Bioinformatics and Next-Generation Data Analysis for Identification of Genes and Molecular Pathways Involved in Subjects with Diabetes and Obesity

Integrative Analyses of Biomarkers and Pathways in Oxidative Stress-Related Genes for Gestational Diabetes Mellitus

PROBLEM

Oxidative stress (OS) plays a key role in the pathogenesis of gestational diabetes mellitus (GDM), but it was not well understood. We aimed to investigate the biomarkers and underlying mechanisms of OS-related genes in GDM.

METHOD OF STUDY

The GSE103552 and GSE70493 datasets of GDM were acquired from the Gene Expression Omnibus (GEO) database. Then, oxidative stress-related differentially expressed genes (OSDEGs) were screened between GDM and normal samples from these two datasets. Further analyses were conducted by gene ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG), and Gene set enrichment analysis (GSEA) for these OSDEGs. Subsequently, protein-protein interaction (PPI) network analyses of these OSDEGs were carried out to screen the hub genes. Eventually, we used single-sample Gene-set enrichment analysis (ssGSEA) to compare the immune cell infiltration between GDM and normal samples.

RESULTS

We identified 26 OSDEGs. Enrichment analysis suggested that the OSDEGs enriched in OS and diabetes-related pathways. GSEA revealed that these OSDEGs enriched in sensory perception of taste and negative regulation of notch4 signaling pathways. Moreover, PPI analysis showed that 15 OSDEGs were the hub gene in GDM. A total of 14 hub genes were highly expressed in GDM and might be used as diagnosis biomarkers. Moreover, many potential agents might target 10 hub genes for GDM treatment. In addition, immune infiltrate analyses revealed that expression of 14 hub genes was positively correlated to immune infiltrates in GDM.

CONCLUSION

OSDEGs are significant in GDM and may provide potential diagnostic biomarkers and treatment targets for GDM.

Identifying Hub Genes and miRNAs Associated with Alzheimer's Disease: A Bioinformatics Pathway to Novel Therapeutic Strategies

Alzheimer's disease (AD) is a neurodegenerative disorder that mainly affects the elderly population. It is characterized by cognitive impairment and dementia due to abnormal levels of amyloid beta peptide (Aβ) and axonal Tau protein in the brain. However, the complex underlying mechanisms affecting this disease are not yet known, and there is a lack of standardized biomarkers and therapeutic targets. Therefore, in this study, by means of bioinformatics analysis, AD-affected brain tissue was analyzed using the GSE138260 dataset, identifying 612 differentially expressed genes (DEGs). Functional analysis revealed 388 upregulated DEGs associated with sensory perception and 224 downregulated DEGs linked to the regulation and modulation of synaptic processes. Protein-protein interaction network analysis identified 20 hub genes. Furthermore, miRNA target gene networks revealed 1767 miRNAs linked to hub genes, among which hsa-mir-106a-5p, hsa-mir-17-5p, hsa-mir-26a-5p, hsa-mir-27a-3p and hsa-mir-34a-5p were the most relevant. This study presents novel biomarkers and therapeutic targets for AD by analyzing the information obtained with a comprehensive literature review, providing new potential targets to study their role in AD.

Novel Insights Into the Causal Effects and Shared Genetics Between Body Fat and Parkinson Disease

AIMS

Existing observational studies examining the effect of body fat on the risk of Parkinson disease (PD) have yielded inconsistent results. We aimed to investigate this causal relationship at the genetic level.

METHODS

We employed two-sample Mendelian randomization (TSMR) to investigate the causal effects of body fat on PD, with multiple sex-specific body fat measures being involved. We performed Bayesian colocalization analysis and cross-trait meta-analysis to reveal pleiotropic genomic loci shared between body mass index (BMI) and PD. Finally, we used the MAGMA tool to perform tissue enrichment analysis of the genome-wide association study hits of BMI.

RESULTS

TSMR analysis suggests that except waist circumference, higher measures of body fatness are associated with a decreased risk of PD, including BMI (OR: 0.83), body fat percentage (OR: 0.69), body fat mass (OR: 0.77), and hip circumference (OR: 0.83). The observed effects were slightly more pronounced in females than males. Colocalization analysis highlighted two colocalized regions (chromosome 3p25.3 and chromosome 17p12) shared by BMI and PD and pointed to some genes as possible players, including SRGAP3, MTMR14, and ADORA2B. Cross-trait meta-analysis successfully identified 10 novel genomic loci, involving genes of TOX3 and MAP4K4. Tissue enrichment analysis showed that BMI-associated genetic variants were enriched in multiple brain tissues.

CONCLUSIONS

We found that nonabdominal body fatness exerts a robust protective effect against PD. Our colocalization analysis and cross-trait meta-analysis identified pleiotropic genetic variation shared between BMI and PD, providing new clues for understanding the association between body fat and PD.

Screening and identification of key biomarkers associated with endometriosis using bioinformatics and next-generation sequencing data analysis

Background

Endometriosis is a common cause of endometrial-type mucosa outside the uterine cavity with symptoms such as painful periods, chronic pelvic pain, pain with intercourse and infertility. However, the early diagnosis of endometriosis is still restricted. The purpose of this investigation is to identify and validate the key biomarkers of endometriosis.

Methods

Next-generation sequencing dataset GSE243039 was obtained from the Gene Expression Omnibus database, and differentially expressed genes (DEGs) between endometriosis and normal control samples were identified. After screening of DEGs, gene ontology (GO) and REACTOME pathway enrichment analyses were performed. Furthermore, a protein–protein interaction (PPI) network was constructed and modules were analyzed using the Human Integrated Protein–Protein Interaction rEference database and Cytoscape software, and hub genes were identified. Subsequently, a network between miRNAs and hub genes, and network between TFs and hub genes were constructed using the miRNet and NetworkAnalyst tool, and possible key miRNAs and TFs were predicted. Finally, receiver operating characteristic curve analysis was used to validate the hub genes.

Results

A total of 958 DEGs, including 479 upregulated genes and 479 downregulated genes, were screened between endometriosis and normal control samples. GO and REACTOME pathway enrichment analyses of the 958 DEGs showed that they were mainly involved in multicellular organismal process, developmental process, signaling by GPCR and muscle contraction. Further analysis of the PPI network and modules identified 10 hub genes, including vcam1, snca, prkcb, adrb2, foxq1, mdfi, actbl2, prkd1, dapk1 and actc1. Possible target miRNAs, including hsa-mir-3143 and hsa-mir-2110, and target TFs, including tcf3 (transcription factor 3) and clock (clock circadian regulator), were predicted by constructing a miRNA-hub gene regulatory network and TF-hub gene regulatory network.

Conclusions

This investigation used bioinformatics techniques to explore the potential and novel biomarkers. These biomarkers might provide new ideas and methods for the early diagnosis, treatment and monitoring of endometriosis.

High-Fat Diets Fed during Pregnancy Cause Changes to Pancreatic Tissue DNA Methylation and Protein Expression in the Offspring: A Multi-Omics Approach

Maternal obesity, caused by diets rich in fats and sugars during pregnancy, can predispose offspring to metabolic diseases such as diabetes. We hypothesized that obesity during pregnancy leads to increased DNA methylation and reduced protein expression in factors regulating β-cell function and apoptosis. Female C57BL/6J mice were fed a high-fat diet (HFD; 42% fat content; n = 3) or a control diet (CON; 16% fat content; n = 3) for fourteen weeks before and during pregnancy. Offspring were euthanized at 8 weeks and pancreatic tissue was collected. Isolated DNA was analyzed using whole-genome bisulfite sequencing. Protein expression was quantified using LC-MS. No significant differences in body weight were observed between HFD and control pups (p = 0.10). Whole-genome bisulfite sequencing identified 91,703 and 88,415 differentially methylated regions (DMRs) in CON vs. HFD male and female offspring. A total of 34 and 4 proteins were determined to have changes in expression that correlated with changes in DNA methylation in CON vs. HFD males and females, respectively. The majority of these factors were grouped into the metabolic function category via pathway analyses. This study illustrates the complex relationship between epigenetics, diet, and sex-specific responses, therefore offering insights into potential therapeutic targets and areas for further research.

Study on Potential Differentially Expressed Genes in Idiopathic Pulmonary Fibrosis by Bioinformatics and Next-Generation Sequencing Data Analysis

Idiopathic pulmonary fibrosis (IPF) is a chronic progressive lung disease with reduced quality of life and earlier mortality, but its pathogenesis and key genes are still unclear. In this investigation, bioinformatics was used to deeply analyze the pathogenesis of IPF and related key genes, so as to investigate the potential molecular pathogenesis of IPF and provide guidance for clinical treatment. Next-generation sequencing dataset GSE213001 was obtained from Gene Expression Omnibus (GEO), and the differentially expressed genes (DEGs) were identified between IPF and normal control group. The DEGs between IPF and normal control group were screened with the DESeq2 package of R language. The Gene Ontology (GO) and REACTOME pathway enrichment analyses of the DEGs were performed. Using the g:Profiler, the function and pathway enrichment analyses of DEGs were performed. Then, a protein-protein interaction (PPI) network was constructed via the Integrated Interactions Database (IID) database. Cytoscape with Network Analyzer was used to identify the hub genes. miRNet and NetworkAnalyst databaseswereused to construct the targeted microRNAs (miRNAs), transcription factors (TFs), and small drug molecules. Finally, receiver operating characteristic (ROC) curve analysis was used to validate the hub genes. A total of 958 DEGs were screened out in this study, including 479 up regulated genes and 479 down regulated genes. Most of the DEGs were significantly enriched in response to stimulus, GPCR ligand binding, microtubule-based process, and defective GALNT3 causes HFTC. In combination with the results of the PPI network, miRNA-hub gene regulatory network and TF-hub gene regulatory network, hub genes including LRRK2, BMI1, EBP, MNDA, KBTBD7, KRT15, OTX1, TEKT4, SPAG8, and EFHC2 were selected. Cyclothiazide and rotigotinethe are predicted small drug molecules for IPF treatment. Our findings will contribute to identification of potential biomarkers and novel strategies for the treatment of IPF, and provide a novel strategy for clinical therapy.

Decoding the genetic relationship between Alzheimer's disease and type 2 diabetes: potential risk variants and future direction for North Africa

Introduction

Alzheimer's disease (AD) and Type 2 diabetes (T2D) are both age-associated diseases. Identification of shared genes could help develop early diagnosis and preventive strategies. Although genetic background plays a crucial role in these diseases, we noticed an underrepresentation tendency of North African populations in omics studies.

Materials and methods

First, we conducted a comprehensive review of genes and pathways shared between T2D and AD through PubMed. Then, the function of the identified genes and variants was investigated using annotation tools including PolyPhen2, RegulomeDB, and miRdSNP. Pathways enrichment analyses were performed with g:Profiler and EnrichmentMap. Next, we analyzed variant distributions in 16 worldwide populations using PLINK2, R, and STRUCTURE software. Finally, we performed an inter-ethnic comparison based on the minor allele frequency of T2D-AD common variants.

Results

A total of 59 eligible papers were included in our study. We found 231 variants and 363 genes shared between T2D and AD. Variant annotation revealed six single nucleotide polymorphisms (SNP) with a high pathogenic score, three SNPs with regulatory effects on the brain, and six SNPs with potential effects on miRNA-binding sites. The miRNAs affected were implicated in T2D, insulin signaling pathways, and AD. Moreover, replicated genes were significantly enriched in pathways related to plasma protein binding, positive regulation of amyloid fibril deposition, microglia activation, and cholesterol metabolism. Multidimensional screening performed based on the 363 shared genes showed that main North African populations are clustered together and are divergent from other worldwide populations. Interestingly, our results showed that 49 SNP associated with T2D and AD were present in North African populations. Among them, 11 variants located in DNM3, CFH, PPARG, ROHA, AGER, CLU, BDNF1, CST9, and PLCG1 genes display significant differences in risk allele frequencies between North African and other populations.

Conclusion

Our study highlighted the complexity and the unique molecular architecture of North African populations regarding T2D-AD shared genes. In conclusion, we emphasize the importance of T2D-AD shared genes and ethnicity-specific investigation studies for a better understanding of the link behind these diseases and to develop accurate diagnoses using personalized genetic biomarkers.