Phenotypically anchored transcriptomics across diverse agrichemicals reveals conserved pathways and unique gene expression signatures in zebrafish

The present study aimed to identify differentially regulated genes between the peritumoral brain zone (PBZ) and tumor core (TC) of glioblastoma (GBM), to elucidate the underlying molecular mechanisms and provide a target for the treatment of tumors. The GSE13276 and GSE116520 datasets were downloaded from the Gene Expression Omnibus (GEO) database. Differentially expressed genes (DEGs) for the PBZ and TC were obtained using the GEO2R tool. The bioinformatics and evolutionary genomics online tool Venn was used to identify common DEGs between the two datasets. The Database for Annotation, Visualization, and Integrated Discovery online tool was used to analyze enriched pathways of the Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) databases. The Search Tool for the Retrieval of Interacting Genes/Proteins online tool was used to construct a protein-protein interaction (PPI) network of DEGs. Hub genes were identified using Cytohubba, a plug-in for Cytoscape. The Gene Expression Profiling Interactive Analysis (GEPIA) database was utilized to perform survival analysis. In total, 75 DEGs, including 12 upregulated and 63 downregulated genes, were identified. In the GO term analysis, these DEGs were mainly enriched in ‘regulation of angiogenesis’ and ‘central nervous system development’. Furthermore, in the KEGG pathway analysis, the DEGs were mainly enriched in ‘bladder cancer’ and ‘endocytosis’. When filtering the results of the PPI network analysis using Cytohubba, a total of 10 hub genes, including proteolipid protein 1, myelin associated oligodendrocyte basic protein, contactin 2, myelin oligodendrocyte glycoprotein, myelin basic protein, myelin associated glycoprotein, SRY-box transcription factor 10, C-X-C motif chemokine ligand 8 (CXCL8), vascular endothelial growth factor A (VEGFA) and plasmolipin, were identified. These hub genes were further subjected to GO term and KEGG pathway analysis, and were revealed to be enriched in ‘central nervous system development’, ‘bladder cancer’ and ‘rheumatoid arthritis’. These hub genes were used to perform survival analysis using the GEPIA database, and it was determined that VEGFA and CXCL8 were significantly associated with a reduction in the overall survival of patients with GBM. In conclusion, the results suggest that the recurrence of GBM is associated with high gene expression levels VEGFA and CXCL8, and the development of the central nervous system.

The present study aimed to analyze the modification of gene expression in bladder cancer (BC) by identifying significant differentially expressed genes (DEGs) and functionally assess them using bioinformatics analysis. To achieve this, two microarray datasets, GSE24152 (which included 10 fresh tumor tissue samples from urothelial bladder carcinoma patients and 7 benign mucosa samples from the bladder), and GSE42089 (which included 10 tissues samples from urothelial cell carcinoma patients and 8 tissues samples from the normal bladder), were downloaded from the Gene Expression Omnibus database for further analysis. Differentially expressed genes (DEGs) were screened between benign the mucosa and control groups in GSE24152 and GSE42089 datasets. Gene ontology (GO) and Kyoto encyclopedia of genes and genomes (KEGG) analysis were performed on overlapping DEGs identified in GSE24152 and GSE42089. Protein-protein interaction (PPI) networks and sub-networks were then constructed to identify key genes and main pathways. GO terms analysis was also performed for the selected clusters. In total, 1,325 DEGs in GSE24152 and 647 DEGs in GSE42089 were screened, in which 619 common DEGs were identified. The DEGs were mainly enriched in pathways and GO terms associated with mitotic and chromosome assembly, including nucleosome assembly, spindle checkpoint and DNA replication. In the interaction network, progesterone receptor (PGR), MAF bZIP transcription factor G (MAFG), cell division cycle 6 (CDC6) and members of the minichromosome maintenance family (MCMs) were identified as key genes. Histones were also considered to be significant factors in BC. Nucleosome assembly and sequence-specific DNA binding were the most significant clustered GO terms. In conclusion, the DEGs, including PGR, MAFG, CDC6 and MCMs, and those encoding the core histone family were closely associated with the development of BC via pathways associated with mitotic and chromosome assembly.

BackgroundImproving feed efficiency (FE) of pigs by genetic selection is of economic and environmental significance. An increasingly accepted measure of feed efficiency is residual feed intake (RFI). Currently, the molecular mechanisms underlying RFI are largely unknown. Additionally, to incorporate RFI into animal breeding programs, feed intake must be recorded on individual pigs, which is costly and time-consuming. Thus, convenient and predictive biomarkers for RFI that can be measured at an early age are greatly desired. In this study, we aimed to explore whether differences exist in the global gene expression profiles of peripheral blood of 35 to 42 day-old pigs with extremely low (more efficient) and high RFI (less efficient) values from two lines that were divergently selected for RFI during the grow-finish phase, to use such information to explore the potential molecular basis of RFI differences, and to initiate development of predictive biomarkers for RFI.ResultsWe identified 1972 differentially expressed genes (DEGs) (q ≤ 0.15) between the low (n = 15) and high (n = 16) RFI groups of animals by using RNA sequencing technology. We validated 24 of 37 selected DEGs by reverse transcription-quantitative PCR (RT-qPCR) in a joint analysis of 24 (12 per line) of the 31 samples already used for RNA-seq plus 24 (12 per line) novel samples from the same contemporary group of pigs. Using an analysis of the 24 novel samples alone, only nine of the 37 selected DEGs were validated. Genes involved in small molecule biosynthetic process, antigen processing and presentation of peptide antigen via major histocompatibility complex (MHC) class I, and steroid biosynthetic process were overrepresented among DEGs that had higher expression in the low versus high RFI animals. Genes known to function in the proteasome complex or mitochondrion were also significantly enriched among genes with higher expression in the low versus high RFI animals. Alternatively, genes involved in signal transduction, bone mineralization and regulation of phosphorylation were overrepresented among DEGs with lower expression in the low versus high RFI animals. The DEGs significantly overlapped with genes associated with disease, including hyperphagia, eating disorders and mitochondrial diseases (q < 1E-05). A weighted gene co-expression network analysis (WGCNA) identified four co-expression modules that were differentially expressed between the low and high RFI groups. Genes involved in lipid metabolism, regulation of bone mineralization, cellular immunity and response to stimulus were overrepresented within the two modules that were most significantly differentially expressed between the low and high RFI groups. We also found five of the DEGs and one of the co-expression modules were significantly associated with the RFI phenotype of individual animals (q < 0.05).ConclusionsThe post-weaning blood transcriptome was clearly different between the low and high RFI groups. The identified DEGs suggested potential differences in mitochondrial and proteasomal activities, small molecule biosynthetic process, and signal transduction between the two RFI groups and provided potential new insights into the molecular basis of RFI in pigs, although the observed relationship between the post-weaning blood gene expression and RFI phenotype measured during the grow-finish phase was not strong. DEGs and representative genes in co-expression modules that were associated with RFI phenotype provide a preliminary list for developing predictive biomarkers for RFI in pigs.Electronic supplementary materialThe online version of this article (doi:10.1186/s12864-016-2395-x) contains supplementary material, which is available to authorized users.

BackgroundCoronary artery disease (CAD) is a common disease with high cost and mortality. Here, we studied the differentially expressed genes (DEGs) between epicardial adipose tissue (EAT) and subcutaneous adipose tissue (SAT) from patients with CAD to explore the possible pathways and mechanisms through which EAT participates in the CAD pathological process.MethodsMicroarray data for EAT and SAT were obtained from the Gene Expression Omnibus database, including three separate expression datasets: GSE24425, GSE64554 and GSE120774. The DEGs between EAT samples and SAT control samples were screened out using the limma package in the R language. Next, we conducted bioinformatic analysis of gene ontology terms and Kyoto Encyclopedia of Genes and Genomes pathways to discover the enriched gene sets and pathways associated with DEGs. Simultaneously, gene set enrichment analysis was carried out to discover enriched gene functions and pathways from all expression data rather than DEGs. The PPI network was constructed to reveal the possible protein interactions consistent with CAD. Mcode and Cytohubba in Cytoscape revealed the possible key CAD genes. In the next step, the corresponding predicted microRNAs (miRNAs) were analysed using miRNA Data Integration Portal. RT-PCR was used to validate the bioinformatic results.ResultsThe three datasets had a total of 89 DEGs (FC log2 > 1 and P value < 0.05). By comparing EAT and SAT, ten common key genes (HOXA5, HOXB5, HOXC6, HOXC8, HOXB7, COL1A1, CCND1, CCL2, HP and TWIST1) were identified. In enrichment analysis, pro-inflammatory and immunological genes and pathways were up-regulated. This could help elucidate the molecular expression mechanism underlying the involvement of EAT in CAD development. Several miRNAs were predicted to regulate these DEGs. In particular, hsa-miR-196a-5p and hsa-miR-196b-5p may be more reliably associated with CAD. Finally, RT-PCR validated the significant difference of OXA5, HOXC6, HOXC8, HOXB7, COL1A1, CCL2 between EAT and SAT (P value < 0.05).ConclusionsBetween EAT and SAT in CAD patients, a total of 89 DEGs, and 10 key genes, including HOXA5, HOXB5, HOXC6, HOXC8, HOXB7, COL1A1, CCND1, CCL2, HP and TWIST1, and miRNAs hsa-miR-196a-5p and hsa-miR-196b-5p were predicted to play essential roles in CAD pathogenesis. Pro-inflammatory and immunological pathways could act as key EAT regulators by participating in the CAD pathological process.

BackgroundAnti-tumor necrosis factor alpha (TNF- α) therapy has made a significant impact on treating psoriasis. Despite these agents being designed to block TNF- α activity, their mechanism of action in the remission of psoriasis is still not fully understood at the molecular level.ResultsTo better understand the molecular mechanisms of Anti-TNF- α therapy, we analysed the global gene expression profile (using mRNA microarray) in peripheral blood mononuclear cells (PBMCs) that were collected from 6 psoriasis patients before and 12 weeks after the treatment of etanercept. First, we identified 176 differentially expressed genes (DEGs) before and after treatment by using paired t-test. Then, we constructed the gene co-expression modules by weighted correlation network analysis (WGCNA), and 22 co-expression modules were found to be significantly correlated with treatment response. Of these 176 DEGs, 79 DEGs (M_DEGs) were the members of these 22 co-expression modules. Of the 287 GO functional processes and pathways that were enriched for these 79 M_DEGs, we identified 30 pathways whose overall gene expression activities were significantly correlated with treatment response. Of the original 176 DEGs, 19 (GO_DEGs) were found to be the members of these 30 pathways, whose expression profiles showed clear discrimination before and after treatment. As expected, of the biological processes and functionalities implicated by these 30 treatment response-related pathways, the inflammation and immune response was the top pathway in response to etanercept treatment, and some known TNF- α related pathways, such as molting cycle process, hair cycle process, skin epidermis development, regulation of hair follicle development, were implicated. Furthermore, additional novel pathways were also suggested, such as heparan sulfate proteoglycan metabolic process, vascular endothelial growth factor production, whose transcriptional regulation may mediate the response to etanercept treatment.ConclusionThrough global gene expression analysis in PBMC of psoriasis patient and subsequent co-expression module based pathway analyses, we have identified a group of functionally coherent and differentially expressed genes (DEGs) and related pathways, which has not only provided new biological insight about the molecular mechanism of anti-TNF- α treatment, but also identified several genes whose expression profiles can be used as potential biomarkers for anti-TNF- α treatment response in psoriasis.

Plants have evolved a sophisticated system to respond to various stresses. Fungal attack or infection is one of the most important biotic stresses for most plants. During the defense response to fungal infection, the plant hormones jasmonic acid (JA) and salicylic acid (SA) play critical roles. Here, gene expression data on JA/SA treatments and Melampsora larici-populina (MLP) infection were generated. Integrated transcriptome analyses of these data were performed, and 943 genes in total were identified as common responsive genes (CRG). Gene ontology (GO) term analysis revealed that the genes from CRG are generally involved in the processes of stress responses, metabolism, and growth and development. The further cluster analysis of the CRG identified a set of core genes that are involved in the JA/SA-mediated response to fungal defense with distinct gene expression profiles upon JA/SA treatment, which highlighted the different effects of these two hormones on plant fungal defenses. The modifications of several pathways relative to metabolism, biotic stress, and plant hormone signal pathways suggest the possible roles of JA/SA on the regulation of growth and defense responses. Co-expression modules (CMs) were also constructed using the poplar expression data on JA, SA, M. larici-populina, Septoria musiva, and Marssonina brunnea treatment or infection. A total of 23 CMs were constructed, and different CMs clearly exhibited distinct biological functions, which conformably regulated the concerted processes in response to fungal defense. Furthermore, the GO term analysis of different CMs confirmed the roles of JA and SA in regulating growth and defense responses, and their expression profiles suggested that the growth ability was reduced when poplar deployed defense responses. Several transcription factors (TFs) among the CRG in the co-expression network were proposed as hub genes in regulating these processes. According to this study, our data finely uncovered the possible roles of JA/SA in regulating the balance between growth and defense responses by integrating multiple hormone signaling pathways. We were also able to provide more knowledge on how the plant hormones JA/SA are involved in the regulation of the balance between growth and plant defense.

Background: Previous studies found that cadmium (Cd) was an environmental toxicant that not only induces toxicological effects but also disrupts cellular biomechanics, affecting cell stiffness, motility, and mechanotransduction pathways. LncRNA-CR848007.6 played an important regulatory role in cadmium toxicology. However, whether its regulatory mechanism involves the biomechanics and changes of the translatome remains to be elucidated. Objective: This study aimed to explore the function and mechanism of LncRNA-CR848007.6 in translation regulation and biomechanical properties during cadmium malignant transformed human bronchial epithelial cells (16HBE cells) through translatome sequencing techniques and bioinformatics methods. Methods: RNA interference technology was applied to silence the expression of LncRNA-CR848007.6 in cadmium malignant transformed 16HBE cells. The libraries of mRNA and RNC were constructed, and Ion Proton™ Sequencer was used for transcriptome and translatome sequencing. Mechanical properties of cells, including stiffness and traction forces, were measured using atomic force microscopy and traction force microscopy. Transcriptome and translatome differential expressions were analyzed using R software. The cell cycle and apoptosis were detected by flow cytometry to verify the functions of LncRNA-CR848007.6 regulating the translatome of cadmium malignant transformed 16HBE cells. Results: Four libraries were obtained after sequencing, including 24062 genes from the transcriptome of the siR mR group cell and NC mR group cell, the translatome of siR RNC group cell and the NC-RNC group cell. It was found that there was little change in the number of transcriptome genes between the siR RNC group cell and NC-RNC group cell, with 19 differentially expressed genes downregulated and 0 differentially expressed genes upregulated. There were 114 genes in the translatome with a ration &lt; −2 and 65 genes in the translatome with a ratio &gt; 2. There was no intersection between the differential TR expression genes and differential mRNA expression genes. The GO analysis results showed significant changes in the translation ratio of cell cycle and mitotic-related pathways, but no enriched KEGG pathway appeared. The cell cycle progression was regulated and cell apoptosis was signficantly inhibited (P &lt; 0.05) after silencing lncRNA-CR848007.6 by siRNA in CdCl2 malignant transformed 16HBE cells. Transcriptome and translatome analyses revealed differential expression of genes involved in cytoskeletal organization and mechanosensitive signaling. Conclusion: LncRNA-CR848007.6 plays a critical role in modulating the biomechanical properties of cadmium-malignant transformed 16HBE cells, influencing cell stiffness and motility through translational regulation. This study provides insights into the biomechanical mechanisms underlying lncRNA-mediated cellular responses to cadmium toxicity.

Immune cascade is one of major factors leading to cardiac dysfunction after burn injury. TLRs are a class of pattern-recognition receptors (PRRs) that initiate the innate immune response by sensing conserved molecular patterns for early immune recognition of a pathogen. The Rat Toll-Like Receptor (TLR) Signaling Pathway RT² Profiler PCR Array profiles the expression of 84 genes central to TLR-mediated signal transduction and innate immunity, and is a validated tool for identifying differentially expressed genes (DEGs). We employed the PCR array to identify burn-induced cardiac TLR-signaling-related DEGs. A total of 38 up-regulated DEGs and 19 down-regulated DEGs were identified. Network analysis determined that all DEGS had 10 clusters, while up-regulated DEGs had 6 clusters and down-regulated DEGs had 5 clusters. Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis showed that DEGs were involved in TLR signaling, the RIG-I-Like receptor signaling pathway, the IL-17 signaling pathway, and the NFkB signaling pathway. Function analysis indicated that DEGs were associated with Toll-like receptor 2 binding, Lipopeptide binding, Toll-like receptor binding, and NAD(P)+ nucleosidase activity. The validation of 18 up-regulated DEGs (≥10-fold change) and 6 down-regulated DEGs (≤5-fold change) demonstrated that the PCR array is a trusted method for identifying DEGs. The analysis of validated DEG-derived protein–protein interaction networks will guide our future investigations. In summary, this study not only identified the TLR-signaling-pathway-related DEGs after burn injury, but also confirmed that the burn-induced cardiac cytokine cascade plays an important role in burn-induced heart dysfunction. The results will provide the novel therapeutic targets to protect the heart after burn injury.

Alcohol consumption is a risk factor for the development of hepatocellular carcinoma (HCC); however, the association between alcohol and HCC remains unknown. The present study aimed to identify key genes related to alcohol-associated HCC to improve the current understanding of the pathology of this disease. Alcohol-associated and non-alcohol-associated HCC samples in the GSE50579 dataset of the Gene Omnibus Database were analyzed to investigate altered gene expression. Integrated bioinformatics methods were employed to clarify the biological functions of the differentially expressed genes (DEGs), including Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG) and protein-protein interactions (PPIs). The present study reported that candidate biomarker micro (mi)RNAs via TargetScan Human 7.1. DEGs and their associated miRNAs (according to bioinformatics analysis) were validated using reverse transcription-quantitative polymerase chain reaction (RT-qPCR). Additionally, 284 EGs from the GSE50579 dataset were revealed. In GO term analysis, DEGs were closely associated with the ‘regulation of nucleic acid metabolism’. KEGG pathway analysis indicated that the DEGs were tightly engaged in the ‘VEGF and VEGF receptor signaling network’, ‘proteoglycan syndecan-mediated signaling events’, ‘erbB receptor signaling’ and ‘β1 integrin cell surface interactions’. According to the results of PPI and heat map analysis, the main hub genes were centrin 3 (CETN3), Toll-like receptor 3 (TLR3), receptor tyrosine-protein kinase (ERBB4), heat shock protein family member 8, actin γ1 (ACTG1) and α-smooth muscle actin. it was demonstrated that the ACTG1, TLR3, miR-6819-3p and miRΝΑ (miR)-6877-3P had undefined associations. Furthermore, RT-qPCR analysis revealed that miR-6819-3p and miR-6877-3P may enhance the expression levels of ACTG1 and inhibit the expression levels of TLR3 in alcohol-associated HCC tissues. TLR3 and ACTG1 were proposed as potential biomarkers of alcohol-associated HCC. Investigation into the regulatory functions of miR-6819-3p and miR-6877-3P may provide novel insights into the treatment of alcohol-associated HCC.

BackgroundAge‐related neurodegenerative disorders (NDDs) continuum includes late‐onset Alzheimer’s disease (LOAD), Dementia with Lewy bodies (DLB), and Parkinson’s disease (PD) exhibit shared and distinct clinicopathological characteristics. Each of the different NDDs is characterized by a complex genetic etiology and although numerous loci have been identified via GWAS, and the causal genes and the specific neuronal and glial cell subtypes through which they exert their pathogenic effects are yet to be fully elucidated. We aimed to untangle the genetic complexity of NDDs, and to identify shared and distinct biological pathways and disease driver cell‐subtypes across NDDs.MethodSingle‐cell transcriptomic profiling was performed using LOAD, DLB, PD and normal brains (12/group). Differential expression was analyzed using the single‐nucleus (sn)RNA‐seq datasets by Nebula. To investigate shared biological pathways, we analyzed the common DEGs across NDD pathologies for each cell‐type using Metascape and aPEAR. To identify distinct DEGs and biological pathways we comparted directly between each two pathologies. The vulnerable and disease driver cell‐subtypes were characterized by beta‐regression and AUCell analyses, respectively.ResultWe identified cell‐subtype specific differential expressed genes (DEGs) for each NDD compared to control. A catalogue of common DEGs across NDDs in each cell type showed the largest number of upregulated‐DEGs in inhibitory neurons (∼700). Pathway analysis using the shared DEGs indicated enrichment for biological pathways including, neurodegeneration, stress response, RNA and protein metabolism, and mitochondria dysfunction. Noteworthy, we observed overlap with pathways detected for NPS comorbidity. Differential gene expression analysis between LOAD vs PD identified &gt;5000 upregulated‐DEGs in microglia followed by Astrocytes and OPC and found enrichment for pathways including oligodendrocytes maturation and myelination, while the majority of upregulated‐DEGs in PD compared to LOAD were identified in oligodendrocytes and enriched in biological terms related to protein biosynthesis and energy metabolism. We showed that microglia and oligodendrocytes cell‐subtypes are drivers of LOAD pathology whereas PD pathogenic effect is driven by excitatory neurons.ConclusionThis study represents the most comprehensive a systematic interrogation of gene dysregulation in a spectrum of NDDs in an unprecedented cell‐subtype resolution. The results enhance our understanding of the shared and distinct genetic factors and biological processes underlying age related NDDs.

Background: Focal and segmental glomerulosclerosis (FSGS) is a clinical-pathologic condition marked by segmental and localized glomerular damages. Despite investigations, the molecular mechanisms behind FSGS development remain to be more clarified. By a comprehensive analysis of an FSGS-related array set, the aim of this study was to unravel the top pathways and molecules involved in the pathogenesis of this disorder. Methods: FSGS-related microarray dataset (GSE129973) from the Gene Expression Omnibus database was quality checked, analyzed, and its differentially expressed genes (DEGs) (log2 fold change > 1) were used for the construction of a protein-protein interaction (PPI) network (STRING). The degree of centrality was considered to select the hub molecules in the network. The weighted gene co-expression network analysis (WGCNA) was utilized to construct co-expression modules. Hub molecules were selected based on module membership and gene significance values in the disease’s most correlated module. After spotting the key molecules considering both strategies, their expression pattern was checked in other FSGS microarray datasets. Gene ontology and Reactome pathway enrichment analyses were performed on the DEGs of the related module. Results: After quality checking, normalization, and analysis of the dataset, 5,296 significant DEGs, including 2,469 upregulated and 2,827 downregulated DEGs were identified. The WGCNA algorithm clustered the DEGs into nine independent co-expression modules. The disease most correlated module (black module) was recognized and considered for further enrichment analysis. The immune system, cell cycle, and vesicle-mediated transports were among the top enriched terms for the identified module’s DEGs. The immune system, cell cycle, and vesicle-mediated transports were among the top enriched terms for the black module’s DEGs. The key molecules (BMP-2 and COL4A1) were identified as common hub molecules extracted from the two methods of PPI and the co-expressed networks. The two identified key molecules were validated in other FSGS datasets, where a similar pattern of expression was observed for both the genes. Conclusions: Two hub molecules (BMP-2 and COL4A) and some pathways (vesicle-mediated transport) were recognized as potential players in the pathogenesis of FSGS.

Gastrointestinal nematode (GIN) infections are considered the most important disease of grazing sheep and due to increasing anthelmintic resistance, chemical control alone is inadequate. Resistance to Gastrointestinal nematode infection is a heritable trait, and through natural selection many sheep breeds have higher resistance. Studying the transcriptome from GIN-exposed and GIN-unexposed sheep using RNA-Sequencing technology can provide measurements of transcript levels associated with the host response to Gastrointestinal nematode infection, and these transcripts may harbor genetic markers that can be used in selective breeding programs to enhance disease resistance. The objective of this study was to compare liver transcriptomes of sheep naturally exposed to Gastrointestinal nematode s, with either high or low parasite burdens, to GIN-unexposed control sheep in order to identify key regulator genes and biological processes associated with Gastrointestinal nematode infection. Differential gene expression analysis revealed no significant differentially expressed genes (DEG) between sheep with a high or low parasite burden (p-value ≤0.01; False Discovery Rate (FDR) ≤ 0.05; and Fold-Change (FC) of > ±2). However, when compared to the control group, low parasite burden sheep showed 146 differentially expressed genes (64 upregulated and 82 downregulated in the low parasite burden group relative to the control), and high parasite burden sheep showed 159 differentially expressed genes (57 upregulated and 102 downregulated in the low parasite burden group relative to the control) (p-value ≤0.01; FDR ≤0.05; and FC of > ±2). Among these two lists of significant differentially expressed genes, 86 differentially expressed genes (34 upregulated, 52 downregulated in the parasited group relative to the control) were found in common between the two parasite burden groups compared to the control (GIN-unexposed sheep). Functional analysis of these significant 86 differentially expressed genes found upregulated genes involved in immune response and downregulated genes involved in lipid metabolism. Results of this study offer insight into the liver transcriptome during natural Gastrointestinal nematode exposure that helps provide a better understanding of the key regulator genes involved in Gastrointestinal nematode infection in sheep.

Idiopathic pulmonary fibrosis (IPF) is a rare but debilitating lung disease characterized by excessive fibrotic tissue accumulation, primarily affecting individuals over 50 years of age. Early diagnosis is challenging, and without intervention, the prognosis remains poor. Understanding the molecular mechanisms underlying IPF pathogenesis is crucial for identifying diagnostic markers and therapeutic targets. We analyzed transcriptomic data from lung tissues of IPF patients using two independent datasets. Differentially expressed genes (DEGs) were identified, and their functional roles were assessed through pathway enrichment and tissue-specific expression analysis. Protein-protein interaction (PPI) networks and co-expression modules were constructed to identify hub genes and their associations with disease severity. Machine learning approaches were applied to identify genes capable of differentiating IPF patients from healthy individuals. Regulatory signatures, including transcription factor and microRNA interactions, were also explored, alongside the identification of potential drug targets. A total of 275 and 167 DEGs were identified across two datasets, with 67 DEGs common to both. These genes exhibited distinct expression patterns across tissues and were associated with pathways such as extracellular matrix organization, collagen fibril formation, and cell adhesion. Co-expression analysis revealed DEG modules correlated with varying IPF severity phenotypes. Machine learning analysis pinpointed a subset of genes with high discriminatory power between IPF and healthy individuals. PPI network analysis identified hub proteins involved in key biological processes, while functional enrichment reinforced their roles in extracellular matrix regulation. Regulatory analysis highlighted interactions with transcription factors and microRNAs, suggesting potential mechanisms driving IPF pathogenesis. Potential drug targets among the DEGs were also identified. This study provides a comprehensive transcriptomic overview of IPF, uncovering DEGs, hub proteins, and regulatory signatures implicated in disease progression. Validation in independent datasets confirmed the relevance of these findings. The insights gained here lay the groundwork for developing diagnostic tools and novel therapeutic strategies for IPF.

The aim of this study was to explore how atrial natriuretic polypeptide (ANP) affects the properties and function of endothelial cells. Gene expression data GSE56976 generated at 0, 1, and 6 hours after ANP incubation in human umbilical vein endothelial cells (HUVEC) was used. Microarray data were preprocessed for differentially expressed genes (DEGs) in each time-dependent group. Next, gene ontology (GO), pathway analysis, and transcriptional regulation were performed. Co-expression clustering analysis of DEGs and functional enrichment analysis of co-expression modules were processed. RT-PCR analysis was performed to validate gene expression. DEGs were obtained and their counts were increased from 0 hours to 6 hours. No overlapping DEGs were obtained among the 3 groups. The DEGs of ANP_6hours, including TGFB2 (transforming growth factor, beta 2), LTF (lactotransferrin/lactoferrin), and ETV7 (Ets variant 7) were mainly related with cell apoptosis and immune responses. The DEGs in the network of ANP_0hour were mainly associated with epithelial ion transport processes. In addition, 3 co-expressed modules were detected. CSF2 (colony stimulating factor 2) and PF4 (platelet factor 4) of the blue module were related with cytolysis, while FXYD1 (FXYD domain containing ion transport regulator 1) and TGFB2 of the yellow module were mainly enriched in ion transport and the ovulation cycle. The expression of TGFB2 obtained by microarray analysis was consistent with that of RT-PCR. Ion transport could be affected promptly after ANP treatment, and subsequently, the cytolysis of vein endothelial cells may be promoted and endothelial permeability would be enhanced, followed by activated immune responses.

BackgroundThe cardiovascular developmental process is a tightly regulated network involving multiple genes. The current understanding of the molecular mechanism behind cardiovascular development is insufficient and requires further research.ResultsTranscriptome sequencing of three developmental stages in zebrafish embryos was performed and revealed three key cardiovascular developmental stages. Then, the differentially expressed genes (DEGs) involved in cardiovascular development were screened out. The three developmental stages were 18 (T1), 24 (T2), and 42 h post fertilization (hpf) (T3), and the three stages were confirmed by detecting differences in expression between cardiomyocyte and endothelial marker genes (cmlc2, fli1) using in situ hybridization, which represents the characteristics of cardiovascular development. Thousands of DEGs were identified using transcriptome analysis. Of them, 2605 DEGs were in T1-vs-T2, including 2003 up-regulated and 602 down-regulated genes, 6446 DEGs were in T1-vs-T3, consisting of 4608 up-regulated and 1838 down-regulated genes, and 3275 DEGs were in T2-vs-T3, including 2420 up-regulated and 855 down-regulated genes. There were 644 common DEGs and 167 common five-fold higher differentially expressed genes (HDEGs) identified, and Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses were performed using the Database for Annotation, Visualization and Integrated Discovery (DAVID). Significant differences was observed in the levels of gene expression among different developmental stages in multiple GO terms and KEGG pathways, such as cell migration to the midline involved in heart development, cardiovascular system development, circulatory system process for biological processes of GO terms; and cardiac muscle contraction, adrenergic signaling in cardiomyocytes for KEGG pathways. These results demonstrated that these three stages were important period for the development of the cardiovascular system. Lastly, we used quantitative real-time PCR (qPCR) to validate the reliability of RNA-sequencing by selecting 21 DEGs.ConclusionsThese results demonstrated that these three stages represented the important periods for cardiovascular system development of zebrafish and some candidate genes was obtained and provided a solid foundation for additional functional studies of the DEGs.