Identification of genetic features that are associated with amplitude of low-frequency fluctuation changes in schizophrenia using omics analysis.

Genetic risk for schizophrenia is thought to trigger variation in clinical features of schizophrenia, but biological processes associated with neuronal activity in brain regions remain elusive. In this study, gene expression features were mapped to various sub-regions of the brain by integrating low-frequency amplitude features and gene expression data from the schizophrenia brain and using gene co-expression network analysis of the Allen Transcriptome Atlas of the human brain from six donors to identify genetic features of brain regions and important associations with neuronal features. The results indicate that changes in the dynamic amplitude of low-frequency fluctuation (dALFF) are mainly associated with transcriptome signature factors such as cortical layer synthesis, immune response, and expanded membrane transport. Further modular disease enrichment analysis revealed that the same set of signature genes associated with dALFF levels was enriched for multiple neurological biological processes. Finally, genetic profiling of individual modules identified multiple core genes closely related to schizophrenia, also potentially associated with neuronal activity. Thus, this paper explores genetic features of brain regions in the schizophrenia closely related to low-frequency amplitude ratio levels based on imaging genetics, which suggests structural endophenotypes associated with schizophrenia.

Pathway integration and annotation: building a puzzle with non-matching pieces and no reference picture.

Biological pathways are a broadly used formalism for representing and interpreting the cascade of biochemical reactions underlying cellular and biological mechanisms. Pathway representation provides an ontological link among biomolecules such as RNA, DNA, small molecules, proteins, protein complexes, hormones and genes. Frequently, pathway annotations are used to identify mechanisms linked to genes within affected biological contexts. This important role and the simplicity and elegance in representing complex interactions led to an explosion of pathway representations and databases. Unfortunately, the lack of overlap across databases results in inconsistent enrichment analysis results, unless databases are integrated. However, due to absence of consensus, guidelines or gold standards in pathway definition and representation, integration of data across pathway databases is not straightforward. Despite multiple attempts to provide consolidated pathways, highly related, redundant, poorly overlapping or ambiguous pathways continue to render pathways analysis inconsistent and hard to interpret. Ontology-based integration will promote unbiased, comprehensive yet streamlined analysis of experiments, and will reduce the number of enriched pathways when performing pathway enrichment analysis. Moreover, appropriate and consolidated pathways provide better training data for pathway prediction algorithms. In this manuscript, we describe the current methods for pathway consolidation, their strengths and pitfalls, and highlight directions for future improvements to this research area.

Dioxin(-like)-Related Biological Effects through Integrated Chemical-wide and Metabolome-wide Analyses.

Dioxin(-like) exposures are linked to adverse health effects, including cancer. However, metabolic alterations induced by these chemicals remain largely unknown. Beyond known dioxin(-like) compounds, we leveraged a chemical-wide approach to assess chlorinated co-exposures and parent compound products [termed dioxin(-like)-related compounds] among 137 occupational workers. Endogenous metabolites were profiled by untargeted metabolomics, namely, reversed-phase chromatography with negative electrospray ionization (C18-negative) and hydrophilic interaction liquid chromatography with positive electrospray ionization (HILIC-positive). We performed a metabolome-wide association study to select dioxin(-like) associated metabolic features using a 20% false discovery rate threshold. Metabolic features were then characterized by pathway enrichment analyses. There are no significant features associated with polychlorinated dibenzo-p-dioxins (PCDDs), a subgroup of known dioxin(-like) compounds. However, 3,110 C18-negative and 2,894 HILIC-positive features were associated with at least one of the PCDD-related compounds. Abundant metabolic changes were also observed for polychlorinated dibenzofuran-related and polychlorinated biphenyl-related compounds. These metabolic features were primarily enriched in pathways of amino acids, lipid and fatty acids, carbohydrates, cofactors, and nucleotides. Our study highlights the potential of chemical-wide analysis for comprehensive exposure assessment beyond targeted chemicals. Coupled with advanced endogenous metabolomics, this approach allows for an in-depth exploration of metabolic alterations induced by environmental chemicals.

Developmental epigenomic effects of maternal financial problems.

Early-life adversity as neglect or low socioeconomic status is associated with negative physical/mental health outcomes and plays an important role in health trajectories through life. The early-life environment has been shown to be encoded as changes in epigenetic markers that are retained for many years.We investigated the effect of maternal major financial problems (MFP) and material deprivation (MD) on their children's epigenome in the Avon Longitudinal Study of Parents and Children (ALSPAC) cohort. Epigenetic aging, measured with epigenetic clocks, was weakly accelerated with increased MFP. In subsequent EWAS, MFP, and MD showed strong, independent programing effects on children's genomes. MFP in the period from birth to age seven was associated with genome-wide epigenetic modifications on children's genome visible at age 7 and partially remaining at age 15.These results support the hypothesis that physiological processes at least partially explain associations between early-life adversity and health problems later in life. Both maternal stressors (MFP/MD) had similar effects on biological pathways, providing preliminary evidence for the mechanisms underlying the effects of low socioeconomic status in early life and disease outcomes later in life. Understanding these associations is essential to explain disease susceptibility, overall life trajectories and the transition from health to disease.

Dissecting loci that underpin the genetic correlations between production, fertility, and urea traits in Australian Holstein cattle.

Unfavorable genetic correlations between milk production, fertility, and urea traits have been reported. However, knowledge of the genomic regions associated with these unfavorable correlations is limited. Here, we used the correlation scan method to identify and investigate the regions driving or antagonizing the genetic correlations between production vs. fertility, urea vs. fertility, and urea vs. production traits. Driving regions produce an estimate of correlation that is in the same direction as the global correlation. Antagonizing regions produce an estimate in the opposite direction of the global estimates. Our dataset comprised 6567, 4700, and 12,658 Holstein cattle with records of production traits (milk yield, fat yield, and protein yield), fertility (calving interval) and urea traits (milk urea nitrogen and blood urea nitrogen predicted using milk-mid-infrared spectroscopy), respectively. Several regions across the genome drive the correlations between production, fertility, and urea traits. Antagonizing regions were confined to certain parts of the genome and the genes within these regions were mostly involved in preventing metabolic dysregulation, liver reprogramming, metabolism remodeling, and lipid homeostasis. The driving regions were enriched for QTL related to puberty, milk, and health-related traits. Antagonizing regions were mostly related to muscle development, metabolic body weight, and milk traits. In conclusion, we have identified genomic regions of potential importance for dairy cattle breeding. Future studies could investigate the antagonizing regions as potential genomic regions to break the unfavorable correlations and improve milk production as well as fertility and urea traits.

[Mechanism of "Trichosanthis Fructus-Allii Macrostemonis Bulbus" in treating cardiovascular diseases with syndrome of combined phlegm and stasis based on serum metabolomics].

This study aimed at investigating the mechanism of Trichosanthis Fructus-Allii Macrostemonis Bulbus(GX) in treating cardiovascular diseases in rats with the syndrome of combined phlegm and stasis. The rat model was established by a high-fat diet, ice-water bath combined with subcutaneous injection of adrenalin hydrochloride, and the syndrome score was determined. The serum samples of rats in the control, model, and GX groups were collected. Ultra-performance liquid chromatography-quadrupole-time-of-flight mass spectrometry(UPLC-Q-TOF-MS) was employed to analyze the metabolic profiles of the serum samples. The differential metabolites were screened and identified by partial least squares-discriminant analysis(PLS-DA) and orthogonal partial least squares-discriminant analysis(OPLS-DA). The intervention targets of GX-regulated metabolites and their metabolic pathways were searched against MetaboAnalyst. Gene Ontology enrichment was carried out to predict the biological pathways associated with the intervention targets of metabolic pathways. A total of 129 potential biomarkers were detected in the rat model with the syndrome of combined phlegm and stasis via metabolomics, and GX regulated 54 metabolites in several metabolic pathways such as linoleic acid metabolism, sphingolipid metabolism, and tricarboxylic acid cycle. The further screening against MetaboAnalyst showed that GX recovered the levels of nine metabolites associated with cardiovascular diseases with the syndrome of combined phlegm and stasis, which involved 69 targets in the pathways regarding cholesterol metabolism, fatty acid metabolism, inflammatory response, and glucose homeostasis and metabolism. The above-mentioned results suggested that GX can alleviate the symptoms of the rat model of cardiovascular diseases with the syndrome of combined phlegm and stasis by regulating the metabolism of linoleic acid, sphingosine, docosahexaenoic acid, rosemary acid, succinic acid, adenine, L-phenylalanine, L-valine and modulating the biological pathways such as cholesterol metabolism, fatty acid metabolism, inflammatory response, and glucose homeostasis and metabolism.

Multi-omics insights and therapeutic implications in polycystic ovary syndrome: a review.

Polycystic ovary syndrome (PCOS) is a common gynecological disease that causes adverse effects in women in their reproductive phase. Nonetheless, the molecular mechanisms remain unclear. Over the last decade, sequencing and omics approaches have advanced at an increased pace. Omics initiatives have come to the forefront of biomedical research by presenting the significance of biological functions and processes. Thus, multi-omics profiling has yielded important insights into understanding the biology of PCOS by identifying potential biomarkers and therapeutic targets. Multi-omics platforms provide high-throughput data to leverage the molecular mechanisms and pathways involving genetic alteration, epigenetic regulation, transcriptional regulation, protein interaction, and metabolic alterations in PCOS. The purpose of this review is to outline the prospects of multi-omics technologies in PCOS research by revealing novel biomarkers and therapeutic targets. Finally, we address the knowledge gaps and emerging treatment strategies for the management of PCOS. Future PCOS research in multi-omics at the single-cell level may enhance diagnostic and treatment options.

CNA2Subpathway: identification of dysregulated subpathway driven by copy number alterations in cancer.

Biological pathways reflect the key cellular mechanisms that dictate disease states, drug response and altered cellular function. The local areas of pathways are defined as subpathways (SPs), whose dysfunction has been reported to be associated with the occurrence and development of cancer. With the development of high-throughput sequencing technology, identifying dysfunctional SPs by using multi-omics data has become possible. Moreover, the SPs are not isolated in the biological system but interact with each other. Here, we propose a network-based calculated method, CNA2Subpathway, to identify dysfunctional SPs is driven by somatic copy number alterations (CNAs) in cancer through integrating pathway topology information, multi-omics data and SP crosstalk. This provides a novel way of SP analysis by using the SP interactions in the system biological level. Using data sets from breast cancer and head and neck cancer, we validate the effectiveness of CNA2Subpathway in identifying cancer-relevant SPs driven by the somatic CNAs, which are also shown to be associated with cancer immune and prognosis of patients. We further compare our results with five pathway or SP analysis methods based on CNA and gene expression data without considering SP crosstalk. With these analyses, we show that CNA2Subpathway could help to uncover dysfunctional SPs underlying cancer via the use of SP crosstalk. CNA2Subpathway is developed as an R-based tool, which is freely available on GitHub (https://github.com/hanjunwei-lab/CNA2Subpathway).

Unraveling the prevalence of various signalling pathways in non-small-cell lung cancer: a review.

Cancer has become a huge public health issue all around the world. The focus of research is on innovative cancer therapy techniques that include the disease's unique targets. Among the cancer-related deaths that occur, lung cancer is considered to be one of the major, accounting for about 1.6 million fatalities globally in 2012, or nearly 20% of all cancer deaths. Non-small-cell lung cancer, a type of lung cancer comprises upto 84% of lung cancer cases, demonstrating the need for a more effective treatment. A novel category of cancer management, known as targeted cancer medicines, has risen to prominence in recent years. Targeted cancer treatments, like traditional chemotherapy, employ pharmacological drugs to slow cancer development, enhance cell death, and prevent it from spreading. Targeted treatments, as the name implies, work by interfering with particular proteins implicated in cancer. Numerous research conducted in the last several decades have led to the conclusion that signalling pathways are involved in the growth of lung cancer. All malignant tumours are produced, spread, invade, and behave in various abnormal ways due to abnormal pathways. Numerous significant signalling pathways, including the RTK/RAS/MAP-Kinase pathway (hence often referred to as RTK-RAS for simplicity), PI3K/Akt signalling, and others, have been discovered as commonly genetically changed. The current developments in research on various signalling pathways, as well as the underlying mechanisms of the molecules implicated in these pathways, are innovatively summarised in this review. To give a good sense of the study that has been done so far, many routes are placed together. Thus, this review includes the detailed description regarding each pathways, the mutations formed, and the present treatment strategy to overcome the resistance.

A radiomics nomogram prediction for survival of patients with "driver gene-negative" lung adenocarcinomas (LUAD).

BACKGROUND: To study the role of computed tomography (CT)-derived radiomics features and clinical characteristics on the prognosis of "driver gene-negative" lung adenocarcinoma (LUAD) and to explore the potential molecular biological which may be helpful for patients' individual postoperative care. METHODS: A total of 180 patients with stage I-III "driver gene-negative" LUAD in the First Affiliated Hospital of Sun Yat-Sen University from September 2003 to June 2015 were retrospectively collected. The Least Absolute Shrinkage and Selection Operator (LASSO) Cox regression model was used to screen radiomics features and calculated the Rad-score. The prediction performance of the nomogram model based on radiomics features and clinical characteristics was validated and then assessed with respect to calibration. Gene set enrichment analysis (GSEA) was used to explore the relevant biological pathways. RESULTS: The radiomics and the clinicopathological characteristics were combined to construct a nomogram resulted in better performance for the estimation of OS (C-index: 0.815; 95% confidence interval [CI]: 0.756-0.874) than the clinicopathological nomogram (C-index: 0.765; 95% CI: 0.692-0.837). Decision curve analysis demonstrated that in terms of clinical usefulness, the radiomics nomogram outperformed the traditional staging system and the clinicopathological nomogram. The clinical prognostic risk score of each patient was calculated based on the radiomics nomogram and divided by X-tile into high-risk (> 65.28) and low-risk (≤ 65.28) groups. GSEA results showed that the low-risk score group was directly related to amino acid metabolism, and the high-risk score group was related to immune and metabolism pathways. CONCLUSIONS: The radiomics nomogram was promising to predict the prognosis of patients with "driver gene-negative" LUAD. The metabolism and immune-related pathways may provide new treatment orientation for this genetically unique subset of patients, which may serve as a potential tool to guide individual postoperative care for those patients.

A statistical network pre-processing method to improve relevance and significance of gene lists in microarray gene expression studies.

BACKGROUND: Microarrays can perform large scale studies of differential expressed gene (DEGs) and even single nucleotide polymorphisms (SNPs), thereby screening thousands of genes for single experiment simultaneously. However, DEGs and SNPs are still just as enigmatic as the first sequence of the genome. Because they are independent from the affected biological context. Pathway enrichment analysis (PEA) can overcome this obstacle by linking both DEGs and SNPs to the affected biological pathways and consequently to the underlying biological functions and processes. RESULTS: To improve the enrichment analysis results, we present a new statistical network pre-processing method by mapping DEGs and SNPs on a biological network that can improve the relevance and significance of the DEGs or SNPs of interest to incorporate pathway topology information into the PEA. The proposed methodology improves the statistical significance of the PEA analysis in terms of computed p value for each enriched pathways and limit the number of enriched pathways. This helps reduce the number of relevant biological pathways with respect to a non-specific list of genes. CONCLUSION: The proposed method provides two-fold enhancements. Network analysis reveals fewer DEGs, by selecting only relevant DEGs and the detected DEGs improve the enriched pathways' statistical significance, rather than simply using a general list of genes.

Integrative omics analysis identifies differential biological pathways that are associated with regional grey matter volume changes in major depressive disorder.

BACKGROUND: Major depressive disorder (MDD) is accompanied by alterations in grey matter volume. However, the biological processes associated with regional structural perturbations remain elusive. METHODS: We applied integrative omics analysis to investigate specialized transcriptome signatures and translational determinants associated with regional grey matter variations in 2737 MDD patients relative to 3098 controls by summarizing the results from gene co-expression network analysis of Allen human brain transcriptome profiles in six donors, enrichment analysis of gene-sets and cellular structure from rodents and mediation analysis of BrainSpan proteome profile in six donors. RESULTS: We found convergent alterations of grey matter volume in MDD were associated with transcriptome profiles enriched for synaptic transmission, metabolism, immune processes and transmembrane transport. Genes with abnormal expression in post-mortem tissue in MDD were also associated with transcriptome signatures. Further gene co-expression network and enrichment analysis of MDD-related genes in these signatures revealed the modules with higher neuronal expression were enriched in the medial temporal cortex and temporo-parietal junction with genes differentially associated with neuronal development and metabolism. Also, the modules with higher non-neuronal (e.g. astrocyte and oligodendrocyte) expression were concentrated in the rostral and dorsal anterior cingulate cortex and were separately associated with immune response and transmembrane transport. Moreover, proteins as the gene expression products mediated the association between transcriptome signatures and brain volume changes in the visual and dorsolateral prefrontal cortex. CONCLUSIONS: Our multidimensional analyses offer a novel approach to detect specific biological pathways that capture regional structural variations in MDD, which suggests structural endophenotypes associated with MDD.

Computational Methods for Drug Repurposing.

The wealth of knowledge and multi-omics data available in drug research has allowed the rise of several computational methods in the drug discovery field, resulting in a novel and exciting strategy called drug repurposing. Drug repurposing consists in finding new applications for existing drugs. Numerous computational methods perform a high-level integration of different knowledge sources to facilitate the discovery of unknown mechanisms. In this chapter, we present a survey of data resources and computational tools available for drug repositioning.

Analysis of microRNAs in Small Urinary Extracellular Vesicles and Their Potential Roles in Pathogenesis of Renal ANCA-Associated Vasculitis.

Antineutrophil cytoplasmic antibodies (ANCA)-associated vasculitis (AAV) represents an autoimmunity disease characterized by high mortality. For successful treatment, the detailed knowledge of its complex pathogenesis and the set of biomarkers for differential diagnostics are desired. Analysis of molecular content of small urinary extracellular vesicles (uEV) offers the possibility to find markers in the form of microRNAs (miRNAs) and study the pathways involved in pathogenesis. We used next-generation sequencing in the first preliminary study to detect the miRNAs with altered expression in uEVs of patients with AAV in comparison with age-matched controls. We confirmed the results using single-target quantitative polymerase chain reaction tests on different sets of samples and found five miRNAs (miR-30a-5p, miR-31-3p, miR-99a-5p, miR-106b-5p, miR-182-5p) with highly elevated levels in uEVs of patients. We performed the comparison of their targets with the differentially expressed proteins in uEVs of patients included in the first phase. We realized that upregulated miRNAs and proteins in uEVs in AAV patients target different biological pathways. The only overlap was detected in pathways regulating the actin cytoskeleton assembly and thus potentially affecting the glomerular functions. The associations of upregulated miRNAs with pathways that were neglected as components of complex AAV pathogenesis, e.g., the epidermal growth factor receptor signaling pathway, were found.

Isolation and identification of O- and N-linked glycoproteins in milk from different mammalian species and their roles in biological pathways which support infant growth.

Milk serves as the sole nutrition for newborns, as well as a medium for the transfer of immunological components from the mother to the baby. This study reveals different glycoprotein profiles obtained from human, bovine, and caprine milk and their potential roles in supporting infant growth. Proteins from these three milk samples are separated and analyzed using two-dimensional gel electrophoresis (2-DE). Glycosylated proteins from all samples are enriched by affinity chromatography using lectins from the seeds of Artocarpus integer before analysis using LC/MS-QTOF. The glycoproteome profiling demonstrates that glycosylated proteins are higher in caprine milk compared to other samples. Analysis using LC/MS-QTOF identified 42 O-glycosylated and 56 N-glycosylated proteins, respectively. Among those identified, human milk has 17 glycoproteins, which are both O- and N-glycosylated, whereas caprine and bovine have 10 and 1, respectively. Only glycoproteins from human milk have shown positive matching to important human biological pathways, such as vesicle-mediated transport, immune system and hemostasis pathways. Human milk remains unique for human babies with the presence of antibodies in the form of immunoglobulins that are lacking in ruminant milk proteomes.

Genome-wide association study and pathway analysis for carcass fatness in Nellore cattle measured by ultrasound.

Identifying genes or genomic regions influencing carcass-quality traits such as fatness (FTN) is essential to optimize the genetic selection processes in beef cattle. The aim of this study was to identify genomic regions associated with FTN in Nellore cattle as well as to elucidate the metabolic pathways related to the phenotypic expression. Ultrasound-based measurements of FTN were collected in 11 750 animals, with 39 903 animals in the pedigree file. Additionally, 1440 animals were genotyped using the GGP-indicus 35K SNP panel, which contained 33 623 SNPs after quality control. Twenty genes related to FTN were found on 11 chromosomes, explaining 12.96% of the total additive genetic variance. Gene ontology revealed seven genes: NR1L2, PKD2, GSK3ß, EXT1, RAD51B, SORCS1 and DPH6, associated with important processes related to FTN. In addition, novel candidate genes (MAATS1, LYPD1, CDK5RAP2, RAD51B, c13H2Oorf96 and TRAPPC11) were detected and could provide further knowledge to uncover genetic regions associated to carcass fatness in beef cattle.

Characterization of Biological Pathways Regulating Acute Cold Resistance of Zebrafish.

Low temperature stress represents a major threat to the lives of both farmed and wild fish species. However, biological pathways determining the development of cold resistance in fish remain largely unknown. Zebrafish larvae at 96 hpf were exposed to lethal cold stress (10 °C) for different time periods to evaluate the adverse effects at organism, tissue and cell levels. Time series RNA sequencing (RNA-seq) experiments were performed to delineate the transcriptomic landscape of zebrafish larvae under cold stress and during the subsequent rewarming phase. The genes regulated by cold stress were characterized by progressively enhanced or decreased expression, whereas the genes associated with rewarming were characterized by rapid upregulation upon return to normal temperature (28 °C). Genes such as trib3, dusp5 and otud1 were identified as the representative molecular markers of cold-induced damages through network analysis. Biological pathways involved in cold stress responses were mined from the transcriptomic data and their functions in regulating cold resistance were validated using specific inhibitors. The autophagy, FoxO and MAPK (mitogen-activated protein kinase) signaling pathways were revealed to be survival pathways for enhancing cold resistance, while apoptosis and necroptosis were the death pathways responsible for cold-induced mortality. Functional mechanisms of the survival-enhancing factors Foxo1, ERK (extracellular signal-regulated kinase) and p38 MAPK were further characterized by inhibiting their activities upon cold stress and analyzing gene expression though RNA-seq. These factors were demonstrated to determine the cold resistance of zebrafish through regulating apoptosis and p53 signaling pathway. These findings have provided novel insights into the stress responses elicited by lethal cold and shed new light on the molecular mechanisms underlying cold resistance of fish.

Hippocampus Proteomics and Brain Lipidomics Reveal Network Dysfunction and Lipid Molecular Abnormalities in APP/PS1 Mouse Model of Alzheimer's Disease.

Alzheimer's disease (AD) is closely associated with protein dysfunction and aberrant lipid metabolism in the brain. Our study could be conducive to the discovery of lipid and protein biomarkers for early diagnosis and therapy. In our current work, novel quantitative proteomic and lipidomic methods were developed for the characterization of molecular perturbation occurring in the brain in early stage AD mice. For this purpose, we performed a proteomic and lipidomic screening by liquid chromatography with mass spectrometry. Significant changes were detected, including 231 proteins and 11 lipid compounds in the AD mouse brain. Early stage AD disturbed biological pathways implicated in neuroactive ligand-receptor, complement and coagulation cascades, PI3K-Akt signaling and metabolic pathways, and glycerophospholipid metabolism. The results in the current study suggest that these significantly altered proteins and lipids may be implicated in early stage AD. Our work raises the possibility of AD diagnosis and therapy by providing new protein targets and lipid biomarkers.

Synaptic and brain-expressed gene sets relate to the shared genetic risk across five psychiatric disorders.

BACKGROUND: Mounting evidence shows genetic overlap between multiple psychiatric disorders. However, the biological underpinnings of shared risk for psychiatric disorders are not yet fully uncovered. The identification of underlying biological mechanisms is crucial for the progress in the treatment of these disorders. METHODS: We applied gene-set analysis including 7372 gene sets, and 53 tissue-type specific gene-expression profiles to identify sets of genes that are involved in the etiology of multiple psychiatric disorders. We included genome-wide meta-association data of the five psychiatric disorders schizophrenia, bipolar disorder, major depressive disorder, autism spectrum disorder, and attention-deficit/hyperactivity disorder. The total dataset contained 159 219 cases and 262 481 controls. RESULTS: We identified 19 gene sets that were significantly associated with the five psychiatric disorders combined, of which we excluded five sets because their associations were likely driven by schizophrenia only. Conditional analyses showed independent effects of several gene sets that in particular relate to the synapse. In addition, we found independent effects of gene expression levels in the cerebellum and frontal cortex. CONCLUSIONS: We obtained novel evidence for shared biological mechanisms that act across psychiatric disorders and we showed that several gene sets that have been related to individual disorders play a role in a broader range of psychiatric disorders.

Transcriptome profiling analysis of caeca in chicks challenged with Salmonella Typhimurium reveals differential expression of genes involved in host mucosal immune response.

Temporal regulation of global gene expression in the caeca of chickens infected with Salmonella Typhimurium has not been investigated previously. In this study, we performed the transcriptome analysis of the caeca of Salmonella Typhimurium challenged chicks to understand the regulation of the mucosal immune system in a temporal manner. The Salmonella infection resulted in the activation of the caecal immune system by the upregulation of the differentially expressed genes (DEGs; false discovery rate (FDR) < 0.05; log2 fold change > 1) involved in biological pathways such as Toll-like receptor signaling pathway, Salmonella infection, cytokine-cytokine receptor interaction, phagosome, apoptosis and intestinal immune network for IgA production. The activation of biological pathways such as RIG-I-like receptor signaling pathway, ErbB signaling pathway and cellular senescence showed a time-dependent response of the host immune system. A 49% increase in the DEGs on day 7 compared with day 3 post-infection (p.i.) suggested a time-dependent role of multiple genes such as AvBD1, AvBD2, AvBD7, IL2, IL10, IL21, SIVA1, CD5, CD14 and GPR142 in the regulation of the immune system. Nested network analysis of the individual biological pathways showed that IL6 played a significant role in the immune system regulation by activating the pathways, including Toll-like receptor signaling pathway, Salmonella infection, intestinal immune network for IgA production and C-type lectin receptor signaling pathway. The downregulated DEGs (FDR < 0.05; log2 fold change < -1) showed that Salmonella challenge affected the functions of pathways, such as tryptophan metabolism, retinol metabolism, folate biosynthesis and pentose and glucoronate interconversions, suggesting the disruption of cellular mechanisms involved in nutrient synthesis, absorption and metabolism. Overall, the immune response was temporally regulated through the activation of Toll-like signaling receptor pathway, cytokine-cytokine interactions and Salmonella infection, where IL6 played a significant role in the modulation of caecal immune system against Salmonella Typhimurium. KEY POINTS: • The immune response to Salmonella Typhimurium challenge was temporally regulated in the caeca of chickens. • Many newly identified genes have been shown to be involved in the activation of the immune system. • Toll-like receptors and interleukins played a key role in immune system regulation.