In-Depth Characterization of bZIP Genes in the Context of Endoplasmic Reticulum (ER) Stress in Brassica campestris ssp. chinensis.

Numerous studies have been conducted to investigate the genomic characterization of bZIP genes and their involvement in the cellular response to endoplasmic reticulum (ER) stress. These studies have provided valuable insights into the coordinated cellular response to ER stress, which is mediated by bZIP transcription factors (TFs). However, a comprehensive and systematic investigations regarding the role of bZIP genes and their involvement in ER stress response in pak choi is currently lacking in the existing literature. To address this knowledge gap, the current study was initiated to elucidate the genomic characteristics of bZIP genes, gain insight into their expression patterns during ER stress in pak choi, and investigate the protein-to-protein interaction of bZIP genes with the ER chaperone BiP. In total, 112 members of the BcbZIP genes were identified through a comprehensive genome-wide analysis. Based on an analysis of sequence similarity, gene structure, conserved domains, and responsive motifs, the identified BcbZIP genes were categorized into 10 distinct subfamilies through phylogenetic analysis. Chromosomal location and duplication events provided insight into their genomic context and evolutionary history. Divergence analysis estimated their evolutionary history with a predicted divergence time ranging from 0.73 to 80.71 million years ago (MYA). Promoter regions of the BcbZIP genes were discovered to exhibit a wide variety of cis-elements, including light, hormone, and stress-responsive elements. GO enrichment analysis further confirmed their roles in the ER unfolded protein response (UPR), while co-expression network analysis showed a strong relationship of BcbZIP genes with ER-stress-responsive genes. Moreover, gene expression profiles and protein-protein interaction with ER chaperone BiP further confirmed their roles and capacity to respond to ER stress in pak choi.

Identification of biomarkers and potential therapeutic targets of kidney stone disease using bioinformatics.

PURPOSE: Kidney stone disease (KSD) is a common urological disease, but its pathogenesis remains unclear. In this study, we screened KSD-related hub genes using bioinformatic methods and predicted the related pathways and potential drug targets. METHODS: The GSE75542 and GSE18160 datasets in the Gene Expression Omnibus (GEO) were selected to identify common differentially expressed genes (DEGs). We conducted Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analyses to identify enriched pathways. Finally, we constructed a hub gene-miRNA network and drug-DEG interaction network. RESULTS: In total, 44 upregulated DEGs and 1 downregulated DEG were selected from the GEO datasets. Signaling pathways, such as leukocyte migration, chemokine activity, NF-κB, TNF, and IL-17, were identified in GO and KEGG. We identified 10 hub genes using Cytohubba. In addition, 21 miRNAs were predicted to regulate 4 or more hub genes, and 10 drugs targeted 2 or more DEGs. LCN2 expression was significantly different between the GEO datasets. Quantitative real-time polymerase chain reaction (qRT-PCR) analyses showed that seven hub gene expressions in HK-2 cells with CaOx treatment were significantly higher than those in the control group. CONCLUSION: The 10 hub genes identified, especially LCN2, may be involved in kidney stone occurrence and development, and may provide new research targets for KSD diagnosis. Furthermore, KSD-related miRNAs may be targeted for the development of novel drugs for KSD treatment.

Exploring gene-patient association to identify personalized cancer driver genes by linear neighborhood propagation.

BACKGROUND: Driver genes play a vital role in the development of cancer. Identifying driver genes is critical for diagnosing and understanding cancer. However, challenges remain in identifying personalized driver genes due to tumor heterogeneity of cancer. Although many computational methods have been developed to solve this problem, few efforts have been undertaken to explore gene-patient associations to identify personalized driver genes. RESULTS: Here we propose a method called LPDriver to identify personalized cancer driver genes by employing linear neighborhood propagation model on individual genetic data. LPDriver builds personalized gene network based on the genetic data of individual patients, extracts the gene-patient associations from the bipartite graph of the personalized gene network and utilizes a linear neighborhood propagation model to mine gene-patient associations to detect personalized driver genes. The experimental results demonstrate that as compared to the existing methods, our method shows competitive performance and can predict cancer driver genes in a more accurate way. Furthermore, these results also show that besides revealing novel driver genes that have been reported to be related with cancer, LPDriver is also able to identify personalized cancer driver genes for individual patients by their network characteristics even if the mutation data of genes are hidden. CONCLUSIONS: LPDriver can provide an effective approach to predict personalized cancer driver genes, which could promote the diagnosis and treatment of cancer. The source code and data are freely available at https://github.com/hyr0771/LPDriver .

Identifying cancer driver genes in individual tumours.

Cancer is a heterogeneous disease with a strong genetic component making it suitable for precision medicine approaches aimed at identifying the underlying molecular drivers within a tumour. Large scale population-level cancer sequencing consortia have identified many actionable mutations common across both cancer types and sub-types, resulting in an increasing number of successful precision medicine programs. Nonetheless, such approaches fail to consider the effects of mutations unique to an individual patient and may miss rare driver mutations, necessitating personalised approaches to driver-gene prioritisation. One approach is to quantify the functional importance of individual mutations in a single tumour based on how they affect the expression of genes in a gene interaction network (GIN). These GIN-based approaches can be broadly divided into those that utilise an existing reference GIN and those that construct de novo patient-specific GINs. These single-tumour approaches have several limitations that likely influence their results, such as use of reference cohort data, network choice, and approaches to mathematical approximation, and more research is required to evaluate the in vitro and in vivo applicability of their predictions. This review examines the current state of the art methods that identify driver genes in single tumours with a focus on GIN-based driver prioritisation.

Identifying Network Biomarkers in Early Diagnosis of Hepatocellular Carcinoma via miRNA-Gene Interaction Network Analysis.

BACKGROUND: Hepatocellular carcinoma (HCC) is a highly heterogeneous cancer at the histological level. Despite the emergence of new biological technology, advanced-stage HCC remains largely incurable. The prediction of a cancer biomarker is a key problem for targeted therapy in the disease. METHODS: We performed a miRNA-gene integrated analysis to identify differentially expressed miRNAs (DEMs) and genes (DEGs) of HCC. The DEM-DEG interaction network was constructed and analyzed. Gene ontology enrichment and survival analyses were also performed in this study. RESULTS: By the analysis of healthy and tumor samples, we found that 94 DEGs and 25 DEMs were significantly differentially expressed in different datasets. Gene ontology enrichment analysis showed that these 94 DEGs were significantly enriched in the term "Liver" with a statistical p-value of 1.71 × 10-26. Function enrichment analysis indicated that these genes were significantly overrepresented in the term "monocarboxylic acid metabolic process" with a p-value = 2.94 × 10-18. Two sets (fourteen genes and five miRNAs) were screened by a miRNA-gene integrated analysis of their interaction network. The statistical analysis of these molecules showed that five genes (CLEC4G, GLS2, H2AFZ, STMN1, TUBA1B) and two miRNAs (hsa-miR-326 and has-miR-331-5p) have significant effects on the survival prognosis of patients. CONCLUSION: We believe that our study could provide critical clinical biomarkers for the targeted therapy of HCC.

Transcriptome alterations in peripheral blood B cells of patients with multiple sclerosis receiving immune reconstitution therapy.

BACKGROUND: Multiple sclerosis (MS) is a chronic, inflammatory and neurodegenerative disease that leads to irreversible damage to the brain and spinal cord. The goal of so-called "immune reconstitution therapies" (IRTs) is to achieve long-term disease remission by eliminating a pathogenic immune repertoire through intense short-term immune cell depletion. B cells are major targets for effective immunotherapy in MS. OBJECTIVES: The aim of this study was to analyze the gene expression pattern of B cells before and during IRT (i.e., before B-cell depletion and after B-cell repopulation) to better understand the therapeutic effects and to identify biomarker candidates of the clinical response to therapy. METHODS: B cells were obtained from blood samples of patients with relapsing-remitting MS (n = 50), patients with primary progressive MS (n = 13) as well as healthy controls (n = 28). The patients with relapsing MS received either monthly infusions of natalizumab (n = 29) or a pulsed IRT with alemtuzumab (n = 15) or cladribine (n = 6). B-cell subpopulation frequencies were determined by flow cytometry, and transcriptome profiling was performed using Clariom D arrays. Differentially expressed genes (DEGs) between the patient groups and controls were examined with regard to their functions and interactions. We also tested for differences in gene expression between patients with and without relapse following alemtuzumab administration. RESULTS: Patients treated with alemtuzumab or cladribine showed on average a > 20% lower proportion of memory B cells as compared to before IRT. This was paralleled by profound transcriptome shifts, with > 6000 significant DEGs after adjustment for multiple comparisons. The top DEGs were found to regulate apoptosis, cell adhesion and RNA processing, and the most highly connected nodes in the network of encoded proteins were ESR2, PHB and RC3H1. Higher mRNA levels of BCL2, IL13RA1 and SLC38A11 were seen in patients with relapse despite IRT, though these differences did not pass the false discovery rate correction. CONCLUSIONS: We show that B cells circulating in the blood of patients with MS undergoing IRT present a distinct gene expression signature, and we delineated the associated biological processes and gene interactions. Moreover, we identified genes whose expression may be an indicator of relapse risk, but further studies are needed to verify their potential value as biomarkers.

Network embedding framework for driver gene discovery by combining functional and structural information.

Comprehensive analysis of multiple data sets can identify potential driver genes for various cancers. In recent years, driver gene discovery based on massive mutation data and gene interaction networks has attracted increasing attention, but there is still a need to explore combining functional and structural information of genes in protein interaction networks to identify driver genes. Therefore, we propose a network embedding framework combining functional and structural information to identify driver genes. Firstly, we combine the mutation data and gene interaction networks to construct mutation integration network using network propagation algorithm. Secondly, the struc2vec model is used for extracting gene features from the mutation integration network, which contains both gene's functional and structural information. Finally, machine learning algorithms are utilized to identify the driver genes. Compared with the previous four excellent methods, our method can find gene pairs that are distant from each other through structural similarities and has better performance in identifying driver genes for 12 cancers in the cancer genome atlas. At the same time, we also conduct a comparative analysis of three gene interaction networks, three gene standard sets, and five machine learning algorithms. Our framework provides a new perspective for feature selection to identify novel driver genes.

Function of CD8+, conventional CD4+, and regulatory CD4+ T cell identification in lung cancer.

Lung cancer is the malignant tumor with the highest mortality rate in the world. There is obvious heterogeneity within the tumor. Single cell sequencing technology enables scholars to obtain information about the cell type, status, subpopulation distribution and communication behavior between cells in the tumor microenvironment from the cellular level. However, due to the problem of sequencing depth, some genes with low expression cannot be detected, which results in that most of the specific genes of immune cells cannot be recognized, and lead to defects in the functional identification of immune cells. In this paper, we used single cell sequencing data of 12346 T cells in 14 treatment-naïve non-small-cell lung cancer patients to identify immune cell-specific genes and infer the function of three types of T cells. The method, named GRAPH-LC, implemented this function by gene interaction network and graph learning methods. Graph learning methods are used to extract genes feature and dense neural network is used to identify immune cell-specific genes. The experiments on 10-cross validation shows that the AUROC and AUPR reached at least 0.802, 0.815 on identifying cell-specific genes of three types of T cells. And we did functional enrichment analysis on the top 15 expressed genes. By functional enrichment analysis, we got 95 GO terms and 39 KEGG pathways that related to three types of T cells. The use of this technology will help to deeply understand the mechanism of the occurrence and development of lung cancer, find new diagnostic markers and therapeutic targets, and provide a theoretical reference for the precise treatment of lung cancer patients in the future.

Translational protein RpsE as an alternative target for novel nucleoside analogues to treat MDR Enterobacter cloacae ATCC 13047: network analysis and molecular dynamics study.

The pathogenic Enterobacter cloacae subsp. cloacae str. ATCC 13047 has contemporarily emerged as a multi-drug resistant strain. To formulate an effective treatment option, alternative therapeutic methods need to be explored. The present study focused on Gene Interaction Network study of 46 antimicrobial resistance genes to reveal the densely interconnecting and functional hub genes in E. cloacae ATCC 13047. The AMR genes were subjected to clustering, topological and functional enrichment analysis, revealing rpsE (RpsE), acrA (AcrA) and arnT (ArnT) as novel therapeutic drug targets for hindering drug resistance in the pathogenic strain. Network topology further indicated translational protein RpsE to be exploited as a promising drug-target candidate for which the structure was predicted, optimized and validated through molecular dynamics simulations (MDS). Absorption, distribution, metabolism and excretion screening recognized ZINC5441082 (N-Isopentyladenosine) (Lead_1) and ZINC1319816 (cyclopentyl-aminopurinyl-hydroxymethyl-oxolanediol) (Lead_2) as orally bioavailable compounds against RpsE. Molecular docking and MDS confirmed the binding efficacy and protein-ligand complex stability. Furthermore, binding free energy (Gbind) calculations, principal component and free energy landscape analyses affirmed the predicted nucleoside analogues against RpsE protein to be comprehensively examined as a potential treatment strategy against E. cloacae ATCC 13047.

Integrated gene network analysis sheds light on understanding the progression of Osteosarcoma.

Introduction: Osteosarcoma is a rare disorder among cancer, but the most frequently occurring among sarcomas in children and adolescents. It has been reported to possess the relapsing capability as well as accompanying collateral adverse effects which hinder the development process of an effective treatment plan. Using networks of omics data to identify cancer biomarkers could revolutionize the field in understanding the cancer. Cancer biomarkers and the molecular mechanisms behind it can both be understood by studying the biological networks underpinning the etiology of the disease. Methods: In our study, we aimed to highlight the hub genes involved in gene-gene interaction network to understand their interaction and how they affect the various biological processes and signaling pathways involved in Osteosarcoma. Gene interaction network provides a comprehensive overview of functional gene analysis by providing insight into how genes cooperatively interact to elicit a response. Because gene interaction networks serve as a nexus to many biological problems, their employment of it to identify the hub genes that can serve as potential biomarkers remain widely unexplored. A dynamic framework provides a clear understanding of biological complexity and a pathway from the gene level to interaction networks. Results: Our study revealed various hub genes viz. TP53, CCND1, CDK4, STAT3, and VEGFA by analyzing various topological parameters of the network, such as highest number of interactions, average shortest path length, high cluster density, etc. Their involvement in key signaling pathways, such as the FOXM1 transcription factor network, FAK-mediated signaling events, and the ATM pathway, makes them significant candidates for studying the disease. The study also highlighted significant enrichment in GO terms (Biological Processes, Molecular Function, and Cellular Processes), such as cell cycle signal transduction, cell communication, kinase binding, transcription factor activity, nucleoplasm, PML body, nuclear body, etc. Conclusion: To develop better therapeutics, a specific approach toward the disease targeting the hub genes involved in various signaling pathways must have opted to unravel the complexity of the disease. Our study has highlighted the candidate hub genes viz. TP53, CCND1 CDK4, STAT3, VEGFA. Their involvement in the major signaling pathways of Osteosarcoma makes them potential candidates to be targeted for drug development. The highly enriched signaling pathways include FOXM1 transcription pathway, ATM signal-ling pathway, FAK mediated signaling events, Arf6 signaling events, mTOR signaling pathway, and Integrin family cell surface interactions. Targeting the hub genes and their associated functional partners which we have reported in our studies may be efficacious in developing novel therapeutic targets.

Identifying cancer driver genes using a two-stage random walk with restart on a gene interaction network.

Cancer development and progression are significantly influenced by cancer driver genes. Understanding cancer driver genes and their mechanisms of action is essential for developing effective cancer treatments. As a result, identifying driver genes is important for drug development, cancer diagnosis, and treatment. Here, we present an algorithm to discover driver genes based on the two-stage random walk with restart (RWR), and the modified method for calculating the transition probability matrix in random walk algorithm. First, we performed the first stage of RWR on the whole gene interaction network, in which we employ a new method for calculating the transition probability matrix and extracted the subnetwork based on nodes that had a high correlation with the seed nodes. The subnetwork was then applied to the second stage of RWR and the nodes were re-ranked in the subnetwork. Our approach outperformed existing methods in identifying driver genes. The outcome of the effect of three gene interaction networks, two rounds of random walk, and the seed nodes' sensitivity were all compared at the same time. In addition, we identified several potential driver genes, some of which are involved in driving cancer development. Overall, our method is efficient in various cancer types, significantly outperforms existing methods, and can identify possible driver genes.

Elucidating the mechanism of antimicrobial resistance in Mycobacterium tuberculosis using gene interaction networks.

Antimicrobial resistance (AMR) in microorganisms is an urgent global health threat. AMR of Mycobacterium tuberculosis is associated with significant morbidity and mortality. It is of great importance to underpin the resistance pathways involved in the mechanisms of AMR and identify the genes that are directly involved in AMR. The focus of the current study was the bacteria M. tuberculosis, which carries AMR genes that give resistance that lead to multidrug resistance. We, therefore, built a network of 43 genes and examined for potential gene-gene interactions. Then we performed a clustering analysis and identified three closely related clusters that could be involved in multidrug resistance mechanisms. Through the bioinformatics pipeline, we consistently identified six-hub genes (dnaN, polA, ftsZ, alr, ftsQ, and murC) that demonstrated the highest number of interactions within the clustering analysis. This study sheds light on the multidrug resistance of MTB and provides a protocol for discovering genes that might be involved in multidrug resistance, which will improve the treatment of resistant strains of TB.

Gene differential co-expression analysis of male infertility patients based on statistical and machine learning methods.

Male infertility has always been one of the important factors affecting the infertility of couples of gestational age. The reasons that affect male infertility includes living habits, hereditary factors, etc. Identifying the genetic causes of male infertility can help us understand the biology of male infertility, as well as the diagnosis of genetic testing and the determination of clinical treatment options. While current research has made significant progress in the genes that cause sperm defects in men, genetic studies of sperm content defects are still lacking. This article is based on a dataset of gene expression data on the X chromosome in patients with azoospermia, mild and severe oligospermia. Due to the difference in the degree of disease between patients and the possible difference in genetic causes, common classical clustering methods such as k-means, hierarchical clustering, etc. cannot effectively identify samples (realize simultaneous clustering of samples and features). In this paper, we use machine learning and various statistical methods such as hypergeometric distribution, Gibbs sampling, Fisher test, etc. and genes the interaction network for cluster analysis of gene expression data of male infertility patients has certain advantages compared with existing methods. The cluster results were identified by differential co-expression analysis of gene expression data in male infertility patients, and the model recognition clusters were analyzed by multiple gene enrichment methods, showing different degrees of enrichment in various enzyme activities, cancer, virus-related, ATP and ADP production, and other pathways. At the same time, as this paper is an unsupervised analysis of genetic factors of male infertility patients, we constructed a simulated data set, in which the clustering results have been determined, which can be used to measure the effect of discriminant model recognition. Through comparison, it finds that the proposed model has a better identification effect.

Adverse Human Health Effects of Chromium by Exposure Route: A Comprehensive Review Based on Toxicogenomic Approach.

Heavy metals are defined as metals with relatively high density and atomic weight, and their various applications have raised serious concerns about the environmental impacts and potential human health effects. Chromium is an important heavy metal that is involved in biological metabolism, but Cr exposure can induce a severe impact on occupational workers or public health. In this study, we explore the toxic effects of Cr exposure through three exposure routes: dermal contact, inhalation, and ingestion. We propose the underlying toxicity mechanisms of Cr exposure based on transcriptomic data and various bioinformatic tools. Our study provides a comprehensive understanding of the toxicity mechanisms of different Cr exposure routes by diverse bioinformatics analyses.

An integrated gene network analysis to decode the multi-drug resistance mechanism in Klebsiella pneumoniae.

Antimicrobial resistance (AMR) among microorganisms has become one of the worldwide concerns of this century and continues to challenge us. To properly understand this problem, it is essential to know the genes that cause AMR and their resistance mechanisms. Our present study focused on Klebsiella pneumoniae, which possesses AMR genes conferring resistance against multiple antibiotics. A gene interaction network of 42 functional partners was constructed and analyzed to broaden our understanding. Three closely related clusters (C1-C3) having an association with multi-drug resistance mechanisms were identified by clustering analysis. The enrichment analysis illustrated 30 genes in biological processes, 24 genes in molecular function, and 25 genes in cellular components having a significant role. The analysis of the gene interaction network revealed genes birA2, folP, pabC, folA, gyrB, glmM, gyrA, thyA_2 had maximum no. of interactions with their functional partners viz. 26, 25, 25, 24, 23, 23, 23, 23 respectively and can be considered as hub genes. Analyzing the enriched pathways and Gene Ontologies provides insight into AMR's molecular basis. In addition, the proposed study could aid the researchers in developing new treatment options to combat multi-drug resistant K. pneumoniae.

Using machine learning to identify gene interaction networks associated with breast cancer.

BACKGROUND: Breast cancer (BC) is one of the most prevalent cancers worldwide but its etiology remains unclear. Obesity is recognized as a risk factor for BC, and many obesity-related genes may be involved in its occurrence and development. Research assessing the complex genetic mechanisms of BC should not only consider the effect of a single gene on the disease, but also focus on the interaction between genes. This study sought to construct a gene interaction network to identify potential pathogenic BC genes. METHODS: The study included 953 BC patients and 963 control individuals. Chi-square analysis was used to assess the correlation between demographic characteristics and BC. The joint density-based non-parametric differential interaction network analysis and classification (JDINAC) was used to build a BC gene interaction network using single nucleotide polymorphisms (SNP). The odds ratio (OR) and 95% confidence interval (95% CI) of hub gene SNPs were evaluated using a logistic regression model. To assess reliability, the hub genes were quantified by edgeR program using BC RNA-seq data from The Cancer Genome Atlas (TCGA) and identical edges were verified by logistic regression using UK Biobank datasets. Go and KEGG enrichment analysis were used to explore the biological functions of interactive genes. RESULTS: Body mass index (BMI) and menopause are important risk factors for BC. After adjusting for potential confounding factors, the BC gene interaction network was identified using JDINAC. LEP, LEPR, XRCC6, and RETN were identified as hub genes and both hub genes and edges were verified. LEPR genetic polymorphisms (rs1137101 and rs4655555) were also significantly associated with BC. Enrichment analysis showed that the identified genes were mainly involved in energy regulation and fat-related signaling pathways. CONCLUSION: We explored the interaction network of genes derived from SNP data in BC progression. Gene interaction networks provide new insight into the underlying mechanisms of BC.

Transcriptome analysis of MYB transcription factors family and PgMYB genes involved in salt stress resistance in Panax ginseng.

BACKGROUND: As the king of all herbs, the medicinal value of ginseng is self-evident. The perennial nature of ginseng causes its quality to be influenced by various factors, one of which is the soil environment. During plant growth and development, MYB transcription factors play an important role in responding to abiotic stresses and regulating the synthesis of secondary metabolites. However, there are relatively few reports on the MYB transcription factor family in Panax ginseng. RESULTS: This study identified 420 PgMYB transcripts under 117 genes ID in the Jilin ginseng transcriptome database. Phylogenetic analysis showed that PgMYB transcripts in Jilin ginseng were classified into 19 functional subclasses. The GO annotation result indicated that the functional differentiation of PgMYB transcripts was annotated to 11 functional nodes at GO Level 2 in ginseng. Expression pattern analysis of PgMYB transcripts based on the expression data (TPM) that PgMYB transcripts were revealed spatiotemporally specific in expression patterns. We performed a weighted network co-expression network analysis on the expression of PgMYB transcripts from different samples. The co-expression network containing 51 PgMYB transcripts was formed under a soft threshold of 0.85, revealing the reciprocal relationship of PgMYB in ginseng. Treatment of adventitious roots of ginseng with different concentrations of NaCl revealed four up-regulated expression of PgMYB transcripts that can candidate genes for salt resistance studies in ginseng. CONCLUSIONS: The present findings provide data resources for the subsequent study of the functions of MYB transcription factor family members in ginseng, and provide an experimental basis for the anti-salt functions of MYB transcription factors in Panax ginseng.

Network metrics, structural dynamics and density functional theory calculations identified a novel Ursodeoxycholic Acid derivative against therapeutic target Parkin for Parkinson's disease.

Parkinson's disease (PD) has been designated as one of the priority neurodegenerative disorders worldwide. Although diagnostic biomarkers have been identified, early onset detection and targeted therapy are still limited. An integrated systems and structural biology approach were adopted to identify therapeutic targets for PD. From a set of 49 PD associated genes, a densely connected interactome was constructed. Based on centrality indices, degree of interaction and functional enrichments, LRRK2, PARK2, PARK7, PINK1 and SNCA were identified as the hub-genes. PARK2 (Parkin) was finalized as a potent theranostic candidate marker due to its strong association (score > 0.99) with α-synuclein (SNCA), which directly regulates PD progression. Besides, modeling and validation of Parkin structure, an extensive virtual-screening revealed small (commercially available) inhibitors against Parkin. Molecule-258 (ZINC5022267) was selected as a potent candidate based on pharmacokinetic profiles, Density Functional Theory (DFT) energy calculations (ΔE = 6.93 eV) and high binding affinity (Binding energy = -6.57 ± 0.1 kcal/mol; Inhibition constant = 15.35 µM) against Parkin. Molecular dynamics simulation of protein-inhibitor complexes further strengthened the therapeutic propositions with stable trajectories (low structural fluctuations), hydrogen bonding patterns and interactive energies (>0kJ/mol). Our study encourages experimental validations of the novel drug candidate to prevent the auto-inhibition of Parkin mediated ubiquitination in PD.

Exploring the multidimensional heterogeneities of glioblastoma multiforme based on sample-specific edge perturbation in gene interaction network.

Glioblastoma multiforme (GBM) is the most malignant brain cancer with great heterogeneities in many aspects, such as prognosis, clinicopathological features, immune landscapes, and immunotherapeutic responses. Considering that gene interaction network is relatively stable in a healthy state but widely perturbed in cancers, we sought to explore the multidimensional heterogeneities of GBM through evaluating the degree of network perturbations. The gene interaction network perturbations of GBM samples (TCGA cohort) and normal samples (GTEx database) were characterized by edge perturbations, which were quantized through evaluating the change in relative gene expression value. An unsupervised consensus clustering analysis was performed to identify edge perturbation-based clusters of GBM samples. Results revealed that the edge perturbation of GBM samples was stronger than that of normal samples. Four edge perturbation-based clusters of GBM samples were identified and showed prominent heterogeneities in prognosis, clinicopathological features, somatic genomic alterations, immune landscapes, and immunotherapeutic responses. In addition, a sample-specific perturbation of gene interaction score (SPGIScore) was constructed based on the differently expressed genes (DEGs) among four clusters, and exhibited a robust ability to predict prognosis. In conclusion, the bioinformatics approach based on sample-specific edge perturbation in gene interaction network provided a new perspective to understanding the multidimensional heterogeneities of GBM.

Regulation of SMAD Signaling Pathway by miRNAs Associated with Myocardial Fibrosis: In silico Analysis of Target Gene Networks.

Hypertrophic cardiomyopathy (HCM) is a hereditary heart disease caused by mutations in the sarcomere genes, which is accompanied by myocardial fibrosis leading to progressive heart failure and arrhythmias. Recent studies suggest that the HCM development involves dysregulation of gene expression. Among the molecules involved in this process are microRNAs (miRNAs), which are short non-coding RNAs. Typically, one miRNA regulates several target genes post-transcriptionally, hence, it might be difficult to determine the role of a particular miRNA in the disease pathogenesis. In this study, using the PubMed database, we selected 15 miRNAs whose expression is associated with myocardial fibrosis, one of the critical pathological processes in HCM. We then used an earlier developed algorithm to search in silico for the signaling pathways regulated by these miRNAs and found that ten of them participate in the regulation of the TGF-ß/SMAD signaling pathway. At the same time, among the SMAD signaling pathway genes, the target of the most identified miRNAs was the MYC gene, which is involved in the development of fibrosis in some tissues. In our earlier work, we found that the TGF-ß/SMAD pathway is also regulated by a set of other miRNAs associated with the myocardial hypertrophy in HCM. The fact that two sets of miRNAs identified in two independent bioinformatic studies are involved in the regulation of the same signaling pathway indicates that the SMAD signaling cascade is indeed a key element in the regulation of pathological processes in HCM. The obtained data might contribute to understanding pathological processes underlying HCM development.