Dept. of Information Communication Technology, Islamic University, Kushtia, Bangladesh.
Dept. of Computer Science Engineering, Islamic University, Kushtia, Bangladesh.
PLoS One. 2023 Jul 26;18(7):e0276820. doi: 10.1371/journal.pone.0276820. eCollection 2023.
Obesity is a chronic multifactorial disease characterized by the accumulation of body fat and serves as a gateway to a number of metabolic-related diseases. Epidemiologic data indicate that Obesity is acting as a risk factor for neuro-psychiatric disorders such as schizophrenia, major depression disorder and vice versa. However, how obesity may biologically interact with neurodevelopmental or neurological psychiatric conditions influenced by hereditary, environmental, and other factors is entirely unknown. To address this issue, we have developed a pipeline that integrates bioinformatics and statistical approaches such as transcriptomic analysis to identify differentially expressed genes (DEGs) and molecular mechanisms in patients with psychiatric disorders that are also common in obese patients. Biomarker genes expressed in schizophrenia, major depression, and obesity have been used to demonstrate such relationships depending on the previous research studies. The highly expressed genes identify commonly altered signalling pathways, gene ontology pathways, and gene-disease associations across disorders. The proposed method identified 163 significant genes and 134 significant pathways shared between obesity and schizophrenia. Similarly, there are 247 significant genes and 65 significant pathways that are shared by obesity and major depressive disorder. These genes and pathways increase the likelihood that psychiatric disorders and obesity are pathogenic. Thus, this study may help in the development of a restorative approach that will ameliorate the bidirectional relation between obesity and psychiatric disorder. Finally, we also validated our findings using genome-wide association study (GWAS) and whole-genome sequence (WGS) data from SCZ, MDD, and OBE. We confirmed the likely involvement of four significant genes both in transcriptomic and GWAS/WGS data. Moreover, we have performed co-expression cluster analysis of the transcriptomic data and compared it with the results of transcriptomic differential expression analysis and GWAS/WGS.
肥胖是一种慢性多因素疾病，其特征是体脂肪的积累，是许多代谢相关疾病的诱因。流行病学数据表明，肥胖是精神神经障碍（如精神分裂症、重度抑郁症等）的一个风险因素，反之亦然。然而，肥胖如何在遗传、环境和其他因素影响下与神经发育或神经精神疾病发生生物学相互作用，目前还完全不清楚。为了解决这个问题，我们开发了一个集成生物信息学和统计方法的管道，例如转录组分析，以识别精神障碍患者中差异表达的基因（DEGs）和分子机制，这些患者与肥胖患者也有共同之处。根据之前的研究，使用在精神分裂症、重度抑郁症和肥胖症中表达的生物标志物基因来证明这种关系。高表达的基因可以确定跨障碍的共同改变的信号通路、基因本体途径和基因疾病关联。所提出的方法确定了 163 个肥胖和精神分裂症之间共有的显著基因和 134 个显著途径。同样，肥胖和重度抑郁症之间有 247 个显著基因和 65 个显著途径是共有的。这些基因和途径增加了精神障碍和肥胖是致病因素的可能性。因此，这项研究可能有助于开发一种恢复性方法，以改善肥胖和精神障碍之间的双向关系。最后，我们还使用精神分裂症、重度抑郁症和肥胖症的全基因组关联研究（GWAS）和全基因组序列（WGS）数据来验证我们的发现。我们在转录组和 GWAS/WGS 数据中都证实了四个显著基因的可能参与。此外，我们对转录组数据进行了共表达聚类分析，并将其与转录组差异表达分析和 GWAS/WGS 的结果进行了比较。
