NetworkAnalyst--integrative approaches for protein-protein interaction network analysis and visual exploration

Rare and low-frequency variants in families with otitis media

Otitis media is a highly frequent diagnosis in children that causes significant morbidity but remains understudied as a genetic trait despite significant heritability in families. To identify rare or low-frequency variants within genes that confer susceptibility to otitis media, exome sequence data of 287 individuals from 243 families were analyzed. Identified variants were tested for co-segregation with otitis media in family members. Genome sequence data from a case-control cohort was imputed and analyzed for association of specific genes with otitis media. Single-cell RNA-sequence data of identified genes were noted in acutely infected mouse middle ears. Thirty-three variants within 24 genes co-segregated with otitis media in 28 families, of which 18 variants were considered pathogenic or likely pathogenic. An additional 81 variants in 21 of the same genes were identified in 83 unrelated probands with otitis media. Of the 24 genes, 12 were associated with otitis media in mouse models, while 15 genes were replicated from previous human studies. A common variant EYA4 c.829G > A was associated with OM in the case-control cohort. Using network analysis, 22 of the 24 genes were connected in a subnetwork enriched in various signaling pathways, Th1/Th2/Th17 cell differentiation, and viral infections. Majority (87.5%) of the identified genes were expressed in mouse middle ear cells, with differential expression after acute infection. The identification of novel genes and variants for susceptibility to otitis media will be useful in future risk screening and clinical management in children that require a more personalized approach due to poor response to standard treatments. KEY MESSAGES: Thirty-three variants in 24 genes were identified in 28 families with otitis media. Eighteen of these variants within 10 genes were considered (likely) pathogenic. A common variant EYA4 c.829G > A was associated with OM in a case-control cohort. The novel genes were differentially expressed in mouse middle ear post-infection. Genetic screening could identify children for targeted treatment for otitis media.

Association Between Altered Microbiota Composition and Immune System-Related Genes in COVID-19 Infection

Microbiota and immunity affect the host's susceptibility to SARS-CoV-2 infection and the severity of COVID-19. This study aimed to identify significant alterations in the microbiota composition, immune signaling pathways, their potential association, and candidate microRNA in COVID-19 patients using an in silico study model. Enrichment online databases and Python programming were utilized to analyze GSE164805, GSE180594, and GSE182279, as well as NGS data of microbiota composition (PRJNA650244 and PRJNA660302) associated with COVID-19, employing amplicon-based/marker gene sequencing methods. C1, TNF, C2, IL1, and CFH genes were found to have a significant impact on immune signaling pathways. Additionally, we observed a notable decrease in Bacteroides spp. and Faecalibacterium sp., while Escherichia coli, Streptococcus spp., and Akkermansia muciniphila showed increased abundance in COVID-19. Notably, A. muciniphila demonstrated an association with immunity through C1 and TNF, while Faecalibacterium sp. was linked to C2 and IL1. The correlation between E. coli and CFH, as well as IL1 and Streptococcus spp. with C2, was identified. hsa-let-7b-5p was identified as a potential candidate that may be involved in the interaction between the microbiota composition, immune response, and COVID-19. In conclusion, integrative in silico analysis shows that these microbiota members are potentially crucial in the immune responses against COVID-19.

Protein subinteractomes of human microsomal cytochromes P450

Microsomal cytochromes P450 (micCYPs) are monooxygenases located in the endoplasmic reticulum and other endomembranes of human cells. micCYPs receive electrons from specific redox partners and perform enzymatic transformations of drugs and different endogenous substrates. The large biodiversity of micCYPs leads to the idea that protein-protein interactions (PPIs) involving micCYPs are not limited to classical redox partners. This review aims to perform a systems biology analysis of the complete set of PPIs for all 33 micCYPs studied, as well as to examine the subinteractome of each micCYP. We have retrieved 287 PPIs from interactomic databases, involving 246 unique protein interactors that share a similar profile of subcellular localization with micCYPs. The number of protein interactors per micCYP unevenly varies from one to 47. Interactors of micCYPs are involved in cellular metabolism, signal transduction, cell-cell junctions, cytoskeleton organization, and intracellular or transmembrane transport. Notably, up to one-third of all interactors belong to the latter group, half of which consists of membrane transporters of compounds, metabolites, and ions (e.g., CACNA2D1, ORAI1, SCN3B, SLC7A2, SLC19A3, and SLC11A2). The CYP2C8 subinteractome is enriched with proteins involved in autophagy; CYP2S1- ERBB2 and EPH-Ephrin signaling; CYP3A4- glucuronidation. Proteins UBC, PGRMC1, and FANCG are the most frequent common interactors across various micCYPs. Nine and 12 interactors of micCYPs are involved in phosphorylation and ubiquitination, respectively; 20 interactors are 'moonlighting' proteins that are represented in the CYP3A4 subinteractome. Furthermore, micCYPs such as CYP2C9, 3A5, 2E1, 2A6, 4F2, and 4A11 may be involved in potentially binary interactions with other micCYPs. The functional implication of these CYP-CYP pairs is likely associated with modulation of their activity. Analysis of transcriptomic data revealed that some micCYP/interactor pairs exhibit tissue-, time-, and disease-specific gene expression patterns. Drugs that are metabolized by micCYPs in some cases can influence the expression of corresponding interactors at the gene or protein levels. These findings suggest that micCYPs may play roles in functions beyond their monooxygenase activity, as indicated by the spectrum of PPIs analyzed.

Significance of Gelsolin Superfamily Genes in Diagnosis, Prognosis and Immune Microenvironment Regulation for Endometrial Cancer

BACKGROUND

Previously, anti-CTLA4 and anti-PD-1/PD-L1 immunotherapies have shown limited efficacy in MSI-H/MMR-D endometrial cancer, leading to poor clinical outcomes. The gelsolin superfamily, which includes GSN, SCIN, VILL, VIL1, CAPG, AVIL, SVIL, and FLII, plays crucial roles in cell motility and gene regulation.

AIMS

The objective of this study is to explore the potential therapeutic and prognostic implications of the gelsolin superfamily in EC.

MATERIALS & METHODS

Data from TCGA, GEPIA, THPA, UALCAN, and Kaplan-Meier plotter databases were analyzed to investigate the expression and clinical relevance of gelsolin superfamily members. Co-expression networks of the gelsolin superfamily were assessed using LinkedOmics, GeneMANIA, and NetworkAnalyst. The relationship between gelsolin superfamily and immune cell infiltration was investigated using TIMER, ImmuCellAI, and GEPIA.

RESULTS

We found that high expressions of CAPG, AVIL, and SVIL were associated with poor prognosis, while high expressions of GSN and FLII were linked to better outcomes in EC. Functional enrichment analysis indicated the involvement of gelsolin superfamily members in pathways related to estrogen response, MYC targets, epithelial-mesenchymal transition, TGF beta signaling, MTORC1 signaling, oxidative phosphorylation, inflammatory response, and IL6-JAK-STAT3 signaling. Furthermore, gelsolin superfamily members demonstrated strong correlations with the levels of monocytes, natural killer T, naive CD4+ T, follicular helper T, and central memory T in EC. In vitro studies showed that silencing CAPG and FLII could inhibit proliferation and metastasis in endometrial cancer cell lines.

CONCLUSION

These findings indicate the significant association of gelsolin superfamily members with prognosis and immunological status in endometrial cancer.

Motif distribution in genomes gives insights into gene clustering and co-regulation

We read the genome as proteins in the cell would - by studying the distributions of 5-6 base motifs of DNA in the whole genome or smaller stretches such as parts of, or whole chromosomes. This led us to some interesting findings about motif clustering and chromosome organization. It is quite clear that the motif distribution in genomes is not random at the length scales we examined: 1 kb to entire chromosomes. The observed-to-expected (OE) ratios of motif distributions show strong correlations in pairs of chromosomes that are susceptible to translocations. With the aid of examples, we suggest that similarity in motif distributions in promoter regions of genes could imply co-regulation. A simple extension of this idea empowers us with the ability to construct gene regulatory networks. Further, we could make inferences about the spatial proximity of genomic fragments using these motif distributions. Spatially proximal regions, as deduced by Hi-C or pcHi-C, were ∼3.5 times more likely to have their motif distributions correlated than non-proximal regions. These correlations had strong contributions from the CTCF protein recognizing motifs which are known markers of topologically associated domains. In general, correlating genomic regions by motif distribution comparisons alone is rife with functional information.

Screening and identification of gene expression in large cohorts of clinical tissue samples unveils the major involvement of EZH2 and SOX2 in lung cancer

Lung adenocarcinoma (LUAD), the primary subtype of Non-Small Cell Lung Cancer (NSCLC), accounts for 80 % to 85 % of cases. Due to suboptimal screening method, LUAD is often detected in late stage, leading to aggressive progression and poor outcomes. Therefore, early disease prognosis for the LUAD is high priority. In order to identify early detection biomarkers, we conducted a meta-analysis of mRNA expression TCGA and GTEx datasets from LUAD patients. A total of 795 differentially expressed genes (DEGs) were identified by exploring the Network-Analyst tool and utilizing combined effect size methods. DEGs refer to genes whose expression levels are significantly different (either higher or lower) compared to their normal baseline expression levels. KEGG pathway enrichment analysis highlighted the TNF signaling pathway as being prominently associated with these DEGs. Subsequently, using the MCODE and CytoHubba plugins in Cytoscape software, we filtered out the top 10 genes. Among these, SOX2 was the only gene exhibiting higher expression, while the others were downregulated. Consequently, our subsequent research focused on SOX2. Further transcription factor-gene network analysis revealed that enhancer of zeste homolog 2 (EZH2) is a significant partner of SOX2, potentially playing a crucial role in euchromatin-heterochromatin dynamics. Structure of SOX2 protein suggest that it is a non-druggable transcription factor, literature survey suggests the same. SOX2 is considered challenging to target directly, or "non-druggable," because of several intrinsic properties that make it difficult to design effective therapeutic agents against it. The primary function of SOX2 is to bind DNA and regulates gene expression. Unlike enzymes or receptors with defined active sites or binding pockets, transcription factors typically have relatively flat or diffuse surfaces that do not offer obvious "pockets" for small molecules to bind effectively. Hence, we drove our focus to investigate on potential drug(s) targeting EZH2. Molecular docking analyses predicted most probable inhibitors of EZH2. We employed several predictive analysis tools and identified GSK343, as a promising inhibitor of EZH2.

Genome-wide identification, characterization and expression profiles of FORMIN gene family in cotton (Gossypium Raimondii L.)

BACKGROUND

Gossypium raimondii serves as a widely used genomic model cotton species. Its genetic influence to enhance fiber quality and ability to adapt to challenging environments both contribute to increasing cotton production. The formins are a large protein family that predominately consists of FH1 and FH2 domains. The presence of the formin domains highly regulates the actin and microtubule filament in the cytoskeleton dynamics confronting various abiotic stresses such as drought, salinity, and cold temperatures.

RESULTS

In this study, 26 formin genes were analyzed and characterized in G. raimondii and mostly were found in the nucleus and chloroplast. According to the evolutionary phylogenetic relationship, GrFH were dispersed and classified into seven different groups and shared an ancestry relationship with MtFH. The GrFH gene structure prediction revealed diverse intron-exon arrangements between groups. The FH2 conserved domain was found in all the GrFH distributed on 12 different chromosomes. Moreover, 11 pairs of GrFH transpired segmental duplication. Among them, GrFH4-GrFH7 evolved 35 million years ago (MYA) according to the evolutionary divergence time. Besides, 57 cis-acting regulatory elements (CAREs) motifs were found to play a potential role in plant growth, development, and in response to various abiotic stresses, including cold stress. The GrFH genes mostly exhibited biological processes resulting in the regulation of actin polymerization. The ERF, GATA, MYB, and LBD, major transcription factors (TFs) families in GrFH, regulated expression in abiotic stress specifically salt as well as defense against certain pathogens. The microRNA of GrFH unveiled the regulatory mechanism to regulate their gene expression in abiotic stresses such as salt and cold. One of the most economic aspects of cotton (G.raimondii) is the production of lint due to its use in manufacturing fabrics and other industrial applications. The expression profiles of GrFH in different tissues particularly during the conversion from ovule to fiber (lint), and the increased levels (up-regulation) of GrFH4, GrFH6, GrFH12, GrFH14, and GrFH26 under cold conditions, along with GrFH19 and GrFH26 in response to salt stress, indicated their potential involvement in combating these environmental challenges. Moreover, these stress-tolerant GrFH linked to cytoskeleton dynamics are essential in producing high-quality lint.

CONCLUSIONS

The findings from this study can contribute to elucidating the evolutionary and functional characterizations of formin genes and deciphering their potential role in abiotic stress such as cold and salt as well as in the future implications in wet lab.

Transcriptomic evidence of black soybean ethanolic extract and 2-aminobutyric acid in suppressing neuroinflammation and enhancing synaptic transmission

INTRODUCTION

Recently, the awareness of the beneficial utilization of natural bioactive compounds in treating neuroinflammation has gained particular attention. We aimed to understand the anti-neuroinflammatory effect of black soybeans (Glycine max (L.) Merr) ethanolic extract (BBEE) and its bioactive compound, 2-aminobutyric acid (2-AB), against LPS-induced SH-SY5Y cells.

METHOD

Cell viability and the optimum therapeutic dose were confirmed by MTT assay. We conducted a whole-transcriptomic analysis of BBEE and 2-AB in LPS-induced SH-SY5Y cells using microarray normalized with SST-RMA. DEGs were selected based on p-value < 0.05 and fold change > 2, and validated by RT-qPCR and immunocytochemical analyses.

RESULTS

We found that both BBEE and 2-AB down-regulated the expression of inflammatory cytokines IL6 and TNFA under LPS-induced conditions. This was also observed in the microarray data, showing downregulation of several inflammatory pathways, such as NF-kB, and IL6-JAK/STAT3-signaling pathways. In contrast, it upregulated the expression of CALML3, GRIN2, and GRIA2 gene expressions, which influence the AMPK and CAMK2 signaling pathways, indicating the potential of BBEE in neurotransmission and synaptic function. Also, immunofluorescence analysis revealed that 2-AB treatment significantly increased PSD-95 and Ca2+ levels, suggesting its effect on synaptic transmission essential for brain function.

CONCLUSION

Our findings suggest the potential anti-neuroinflammatory effects of BBEE and 2-AB, which may offer therapeutic and preventive benefits in mitigating neurological disorders. Given that BB is widely consumed in many Asian countries, our study may encourage its incorporation into the daily diet to slow inflammation-induced neurodegenerative disorders, reduce age-related cognitive decline, and enhance overall brain function.

A Multistep In Silico Approach Identifies Potential Glioblastoma Drug Candidates via Inclusive Molecular Targeting of Glioblastoma Stem Cells

Glioblastoma (GBM) is the highest grade of glioma for which no effective therapy is currently available. Despite extensive research in diagnosis and therapy, there has been no significant improvement in GBM outcomes, with a median overall survival continuing at a dismal 15-18 months. In recent times, glioblastoma stem cells (GSCs) have been identified as crucial drivers of treatment resistance and tumor recurrence, and GBM therapies targeting GSCs are expected to improve patient outcomes. We used a multistep in silico screening strategy to identify repurposed candidate drugs against selected therapeutic molecular targets in GBM with potential to concomitantly target GSCs. Common differentially expressed genes (DEGs) were identified through analysis of multiple GBM and GSC datasets from the Gene Expression Omnibus (GEO) and The Cancer Genome Atlas (TCGA). For identification of target genes, we selected the genes with most significant effect on overall patient survival. The relative mRNA and protein expression of the selected genes in TCGA control versus GBM samples was also validated and their cancer dependency scores were assessed. Drugs targeting these genes and their corresponding proteins were identified from LINCS database using Connectivity Map (CMap) portal and by in silico molecular docking against each individual target using FDA-approved drug library from the DrugBank database, respectively. The molecules thus obtained were further evaluated for their ability to cross blood brain barrier (BBB) and their likelihood of resulting in drug resistance by acting as p-glycoprotein (p-Gp) substrates. The growth inhibitory effect of these final shortlisted compounds was examined on a panel of GBM cell lines and compared with temozolomide through the drug sensitivity EC50 values and AUC from the PRISM Repurposing Secondary Screen, and the IC50 values were obtained from GDSC portal. We identified RPA3, PSMA2, PSMC2, BLVRA, and HUS1 as molecular targets in GBM including GSCs with significant impact on patient survival. Our results show GSK-2126458/omipalisib, linifanib, drospirenone, eltrombopag, nilotinib, and PD198306 as candidate drugs which can be further evaluated for their anti-tumor potential against GBM. Through this work, we identified repurposed candidate therapeutics against GBM utilizing a GSC inclusive targeting approach, which demonstrated high in vitro efficacy and can prospectively evade drug resistance. These drugs have the potential to be developed as individual or combination therapy to improve GBM outcomes.

APOBR Is Downregulated in EBV+ Tonsils of Children with Obstructive Sleep-Disordered Breathing

Background: Obstructive sleep-disordered breathing (oSDB) is a heterogeneous phenotype that is increasing in prevalence worldwide and has many potential comorbidities that could severely affect quality of life. There is a need to identify biomarkers for oSDB and its comorbidities to improve clinical management, particularly in children. Methods: We performed bulk mRNA-sequencing, differential expression analysis, and qPCR replication of selected differentially expressed genes (DEGs) using RNA samples extracted from tonsils of children with oSDB. Two variables were used as classifier, namely, detection of Epstein-Barr virus (EBV) in tonsils and need for continuous positive airway pressure (CPAP) treatment. Standard statistical tests were used to determine associations across clinical, EBV, and DEG variables. Results: Nineteen genes were dysregulated in tonsils that are EBV+ or from children needing CPAP. Of these genes, APOBR was downregulated in both EBV+ and CPAP+ tonsils, and this downregulation was replicated by qPCR in an independent set of pediatric samples. In the tonsils of adult patients with oSDB, APOBR was positively correlated with age, and potentially with diastolic blood pressure. Conclusions: Taken together, APOBR and DEGs in tonsillar tissues may be useful as potential biomarkers of oSDB severity and comorbidity across the lifespan, with APOBR levels being dependent on latent EBV infection.

A Network and Pathway Analysis of Genes Associated With Atrial Fibrillation

Background: Atrial fibrillation (AF) is affected by both environmental and genetic factors. Previous genetic association studies, especially genome-wide association studies, revealed a large group of AF-associated genes. However, little is known about the functions and interactions of these genes. Moreover, established genetic variants of AF contribute modestly to AF variance, implying that numerous additional AF-associated genetic variations need to be identified. Hence, a systematic network and pathway analysis is needed. Methods: We retrieved all AF-associated genes from genetic association studies in various databases and performed integrative analyses including pathway enrichment analysis, pathway crosstalk analysis, network analysis, and microarray meta-analysis. Results: We collected 254 AF-associated genes from genetic association studies in various databases. Pathway enrichment analysis revealed the top biological pathways that were enriched in the AF-associated genes related to cardiac electromechanical activity. Pathway crosstalk analysis showed that numerous neuro-endocrine-immune pathways connected AF with various diseases including cancers, inflammatory diseases, and cardiovascular diseases. Furthermore, an AF-specific subnetwork was constructed with the prize-collecting Steiner forest algorithm based on the AF-associated genes, and 24 novel genes that were potentially associated with AF were inferred by the subnetwork. In the microarray meta-analysis, six of the 24 novel genes (APLP1, CREB1, CREBBP, PRMT1, IRAK1, and PLXND1) were expressed differentially in patients with AF and sinus rhythm. Conclusions: AF is not only an isolated disease with abnormal electrophysiological activity but might also share a common genetic basis and biological process with tumors and inflammatory diseases as well as cardiovascular diseases. Moreover, the six novel genes inferred from network analysis might help detect the missing AF risk loci.

Graph databases in systems biology: a systematic review

Graph databases are becoming increasingly popular across scientific disciplines, being highly suitable for storing and connecting complex heterogeneous data. In systems biology, they are used as a backend solution for biological data repositories, ontologies, networks, pathways, and knowledge graph databases. In this review, we analyse all publications using or mentioning graph databases retrieved from PubMed and PubMed Central full-text search, focusing on the top 16 available graph databases, Publications are categorized according to their domain and application, focusing on pathway and network biology and relevant ontologies and tools. We detail different approaches and highlight the advantages of outstanding resources, such as UniProtKB, Disease Ontology, and Reactome, which provide graph-based solutions. We discuss ongoing efforts of the systems biology community to standardize and harmonize knowledge graph creation and the maintenance of integrated resources. Outlining prospects, including the use of graph databases as a way of communication between biological data repositories, we conclude that efficient design, querying, and maintenance of graph databases will be key for knowledge generation in systems biology and other research fields with heterogeneous data.

Bibliometric Analysis of the Role of Occludin in the Pathogenesis of Stroke

Over the past decade, there has been a notable surge in research dedicated to unraveling the intricate role of tight junction proteins in blood-brain barrier (BBB) damage associated with ischemic stroke. This bibliometric analysis explores the expansive landscape of occludin research, a key tight junction protein, during the years 2000-2023, shedding light on the global scientific contributions, collaborations, and emerging trends in this critical area of stroke pathogenesis. China and the United States emerge as significant contributors, underscoring their prominence in advancing our understanding of tight junction proteins. Occludin, identified as a linchpin in regulating BBB integrity, proves to be a pivotal player, with implications extending to the diagnosis of hemorrhagic transformation in ischemic stroke. This study identifies occludin as a potential biomarker, offering promise for early diagnosis and paving the way for novel diagnostic strategies. The analysis highlights the necessity for a more comprehensive exploration of tight junction proteins, including occludin and claudin-5, particularly in the context of acute cerebral ischemia. The unique healthcare landscape in Kazakhstan adds urgency to the call for further scientific research in this region, emphasizing the need for tailored investigations to address specific regional challenges. This comprehensive overview not only delineates the current state of occludin research but also signals the direction for future investigations. The identified knowledge gaps and emerging trends provide a roadmap for researchers and policymakers alike, with implications for both scientific discourse and clinical practice. Moving forward, a deeper understanding of tight junction proteins, informed by the insights gleaned from this study, holds the potential to shape targeted therapeutic interventions and diagnostic strategies, ultimately contributing to advancements in global stroke care.

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.

Integrated Transcriptomics and Network Analysis Identified Altered Neural Mechanisms in Frontal Aging Brain-Associated Alzheimer's Disease

Alzheimer's disease (AD) is the most common neurodegenerative disease. The late stage of AD typically develops after 60 years of age and AD pathogenesis can be detected predominately in the frontal lobe, which is responsible for memory. Multiple alterations in cellular mechanisms have been associated with AD, but there is no clear information on AD pathogenesis during brain aging. This study aimed to explore the differentially expressed genes (DEGs) in the frontal lobe of aging brains and to identify shared crucial mechanisms in the aging brain linked to AD pathogenesis. Three datasets were downloaded from the Gene Expression Omnibus (GEO). Biological function analysis was performed by DAVID and KEGG databases. An AD patient's cohort (GSE150696) was collected for verification of the enriched pathway. The results demonstrated that multiple neurochemical synapsis and regulation of the cytoskeleton are linked to AD pathogenesis during aging. Taken together, this study contributes to our further understanding of neural alterations during aging in AD that could be used to develop therapeutics for early intervention to prevent or slow progression.

ELANE is a promising prognostic biomarker that mediates pyroptosis in gastric cancer

Background

Gastric cancer (GC) is a typical malignant tumor and the main cause of cancer-related deaths. Its pathogenesis involves multiple steps, including pyroptosis, although these steps are still uncertain. Pyroptosis, also known as gasdermin-mediated programmed necrosis, participates in various pathological processes in tumors, including GC. ELANE, which encodes neutrophil elastase, is closely associated with GC. Additionally, ELANE has been implicated in GC cell pyroptosis, but this has not been confirmed. Therefore, investigating the link between ELANE and pyroptosis in GC is warranted. This research uses bioinformatics and experiments to examine the relationship between ELANE, pyroptosis, and GC prognosis.

Methods

The GEO and TCGA databases, along with pyroptosis-related genes, were applied to identify pyroptosis-related differentially expressed genes (DEGs). ELANE was selected via primary screening. Using the median expression level of ELANE as the threshold, pyroptosis-related DEGs were divided into low- and high-ELANE groups. Based on the DEGs in these two groups, GO, KEGG and GSEA analyses were conducted to elucidate the mechanisms of ELANE in GC. Furthermore, we plotted ROC and Kaplan-Meier curves to analyze the clinical and pathological features of ELANE expression. The Nomograms tool was applied to calculate the predictive value of ELANE for the clinical outcomes of GC cases. Immunohistochemical analysis was performed to detect the level of ELANE in GC tissues and to validate whether ELANE was involved in pyroptosis in GC cells through cell experiments. Finally, the immune infiltration of ELANE was investigated, and interaction networks (proteins-ELANE, microRNA-ELANE, and small-molecule drug-ELANE) were constructed.

Results

We aimed to investigate the expression of the ELANE gene in GC and study the relationship among ELANE, pyroptosis, and the prognosis of patients with GC. Differential expression analysis of gene-expression datasets from TCGA-STAD and GSE49051 revealed that the expression of the ELANE gene was significantly up-regulated in GC. Using STRING network analysis, we identified multiple proteins involved in the occurrence and development of GC, including interactions between ELANE and GSDMC, a member of the gasdermin protein family. Survival analysis showed that ELANE expression levels significantly affected overall survival (OS), disease-free survival (DFS), and progression-free survival (PFS) in patients with GC. Additionally, ROC analysis demonstrated that ELANE was effective in distinguishing GC patients from normal controls (AUC = 0.812). Immunohistochemical analysis showed that ELANE was highly expressed in gastric cancer tissues and was closely related to age, tumor grade, and stage. The cell experiments further confirmed that the high expression of ELANE in gastric cancer cells was associated with pyroptosis. Comprehensive analysis indicated that ELANE could be used as a potential prognostic marker for GC and plays an important role in pyroptosis.

Conclusion

High ELANE expression is related to poor survival and prognosis of patients with GC. It participates in pyroptosis and immune infiltration in GC. Therefore, ELANE is a promising prognostic biomarker for pyroptosis in GC.

The preliminary data of gene expressions and bioinformatics analysis of miR-146b-5p and miR-4510 in the Turkish population in HBV-related hepatocellular carcinoma

Background and Aim

It is reported that miRNAs play an important role in hepatocellular carcinogenesis and may serve as non-invasive biomarkers for hepatocellular carcinoma (HCC). MiR-4510 and miR-146b-5p expression levels have been found to be associated with HCC. However, their associations with hepatitis B virus (HBV)-related HCC (HBV-HCC) are yet to be explored. We aimed to assess the predictive value of expression levels of serum miR-4510 and miR-146b-5p in patients with HBV-HCC and performed bioinformatics analyses based on the miRNA expression profile.

Materials and Methods

This cross-sectional study used the serum of 16 patients with Chronic Hepatitis B (CHB), 15 hepatitis B virus-related cirrhosis (HBV-cirrhosis), 15 HBV-HCC, and 16 healthy subjects. The total RNA was isolated from serum, and the expression of miRNAs was measured by qRT-PCR, calculated using the 2-ΔΔCt methods. MIENTURNET was used to predict miRNA-target gene interactions. The Network Analyst was used to build protein-protein interactions.

Results

There was a significant difference in miR-146b-5p between study groups (p=0.009). MiR-146b-5p expression was found to be significantly reduced in HBV-HCC compared to the HBV-cirrhosis group and healthy controls (p=0.005 and p=0.006, respectively).

Conclusion

The serum miR-146b-5p levels might be a promising tool to be used as a non-invasive diagnostic biomarker for HCC. Our findings shed light on potential biomarkers for the diagnosis of HBV-HCC in terms of selected miRNAs. The target pathways of miR-146b-5p identified by our in-silico analysis to reveal the functional mechanism are "MAPK signaling pathways" and "Pathways in cancer."

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

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

Proteomics Analysis of Duck Lung Tissues in Response to Highly Pathogenic Avian Influenza Virus

Domestic ducks (Anas platyrhynchos domesticus) are resistant to most of the highly pathogenic avian influenza virus (HPAIV) infections. In this study, we characterized the lung proteome and phosphoproteome of ducks infected with the HPAI H5N1 virus (A/duck/India/02CA10/2011/Agartala) at 12 h, 48 h, and 5 days post-infection. A total of 2082 proteins were differentially expressed and 320 phosphorylation sites mapping to 199 phosphopeptides, corresponding to 129 proteins were identified. The functional annotation of the proteome data analysis revealed the activation of the RIG-I-like receptor and Jak-STAT signaling pathways, which led to the induction of interferon-stimulated gene (ISG) expression. The pathway analysis of the phosphoproteome datasets also confirmed the activation of RIG-I, Jak-STAT signaling, NF-kappa B signaling, and MAPK signaling pathways in the lung tissues. The induction of ISG proteins (STAT1, STAT3, STAT5B, STAT6, IFIT5, and PKR) established a protective anti-viral immune response in duck lung tissue. Further, the protein-protein interaction network analysis identified proteins like AKT1, STAT3, JAK2, RAC1, STAT1, PTPN11, RPS27A, NFKB1, and MAPK1 as the main hub proteins that might play important roles in disease progression in ducks. Together, the functional annotation of the proteome and phosphoproteome datasets revealed the molecular basis of the disease progression and disease resistance mechanism in ducks infected with the HPAI H5N1 virus.

Unraveling malignant phenotype of peritumoral tissue: transcriptomic insights into early-stage breast cancer

BACKGROUND

Early-stage invasive ductal carcinoma displays high survival rates due to early detection and treatments. However, there is still a chance of relapse of 3-15% after treatment. The aim of this study was to uncover the distinctive transcriptomic characteristics and monitoring prognosis potential of peritumoral tissue in early-stage cases.

METHODS

RNA was isolated from tumoral, peritumoral, and non-tumoral breast tissue from surgical resection of 10 luminal early-stage invasive ductal carcinoma patients. Transcriptome expression profiling for differentially expressed genes (DEGs) identification was carried out through microarray analysis. Gene Ontology and KEGG pathways enrichment analysis were explored for functional characterization of identified DEGs. Protein-Protein Interactions (PPI) networks analysis was performed to identify hub nodes of peritumoral tissue alterations and correlated with Overall Survival and Relapse Free Survival.

RESULTS

DEGs closely related with cell migration, extracellular matrix organization, and cell cycle were upregulated in peritumoral tissue compared to non-tumoral. Analyzing PPI networks, we observed that the proximity to tumor leads to the alteration of gene modules involved in cell proliferation and differentiation signaling pathways. In fact, in the peritumoral area were identified the top ten upregulated hub nodes including CDK1, ESR1, NOP58, PCNA, EZH2, PPP1CA, BUB1, TGFBR1, CXCR4, and CCND1. A signature performed by four of these hub nodes (CDK1, PCNA, EZH2, and BUB1) was associated with relapse events in untreated luminal breast cancer patients.

CONCLUSIONS

In conclusion, our study characterizes in depth breast peritumoral tissue providing clues on the changes that tumor signaling could cause in patients with early-stage breast cancer. We propose that the use of a four gene signature could help to predict local relapse. Overall, our results highlight the value of peritumoral tissue as a potential source of new biomarkers for early detection of relapse and improvement in invasive ductal carcinoma patient's prognosis.

Analysis of gene expression profiles to elucidate racial differences in African American and White patients with Triple-negative breast cancer

Triple-negative breast cancer (TNBC) is the second most diagnosed subtype of breast cancer. It is known to be the most aggressive one that lacks known targetable receptors. One of the concerns in TNBC is the disparities in its prevalence and tumor pathogenesis among women with non-Hispanic African American backgrounds. Despite extensive research, the genetic underpinnings that lead to these disparities remain elusive. The current study aims to provide initiative for further clinical research in the development of targeted therapy for TNBC. Gene expression profiles from African American (AA) and European American (EA) patients with TNBC were collected from Gene Expression Omnibus and performed differential gene expression (DEG)analysis. Candidate genes for a significant correlation between expression and survival rates for breast invasive carcinoma were analyzed using UALCAN. The DAVID annotation tool, Enrichr web server, KEGG database, and Gene Ontology (GO) database were used for functional enrichment analysis of target genes. The Network Analyst server was used to identify ligands with strong affinities, SeamDock server for molecular docking between the biomarkers/associated ligands and examined protein-protein interactions (PPI) from the STRING server. Data from public breast cancer cohorts was utilized to identify expression patterns associated with poor survival outcomes of AA patients with TNBC. Our results showed three genes of interest ( CCT3 , LSM2 , and MRPS16 ) and potential ligands for molecular docking. Molecular docking was performed for the ICG001 ligand to CCT3 (binding affinities of -9.3 kcal/mol and -8.9 kcal/mol) and other interacting proteins ( CDC20 and PPP2CA ) with high degrees of connectivity. The results determined molecular docking of ICG001 to the CDC20 protein resulted in the highest binding affinity. Our results demonstrated that CCT3 and its interacting partners could serve as potential biomarkers due to their association with the survival outcome of AA patients with TNBC and ICG 001 could be the therapeutic lead for these biomarkers.

System biology approach to identify the novel biomarkers in glioblastoma multiforme tumors by using computational analysis

Introduction: The most common primary brain tumor in adults is glioblastoma multiforme (GBM), accounting for 45.2% of all cases. The characteristics of GBM, a highly aggressive brain tumor, include rapid cell division and a propensity for necrosis. Regretfully, the prognosis is extremely poor, with only 5.5% of patients surviving after diagnosis. Methodology: To eradicate these kinds of complicated diseases, significant focus is placed on developing more effective drugs and pinpointing precise pharmacological targets. Finding appropriate biomarkers for drug discovery entails considering a variety of factors, including illness states, gene expression levels, and interactions between proteins. Using statistical techniques like p-values and false discovery rates, we identified differentially expressed genes (DEGs) as the first step in our research for identifying promising biomarkers in GBM. Of the 132 genes, 13 showed upregulation, and only 29 showed unique downregulation. No statistically significant changes in the expression of the remaining genes were observed. Results: Matrix metallopeptidase 9 (MMP9) had the greatest degree in the hub biomarker gene identification, followed by (periostin (POSTN) at 11 and Hes family BHLH transcription factor 5 (HES5) at 9. The significance of the identification of each hub biomarker gene in the initiation and advancement of glioblastoma multiforme was brought to light by the survival analysis. Many of these genes participate in signaling networks and function in extracellular areas, as demonstrated by the enrichment analysis.We also identified the transcription factors and kinases that control proteins in the proteinprotein interactions (PPIs) of the DEGs. Discussion: We discovered drugs connected to every hub biomarker. It is an appealing therapeutic target for inhibiting MMP9 involved in GBM. Molecular docking investigations indicated that the chosen complexes (carmustine, lomustine, marimastat, and temozolomide) had high binding affinities of -6.3, -7.4, -7.7, and -8.7 kcal/mol, respectively, the mean root-mean-square deviation (RMSD) value for the carmustine complex and marimastat complex was 4.2 Å and 4.9 Å, respectively, and the lomustine and temozolomide complex system showed an average RMSD of 1.2 Å and 1.6 Å, respectively. Additionally, high stability in root-mean-square fluctuation (RMSF) analysis was observed with no structural conformational changes among the atomic molecules. Thus, these in silico investigations develop a new way for experimentalists to target lethal diseases in future.

A Tiny Viral Protein, SARS-CoV-2-ORF7b: Functional Molecular Mechanisms

This study presents the interaction with the human host metabolism of SARS-CoV-2 ORF7b protein (43 aa), using a protein-protein interaction network analysis. After pruning, we selected from BioGRID the 51 most significant proteins among 2753 proven interactions and 1708 interactors specific to ORF7b. We used these proteins as functional seeds, and we obtained a significant network of 551 nodes via STRING. We performed topological analysis and calculated topological distributions by Cytoscape. By following a hub-and-spoke network architectural model, we were able to identify seven proteins that ranked high as hubs and an additional seven as bottlenecks. Through this interaction model, we identified significant GO-processes (5057 terms in 15 categories) induced in human metabolism by ORF7b. We discovered high statistical significance processes of dysregulated molecular cell mechanisms caused by acting ORF7b. We detected disease-related human proteins and their involvement in metabolic roles, how they relate in a distorted way to signaling and/or functional systems, in particular intra- and inter-cellular signaling systems, and the molecular mechanisms that supervise programmed cell death, with mechanisms similar to that of cancer metastasis diffusion. A cluster analysis showed 10 compact and significant functional clusters, where two of them overlap in a Giant Connected Component core of 206 total nodes. These two clusters contain most of the high-rank nodes. ORF7b acts through these two clusters, inducing most of the metabolic dysregulation. We conducted a co-regulation and transcriptional analysis by hub and bottleneck proteins. This analysis allowed us to define the transcription factors and miRNAs that control the high-ranking proteins and the dysregulated processes within the limits of the poor knowledge that these sectors still impose.

Cross-platform gene expression profiling of breast cancer: Exploring the relationship between breast cancer grades and gene expression pattern

Gene expression profiling is a powerful tool that has been extensively used to investigate the underlying biology and etiology of diseases, including cancer. Microarray gene expression analysis enables simultaneous measurement of thousands of mRNA levels. Sophisticated computational approaches have evolved in parallel with the rapid progress in bioassay technologies, enabling more effective analysis of the large and complex datasets that these technologies produce. In this study, we utilized systems biology approaches to examine gene expression profiles across different grades of breast cancer progression. We conducted a meta-analysis of publicly available microarray data to elucidate the molecular mechanisms underlying breast cancer grade classification. Our results suggest that while grade index is commonly used for evaluating cancer progression status in the clinic, the complexity of molecular mechanisms, histological characteristics, and other factors related to patient outcomes raises doubts about the utility of breast cancer grades as a foundation for formulating treatment protocols. Our study underscores the importance of advancing personalized strategies for breast cancer classification and management. More research is crucial to refine diagnostic tools and treatment modalities, aiming for greater precision and tailored care in patient outcomes.

Exploration of the shared gene signatures and biological mechanisms between ischemia-reperfusion injury and antibody-mediated rejection in renal transplantation

BACKGROUND

Antibody-mediated rejection (ABMR) plays a crucial role in graft loss during allogeneic renal transplantation. In renal transplantation, ischemia-reperfusion injury (IRI) is unavoidable, serves as a major contributor to acute rejection, and is linked to graft loss. However, the mechanisms underlying IRI and ABMR are unclear. Therefore, this study aimed to investigate the shared genetic characteristics and biological mechanisms between IRI and ABMR.

METHODS

Gene expressions for IRI (GSE43974) and ABMR (GSE129166 and GSE36059) were retrieved from the Gene Expression Omnibus database. The shared differentially expressed genes (DEGs) of IRI and ABMR were identified, and subsequent functional enrichment analysis was performed. Immune cell infiltration in ABMR and its relationship with the shared DEGs were investigated using the CIBERSORT method. Random forest analysis, a protein-protein interaction network, and Cytoscape were used to screen hub genes, which were subsequently subjected to gene set enrichment analysis, miRNA prediction, and transcription factors analysis. The survival analysis was performed through Kaplan-Meier curves. Finally, drug compound prediction was performed on the shared DEGs using the Drug Signature Database.

RESULTS

Overall, 27 shared DEGs were identified between the renal IRI and ABMR groups. Among these, 24 genes exhibited increased co-expression, whereas none showed decreased co-expression. The shared DEGs were primarily enriched in the inflammation signaling pathways. Notably, CD4 memory T cells were identified as potential critical mediators of IRI, leading to ABMR. Tumor necrosis factor alpha-induced protein 3 (TNFAIP3), interferon regulatory factor 1 (IRF1), and early growth response 2 (EGR2) were identified as key components in the potential mechanism that link IRI and ABMR. Patients undergoing renal transplantation with higher expression levels of TNFAIP3, IRF1, and EGR2 exhibited decreased survival rates compared to those with lower expression levels.

CONCLUSION

Inflammation is a key mechanism that links IRI and ABMR, with a potential role played by CD4 memory T cells. Furthermore, TNFAIP3, IRF1, and EGR2 are implicated in the underlying mechanism between IRI and ABMR.

Transcriptomics and biochemical evidence of trigonelline ameliorating learning and memory decline in the senescence-accelerated mouse prone 8 (SAMP8) model by suppressing proinflammatory cytokines and elevating neurotransmitter release

In recent years, exploring natural compounds with functional properties to ameliorate aging-associated cognitive decline has become a research priority to ensure healthy aging. In the present study, we investigated the effects of Trigonelline (TG), a plant alkaloid, on memory and spatial learning in 16-week-old senescence-accelerated mouse model SAMP8 using an integrated approach for cognitive and molecular biology aspects. After 30 days of oral administration of TG at the dose of 5 mg/kg/day, the mice were trained in Morris Water Maze task. TG-treated SAMP8 mice exhibited significant improvement in the parameters of escape latency, distance moved, and annulus crossing index. Next, we performed a whole-genome transcriptome profiling of the mouse hippocampus using microarrays. Gene ontology analyses showed that a wide range of biological processes, including nervous system development, mitochondrial function, ATP synthesis, and several signaling pathways related to inflammation, autophagy, and neurotransmitter release, were significantly enriched in TG-treated SAMP8 compared to nontreated. Further, a nonlinear dimensionality reduction technique, Uniform Manifold Approximation and Projection (UMAP), was applied to identify clusters of functions that revealed TG primarily regulated pathways related to inflammation, followed by those involved in neurotransmitter release. In addition, a protein-protein interaction network analysis indicated that TG may exert its biological effects through negatively modulating Traf6-mediated NF-κB activation. Finally, ELISA test showed that TG treatment significantly decreased proinflammatory cytokines- TNFα and IL6 and increased neurotransmitters- dopamine, noradrenaline, and serotonin in mouse hippocampus. Altogether, our integrated bio-cognitive approach highlights the potential of TG in alleviating age-related memory and spatial impairment.

A comparative transcriptomics analysis reveals ethylene glycol derivatives of squalene ameliorate excessive lipogenesis and inflammatory response in 3T3-L1 preadipocytes

Squalene (SQ) is a natural compound with anti-inflammatory, anti-cancer, and anti-oxidant effects, but due to its low solubility, its biological properties have been greatly underestimated. This study aims to explore the differences in gene expression patterns of four newly synthesized amphipathic ethylene glycol (EG) derivatives of SQ by whole-genome transcriptomics analysis using DNA microarray to examine the mRNA expression profile of adipocytes differentiated from 3T3-L1 cells treated with SQ and its EG derivatives. Enrichment analyses of the transcriptional data showed that compared with SQ, its EG derivatives exerted different, in most cases desirable, biological responses. EG derivatives showed increased enrichment of mitochondrial functions, lipid and glucose metabolism, and inflammatory response. Mono-, di-, and tetra-SQ showed higher enrichment of the cellular component-ribosome. Histological staining showed EG derivatives prevented excessive lipid accumulation. Additionally, mitochondrial transcription factors showed upregulation in tetra-SQ-treated cells. Notably, EG derivatives showed better anti-inflammatory effects. Further, gene-disease association analysis predicted substantial improvement in the bioactivities of SQ derivatives in metabolic diseases. Cluster analyses revealed di- and tetra-SQ had more functional similarities than others, reflected in their scanning electron microscopy images; both di- and tetra-SQ self-organized into similar sizes and shapes of vesicles, subsequently improving their cation binding activities. Protein-protein interaction networks further revealed that cation binding activity might explain a major part, if not all, of the differences observed in functional analyses. Altogether, the addition of EG derivatives may improve the biological responses of SQ and thus may enhance its health-promoting potential.

System biology approaches to identify hub genes linked with ECM organization and inflammatory signaling pathways in schizophrenia pathogenesis

Schizophrenia (SZ) is a chronic and devastating mental illness that affects around 20 million individuals worldwide. Cognitive deficits and structural and functional changes of the brain, abnormalities of brain ECM components, chronic neuroinflammation, and devastating clinical manifestation during SZ are likely etiological factors shown by affected individuals. However, the pathophysiological events associated with multiple regulatory pathways involved in the brain of this complex disorder are still unclear. This study aimed to develop a pipeline based on bioinformatics and systems biology approaches for identifying potential therapeutic targets involving possible biological mechanisms from SZ patients and healthy volunteers. About 420 overlapping differentially expressed genes (DEGs) from three RNA-seq datasets were identified. Gene ontology (GO), and pathways analysis showed several biological mechanisms enriched by the commonly shared DEGs, including extracellular matrix organization (ECM) organization, collagen fibril organization, integrin signaling pathway, inflammation mediated by chemokines and cytokines signaling pathway, and GABA-B receptor II and IL4 mediated signaling. Besides, 15 hub genes (FN1, COL1A1, COL3A1, COL1A2, COL5A1, COL2A1, COL6A2, COL6A3, MMP2, THBS1, DCN, LUM, HLA-A, HLA-C, and FBN1) were discovered by comprehensive analysis, which was mainly involved in the ECM organization and inflammatory signaling pathway. Furthermore, the miRNA target of the hub genes was analyzed with the random-forest-based approach software miRTarBase. In addition, the transcriptional factors and protein kinases regulating overlapping DEGs in SZ, namely, SUZ12, EZH2, TRIM28, TP53, EGR1, CSNK2A1, GSK3B, CDK1, and MAPK14, were also identified. The results point to a new understanding that the hub genes (fibronectin 1, collagen, matrix metalloproteinase-2, and lumican) in the ECM organization and inflammatory signaling pathways may be involved in the SZ occurrence and pathogenesis.

Downregulation of nutrition sensor GCN2 in macrophages contributes to poor wound healing in diabetes

Poor skin wound healing, which is common in patients with diabetes, is related to imbalanced macrophage polarization. Here, we find that nutrition sensor GCN2 (general control nonderepressible 2) and its downstream are significantly upregulated in human skin wound tissue and mouse skin wound macrophages, but skin wound-related GCN2 expression and activity are significantly downregulated by diabetes and hyperglycemia. Using wound healing models of GCN2-deleted mice, bone marrow chimeric mice, and monocyte-transferred mice, we show that GCN2 deletion in macrophages significantly delays skin wound healing compared with wild-type mice by altering M1 and M2a/M2c polarization. Mechanistically, GCN2 inhibits M1 macrophages via OXPHOS-ROS-NF-κB pathway and promotes tissue-repairing M2a/M2c macrophages through eukaryotic translation initiation factor 2 (eIF2α)-hypoxia-inducible factor 1α (HIF1α)-glycolysis pathway. Importantly, local supplementation of GCN2 activator halofuginone efficiently restores wound healing in diabetic mice with re-balancing M1 and M2a/2c polarization. Thus, the decreased macrophage GCN2 expression and activity contribute to poor wound healing in diabetes and targeting GCN2 improves wound healing in diabetes.

Novel lncRNA regulatory elements in milk somatic cells of Holstein dairy cows associated with mastitis

Despite regulatory elements such as long non - coding RNAs representing most of the transcriptome, the functional understanding of long non - coding RNAs in relation to major health conditions including bovine mastitis is limited. This study examined the milk somatic cell transcriptome from udder quarters of 6 Holstein dairy cows to identify differentially expressed long non - coding RNAs using RNA - Sequencing. Ninety - four differentially expressed long non - coding RNAs are identified, 5 of which are previously annotated for gene name and length, 11 are annotated for gene name and 78 are novel, having no gene name or length previously annotated. Significant inflammatory response and regulation of immune response pathways (false discovery rate < 0.05) are associated with the differentially expressed long non - coding RNAs. QTL annotation analysis revealed 31 QTL previously annotated in the genomic regions of the 94 differentially expressed long non - coding RNAs, and the majority are associated with milk traits. This research provides a better understanding of long non - coding RNAs regulatory elements in milk somatic cells, which may enhance current breeding strategies for more adaptable or high mastitis resistant cattle.

Modulation of mitochondrial activity by sugarcane (Saccharum officinarum L.) top extract and its bioactive polyphenols: a comprehensive transcriptomics analysis in C2C12 myotubes and HepG2 hepatocytes

Age-related mitochondrial dysfunction leads to defects in cellular energy metabolism and oxidative stress defense systems, which can contribute to tissue damage and disease development. Among the key regulators responsible for mitochondrial quality control, peroxisome proliferator-activated receptor gamma coactivator 1-alpha (PGC-1α) is an important target for mitochondrial dysfunction. We have previously reported that bioactive polyphenols extracted from sugarcane top (ST) ethanol extract (STEE) could activate neuronal energy metabolism and increase astrocyte PGC-1α transcript levels. However, their potential impact on the mitochondria activity in muscle and liver cells has not yet been investigated. To address this gap, our current study examined the effects of STEE and its polyphenols on cultured myotubes and hepatocytes in vitro. Rhodamine 123 assay revealed that the treatment with STEE and its polyphenols resulted in an increase in mitochondrial membrane potential in C2C12 myotubes. Furthermore, a comprehensive examination of gene expression patterns through transcriptome-wide microarray analysis indicated that STEE altered gene expressions related to mitochondrial functions, fatty acid metabolism, inflammatory cytokines, mitogen-activated protein kinase (MAPK) signaling, and cAMP signaling in both C2C12 myotubes and HepG2 hepatocytes. Additionally, protein-protein interaction analysis identified the PGC-1α interactive-transcription factors-targeted regulatory network of the genes regulated by STEE, and the quantitative polymerase chain reaction results confirmed that STEE and its polyphenols upregulated the transcript levels of PGC-1α in both C2C12 and HepG2 cells. These findings collectively suggest the potential beneficial effects of STEE on muscle and liver tissues and offer novel insights into the potential nutraceutical applications of this material.

Preclinical Repurposing of Sitagliptin as a Drug Candidate for Colorectal Cancer by Targeting CD24/CTNNB1/SOX4-Centered Signaling Hub

Despite significant advances in treatment modalities, colorectal cancer (CRC) remains a poorly understood and highly lethal malignancy worldwide. Cancer stem cells (CSCs) and the tumor microenvironment (TME) have been shown to play critical roles in initiating and promoting CRC progression, metastasis, and treatment resistance. Therefore, a better understanding of the underlying mechanisms contributing to the generation and maintenance of CSCs is crucial to developing CSC-specific therapeutics and improving the current standard of care for CRC patients. To this end, we used a bioinformatics approach to identify increased CD24/SOX4 expression in CRC samples associated with poor prognosis. We also discovered a novel population of tumor-infiltrating CD24+ cancer-associated fibroblasts (CAFs), suggesting that the CD24/SOX4-centered signaling hub could be a potential therapeutic target. Pathway networking analysis revealed a connection between the CD24/SOX4-centered signaling, β-catenin, and DPP4. Emerging evidence indicates that DPP4 plays a role in CRC initiation and progression, implicating its involvement in generating CSCs. Based on these bioinformatics data, we investigated whether sitagliptin, a DPP4 inhibitor and diabetic drug, could be repurposed to inhibit colon CSCs. Using a molecular docking approach, we demonstrated that sitagliptin targeted CD24/SOX4-centered signaling molecules with high affinity. In vitro experimental data showed that sitagliptin treatment suppressed CRC tumorigenic properties and worked in synergy with 5FU and this study thus provided preclinical evidence to support the alternative use of sitagliptin for treating CRC.

Integrated analysis of bulk RNA-seq and single-cell RNA-seq reveals the function of pyrocytosis in the pathogenesis of abdominal aortic aneurysm

Pyrocytosis is involved in the development of abdominal aortic aneurysm (AAA), we explored the pyrocytosis-related hub genes in AAA and conducted a diagnostic model based on the pyrocytosis-related genes score (PRGs). A total of 2 bulk RNA-seq (GSE57691 and GSE47472) datasets and pyrocytosis-related genes were integrated to obtain 24 pyrocytosis-related different expression genes (DEGs). The LASSO Cox regression analysis was conducted to filter out 7 genes and further establish the nomogram signature based on the PRGs that exhibited a good diagnosis value. Weighted gene co-expression network analysis (WGCNA) established 14 gene modules and further identified 6 hub genes which were involved in the regulatory process of pyrocytosis in AAA. At the single cell level, we further identified 3 immune cells were highly associated with the pyrocytosis process in AAA. Finally, the cell-cell communication demonstrated that fibroblasts and endothelial cells and myeloid cells maintained close communications. Here, we identified the dysfunctional expressed pyrocytosis-related genes and immune cells in AAA, which provide a comprehensive understanding of the pathogenesis of AAA.

Identification of CDK1, PBK, and CHEK1 as an Oncogenic Signature in Glioblastoma: A Bioinformatics Approach to Repurpose Dapagliflozin as a Therapeutic Agent

Glioblastoma multiforme (GBM) is the most aggressive and lethal primary brain tumor whose median survival is less than 15 months. The current treatment regimen comprising surgical resectioning, chemotherapy with Temozolomide (TMZ), and adjuvant radiotherapy does not achieve total patient cure. Stem cells' presence and GBM tumor heterogeneity increase their resistance to TMZ, hence the poor overall survival of patients. A dysregulated cell cycle in glioblastoma enhances the rapid progression of GBM by evading senescence or apoptosis through an over-expression of cyclin-dependent kinases and other protein kinases that are the cell cycle's main regulatory proteins. Herein, we identified and validated the biomarker and predictive properties of a chemoradio-resistant oncogenic signature in GBM comprising CDK1, PBK, and CHEK1 through our comprehensive in silico analysis. We found that CDK1/PBK/CHEK1 overexpression drives the cell cycle, subsequently promoting GBM tumor progression. In addition, our Kaplan-Meier survival estimates validated the poor patient survival associated with an overexpression of these genes in GBM. We used in silico molecular docking to analyze and validate our objective to repurpose Dapagliflozin against CDK1/PBK/CHEK1. Our results showed that Dapagliflozin forms putative conventional hydrogen bonds with CDK1, PBK, and CHEK1 and arrests the cell cycle with the lowest energies as Abemaciclib.

Comprehensive analysis for the immune related biomarkers of platinum-based chemotherapy in ovarian cancer

BACKGROUND

Ovarian cancer (OC) is one of the most lethal gynecological malignancies. This study aimed to identify biomarkers that were sensitive to platinum-based chemotherapeutic agents and can be used in immunotherapy and explore the importance of their mechanisms of action.

METHODS

RNA-seq profiles and clinicopathological data for OC samples were obtained from The Cancer Genome Atlas (TCGA) and cBioPortal platform, respectively. Platinum-sensitive and platinum-resistant OC samples in the TCGA cohort were selected based on the clinical information. RNA-seq data for 70 OC samples withSingle-sample gene set enrichment analysis (ssGSEA) and unsupervised clustering were used to classify OC patients from the TCGA cohort into clusters with different proportions of infiltrating immune cells. ESTIMATE analysis was used to assess the immune landscape among clusters. Differential expression, univariate Cox regression, and LASSO regression analyses were performed to construct prognostic model. Spearman correlation analysis was conducted to investigate the correlations among immune checkpoint inhibitors (ICIs) and risk score, half-maximal drug inhibitory concentration (IC50) and risk score.

RESULTS

Using ssGSEA and unsupervised clustering, OC samples were divided into two clusters with different immune cell infiltration. Then, 1715 differentially expressed immune-related genes (DEIRGs) were identified between two clusters, 984 differentially expressed platinum-sensitive related genes (DEPSRGs) between 149 platinum-sensitive and 63 platinum-resistant OC samples were identified, and 5384 differentially expressed genes (DEGs) between 380 OC and 194 normal samples were detected from the TCGA cohort. Six biomarkers (GMPPB, SRPK1, STC1, PRSS16, HPDL, and SPTSSB) were detected to establish a prognostic model. The OC patients in the TCGA cohort were classified into high- and low-risk groups. The receive operating characteristic (ROC) curve was plotted and demonstrated that the prognostic model performed well with the area under ROC curve (AUC) greater than 0.6. The expressions of 5 ICIs, including CD200, TNFRSF18, CD160, CD200R1, and CD274 (PD-L1), were significantly different between two risk groups, and the risk score was significant negative associated with CTLA4, TNFRSF4, TNFRSF18, and CD274. Moreover, there were significant differences in IC50 of 10 chemo drugs between two risk groups, patients in the high-risk group could be more resistant to po0tinib, dasatinib, and neratinib.

CONCLUSION

In summary, this study constructed a novel prognostic model based on six prognostic biomarkers, including GMPPB, SRPK1, STC1, PRSS16, HPDL, and SPTSSB, which can be utilized for predicting the prognosis of OC patients. These biomarkers were the potential therapeutic targets.

A meta-analysis of differentially expressed and regulatory genes with their functional enrichment analysis for brain transcriptome data in autism spectrum disorder

Autism spectrum disorder (ASD) is a complex neurodevelopmental condition characterized by persistent challenges in social interactions and repetitive behavioral patterns. It is a significant problem emerging worldwide, as one in 100 children is affected by this disorder globally. In this study, a meta-analysis was performed for the identification of differentially expressed genes (DEGs) along with the expression analysis of regulatory genes. Functional enrichment analysis was an integral part of current findings to notify the significant pathways of this complex disorder. The study was conducted with two RNA-Seq datasets, viz., GSE64018 and GSE62098, for ASD patients and control samples from the GEO database. The identification of up-regulatory and down-regulatory genes was performed by the interaction analysis of transcription factors (TF) and DEGs. As an outcome of the meta-analysis, 2543 DEGs were identified as common across both of the datasets in which 1402 DEGs exhibited upregulation and 1130 genes have shown downregulation. In network analysis, upregulatory genes have shown strong interaction while downregulatory genes exhibit weak or null interaction. Further, in the enrichment analysis of screened upregulatory DEGs, three major significant pathways were identified namely the ATP synthesis pathway, FAS signaling pathway, and the Huntington's disease pathway. The common expression of CYC 1 gene in all the identified pathways has indicated that it is an important key regulator gene for the majorly associated pathways. The study concludes that all the potential DEGs were found to show their related high expression in neurobiological regulations specifically with ASD.Communicated by Ramaswamy S. Sarma.

The Novel Link between Gene Expression Profiles of Adult T-Cell Leukemia/Lymphoma Patients' Peripheral Blood Lymphocytes and Ferroptosis Susceptibility

Ferroptosis, a regulated cell death dependent on iron, has garnered attention as a potential broad-spectrum anticancer approach in leukemia research. However, there has been limited ferroptosis research on ATL, an aggressive T-cell malignancy caused by HTLV-1 infection. Our study employs bioinformatic analysis, utilizing dataset GSE33615, to identify 46 ferroptosis-related DEGs and 26 autophagy-related DEGs in ATL cells. These DEGs are associated with various cellular responses, chemical stress, and iron-related pathways. Autophagy-related DEGs are linked to autophagy, apoptosis, NOD-like receptor signaling, TNF signaling, and the insulin resistance pathway. PPI network analysis revealed 10 hub genes and related biomolecules. Moreover, we predicted crucial miRNAs, transcription factors, and potential pharmacological compounds. We also screened the top 20 medications based on upregulated DEGs. In summary, our study establishes an innovative link between ATL treatment and ferroptosis, offering promising avenues for novel therapeutic strategies in ATL.

Comprehensive analysis and validation reveal potential MYCN regulatory biomarkers associated with neuroblastoma prognosis

Neuroblastoma (NB) is an embryonic malignant tumor that occurs in the sympathetic nervous system. The treatment results of patients in the high-risk group are poor, and relapse and treatment failure can occur even with multiple combination treatments. The proto-oncogene MYCN is a BHLH Transcription Factor used as an independent prognostic factor for NB. The proportion of MYCN amplification in tumor tissues of high-risk patients reaches 40-50%. Hence, exploring new MYCN target genes is a meaningful approach in developing treatment for high-risk NB patients. The microarray datasets were obtained from Gene Expression Omnibus (GEO), and differentially expressed genes (DEGs) were identified. Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG), and miRPathDB were used for enrichment analysis. STRING and Cytoscape were used to construct a protein-protein interaction (PPI) network and for modular analysis. The miRNet and NetworkAnalyst databases were used to predict and construct gene-miRNA and gene-TFs networks. The R2 database was used for expression, correlation, and prognostic analyses. The diagnostic value of the biomarkers was predicted by ROC analysis, and RT-qPCR was used to validate the identified hub genes. Finally, using specific MYCN siRNA and overexpressing plasmids, the correlation between the identified hub genes and MYCN was investigated. Our results showed that FBXO9, HECW2, MIB2, RNF19B, RNF213, TRIM36, and ZBTB16 are novel biomarkers that affect the prognosis of the NB patients. In addition, FBXO9, RNF19B, and TRIM36 were preliminarily confirmed as potential target genes of MYCN. Overall, FBXO9, HECW2, MIB2, RNF19B, RNF213, TRIM36, and ZBTB16 are expected to become novel biomarkers for the treatment of high-risk NB patients.Communicated by Ramaswamy H. Sarma.

Integrated gene expression profiling and functional enrichment analyses to discover biomarkers and pathways associated with Guillain-Barré syndrome and autism spectrum disorder to identify new therapeutic targets

Guillain-Barré syndrome (GBS) is one of the most prominent and acute immune-mediated peripheral neuropathy, while autism spectrum disorders (ASD) are a group of heterogeneous neurodevelopmental disorders. The complete mechanism regarding the neuropathophysiology of these disorders is still ambiguous. Even after recent breakthroughs in molecular biology, the link between GBS and ASD remains a mystery. Therefore, we have implemented well-established bioinformatic techniques to identify potential biomarkers and drug candidates for GBS and ASD. 17 common differentially expressed genes (DEGs) were identified for these two disorders, which later guided the rest of the research. Common genes identified the protein-protein interaction (PPI) network and pathways associated with both disorders. Based on the PPI network, the constructed hub gene and module analysis network determined two common DEGs, namely CXCL9 and CXCL10, which are vital in predicting the top drug candidates. Furthermore, coregulatory networks of TF-gene and TF-miRNA were built to detect the regulatory biomolecules. Among drug candidates, imatinib had the highest docking and MM-GBSA score with the well-known chemokine receptor CXCR3 and remained stable during the 100 ns molecular dynamics simulation validated by the principal component analysis and the dynamic cross-correlation map. This study predicted the gene-based disease network for GBS and ASD and suggested prospective drug candidates. However, more in-depth research is required for clinical validation.Communicated by Ramaswamy H. Sarma.

Investigation into Molecular Brain Aging in Senescence-Accelerated Mouse (SAM) Model Employing Whole Transcriptomic Analysis in Search of Potential Molecular Targets for Therapeutic Interventions

With the progression of an aging society, cognitive aging has emerged as a pressing concern necessitating attention. The senescence-accelerated mouse-prone 8 (SAMP8) model has proven instrumental in investigating the early stages of cognitive aging. Through an extensive examination of molecular changes in the brain cortex, utilizing integrated whole-genome transcriptomics, our principal aim was to uncover potential molecular targets with therapeutic applications and relevance to drug screening. Our investigation encompassed four distinct conditions, comparing the same strain at different time points (1 year vs. 16 weeks) and the same time point across different strains (SAMP8 vs. SAMR1), namely: physiological aging, accelerated aging, early events in accelerated aging, and late events in accelerated aging. Focusing on key functional alterations associated with aging in the brain, including neurogenesis, synapse dynamics, neurometabolism, and neuroinflammation, we identified candidate genes linked to these processes. Furthermore, employing protein-protein interaction (PPI) analysis, we identified pivotal hub genes involved in interactions within these functional domains. Additionally, gene-set perturbation analysis allowed us to uncover potential upstream genes or transcription factors that exhibited activation or inhibition across the four conditions. In summary, our comprehensive analysis of the SAMP8 mouse brain through whole-genome transcriptomics not only deepens our understanding of age-related changes but also lays the groundwork for a predictive model to facilitate drug screening for cognitive aging.

Proteomic Profiles and Protein Network Analysis of Primary Human Leukocytes Revealed Possible Clearance Biomarkers for Staphylococcus aureus Infection

Staphylococcus aureus is a serious pathogen that can survive within host cells after a typical course of treatment completion, leading to chronic infection. Knowledge of host proteomic patterns after clearance of this pathogen from cells is limited. Here, we looked for S. aureus clearance biomarkers produced by in vitro-infected leukocytes. Extracellular proteins from primary human leukocytes infected with S. aureus ATCC 25923 were investigated as possible treatment-monitoring clearance biomarkers by applying a proteomics approach combining liquid chromatography with tandem mass spectrometry (LC-MS/MS) and protein interaction network analysis. It was found that the expression patterns of proteins secreted by S. aureus-infected leukocytes differed among stages of infection. Proteomic profiles showed that an ATPase, aminophospholipid transporter-like, Class I, type 8A, member 2 (ATP8A2) was expressed in the clearance stage and was not detected at any earlier stage or in uninfected controls. Protein network analysis showed that TERF2 (telomeric repeat-binding factor 2), ZNF440 (zinc finger protein 440), and PPP1R14A (phosphatase 1 regulatory subunit 14A) were up-regulated, while GLE1, an essential RNA-export mediator, was suppressed in both infection and clearance stages, suggesting their potential roles in S. aureus infection and clearance. These findings are the first to report that the ATP8A2 has potential as a clearance biomarker for S. aureus infection.

Blood Transcriptomic Analyses Reveal Functional Pathways Associated with Thermotolerance in Pregnant Ewes Exposed to Environmental Heat Stress

Environmental heat stress triggers a series of compensatory mechanisms in sheep that are dependent on their genetic regulation of thermotolerance. Our objective was to identify genes and regulatory pathways associated with thermotolerance in ewes exposed to heat stress. We performed next-generation RNA sequencing on blood collected from 16 pregnant ewes, which were grouped as tolerant and non-tolerant to heat stress according to a physiological indicator. Additional samples were collected to measure complete blood count. A total of 358 differentially expressed genes were identified after applying selection criteria. Gene expression analysis detected 46 GO terms and 52 KEGG functional pathways. The top-three signaling pathways were p53, RIG-I-like receptor and FoxO, which suggested gene participation in biological processes such as apoptosis, cell signaling and immune response to external stressors. Network analysis revealed ATM, ISG15, IRF7, MDM4, DHX58 and TGFβR1 as over-expressed genes with high regulatory potential. A co-expression network involving the immune-related genes ISG15, IRF7 and DXH58 was detected in lymphocytes and monocytes, which was consistent with hematological findings. In conclusion, transcriptomic analysis revealed a non-viral immune mechanism involving apoptosis, which is induced by external stressors and appears to play an important role in the molecular regulation of heat stress tolerance in ewes.

Development of a novel air-liquid interface airway tissue equivalent model for in vitro respiratory modeling studies

The human airways are complex structures with important interactions between cells, extracellular matrix (ECM) proteins and the biomechanical microenvironment. A robust, well-differentiated in vitro culture system that accurately models these interactions would provide a useful tool for studying normal and pathological airway biology. Here, we report the development and characterization of a physiologically relevant air-liquid interface (ALI) 3D airway 'organ tissue equivalent' (OTE) model with three novel features: native pulmonary fibroblasts, solubilized lung ECM, and hydrogel substrate with tunable stiffness and porosity. We demonstrate the versatility of the OTE model by evaluating the impact of these features on human bronchial epithelial (HBE) cell phenotype. Variations of this model were analyzed during 28 days of ALI culture by evaluating epithelial confluence, trans-epithelial electrical resistance, and epithelial phenotype via multispectral immuno-histochemistry and next-generation sequencing. Cultures that included both solubilized lung ECM and native pulmonary fibroblasts within the hydrogel substrate formed well-differentiated ALI cultures that maintained a barrier function and expressed mature epithelial markers relating to goblet, club, and ciliated cells. Modulation of hydrogel stiffness did not negatively impact HBE differentiation and could be a valuable variable to alter epithelial phenotype. This study highlights the feasibility and versatility of a 3D airway OTE model to model the multiple components of the human airway 3D microenvironment.

A Rare Olive Compound Oleacein Improves Lipid and Glucose Metabolism, and Inflammatory Functions: A Comprehensive Whole-Genome Transcriptomics Analysis in Adipocytes Differentiated from Healthy and Diabetic Adipose Stem Cells

Oleacein (OLE), a rare natural compound found in unfiltered extra virgin olive oil, has been shown to have anti-inflammatory and anti-obesity properties. However, little is known regarding the mechanisms by which OLE influences metabolic processes linked to disease targets, particularly in the context of lipid metabolism. In the present study, we conducted whole-genome DNA microarray analyses in adipocytes differentiated from human adipose-derived stem cells (hASCs) and diabetic hASCs (d-hASCs) to examine the effects of OLE on modulating metabolic pathways. We found that OLE significantly inhibited lipid formation in adipocytes differentiated from both sources. In addition, microarray analysis demonstrated that OLE treatment could significantly downregulate lipid-metabolism-related genes and modulate glucose metabolism in both adipocyte groups. Transcription factor enrichment and protein-protein interaction (PPI) analyses identified potential regulatory gene targets. We also found that OLE treatment enhanced the anti-inflammatory properties in adipocytes. Our study findings suggest that OLE exhibits potential benefits in improving lipid and glucose metabolism, thus holding promise for its application in the management of metabolic disorders.

Transcriptomics for Clinical and Experimental Biology Research: Hang on a Seq

Sequencing the human genome empowers translational medicine, facilitating transcriptome-wide molecular diagnosis, pathway biology, and drug repositioning. Initially, microarrays are used to study the bulk transcriptome; but now short-read RNA sequencing (RNA-seq) predominates. Positioned as a superior technology, that makes the discovery of novel transcripts routine, most RNA-seq analyses are in fact modeled on the known transcriptome. Limitations of the RNA-seq methodology have emerged, while the design of, and the analysis strategies applied to, arrays have matured. An equitable comparison between these technologies is provided, highlighting advantages that modern arrays hold over RNA-seq. Array protocols more accurately quantify constitutively expressed protein coding genes across tissue replicates, and are more reliable for studying lower expressed genes. Arrays reveal long noncoding RNAs (lncRNA) are neither sparsely nor lower expressed than protein coding genes. Heterogeneous coverage of constitutively expressed genes observed with RNA-seq, undermines the validity and reproducibility of pathway analyses. The factors driving these observations, many of which are relevant to long-read or single-cell sequencing are discussed. As proposed herein, a reappreciation of bulk transcriptomic methods is required, including wider use of the modern high-density array data-to urgently revise existing anatomical RNA reference atlases and assist with more accurate study of lncRNAs.

Using system biology and bioinformatics to identify the influences of COVID-19 co-infection with influenza virus on COPD

Coronavirus disease 2019 (COVID-19) has speedily increased mortality globally. Although they are risk factors for severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), less is known about the common molecular mechanisms behind COVID-19, influenza virus A (IAV), and chronic obstructive pulmonary disease (COPD). This research used bioinformatics and systems biology to find possible medications for treating COVID-19, IAV, and COPD via identifying differentially expressed genes (DEGs) from gene expression datasets (GSE171110, GSE76925, GSE106986, and GSE185576). A total of 78 DEGs were subjected to functional enrichment, pathway analysis, protein-protein interaction (PPI) network construct, hub gene extraction, and other potentially relevant disorders. Then, DEGs were discovered in networks including transcription factor (TF)-gene connections, protein-drug interactions, and DEG-microRNA (miRNA) coregulatory networks by using NetworkAnalyst. The top 12 hub genes were MPO, MMP9, CD8A, HP, ELANE, CD5, CR2, PLA2G7, PIK3R1, SLAMF1, PEX3, and TNFRSF17. We found that 44 TFs-genes, as well as 118 miRNAs, are directly linked to hub genes. Additionally, we searched the Drug Signatures Database (DSigDB) and identified 10 drugs that could potentially treat COVID-19, IAV, and COPD. Therefore, we evaluated the top 12 hub genes that could be promising DEGs for targeted therapy for SARS-CoV-2 and identified several prospective medications that may benefit COPD patients with COVID-19 and IAV co-infection.

Analysis of PPI networks of transcriptomic expression identifies hub genes associated with Newcastle disease virus persistent infection in bladder cancer

Bladder cancer cells can acquire persistent infection of oncolytic Newcastle disease virus (NDV) but the molecular mechanism(s) remain unelucidated. This poses a major barrier to the effective clinical translation of oncolytic NDV virotherapy of cancers. To improve our understanding of the molecular mechanism(s) associated with the development of NDV persistent infection in bladder cancer, we used mRNA expression profiles of persistently infected bladder cancer cells to construct PPI networks. Based on paths and modules in the PPI network, the bridges were found mainly in the upregulated mRNA-pathways of p53 signalling, ECM-receptor interaction, and TGF-beta signalling and downregulated mRNA-pathways of antigen processing and presentation, protein processing in endoplasmic reticulum, completement and coagulation cascades in persistent TCCSUPPi cells. In persistent EJ28Pi cells, connections were identified mainly through upregulated mRNA-pathways of renal carcinoma, viral carcinogenesis, Ras signalling and cell cycle and the downregulated mRNA-pathways of Wnt signalling, HTLV-I infection and pathways in cancers. These connections were mainly dependent on RPL8-HSPA1A/HSPA4 in TCCSUPPi cells and EP300, PTPN11, RAC1-TP53, SP1, CCND1 and XPO1 in EJ28Pi cells. Oncomine validation showed that the top hub genes identified in the networks that include RPL8, THBS1, F2 from TCCSUPPi and TP53 and RAC1 from EJ28Pi are involved in the development and progression of bladder cancer. Protein-drug interaction networks identified several putative drug targets that could be used to disrupt the linkages between the modules and prevent bladder cancer cells from acquiring NDV persistent infection. This novel PPI network analysis of differentially expressed mRNAs of NDV persistently infected bladder cancer cell lines provide an insight into the molecular mechanisms of NDV persistency of infection in bladder cancers and the future screening of drugs that can be used together with NDV to enhance its oncolytic efficacy.

A Descriptive Whole-Genome Transcriptomics Study in a Stem Cell-Based Tool Predicts Multiple Tissue-Specific Beneficial Potential and Molecular Targets of Carnosic Acid

Carnosic acid (CA) is a phenolic diterpene widely distributed in herbal plants, rosemary and sage. Although its medicinal properties, such as antioxidant, antimicrobial, and neuroprotective effects, have been well-documented, its relevant biochemical processes and molecular targets have not been fully explored yet. In the present study, we conducted an untargeted whole-genome transcriptomics analysis to investigate CA-induced early biological and molecular events in human amniotic epithelial stem cells (hAESCs) with the aim of exploring its multiple tissue-specific functionalities and potential molecular targets. We found that seven days of CA treatment in hAESCs could induce mesoderm-lineage-specific differentiation. Tissue enrichment analysis revealed that CA significantly enriched lateral plate mesoderm-originated cardiovascular and adipose tissues. Further tissue-specific PPI analysis and kinase and transcription factor enrichment analyses identified potential upstream regulators and molecular targets of CA in a tissue-specific manner. Gene ontology enrichment analyses revealed the metabolic, antioxidant, and antifibrotic activities of CA. Altogether, our comprehensive whole-genome transcriptomics analyses offer a thorough understanding of the possible underlying molecular mechanism of CA.

Bioinformatics analysis of the pathogenic link between Epstein-Barr virus infection, systemic lupus erythematosus and diffuse large B cell lymphoma

Epstein-Barr virus (EBV) is a risk factor for diffuse large B-cell lymphoma (DLBCL) and systemic lupus erythematosus (SLE). While prior research has suggested a potential correlation between SLE and DLBCL, the molecular mechanisms remain unclear. The present study aimed to explore the contribution of EBV infection to the pathogenesis of DLBCL in the individuals with SLE using bioinformatics approaches. The Gene Expression Omnibus database was used to compile the gene expression profiles of EBV-infected B cells (GSE49628), SLE (GSE61635), and DLBCL (GSE32018). Altogether, 72 shared common differentially expressed genes (DEGs) were extracted and enrichment analysis of the shared genes showed that p53 signaling pathway was a common feature of the pathophysiology. Six hub genes were selected using protein-protein interaction (PPI) network analysis, including CDK1, KIF23, NEK2, TOP2A, NEIL3 and DEPDC1, which showed preferable diagnostic values for SLE and DLBCL and involved in immune cell infiltration and immune responses regulation. Finally, TF-gene and miRNA-gene regulatory networks and 10 potential drugs molecule were predicted. Our study revealed the potential molecular mechanisms by which EBV infection contribute to the susceptibility of DLBCL in SLE patients for the first time and identified future biomarkers and therapeutic targets for SLE and DLBCL.

Targeting Essential Hypothetical Proteins of Pseudomonas aeruginosa PAO1 for Mining of Novel Therapeutics: An In Silico Approach

As an omnipresent opportunistic bacterium, Pseudomonas aeruginosa PAO1 is responsible for acute and chronic infection in immunocompromised individuals. Currently, this bacterium is on WHO's red list where new antibiotics are urgently required for the treatment. Finding essential genes and essential hypothetical proteins (EHP) can be crucial in identifying novel druggable targets and therapeutics. This study is aimed at characterizing these EHPs and analyzing subcellular and physiochemical properties, PPI network, nonhomologous analysis against humans, virulence factor and novel drug target prediction, and finally structural analysis of the identified target employing around 42 robust bioinformatics tools/databases, the output of which was evaluated using the ROC analysis. The study discovered 18 EHPs from 336 essential genes, with domain and functional annotation revealing that 50% of these proteins belong to the enzyme category. The majority are cytoplasmic and cytoplasmic membrane proteins, with half being stable proteins subjected to PPIs network analysis. The network contains 261 nodes and 269 edges for 9 proteins of interest, with 11 hubs containing at least three nodes each. Finally, a pipeline builder predicts 7 proteins with novel drug targets, 5 nonhomologous proteins against human proteome, human antitargets, and human gut flora, and 3 virulent proteins. Among these, homology modeling of NP_249450 and NP_251676 was done, and the Ramachandran plot analysis revealed that more than 94% of the residues were in the preferred region. By analyzing functional attributes and virulence characteristics, the findings of this study may facilitate the development of innovative antibacterial drug targets and drugs of Pseudomonas aeruginosa PAO1.