Network-based identification of genetic factors in ageing, lifestyle and type 2 diabetes that influence to the progression of Alzheimer's disease

Seven Hub Genes Associated with Huntington's Disease and Diagnostic and Therapeutic Potentials Identified by Computational Biology

Huntington's disease (HD) is characterized by progressive motor dysfunction and cognitive decline. Early diagnosis and new therapeutic targets are essential for effective interventions. We performed integrative analyses of mRNA profiles from three microarrays and one RNA-seq dataset from the Gene Expression Omnibus database. The datasets included were GSE8762, GSE24250, GSE45516, and GSE64810. Data pre-processing included background correction, normalization, log2 transformation, probe-to-gene symbol mapping, and differential expression analysis. We identified 80 differentially expressed genes (DEGs) based on a significance threshold (p < 0.05) and absolute log fold change (logFC) >0.65. Additionally, we conducted Gene Ontology (GO) and pathway analyses of the identified genes. Protein-protein interactions among DEGs revealed a network from which seven hub genes (VIM, COL1A1, COL3A1, COL1A2, DCN, CXCR2, and S100A9) were identified using the cytoHubba plugin in Cytoscape software. Two top DEGs, IGHG1 (up-regulated) and PITX1 (up-regulated), also hold potential as therapeutic targets. Insofar as biological contextualization of the findings is concerned, the top enriched GO terms were skeletal system development, blood vessel development, and vasculature development. Molecular function terms highlighted signaling receptor binding, extracellular matrix structural constituent, and platelet-derived growth factor binding. Notably, the significant KEGG pathways included amoebiasis, the AGE-RAGE signaling pathway in diabetic complications, and the relaxin signaling pathway. In conclusion, the present computational biology integrative analyses of multiple datasets discovered new DEGs and seven hub genes, shedding light on molecular mechanisms of HD. These findings call for translational clinical omics research and may potentially lead to future precision medicine interventions and novel diagnostic biomarkers and therapeutic targets.

The expression of insulin signaling and N-methyl-D-aspartate receptor genes in areas of gray matter atrophy is associated with cognitive function in type 2 diabetes

Type 2 diabetes (T2DM) is associated with brain abnormalities and cognitive dysfunction, including increased risk for Alzheimer's disease. However, the mechanisms of T2DM-related dementia remain poorly understood. We obtained retrospective data from the Mayo Clinic Study of Aging for 271 individuals with T2DM and 542 demographically matched non-diabetic controls (age 51-89, 62% male). We identified regions of significant gray matter atrophy in the T2DM group and then determined which genes were significantly expressed in these brain regions using imaging transcriptomics. We selected 15 candidate genes involved in insulin signaling, lipid metabolism, amyloid processing, N-methyl-D-aspartate-mediated neurotransmission, and calcium signaling. The T2DM group demonstrated significant gray matter atrophy in regions of the default mode, frontal-parietal, and sensorimotor networks (p < 0.05 cluster threshold corrected for false discovery rate, FDR). IRS1, AKT1, PPARG, PRKAG2, and GRIN2B genes were significantly expressed in these same regions (R2 > 0.10, p < 0.03, FDR corrected). Bayesian network analysis indicated significant directional paths among all 5 genes as well as the Clinical Dementia Rating score. Directional paths among genes were significantly altered in the T2DM group (Structural Hamming Distance = 12, p = 0.004), with PPARG expression becoming more important in the context of T2DM-related pathophysiology. Alterations of brain transcriptome patterns occurred in the absence of significant cognitive deficit or amyloid accumulation, potentially representing an early biomarker of T2DM-related dementia.

Exploring gene regulatory interaction networks and predicting therapeutic molecules for hypopharyngeal cancer and EGFR-mutated lung adenocarcinoma

Hypopharyngeal cancer is a disease that is associated with EGFR-mutated lung adenocarcinoma. Here we utilized a bioinformatics approach to identify genetic commonalities between these two diseases. To this end, we examined microarray datasets from GEO (Gene Expression Omnibus) to identify differentially expressed genes, common genes, and hub genes between the selected two diseases. Our analyses identified potential therapeutic molecules for the selected diseases based on 10 hub genes with the highest interactions according to the degree topology method and the maximum clique centrality (MCC). These therapeutic molecules may have the potential for simultaneous treatment of these diseases.

Proteomics and its application in the research of acupuncture: An updated review

As a complementary and alternative therapy, acupuncture is widely used in the prevention and treatment of various diseases. However, the understanding of the mechanism of acupuncture effects is still limited due to the lack of systematic biological validation. Notably, proteomics technologies in the field of acupuncture are rapidly evolving, and these advances are greatly contributing to the research of acupuncture. In this study, we review the progress of proteomics research in analyzing the molecular mechanisms of acupuncture for neurological disorders, pain, circulatory disorders, digestive disorders, and other diseases, with an in-depth discussion around acupoint prescription and acupuncture manipulation modalities. The study found that proteomics has great potential in understanding the mechanisms of acupuncture. This study will help explore the mechanisms of acupuncture from a proteomic perspective and provide information to support future clinical decisions.

Identification of disease-specific bio-markers through network-based analysis of gene co-expression: A case study on Alzheimer's disease

Finding biomarker genes for complex diseases attracts persistent attention due to its application in clinics. In this paper, we propose a network-based method to obtain a set of biomarker genes. The key idea is to construct a gene co-expression network among sensitive genes and cluster the genes into different modules. For each module, we can identify its representative, i.e., the gene with the largest connectivity and the smallest average shortest path length to other genes within the module. We believe these representative genes could serve as a new set of potential biomarkers for diseases. As a typical example, we investigated Alzheimer's disease, obtaining a total of 16 potential representative genes, three of which belong to the non-transcriptome. A total of 11 out of these genes are found in literature from different perspectives and methods. The incipient groups were classified into two different subtypes using machine learning algorithms. We subjected the two subtypes to Gene Ontology analysis and Kyoto Encyclopedia of Genes and Genomes analysis with healthy groups and moderate groups, respectively. The two sub-type groups were involved in two different biological processes, demonstrating the validity of this approach. This method is disease-specific and independent; hence, it can be extended to classify other kinds of complex diseases.

Novel diagnostic biomarkers related to immune infiltration in Parkinson's disease by bioinformatics analysis

Background

Parkinson's disease (PD) is Pengfei Zhang Liwen Zhao Pengfei Zhang Liwen Zhao a common neurological disorder involving a complex relationship with immune infiltration. Therefore, we aimed to explore PD immune infiltration patterns and identify novel immune-related diagnostic biomarkers.

Materials and methods

Three substantia nigra expression microarray datasets were integrated with elimination of batch effects. Differentially expressed genes (DEGs) were screened using the "limma" package, and functional enrichment was analyzed. Weighted gene co-expression network analysis (WGCNA) was performed to explore the key module most significantly associated with PD; the intersection of DEGs and the key module in WGCNA were considered common genes (CGs). The CG protein-protein interaction (PPI) network was constructed to identify candidate hub genes by cytoscape. Candidate hub genes were verified by another two datasets. Receiver operating characteristic curve analysis was used to evaluate the hub gene diagnostic ability, with further gene set enrichment analysis (GSEA). The immune infiltration level was evaluated by ssGSEA and CIBERSORT methods. Spearman correlation analysis was used to evaluate the hub genes association with immune cells. Finally, a nomogram model and microRNA-TF-mRNA network were constructed based on immune-related biomarkers.

Results

A total of 263 CGs were identified by the intersection of 319 DEGs and 1539 genes in the key turquoise module. Eleven candidate hub genes were screened by the R package "UpSet." We verified the candidate hub genes based on two validation sets and identified six (SYT1, NEFM, NEFL, SNAP25, GAP43, and GRIA1) that distinguish the PD group from healthy controls. Both CIBERSORT and ssGSEA revealed a significantly increased proportion of neutrophils in the PD group. Correlation between immune cells and hub genes showed SYT1, NEFM, GAP43, and GRIA1 to be significantly related to immune cells. Moreover, the microRNA-TFs-mRNA network revealed that the microRNA-92a family targets all four immune-related genes in PD pathogenesis. Finally, a nomogram exhibited a reliable capability of predicting PD based on the four immune-related genes (AUC = 0.905).

Conclusion

By affecting immune infiltration, SYT1, NEFM, GAP43, and GRIA1, which are regulated by the microRNA-92a family, were identified as diagnostic biomarkers of PD. The correlation of these four genes with neutrophils and the microRNA-92a family in PD needs further investigation.

Identification of Comorbidities, Genomic Associations, and Molecular Mechanisms for COVID-19 Using Bioinformatics Approaches

Several studies have been done to identify comorbidities of COVID-19. In this work, we developed an analytical bioinformatics framework to reveal COVID-19 comorbidities, their genomic associations, and molecular mechanisms accomplishing transcriptomic analyses of the RNA-seq datasets provided by the Gene Expression Omnibus (GEO) database, where normal and infected tissues were evaluated. Using the framework, we identified 27 COVID-19 correlated diseases out of 7,092 collected diseases. Analyzing clinical and epidemiological research, we noticed that our identified 27 diseases are associated with COVID-19, where hypertension, diabetes, obesity, and lung cancer are observed several times in COVID-19 patients. Therefore, we selected the above four diseases and performed assorted analyses to demonstrate the association between COVID-19 and hypertension, diabetes, obesity, and lung cancer as comorbidities. We investigated genomic associations with the cross-comparative analysis and Jaccard's similarity index, identifying shared differentially expressed genes (DEGs) and linking DEGs of COVID-19 and the comorbidities, in which we identified hypertension as the most associated illness. We also revealed molecular mechanisms by identifying statistically significant ten pathways and ten ontologies. Moreover, to understand cellular physiology, we did protein-protein interaction (PPI) analyses among the comorbidities and COVID-19. We also used the degree centrality method and identified ten biomarker hub proteins (IL1B, CXCL8, FN1, MMP9, CXCL10, IL1A, IRF7, VWF, CXCL9, and ISG15) that associate COVID-19 with the comorbidities. Finally, we validated our findings by searching the published literature. Thus, our analytical approach elicited interconnections between COVID-19 and the aforementioned comorbidities in terms of remarkable DEGs, pathways, ontologies, PPI, and biomarker hub proteins.

Revealing genetic links of Type 2 diabetes that lead to the development of Alzheimer's disease

Background

A factor leading to Alzheimer's Disease (AD), portrayed by peripheral insulin resistance, is Type 2 diabetes mellitus (T2D). The likelihood of T2D cases would be at boosted danger in alternating AD cases has severe social consequences. Several genes have been detected via gene expression profiling or different techniques; despite the consideration of the utility of numerous of these genes stays insufficient.

Methods

This project is designed to uncover the mutual genomics motifs between AD and T2D via non-negative matrix factorization (NMF) of differentially expressed genes (DEGs) of T2D Mellitus of human cortical neurons of the neurovascular unit gene expression data. A rank factorization value is calculated by employing the combination of the NMF model with the unit invariant knee (UIK) point method. The metagenes are further determined by remarking the enriched Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway and gene ontology (GO) enrichment tools. In this study, the most highly expressed genes of metagenes are subjected to protein-protein interaction (PPI) network study to discover the most significant biomarkers of T2D Mellitus in the ageing brain.

Results

We screened the most important shared genes (CDKN1A, COL22A1, EIF4A, GFAP, SLC1A1, and VIM) and essential human molecular pathways that motivate these diseases. The study aimed to validate the most significant hub genes using network-based methods which detected the corresponding relationship between AD and T2D.

Conclusions

Using in silico tools, the computational pipeline has broadly examined transformed pathways and discovered promising biomarkers and drug targets. We validated the most significant hub genes using network-based methods which detected the corresponding relationship between AD and T2D. These consequences on brain cells hypothetically reserve to diabetic Alzheimer's so-called type 3 diabetes (T3D) and may offer promising methodologies for curative intrusion.

Identification of Novel Noninvasive Diagnostics Biomarkers in the Parkinson’s Diseases and Improving the Disease Classification Using Support Vector Machine

Background

Parkinson's disease (PD) is a neurological disorder that is marked by the deficit of neurons in the midbrain that changes motor and cognitive function. In the substantia nigra, the selective demise of dopamine-producing neurons was the main cause of this disease. The purpose of this research was to discover genes involved in PD development.

Methods

In this study, the microarray dataset (GSE22491) provided by GEO was used for further analysis. The Limma package under R software was used to examine and assess gene expression and identify DEGs. The DAVID online tool was used to accomplish GO enrichment analysis and KEGG pathway for DEGs. Furthermore, the PPI network of these DEGs was depicted using the STRING database and analyzed through the Cytoscape to identify hub genes. Support vector machine (SVM) classifier was subsequently employed to predict the accuracy of genes.

Result

PPI network consisted of 264 nodes as well as 502 edges was generated using the DEGs recognized from the Limma package under the R software. Moreover, three genes were identified as hubs: GNB5, GNG11, and ELANE. By using 3-gene combination, SVM found that prediction accuracy of 88% can be achieved.

Conclusion

According to the findings of the study, the 3 hub genes GNB5, GNG11, and ELANE may be used as PD detection biomarkers. Moreover, the results obtained from SVM with high accuracy can be considered as PD biomarkers in further investigations.

Machine learning models for classification and identification of significant attributes to detect type 2 diabetes

Type 2 Diabetes (T2D) is a chronic disease characterized by abnormally high blood glucose levels due to insulin resistance and reduced pancreatic insulin production. The challenge of this work is to identify T2D-associated features that can distinguish T2D sub-types for prognosis and treatment purposes. We thus employed machine learning (ML) techniques to categorize T2D patients using data from the Pima Indian Diabetes Dataset from the Kaggle ML repository. After data preprocessing, several feature selection techniques were used to extract feature subsets, and a range of classification techniques were used to analyze these. We then compared the derived classification results to identify the best classifiers by considering accuracy, kappa statistics, area under the receiver operating characteristic (AUROC), sensitivity, specificity, and logarithmic loss (logloss). To evaluate the performance of different classifiers, we investigated their outcomes using the summary statistics with a resampling distribution. Therefore, Generalized Boosted Regression modeling showed the highest accuracy (90.91%), followed by kappa statistics (78.77%) and specificity (85.19%). In addition, Sparse Distance Weighted Discrimination, Generalized Additive Model using LOESS and Boosted Generalized Additive Models also gave the maximum sensitivity (100%), highest AUROC (95.26%) and lowest logarithmic loss (30.98%) respectively. Notably, the Generalized Additive Model using LOESS was the top-ranked algorithm according to non-parametric Friedman testing. Of the features identified by these machine learning models, glucose levels, body mass index, diabetes pedigree function, and age were consistently identified as the best and most frequently accurate outcome predictors. These results indicate the utility of ML methods in constructing improved prediction models for T2D and successfully identified outcome predictors for this Pima Indian population.

Detection of molecular signatures and pathways shared by Alzheimer's disease and type 2 diabetes

Alzheimer's disease (AD) and type 2 diabetes (T2D) are common in the general elderly population, conferring heavy individual, social, and economic stresses on families and society. Accumulating evidence indicates T2D to be a risk factor for AD. However, the underlying mechanisms for this association are largely unknown. This study aimed to identify the shared molecular signatures between AD and T2D through integrated analysis of temporal cortex gene expression data. Gene Ontology (GO) and pathway enrichment analysis, protein over-representation analysis, protein-protein interaction, DEG-transcription factor interactions, DEG-microRNA interactions, protein-drug interactions, gene-disease association analysis, and protein subcellular localization analysis of the common DEGs were performed. We identified 16 common DEGs between the two datasets, which were mainly enriched in the biological processes of apoptosis, autophagy, inflammation, and hemostasis. We also identified five hub proteins encoded by the DEGs, five central regulatory transcription factors, and six microRNAs. Protein-drug interaction analysis showed C1QB to be associated with different drugs. Gene-disease association analysis revealed that hub genes, SFN and ITGB2, were actively engaged in other diseases. Collectively, these findings provide new insights into shared molecular mechanisms between AD and T2D and provide novel candidate targets for therapeutic intervention.

Network based systems biology approach to identify diseasome and comorbidity associations of Systemic Sclerosis with cancers

Systemic Sclerosis (SSc) is an autoimmune disease associated with changes in the skin's structure in which the immune system attacks the body. A recent meta-analysis has reported a high incidence of cancer prognosis including lung cancer (LC), leukemia (LK), and lymphoma (LP) in patients with SSc as comorbidity but its underlying mechanistic details are yet to be revealed. To address this research gap, bioinformatics methodologies were developed to explore the comorbidity interactions between a pair of diseases. Firstly, appropriate gene expression datasets from different repositories on SSc and its comorbidities were collected. Then the interconnection between SSc and its cancer comorbidities was identified by applying the developed pipelines. The pipeline was designed as a generic workflow to demonstrate a premise comorbid condition that integrate regarding gene expression data, tissue/organ meta-data, Gene Ontology (GO), Molecular pathways, and other online resources, and analyze them with Gene Set Enrichment Analysis (GSEA), Pathway enrichment and Semantic Similarity (SS). The pipeline was implemented in R and can be accessed through our Github repository: https://github.com/hiddenntreasure/comorbidity. Our result suggests that SSc and its cancer comorbidities share differentially expressed genes, functional terms (gene ontology), and pathways. The findings have led to a better understanding of disease pathways and our developed methodologies may be applied to any set of diseases for finding any association between them. This research may be used by physicians, researchers, biologists, and others.

Identifying molecular insight of synergistic complexities for SARS-CoV-2 infection with pre-existing type 2 diabetes

The ongoing COVID-19 outbreak, caused by SARS-CoV-2, has posed a massive threat to global public health, especially to people with underlying health conditions. Type 2 diabetes (T2D) is lethal comorbidity of COVID-19. However, its pathogenetic link remains unclear. This research aims to determine the genetic factors and processes contributing to the synergistic severity of SARS-CoV-2 infection among T2D patients through bioinformatics approaches. We analyzed two sets of transcriptomic data of SARS-CoV-2 infection obtained from lung epithelium cells and PBMCs, and two sets of T2D data from pancreatic islet cells and PBMCs to identify the associated differentially expressed genes (DEGs) followed by their functional enrichment analyses in terms of protein-protein interaction (PPI) to detect hub-proteins and associated comorbidities, transcription factors (TFs), microRNAs (miRNAs) as well as the potential drug candidates. In PPI analysis, four potential hub-proteins (i.e., BIRC3, C3, MME, and IL1B) were identified among 25 DEGs shared between the disease pair. Enrichment analyses using the mutually overlapped DEGs revealed the most prevalent GO and cell signalling pathways, including TNF signalling, cytokine-cytokine receptor interaction, and IL-17 signalling, which are related to cytokine activities. Furthermore, as significant TFs, we identified IRF1, KLF11, FOSL1, and CREB3L1 while miRNAs including miR-1-3p, 34a-5p, 16–5p, 155–5p, 20a-5p, and let-7b-5p were found to be noteworthy. The findings illustrated the significant association between COVID-19 and T2D at the molecular level. These genetic determinants can further be explored for their specific roles in disease progression and therapeutic intervention, while significant pathways can also be studied as molecular checkpoints. Finally, the identified drug candidates may be evaluated for their potency to minimize the severity of COVID-19 patients with pre-existing T2D.

System biology and bioinformatics pipeline to identify comorbidities risk association: Neurodegenerative disorder case study

Alzheimer's disease (AD) is the commonest progressive neurodegenerative condition in humans, and is currently incurable. A wide spectrum of comorbidities, including other neurodegenerative diseases, are frequently associated with AD. How AD interacts with those comorbidities can be examined by analysing gene expression patterns in affected tissues using bioinformatics tools. We surveyed public data repositories for available gene expression data on tissue from AD subjects and from people affected by neurodegenerative diseases that are often found as comorbidities with AD. We then utilized large set of gene expression data, cell-related data and other public resources through an analytical process to identify functional disease links. This process incorporated gene set enrichment analysis and utilized semantic similarity to give proximity measures. We identified genes with abnormal expressions that were common to AD and its comorbidities, as well as shared gene ontology terms and molecular pathways. Our methodological pipeline was implemented in the R platform as an open-source package and available at the following link: https://github.com/unchowdhury/AD_comorbidity. The pipeline was thus able to identify factors and pathways that may constitute functional links between AD and these common comorbidities by which they affect each others development and progression. This pipeline can also be useful to identify key pathological factors and therapeutic targets for other diseases and disease interactions.

Identification of biomarkers and pathways for the SARS-CoV-2 infections that make complexities in pulmonary arterial hypertension patients

This study aimed to identify significant gene expression profiles of the human lung epithelial cells caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infections. We performed a comparative genomic analysis to show genomic observations between SARS-CoV and SARS-CoV-2. A phylogenetic tree has been carried for genomic analysis that confirmed the genomic variance between SARS-CoV and SARS-CoV-2. Transcriptomic analyses have been performed for SARS-CoV-2 infection responses and pulmonary arterial hypertension (PAH) patients' lungs as a number of patients have been identified who faced PAH after being diagnosed with coronavirus disease 2019 (COVID-19). Gene expression profiling showed significant expression levels for SARS-CoV-2 infection responses to human lung epithelial cells and PAH lungs as well. Differentially expressed genes identification and integration showed concordant genes (SAA2, S100A9, S100A8, SAA1, S100A12 and EDN1) for both SARS-CoV-2 and PAH samples, including S100A9 and S100A8 genes that showed significant interaction in the protein-protein interactions network. Extensive analyses of gene ontology and signaling pathways identification provided evidence of inflammatory responses regarding SARS-CoV-2 infections. The altered signaling and ontology pathways that have emerged from this research may influence the development of effective drugs, especially for the people with preexisting conditions. Identification of regulatory biomolecules revealed the presence of active promoter gene of SARS-CoV-2 in Transferrin-micro Ribonucleic acid (TF-miRNA) co-regulatory network. Predictive drug analyses provided concordant drug compounds that are associated with SARS-CoV-2 infection responses and PAH lung samples, and these compounds showed significant immune response against the RNA viruses like SARS-CoV-2, which is beneficial in therapeutic development in the COVID-19 pandemic.

Detection of molecular signatures and pathways shared in inflammatory bowel disease and colorectal cancer: A bioinformatics and systems biology approach

Emerging evidence indicates IBD is a risk factor for the increasing incidence of colorectal cancer (CRC) development. We used a system biology approach to identify common molecular signatures and pathways that interact between IBD and CRC and the indispensable pathological mechanisms. First, we identified 177 common differentially expressed genes (DEGs) between IBD and CRC. Gene set enrichment, protein-protein, DEGs-transcription factors, DEGs-microRNAs, protein-drug interaction, gene-disease association, Gene Ontology, pathway enrichment analyses were conducted to these common genes. The inclusion of common DEGs with bimolecular networks disclosed hub proteins (LYN, PLCB1, NPSR1, WNT5A, CDC25B, CD44, RIPK2, ASAP1), transcription factors (SCD, SLC7A5, IKZF3, SLC16A1, SLC7A11) and miRNAs (mir-335-5p, mir-26b-5p, mir-124-3p, mir-16-5p, mir-192-5p, mir-548c-3p, mir-29b-3p, mir-155-5p, mir-21-5p, mir-15a-5p). Analysis of the interaction between protein and drug discovered ASAP1 interacts with cysteine sulfonic acid and double oxidized cysteine drug compounds. Gene-disease association analysis retrieved ASAP1 also associated with pulmonary and bladder neoplasm diseases.