Initial findings of striatum tripartite model in OCD brain samples based on transcriptome analysis.

Obsessive-compulsive disorder (OCD) is a psychiatric disorder characterized by obsessions and/or compulsions. Different striatal subregions belonging to the cortico-striato-thalamic circuitry (CSTC) play an important role in the pathophysiology of OCD. The transcriptomes of 3 separate striatal areas (putamen (PT), caudate nucleus (CN) and accumbens nucleus (NAC)) from postmortem brain tissue were compared between 6 OCD and 8 control cases. In addition to network connectivity deregulation, different biological processes are specific to each striatum region according to the tripartite model of the striatum and contribute in various ways to OCD pathophysiology. Specifically, regulation of neurotransmitter levels and presynaptic processes involved in chemical synaptic transmission were shared between NAC and PT. The Gene Ontology terms cellular response to chemical stimulus, response to external stimulus, response to organic substance, regulation of synaptic plasticity, and modulation of synaptic transmission were shared between CN and PT. Most genes harboring common and/or rare variants previously associated with OCD that were differentially expressed or part of a least preserved coexpression module in our study also suggest striatum subregion specificity. At the transcriptional level, our study supports differences in the 3 circuit CSTC model associated with OCD.

Transcriptome Analysis of Mesenchymal Stem Cells from Multiple Myeloma Patients Reveals Downregulation of Genes Involved in Cell Cycle Progression, Immune Response, and Bone Metabolism.

A growing body of evidence suggests a key role of tumor microenvironment, especially for bone marrow mesenchymal stem cells (MSC), in the maintenance and progression of multiple myeloma (MM), through direct and indirect interactions with tumor plasma cells. Thus, this study aimed to investigate the gene expression and functional alterations of MSC from MM patients (MM-MSC) in comparison with their normal counterparts from normal donors (ND-MSC). Gene expression analysis (Affymetrix) was performed in MM-MSC and ND-MSC after in vitro expansion. To validate these findings, some genes were selected to be evaluated by quantitative real time PCR (RT-qPCR), and also functional in vitro analyses were performed. We demonstrated that MM-MSC have a distinct gene expression profile than ND-MSC, with 485 differentially expressed genes (DEG) - 280 upregulated and 205 downregulated. Bioinformatics analyses revealed that the main enriched functions among downregulated DEG were related to cell cycle progression, immune response activation and bone metabolism. Four genes were validated by qPCR - ZNF521 and SEMA3A, which are involved in bone metabolism, and HLA-DRA and CHIRL1, which are implicated in the activation of immune response. Taken together, our results suggest that MM-MSC have constitutive abnormalities that remain present even in the absence of tumors cells. The alterations found in cell cycle progression, immune system activation, and osteoblastogenesis suggest, respectively, that MM-MSC are permanently dependent of tumor cells, might contribute to immune evasion and play an essential role in bone lesions frequently found in MM patients.

Assessment of complementarity of WGCNA and NERI results for identification of modules associated to schizophrenia spectrum disorders.

Psychiatric disorders involve both changes in multiple genes as well different types of variations. As such, gene co-expression networks allowed the comparison of different stages and parts of the brain contributing to an integrated view of genetic variation. Two methods based on co-expression networks presents appealing results: Weighted Gene Correlation Network Analysis (WGCNA) and Network-Medicine Relative Importance (NERI). By selecting two different gene expression databases related to schizophrenia, we evaluated the biological modules selected by both WGCNA and NERI along these databases as well combining both WGCNA and NERI results (WGCNA-NERI). Also we conducted a enrichment analysis for the identification of partial biological function of each result (as well a replication analysis). To appraise the accuracy of whether both algorithms (as well our approach, WGCNA-NERI) were pointing to genes related to schizophrenia and its complex genetic architecture we conducted the MSET analysis, based on a reference gene list of schizophrenia database (SZDB) related to DNA Methylation, Exome, GWAS as well as copy number variation mutation studies. The WGCNA results were more associated with inflammatory pathways and immune system response; NERI obtained genes related with cellular regulation, embryological pathways e cellular growth factors. Only NERI were able to provide a statistical meaningful results to the MSET analysis (for Methylation and de novo mutations data). However, combining WGCNA and NERI provided a much more larger overlap in these two categories and additionally on Transcriptome database. Our study suggests that using both methods in combination is better for establishing a group of modules and pathways related to a complex disease than using each method individually. NERI is available at: https://bitbucket.org/sergionery/neri.

Multidimensional integrative analysis uncovers driver candidates and biomarkers in penile carcinoma.

Molecular data generation and their combination in penile carcinomas (PeCa), a significant public health problem in poor and underdeveloped countries, remain virtually unexplored. An integrativemethodology combin ing genome-wide copy number alteration, DNA methylation, miRNA and mRNA expression analysis was performed in a set of 20 usual PeCa. The well-ranked 16 driver candidates harboring genomic alterations and regulated by a set of miRNAs, including hsa-miR-31, hsa-miR-34a and hsa-miR-130b, were significantly associated with over-represented pathways in cancer, such as immune-inflammatory system, apoptosis and cell cycle. Modules of co-expressed genes generated from expression matrix were associated with driver candidates and classified according to the over-representation of passengers, thus suggesting an alteration of the pathway dynamics during the carcinogenesis. This association resulted in 10 top driver candidates (AR, BIRC5, DNMT3B, ERBB4, FGFR1, PML, PPARG, RB1, TNFSF10 and STAT1) selected and confirmed as altered in an independent set of 33 PeCa samples. In addition to the potential driver genes herein described, shorter overall survival was associated with BIRC5 and DNMT3B overexpression (log-rank test, P = 0.026 and P = 0.002, respectively) highlighting its potential as novel prognostic marker for penile cancer.

GeNICE: A Novel Framework for Gene Network Inference by Clustering, Exhaustive Search, and Multivariate Analysis.

Gene network (GN) inference from temporal gene expression data is a crucial and challenging problem in systems biology. Expression data sets usually consist of dozens of temporal samples, while networks consist of thousands of genes, thus rendering many inference methods unfeasible in practice. To improve the scalability of GN inference methods, we propose a novel framework called GeNICE, based on probabilistic GNs; the main novelty is the introduction of a clustering procedure to group genes with related expression profiles and to provide an approximate solution with reduced computational complexity. We use the defined clusters to perform an exhaustive search to retrieve the best predictor gene subsets for each target gene, according to multivariate criterion functions. GeNICE greatly reduces the search space because predictor candidates are restricted to one gene per cluster. Finally, a multivariate analysis is performed for each defined predictor subset to retrieve minimal subsets and to simplify the network. In our experiments with in silico generated data sets, GeNICE achieved substantial computational time reduction when compared to solutions without the clustering step, while preserving the gene expression prediction accuracy even when the number of clusters is small (about 50) relative to the number of genes (order of thousands). For a Plasmodium falciparum microarray data set, the prediction accuracy achieved by GeNICE was roughly 97%, while the respective topologies involving glycolytic and apicoplast seed genes had a very large intramodularity, very small interconnection between modules, and some module hub genes, reflecting small-world and scale-free topological properties, as expected.

Genome-wide analysis of the human malaria parasite Plasmodium falciparum transcription factor PfNF-YB shows interaction with a CCAAT motif.

Little is known about transcription factor regulation during the Plasmodium falciparum intraerythrocytic cycle. In order to elucidate the role of the P. falciparum (Pf)NF-YB transcription factor we searched for target genes in the entire genome. PfNF-YB mRNA is highly expressed in late trophozoite and schizont stages relative to the ring stage. In order to determine the candidate genes bound by PfNF-YB a ChIP-on-chip assay was carried out and 297 genes were identified. Ninety nine percent of PfNF-YB binding was to putative promoter regions of protein coding genes of which only 16% comprise proteins of known function. Interestingly, our data reveal that PfNF-YB binding is not exclusively to a canonical CCAAT box motif. PfNF-YB binds to genes coding for proteins implicated in a range of different biological functions, such as replication protein A large subunit (DNA replication), hypoxanthine phosphoribosyltransferase (nucleic acid metabolism) and multidrug resistance protein 2 (intracellular transport).

Feature selection environment for genomic applications.

BACKGROUND: Feature selection is a pattern recognition approach to choose important variables according to some criteria in order to distinguish or explain certain phenomena (i.e., for dimensionality reduction). There are many genomic and proteomic applications that rely on feature selection to answer questions such as selecting signature genes which are informative about some biological state, e.g., normal tissues and several types of cancer; or inferring a prediction network among elements such as genes, proteins and external stimuli. In these applications, a recurrent problem is the lack of samples to perform an adequate estimate of the joint probabilities between element states. A myriad of feature selection algorithms and criterion functions have been proposed, although it is difficult to point the best solution for each application. RESULTS: The intent of this work is to provide an open-source multiplatform graphical environment for bioinformatics problems, which supports many feature selection algorithms, criterion functions and graphic visualization tools such as scatterplots, parallel coordinates and graphs. A feature selection approach for growing genetic networks from seed genes (targets or predictors) is also implemented in the system. CONCLUSION: The proposed feature selection environment allows data analysis using several algorithms, criterion functions and graphic visualization tools. Our experiments have shown the software effectiveness in two distinct types of biological problems. Besides, the environment can be used in different pattern recognition applications, although the main concern regards bioinformatics tasks.