Motif enrichment tool

SP140 regulates the expression of immune-related genes associated with multiple sclerosis and other autoimmune diseases by NF-κB inhibition

SP140 locus has been associated with multiple sclerosis (MS) as well as other autoimmune diseases by genome-wide association studies (GWAS). The causal variant of these associations (rs28445040-T) alters the splicing of the SP140 gene transcripts reducing the protein expression. We aimed to understand why the reduction of SP140 expression produced by the risk variant can increase the susceptibility to MS. To this end, we determined by RNA sequencing (RNA-seq) analysis the differentially expressed genes after SP140 silencing in lymphoblastoid cell lines (LCLs). We analyzed these genes by gene ontology (GO), comparative transcriptome profiles, enrichment of transcription factors (TFs) in the promoters of these genes and colocalization with GWAS risk variants. We also monitored the activity of the nuclear factor kappa-light-chain-enhancer of activated B cells (NF-κB) in SP140-silenced cells by luciferase reporter system. We identified 100 genes that were up-regulated and 22 genes down-regulated in SP140-silenced LCLs. GO analysis revealed that genes affected by SP140 were involved in regulation of cytokine production, inflammatory response and cell-cell adhesion. We observed enrichment of NF-κB TF in the promoter of up-regulated genes and NF-κB-increased activity in SP140-silenced cell lines. We showed enrichment of genes regulated by SP140 in GWAS-detected risk loci for MS (14.63 folds), Crohn's disease (4.82 folds) and inflammatory bowel disease (4.47 folds), not observed in other unrelated immune diseases. Our findings showed that SP140 is an important repressor of genes implicated in inflammation, suggesting that decreased expression of SP140, promoted by the rs28445040-T risk variant, may lead to up-regulation of these genes by means of NF-κB inhibition in B cells.

Epigenetic regulation of transcriptional plasticity associated with developmental song learning

Ethologists discovered over 100 years ago that some lifelong behavioural patterns were acquired exclusively during restricted developmental phases called critical periods (CPs). Developmental song learning in zebra finches is one of the most striking examples of a CP for complex learned behaviour. After post-hatch day 65, whether or not a juvenile male can memorize the song of a 'tutor' depends on his experiences in the month prior. If he experienced a tutor, he can no longer learn, but if he has been isolated from hearing a tutor the learning period is extended. We aimed to identify how tutor experience alters the brain and controls the ability to learn. Epigenetic landscapes are modulated by experience and are able to regulate the transcription of sets of genes, thereby affecting cellular function. Thus, we hypothesized that tutor experiences determine the epigenetic landscape in the auditory forebrain, a region required for tutor song memorization. Using ChIPseq, RNAseq and molecular biology, we provide evidence that naturalistic experiences associated with the ability to learn can induce epigenetic changes, and propose transcriptional plasticity as a mediator of CP learning potential.

SeqEnrich: A tool to predict transcription factor networks from co-expressed Arabidopsis and Brassica napus gene sets

Transcription factors and their associated DNA binding sites are key regulatory elements of cellular differentiation, development, and environmental response. New tools that predict transcriptional regulation of biological processes are valuable to researchers studying both model and emerging-model plant systems. SeqEnrich predicts transcription factor networks from co-expressed Arabidopsis or Brassica napus gene sets. The networks produced by SeqEnrich are supported by existing literature and predicted transcription factor–DNA interactions that can be functionally validated at the laboratory bench. The program functions with gene sets of varying sizes and derived from diverse tissues and environmental treatments. SeqEnrich presents as a powerful predictive framework for the analysis of Arabidopsis and Brassica napus co-expression data, and is designed so that researchers at all levels can easily access and interpret predicted transcriptional circuits. The program outperformed its ancestral program ChipEnrich, and produced detailed transcription factor networks from Arabidopsis and Brassica napus gene expression data. The SeqEnrich program is ideal for generating new hypotheses and distilling biological information from large-scale expression data.

Contribution of Sequence Motif, Chromatin State, and DNA Structure Features to Predictive Models of Transcription Factor Binding in Yeast

Transcription factor (TF) binding is determined by the presence of specific sequence motifs (SM) and chromatin accessibility, where the latter is influenced by both chromatin state (CS) and DNA structure (DS) properties. Although SM, CS, and DS have been used to predict TF binding sites, a predictive model that jointly considers CS and DS has not been developed to predict either TF-specific binding or general binding properties of TFs. Using budding yeast as model, we found that machine learning classifiers trained with either CS or DS features alone perform better in predicting TF-specific binding compared to SM-based classifiers. In addition, simultaneously considering CS and DS further improves the accuracy of the TF binding predictions, indicating the highly complementary nature of these two properties. The contributions of SM, CS, and DS features to binding site predictions differ greatly between TFs, allowing TF-specific predictions and potentially reflecting different TF binding mechanisms. In addition, a "TF-agnostic" predictive model based on three DNA “intrinsic properties” (in silico predicted nucleosome occupancy, major groove geometry, and dinucleotide free energy) that can be calculated from genomic sequences alone has performance that rivals the model incorporating experiment-derived data. This intrinsic property model allows prediction of binding regions not only across TFs, but also across DNA-binding domain families with distinct structural folds. Furthermore, these predicted binding regions can help identify TF binding sites that have a significant impact on target gene expression. Because the intrinsic property model allows prediction of binding regions across DNA-binding domain families, it is TF agnostic and likely describes general binding potential of TFs. Thus, our findings suggest that it is feasible to establish a TF agnostic model for identifying functional regulatory regions in potentially any sequenced genome.

Identification of transcription factor binding sites based on sequence motifs is typically accompanied by a high false positive rate. Increasing evidence suggests that there are many other factors besides DNA sequence that may affect the binding and interaction of TFs with DNA. Through the integration of sequence motif, chromatin state, and DNA structure properties, we show that TF binding can be better predicted. Moreover, considering chromatin state and DNA structure properties simultaneously yields a significant improvement. While the binding of some TFs can be readily predicted using either chromatin state information or DNA structure, other TFs need both. Thus, our findings provide insights on how different histone modifications and DNA structure properties may influence the binding of a particular TF and thus how TFs regulate gene expression. These features are referred to as sequence “intrinsic properties” because they can be predicted from sequences alone. These intrinsic properties can be used to build a TF binding prediction model that has a similar performance to considering all features. Moreover, the intrinsic property model allows TFBS predictions not only across TFs, but also across DNA-binding domain families that are present in most eukaryotes, suggesting that the model likely can be used across species.

Genomic evidence for a globally distributed, bimodal population in the ovine footrot pathogen Dichelobacter nodosus

Footrot is a contagious, debilitating disease of sheep, causing major economic losses in most sheep-producing countries. The causative agent is the Gram-negative anaerobe Dichelobacter nodosus. Depending on the virulence of the infective bacterial strain, clinical signs vary from a mild interdigital dermatitis (benign footrot) to severe underrunning of the horn of the hoof (virulent footrot). The aim of this study was to investigate the genetic relationship between D. nodosus strains of different phenotypic virulences and between isolates from different geographic regions. Genome sequencing was performed on 103 D. nodosus isolates from eight different countries. Comparison of these genome sequences revealed that they were highly conserved, with >95% sequence identity. However, single nucleotide polymorphism analysis of the 31,627 nucleotides that were found to differ in one or more of the 103 sequenced isolates divided them into two distinct clades. Remarkably, this division correlated with known virulent and benign phenotypes, as well as with the single amino acid difference between the AprV2 and AprB2 proteases, which are produced by virulent and benign strains, respectively. This division was irrespective of the geographic origin of the isolates. However, within one of these clades, isolates from different geographic regions generally belonged to separate clusters. In summary, we have shown that D. nodosus has a bimodal population structure that is globally conserved and provide evidence that virulent and benign isolates represent two distinct forms of D. nodosus strains. These data have the potential to improve the diagnosis and targeted control of this economically significant disease.

The Gram-negative anaerobic bacterium Dichelobacter nodosus is the causative agent of ovine footrot, a disease of major importance to the worldwide sheep industry. The known D. nodosus virulence factors are its type IV fimbriae and extracellular serine proteases. D. nodosus strains are designated virulent or benign based on the type of disease caused under optimal climatic conditions. These isolates have similar fimbriae but distinct extracellular proteases. To determine the relationship between virulent and benign isolates and the relationship of isolates from different geographical regions, a genomic study that involved the sequencing and subsequent analysis of 103 D. nodosus isolates was undertaken. The results showed that D. nodosus isolates are highly conserved at the genomic level but that they can be divided into two distinct clades that correlate with their disease phenotypes and with a single amino acid substitution in one of the extracellular proteases.