Markov Chain-based Promoter Structure Modeling for Tissue-specific Expression Pattern Prediction

Transcriptional regulation is the first level of regulation of gene expression and is therefore a major topic in computational biology. Genes with similar expression patterns can be assumed to be co-regulated at the transcriptional level by promoter sequences with a similar structure. Current approaches for modeling shared regulatory features tend to focus mainly on clustering of cis-regulatory sites. Here we introduce a Markov chain-based promoter structure model that uses both shared motifs and shared features from an input set of promoter sequences to predict candidate genes with similar expression. The model uses positional preference, order, and orientation of motifs. The trained model is used to score a genomic set of promoter sequences: high-scoring promoters are assumed to have a structure similar to the input sequences and are thus expected to drive similar expression patterns. We applied our model on two datasets in Caenorhabditis elegans and in Ciona intestinalis. Both computational and experimental verifications indicate that this model is capable of predicting candidate promoters driving similar expression patterns as the input-regulatory sequences. This model can be useful for finding promising candidate genes for wet-lab experiments and for increasing our understanding of transcriptional regulation

MyoMiner: explore gene co-expression in normal and pathological muscle

International audienceBackground: High-throughput transcriptomics measures mRNA levels for thousands of genes in a biological sample. Most gene expression studies aim to identify genes that are differentially expressed between different biological conditions, such as between healthy and diseased states. However, these data can also be used to identify genes that are co-expressed within a biological condition. Gene co-expression is used in a guilt-by-association approach to prioritize candidate genes that could be involved in disease, and to gain insights into the functions of genes, protein relations, and signaling pathways. Most existing gene co-expression databases are generic, amalgamating data for a given organism regardless of tissue-type.Methods: To study muscle-specific gene co-expression in both normal and pathological states, publicly available gene expression data were acquired for 2376 mouse and 2228 human striated muscle samples, and separated into 142 categories based on species (human or mouse), tissue origin, age, gender, anatomic part, and experimental condition. Co-expression values were calculated for each category to create the MyoMiner database.Results: Within each category, users can select a gene of interest, and the MyoMiner web interface will return all correlated genes. For each co-expressed gene pair, adjusted p-value and confidence intervals are provided as measures of expression correlation strength. A standardized expression-level scatterplot is available for every gene pair r-value. MyoMiner has two extra functions: (a) a network interface for creating a 2-shell correlation network, based either on the most highly correlated genes or from a list of genes provided by the user with the option to include linked genes from the database and (b) a comparison tool from which the users can test whether any two correlation coefficients from different conditions are significantly different.Conclusions: These co-expression analyses will help investigators to delineate the tissue-, cell-, and pathology-specific elements of muscle protein interactions, cell signaling and gene regulation. Changes in co-expression between pathologic and healthy tissue may suggest new disease mechanisms and help define novel therapeutic targets. Thus, MyoMiner is a powerful muscle-specific database for the discovery of genes that are associated with related functions based on their co-expression. MyoMiner is freely available at https://www.sys-myo.com/myominer

Differential roles of epigenetic changes and Foxp3 expression in regulatory T cell-specific transcriptional regulation

Naturally occurring regulatory T (Treg) cells, which specifically express the transcription factor forkhead box P3 (Foxp3), are engaged in the maintenance of immunological self-tolerance and homeostasis. By transcriptional start site cluster analysis, we assessed here how genome-wide patterns of DNA methylation or Foxp3 binding sites were associated with Treg-specific gene expression. We found that Treg-specific DNA hypomethylated regions were closely associated with Treg up-regulated transcriptional start site clusters, whereas Foxp3 binding regions had no significant correlation with either up- or down-regulated clusters in nonactivated Treg cells. However, in activated Treg cells, Foxp3 binding regions showed a strong correlation with down-regulated clusters. In accordance with these findings, the above two features of activation-dependent gene regulation in Treg cells tend to occur at different locations in the genome. The results collectively indicate that Treg-specific DNA hypomethylation is instrumental in gene up-regulation in steady state Treg cells, whereas Foxp3 down-regulates the expression of its target genes in activated Treg cells. Thus, the two events seem to play distinct but complementary roles in Treg-specific gene expression

A qualitative host-pathogen interaction in the Theobroma cacao-Moniliophthora perniciosa pathosystem

The aim of this study was to test whether resistance of clones of Theobroma cacao ( cocoa) varied between isolates of Moniliophthora (formerly Crinipellis) perniciosa, the cause of witches' broom disease. Developing buds of vegetatively propagated T. cacao grown in greenhouses in the UK were inoculated with 16 000 spores of M. perniciosa per meristem in water, under conditions where water condensed on the inoculated shoot for at least 12 h after inoculation. The proportion of successful inoculations varied between clones and was inversely correlated with time to symptom production or broom formation. A specific interaction was demonstrated among three single-spore isolates of M. perniciosa and the clone Scavina 6 (SCA 6) and a variety of susceptible clones. Isolates Castenhal-I and APC3 were equally likely to infect SCA 6 and the other clones, but isolate Gran Couva A9 never infected SCA 6, although it was as virulent on the other clones. The interaction was maintained when the wetness period was extended to 70 h. Offspring of SCA 6 x Amelonado matings were all susceptible to both Castenhal-I and GC-A5, with no evidence of greater variability in susceptibility to GC-A5 than Castanhal-I. This suggests recessive inheritance of a single homozygous factor conferring resistance to GC-A5, from SCA 6. The progenies were slightly more susceptible to Castanhal-I than GC-A5. The implications for managing the disease are discussed

Akirin2 is critical for inducing inflammatory genes by bridging IκB-ζ and the SWI/SNF complex.

Transcription of inflammatory genes in innate immune cells is coordinately regulated by transcription factors, including NF-κB, and chromatin modifiers. However, it remains unclear how microbial sensing initiates chromatin remodeling. Here, we show that Akirin2, an evolutionarily conserved nuclear protein, bridges NF-κB and the chromatin remodeling SWI/SNF complex by interacting with BRG1-Associated Factor 60 (BAF60) proteins as well as IκB-ζ, which forms a complex with the NF-κB p50 subunit. These interactions are essential for Toll-like receptor-, RIG-I-, and Listeria-mediated expression of proinflammatory genes including Il6 and Il12b in macrophages. Consistently, effective clearance of Listeria infection required Akirin2. Furthermore, Akirin2 and IκB-ζ recruitment to the Il6 promoter depend upon the presence of IκB-ζ and Akirin2, respectively, for regulation of chromatin remodeling. BAF60 proteins were also essential for the induction of Il6 in response to LPS stimulation. Collectively, the IκB-ζ-Akirin2-BAF60 complex physically links the NF-κB and SWI/SNF complexes in innate immune cell activation. By recruiting SWI/SNF chromatin remodellers to IκB-ζ, transcriptional coactivator for NF-κB, the conserved nuclear protein Akirin2 stimulates pro-inflammatory gene promoters in mouse macrophages during innate immune responses to viral or bacterial infection

Mapping circulating serum miRNAs to their immune-related target mRNAs

Bakhtiyor Nosirov,1 Joël Billaud,1 Alexis Vandenbon,2 Diego Diez,3 Edward Wijaya,1 Ken J Ishii,4,5 Shunsuke Teraguchi,3 Daron M Standley1,6 1Systems Immunology Lab, 2Immuno-Genomics Research Unit, 3Quantitative Immunology Research Unit, 4Laboratory of Vaccine Science, WPI Immunology Frontier Research Center, Osaka University, Suita, 5Laboratory of Adjuvant Innovation, National Institute of Biomedical Innovation, Osaka, 6Lab of Integrated Biological Information, Institute for Virus Research Kyoto University, Kyoto, Japan Purpose: Evidence suggests that circulating serum microRNAs (miRNAs) might preferentially target immune-related mRNAs. If this were the case, we hypothesized that immune-related mRNAs would have more predicted serum miRNA binding sites than other mRNAs and, reciprocally, that serum miRNAs would have more immune-related mRNA targets than non-serum miRNAs.Materials and methods: We developed a consensus target predictor using the random forest framework and calculated the number of predicted miRNA–mRNA interactions in various subsets of miRNAs (serum, non-serum) and mRNAs (immune related, nonimmune related).Results: Immune-related mRNAs were predicted to be targeted by serum miRNA more than other mRNAs. Moreover, serum miRNAs were predicted to target many more immune-related mRNA targets than non-serum miRNAs; however, these two biases in immune-related mRNAs and serum miRNAs appear to be completely independent.Conclusion: Immune-related mRNAs have more miRNA binding sites in general, not just for serum miRNAs; likewise, serum miRNAs target many more mRNAs than non-serum miRNAs overall, regardless of whether they are immune related or not. Nevertheless, these two independent phenomena result in a significantly larger number of predicted serum miRNA–immune mRNA interactions than would be expected by chance. Keywords: biomarker, posttranscriptional regulation, random forest, target predictio