Investigating the impact human protein–protein interaction networks have on disease-gene analysis

Identification of key regulators in pancreatic ductal adenocarcinoma using network theoretical approach

Pancreatic Ductal Adenocarcinoma (PDAC) is a devastating disease with poor clinical outcomes, which is mainly because of delayed disease detection, resistance to chemotherapy, and lack of specific targeted therapies. The disease's development involves complex interactions among immunological, genetic, and environmental factors, yet its molecular mechanism remains elusive. A major challenge in understanding PDAC etiology lies in unraveling the genetic profiling that governs the PDAC network. To address this, we examined the gene expression profile of PDAC and compared it with that of healthy controls, identifying differentially expressed genes (DEGs). These DEGs formed the basis for constructing the PDAC protein interaction network, and their network topological properties were calculated. It was found that the PDAC network self-organizes into a scale-free fractal state with weakly hierarchical organization. Newman and Girvan's algorithm (leading eigenvector (LEV) method) of community detection enumerated four communities leading to at least one motif defined by G (3,3). Our analysis revealed 33 key regulators were predominantly enriched in neuroactive ligand-receptor interaction, Cell adhesion molecules, Leukocyte transendothelial migration pathways; positive regulation of cell proliferation, positive regulation of protein kinase B signaling biological functions; G-protein beta-subunit binding, receptor binding molecular functions etc. Transcription Factor and mi-RNA of the key regulators were obtained. Recognizing the therapeutic potential and biomarker significance of PDAC Key regulators, we also identified approved drugs for specific genes. However, it is imperative to subject Key regulators to experimental validation to establish their efficacy in the context of PDAC.

Data mining and network analysis reveals C-X-C chemokine receptor type 5 is involved in the pathophysiology of age-related macular degeneration

Our previous studies found that the C-X-C motif chemokine receptor 5 (CXCR5) loss leads to retinal pigment epithelium (RPE) dysfunction and AMD pathogenesis. The current study aimed to characterize the G protein-coupled receptor (GPCR) structure of CXCR5 and analyze its interactions with AMD-related risk genes. The sequence alignments, homology model of CXCR5 and structural assessment analysis were performed. Data and text mining were then performed to identify AMD-related risk genes and their interaction with CXCR5 using statistical and mathematical algorithms. Sequence alignment and phylogenetic tree analysis revealed that human CXCR5 was highly similar (85.4839%) to the rabbit. The least similarity (33.871%) was found to be in zebrafish compared to the other species. The CXCR5 model structural assessment and secondary structure analysis exhibited an excellent model. Network analysis revealed that IL10, TNF, ICAM1, CXCL1, CXCL8, APP, TLR4, SELL, C3, IL17A and CCR2 were the most connected genes CXCR5. These findings suggest that CXCR5 signaling may regulate the biological function of RPE and modulate AMD pathophysiology via GPCR signaling and interacting with identified AMD risk genes. In summary, the data presented here provide novel and crucial insights into the molecular mechanisms of CXCR5 involvement in AMD.Communicated by Ramaswamy H. Sarma.

Transcriptome-wide analysis reveals core sets of transcriptional regulators of sensome and inflammation genes in retinal microglia

BACKGROUND

Retinal microglial cells (RMCs) play crucial roles in maintaining normal visual functions in a healthy eye. However, the underlying mechanisms of RMCs over-activation manifesting the alterations of sensome profile and inflammation state, which contribute to various retinal neurodegenerative diseases, remain elusive. Here, we aimed to identify the core set of sensome and pro-inflammatory genes and their regulators using transcriptome and data mining approaches.

METHODS

We performed paired-end RNA-sequencing in primary microglial cell cultures treated with TNFα/IFNϒ (10 ng/ml for 12 h) and PBS as a control. Gene enrichment analysis and hierarchical clustering for the differentially expressed transcripts highlight functional pathways and network perturbations. We examined overlaps of the mouse microglial gene expression profiles with the data-mined human sensome and pro-inflammatory marker genes. The core sets of sensome and pro-inflammatory genes were selected and predicted for transcription factors (TFs). The identified TFs in RNA-Seq are validated by the quantitative PCR method.

RESULTS

TNFα/IFNϒ induced 668 differentially expressed transcripts in retinal microglial cells relative to the control. Furthermore, gene enrichment analysis and the gene expression network revealed activated microglial genes, biological, molecular and inflammatory pathways. The overlapping analysis of the TNFα/IFNϒ-activated microglia genes and the data-mined human gene sets revealed 22 sensome and 61 pro-inflammatory genes. Based on network analysis, we determined 10 genes as the core sets of sensome and pro-inflammatory genes and predicted the top ten TFs that regulate them. The SP110, IRF1, FLI1, SP140 (sensome) and RELB, BATF2, NFKB2, TRAFD1, SP100, NFKB1 (inflammation) are differentially expressed between the TNFα/IFNϒ activated and the non-activated microglia which were validated by quantitative PCR. The outcomes indicate that these transcriptional regulators are highly expressed and may regulate the sensome and inflammatory genes of RMCs and switch them to over-activation.

CONCLUSION

Our results comprise a powerful, cross-species functional genomics resource for sensome and inflammation of RMCs, which may provide novel therapeutic approaches to prevent retinal neurodegenerative diseases.

Identification of the Key Regulators of Spina Bifida Through Graph-Theoretical Approach

Spina Bifida (SB) is a congenital spinal cord malformation. Efforts to discern the key regulators (KRs) of the SB protein-protein interaction (PPI) network are requisite for developing its successful interventions. The architecture of the SB network, constructed from 117 manually curated genes was found to self-organize into a scale-free fractal state having a weak hierarchical organization. We identified three modules/motifs consisting of ten KRs, namely, TNIP1, TNF, TRAF1, TNRC6B, KMT2C, KMT2D, NCOA3, TRDMT1, DICER1, and HDAC1. These KRs serve as the backbone of the network, they propagate signals through the different hierarchical levels of the network to conserve the network’s stability while maintaining low popularity in the network. We also observed that the SB network exhibits a rich-club organization, the formation of which is attributed to our key regulators also except for TNIP1 and TRDMT1. The KRs that were found to ally with each other and emerge in the same motif, open up a new dimension of research of studying these KRs together. Owing to the multiple etiology and mechanisms of SB, a combination of several biomarkers is expected to have higher diagnostic accuracy for SB as compared to using a single biomarker. So, if all the KRs present in a single module/motif are targetted together, they can serve as biomarkers for the diagnosis of SB. Our study puts forward some novel SB-related genes that need further experimental validation to be considered as reliable future biomarkers and therapeutic targets.

Gene biomarker discovery at different stages of Alzheimer using gene co-expression network approach

Alzheimer's disease (AD) is a chronic neurodegenerative disorder. It is the most common type of dementia that has remained as an incurable disease in the world, which destroys the brain cells irreversibly. In this study, a systems biology approach was adopted to discover novel micro-RNA and gene-based biomarkers of the diagnosis of Alzheimer's disease. The gene expression data from three AD stages (Normal, Mild Cognitive Impairment, and Alzheimer) were used to reconstruct co-expression networks. After preprocessing and normalization, Weighted Gene Co-Expression Network Analysis (WGCNA) was used on a total of 329 samples, including 145 samples of Alzheimer stage, 80 samples of Mild Cognitive Impairment (MCI) stage, and 104 samples of the Normal stage. Next, three gene-miRNA bipartite networks were reconstructed by comparing the changes in module groups. Then, the functional enrichment analyses of extracted genes of three bipartite networks and miRNAs were done, respectively. Finally, a detailed analysis of the authentic studies was performed to discuss the obtained biomarkers. The outcomes addressed proposed novel genes, including MBOAT1, ARMC7, RABL2B, HNRNPUL1, LAMTOR1, PLAGL2, CREBRF, LCOR, and MRI1and novel miRNAs comprising miR-615-3p, miR-4722-5p, miR-4768-3p, miR-1827, miR-940 and miR-30b-3p which were related to AD. These biomarkers were proposed to be related to AD for the first time and should be examined in future clinical studies.