New, previously unreported correlations between latent Toxoplasma gondii infection and excessive ethanol consumption.

A number of world literature reports indicate that a latent Toxoplasma gondii infection leads to development of central nervous system disorders, which in turn may lead to altered behavior in the affected individuals. T. gondii infection has been observed to play the greatest role in drivers, suicides, and psychiatric patients. Studies conducted for this manuscript involve a different, never before really reported correlation between latent T. gondii infection and ethanol abuse. A total of 538 decedents with a known cause of death were included in the study. These individuals were divided into three groups: the risky behavior group, inconclusively risky behavior group, and control group. The criterion for this division was the likely effect of the individual's behavior on the mechanism and cause of his/her death. The material used for analyses were blood samples collected during routine medico-legal examinations in these cases. The blood samples were used to measure anti-T. gondii IgG antibodies with an enzyme-linked immunosorbent assay (ELISA). Moreover, the following data were recorded for each decedent: sex, age, circumstances of death, cause of death, time from death to autopsy, and (if provided) substance abuse status (alcohol, illicit drugs). In those cases where blood alcohol level or toxicology tests were requested by the Prosecutor's Office, their results were also included in our analysis. Test results demonstrated a strong correlation between latent T. gondii infection and engaging in risky behaviors leading to death. Moreover, analyses demonstrated a positive correlation between the presence of anti-T. gondii IgG antibodies and psychoactive substance (especially ethanol) abuse, however, the causal relationship remains unclear. Due to the fact that alcohol abuse constitutes a significant social problem, searching for eliminable risk factors for addiction is extremely important. Our analyses provided new important information on the possible effects of latent T. gondii infection in humans.

Maps of context-dependent putative regulatory regions and genomic signal interactions.

Gene transcription is regulated mainly by transcription factors (TFs). ENCODE and Roadmap Epigenomics provide global binding profiles of TFs, which can be used to identify regulatory regions. To this end we implemented a method to systematically construct cell-type and species-specific maps of regulatory regions and TF-TF interactions. We illustrated the approach by developing maps for five human cell-lines and two other species. We detected ∼144k putative regulatory regions among the human cell-lines, with the majority of them being ∼300 bp. We found ∼20k putative regulatory elements in the ENCODE heterochromatic domains suggesting a large regulatory potential in the regions presumed transcriptionally silent. Among the most significant TF interactions identified in the heterochromatic regions were CTCF and the cohesin complex, which is in agreement with previous reports. Finally, we investigated the enrichment of the obtained putative regulatory regions in the 3D chromatin domains. More than 90% of the regions were discovered in the 3D contacting domains. We found a significant enrichment of GWAS SNPs in the putative regulatory regions. These significant enrichments provide evidence that the regulatory regions play a crucial role in the genomic structural stability. Additionally, we generated maps of putative regulatory regions for prostate and colorectal cancer human cell-lines.

A Significant Regulatory Mutation Burden at a High-Affinity Position of the CTCF Motif in Gastrointestinal Cancers.

Somatic mutations drive cancer and there are established ways to study those in coding sequences. It has been shown that some regulatory mutations are over-represented in cancer. We develop a new strategy to find putative regulatory mutations based on experimentally established motifs for transcription factors (TFs). In total, we find 1,552 candidate regulatory mutations predicted to significantly reduce binding affinity of many TFs in hepatocellular carcinoma and affecting binding of CTCF also in esophagus, gastric, and pancreatic cancers. Near mutated motifs, there is a significant enrichment of (1) genes mutated in cancer, (2) tumor-suppressor genes, (3) genes in KEGG cancer pathways, and (4) sets of genes previously associated to cancer. Experimental and functional validations support the findings. The strategy can be applied to identify regulatory mutations in any cell type with established TF motifs and will aid identifications of genes contributing to cancer.

Integration of genome-wide of Stat3 binding and epigenetic modification mapping with transcriptome reveals novel Stat3 target genes in glioma cells.

BACKGROUND: Signal transducer and activator of transcription 3 (STAT3) is constitutively activated in many human tumors, including gliomas, and regulates the expression of genes implicated in proliferation, survival, apoptosis, angiogenesis and immune regulation. Only a small fraction of those genes has been proven to be direct STAT3 targets. In gliomas, STAT3 can play tumor suppressive or oncogenic roles depending on the tumor genetic background with target genes being largely unknown. RESULTS: We used chromatin immunoprecipitation, promoter microarrays and deep sequencing to assess the genome-wide occupancy of phospho (p)-Stat3 and epigenetic modifications of H3K4me3 and H3ac in C6 glioma cells. This combined assessment identified a list of 1200 genes whose promoters have both Stat3 binding sites and epigenetic marks characteristic for actively transcribed genes. The Stat3 and histone markings data were also intersected with a set of microarray data from C6 glioma cells after inhibition of Jak2/Stat3 signaling. Subsequently, we found 284 genes characterized by p-Stat3 occupancy, activating histone marks and transcriptional changes. Novel genes were screened for their potential involvement in oncogenesis, and the most interesting hits were verified by ChIP-PCR and STAT3 knockdown in human glioma cells. CONCLUSIONS: Non-random association between silent genes, histone marks and p-Stat3 binding near transcription start sites was observed, consistent with its repressive role in transcriptional regulation of target genes in glioma cells with specific genetic background.

Peak Finder Metaserver - a novel application for finding peaks in ChIP-seq data.

BACKGROUND: Finding peaks in ChIP-seq is an important process in biological inference. In some cases, such as positioning nucleosomes with specific histone modifications or finding transcription factor binding specificities, the precision of the detected peak plays a significant role. There are several applications for finding peaks (called peak finders) based on different algorithms (e.g. MACS, Erange and HPeak). Benchmark studies have shown that the existing peak finders identify different peaks for the same dataset and it is not known which one is the most accurate. We present the first meta-server called Peak Finder MetaServer (PFMS) that collects results from several peak finders and produces consensus peaks. Our application accepts three standard ChIP-seq data formats: BED, BAM, and SAM. RESULTS: Sensitivity and specificity of seven widely used peak finders were examined. For the experiments we used three previously studied Transcription Factors (TF) ChIP-seq datasets and identified three of the selected peak finders that returned results with high specificity and very good sensitivity compared to the remaining four. We also ran PFMS using the three selected peak finders on the same TF datasets and achieved higher specificity and sensitivity than the peak finders individually. CONCLUSIONS: We show that combining outputs from up to seven peak finders yields better results than individual peak finders. In addition, three of the seven peak finders outperform the remaining four, and running PFMS with these three returns even more accurate results. Another added value of PFMS is a separate report of the peaks returned by each of the included peak finders.

Monte Carlo feature selection and rule-based models to predict Alzheimer's disease in mild cognitive impairment.

The objective of the present study was to evaluate a Monte Carlo feature selection (MCFS) and rough set Rosetta pipeline for generating rule-based models as a tool for comprehensive risk estimates for future Alzheimer's disease (AD) in individual patients with mild cognitive impairment (MCI). Risk estimates were generated on the basis of age, gender, Mini-Mental State Examination scores, apolipoprotein E (APOE) genotype and the cerebrospinal fluid (CSF) biomarkers total tau (T-tau), phospho-tau(181) (P-tau) and the 42 amino acid form of amyloid ß (Aß42) in two sets of longitudinally followed MCI patients (n = 217 in total). The predictive model was created in Rosetta, evaluated with the standard tenfold cross-validation approach and tested on an external set. Features were ranked and selected by the MCFS algorithm. Using the combined pipeline of MCFS and Rosetta, it was possible to predict AD among patients with MCI with an area under the receiver operating characteristics curve of 0.92. Risk estimates were produced for the individual patients and showed good correlation with actual diagnosis in cross validation, and on an external dataset from a new study. Analysis of the importance of attributes showed that the biochemical CSF markers contributed the most to the predictions, and that added value was gained by combining several biochemical markers. Despite a correlation with the biochemical markers, the genetic marker APOE ε4 did not contribute to the predictive power of the model.