Differential Co-expression Network Research Articles

A 19-Gene Signature of Serous Ovarian Cancer Identified by Machine Learning and Systems Biology: Prospects for Diagnostics and Personalized Medicine.

Ovarian cancer is a major cause of cancer deaths among women. Early diagnosis and precision/personalized medicine are essential to reduce mortality and morbidity of ovarian cancer, as with new molecular targets to accelerate drug discovery. We report here an integrated systems biology and machine learning (ML) approach based on the differential coexpression analysis to identify candidate systems biomarkers (i.e., gene modules) for serous ovarian cancer. Accordingly, four independent transcriptome datasets were statistically analyzed independently and common differentially expressed genes (DEGs) were identified. Using these DEGs, coexpressed gene pairs were unraveled. Subsequently, differential coexpression networks between the coexpressed gene pairs were reconstructed so as to identify the differentially coexpressed gene modules. Based on the established criteria, "SOV-module" was identified as being significant, consisting of 19 genes. Using independent datasets, the diagnostic capacity of the SOV-module was evaluated using principal component analysis (PCA) and ML techniques. PCA showed a sensitivity and specificity of 96.7% and 100%, respectively, and ML analysis showed an accuracy of up to 100% in distinguishing phenotypes in the present study sample. The prognostic capacity of the SOV-module was evaluated using survival and ML analyses. We found that the SOV-module's performance for prognostics was significant (p-value = 1.36 × 10-4) with an accuracy of 63% in discriminating between survival and death using ML techniques. In summary, the reported genomic systems biomarker candidate offers promise for personalized medicine in diagnosis and prognosis of serous ovarian cancer and warrants further experimental and translational clinical studies.

In-depth analysis of immune cell landscapes reveals differences between lung adenocarcinoma and lung squamous cell carcinoma

BackgroundLung cancer is the leading cause of cancer deaths globally, with lung adenocarcinoma (LUAD) and squamous cell carcinoma (LUSC) being major subtypes. Immunotherapy has emerged as a promising approach for the treatment of lung cancer, but understanding the underlying mechanisms of immune dysregulation is crucial for the development of effective therapies. This study aimed to investigate the distinctive cellular features of LUAD and LUSC and identify potential biomarkers associated with the pathogenesis and clinical outcomes of each subtype.MethodsWe used digital cytometry techniques to analyze the RNA-Seq data of 1128 lung cancer patients from The Cancer Genome Atlas (TCGA) database. The abundance of cell subtypes and ecotypes in LUAD and LUSC patients was quantified. Univariate survival analysis was used to investigate their associations with patient overall survival (OS). Differential gene expression analysis and gene co-expression network construction were carried out to explore the gene expression patterns of LUSC patients with distinct survival outcomes. Scratch wound-healing assay, colony formation assay, and transwell assay were used to validate the candidate drugs for LUSC treatment.ResultsWe found differential expression of cell subtypes between LUAD and LUSC, with certain cell subtypes being prognostic for survival in both subtypes. We also identified differential gene expression and gene co-expression modules associated with macrophages.3/PCs.2 ratio in LUSC patients with distinct survival outcomes. Furthermore, ecotype ratios were found to be prognostic in both subtypes and machine learning models showed that certain cell subtypes, such as epithelial.cells.1, epithelial.cells.5, and endothelial.cells.2 are important for predicting LUSC. Ginkgolide B and triamterene can inhibit the proliferation, invasion, and migration of LUSC cell lines.ConclusionWe provide insight into the distinctive cellular features of LUAD and LUSC, and identify potential biomarkers associated with the pathogenesis and clinical outcomes of each subtype. Ginkgolide B and triamterene could be promising drugs for LUSC treatment.

Differential co-expression network analysis elucidated genes associated with sensitivity to farnesyltransferase inhibitor and prognosis of acute myeloid leukemia.

Acute myeloid leukemia (AML) is a heterogeneous disease and the most common form of acute leukemia with a poor prognosis. Due to its complexity, the disease requires the identification of biomarkers for reliable prognosis. To identify potential disease genes that regulate patient prognosis, we used differential co-expression network analysis and transcriptomics data from relapsed, refractory, and previously untreated AML patients based on their response to treatment in the present study. In addition, we combined functional genomics and transcriptomics data to identify novel and therapeutically potential systems biomarkers for patients who do or do not respond to treatment. As a result, we constructed co-expression networks for response and non-response cases and identified a highly interconnected group of genes consisting of SECISBP2L, MAN1A2, PRPF31, VASP, and SNAPC1 in the response network and a group consisting of PHTF2, SLC11A2, PDLIM5, OTUB1, and KLRD1 in the non-response network, both of which showed high prognostic performance with hazard ratios of 4.12 and 3.66, respectively. Remarkably, ETS1, GATA2, AR, YBX1, and FOXP3 were found to be important transcription factors in both networks. The prognostic indicators reported here could be considered as a resource for identifying tumorigenesis and chemoresistance to farnesyltransferase inhibitor. They could help identify important research directions for the development of new prognostic and therapeutic techniques for AML.

Exploring explainable machine learning and Shapley additive exPlanations (SHAP) technique to uncover key factors of HNSC cancer: An analysis of the best practices

ObjectiveHead and Neck Squamous Cell Carcinoma (HNSC) is characterized by poor prognosis and easy recurrence. Aberrant induction of autophagy and long non-coding RNAs (LncRNAs) have emerged as crucial factors in diagnosing and predicting HNSC. The goal of this study is to show how machine learning may help categorize HNSC subjects into risk categories. Moreover, we used SHAP architectures to offer explanations and interpretations. MethodsIn this study, we screened autophagy-related LncRNAs (DEARLs) using differential analysis and co-expression network analysis, identified DEARLs with prognostic ability using LASSO regression and RF classification, built a model based on multivariate Cox regression, subsequently validated its predictive capability. Eight machine learning methods were used to construct prediction models. SHAP analysis was established based on the best model, and the output of the ARLGS model was interpreted to filter out significant DEARLs. Finally, the ARLGS model was used for pan-cancer analysis. ResultsWe determined Nine DEARLs with predictive ability, and then these DEARLs were utilized to construct an ARLGS model. Kaplan-Meier (KM) analysis was performed to verify that cohorts with high ARLGS exhibited lower overall survival (OS) compared to those with low ARLGS, both in the train and test set. Furthermore, ROC curve analysis demonstrated the model's strong performance for HNSC patients at 1-, 3-, and 5-year (AUC > 70 %). Comparing the prediction models established by eight machine learning methods, we found that the xgboost model has the highest accuracy. Based on this, we performed SHAP analysis and found that the five DEARLs with the highest SHAP values were MIAT, LINC01343, LINC01305, and SLCO4A1.AS1 and GATA2.AS1 reflects the importance of these five DEARLs. Finally, we obtained the differential expression, immune signature, mutation, and drug sensitivity signatures of DEARLs in 33 cancers using pan-cancer analysis. ConclusionWe developed an ARLGS model with good reliability using LASSO and RF. We also found that the Xgboost model was the best, and can be used for SHAP analysis to find important DEARLs, solving the problem of the uninterpretable nature of machine learning methods. Thus, such an approach provided important guidance for assessing the risk of HNSC patients and developing customized treatments.

Patient-specific analysis of co-expression to measure biological network rewiring in individuals.

To effectively understand the underlying mechanisms of disease and inform the development of personalized therapies, it is critical to harness the power of differential co-expression (DCE) network analysis. Despite the promise of DCE network analysis in precision medicine, current approaches have a major limitation: they measure an average differential network across multiple samples, which means the specific etiology of individual patients is often overlooked. To address this, we present Cosinet, a DCE-based single-sample network rewiring degree quantification tool. By analyzing two breast cancer datasets, we demonstrate that Cosinet can identify important differences in gene co-expression patterns between individual patients and generate scores for each individual that are significantly associated with overall survival, recurrence-free interval, and other clinical outcomes, even after adjusting for risk factors such as age, tumor size, HER2 status, and PAM50 subtypes. Cosinet represents a remarkable development toward unlocking the potential of DCE analysis in the context of precision medicine.

PRANA: an R package for differential co-expression network analysis with the presence of additional covariates

BackgroundAdvances in sequencing technology and cost reduction have enabled an emergence of various statistical methods used in RNA-sequencing data, including the differential co-expression network analysis (or differential network analysis). A key benefit of this method is that it takes into consideration the interactions between or among genes and do not require an established knowledge in biological pathways. As of now, none of existing softwares can incorporate covariates that should be adjusted if they are confounding factors while performing the differential network analysis.ResultsWe develop an R package PRANA which a user can easily include multiple covariates. The main R function in this package leverages a novel pseudo-value regression approach for a differential network analysis in RNA-sequencing data. This software is also enclosed with complementary R functions for extracting adjusted p-values and coefficient estimates of all or specific variable for each gene, as well as for identifying the names of genes that are differentially connected (DC, hereafter) between subjects under biologically different conditions from the output.ConclusionHerewith, we demonstrate the application of this package in a real data on chronic obstructive pulmonary disease. PRANA is available through the CRAN repositories under the GPL-3 license: https://cran.r-project.org/web/packages/PRANA/index.html.

Transcriptomic characterization of Trichoderma harzianum T34 primed tomato plants: assessment of biocontrol agent induced host specific gene expression and plant growth promotion

In this study, we investigated the intricate interplay between Trichoderma and the tomato genome, focusing on the transcriptional and metabolic changes triggered during the late colonization event. Microarray probe set (GSE76332) was utilized to analyze the gene expression profiles changes of the un-inoculated control (tomato) and Trichoderma-tomato interactions for identification of the differentially expressed significant genes. Based on principal component analysis and R-based correlation, we observed a positive correlation between the two cross-comaparable groups, corroborating the existence of transcriptional responses in the host triggered by Trichoderma priming. The statistically significant genes based on different p-value cut-off scores [(padj-values or q-value); padj-value < 0.05], [(pcal-values); pcal-value < 0.05; pcal < 0.01; pcal < 0.001)] were cross compared. Through cross-comparison, we identified 156 common genes that were consistently significant across all probability thresholds, and showing a strong positive corelation between p-value and q-value in the selected probe sets. We reported TD2, CPT1, pectin synthase, EXT-3 (extensin-3), Lox C, and pyruvate kinase (PK), which exhibited upregulated expression, and Glb1 and nitrate reductase (nii), which demonstrated downregulated expression during Trichoderma-tomato interaction. In addition, microbial priming with Trichoderma resulted into differential expression of transcription factors related to systemic defense and flowering including MYB13, MYB78, ERF2, ERF3, ERF5, ERF-1B, NAC, MADS box, ZF3, ZAT10, A20/AN1, polyol sugar transporter like zinc finger proteins, and a novel plant defensin protein. The potential bottleneck and hub genes involved in this dynamic response were also identified. The protein–protein interaction (PPI) network analysis based on 25 topmost DEGS (pcal-value < 0.05) and the Weighted Correlation Gene Network Analysis (WGCNA) of the 1786 significant DEGs (pcal-value < 0.05) we reported the hits associated with carbohydrate metabolism, secondary metabolite biosynthesis, and the nitrogen metabolism. We conclude that the Trichoderma-induced microbial priming re-programmed the host genome for transcriptional response during the late colonization event and were characterized by metabolic shifting and biochemical changes specific to plant growth and development. The work also highlights the relevance of statistical parameters in understanding the gene regulatory dynamics and complex regulatory networks based on differential expression, co-expression, and protein interaction networks orchestrating the host responses to beneficial microbial interactions.

Transcriptional dissection of symptomatic profiles across the brain of men and women with depression

Major depressive disorder (MDD) is one of the most important causes of disability worldwide. While recent work provides insights into the molecular alterations in the brain of patients with MDD, whether these molecular signatures can be associated with the expression of specific symptom domains remains unclear. Here, we identified sex-specific gene modules associated with the expression of MDD, combining differential gene expression and co-expression network analyses in six cortical and subcortical brain regions. Our results show varying levels of network homology between males and females across brain regions, although the associations between these structures and the expression of MDD remain highly sex specific. We refined these associations to several symptom domains and identified transcriptional signatures associated with distinct functional pathways, including GABAergic and glutamatergic neurotransmission, metabolic processes and intracellular signal transduction, across brain regions associated with distinct symptomatic profiles in a sex-specific fashion. In most cases, these associations were specific to males or to females with MDD, although a subset of gene modules associated with common symptomatic features in both sexes were also identified. Together, our findings suggest that the expression of distinct MDD symptom domains associates with sex-specific transcriptional structures across brain regions.

Integrated study of systemic and local airway transcriptomes in asthma reveals causal mediation of systemic effects by airway key drivers

BackgroundSystemic and local profiles have each been associated with asthma, but parsing causal relationships between system-wide and airway-specific processes can be challenging. We sought to investigate systemic and airway processes in asthma and their causal relationships.MethodsThree hundred forty-one participants with persistent asthma and non-asthmatic controls were recruited and underwent peripheral blood mononuclear cell (PBMC) collection and nasal brushing. Transcriptome-wide RNA sequencing of the PBMC and nasal samples and a series of analyses were then performed using a discovery and independent test set approach at each step to ensure rigor. Analytic steps included differential expression analyses, coexpression and probabilistic causal (Bayesian) network constructions, key driver analyses, and causal mediation models.ResultsAmong the 341 participants, the median age was 13 years (IQR = 10–16), 164 (48%) were female, and 200 (58.7%) had persistent asthma with mean Asthma Control Test (ACT) score 16.6 (SD = 4.2). PBMC genes associated with asthma were enriched in co-expression modules for NK cell-mediated cytotoxicity (fold enrichment = 4.5, FDR = 6.47 × 10−32) and interleukin production (fold enrichment = 2.0, FDR = 1.01 × 10−15). Probabilistic causal network and key driver analyses identified NK cell granule protein (NKG7, fold change = 22.7, FDR = 1.02 × 10−31) and perforin (PRF1, fold change = 14.9, FDR = 1.31 × 10−22) as key drivers predicted to causally regulate PBMC asthma modules. Nasal genes associated with asthma were enriched in the tricarboxylic acid (TCA) cycle module (fold enrichment = 7.5 FDR = 5.09 × 10−107), with network analyses identifying G3BP stress granule assembly factor 1 (G3BP1, fold change = 9.1 FDR = 2.77 × 10−5) and InaD-like protein (INADL, fold change = 5.3 FDR = 2.98 × 10−9) as nasal key drivers. Causal mediation analyses revealed that associations between PBMC key drivers and asthma are causally mediated by nasal key drivers (FDR = 0.0076 to 0.015).ConclusionsIntegrated study of the systemic and airway transcriptomes in a well-phenotyped asthma cohort identified causal key drivers of asthma among PBMC and nasal transcripts. Associations between PBMC key drivers and asthma are causally mediated by nasal key drivers.

MiRNA-103-3p-Hlf regulates apoptosis and autophagy by targeting hepatic leukaemia factor in heart failure.

Cardiomyocyte apoptosis is an important factor leading to the occurrence and development of heart failure (HF), which is associated with high mortality of patients with cardiovascular diseases. This study aims to investigate the underlying mechanisms of HF in terms of expression and regulation patterns using bioinformatics and experimental validation. Two HF datasets were collected: a dataset GSE112056 downloaded from the GEO database (including mRNA and miRNA sequencing data) and another is the laboratory-owned mRNA dataset. Differential mRNAs and miRNAs in the two datasets were screened using the raw Bayesian approach method. Gene Ontology was used to perform functional enrichment analysis of the differential mRNAs and co-expression network analysis of the differential mRNAs, combined with nuclear transcription factors in the differential miRNAs and mRNAs for target gene prediction. A HF cell model was constructed using mouse cardiomyocytes (HL-1), and the role and mechanism of miRNA-103-3p-Hlf (hepatic leukaemia factor) in the process of HF was verified by cell transfection, luciferase reporter gene, WB, and qPCR. We found that Hlf gene expression was decreased in the HF model group and strongly correlated with FYCO1 (FYVE and coiled-coil domain-containing protein 1) gene, a phenomenon enriched in apoptotic autophagy-related pathways. MiR-103-3p expression was up-regulated in the HF model group, and its targeting correlation with Hlf was confirmed by luciferase activity assay. In the HL-1 cell model, miR-103-3p significantly promoted apoptosis and inhibited autophagy in HL-1 cells (all P<0.05), and overexpression of the Hlf gene reversed this phenomenon, inhibiting apoptosis and promoting autophagy in HL-1 cells (all P<0.05). MiR-103-3p affects myocardial cells apoptosis and autophagy by targeting Hlf, playing as a potential therapeutic biomarker for HF progression.

The proteome and phosphoproteome of circulating extracellular vesicle-enriched preparations are associated with characteristic clinical features in type 1 diabetes.

There are no validated clinical or laboratory biomarkers to identify and differentiate endotypes of type 1 diabetes (T1D) or the risk of progression to chronic complications. Extracellular vesicles (EVs) have been studied as biomarkers in several different disease states but have not been well studied in T1D. As the initial step towards circulating biomarker identification in T1D, this pilot study aimed to provide an initial characterization of the proteomic and phosphoproteomic landscape of circulating EV-enriched preparations in participants with established T1D (N=10) and healthy normal volunteers (Controls) (N=7) (NCT03379792) carefully matched by age, race/ethnicity, sex, and BMI. EV-enriched preparations were obtained using EVtrap® technology. Proteins were identified and quantified by LC-MS analysis. Differential abundance and coexpression network (WGCNA), and pathway enrichment analyses were implemented. The detected proteins and phosphoproteins were enriched (75%) in exosomal proteins cataloged in the ExoCarta database. A total of 181 proteins and 8 phosphoproteins were differentially abundant in participants with T1D compared to controls, including some well-known EVproteins (i.e., CD63, RAB14, BSG, LAMP2, and EZR). Enrichment analyses of differentially abundant proteins and phosphoproteins of EV-enriched preparations identified associations with neutrophil, platelet, and immune response functions, as well as prion protein aggregation. Downregulated proteins were involved in MHC class II signaling and the regulation of monocyte differentiation. Potential key roles in T1D for C1q, plasminogen, IL6ST, CD40, HLA-DQB1, HLA-DRB1, CD74, NUCB1, and SAP, are highlighted. Remarkably, WGCNA uncovered two protein modules significantly associated with pancreas size, which may be implicated in the pathogenesis of T1D. Similarly, these modules showed significant enrichment for membrane compartments, processes associated with inflammation and the immune response, and regulation of viral processes, among others. This study demonstrates the potential of proteomic and phosphoproteomic signatures of EV-enriched preparations to provide insight into the pathobiology of T1D. The WGCNA analysis could be a powerful tool to discriminate signatures associated with different pathobiological components of the disease.

Domestication over Speciation in Allopolyploid Cotton Species: A Stronger Transcriptomic Pull.

Cotton has been domesticated independently four times for its fiber, but the genomic targets of selection during each domestication event are mostly unknown. Comparative analysis of the transcriptome during cotton fiber development in wild and cultivated materials holds promise for revealing how independent domestications led to the superficially similar modern cotton fiber phenotype in upland (G. hirsutum) and Pima (G. barbadense) cotton cultivars. Here we examined the fiber transcriptomes of both wild and domesticated G. hirsutum and G. barbadense to compare the effects of speciation versus domestication, performing differential gene expression analysis and coexpression network analysis at four developmental timepoints (5, 10, 15, or 20 days after flowering) spanning primary and secondary wall synthesis. These analyses revealed extensive differential expression between species, timepoints, domestication states, and particularly the intersection of domestication and species. Differential expression was higher when comparing domesticated accessions of the two species than between the wild, indicating that domestication had a greater impact on the transcriptome than speciation. Network analysis showed significant interspecific differences in coexpression network topology, module membership, and connectivity. Despite these differences, some modules or module functions were subject to parallel domestication in both species. Taken together, these results indicate that independent domestication led G. hirsutum and G. barbadense down unique pathways but that it also leveraged similar modules of coexpression to arrive at similar domesticated phenotypes.

423-P: Urinary Extracellular Vesicles in African Americans with Chronic Kidney Disease With and Without Type 2 Diabetes Mellitus

Introduction: Type 2 diabetes (T2D) is the leading cause for chronic kidney disease (CKD). Novel biomarkers are critical in early detection of CKD. Methods: Healthy Eatonville Project (HEP) was a prospective study (n=300, females (F)=71%, T2D 32%, CKD 25%) in historically black community of Eatonville, FL. We characterized EVtrap®-isolated urinary extracellular vesicles (uEV) proteins from a balanced HEP cohort of 48 African Americans (mean age 62y, F=73%, BMI 32.2 kg/m2, T2D 46%) with CKD risk ranging from low, intermediate, to high-very high (H-VH), with and without T2D. Differential expression and coexpression network analyses were implemented. Results: We detected 5265 uEV proteins, 46 of which were significantly (fold change&amp;gt;1.5, P&amp;lt;0.05, FDR&amp;lt;0.1) differentially expressed (DE) in CKD risk groups. Of those, 24 were significantly upregulated in H-VH risk CKD (Figure Module Pr.1). A set of 15 DE proteins appeared to represent a protective pathway against CKD (Figure Module Pr.2). Key biomarkers of acute kidney injury IGFBP7, TIMP2, and IL18 had significant negative correlations with CKD risk (-0.59 ≤ r ≤ -0.44, P&amp;lt;0.002). Supporting our findings, several of these biomarkers had been previously reported in kidney disease studies. Conclusion: The uEV proteomic landscape is representative of kidney pathophysiology and suggests potential biomarkers of CKD in Africans Americans. Disclosure T.K.Thethi: Other Relationship; Bayer Inc., Novo Nordisk. A.Bilal: None. M.S.Zaino: None. A.Iliuk: None. R.E.Pratley: Other Relationship; Bayer Inc., Corcept Therapeutics, Dexcom, Inc., Gasherbrum Bio, Inc., Hanmi Pharm. Co., Ltd., Hengrui (USA) Ltd., Merck Sharp &amp; Dohme Corp., Novo Nordisk, Pfizer Inc., Rivus Pharmaceuticals Inc., Sanofi, Scohia Pharma Inc., Sun Pharmaceutical Industries Ltd. Y.O.Nunez lopez: None.

1509-P: Global Metabolomic Profiling and the Pathobiology of Prediabetes and Type 2 Diabetes

Introduction: Metabolomics has provided valuable insights into metabolic alterations associated with diabetes. However, prior studies profiled only a small subset of metabolites that was possible with the contemporaneous technology. We aimed to reassess and extend the current knowledge by implementing comprehensive LC-MS/MS metabolomic analyses. Methods: Untargeted metabolomic analysis was performed using serum samples from people with normal glucose tolerance, prediabetes, and type 2 diabetes (n=30, balanced ORIGINS cohort subset, NCT02226640), at baseline and 2 hours (2h) after an oral glucose tolerance test (OGTT). Differential expression (MetaboAnalyst) and coexpression network (WGCNA) analyses were implemented. Random Forest classification was used to further assess variable importance. Results: A total of 697 known metabolites were detected in more than 80% of the samples. This represents a significant increase in the number of detected circulating metabolites compared to previous studies in diabetes. WGCNA identified modules of correlated metabolites that associated with glucose tolerance at baseline and 2h. The top module for each timepoint was enriched in metabolites that significantly changed in response to the glucose challenge and highlighted an underappreciated role of glyoxylate and dicarboxylate metabolism in early stages of diabetes evolution. Elevation of hexoses and bile acids, and downregulation of amino acids, lipids, and free fatty acids were demonstrated after the glucose challenge. Random forest classification further unveiled the biomarker potential of baseline levels of aminoadipic and methyluric acids. Conclusions: Comprehensive profiling of serum metabolites in humans underscored the role of carbohydrate metabolism, the citric acid and urea cycles, suppression of lipolysis, ketogenesis, and proteolysis in response to a glucose challenge. Potential biomarkers of early diabetes development and progression were identified. Disclosure D.Dubey: None. T.Dutta: None. Y.O.Nunez lopez: None. S.J.Gardell: None. R.E.Pratley: Other Relationship; Bayer Inc., Corcept Therapeutics, Dexcom, Inc., Gasherbrum Bio, Inc., Hanmi Pharm. Co., Ltd., Hengrui (USA) Ltd., Merck Sharp &amp; Dohme Corp., Novo Nordisk, Pfizer Inc., Rivus Pharmaceuticals Inc., Sanofi, Scohia Pharma Inc., Sun Pharmaceutical Industries Ltd. Funding AdventHealth Translational Research Institute

Integrating differential expression, co-expression and gene network analysis for the identification of common genes associated with tumor angiogenesis deregulation

Angiogenesis is essential for tumor growth and cancer metastasis. Identifying the molecular pathways involved in this process is the first step in the rational design of new therapeutic strategies to improve cancer treatment. In recent years, RNA-seq data analysis has helped to determine the genetic and molecular factors associated with different types of cancer. In this work we performed integrative analysis using RNA-seq data from human umbilical vein endothelial cells (HUVEC) and patients with angiogenesis-dependent diseases to find genes that serve as potential candidates to improve the prognosis of tumor angiogenesis deregulation and understand how this process is orchestrated at the genetic and molecular level.We downloaded four RNA-seq datasets (including cellular models of tumor angiogenesis and ischaemic heart disease) from the Sequence Read Archive. Our integrative analysis includes a first step to determine differentially and co-expressed genes. For this, we used the ExpHunter Suite, an R package that performs differential expression, co-expression and functional analysis of RNA-seq data. We used both differentially and co-expressed genes to explore the human gene interaction network and determine which genes were found in the different datasets that may be key for the angiogenesis deregulation. Finally, we performed drug repositioning analysis to find potential targets related to angiogenesis inhibition.We found that that among the transcriptional alterations identified, SEMA3D and IL33 genes are deregulated in all datasets. Microenvironment remodeling, cell cycle, lipid metabolism and vesicular transport are the main molecular pathways affected. In addition to this, interacting genes are involved in intracellular signaling pathways, especially in immune system and semaphorins, respiratory electron transport and fatty acid metabolism. The methodology presented here can be used for finding common transcriptional alterations in other genetically-based diseases.

Transcriptomics reveal the genetic coordination of early defense to Armillaria root rot (ARR) in Prunus spp.

Armillaria root rot (ARR) poses a significant threat to the long-term productivity of stone-fruit and nut crops in the predominant production area of the United States. To mitigate this issue, the development of ARR-resistant and horticulturally-acceptable rootstocks is a crucial step towards the maintenance of production sustainability. To date, genetic resistance to ARR has been found in exotic plum germplasm and a peach/plum hybrid rootstock, 'MP-29'. However, the widely-used peach rootstock Guardian® is susceptible to the pathogen. To understand the molecular defense mechanisms involved in ARR resistance in Prunus rootstocks, transcriptomic analyses of one susceptible and two resistant Prunus spp. were performed using two causal agents of ARR, including Armillaria mellea and Desarmillaria tabescens. The results of in vitro co-culture experiments revealed that the two resistant genotypes showed different temporal response dynamics and fungus-specific responses, as seen in the genetic response. Gene expression analysis over time indicated an enrichment of defense-related ontologies, including glucosyltransferase activity, monooxygenase activity, glutathione transferase activity, and peroxidase activity. Differential gene expression and co-expression network analysis highlighted key hub genes involved in the sensing and enzymatic degradation of chitin, GSTs, oxidoreductases, transcription factors, and biochemical pathways likely involved in Armillaria resistance. These data provide valuable resources for the improvement of ARR resistance in Prunus rootstocks through breeding.

The fusiform gyrus exhibits differential gene-gene co-expression in Alzheimer's disease.

Alzheimer's Disease (AD) is an irreversible neurodegenerative disease clinically characterized by the presence of β-amyloid plaques and tau deposits in various regions of the brain. However, the underlying factors that contribute to the development of AD remain unclear. Recently, the fusiform gyrus has been identified as a critical brain region associated with mild cognitive impairment, which may increase the risk of AD development. In our study, we performed gene co-expression and differential co-expression network analyses, as well as gene-expression-based prediction, using RNA-seq transcriptome data from post-mortem fusiform gyrus tissue samples collected from both cognitively healthy individuals and those with AD. We accessed differential co-expression networks in large cohorts such as ROSMAP, MSBB, and Mayo, and conducted over-representation analyses of gene pathways and gene ontology. Our results comprise four exclusive gene hubs in co-expression modules of Alzheimer's Disease, including FNDC3A, MED23, NRIP1, and PKN2. Further, we identified three genes with differential co-expressed links, namely FAM153B, CYP2C8, and CKMT1B. The differential co-expressed network showed moderate predictive performance for AD, with an area under the curve ranging from 0.71 to 0.76 (+/- 0.07). The over-representation analysis identified enrichment for Toll-Like Receptors Cascades and signaling pathways, such as G protein events, PIP2 hydrolysis and EPH-Epherin mechanism, in the fusiform gyrus. In conclusion, our findings shed new light on the molecular pathophysiology of AD by identifying new genes and biological pathways involved, emphasizing the crucial role of gene regulatory networks in the fusiform gyrus.

Tissues and mechanisms associated with Verticillium wilt resistance in tomato using bi-grafted near-isogenic lines.

Host resistance is the primary means to control Verticillium dahliae, a soil-borne pathogen causing major losses on a broad range of plants, including tomato. The tissues and mechanisms responsible for resistance remain obscure. In the field, resistant tomato used as rootstocks does not confer resistance. Here, we created bi-grafted plants with near-isogenic lines (NILs) exhibiting (Ve1) or lacking (ve1) resistance to V. dahliae race 1. Ten days after inoculation, scion and rootstock tissues were subjected to differential gene expression and co-expression network analyses. Symptoms only developed in susceptible scions regardless of the rootstock. Infection caused more dramatic alteration of tomato gene expression in susceptible compared to resistant tissues, including pathogen receptor, signaling pathway, PR protein, and cell wall modification genes. Differences were observed between scions and rootstocks, primarily related to physiological processes in these tissues. Gene expression in scions was influenced by the rootstock genotype. A few genes were associated with the Ve1 genotype, which was independent of infection or tissue type. Several were physically clustered, some near the Ve1 locus on chromosome 9. Transcripts mapped to V. dahliae were dominated by secreted candidate effector proteins. These findings advance knowledge of molecular mechanisms underlying the tomato-V. dahliae interaction.

Host immune responses to enzootic and invasive pathogen lineages vary in magnitude, timing, and efficacy.

Infectious diseases of wildlife continue to pose a threat to biodiversity worldwide, yet pathogens are far from uniform in virulence or host disease outcome. Within the same pathogen species, virulence can vary considerably depending on strain or lineage, in turn eliciting variable host responses. One pathogen that has caused extensive biodiversity loss is the amphibian-killing fungus, Batrachochytrium dendrobatidis (Bd), which is comprised of a globally widespread hypervirulent lineage (Bd-GPL), and multiple geographically restricted, enzootic lineages. Whereas host immunogenomic responses to Bd-GPL have been characterized in a number of amphibian species, immunogenomic responses to geographically restricted, enzootic Bd lineages are less clear. To examine lineage-specific host immune responses to Bd, we exposed a species of pumpkin toadlet, Brachycephalus pitanga, which is endemic to Brazil's Southern Atlantic Forest, to either the Bd-GPL or the enzootic Bd-Asia-2/Brazil (hereafter Bd-Brazil) lineage. Using temporal samples from early, mid, and late infection stages, we quantified functional immunogenomic responses over the course of infection using differential gene expression tests and coexpression network analyses. Host immune responses varied significantly with Bd lineage. Relative to controls, toadlet responses to Bd-Brazil were weak at early infection (25 genes significantly differentially expressed), peaked by mid-stage infection (414 genes), and were nearly fully resolved by late-stage infection (nine genes). In contrast, responses to Bd-GPL were magnified and delayed; toadlets significantly differentially expressed 111 genes early, 87 genes at mid-stage infection, and 726 genes by late-stage infection relative to controls. Given that infection intensity did not vary between mid- and late-stage disease in either Bd-Brazil or Bd-GPL treatments, this suggests that pumpkin toadlets may be at least partially tolerant to the enzootic Bd-Brazil lineage. In contrast, late-stage immune activation against Bd-GPL was consistent with immune dysregulation previously observed in other species. Our results demonstrate that both the timing of immune response and the particular immune pathways activated are specific to Bd lineage. Within regions where multiple Bd lineages co-occur, and given continued global Bd movement, these differential host responses may influence not only individual disease outcome, but transmission dynamics at the population and community levels.

Bioinformatics-based diagnosis and evaluation of several pivotal genes and pathways associated with immune infiltration at different time points in spinal cord injury

ABSTRACT Spinal Cord Injury (SCI) is a devastating neurological event. To assess the degree of spinal cord damage and classify the injury, it is recommended to use the 2019 version of the AIS standard. The severity of trauma was evaluated using the Trauma Severity Score, and various classification systems have been proposed for injuries at different parts and segments of the spine. Understanding the regulated signaling pathways and immune processes following SCI can lead to a better understanding of SCI-induced biomarkers and their underlying mechanisms. In this study, two gene expression datasets (GSE464 and GSE45006) from the Gene Expression Omnibus database were utilized. Differential gene expression and co-expression network analysis were performed, revealing 370 shared genes in the 3-day group and 111 shared genes in the 14-day group after SCI. The study used functional enrichment analysis methods such as Gene Set Enrichment Analysis, Gene Ontology, and Kyoto Encyclopedia of Genes and Genomes. The ssGSEA method was used to assess the levels and composition of immune infiltration in both the sham (control) and SCI groups. The single-cell transcriptomics dataset GSE182803 was analyzed to identify genes associated with immune marker cells. Four key genes (Ptgs2, Fn1, Ccl2, and Icam1) were identified in the 3-day group, while only one gene (Cyp51) was identified in the 14-day group after SCI. The findings offer significant insights into the immune-related genes and signaling pathways involved in secondary SCI at different time points and hold potential for the development of intervention strategies for acute and chronic post-SCI.