Two T helper (Th) cell subsets, namely Th1 and Th2 cells, play an important role in inflammatory diseases. The two subsets are thought to counter-regulate each other, and alterations in their balance result in different diseases. This paradigm has been challenged by recent clinical and experimental data. Because of the large number of genes involved in regulating Th1 and Th2 cells, assessment of this paradigm by modeling or experiments is difficult. Novel algorithms based on formal methods now permit the analysis of large gene regulatory networks. By combining these algorithms with in silico knockouts and gene expression microarray data from human T cells, we examined if the results were compatible with a counter-regulatory role of Th1 and Th2 cells. We constructed a directed network model of genes regulating Th1 and Th2 cells through text mining and manual curation. We identified four attractors in the network, three of which included genes that corresponded to Th0, Th1 and Th2 cells. The fourth attractor contained a mixture of Th1 and Th2 genes. We found that neither in silico knockouts of the Th1 and Th2 attractor genes nor gene expression microarray data from patients with immunological disorders and healthy subjects supported a counter-regulatory role of Th1 and Th2 cells. By combining network modeling with transcriptomic data analysis and in silico knockouts, we have devised a practical way to help unravel complex regulatory network topology and to increase our understanding of how network actions may differ in health and disease.
Abstract Monozygotic ( MZ ) twins discordant for complex diseases may help to find disease mechanisms that are not due to genetic variants. Intermittent allergic rhinitis ( IAR ) is an optimal disease model because it occurs at defined time points each year, owing to known external antigens. We hypothesized that MZ twins discordant for IAR could help to find gene expression differences that are not dependent on genetic variants. We collected blood outside of the season from MZ twins discordant for IAR , challenged their peripheral blood mononuclear cells ( PBMC ) with pollen allergen in vitro , collected supernatants and isolated CD 4+ T cells. We identified disease‐relevant mRNA s and proteins that differed between the discordant MZ twins. By contrast, no differences in micro RNA expression were found. Our results indicate that MZ twins discordant for IAR is an optimal model to identify disease mechanisms that are not due to genetic variants.
Vesicular stomatitis virus expressing Zaire Ebola virus (EBOV) glycoprotein (VSVΔG/EBOVgp) could be used as a vaccine to meet the 2014 Ebola virus outbreak. To characterize the host response to this vaccine, we used mRNA sequencing to analyze peripheral blood mononuclear cells (PBMCs) from cynomolgus macaques after VSVΔG/EBOVgp immunization and subsequent EBOV challenge. We found a controlled transcriptional response that transitioned to immune regulation as the EBOV was cleared. This observation supports the safety of the vaccine.
Altered DNA methylation patterns in CD4+ T-cells indicate the importance of epigenetic mechanisms in inflammatory diseases. However, the identification of these alterations is complicated by the heterogeneity of most inflammatory diseases. Seasonal allergic rhinitis (SAR) is an optimal disease model for the study of DNA methylation because of its well-defined phenotype and etiology. We generated genome-wide DNA methylation (Npatients = 8, Ncontrols = 8) and gene expression (Npatients = 9, Ncontrols = 10) profiles of CD4+ T-cells from SAR patients and healthy controls using Illumina's HumanMethylation450 and HT-12 microarrays, respectively. DNA methylation profiles clearly and robustly distinguished SAR patients from controls, during and outside the pollen season. In agreement with previously published studies, gene expression profiles of the same samples failed to separate patients and controls. Separation by methylation (Npatients = 12, Ncontrols = 12), but not by gene expression (Npatients = 21, Ncontrols = 21) was also observed in an in vitro model system in which purified PBMCs from patients and healthy controls were challenged with allergen. We observed changes in the proportions of memory T-cell populations between patients (Npatients = 35) and controls (Ncontrols = 12), which could explain the observed difference in DNA methylation. Our data highlight the potential of epigenomics in the stratification of immune disease and represents the first successful molecular classification of SAR using CD4+ T cells.
Simian immunodeficiency virus (SIV) challenge of rhesus macaques (RMs) vaccinated with strain 68–1 Rhesus Cytomegalovirus (RhCMV) vectors expressing SIV proteins (RhCMV/SIV) results in a binary outcome: stringent control and subsequent clearance of highly pathogenic SIV in ~55% of vaccinated RMs with no protection in the remaining 45%. Although previous work indicates that unconventionally restricted, SIV-specific, effector-memory (EM)-biased CD8 + T cell responses are necessary for efficacy, the magnitude of these responses does not predict efficacy, and the basis of protection vs. non-protection in 68–1 RhCMV/SIV vector-vaccinated RMs has not been elucidated. Here, we report that 68–1 RhCMV/SIV vector administration strikingly alters the whole blood transcriptome of vaccinated RMs, with the sustained induction of specific immune-related pathways, including immune cell, toll-like receptor (TLR), inflammasome/cell death, and interleukin-15 (IL-15) signaling, significantly correlating with subsequent vaccine efficacy. Treatment of a separate RM cohort with IL-15 confirmed the central involvement of this cytokine in the protection signature, linking the major innate and adaptive immune gene expression networks that correlate with RhCMV/SIV vaccine efficacy. This change-from-baseline IL-15 response signature was also demonstrated to significantly correlate with vaccine efficacy in an independent validation cohort of vaccinated and challenged RMs. The differential IL-15 gene set response to vaccination strongly correlated with the pre-vaccination activity of this pathway, with reduced baseline expression of IL-15 response genes significantly correlating with higher vaccine-induced induction of IL-15 signaling and subsequent vaccine protection, suggesting that a robust de novo vaccine-induced IL-15 signaling response is needed to program vaccine efficacy. Thus, the RhCMV/SIV vaccine imparts a coordinated and persistent induction of innate and adaptive immune pathways featuring IL-15, a known regulator of CD8 + T cell function, that support the ability of vaccine-elicited unconventionally restricted CD8 + T cells to mediate protection against SIV challenge.
Background Previous studies of network properties of human disease genes have mainly focused on monogenic diseases or cancers and have suffered from discovery bias. Here we investigated the network properties of complex disease genes identified by genome-wide association studies (GWAs), thereby eliminating discovery bias. Principal findings We derived a network of complex diseases (n = 54) and complex disease genes (n = 349) to explore the shared genetic architecture of complex diseases. We evaluated the centrality measures of complex disease genes in comparison with essential and monogenic disease genes in the human interactome. The complex disease network showed that diseases belonging to the same disease class do not always share common disease genes. A possible explanation could be that the variants with higher minor allele frequency and larger effect size identified using GWAs constitute disjoint parts of the allelic spectra of similar complex diseases. The complex disease gene network showed high modularity with the size of the largest component being smaller than expected from a randomized null-model. This is consistent with limited sharing of genes between diseases. Complex disease genes are less central than the essential and monogenic disease genes in the human interactome. Genes associated with the same disease, compared to genes associated with different diseases, more often tend to share a protein-protein interaction and a Gene Ontology Biological Process. Conclusions This indicates that network neighbors of known disease genes form an important class of candidates for identifying novel genes for the same disease.
Complex diseases are associated with altered interactions between thousands of genes. We developed a novel method to identify and prioritize disease genes, which was generally applicable to complex diseases. We identified modules of highly interconnected genes in disease-specific networks derived from integrating gene-expression and protein interaction data. We examined if those modules were enriched for disease-associated SNPs, and could be used to find novel genes for functional studies. First, we analyzed publicly available gene expression microarray and genome-wide association study (GWAS) data from 13, highly diverse, complex diseases. In each disease, highly interconnected genes formed modules, which were significantly enriched for genes harboring disease-associated SNPs. To test if such modules could be used to find novel genes for functional studies, we repeated the analyses using our own gene expression microarray and GWAS data from seasonal allergic rhinitis. We identified a novel gene, FGF2, whose relevance was supported by functional studies using combined small interfering RNA-mediated knock-down and gene expression microarrays. The modules in the 13 complex diseases analyzed here tended to overlap and were enriched for pathways related to oncological, metabolic and inflammatory diseases. This suggested that this union of the modules would be associated with a general increase in susceptibility for complex diseases. Indeed, we found that this union was enriched with GWAS genes for 145 other complex diseases. Modules of highly interconnected complex disease genes were enriched for disease-associated SNPs, and could be used to find novel genes for functional studies.
Study Objective Obesity is a predisposing factor for cardiometabolic and kidney disease. Inflammation has been identified as an underlying risk factor. However, studies report conflicting results on the impact of short‐ and long‐term weight loss on obesity‐related inflammation. Here we investigate levels of inflammation in lean, metabolically healthy obese (MHO) and metabolically unhealthy obese (MUO) patients, and the impact of short‐ and long‐term weight loss, using multiplex arrays and bioinformatics analysis. Methods Obese patients scheduled to undergo gastric bypass surgery were age‐ and sex‐matched to lean controls (ClinicalTrials.gov NCT02322073), and sub‐classified as metabolically healthy (MHO: n=4) or unhealthy (MUO: n=7) using International Diabetes Guidelines (i.e. metabolically unhealthy is defined as having more than two cardiometabolic risk factors: central adiposity, hyperglycemia, elevated triglycerides, decrease HDL‐cholesterol, elevated blood pressure). Gender was not a part off inclusion/exclusion criteria, thus three patients were male and eight were female. Plasma was collected at baseline and following short‐ and long‐term weight loss. The short‐term weight loss consisted of a 3‐week diet regime, which takes place before surgery and resulted in 8.7 kg weight loss. The long‐term weight loss was measured 1‐year post‐surgery, at which time the patients had lost on average 46.7 kg. A multiplex array of 96 inflammation‐related plasma proteins (Olink Inflammation Panel) was analyzed using R‐studio program software version 1.1.38 and Graph Pad Prism. Results Inflammation (measured as C‐reactive protein) was higher in both MHO and MUO patients than in lean controls, although multivariate analysis of the multiplex array identified 11 proteins that were significantly different between the MHO/MUO groups at baseline. Hierarchal cluster analysis further revealed that the MHO group clustered together at baseline and after short‐term dieting, but not after long‐term weight loss. Both MHO and MUO groups presented with higher levels of macrophage inflammatory protein (MIP)‐1α, C‐C motif chemokine (CCL)‐4, oncostatin‐M and TNSF‐14, as compared to lean controls. CCL‐4 was not affected by weight loss, but oncostatin‐M and TNSF‐14 were significantly lowered by long‐term weight loss in both obese groups. MIP‐1α was further elevated by short‐term dieting, but reduced following long‐term weight loss. IL‐8 and CDCP1 were only increased in the MUO group, while IL‐18 and the anti‐inflammatory LAP TGF‐β1 were only increased in the MHO group; none of these proteins were attenuated by weight loss. Surprisingly, CCL25 and Fractalkine (chemotactics for T‐cells and monocytes) were not elevated in MHO or MUO groups at baseline compared to lean controls, but they were significantly higher 1‐year post‐surgery. Conclusion In summary, our study revealed that although obesity increases inflammatory markers, these may be differently regulated in MHO versus MUO patients and by short‐ and long‐term weight loss. Currently ongoing investigations are delineating the specific pathways involved. Support or Funding Information The Borgeson lab is supported by the Knut & Alice Wallenberg Foundation, Wallenberg Centre for Molecular & Translational Medicine, the Swedish Research Council, (no. 2016/82), the Swedish Society for Medical Research (no. S150086), Ake Wiberg's Foundation (no. M15‐0058), Konrad & Helfrid Johansson's foundation. This abstract is from the Experimental Biology 2018 Meeting. There is no full text article associated with this abstract published in The FASEB Journal .
Rhesus Macaques and African Green Monkeys Exhibit Striking Differences in Extracellular Matrix and Cell Adhesion Gene Expression During the Eclipse Phase of Siv Infection
