Steroidogenic Acute Regulatory Protein (StAR) Overexpression Reduces Inflammation and Insulin Resistance in Obese Mice

Qiu, Yanyan; Sui, Xianxian; Cao, Shengxuan; Li, Xiaobo; Ning, Yanxia; Wang, Songmei; Yin, Lu; Zhi, Xiuling

doi:10.1002/jcb.26046

Cited by 10 publications

(6 citation statements)

References 53 publications

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“…The worst hit enzymes were CYP17A1 which catalyses the synthesis of dehydroepiandrosterone (DHEA) from pregnenolone, as well as 3β‐HSD and 17β‐HSD that catalyse the downstream reactions leading to the synthesis of testosterone from DHEA. Earlier study corroborated our findings that the transcript levels of 17β‐HSD and 3β‐HSD are decreased in obese rats 66 …”

Section: Discussionsupporting

confidence: 91%

Orlistat attenuates obesity‐induced decline in steroidogenesis and spermatogenesis by up‐regulating steroidogenic genes

et al. 2020

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Background Steroidogenesis decline is reported to be one of the mechanisms associated with obesity‐induced male factor subfertility/infertility. Objectives We explored the possible preventive/therapeutic effects of orlistat (a medication prescribed for weight loss) on obesity‐induced steroidogenesis and spermatogenesis decline. Materials and methods Twenty‐four adult male Sprague Dawley rats weighing 250‐300 g were randomized into four groups (n = 6/group), namely; normal control, high‐fat diet, high‐fat diet plus orlistat preventive group and high‐fat diet plus orlistat treatment group. Orlistat (10 mg/kg/b.w./d suspended in distilled water) was either concurrently administered with high‐fat diet for 12 weeks (high‐fat diet plus orlistat preventive group) or administered from week 7‐12 post‐ high‐fat diet feeding (high‐fat diet plus orlistat treatment group). Thereafter, serum, testes and epididymis were collected for analyses. Results Obesity increased serum leptin and decreased adiponectin levels, decreased serum and intra‐testicular levels of follicle stimulating hormone, luteinising hormone and testosterone, sperm count, motility, viability, normal morphology and epididymal antioxidants, but increased epididymal malondialdehyde level and sperm nDNA fragmentation. Testicular mRNA transcript levels of androgen receptor, luteinizing hormone receptor, steroidogenic acute regulatory protein, cytochrome P450 enzyme (CYP11A1), 3β‐hydroxysteroid dehydrogenase and 17β‐hydroxysteroid dehydrogenase were significantly decreased in the testes of the high‐fat diet group. Further, the levels of steroidogenic acute regulatory protein protein and enzymatic activities of CYP11A1, 3β‐hydroxysteroid dehydrogenase and 17β‐hydroxysteroid dehydrogenase were also significantly decreased in the testes of the high‐fat diet group. Treatment with orlistat significantly decreased leptin and increased adiponectin levels, improved sperm parameters, decreased sperm DNA fragmentation, increased the levels of steroidogenic hormones, proteins and associated genes in high‐fat diet‐induced obese male rats, with the preventive group (high‐fat diet plus orlistat preventive group) having better results relative to the treatment group (high‐fat diet plus orlistat treatment group). Discussion and Conclusion Orlistat attenuated impaired spermatogenesis and steroidogenesis decline by up‐regulating steroidogenic genes. This may not be unconnected to its significant effect in lowering serum leptin levels, since the hormone is known to dampen fertility potential. Therefore, orlistat may improve fertility potential in overweight/obese men.

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Section: Discussionsupporting

confidence: 91%

Orlistat attenuates obesity‐induced decline in steroidogenesis and spermatogenesis by up‐regulating steroidogenic genes

et al. 2020

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“…Previous studies have shown that genes include KCP (kielin cysteine rich BMP regulator) 116 , NOG (noggin) 117 , COL6A3 118 , BTG2 119 , RPS6 120 , KLF15 121 , KLF3 122 , ZFP36 123 , ETV5 124 , TLE3 125 , NNMT (nicotinamide N-methyltransferase) 126 , WDTC1 127 , ZFHX3 128 , SIAH2 129 , MBOAT7 130 , RUNX1T1 131 , MAPK4 132 , KLF9 133 , SELENBP1 134 , HELZ2 135 , ELK1 136 , SERTAD2 137 , CRTC3 138 , ABCB11 139 , TACR1 140 , SLC22A11 141 , PER3 142 , P2RX5 143 , MFAP5 144 , FGL1 145 , OLFM4 146 , NTN1 147 , ESR1 148 , ABCB1 149 , VAV3 150 and LAMB3 151 can be used as clinical prognostic biomarkers for obesity. Genes include STAR (steroidogenic acute regulatory protein) 152 , IL1RN 153 , AQP5 154 , EGR1 155 , SFTPD (surfactant protein D) 156 , KLF10 157 , PODXL (podocalyxin like) 158 , FOXN3 159 , IL6R 160 , PBX1 161 , APOD (apolipoprotein D) 162 , ACVR2B 163 , CD34 164 , INSR (insulin receptor) 165 , APOA5 166 , STAR (steroidogenic acute regulatory protein) 167 , PDK4 168 , GLS (glutaminase) 169 , FKBP5 170 , SLC6A15 …”

Section: Discussionmentioning

confidence: 99%

Identification of candidate biomarkers and pathways associated with type 1 diabetes mellitus using bioinformatics analysis

Pujar¹,

Vastrad²,

Kavatagimath

et al. 2022

Sci Rep

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Type 1 diabetes mellitus (T1DM) is a metabolic disorder for which the underlying molecular mechanisms remain largely unclear. This investigation aimed to elucidate essential candidate genes and pathways in T1DM by integrated bioinformatics analysis. In this study, differentially expressed genes (DEGs) were analyzed using DESeq2 of R package from GSE162689 of the Gene Expression Omnibus (GEO). Gene ontology (GO) enrichment analysis, REACTOME pathway enrichment analysis, and construction and analysis of protein–protein interaction (PPI) network, modules, miRNA-hub gene regulatory network and TF-hub gene regulatory network, and validation of hub genes were performed. A total of 952 DEGs (477 up regulated and 475 down regulated genes) were identified in T1DM. GO and REACTOME enrichment result results showed that DEGs mainly enriched in multicellular organism development, detection of stimulus, diseases of signal transduction by growth factor receptors and second messengers, and olfactory signaling pathway. The top hub genes such as MYC, EGFR, LNX1, YBX1, HSP90AA1, ESR1, FN1, TK1, ANLN and SMAD9 were screened out as the critical genes among the DEGs from the PPI network, modules, miRNA-hub gene regulatory network and TF-hub gene regulatory network. Receiver operating characteristic curve (ROC) analysis confirmed that these genes were significantly associated with T1DM. In conclusion, the identified DEGs, particularly the hub genes, strengthen the understanding of the advancement and progression of T1DM, and certain genes might be used as candidate target molecules to diagnose, monitor and treat T1DM.

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“…Recent studies found that KCP (kielin cysteine rich BMP regulator) [117], NOG (noggin) [118], COL6A3 [119], BTG2 [120], RPS6 [121], KLF15 [122], KLF3 [123], ZFP36 [124], ETV5 [125], TLE3 [126], NNMT (nicotinamide N-methyltransferase) [127], WDTC1 [128], ZFHX3 [129], SIAH2 [130], MBOAT7 [131], RUNX1T1 [132], MAPK4 [133], KLF9 [134], SELENBP1 [135], HELZ2 [136], ELK1 [137], SERTAD2 [138], CRTC3 [139], ABCB11 [140], TACR1 [141], SLC22A11 [142], PER3 [143], P2RX5 [144], MFAP5 [145], FGL1 [146], OLFM4 [147], NTN1 [148], ESR1 [149], ABCB1 [150], VAV3 [151] and LAMB3 [152] plays an important role in the occurrence and development of obesity, but these genes might be novel target for T1DM. STAR (steroidogenic acute regulatory protein) [153], IL1RN [154], AQP5 [155], EGR1 [156], SFTPD (surfactant protein D) [157], KLF10 [158], PODXL (podocalyxin like) [159], FOXN3 [160], IL6R [161], PBX1 [162], APOD (apolipoprotein D) [163], ACVR2B [164], CD34 [165], INSR (insulin receptor) [166], APOA5 [167], STAR (steroidogenic acute regulatory protein) [168], PDK4 [169], GLS (glutaminase) [170], FKBP5 [171], SLC6A15 [172], MT2A [173], SLC38A4 [174], AQP7 [175], ABHD15 [176], ABCA1 [177], ZNRF1 [178], PPP1R3B [179], MAOA (monoamine oxidase A) [180], UBE2E2 [181], RNASEK (ribonuclease K) [182], PREX1 [183], DGKG (diacylglycerol kinase gamma) [184], POSTN (periostin) [185], COMP (cartilage oligomeric matrix protein) [186], GAP43 […”

Section: Discussionmentioning

confidence: 99%

Identification of candidate biomarkers and pathways associated with type 1 diabetes mellitus using bioinformatics analysis

Bm¹,

Cm²

2021

Preprint

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Type 1 diabetes mellitus (T1DM) is a metabolic disorder for which the underlying molecular mechanisms remain largely unclear. This investigation aimed to elucidate essential candidate genes and pathways in T1DM by integrated bioinformatics analysis. In this study, differentially expressed genes (DEGs) were analyzed using DESeq2 of R package from GSE162689 of the Gene Expression Omnibus (GEO). Gene ontology (GO) enrichment analysis, REACTOME pathway enrichment analysis, and construction and analysis of protein-protein interaction (PPI) network, modules, miRNA-hub gene regulatory network and TF-hub gene regulatory network, and validation of hub genes were then performed. A total of 952 DEGs (477 up regulated and 475 down regulated genes) were identified in T1DM. GO and REACTOME enrichment result results showed that DEGs mainly enriched in multicellular organism development, detection of stimulus, diseases of signal transduction by growth factor receptors and second messengers, and olfactory signaling pathway. The top hub genes such as MYC, EGFR, LNX1, YBX1, HSP90AA1, ESR1, FN1, TK1, ANLN and SMAD9 were screened out as the critical genes among the DEGs from the PPI network, modules, miRNA-hub gene regulatory network and TF-hub gene regulatory network. Receiver operating characteristic curve (ROC) analysis and RT-PCR confirmed that these genes were significantly associated with T1DM. In conclusion, the identified DEGs, particularly the hub genes, strengthen the understanding of the advancement and progression of T1DM, and certain genes might be used as candidate target molecules to diagnose, monitor and treat T1DM.

show abstract

Steroidogenic Acute Regulatory Protein (StAR) Overexpression Reduces Inflammation and Insulin Resistance in Obese Mice

Cited by 10 publications

References 53 publications

Orlistat attenuates obesity‐induced decline in steroidogenesis and spermatogenesis by up‐regulating steroidogenic genes

Orlistat attenuates obesity‐induced decline in steroidogenesis and spermatogenesis by up‐regulating steroidogenic genes

Identification of candidate biomarkers and pathways associated with type 1 diabetes mellitus using bioinformatics analysis

Identification of candidate biomarkers and pathways associated with type 1 diabetes mellitus using bioinformatics analysis

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