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Redhwan A, Adnan M, Bakhsh HR, Alshammari N, Surti M, Parashar M, Patel M, Patel M, Manjegowda DS, Sharma S. Computational Identification and Functional Analysis of Potentially Pathogenic nsSNPs in the NLRP3 Gene Linked to Alzheimer's Disease. Cell Biochem Biophys 2024:10.1007/s12013-024-01465-9. [PMID: 39167281 DOI: 10.1007/s12013-024-01465-9] [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] [Accepted: 07/25/2024] [Indexed: 08/23/2024]
Abstract
Single Nucleotide Polymorphisms (SNPs) are key in understanding complex diseases. Nonsynonymous single-nucleotide polymorphisms (nsSNPs) occur in protein-coding regions, potentially altering amino acid sequences, protein structure and function. Computational methods are vital for distinguishing deleterious nsSNPs from neutral ones. We investigated the role of NLRP3 gene in neuroinflammation associated with Alzheimer's disease (AD) pathogenesis. A total of 893 missense (nsSNPs) were obtained from the dbSNP database and subjected to rigorous filtering using bioinformatics tools like SIFT, Align GVGD, PolyPhen-2, and PANTHER to identify potentially damaging variants. Of these, 18 nsSNPs were consistently predicted to have deleterious effects across all tools. Notably, 16 of these variants exhibited reduced protein stability, while only 4 were predicted to be buried within the protein structure. Among the identified nsSNPs, rs180177442 (R262L and R262P), rs201875324 (T659I), and rs139814109 (T897M) were classified as high-risk variants due to their significant deleterious impact, probable damaging effects, and association with decreased protein stability. Molecular docking and simulation analyses were conducted utilizing Memantine, a standard drug utilized in AD treatment, to investigate potential interactions with the altered protein structures. Additional clinical and genetic investigations are necessary to elucidate the underlying mechanisms that link NLRP3 polymorphisms with the initiation of AD.
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Affiliation(s)
- Alya Redhwan
- Department of Health, College of Health and Rehabilitation Sciences, Princess Nourah bint Abdulrahman University, Riyadh, 11671, Saudi Arabia
| | - Mohd Adnan
- Department of Biology, College of Science, University of Ha'il, Ha'il, Saudi Arabia
| | - Hadeel R Bakhsh
- Department of Rehabilitation Sciences, College of Health and Rehabilitation Sciences, Princess Nourah bint Abdulrahman University, Riyadh, Saudi Arabia
| | - Nawaf Alshammari
- Department of Health, College of Health and Rehabilitation Sciences, Princess Nourah bint Abdulrahman University, Riyadh, 11671, Saudi Arabia
| | - Malvi Surti
- 4Research and Development Cell, Department of Biotechnology, Parul Institute of Applied Sciences, Parul University, Vadodara, Gujarat, India
| | - Mansi Parashar
- 4Research and Development Cell, Department of Biotechnology, Parul Institute of Applied Sciences, Parul University, Vadodara, Gujarat, India
| | - Mirav Patel
- 4Research and Development Cell, Department of Biotechnology, Parul Institute of Applied Sciences, Parul University, Vadodara, Gujarat, India
| | - Mitesh Patel
- 4Research and Development Cell, Department of Biotechnology, Parul Institute of Applied Sciences, Parul University, Vadodara, Gujarat, India
| | - Dinesh Sosalagere Manjegowda
- 4Department of Human Genetics, School of Basic and Applied Sciences, Dayananda Sagar University, Bangalore, 560078, India
| | - Sameer Sharma
- Department of Bioinformatics, BioNome, Bangalore, India.
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Risha KS, Rasal KD, Reang D, Iquebal MA, Sonwane A, Brahmane M, Chaudhari A, Nagpure N. DNA Methylation Profiling in Genetically Selected Clarias magur (Hamilton, 1822) Provides Insights into the Epigenetic Regulation of Growth and Development. MARINE BIOTECHNOLOGY (NEW YORK, N.Y.) 2024; 26:776-789. [PMID: 39037491 DOI: 10.1007/s10126-024-10346-4] [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: 05/29/2024] [Accepted: 07/13/2024] [Indexed: 07/23/2024]
Abstract
DNA methylation is an epigenetic alteration that impacts gene expression without changing the DNA sequence affecting an organism's phenotype. This study utilized a reduced representation bisulfite sequencing (RRBS) approach to investigate the patterns of DNA methylation in genetically selected Clarias magur stocks. RRBS generated 249.22 million reads, with an average of 490,120 methylation sites detected in various parts of genes, including exons, introns, and intergenic regions. A total of 896 differentially methylated regions (DMRs) were identified; 356 and 540 were detected as hyper-methylated and hypo-methylated regions, respectively. The DMRs and their association with overlapping genes were explored using whole genome data of magur, which revealed 205 genes in exonic, 210 in intronic, and 480 in intergenic regions. The analysis identified the maximum number of genes enriched in biological processes such as RNA biosynthetic process, response to growth factors, nervous system development, neurogenesis, and anatomical structure morphogenesis. Differentially methylated genes (DMGs) such as myrip, mylk3, mafb, egr3, ndnf, meis2a, foxn3, bmp1a, plxna3, fgf6, sipa1l1, mcu, cnot8, trim55b, and myof were associated with growth and development. The selected DMGs were analyzed using real-time PCR, which showed altered mRNA expression levels. This work offers insights into the epigenetic mechanisms governing growth performance regulation in magur stocks. This work provides a valuable resource of epigenetic data that could be integrated into breeding programs to select high-performing individuals.
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Affiliation(s)
- K Shasti Risha
- Fish Genetics and Biotechnology, ICAR - Central Institute of Fisheries Education, Mumbai, Maharashtra, 400061, India
| | - Kiran D Rasal
- Fish Genetics and Biotechnology, ICAR - Central Institute of Fisheries Education, Mumbai, Maharashtra, 400061, India.
| | - Dhalongsaih Reang
- Fish Genetics and Biotechnology, ICAR - Central Institute of Fisheries Education, Mumbai, Maharashtra, 400061, India
| | - Mir Asif Iquebal
- Centre for Agricultural Bioinformatics, ICAR-Indian Agricultural Statistics Research Institute, New Delhi, India
| | - Arvind Sonwane
- Fish Genetics and Biotechnology, ICAR - Central Institute of Fisheries Education, Mumbai, Maharashtra, 400061, India
| | - Manoj Brahmane
- Fish Genetics and Biotechnology, ICAR - Central Institute of Fisheries Education, Mumbai, Maharashtra, 400061, India
| | - Aparna Chaudhari
- Fish Genetics and Biotechnology, ICAR - Central Institute of Fisheries Education, Mumbai, Maharashtra, 400061, India
| | - Naresh Nagpure
- Fish Genetics and Biotechnology, ICAR - Central Institute of Fisheries Education, Mumbai, Maharashtra, 400061, India
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Mozibullah M, Khatun M, Sikder M, Islam M, Sharmin M. Identifying Oncogenic Missense Single Nucleotide Polymorphisms in Human SAT1 Gene Using Computational Algorithms and Molecular Dynamics Tools. Cancer Rep (Hoboken) 2024; 7:e2130. [PMID: 39041636 PMCID: PMC11264109 DOI: 10.1002/cnr2.2130] [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: 10/29/2023] [Revised: 05/04/2024] [Accepted: 06/30/2024] [Indexed: 07/24/2024] Open
Abstract
BACKGROUND The human SAT1 gene encodes spermidine/spermine N1-acetyltransferase 1 (SSAT1), a regulatory biological catalyst of polyamine catabolism. Numerous essential biological processes, such as cellular proliferation, differentiation, and survival, depend on polyamines like spermidine and spermine. Thus, SSAT1 is involved in key cellular activities such as proliferation and survival of cells and mediates various diseases including cancer. A plethora of studies established the involvement of missense single nucleotide polymorphisms (SNPs) in numerous pathological conditions due to their ability to adversely affect the structure and subsequent function of the protein. AIMS To date, an in silico study to identify the pathogenic missense SNPs of the human SAT1 gene has not been accomplished yet. This study aimed to filter the missense SNPs that were functionally detrimental and pathogenic. METHODS AND RESULTS The rs757435207 (I21N) was ascertained to be the most deleterious and pathogenic by all algorithmic tools. Stability and evolutionary conservation analysis tools also stated that I21N variant decreased the stability and was located in the highly conserved residue. Molecular dynamics simulation revealed that I21N caused substantial alterations in the conformational stability and dynamics of the SSAT1 protein. Consequently, the I21N variant could disrupt the native functional roles of the SSAT1 enzyme. CONCLUSION Therefore, the I21N variant was identified and concluded to be an oncogenic missense variant of the human SAT1 gene. Overall, the findings of this study would be a great directory of future experimental research to develop personalized medicine.
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Affiliation(s)
- Md. Mozibullah
- Department of Biochemistry and Molecular BiologyMawlana Bhashani Science and Technology UniversityTangailBangladesh
| | - Marina Khatun
- Department of Biochemistry and Molecular BiologyMawlana Bhashani Science and Technology UniversityTangailBangladesh
| | - Md. Asaduzzaman Sikder
- Department of Biochemistry and Molecular BiologyMawlana Bhashani Science and Technology UniversityTangailBangladesh
| | - Mohammod Johirul Islam
- Department of Biochemistry and Molecular BiologyMawlana Bhashani Science and Technology UniversityTangailBangladesh
| | - Mehbuba Sharmin
- Department of Biochemistry and Molecular BiologyMawlana Bhashani Science and Technology UniversityTangailBangladesh
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Mahur P, Sharma A, Jahan G, S G A, Kumar Singh A, Muthukumaran J, Jain M. Understanding Genetic Risks: Computational Exploration of Human β-Synuclein nsSNPs and their Potential Impact on Structural Alteration. Neurosci Lett 2024; 833:137826. [PMID: 38768940 DOI: 10.1016/j.neulet.2024.137826] [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: 04/12/2024] [Revised: 05/16/2024] [Accepted: 05/17/2024] [Indexed: 05/22/2024]
Abstract
Synucleins are pivotal in neurodegenerative conditions. Beta-synuclein (β-synuclein) is part of the synuclein protein family alongside alpha-synuclein (α-synuclein) and gamma-synuclein (γ-synuclein). These proteins, found mainly in brain tissue and cancers, are soluble and unstructured. β-synuclein shares significant similarity with α-synuclein, especially in their N-terminus, with a 90% match. However, their aggregation tendencies differ significantly. While α-synuclein aggregation is believed to be counteracted by β-synuclein, which occurs in conditions like Parkinson's disease, β-synuclein may counteract α-synuclein's toxic effects on the nervous system, offering potential treatment for neurodegenerative diseases. Under normal circumstances, β-synuclein may guard against disease by interacting with α-synuclein. Yet, in pathological environments with heightened levels or toxic substances, it might contribute to disease. Our research aims to explore potential harmful mutations in the β-synuclein using computational tools to predict their destabilizing impact on protein structure. Consensus analysis revealed rs1207608813 (A63P), rs1340051870 (S72F), and rs1581178262 (G36C) as deleterious. These findings highlight the intricate relationship between nsSNPs and protein function, shedding light on their potential implications in disease pathways. Understanding the structural consequences of nsSNPs is crucial for elucidating their role in pathogenesis and developing targeted therapeutic interventions. Our results offer a robust computational framework for identifying neurodegenerative disorder-related mutations from SNP datasets, potentially reducing the costs associated with experimental characterization.
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Affiliation(s)
- Pragati Mahur
- Department of Biotechnology, Sharda School of Engineering and Technology, Sharda University, Greater Noida, Uttar Pradesh, India
| | - Abhishek Sharma
- Department of Biotechnology, Sharda School of Engineering and Technology, Sharda University, Greater Noida, Uttar Pradesh, India
| | - Gulnaz Jahan
- Department of Biotechnology, Sharda School of Engineering and Technology, Sharda University, Greater Noida, Uttar Pradesh, India
| | - Adithya S G
- Department of Biotechnology, Sharda School of Engineering and Technology, Sharda University, Greater Noida, Uttar Pradesh, India
| | - Amit Kumar Singh
- Department of Biotechnology, Sharda School of Engineering and Technology, Sharda University, Greater Noida, Uttar Pradesh, India
| | - Jayaraman Muthukumaran
- Department of Biotechnology, Sharda School of Engineering and Technology, Sharda University, Greater Noida, Uttar Pradesh, India.
| | - Monika Jain
- Department of Biotechnology, Sharda School of Engineering and Technology, Sharda University, Greater Noida, Uttar Pradesh, India.
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Mozibullah M, Eslampanah Seyedi H, Khatun M, Solayman M. Identification and analysis of oncogenic non-synonymous single nucleotide polymorphisms in the human NRAS gene: An exclusive in silico study. J Genet Eng Biotechnol 2024; 22:100378. [PMID: 38797553 PMCID: PMC11087716 DOI: 10.1016/j.jgeb.2024.100378] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/02/2023] [Accepted: 04/19/2024] [Indexed: 05/29/2024]
Abstract
BACKGROUND N-ras protein is encoded by the NRAS gene and operates as GDP-GTP-controlled on/off switching. N-ras interacts with cellular signaling networks that regulate various cellular activities including cell proliferation and survival. The nonsynonymous single nucleotide polymorphism (nsSNPs)-mediated alteration can substantially disrupt the structure and activity of the corresponding protein. N-ras has been reported to be associated with numerous diseases including cancers due to the nsSNPs. A comprehensive study on the NRAS gene to unveil the potentially damaging and oncogenic nsSNPs is yet to be accomplished. Hence, this extensive in silico study is intended to identify the disease-associated, specifically oncogenic nsSNPs of the NRAS gene. RESULTS Out of 140 missense variants, 7 nsSNPs (I55R, G60E, G60R, Y64D, L79F, D119G, and V152F) were identified to be damaging utilizing 10 computational tools that works based on different algorithms with high accuracy. Among those, G60E, G60R, and D119G variants were further filtered considering their location in the highly conserved region and later identified as oncogenic variants. Interestingly, G60E and G60R variants were revealed to be particularly associated with lung adenocarcinoma, rhabdomyosarcoma, and prostate adenocarcinoma. Therefore, D119G could be subjected to detailed investigation for identifying its association with specific cancer. CONCLUSION This in silico study identified the deleterious and oncogenic missense variants of the human NRAS gene that could be utilized for designing further experimental investigation. The outcomes of this study would be worthwhile in future research for developing personalized medicine.
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Affiliation(s)
- Md Mozibullah
- Department of Biochemistry and Molecular Biology, Mawlana Bhashani Science and Technology University, Santosh, Tangail 1902, Bangladesh
| | | | - Marina Khatun
- Department of Biochemistry and Molecular Biology, Mawlana Bhashani Science and Technology University, Santosh, Tangail 1902, Bangladesh
| | - Md Solayman
- Department of Biochemistry and Molecular Biology, Primeasia University, Bangladesh.
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Thayyil Menambath D, Adiga U, Rai T, Adiga S, Shetty V. Identification of the SIRT1 gene's most harmful non-synonymous SNPs and their effects on functional and structural features-an in silico analysis. F1000Res 2024; 12:66. [PMID: 38283900 PMCID: PMC10822041 DOI: 10.12688/f1000research.128706.2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Accepted: 01/16/2024] [Indexed: 01/30/2024] Open
Abstract
Introduction The sirtuin (Silent mating type information regulation 2 homolog)1(SIRT1) protein plays a vital role in many disorders such as diabetes, cancer, obesity, inflammation, and neurodegenerative and cardiovascular diseases. The objective of this in silico analysis of SIRT1's functional single nucleotide polymorphisms (SNPs) was to gain valuable insight into the harmful effects of non-synonymous SNPs (nsSNPs) on the protein. The objective of the study was to use bioinformatics methods to investigate the genetic variations and modifications that may have an impact on the SIRT1 gene's expression and function. Methods nsSNPs of SIRT1 protein were collected from the dbSNP site, from its three (3) different protein accession IDs. These were then fed to various bioinformatic tools such as SIFT, Provean, and I- Mutant to find the most deleterious ones. Functional and structural effects were examined using the HOPE server and I-Tasser. Gene interactions were predicted by STRING software. The SIFT, Provean, and I-Mutant tools detected the most deleterious three nsSNPs (rs769519031, rs778184510, and rs199983221). Results Out of 252 nsSNPs, SIFT analysis showed that 94 were deleterious, Provean listed 67 dangerous, and I-Mutant found 58 nsSNPs resulting in lowered stability of proteins. HOPE modelling of rs199983221 and rs769519031 suggested reduced hydrophobicity due to Ile 4Thr and Ile223Ser resulting in decreased hydrophobic interactions. In contrast, on modelling rs778184510, the mutant protein had a higher hydrophobicity than the wild type. Conclusions Our study reports that three nsSNPs (D357A, I223S, I4T) are the most damaging mutations of the SIRT1 gene. Mutations may result in altered protein structure and functions. Such altered protein may be the basis for various disorders. Our findings may be a crucial guide in establishing the pathogenesis of various disorders.
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Affiliation(s)
| | - Usha Adiga
- Biochemistry, KS Hegde Medical Academy, NITTE (DU), Mangalore, Karnataka, 575018, India
| | - Tirthal Rai
- Biochemistry, KS Hegde Medical Academy, NITTE (DU), Mangalore, Karnataka, 575018, India
| | - Sachidananda Adiga
- Pharmacology, KS Hegde Medical Academy, NITTE(DU), Mangalore, Karnataka, 575018, India
| | - Vijith Shetty
- Oncology, KS Hegde Medical Academy, NITTE(DU), Mangalore, Karnataka, 575018, India
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Lee Y, Kim J. Unraveling the mystery of oligogenic inheritance under way? Mol Cells 2024; 47:100003. [PMID: 38376484 PMCID: PMC10880077 DOI: 10.1016/j.mocell.2023.10.002] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/09/2023] [Revised: 10/22/2023] [Accepted: 10/23/2023] [Indexed: 02/21/2024] Open
Affiliation(s)
- Yerim Lee
- Department of Life Science, Ewha Womans University, Seoul 03760, Korea
| | - Jaesang Kim
- Department of Life Science, Ewha Womans University, Seoul 03760, Korea.
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Ning Y, Xiao M, Perlin DS, Zhao Y, Lu M, Li Y, Luo Z, Dai R, Li S, Xu J, Liu L, He H, Liu Y, Li F, Guo Y, Chen Z, Xu Y, Sun T, Zhang L. Decreased echinocandin susceptibility in Candida parapsilosis causing candidemia and emergence of a pan-echinocandin resistant case in China. Emerg Microbes Infect 2023; 12:2153086. [PMID: 36440795 PMCID: PMC9793909 DOI: 10.1080/22221751.2022.2153086] [Citation(s) in RCA: 17] [Impact Index Per Article: 17.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
Abstract
Candida parapsilosis is becoming a predominant non-albicans cause of invasive candidiasis (IC). Echinocandins are the preferred choice for IC treatment and prophylaxis. Resistance to echinocandins in C. parapsilosis has emerged in several countries, but little is known about the susceptibility profile in China or about mechanisms of resistance. Here, we investigated the echinocandin susceptibilities of 2523 C. parapsilosis isolates collected from China and further explored the resistance mechanism among echinocandin-resistant isolates. Anidulafungin exhibited the highest MICs (MIC50/90, 1 and 2 µg/mL; GM, 0.948 µg/mL), while caspofungin showed better activity (0.5 and 1 µg/mL; 0.498 µg/mL). Significantly higher echinocandin MICs were observed among blood-derived isolates compared to others, especially for caspofungin (GM, 1.348 µg/mL vs 0.478 µg/mL). Isolates from ICU and surgical wards also showed higher MICs. Twenty isolates showed intermediate phenotypes for at least one echinocandin. One was resistant to all three echinocandins, fluconazole and voriconazole, which caused breakthrough IC during long-term exposure to micafungin. WGS revealed this isolate carried a mutation S656P in hotspot1 region of Fks1. Bioinformatics analyses suggested that this mutation might lead to an altered protein conformation. CRISPR Cas9-mediated introduction of this mutation into a susceptible reference C. parapsilosis strain increased MICs of all echinocandins 64-fold, with similar results found in the subspecies, C. orthopsilosis and C. metapsilosis. This is the first report of a multi-azole resistant and pan-echinocandin resistant C. parapsilosis isolate, and the identification of a FKS1S656P conferring pan-echinocandin resistance. Our study underscores the necessity of rigorous management of antifungal use and of monitoring for antifungal susceptibility.
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Affiliation(s)
- Yating Ning
- Department of Laboratory Medicine, State Key Laboratory of Complex Severe and Rare Diseases, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, People’s Republic of China,Graduate School, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, People’s Republic of China,Beijing Key Laboratory for Mechanisms Research and Precision Diagnosis of Invasive Fungal Diseases, Beijing, People’s Republic of China
| | - Meng Xiao
- Department of Laboratory Medicine, State Key Laboratory of Complex Severe and Rare Diseases, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, People’s Republic of China,Beijing Key Laboratory for Mechanisms Research and Precision Diagnosis of Invasive Fungal Diseases, Beijing, People’s Republic of China
| | - David S. Perlin
- Center for Discovery and Innovation, Hackensack Meridian Health, Nutley, NJ, USA
| | - Yanan Zhao
- Center for Discovery and Innovation, Hackensack Meridian Health, Nutley, NJ, USA
| | - Minya Lu
- Department of Laboratory Medicine, State Key Laboratory of Complex Severe and Rare Diseases, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, People’s Republic of China,Beijing Key Laboratory for Mechanisms Research and Precision Diagnosis of Invasive Fungal Diseases, Beijing, People’s Republic of China
| | - Yi Li
- Department of Laboratory Medicine, State Key Laboratory of Complex Severe and Rare Diseases, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, People’s Republic of China,Graduate School, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, People’s Republic of China,Beijing Key Laboratory for Mechanisms Research and Precision Diagnosis of Invasive Fungal Diseases, Beijing, People’s Republic of China
| | - Zhengyu Luo
- Department of Laboratory Medicine, State Key Laboratory of Complex Severe and Rare Diseases, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, People’s Republic of China,Graduate School, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, People’s Republic of China,Beijing Key Laboratory for Mechanisms Research and Precision Diagnosis of Invasive Fungal Diseases, Beijing, People’s Republic of China
| | - Rongchen Dai
- School of Public Health, Zhejiang Chinese Medical University, Hangzhou, People’s Republic of China
| | - Shengjie Li
- Medical Research Centre, State Key Laboratory of Complex Severe and Rare Diseases, Peking Union Medical College Hospital, Chinese Academy of Medical Science, Beijing, People’s Republic of China
| | - Jiajun Xu
- Department of Laboratory Medicine, State Key Laboratory of Complex Severe and Rare Diseases, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, People’s Republic of China
| | - Lingli Liu
- Department of Laboratory Medicine, State Key Laboratory of Complex Severe and Rare Diseases, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, People’s Republic of China
| | - Hong He
- Department of Clinical Laboratory, the Affiliated Hospital of Qingdao University, Qingdao, People’s Republic of China
| | - Yun Liu
- Department of Laboratory Medicine, Changhai Hospital, Second Military Medical University, Shanghai, People’s Republic of China
| | - Fushun Li
- Department of Laboratory Medicine, The First Hospital of China Medical University, Shenyang, People’s Republic of China
| | - Yuguang Guo
- Department of Laboratory Medicine, Liaoning Provincial People’s Hospital, Shenyang, People’s Republic of China
| | - Zhongju Chen
- Department of Laboratory Medicine, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, People’s Republic of China,Zhongju Chen
| | - Yingchun Xu
- Department of Laboratory Medicine, State Key Laboratory of Complex Severe and Rare Diseases, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, People’s Republic of China,Beijing Key Laboratory for Mechanisms Research and Precision Diagnosis of Invasive Fungal Diseases, Beijing, People’s Republic of China,Yingchun Xu
| | - Tianshu Sun
- Beijing Key Laboratory for Mechanisms Research and Precision Diagnosis of Invasive Fungal Diseases, Beijing, People’s Republic of China,Medical Research Centre, State Key Laboratory of Complex Severe and Rare Diseases, Peking Union Medical College Hospital, Chinese Academy of Medical Science, Beijing, People’s Republic of China,Tianshu Sun
| | - Li Zhang
- Department of Laboratory Medicine, State Key Laboratory of Complex Severe and Rare Diseases, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, People’s Republic of China,Beijing Key Laboratory for Mechanisms Research and Precision Diagnosis of Invasive Fungal Diseases, Beijing, People’s Republic of China, Li Zhang
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Farajzadeh-Dehkordi M, Mafakher L, Harifi A, Samiee-Rad F, Rahmani B. Computational analysis of the functional and structural impact of the most deleterious missense mutations in the human Protein C. PLoS One 2023; 18:e0294417. [PMID: 38015884 PMCID: PMC10683990 DOI: 10.1371/journal.pone.0294417] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/10/2023] [Accepted: 11/01/2023] [Indexed: 11/30/2023] Open
Abstract
Protein C (PC) is a vitamin K-dependent factor that plays a crucial role in controlling anticoagulant processes and acts as a cytoprotective agent to promote cell survival. Several mutations in human PC are associated with decreased protein production or altered protein structure, resulting in PC deficiency. In this study, we conducted a comprehensive analysis of nonsynonymous single nucleotide polymorphisms in human PC to prioritize and confirm the most high-risk mutations predicted to cause disease. Of the 340 missense mutations obtained from the NCBI database, only 26 were classified as high-risk mutations using various bioinformatic tools. Among these, we identified that 12 mutations reduced the stability of protein, and thereby had the greatest potential to disturb protein structure and function. Molecular dynamics simulations revealed moderate alterations in the structural stability, flexibility, and secondary structural organization of the serine protease domain of human PC for five missense mutations (L305R, W342C, G403R, V420E, and W444C) when compared to the native structure that could maybe influence its interaction with other molecules. Protein-protein interaction analyses demonstrated that the occurrence of these five mutations can affect the regular interaction between PC and activated factor V. Therefore, our findings assume that these mutants can be used in the identification and development of therapeutics for diseases associated with PC dysfunction, although assessment the effect of these mutations need to be proofed in in-vitro.
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Affiliation(s)
- Mahvash Farajzadeh-Dehkordi
- Cellular and Molecular Research Center, Institute for Prevention of Non-Communicable Diseases, Qazvin University of Medical Sciences, Qazvin, Iran
- Department of Molecular Medicine, Qazvin University of Medical Sciences, Qazvin, Iran
| | - Ladan Mafakher
- Thalassemia & Hemoglobinopathy Research Center, Health Research Institute, Ahvaz Jundishapur University of Medical Sciences, Ahvaz, Iran
| | - Abbas Harifi
- Department of Electrical and Computer Engineering, University of Hormozgan, Bandar Abbas, Iran
| | - Fatemeh Samiee-Rad
- Cellular and Molecular Research Center, Institute for Prevention of Non-Communicable Diseases, Qazvin University of Medical Sciences, Qazvin, Iran
- Department of Pathobiology, Faculty of Medical School, Qazvin University of Medical Sciences, Qazvin, Iran
| | - Babak Rahmani
- Cellular and Molecular Research Center, Institute for Prevention of Non-Communicable Diseases, Qazvin University of Medical Sciences, Qazvin, Iran
- Department of Molecular Medicine, Qazvin University of Medical Sciences, Qazvin, Iran
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Azmi MB, Sehgal SA, Asif U, Musani S, Abedin MFE, Suri A, Ahmed SDH, Qureshi SA. Genetic insights into obesity: in silico identification of pathogenic SNPs in MBOAT4 gene and their structural molecular dynamics consequences. J Biomol Struct Dyn 2023:1-17. [PMID: 37921712 DOI: 10.1080/07391102.2023.2274970] [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: 07/04/2023] [Accepted: 10/18/2023] [Indexed: 11/04/2023]
Abstract
Membrane Bound O-Acyltransferase Domain-Containing 4 (MBOAT4) protein catalyzes ghrelin acylation, leading to prominent ghrelin activity, hence characterizing its role as an anti-obesity target. We extracted 625 exonic SNPs from the ENSEMBL database and one phenotype-based missense mutation associated with obesity (A46T) from the HGMD (Human Gene Mutation Database). These were differentiated on deleterious missense SNPs of the MBOAT4 gene through MAF (minor allele frequency: <0.01) cut-off criteria in relation to some bioinformatics-based supervised machine learning tools. We found 8 rare-coding and harmful missense SNPs. The consensus classifier (PredictSNP) tool predicted that the SNP (G57S, C: rs561065025) was the most pathogenic. Several trained in silico algorithms have predicted decreased protein stability [ΔΔG (kcal/mol)] function in the presence of these rare-coding pathogenic mutations in the MBOAT4 gene. Then, a stereochemical quality check (i.e. validation and assessment) of the 3D model was performed, followed by a blind cavity docking approach, used to search for druggable cavities and molecular interactions with citrus flavonoids of the Rutaceae family, ranked with energetic estimations. Significant interactions with Phloretin 3',5'-Di-C-Glucoside were also observed at R304, W306, N307, A311, L314 and H338 with (iGEMDOCK: -95.82 kcal/mol and AutoDock: -7.80 kcal/mol). The RMSD values and other variables of MD simulation analyses on this protein further validated its significant interactions with the above flavonoids. The MBOAT4 gene and its molecular interactions could serve as an interventional future anti-obesity target. The current study's findings will benefit future prospects for large population-based studies and drug development, particularly for generating personalized medicine.Communicated by Ramaswamy H. Sarma.
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Affiliation(s)
- Muhammad Bilal Azmi
- Department of Biochemistry, Dow Medical College, Dow University of Health Sciences, Karachi, Pakistan
| | - Sheikh Arslan Sehgal
- Department of Bioinformatics, Institute of Biochemistry, Biotechnology and Bioinformatics, The Islamia University of Bahawalpur, Bahawalpur, Pakistan
| | - Uzma Asif
- Department of Biochemistry, Medicine Program, Batterjee Medical College, Jeddah, Saudi Arabia
| | - Sarah Musani
- Dow Medical College, Dow University of Health Sciences, Karachi, Pakistan
| | | | - Azeema Suri
- Dow Medical College, Dow University of Health Sciences, Karachi, Pakistan
| | - Syed Danish Haseen Ahmed
- Department of Biochemistry, Dow Medical College, Dow University of Health Sciences, Karachi, Pakistan
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11
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Khan N, Khan K, Badshah Y, Trembley JH, Ashraf NM, Shabbir M, Danish L, Afsar T, Almajwal A, Justin S, Hasan Z, Razak S. Investigating pathogenic SNP of PKCι in HCV-induced hepatocellular carcinoma. Sci Rep 2023; 13:12504. [PMID: 37532886 PMCID: PMC10397245 DOI: 10.1038/s41598-023-39804-0] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/02/2023] [Accepted: 07/31/2023] [Indexed: 08/04/2023] Open
Abstract
Hepatocellular carcinoma is a leading cause of cancer-related deaths due to its complexity in diagnosis, chemo-resistance, and aggressive nature. Identifying pathogenic single nucleotide polymorphism (SNP) in protein kinase C iota (PKCι) can be a potential biomarker in the prognosis and treatment of HCC. This study investigated the association between a SNP in PRKCI and the Pakistani population's hepatocellular carcinoma (HCC) risk. Obtained samples were first evaluated for ALT measurements and viral load quantification through reverse transcriptase-PCR. The PKCι nsSNP rs1199520604 was evaluated computationally by multiple consensus bioinformatics tools for predicting its potential deleterious effects. Its association with hepatitis C virus- (HCV) mediated HCC was then investigated through ARMS-PCR (Amplification Refractory Mutation System Polymerase Chain Reaction). SNP analysis of rs1199520604 was performed in 100 cases and 100 controls. Variant rs1199520604's homozygous T genotype is a risk factor allele for the HCV-induced HCC (odds ratio: 4.13, relative risk: 2.01, P-value < 0.0001). The heterozygous genotype is determined to protect HCV patients from HCC development (P < 0.001). The study highlighted the disease association of variant rs1199520604 with HCV-induced HCC in the Pakistani populations. This variant, after further validation through high-throughput investigation on a larger cohort, has the potential to be translated at the clinical level.
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Affiliation(s)
- Naila Khan
- Department of Healthcare Biotechnology, Atta-Ur-Rahman School of Applied Biosciences (ASAB), National University of Sciences and Technology (NUST), Islamabad, Pakistan
| | - Khushbukhat Khan
- Department of Healthcare Biotechnology, Atta-Ur-Rahman School of Applied Biosciences (ASAB), National University of Sciences and Technology (NUST), Islamabad, Pakistan
| | - Yasmin Badshah
- Department of Healthcare Biotechnology, Atta-Ur-Rahman School of Applied Biosciences (ASAB), National University of Sciences and Technology (NUST), Islamabad, Pakistan
| | - Janeen H Trembley
- Research Service, Minneapolis VA Health Care System, Minneapolis, MN, USA
- Department of Laboratory Medicine and Pathology, University of Minnesota, Minneapolis, MN, USA
- Masonic Cancer Center, University of Minnesota, Minneapolis, MN, USA
| | - Naeem Mahmood Ashraf
- School of Biochemistry and Biotechnology, University of the Punjab, Lahore, Pakistan
| | - Maria Shabbir
- Department of Healthcare Biotechnology, Atta-Ur-Rahman School of Applied Biosciences (ASAB), National University of Sciences and Technology (NUST), Islamabad, Pakistan.
| | - Lubna Danish
- Agricultural Research Institute, Tarnab, Peshawar, Pakistan
| | - Tayyaba Afsar
- Department of Community Health Sciences, College of Applied Medical Sciences, King Saud University, Riyadh, Saudi Arabia
| | - Ali Almajwal
- Department of Community Health Sciences, College of Applied Medical Sciences, King Saud University, Riyadh, Saudi Arabia
| | - Saira Justin
- Department of Healthcare Biotechnology, Atta-Ur-Rahman School of Applied Biosciences (ASAB), National University of Sciences and Technology (NUST), Islamabad, Pakistan
| | - Zafarul Hasan
- College of Nursing, Sciences, King Saud University, Riyadh, Saudi Arabia
| | - Suhail Razak
- Department of Community Health Sciences, College of Applied Medical Sciences, King Saud University, Riyadh, Saudi Arabia.
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12
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Sadee W, Wang D, Hartmann K, Toland AE. Pharmacogenomics: Driving Personalized Medicine. Pharmacol Rev 2023; 75:789-814. [PMID: 36927888 PMCID: PMC10289244 DOI: 10.1124/pharmrev.122.000810] [Citation(s) in RCA: 12] [Impact Index Per Article: 12.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/18/2022] [Revised: 03/09/2023] [Accepted: 03/10/2023] [Indexed: 03/18/2023] Open
Abstract
Personalized medicine tailors therapies, disease prevention, and health maintenance to the individual, with pharmacogenomics serving as a key tool to improve outcomes and prevent adverse effects. Advances in genomics have transformed pharmacogenetics, traditionally focused on single gene-drug pairs, into pharmacogenomics, encompassing all "-omics" fields (e.g., proteomics, transcriptomics, metabolomics, and metagenomics). This review summarizes basic genomics principles relevant to translation into therapies, assessing pharmacogenomics' central role in converging diverse elements of personalized medicine. We discuss genetic variations in pharmacogenes (drug-metabolizing enzymes, drug transporters, and receptors), their clinical relevance as biomarkers, and the legacy of decades of research in pharmacogenetics. All types of therapies, including proteins, nucleic acids, viruses, cells, genes, and irradiation, can benefit from genomics, expanding the role of pharmacogenomics across medicine. Food and Drug Administration approvals of personalized therapeutics involving biomarkers increase rapidly, demonstrating the growing impact of pharmacogenomics. A beacon for all therapeutic approaches, molecularly targeted cancer therapies highlight trends in drug discovery and clinical applications. To account for human complexity, multicomponent biomarker panels encompassing genetic, personal, and environmental factors can guide diagnosis and therapies, increasingly involving artificial intelligence to cope with extreme data complexities. However, clinical application encounters substantial hurdles, such as unknown validity across ethnic groups, underlying bias in health care, and real-world validation. This review address the underlying science and technologies germane to pharmacogenomics and personalized medicine, integrated with economic, ethical, and regulatory issues, providing insights into the current status and future direction of health care. SIGNIFICANCE STATEMENT: Personalized medicine aims to optimize health care for the individual patients with use of predictive biomarkers to improve outcomes and prevent adverse effects. Pharmacogenomics drives biomarker discovery and guides the development of targeted therapeutics. This review addresses basic principles and current trends in pharmacogenomics, with large-scale data repositories accelerating medical advances. The impact of pharmacogenomics is discussed, along with hurdles impeding broad clinical implementation, in the context of clinical care, ethics, economics, and regulatory affairs.
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Affiliation(s)
- Wolfgang Sadee
- Department of Cancer Biology and Genetics, College of Medicine, The Ohio State University, Columbus Ohio (W.S., A.E.T.); Department of Pharmacotherapy and Translational Research, College of Pharmacy, University of Florida, Gainesville, Florida (D.W.); Department of Radiology, Hospital of the University of Pennsylvania, Philadelphia, Pennsylvania (K.H.); Department of Bioengineering and Therapeutic Sciences, Schools of Pharmacy and Medicine, University of California San Francisco, San Francisco, California (W.S.); and Aether Therapeutics, Austin, Texas (W.S.)
| | - Danxin Wang
- Department of Cancer Biology and Genetics, College of Medicine, The Ohio State University, Columbus Ohio (W.S., A.E.T.); Department of Pharmacotherapy and Translational Research, College of Pharmacy, University of Florida, Gainesville, Florida (D.W.); Department of Radiology, Hospital of the University of Pennsylvania, Philadelphia, Pennsylvania (K.H.); Department of Bioengineering and Therapeutic Sciences, Schools of Pharmacy and Medicine, University of California San Francisco, San Francisco, California (W.S.); and Aether Therapeutics, Austin, Texas (W.S.)
| | - Katherine Hartmann
- Department of Cancer Biology and Genetics, College of Medicine, The Ohio State University, Columbus Ohio (W.S., A.E.T.); Department of Pharmacotherapy and Translational Research, College of Pharmacy, University of Florida, Gainesville, Florida (D.W.); Department of Radiology, Hospital of the University of Pennsylvania, Philadelphia, Pennsylvania (K.H.); Department of Bioengineering and Therapeutic Sciences, Schools of Pharmacy and Medicine, University of California San Francisco, San Francisco, California (W.S.); and Aether Therapeutics, Austin, Texas (W.S.)
| | - Amanda Ewart Toland
- Department of Cancer Biology and Genetics, College of Medicine, The Ohio State University, Columbus Ohio (W.S., A.E.T.); Department of Pharmacotherapy and Translational Research, College of Pharmacy, University of Florida, Gainesville, Florida (D.W.); Department of Radiology, Hospital of the University of Pennsylvania, Philadelphia, Pennsylvania (K.H.); Department of Bioengineering and Therapeutic Sciences, Schools of Pharmacy and Medicine, University of California San Francisco, San Francisco, California (W.S.); and Aether Therapeutics, Austin, Texas (W.S.)
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13
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Lagniton PNP, Tam B, Wang SM. DARVIC: Dihedral angle-reliant variant impact classifier for functional prediction of missense VUS. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2023; 238:107596. [PMID: 37201251 DOI: 10.1016/j.cmpb.2023.107596] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/11/2022] [Revised: 04/19/2023] [Accepted: 05/10/2023] [Indexed: 05/20/2023]
Abstract
BACKGROUND Of the large number of genetic variants identified, the functional impact for most of them remains unknown. Mutations in DNA damage repair genes such as MUTYH, which is involved in repairing A:8-oxoG mismatches caused by reactive oxygen species, can cause a higher risk of cancer. Mutations happening in other key genes such as TP53 also pose huge health threats and risk of cancer. The interpretation of genetic variants' functional impact is a forefront issue that needs to be addressed. Many different in silico methods based on different principles have been developed and applied in interpreting genetic variants. However, a current challenge is that many existing methods tend to overpredict the pathogenicity of benign variants. A new approach is needed to tackle this issue to improve genetic variant interpretation through the use of in silico methods. METHODS In this study, we developed another protein structural-based approach called Dihedral angle-reliant variant impact classifier (DARVIC) to predict the deleterious impact of the coding-changing missense variants. DARVIC uses Ramachandran's principle of protein stereochemistry as the theoretical foundation and uses molecular dynamics simulations coupled with a supervised machine learning algorithm XGBoost to determine the functional impact of missense variants on protein structural stability. RESULTS We characterized the features of dihedral angles in dynamic protein structures. We also tested the performance of DARVIC in MUTYH and TP53 missense variants and achieved satisfactory results in reflecting the functional impacts of the variants on protein structure. The method achieved a balanced accuracy of 84% in a functionally validated MUTYH dataset containing both benign and pathogenic missense variants, higher than other existing in silico methods. Along with that, DARVIC was able to predict 119 (47%) deleterious variants from a dataset of 254 MUTYH VUS. Further application of DARVIC to a functionally validated TP53 dataset had a balanced accuracy of 94%, topping other methods, demonstrating DARVIC's robustness. CONCLUSION DARVIC provides a valuable tool to predict the functional impacts of missense variants based on their effects on protein structural stability and motion. At its current state, DARVIC performed well in predicting the functional impact of the missense variants both in MUTYH and TP53. We expect its high potential to predict functional impact for the missense variants in other genes.
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Affiliation(s)
- Philip Naderev P Lagniton
- Cancer Centre and Institute of Translational Medicine, Department of Public Health and Medical Administration, Faculty of Health Sciences, Ministry of Education Frontiers Science Center for Precision Oncology, University of Macau, Macao
| | - Benjamin Tam
- Cancer Centre and Institute of Translational Medicine, Department of Public Health and Medical Administration, Faculty of Health Sciences, Ministry of Education Frontiers Science Center for Precision Oncology, University of Macau, Macao
| | - San Ming Wang
- Cancer Centre and Institute of Translational Medicine, Department of Public Health and Medical Administration, Faculty of Health Sciences, Ministry of Education Frontiers Science Center for Precision Oncology, University of Macau, Macao; Senior Author, Macao.
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14
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Tan WX, Sim X, Khoo CM, Teo AKK. Prioritization of genes associated with type 2 diabetes mellitus for functional studies. Nat Rev Endocrinol 2023:10.1038/s41574-023-00836-1. [PMID: 37169822 DOI: 10.1038/s41574-023-00836-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Accepted: 03/28/2023] [Indexed: 05/13/2023]
Abstract
Existing therapies for type 2 diabetes mellitus (T2DM) show limited efficacy or have adverse effects. Numerous genetic variants associated with T2DM have been identified, but progress in translating these findings into potential drug targets has been limited. Here, we describe the tools and platforms available to identify effector genes from T2DM-associated coding and non-coding variants and prioritize them for functional studies. We discuss QSER1 and SLC12A8 as examples of genes that have been identified as possible T2DM candidate genes using these tools and platforms. We suggest further approaches, including the use of sequencing data with increased sample size and ethnic diversity, single-cell omics data for analyses, glycaemic trait associations to predict gene function and, potentially, human induced pluripotent stem cell 'village' cultures, to strengthen current gene functionalization workflows. Effective prioritization of T2DM-associated genes for experimental validation could expedite our understanding of the genetic mechanisms responsible for T2DM to facilitate the use of precision medicine in its treatment.
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Affiliation(s)
- Wei Xuan Tan
- Stem Cells and Diabetes Laboratory, Institute of Molecular and Cell Biology (IMCB), Agency for Science, Technology and Research (A*STAR), Singapore, Singapore
- Department of Medicine, Yong Loo Lin School of Medicine, National University of Singapore, Singapore, Singapore
| | - Xueling Sim
- Saw Swee Hock School of Public Health, National University of Singapore and National University Health System, Singapore, Singapore
| | - Chin Meng Khoo
- Department of Medicine, Yong Loo Lin School of Medicine, National University of Singapore, Singapore, Singapore
| | - Adrian K K Teo
- Stem Cells and Diabetes Laboratory, Institute of Molecular and Cell Biology (IMCB), Agency for Science, Technology and Research (A*STAR), Singapore, Singapore.
- Department of Medicine, Yong Loo Lin School of Medicine, National University of Singapore, Singapore, Singapore.
- Precision Medicine Translational Research Programme, Yong Loo Lin School of Medicine, National University of Singapore, Singapore, Singapore.
- Department of Biochemistry, Yong Loo Lin School of Medicine, National University of Singapore, Singapore, Singapore.
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15
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Gharemirshamloo FR, Majumder R, Kumar S U, Doss C GP, Bamdad K, Frootan F, Un C. Effects of the pathological E200K mutation on human prion protein: A computational screening and molecular dynamics approach. J Cell Biochem 2023; 124:254-265. [PMID: 36565210 DOI: 10.1002/jcb.30359] [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: 10/10/2022] [Revised: 12/06/2022] [Accepted: 12/08/2022] [Indexed: 12/25/2022]
Abstract
The human prion protein gene (PRNP) is mapped to the short arm of chromosome 20 (20pter-12). Prion disease is associated with mutations in the prion protein-encoding gene sequence. Earlier studies found that the mutation G127V in the PRNP increases protein stability. In contrast, the mutation E200K, which has the highest mutation rate in the prion protein, causes Creutzfeldt-Jakob disease (CJD) in humans and induces protein aggregation. We aimed to identify the structural mechanisms of E200k and G127V mutations causing CJD. We used a variety of bioinformatic algorithms, including SIFT, PolyPhen, I-Mutant, PhD-SNP, and SNP& GO, to predict the association of the E200K mutation with prion disease. MD simulation is performed, and graphs for root mean square deviation, root mean square fluctuation, radius of gyration, DSSP, principal component analysis, porcupine, and free energy landscape are generated to confirm and prove the stability of the wild-type and mutant protein structures. The protein is analyzed for aggregation, and the results indicate more fluctuations in the protein structure during the simulation owing to the E200K mutation; however, the G127V mutation makes the protein structure stable against aggregation during the simulation.
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Affiliation(s)
| | - Ranabir Majumder
- School of Medical Science and Technology, Indian Institute of Technology Kharagpur, Kharagpur, India
| | - Udhaya Kumar S
- Department of Integrative Biology, Laboratory of Integrative Genomics, School of Bio Sciences and Technology, Vellore Institute of Technology (VIT), Vellore, Tamil Nadu, India
| | - George Priya Doss C
- Department of Integrative Biology, Laboratory of Integrative Genomics, School of Bio Sciences and Technology, Vellore Institute of Technology (VIT), Vellore, Tamil Nadu, India
| | - Kourosh Bamdad
- Department of Biology, Payame Noor University, Tehran, Iran
| | - Fateme Frootan
- Institute of Agricultural Biotechnology, National Institute of Genetic Engineering & Biotechnology (NIGEB), Tehran, Iran
| | - Cemal Un
- Department of Biology, Division of Molecular Biology, Ege University, Izmir, Turkey
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16
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Azmi MB, Khan W, Azim MK, Nisar MI, Jehan F. Identification of potential therapeutic intervening targets by in-silico analysis of nsSNPs in preterm birth-related genes. PLoS One 2023; 18:e0280305. [PMID: 36881567 PMCID: PMC9990928 DOI: 10.1371/journal.pone.0280305] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/23/2022] [Accepted: 12/27/2022] [Indexed: 03/08/2023] Open
Abstract
Prematurity is the foremost cause of death in children under 5 years of age. Genetics contributes to 25-40% of all preterm births (PTB) yet we still need to identify specific targets for intervention based on genetic pathways. This study involved the effect of region-specific non-synonymous variations and their transcript level mutational impact on protein functioning and stability by various in-silico tools. This investigation identifies potential therapeutic targets to manage the challenge of PTB, corresponding protein cavities and explores their binding interactions with intervening compounds. We searched 20 genes coding 55 PTB proteins from NCBI. Single Nucleotide Polymorphisms (SNPs) of concerned genes were extracted from ENSEMBL, and filtration of exonic variants (non-synonymous) was performed. Several in-silico downstream protein functional effect prediction tools were used to identify damaging variants. Rare coding variants were selected with an allele frequency of ≤1% in 1KGD, further supported by South Asian ALFA frequencies and GTEx gene/tissue expression database. CNN1, COL24A1, IQGAP2 and SLIT2 were identified with 7 rare pathogenic variants found in 17 transcript sequences. The functional impact analyses of rs532147352 (R>H) of CNN1 computed through PhD-SNP, PROVEAN, SNP&GO, PMut and MutPred2 algorithms showed impending deleterious effects, and the presence of this pathogenic mutation in CNN1 resulted in large decrease in protein structural stability (ΔΔG (kcal/mol). After structural protein identification, homology modelling of CNN1, which has been previously reported as a biomarker for the prediction of PTB, was performed, followed by the stereochemical quality checks of the 3D model. Blind docking approach were used to search the binding cavities and molecular interactions with progesterone, ranked with energetic estimations. Molecular interactions of CNN1 with progesterone were investigated through LigPlot 2D. Further, molecular docking experimentation of CNN1 showed the significant interactions at S102, L105, A106, K123, Y124 with five selected PTB-drugs, Allylestrenol (-7.56 kcal/mol), Hydroxyprogesterone caproate (-8.19 kcal/mol), Retosiban (-9.43 kcal/mol), Ritodrine (-7.39 kcal/mol) and Terbutaline (-6.87 kcal/mol). Calponin-1 gene and its molecular interaction analysis could serve as an intervention target for the prevention of PTB.
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Affiliation(s)
- Muhammad Bilal Azmi
- Department of Biochemistry, Dow Medical College, Dow University of Health Sciences, Karachi, Pakistan
- Department of Biosciences, Faculty of Life Sciences, Mohammad Ali Jinnah University, Karachi, Pakistan
| | - Waqasuddin Khan
- Biorepositroy and Omics Research Group, Department of Pediatrics and Child Health, Faculty of Health Sciences, Medical College, The Aga Khan University, Karachi, Pakistan
- Department of Pediatrics and Child Health, Faculty of Health Sciences, Medical College, The Aga Khan University, Karachi, Pakistan
- CITRIC Center for Bioinformatics and Computational Biology, Department of Pediatrics and Child Health, Faculty of Health Sciences, Medical College, The Aga Khan University, Karachi, Pakistan
- * E-mail:
| | - M. Kamran Azim
- Department of Biosciences, Faculty of Life Sciences, Mohammad Ali Jinnah University, Karachi, Pakistan
| | - Muhammad Imran Nisar
- Biorepositroy and Omics Research Group, Department of Pediatrics and Child Health, Faculty of Health Sciences, Medical College, The Aga Khan University, Karachi, Pakistan
- Department of Pediatrics and Child Health, Faculty of Health Sciences, Medical College, The Aga Khan University, Karachi, Pakistan
- CITRIC Center for Bioinformatics and Computational Biology, Department of Pediatrics and Child Health, Faculty of Health Sciences, Medical College, The Aga Khan University, Karachi, Pakistan
| | - Fyezah Jehan
- Biorepositroy and Omics Research Group, Department of Pediatrics and Child Health, Faculty of Health Sciences, Medical College, The Aga Khan University, Karachi, Pakistan
- Department of Pediatrics and Child Health, Faculty of Health Sciences, Medical College, The Aga Khan University, Karachi, Pakistan
- CITRIC Center for Bioinformatics and Computational Biology, Department of Pediatrics and Child Health, Faculty of Health Sciences, Medical College, The Aga Khan University, Karachi, Pakistan
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Ali S, Ali U, Qamar A, Zafar I, Yaqoob M, Ain QU, Rashid S, Sharma R, Nafidi HA, Bin Jardan YA, Bourhia M. Predicting the effects of rare genetic variants on oncogenic signaling pathways: A computational analysis of HRAS protein function. Front Chem 2023; 11:1173624. [PMID: 37153521 PMCID: PMC10160440 DOI: 10.3389/fchem.2023.1173624] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/24/2023] [Accepted: 04/10/2023] [Indexed: 05/09/2023] Open
Abstract
The HRAS gene plays a crucial role in regulating essential cellular processes for life, and this gene's misregulation is linked to the development of various types of cancers. Nonsynonymous single nucleotide polymorphisms (nsSNPs) within the coding region of HRAS can cause detrimental mutations that disrupt wild-type protein function. In the current investigation, we have employed in-silico methodologies to anticipate the consequences of infrequent genetic variations on the functional properties of the HRAS protein. We have discovered a total of 50 nsSNPs, of which 23 were located in the exon region of the HRAS gene and denoting that they were expected to cause harm or be deleterious. Out of these 23, 10 nsSNPs ([G60V], [G60D], [R123P], [D38H], [I46T], [G115R], [R123G], [P11OL], [A59L], and [G13R]) were identified as having the most delterious effect based on results of SIFT analysis and PolyPhen2 scores ranging from 0.53 to 69. The DDG values -3.21 kcal/mol to 0.87 kcal/mol represent the free energy change associated with protein stability upon mutation. Interestingly, we identified that the three mutations (Y4C, T58I, and Y12E) were found to improve the structural stability of the protein. We performed molecular dynamics (MD) simulations to investigate the structural and dynamic effects of HRAS mutations. Our results showed that the stable model of HRAS had a significantly lower energy value of -18756 kj/mol compared to the initial model of -108915 kj/mol. The RMSD value for the wild-type complex was 4.40 Å, and the binding energies for the G60V, G60D, and D38H mutants were -107.09 kcal/mol, -109.42 kcal/mol, and -107.18 kcal/mol, respectively as compared to wild-type HRAS protein had -105.85 kcal/mol. The result of our investigation presents convincing corroboration for the potential functional significance of nsSNPs in augmenting HRAS expression and adding to the activation of malignant oncogenic signalling pathways.
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Affiliation(s)
- Sadaqat Ali
- Medical Department, DHQ Hospital Bhawalnagr, Punjab, Pakistan
| | | | - Adeem Qamar
- Department of Pathology, Sahiwal Medical College Sahiwal, Punjab, Pakistan
| | - Imran Zafar
- Department of Bioinformatics and Computational Biology, Virtual University of Pakistan, Punjab, Pakistan
| | - Muhammad Yaqoob
- Department of Life Sciences, ARID University-Barani Institute of Sciences Burewala Campus, Punjab, Pakistan
| | - Qurat ul Ain
- Department of Chemistry, Government College Women University, Faisalabad, Pakistan
| | - Summya Rashid
- Department of Bioinformatics and Computational Biology, Virtual University of Pakistan, Punjab, Pakistan
| | - Rohit Sharma
- Department of Rasa Shastra and Bhaishajya Kalpana, Faculty of Ayurveda, Institute of Medical Sciences, Banaras Hindu University, Varanasi, Uttar Pradesh, India
- *Correspondence: Mohammed Bourhia, ; Rohit Sharma,
| | - Hiba-Allah Nafidi
- Department of Food Science, Faculty of Agricultural and Food Sciences, Laval University, Quebec City, QC, Canada
| | - Yousef A. Bin Jardan
- Department of Pharmaceutics, College of Pharmacy, King Saud University, Riyadh, Saudi Arabia
| | - Mohammed Bourhia
- Laboratory of Chemistry and Biochemistry, Faculty of Medicine and Pharmacy, Ibn Zohr University, Agadir, Morocco
- *Correspondence: Mohammed Bourhia, ; Rohit Sharma,
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18
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Liu Y, Yeung WSB, Chiu PCN, Cao D. Computational approaches for predicting variant impact: An overview from resources, principles to applications. Front Genet 2022; 13:981005. [PMID: 36246661 PMCID: PMC9559863 DOI: 10.3389/fgene.2022.981005] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/29/2022] [Accepted: 08/08/2022] [Indexed: 11/13/2022] Open
Abstract
One objective of human genetics is to unveil the variants that contribute to human diseases. With the rapid development and wide use of next-generation sequencing (NGS), massive genomic sequence data have been created, making personal genetic information available. Conventional experimental evidence is critical in establishing the relationship between sequence variants and phenotype but with low efficiency. Due to the lack of comprehensive databases and resources which present clinical and experimental evidence on genotype-phenotype relationship, as well as accumulating variants found from NGS, different computational tools that can predict the impact of the variants on phenotype have been greatly developed to bridge the gap. In this review, we present a brief introduction and discussion about the computational approaches for variant impact prediction. Following an innovative manner, we mainly focus on approaches for non-synonymous variants (nsSNVs) impact prediction and categorize them into six classes. Their underlying rationale and constraints, together with the concerns and remedies raised from comparative studies are discussed. We also present how the predictive approaches employed in different research. Although diverse constraints exist, the computational predictive approaches are indispensable in exploring genotype-phenotype relationship.
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Affiliation(s)
- Ye Liu
- Shenzhen Key Laboratory of Fertility Regulation, Reproductive Medicine Center, The University of Hong Kong-Shenzhen Hospital, Shenzhen, China
| | - William S. B. Yeung
- Shenzhen Key Laboratory of Fertility Regulation, Reproductive Medicine Center, The University of Hong Kong-Shenzhen Hospital, Shenzhen, China
- Department of Obstetrics and Gynaecology, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong SAR, China
| | - Philip C. N. Chiu
- Shenzhen Key Laboratory of Fertility Regulation, Reproductive Medicine Center, The University of Hong Kong-Shenzhen Hospital, Shenzhen, China
- Department of Obstetrics and Gynaecology, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong SAR, China
- *Correspondence: Philip C. N. Chiu, ; Dandan Cao,
| | - Dandan Cao
- Shenzhen Key Laboratory of Fertility Regulation, Reproductive Medicine Center, The University of Hong Kong-Shenzhen Hospital, Shenzhen, China
- *Correspondence: Philip C. N. Chiu, ; Dandan Cao,
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Yazar M, Ozbek P. Assessment of 13 in silico pathogenicity methods on cancer-related variants. Comput Biol Med 2022; 145:105434. [DOI: 10.1016/j.compbiomed.2022.105434] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/04/2022] [Revised: 03/04/2022] [Accepted: 03/20/2022] [Indexed: 11/03/2022]
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Katsonis P, Wilhelm K, Williams A, Lichtarge O. Genome interpretation using in silico predictors of variant impact. Hum Genet 2022; 141:1549-1577. [PMID: 35488922 PMCID: PMC9055222 DOI: 10.1007/s00439-022-02457-6] [Citation(s) in RCA: 18] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/02/2021] [Accepted: 04/17/2022] [Indexed: 02/06/2023]
Abstract
Estimating the effects of variants found in disease driver genes opens the door to personalized therapeutic opportunities. Clinical associations and laboratory experiments can only characterize a tiny fraction of all the available variants, leaving the majority as variants of unknown significance (VUS). In silico methods bridge this gap by providing instant estimates on a large scale, most often based on the numerous genetic differences between species. Despite concerns that these methods may lack reliability in individual subjects, their numerous practical applications over cohorts suggest they are already helpful and have a role to play in genome interpretation when used at the proper scale and context. In this review, we aim to gain insights into the training and validation of these variant effect predicting methods and illustrate representative types of experimental and clinical applications. Objective performance assessments using various datasets that are not yet published indicate the strengths and limitations of each method. These show that cautious use of in silico variant impact predictors is essential for addressing genome interpretation challenges.
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Affiliation(s)
- Panagiotis Katsonis
- Department of Molecular and Human Genetics, Baylor College of Medicine, One Baylor Plaza, Houston, TX, 77030, USA.
| | - Kevin Wilhelm
- Graduate School of Biomedical Sciences, Baylor College of Medicine, One Baylor Plaza, Houston, TX, 77030, USA
| | - Amanda Williams
- Department of Molecular and Human Genetics, Baylor College of Medicine, One Baylor Plaza, Houston, TX, 77030, USA
| | - Olivier Lichtarge
- Department of Molecular and Human Genetics, Baylor College of Medicine, One Baylor Plaza, Houston, TX, 77030, USA. .,Department of Biochemistry, Human Genetics and Molecular Biology, Baylor College of Medicine, One Baylor Plaza, Houston, TX, 77030, USA. .,Department of Pharmacology, Baylor College of Medicine, One Baylor Plaza, Houston, TX, 77030, USA. .,Computational and Integrative Biomedical Research Center, Baylor College of Medicine, One Baylor Plaza, Houston, TX, 77030, USA.
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21
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Alam MS, Saleh MA, Mozibullah M, Riham AT, Solayman M, Gan SH. Computational algorithmic and molecular dynamics study of functional and structural impacts of non-synonymous single nucleotide polymorphisms in human DHFR gene. Comput Biol Chem 2021; 95:107587. [PMID: 34710812 DOI: 10.1016/j.compbiolchem.2021.107587] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/02/2021] [Revised: 08/21/2021] [Accepted: 10/01/2021] [Indexed: 11/26/2022]
Abstract
Human dihydrofolate reductase (DHFR) is a conserved enzyme that is central to folate metabolism and is widely targeted in pathogenic diseases as well as cancers. Although studies have reported the fact that genetic mutations in DHFR leads to a rare autosomal recessive inborn error of folate metabolism and drug resistance, there is a lack of an extensive study on how the deleterious non-synonymous SNPs (nsSNPs) disrupt its phenotypic effects. In this study, we aim at discovering the structural and functional consequences of nsSNPs in DHFR by employing a combined computational approach consisting of ten recently developed in silico tools for identification of damaging nsSNPs and molecular dynamics (MD) simulation for getting deeper insights into the magnitudes of damaging effects. Our study revealed the presence of 12 most deleterious nsSNPs affecting the native phenotypic effects, with three (R71T, G118D, Y122D) identified in the co-factor and ligand binding active sites. MD simulations also suggested that these three SNPs particularly Y122D, alter the overall structural flexibility and dynamics of the native DHFR protein which can provide more understandings into the crucial roles of these mutants in influencing the loss of DHFR function.
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Affiliation(s)
- Md Shahed Alam
- Department of Biochemistry and Molecular Biology, Jahangirnagar University, Savar, Dhaka 1342, Bangladesh
| | - Md Abu Saleh
- Department of Biochemistry and Molecular Biology, Jahangirnagar University, Savar, Dhaka 1342, Bangladesh
| | - Md Mozibullah
- Department of Biochemistry and Molecular Biology, Mawlana Bhashani Science and Technology University, Santosh, Tangail 1902, Bangladesh
| | - Ashik Tanvir Riham
- Department of Biochemistry and Molecular Biology, Jahangirnagar University, Savar, Dhaka 1342, Bangladesh
| | - Md Solayman
- Institute for Glycomics, Griffith University, Parklands Dr. Southport, QLD 4222, Australia.
| | - Siew Hua Gan
- School of Pharmacy, Monash University Malaysia, Bandar Sunway 47500, Selangor Darul Ehsan, Malaysia
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22
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Gong T, Yang L, Shen F, Chen H, Pan Z, Zhang Q, Jiang Y, Zhong F, Yang P, Zhang Y. Computational and Mass Spectrometry-Based Approach Identify Deleterious Non-Synonymous Single Nucleotide Polymorphisms (nsSNPs) in JMJD6. Molecules 2021; 26:molecules26154653. [PMID: 34361805 PMCID: PMC8347302 DOI: 10.3390/molecules26154653] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/07/2021] [Revised: 07/26/2021] [Accepted: 07/27/2021] [Indexed: 12/30/2022] Open
Abstract
The jumonji domain-containing protein 6 (JMJD6) gene catalyzes the arginine demethylation and lysine hydroxylation of histone and a growing list of its known substrate molecules, including p53 and U2AF65, suggesting a possible role in mRNA splicing and transcription in cancer progression. Mass spectrometry-based technology offers the opportunity to detect SNP variants accurately and effectively. In our study, we conducted a combined computational and filtration workflow to predict the nonsynonymous single nucleotide polymorphisms (nsSNPs) present in JMJD6, followed by a liquid chromatography-tandem mass spectrometry (LC-MS/MS) analysis and validation. The computational approaches SIFT, PolyPhen-2, SNAP, I-Mutant 2.0, PhD-SNP, PANTHER, and SNPS&GO were integrated to screen out the predicted damaging/deleterious nsSNPs. Through the three-dimensional structure of JMJD6, H187R (rs1159480887) was selected as a candidate for validation. The validation experiments showed that the mutation of this nsSNP in JMJD6 obviously affected mRNA splicing or the transcription of downstream genes through the reduced lysyl-hydroxylase activity of its substrates, U2AF65 and p53, further indicating the accuracy of this prediction method. This research provides an effective computational workflow for researchers with an opportunity to select prominent deleterious nsSNPs and, thus, remains promising for examining the dysfunction of proteins.
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Affiliation(s)
- Tianqi Gong
- Department of Systems Biology for Medicine, Institutes of Biomedical Sciences, Shanghai Medical College, Fudan University, Shanghai 200032, China; (T.G.); (L.Y.); (F.S.); (Z.P.); (Y.J.)
| | - Lujie Yang
- Department of Systems Biology for Medicine, Institutes of Biomedical Sciences, Shanghai Medical College, Fudan University, Shanghai 200032, China; (T.G.); (L.Y.); (F.S.); (Z.P.); (Y.J.)
| | - Fenglin Shen
- Department of Systems Biology for Medicine, Institutes of Biomedical Sciences, Shanghai Medical College, Fudan University, Shanghai 200032, China; (T.G.); (L.Y.); (F.S.); (Z.P.); (Y.J.)
| | - Hao Chen
- College of Bioscience and Biotechnology, Yangzhou University, Yangzhou 225009, China;
| | - Ziyue Pan
- Department of Systems Biology for Medicine, Institutes of Biomedical Sciences, Shanghai Medical College, Fudan University, Shanghai 200032, China; (T.G.); (L.Y.); (F.S.); (Z.P.); (Y.J.)
| | - Quanqing Zhang
- Department of Chemistry, University of California, Riverside, CA 92521, USA;
| | - Yan Jiang
- Department of Systems Biology for Medicine, Institutes of Biomedical Sciences, Shanghai Medical College, Fudan University, Shanghai 200032, China; (T.G.); (L.Y.); (F.S.); (Z.P.); (Y.J.)
| | - Fan Zhong
- Department of Systems Biology for Medicine, Institutes of Biomedical Sciences, Shanghai Medical College, Fudan University, Shanghai 200032, China; (T.G.); (L.Y.); (F.S.); (Z.P.); (Y.J.)
- Correspondence: (F.Z.); (P.Y.); (Y.Z.)
| | - Pengyuan Yang
- Department of Systems Biology for Medicine, Institutes of Biomedical Sciences, Shanghai Medical College, Fudan University, Shanghai 200032, China; (T.G.); (L.Y.); (F.S.); (Z.P.); (Y.J.)
- Correspondence: (F.Z.); (P.Y.); (Y.Z.)
| | - Yang Zhang
- Department of Systems Biology for Medicine, Institutes of Biomedical Sciences, Shanghai Medical College, Fudan University, Shanghai 200032, China; (T.G.); (L.Y.); (F.S.); (Z.P.); (Y.J.)
- Correspondence: (F.Z.); (P.Y.); (Y.Z.)
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