Genome-wide analysis of the mouse lung transcriptome reveals novel molecular gene interaction networks and cell-specific expression signatures

Tankyrase inhibition preserves osteoarthritic cartilage by coordinating cartilage matrix anabolism via effects on SOX9 PARylation

Osteoarthritis (OA) is a prevalent degenerative disease, which involves progressive and irreversible destruction of cartilage matrix. Despite efforts to reconstruct cartilage matrix in osteoarthritic joints, it has been a difficult task as adult cartilage exhibits marginal repair capacity. Here we report the identification of tankyrase as a regulator of the cartilage anabolism axis based on systems-level factor analysis of mouse reference populations. Tankyrase inhibition drives the expression of a cartilage-signature matrisome and elicits a transcriptomic pattern that is inversely correlated with OA progression. Furthermore, tankyrase inhibitors ameliorate surgically induced OA in mice, and stem cell transplantation coupled with tankyrase knockdown results in superior regeneration of cartilage lesions. Mechanistically, the pro-regenerative features of tankyrase inhibition are mainly triggered by uncoupling SOX9 from a poly(ADP-ribosyl)ation (PARylation)-dependent protein degradation pathway. Our findings provide insights into the development of future OA therapies aimed at reconstruction of articular cartilage.

Osteoarthritis results from the progressive destruction of cartilage matrix. Here, Kim et al. identify tankyrase as a regulator of cartilage matrix anabolism, and find that tankyrase inhibition, by preventing SOX9 PARylation, protects from cartilage destruction in a mouse model of osteoarthritis.

Development of a tissue augmented Bayesian model for expression quantitative trait loci analysis

Expression quantitative trait loci (eQTL) analyses detect genetic variants (SNPs) associated with RNA expression levels of genes. The conventional eQTL analysis is to perform individual tests for each gene-SNP pair using simple linear regression and to perform the test on each tissue separately ignoring the extensive information known about RNA expression in other tissue(s). Although Bayesian models have been recently developed to improve eQTL prediction on multiple tissues, they are often based on uninformative priors or treat all tissues equally. In this study, we develop a novel tissue augmented Bayesian model for eQTL analysis (TA-eQTL), which takes prior eQTL information from a different tissue into account to better predict eQTL for another tissue. We demonstrate that our modified Bayesian model has comparable performance to several existing methods in terms of sensitivity and specificity using allele-specific expression (ASE) as the gold standard. Furthermore, the tissue augmented Bayesian model improves the power and accuracy for local-eQTL prediction especially when the sample size is small. In summary, TA-eQTL's performance is comparable to existing methods but has additional flexibility to evaluate data from different platforms, can focus prediction on one tissue using only summary statistics from the secondary tissue(s), and provides a closed form solution for estimation.

Sex Differences in Correlation with Gene Expression Levels between Ifi200 Family Genes and Four Sets of Immune Disease-Relevant Genes

Background

The HIN-200 family genes in humans have been linked to several autoimmune diseases—particularly to systemic lupus erythematosus (SLE) and rheumatoid arthritis (RA). Recently, its human counterpart gene cluster, the Ifi200 family in mice, has been linked to spontaneous arthritis disease (SAD). However, many immune-mediated diseases (including RA and SLE) show gender difference. Understanding whether or not and how these genes play a role in sex difference in immune-mediated diseases is essential for diagnosis/treatment.

Methods

This study takes advantage of the whole genome gene expression profiles of recombinant inbred (RI) strain populations from female and male mice to analyze potential sex differences in a variety of genes in disease pathways. Expression levels and regulatory QTL of Ifi200 family genes between female and male mice were first examined in a large mouse population, including RI strains derived from C57BL/6J, DBA/2J (BXD), and classic inbred strains. Sex similarities and differences were then analyzed for correlations with gene expression levels between genes in the Ifi200 family and four selected gene sets: known immune Ifi200 pathway-related genes, lupus-relevant genes, osteoarthritis- (OA-) and RA-relevant genes, and sex hormone-related genes.

Results

The expression level of Ifi202b showed the most sex difference in correlation with known immune-related genes (the P value for Ifi202b is 0.0004). Ifi202b also showed gender difference in correlation with selected sex hormone genes, with a P value of 0.0243. When comparing coexpression levels between Ifi200 genes and lupus-relevant genes, Ifi203 and Ifi205 showed significant sex difference (P values: 0.0303 and 0.002, resp.). Furthermore, several key genes (e.g., Csf1r, Ifnb1, IL-20, IL-22, IL-24, Jhdm1d, Csf1r, Ifnb1, IL-20, IL-22, IL-24, and Tgfb2 that regulate sex differences in immune diseases) were discovered.

Conclusions

Different genes in the Ifi200 family play different roles in sex difference among dissimilar pathways of these four gene groups.

Integrated analysis of mRNA-seq and miRNA-seq for host susceptibilities to influenza A (H7N9) infection in inbred mouse lines

Host genetic factors play an important role in diverse host outcomes after influenza A (H7N9) infection. Studying differential responses of inbred mouse lines with distinct genetic backgrounds to influenza virus infection could substantially increase our understanding of the contributory roles of host genetic factors to disease severity. Here, we utilized an integrated approach of mRNA-seq and miRNA-seq to investigate the transcriptome expression and regulation of host genes in C57BL/6J and DBA/2J mouse strains during influenza virus infection. The differential pathogenicity of influenza virus in C57BL/6J and DBA/2J has been fully demonstrated through immunohistochemical staining, histopathological analyses, and viral replication assessment. A transcriptional molecular signature correlates to differential host response to infection has been uncovered. With the introduction of temporal expression pattern analysis, we demonstrated that host factors responsible for influenza virus replication and host-virus interaction were significantly enriched in genes exhibiting distinct temporal dynamics between different inbred mouse lines. A combination of time-series expression analysis and temporal expression pattern analysis has provided a list of promising candidate genes for future studies. An integrated miRNA regulatory network from both mRNA-seq and miRNA-seq revealed several regulatory modules responsible for regulating host susceptibilities and disease severity. Overall, a comprehensive framework for analyzing host susceptibilities to influenza infection was established by integrating mRNA-seq and miRNA-seq data of inbred mouse lines. This work suggests novel putative molecular targets for therapeutic interventions in seasonal and pandemic influenza.

Precise Network Modeling of Systems Genetics Data Using the Bayesian Network Webserver

The Bayesian Network Webserver (BNW, http://compbio.uthsc.edu/BNW ) is an integrated platform for Bayesian network modeling of biological datasets. It provides a web-based network modeling environment that seamlessly integrates advanced algorithms for probabilistic causal modeling and reasoning with Bayesian networks. BNW is designed for precise modeling of relatively small networks that contain less than 20 nodes. The structure learning algorithms used by BNW guarantee the discovery of the best (most probable) network structure given the data. To facilitate network modeling across multiple biological levels, BNW provides a very flexible interface that allows users to assign network nodes into different tiers and define the relationships between and within the tiers. This function is particularly useful for modeling systems genetics datasets that often consist of multiscalar heterogeneous genotype-to-phenotype data. BNW enables users to, within seconds or minutes, go from having a simply formatted input file containing a dataset to using a network model to make predictions about the interactions between variables and the potential effects of experimental interventions. In this chapter, we will introduce the functions of BNW and show how to model systems genetics datasets with BNW.

Identification of lung-specific genes by meta-analysis of multiple tissue RNA-seq data

Lung-specific genes play critically important roles in lung development, lung physiology, and pathogenesis of lung-associated diseases. We performed a meta-analysis of multiple tissue RNA-seq data to identify lung-specific genes in order to better investigate their lung-specific functions and pathological roles. We identified 83 lung-specific genes consisting of 62 protein-coding genes, five pseudogenes and 16 noncoding RNA genes. About 49.4% of lung-specific genes were implicated in the pathogenesis of lung diseases and 21.7% were involved with lung development. The identification of genes with enriched expression in the lung will facilitate the elucidation of lung-specific functions and their roles in disease pathogenesis.

Exploiting Gene-Expression Deconvolution to Probe the Genetics of the Immune System

Sequence variation can affect the physiological state of the immune system. Major experimental efforts targeted at understanding the genetic control of the abundance of immune cell subpopulations. However, these studies are typically focused on a limited number of immune cell types, mainly due to the use of relatively low throughput cell-sorting technologies. Here we present an algorithm that can reveal the genetic basis of inter-individual variation in the abundance of immune cell types using only gene expression and genotyping measurements as input. Our algorithm predicts the abundance of immune cell subpopulations based on the RNA levels of informative marker genes within a complex tissue, and then provides the genetic control on these predicted immune traits as output. A key feature of the approach is the integration of predictions from various sets of marker genes and refinement of these sets to avoid spurious signals. Our evaluation of both synthetic and real biological data shows the significant benefits of the new approach. Our method, VoCAL, is implemented in the freely available R package ComICS.

Quantitative trait locus (QTL) studies have identified a plethora of genetic variants that lead to inter-individual variation in the abundance of immune cell subpopulations, both in normal and disease states. Cell sorting is an effective method of monitoring immune cell type quantities; however, owing to the large number of possible immune cell subsets, it can be difficult to apply this method to each cell type over multiple individuals. Recent QTL studies dealt with this difficulty by focusing on an a priori selection of one or a few cell subsets. Here we introduce VoCAL, a deconvolution-based method that utilizes transcriptome data to infer the quantities of immune cell types, and then uses these quantitative traits to uncover the underlying DNA loci. Our results in synthetic data and lung cohorts show that the VoCAL method outperforms other alternatives in revealing the genetic basis of immune physiology.

RNAseq expression analysis of resistant and susceptible mice after influenza A virus infection identifies novel genes associated with virus replication and important for host resistance to infection

Background

The host response to influenza A infections is strongly influenced by host genetic factors. Animal models of genetically diverse mouse strains are well suited to identify host genes involved in severe pathology, viral replication and immune responses. Here, we have utilized a dual RNAseq approach that allowed us to investigate both viral and host gene expression in the same individual mouse after H1N1 infection.

Results

We performed a detailed expression analysis to identify (i) correlations between changes in expression of host and virus genes, (ii) host genes involved in viral replication, and (iii) genes showing differential expression between two mouse strains that strongly differ in resistance to influenza infections. These genes may be key players involved in regulating the differences in pathogenesis and host defense mechanisms after influenza A infections. Expression levels of influenza segments correlated well with the viral load and may thus be used as surrogates for conventional viral load measurements. Furthermore, we investigated the functional role of two genes, Reg3g and Irf7, in knock-out mice and found that deletion of the Irf7 gene renders the host highly susceptible to H1N1 infection.

Conclusions

Using RNAseq analysis we identified novel genes important for viral replication or the host defense. This study adds further important knowledge to host-pathogen-interactions and suggests additional candidates that are crucial for host susceptibility or survival during influenza A infections.

Electronic supplementary material

The online version of this article (doi:10.1186/s12864-015-1867-8) contains supplementary material, which is available to authorized users.

A novel genetic locus linked to pro-inflammatory cytokines after virulent H5N1 virus infection in mice

Background

Genetic variation in the human population is a key determinant of influenza disease severity. A single nucleotide polymorphism in the antiviral gene IFITM3 was linked to outcomes during the 2009 H1N1 pandemic. To identify variant host genes associated with increased virus replication and severe disease, we performed a quantitative trait locus analysis on pro-inflammatory cytokine production 48 hours after intranasal infection with highly pathogenic H5N1 influenza virus.

Results

Pro-inflammatory cytokines CCL2, TNFα and IFN-α, were measured by ELISA in lung homogenates of DBA/2J (D2), C57BL/6J (B6) and 44 different BXD recombinant inbred mouse strains. Virus titer was also assessed in a subset of these animals. CCL2 (8-fold), TNFα (24-fold) and IFN-α (8-fold) concentrations varied significantly among the different BXD RI strains. Importantly, cytokine concentration correlated very well (r =0.86-0.96, P <0.0001) with virus titer suggesting that early cytokine production is due to increased virus infection and replication. Linkage analysis of cytokine concentration revealed a significant locus on chromosome 6 associated with differences in TNFα, IFN-α and CCL2 cytokine concentration (LRS =26). This locus accounted for nearly 20% of the observed phenotypic variation in the BXD population studied. Sequence and RNA expression analysis identified several candidate host genes containing missense mutations or deletions; Samd9l, Ica1, and Slc25a13. To study the role of Slc25a13, we obtained Slc25a13 knockout line, but upon challenge with H5N1 influenza virus observed no effect on CCL2 production, or morbidity and mortality.

Conclusion

A novel genetic locus on chromosome 6 modulates early pro-inflammatory cytokine production and virus replication after highly pathogenic influenza virus infection. Candidate genes, Samd9l and Ica1, may be important for the control of influenza virus infection and pathogenesis.

Electronic supplementary material

The online version of this article (doi:10.1186/1471-2164-15-1017) contains supplementary material, which is available to authorized users.

Pharmacogenomic characterization of gemcitabine response--a framework for data integration to enable personalized medicine

Supplemental Digital Content is available in the text.

Objectives

Response to the oncology drug gemcitabine may be variable in part due to genetic differences in the enzymes and transporters responsible for its metabolism and disposition. The aim of our in-silico study was to identify gene variants significantly associated with gemcitabine response that may help to personalize treatment in the clinic.

Methods

We analyzed two independent data sets: (a) genotype data from NCI-60 cell lines using the Affymetrix DMET 1.0 platform combined with gemcitabine cytotoxicity data in those cell lines, and (b) genome-wide association studies (GWAS) data from 351 pancreatic cancer patients treated on an NCI-sponsored phase III clinical trial. We also performed a subset analysis on the GWAS data set for 135 patients who were given gemcitabine+placebo. Statistical and systems biology analyses were performed on each individual data set to identify biomarkers significantly associated with gemcitabine response.

Results

Genetic variants in the ABC transporters (ABCC1, ABCC4) and the CYP4 family members CYP4F8 and CYP4F12, CHST3, and PPARD were found to be significant in both the NCI-60 and GWAS data sets. We report significant association between drug response and variants within members of the chondroitin sulfotransferase family (CHST) whose role in gemcitabine response is yet to be delineated.

Conclusion

Biomarkers identified in this integrative analysis may contribute insights into gemcitabine response variability. As genotype data become more readily available, similar studies can be conducted to gain insights into drug response mechanisms and to facilitate clinical trial design and regulatory reviews.

Modeling genome-wide dynamic regulatory network in mouse lungs with influenza infection using high-dimensional ordinary differential equations

The immune response to viral infection is regulated by an intricate network of many genes and their products. The reverse engineering of gene regulatory networks (GRNs) using mathematical models from time course gene expression data collected after influenza infection is key to our understanding of the mechanisms involved in controlling influenza infection within a host. A five-step pipeline: detection of temporally differentially expressed genes, clustering genes into co-expressed modules, identification of network structure, parameter estimate refinement, and functional enrichment analysis, is developed for reconstructing high-dimensional dynamic GRNs from genome-wide time course gene expression data. Applying the pipeline to the time course gene expression data from influenza-infected mouse lungs, we have identified 20 distinct temporal expression patterns in the differentially expressed genes and constructed a module-based dynamic network using a linear ODE model. Both intra-module and inter-module annotations and regulatory relationships of our inferred network show some interesting findings and are highly consistent with existing knowledge about the immune response in mice after influenza infection. The proposed method is a computationally efficient, data-driven pipeline bridging experimental data, mathematical modeling, and statistical analysis. The application to the influenza infection data elucidates the potentials of our pipeline in providing valuable insights into systematic modeling of complicated biological processes.

A module of human peripheral blood mononuclear cell transcriptional network containing primitive and differentiation markers is related to specific cardiovascular health variables

Peripheral blood mononuclear cells (PBMCs), including rare circulating stem and progenitor cells (CSPCs), have important yet poorly understood roles in the maintenance and repair of blood vessels and perfused organs. Our hypothesis was that the identities and functions of CSPCs in cardiovascular health could be ascertained by analyzing the patterns of their co-expressed markers in unselected PBMC samples. Because gene microarrays had failed to detect many stem cell-associated genes, we performed quantitative real-time PCR to measure the expression of 45 primitive and tissue differentiation markers in PBMCs from healthy and hypertensive human subjects. We compared these expression levels to the subjects' demographic and cardiovascular risk factors, including vascular stiffness. The tested marker genes were expressed in all of samples and organized in hierarchical transcriptional network modules, constructed by a bottom-up approach. An index of gene expression in one of these modules (metagene), defined as the average standardized relative copy numbers of 15 pluripotency and cardiovascular differentiation markers, was negatively correlated (all p<0.03) with age (R² = −0.23), vascular stiffness (R² = −0.24), and central aortic pressure (R² = −0.19) and positively correlated with body mass index (R² = 0.72, in women). The co-expression of three neovascular markers was validated at the single-cell level using mRNA in situ hybridization and immunocytochemistry. The overall gene expression in this cardiovascular module was reduced by 72±22% in the patients compared with controls. However, the compactness of both modules was increased in the patients' samples, which was reflected in reduced dispersion of their nodes' degrees of connectivity, suggesting a more primitive character of the patients' CSPCs. In conclusion, our results show that the relationship between CSPCs and vascular function is encoded in modules of the PBMCs transcriptional network. Furthermore, the coordinated gene expression in these modules can be linked to cardiovascular risk factors and subclinical cardiovascular disease; thus, this measure may be useful for their diagnosis and prognosis.

Genetic control of weight loss during pneumonic Burkholderia pseudomallei infection

Burkholderia pseudomallei (Bp) is the causal agent of a high-morbidity/mortality disease syndrome known as melioidosis. This syndrome can range from acute fulminate disease to chronic, local, and disseminated infections that are often difficult to treat because Bp exhibits resistance to many antibiotics. Bp is a prime candidate for use in biologic warfare/terrorism and is classified as a Tier-1 select agent by HHS and APHIS. It is known that inbred mouse strains display a range of susceptibility to Bp and that the murine infection model is ideal for studying acute melioidosis. Here, we exploit a powerful mouse genetics resource that consists of a large family of BXD-type recombinant inbred strains, to perform genome-wide linkage analysis of the weight loss phenotype following pneumonic infection with Bp. We infected parental mice and 32 BXD strains with 50-100 CFU of Bp (strain 1026b) and monitored weight retention each day over an eleven-day time course. Using the computational tools in GeneNetwork, we performed genome-wide linkage analysis to identify an interval on chromosome 12 that appears to control the weight retention trait. We then analyzed and ranked positional candidate genes in this interval, several of which have intriguing connections with innate immunity, calcium homeostasis, lipid transport, host cell growth and development, and autophagy.

OLYMPUS: an automated hybrid clustering method in time series gene expression. Case study: host response after Influenza A (H1N1) infection

The increasing flow of short time series microarray experiments for the study of dynamic cellular processes poses the need for efficient clustering tools. These tools must deal with three primary issues: first, to consider the multi-functionality of genes; second, to evaluate the similarity of the relative change of amplitude in the time domain rather than the absolute values; third, to cope with the constraints of conventional clustering algorithms such as the assignment of the appropriate cluster number. To address these, we propose OLYMPUS, a novel unsupervised clustering algorithm that integrates Differential Evolution (DE) method into Fuzzy Short Time Series (FSTS) algorithm with the scope to utilize efficiently the information of population of the first and enhance the performance of the latter. Our hybrid approach provides sets of genes that enable the deciphering of distinct phases in dynamic cellular processes. We proved the efficiency of OLYMPUS on synthetic as well as on experimental data. The discriminative power of OLYMPUS provided clusters, which refined the so far perspective of the dynamics of host response mechanisms to Influenza A (H1N1). Our kinetic model sets a timeline for several pathways and cell populations, implicated to participate in host response; yet no timeline was assigned to them (e.g. cell cycle, homeostasis). Regarding the activity of B cells, our approach revealed that some antibody-related mechanisms remain activated until day 60 post infection. The Matlab codes for implementing OLYMPUS, as well as example datasets, are freely accessible via the Web (http://biosignal.med.upatras.gr/wordpress/biosignal/).

Analysis of the lung transcriptome in Mycobacterium tuberculosis-infected mice reveals major differences in immune response pathways between TB-susceptible and resistant hosts

Using whole genome microarrays, we compared changes in gene expression patterns in the lungs of TB-resistant A/Sn and TB-susceptible I/St mice at day 14 following infection with Mycobacterium tuberculosis H37Rv. Analyses of differentially expressed genes for representation of gene ontology terms and activation of regulatory pathways revealed interstrain differences in antigen presentation, NK, T and B cell activation pathways. In general, resistant A/Sn mice exhibited a more complex pattern and stronger activation of host defense pathways compared to the TB-susceptible I/St mouse strain. In addition, in I/St mice elevated activation of genes involved in neutrophil response was observed and confirmed by quantitative RT-PCR and histopathology. Furthermore, a specific post infection upregulation of cysteine protease inhibitors was found in susceptible I/St mice.

Distinct gene loci control the host response to influenza H1N1 virus infection in a time-dependent manner

Background

There is strong but mostly circumstantial evidence that genetic factors modulate the severity of influenza infection in humans. Using genetically diverse but fully inbred strains of mice it has been shown that host sequence variants have a strong influence on the severity of influenza A disease progression. In particular, C57BL/6J, the most widely used mouse strain in biomedical research, is comparatively resistant. In contrast, DBA/2J is highly susceptible.

Results

To map regions of the genome responsible for differences in influenza susceptibility, we infected a family of 53 BXD-type lines derived from a cross between C57BL/6J and DBA/2J strains with influenza A virus (PR8, H1N1). We monitored body weight, survival, and mean time to death for 13 days after infection. Qivr5 (quantitative trait for influenza virus resistance on chromosome 5) was the largest and most significant QTL for weight loss. The effect of Qivr5 was detectable on day 2 post infection, but was most pronounced on days 5 and 6. Survival rate mapped to Qivr5, but additionally revealed a second significant locus on chromosome 19 (Qivr19). Analysis of mean time to death affirmed both Qivr5 and Qivr19. In addition, we observed several regions of the genome with suggestive linkage. There are potentially complex combinatorial interactions of the parental alleles among loci. Analysis of multiple gene expression data sets and sequence variants in these strains highlights about 30 strong candidate genes across all loci that may control influenza A susceptibility and resistance.

Conclusions

We have mapped influenza susceptibility loci to chromosomes 2, 5, 16, 17, and 19. Body weight and survival loci have a time-dependent profile that presumably reflects the temporal dynamic of the response to infection. We highlight candidate genes in the respective intervals and review their possible biological function during infection.

Global transcriptome analysis in influenza-infected mouse lungs reveals the kinetics of innate and adaptive host immune responses

An infection represents a highly dynamic process involving complex biological responses of the host at many levels. To describe such processes at a global level, we recorded gene expression changes in mouse lungs after a non-lethal infection with influenza A virus over a period of 60 days. Global analysis of the large data set identified distinct phases of the host response. The increase in interferon genes and up-regulation of a defined NK-specific gene set revealed the initiation of the early innate immune response phase. Subsequently, infiltration and activation of T and B cells could be observed by an augmentation of T and B cell specific signature gene expression. The changes in B cell gene expression and preceding chemokine subsets were associated with the formation of bronchus-associated lymphoid tissue. In addition, we compared the gene expression profiles from wild type mice with Rag2 mutant mice. This analysis readily demonstrated that the deficiency in the T and B cell responses in Rag2 mutants could be detected by changes in the global gene expression patterns of the whole lung. In conclusion, our comprehensive gene expression study describes for the first time the entire host response and its kinetics to an acute influenza A infection at the transcriptome level.

Deciphering the complexities of human diseases and disorders by coupling induced-pluripotent stem cells and systems genetics

The recent discovery that adult mouse and human somatic cells can be 'reprogrammed' to a state of pluripotency by ectopic expression of only a few transcription factors has already made a major impact on the biomedical community. For the first time, it is possible to study diseases on an individual patient basis, which may eventually lead to the realization of personalized medicine. The utility of induced-pluripotent stem cells (iPSCs) for modeling human diseases has greatly benefitted from established human embryonic stem cell (ESC) differentiation and tissue engineering protocols developed to generate many different cell and tissue types. The limited access to preimplantation genetic tested embryos and the difficulty in gene targeting human ESCs have restricted the use of human ESCs in modeling human disease. Afforded by iPSC technology is the ability to study disease pathogenesis as it unfolds during tissue morphogenesis. The complexities of molecular signaling and interplay with protein transduction during disease progression necessitate a systems approach to studying human diseases, whereby data can be statistically integrated by sorting out the signal to noise issues that arise from global biological changes associated with disease versus experimental noise. Using a systems approach, biomarkers can be identified that define the initiation or progression of disease and likewise can serve as putative therapeutic targets.

NCoR1 is a conserved physiological modulator of muscle mass and oxidative function

Transcriptional coregulators control the activity of many transcription factors and are thought to have wide-ranging effects on gene expression patterns. We show here that muscle-specific loss of nuclear receptor corepressor 1 (NCoR1) in mice leads to enhanced exercise endurance due to an increase of both muscle mass and of mitochondrial number and activity. The activation of selected transcription factors that control muscle function, such as MEF2, PPARβ/δ, and ERRs, underpins these phenotypic alterations. NCoR1 levels are decreased in conditions that require fat oxidation, resetting transcriptional programs to boost oxidative metabolism. Knockdown of gei-8, the sole C. elegans NCoR homolog, also robustly increased muscle mitochondria and respiration, suggesting conservation of NCoR1 function. Collectively, our data suggest that NCoR1 plays an adaptive role in muscle physiology and that interference with NCoR1 action could be used to improve muscle function.

Expression QTL mapping in regulatory and helper T cells from the BXD family of strains reveals novel cell-specific genes, gene-gene interactions and candidate genes for auto-immune disease

BACKGROUND

Regulatory T cells (Tregs) play an essential role in the control of the immune response. Treg cells represent important targets for therapeutic interventions of the immune system. Therefore, it will be very important to understand in more detail which genes are specifically activated in Treg cells versus T helper (Th) cells, and which gene regulatory circuits may be involved in specifying and maintaining Treg cell homeostasis.

RESULTS

We isolated Treg and Th cells from a genetically diverse family of 31 BXD type recombinant inbred strains and the fully inbred parental strains of this family--C57BL/6J and DBA/2J. Subsequently genome-wide gene expression studies were performed from the isolated Treg and Th cells. A comparative analysis of the transcriptomes of these cell populations allowed us to identify many novel differentially expressed genes. Analysis of cis- and trans-expression Quantitative Trait Loci (eQTLs) highlighted common and unique regulatory mechanisms that are active in the two cell types. Trans-eQTL regions were found for the Treg functional genes Nrp1, Stat3 and Ikzf4. Analyses of the respective QTL intervals suggested several candidate genes that may be involved in regulating these genes in Treg cells. Similarly, possible candidate genes were found which may regulate the expression of F2rl1, Ctla4, Klrb1f. In addition, we identified a focused group of candidate genes that may be important for the maintenance of self-tolerance and the prevention of allergy.

CONCLUSIONS

Variation of expression across the strains allowed us to find many novel gene-interaction networks in both T cell subsets. In addition, these two data sets enabled us to identify many differentially expressed genes and to nominate candidate genes that may have important functions for the maintenance of self-tolerance and the prevention of allergy.