Research, Children’s National Hospital, 111, Michigan Ave NW, Washington, DC 20010, USA
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Liu B, Peng G, Yin Y, Shen C, Yin X, Cai Z, Zhang B. Genetic evidence for repurposing of glucagon-like peptide-1 receptor agonists to prevent chronic liver diseases. Gut 2024; 73:879-882. [PMID: 38490731 DOI: 10.1136/gutjnl-2024-332254] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/20/2024] [Accepted: 03/09/2024] [Indexed: 03/17/2024]
Affiliation(s)
- Baike Liu
- Gastric Cancer Center, Department of General Surgery, West China Hospital, Sichuan University, Chengdu, People's Republic of China
| | - Ge Peng
- Department of Endocrinology and Metabolism, West China Hospital, Sichuan University, Chengdu, People's Republic of China
| | - Yuan Yin
- Gastric Cancer Center, Department of General Surgery, West China Hospital, Sichuan University, Chengdu, People's Republic of China
| | - Chaoyong Shen
- Gastric Cancer Center, Department of General Surgery, West China Hospital, Sichuan University, Chengdu, People's Republic of China
| | - Xiaonan Yin
- Gastric Cancer Center, Department of General Surgery, West China Hospital, Sichuan University, Chengdu, People's Republic of China
| | - Zhaolun Cai
- Gastric Cancer Center, Department of General Surgery, West China Hospital, Sichuan University, Chengdu, People's Republic of China
| | - Bo Zhang
- Gastric Cancer Center, Department of General Surgery, West China Hospital, Sichuan University, Chengdu, People's Republic of China
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Sahu M, Vashishth S, Kukreti N, Gulia A, Russell A, Ambasta RK, Kumar P. Synergizing drug repurposing and target identification for neurodegenerative diseases. PROGRESS IN MOLECULAR BIOLOGY AND TRANSLATIONAL SCIENCE 2024; 205:111-169. [PMID: 38789177 DOI: 10.1016/bs.pmbts.2024.03.023] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/26/2024]
Abstract
Despite dedicated research efforts, the absence of disease-curing remedies for neurodegenerative diseases (NDDs) continues to jeopardize human society and stands as a challenge. Drug repurposing is an attempt to find new functionality of existing drugs and take it as an opportunity to discourse the clinically unmet need to treat neurodegeneration. However, despite applying this approach to rediscover a drug, it can also be used to identify the target on which a drug could work. The primary objective of target identification is to unravel all the possibilities of detecting a new drug or repurposing an existing drug. Lately, scientists and researchers have been focusing on specific genes, a particular site in DNA, a protein, or a molecule that might be involved in the pathogenesis of the disease. However, the new era discusses directing the signaling mechanism involved in the disease progression, where receptors, ion channels, enzymes, and other carrier molecules play a huge role. This review aims to highlight how target identification can expedite the whole process of drug repurposing. Here, we first spot various target-identification methods and drug-repositioning studies, including drug-target and structure-based identification studies. Moreover, we emphasize various drug repurposing approaches in NDDs, namely, experimental-based, mechanism-based, and in silico approaches. Later, we draw attention to validation techniques and stress on drugs that are currently undergoing clinical trials in NDDs. Lastly, we underscore the future perspective of synergizing drug repurposing and target identification in NDDs and present an unresolved question to address the issue.
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Affiliation(s)
- Mehar Sahu
- Molecular Neuroscience and Functional Genomics Laboratory, Department of Biotechnology, Delhi Technological University, Delhi, India
| | - Shrutikirti Vashishth
- Molecular Neuroscience and Functional Genomics Laboratory, Department of Biotechnology, Delhi Technological University, Delhi, India
| | - Neha Kukreti
- Molecular Neuroscience and Functional Genomics Laboratory, Department of Biotechnology, Delhi Technological University, Delhi, India
| | - Ashima Gulia
- Molecular Neuroscience and Functional Genomics Laboratory, Department of Biotechnology, Delhi Technological University, Delhi, India
| | - Ashish Russell
- Molecular Neuroscience and Functional Genomics Laboratory, Department of Biotechnology, Delhi Technological University, Delhi, India
| | - Rashmi K Ambasta
- Department of Biotechnology and Microbiology, SRM University, Sonepat, Haryana, India
| | - Pravir Kumar
- Molecular Neuroscience and Functional Genomics Laboratory, Department of Biotechnology, Delhi Technological University, Delhi, India.
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Zhang W, Ma L, Zhou Q, Gu T, Zhang X, Xing H. Therapeutic Targets for Diabetic Kidney Disease: Proteome-Wide Mendelian Randomization and Colocalization Analyses. Diabetes 2024; 73:618-627. [PMID: 38211557 PMCID: PMC10958583 DOI: 10.2337/db23-0564] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/19/2023] [Accepted: 12/23/2023] [Indexed: 01/13/2024]
Abstract
At present, safe and effective treatment drugs are urgently needed for diabetic kidney disease (DKD). Circulating protein biomarkers with causal genetic evidence represent promising drug targets, which provides an opportunity to identify new therapeutic targets. Summary data from two protein quantitative trait loci studies are presented, one involving 4,907 plasma proteins data from 35,559 individuals and the other encompassing 4,657 plasma proteins among 7,213 European Americans. Summary statistics for DKD were obtained from a large genome-wide association study (3,345 cases and 2,372 controls) and the FinnGen study (3,676 cases and 283,456 controls). Mendelian randomization (MR) analysis was conducted to examine the potential targets for DKD. The colocalization analysis was used to detect whether the potential proteins exist in the shared causal variants. To enhance the credibility of the results, external validation was conducted. Additionally, enrichment analysis, assessment of protein druggability, and the protein-protein interaction networks were used to further enrich the research findings. The proteome-wide MR analyses identified 21 blood proteins that may causally be associated with DKD. Colocalization analysis further supported a causal relationship between 12 proteins and DKD, with external validation confirming 4 of these proteins, and TGFBI was affirmed through two separate group data sets. These results indicate that targeting these four proteins could be a promising approach for treating DKD, and warrant further clinical investigations. ARTICLE HIGHLIGHTS
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Affiliation(s)
- Wei Zhang
- Department of Nephrology, First Teaching Hospital of Tianjin University of Traditional Chinese Medicine, National Clinical Research Center for Chinese Medicine Acupuncture and Moxibustion, Tianjin, China
| | - Leilei Ma
- Department of Nephrology, First Teaching Hospital of Tianjin University of Traditional Chinese Medicine, National Clinical Research Center for Chinese Medicine Acupuncture and Moxibustion, Tianjin, China
| | - Qianyi Zhou
- Department of Nephrology, First Teaching Hospital of Tianjin University of Traditional Chinese Medicine, National Clinical Research Center for Chinese Medicine Acupuncture and Moxibustion, Tianjin, China
| | - Tianjiao Gu
- Department of Endocrinology, First Teaching Hospital of Tianjin University of Traditional Chinese Medicine, National Clinical Research Center for Chinese Medicine Acupuncture and Moxibustion, Tianjin, China
| | - Xiaotian Zhang
- Department of Nephrology, First Teaching Hospital of Tianjin University of Traditional Chinese Medicine, National Clinical Research Center for Chinese Medicine Acupuncture and Moxibustion, Tianjin, China
| | - Haitao Xing
- Department of Nephrology, First Teaching Hospital of Tianjin University of Traditional Chinese Medicine, National Clinical Research Center for Chinese Medicine Acupuncture and Moxibustion, Tianjin, China
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Reay WR, Clarke E, Eslick S, Riveros C, Holliday EG, McEvoy MA, Peel R, Hancock S, Scott RJ, Attia JR, Collins CE, Cairns MJ. Using Genetics to Inform Interventions Related to Sodium and Potassium in Hypertension. Circulation 2024; 149:1019-1032. [PMID: 38131187 PMCID: PMC10962430 DOI: 10.1161/circulationaha.123.065394] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/02/2023] [Accepted: 11/28/2023] [Indexed: 12/23/2023]
Abstract
BACKGROUND Hypertension is a key risk factor for major adverse cardiovascular events but remains difficult to treat in many individuals. Dietary interventions are an effective approach to lower blood pressure (BP) but are not equally effective across all individuals. BP is heritable, and genetics may be a useful tool to overcome treatment response heterogeneity. We investigated whether the genetics of BP could be used to identify individuals with hypertension who may receive a particular benefit from lowering sodium intake and boosting potassium levels. METHODS In this observational genetic study, we leveraged cross-sectional data from up to 296 475 genotyped individuals drawn from the UK Biobank cohort for whom BP and urinary electrolytes (sodium and potassium), biomarkers of sodium and potassium intake, were measured. Biologically directed genetic scores for BP were constructed specifically among pathways related to sodium and potassium biology (pharmagenic enrichment scores), as well as unannotated genome-wide scores (conventional polygenic scores). We then tested whether there was a gene-by-environment interaction between urinary electrolytes and these genetic scores on BP. RESULTS Genetic risk and urinary electrolytes both independently correlated with BP. However, urinary sodium was associated with a larger BP increase among individuals with higher genetic risk in sodium- and potassium-related pathways than in those with comparatively lower genetic risk. For example, each SD in urinary sodium was associated with a 1.47-mm Hg increase in systolic BP for those in the top 10% of the distribution of genetic risk in sodium and potassium transport pathways versus a 0.97-mm Hg systolic BP increase in the lowest 10% (P=1.95×10-3). This interaction with urinary sodium remained when considering estimated glomerular filtration rate and indexing sodium to urinary creatinine. There was no strong evidence of an interaction between urinary sodium and a standard genome-wide polygenic score of BP. CONCLUSIONS The data suggest that genetic risk in sodium and potassium pathways could be used in a precision medicine model to direct interventions more specifically in the management of hypertension. Intervention studies are warranted.
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Affiliation(s)
- William R. Reay
- Schools of Biomedical Sciences and Pharmacy (W.R.R., R.J.S., M.J.C.), The University of Newcastle, Callaghan, NSW, Australia
- Precision Medicine Research Program (W.R.R., M.J.C.), New Lambton, NSW, Australia
| | - Erin Clarke
- Health Sciences (E.C., S.E., C.E.C.), The University of Newcastle, Callaghan, NSW, Australia
- Food and Nutrition Research Program (E.C., C.E.C.), New Lambton, NSW, Australia
| | - Shaun Eslick
- Health Sciences (E.C., S.E., C.E.C.), The University of Newcastle, Callaghan, NSW, Australia
| | - Carlos Riveros
- Hunter Medical Research Institute (C.R., E.G.H., J.R.A.), New Lambton, NSW, Australia
| | - Elizabeth G. Holliday
- Medicine and Public Health (E.G.H., R.P., S.H., J.R.A.), The University of Newcastle, Callaghan, NSW, Australia
- Hunter Medical Research Institute (C.R., E.G.H., J.R.A.), New Lambton, NSW, Australia
| | - Mark A. McEvoy
- Rural Health School, La Trobe University, Bendigo, Victoria, Australia (M.A.M.)
| | - Roseanne Peel
- Medicine and Public Health (E.G.H., R.P., S.H., J.R.A.), The University of Newcastle, Callaghan, NSW, Australia
| | - Stephen Hancock
- Medicine and Public Health (E.G.H., R.P., S.H., J.R.A.), The University of Newcastle, Callaghan, NSW, Australia
| | - Rodney J. Scott
- Schools of Biomedical Sciences and Pharmacy (W.R.R., R.J.S., M.J.C.), The University of Newcastle, Callaghan, NSW, Australia
- Cancer Detection and Therapy Research Program (R.J.S.), New Lambton, NSW, Australia
| | - John R. Attia
- Medicine and Public Health (E.G.H., R.P., S.H., J.R.A.), The University of Newcastle, Callaghan, NSW, Australia
- Hunter Medical Research Institute (C.R., E.G.H., J.R.A.), New Lambton, NSW, Australia
| | - Clare E. Collins
- Health Sciences (E.C., S.E., C.E.C.), The University of Newcastle, Callaghan, NSW, Australia
- Food and Nutrition Research Program (E.C., C.E.C.), New Lambton, NSW, Australia
| | - Murray J. Cairns
- Schools of Biomedical Sciences and Pharmacy (W.R.R., R.J.S., M.J.C.), The University of Newcastle, Callaghan, NSW, Australia
- Precision Medicine Research Program (W.R.R., M.J.C.), New Lambton, NSW, Australia
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Chen B, Wang L, Pu S, Guo L, Chai N, Sun X, Tang X, Ren Y, He J, Hao N. Unveiling potential drug targets for hyperparathyroidism through genetic insights via Mendelian randomization and colocalization analyses. Sci Rep 2024; 14:6435. [PMID: 38499600 PMCID: PMC10948885 DOI: 10.1038/s41598-024-57100-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/24/2024] [Accepted: 03/14/2024] [Indexed: 03/20/2024] Open
Abstract
Hyperparathyroidism (HPT) manifests as a complex condition with a substantial disease burden. While advances have been made in surgical interventions and non-surgical pharmacotherapy for the management of hyperparathyroidism, radical options to halt underlying disease progression remain lacking. Identifying putative genetic drivers and exploring novel drug targets that can impede HPT progression remain critical unmet needs. A Mendelian randomization (MR) analysis was performed to uncover putative therapeutic targets implicated in hyperparathyroidism pathology. Cis-expression quantitative trait loci (cis-eQTL) data serving as genetic instrumental variables were obtained from the eQTLGen Consortium and Genotype-Tissue Expression (GTEx) portal. Hyperparathyroidism summary statistics for single nucleotide polymorphism (SNP) associations were sourced from the FinnGen study (5590 cases; 361,988 controls). Colocalization analysis was performed to determine the probability of shared causal variants underlying SNP-hyperparathyroidism and SNP-eQTL links. Five drug targets (CMKLR1, FSTL1, IGSF11, PIK3C3 and SLC40A1) showed significant causation with hyperparathyroidism in both eQTLGen and GTEx cohorts by MR analysis. Specifically, phosphatidylinositol 3-kinase catalytic subunit type 3 (PIK3C3) and solute carrier family 40 member 1 (SLC40A1) showed strong evidence of colocalization with HPT. Multivariable MR and Phenome-Wide Association Study analyses indicated these two targets were not associated with other traits. Additionally, drug prediction analysis implies the potential of these two targets for future clinical applications. This study identifies PIK3C3 and SLC40A1 as potential genetically proxied druggable genes and promising therapeutic targets for hyperparathyroidism. Targeting PIK3C3 and SLC40A1 may offer effective novel pharmacotherapies for impeding hyperparathyroidism progression and reducing disease risk. These findings provide preliminary genetic insight into underlying drivers amenable to therapeutic manipulation, though further investigation is imperative to validate translational potential from preclinical models through clinical applications.
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Affiliation(s)
- Bohong Chen
- Department of Urology, The First Affiliated Hospital of Xi'an Jiaotong University, Xi'an, 710061, Shaan'xi Province, China
| | - Lihui Wang
- Department of Obstetrics and Gynecology, the First Affiliated Hospital of Xi'an Jiaotong University, Xi'an, 710061, Shaan'xi Province, China
| | - Shengyu Pu
- Department of Breast Surgery, First Affiliated Hospital, School of Medicine, Xi'an Jiaotong University, 277 Yanta Western Rd., Xi'an 710061, Shaan'xi Province, China
| | - Li Guo
- Department of Urology, The First Affiliated Hospital of Xi'an Jiaotong University, Xi'an, 710061, Shaan'xi Province, China
| | - Na Chai
- Department of Breast Surgery, First Affiliated Hospital, School of Medicine, Xi'an Jiaotong University, 277 Yanta Western Rd., Xi'an 710061, Shaan'xi Province, China
| | - Xinyue Sun
- Department of Neurology, The First Affiliated Hospital of Xi'an Jiaotong University, Xi'an, 710061, Shaan'xi Province, China
| | - Xiaojiang Tang
- Department of Breast Surgery, First Affiliated Hospital, School of Medicine, Xi'an Jiaotong University, 277 Yanta Western Rd., Xi'an 710061, Shaan'xi Province, China
| | - Yu Ren
- Department of Breast Surgery, First Affiliated Hospital, School of Medicine, Xi'an Jiaotong University, 277 Yanta Western Rd., Xi'an 710061, Shaan'xi Province, China
| | - Jianjun He
- Department of Breast Surgery, First Affiliated Hospital, School of Medicine, Xi'an Jiaotong University, 277 Yanta Western Rd., Xi'an 710061, Shaan'xi Province, China.
| | - Na Hao
- Department of Breast Surgery, First Affiliated Hospital, School of Medicine, Xi'an Jiaotong University, 277 Yanta Western Rd., Xi'an 710061, Shaan'xi Province, China.
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Ng II, Zhang J, Tian T, Peng Q, Huang Z, Xiao K, Yao X, Ng L, Zeng J, Tang H. Network-based screening identifies sitagliptin as an antitumor drug targeting dendritic cells. J Immunother Cancer 2024; 12:e008254. [PMID: 38458637 PMCID: PMC10921530 DOI: 10.1136/jitc-2023-008254] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 02/19/2024] [Indexed: 03/10/2024] Open
Abstract
BACKGROUND Dendritic cell (DC)-mediated antigen presentation is essential for the priming and activation of tumor-specific T cells. However, few drugs that specifically manipulate DC functions are available. The identification of drugs targeting DC holds great promise for cancer immunotherapy. METHODS We observed that type 1 conventional DCs (cDC1s) initiated a distinct transcriptional program during antigen presentation. We used a network-based approach to screen for cDC1-targeting therapeutics. The antitumor potency and underlying mechanisms of the candidate drug were investigated in vitro and in vivo. RESULTS Sitagliptin, an oral gliptin widely used for type 2 diabetes, was identified as a drug that targets DCs. In mouse models, sitagliptin inhibited tumor growth by enhancing cDC1-mediated antigen presentation, leading to better T-cell activation. Mechanistically, inhibition of dipeptidyl peptidase 4 (DPP4) by sitagliptin prevented the truncation and degradation of chemokines/cytokines that are important for DC activation. Sitagliptin enhanced cancer immunotherapy by facilitating the priming of antigen-specific T cells by DCs. In humans, the use of sitagliptin correlated with a lower risk of tumor recurrence in patients with colorectal cancer undergoing curative surgery. CONCLUSIONS Our findings indicate that sitagliptin-mediated DPP4 inhibition promotes antitumor immune response by augmenting cDC1 functions. These data suggest that sitagliptin can be repurposed as an antitumor drug targeting DC, which provides a potential strategy for cancer immunotherapy.
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Affiliation(s)
- Ian-Ian Ng
- State Key Laboratory of Molecular Oncology, School of Pharmaceutical Sciences, Tsinghua University, Beijing, China
| | - Jiaqi Zhang
- State Key Laboratory of Molecular Oncology, School of Pharmaceutical Sciences, Tsinghua University, Beijing, China
| | - Tingzhong Tian
- Institute for Interdisciplinary Information Sciences, Tsinghua University, Beijing, China
| | - Qi Peng
- State Key Laboratory of Molecular Oncology, School of Pharmaceutical Sciences, Tsinghua University, Beijing, China
- Joint Graduate Program of Peking-Tsinghua-NIBS, School of Life Sciences, Tsinghua University, Beijing, China
| | - Zheng Huang
- Department of Surgery, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong, China
| | - Kaimin Xiao
- State Key Laboratory of Molecular Oncology, School of Pharmaceutical Sciences, Tsinghua University, Beijing, China
- Joint Graduate Program of Peking-Tsinghua-NIBS, School of Life Sciences, Tsinghua University, Beijing, China
| | - Xiyue Yao
- State Key Laboratory of Molecular Oncology, School of Pharmaceutical Sciences, Tsinghua University, Beijing, China
| | - Lui Ng
- Department of Surgery, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong, China
| | - Jianyang Zeng
- Institute for Interdisciplinary Information Sciences, Tsinghua University, Beijing, China
| | - Haidong Tang
- State Key Laboratory of Molecular Oncology, School of Pharmaceutical Sciences, Tsinghua University, Beijing, China
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Ma Y, Zhou Y, Jiang D, Dai W, Li J, Deng C, Chen C, Zheng G, Zhang Y, Qiu F, Sun H, Xing S, Han H, Qu J, Wu N, Yao Y, Su J. Integration of human organoids single-cell transcriptomic profiles and human genetics repurposes critical cell type-specific drug targets for severe COVID-19. Cell Prolif 2024; 57:e13558. [PMID: 37807299 PMCID: PMC10905359 DOI: 10.1111/cpr.13558] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/31/2023] [Revised: 08/31/2023] [Accepted: 09/18/2023] [Indexed: 10/10/2023] Open
Abstract
Human organoids recapitulate the cell type diversity and function of their primary organs holding tremendous potentials for basic and translational research. Advances in single-cell RNA sequencing (scRNA-seq) technology and genome-wide association study (GWAS) have accelerated the biological and therapeutic interpretation of trait-relevant cell types or states. Here, we constructed a computational framework to integrate atlas-level organoid scRNA-seq data, GWAS summary statistics, expression quantitative trait loci, and gene-drug interaction data for distinguishing critical cell populations and drug targets relevant to coronavirus disease 2019 (COVID-19) severity. We found that 39 cell types across eight kinds of organoids were significantly associated with COVID-19 outcomes. Notably, subset of lung mesenchymal stem cells increased proximity with fibroblasts predisposed to repair COVID-19-damaged lung tissue. Brain endothelial cell subset exhibited significant associations with severe COVID-19, and this cell subset showed a notable increase in cell-to-cell interactions with other brain cell types, including microglia. We repurposed 33 druggable genes, including IFNAR2, TYK2, and VIPR2, and their interacting drugs for COVID-19 in a cell-type-specific manner. Overall, our results showcase that host genetic determinants have cellular-specific contribution to COVID-19 severity, and identification of cell type-specific drug targets may facilitate to develop effective therapeutics for treating severe COVID-19 and its complications.
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Affiliation(s)
- Yunlong Ma
- National Engineering Research Center of Ophthalmology and Optometry, Eye Hospital, Wenzhou Medical University, Wenzhou, China
- Department of Biomedical Informatics, Institute of Biomedical Big Data, Wenzhou Medical University, Wenzhou, China
- Oujiang Laboratory, Zhejiang Lab for Regenerative Medicine, Vision and Brain Health, Zhejiang, China
| | - Yijun Zhou
- National Engineering Research Center of Ophthalmology and Optometry, Eye Hospital, Wenzhou Medical University, Wenzhou, China
- Department of Biomedical Informatics, Institute of Biomedical Big Data, Wenzhou Medical University, Wenzhou, China
| | - Dingping Jiang
- National Engineering Research Center of Ophthalmology and Optometry, Eye Hospital, Wenzhou Medical University, Wenzhou, China
- Oujiang Laboratory, Zhejiang Lab for Regenerative Medicine, Vision and Brain Health, Zhejiang, China
| | - Wei Dai
- Wenzhou Institute, University of Chinese Academy of Sciences, Wenzhou, China
| | - Jingjing Li
- National Engineering Research Center of Ophthalmology and Optometry, Eye Hospital, Wenzhou Medical University, Wenzhou, China
- Department of Biomedical Informatics, Institute of Biomedical Big Data, Wenzhou Medical University, Wenzhou, China
| | - Chunyu Deng
- School of Life Science and Technology, Harbin Institute of Technology, Harbin, China
| | - Cheng Chen
- National Engineering Research Center of Ophthalmology and Optometry, Eye Hospital, Wenzhou Medical University, Wenzhou, China
- Department of Biomedical Informatics, Institute of Biomedical Big Data, Wenzhou Medical University, Wenzhou, China
| | - Gongwei Zheng
- National Engineering Research Center of Ophthalmology and Optometry, Eye Hospital, Wenzhou Medical University, Wenzhou, China
- Department of Biomedical Informatics, Institute of Biomedical Big Data, Wenzhou Medical University, Wenzhou, China
| | - Yaru Zhang
- National Engineering Research Center of Ophthalmology and Optometry, Eye Hospital, Wenzhou Medical University, Wenzhou, China
- Department of Biomedical Informatics, Institute of Biomedical Big Data, Wenzhou Medical University, Wenzhou, China
- Oujiang Laboratory, Zhejiang Lab for Regenerative Medicine, Vision and Brain Health, Zhejiang, China
| | - Fei Qiu
- National Engineering Research Center of Ophthalmology and Optometry, Eye Hospital, Wenzhou Medical University, Wenzhou, China
- Department of Biomedical Informatics, Institute of Biomedical Big Data, Wenzhou Medical University, Wenzhou, China
| | - Haojun Sun
- National Engineering Research Center of Ophthalmology and Optometry, Eye Hospital, Wenzhou Medical University, Wenzhou, China
- Department of Biomedical Informatics, Institute of Biomedical Big Data, Wenzhou Medical University, Wenzhou, China
| | - Shilai Xing
- National Engineering Research Center of Ophthalmology and Optometry, Eye Hospital, Wenzhou Medical University, Wenzhou, China
- Department of Biomedical Informatics, Institute of Biomedical Big Data, Wenzhou Medical University, Wenzhou, China
| | - Haijun Han
- School of Medicine, Hangzhou City University, Hangzhou, China
| | - Jia Qu
- National Engineering Research Center of Ophthalmology and Optometry, Eye Hospital, Wenzhou Medical University, Wenzhou, China
| | - Nan Wu
- Beijing Key Laboratory for Genetic Research of Skeletal Deformity, Key Laboratory of Big Data for Spinal Deformities, Department of Orthopedic Surgery, Peking Union Medical College Hospital, Peking Union Medical College and Chinese Academy of Medical Sciences, Beijing, China
| | - Yinghao Yao
- Oujiang Laboratory, Zhejiang Lab for Regenerative Medicine, Vision and Brain Health, Zhejiang, China
| | - Jianzhong Su
- National Engineering Research Center of Ophthalmology and Optometry, Eye Hospital, Wenzhou Medical University, Wenzhou, China
- Department of Biomedical Informatics, Institute of Biomedical Big Data, Wenzhou Medical University, Wenzhou, China
- Oujiang Laboratory, Zhejiang Lab for Regenerative Medicine, Vision and Brain Health, Zhejiang, China
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21
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Hassan MM, Li D, Han Y, Byun J, Hatia RI, Long E, Choi J, Kelley RK, Cleary SP, Lok AS, Bracci P, Permuth JB, Bucur R, Yuan JM, Singal AG, Jalal PK, Ghobrial RM, Santella RM, Kono Y, Shah DP, Nguyen MH, Liu G, Parikh ND, Kim R, Wu HC, El-Serag H, Chang P, Li Y, Chun YS, Lee SS, Gu J, Hawk E, Sun R, Huff C, Rashid A, Amin HM, Beretta L, Wolff RA, Antwi SO, Patt Y, Hwang LY, Klein AP, Zhang K, Schmidt MA, White DL, Goss JA, Khaderi SA, Marrero JA, Cigarroa FG, Shah PK, Kaseb AO, Roberts LR, Amos CI. Genome-wide association study identifies high-impact susceptibility loci for HCC in North America. Hepatology 2024:01515467-990000000-00763. [PMID: 38381705 DOI: 10.1097/hep.0000000000000800] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/20/2023] [Accepted: 12/18/2023] [Indexed: 02/23/2024]
Abstract
BACKGROUND AND AIMS Despite the substantial impact of environmental factors, individuals with a family history of liver cancer have an increased risk for HCC. However, genetic factors have not been studied systematically by genome-wide approaches in large numbers of individuals from European descent populations (EDP). APPROACH AND RESULTS We conducted a 2-stage genome-wide association study (GWAS) on HCC not affected by HBV infections. A total of 1872 HCC cases and 2907 controls were included in the discovery stage, and 1200 HCC cases and 1832 controls in the validation. We analyzed the discovery and validation samples separately and then conducted a meta-analysis. All analyses were conducted in the presence and absence of HCV. The liability-scale heritability was 24.4% for overall HCC. Five regions with significant ORs (95% CI) were identified for nonviral HCC: 3p22.1, MOBP , rs9842969, (0.51, [0.40-0.65]); 5p15.33, TERT , rs2242652, (0.70, (0.62-0.79]); 19q13.11, TM6SF2 , rs58542926, (1.49, [1.29-1.72]); 19p13.11 MAU2 , rs58489806, (1.53, (1.33-1.75]); and 22q13.31, PNPLA3 , rs738409, (1.66, [1.51-1.83]). One region was identified for HCV-induced HCC: 6p21.31, human leukocyte antigen DQ beta 1, rs9275224, (0.79, [0.74-0.84]). A combination of homozygous variants of PNPLA3 and TERT showing a 6.5-fold higher risk for nonviral-related HCC compared to individuals lacking these genotypes. This observation suggests that gene-gene interactions may identify individuals at elevated risk for developing HCC. CONCLUSIONS Our GWAS highlights novel genetic susceptibility of nonviral HCC among European descent populations from North America with substantial heritability. Selected genetic influences were observed for HCV-positive HCC. Our findings indicate the importance of genetic susceptibility to HCC development.
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Affiliation(s)
- Manal M Hassan
- Department of Epidemiology, The University of Texas MD Anderson Cancer Center, Houston, Texas, USA
| | - Donghui Li
- Department of Gastrointestinal Medical Oncology, The University of Texas MD Anderson Cancer Center, Houston, Texas, USA
| | - Younghun Han
- Institute for Clinical and Translational Research, Baylor College of Medicine, Houston, Texas, USA
| | - Jinyoung Byun
- Institute for Clinical and Translational Research, Baylor College of Medicine, Houston, Texas, USA
| | - Rikita I Hatia
- Department of Epidemiology, The University of Texas MD Anderson Cancer Center, Houston, Texas, USA
| | - Erping Long
- Division of Cancer Epidemiology and Genetics, National Cancer Institute, National Institutes of Health, Bethesda, Maryland, USA
| | - Jiyeon Choi
- Division of Cancer Epidemiology and Genetics, National Cancer Institute, National Institutes of Health, Bethesda, Maryland, USA
| | - Robin Kate Kelley
- Helen Diller Family Comprehensive Cancer Center, University of California San Francisco, San Francisco, California, USA
| | - Sean P Cleary
- Division of Hepatobiliary and Pancreas Surgery, Mayo Clinic, Rochester, Minnesota, USA
| | - Anna S Lok
- Division of Gastroenterology and Hepatology, Department of Internal Medicine, University of Michigan, Ann Arbor, Michigan, USA
| | - Paige Bracci
- Department of Epidemiology and Biostatistics, University of California San Francisco, San Francisco, California, USA
| | - Jennifer B Permuth
- Department of Gastrointestinal Oncology, Moffitt Cancer Center, Tampa, Florida, USA
- Department of Cancer Epidemiology, Moffitt Cancer Center, Tampa, Florida, USA
| | - Roxana Bucur
- Princess Margaret Cancer Center and Toronto General Hospital, University Health Network, Toronto, Ontario, Canada
| | - Jian-Min Yuan
- Cancer Epidemiology and Prevention Program, UPMC Hillman Cancer Center, University of Pittsburgh, Pittsburgh, Pennsylvania, USA
- Department of Epidemiology, Graduate School of Public Health, University of Pittsburgh, Pittsburgh, Pennsylvania, USA
| | - Amit G Singal
- Division of Digestive and Liver Diseases, The University of Texas Southwestern Medical Center, Dallas, Texas, USA
| | - Prasun K Jalal
- Department of Gastroenterology and Hepatology, Baylor College of Medicine, Houston, Texas, USA
| | - R Mark Ghobrial
- J.C. Walter Jr. Transplant Center, Houston Methodist Hospital, Houston, Texas, USA
| | - Regina M Santella
- Department of Environmental Health Sciences, Mailman School of Public Health, Columbia University, New York City, New York, USA
| | - Yuko Kono
- Division of Gastroenterology and Hepatology, University of California San Diego, San Diego, California, USA
| | - Dimpy P Shah
- Mays Cancer Center, The University of Texas Health Science Center San Antonio MD Anderson, San Antonio, Texas, USA
| | - Mindie H Nguyen
- Division of Gastroenterology and Hepatology, Department of Epidemiology and Population Health, Stanford University Medical Center, Palo Alto, California, USA
| | - Geoffrey Liu
- Medical Oncology and Hematology, Princess Margaret Cancer Centre, University of Toronto, Toronto, ON, Canada
| | - Neehar D Parikh
- Division of Gastroenterology and Hepatology, Department of Internal Medicine, University of Michigan, Ann Arbor, Michigan, USA
| | - Richard Kim
- Department of Gastrointestinal Oncology, Moffitt Cancer Center, Tampa, Florida, USA
| | - Hui-Chen Wu
- Department of Environmental Health Sciences, Mailman School of Public Health, Columbia University, New York City, New York, USA
| | - Hashem El-Serag
- Department of Medicine, Baylor College of Medicine, Houston, Texas, USA
| | - Ping Chang
- Department of Gastrointestinal Medical Oncology, The University of Texas MD Anderson Cancer Center, Houston, Texas, USA
| | - Yanan Li
- Department of Gastrointestinal Medical Oncology, The University of Texas MD Anderson Cancer Center, Houston, Texas, USA
| | - Yun Shin Chun
- Division of Surgery, Department of Surgical Oncology, The University of Texas MD Anderson Cancer Center, Houston, Texas, USA
| | - Sunyoung S Lee
- Department of Gastrointestinal Medical Oncology, The University of Texas MD Anderson Cancer Center, Houston, Texas, USA
| | - Jian Gu
- Department of Epidemiology, The University of Texas MD Anderson Cancer Center, Houston, Texas, USA
| | - Ernest Hawk
- Division of Cancer Prevention and Population Sciences, The University of Texas MD Anderson Cancer Center, Houston, Texas, USA
| | - Ryan Sun
- Department of Biostatistics, The University of Texas MD Anderson Cancer Center, Houston, Texas, USA
| | - Chad Huff
- Department of Epidemiology, The University of Texas MD Anderson Cancer Center, Houston, Texas, USA
| | - Asif Rashid
- Department of Pathology, The University of Texas MD Anderson Cancer Center, Houston, Texas, USA
| | - Hesham M Amin
- Department of Hematopathology, The University of Texas MD Anderson Cancer Center, Houston, Texas, USA
| | - Laura Beretta
- Department of Molecular and Cellular Oncology, The University of Texas MD Anderson Cancer Center, Houston, Texas, USA
| | - Robert A Wolff
- Department of Gastrointestinal Medical Oncology, The University of Texas MD Anderson Cancer Center, Houston, Texas, USA
| | - Samuel O Antwi
- Division of Epidemiology, Department of Quantitative Health Sciences, Mayo Clinic, Jacksonville, Florida, USA
| | - Yehuda Patt
- Division of Hematology/Oncology, Department of Internal Medicine, University of New Mexico Health Sciences Center, Albuquerque, New Mexico, USA
| | - Lu-Yu Hwang
- Department of Epidemiology, Human Genetics, and Environment Science, The University of Texas Health Science Center at Houston, Houston, Texas, USA
| | - Alison P Klein
- Department of Oncology, Sidney Kimmel Comprehensive Cancer Center at Johns Hopkins, Baltimore, Maryland, USA
| | - Karen Zhang
- Helen Diller Family Comprehensive Cancer Center, University of California San Francisco, San Francisco, California, USA
| | - Mikayla A Schmidt
- Division of Gastroenterology and Hepatology, Mayo Clinic, Rochester, Minnesota, USA
| | - Donna L White
- Sections of Gastroenterology and Hepatology and Health Services Research, Baylor College of Medicine, Houston, Texas, USA
| | - John A Goss
- Division of Abdominal Transplantation, Michael E. DeBakey School of Medicine, Baylor College of Medicine, Houston, Texas, USA
| | - Saira A Khaderi
- Division of Abdominal Transplantation, Baylor College of Medicine, Houston, Texas, USA
| | - Jorge A Marrero
- Division of Digestive and Liver Diseases, The University of Texas Southwestern Medical Center, Dallas, Texas, USA
| | - Francisco G Cigarroa
- Transplant Center, The University of Texas Health Science Center at San Antonio, San Antonio, Texas, USA
| | - Pankil K Shah
- Mays Cancer Center, The University of Texas Health Science Center San Antonio MD Anderson, San Antonio, Texas, USA
| | - Ahmed O Kaseb
- Department of Gastrointestinal Medical Oncology, The University of Texas MD Anderson Cancer Center, Houston, Texas, USA
| | - Lewis R Roberts
- Division of Gastroenterology and Hepatology, Mayo Clinic, Rochester, Minnesota, USA
| | - Christopher I Amos
- Institute for Clinical and Translational Research, Baylor College of Medicine, Houston, Texas, USA
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22
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Reay WR, Kiltschewskij DJ, Di Biase MA, Gerring ZF, Kundu K, Surendran P, Greco LA, Clarke ED, Collins CE, Mondul AM, Albanes D, Cairns MJ. Genetic influences on circulating retinol and its relationship to human health. Nat Commun 2024; 15:1490. [PMID: 38374065 PMCID: PMC10876955 DOI: 10.1038/s41467-024-45779-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/23/2023] [Accepted: 02/04/2024] [Indexed: 02/21/2024] Open
Abstract
Retinol is a fat-soluble vitamin that plays an essential role in many biological processes throughout the human lifespan. Here, we perform the largest genome-wide association study (GWAS) of retinol to date in up to 22,274 participants. We identify eight common variant loci associated with retinol, as well as a rare-variant signal. An integrative gene prioritisation pipeline supports novel retinol-associated genes outside of the main retinol transport complex (RBP4:TTR) related to lipid biology, energy homoeostasis, and endocrine signalling. Genetic proxies of circulating retinol were then used to estimate causal relationships with almost 20,000 clinical phenotypes via a phenome-wide Mendelian randomisation study (MR-pheWAS). The MR-pheWAS suggests that retinol may exert causal effects on inflammation, adiposity, ocular measures, the microbiome, and MRI-derived brain phenotypes, amongst several others. Conversely, circulating retinol may be causally influenced by factors including lipids and serum creatinine. Finally, we demonstrate how a retinol polygenic score could identify individuals more likely to fall outside of the normative range of circulating retinol for a given age. In summary, this study provides a comprehensive evaluation of the genetics of circulating retinol, as well as revealing traits which should be prioritised for further investigation with respect to retinol related therapies or nutritional intervention.
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Affiliation(s)
- William R Reay
- School of Biomedical Sciences and Pharmacy, The University of Newcastle, Callaghan, NSW, Australia.
- Precision Medicine Research Program, Hunter Medical Research Institute, New Lambton, NSW, Australia.
- Melbourne Neuropsychiatry Centre, Department of Psychiatry, The University of Melbourne, Melbourne, VIC, Australia.
| | - Dylan J Kiltschewskij
- School of Biomedical Sciences and Pharmacy, The University of Newcastle, Callaghan, NSW, Australia
- Precision Medicine Research Program, Hunter Medical Research Institute, New Lambton, NSW, Australia
| | - Maria A Di Biase
- Melbourne Neuropsychiatry Centre, Department of Psychiatry, The University of Melbourne, Melbourne, VIC, Australia
- Department of Anatomy and Physiology, The University of Melbourne, Melbourne, VIC, Australia
- Department of Psychiatry, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
| | - Zachary F Gerring
- QIMR Berghofer Medical Research Institute, Brisbane, Queensland, Australia
| | - Kousik Kundu
- Human Genetics, Wellcome Sanger Institute, Wellcome Genome Campus, Hinxton, Cambridge, UK
- Department of Haematology, University of Cambridge, Cambridge Biomedical Campus, Cambridge, UK
| | - Praveen Surendran
- British Heart Foundation Cardiovascular Epidemiology Unit, Department of Public Health and Primary Care, University of Cambridge, Cambridge, UK
- British Heart Foundation Centre of Research Excellence, School of Clinical Medicine, Addenbrooke's Hospital, University of Cambridge, Cambridge, UK
- Health Data Research UK, Wellcome Genome Campus and University of Cambridge, Hinxton, UK
| | - Laura A Greco
- School of Biomedical Sciences and Pharmacy, The University of Newcastle, Callaghan, NSW, Australia
- Precision Medicine Research Program, Hunter Medical Research Institute, New Lambton, NSW, Australia
| | - Erin D Clarke
- School of Health Sciences, The University of Newcastle, Callaghan, NSW, Australia
- Food and Nutrition Research Program, Hunter Medical Research Institute, New Lambton, NSW, Australia
| | - Clare E Collins
- School of Health Sciences, The University of Newcastle, Callaghan, NSW, Australia
- Food and Nutrition Research Program, Hunter Medical Research Institute, New Lambton, NSW, Australia
| | - Alison M Mondul
- Department of Epidemiology, University of Michigan School of Public Health, Ann Arbor, MI, USA
| | - Demetrius Albanes
- Division of Cancer Epidemiology and Genetics, National Cancer Institute, NIH, Department of Health and Human Services, Bethesda, MD, USA
| | - Murray J Cairns
- School of Biomedical Sciences and Pharmacy, The University of Newcastle, Callaghan, NSW, Australia.
- Precision Medicine Research Program, Hunter Medical Research Institute, New Lambton, NSW, Australia.
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23
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Zhang Z, Wang S, Jiang L, Wei J, Lu C, Li S, Diao Y, Fang Z, He S, Tan T, Yang Y, Zou K, Shi J, Lin J, Chen L, Bao C, Fei J, Fang H. Priority index for critical Covid-19 identifies clinically actionable targets and drugs. Commun Biol 2024; 7:189. [PMID: 38366110 PMCID: PMC10873402 DOI: 10.1038/s42003-024-05897-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/22/2023] [Accepted: 02/07/2024] [Indexed: 02/18/2024] Open
Abstract
While genome-wide studies have identified genomic loci in hosts associated with life-threatening Covid-19 (critical Covid-19), the challenge of resolving these loci hinders further identification of clinically actionable targets and drugs. Building upon our previous success, we here present a priority index solution designed to address this challenge, generating the target and drug resource that consists of two indexes: the target index and the drug index. The primary purpose of the target index is to identify clinically actionable targets by prioritising genes associated with Covid-19. We illustrate the validity of the target index by demonstrating its ability to identify pre-existing Covid-19 phase-III drug targets, with the majority of these targets being found at the leading prioritisation (leading targets). These leading targets have their evolutionary origins in Amniota ('four-leg vertebrates') and are predominantly involved in cytokine-cytokine receptor interactions and JAK-STAT signaling. The drug index highlights opportunities for repurposing clinically approved JAK-STAT inhibitors, either individually or in combination. This proposed strategic focus on the JAK-STAT pathway is supported by the active pursuit of therapeutic agents targeting this pathway in ongoing phase-II/III clinical trials for Covid-19.
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Affiliation(s)
- Zhiqiang Zhang
- Shanghai Institute of Hematology, State Key Laboratory of Medical Genomics, National Research Center for Translational Medicine at Shanghai, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, 200025, China
- School of Life Sciences and Biotechnology, Shanghai Jiao Tong University, Shanghai, 200240, China
| | - Shan Wang
- Shanghai Institute of Hematology, State Key Laboratory of Medical Genomics, National Research Center for Translational Medicine at Shanghai, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, 200025, China
| | - Lulu Jiang
- Translational Health Sciences, University of Bristol, Bristol, BS1 3NY, UK
| | - Jianwen Wei
- Network and Information Center, Shanghai Jiao Tong University, Shanghai, 200240, China
| | - Chang Lu
- MRC London Institute of Medical Sciences, Imperial College London, London, W12 0HS, UK
| | - Shengli Li
- Precision Research Center for Refractory Diseases, Institute for Clinical Research, Shanghai General Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, 201620, China
| | - Yizhu Diao
- College of Finance and Statistics, Hunan University, Changsha, 410079, Hunan, China
| | - Zhongcheng Fang
- Shanghai Institute of Hematology, State Key Laboratory of Medical Genomics, National Research Center for Translational Medicine at Shanghai, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, 200025, China
- School of Life Sciences and Biotechnology, Shanghai Jiao Tong University, Shanghai, 200240, China
| | - Shuo He
- College of Health Science and Technology, Shanghai Jiao Tong University School of Medicine, Shanghai, 200025, China
| | - Tingting Tan
- Shanghai Institute of Hematology, State Key Laboratory of Medical Genomics, National Research Center for Translational Medicine at Shanghai, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, 200025, China
| | - Yisheng Yang
- Shanghai Institute of Hematology, State Key Laboratory of Medical Genomics, National Research Center for Translational Medicine at Shanghai, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, 200025, China
| | - Kexin Zou
- Shanghai Institute of Hematology, State Key Laboratory of Medical Genomics, National Research Center for Translational Medicine at Shanghai, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, 200025, China
- School of Life Sciences and Biotechnology, Shanghai Jiao Tong University, Shanghai, 200240, China
| | - Jiantao Shi
- Key Laboratory of RNA Science and Engineering, Shanghai Institute of Biochemistry and Cell Biology, Center for Excellence in Molecular Cell Science, Chinese Academy of Sciences, Shanghai, 200031, China
| | - James Lin
- Network and Information Center, Shanghai Jiao Tong University, Shanghai, 200240, China
| | - Liye Chen
- Nuffield Department of Orthopaedics, Rheumatology and Musculoskeletal Sciences, University of Oxford, Oxford, OX3 7LD, UK.
| | - Chaohui Bao
- Shanghai Institute of Hematology, State Key Laboratory of Medical Genomics, National Research Center for Translational Medicine at Shanghai, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, 200025, China.
- Department of General Surgery, Ruijin Hospital Luwan Branch, Shanghai Jiao Tong University School of Medicine, Shanghai, 200020, China.
| | - Jian Fei
- Department of General Surgery, Ruijin Hospital Luwan Branch, Shanghai Jiao Tong University School of Medicine, Shanghai, 200020, China.
- Department of General Surgery, Pancreatic Disease Center, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, 200025, China.
| | - Hai Fang
- Shanghai Institute of Hematology, State Key Laboratory of Medical Genomics, National Research Center for Translational Medicine at Shanghai, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, 200025, China.
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24
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Frison E, Breban M, Costantino F. How to translate genetic findings into clinical applications in spondyloarthritis? Front Immunol 2024; 15:1301735. [PMID: 38327520 PMCID: PMC10847566 DOI: 10.3389/fimmu.2024.1301735] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/25/2023] [Accepted: 01/08/2024] [Indexed: 02/09/2024] Open
Abstract
Spondyloarthritis (SpA) is characterized by a strong genetic predisposition evidenced by the identification of up to 50 susceptibility loci, in addition to HLA-B27, the major genetic factor associated with the disease. These loci have not only deepened our understanding of disease pathogenesis but also offer the potential to improve disease management. Diagnostic delay is a major issue in SpA. HLA-B27 testing is widely used as diagnostic biomarker in SpA but its predictive value is limited. Several attempts have been made to develop more sophisticated polygenic risk score (PRS). However, these scores currently offer very little improvement as compared to HLA-B27 and are still difficult to implement in clinical routine. Genetics might also help to predict disease outcome including treatment response. Several genetic variants have been reported to be associated with radiographic damage or with poor response to TNF blockers, unfortunately with lack of coherence across studies. Large-scale studies should be conducted to obtain more robust findings. Genetic and genomic evidence in complex diseases can be further used to support the identification of new drug targets and to repurpose existing drugs. Although not fully driven by genetics, development of IL-17 blockers has been facilitated by the discovery of the association between IL23R variants and SpA. Development of recent approaches combining GWAS findings with functional genomics will help to prioritize new drug targets in the future. Although very promising, translational genetics in SpA remains challenging and will require a multidisciplinary approach that integrates genetics, genomics, immunology, and clinical research.
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Affiliation(s)
- Eva Frison
- UMR1173, INSERM, UFR Simone Veil, Versailles-Saint-Quentin-Paris-Saclay University, Saint-Quentin-en-Yvelines, France
- Labex Inflamex, Paris Diderot Sorbonne Paris-Cité University, Paris, France
| | - Maxime Breban
- UMR1173, INSERM, UFR Simone Veil, Versailles-Saint-Quentin-Paris-Saclay University, Saint-Quentin-en-Yvelines, France
- Labex Inflamex, Paris Diderot Sorbonne Paris-Cité University, Paris, France
- Rheumatology Division, Ambroise Paré Hospital, Assistance Publique des Hôpitaux de Paris (AP-HP), Boulogne-Billancourt, France
| | - Félicie Costantino
- UMR1173, INSERM, UFR Simone Veil, Versailles-Saint-Quentin-Paris-Saclay University, Saint-Quentin-en-Yvelines, France
- Labex Inflamex, Paris Diderot Sorbonne Paris-Cité University, Paris, France
- Rheumatology Division, Ambroise Paré Hospital, Assistance Publique des Hôpitaux de Paris (AP-HP), Boulogne-Billancourt, France
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25
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Wang L, Lu Y, Li D, Zhou Y, Yu L, Mesa Eguiagaray I, Campbell H, Li X, Theodoratou E. The landscape of the methodology in drug repurposing using human genomic data: a systematic review. Brief Bioinform 2024; 25:bbad527. [PMID: 38279645 PMCID: PMC10818097 DOI: 10.1093/bib/bbad527] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/17/2023] [Revised: 11/24/2023] [Accepted: 12/19/2023] [Indexed: 01/28/2024] Open
Abstract
The process of drug development is expensive and time-consuming. In contrast, drug repurposing can be introduced to clinical practice more quickly and at a reduced cost. Over the last decade, there has been a significant expansion of large biobanks that link genomic data to electronic health record data, public availability of various databases containing biological and clinical information and rapid development of novel methodologies and algorithms in integrating different sources of data. This review aims to provide a thorough summary of different strategies that utilize genomic data to seek drug-repositioning opportunities. We searched MEDLINE and EMBASE databases to identify eligible studies up until 1 May 2023, with a total of 102 studies finally included after two-step parallel screening. We summarized commonly used strategies for drug repurposing, including Mendelian randomization, multi-omic-based and network-based studies and illustrated each strategy with examples, as well as the data sources implemented. By leveraging existing knowledge and infrastructure to expedite the drug discovery process and reduce costs, drug repurposing potentially identifies new therapeutic uses for approved drugs in a more efficient and targeted manner. However, technical challenges when integrating different types of data and biased or incomplete understanding of drug interactions are important hindrances that cannot be disregarded in the pursuit of identifying novel therapeutic applications. This review offers an overview of drug repurposing methodologies, providing valuable insights and guiding future directions for advancing drug repurposing studies.
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Affiliation(s)
- Lijuan Wang
- Centre for Global Health, Usher Institute, The University of Edinburgh, Edinburgh, UK
- School of Public Health and the Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, Zhejiang, China
| | - Ying Lu
- School of Public Health and the Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, Zhejiang, China
| | - Doudou Li
- School of Public Health and the Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, Zhejiang, China
| | - Yajing Zhou
- School of Public Health and the Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, Zhejiang, China
| | - Lili Yu
- School of Public Health and the Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, Zhejiang, China
| | - Ines Mesa Eguiagaray
- Centre for Global Health, Usher Institute, The University of Edinburgh, Edinburgh, UK
| | - Harry Campbell
- Centre for Global Health, Usher Institute, The University of Edinburgh, Edinburgh, UK
| | - Xue Li
- School of Public Health and the Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, Zhejiang, China
| | - Evropi Theodoratou
- Centre for Global Health, Usher Institute, The University of Edinburgh, Edinburgh, UK
- Cancer Research UK Edinburgh Centre, The University of Edinburgh MRC Institute of Genetics and Cancer, Edinburgh, UK
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26
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Kang H, Pan S, Lin S, Wang YY, Yuan N, Jia P. PharmGWAS: a GWAS-based knowledgebase for drug repurposing. Nucleic Acids Res 2024; 52:D972-D979. [PMID: 37831083 PMCID: PMC10767932 DOI: 10.1093/nar/gkad832] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/14/2023] [Revised: 09/12/2023] [Accepted: 09/21/2023] [Indexed: 10/14/2023] Open
Abstract
Leveraging genetics insights to promote drug repurposing has become a promising and active strategy in pharmacology. Indeed, among the 50 drugs approved by FDA in 2021, two-thirds have genetically supported evidence. In this regard, the increasing amount of widely available genome-wide association studies (GWAS) datasets have provided substantial opportunities for drug repurposing based on genetics discoveries. Here, we developed PharmGWAS, a comprehensive knowledgebase designed to identify candidate drugs through the integration of GWAS data. PharmGWAS focuses on novel connections between diseases and small-molecule compounds derived using a reverse relationship between the genetically-regulated expression signature and the drug-induced signature. Specifically, we collected and processed 1929 GWAS datasets across a diverse spectrum of diseases and 724 485 perturbation signatures pertaining to a substantial 33609 molecular compounds. To obtain reliable and robust predictions for the reverse connections, we implemented six distinct connectivity methods. In the current version, PharmGWAS deposits a total of 740 227 genetically-informed disease-drug pairs derived from drug-perturbation signatures, presenting a valuable and comprehensive catalog. Further equipped with its user-friendly web design, PharmGWAS is expected to greatly aid the discovery of novel drugs, the exploration of drug combination therapies and the identification of drug resistance or side effects. PharmGWAS is available at https://ngdc.cncb.ac.cn/pharmgwas.
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Affiliation(s)
- Hongen Kang
- CAS Key Laboratory of Genomic and Precision Medicine, Beijing Institute of Genomics, Chinese Academy of Sciences and China National Center for Bioinformation, Beijing 100101, China
- University of Chinese Academy of Sciences, Beijing 100049, China
| | - Siyu Pan
- CAS Key Laboratory of Genomic and Precision Medicine, Beijing Institute of Genomics, Chinese Academy of Sciences and China National Center for Bioinformation, Beijing 100101, China
- University of Chinese Academy of Sciences, Beijing 100049, China
| | - Shiqi Lin
- CAS Key Laboratory of Genomic and Precision Medicine, Beijing Institute of Genomics, Chinese Academy of Sciences and China National Center for Bioinformation, Beijing 100101, China
- University of Chinese Academy of Sciences, Beijing 100049, China
| | - Yin-Ying Wang
- CAS Key Laboratory of Genomic and Precision Medicine, Beijing Institute of Genomics, Chinese Academy of Sciences and China National Center for Bioinformation, Beijing 100101, China
| | - Na Yuan
- CAS Key Laboratory of Genomic and Precision Medicine, Beijing Institute of Genomics, Chinese Academy of Sciences and China National Center for Bioinformation, Beijing 100101, China
| | - Peilin Jia
- CAS Key Laboratory of Genomic and Precision Medicine, Beijing Institute of Genomics, Chinese Academy of Sciences and China National Center for Bioinformation, Beijing 100101, China
- University of Chinese Academy of Sciences, Beijing 100049, China
- National Genomics Data Center, Beijing Institute of Genomics, Chinese Academy of Sciences and China National Center for Bioinformation, Beijing 100101, China
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27
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Sigala RE, Lagou V, Shmeliov A, Atito S, Kouchaki S, Awais M, Prokopenko I, Mahdi A, Demirkan A. Machine Learning to Advance Human Genome-Wide Association Studies. Genes (Basel) 2023; 15:34. [PMID: 38254924 PMCID: PMC10815885 DOI: 10.3390/genes15010034] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/16/2023] [Revised: 12/19/2023] [Accepted: 12/22/2023] [Indexed: 01/24/2024] Open
Abstract
Machine learning, including deep learning, reinforcement learning, and generative artificial intelligence are revolutionising every area of our lives when data are made available. With the help of these methods, we can decipher information from larger datasets while addressing the complex nature of biological systems in a more efficient way. Although machine learning methods have been introduced to human genetic epidemiological research as early as 2004, those were never used to their full capacity. In this review, we outline some of the main applications of machine learning to assigning human genetic loci to health outcomes. We summarise widely used methods and discuss their advantages and challenges. We also identify several tools, such as Combi, GenNet, and GMSTool, specifically designed to integrate these methods for hypothesis-free analysis of genetic variation data. We elaborate on the additional value and limitations of these tools from a geneticist's perspective. Finally, we discuss the fast-moving field of foundation models and large multi-modal omics biobank initiatives.
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Affiliation(s)
- Rafaella E. Sigala
- Section of Statistical Multi-Omics, Department of Clinical and Experimental Medicine, Guildford GU2 7XH, Surrey, UK; (R.E.S.); (V.L.); (A.S.); (I.P.)
| | - Vasiliki Lagou
- Section of Statistical Multi-Omics, Department of Clinical and Experimental Medicine, Guildford GU2 7XH, Surrey, UK; (R.E.S.); (V.L.); (A.S.); (I.P.)
| | - Aleksey Shmeliov
- Section of Statistical Multi-Omics, Department of Clinical and Experimental Medicine, Guildford GU2 7XH, Surrey, UK; (R.E.S.); (V.L.); (A.S.); (I.P.)
| | - Sara Atito
- Surrey Institute for People-Centred Artificial Intelligence, University of Surrey, Guildford GU2 7XH, Surrey, UK; (S.A.); (S.K.); (M.A.)
- Centre for Vision, Speech and Signal Processing, University of Surrey, Guildford GU2 7XH, Surrey, UK
| | - Samaneh Kouchaki
- Surrey Institute for People-Centred Artificial Intelligence, University of Surrey, Guildford GU2 7XH, Surrey, UK; (S.A.); (S.K.); (M.A.)
- Centre for Vision, Speech and Signal Processing, University of Surrey, Guildford GU2 7XH, Surrey, UK
| | - Muhammad Awais
- Surrey Institute for People-Centred Artificial Intelligence, University of Surrey, Guildford GU2 7XH, Surrey, UK; (S.A.); (S.K.); (M.A.)
- Centre for Vision, Speech and Signal Processing, University of Surrey, Guildford GU2 7XH, Surrey, UK
| | - Inga Prokopenko
- Section of Statistical Multi-Omics, Department of Clinical and Experimental Medicine, Guildford GU2 7XH, Surrey, UK; (R.E.S.); (V.L.); (A.S.); (I.P.)
- Surrey Institute for People-Centred Artificial Intelligence, University of Surrey, Guildford GU2 7XH, Surrey, UK; (S.A.); (S.K.); (M.A.)
| | - Adam Mahdi
- Oxford Internet Institute, University of Oxford, Oxford OX1 3JS, Oxfordshire, UK;
| | - Ayse Demirkan
- Section of Statistical Multi-Omics, Department of Clinical and Experimental Medicine, Guildford GU2 7XH, Surrey, UK; (R.E.S.); (V.L.); (A.S.); (I.P.)
- Surrey Institute for People-Centred Artificial Intelligence, University of Surrey, Guildford GU2 7XH, Surrey, UK; (S.A.); (S.K.); (M.A.)
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28
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Jiang M, Hao X, Jiang Y, Li S, Wang C, Cheng S. Genetic and observational associations of lung function with gastrointestinal tract diseases: pleiotropic and mendelian randomization analysis. Respir Res 2023; 24:315. [PMID: 38102678 PMCID: PMC10724909 DOI: 10.1186/s12931-023-02621-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/19/2023] [Accepted: 11/29/2023] [Indexed: 12/17/2023] Open
Abstract
BACKGROUND The two-way communications along the gut-lung axis influence the immune function in both gut and lung. However, the shared genetic characteristics of lung function with gastrointestinal tract (GIT) diseases remain to be investigated. METHODS We first investigated the genetic correlations between three lung function traits and four GIT diseases. Second, we illustrated the genetic overlap by genome-wide pleiotropic analysis (PLACO) and further pinpointed the relevant tissue and cell types by partitioning heritability. Furthermore, we proposed pleiotropic genes as potential drug targets by drug database mining. Finally, we evaluated the causal relationships by epidemiologic observational study and Mendelian randomization (MR) analysis. RESULTS We found lung function and GIT diseases were genetically correlated. We identified 258 pleiotropic loci, which were enriched in gut- and lung-specific regions marked by H3K4me1. Among these, 16 pleiotropic genes were targets of drugs, such as tofacitinib and baricitinib targeting TYK2 for the treatment of ulcer colitis and COVID-19, respectively. We identified a missense variant in TYK2, exhibiting a shared causal effect on FEV1/FVC and inflammatory bowel disease (rs12720356, PPLACO=1.38 × 10- 8). These findings suggested TYK2 as a promising drug target. Although the epidemiologic observational study suggested the protective role of lung function in the development of GIT diseases, no causalities were found by MR analysis. CONCLUSIONS Our study suggested the shared genetic characteristics between lung function and GIT diseases. The pleiotropic variants could exert their effects by modulating gene expression marked by histone modifications. Finally, we highlighted the potential of pleiotropic analyses in drug repurposing.
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Affiliation(s)
- Minghui Jiang
- Department of Epidemiology and Biostatistics, School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, 430030, China
- Ministry of Education Key Laboratory of Environment and Health, School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, 430030, China
| | - Xingjie Hao
- Department of Epidemiology and Biostatistics, School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, 430030, China
- Ministry of Education Key Laboratory of Environment and Health, School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, 430030, China
| | - Yi Jiang
- Department of Epidemiology and Biostatistics, School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, 430030, China
- Ministry of Education Key Laboratory of Environment and Health, School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, 430030, China
| | - Si Li
- Department of Epidemiology and Biostatistics, School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, 430030, China
- Ministry of Education Key Laboratory of Environment and Health, School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, 430030, China
| | - Chaolong Wang
- Department of Epidemiology and Biostatistics, School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, 430030, China
- Ministry of Education Key Laboratory of Environment and Health, School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, 430030, China
| | - Shanshan Cheng
- Department of Epidemiology and Biostatistics, School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, 430030, China.
- Ministry of Education Key Laboratory of Environment and Health, School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, 430030, China.
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29
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Baukmann HA, Cope JL, Bannard C, Schwinges AR, Lamparter MR, Groves S, Ravarani CN, Amulic B, Klinger JE, Schmidt MF. Exploring disease-causing traits for drug repurposing in critically ill COVID-19 patients: A causal inference approach. iScience 2023; 26:108185. [PMID: 37965141 PMCID: PMC10641251 DOI: 10.1016/j.isci.2023.108185] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/01/2023] [Revised: 06/16/2023] [Accepted: 10/09/2023] [Indexed: 11/16/2023] Open
Abstract
Despite recent development of vaccines to prevent SARS-CoV-2 infection, treatment of critically ill COVID-19 patients remains an important goal. In principle, genome-wide association studies (GWASs) provide a shortcut to the clinical evidence needed to repurpose existing drugs; however, genes identified frequently lack a causal disease link. We report an alternative method for finding drug repurposing targets, focusing on disease-causing traits beyond immediate disease genetics. Sixty blood cell types and biochemistries, and body mass index, were screened on a cohort of critically ill COVID-19 cases and controls that exhibited mild symptoms after infection, yielding high neutrophil cell count as a possible causal trait for critical illness. Our methodology identified CDK6 and janus kinase (JAK) inhibitors as treatment targets that were validated in an ex vivo neutrophil extracellular trap (NET) formation assay. Our methodology demonstrates the increased power for drug target identification by leveraging large disease-causing trait datasets.
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Affiliation(s)
| | | | - Colin Bannard
- University of Manchester, Oxford Road, Manchester M13 9PL, UK
| | | | | | - Sarah Groves
- School of Cellular and Molecular Medicine, Faculty of Life Sciences, University of Bristol, Bristol BS8 1DT, UK
| | | | - Borko Amulic
- School of Cellular and Molecular Medicine, Faculty of Life Sciences, University of Bristol, Bristol BS8 1DT, UK
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30
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Franks PW, Cefalu WT, Dennis J, Florez JC, Mathieu C, Morton RW, Ridderstråle M, Sillesen HH, Stehouwer CDA. Precision medicine for cardiometabolic disease: a framework for clinical translation. Lancet Diabetes Endocrinol 2023; 11:822-835. [PMID: 37804856 DOI: 10.1016/s2213-8587(23)00165-1] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/27/2022] [Revised: 06/01/2023] [Accepted: 06/01/2023] [Indexed: 10/09/2023]
Abstract
Cardiometabolic disease is a major threat to global health. Precision medicine has great potential to help to reduce the burden of this common and complex disease cluster, and to enhance contemporary evidence-based medicine. Its key pillars are diagnostics; prediction (of the primary disease); prevention (of the primary disease); prognosis (prediction of complications of the primary disease); treatment (of the primary disease or its complications); and monitoring (of risk exposure, treatment response, and disease progression or remission). To contextualise precision medicine in both research and clinical settings, and to encourage the successful translation of discovery science into clinical practice, in this Series paper we outline a model (the EPPOS model) that builds on contemporary evidence-based approaches; includes precision medicine that improves disease-related predictions by stratifying a cohort into subgroups of similar characteristics, or using participants' characteristics to model treatment outcomes directly; includes personalised medicine with the use of a person's data to objectively gauge the efficacy, safety, and tolerability of therapeutics; and subjectively tailors medical decisions to the individual's preferences, circumstances, and capabilities. Precision medicine requires a well functioning system comprised of multiple stakeholders, including health-care recipients, health-care providers, scientists, health economists, funders, innovators of medicines and technologies, regulators, and policy makers. Powerful computing infrastructures supporting appropriate analysis of large-scale, well curated, and accessible health databases that contain high-quality, multidimensional, time-series data will be required; so too will prospective cohort studies in diverse populations designed to generate novel hypotheses, and clinical trials designed to test them. Here, we carefully consider these topics and describe a framework for the integration of precision medicine in cardiometabolic disease.
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Affiliation(s)
- Paul W Franks
- Department of Translational Medicine, Medical Science, Novo Nordisk Foundation, Hellerup, Denmark; Lund University Diabetes Centre, Department of Clinical Sciences, Lund University, Malmö, Sweden; Oxford Centre for Diabetes, Endocrinology and Metabolism, Radcliffe Department of Medicine, University of Oxford, Oxford, UK; Harvard T H Chan School of Public Health, Boston, MA, USA.
| | - William T Cefalu
- Division of Diabetes, Endocrinology and Metabolic Diseases, National Institute of Diabetes and Digestive and Kidney Diseases, National Institutes of Health, Bethesda, MD, USA
| | - John Dennis
- Institute of Biomedical and Clinical Science, Royal Devon and Exeter Hospital, University of Exeter, Exeter, UK
| | - Jose C Florez
- Diabetes Unit and Center for Genomic Medicine, Massachusetts General Hospital, Boston, MA, USA; Programs in Metabolism and Medical & Population Genetics, Broad Institute of Harvard and Massachusetts Institute of Technology, Cambridge, MA, USA; Department of Medicine, Harvard Medical School, Boston, MA, USA
| | - Chantal Mathieu
- Clinical and Experimental Endocrinology, UZ Gasthuisberg, KU Leuven, Leuven, Belgium
| | - Robert W Morton
- Department of Translational Medicine, Medical Science, Novo Nordisk Foundation, Hellerup, Denmark
| | | | - Henrik H Sillesen
- Department of Clinical Medicine, Medical Science, Novo Nordisk Foundation, Hellerup, Denmark
| | - Coen D A Stehouwer
- CARIM School for Cardiovascular Diseases, Maastricht University, Maastricht, Netherlands; Department of Internal Medicine, Maastricht University Medical Centre, Maastricht, Netherlands
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31
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He C, Xu Y, Zhou Y, Fan J, Cheng C, Meng R, Gamazon ER, Zhou D. Integrating population-level and cell-based signatures for drug repositioning. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.10.25.564079. [PMID: 37961219 PMCID: PMC10634827 DOI: 10.1101/2023.10.25.564079] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/15/2023]
Abstract
Drug repositioning presents a streamlined and cost-efficient way to expand the range of therapeutic possibilities. Furthermore, drugs with genetic evidence are more likely to progress successfully through clinical trials towards FDA approval. Exploiting these developments, single gene-based drug repositioning methods have been implemented, but approaches leveraging the entire spectrum of molecular signatures are critically underexplored. Most multi-gene-based approaches rely on differential gene expression (DGE) analysis, which is prone to identify the molecular consequence of disease and renders causal inference challenging. We propose a framework TReD (Transcriptome-informed Reversal Distance) that integrates population-level disease signatures robust to reverse causality and cell-based drug-induced transcriptome response profiles. TReD embeds the disease signature and drug profile in a high-dimensional normed space, quantifying the reversal potential of candidate drugs in a disease-related cell screen assay. The robustness is ensured by evaluation in additional cell screens. For an application, we implement the framework to identify potential drugs against COVID-19. Taking transcriptome-wide association study (TWAS) results from four relevant tissues and three DGE results as disease features, we identify 37 drugs showing potential reversal roles in at least four of the seven disease signatures. Notably, over 70% (27/37) of the drugs have been linked to COVID-19 from other studies, and among them, eight drugs are supported by ongoing/completed clinical trials. For example, TReD identifies the well-studied JAK1/JAK2 inhibitor baricitinib, the first FDA-approved immunomodulatory treatment for COVID-19. Novel potential candidates, including enzastaurin, a selective inhibitor of PKC-beta which can be activated by SARS-CoV-2, are also identified. In summary, we propose a comprehensive genetics-anchored framework integrating population-level signatures and cell-based screens that can accelerate the search for new therapeutic strategies.
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32
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Michoel T, Zhang JD. Causal inference in drug discovery and development. Drug Discov Today 2023; 28:103737. [PMID: 37591410 DOI: 10.1016/j.drudis.2023.103737] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/11/2022] [Revised: 07/31/2023] [Accepted: 08/10/2023] [Indexed: 08/19/2023]
Abstract
To discover new drugs is to seek and to prove causality. As an emerging approach leveraging human knowledge and creativity, data, and machine intelligence, causal inference holds the promise of reducing cognitive bias and improving decision-making in drug discovery. Although it has been applied across the value chain, the concepts and practice of causal inference remain obscure to many practitioners. This article offers a nontechnical introduction to causal inference, reviews its recent applications, and discusses opportunities and challenges of adopting the causal language in drug discovery and development.
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Affiliation(s)
- Tom Michoel
- Computational Biology Unit, Department of Informatics, University of Bergen, Postboks 7803, 5020 Bergen, Norway
| | - Jitao David Zhang
- Pharma Early Research and Development, Roche Innovation Centre Basel, F. Hoffmann-La Roche, Grenzacherstrasse 124, 4070 Basel, Switzerland; Department of Mathematics and Computer Science, University of Basel, Spiegelgasse 1, 4051 Basel, Switzerland.
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33
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Malik MA, Faraone SV, Michoel T, Haavik J. Use of big data and machine learning algorithms to extract possible treatment targets in neurodevelopmental disorders. Pharmacol Ther 2023; 250:108530. [PMID: 37708996 DOI: 10.1016/j.pharmthera.2023.108530] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/13/2023] [Revised: 08/30/2023] [Accepted: 09/11/2023] [Indexed: 09/16/2023]
Abstract
Neurodevelopmental disorders (NDDs) impact multiple aspects of an individual's functioning, including social interactions, communication, and behaviors. The underlying biological mechanisms of NDDs are not yet fully understood, and pharmacological treatments have been limited in their effectiveness, in part due to the complex nature of these disorders and the heterogeneity of symptoms across individuals. Identifying genetic loci associated with NDDs can help in understanding biological mechanisms and potentially lead to the development of new treatments. However, the polygenic nature of these complex disorders has made identifying new treatment targets from genome-wide association studies (GWAS) challenging. Recent advances in the fields of big data and high-throughput tools have provided radically new insights into the underlying biological mechanism of NDDs. This paper reviews various big data approaches, including classical and more recent techniques like deep learning, which can identify potential treatment targets from GWAS and other omics data, with a particular emphasis on NDDs. We also emphasize the increasing importance of explainable and causal machine learning (ML) methods that can aid in identifying genes, molecular pathways, and more complex biological processes that may be future targets of intervention in these disorders. We conclude that these new developments in genetics and ML hold promise for advancing our understanding of NDDs and identifying novel treatment targets.
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Affiliation(s)
- Muhammad Ammar Malik
- Computational Biology Unit, Department of Informatics, University of Bergen, PO BOX 7803, 5020 Bergen, Norway
| | - Stephen V Faraone
- Department of Psychiatry, Norton College of Medicine at SUNY Upstate Medical University, 13210, NY, USA
| | - Tom Michoel
- Computational Biology Unit, Department of Informatics, University of Bergen, PO BOX 7803, 5020 Bergen, Norway
| | - Jan Haavik
- Department of Biomedicine, University of Bergen, PO BOX 7804, 5020 Bergen, Norway; Bergen Center for Brain Plasticity, Division of Psychiatry, Haukeland University Hospital, PO BOX 1400, 5021 Bergen, Norway.
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34
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Sun BB, Chiou J, Traylor M, Benner C, Hsu YH, Richardson TG, Surendran P, Mahajan A, Robins C, Vasquez-Grinnell SG, Hou L, Kvikstad EM, Burren OS, Davitte J, Ferber KL, Gillies CE, Hedman ÅK, Hu S, Lin T, Mikkilineni R, Pendergrass RK, Pickering C, Prins B, Baird D, Chen CY, Ward LD, Deaton AM, Welsh S, Willis CM, Lehner N, Arnold M, Wörheide MA, Suhre K, Kastenmüller G, Sethi A, Cule M, Raj A, Burkitt-Gray L, Melamud E, Black MH, Fauman EB, Howson JMM, Kang HM, McCarthy MI, Nioi P, Petrovski S, Scott RA, Smith EN, Szalma S, Waterworth DM, Mitnaul LJ, Szustakowski JD, Gibson BW, Miller MR, Whelan CD. Plasma proteomic associations with genetics and health in the UK Biobank. Nature 2023; 622:329-338. [PMID: 37794186 PMCID: PMC10567551 DOI: 10.1038/s41586-023-06592-6] [Citation(s) in RCA: 29] [Impact Index Per Article: 29.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/17/2022] [Accepted: 08/31/2023] [Indexed: 10/06/2023]
Abstract
The Pharma Proteomics Project is a precompetitive biopharmaceutical consortium characterizing the plasma proteomic profiles of 54,219 UK Biobank participants. Here we provide a detailed summary of this initiative, including technical and biological validations, insights into proteomic disease signatures, and prediction modelling for various demographic and health indicators. We present comprehensive protein quantitative trait locus (pQTL) mapping of 2,923 proteins that identifies 14,287 primary genetic associations, of which 81% are previously undescribed, alongside ancestry-specific pQTL mapping in non-European individuals. The study provides an updated characterization of the genetic architecture of the plasma proteome, contextualized with projected pQTL discovery rates as sample sizes and proteomic assay coverages increase over time. We offer extensive insights into trans pQTLs across multiple biological domains, highlight genetic influences on ligand-receptor interactions and pathway perturbations across a diverse collection of cytokines and complement networks, and illustrate long-range epistatic effects of ABO blood group and FUT2 secretor status on proteins with gastrointestinal tissue-enriched expression. We demonstrate the utility of these data for drug discovery by extending the genetic proxied effects of protein targets, such as PCSK9, on additional endpoints, and disentangle specific genes and proteins perturbed at loci associated with COVID-19 susceptibility. This public-private partnership provides the scientific community with an open-access proteomics resource of considerable breadth and depth to help to elucidate the biological mechanisms underlying proteo-genomic discoveries and accelerate the development of biomarkers, predictive models and therapeutics1.
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Affiliation(s)
- Benjamin B Sun
- Translational Sciences, Research & Development, Biogen, Cambridge, MA, USA.
| | - Joshua Chiou
- Internal Medicine Research Unit, Worldwide Research, Development and Medical, Pfizer, Cambridge, MA, USA
| | - Matthew Traylor
- Human Genetics Centre of Excellence, Novo Nordisk Research Centre Oxford, Oxford, UK
| | | | | | - Tom G Richardson
- Human Genetics Centre of Excellence, Novo Nordisk Research Centre Oxford, Oxford, UK
- Genomic Sciences, GlaxoSmithKline, Stevenage, UK
| | | | | | - Chloe Robins
- Genomic Sciences, GlaxoSmithKline, Collegeville, PA, USA
| | | | - Liping Hou
- Population Analytics, Janssen Research & Development, Spring House, PA, USA
| | | | - Oliver S Burren
- Centre for Genomics Research, Discovery Sciences, BioPharmaceuticals R&D, AstraZeneca, Cambridge, UK
| | | | - Kyle L Ferber
- Biostatistics, Research and Development, Biogen, Cambridge, MA, USA
| | | | - Åsa K Hedman
- External Science and Innovation Target Sciences, Worldwide Research, Development and Medical, Pfizer, Stockholm, Sweden
| | - Sile Hu
- Human Genetics Centre of Excellence, Novo Nordisk Research Centre Oxford, Oxford, UK
| | - Tinchi Lin
- Analytics and Data Sciences, Biogen, Cambridge, MA, USA
| | - Rajesh Mikkilineni
- Data Science Institute, Takeda Development Center Americas, Cambridge, MA, USA
| | | | | | - Bram Prins
- Centre for Genomics Research, Discovery Sciences, BioPharmaceuticals R&D, AstraZeneca, Cambridge, UK
| | - Denis Baird
- Translational Sciences, Research & Development, Biogen, Cambridge, MA, USA
| | - Chia-Yen Chen
- Translational Sciences, Research & Development, Biogen, Cambridge, MA, USA
| | - Lucas D Ward
- Alnylam Human Genetics, Discovery & Translational Research, Alnylam Pharmaceuticals, Cambridge, MA, USA
| | - Aimee M Deaton
- Alnylam Human Genetics, Discovery & Translational Research, Alnylam Pharmaceuticals, Cambridge, MA, USA
| | | | - Carissa M Willis
- Alnylam Human Genetics, Discovery & Translational Research, Alnylam Pharmaceuticals, Cambridge, MA, USA
| | - Nick Lehner
- Institute of Computational Biology, Helmholtz Zentrum München, German Research Center for Environmental Health, Neuherberg, Germany
| | - Matthias Arnold
- Institute of Computational Biology, Helmholtz Zentrum München, German Research Center for Environmental Health, Neuherberg, Germany
- Department of Psychiatry and Behavioral Sciences, Duke University, Durham, NC, USA
| | - Maria A Wörheide
- Institute of Computational Biology, Helmholtz Zentrum München, German Research Center for Environmental Health, Neuherberg, Germany
| | - Karsten Suhre
- Bioinformatics Core, Weill Cornell Medicine-Qatar, Doha, Qatar
| | - Gabi Kastenmüller
- Institute of Computational Biology, Helmholtz Zentrum München, German Research Center for Environmental Health, Neuherberg, Germany
| | | | | | - Anil Raj
- Calico Life Sciences, San Francisco, CA, USA
| | | | | | - Mary Helen Black
- Population Analytics, Janssen Research & Development, Spring House, PA, USA
| | - Eric B Fauman
- Internal Medicine Research Unit, Worldwide Research, Development and Medical, Pfizer, Cambridge, MA, USA
| | - Joanna M M Howson
- Human Genetics Centre of Excellence, Novo Nordisk Research Centre Oxford, Oxford, UK
| | | | | | - Paul Nioi
- Alnylam Human Genetics, Discovery & Translational Research, Alnylam Pharmaceuticals, Cambridge, MA, USA
| | - Slavé Petrovski
- Centre for Genomics Research, Discovery Sciences, BioPharmaceuticals R&D, AstraZeneca, Cambridge, UK
- Department of Medicine, University of Melbourne, Austin Health, Melbourne, Victoria, Australia
| | | | - Erin N Smith
- Takeda Development Center Americas, San Diego, CA, USA
| | - Sándor Szalma
- Takeda Development Center Americas, San Diego, CA, USA
| | | | | | | | | | - Melissa R Miller
- Internal Medicine Research Unit, Worldwide Research, Development and Medical, Pfizer, Cambridge, MA, USA
| | - Christopher D Whelan
- Translational Sciences, Research & Development, Biogen, Cambridge, MA, USA.
- Neuroscience Data Science, Janssen Research & Development, Cambridge, MA, USA.
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Wen J, Zhao B, Yang Z, Erus G, Skampardoni I, Mamourian E, Cui Y, Hwang G, Bao J, Boquet-Pujadas A, Zhou Z, Veturi Y, Ritchie MD, Shou H, Thompson PM, Shen L, Toga AW, Davatzikos C. The Genetic Architecture of Multimodal Human Brain Age. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.04.13.536818. [PMID: 37333190 PMCID: PMC10274645 DOI: 10.1101/2023.04.13.536818] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/20/2023]
Abstract
The complex biological mechanisms underlying human brain aging remain incompletely understood, involving multiple body organs and chronic diseases. In this study, we used multimodal magnetic resonance imaging and artificial intelligence to examine the genetic architecture of the brain age gap (BAG) derived from gray matter volume (GM-BAG, N=31,557 European ancestry), white matter microstructure (WM-BAG, N=31,674), and functional connectivity (FC-BAG, N=32,017). We identified sixteen genomic loci that reached genome-wide significance (P-value<5×10-8). A gene-drug-disease network highlighted genes linked to GM-BAG for treating neurodegenerative and neuropsychiatric disorders and WM-BAG genes for cancer therapy. GM-BAG showed the highest heritability enrichment for genetic variants in conserved regions, whereas WM-BAG exhibited the highest heritability enrichment in the 5' untranslated regions; oligodendrocytes and astrocytes, but not neurons, showed significant heritability enrichment in WM and FC-BAG, respectively. Mendelian randomization identified potential causal effects of several exposure variables on brain aging, such as type 2 diabetes on GM-BAG (odds ratio=1.05 [1.01, 1.09], P-value=1.96×10-2) and AD on WM-BAG (odds ratio=1.04 [1.02, 1.05], P-value=7.18×10-5). Overall, our results provide valuable insights into the genetics of human brain aging, with clinical implications for potential lifestyle and therapeutic interventions. All results are publicly available at the MEDICINE knowledge portal: https://labs.loni.usc.edu/medicine.
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Affiliation(s)
- Junhao Wen
- Laboratory of AI and Biomedical Science (LABS), Stevens Neuroimaging and Informatics Institute, Keck School of Medicine of USC, University of Southern California, Los Angeles, California, USA
| | - Bingxin Zhao
- Department of Statistics and Data Science, University of Pennsylvania, Philadelphia, PA, USA
| | - Zhijian Yang
- Artificial Intelligence in Biomedical Imaging Laboratory (AIBIL), Center for AI and Data Science for Integrated Diagnostics (AID), Perelman School of Medicine, University of Pennsylvania, Philadelphia, USA
| | - Guray Erus
- Artificial Intelligence in Biomedical Imaging Laboratory (AIBIL), Center for AI and Data Science for Integrated Diagnostics (AID), Perelman School of Medicine, University of Pennsylvania, Philadelphia, USA
| | - Ioanna Skampardoni
- Artificial Intelligence in Biomedical Imaging Laboratory (AIBIL), Center for AI and Data Science for Integrated Diagnostics (AID), Perelman School of Medicine, University of Pennsylvania, Philadelphia, USA
| | - Elizabeth Mamourian
- Artificial Intelligence in Biomedical Imaging Laboratory (AIBIL), Center for AI and Data Science for Integrated Diagnostics (AID), Perelman School of Medicine, University of Pennsylvania, Philadelphia, USA
| | - Yuhan Cui
- Artificial Intelligence in Biomedical Imaging Laboratory (AIBIL), Center for AI and Data Science for Integrated Diagnostics (AID), Perelman School of Medicine, University of Pennsylvania, Philadelphia, USA
| | - Gyujoon Hwang
- Artificial Intelligence in Biomedical Imaging Laboratory (AIBIL), Center for AI and Data Science for Integrated Diagnostics (AID), Perelman School of Medicine, University of Pennsylvania, Philadelphia, USA
| | - Jingxuan Bao
- Department of Biostatistics, Epidemiology and Informatics, University of Pennsylvania Perelman School of Medicine, Philadelphia, USA
| | | | - Zhen Zhou
- Artificial Intelligence in Biomedical Imaging Laboratory (AIBIL), Center for AI and Data Science for Integrated Diagnostics (AID), Perelman School of Medicine, University of Pennsylvania, Philadelphia, USA
| | - Yogasudha Veturi
- Department of Biobehavioral Health and Statistics, Penn State University, University Park, PA, USA
| | - Marylyn D Ritchie
- Department of Genetics and Institute for Biomedical Informatics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Haochang Shou
- Artificial Intelligence in Biomedical Imaging Laboratory (AIBIL), Center for AI and Data Science for Integrated Diagnostics (AID), Perelman School of Medicine, University of Pennsylvania, Philadelphia, USA
| | - Paul M Thompson
- Imaging Genetics Center, Mark and Mary Stevens Neuroimaging and Informatics Institute, Keck School of Medicine of USC, University of Southern California, Marina del Rey, California
| | - Li Shen
- Department of Biostatistics, Epidemiology and Informatics, University of Pennsylvania Perelman School of Medicine, Philadelphia, USA
| | - Arthur W Toga
- Laboratory of Neuro Imaging (LONI), Stevens Neuroimaging and Informatics Institute, Keck School of Medicine of USC, University of Southern California, Los Angeles, California, USA
| | - Christos Davatzikos
- Artificial Intelligence in Biomedical Imaging Laboratory (AIBIL), Center for AI and Data Science for Integrated Diagnostics (AID), Perelman School of Medicine, University of Pennsylvania, Philadelphia, USA
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Ren F, Jin Q, Liu T, Ren X, Zhan Y. Proteome-wide mendelian randomization study implicates therapeutic targets in common cancers. J Transl Med 2023; 21:646. [PMID: 37735436 PMCID: PMC10512580 DOI: 10.1186/s12967-023-04525-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/17/2023] [Accepted: 09/13/2023] [Indexed: 09/23/2023] Open
Abstract
BACKGROUND The interest in targeted cancer therapies has been growing rapidly. While numerous cancer biomarkers and targeted treatment strategies have been developed and employed, there are still significant limitations and challenges in the early diagnosis and targeted treatment of cancers. Accordingly, there is an urgent need to identify novel targets and develop new targeted drugs. METHODS The study was conducted using combined cis-Mendelian randomization (cis-MR) and colocalization analysis. We analyzed data from 732 plasma proteins to identify potential drug targets associated with eight site-specific cancers. These findings were further validated using the UK Biobank dataset. Then, a protein-protein interaction network was also constructed to examine the interplay between the identified proteins and the targets of existing cancer medications. RESULTS This MR analysis revealed associations between five plasma proteins and prostate cancer, five with breast cancer, and three with lung cancer. Subsequently, these proteins were classified into four distinct target groups, with a focus on tier 1 and 2 targets due to their higher potential to become drug targets. Our study indicatied that genetically predicted KDELC2 (OR: 0.89, 95% CI 0.86-0.93) and TNFRSF10B (OR: 0.74, 95% CI 0.65-0.83) are inversely associated with prostate cancer. Furthermore, we observed an inverse association between CPNE1 (OR: 0.96, 95% CI 0.94-0.98) and breast cancer, while PDIA3 (OR: 1.19, 95% CI 1.10-1.30) were found to be associated with the risk of breast cancer. In addition, we also propose that SPINT2 (OR: 1.05, 95% CI 1.03-1.06), GSTP1 (OR: 0.82, 95% CI 0.74-0.90), and CTSS (OR: 0.91, 95% CI 0.88-0.95) may serve as potential therapeutic targets in prostate cancer. Similarly, GDI2 (OR: 0.85, 95% CI 0.80-0.91), ISLR2 (OR: 0.87, 95% CI 0.82-0.93), and CTSF (OR: 1.14, 95% CI 1.08-1.21) could potentially be targets for breast cancer. Additionally, we identified SFTPB (OR: 0.93, 95% CI 0.91-0.95), ICAM5 (OR: 0.95, 95% CI 0.93-0.97), and FLRT3 (OR: 1.10, 95% CI 1.05-1.15) as potential targets for lung cancer. Notably, TNFRSF10B, GSTP1, and PDIA3 were found to interact with the target proteins of current medications used in prostate or breast cancer treatment. CONCLUSIONS This comprehensive analysis has highlighted thirteen plasma proteins with potential roles in three site-specific cancers. Continued research in this area may reveal their therapeutic potential, particularly KDELC2, TNFRSF10B, CPNE1, and PDIA3, paving the way for more effective cancer treatments.
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Affiliation(s)
- Feihong Ren
- Guang'anmen Hospital, China Academy of Chinese Medical Sciences, Beijing, 100053, China
- Graduate School, Beijing University of Chinese Medicine, Beijing, 100029, China
| | - Qiubai Jin
- Guang'anmen Hospital, China Academy of Chinese Medical Sciences, Beijing, 100053, China
| | - Tongtong Liu
- Guang'anmen Hospital, China Academy of Chinese Medical Sciences, Beijing, 100053, China
| | - Xuelei Ren
- Guang'anmen Hospital, China Academy of Chinese Medical Sciences, Beijing, 100053, China
| | - Yongli Zhan
- Guang'anmen Hospital, China Academy of Chinese Medical Sciences, Beijing, 100053, China.
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Wu Y, Wang Z, Yang Y, Han C, Wang L, Kang K, Zhao A. Exploration of potential novel drug targets and biomarkers for small cell lung cancer by plasma proteome screening. Front Pharmacol 2023; 14:1266782. [PMID: 37745050 PMCID: PMC10511877 DOI: 10.3389/fphar.2023.1266782] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/25/2023] [Accepted: 08/28/2023] [Indexed: 09/26/2023] Open
Abstract
Background: Small cell lung cancer (SCLC) is characterized by extreme invasiveness and lethality. There have been very few developments in its diagnosis and treatment over the past decades. It is urgently needed to explore potential novel biomarkers and drug targets for SCLC. Methods: Two-sample Mendelian Randomization (MR) was performed to investigate causal associations between SCLC and plasma proteins using genome-wide association studies (GWAS) summary statistics of SCLC from Transdisciplinary Research Into Cancer of the Lung Consortium (nCase = 2,791 vs. nControl = 20,580), and was validated in another cohort (nCase = 2,664 vs. nControl = 21,444). 734 plasma proteins and their genetic instruments of cis-acting protein quantitative trait loci (pQTL) were used, whereas external plasma proteome data was retrieved from deCODE database. Bidirectional MR, Steiger filtering and phenotype scanning were applied to further verify the associations. Results: Seven significant (p < 6.81 × 10-5) plasma protein-SCLC pairs were identified by MR analysis, including ACP5 (OR = 0.76, 95% CI: 0.67-0.86), CPB2 (OR = 0.90, 95% CI: 0.86-0.95), GSTM3 (OR = 0.45, 95% CI: 0.33-0.63), SHMT1 (OR = 0.74, 95% CI: 0.64-0.86), CTSB (OR = 0.79, 95% CI: 0.71-0.88), NTNG1 (OR = 0.81, 95% CI: 0.74-0.90) and FAM171B (OR = 1.40, 95% CI: 1.21-1.62). The external validation confirmed that CPB2, GSTM3 and NTNG1 had protective effects against SCLC, while FAM171B increased SCLC risk. However, the reverse causality analysis revealed that SCLC caused significant changes in plasma levels of most of these proteins, including decreases of ACP5, CPB2, GSTM3 and NTNG1, and the increase of FAM171B. Conclusion: This integrative analysis firstly suggested the causal associations between SCLC and plasma proteins, and the identified several proteins may be promising novel drug targets or biomarkers for SCLC.
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Affiliation(s)
- Yijun Wu
- Cancer Center, West China Hospital, Sichuan University, Chengdu, China
| | - Zhile Wang
- Department of Thoracic Surgery, West China Hospital, Sichuan University, Chengdu, China
| | - Yuqi Yang
- West China School of Medicine, Sichuan University, Chengdu, Sichuan, China
| | - Chang Han
- Cancer Center, West China Hospital, Sichuan University, Chengdu, China
| | - Li Wang
- Department of Hematology, West China Hospital, Sichuan University, Chengdu, China
| | - Kai Kang
- Cancer Center, West China Hospital, Sichuan University, Chengdu, China
| | - Ailin Zhao
- Department of Hematology, West China Hospital, Sichuan University, Chengdu, China
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Stevelink R, Campbell C, Chen S, Abou-Khalil B, Adesoji OM, Afawi Z, Amadori E, Anderson A, Anderson J, Andrade DM, Annesi G, Auce P, Avbersek A, Bahlo M, Baker MD, Balagura G, Balestrini S, Barba C, Barboza K, Bartolomei F, Bast T, Baum L, Baumgartner T, Baykan B, Bebek N, Becker AJ, Becker F, Bennett CA, Berghuis B, Berkovic SF, Beydoun A, Bianchini C, Bisulli F, Blatt I, Bobbili DR, Borggraefe I, Bosselmann C, Braatz V, Bradfield JP, Brockmann K, Brody LC, Buono RJ, Busch RM, Caglayan H, Campbell E, Canafoglia L, Canavati C, Cascino GD, Castellotti B, Catarino CB, Cavalleri GL, Cerrato F, Chassoux F, Cherny SS, Cheung CL, Chinthapalli K, Chou IJ, Chung SK, Churchhouse C, Clark PO, Cole AJ, Compston A, Coppola A, Cosico M, Cossette P, Craig JJ, Cusick C, Daly MJ, Davis LK, de Haan GJ, Delanty N, Depondt C, Derambure P, Devinsky O, Di Vito L, Dlugos DJ, Doccini V, Doherty CP, El-Naggar H, Elger CE, Ellis CA, Eriksson JG, Faucon A, Feng YCA, Ferguson L, Ferraro TN, Ferri L, Feucht M, Fitzgerald M, Fonferko-Shadrach B, Fortunato F, Franceschetti S, Franke A, French JA, Freri E, Gagliardi M, Gambardella A, Geller EB, Giangregorio T, Gjerstad L, Glauser T, Goldberg E, Goldman A, Granata T, Greenberg DA, Guerrini R, Gupta N, Haas KF, Hakonarson H, Hallmann K, Hassanin E, Hegde M, Heinzen EL, Helbig I, Hengsbach C, Heyne HO, Hirose S, Hirsch E, Hjalgrim H, Howrigan DP, Hucks D, Hung PC, Iacomino M, Imbach LL, Inoue Y, Ishii A, Jamnadas-Khoda J, Jehi L, Johnson MR, Kälviäinen R, Kamatani Y, Kanaan M, Kanai M, Kantanen AM, Kara B, Kariuki SM, Kasperavičiūte D, Kasteleijn-Nolst Trenite D, Kato M, Kegele J, Kesim Y, Khoueiry-Zgheib N, King C, Kirsch HE, Klein KM, Kluger G, Knake S, Knowlton RC, Koeleman BPC, Korczyn AD, Koupparis A, Kousiappa I, Krause R, Krenn M, Krestel H, Krey I, Kunz WS, Kurki MI, Kurlemann G, Kuzniecky R, Kwan P, Labate A, Lacey A, Lal D, Landoulsi Z, Lau YL, Lauxmann S, Leech SL, Lehesjoki AE, Lemke JR, Lerche H, Lesca G, Leu C, Lewin N, Lewis-Smith D, Li GHY, Li QS, Licchetta L, Lin KL, Lindhout D, Linnankivi T, Lopes-Cendes I, Lowenstein DH, Lui CHT, Madia F, Magnusson S, Marson AG, May P, McGraw CM, Mei D, Mills JL, Minardi R, Mirza N, Møller RS, Molloy AM, Montomoli M, Mostacci B, Muccioli L, Muhle H, Müller-Schlüter K, Najm IM, Nasreddine W, Neale BM, Neubauer B, Newton CRJC, Nöthen MM, Nothnagel M, Nürnberg P, O’Brien TJ, Okada Y, Ólafsson E, Oliver KL, Özkara C, Palotie A, Pangilinan F, Papacostas SS, Parrini E, Pato CN, Pato MT, Pendziwiat M, Petrovski S, Pickrell WO, Pinsky R, Pippucci T, Poduri A, Pondrelli F, Powell RHW, Privitera M, Rademacher A, Radtke R, Ragona F, Rau S, Rees MI, Regan BM, Reif PS, Rhelms S, Riva A, Rosenow F, Ryvlin P, Saarela A, Sadleir LG, Sander JW, Sander T, Scala M, Scattergood T, Schachter SC, Schankin CJ, Scheffer IE, Schmitz B, Schoch S, Schubert-Bast S, Schulze-Bonhage A, Scudieri P, Sham P, Sheidley BR, Shih JJ, Sills GJ, Sisodiya SM, Smith MC, Smith PE, Sonsma ACM, Speed D, Sperling MR, Stefansson H, Stefansson K, Steinhoff BJ, Stephani U, Stewart WC, Stipa C, Striano P, Stroink H, Strzelczyk A, Surges R, Suzuki T, Tan KM, Taneja RS, Tanteles GA, Taubøll E, Thio LL, Thomas GN, Thomas RH, Timonen O, Tinuper P, Todaro M, Topaloğlu P, Tozzi R, Tsai MH, Tumiene B, Turkdogan D, Unnsteinsdóttir U, Utkus A, Vaidiswaran P, Valton L, van Baalen A, Vetro A, Vining EPG, Visscher F, von Brauchitsch S, von Wrede R, Wagner RG, Weber YG, Weckhuysen S, Weisenberg J, Weller M, Widdess-Walsh P, Wolff M, Wolking S, Wu D, Yamakawa K, Yang W, Yapıcı Z, Yücesan E, Zagaglia S, Zahnert F, Zara F, Zhou W, Zimprich F, Zsurka G, Zulfiqar Ali Q. GWAS meta-analysis of over 29,000 people with epilepsy identifies 26 risk loci and subtype-specific genetic architecture. Nat Genet 2023; 55:1471-1482. [PMID: 37653029 PMCID: PMC10484785 DOI: 10.1038/s41588-023-01485-w] [Citation(s) in RCA: 13] [Impact Index Per Article: 13.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/07/2022] [Accepted: 07/21/2023] [Indexed: 09/02/2023]
Abstract
Epilepsy is a highly heritable disorder affecting over 50 million people worldwide, of which about one-third are resistant to current treatments. Here we report a multi-ancestry genome-wide association study including 29,944 cases, stratified into three broad categories and seven subtypes of epilepsy, and 52,538 controls. We identify 26 genome-wide significant loci, 19 of which are specific to genetic generalized epilepsy (GGE). We implicate 29 likely causal genes underlying these 26 loci. SNP-based heritability analyses show that common variants explain between 39.6% and 90% of genetic risk for GGE and its subtypes. Subtype analysis revealed markedly different genetic architectures between focal and generalized epilepsies. Gene-set analyses of GGE signals implicate synaptic processes in both excitatory and inhibitory neurons in the brain. Prioritized candidate genes overlap with monogenic epilepsy genes and with targets of current antiseizure medications. Finally, we leverage our results to identify alternate drugs with predicted efficacy if repurposed for epilepsy treatment.
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Sakai N, Kamimura K, Terai S. Repurposable Drugs for Immunotherapy and Strategies to Find Candidate Drugs. Pharmaceutics 2023; 15:2190. [PMID: 37765160 PMCID: PMC10536625 DOI: 10.3390/pharmaceutics15092190] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/03/2023] [Revised: 08/20/2023] [Accepted: 08/22/2023] [Indexed: 09/29/2023] Open
Abstract
Conventional drug discovery involves significant steps, time, and expenses; therefore, novel methods for drug discovery remain unmet, particularly for patients with intractable diseases. For this purpose, the drug repurposing method has been recently used to search for new therapeutic agents. Repurposed drugs are mostly previously approved drugs, which were carefully tested for their efficacy for other diseases and had their safety for the human body confirmed following careful pre-clinical trials, clinical trials, and post-marketing surveillance. Therefore, using these approved drugs for other diseases that cannot be treated using conventional therapeutic methods could save time and economic costs for testing their clinical applicability. In this review, we have summarized the methods for identifying repurposable drugs focusing on immunotherapy.
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Affiliation(s)
- Norihiro Sakai
- Division of Gastroenterology and Hepatology, Graduate School of Medical and Dental Sciences, Niigata University, 1-757, Aasahimachi-Dori, Chuo-Ku, Niigata 951-8510, Japan; (N.S.); (S.T.)
| | - Kenya Kamimura
- Division of Gastroenterology and Hepatology, Graduate School of Medical and Dental Sciences, Niigata University, 1-757, Aasahimachi-Dori, Chuo-Ku, Niigata 951-8510, Japan; (N.S.); (S.T.)
- Department of General Medicine, Niigata University School of Medicine, 1-757, Aasahimachi-Dori, Chuo-Ku, Niigata 951-8510, Japan
| | - Shuji Terai
- Division of Gastroenterology and Hepatology, Graduate School of Medical and Dental Sciences, Niigata University, 1-757, Aasahimachi-Dori, Chuo-Ku, Niigata 951-8510, Japan; (N.S.); (S.T.)
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40
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Woodward DJ, Thorp JG, Akosile W, Ong JS, Gamazon ER, Derks EM, Gerring ZF. Identification of drug repurposing candidates for the treatment of anxiety: A genetic approach. Psychiatry Res 2023; 326:115343. [PMID: 37473490 PMCID: PMC10493169 DOI: 10.1016/j.psychres.2023.115343] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/28/2023] [Revised: 07/03/2023] [Accepted: 07/10/2023] [Indexed: 07/22/2023]
Abstract
Anxiety disorders are a group of prevalent and heritable neuropsychiatric diseases. We previously conducted a genome-wide association study (GWAS) which identified genomic loci associated with anxiety; however, the biological consequences underlying the genetic associations are largely unknown. Integrating GWAS and functional genomic data may improve our understanding of the genetic effects on intermediate molecular phenotypes such as gene expression. This can provide an opportunity for the discovery of drug targets for anxiety via drug repurposing. We used the GWAS summary statistics to determine putative causal genes for anxiety using MAGMA and colocalization analyses. A transcriptome-wide association study was conducted to identify genes with differential genetically regulated levels of gene expression in human brain tissue. The genes were integrated with a large drug-gene expression database (Connectivity Map), discovering compounds that are predicted to "normalise" anxiety-associated expression changes. The study identified 64 putative causal genes associated with anxiety (35 genes upregulated; 29 genes downregulated). Drug mechanisms adrenergic receptor agonists, sigma receptor agonists, and glutamate receptor agonists gene targets were enriched in anxiety-associated genetic signal and exhibited an opposing effect on the anxiety-associated gene expression signature. The significance of the project demonstrated genetic links for novel drug candidates to potentially advance anxiety therapeutics.
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Affiliation(s)
- Damian J Woodward
- Mental Health and Neuroscience, QIMR Berghofer Medical Research Institute, Herston, QLD, Australia; School of Biomedical Science, Queensland University of Technology, Kelvin Grove, QLD, Australia.
| | - Jackson G Thorp
- Mental Health and Neuroscience, QIMR Berghofer Medical Research Institute, Herston, QLD, Australia
| | - Wole Akosile
- School of Medicine, University of Queensland, St Lucia, QLD, Australia
| | - Jue-Sheng Ong
- Population Health Department, QIMR Berghofer Medical Research Institute, Herston, QLD, Australia
| | - Eric R Gamazon
- Vanderbilt Genetics Institute, Vanderbilt University Medical Centre, Nashville, TN, USA; Clare Hall, University of Cambridge, Cambridge, UK
| | - Eske M Derks
- Mental Health and Neuroscience, QIMR Berghofer Medical Research Institute, Herston, QLD, Australia
| | - Zachary F Gerring
- Mental Health and Neuroscience, QIMR Berghofer Medical Research Institute, Herston, QLD, Australia.
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Bao C, Gu L, Wang S, Zou K, Zhang Z, Jiang L, Chen L, Fang H. Priority index for asthma (PIA): In silico discovery of shared and distinct drug targets for adult- and childhood-onset disease. Comput Biol Med 2023; 162:107095. [PMID: 37285660 DOI: 10.1016/j.compbiomed.2023.107095] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/30/2023] [Revised: 04/30/2023] [Accepted: 05/27/2023] [Indexed: 06/09/2023]
Abstract
Asthma is a chronic disease that is caused by a combination of genetic risks and environmental triggers and can affect both adults and children. Genome-wide association studies have revealed partly distinct genetic architectures for its two age-of-onset subtypes (namely, adult-onset and childhood-onset). We reason that identifying shared and distinct drug targets between these subtypes may inform the development of subtype-specific therapeutic strategies. In attempting this, we here introduce Priority Index for Asthma or PIA, a genetics-led and network-driven drug target prioritisation tool for asthma. We demonstrate the validity of the tool in improving drug target prioritisation for asthma compared to the status quo methods, as well as in capturing the underlying etiology and existing therapeutics for the disease. We also illustrate how PIA can be used to prioritise drug targets for adult- and childhood-onset asthma, as well as to identify shared and distinct pathway crosstalk genes. Shared crosstalk genes are mostly involved in JAK-STAT signaling, with clinical evidence supporting that targeting this pathway may be a promising drug repurposing opportunity for both subtypes. Crosstalk genes specific to childhood-onset asthma are enriched for PI3K-AKT-mTOR signaling, and we identify genes that are already targeted by licensed medications as repurposed drug candidates for this subtype. We make all our results accessible and reproducible at http://www.genetictargets.com/PIA. Collectively, our study has significant implications for asthma computational medicine research and can guide the future development of subtype-specific therapeutic strategies for the disease.
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Affiliation(s)
- Chaohui Bao
- Shanghai Institute of Hematology, State Key Laboratory of Medical Genomics, National Research Center for Translational Medicine at Shanghai, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Leyao Gu
- Shanghai Institute of Hematology, State Key Laboratory of Medical Genomics, National Research Center for Translational Medicine at Shanghai, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China; Faculty of Medical Laboratory Science, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Shan Wang
- Shanghai Institute of Hematology, State Key Laboratory of Medical Genomics, National Research Center for Translational Medicine at Shanghai, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Kexin Zou
- School of Life Sciences, Central South University, Hunan, China
| | - Zhiqiang Zhang
- Shanghai Institute of Hematology, State Key Laboratory of Medical Genomics, National Research Center for Translational Medicine at Shanghai, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China; School of Life Sciences and Biotechnology, Shanghai Jiao Tong University, Shanghai, China
| | - Lulu Jiang
- Translational Health Sciences, University of Bristol, Bristol, UK
| | - Liye Chen
- Nuffield Department of Orthopaedics, Rheumatology and Musculoskeletal Sciences, University of Oxford, Oxford, UK
| | - Hai Fang
- Shanghai Institute of Hematology, State Key Laboratory of Medical Genomics, National Research Center for Translational Medicine at Shanghai, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China.
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Grotzinger AD, Singh K, Miller-Fleming TW, Lam M, Mallard TT, Chen Y, Liu Z, Ge T, Smoller JW. Transcriptome-Wide Structural Equation Modeling of 13 Major Psychiatric Disorders for Cross-Disorder Risk and Drug Repurposing. JAMA Psychiatry 2023; 80:811-821. [PMID: 37314780 PMCID: PMC10267850 DOI: 10.1001/jamapsychiatry.2023.1808] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/01/2022] [Accepted: 04/05/2023] [Indexed: 06/15/2023]
Abstract
Importance Psychiatric disorders display high levels of comorbidity and genetic overlap, necessitating multivariate approaches for parsing convergent and divergent psychiatric risk pathways. Identifying gene expression patterns underlying cross-disorder risk also stands to propel drug discovery and repurposing in the face of rising levels of polypharmacy. Objective To identify gene expression patterns underlying genetic convergence and divergence across psychiatric disorders along with existing pharmacological interventions that target these genes. Design, Setting, and Participants This genomic study applied a multivariate transcriptomic method, transcriptome-wide structural equation modeling (T-SEM), to investigate gene expression patterns associated with 5 genomic factors indexing shared risk across 13 major psychiatric disorders. Follow-up tests, including overlap with gene sets for other outcomes and phenome-wide association studies, were conducted to better characterize T-SEM results. The Broad Institute Connectivity Map Drug Repurposing Database and Drug-Gene Interaction Database public databases of drug-gene pairs were used to identify drugs that could be repurposed to target genes found to be associated with cross-disorder risk. Data were collected from database inception up to February 20, 2023. Main Outcomes and Measures Gene expression patterns associated with genomic factors or disorder-specific risk and existing drugs that target these genes. Results In total, T-SEM identified 466 genes whose expression was significantly associated (z ≥ 5.02) with genomic factors and 36 genes with disorder-specific effects. Most associated genes were found for a thought disorders factor, defined by bipolar disorder and schizophrenia. Several existing pharmacological interventions were identified that could be repurposed to target genes whose expression was associated with the thought disorders factor or a transdiagnostic p factor defined by all 13 disorders. Conclusions and Relevance The findings from this study shed light on patterns of gene expression associated with genetic overlap and uniqueness across psychiatric disorders. Future versions of the multivariate drug repurposing framework outlined here have the potential to identify novel pharmacological interventions for increasingly common, comorbid psychiatric presentations.
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Affiliation(s)
- Andrew D. Grotzinger
- Institute for Behavioral Genetics, University of Colorado Boulder, Boulder
- Department of Psychology and Neuroscience, University of Colorado Boulder, Boulder
| | - Kritika Singh
- Division of Genetic Medicine, Department of Medicine, Vanderbilt University Medical Center, Nashville, Tennessee
- Vanderbilt Genetics Institute, Vanderbilt University Medical Center, Nashville, Tennessee
| | - Tyne W. Miller-Fleming
- Division of Genetic Medicine, Department of Medicine, Vanderbilt University Medical Center, Nashville, Tennessee
- Vanderbilt Genetics Institute, Vanderbilt University Medical Center, Nashville, Tennessee
| | - Max Lam
- Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard, Cambridge, Massachusetts
- Analytic and Translational Genetics Unit, Massachusetts General Hospital, Boston
- Division of Psychiatry Research, The Zucker Hillside Hospital, Northwell Health, Glen Oaks, New York
- Research Division, Institute of Mental Health Singapore, Singapore
- Human Genetics, Genome Institute of Singapore, Singapore
| | - Travis T. Mallard
- Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard, Cambridge, Massachusetts
- Psychiatric and Neurodevelopmental Genetics Unit, Massachusetts General Hospital, Boston
- Center for Precision Psychiatry, Department of Psychiatry, Massachusetts General Hospital, Boston
| | - Yu Chen
- Analytic and Translational Genetics Unit, Massachusetts General Hospital, Boston
- Center for Medical Genetics, School of Life Sciences, Central South University, Changsha, Hunan, China
| | - Zhaowen Liu
- Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard, Cambridge, Massachusetts
- Psychiatric and Neurodevelopmental Genetics Unit, Massachusetts General Hospital, Boston
- Center for Precision Psychiatry, Department of Psychiatry, Massachusetts General Hospital, Boston
| | - Tian Ge
- Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard, Cambridge, Massachusetts
- Psychiatric and Neurodevelopmental Genetics Unit, Massachusetts General Hospital, Boston
- Center for Precision Psychiatry, Department of Psychiatry, Massachusetts General Hospital, Boston
| | - Jordan W. Smoller
- Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard, Cambridge, Massachusetts
- Psychiatric and Neurodevelopmental Genetics Unit, Massachusetts General Hospital, Boston
- Center for Precision Psychiatry, Department of Psychiatry, Massachusetts General Hospital, Boston
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43
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Pinakhina D, Loboda A, Sergushichev A, Artomov M. Gene, cell type, and drug prioritization analysis suggest genetic basis for the utility of diuretics in treating Alzheimer disease. HGG ADVANCES 2023; 4:100203. [PMID: 37250495 PMCID: PMC10209737 DOI: 10.1016/j.xhgg.2023.100203] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/09/2022] [Accepted: 04/25/2023] [Indexed: 05/31/2023] Open
Abstract
We introduce a user-friendly tool for risk gene, cell type, and drug prioritization for complex traits: GCDPipe. It uses gene-level GWAS-derived data and gene expression data to train a model for the identification of disease risk genes and relevant cell types. Gene prioritization information is then coupled with known drug target data to search for applicable drug agents based on their estimated functional effects on the identified risk genes. We illustrate the utility of our approach in different settings: identification of the cell types, implicated in disease pathogenesis, was tested in inflammatory bowel disease (IBD) and Alzheimer disease (AD); gene target and drug prioritization was tested in IBD and schizophrenia. The analysis of phenotypes with known disease-affected cell types and/or existing drug candidates shows that GCDPipe is an effective tool to unify genetic risk factors with cellular context and known drug targets. Next, analysis of the AD data with GCDPipe suggested that gene targets of diuretics, as an Anatomical Therapeutic Chemical drug subgroup, are significantly enriched among the genes prioritized by GCDPipe, indicating their possible effect on the course of the disease.
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Affiliation(s)
- Daria Pinakhina
- ITMO University, 197101 Saint Petersburg, Russia
- Bekhterev National Medical Research Center, 192019 Saint Petersburg, Russia
| | - Alexander Loboda
- ITMO University, 197101 Saint Petersburg, Russia
- Almazov National Medical Research Center, 191014 Saint Petersburg, Russia
| | | | - Mykyta Artomov
- ITMO University, 197101 Saint Petersburg, Russia
- Broad Institute, Cambridge, MA 02142, USA
- Massachusetts General Hospital, Boston, MA 02114, USA
- The Steve and Cindy Rasmussen Institute for Genomic Medicine, Nationwide Children’s Hospital, Columbus, OH 43205, USA
- Department of Pediatrics, The Ohio State University College of Medicine, Columbus, OH 43210, USA
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Ciochetti NP, Lugli-Moraes B, da Silva BS, Rovaris DL. Genome-wide association studies: utility and limitations for research in physiology. J Physiol 2023; 601:2771-2799. [PMID: 37208942 PMCID: PMC10527550 DOI: 10.1113/jp284241] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/31/2023] [Accepted: 05/10/2023] [Indexed: 05/21/2023] Open
Abstract
Physiological systems are subject to interindividual variation encoded by genetics. Genome-wide association studies (GWAS) operate by surveying thousands of genetic variants from a substantial number of individuals and assessing their association to a trait of interest, be it a physiological variable, a molecular phenotype (e.g. gene expression), or even a disease or condition. Through a myriad of methods, GWAS downstream analyses then explore the functional consequences of each variant and attempt to ascertain a causal relationship to the phenotype of interest, as well as to delve into its links to other traits. This type of investigation allows mechanistic insights into physiological functions, pathological disturbances and shared biological processes between traits (i.e. pleiotropy). An exciting example is the discovery of a new thyroid hormone transporter (SLC17A4) and hormone metabolising enzyme (AADAT) from a GWAS on free thyroxine levels. Therefore, GWAS have substantially contributed with insights into physiology and have been shown to be useful in unveiling the genetic control underlying complex traits and pathological conditions; they will continue to do so with global collaborations and advances in genotyping technology. Finally, the increasing number of trans-ancestry GWAS and initiatives to include ancestry diversity in genomics will boost the power for discoveries, making them also applicable to non-European populations.
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Affiliation(s)
- Nicolas Pereira Ciochetti
- Laboratory of Physiological Genomics of Mental Health (PhysioGen Lab), Instituto de Ciencias Biomedicas Universidade de Sao Paulo, São Paulo, Brazil
| | - Beatriz Lugli-Moraes
- Laboratory of Physiological Genomics of Mental Health (PhysioGen Lab), Instituto de Ciencias Biomedicas Universidade de Sao Paulo, São Paulo, Brazil
| | - Bruna Santos da Silva
- Laboratory of Physiological Genomics of Mental Health (PhysioGen Lab), Instituto de Ciencias Biomedicas Universidade de Sao Paulo, São Paulo, Brazil
- Laboratory of Developmental Psychiatry, Center of Experimental Research, Hospital de Clínicas de Porto Alegre, Porto Alegre, Rio Grande do Sul, Brazil
| | - Diego Luiz Rovaris
- Laboratory of Physiological Genomics of Mental Health (PhysioGen Lab), Instituto de Ciencias Biomedicas Universidade de Sao Paulo, São Paulo, Brazil
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45
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Adikusuma W, Zakaria ZA, Irham LM, Nopitasari BL, Pradiningsih A, Firdayani F, Septama AW, Chong R. Transcriptomics-driven drug repositioning for the treatment of diabetic foot ulcer. Sci Rep 2023; 13:10032. [PMID: 37340026 DOI: 10.1038/s41598-023-37120-1] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/05/2023] [Accepted: 06/15/2023] [Indexed: 06/22/2023] Open
Abstract
Diabetic foot ulcers (DFUs) are a common complication of diabetes and can lead to severe disability and even amputation. Despite advances in treatment, there is currently no cure for DFUs and available drugs for treatment are limited. This study aimed to identify new candidate drugs and repurpose existing drugs to treat DFUs based on transcriptomics analysis. A total of 31 differentially expressed genes (DEGs) were identified and used to prioritize the biological risk genes for DFUs. Further investigation using the database DGIdb revealed 12 druggable target genes among 50 biological DFU risk genes, corresponding to 31 drugs. Interestingly, we highlighted that two drugs (urokinase and lidocaine) are under clinical investigation for DFU and 29 drugs are potential candidates to be repurposed for DFU therapy. The top 5 potential biomarkers for DFU from our findings are IL6ST, CXCL9, IL1R1, CXCR2, and IL10. This study highlights IL1R1 as a highly promising biomarker for DFU due to its high systemic score in functional annotations, that can be targeted with an existing drug, Anakinra. Our study proposed that the integration of transcriptomic and bioinformatic-based approaches has the potential to drive drug repurposing for DFUs. Further research will further examine the mechanisms by which targeting IL1R1 can be used to treat DFU.
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Affiliation(s)
- Wirawan Adikusuma
- Borneo Research on Algesia, Inflammation, and Neurodegeneration (BRAIN) Group, Department of Biomedical Sciences, Faculty of Medicines and Health Sciences, University Malaysia Sabah, Jalan UMS, 88400, Kota Kinabalu, Sabah, Malaysia.
- Departement of Pharmacy, University of Muhammadiyah Mataram, Mataram, Indonesia.
- Research Center for Vaccine and Drugs, National Research and Innovation Agency (BRIN), South Tangerang, Indonesia.
| | - Zainul Amiruddin Zakaria
- Borneo Research on Algesia, Inflammation, and Neurodegeneration (BRAIN) Group, Department of Biomedical Sciences, Faculty of Medicines and Health Sciences, University Malaysia Sabah, Jalan UMS, 88400, Kota Kinabalu, Sabah, Malaysia.
| | - Lalu Muhammad Irham
- Faculty of Pharmacy, Universitas Ahmad Dahlan, Yogyakarta, Indonesia
- Research Center for Pharmaceutical Ingredients and Traditional Medicine, National Research and Innovation Agency (BRIN), South Tangerang, Indonesia
| | | | - Anna Pradiningsih
- Departement of Pharmacy, University of Muhammadiyah Mataram, Mataram, Indonesia
| | - Firdayani Firdayani
- Research Center for Vaccine and Drugs, National Research and Innovation Agency (BRIN), South Tangerang, Indonesia
| | - Abdi Wira Septama
- Research Center for Pharmaceutical Ingredients and Traditional Medicine, National Research and Innovation Agency (BRIN), South Tangerang, Indonesia
| | - Rockie Chong
- Department of Chemistry and Biochemistry, University of California, Los Angeles, USA
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Long E, Wan P, Chen Q, Lu Z, Choi J. From function to translation: Decoding genetic susceptibility to human diseases via artificial intelligence. CELL GENOMICS 2023; 3:100320. [PMID: 37388909 PMCID: PMC10300605 DOI: 10.1016/j.xgen.2023.100320] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 07/01/2023]
Abstract
While genome-wide association studies (GWAS) have discovered thousands of disease-associated loci, molecular mechanisms for a considerable fraction of the loci remain to be explored. The logical next steps for post-GWAS are interpreting these genetic associations to understand disease etiology (GWAS functional studies) and translating this knowledge into clinical benefits for the patients (GWAS translational studies). Although various datasets and approaches using functional genomics have been developed to facilitate these studies, significant challenges remain due to data heterogeneity, multiplicity, and high dimensionality. To address these challenges, artificial intelligence (AI) technology has demonstrated considerable promise in decoding complex functional datasets and providing novel biological insights into GWAS findings. This perspective first describes the landmark progress driven by AI in interpreting and translating GWAS findings and then outlines specific challenges followed by actionable recommendations related to data availability, model optimization, and interpretation, as well as ethical concerns.
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Affiliation(s)
- Erping Long
- Institute of Basic Medical Sciences, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
- Division of Cancer Epidemiology and Genetics, National Cancer Institute, National Institutes of Health, Bethesda, MD, USA
| | - Peixing Wan
- Center for Cancer Research, National Cancer Institute, National Institutes of Health, Bethesda, MD, USA
| | - Qingyu Chen
- National Center for Biotechnology Information, National Library of Medicine, National Institutes of Health, Bethesda, MD, USA
| | - Zhiyong Lu
- National Center for Biotechnology Information, National Library of Medicine, National Institutes of Health, Bethesda, MD, USA
| | - Jiyeon Choi
- Division of Cancer Epidemiology and Genetics, National Cancer Institute, National Institutes of Health, Bethesda, MD, USA
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Ortiz-Fernández L, Carmona EG, Kerick M, Lyons P, Carmona FD, López Mejías R, Khor CC, Grayson PC, Tombetti E, Jiang L, Direskeneli H, Saruhan-Direskeneli G, Callejas-Rubio JL, Vaglio A, Salvarani C, Hernández-Rodríguez J, Cid MC, Morgan AW, Merkel PA, Burgner D, Smith KG, Gonzalez-Gay MA, Sawalha AH, Martin J, Marquez A. Identification of new risk loci shared across systemic vasculitides points towards potential target genes for drug repurposing. Ann Rheum Dis 2023; 82:837-847. [PMID: 36797040 PMCID: PMC10314028 DOI: 10.1136/ard-2022-223697] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/30/2022] [Accepted: 02/04/2023] [Indexed: 02/18/2023]
Abstract
OBJECTIVES The number of susceptibility loci currently associated with vasculitis is lower than in other immune-mediated diseases due in part to small cohort sizes, a consequence of the low prevalence of vasculitides. This study aimed to identify new genetic risk loci for the main systemic vasculitides through a comprehensive analysis of their genetic overlap. METHODS Genome-wide data from 8467 patients with any of the main forms of vasculitis and 29 795 healthy controls were meta-analysed using ASSET. Pleiotropic variants were functionally annotated and linked to their target genes. Prioritised genes were queried in DrugBank to identify potentially repositionable drugs for the treatment of vasculitis. RESULTS Sixteen variants were independently associated with two or more vasculitides, 15 of them representing new shared risk loci. Two of these pleiotropic signals, located close to CTLA4 and CPLX1, emerged as novel genetic risk loci in vasculitis. Most of these polymorphisms appeared to affect vasculitis by regulating gene expression. In this regard, for some of these common signals, potential causal genes were prioritised based on functional annotation, including CTLA4, RNF145, IL12B, IL5, IRF1, IFNGR1, PTK2B, TRIM35, EGR2 and ETS2, each of which has key roles in inflammation. In addition, drug repositioning analysis showed that several drugs, including abatacept and ustekinumab, could be potentially repurposed in the management of the analysed vasculitides. CONCLUSIONS We identified new shared risk loci with functional impact in vasculitis and pinpointed potential causal genes, some of which could represent promising targets for the treatment of vasculitis.
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Affiliation(s)
| | - Elio G Carmona
- Institute of Parasitology and Biomedicine "López- Neyra", CSIC, Granada, Spain
- Unidad de Enfermedades Autoinmunes Sistémicas, Hospital Clínico San Cecilio, Instituto de Investigación Biosanitaria de Granada ibs.GRANADA, Granada, Spain
| | - Martin Kerick
- Institute of Parasitology and Biomedicine "López- Neyra", CSIC, Granada, Spain
| | - Paul Lyons
- Department of Medicine, University of Cambridge School of Clinical Medicine, Cambridge, UK
- Cambridge Institute of Therapeutic Immunology and Infectious Disease, Jeffrey Cheah Biomedical Centre, University of Cambridge, Cambridge, UK
| | - Francisco David Carmona
- Departamento de Genética e Instituto de Biotecnología, Centro de Investigación Biomédica, Universidad de Granada, Granada, Spain
- Instituto de Investigación Biosanitaria ibs.GRANADA, Granada, Spain
| | - Raquel López Mejías
- Research Group on Genetic Epidemiology and Atherosclerosis in Systemic Diseases and in Metabolic Bone Diseases of the Musculoskeletal System, IDIVAL, Santander, Spain
| | - Chiea Chuen Khor
- Duke-NUS Medical School, Genome Institute of Singapore, Agency for Science, Technology and Research, Singapore Eye Research Institute, Singapore National Eye Centre, Singapore
| | - Peter C Grayson
- Systemic Autoimmunity Branch, NIAMS, National Institutes of Health, Bethesda, Maryland, USA
| | - Enrico Tombetti
- Division of Immunology, Transplantation and Infectious Diseases, San Raffaele Hospital, Milan, Italy
- Department of Biomedical and Clinical Sciences L. Sacco, Milan University, Milan, Italy
| | - Lindi Jiang
- Department of Rheumatology, Zhongshan Hospital, Fudan University, Shanghai, China
- Evidence-Based Medicine Center, Fudan University, Shanghai, China
| | - Haner Direskeneli
- Department of Internal Medicine, Division of Rheumatology, Marmara University, Faculty of Medicine, Istanbul, Turkey
| | | | - José-Luis Callejas-Rubio
- Systemic Autoimmune Diseases Unit, San Cecilio University Hospital, Instituto de Investigación Biosanitaria ibs.GRANADA, Granada, Spain
| | - Augusto Vaglio
- Department of Biomedical Experimental and Clinical Sciences "Mario Serio", University of Florence, Florence, Italy
- Nephrology and Dialysis Unit, Meyer Children's Hospital, Florence, Italy
| | - Carlo Salvarani
- Rheumatology Unit, Azienda USL-IRCCS di Reggio Emilia and Azienda Ospedaliero - Universitaria di Modena, Università di Modena and Reggio Emilia, Reggio Emilia, Italy
| | - Jose Hernández-Rodríguez
- Department of Autoimmune Diseases, Hospital Clinic, University of Barcelona, Institut d'Investigacions Biomèdiques August Pi i Sunyer (IDIBAPS), Barcelona, Spain
| | - Maria Cinta Cid
- Department of Autoimmune Diseases, Hospital Clinic, University of Barcelona, Institut d'Investigacions Biomèdiques August Pi i Sunyer (IDIBAPS), Barcelona, Spain
| | - Ann W Morgan
- School of Medicine and Leeds Institute for Data Analytics, University of Leeds, Leeds, UK
- NIHR Leeds Biomedical Research Centre and NIHR Leeds Medtech and In vitro Diagnostics Co-Operative, Leeds Teaching Hospitals NHS Trust, Leeds, UK
| | - Peter A Merkel
- Division of Rheumatology, Department of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania, USA
- Division of Epidemiology, Department of Biostatistics, Epidemiology, and Informatics, University of Pennsylvania, Philadelphia, Pennsylvania, USA
| | - David Burgner
- Murdoch Children's Research Institute, Royal Children's Hospital, Parkville, Victoria, Australia
- Department of Paediatrics, University of Melbourne, Parkville, Victoria, Australia
- Department of General Medicine, Royal Children's Hospital, Parkville, Victoria, Australia
- Department of Paediatrics, Monash University, Clayton, Victoria, Australia
| | - Kenneth Gc Smith
- Department of Medicine, University of Cambridge School of Clinical Medicine, Cambridge, UK
- Cambridge Institute of Therapeutic Immunology and Infectious Disease, Jeffrey Cheah Biomedical Centre, University of Cambridge, Cambridge, UK
| | - Miguel Angel Gonzalez-Gay
- Research Group on Genetic Epidemiology and Atherosclerosis in Systemic Diseases and in Metabolic Bone Diseases of the Musculoskeletal System, IDIVAL, University of Cantabria, Santander, Spain
| | - Amr H Sawalha
- Division of Rheumatology, Department of Pediatrics, University of Pittsburgh, Pittsburgh, Pennsylvania, USA
- Division of Rheumatology and Clinical Immunology, Department of Medicine, University of Pittsburgh, Pittsburgh, Pennsylvania, USA
- Lupus Center of Excellence, University of Pittsburgh School of Medicine, Pittsburgh, Pennsylvania, USA
- Department of Immunology, University of Pittsburgh, Pittsburgh, Pennsylvania, USA
| | - Javier Martin
- Institute of Parasitology and Biomedicine "López- Neyra", CSIC, Granada, Spain
| | - Ana Marquez
- Institute of Parasitology and Biomedicine "López- Neyra", CSIC, Granada, Spain
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48
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Xia C, Pickett SJ, Liewald DCM, Weiss A, Hudson G, Hill WD. The contributions of mitochondrial and nuclear mitochondrial genetic variation to neuroticism. Nat Commun 2023; 14:3146. [PMID: 37253732 DOI: 10.1038/s41467-023-38480-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/09/2021] [Accepted: 05/04/2023] [Indexed: 06/01/2023] Open
Abstract
Neuroticism is a heritable trait composed of separate facets, each conferring different levels of protection or risk, to health. By examining mitochondrial DNA in 269,506 individuals, we show mitochondrial haplogroups explain 0.07-0.01% of variance in neuroticism and identify five haplogroup and 15 mitochondria-marker associations across a general factor of neuroticism, and two special factors of anxiety/tension, and worry/vulnerability with effect sizes of the same magnitude as autosomal variants. Within-haplogroup genome-wide association studies identified H-haplogroup-specific autosomal effects explaining 1.4% variance of worry/vulnerability. These H-haplogroup-specific autosomal effects show a pleiotropic relationship with cognitive, physical and mental health that differs from that found when assessing autosomal effects across haplogroups. We identify interactions between chromosome 9 regions and mitochondrial haplogroups at P < 5 × 10-8, revealing associations between general neuroticism and anxiety/tension with brain-specific gene co-expression networks. These results indicate that the mitochondrial genome contributes toward neuroticism and the autosomal links between neuroticism and health.
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Affiliation(s)
- Charley Xia
- Lothian Birth Cohort studies, University of Edinburgh, 7 George Square, Edinburgh, EH8 9JZ, UK
- Department of Psychology, School of Philosophy, Psychology and Language Sciences, University of Edinburgh, 7 George Square, Edinburgh, EH8 9JZ, UK
| | - Sarah J Pickett
- Wellcome Centre for Mitochondrial Research and Translational and Clinical Research Institute, The Medical School, Newcastle University, Newcastle upon Tyne, NE2 4HH, UK
| | - David C M Liewald
- Lothian Birth Cohort studies, University of Edinburgh, 7 George Square, Edinburgh, EH8 9JZ, UK
- Department of Psychology, School of Philosophy, Psychology and Language Sciences, University of Edinburgh, 7 George Square, Edinburgh, EH8 9JZ, UK
| | - Alexander Weiss
- Department of Psychology, School of Philosophy, Psychology and Language Sciences, University of Edinburgh, 7 George Square, Edinburgh, EH8 9JZ, UK
| | - Gavin Hudson
- Wellcome Centre for Mitochondrial Research and Biosciences Institute, Newcastle University, Newcastle upon Tyne, NE2 4HH, UK
| | - W David Hill
- Lothian Birth Cohort studies, University of Edinburgh, 7 George Square, Edinburgh, EH8 9JZ, UK.
- Department of Psychology, School of Philosophy, Psychology and Language Sciences, University of Edinburgh, 7 George Square, Edinburgh, EH8 9JZ, UK.
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49
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Qin C, Diaz-Gallo LM, Tang B, Wang Y, Nguyen TD, Harder A, Lu Y, Padyukov L, Askling J, Hägg S. Repurposing antidiabetic drugs for rheumatoid arthritis: results from a two-sample Mendelian randomization study. Eur J Epidemiol 2023:10.1007/s10654-023-01000-9. [PMID: 37052755 DOI: 10.1007/s10654-023-01000-9] [Citation(s) in RCA: 7] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/26/2022] [Accepted: 03/30/2023] [Indexed: 04/14/2023]
Abstract
Despite increasing therapeutic options to treat rheumatoid arthritis (RA), many patients fail to reach treatment targets. The use of antidiabetic drugs like thiazolidinediones has been associated with lower RA risk. We aimed to explore the repurposing potential of antidiabetic drugs in RA prevention by assessing associations between genetic variation in antidiabetic drug target genes and RA using Mendelian randomization (MR). A two-sample MR design was used to estimate the association between the antidiabetic drug and RA risk using summary statistics from genome-wide association studies (GWAS). We selected independent genetic variants from the gene(s) that encode the target protein(s) of the investigated antidiabetic drug as instruments. We extracted the associations of instruments with blood glucose concentration and RA from the UK Biobank and a GWAS meta-analysis of clinically diagnosed RA, respectively. The effect of genetic variation in the drug target(s) on RA risk was estimated by the Wald ratio test or inverse-variance weighted method. Insulin and its analogues, thiazolidinediones, and sulfonylureas had valid genetic instruments (n = 1, 1, and 2, respectively). Genetic variation in thiazolidinedione target (gene: PPARG) was inversely associated with RA risk (odds ratio [OR] 0.38 per 0.1mmol/L glucose lowering, 95% confidence interval [CI] 0.20-0.73). Corresponding ORs (95%CIs) were 0.83 (0.44-1.55) for genetic variation in the targets of insulin and its analogues (gene: INSR), and 1.12 (0.83, 1.49) 1.25 (0.78-2.00) for genetic variation in the sulfonylurea targets (gene: ABCC8 and KCNJ11). In conclusion, genetic variation in the thiazolidinedione target is associated with a lower RA risk. The underlying mechanisms warrant further exploration.
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Affiliation(s)
- Chenxi Qin
- Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden
| | - Lina-Marcela Diaz-Gallo
- Division of Rheumatology, Department of Medicine Solna, Karolinska Institutet, Karolinksa University Hospital, Stockholm, Sweden
- Center for Molecular Medicine, Karolinska Institutet, Karolinska University Hospital, Stockholm, Sweden
| | - Bowen Tang
- Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden
| | - Yunzhang Wang
- Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden
| | - Thuy-Dung Nguyen
- Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden
| | - Arvid Harder
- Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden
| | - Yi Lu
- Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden
| | - Leonid Padyukov
- Division of Rheumatology, Department of Medicine Solna, Karolinska Institutet, Karolinksa University Hospital, Stockholm, Sweden
- Center for Molecular Medicine, Karolinska Institutet, Karolinska University Hospital, Stockholm, Sweden
| | - Johan Askling
- Clinical Epidemiology Division, Department of Medicine Solna, Karolinska Institutet, Stockholm, Sweden
- Rheumatology, Theme Inflammation and Infection, Karolinska University Hospital, Stockholm, Sweden
| | - Sara Hägg
- Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden.
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Jiang JC, Hu C, McIntosh AM, Shah S. Investigating the potential anti-depressive mechanisms of statins: a transcriptomic and Mendelian randomization analysis. Transl Psychiatry 2023; 13:110. [PMID: 37015906 PMCID: PMC10073189 DOI: 10.1038/s41398-023-02403-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/08/2022] [Revised: 03/17/2023] [Accepted: 03/20/2023] [Indexed: 04/06/2023] Open
Abstract
Observational studies and randomized controlled trials presented inconsistent findings on the effects of cholesterol-lowering statins on depression. It therefore remains unclear whether statins have any beneficial effects on depression, and if so, what the underlying molecular mechanisms are. Here, we aimed to use genomic approaches to investigate this further. Using Connectivity Map (CMap), we first investigated whether statins and antidepressants shared pharmacological effects by interrogating gene expression responses to drug exposure in human cell lines. Second, using Mendelian randomization analysis, we investigated both on-target (through HMGCR inhibition) and potential off-target (through ITGAL and HDAC2 inhibition) causal effects of statins on depression risk and depressive symptoms, and traits related to the shared biological pathways identified from CMap analysis. Compounds inducing highly similar gene expression responses to statins in HA1E cells (indicated by an average connectivity score with statins > 90) were found to be enriched for antidepressants (12 out of 38 antidepressants; p = 9E-08). Genes perturbed in the same direction by both statins and antidepressants were significantly enriched for diverse cellular and metabolic pathways, and various immune activation, development and response processes. MR analysis did not identify any significant associations between statin exposure and depression risk or symptoms after multiple testing correction. However, genetically proxied HMGCR inhibition was strongly associated with alterations in platelets (a prominent serotonin reservoir) and monocyte percentage, which have previously been implicated in depression. Genetically proxied ITGAL inhibition was strongly associated with basophil, monocyte and neutrophil counts. We identified biological pathways that are commonly perturbed by both statins and antidepressants, and haematological biomarkers genetically associated with statin targets. Our findings warrant pre-clinical investigation of the causal role of these shared pathways in depression and potential as therapeutic targets, and investigation of whether blood biomarkers may be important considerations in clinical trials investigating effects of statins on depression.
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Affiliation(s)
- Jiayue-Clara Jiang
- Institute for Molecular Bioscience, The University of Queensland, St Lucia, QLD, Australia
| | - Chenwen Hu
- The University of Queensland, St Lucia, QLD, Australia
| | | | - Sonia Shah
- Institute for Molecular Bioscience, The University of Queensland, St Lucia, QLD, Australia.
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