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Bandesh K, Pal M, Balakrishnan A, Gautam P, Jha P, Tandon N, Pillai B, Bharadwaj D. A novel antisense lncRNA, ARBAG harboring an RNA destabilizing GWAS variant for C-peptide dictates the transcript isoforms of GABRA6 in cerebellum. Hum Mol Genet 2023; 32:2929-2939. [PMID: 37498167 DOI: 10.1093/hmg/ddad119] [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: 12/16/2022] [Revised: 06/26/2023] [Accepted: 07/17/2023] [Indexed: 07/28/2023] Open
Abstract
Human disease-associated genetic variations often map to long non-coding RNA (lncRNA) genes; however, elucidation of their functional impact is challenging. We previously identified a new genetic variant rs4454083 (A/G) residing in exon of an uncharacterized lncRNA ARBAG that strongly associates with plasma levels of C-peptide, a hormone that regulates insulin bioavailability. On the opposite strand, rs4454083 also corresponds to an intron of a cerebellum-specific GABA receptor subunit gene GABRA6 that mediates strengthening of inhibitory synapses by insulin. Here, we show that alleles of rs4454083 modulate transcript levels of the antisense gene, ARBAG, which then controls the expression of the sense gene, GABRA6. Predisposing to low C-peptide, GG (a minor allele genotype across ethnicities) stabilizes ARBAG lncRNA causing higher transcript levels in cerebellum. ARBAG lncRNA abundance leads to cleavage of GABRA6 mRNA at the complementary region, resulting in a dysfunctional GABRA6 protein that would not be recruited for synapse strengthening. Together, our findings in human cerebellar cell-line and induced Pluripotent Stem Cells (iPSCs) demonstrate biological role of a novel lncRNA in determining the ratio of mRNA isoforms of a protein-coding gene and the ability of an embedded variant in modulating lncRNA stability leading to inter-individual differences in protein expression.
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Affiliation(s)
- Khushdeep Bandesh
- CSIR-Institute of Genomics and Integrative Biology, Delhi 110025, India
- Academy of Scientific and Innovative Research (AcSIR), Ghaziabad 201002, India
| | - Muneesh Pal
- CSIR-Institute of Genomics and Integrative Biology, Delhi 110025, India
| | | | - Pradeep Gautam
- CSIR-Institute of Genomics and Integrative Biology, Delhi 110025, India
| | - Punam Jha
- CSIR-Institute of Genomics and Integrative Biology, Delhi 110025, India
- Academy of Scientific and Innovative Research (AcSIR), Ghaziabad 201002, India
| | - Nikhil Tandon
- Department of Endocrinology and Metabolism, All India Institute of Medical Sciences, New Delhi 110029, India
| | - Beena Pillai
- CSIR-Institute of Genomics and Integrative Biology, Delhi 110025, India
- Academy of Scientific and Innovative Research (AcSIR), Ghaziabad 201002, India
| | - Dwaipayan Bharadwaj
- Systems Genomics Laboratory, School of Biotechnology, Jawaharlal Nehru University, New Delhi 110067, India
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Pan Y, Du R, Han X, Zhu W, Peng D, Tu Y, Han J, Bao Y, Yu H. Machine Learning Prediction of Iron Deficiency Anemia in Chinese Premenopausal Women 12 Months after Sleeve Gastrectomy. Nutrients 2023; 15:3385. [PMID: 37571322 PMCID: PMC10421369 DOI: 10.3390/nu15153385] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/07/2023] [Revised: 07/07/2023] [Accepted: 07/21/2023] [Indexed: 08/13/2023] Open
Abstract
Premenopausal women, who account for more than half of patients for bariatric surgery, are at higher risk of developing postoperative iron deficiency anemia (IDA) than postmenopausal women and men. We aimed at establishing a machine learning model to evaluate the risk of newly onset IDA in premenopausal women 12 months after sleeve gastrectomy (SG). Premenopausal women with complete clinical records and undergoing SG were enrolled in this retrospective study. Newly onset IDA after surgery, the main outcome, was defined according to the age- and gender-specific World Health Organization criteria. A linear support vector machine model was developed to predict the risk of IDA after SG with the top five important features identified during feature selection. Four hundred and seven subjects aged 31.0 (Interquartile range (IQR): 26.0-36.0) years with a median follow-up period of 12 (IQR 7-13) months were analyzed. They were divided into a training set and a validation set with 285 and 122 individuals, respectively. Preoperative ferritin, age, hemoglobin, creatinine, and fasting C-peptide were included. The model showed moderate discrimination in both sets (area under curve 0.858 and 0.799, respectively, p < 0.001). The calibration curve indicated acceptable consistency between observed and predicted results in both sets. Moreover, decision curve analysis showed substantial clinical benefits of the model in both sets. Our machine learning model could accurately predict newly onset IDA in Chinese premenopausal women with obesity 12 months after SG. External validation was required before the model was used in clinical practice.
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Affiliation(s)
- Yunhui Pan
- Department of Endocrinology and Metabolism, Shanghai Sixth People’s Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai Diabetes Institute, Shanghai Clinical Center of Diabetes, Shanghai Key Laboratory of Diabetes Mellitus, Shanghai Key Clinical Center for Metabolic Disease, Shanghai 200233, China
| | - Ronghui Du
- Department of Endocrinology and Metabolism, Shanghai Sixth People’s Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai Diabetes Institute, Shanghai Clinical Center of Diabetes, Shanghai Key Laboratory of Diabetes Mellitus, Shanghai Key Clinical Center for Metabolic Disease, Shanghai 200233, China
| | - Xiaodong Han
- Department of General Surgery, Shanghai Sixth People’s Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai 200233, China
| | - Wei Zhu
- Department of Endocrinology and Metabolism, Shanghai Sixth People’s Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai Diabetes Institute, Shanghai Clinical Center of Diabetes, Shanghai Key Laboratory of Diabetes Mellitus, Shanghai Key Clinical Center for Metabolic Disease, Shanghai 200233, China
| | - Danfeng Peng
- Department of Endocrinology and Metabolism, Shanghai Sixth People’s Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai Diabetes Institute, Shanghai Clinical Center of Diabetes, Shanghai Key Laboratory of Diabetes Mellitus, Shanghai Key Clinical Center for Metabolic Disease, Shanghai 200233, China
| | - Yinfang Tu
- Department of Endocrinology and Metabolism, Shanghai Sixth People’s Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai Diabetes Institute, Shanghai Clinical Center of Diabetes, Shanghai Key Laboratory of Diabetes Mellitus, Shanghai Key Clinical Center for Metabolic Disease, Shanghai 200233, China
- Department of Endocrinology and Metabolism, Haikou Orthopedic and Diabetes Hospital of Shanghai Sixth People’s Hospital, Haikou 570300, China
| | - Junfeng Han
- Department of Endocrinology and Metabolism, Tongji Hospital, School of Medicine, Tongji University, Shanghai 200065, China
| | - Yuqian Bao
- Department of Endocrinology and Metabolism, Shanghai Sixth People’s Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai Diabetes Institute, Shanghai Clinical Center of Diabetes, Shanghai Key Laboratory of Diabetes Mellitus, Shanghai Key Clinical Center for Metabolic Disease, Shanghai 200233, China
| | - Haoyong Yu
- Department of Endocrinology and Metabolism, Shanghai Sixth People’s Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai Diabetes Institute, Shanghai Clinical Center of Diabetes, Shanghai Key Laboratory of Diabetes Mellitus, Shanghai Key Clinical Center for Metabolic Disease, Shanghai 200233, China
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May C-peptide index be a new marker to predict proteinuria in anemic patients with type 2 diabetes mellitus? Endocr Regul 2021; 54:1-5. [PMID: 32597149 DOI: 10.2478/enr-2020-0001] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 11/20/2022] Open
Abstract
OBJECTIVE C-peptide is a reliable marker of beta cell reserve and is associated with diabetic complications. Furthermore, HbA1c level is associated with micro- and macro-vascular complications in diabetic patients. HbA1c measurement of diabetic patients with anemia may be misleading because HbA1c is calculated in percent by taking reference to hemoglobin measurements. We hypothesized that there may be a relationship between C-peptide index (CPI) and proteinuria in anemic patients with type 2 diabetes mellitus (T2DM). Therefore, the aim of the present study was to investigate the association between C-peptide levels and CPI in anemic patients with T2DM and proteinuria. METHODS The patients over 18 years of age with T2DM whose C-peptide levels were analyzed in Endocrinology and Internal medicine clinics between 2014 and 2018 with normal kidney functions (GFR>60 ml/min) and who do not use any insulin secretagogue oral antidiabetic agent (i.e. sulfonylurea) were enrolled into the study. RESULTS Hemoglobin levels were present in 342 patients with T2DM. Among these 342 cases, 258 (75.4%) were non-anemic whereas 84 (24.6%) were anemic. The median DM duration of the anemic group was statistically significantly higher in T2DM (p=0.003). There was no statistically significant difference found in proteinuria prevalence between non-anemic and anemic patient groups (p=0.690 and p=0.748, respectively). Anemic T2DM cases were corrected according to the age, gender, and duration of DM. C-peptide and CPI levels were not statistically significant to predict proteinuria (p=0.449 and p=0.465, respectively). CONCLUSION The present study sheds light to the association between C-peptide, CPI, and anemic diabetic nephropathy in T2DM patients and indicates that further prospective studies are needed to clarify this issue.
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