A comprehensive meta-analysis of transcriptome data to identify signature genes associated with pancreatic ductal adenocarcinoma.

PURPOSE: Pancreatic ductal adenocarcinoma (PDAC) has a five-year survival rate of less than 5%. Absence of symptoms at primary tumor stages, as well as high aggressiveness of the tumor can lead to high mortality in cancer patients. Most patients are recognized at the advanced or metastatic stage without surgical symptom, because of the lack of reliable early diagnostic biomarkers. The objective of this work was to identify potential cancer biomarkers by integrating transcriptome data. METHODS: Several transcriptomic datasets comprising of 11 microarrays were retrieved from the GEO database. After pre-processing, a meta-analysis was applied to identify differentially expressed genes (DEGs) between tumor and nontumor samples for datasets. Next, co-expression analysis, functional enrichment and survival analyses were used to determine the functional properties of DEGs and identify potential prognostic biomarkers. In addition, some regulatory factors involved in PDAC including transcription factors (TFs), protein kinases (PKs), and miRNAs were identified. RESULTS: After applying meta-analysis, 1074 DEGs including 539 down- and 535 up-regulated genes were identified. Pathway enrichment analyzes using Gene Ontology (GO) and the Kyoto Encyclopedia of Genes and Genomes (KEGG) revealed that DEGs were significantly enriched in the HIF-1 signaling pathway and focal adhesion. The results also showed that some of the DEGs were assigned to TFs that belonged to 23 conserved families. Sixty-four PKs were identified among the DEGs that showed the CAMK family was the most abundant group. Moreover, investigation of corresponding upstream regions of DEGs identified 11 conserved sequence motifs. Furthermore, weighted gene co-expression network analysis (WGCNA) identified 8 modules, more of them were significantly enriched in Ras signaling, p53 signaling, MAPK signaling pathways. In addition, several hubs in modules were identified, including EMP1, EVL, ELP5, DEF8, MTERF4, GLUP1, CAPN1, IGF1R, HSD17B14, TOM1L2 and RAB11FIP3. According to survival analysis, it was identified that the expression levels of two genes, EMP1 and RAB11FIP3 are related to prognosis. CONCLUSION: We identified several genes critical for PDAC based on meta-analysis and system biology approach. These genes may serve as potential targets for the treatment and prognosis of PDAC.

Unveiling promising breast cancer biomarkers: an integrative approach combining bioinformatics analysis and experimental verification.

BACKGROUND: Breast cancer remains a significant health challenge worldwide, necessitating the identification of reliable biomarkers for early detection, accurate prognosis, and targeted therapy. MATERIALS AND METHODS: Breast cancer RNA expression data from the TCGA database were analyzed to identify differentially expressed genes (DEGs). The top 500 up-regulated DEGs were selected for further investigation using random forest analysis to identify important genes. These genes were evaluated based on their potential as diagnostic biomarkers, their overexpression in breast cancer tissues, and their low median expression in normal female tissues. Various validation methods, including online tools and quantitative Real-Time PCR (qRT-PCR), were used to confirm the potential of the identified genes as breast cancer biomarkers. RESULTS: The study identified four overexpressed genes (CACNG4, PKMYT1, EPYC, and CHRNA6) among 100 genes with higher importance scores. qRT-PCR analysis confirmed the significant upregulation of these genes in breast cancer patients compared to normal samples. CONCLUSIONS: These findings suggest that CACNG4, PKMYT1, EPYC, and CHRNA6 may serve as valuable biomarkers for breast cancer diagnosis, and PKMYT1 may also have prognostic significance. Furthermore, CACNG4, CHRNA6, and PKMYT1 show promise as potential therapeutic targets. These findings have the potential to advance diagnostic methods and therapeutic approaches for breast cancer.

Using meta-analysis and machine learning to investigate the transcriptional response of immune cells to Leishmania infection.

BACKGROUND: Leishmaniasis is a parasitic disease caused by the Leishmania protozoan affecting millions of people worldwide, especially in tropical and subtropical regions. The immune response involves the activation of various cells to eliminate the infection. Understanding the complex interplay between Leishmania and the host immune system is crucial for developing effective treatments against this disease. METHODS: This study collected extensive transcriptomic data from macrophages, dendritic, and NK cells exposed to Leishmania spp. Our objective was to determine the Leishmania-responsive genes in immune system cells by applying meta-analysis and feature selection algorithms, followed by co-expression analysis. RESULTS: As a result of meta-analysis, we discovered 703 differentially expressed genes (DEGs), primarily associated with the immune system and cellular metabolic processes. In addition, we have substantiated the significance of transcription factor families, such as bZIP and C2H2 ZF, in response to Leishmania infection. Furthermore, the feature selection techniques revealed the potential of two genes, namely G0S2 and CXCL8, as biomarkers and therapeutic targets for Leishmania infection. Lastly, our co-expression analysis has unveiled seven hub genes, including PFKFB3, DIAPH1, BSG, BIRC3, GOT2, EIF3H, and ATF3, chiefly related to signaling pathways. CONCLUSIONS: These findings provide valuable insights into the molecular mechanisms underlying the response of immune system cells to Leishmania infection and offer novel potential targets for the therapeutic goals.

Integrated co-expression analysis of regulatory elements (miRNA, lncRNA, and TFs) in bovine monocytes induced by Str. uberis.

Non-coding RNAs, including long non-coding RNAs (lncRNAs) and microRNAs (miRNAs), together with transcription factors, are critical pre-, co-, and post-transcriptional regulators. In addition to their criteria as ideal biomarkers, they have great potential in disease prognosis, diagnosis, and treatment of complex diseases. Investigation of regulatory mechanisms in the context of bovine mastitis, as most common and economic disease in the dairy industry, to identify elements influencing the expression of candidate genes as key regulators of the mammary immune response is not yet fully understood. Transcriptome profiles (50 RNA-Seq and 50 miRNA-Seq samples) of bovine monocytes induced by Str. uberis were used for co-expression module detection and preservation analysis using the weighted gene co-expression network analysis (WGCNA) approach. Assigned mi-, lnc-, and m-modules used to construct the integrated regulatory networks and miRNA-lncRNA-mRNA regulatory sub-networks. Remarkably, we have identified 18 miRNAs, five lncRNAs, and seven TFs as key regulators of str. uberis-induced mastitis. Most of the genes introduced here, mainly involved in immune response, inflammation, and apoptosis, were new to mastitis. These findings may help to further elucidate the underlying mechanisms of bovine mastitis, and the discovered genes may serve as signatures for early diagnosis and treatment of the disease.

Construction of lncRNA/Pseudogene-miRNA Network Based on In Silico Approaches for Glycolysis Pathway to Identify Prostate Adenocarcinoma-Related Potential Biomarkers.

LncRNAs, pseudogenes, and miRNAs participate a fundamental function in tumorigenesis, metabolism, and invasion of cancer cells, although their regulation of tumor glycolysis in prostate adenocarcinoma (PRAD) is thoroughly not well studied. In this study, we applied transcriptomic, proteomic, and medical information to identify glycolysis-related key genes and modules associated with PRAD. Then, the glycolysis-related lncRNA/lncRNAs/pseudogenes-miRNA-mRNA network was constructed. Analysis of DNA methylation status and expression data determined a DNA methylation-dysregulated three-DE-mRNAs signature for predicting diagnosis, ANGPTL4, GNE, and HSPA in PRAD patients and healthy control. Several lncRNAs/pseudogenes, significantly correlated with the overall survival PVT1, CA5BP1, MIRLET7BHG, SNHG12, and ZNF37BP and disease-free survival status, MALAT1, GUSBP11, MIRLET7BHG, and SNHG1, of patients with PRAD were determined. The methylation profile of DE-lncRNA/pseudogenes was significantly proper for predicting PRAD prognostic model. The transcription level of 6 DE-mRNA ANGPTL4, QSOX1, BIK, CLDN3, DDIT4, and TFF3 was correlated with cancer-related fibroblast infiltration in PRAD. The mutated form of 7 mRNAs, COL5A1, IDH1, HK2, DDIT4, GNE, and QSOX1, was associated with PRAD. In addition to the glycolysis pathway, DE-RNAs play regulatory roles on several pathways, including DNA damage, RTK, cell cycle, RAS/MAPK, TSC/mTOR and PI3K/AKT, AR hormone, and EMT. Overall, our study improves our knowledge of the relation between lncRNAs/pseudogenes and miRNA related to glycolysis and PRAD pathogenesis. This schematics presents shows the websites and databases implemented in this research.

Integrative systems biology analysis of barley transcriptome ─ hormonal signaling against biotic stress.

Biotic stresses are pests and pathogens that cause a variety of crop diseases and damages. In response to these agents, crops trigger specific defense signal transduction pathways in which hormones play a central role. To recognize hormonal signaling, we integrated barley transcriptome datasets related to hormonal treatments and biotic stresses. In the meta-analysis of each dataset, 308 hormonal and 1232 biotic DEGs were identified respectively. According to the results, 24 biotic TFs belonging to 15 conserved families and 6 hormonal TFs belonging to 6 conserved families were identified, with the NF-YC, GNAT, and WHIRLY families being the most prevalent. Additionally, gene enrichment and pathway analyses revealed that over-represented cis-acting elements were recognized in response to pathogens and hormones. Based on the co-expression analysis, 6 biotic and 7 hormonal modules were uncovered. Finally, the hub genes of PKT3, PR1, SSI2, LOX2, OPR3, and AOS were candidates for further study in JA- or SA-mediated plant defense. The qPCR confirmed that the expression of these genes was induced from 3 to 6 h following exposure to 100 µM MeJA, with peak expression occurring between 12 h and 24 h and decreasing after 48 h. Overexpression of PR1 was one of the first steps toward SAR. As well as regulating SAR, NPR1 has also been shown to be involved in the activation of ISR by the SSI2. LOX2 catalyzes the first step of JA biosynthesis, PKT3 plays an important role in wound-activated responses, and OPR3 and AOS are involved in JA biosynthesis. In addition, many unknown genes were introduced that can be used by crop biotechnologists to accelerate barley genetic engineering.

System network analysis of Rosmarinus officinalis transcriptome and metabolome-Key genes in biosynthesis of secondary metabolites.

Medicinal plants contain valuable compounds that have attracted worldwide interest for their use in the production of natural drugs. The presence of compounds such as rosmarinic acid, carnosic acid, and carnosol in Rosmarinus officinalis has made it a plant with unique therapeutic effects. The identification and regulation of the biosynthetic pathways and genes will enable the large-scale production of these compounds. Hence, we studied the correlation between the genes involved in biosynthesis of the secondary metabolites in R. officinalis using proteomics and metabolomics data by WGCNA. We identified three modules as having the highest potential for the metabolite engineering. Moreover, the hub genes highly connected to particular modules, TFs, PKs, and transporters were identified. The TFs of MYB, C3H, HB, and C2H2 were the most likely candidates associated with the target metabolic pathways. The results indicated that the hub genes including Copalyl diphosphate synthase (CDS), Phenylalanine ammonia lyase (PAL), Cineole synthase (CIN), Rosmarinic acid synthase (RAS), Tyrosine aminotransferase (TAT), Cinnamate 4-hydroxylase (C4H), and MYB58 are responsible for biosynthesis of important secondary metabolites. Thus, we confirmed these results using qRT-PCR after treating R. officinalis seedlings with methyl jasmonate. These candidate genes may be employed for genetic and metabolic engineering research to increase R. officinalis metabolite production.

Identification of key genes involved in secondary metabolite biosynthesis in Digitalis purpurea.

The medicinal plant Digitalis purpurea produces cardiac glycosides that are useful in the pharmaceutical industry. These bioactive compounds are in high demand due to ethnobotany's application to therapeutic procedures. Recent studies have investigated the role of integrative analysis of multi-omics data in understanding cellular metabolic status through systems metabolic engineering approach, as well as its application to genetically engineering metabolic pathways. In spite of numerous omics experiments, most molecular mechanisms involved in metabolic pathways biosynthesis in D. purpurea remain unclear. Using R Package Weighted Gene Co-expression Network Analysis, co-expression analysis was performed on the transcriptome and metabolome data. As a result of our study, we identified transcription factors, transcriptional regulators, protein kinases, transporters, non-coding RNAs, and hub genes that are involved in the production of secondary metabolites. Since jasmonates are involved in the biosynthesis of cardiac glycosides, the candidate genes for Scarecrow-Like Protein 14 (SCL14), Delta24-sterol reductase (DWF1), HYDRA1 (HYD1), and Jasmonate-ZIM domain3 (JAZ3) were validated under methyl jasmonate treatment (MeJA, 100 µM). Despite early induction of JAZ3, which affected downstream genes, it was dramatically suppressed after 48 hours. SCL14, which targets DWF1, and HYD1, which induces cholesterol and cardiac glycoside biosynthesis, were both promoted. The correlation between key genes and main metabolites and validation of expression patterns provide a unique insight into the biosynthesis mechanisms of cardiac glycosides in D. purpurea.

Integration of mRNA and protein expression data for the identification of potential biomarkers associated with pancreatic ductal adenocarcinoma.

Pancreatic ductal adenocarcinoma (PDAC) is one of the most death-dealing tumors, with a tremendously poor prognosis. Here, we, through interrogation of mRNA and protein data combined with a system biology approach, identified several key genes, functional processes, and pathways that can have critical roles in PDAC. We detected an interesting module related to the clinical traits that enriched in the ribosome, hematopoietic cell lineage, and cell adhesion molecules-related pathways. We also identified several hub genes in important modules that are associated with immune system processes. The results also indicated some lncRNAs, such as FAM30A, and MIR223HG with essential functions that are involved in PDAC. Additionally, five genes, including CD53, ITGAL, WDFY4, TLX1, and LMAN1L were screened by survival analysis and can be considered as candidate biomarkers or therapeutic targets. According to our strategy, the findings of this study may provide a better understanding of the molecular mechanisms and suggest potential prognostic and therapeutic targets for PDAC.

Integration of meta-analysis, machine learning and systems biology approach for investigating the transcriptomic response to drought stress in Populus species.

In Populus, drought is a major problem affecting plant growth and development which can be closely reflected by corresponding transcriptomic changes. Nevertheless, how these changes in Populus are not fully understood. Here, we first used meta-analysis and machine learning methods to identify water stress-responsive genes and then performed a systematic approach to discover important gene networks. Our analysis revealed that large transcriptional variations occur during drought stress. These changes were more associated with the response to stress, cellular catabolic process, metabolic pathways, and hormone-related genes. The differential gene coexpression analysis highlighted two acetyltransferase NATA1-like and putative cytochrome P450 genes that have a special contribution in response to drought stress. In particular, the findings showed that MYBs and MAPKs have a prominent role in the drought stress response that could be considered to improve the drought tolerance of Populus. We also suggest ARF2-like and PYL4-like genes as potential markers for use in breeding programs. This study provides a better understanding of how Populus responses to drought that could be useful for improving tolerance to stress in Populus.

Integration of transcriptomic and metabolomic analyses provides insights into response mechanisms to nitrogen and phosphorus deficiencies in soybean.

Nitrogen (N) and phosphorus (P) are two essential plant macronutrients that can limit plant growth by different mechanisms. We aimed to shed light on how soybean respond to low nitrogen (LN), low phosphorus (LP) and their combined deficiency (LNP). Generally, these conditions triggered changes in gene expression of the same processes, including cell wall organization, defense response, response to oxidative stress, and photosynthesis, however, response was different in each condition. A typical primary response to LN and LP was detected also in soybean, i.e., the enhanced uptake of N and P, respectively, by upregulation of genes for the corresponding transporters. The regulation of genes involved in cell wall organization showed that in LP roots tended to produce more casparian strip, in LN more secondary wall biosynthesis occurred, and in LNP reduction in expression of genes involved in secondary wall production accompanied by cell wall loosening was observed. Flavonoid biosynthesis also showed distinct pattern of regulation in different conditions: more anthocyanin production in LP, and more isoflavonoid production in LN and LNP, which we confirmed also on the metabolite level. Interestingly, in soybean the nutrient deficiencies reduced defense response by lowering expression of genes involved in defense response, suggesting a role of N and P nutrition in plant disease resistance. In conclusion, we provide detailed information on how LN, LP, and LNP affect different processes in soybean roots on the molecular and physiological levels.

MicroRNA miR1118 contributes to wheat (Triticum aestivum L.) salinity tolerance by regulating the Plasma Membrane Intrinsic Proteins1;5 (PIP1;5) gene.

microRNAs (miRNAs) are important regulators of various adaptive stress responses in crops; however, many details about associations among miRNAs, their target genes and physiochemical responses of crops under salinity stress remain poorly understood. We designed this study in a systems biology context and used a collection of computational, experimental and statistical procedures to uncover some regulatory functions of miRNAs in the response of the important crop, wheat, to salinity stress. Accordingly, under salinity conditions, wheat roots' Expressed Sequence Tag (EST) libraries were computationally mined to identify the most reliable differentially expressed miRNA and its related target gene(s). Then, molecular and physiochemical evaluations were carried out in a separate salinity experiment using two contrasting wheat genotypes. Finally, the association between changes in measured characteristics and wheat salinity tolerance was determined. From the results, miR1118 was assigned as a reliable salinity-responsive miRNA in wheat roots. The expression profiles of miR1118 and its predicted target gene, Plasma Membrane Intrinsic Proteins1,5 (PIP1;5), significantly differed between wheat genotypes. Moreover, results revealed that expression profiles of miR1118 and PIP1;5 significantly correlate to Relative Water Content (RWC), root hydraulic conductance (Lp), photosynthetic activities, plasma membrane damages, osmolyte accumulation and ion homeostasis of wheat. Our results suggest a plausible regulatory node through miR1118 adjusting the wheat water status, maintaining ion homeostasis and mitigating membrane damages, mainly through the PIP1;5 gene, under salinity conditions. To our knowledge, this is the first report on the role of miR1118 and PIP1;5 in wheat salinity response.

Genome-wide post-transcriptional regulation of bovine mammary gland response to Streptococcus uberis.

MicroRNAs (miRNAs) as post-transcriptionally regulators of gene expression have been shown to be critical regulators to fine-tuning immune responses, besides their criteria for being an ideal biomarker. The regulatory role of miRNAs in responses to most mastitis-causing pathogens is not well understood. Gram-positive Streptococcus uberis (Str. uberis), the leading pathogen in dairy herds, cause both clinical and subclinical infections. In this study, a system biology approach was used to better understand the main post-transcriptional regulatory functions and elements of bovine mammary gland response to Str. uberis infection. Publicly available miRNA-Seq data containing 50 milk samples of the ten dairy cows (five controls and five infected) were retrieved for this current research. Functional enrichment analysis of predicted targets revealed that highly confident responsive miRNAs (4 up- and 19 downregulated) mainly regulate genes involved in the regulation of transcription, apoptotic process, regulation of cell adhesion, and pro-inflammatory signaling pathways. Time series analysis showed that six gene clusters significantly differed in comparisons between Str. uberis-induced samples with controls. Additionally, other bioinformatic analysis, including upstream network analysis, showed essential genes, including TP53 and TGFB1 and some small molecules, including glucose, curcumin, and LPS, commonly regulate most of the downregulated miRNAs. Upregulated miRNAs are commonly controlled by the most important genes, including IL1B, NEAT1, DICER1 enzyme and small molecules including estradiol, tamoxifen, estrogen, LPS, and epigallocatechin. Our study used results of next-generation sequencing to reveal key miRNAs as the main regulator of gene expression responses to a Gram-positive bacterial infection. Furthermore, by gene regulatory network (GRN) analysis, we can introduce the common upregulator transcription factor of these miRNAs. Such milk-based miRNA signature(s) would facilitate risk stratification for large-scale prevention programs and provide an opportunity for early diagnosis and therapeutic intervention.

Transcriptome Meta-Analysis Identifies Candidate Hub Genes and Pathways of Pathogen Stress Responses in Arabidopsis thaliana.

Following a pathogen attack, plants defend themselves using multiple defense mechanisms to prevent infections. We used a meta-analysis and systems-biology analysis to search for general molecular plant defense responses from transcriptomic data reported from different pathogen attacks in Arabidopsis thaliana. Data from seven studies were subjected to meta-analysis, which revealed a total of 3694 differentially expressed genes (DEGs), where both healthy and infected plants were considered. Gene Ontology and Kyoto Encyclopedia of Genes and Genomes pathway enrichment analysis further suggested that the DEGs were involved in several biosynthetic metabolic pathways, including those responsible for the biosynthesis of secondary metabolites and pathways central to photosynthesis and plant-pathogen interactions. Using network analysis, we highlight the importance of WRKY40, WRKY46 and STZ, and suggest that they serve as major points in protein-protein interactions. This is especially true regarding networks of composite-metabolic responses by pathogens. In summary, this research provides a new approach that illuminates how different mechanisms of transcriptome responses can be activated in plants under pathogen infection and indicates that common genes vary in their ability to regulate plant responses to the pathogens studied herein.

Network for network concept offers new insights into host- SARS-CoV-2 protein interactions and potential novel targets for developing antiviral drugs.

SARS-CoV-2, the causal agent of COVID-19, is primarily a pulmonary virus that can directly or indirectly infect several organs. Despite many studies carried out during the current COVID-19 pandemic, some pathological features of SARS-CoV-2 have remained unclear. It has been recently attempted to address the current knowledge gaps on the viral pathogenicity and pathological mechanisms via cellular-level tropism of SARS-CoV-2 using human proteomics, visualization of virus-host protein-protein interactions (PPIs), and enrichment analysis of experimental results. The synergistic use of models and methods that rely on graph theory has enabled the visualization and analysis of the molecular context of virus/host PPIs. We review current knowledge on the SARS-COV-2/host interactome cascade involved in the viral pathogenicity through the graph theory concept and highlight the hub proteins in the intra-viral network that create a subnet with a small number of host central proteins, leading to cell disintegration and infectivity. Then we discuss the putative principle of the "gene-for-gene and "network for network" concepts as platforms for future directions toward designing efficient anti-viral therapies.

LncRNA-miRNA network analysis across the Th17 cell line reveals biomarker potency of lncRNA NEAT1 and KCNQ1OT1 in multiple sclerosis.

Differentiation of CD4+ T cells into Th17 cells is an important factor in the onset and progression of multiple sclerosis (MS) and Th17/Treg imbalance. Little is known about the role of lncRNAs in the differentiation of CD4+ cells from Th17 cells. This study aimed to analyse the lncRNA-miRNAs network involved in MS disease and its role in the differentiation of Th17 cells. The lncRNAs in Th17 differentiation were obtained from GSE66261 using the GEO datasets. Differential expression of lncRNAs in Th17 primary cells compared to Th17 effector cells was investigated by RNA-seq analysis. Next, the most highlighted lncRNAs in autoimmune diseases were downloaded from the lncRNAs disease database, and the most critical miRNA was extracted by literature search. Then, the lncRNA-miRNA interaction was achieved by the Starbase database, and the ceRNA network was designed by Cytoscape. Finally, using the CytoHubba application, two hub lncRNAs with the most interactions with miRNAs were identified by the MCODE plug-in. The expression level of genes was measured by qPCR, and the plasma level of cytokines was analysed by ELISA kits. The results showed an increase in the expression of NEAT1, KCNQ1OT1 and RORC and a decrease in the expression of FOXP3. In plasma, an upregulation of IL17 and a downregulation of TGFB inflammatory cytokines were detected. The dysregulated expression of these genes could be attributed to relapsing-remitting MS (RR-MS) patients and help us understand MS pathogenesis better.

Meta-Analysis of Common and Differential Transcriptomic Responses to Biotic and Abiotic Stresses in Arabidopsis thaliana.

Environmental stresses adversely affect crop growth and yield, resulting in major losses to plants. These stresses occur simultaneously in nature, and we therefore conducted a meta-analysis in this study to identify differential and shared genes, pathways, and transcriptomic mechanisms involved in Arabidopsis response to biotic and abiotic stresses. The results showed a total of 436/21 significant up-/downregulated differentially expressed genes (DEGs) in response to biotic stresses, while 476 and 71 significant DEGs were respectively up- and downregulated in response to abiotic stresses in Arabidopsis thaliana. In addition, 21 DEGs (2.09%) were commonly regulated in response to biotic and abiotic stresses. Except for WRKY45 and ATXTH22, which were respectively up-/down- and down-/upregulated in response to biotic and abiotic stresses, other common DEGs were upregulated in response to all biotic and abiotic treatments. Moreover, the transcription factors (TFs) bHLH, MYB, and WRKY were the common TFs in response to biotic and abiotic stresses. In addition, ath-miR414 and ath-miR5658 were identified to be commonly expressed in response to both biotic and abiotic stresses. The identified common genes and pathways during biotic and abiotic stresses may provide potential candidate targets for the development of stress resistance breeding programs and for the genetic manipulation of crop plants.