Identification of Transcriptional Modules and Key Genes in Chickens Infected with Salmonella enterica Serovar Pullorum Using Integrated Coexpression Analyses

Using transcriptomics to predict and visualize disease status in bighorn sheep (Ovis canadensis)

Increasing risk of pathogen spillover coupled with overall declines in wildlife population abundance in the Anthropocene make infectious disease a relevant concern for species conservation worldwide. While emerging molecular tools could improve our diagnostic capabilities and give insight into mechanisms underlying wildlife disease risk, they have rarely been applied in practice. Here, employing a previously reported gene transcription panel of common immune markers to track physiological changes, we present a detailed analysis over the course of both acute and chronic infection in one wildlife species where disease plays a critical role in conservation, bighorn sheep (Ovis canadensis). Differential gene transcription patterns distinguished between infection statuses over the course of acute infection and differential correlation (DC) analyses identified clear changes in gene co-transcription patterns over the early stages of infection, with transcription of four genes-TGFb, AHR, IL1b and MX1-continuing to increase even as transcription of other immune-associated genes waned. In a separate analysis, we considered the capacity of the same gene transcription panel to aid in differentiating between chronically infected animals and animals in other disease states outside of acute disease events (an immediate priority for wildlife management in this system). We found that this transcription panel was capable of accurately identifying chronically infected animals in the test dataset, though additional data will be required to determine how far this ability extends. Taken together, our results showcase the successful proof of concept and breadth of potential utilities that gene transcription might provide to wildlife disease management, from direct insight into mechanisms associated with differential disease response to improved diagnostic capacity in the field.

Identification of Potential Prognostic Biomarkers Associated With Macrophage M2 Infiltration in Gastric Cancer

Gastric cancer is a common cancer afflicting people worldwide. Although incremental progress has been achieved in gastric cancer research, the molecular mechanisms underlying remain unclear. In this study, we conducted bioinformatics methods to identify prognostic marker genes associated with gastric cancer progression. Three hundred and twenty-seven overlapping DEGs were identified from three GEO microarray datasets. Functional enrichment analysis revealed that these DEGs are involved in extracellular matrix organization, tissue development, extracellular matrix–receptor interaction, ECM-receptor interaction, PI3K-Akt signaling pathway, focal adhesion, and protein digestion and absorption. A protein–protein interaction network (PPI) was constructed for the DEGs in which 25 hub genes were obtained. Furthermore, the turquoise module was identified to be significantly positively coexpressed with macrophage M2 infiltration by weighted gene coexpression network analysis (WGCNA). Hub genes of COL1A1, COL4A1, COL12A1, and PDGFRB were overlapped in both PPI hub gene list and the turquoise module with significant association with the prognosis in gastric cancer. Moreover, functional analysis demonstrated that these hub genes play pivotal roles in cancer cell proliferation and invasion. The investigation of the gene markers can help deepen our understanding of the molecular mechanisms of gastric cancer. In addition, these genes may serve as potential prognostic biomarkers for gastric cancer diagnosis.

Integrated Network Analysis to Identify Key Modules and Potential Hub Genes Involved in Bovine Respiratory Disease: A Systems Biology Approach

Background: Bovine respiratory disease (BRD) is the most common disease in the beef and dairy cattle industry. BRD is a multifactorial disease resulting from the interaction between environmental stressors and infectious agents. However, the molecular mechanisms underlying BRD are not fully understood yet. Therefore, this study aimed to use a systems biology approach to systematically evaluate this disorder to better understand the molecular mechanisms responsible for BRD. Methods: Previously published RNA-seq data from whole blood of 18 healthy and 25 BRD samples were downloaded from the Gene Expression Omnibus (GEO) and then analyzed. Next, two distinct methods of weighted gene coexpression network analysis (WGCNA), i.e., module-trait relationships (MTRs) and module preservation (MP) analysis were used to identify significant highly correlated modules with clinical traits of BRD and non-preserved modules between healthy and BRD samples, respectively. After identifying respective modules by the two mentioned methods of WGCNA, functional enrichment analysis was performed to extract the modules that are biologically related to BRD. Gene coexpression networks based on the hub genes from the candidate modules were then integrated with protein-protein interaction (PPI) networks to identify hub-hub genes and potential transcription factors (TFs). Results: Four significant highly correlated modules with clinical traits of BRD as well as 29 non-preserved modules were identified by MTRs and MP methods, respectively. Among them, two significant highly correlated modules (identified by MTRs) and six nonpreserved modules (identified by MP) were biologically associated with immune response, pulmonary inflammation, and pathogenesis of BRD. After aggregation of gene coexpression networks based on the hub genes with PPI networks, a total of 307 hub-hub genes were identified in the eight candidate modules. Interestingly, most of these hub-hub genes were reported to play an important role in the immune response and BRD pathogenesis. Among the eight candidate modules, the turquoise (identified by MTRs) and purple (identified by MP) modules were highly biologically enriched in BRD. Moreover, STAT1, STAT2, STAT3, IRF7, and IRF9 TFs were suggested to play an important role in the immune system during BRD by regulating the coexpressed genes of these modules. Additionally, a gene set containing several hub-hub genes was identified in the eight candidate modules, such as TLR2, TLR4, IL10, SOCS3, GZMB, ANXA1, ANXA5, PTEN, SGK1, IFI6, ISG15, MX1, MX2, OAS2, IFIH1, DDX58, DHX58, RSAD2, IFI44, IFI44L, EIF2AK2, ISG20, IFIT5, IFITM3, OAS1Y, HERC5, and PRF1, which are potentially critical during infection with agents of bovine respiratory disease complex (BRDC). Conclusion: This study not only helps us to better understand the molecular mechanisms responsible for BRD but also suggested eight candidate modules along with several promising hub-hub genes as diagnosis biomarkers and therapeutic targets for BRD.

Construction and Analysis of Coexpression Network to Understand Biological Responses in Chickens Infected by Eimeria tenella

Coccidiosis, caused by various Eimeria species, is a major parasitic disease in chickens. Our understanding of how chickens respond to coccidian infections is highly limited at both the molecular and cellular levels. In this study, coexpression modules were identified by weighted gene coexpression network analysis in chickens infected with Eimeria tenella. A total of 15 correlation modules were identified using 5,175 genes with 24 chicken samples, 12 with primary and 12 with secondary E. tenella infection. The analysis of the interactions between these modules showed a high degree of scale independence. Gene Ontology and Kyoto Encyclopedia of Gene and Genomes enrichment analyses revealed that genes in these functional modules were involved in a broad categories of functions, such as immune response, amino acid metabolism, cellular responses to lipids, sterol biosynthetic processes, and RNA transport. Two modules viz yellow and magenta were identified significantly associating with infection status. Preservation analysis showed that most of the modules identified in E. tenella infections were highly or moderately preserved in chickens infected with either Eimeria acervulina or Eimeria maxima. These analyses outline a biological responses landscape for chickens infected by E. tenella, and also indicates that infections with these three Eimeria species elicit similar biological responses in chickens at the system level. These findings provide new clues and ideas for investigating the relationship between parasites and host, and the control of parasitic diseases.

Effects of inulin supplementation on intestinal barrier function and immunity in specific pathogen-free chickens with Salmonella infection

We investigated the effects of inulin on intestinal barrier function and mucosal immunity in Salmonella enterica serovar Enteritidis (SE)-infected specific pathogen-free (SPF) chickens. SPF chickens (n = 240, 1-d-old) were divided into 4 groups (6 replicates per group, 10 chickens per replicate): a control group (CON) fed a basal diet without inulin supplementation and 3 SE-infected groups fed a basal diet supplemented with inulin 0% (SE group), 0.5% (0.5% InSE group), and 1% (1% InSE group), respectively. At 28 d of age, the chickens in SE-infected groups were orally infected with SE and in CON group were administrated with phosphated-buffered saline (PBS). Intestinal morphology, mucosal immunity, and intestinal barrier function-related gene expression were analyzed at 1- and 3-d post-infection (dpi). SE challenge significantly increased the mucosal gene expression, such as interleukin-1β (IL-1β), lipopolysaccharide-induced tumor necrosis factor factor (LITAF), interferon-γ (IFN-γ), and interleukin-6 (IL-6), and increased serum IFN-γ, secretory IgA (sIgA), and IgG concentration, and significantly decreased the gene expression levels of mucin 2 (MUC2) and claudin-1 at 3 dpi compared with the CON group (P < 0.05). Inulin supplementation improved the expression levels of these immunity- and intestinal barrier function-related genes, increased villus height (VH), and decreased crypt depth (CD) in the duodenum, jejunum, and ileum at 1 and 3 dpi within the SE-challenged groups (P < 0.05). SE challenge significantly increased ileal Toll-like receptor 4 (TLR4) mRNA at 1 and 3 dpi, suppressor of cytokine signaling 3 (SOCS3) mRNA at 1 dpi, and phospho-signal transducer and activator of transcription 3 (p-STAT3) and Janus kinase1 (JAK1) protein expression at 3 dpi compared with the CON group (P < 0.05). Inulin supplementation suppressed p-STAT3 and JAK1 protein expression and promoted ileal TLR4 and SOCS3 mRNA expression at 3 dpi compared with SE group (P < 0.05). In conclusion, inulin alleviated SE-induced gut injury by decreasing the proinflammatory response and enhancing mucosal immunity in chickens.

Interaction Differences of the Avian Host-Specific Salmonella enterica Serovar Gallinarum, the Host-Generalist S. Typhimurium, and the Cattle Host-Adapted S. Dublin with Chicken Primary Macrophage

Most Salmonella serovars cause disease in many host species, while a few serovars have evolved to be host specific. Very little is known about the mechanisms that contribute to Salmonella host specificity. We compared the interactions between chicken primary macrophages (CDPM) and host-generalist serovar Salmonella enterica serovar Typhimurium, host-adapted Salmonella enterica serovar Dublin, and avian host-specific Salmonella enterica serovar Gallinarum. S Gallinarum was taken up in lower numbers by CDPM than S Typhimurium and S Dublin; however, a higher survival rate was observed for this serovar. In addition, S Typhimurium and S Dublin caused substantially higher levels of cell death to the CDPM, while significantly higher concentrations of NO were produced by S Gallinarum-infected cells. Global transcriptome analysis performed 2 h postinfection showed that S Gallinarum infection triggered a more comprehensive response in CDPM with 1,114 differentially expressed genes (DEGs) compared to the responses of S Typhimurium (625 DEGs) and S Dublin (656 DEGs). Comparable levels of proinflammation responses were observed in CDPM infected by these three different serovars at the initial infection phase, but a substantially quicker reduction in levels of interleukin-1β (IL-1β), CXCLi1, and CXCLi2 gene expression was detected in the S Gallinarum-infected macrophages than that of two other groups as infections proceeded. KEGG cluster analysis for unique DEGs after S Gallinarum infection showed that the JAK-STAT signaling pathway was top enriched, indicating a specific role for this pathway in response to S Gallinarum infection of CDPM. Together, these findings provide new insights into the interaction between Salmonella and the host and increase our understanding of S Gallinarum host specificity.