SUMMARY Protein-DNA interactions (PDIs) mediate a broad range of functions essential for cellular differentiation, function, and survival. However, it is still a daunting task to comprehensively identify and profile sequence-specific PDIs in complex genomes. Here, we have used a combined bioinformatics and protein microarray-based strategy to systematically characterize the human protein-DNA interactome. We identified 17,718 PDIs between 460 DNA motifs predicted to regulate transcription and 4,191 human proteins of various functional classes. Among them, we recovered many known PDIs for transcription factors (TFs). We also identified a large number of new PDIs for known TFs, as well as for previously uncharacterized TFs. Remarkably, we found that over three hundred proteins not previously annotated as TFs also showed sequence-specific PDIs, including RNA binding proteins, mitochondrial proteins, and protein kinases. One of such unconventional DNA-binding proteins, MAPK1, acts as a transcriptional repressor for interferon gamma-induced genes.

BackgroundUnderstanding how genes are expressed and regulated in different tissues is a fundamental and challenging question. However, most of currently available biological databases do not focus on tissue-specific gene regulation.ResultsThe recent development of computational methods for tissue-specific combinational gene regulation, based on transcription factor binding sites, enables us to perform a large-scale analysis of tissue-specific gene regulation in human tissues. The results are stored in a web database called TiGER (Tissue-specific Gene Expression and Regulation). The database contains three types of data including tissue-specific gene expression profiles, combinatorial gene regulations, and cis-regulatory module (CRM) detections. At present the database contains expression profiles for 19,526 UniGene genes, combinatorial regulations for 7,341 transcription factor pairs and 6,232 putative CRMs for 2,130 RefSeq genes.ConclusionWe have developed and made publicly available a database, TiGER, which summarizes and provides large scale data sets for tissue-specific gene expression and regulation in a variety of human tissues. This resource is available at [1].

Tissue-specific gene expression is generally regulated by more than a single transcription factor (TF). Multiple TFs work in concert to achieve tissue specificity. In order to explore these complex TF interaction networks, we performed a large-scale analysis of TF interactions for 30 human tissues. We first identified tissue-specific genes for 30 tissues based on gene expression databases. We then evaluated the relationships between TFs using the relative position and co-occurrence of their binding sites in the promoters of tissue-specific genes. The predicted TF–TF interactions were validated by both known protein–protein interactions and co-expression of their target genes. We found that our predictions are enriched in known protein–protein interactions (>80 times that of random expectation). In addition, we found that the target genes show the highest co-expression in the tissue of interest. Our findings demonstrate that non-tissue specific TFs play a large role in regulation of tissue-specific genes. Furthermore, they show that individual TFs can contribute to tissue specificity in different tissues by interacting with distinct TF partners. Lastly, we identified several tissue-specific TF clusters that may play important roles in tissue-specific gene regulation.

MicroRNAs (miRNAs) negatively regulate the expression of target genes at the post-transcriptional level. Little is known about the crosstalk between miRNAs and transcription factors (TFs). Here we provide data suggesting that the interaction patterns between TFs and miRNAs can influence the biological functions of miRNAs. From this global survey, we find that a regulated feedback loop, in which two TFs regulate each other and one miRNA regulates both of the factors, is the most significantly overrepresented network motif. Mathematical modeling shows that the miRNA in this motif stabilizes the feedback loop to resist environmental perturbation, providing one mechanism to explain the robustness of developmental programs that is contributed by miRNAs. Furthermore, on the basis of a network motif profile analysis, we demonstrate the existence of two classes of miRNAs with distinct network topological properties. The first class of miRNAs is regulated by a large number of TFs, whereas the second is regulated by only a few TFs. The differential expression level of the two classes of miRNAs in embryonic developmental stages versus adult tissues suggests that the two classes may have fundamentally different biological functions. Our results demonstrate that the TFs and miRNAs extensively interact with each other and the biological functions of miRNAs may be wired in the regulatory network topology.

Combinatorial regulation by transcription factor complexes is an important feature of eukaryotic gene regulation. Here, we propose a new method for identification of interactions between transcription factors (TFs) that relies on the relationship of their binding sites, and we test it using Saccharomyces cerevisiae as a model system. The algorithm predicts interacting TF pairs based on the co-occurrence of their binding motifs and the distance between the motifs in promoter sequences. This allows investigation of interactions between TFs without known binding motifs or expression data. With this approach, 300 significant interactions involving 77 TFs were identified. These included more than 70% of the known protein–protein interactions. Approximately half of the detected interacting motif pairs showed strong preferences for particular distances and orientations in the promoter sequences. These one dimensional features may reflect constraints on allowable spatial arrangements for protein–protein interactions. Evidence for biological relevance of the observed characteristic distances is provided by the finding that target genes with the same characteristic distances show significantly higher co-expression than those without preferred distances. Furthermore, the observed interactions were dynamic: most of the TF pairs were not constitutively active, but rather showed variable activity depending on the physiological condition of the cells. Interestingly, some TF pairs active in multiple conditions showed preferences for different distances and orientations depending on the condition. Our prediction and characterization of TF interactions may help to understand the transcriptional regulatory networks in eukaryotic systems.

Burkholderia mallei is an infectious intracellular pathogen whose virulence and resistance to antibiotics makes it a potential bioterrorism agent. Given its genetic origin as a commensal soil organism, it is equipped with an extensive and varied set of adapted mechanisms to cope with and modulate host-cell environments. One essential virulence mechanism constitutes the specialized secretion systems that are designed to penetrate host-cell membranes and insert pathogen proteins directly into the host cell's cytosol. However, the secretion systems' proteins and, in particular, their host targets are largely uncharacterized. Here, we used a combined in silico, in vitro, and in vivo approach to identify B. mallei proteins required for pathogenicity. We used bioinformatics tools, including orthology detection and ab initio predictions of secretion system proteins, as well as published experimental Burkholderia data to initially select a small number of proteins as putative virulence factors. We then used yeast two-hybrid assays against normalized whole human and whole murine proteome libraries to detect and identify interactions among each of these bacterial proteins and host proteins. Analysis of such interactions provided both verification of known virulence factors and identification of three new putative virulence proteins. We successfully created insertion mutants for each of these three proteins using the virulent B. mallei ATCC 23344 strain. We exposed BALB/c mice to mutant strains and the wild-type strain in an aerosol challenge model using lethal B. mallei doses. In each set of experiments, mice exposed to mutant strains survived for the 21-day duration of the experiment, whereas mice exposed to the wild-type strain rapidly died. Given their in vivo role in pathogenicity, and based on the yeast two-hybrid interaction data, these results point to the importance of these pathogen proteins in modulating host ubiquitination pathways, phagosomal escape, and actin-cytoskeleton rearrangement processes.

Aim: Chronic hepatitis B (CHB) patients with normal alanine aminotransferase (ALT) levels are not free from significant hepatic lesions. Recently, there has been an improved understanding of the clinical significance of quantitative hepatitis B core antibody levels (qAnti-HBc) during CHB management. In this cross-sectional study, we evaluated the utility of qAnti-HBc in identifying significant liver inflammation in CHB patients.Methods: A total of 469 patients (training set, n = 363; validation set, n = 106) who underwent liver biopsy (LB) were included. The qAnti-HBc levels were quantified and the relationship between histology and serum markers was systematically analyzed.Results: In the training set, qAnti-HBc levels were found to have significant diagnostic value for moderate to severe liver inflammation (≥G2) in all patients (area under the receiver operating characteristic curve [AUROC] = 0.768; 95% confidence interval [CI], 0.721-0.810; P < 0.001) and in patients with normal or nearnormal ALT levels (AUROC = 0.767; 95% CI, 0.697-0.828; P < 0.001). Our novel index (AC index) for the identification of ≥G2 inflammation, which combined the qAnti-HBc and ALT levels, significantly improved diagnostic performance (AUROC = 0.813; 95% CI, 0.768-0.852) compared to the use of ALT alone (AUROC = 0.779; 95% CI, 0.732-0.821) in all patients. In the validation set, the AC index showed an improved AUROC of 0.890 (95% CI, 0.814-0.942) and 0.867 (95% CI, 0.749-0.943) in all patients and patients with normal ALT levels, respectively. Conclusions:The qAnti-HBc level predicts significant liver inflammation well, even in patients with normal or near-normal ALT levels. Compared with the conventional ALT level, the AC index is a more reliable non-invasive biomarker for significant liver inflammation in CHB patients.

BackgroundThe use of structural alerts to de-prioritize compounds with undesirable features as drug candidates has been gaining in popularity. Hundreds of molecular structural moieties have been proposed as structural alerts. An emerging issue is that strict application of these alerts will result in a significant reduction of the chemistry space for new drug discovery, as more than half of the oral drugs on the market match at least one of the alerts. To mitigate this issue, we propose to apply a rigorous statistical analysis to derive/validate structural alerts before use.MethodTo derive human liver toxicity structural alerts, we retrieved all small-molecule entries from LiverTox, a U.S. National Institutes of Health online resource for information on human liver injuries induced by prescription and over-the-counter drugs and dietary supplements. We classified the compounds into hepatotoxic, nonhepatotoxic, and possible hepatotoxic classes, and performed detailed statistical analyses to identify molecular structural fragments highly enriched in the hepatotoxic class beyond random distribution as structural alerts for human liver injuries.ResultsWe identified 12 molecular fragments present in multiple marketed drugs that one can consider as common “drug-like” fragments, yet they are strongly associated with drug-induced human liver injuries. Thus, these fragments may be considered as robust hepatotoxicity structural alerts suitable for use in drug discovery screening programs.ConclusionsThe use of structural alerts has contributed to the identification of many compounds with potential toxicity issues in modern drug discovery. However, with a large number of structural alerts published to date without proper validation, application of these alerts may restrict the chemistry space and prevent discovery of valuable drugs. To mitigate this issue, we showed how to use statistical analyses to develop a small, robust, and broadly applicable set of structural alerts.Graphical abstractHepatotoxic-specific filters for flagging high risk compounds.Electronic supplementary materialThe online version of this article (doi:10.1186/s13321-015-0053-y) contains supplementary material, which is available to authorized users.