Exploring Diverse Coagulation Factor XIII Subunit Expression Datasets: A Bioinformatic Analysis.

Coagulation factor XIII (FXIII) circulates in plasma as a pro-transglutaminase heterotetrameric complex (FXIIIA2B2), which upon activation by thrombin and calcium covalently crosslinks preformed fibrin polymers. The heterotetrameric complex is composed of a catalytic FXIIIA2 subunit and a protective/regulatory FXIII-B2 subunit coded by F13A1 and F13B genes, respectively. The catalytic FXIIIA2 subunit is encoded by the F13A1 gene, expressed primarily in cells of mesenchymal origin, whereas the FXIIIB subunit encoded by the F13B gene is expressed and secreted from hepatocytes. The plasma FXIIIA2 subunit, which earlier was believed to be secreted from cells of megakaryocytic lineage, is now understood to result primarily from resident macrophages. The regulation of the FXIII subunits at the genetic level is still poorly understood. The current study adopts a purely bioinformatic approach to analyze the temporal, time-specific expression array-data corresponding to both the subunits in specific cell lineages, with respect to the gene promoters. We analyze the differentially expressed genes correlated with F13A1 and F13B expression levels in an array of cell types, utilizing publicly available microarray data. We attempt to understand the regulatory mechanism underlying the variable expression of FXIIIA2 subunit in macrophages (M0, M1, M2 and aortic resident macrophages). Similarly, the FXIIIB2 subunit expression data from adult, fetal hepatocytes and embryonic stem cells derived hepatoblasts (hESC-hepatoblast) was analyzed. The results suggest regulatory dependence between the two FXIII subunits at the transcript level. Our analysis also predicts the involvement of the FXIIIA2 subunit in macrophage polarization, plaque stability, and inflammation.

Computational master-regulator search reveals mTOR and PI3K pathways responsible for low sensitivity of NCI-H292 and A427 lung cancer cell lines to cytotoxic action of p53 activator Nutlin-3.

BACKGROUND: Small molecule Nutlin-3 reactivates p53 in cancer cells by interacting with the complex between p53 and its repressor Mdm-2 and causing an increase in cancer cell apoptosis. Therefore, Nutlin-3 has potent anticancer properties. Clinical and experimental studies of Nutlin-3 showed that some cancer cells may lose sensitivity to this compound. Here we analyze possible mechanisms for insensitivity of cancer cells to Nutlin-3. METHODS: We applied upstream analysis approach implemented in geneXplain platform ( genexplain.com ) using TRANSFAC® database of transcription factors and their binding sites in genome and using TRANSPATH® database of signal transduction network with associated software such as Match™ and Composite Module Analyst (CMA). RESULTS: Using genome-wide gene expression profiling we compared several lung cancer cell lines and showed that expression programs executed in Nutlin-3 insensitive cell lines significantly differ from that of Nutlin-3 sensitive cell lines. Using artificial intelligence approach embed in CMA software, we identified a set of transcription factors cooperatively binding to the promoters of genes up-regulated in the Nutlin-3 insensitive cell lines. Graph analysis of signal transduction network upstream of these transcription factors allowed us to identify potential master-regulators responsible for maintaining such low sensitivity to Nutlin-3 with the most promising candidate mTOR, which acts in the context of activated PI3K pathway. These finding were validated experimentally using an array of chemical inhibitors. CONCLUSIONS: We showed that the Nutlin-3 insensitive cell lines are actually highly sensitive to the dual PI3K/mTOR inhibitor NVP-BEZ235, while no responding to either PI3K -specific LY294002 nor Bcl-XL specific 2,3-DCPE compounds.

EMQIT: a machine learning approach for energy based PWM matrix quality improvement.

BACKGROUND: Transcription factor binding affinities to DNA play a key role for the gene regulation. Learning the specificity of the mechanisms of binding TFs to DNA is important both to experimentalists and theoreticians. With the development of high-throughput methods such as, e.g., ChiP-seq the need to provide unbiased models of binding events has been made apparent. We present EMQIT a modification to the approach introduced by Alamanova et al. and later implemented as 3DTF server. We observed that tuning of Boltzmann factor weights, used for conversion of calculated energies to nucleotide probabilities, has a significant impact on the quality of the associated PWM matrix. RESULTS: Consequently, we proposed to use receiver operator characteristics curves and the 10-fold cross-validation to learn best weights using experimentally verified data from TRANSFAC database. We applied our method to data available for various TFs. We verified the efficiency of detecting TF binding sites by the 3DTF matrices improved with our technique using experimental data from the TRANSFAC database. The comparison showed a significant similarity and comparable performance between the improved and the experimental matrices (TRANSFAC). Improved 3DTF matrices achieved significantly higher AUC values than the original 3DTF matrices (at least by 0.1) and, at the same time, detected notably more experimentally verified TFBSs. CONCLUSIONS: The resulting new improved PWM matrices for analyzed factors show similarity to TRANSFAC matrices. Matrices had comparable predictive capabilities. Moreover, improved PWMs achieve better results than matrices downloaded from 3DTF server. Presented approach is general and applicable to any energy-based matrices. EMQIT is available online at http://biosolvers.polsl.pl:3838/emqit . REVIEWERS: This article was reviewed by Oliviero Carugo, Marek Kimmel and István Simon.

An integrated transcriptomics-guided genome-wide promoter analysis and next-generation proteomics approach to mine factor(s) regulating cellular differentiation.

Differential next-generation-omics approaches aid in the visualization of biological processes and pave the way for divulging important events and/or interactions leading to a functional output at cellular or systems level. To this end, we undertook an integrated Nextgen transcriptomics and proteomics approach to divulge differential gene expression of infant and pubertal rat Sertoli cells (Sc).Unlike, pubertal Sc, infant Sc are immature and fail to support spermatogenesis. We found exclusive association of 14 and 19 transcription factor binding sites to infantile and pubertal states of Sc, respectively, using differential transcriptomics-guided genome-wide computational analysis of relevant promoters employing 220 Positional Weight Matrices from the TRANSFAC database. Proteomic SWATH-MS analysis provided extensive quantification of nuclear and cytoplasmic protein fractions revealing 1,670 proteins differentially located between the nucleus and cytoplasm of infant Sc and 890 proteins differentially located within those of pubertal Sc. Based on our multi-omics approach, the transcription factor YY1 was identified as one of the lead candidates regulating differentiation of Sc.YY1 was found to have abundant binding sites on promoters of genes upregulated during puberty. To determine its significance, we generated transgenic rats with Sc specific knockdown of YY1 that led to compromised spermatogenesis.

mRNA Transcriptomics of Galectins Unveils Heterogeneous Organization in Mouse and Human Brain.

Background: Galectins, a family of non-classically secreted, ß-galactoside binding proteins is involved in several brain disorders; however, no systematic knowledge on the normal neuroanatomical distribution and functions of galectins exits. Hence, the major purpose of this study was to understand spatial distribution and predict functions of galectins in brain and also compare the degree of conservation vs. divergence between mouse and human species. The latter objective was required to determine the relevance and appropriateness of studying galectins in mouse brain which may ultimately enable us to extrapolate the findings to human brain physiology and pathologies. Results: In order to fill this crucial gap in our understanding of brain galectins, we analyzed the in situ hybridization and microarray data of adult mouse and human brain respectively, from the Allen Brain Atlas, to resolve each galectin-subtype's spatial distribution across brain distinct cytoarchitecture. Next, transcription factors (TFs) that may regulate galectins were identified using TRANSFAC software and the list obtained was further curated to sort TFs on their confirmed transcript expression in the adult brain. Galectin-TF cluster analysis, gene-ontology annotations and co-expression networks were then extrapolated to predict distinct functional relevance of each galectin in the neuronal processes. Data shows that galectins have highly heterogeneous expression within and across brain sub-structures and are predicted to be the crucial targets of brain enriched TFs. Lgals9 had maximal spatial distribution across mouse brain with inferred predominant roles in neurogenesis while LGALS1 was ubiquitously expressed in human. Limbic region associated with learning, memory and emotions and substantia nigra associated with motor movements showed strikingly high expression of LGALS1 and LGALS8 in human vs. mouse brain. The overall expression profile of galectin-8 was most preserved across both these species, however, galectin-9 showed maximal preservation only in the cerebral cortex. Conclusion: It is for the first time that a comprehensive description of galectins' mRNA expression profile in brain is presented. Results suggests that spatial transcriptome changes in galectins may contribute to differential brain functions and evolution across species that highlights galectins as novel signatures of brain heterogeneity and functions, which if disturbed, can promote several brain disorders.

Establishing and validating regulatory regions for variant annotation and expression analysis.

BACKGROUND: The regulatory effect of inherited or de novo genetic variants occurring in promoters as well as in transcribed or even coding gene regions is gaining greater recognition as a contributing factor to disease processes in addition to mutations affecting protein functionality. Thousands of such regulatory mutations are already recorded in HGMD, OMIM, ClinVar and other databases containing published disease causing and associated mutations. It is therefore important to properly annotate genetic variants occurring in experimentally verified and predicted transcription factor binding sites (TFBS) that could thus influence the factor binding event. Selection of the promoter sequence used is an important factor in the analysis as it directly influences the composition of the sequence available for transcription factor binding analysis. RESULTS: In this study we first establish genomic regions likely to be involved in regulation of gene expression. TRANSFAC uses a method of virtual transcription start sites (vTSS) calculation to define the best supported promoter for a gene. We have performed a comparison of the virtually calculated promoters between the best supported and secondary promoters in hg19 and hg38 reference genomes to test and validate the approach. Next we create and utilize a workflow for systematic analysis of casual disease associated variants in TFBS using Genome Trax and TRANSFAC databases. A total of 841 and 736 experimentally verified TFBSs within best supported promoters were mapped over HGMD and ClinVar mutation sites respectively. Tens of thousands of predicted ChIP-Seq derived TFBSs were mapped over mutations as well. We have further analyzed some of these mutations for potential gain or loss in transcription factor binding. CONCLUSIONS: We have confirmed the validity of TRANSFAC's approach to define the best supported promoters and established a workflow of their use in annotation of regulatory genetic variants.

Transcriptional regulation of human and murine short-chain dehydrogenase/reductases (SDRs) - an in silico approach.

Numerous physiological functions of the body are controlled by endogenous (e.g. steroids, retinoids, lipid mediators) or exogenous molecules (e.g. drugs, xenobiotics) that bind to transcription factors (TF). The biosynthesis and catabolism of these signaling molecules depend, apart from CYPs, on enzymes belonging to the short-chain dehydrogenase/reductase (SDR) superfamily. Moreover, the contribution of SDRs to the metabolism of therapeutic drugs and xenobiotics is increasingly recognized. However, only scarce information exists regarding the transcriptional regulation of most SDR proteins. This work aims to illustrate the role of nuclear receptors (NR) and TF related to oxidative stress, inflammation, hypoxia, and xenobiotics in the regulation of selected human and murine SDRs that play crucial roles in steroid, retinoid, eicosanoid, fatty acid, and xenobiotic metabolism. These include, for example, 17ß-hydroxysteroid dehydrogenases, retinol dehydrogenases, and carbonyl reductases. Because existing experimental data are limited, an in silico analysis (TRANSFAC(®) Professional database) of the 5'-upstream sequences for putative response elements was performed. Experimental and in silico data suggest that pharmaceutical, environmental, or dietary NR ligands may alter SDR-mediated retinoid, steroid, and xenobiotic metabolism, likely affecting basic cellular events like energy expenditure, cell proliferation/differentiation, or aging processes. Also, some SDRs are possibly induced by their own substrates. Further experimental work is urgently needed to fully understand the NR-mediated transcriptional regulation of SDRs. This is essential for deducing their possible involvement in drug side effects and will help to identify new substrates and further physiological functions of these SDRs.

Novel polymorphisms in UTR and coding region of inducible heat shock protein 70.1 gene in tropically adapted Indian zebu cattle (Bos indicus) and riverine buffalo (Bubalus bubalis).

Due to evolutionary divergence, cattle (taurine, and indicine) and buffalo are speculated to have different responses to heat stress condition. Variation in candidate genes associated with a heat-shock response may provide an insight into the dissimilarity and suggest targets for intervention. The present work was undertaken to characterize one of the inducible heat shock protein genes promoter and coding regions in diverse breeds of Indian zebu cattle and buffaloes. The genomic DNA from a panel of 117 unrelated animals representing 14 diversified native cattle breeds and 6 buffalo breeds were utilized to determine the complete sequence and gene diversity of HSP70.1 gene. The coding region of HSP70.1 gene in Indian zebu cattle, Bos taurus and buffalo was similar in length (1,926 bp) encoding a HSP70 protein of 641 amino acids with a calculated molecular weight (Mw) of 70.26 kDa. However buffalo had a longer 5' and 3' untranslated region (UTR) of 204 and 293 nucleotides respectively, in comparison to Indian zebu cattle and Bos taurus wherein length of 5' and 3'-UTR was 172 and 286 nucleotides, respectively. The increased length of buffalo HSP70.1 gene compared to indicine and taurine gene was due to two insertions each in 5' and 3'-UTR. Comparative sequence analysis of cattle (taurine and indicine) and buffalo HSP70.1 gene revealed a total of 54 gene variations (50 SNPs and 4 INDELs) among the three species in the HSP70.1 gene. The minor allele frequencies of these nucleotide variations varied from 0.03 to 0.5 with an average of 0.26. Among the 14 B. indicus cattle breeds studied, a total of 19 polymorphic sites were identified: 4 in the 5'-UTR and 15 in the coding region (of these 2 were non-synonymous). Analysis among buffalo breeds revealed 15 SNPs throughout the gene: 6 at the 5' flanking region and 9 in the coding region. In bubaline 5'-UTR, 2 additional putative transcription factor binding sites (Elk-1 and C-Re1) were identified, other than three common sites (CP2, HSE and Pax-4) observed across all the analyzed animals. No polymorphism was found within the 3'-UTR of Indian cattle or buffalo as it was found to be monomorphic. The promoter sequences generated in 117 individuals showed a rich array of sequence elements known to be involved in transcription regulation. A total of 11 nucleotide changes were observed in the promoter sequence across the analyzed species, 3 of these changes were located within the potential transcription factor binding domains. We also identified 4 microsatellite markers within the buffalo HSP70.1 gene and 3 microsatellites within bovine HSP70.1. The present study identified several distinct changes across indicine, taurine and bubaline HSP70.1 genes that could further be evaluated as molecular markers for thermotolerance.