Transcriptional responses to fatty acid are coordinated by combinatorial control

In transcriptional regulatory networks, the coincident binding of a combination of factors to regulate a gene implies the existence of complex mechanisms to control both the gene expression profile and specificity of the response. Unraveling this complexity is a major challenge to biologists. Here, a novel network topology-based clustering approach was applied to condition-specific genome-wide chromatin localization and expression data to characterize a dynamic transcriptional regulatory network responsive to the fatty acid oleate. A network of four (predicted) regulators of the response (Oaf1p, Pip2p, Adr1p and Oaf3p) was investigated. By analyzing trends in the network structure, we found that two groups of multi-input motifs form in response to oleate, each controlling distinct functional classes of genes. This functionality is contributed in part by Oaf1p, which is a component of both types of multi-input motifs and has two different regulatory activities depending on its binding context. The dynamic cooperation between Oaf1p and Pip2p appears to temporally synchronize the two different responses. Together, these data suggest a network mechanism involving dynamic combinatorial control for coordinating transcriptional responses

Determining gene expression on a single pair of microarrays

<p>Abstract</p> <p>Background</p> <p>In microarray experiments the numbers of replicates are often limited due to factors such as cost, availability of sample or poor hybridization. There are currently few choices for the analysis of a pair of microarrays where N = 1 in each condition. In this paper, we demonstrate the effectiveness of a new algorithm called PINC (PINC is Not Cyber-T) that can analyze Affymetrix microarray experiments.</p> <p>Results</p> <p>PINC treats each pair of probes within a probeset as an independent measure of gene expression using the Bayesian framework of the Cyber-T algorithm and then assigns a corrected p-value for each gene comparison.</p> <p>The p-values generated by PINC accurately control False Discovery rate on Affymetrix control data sets, but are small enough that family-wise error rates (such as the Holm's step down method) can be used as a conservative alternative to false discovery rate with little loss of sensitivity on control data sets.</p> <p>Conclusion</p> <p>PINC outperforms previously published methods for determining differentially expressed genes when comparing Affymetrix microarrays with N = 1 in each condition. When applied to biological samples, PINC can be used to assess the degree of variability observed among biological replicates in addition to analyzing isolated pairs of microarrays.</p

Predicting functional associations from metabolism using bi-partite network algorithms

<p>Abstract</p> <p>Background</p> <p>Metabolic reconstructions contain detailed information about metabolic enzymes and their reactants and products. These networks can be used to infer functional associations between metabolic enzymes. Many methods are based on the number of metabolites shared by two enzymes, or the shortest path between two enzymes. Metabolite sharing can miss associations between non-consecutive enzymes in a serial pathway, and shortest-path algorithms are sensitive to high-degree metabolites such as water and ATP that create connections between enzymes with little functional similarity.</p> <p>Results</p> <p>We present new, fast methods to infer functional associations in metabolic networks. A local method, the degree-corrected Poisson score, is based only on the metabolites shared by two enzymes, but uses the known metabolite degree distribution. A global method, based on graph diffusion kernels, predicts associations between enzymes that do not share metabolites. Both methods are robust to high-degree metabolites. They out-perform previous methods in predicting shared Gene Ontology (GO) annotations and in predicting experimentally observed synthetic lethal genetic interactions. Including cellular compartment information improves GO annotation predictions but degrades synthetic lethal interaction prediction. These new methods perform nearly as well as computationally demanding methods based on flux balance analysis.</p> <p>Conclusions</p> <p>We present fast, accurate methods to predict functional associations from metabolic networks. Biological significance is demonstrated by identifying enzymes whose strong metabolic correlations are missed by conventional annotations in GO, most often enzymes involved in transport vs. synthesis of the same metabolite or other enzyme pairs that share a metabolite but are separated by conventional pathway boundaries. More generally, the methods described here may be valuable for analyzing other types of networks with long-tailed degree distributions and high-degree hubs.</p

Establishing the baseline level of repetitive element expression in the human cortex

Background: Although nearly half of the human genome is comprised of repetitive sequences, the expression profile of these elements remains largely uncharacterized. Recently developed high throughput sequencing technologies provide us with a powerful new set of tools to study repeat elements. Hence, we performed whole transcriptome sequencing to investigate the expression of repetitive elements in human frontal cortex using postmortem tissue obtained from the Stanley Medical Research Institute. Results: We found a significant amount of reads from the human frontal cortex originate from repeat elements. We also noticed that Alu elements were expressed at levels higher than expected by random or background transcription. In contrast, L1 elements were expressed at lower than expected amounts. Conclusions: Repetitive elements are expressed abundantly in the human brain. This expression pattern appears to be element specific and can not be explained by random or background transcription. These results demonstrate that our knowledge about repetitive elements is far from complete. Further characterization is required to determine the mechanism, the control, and the effects of repeat element expression

Complex trait subtypes identification using transcriptome profiling reveals an interaction between two QTL affecting adiposity in chicken

<p>Abstract</p> <p>Background</p> <p>Integrative genomics approaches that combine genotyping and transcriptome profiling in segregating populations have been developed to dissect complex traits. The most common approach is to identify genes whose eQTL colocalize with QTL of interest, providing new functional hypothesis about the causative mutation. Another approach includes defining subtypes for a complex trait using transcriptome profiles and then performing QTL mapping using some of these subtypes. This approach can refine some QTL and reveal new ones.</p> <p>In this paper we introduce Factor Analysis for Multiple Testing (FAMT) to define subtypes more accurately and reveal interaction between QTL affecting the same trait. The data used concern hepatic transcriptome profiles for 45 half sib male chicken of a sire known to be heterozygous for a QTL affecting abdominal fatness (AF) on chromosome 5 distal region around 168 cM.</p> <p>Results</p> <p>Using this methodology which accounts for hidden dependence structure among phenotypes, we identified 688 genes that are significantly correlated to the AF trait and we distinguished 5 subtypes for AF trait, which are not observed with gene lists obtained by classical approaches. After exclusion of one of the two lean bird subtypes, linkage analysis revealed a previously undetected QTL on chromosome 5 around 100 cM. Interestingly, the animals of this subtype presented the same q paternal haplotype at the 168 cM QTL. This result strongly suggests that the two QTL are in interaction. In other words, the "q configuration" at the 168 cM QTL could hide the QTL existence in the proximal region at 100 cM. We further show that the proximal QTL interacts with the previous one detected on the chromosome 5 distal region.</p> <p>Conclusion</p> <p>Our results demonstrate that stratifying genetic population by molecular phenotypes followed by QTL analysis on various subtypes can lead to identification of novel and interacting QTL.</p

Thyroid Hormone May Regulate mRNA Abundance in Liver by Acting on MicroRNAs

MicroRNAs (miRNAs) are extensively involved in diverse biological processes. However, very little is known about the role of miRNAs in mediating the action of thyroid hormones (TH). Appropriate TH levels are known to be critically important for development, differentiation and maintenance of metabolic balance in mammals. We induced transient hypothyroidism in juvenile mice by short-term exposure to methimazole and perchlorate from post natal day (PND) 12 to 15. The expression of miRNAs in the liver was analyzed using Taqman Low Density Arrays (containing up to 600 rodent miRNAs). We found the expression of 40 miRNAs was significantly altered in the livers of hypothyroid mice compared to euthyroid controls. Among the miRNAs, miRs-1, 206, 133a and 133b exhibited a massive increase in expression (50- to 500-fold). The regulation of TH on the expression of miRs-1, 206, 133a and 133b was confirmed in various mouse models including: chronic hypothyroid, short-term hyperthyroid and short-term hypothyroid followed by TH supplementation. TH regulation of these miRNAs was also confirmed in mouse hepatocyte AML 12 cells. The expression of precursors of miRs-1, 206, 133a and 133b were examined in AML 12 cells and shown to decrease after TH treatment, only pre-mir-206 and pre-mir-133b reached statistical significance. To identify the targets of these miRNAs, DNA microarrays were used to examine hepatic mRNA levels in the short-term hypothyroid mouse model relative to controls. We found transcripts from 92 known genes were significantly altered in these hypothyroid mice. Web-based target predication software (TargetScan and Microcosm) identified 14 of these transcripts as targets of miRs-1, 206, 133a and 133b. The vast majority of these mRNA targets were significantly down-regulated in hypothyroid mice, corresponding with the up-regulation of miRs-1, 206, 133a and 133b in hypothyroid mouse liver. To further investigate target genes, miR-206 was over-expressed in AML 12 cells. TH treatment of cells over-expressing miR-206 resulted in decreased miR-206 expression, and a significant increase in two predicted target genes, Mup1 and Gpd2. The results suggest that TH regulation of these genes may occur secondarily via miR-206. These studies provide new insight into the role of miRNAs in mediating TH regulation of gene expression

Discovery of T Cell Antigens by High-Throughput Screening of Synthetic Minigene Libraries

The identification of novel T cell antigens is central to basic and translational research in autoimmunity, tumor immunology, transplant immunology, and vaccine design for infectious disease. However, current methods for T cell antigen discovery are low throughput, and fail to explore a wide range of potential antigen-receptor interactions. To overcome these limitations, we developed a method in which programmable microarrays are used to cost-effectively synthesize complex libraries of thousands of minigenes that collectively encode the content of hundreds of candidate protein targets. Minigene-derived mRNA are transfected into autologous antigen presenting cells and used to challenge complex populations of purified peripheral blood CD8+ T cells in multiplex, parallel ELISPOT assays. In this proof-of-concept study, we apply synthetic minigene screening to identify two novel pancreatic islet autoantigens targeted in a patient with Type I Diabetes. To our knowledge, this is the first successful screen of a highly complex, synthetic minigene library for identification of a T cell antigen. In principle, responses against the full protein complement of any tissue or pathogen can be assayed by this approach, suggesting that further optimization of synthetic libraries holds promise for high throughput antigen discovery

TMPRSS2-ERG -specific transcriptional modulation is associated with prostate cancer biomarkers and TGF-β signaling

<p>Abstract</p> <p>Background</p> <p><it>TMPRSS2-ERG </it>gene fusions occur in about 50% of all prostate cancer cases and represent promising markers for molecular subtyping. Although <it>TMPRSS2-ERG </it>fusion seems to be a critical event in prostate cancer, the precise functional role in cancer development and progression is still unclear.</p> <p>Methods</p> <p>We studied large-scale gene expression profiles in 47 prostate tumor tissue samples and in 48 normal prostate tissue samples taken from the non-suspect area of clinical low-risk tumors using Affymetrix GeneChip Exon 1.0 ST microarrays.</p> <p>Results</p> <p>Comparison of gene expression levels among <it>TMPRSS2-ERG </it>fusion-positive and negative tumors as well as benign samples demonstrated a distinct transcriptional program induced by the gene fusion event. Well-known biomarkers for prostate cancer detection like <it>CRISP3 </it>were found to be associated with the gene fusion status. WNT and TGF-β/BMP signaling pathways were significantly associated with genes upregulated in <it>TMPRSS2-ERG </it>fusion-positive tumors.</p> <p>Conclusions</p> <p>The <it>TMPRSS2-ERG </it>gene fusion results in the modulation of transcriptional patterns and cellular pathways with potential consequences for prostate cancer progression. Well-known biomarkers for prostate cancer detection were found to be associated with the gene fusion. Our results suggest that the fusion status should be considered in retrospective and future studies to assess biomarkers for prostate cancer detection, progression and targeted therapy.</p

Temporal transcriptome changes induced by MDV in marek's disease-resistant and -susceptible inbred chickens

<p>Abstract</p> <p>Background</p> <p>Marek's disease (MD) is a lymphoproliferative disease in chickens caused by Marek's disease virus (MDV) and characterized by T cell lymphoma and infiltration of lymphoid cells into various organs such as liver, spleen, peripheral nerves and muscle. Resistance to MD and disease risk have long been thought to be influenced both by genetic and environmental factors, the combination of which contributes to the observed outcome in an individual. We hypothesize that after MDV infection, genes related to MD-resistance or -susceptibility may exhibit different trends in transcriptional activity in chicken lines having a varying degree of resistance to MD.</p> <p>Results</p> <p>In order to study the mechanisms of resistance and susceptibility to MD, we performed genome-wide temporal expression analysis in spleen tissues from MD-resistant line 6₃, susceptible line 7₂and recombinant congenic strain M (RCS-M) that has a phenotype intermediate between lines 6₃and 7₂after MDV infection. Three time points of the MDV life cycle in chicken were selected for study: 5 days post infection (dpi), 10dpi and 21dpi, representing the early cytolytic, latent and late cytolytic stages, respectively. We observed similar gene expression profiles at the three time points in line 6₃and RCS-M chickens that are both different from line 7₂. Pathway analysis using Ingenuity Pathway Analysis (IPA) showed that MDV can broadly influence the chickens irrespective of whether they are resistant or susceptible to MD. However, some pathways like cardiac arrhythmia and cardiovascular disease were found to be affected only in line 7₂; while some networks related to cell-mediated immune response and antigen presentation were enriched only in line 6₃and RCS-M. We identified 78 and 30 candidate genes associated with MD resistance, at 10 and 21dpi respectively, by considering genes having the same trend of expression change after MDV infection in lines 6₃and RCS-M. On the other hand, by considering genes with the same trend of expression change after MDV infection in lines 7₂and RCS-M, we identified 78 and 43 genes at 10 and 21dpi, respectively, which may be associated with MD-susceptibility.</p> <p>Conclusions</p> <p>By testing temporal transcriptome changes using three representative chicken lines with different resistance to MD, we identified 108 candidate genes for MD-resistance and 121 candidate genes for MD-susceptibility over the three time points. Genes included in our resistance or susceptibility genes lists that are also involved in more than 5 biofunctions, such as <it>CD8α</it>, <it>IL8</it>, <it>USP18</it>, and <it>CTLA4</it>, are considered to be important genes involved in MD-resistance or -susceptibility. We were also able to identify several biofunctions related with immune response that we believe play an important role in MD-resistance.</p

Acetaminophen Modulates the Transcriptional Response to Recombinant Interferon-β

BACKGROUND: Recombinant interferon treatment can result in several common side effects including fever and injection-site pain. Patients are often advised to use acetaminophen or other over-the-counter pain medications as needed. Little is known regarding the transcriptional changes induced by such co-administration. METHODOLOGY/PRINCIPAL FINDINGS: We tested whether the administration of acetaminophen causes a change in the response normally induced by interferon-beta treatment. CD-1 mice were administered acetaminophen (APAP), interferon-beta (IFN-beta) or a combination of IFN-beta+APAP and liver and serum samples were collected for analysis. Differential gene expression was determined using an Agilent 22 k whole mouse genome microarray. Data were analyzed by several methods including Gene Ontology term clustering and Gene Set Enrichment Analysis. We observed a significant change in the transcription profile of hepatic cells when APAP was co-administered with IFN-beta. These transcriptional changes included a marked up-regulation of genes involved in signal transduction and cell differentiation and down-regulation of genes involved in cellular metabolism, trafficking and the IkappaBK/NF-kappaB cascade. Additionally, we observed a large decrease in the expression of several IFN-induced genes including Ifit-3, Isg-15, Oasl1, Zbp1 and predicted gene EG634650 at both early and late time points. CONCLUSIONS/SIGNIFICANCE: A significant change in the transcriptional response was observed following co-administration of IFN-beta+APAP relative to IFN-beta treatment alone. These results suggest that administration of acetaminophen has the potential to modify the efficacy of IFN-beta treatment