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Abu-Halima M, Becker LS, Ayesh BM, Meese E. MicroRNA-targeting in male infertility: Sperm microRNA-19a/b-3p and its spermatogenesis related transcripts content in men with oligoasthenozoospermia. Front Cell Dev Biol 2022; 10:973849. [PMID: 36211460 PMCID: PMC9533736 DOI: 10.3389/fcell.2022.973849] [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: 06/20/2022] [Accepted: 09/08/2022] [Indexed: 11/21/2022] Open
Abstract
Objective: To elucidate and validate the potential regulatory function of miR-19a/b-3p and its spermatogenesis-related transcripts content in sperm samples collected from men with oligoasthenozoospermia. Methods: Men presenting at an infertility clinic were enrolled. MicroRNA (miRNA) and target genes evaluation were carried out using in silico prediction analysis, Reverse transcription-quantitative PCR (RT-qPCR) validation, and Western blot confirmation. Results: The expression levels of miRNA-19a/b-3p were significantly up-regulated and 51 target genes were significantly down-regulated in oligoasthenozoospermic men compared with age-matched normozoospermic men as determined by RT-qPCR. Correlation analysis highlighted that sperm count, motility, and morphology were negatively correlated with miRNA-19a/b-3p and positively correlated with the lower expression level of 51 significantly identified target genes. Furthermore, an inverse correlation between higher expression levels of miRNA-19a/b-3p and lower expression levels of 51 target genes was observed. Consistent with the results of the RT-qPCR, reduced expression levels of STK33 and DNAI1 protein levels were identified in an independent cohort of sperm samples collected from men with oligoasthenozoospermia. Conclusion: Findings suggest that the higher expression of miRNA-19a/b-3p or the lower expression of target genes are associated with oligoasthenozoospermia and male infertility, probably through influencing basic semen parameters. This study lay the groundwork for future studies focused on investigating therapies for male infertility.
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Affiliation(s)
| | | | - Basim M Ayesh
- Department of Laboratory Medical Sciences, Alaqsa University, Gaza, Palestine
| | - Eckart Meese
- Institute of Human Genetics, Saarland University, Homburg, Germany
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Chen J, Ding Q, An L, Wang H. Ca2+-stimulated adenylyl cyclases as therapeutic targets for psychiatric and neurodevelopmental disorders. Front Pharmacol 2022; 13:949384. [PMID: 36188604 PMCID: PMC9523369 DOI: 10.3389/fphar.2022.949384] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/20/2022] [Accepted: 09/05/2022] [Indexed: 11/13/2022] Open
Abstract
As the main secondary messengers, cyclic AMP (cAMP) and Ca2+ trigger intracellular signal transduction cascade and, in turn, regulate many aspects of cellular function in developing and mature neurons. The group I adenylyl cyclase (ADCY, also known as AC) isoforms, including ADCY1, 3, and 8 (also known as AC1, AC3, and AC8), are stimulated by Ca2+ and thus functionally positioned to integrate cAMP and Ca2+ signaling. Emerging lines of evidence have suggested the association of the Ca2+-stimulated ADCYs with bipolar disorder, schizophrenia, major depressive disorder, post-traumatic stress disorder, and autism. In this review, we discuss the molecular and cellular features as well as the physiological functions of ADCY1, 3, and 8. We further discuss the recent therapeutic development to target the Ca2+-stimulated ADCYs for potential treatments of psychiatric and neurodevelopmental disorders.
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Wu J, Li D, Liu X, Li Q, He X, Wei J, Li X, Li M, Rehman AU, Xia Y, Wu C, Zhang J, Lu X. IDDB: a comprehensive resource featuring genes, variants and characteristics associated with infertility. Nucleic Acids Res 2021; 49:D1218-D1224. [PMID: 32941628 PMCID: PMC7779019 DOI: 10.1093/nar/gkaa753] [Citation(s) in RCA: 26] [Impact Index Per Article: 8.7] [Reference Citation Analysis] [Abstract] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/31/2020] [Revised: 08/24/2020] [Accepted: 08/29/2020] [Indexed: 12/26/2022] Open
Abstract
Infertility is a complex multifactorial disease that affects up to 10% of couples across the world. However, many mechanisms of infertility remain unclear due to the lack of studies based on systematic knowledge, leading to ineffective treatment and/or transmission of genetic defects to offspring. Here, we developed an infertility disease database to provide a comprehensive resource featuring various factors involved in infertility. Features in the current IDDB version were manually curated as follows: (i) a total of 307 infertility-associated genes in human and 1348 genes associated with reproductive disorder in 9 model organisms; (ii) a total of 202 chromosomal abnormalities leading to human infertility, including aneuploidies and structural variants; and (iii) a total of 2078 pathogenic variants from infertility patients’ samples across 60 different diseases causing infertility. Additionally, the characteristics of clinically diagnosed infertility patients (i.e. causative variants, laboratory indexes and clinical manifestations) were collected. To the best of our knowledge, the IDDB is the first infertility database serving as a systematic resource for biologists to decipher infertility mechanisms and for clinicians to achieve better diagnosis/treatment of patients from disease phenotype to genetic factors. The IDDB is freely available at http://mdl.shsmu.edu.cn/IDDB/.
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Affiliation(s)
- Jing Wu
- Department of Assisted Reproduction, Shanghai Ninth People's Hospital, Shanghai Jiao Tong University School of Medicine (SJTU-SM), Shanghai 200011, China.,Medicinal Bioinformatics Center, Shanghai Jiao Tong University School of Medicine (SJTU-SM), Shanghai 200025, China
| | - Danjun Li
- Department of Assisted Reproduction, Shanghai Ninth People's Hospital, Shanghai Jiao Tong University School of Medicine (SJTU-SM), Shanghai 200011, China.,Medicinal Bioinformatics Center, Shanghai Jiao Tong University School of Medicine (SJTU-SM), Shanghai 200025, China
| | - Xinyi Liu
- Medicinal Bioinformatics Center, Shanghai Jiao Tong University School of Medicine (SJTU-SM), Shanghai 200025, China
| | - Qian Li
- Medicinal Bioinformatics Center, Shanghai Jiao Tong University School of Medicine (SJTU-SM), Shanghai 200025, China
| | - Xinheng He
- Medicinal Bioinformatics Center, Shanghai Jiao Tong University School of Medicine (SJTU-SM), Shanghai 200025, China
| | - Jiale Wei
- Medicinal Bioinformatics Center, Shanghai Jiao Tong University School of Medicine (SJTU-SM), Shanghai 200025, China
| | - Xinyi Li
- Medicinal Bioinformatics Center, Shanghai Jiao Tong University School of Medicine (SJTU-SM), Shanghai 200025, China
| | - Mingyu Li
- Medicinal Bioinformatics Center, Shanghai Jiao Tong University School of Medicine (SJTU-SM), Shanghai 200025, China
| | - Ashfaq Ur Rehman
- Medicinal Bioinformatics Center, Shanghai Jiao Tong University School of Medicine (SJTU-SM), Shanghai 200025, China
| | - Yujia Xia
- Medicinal Bioinformatics Center, Shanghai Jiao Tong University School of Medicine (SJTU-SM), Shanghai 200025, China
| | - Chengwei Wu
- Medicinal Bioinformatics Center, Shanghai Jiao Tong University School of Medicine (SJTU-SM), Shanghai 200025, China
| | - Jian Zhang
- Medicinal Bioinformatics Center, Shanghai Jiao Tong University School of Medicine (SJTU-SM), Shanghai 200025, China.,School of Pharmaceutical Sciences, Zhengzhou University, Zhengzhou 450001, China
| | - Xuefeng Lu
- Department of Assisted Reproduction, Shanghai Ninth People's Hospital, Shanghai Jiao Tong University School of Medicine (SJTU-SM), Shanghai 200011, China
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Sundararajan T, Manzardo AM, Butler MG. Functional analysis of schizophrenia genes using GeneAnalytics program and integrated databases. Gene 2018; 641:25-34. [PMID: 29032150 PMCID: PMC6706854 DOI: 10.1016/j.gene.2017.10.035] [Citation(s) in RCA: 28] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/25/2017] [Revised: 10/06/2017] [Accepted: 10/11/2017] [Indexed: 12/14/2022]
Abstract
Schizophrenia (SCZ) is a chronic debilitating neuropsychiatric disorder with multiple risk factors involving numerous complex genetic influences. We examined and updated a master list of clinically relevant and susceptibility genes associated with SCZ reported in the literature and genomic databases dedicated to gene discovery for characterization of SCZ genes. We used the commercially available GeneAnalytics computer-based gene analysis program and integrated genomic databases to create a molecular profile of the updated list of 608 SCZ genes to model their impact in select categories (tissues and cells, diseases, pathways, biological processes, molecular functions, phenotypes and compounds) using specialized GeneAnalytics algorithms. Genes for schizophrenia were predominantly expressed in the cerebellum, cerebral cortex, medulla oblongata, thalamus and hypothalamus. Psychiatric/behavioral disorders incorporating SCZ genes included ADHD, bipolar disorder, autism spectrum disorder and alcohol dependence as well as cancer, Alzheimer's and Parkinson's disease, sleep disturbances and inflammation. Function based analysis of major biological pathways and mechanisms associated with SCZ genes identified glutaminergic receptors (e.g., GRIA1, GRIN2, GRIK4, GRM5), serotonergic receptors (e.g., HTR2A, HTR2C), GABAergic receptors (e.g., GABRA1, GABRB2), dopaminergic receptors (e.g., DRD1, DRD2), calcium-related channels (e.g., CACNA1H, CACNA1B), solute transporters (e.g., SLC1A1, SLC6A2) and for neurodevelopment (e.g., ADCY1, MEF2C, NOTCH2, SHANK3). Biological mechanisms involving synaptic transmission, regulation of membrane potential and transmembrane ion transport were identified as leading molecular functions associated with SCZ genes. Our approach to interrogate SCZ genes and their interactions at various levels has increased our knowledge and insight into the disease process possibly opening new avenues for therapeutic intervention.
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Affiliation(s)
- Tharani Sundararajan
- Department of Psychiatry and Behavioral Sciences, University of Kansas Medical Center, Kansas City, KS, United States
| | - Ann M Manzardo
- Department of Psychiatry and Behavioral Sciences, University of Kansas Medical Center, Kansas City, KS, United States
| | - Merlin G Butler
- Department of Psychiatry and Behavioral Sciences, University of Kansas Medical Center, Kansas City, KS, United States; Department of Pediatrics, University of Kansas Medical Center, Kansas City, KS, United States.
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Gabrielli AP, Manzardo AM, Butler MG. Exploring genetic susceptibility to obesity through genome functional pathway analysis. Obesity (Silver Spring) 2017; 25:1136-1143. [PMID: 28474384 PMCID: PMC5444946 DOI: 10.1002/oby.21847] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/16/2017] [Revised: 03/16/2017] [Accepted: 03/21/2017] [Indexed: 12/24/2022]
Abstract
OBJECTIVE Obesity has been reaching epidemic levels in recent decades, with a growing body of research identifying predisposing genetic components. To explore the relationship of genetic factors contributing to obesity, an analytical computer-based gene-profiling approach utilizing an updated list of clinically relevant and known obesity-related genes was undertaken. METHODS An updated list of 494 genes reportedly associated with obesity was compiled, and the GeneAnalytics profiling software was utilized to interrogate genomic databases from GeneCards® to cross-reference obesity gene sets against tissues and cells, diseases, genetic pathways, gene ontology (GO)-biological processes and GO-molecular functions, phenotypes, and compounds. RESULTS Obesity-related fields identified by GeneAnalytics algorithms included 8 diseases, 46 pathways, 62 biological processes, 22 molecular functions, 148 phenotypes, and 286 compounds impacting adipogenesis, signal transduction by G-protein coupled receptors, and lipid metabolism involving insulin-related genes (IGF1, INS, IRS1). GO-biological processes identified feeding behavior, cholesterol metabolic process, and glucose and cholesterol homeostasis pathways, while GO-molecular processes pertained to receptor binding, affecting glucose homeostasis, body weight, and circulating insulin and triglyceride levels. CONCLUSIONS The gene-profiling model suggests that pathogenesis of obesity relates to the coordination of biological responses to glucose and intracellular lipids possibly through a disruption of biochemical cascades and cellular signaling arising from affected receptors.
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Affiliation(s)
- Alexander P Gabrielli
- Departments of Psychiatry and Behavioral Sciences and Pediatrics, University of Kansas Medical Center, Kansas City, Kansas, USA
| | - Ann M Manzardo
- Departments of Psychiatry and Behavioral Sciences and Pediatrics, University of Kansas Medical Center, Kansas City, Kansas, USA
| | - Merlin G Butler
- Departments of Psychiatry and Behavioral Sciences and Pediatrics, University of Kansas Medical Center, Kansas City, Kansas, USA
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El Yakoubi W, Wassmann K. Meiotic Divisions: No Place for Gender Equality. ADVANCES IN EXPERIMENTAL MEDICINE AND BIOLOGY 2017; 1002:1-17. [PMID: 28600780 DOI: 10.1007/978-3-319-57127-0_1] [Citation(s) in RCA: 21] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/12/2022]
Abstract
In multicellular organisms the fusion of two gametes with a haploid set of chromosomes leads to the formation of the zygote, the first cell of the embryo. Accurate execution of the meiotic cell division to generate a female and a male gamete is required for the generation of healthy offspring harboring the correct number of chromosomes. Unfortunately, meiosis is error prone. This has severe consequences for fertility and under certain circumstances, health of the offspring. In humans, female meiosis is extremely error prone. In this chapter we will compare male and female meiosis in humans to illustrate why and at which frequency errors occur, and describe how this affects pregnancy outcome and health of the individual. We will first introduce key notions of cell division in meiosis and how they differ from mitosis, followed by a detailed description of the events that are prone to errors during the meiotic divisions.
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Affiliation(s)
- Warif El Yakoubi
- Sorbonne Universités, UPMC Univ Paris 06, Institut de Biologie Paris Seine (IBPS), UMR7622, Paris, 75252, France.,CNRS, IBPS, UMR7622 Developmental Biology Lab, Paris, 75252, France
| | - Katja Wassmann
- Sorbonne Universités, UPMC Univ Paris 06, Institut de Biologie Paris Seine (IBPS), UMR7622, Paris, 75252, France. .,CNRS, IBPS, UMR7622 Developmental Biology Lab, Paris, 75252, France.
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