Enhanced construction of gene regulatory networks using hub gene information

Network-based multi-class classifier to identify optimized gene networks for acute leukemia cell line classification

Unraveling the genetic regulatory networks that underlie diseases is essential for comprehending the intricate mechanisms of these conditions. While various computational strategies were developed, the approaches in the existing studies concerning network-based prediction and classification are based on the pre-estimated gene networks. However, the gene network that is pre-estimated fails to yield biologically meaningful explanations for classifying cell lines into particular clinical states. The reason for this limitation is the lack of inclusion of any information about the clinical status of cell lines during the process of network estimation. To achieve effective cell line classification and ensure the biological validity of the cell lines classification, we develop a computational strategy referred to as GRN-multiClassifier for network-based multi-class classification. The GRN-multiClassifier estimates gene network in a manner that simultaneously minimizes both the network estimation error and the negative log-likelihood function of multinomial logistic regression. That is, our strategy estimates optimized gene network to enable the multi-class classification of cell lines into specific clinical conditions. Monte Carlo simulations demonstrate the efficacy of the GRN-multiClassifier. We applied our strategy to network-based classification of acute leukemia cell lines into three distinct categories of acute leukemia. Our strategy shows outstanding performance in the classification of acute leukemia cell lines. The results for the acute leukemia marker identification are strongly supported by existing literature. The implications of our findings suggest that potential pathways involving the inhibition of ACTB and the molecular interactions between "HBA1&HBB," "HBB&HBA1," "IGKV1-5&IGHV4-31," "IGHV4-31&IGKV1-5," "HLA-DRA&CD74" and "ACTB&ACTB" could offer significant insights into the underlying mechanism of acute leukemia.

HORNET: tools to find genes with causal evidence and their regulatory networks using eQTLs

Motivation

Nearly two decades of genome-wide association studies (GWAS) have identify thousands of disease-associated genetic variants, but very few genes with evidence of causality. Recent methodological advances demonstrate that Mendelian randomization (MR) using expression quantitative loci (eQTLs) as instrumental variables can detect potential causal genes. However, existing MR approaches are not well suited to handle the complexity of eQTL GWAS data structure and so they are subject to bias, inflation, and incorrect inference.

Results

We present a whole-genome regulatory network analysis tool (HORNET), which is a comprehensive set of statistical and computational tools to perform genome-wide searches for causal genes using summary level GWAS data, i.e. robust to biases from multiple sources. Applying HORNET to schizophrenia, eQTL effects in the cerebellum were spread throughout the genome, and in the cortex were more localized to select loci.

Availability and implementation

Freely available at https://github.com/noahlorinczcomi/HORNET or Mac, Windows, and Linux users.

Transcriptomic analysis of human cartilage identified potential therapeutic targets for hip osteoarthritis

Cartilage degradation is the hallmark of osteoarthritis (OA). The purpose of this study was to identify and validate differentially expressed genes (DEGs) in human articular cartilage that could serve as potential therapeutic targets for hip OA. We performed transcriptomic profiling in a discovery cohort (12 OA-free and 72 hip OA-affected cartilage) and identified 179 DEGs between OA-free and OA-affected cartilage after correcting for multiple testing (P < 2.97 × 10-6). Pathway and network analyses found eight hub genes to be associated with hip OA (ASPN, COL1A2, MXRA5, P3H1, PCOLCE, SDC1, SPARC, and TLR2), which were all confirmed using qPCR in a validation cohort (36 OA-free and 62 hip OA-affected cartilage) (P < 6.25 × 10-3). Our data showed that dysregulation of extracellular matrix formation and imbalance in the proportion of collagen chains may contribute to the development of hip OA, and SDC1 could be a promising potential therapeutic target. These findings provided a better understanding of the molecular mechanisms for hip OA and may assist in developing targeted treatment strategies.

Patient-centered brain transcriptomic and multimodal imaging determinants of clinical progression, physical activity, and treatment needs in Parkinson's disease

We continue to lack a clear understanding on how the biological and clinical complexity of Parkinson's disease emerges from molecular to macroscopic brain interactions. Here, we use personalized multiscale spatiotemporal computational brain models to characterize for the first time the synergistic links between genes, several multimodal neuroimaging-derived biological factors, clinical profiles, and therapeutic needs in PD. We identified genes modulating PD-caused brain reorganization in dopamine transporter level, neuronal activity integrity, microstructure, dendrite density and tissue atrophy. Inter-individual heterogeneity in the identified gene-mediated biological mechanisms was associated with five distinct configurations of PD motor and non-motor symptoms. Notably, the protein-protein interaction networks underlying both brain phenotypic and symptom configurations in PD revealed distinct hub genes including MYC, CCNA2, CCDK1, SRC, STAT3 and PSMD4. We also studied the biological mechanisms associated with physical activities performance, observing that leisure and work activities are strongly related to neurotypical cholesterol homeostasis and inflammatory response processes, respectively. Finally, patient-tailored in silico gene perturbations revealed a set of putative disease-modifying drugs with potential to effectively treat PD across different biological levels, most of which are associated with dopamine reuptake and anti-inflammation. Our study constitutes the first self-contained multiscale spatiotemporal computational approach providing comprehensive insights into the complex multifactorial pathogenesis of PD, unraveling key biological modulators of physical and clinical deterioration, and serving as a blueprint for optimum drug selection at personalized level.

Covariate-adjusted construction of gene regulatory networks using a combination of generalized linear model and penalized maximum likelihood

Many machine learning techniques have been used to construct gene regulatory networks (GRNs) through precision matrix that considers conditional independence among genes, and finally produces sparse version of GRNs. This construction can be improved using the auxiliary information like gene expression profile of the related species or gene markers. To reach out this goal, we apply a generalized linear model (GLM) in first step and later a penalized maximum likelihood to construct the gene regulatory network using Glasso technique for the residuals of a multi-level multivariate GLM among the gene expressions of one species as a multi-levels response variable and the gene expression of related species as a multivariate covariates. By considering the intrinsic property of the gene data which the number of variables is much greater than the number of available samples, a bootstrap version of multi-response multivariate GLM is used. To find most appropriate related species, a cross-validation technique has been used to compute the minimum square error of the fitted GLM under different regularization. The penalized maximum likelihood under a lasso or elastic net penalty is applied on the residual of fitted GLM to find the sparse precision matrix. Finally, we show that the presented algorithm which is a combination of fitted GLM and applying the penalized maximum likelihood on the residual of the model is extremely fast, and can exploit sparsity in the constructed GRNs. Also, we exhibit flexibility of the proposed method presented in this paper by comparing with the other methods to demonstrate the super validity of our approach.

Mapping integral cell-type-specific interferon-induced gene regulatory networks (GRNs) involved in systemic lupus erythematosus using systems and computational analysis

Systemic lupus erythematosus (SLE) is a systemic autoimmune disorder characterized by the production of autoantibodies, resulting in inflammation and organ damage. Although extensive research has been conducted on SLE pathogenesis, a comprehensive understanding of its molecular landscape in different cell types has not been achieved. This study uncovers the molecular mechanisms of the disease by thoroughly examining gene regulatory networks within neutrophils, dendritic cells, T cells, and B cells. Firstly, we identified genes and ncRNAs with differential expression in SLE patients compared to controls for different cell types. Furthermore, the derived differentially expressed genes were curated based on immune functions using functional enrichment analysis to create a protein-protein interaction network. Topological network analysis of the formed genes revealed key hub genes associated with each of the cell types. To understand the regulatory mechanism through which these hub genes function in the diseased state, their associations with transcription factors, and non-coding RNAs in different immune cell types were investigated through correlation analysis and regression models. Finally, by integrating these findings, distinct gene regulatory networks were constructed, which provide a novel perspective on the molecular, cellular, and immunological landscapes of SLE. Importantly, we reveal the crucial role of IRF3, IRF7, and STAT1 in neutrophils, dendritic cells, and T cells, where their aberrant upregulation in disease states might enhance the production of type I IFN. Furthermore, we found MYB to be a crucial regulator that might activate T cells toward autoimmune responses in SLE. Similarly, in B-cell lymphocytes, we found FOXO1 to be a key player in autophagy and chemokine regulation. These findings were also validated using single-cell RNASeq analysis using an independent dataset. Genotype variations of these genes were also explored using the GWAS central database to ensure their targetability. These findings indicate that IRF3, IRF7, STAT1, MYB, and FOXO1 are promising targets for therapeutic interventions for SLE.

Big Data and Artificial Intelligence in Drug Discovery for Gastric Cancer: Current Applications and Future Perspectives

Gastric cancer (GC) represents a significant global health burden, ranking as the fifth most common malignancy and the fourth leading cause of cancer-related death worldwide. Despite recent advancements in GC treatment, the five-year survival rate for advanced-stage GC patients remains low. Consequently, there is an urgent need to identify novel drug targets and develop effective therapies. However, traditional drug discovery approaches are associated with high costs, time-consuming processes, and a high failure rate, posing challenges in meeting this critical need. In recent years, there has been a rapid increase in the utilization of artificial intelligence (AI) algorithms and big data in drug discovery, particularly in cancer research. AI has the potential to improve the drug discovery process by analyzing vast and complex datasets from multiple sources, enabling the prediction of compound efficacy and toxicity, as well as the optimization of drug candidates. This review provides an overview of the latest AI algorithms and big data employed in drug discovery for GC. Additionally, we examine the various applications of AI in this field, with a specific focus on therapeutic discovery. Moreover, we discuss the challenges, limitations, and prospects of emerging AI methods, which hold significant promise for advancing GC research in the future.

Nei 6 You 7075, a hybrid rice cultivar, exhibits enhanced disease resistance and drought tolerance traits

BACKGROUND

Rice is the main food crop for much of the population in China. Therefore, selecting and breeding new disease resistance and drought tolerance in rice is essential to ensure national food security. The utilization of heterosis has significantly enhanced rice productivity, yet many of the molecular mechanisms underlying this phenomenon remain largely unexplored. 'Nei 6 You 7075' ('N6Y7075') is a novel hybrid rice cultivar with exceptional quality, developed through the crossbreeding of 'Fuhui 7075' ('FH7075') and 'Neixiang 6 A' ('NX6A'). However, the precise mechanisms underlying the disease resistance and drought tolerance in 'N6Y7075' are poorly understood. In this study, we investigated the resistance of hybrid rice 'N6Y7075' to bacterial blight (Xanthomonas oryzae pv. oryzae), rice blast (Magnaporthe oryzae), and drought and identified differentially expressed genes between hybrid rice 'N6Y7075' and its parents through RNA-seq analysis.

RESULTS

Our research found that the hybrid 'N6Y7075' and its female parent 'NX6A' were less susceptible to bacterial blight and rice blast than the male parent 'FH7075', while 'FH7075' showed better drought tolerance than 'NX6A'. The hybrid 'N6Y7075' exhibited heterosis. Clustering results revealed that the expression profiles of the F1 hybrid closely resembled those of its parental lines rather than exhibiting an intermediate profile between the two parental lines. The disease resistance of hybrid rice 'N6Y7075' may be attributed to the plant-pathogen interaction pathways involving Xa21, CDPK, and RPM1-mediated hypersensitive response and WRKY1-induced defense-related gene expression and programmed cell death. The MAPK signaling pathway PR1 could also be associated with plant defense responses. Hybrid rice 'N6Y7075' may enhance drought tolerance by regulating MAPKKK17 and WAK60 in the MAPK signaling pathway. These proteins affect ABA stress adaptation and stomatal development in plants, respectively.

CONCLUSIONS

Our results provide a preliminary exploration of 'N6Y7075' disease resistance and drought tolerance and provide a relevant theoretical basis for its further study and use. This study provides insights into the molecular mechanisms of heterosis in hybrid rice and identifies potential associated genes.

Interplay of cell death pathways and immune responses in ischemic stroke: insights into novel biomarkers

Stroke is a severe neurological disease and a major worldwide issue, mostly manifesting as ischemic stroke (IS). In order to create effective treatments for IS, it is imperative to fully understand the underlying pathologies, as the existing therapeutic choices are inadequate. Recent investigations have shown the complex relationships between several programmed cell death (PCD) pathways, including necroptosis, ferroptosis, and pyroptosis, and their correlation with immune responses during IS. However, this relationship is still unclear. To address this gap, this review study explored the cellular interactions in the immune microenvironment of IS. Then, to validate prior findings and uncover biomarkers, the study investigated bioinformatics studies. Several pathways, including nuclear factor kappa-light-chain-enhancer of activated B cells (NF-κB), Toll-like receptor 4 (TLR4), and receptor-interacting protein kinase (RIPK), were involved in PCD-immune interactions. The bioinformatics studies reported key biomarkers such as glutathione peroxidase 4 (GPX4), NOD-like receptor family pyrin domain containing 3 (NLRP3), gasdermin D (GSDMD), and TLR4, which have important implications in ferroptosis, cuproptosis, pyroptosis, and necroptosis respectively. These biomarkers were associated with PCD mechanisms such as oxidative stress and inflammatory reactions. The immune infiltration analysis consistently revealed a significant correlation between PCD pathways and detrimental immune cells, such as neutrophils and γδ T cells. Conversely, M2 macrophages and T helper cells showed protective effects. In conclusion, considering the intricate network of interactions between immune responses and PCD pathways, this study emphasized the necessity of a paradigm shift in therapeutic approaches to address the injuries that are related to this complex network.

Key genes and pathways in the molecular landscape of pancreatic ductal adenocarcinoma: A bioinformatics and machine learning study

Pancreatic ductal adenocarcinoma (PDAC) is recognized for its aggressive nature, dismal prognosis, and a notably low five-year survival rate, underscoring the critical need for early detection methods and more effective therapeutic approaches. This research rigorously investigates the molecular mechanisms underlying PDAC, with a focus on the identification of pivotal genes and pathways that may hold therapeutic relevance and prognostic value. Through the construction of a protein-protein interaction (PPI) network and the examination of differentially expressed genes (DEGs), the study uncovers key hub genes such as CDK1, KIF11, and BUB1, demonstrating their substantial role in the pathogenesis of PDAC. Notably, the dysregulation of these genes is consistent across a spectrum of cancers, positing them as potential targets for wide-ranging cancer therapeutics. This study also brings to the fore significant genes encoding intrinsically disordered proteins, in particular GPRC5A and KRT7, unveiling promising new pathways for therapeutic intervention. Advanced machine learning techniques were harnessed to classify PDAC patients with high accuracy, utilizing the key genetic markers as a dataset. The Support Vector Machine (SVM) model leveraged the hub genes to achieve a sensitivity of 91 % and a specificity of 85 %, while the RandomForest model notched a sensitivity of 91 % and specificity of 92.5 %. Crucially, when the identified genes were cross-referenced with TCGA-PAAD clinical datasets, a tangible correlation with patient survival rates was discovered, reinforcing the potential of these genes as prognostic biomarkers and their viability as targets for therapeutic intervention. This study's findings serve as a potent testament to the value of molecular analysis in enhancing the understanding of PDAC and in advancing the pursuit for more effective diagnostic and treatment strategies.

Network-Constrained Eigen-Single-Cell Profile Estimation for Uncovering Crucial Immunogene Regulatory Systems in Human Bone Marrow

We focus on characterizing cell lines from young and aged-healthy and -AML (acute myeloid leukemia) cell lines, and our goal is to identify the key markers associated with the progression of AML. To characterize the age-related phenotypes in AML cell lines, we consider eigenCell analysis that effectively encapsulates the primary expression level patterns across the cell lines. However, earlier investigations utilizing eigenGenes and eigenCells analysis were based on linear combination of all features, leading to the disturbance from noise features. Moreover, the analysis based on a fully dense loading matrix makes it challenging to interpret the results of eigenCells analysis. In order to address these challenges, we develop a novel computational approach termed network-constrained eigenCells profile estimation, which employs a sparse learning strategy. The proposed method estimates eigenCell based on not only the lasso but also network constrained penalization. The use of the network-constrained penalization enables us to simultaneously select neighborhood genes. Furthermore, the hub genes and their regulator/target genes are easily selected as crucial markers for eigenCells estimation. That is, our method can incorporate insights from network biology into the process of sparse loading estimation. Through our methodology, we estimate sparse eigenCells profiles, where only critical markers exhibit expression levels. This allows us to identify the key markers associated with a specific phenotype. Monte Carlo simulations demonstrate the efficacy of our method in reconstructing the sparse structure of eigenCells profiles. We employed our approach to unveil the regulatory system of immunogenes in both young/aged-healthy and -AML cell lines. The markers we have identified for the age-related phenotype in both healthy and AML cell lines have garnered strong support from previous studies. Specifically, our findings, in conjunction with the existing literature, indicate that the activities within this subnetwork of CD79A could be pivotal in elucidating the mechanism driving AML progression, particularly noting the significant role played by the diminished activities in the CD79A subnetwork. We expect that the proposed method will be a useful tool for characterizing disease-related subsets of cell lines, encompassing phenotypes and clones.

An integrated bioinformatics approach reveals the potential role of microRNA-30b-5p and let-7a-5p during SARS CoV-2 spike-1 mediated neuroinflammation

SARS-CoV-2 induced neuroinflammation contributing to neurological sequelae is one of the critical outcomes of long-COVID, however underlying regulatory mechanisms involved therein are poorly understood. We deciphered the profile of dysregulated microRNAs, their targets, associated pathways, protein-protein interactions (PPI), transcription factor-hub genes interaction networks, hub genes-microRNA co-regulatory networks in SARS-CoV-2 Spike-1 (S1) stimulated microglial cells along with candidate drug prediction using RNA-sequencing and multiple bioinformatics approaches. We identified 11 dysregulated microRNAs in the S1-stimulated microglial cells (p < 0.05). KEGG analysis revealed involvement of important neuroinflammatory pathways such as MAPK signalling, PI3K-AKT signalling, Ras signalling and axon guidance. PPI analysis further identified 11 hub genes involved in these pathways. Real time PCR validation confirmed a significant upregulation of microRNA-30b-5p and let-7a-5p; proinflammatory cytokines- IL-6, TNF-α, IL-1β, GM-CSF; and inflammatory genes- PIK3CA and AKT in the S1-stimulated microglial cells, while PTEN and SHIP1 expression was decreased as compared to the non-stimulated cells. Drug prediction analysis further indicated resveratrol, diclofenac and rapamycin as the potential drugs based on their degree of interaction with hub genes. Thus, targeting of these microRNAs and/or their intermediate signalling molecules would be a prospective immunotherapeutic approach in alleviating SARS-CoV-2-S1 mediated neuroinflammation; and needs further investigations.

The Effect of Vitamin D Deficiency on Immune-Related Hub Genes: A Network Analysis Associated With Type 1 Diabetes

Background Type 1 diabetes (T1D) is an autoimmune disorder that results in the destruction of pancreatic beta cells, causing a shortage of insulin secretion. The development of T1D is influenced by both genetic predisposition and environmental factors, such as vitamin D. This vitamin is known for its ability to regulate the immune system and has been associated with a decreased risk of T1D. However, the specific ways in which vitamin D affects immune regulation and the preservation of beta cells in T1D are not yet fully understood. Gaining a better understanding of these interactions is essential for identifying potential targets for preventing and treating T1D. Methods The analysis focused on two Gene Expression Omnibus (GEO) datasets, namely, GSE55098 and GSE50012, to detect differentially expressed genes (DEGs). Enrichr (Ma'ayan Laboratory, New York, NY) was used to perform enrichment analysis for the Gene Ontology (GO) biological process and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways. The Search Tool for the Retrieval of Interacting Genes 12.0 (STRING) database was used to generate a protein-protein interaction (PPI) network. The Cytoscape 3.10.1 (Cytoscape Team, San Diego, CA) was used to analyze the PPI network and discover the hub genes. Results The DEGs in both datasets were identified using the GEO2R tool, with a particular focus on genes exhibiting contrasting regulations. Enrichment analysis unveiled the participation of these oppositely regulated DEGs in processes relevant to the immune system. Cytoscape analysis of the PPI network revealed five hub genes, MNDA, LILRB2, FPR2, HCK, and FCGR2A, suggesting their potential role in the pathogenesis of T1D and the response to vitamin D. Conclusion The study elucidates the complex interaction between vitamin D metabolism and immune regulation in T1D. The identified hub genes provide important knowledge on the molecular pathways that underlie T1D and have the potential to be targeted for therapeutic intervention. This research underscores the importance of vitamin D in the immune system's modulation and its impact on T1D development.

Gene expression analysis identifies hub genes and pathways distinguishing fatal from survivor outcomes of Ebola virus disease

The Ebola virus poses a severe public health threat, yet understanding factors influencing disease outcomes remains incomplete. Our study aimed to identify critical pathways and hub genes associated with fatal and survivor Ebola disease outcomes. We analyzed differentially expressed hub genes (DEGs) between groups with fatal and survival outcomes, as well as a healthy control group. We conducted additional analysis to determine the functions and pathways associated with these DEGs. We found 13,198 DEGs in the fatal and 12,039 DEGs in the survival group compared to healthy controls, and 1873 DEGs in the acute fatal and survivor groups comparison. Upregulated DEGs in the comparison between the acute fatal and survivor groups were linked to ECM receptor interaction, complement and coagulation cascades, and PI3K-Akt signaling. Upregulated hub genes identified from the acute fatal and survivor comparison (FGB, C1QA, SERPINF2, PLAT, C9, SERPINE1, F3, VWF) were enriched in complement and coagulation cascades; the downregulated hub genes (IL1B, 1L17RE, XCL1, CXCL6, CCL4, CD8A, CD8B, CD3D) were associated with immune cell processes. Hub genes CCL2 and F2 were unique to fatal outcomes, while CXCL1, HIST1H4F, and IL1A were upregulated hub genes unique to survival outcomes compared to healthy controls. Our results demonstrate for the first time the association of EVD outcomes to specific hub genes and their associated pathways and biological processes. The identified hub genes and pathways could help better elucidate Ebola disease pathogenesis and contribute to the development of targeted interventions and personalized treatment for distinct EVD outcomes.

Meta graphical lasso: uncovering hidden interactions among latent mechanisms

In complex systems, it's crucial to uncover latent mechanisms and their context-dependent relationships. This is especially true in medical research, where identifying unknown cancer mechanisms and their impact on phenomena like drug resistance is vital. Directly observing these mechanisms is challenging due to measurement complexities, leading to an approach that infers latent mechanisms from observed variable distributions. Despite machine learning advancements enabling sophisticated generative models, their black-box nature complicates the interpretation of complex latent mechanisms. A promising method for understanding these mechanisms involves estimating latent factors through linear projection, though there's no assurance that inferences made under specific conditions will remain valid across contexts. We propose a novel solution, suggesting data, even from systems appearing complex, can often be explained by sparse dependencies among a few common latent factors, regardless of the situation. This simplification allows for modeling that yields significant insights across diverse fields. We demonstrate this with datasets from finance, where we capture societal trends from stock price movements, and medicine, where we uncover new insights into cancer drug resistance through gene expression analysis.

The Gene Network Correlation Analysis of Obesity to Type 1 Diabetes and Cardiovascular Disorders: An Interactome-Based Bioinformatics Approach

The current study focuses on the importance of Protein-Protein Interactions (PPIs) in biological processes and the potential of targeting PPIs as a new treatment strategy for diseases. Specifically, the study explores the cross-links of PPIs network associated with obesity, type 1 diabetes mellitus (T1DM), and cardiac disease (CD), which is an unexplored area of research. The research aimed to understand the role of highly connected proteins in the network and their potential as drug targets. The methodology for this research involves retrieving genes from the NCBI online gene database, intersecting genes among three diseases (type 1 diabetes, obesity, and cardiovascular) using Interactivenn, determining suitable drug molecules using NetworkAnalyst, and performing various bioinformatics analyses such as Generic Protein-Protein Interactions, topological properties analysis, function enrichment analysis in terms of GO, and Kyoto Encyclopedia of Genes and Genomes (KEGG), gene co-expression network, and protein drug as well as protein chemical interaction network. The study focuses on human subjects. The results of this study identified 12 genes [VEGFA (Vascular Endothelial Growth Factor A), IL6 (Interleukin 6), MTHFR (Methylenetetrahydrofolate reductase), NPPB (Natriuretic Peptide B), RAC1 (Rac Family Small GTPase 1), LMNA (Lamin A/C), UGT1A1 (UDP-glucuronosyltransferase family 1 membrane A1), RETN (Resistin), GCG (Glucagon), NPPA (Natriuretic Peptide A), RYR2 (Ryanodine receptor 2), and PRKAG2 (Protein Kinase AMP-Activated Non-Catalytic Subunit Gamma 2)] that were shared across the three diseases and could be used as key proteins for protein-drug/chemical interaction. Additionally, the study provides an in-depth understanding of the complex molecular and biological relationships between the three diseases and the cellular mechanisms that lead to their development. Potentially significant implications for the therapy and management of various disorders are highlighted by the findings of this study by improving treatment efficacy, simplifying treatment regimens, cost-effectiveness, better understanding of the underlying mechanism of these diseases, early diagnosis, and introducing personalized medicine. In conclusion, the current study provides new insights into the cross-links of PPIs network associated with obesity, T1DM, and CD, and highlights the potential of targeting PPIs as a new treatment strategy for these prevalent diseases.

Transcriptome Profiling of Mouse Embryonic Fibroblast Spontaneous Immortalization: A Comparative Analysis

Cell immortalization, a hallmark of cancer development, is a process that cells can undergo on their path to carcinogenesis. Spontaneously immortalized mouse embryonic fibroblasts (MEFs) have been used for decades; however, changes in the global transcriptome during this process have been poorly described. In our research, we characterized the poly-A RNA transcriptome changes after spontaneous immortalization. To this end, differentially expressed genes (DEGs) were screened using DESeq2 and characterized by gene ontology enrichment analysis and protein-protein interaction (PPI) network analysis to identify the potential hub genes. In our study, we identified changes in the expression of genes involved in proliferation regulation, cell adhesion, immune response and transcriptional regulation in immortalized MEFs. In addition, we performed a comparative analysis with previously reported MEF immortalization data, where we propose a predicted gene regulatory network model in immortalized MEFs based on the altered expression of Mapk11, Cdh1, Chl1, Zic1, Hoxd10 and the novel hub genes Il6 and Itgb2.

Sparse spectral graph analysis and its application to gastric cancer drug resistance-specific molecular interplays identification

Uncovering acquired drug resistance mechanisms has garnered considerable attention as drug resistance leads to treatment failure and death in patients with cancer. Although several bioinformatics studies developed various computational methodologies to uncover the drug resistance mechanisms in cancer chemotherapy, most studies were based on individual or differential gene expression analysis. However the single gene-based analysis is not enough, because perturbations in complex molecular networks are involved in anti-cancer drug resistance mechanisms. The main goal of this study is to reveal crucial molecular interplay that plays key roles in mechanism underlying acquired gastric cancer drug resistance. To uncover the mechanism and molecular characteristics of drug resistance, we propose a novel computational strategy that identified the differentially regulated gene networks. Our method measures dissimilarity of networks based on the eigenvalues of the Laplacian matrix. Especially, our strategy determined the networks' eigenstructure based on sparse eigen loadings, thus, the only crucial features to describe the graph structure are involved in the eigenanalysis without noise disturbance. We incorporated the network biology knowledge into eigenanalysis based on the network-constrained regularization. Therefore, we can achieve a biologically reliable interpretation of the differentially regulated gene network identification. Monte Carlo simulations show the outstanding performances of the proposed methodology for differentially regulated gene network identification. We applied our strategy to gastric cancer drug-resistant-specific molecular interplays and related markers. The identified drug resistance markers are verified through the literature. Our results suggest that the suppression and/or induction of COL4A1, PXDN and TGFBI and their molecular interplays enriched in the Extracellular-related pathways may provide crucial clues to enhance the chemosensitivity of gastric cancer. The developed strategy will be a useful tool to identify phenotype-specific molecular characteristics that can provide essential clues to uncover the complex cancer mechanism.

Key gene modules and hub genes associated with pyrethroid and organophosphate resistance in Anopheles mosquitoes: a systems biology approach

Indoor residual spraying (IRS) and insecticide-treated nets (ITNs) are the main methods used to control mosquito populations for malaria prevention. The efficacy of these strategies is threatened by the spread of insecticide resistance (IR), limiting the success of malaria control. Studies of the genetic evolution leading to insecticide resistance could enable the identification of molecular markers that can be used for IR surveillance and an improved understanding of the molecular mechanisms associated with IR. This study used a weighted gene co-expression network analysis (WGCNA) algorithm, a systems biology approach, to identify genes with similar co-expression patterns (modules) and hub genes that are potential molecular markers for insecticide resistance surveillance in Kenya and Benin. A total of 20 and 26 gene co-expression modules were identified via average linkage hierarchical clustering from Anopheles arabiensis and An. gambiae, respectively, and hub genes (highly connected genes) were identified within each module. Three specific genes stood out: serine protease, E3 ubiquitin-protein ligase, and cuticular proteins, which were top hub genes in both species and could serve as potential markers and targets for monitoring IR in these malaria vectors. In addition to the identified markers, we explored molecular mechanisms using enrichment maps that revealed a complex process involving multiple steps, from odorant binding and neuronal signaling to cellular responses, immune modulation, cellular metabolism, and gene regulation. Incorporation of these dynamics into the development of new insecticides and the tracking of insecticide resistance could improve the sustainable and cost-effective deployment of interventions.

Identification of Novel Genes and Pathways of Ovarian Cancer Using a Comprehensive Bioinformatic Framework

Ovarian cancer (OC) is a significant contributor to gynecological cancer-related deaths worldwide, with a high mortality rate. Despite several advances in understanding the pathogenesis of OC, the molecular mechanisms underlying its development and prognosis remain poorly understood. Therefore, the current research study aimed to identify hub genes involved in the pathogenesis of OC that could serve as selective diagnostic and therapeutic targets. To achieve this, the dataset GEO2R was used to retrieve differentially expressed genes. The study identified a total of five genes (CDKN1A, DKK1, CYP1B1, NTS, and GDF15) that were differentially expressed in OC. Subsequently, a network analysis was performed using the STRING database, followed by the construction of a network using Cytoscape. The network analyzer tool in Cytoscape predicted 276 upregulated and 269 downregulated genes. Furthermore, KEGG analysis was conducted to identify different pathways related to OC. Subsequently, survival analysis was performed to validate gene expression alterations and predict hub genes, using a p-value of 0.05 as a threshold. Four genes (CDKN1A, DKK1, CYP1B1, and NTS) were predicted as significant hub genes, while one gene (GDF15) was predicted as non-significant. The adjusted P values of said predicted genes are 2.85E - 07, 5.49E - 06, 4.28E - 07, 1.43E - 07, and 3.70E - 07 for CDKN1A, DKK1, NTS, GDF15, and CYP1B1 respectively; additionally 6.08, 5.76, 5.74, 5.01, and 4.9 LogFc values of the said genes were predicted in GEO data set. In a boxplot analysis, the expression of these genes was analyzed in normal and tumor cells. The study found that three genes were highly expressed in tumor cells, while two genes (CDKN1A and DKK1) were more elevated in normal cells. According to the boxplot analysis for CDKN1A, 50% of tumor cells ranged between approx 3.8 and 5, while 50% of normal cells ranged between approx 6.9 and 7.9, which is greater than tumor cells. This shows that in normal cells, the CYP1B1 has a high expression level according to the GEPIA boxplot; addtionally the boxplot for DKK1 indicated that 50% of tumor cells ranged between approx 0 and 0.5, which was less than that of normal cells which ranged between approx 0.3 and 0.9. It shows that DKK1 is highly expressed in normal genes. Overall, the current study provides novel insights into the molecular mechanisms underlying OC. The identified hub genes and drug candidate targets could potentially serve as alternative diagnostic and therapeutic options for OC patients. Further research is needed to investigate the clinical significance of these findings and develop effective interventions that can improve the prognosis of patients with OC.

CX3CL1 (Fractalkine)-CX3CR1 Axis in Inflammation-Induced Angiogenesis and Tumorigenesis

The chemotactic cytokine fractalkine (FKN, chemokine CX3CL1) has unique properties resulting from the combination of chemoattractants and adhesion molecules. The soluble form (sFKN) has chemotactic properties and strongly attracts T cells and monocytes. The membrane-bound form (mFKN) facilitates diapedesis and is responsible for cell-to-cell adhesion, especially by promoting the strong adhesion of leukocytes (monocytes) to activated endothelial cells with the subsequent formation of an extracellular matrix and angiogenesis. FKN signaling occurs via CX3CR1, which is the only known member of the CX3C chemokine receptor subfamily. Signaling within the FKN-CX3CR1 axis plays an important role in many processes related to inflammation and the immune response, which often occur simultaneously and overlap. FKN is strongly upregulated by hypoxia and/or inflammation-induced inflammatory cytokine release, and it may act locally as a key angiogenic factor in the highly hypoxic tumor microenvironment. The importance of the FKN/CX3CR1 signaling pathway in tumorigenesis and cancer metastasis results from its influence on cell adhesion, apoptosis, and cell migration. This review presents the role of the FKN signaling pathway in the context of angiogenesis in inflammation and cancer. The mechanisms determining the pro- or anti-tumor effects are presented, which are the cause of the seemingly contradictory results that create confusion regarding the therapeutic goals.

Exploring protein profiles and hub genes in ameloblastoma

Ameloblastoma (AM) is a prominent benign odontogenic tumor characterized by aggressiveness, likely originating from tooth-generating tissue or the dental follicle (DF). However, proteomic distinctions between AM and DF remain unclear. In the present study, the aim was to identify the distinction between AM and DF in terms of their proteome and to determine the associated hub genes. Shotgun proteomics was used to compare the proteomes of seven fresh-frozen AM tissues and five DF tissues. Differentially expressed proteins (DEPs) were quantified and subsequently analyzed through Gene Ontology-based functional analysis, protein-protein interaction (PPI) analysis and hub gene identification. Among 7,550 DEPs, 520 and 216 were exclusive to AM and DF, respectively. Significant biological pathways included histone H2A monoubiquitination and actin filament-based movement in AM, as well as pro-B cell differentiation in DF. According to PPI analysis, the top-ranked upregulated hub genes were ubiquitin C (UBC), breast cancer gene 1 (BRCA1), lymphocyte cell-specific protein-tyrosine kinase (LCK), Janus kinase 1 and ATR serine/threonine kinase, whereas the top-ranked downregulated hub genes were UBC, protein kinase, DNA-activated, catalytic subunit (PRKDC), V-Myc avian myelocytomatosis viral oncogene homolog (MYC), tumor protein P53 and P21 (RAC1) activated kinase 1. When combining upregulated and downregulated genes, UBC exhibited the highest degree and betweenness values, followed by MYC, BRCA1, PRKDC, embryonic lethal, abnormal vision, Drosophila, homolog-like 1, myosin heavy chain 9, amyloid beta precursor protein, telomeric repeat binding factor 2, LCK and filamin A. In summary, these findings contributed to the knowledge on AM protein profiles, potentially aiding future research regarding AM etiopathogenesis and leading to AM prevention and treatment.

Comparative transcriptome profiling and co-expression network analysis uncover the key genes associated with pear petal defense responses against Monilinia laxa infection

Pear brown rot and blossom blight caused by Monilinia laxa seriously affect pear production worldwide. Here, we compared the transcriptomic profiles of petals after inoculation with M. laxa using two pear cultivars with different levels of sensitivity to disease (Sissy, a relatively tolerant cultivar, and Kristalli, a highly susceptible cultivar). Physiological indexes were also monitored in the petals of both cultivars at 2 h and 48 h after infection (2 HAI and 48 HAI). RNA-seq data and weighted gene co-expression network analysis (WGCNA) allowed the identification of key genes and pathways involved in immune- and defense-related responses that were specific for each cultivar in a time-dependent manner. In particular, in the Kristalli cultivar, a significant transcriptome reprogramming occurred early at 2 HAI and was accompanied either by suppression of key differentially expressed genes (DEGs) involved in the modulation of any defense responses or by activation of DEGs acting as sensitivity factors promoting susceptibility. In contrast to the considerably high number of DEGs induced early in the Kristalli cultivar, upregulation of specific DEGs involved in pathogen perception and signal transduction, biosynthesis of secondary and primary metabolism, and other defense-related responses was delayed in the Sissy cultivar, occurring at 48 HAI. The WGCNA highlighted one module that was significantly and highly correlated to the relatively tolerant cultivar. Six hub genes were identified within this module, including three WRKY transcription factor-encoding genes: WRKY 65 (pycom05g27470), WRKY 71 (pycom10g22220), and WRKY28 (pycom17g13130), which may play a crucial role in enhancing the tolerance of pear petals to M. laxa. Our results will provide insights into the interplay of the molecular mechanisms underlying immune responses of petals at the pear-M. laxa pathosystem.

Unveiling Gene Regulatory Networks That Characterize Difference of Molecular Interplays Between Gastric Cancer Drug Sensitive and Resistance Cell Lines

Gastric cancer is a leading cause of cancer-related deaths globally and chemotherapy is widely accepted as the standard treatment for gastric cancer. However, drug resistance in cancer cells poses a significant obstacle to the success of chemotherapy, limiting its effectiveness in treating gastric cancer. Although many studies have been conducted to unravel the mechanisms of acquired drug resistance, the existing studies were based on abnormalities of a single gene, that is, differential gene expression (DGE) analysis. Single gene-based analysis alone is insufficient to comprehensively understand the mechanisms of drug resistance in cancer cells, because the underlying processes of the mechanism involve perturbations of the molecular interactions. To uncover the mechanism of acquired gastric cancer drug resistance, we perform for identification of differentially regulated gene networks between drug-sensitive and drug-resistant cell lines. We develop a computational strategy for identifying phenotype-specific gene networks by extending the existing method, CIdrgn, that quantifies the dissimilarity of gene networks based on comprehensive information of network structure, that is, regulatory effect between genes, structure of edge, and expression levels of genes. To enhance the efficiency of identifying differentially regulated gene networks and improve the biological relevance of our findings, we integrate additional information and incorporate knowledge of network biology, such as hubness of genes and weighted adjacency matrices. The outstanding capabilities of the developed strategy are validated through Monte Carlo simulations. By using our strategy, we uncover gene regulatory networks that specifically capture the molecular interplays distinguishing drug-sensitive and drug-resistant profiles in gastric cancer. The reliability and significance of the identified drug-sensitive and resistance-specific gene networks, as well as their related markers, are verified through literature. Our analysis for differentially regulated gene network identification has the capacity to characterize the drug-sensitive and resistance-specific molecular interplays related to mechanisms of acquired drug resistance that cannot be revealed by analysis based solely on abnormalities of a single gene, for example, DGE analysis. Through our analysis and comprehensive examination of relevant literature, we suggest that targeting the suppressors of the identified drug-resistant markers, such as the Melanoma Antigen (MAGE) family, Trefoil Factor (TFF) family, and Ras-Associated Binding 25 (RAB25), while enhancing the expression of inducers of the drug sensitivity markers [e.g., Serum Amyloid A (SAA) family], could potentially reduce drug resistance and enhance the effectiveness of chemotherapy for gastric cancer. We expect that the developed strategy will serve as a useful tool for uncovering cancer-related phenotype-specific gene regulatory networks that provide essential clues for uncovering not only drug resistance mechanisms but also complex biological systems of cancer.

Unraveling the transcriptional features and gene expression networks of pathogenic and saprotrophic Ophiostoma species during the infection of Ulmus americana

American elm (Ulmus americana), highly prized for its ornamental value, has suffered two successive outbreaks of Dutch elm disease (DED) caused by ascomycete fungi belonging to the genus Ophiostoma. To identify the genes linked to the pathogenicity of different species and lineages of Ophiostoma, we inoculated 2-year-old U. americana saplings with six strains representing three species of DED fungi, and one strain of the saprotroph Ophiostoma quercus. Differential expression analyses were performed following RNA sequencing of fungal transcripts recovered at 3- and 10-days post-infection. Based on a total of 8,640 Ophiostoma genes, we observed a difference in fungal gene expression depending on the strain inoculated and the time of incubation in host tissue. Some genes overexpressed in the more virulent strains of Ophiostoma encode hydrolases that possibly act synergistically. A mutant of Ophiostoma novo-ulmi in which the gene encoding the ogf1 transcription factor had been deleted did not produce transcripts for the gene encoding the hydrophobin cerato-ulmin and was less virulent. Weighted gene correlation network analyses identified several candidate pathogenicity genes distributed among 13 modules of interconnected genes.IMPORTANCEOphiostoma is a genus of cosmopolitan fungi that belongs to the family Ophiostomataceae and includes the pathogens responsible for two devastating pandemics of Dutch elm disease (DED). As the mechanisms of action of DED agents remain unclear, we carried out the first comparative transcriptomic study including representative strains of the three Ophiostoma species causing DED, along with the phylogenetically close saprotrophic species Ophiostoma quercus. Statistical analyses of the fungal transcriptomes recovered at 3 and 10 days following infection of Ulmus americana saplings highlighted several candidate genes associated with virulence and host-pathogen interactions wherein each strain showed a distinct transcriptome. The results of this research underscore the importance of investigating the transcriptional behavior of different fungal taxa to understand their pathogenicity and virulence in relation to the timeline of infection.

Molecular mechanisms of cellular dysfunction in testes from men with non-obstructive azoospermia

Male factor infertility affects 50% of infertile couples worldwide; the most severe form, non-obstructive azoospermia (NOA), affects 10-15% of infertile males. Treatment for individuals with NOA is limited to microsurgical sperm extraction paired with in vitro fertilization intracytoplasmic sperm injection. Unfortunately, spermatozoa are only retrieved in ~50% of patients, resulting in live birth rates of 21-46%. Regenerative therapies could provide a solution; however, understanding the cell-type-specific mechanisms of cellular dysfunction is a fundamental necessity to develop precision medicine strategies that could overcome these abnormalities and promote regeneration of spermatogenesis. A number of mechanisms of cellular dysfunction have been elucidated in NOA testicular cells. These mechanisms include abnormalities in both somatic cells and germ cells in NOA testes, such as somatic cell immaturity, aberrant growth factor signalling, increased inflammation, increased apoptosis and abnormal extracellular matrix regulation. Future cell-type-specific investigations in identifying modulators of cellular transcription and translation will be key to understanding upstream dysregulation, and these studies will require development of in vitro models to functionally interrogate spermatogenic niche dysfunction in both somatic and germ cells.

Targeting IKZF1 via HDAC1: Combating Acute Myeloid Leukemia

Acute myeloid leukemia (AML) accounts for 1.3% of all cancers, with a limited survival of only 30%, and treating AML is a continuous challenge in medicine. IKZF1 is a DNA-binding protein that is highly mutated and undruggable but significant in causing AML. The current study aims to target its transcription factors (TFs) modulating IKZF1 activity. The TF network was constructed and analyzed which revealed a dense Markov cluster (MCL) cluster and five hub genes namely, HDAC1, EP300, CREBBP, TP53, and MYC; the first node clusters were generated for the hub genes. Functional enrichment analysis found AML pathway enriched in all the clusters. Gene ontology terms were majorly related to transcription regulation terms including RNA polymerase transcription regulation, DNA binding activity, DNA templated transcription, and transcription factor binding. Further, the mutation profile of all the TFs found HDAC1 with a very low mutation profile of 0.1% and the survival plot found HDAC1 with a hazard ratio of 1.17 with increased survival upon low expression. Also, among the hub genes, HDAC1 was the only first node interactor with IKZF1. Thus, HDAC1 could be a potential biomarker candidate as well as a key target in treating AML. Insight Box The study has an integrated approach for identifying a potential target through network analysis, functional enrichment analysis, mutation profiling survival prognosis, and target screening. The study employs a better strategy for targeting IKZF1, a significantly upregulated gene in AML by regulating its transcription factors. The analysis revealed a network of TFs regulating IKZF1, among which HDAC1 emerged as a promising candidate due to its low mutation rate, association with better survival outcomes, and direct interaction with IKZF1. This suggests HDAC1 could be a valuable biomarker and therapeutic target for AML treatment.

Inhibitory effects of Acanthopanax sessiliflorus Harms extract on the etiology of rheumatoid arthritis in a collagen-induced arthritis mouse model

BACKGROUND

The biological function of Acanthopanax sessiliflorus Harm (ASH) has been investigated on various diseases; however, the effects of ASH on arthritis have not been investigated so far. This study investigates the effects of ASH on rheumatoid arthritis (RA).

METHODS

Supercritical carbon dioxide (CO2) was used for ASH extract preparation, and its primary components, pimaric and kaurenoic acids, were identified using gas chromatography-mass spectrometer (GC-MS). Collagenase-induced arthritis (CIA) was used as the RA model, and primary cultures of articular chondrocytes were used to examine the inhibitory effects of ASH extract on arthritis in three synovial joints: ankle, sole, and knee.

RESULTS

Pimaric and kaurenoic acids attenuated pro-inflammatory cytokine-mediated increase in the catabolic factors and retrieved pro-inflammatory cytokine-mediated decrease in related anabolic factors in vitro; however, they did not affect pro-inflammatory cytokine (IL-1β, TNF-α, and IL-6)-mediated cytotoxicity. ASH effectively inhibited cartilage degradation in the knee, ankle, and toe in the CIA model and decreased pannus development in the knee. Immunohistochemistry demonstrated that ASH mostly inhibited the IL-6-mediated matrix metalloproteinase. Gene Ontology and pathway studies bridge major gaps in the literature and provide insights into the pathophysiology and in-depth mechanisms of RA-like joint degeneration.

CONCLUSIONS

To the best of our knowledge, this is the first study to conduct extensive research on the efficacy of ASH extract in inhibiting the pathogenesis of RA. However, additional animal models and clinical studies are required to validate this hypothesis.

Integrated whole transcriptome profiling revealed a convoluted circular RNA-based competing endogenous RNAs regulatory network in colorectal cancer

Recently, it has been identified that circRNAs can act as miRNA sponge to regulate gene expression in various types of cancers, associating them with cancer initiation and progression. The present study aims to identify colorectal cancer-related circRNAs and the underpinning mechanisms of circRNA/miRNA/mRNA networks in the development and progress of Colorectal Cancer. Differentially expressed circRNAs, miRNAs, and mRNAs were identified in GEO microarray datasets using the Limma package of R. The analysis of differentially expressed circRNAs resulted in 23 upregulated and 31 downregulated circRNAs. CeRNAs networks were constructed by intersecting the results of predicted and experimentally validated databases, circbank and miRWalk, and by performing DEMs and DEGs analysis using Cytoscape. Next, functional enrichment analysis was performed for DEGs included in ceRNA networks. Followed by survival analysis, expression profile assessment using TCGA and GEO data, and ROC curve analysis we identified a ceRNA sub-networks that revealed the potential regulatory effect of hsa_circ_0001955 and hsa_circ_0071681 on survival-related genes, namely KLF4, MYC, CCNA2, RACGAP1, and CD44. Overall, we constructed a convoluted regulatory network and outlined its likely mechanisms of action in CRC, which may contribute to the development of more effective approaches for early diagnosis, prognosis, and treatment of CRC.

MicroRNAs in Small Extracellular Vesicles from Amniotic Fluid and Maternal Plasma Associated with Fetal Palate Development in Mice

Cleft palate (CP) is a common congenital birth defect. Cellular and morphological processes change dynamically during palatogenesis, and any disturbance in this process could result in CP. However, the molecular mechanisms steering this fundamental phase remain unclear. One study suggesting a role for miRNAs in palate development via maternal small extracellular vesicles (SEVs) drew our attention to their potential involvement in palatogenesis. In this study, we used an in vitro model to determine how SEVs derived from amniotic fluid (ASVs) and maternal plasma (MSVs) influence the biological behaviors of mouse embryonic palatal mesenchyme (MEPM) cells and medial edge epithelial (MEE) cells; we also compared time-dependent differential expression (DE) miRNAs in ASVs and MSVs with the DE mRNAs in palate tissue from E13.5 to E15.5 to study the dynamic co-regulation of miRNAs and mRNAs during palatogenesis in vivo. Our results demonstrate that some pivotal biological activities, such as MEPM proliferation, migration, osteogenesis, and MEE apoptosis, might be directed, in part, by stage-specific MSVs and ASVs. We further identified interconnected networks and key miRNAs such as miR-744-5p, miR-323-5p, and miR-3102-5p, offering a roadmap for mechanistic investigations and the identification of early CP biomarkers.

In silico based analysis to explore genetic linkage between atherosclerosis and its potential risk factors

Atherosclerosis (ATH) is a chronic cardiovascular disease characterized by plaque formation in arteries, and it is a major cause of illness and death. Although therapeutic advances have significantly improved the prognosis of ATH, missing therapeutic targets pose a significant residual threat. This research used a systems biology approach to identify the molecular biomarkers involved in the onset and progression of ATH, analysing microarray gene expression datasets from ATH and tissues impacted by risk factors such as high cholesterol, adipose tissue, smoking, obesity, sedentary lifestyle, stress, alcohol consumption, hypertension, hyperlipidaemia, high fat, diabetes to find the differentially expressed genes (DEGs). Bioinformatic analyses of Protein-Protein Interaction (PPI), Gene Ontology (GO), and Kyoto Encyclopedia of Genes and Genomes (KEGG) were conducted on differentially expressed genes, revealing metabolic and signaling pathways (the chemokine signaling pathway, cytokine-cytokine receptor interaction, the cytosolic DNA-sensing pathway, the peroxisome proliferator-activated receptors signaling pathway, and the nuclear factor-kappa B signaling pathway), ten hubs proteins (CCL5, CCR1, TLR1, CCR2, FCGR2A, IL1B, CD163, AIF1, CXCL-1 and TNF), five transcription factors (YY1, FOXL1, FOXC1, SRF, and GATA2), and five miRNAs (mir-27a-3p, mir-124-3p, mir-16-5p, mir-129-2-3p, mir-1-3p). These findings identify potential biomarkers that may increase knowledge of the mechanisms underlying ATH and their connection to risk factors, aiding in the development of new therapies.

Arabidopsis cell suspension culture and RNA sequencing reveal regulatory networks underlying plant-programmed cell death

Programmed cell death (PCD) facilitates selective, genetically controlled elimination of redundant, damaged, or infected cells. In plants, PCD is often an essential component of normal development and can mediate responses to abiotic and biotic stress stimuli. However, studying the transcriptional regulation of PCD is hindered by difficulties in sampling small groups of dying cells that are often buried within the bulk of living plant tissue. We addressed this challenge by using RNA sequencing and Arabidopsis thaliana suspension cells, a model system that allows precise monitoring of PCD rates. The use of three PCD-inducing treatments (salicylic acid, heat, and critical dilution), in combination with three cell death modulators (3-methyladenine, lanthanum chloride, and conditioned medium), enabled isolation of candidate core- and stimuli-specific PCD genes, inference of underlying regulatory networks and identification of putative transcriptional regulators of PCD in plants. This analysis underscored a disturbance of the cell cycle and mitochondrial retrograde signaling, and repression of pro-survival stress responses, as key elements of the PCD-associated transcriptional signature. Further, phenotyping of Arabidopsis T-DNA insertion mutants in selected candidate genes validated the potential of generated resources to identify novel genes involved in plant PCD pathways and/or stress tolerance.

Integrated multiomic analysis and high-throughput screening reveal potential gene targets and synergetic drug combinations for osteosarcoma therapy

Although great advances have been made over the past decades, therapeutics for osteosarcoma are quite limited. We performed long-read RNA sequencing and tandem mass tag (TMT)-based quantitative proteome on osteosarcoma and the adjacent normal tissues, next-generation sequencing (NGS) on paired osteosarcoma samples before and after neoadjuvant chemotherapy (NACT), and high-throughput drug combination screen on osteosarcoma cell lines. Single-cell RNA sequencing data were analyzed to reveal the heterogeneity of potential therapeutic target genes. Additionally, we clarified the synergistic mechanisms of doxorubicin (DOX) and HDACs inhibitors for osteosarcoma treatment. Consequently, we identified 2535 osteosarcoma-specific genes and several alternative splicing (AS) events with osteosarcoma specificity and/or patient heterogeneity. Hundreds of potential therapeutic targets were identified among them, which showed the core regulatory roles in osteosarcoma. We also identified 215 inhibitory drugs and 236 synergistic drug combinations for osteosarcoma treatment. More interestingly, the multiomic analysis pointed out the pivotal role of HDAC1 and TOP2A in osteosarcoma. HDAC inhibitors synergized with DOX to suppress osteosarcoma both in vitro and in vivo. Mechanistically, HDAC inhibitors synergized with DOX by downregulating SP1 to transcriptionally modulate TOP2A expression. This study provided a comprehensive view of molecular features, therapeutic targets, and synergistic drug combinations for osteosarcoma.

Molecular crosstalk between COVID-19 and Alzheimer's disease using microarray and RNA-seq datasets: A system biology approach

Objective

Coronavirus disease 2019 (COVID-19) is an infectious disease caused by Severe Acute Respiratory Syndrome Coronavirus-2 (SARS-CoV-2). The clinical and epidemiological analysis reported the association between SARS-CoV-2 and neurological diseases. Among neurological diseases, Alzheimer's disease (AD) has developed as a crucial comorbidity of SARS-CoV-2. This study aimed to understand the common transcriptional signatures between SARS-CoV-2 and AD.

Materials and methods

System biology approaches were used to compare the datasets of AD and COVID-19 to identify the genetic association. For this, we have integrated three human whole transcriptomic datasets for COVID-19 and five microarray datasets for AD. We have identified differentially expressed genes for all the datasets and constructed a protein-protein interaction (PPI) network. Hub genes were identified from the PPI network, and hub genes-associated regulatory molecules (transcription factors and miRNAs) were identified for further validation.

Results

A total of 9,500 differentially expressed genes (DEGs) were identified for AD and 7,000 DEGs for COVID-19. Gene ontology analysis resulted in 37 molecular functions, 79 cellular components, and 129 biological processes were found to be commonly enriched in AD and COVID-19. We identified 26 hub genes which includes AKT1, ALB, BDNF, CD4, CDH1, DLG4, EGF, EGFR, FN1, GAPDH, INS, ITGB1, ACTB, SRC, TP53, CDC42, RUNX2, HSPA8, PSMD2, GFAP, VAMP2, MAPK8, CAV1, GNB1, RBX1, and ITGA2B. Specific miRNA targets associated with Alzheimer's disease and COVID-19 were identified through miRNA target prediction. In addition, we found hub genes-transcription factor and hub genes-drugs interaction. We also performed pathway analysis for the hub genes and found that several cell signaling pathways are enriched, such as PI3K-AKT, Neurotrophin, Rap1, Ras, and JAK-STAT.

Conclusion

Our results suggest that the identified hub genes could be diagnostic biomarkers and potential therapeutic drug targets for COVID-19 patients with AD comorbidity.

Bone marrow fatty acids affect osteoblastic differentiation through miR-92b-3p in the early stages of postmenopausal osteoporosis

Osteoporosis is partially caused by dysfunctions in the commitment, differentiation or survival of osteoblasts. Bone marrow fatty acids affect bone resorption and formation. In this study, we aimed to explore the role of fatty acids in the early stages of postmenopausal osteoporosis and determine whether they influence osteogenic differentiation through microRNAs. A quantitative analysis of bone marrow fatty acids early after ovariectomy or sham surgery in a rat osteoporotic model was performed using gas chromatography/mass spectrometry. The results showed that palmitoleate was significantly decreased on postoperative day 3 while both pentadecanoate and palmitoleate were significantly decreased on postoperative day 5 in rats in the ovariectomized group compared with those in the sham group. Palmitoleate promotes osteogenic differentiation, whereas pentadecanoate inhibits this process. Palmitoleate levels were higher than those of pentadecanoate; therefore, the early overall effect of significant bone marrow fatty acid changes was a decrease in osteogenic differentiation. We also found that miR-92b-3p inhibited osteoblastogenesis via the miR-92b-3p/phosphatase and tensin homolog regulatory axis. Palmitoleate, pentadecanoate, and palmitate influenced the osteoblastogenesis of MC3T3-E1 cells through miR-92b-3p. Taken together, we propose that miR-92b-3p mediates the effect of bone marrow fatty acids on osteoblast differentiation in the early stages of osteoporosis. These findings may provide molecular insights for the treatment of osteoporosis.

Bayesian Machine Learning Enables Identification of Transcriptional Network Disruptions Associated with Drug-Resistant Prostate Cancer

Survival rates of patients with metastatic castration-resistant prostate cancer (mCRPC) are low due to lack of response or acquired resistance to available therapies, such as abiraterone (Abi). A better understanding of the underlying molecular mechanisms is needed to identify effective targets to overcome resistance. Given the complexity of the transcriptional dynamics in cells, differential gene expression analysis of bulk transcriptomics data cannot provide sufficient detailed insights into resistance mechanisms. Incorporating network structures could overcome this limitation to provide a global and functional perspective of Abi resistance in mCRPC. Here, we developed TraRe, a computational method using sparse Bayesian models to examine phenotypically driven transcriptional mechanistic differences at three distinct levels: transcriptional networks, specific regulons, and individual transcription factors (TF). TraRe was applied to transcriptomic data from 46 patients with mCRPC with Abi-response clinical data and uncovered abrogated immune response transcriptional modules that showed strong differential regulation in Abi-responsive compared with Abi-resistant patients. These modules were replicated in an independent mCRPC study. Furthermore, key rewiring predictions and their associated TFs were experimentally validated in two prostate cancer cell lines with different Abi-resistance features. Among them, ELK3, MXD1, and MYB played a differential role in cell survival in Abi-sensitive and Abi-resistant cells. Moreover, ELK3 regulated cell migration capacity, which could have a direct impact on mCRPC. Collectively, these findings shed light on the underlying transcriptional mechanisms driving Abi response, demonstrating that TraRe is a promising tool for generating novel hypotheses based on identified transcriptional network disruptions.

SIGNIFICANCE

The computational method TraRe built on Bayesian machine learning models for investigating transcriptional network structures shows that disruption of ELK3, MXD1, and MYB signaling cascades impacts abiraterone resistance in prostate cancer.

Integrated Bioinformatics Analysis of Shared Genes, miRNA, Biological Pathways and Their Potential Role as Therapeutic Targets in Huntington's Disease Stages

Huntington's Disease (HD) is a progressive neurodegenerative disease caused by CAG repeat expansion in the huntingtin gene (HTT). The HTT gene was the first disease-associated gene mapped to a chromosome, but the pathophysiological mechanisms, genes, proteins or miRNAs involved in HD remain poorly understood. Systems bioinformatics approaches can divulge the synergistic relationships of multiple omics data and their integration, and thus provide a holistic approach to understanding diseases. The purpose of this study was to identify the differentially expressed genes (DEGs), HD-related gene targets, pathways and miRNAs in HD and, more specifically, between the pre-symptomatic and symptomatic HD stages. Three publicly available HD datasets were analysed to obtain DEGs for each HD stage from each dataset. In addition, three databases were used to obtain HD-related gene targets. The shared gene targets between the three public databases were compared, and clustering analysis was performed on the common shared genes. Enrichment analysis was performed on (i) DEGs identified for each HD stage in each dataset, (ii) gene targets from the public databases and (iii) the clustering analysis results. Furthermore, the hub genes shared between the public databases and the HD DEGs were identified, and topological network parameters were applied. Identification of HD-related miRNAs and their gene targets was obtained, and a miRNA-gene network was constructed. Enriched pathways identified for the 128 common genes revealed pathways linked to multiple neurodegeneration diseases (HD, Parkinson's disease, Spinocerebellar ataxia), MAPK and HIF-1 signalling pathways. Eighteen HD-related hub genes were identified based on network topological analysis of MCC, degree and closeness. The highest-ranked genes were FoxO3 and CASP3, CASP3 and MAP2 were found for betweenness and eccentricity and CREBBP and PPARGC1A were identified for the clustering coefficient. The miRNA-gene network identified eleven miRNAs (mir-19a-3p, mir-34b-3p, mir-128-5p, mir-196a-5p, mir-34a-5p, mir-338-3p, mir-23a-3p and mir-214-3p) and eight genes (ITPR1, CASP3, GRIN2A, FoxO3, TGM2, CREBBP, MTHFR and PPARGC1A). Our work revealed that various biological pathways seem to be involved in HD either during the pre-symptomatic or symptomatic stages of HD. This may offer some clues for the molecular mechanisms, pathways and cellular components underlying HD and how these may act as potential therapeutic targets for HD.

Identification of Novel Core Genes Involved in Malignant Transformation of Inflamed Colon Tissue Using a Computational Biology Approach and Verification in Murine Models

Inflammatory bowel disease (IBD) is a complex and multifactorial systemic disorder of the gastrointestinal tract and is strongly associated with the development of colorectal cancer. Despite extensive studies of IBD pathogenesis, the molecular mechanism of colitis-driven tumorigenesis is not yet fully understood. In the current animal-based study, we report a comprehensive bioinformatics analysis of multiple transcriptomics datasets from the colon tissue of mice with acute colitis and colitis-associated cancer (CAC). We performed intersection of differentially expressed genes (DEGs), their functional annotation, reconstruction, and topology analysis of gene association networks, which, when combined with the text mining approach, revealed that a set of key overexpressed genes involved in the regulation of colitis (C3, Tyrobp, Mmp3, Mmp9, Timp1) and CAC (Timp1, Adam8, Mmp7, Mmp13) occupied hub positions within explored colitis- and CAC-related regulomes. Further validation of obtained data in murine models of dextran sulfate sodium (DSS)-induced colitis and azoxymethane/DSS-stimulated CAC fully confirmed the association of revealed hub genes with inflammatory and malignant lesions of colon tissue and demonstrated that genes encoding matrix metalloproteinases (acute colitis: Mmp3, Mmp9; CAC: Mmp7, Mmp13) can be used as a novel prognostic signature for colorectal neoplasia in IBD. Finally, using publicly available transcriptomics data, translational bridge interconnecting of listed colitis/CAC-associated core genes with the pathogenesis of ulcerative colitis, Crohn's disease, and colorectal cancer in humans was identified. Taken together, a set of key genes playing a core function in colon inflammation and CAC was revealed, which can serve both as promising molecular markers and therapeutic targets to control IBD and IBD-associated colorectal neoplasia.

CYSRT1: an antimicrobial epidermal protein that can interact with late cornified envelope (LCE) proteins

Late cornified envelope (LCE) proteins are small cationic epidermal proteins with antimicrobial properties, and the combined deletion of LCE3B and LCE3C genes is a risk factor for psoriasis that affects skin microbiome composition. In a yeast two-hybrid screen we identified cysteine-rich tail 1 protein (CYSRT1) as an interacting partner of members of all LCE groups except LCE6. These interactions were confirmed in a mammalian cell system by co-immunoprecipitation. CYSRT1 is a protein of unknown function that is specifically expressed in cutaneous and oral epithelia and spatially colocalizes with LCE proteins in the upper layers of the suprabasal epidermis. Constitutive CYSRT1 expression is present in fully differentiated epidermis and can be further induced in vivo by disruption of the skin barrier upon stratum corneum removal. Transcriptional regulation correlates to keratinocyte terminal differentiation but not to skin bacteria exposure. Similar to LCEs, CYSRT1 was found to have antibacterial activity against Pseudomonas aeruginosa. Comparative gene sequence analysis and protein amino acid alignment indicates that CYSRT1 is highly conserved among vertebrates and has putative antimicrobial activity. To summarize, we identified CYSRT1 in the outer skin layer, where it colocalizes with LCE proteins and contributes to the constitutive epidermal antimicrobial host defense repertoire.

Transcriptome Analysis Revealed Osmoregulation Related Regulatory Networks and Hub Genes in the Gills of Hilsa shad, Tenualosa ilisha, during the Migratory Osmotic Stress

Tenualosa ilisha (Hilsa shad), an anadromous fish, usually inhabits coastal and estuarine waters, and migrates to freshwater for spawning. In this study, large-scale gill transcriptome analyses from three salinity regions, i.e., fresh, brackish and marine water, revealed 3277 differentially expressed genes (DEGs), out of which 232 were found to be common between marine vs freshwater and brackish vs freshwater. These genes were mapped into 54 KEGG Pathways, and the most significant of these were focal adhesion, adherens junction, tight junction, and PI3K-Akt signaling pathways. A total of 24 osmoregulatory genes were found to be differentially expressed in different habitats. The gene members of slc16 and slc2 families showed a dissimilar pattern of expressions, while two claudin genes (cldn11 & cldn10), transmembrane tm56b, and voltage-gated potassium channel gene kcna10 were downregulated in freshwater samples, as compared to that of brackish and marine environment. Protein-protein interaction (PPI) network analysis of 232 DEGs showed 101 genes to be involved in PPI, while fn1 gene was found to be interacting with the highest number of genes (36). Twenty-five hub genes belonged to 12 functional groups, with muscle structure development with seven genes, forming the major group. These results provided valuable information about the genes, potentially involved in the molecular mechanisms regulating water homeostasis in gills, during migration for spawning and low-salinity adaptation in Hilsa shad. These genes may form the basis for the bio-marker development for adaptation to the stress levied by major environmental changes, due to hatchery/culture conditions.

Screening and identification of potential hub genes and immune cell infiltration in the synovial tissue of rheumatoid arthritis by bioinformatic approach

Background

Rheumatoid arthritis (RA) is an autoimmune disease that affects individuals of all ages. The basic pathological manifestations are synovial inflammation, pannus formation, and erosion of articular cartilage, bone destruction will eventually lead to joint deformities and loss of function. However, the specific molecular mechanisms of synovitis tissue in RA are still unclear. Therefore, this study aimed to screen and explore the potential hub genes and immune cell infiltration in RA.

Methods

Three microarray datasets (GSE12021, GSE55457, and GSE55235), from the Gene Expression Omnibus (GEO) database, have been analyzed to explore the potential hub genes and immune cell infiltration in RA. First, the LIMMA package was used to screen the differentially expression genes (DEGs) after removing the batch effect. Then the clusterProfiler package was used to perform functional enrichment analyses. Second, through weighted coexpression network analysis (WGCNA), the key module was identified in the coexpression network of the gene set. Third, the protein-protein interaction (PPI) network was constructed through STRING website and the module analysis was performed using Cytoscape software. Fourth, the CIBERSORT and ssGSEA algorithm were used to analyze the immune status of RA and healthy synovial tissue, and the associations between immune cell infiltration and RA-related diagnostic biomarkers were evaluated. Fifth, we used the quantitative reverse transcription-polymerase chain reaction (qRT-PCR) to validate the expression levels of the hub genes, and ROC curve analysis of hub genes for discriminating between RA and healthy tissue. Finally, the gene-drug interaction network was constructed using DrugCentral database, and identification of drug molecules based on hub genes using the Drug Signature Database (DSigDB) by Enrichr.

Results

A total of 679 DEGs were identified, containing 270 downregulated genes and 409 upregulated genes. DEGs were primarily enriched in immune response and chemokine signaling pathways, according to functional enrichment analysis of DEGs. WGCNA explored the co-expression network of the gene set and identified key modules, the blue module was selected as the key module associated with RA. Seven hub genes are identified when PPI network and WGCNA core modules are intersected. Immune infiltration analysis using CIBERSORT and ssGSEA algorithms revealed that multiple types of immune infiltration were found to be upregulated in RA tissue compared to normal tissue. Furthermore, the levels of 7 hub genes were closely related to the relative proportions of multiple immune cells in RA. The results of the qRT-PCR demonstrated that the relative expression levels of 6 hub genes (CD27, LCK, CD2, GZMB, IL7R, and IL2RG) were up-regulated in RA synovial tissue, compared with normal tissue. Simultaneously, ROC curves indicated that the above 6 hub genes had strong biomarker potential for RA (AUC >0.8).

Conclusions

Through bioinformatics analysis and qRT-PCR experiment, our study ultimately discovered 6 hub genes (CD27, LCK, CD2, GZMB, IL7R, and IL2RG) that closely related to RA. These findings may provide valuable direction for future RA clinical diagnosis, treatment, and associated research.

Differentially Expressed Cell Cycle Genes and STAT1/3-Driven Multiple Cancer Entanglement in Psoriasis, Coupled with Other Comorbidities

Psoriasis is a persistent T-cell-supported inflammatory cutaneous disorder, which is defined by a significant expansion of basal cells in the epidermis. Cell cycle and STAT genes that control cell cycle progression and viral infection have been revealed to be comorbid with the development of certain cancers and other disorders, due to their abnormal or scanty expression. The purpose of this study is to evaluate the expression of certain cell cycle and STAT1/3 genes in psoriasis patients and to determine the types of comorbidities associated with these genes. To do so, we opted to adopt the in silico methodology, since it is a quick and easy way to discover any potential comorbidity risks that may exist in psoriasis patients. With the genes collected from early research groups, protein networks were created in this work using the NetworkAnalyst program. The crucial hub genes were identified by setting the degree parameter, and they were then used in gene ontology and pathway assessments. The transcription factors that control the hub genes were detected by exploring TRRUST, and DGIdb was probed for remedies that target transcription factors and hubs. Using the degree filter, the first protein subnetwork produced seven hub genes, including STAT3, CCNB1, STAT1, CCND1, CDC20, HSPA4, and MAD2L1. The hub genes were shown to be implicated in cell cycle pathways by the gene ontology and Reactome annotations. The former four hubs were found in signaling pathways, including prolactin, FoxO, JAK/STAT, and p53, according to the KEGG annotation. Furthermore, they enhanced several malignancies, including pancreatic cancer, Kaposi's sarcoma, non-small cell lung cancer, and acute myeloid leukemia. Viral infections, including measles, hepatitis C, Epstein-Barr virus, and HTLV-1 and viral carcinogenesis were among the other susceptible diseases. Diabetes and inflammatory bowel disease were conjointly annotated. In total, 129 medicines were discovered in DGIdb to be effective against the transcription factors BRCA1, RELA, TP53, and MYC, as opposed to 10 medications against the hubs, STAT3 and CCND1, in tandem with 8 common medicines. The study suggests that the annotated medications should be tested in suitable psoriatic cell lines and animal models to optimize the drugs used based on the kind, severity, and related comorbidities of psoriasis. Furthermore, a personalized medicine protocol must be designed for each psoriasis patient that displays different comorbidities.

[MiR-4772 modulates tumor immune microenvironment by regulating immune- related genes in ovarian cancer]

OBJECTIVE

To explore the regulatory role of miR-4772 in the formation of tumor immune microenvironment in ovarian cancer.

METHODS

The optimal cutoff level of PD-L1 expression was calculated based on data from 294 ovarian cancer patients in the TCGA database. The differentially expressed genes (DEGs) between high and low PD-L1 expression groups were screened, and the important DEGs were identified by correlation analysis. WGCNA analysis was performed to select the weighted genes and PD-L1-related miRNAs, from which the hub genes were obtained by intersection analysis. ssGSEA analysis was used to evaluate the effect of PD-L1 and miR-4772 expressions on the tumor immune microenvironment in ovarian cancer. KEGG analysis was used to identify the involved signal pathways, and the interactions between the hub genes were mapped by protein-protein interaction (PPI) analysis. Survival analysis was carried out to identify the survival-related hub genes, and the results were validated using the data of 399 patients with ovarian cancer from GEO database and the sequencing results of SKOV3 cells transfected with miR-4772 mimics or inhibitor.

RESULTS

According the optimal cutoff level of PD-L1 expression of 1.31582 (90th quantile), the patients were divided into high- and low-PD-L1 expression groups. A total of 840 DEGs were identified, including 549 significantly up-regulated genes and 291 down-regulated genes. Among them, 20 important DEGs were found to closely correlate with miR-4772 expression, and WGCNA analysis identified 48 weighted genes significantly correlated with miR-4772. Twelve genes were identified as both key DEGs and weighted genes and were treated as the hub genes. ssGSEA analysis showed that both the patients with high PD-L1 expressions and those with high miR-4772 expressions showed more active immune infiltration and functional activity. The 12 hub genes were involved mainly in immune-related signaling pathways, and PPI analysis suggested significant interactions among the hub genes. The two hub genes CD96 and TBX21 showed close correlation with the survival of ovarian cancer patients. The sequencing results of SKOV3 cells transfected with miR-4772 mimics or inhibitor showed that the changes in miR-4772 expression level caused obvious changes in the expressions of the 12 hub genes and PD-L1.

CONCLUSION

MiR-4772 plays a regulatory role in the formation of tumor immune microenvironment in ovarian cancer by regulating 12 hub genes.

Learning complex dependency structure of gene regulatory networks from high dimensional microarray data with Gaussian Bayesian networks

Reconstruction of Gene Regulatory Networks (GRNs) of gene expression data with Probabilistic Network Models (PNMs) is an open problem. Gene expression datasets consist of thousand of genes with relatively small sample sizes (i.e. are large-p-small-n). Moreover, dependencies of various orders coexist in the datasets. On the one hand transcription factor encoding genes act like hubs and regulate target genes, on the other hand target genes show local dependencies. In the field of Undirected Network Models (UNMs)-a subclass of PNMs-the Glasso algorithm has been proposed to deal with high dimensional microarray datasets forcing sparsity. To overcome the problem of the complex structure of interactions, modifications of the default Glasso algorithm have been developed that integrate the expected dependency structure in the UNMs beforehand. In this work we advocate the use of a simple score-based Hill Climbing algorithm (HC) that learns Gaussian Bayesian networks leaning on directed acyclic graphs. We compare HC with Glasso and variants in the UNM framework based on their capability to reconstruct GRNs from microarray data from the benchmarking synthetic dataset from the DREAM5 challenge and from real-world data from the Escherichia coli genome. We conclude that dependencies in complex data are learned best by the HC algorithm, presenting them most accurately and efficiently, simultaneously modelling strong local and weaker but significant global connections coexisting in the gene expression dataset. The HC algorithm adapts intrinsically to the complex dependency structure of the dataset, without forcing a specific structure in advance.

Spatiotemporal MicroRNA-Gene Expression Network Related to Orofacial Clefts

Craniofacial structures change dynamically in morphology during development through the coordinated regulation of various cellular molecules. However, it remains unclear how these complex mechanisms are regulated in a spatiotemporal manner. Here we applied natural cubic splines to model gene and microRNA (miRNA) expression from embryonic day (E) 10.5 to E14.5 in the proximal and distal regions of the maxillary processes to identify spatiotemporal patterns of gene and miRNA expression, followed by constructing corresponding regulatory networks. Three major groups of differentially expressed genes (DEGs) were identified, including 3,927 temporal, 314 spatial, and 494 spatiotemporal DEGs. Unsupervised clustering further resolved these spatiotemporal DEGs into 8 clusters with distinct expression patterns. Interestingly, we found 2 clusters of differentially expressed miRNAs: 1 had 80 miRNAs monotonically decreasing and the other had 97 increasing across developmental stages. To evaluate the phenotypic relevance of these DEGs during craniofacial development, we integrated data from the CleftGeneDB database and constructed the regulatory networks of genes related to orofacial clefts. Our analysis revealed 2 hub miRNAs, mmu-miR-325-3p and mmu-miR-384-5p, that repressed cleft-related genes Adamts3, Runx2, Fgfr2, Acvr1, and Edn2, while their expression increased over time. On the contrary, 2 hub miRNAs, mmu-miR-218-5p and mmu-miR-338-5p, repressed cleft-related genes Pbx2, Ermp1, Snai1, Tbx2, and Bmi1, while their expression decreased over time. Our experiments indicated that these miRNA mimics significantly inhibited cell proliferation in mouse embryonic palatal mesenchymal (MEPM) cells and O9-1 cells through the regulation of genes associated with cleft palate and validated the role of our regulatory networks in orofacial clefts. To facilitate interactive exploration of these data, we developed a user-friendly web tool to visualize the gene and miRNA expression patterns across developmental stages, as well as the regulatory networks (https://fyan.shinyapps.io/facebase_shiny/). Taken together, our results provide a valuable resource that serves as a reference map for future research in craniofacial development.

Network approaches for omics studies of neurodegenerative diseases

The recent methodological advances in multi-omics approaches, including genomic, transcriptomic, metabolomic, lipidomic, and proteomic, have revolutionized the research field by generating “big data” which greatly enhanced our understanding of the molecular complexity of the brain and disease states. Network approaches have been routinely applied to single-omics data to provide critical insight into disease biology. Furthermore, multi-omics integration has emerged as both a vital need and a new direction to connect the different layers of information underlying disease mechanisms. In this review article, we summarize popular network analytic approaches for single-omics data and multi-omics integration and discuss how these approaches have been utilized in studying neurodegenerative diseases.

Identification and validation of potential hub genes in rheumatoid arthritis by bioinformatics analysis

OBJECTIVE

Rheumatoid arthritis (RA) is considered to be a chronic immune disease pathologically characterized by synovial inflammation and bone destruction. At present, the potential pathogenesis of RA is still unclear. Hub genes are recognized to play a pivotal role in the occurrence and progression of RA.

METHODS

Firstly, we attempted to screen hub genes that are associated with RA, to clarify the underlying pathological mechanisms of RA, and to offer potential treatment methods for RA. We acquired these datasets (GSE12021, GSE55235, and GSE55457) of RA patients and healthy samples from the Gene Expression Omnibus (GEO) database. Differentially expressed genes (DEGs) were recognized via R software. Then, Gene ontology (GO) functional analysis and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis were utilized to deeply explore the underlying biological functions and pathways closely associated with RA. In addition, a protein-protein interaction (PPI) network was built to further evaluate and screen for hub genes. Finally, on the basis of the results of PPI analysis, we confirmed the mRNA expression levels of five hub genes in the synovial tissue of rats modeled with RA.

RESULTS

In the human microarray datasets, LCK, JAK2, SOCS3, STAT1, and EGFR were identified as hub genes associated with RA by bioinformatics analysis. Furthermore, we verified the differential expression levels of hub genes in rat synovial tissues via qRT-PCR (P < 0.05).

CONCLUSIONS

Our findings suggest that the hub genes LCK, JAK2, SOCS3, STAT1, and EGFR might have vital roles in the progression of RA and may offer novel therapeutic treatments for RA.

PredictiveNetwork: predictive gene network estimation with application to gastric cancer drug response-predictive network analysis

Background

Gene regulatory networks have garnered a large amount of attention to understand disease mechanisms caused by complex molecular network interactions. These networks have been applied to predict specific clinical characteristics, e.g., cancer, pathogenicity, and anti-cancer drug sensitivity. However, in most previous studies using network-based prediction, the gene networks were estimated first, and predicted clinical characteristics based on pre-estimated networks. Thus, the estimated networks cannot describe clinical characteristic-specific gene regulatory systems. Furthermore, existing computational methods were developed from algorithmic and mathematics viewpoints, without considering network biology.

Results

To effectively predict clinical characteristics and estimate gene networks that provide critical insights into understanding the biological mechanisms involved in a clinical characteristic, we propose a novel strategy for predictive gene network estimation. The proposed strategy simultaneously performs gene network estimation and prediction of the clinical characteristic. In this strategy, the gene network is estimated with minimal network estimation and prediction errors. We incorporate network biology by assuming that neighboring genes in a network have similar biological functions, while hub genes play key roles in biological processes. Thus, the proposed method provides interpretable prediction results and enables us to uncover biologically reliable marker identification. Monte Carlo simulations shows the effectiveness of our method for feature selection in gene estimation and prediction with excellent prediction accuracy. We applied the proposed strategy to construct gastric cancer drug-responsive networks.

Conclusion

We identified gastric drug response predictive markers and drug sensitivity/resistance-specific markers, AKR1B10, AKR1C3, ANXA10, and ZNF165, based on GDSC data analysis. Our results for identifying drug sensitive and resistant specific molecular interplay are strongly supported by previous studies. We expect that the proposed strategy will be a useful tool for uncovering crucial molecular interactions involved a specific biological mechanism, such as cancer progression or acquired drug resistance.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12859-022-04871-z.

Molecular Pathways and Key Genes Associated With Breast Width and Protein Content in White Striping and Wooden Breast Chicken Pectoral Muscle

Growth-related abnormalities affecting modern chickens, known as White Striping (WS) and Wooden Breast (WB), have been deeply investigated in the last decade. Nevertheless, their precise etiology remains unclear. The present study aimed at providing new insights into the molecular mechanisms involved in their onset by identifying clusters of co-expressed genes (i.e., modules) and key loci associated with phenotypes highly related to the occurrence of these muscular disorders. The data obtained by a Weighted Gene Co-expression Network Analysis (WGCNA) were investigated to identify hub genes associated with the parameters breast width (W) and total crude protein content (PC) of Pectoralis major muscles (PM) previously harvested from 12 fast-growing broilers (6 normal vs. 6 affected by WS/WB). W and PC can be considered markers of the high breast yield of modern broilers and the impaired composition of abnormal fillets, respectively. Among the identified modules, the turquoise (r = -0.90, p &lt; 0.0001) and yellow2 (r = 0.91, p &lt; 0.0001) were those most significantly related to PC and W, and therefore respectively named “protein content” and “width” modules. Functional analysis of the width module evidenced genes involved in the ubiquitin-mediated proteolysis and inflammatory response. GTPase activator activity, PI3K-Akt signaling pathway, collagen catabolic process, and blood vessel development have been detected among the most significant functional categories of the protein content module. The most interconnected hub genes detected for the width module encode for proteins implicated in the adaptive responses to oxidative stress (i.e., THRAP3 and PRPF40A), and a member of the inhibitor of apoptosis family (i.e., BIRC2) involved in contrasting apoptotic events related to the endoplasmic reticulum (ER)-stress. The protein content module showed hub genes coding for different types of collagens (such as COL6A3 and COL5A2), along with MMP2 and SPARC, which are implicated in Collagen type IV catabolism and biosynthesis. Taken together, the present findings suggested that an ER stress condition may underly the inflammatory responses and apoptotic events taking place within affected PM muscles. Moreover, these results support the hypothesis of a role of the Collagen type IV in the cascade of events leading to the occurrence of WS/WB and identify novel actors probably involved in their onset.