Interrelationship between 2019-nCov receptor DPP4 and diabetes mellitus targets based on protein interaction network

Patients with diabetes are more likely to be infected with Coronavirus disease 2019 (COVID-19), and the risk of death is significantly higher than ordinary patients. Dipeptidyl peptidase-4 (DPP4) is one of the functional receptor of human coronavirus. Exploring the relationship between diabetes mellitus targets and DPP4 is particularly important for the management of patients with diabetes and COVID-19. We intend to study the protein interaction through the protein interaction network in order to find a new clue for the management of patients with diabetes with COVID-19. Diabetes mellitus targets were obtained from GeneCards database. Targets with a relevance score exceeding 20 were included, and DPP4 protein was added manually. The initial protein interaction network was obtained through String. The targets directly related to DPP4 were selected as the final analysis targets. Importing them into String again to obtain the protein interaction network. Module identification, gene ontology (GO) analysis and Kyoto encyclopedia of genes and genomes (KEGG) pathway analysis were carried out respectively. The impact of DPP4 on the whole network was analyzed by scoring the module where it located. 43 DPP4-related proteins were finally selected from the diabetes mellitus targets and three functional modules were found by the cluster analysis. Module 1 was involved in insulin secretion and glucagon signaling pathway, module 2 and module 3 were involved in signaling receptor binding. The scoring results showed that LEP and apoB in module 1 were the highest, and the scores of INS, IL6 and ALB of cross module associated proteins of module 1 were the highest. DPP4 is widely associated with key proteins in diabetes mellitus. COVID-19 may affect DPP4 in patients with diabetes mellitus, leading to high mortality of diabetes mellitus combined with COVID-19. DPP4 inhibitors and IL-6 antagonists can be considered to reduce the effect of COVID-19 infection on patients with diabetes.

Co-expression analysis of placental genes in the search for key signaling pathways and biomarkers of the great obstetrical syndromes

Objective. To study the molecular mechanisms responsible for the development of diseases grouped within the great obstetrical syndromes (GOS) at the level of the transcriptome of human maternal placenta.Material and Methods. We gathered the results of genome-wide transcriptome studies of the human placental tissue using Gene Expression Omnibus (GEO) data repository for the following phenotypes: physiological pregnancy, preeclampsia (PE), premature birth, and intrauterine growth restriction (IUGR). Eleven data sets were selected and supplemented with our experimental data; a total of 481 samples of human placental tissue were included in the integrative analysis. Bioinformatic data processing and statistical analyses were performed in the R v3.6.1 software environment using the Bioconductor packages. The pooled dataset was used to search for common molecular targets for GOS via weighted gene co-expression network analysis (WGCNA). The functional annotation of genes and the resulting clusters was carried out with the DAVID database; protein-protein interaction network was built using the STRING software; and the hub genes for the network were identified using the MCC analysis with plugin cytoHubba in Cytoscape software 3.7.2.Results. We obtained a table of expression levels for 15,167 genes in 246 samples. Hierarchical clustering of this network allowed to find 55 modules of co-expressed genes in the group with PE, 109 modules in the group with PB, 75 modules in patients with IUGR, and 56 modules in the control group. The preservation analysis of co-expressed modules for the studied phenotypes suggested the presence of a common cluster comprising eight genes specific only for patients with PE and IUGR, as well as the module of 23 co-expressed genes typical only for patients with PB and IUGR. Protein-protein interaction network was built for these gene sets, and the SOD1, TXNRD1, and UBB genes were the central nodes in the network. Based on network topology evaluation with cytoHubba, six hub genes (rank ˂ 5) were identified as follows: SOD1, TKT, TXNRD1, GCLM, GOT1, and ACO1.Conclusion. The obtained results allowed to identify promising genetic markers for preeclampsia, intrauterine growth restriction, and miscarriage. Moreover, the study also made it possible to identify the most important overlapping molecular mechanisms of these diseases occurring in the placental tissue.

Super.Complex: A supervised machine learning pipeline for molecular complex detection in protein-interaction networks

Characterization of protein complexes, i.e. sets of proteins assembling into a single larger physical entity, is important, as such assemblies play many essential roles in cells such as gene regulation. From networks of protein-protein interactions, potential protein complexes can be identified computationally through the application of community detection methods, which flag groups of entities interacting with each other in certain patterns. Most community detection algorithms tend to be unsupervised and assume that communities are dense network subgraphs, which is not always true, as protein complexes can exhibit diverse network topologies. The few existing supervised machine learning methods are serial and can potentially be improved in terms of accuracy and scalability by using better-suited machine learning models and parallel algorithms. Here, we present Super.Complex, a distributed, supervised AutoML-based pipeline for overlapping community detection in weighted networks. We also propose three new evaluation measures for the outstanding issue of comparing sets of learned and known communities satisfactorily. Super.Complex learns a community fitness function from known communities using an AutoML method and applies this fitness function to detect new communities. A heuristic local search algorithm finds maximally scoring communities, and a parallel implementation can be run on a computer cluster for scaling to large networks. On a yeast protein-interaction network, Super.Complex outperforms 6 other supervised and 4 unsupervised methods. Application of Super.Complex to a human protein-interaction network with ~8k nodes and ~60k edges yields 1,028 protein complexes, with 234 complexes linked to SARS-CoV-2, the COVID-19 virus, with 111 uncharacterized proteins present in 103 learned complexes. Super.Complex is generalizable with the ability to improve results by incorporating domain-specific features. Learned community characteristics can also be transferred from existing applications to detect communities in a new application with no known communities. Code and interactive visualizations of learned human protein complexes are freely available at: https://sites.google.com/view/supercomplex/super-complex-v3-0.

Integrated analysis and the identification of a circRNA-miRNA-mRNA network in the progression of abdominal aortic aneurysm

Background Abdominal aortic aneurysm (AAA) is a disease commonly seen in the elderly. The aneurysm diameter increases yearly, and the larger the AAA the higher the risk of rupture, increasing the risk of death. However, there are no current effective interventions in the early stages of AAA. Methods Four gene expression profiling datasets, including 23 normal artery (NOR) tissue samples and 97 AAA tissue samples, were integrated in order to explore potential molecular biological targets for early intervention. After preprocessing, differentially expressed genes (DEGs) between AAA and NOR were identified using LIMMA package. Gene Ontology and Kyoto Encyclopedia of Genes and Genomes analysis were conducted using the DAVID database. The protein-protein interaction network was constructed and hub genes were identified using the STRING database and plugins in Cytoscape. A circular RNA (circRNA) profile of four NOR tissues versus four AAA tissues was then reanalyzed. A circRNA-miRNA-mRNA interaction network was constructed after predictions were made using the Targetscan and Circinteractome databases. Results A total of 440 DEGs (263 up-regulated and 177 down-regulated) were identified in the AAA group, compared with the NOR group. The majority were associated with the extracellular matrix, tumor necrosis factor-α, and transforming growth factor-β. Ten hub gene-encoded proteins (namely IL6, RPS27A, JUN, UBC, UBA52, FOS, IL1B, MMP9, SPP1 and CCL2) coupled with a higher degree of connectivity hub were identified after protein‐protein interaction network analysis. Our results, in combination with the results of previous studies revealed that miR-635, miR-527, miR-520h, miR-938 and miR-518a-5p may be affected by circ_0005073 and impact the expression of hub genes such as CCL2, SPP1 and UBA52. The miR-1206 may also be affected by circ_0090069 and impact RPS27A expression. Conclusions This circRNA-miRNA-mRNA network may perform critical roles in AAA and may be a novel target for early intervention.

Comparative analysis of aneurysm subtypes associated genes based on protein–protein interaction network

The arterial aneurysm refers to localized dilation of blood vessel wall and is common in general population. The majority of aneurysm cases remains asymptomatic until a sudden rupture which is usually fatal and of extremely high mortality (~ 50–60%). Therefore, early diagnosis, prevention and management of aneurysm are in urgent need. Unfortunately, current understanding of disease driver genes of various aneurysm subtypes is still limited, and without appropriate biomarkers and drug targets no specialized drug has been developed for aneurysm treatment. In this research, aneurysm subtypes were analyzed based on protein–protein interaction network to better understand aneurysm pathogenesis. By measuring network-based proximity of aneurysm subtypes, we identified a relevant closest relationship between aortic aneurysm and aortic dissection. An improved random walk method was performed to prioritize candidate driver genes of each aneurysm subtype. Thereafter, transcriptomes of 6 human aneurysm subtypes were collected and differential expression genes were identified to further filter potential driver genes. Functional enrichment of above driver genes indicated a general role of ubiquitination and programmed cell death in aneurysm pathogenesis. Especially, we further observed participation of BCL-2-mediated apoptosis pathway and caspase-1 related pyroptosis in the development of cerebral aneurysm and aneurysmal subarachnoid hemorrhage in corresponding transcriptomes.

Crosstalk between Inflammatory Bowel Disease Proteins and Neuroinflammatory Proteins for Understanding Involvement of Gut-Brain Axis in Parkinson’s Disease and Identified Drug Receptors: In-Silico Approach

Parkinson’s disease (PD) is a chronic, progressive and second most prevalent neurological disorder affecting the motor system. It has been found that people suffering with inflammatory bowel disease (IBD) are 22% more prone to PD. In the current study, we have framed a hypothesis and deciphered protein-protein interaction network between the IBD and PD and therefore, have proposed a role of gut-brain axis in PD. Text mining for retrieval of Differentially Expressed protein (DEPs; specific to Homo sapiens) associated with PD and IBD was done using Science Direct, Pub Med Central, Sci ELO and JSTOR. The protein interaction network was constructed in Cytoscape (version 3.7.1) by using above 58 DEPs. The generated master network was further analyzed using BiNGO plugin for retrieval of overrepresented biological processes in IBD-PD pathologies. Hub nodes were also generated in the network. In the present study the gutbrain hypothesis was designed which demonstrates the series of protein interactions that ought to link IBD to PD. Major proteins involved in this connection were LRRK2, APOE, SNCA, IL6, HIF-1α, ABCA, TLR4, CREB1, IL10, ADORA2A, DRD2, INOS, CCL2, SLC6A3 and CASP3. These proteins could be used as druggable targets to halt the progression of PD pathogenesis initiating via IBD. The common biological pathways linking both the pathologies were found to be HIF-signaling, Cytokines interactions, JAK-STAT pathway, Cholesterol metabolism, cAMP mediated signaling and apoptosis. This study also suggests the role ABC transporters and APOE in linking IBD and PD via disturbance of cerebral homeostasis.

In silico investigation of the mechanism of action of kojic acid effects via protein-protein interaction network

Purpose: To evaluate the molecular mechanism of kojic acid by network pharmacology.Methods: This study was conducted by designing a protein-protein interaction network through the STITCH database and analyzing biological processes via Cytoscape plugin ClueGO.Results: A total of 19 protein targets of kojic acid including TYR, NOS3, NOS2, and NOS1 were found. The PPI network helped to understand the mode of action of kojic acid at a molecular level. Gene Ontology (GO) analysis resulted in the retrieval of 104 GO terms which were related to variousphysiological processes. GO analysis revealed that kojic acid might be involved in the regulation of several biological processes such as circadian gene expression and transcription initiation of RNA polymerase 2.Conclusion: The findings from this study reveal that the retrieved GO pathways are known to be involved in several diseases such as inflammation, cancer, aging, pigmentation, and melisma. Furthermore, these pathways are directly or indirectly related to kojic acid. Thus, this study has contributed to a better understanding of the mode of action of kojic acid.