Andrés Aravena

Calcium/calmodulin-dependent protein kinase type IV Is a target gene of the Wnt/β-catenin signaling pathway

Calcium/calmodulin‐dependent protein kinase IV (CaMKIV) plays a key role in the regulation of calcium‐dependent gene expression. The expression of CaMKIV and the activation of CREB regulated genes are involved in memory and neuronal survival. We report here that: (a) a bioinformatic analysis of 15,476 promoters of the human genome predicted several Wnt target genes, being CaMKIV a very interesting candidate; (b) CaMKIV promoter contains TCF/LEF transcription motifs similar to those present in Wnt target genes; (c) biochemical studies indicate that lithium and the canonical ligand Wnt‐3a induce CaMKIV mRNA and protein expression levels in rat hippocampal neurons as well as CaMKIV promoter activity; (d) treatment of hippocampal neurons with Wnt‐3a increases the binding of β‐catenin to the CaMKIV promoter: (e) In vivo activation of the Wnt signaling improve spatial memory impairment and restores the expression of CaMKIV in a mice double transgenic model for Alzheimers disease which shows decreased levels of the kinase. We conclude that CaMKIV is regulated by the Wnt signaling pathway and that its expression could play a role in the neuroprotective function of the Wnt signaling against the Alzheimers amyloid peptide. J. Cell. Physiol. 221: 658–667, 2009.

Genome-wide identification of new Wnt/β-catenin target genes in the human genome using CART method

BackgroundThe importance of in silico predictions for understanding cellular processes is now widely accepted, and a variety of algorithms useful for studying different biological features have been designed. In particular, the prediction of cis regulatory modules in non-coding human genome regions represents a major challenge for understanding gene regulation in several diseases. Recently, studies of the Wnt signaling pathway revealed a connection with neurodegenerative diseases such as Alzheimers. In this article, we construct a classification tool that uses the transcription factor binding site motifs composition of some gene promoters to identify new Wnt/β-catenin pathway target genes potentially involved in brain diseases.ResultsIn this study, we propose 89 new Wnt/β-catenin pathway target genes predicted in silico by using a method based on multiple Classification and Regression Tree (CART) analysis. We used as decision variables the presence of transcription factor binding site motifs in the upstream region of each gene. This prediction was validated by RT-qPCR in a sample of 9 genes. As expected, LEF1, a member of the T-cell factor/lymphoid enhancer-binding factor family (TCF/LEF1), was relevant for the classification algorithm and, remarkably, other factors related directly or indirectly to the inflammatory response and amyloidogenic processes also appeared to be relevant for the classification. Among the 89 new Wnt/β-catenin pathway targets, we found a group expressed in brain tissue that could be involved in diverse responses to neurodegenerative diseases, like Alzheimers disease (AD). These genes represent new candidates to protect cells against amyloid β toxicity, in agreement with the proposed neuroprotective role of the Wnt signaling pathway.ConclusionsOur multiple CART strategy proved to be an effective tool to identify new Wnt/β-catenin pathway targets based on the study of their regulatory regions in the human genome. In particular, several of these genes represent a new group of transcriptional dependent targets of the canonical Wnt pathway. The functions of these genes indicate that they are involved in pathophysiology related to Alzheimers disease or other brain disorders.

SalmonDB: a bioinformatics resource for Salmo salar and Oncorhynchus mykiss

SalmonDB is a new multiorganism database containing EST sequences from Salmo salar, Oncorhynchus mykiss and the whole genome sequence of Danio rerio, Gasterosteus aculeatus, Tetraodon nigroviridis, Oryzias latipes and Takifugu rubripes, built with core components from GMOD project, GOPArc system and the BioMart project. The information provided by this resource includes Gene Ontology terms, metabolic pathways, SNP prediction, CDS prediction, orthologs prediction, several precalculated BLAST searches and domains. It also provides a BLAST server for matching user-provided sequences to any of the databases and an advanced query tool (BioMart) that allows easy browsing of EST databases with user-defined criteria. These tools make SalmonDB database a valuable resource for researchers searching for transcripts and genomic information regarding S. salar and other salmonid species. The database is expected to grow in the near feature, particularly with the S. salar genome sequencing project. Database URL: http://genomicasalmones.dim.uchile.cl/

Global transcriptional responses of Acidithiobacillus ferrooxidans Wenelen under different sulfide minerals

In order to provide new information about the adaptation of Acidithiobacillus ferrooxidans during the bioleaching process, the current analysis presents the first report of the global transcriptional response of the native copper mine strain Wenelen (DSM 16786) oxidized under different sulfide minerals. Microarrays were used to measure the response of At. ferrooxidans Wenelen to shifts from iron supplemented liquid cultures (reference state) to the addition of solid substrates enriched in pyrite or chalcopyrite. Genes encoding for energy metabolism showed a similar transcriptional profile for the two sulfide minerals. Interestingly, four operons related to sulfur metabolism were over-expressed during growth on a reduced sulfur source. Genes associated with metal tolerance (RND and ATPases type P) were up-regulated in the presence of pyrite or chalcopyrite. These results suggest that At. ferrooxidans Wenelen presents an efficient transcriptional system developed to respond to environmental conditions, namely the ability to withstand high copper concentrations.

Different Transcriptional Responses from Slow and Fast Growth Rate Strains of Listeria monocytogenes Adapted to Low Temperature.

Listeria monocytogenes has become one of the principal foodborne pathogens worldwide. The capacity of this bacterium to grow at low temperatures has opened an interesting field of study in terms of the identification and classification of new strains of L. monocytogenes with different growth capacities at low temperatures. We determined the growth rate at 8°C of 110 strains of L. monocytogenes isolated from different food matrices. We identified a group of slow and fast strains according to their growth rate at 8°C and performed a global transcriptomic assay in strains previously adapted to low temperature. We then identified shared and specific transcriptional mechanisms, metabolic and cellular processes of both groups; bacterial motility was the principal process capable of differentiating the adaptation capacity of L. monocytogenes strains with different ranges of tolerance to low temperatures. Strains belonging to the fast group were less motile, which may allow these strains to achieve a greater rate of proliferation at low temperature.

Different Array CGH profiles within hereditary breast cancer tumors associated to BRCA1 expression and overall survival

BackgroundArray CGH analysis of breast tumors has contributed to the identification of different genomic profiles in these tumors. Loss of DNA repair by BRCA1 functional deficiency in breast cancer has been proposed as a relevant contribution to breast cancer progression for tumors with no germline mutation. Identifying the genomic alterations taking place in BRCA1 not expressing tumors will lead us to a better understanding of the cellular functions affected in this heterogeneous disease. Moreover, specific genomic alterations may contribute to the identification of potential therapeutic targets and offer a more personalized treatment to breast cancer patients.MethodsForty seven tumors from hereditary breast cancer cases, previously analyzed for BRCA1 expression, and screened for germline BRCA1 and 2 mutations, were analyzed by Array based Comparative Genomic Hybridization (aCGH) using Agilent 4x44K arrays. Overall survival was established for tumors in different clusters using Log-rank (Mantel-Cox) Test. Gene lists obtained from aCGH analysis were analyzed for Gene Ontology enrichment using GOrilla and DAVID tools.ResultsGenomic profiling of the tumors showed specific alterations associated to BRCA1 or 2 mutation status, and BRCA1 expression in the tumors, affecting relevant cellular processes. Similar cellular functions were found affected in BRCA1 not expressing and BRCA1 or 2 mutated tumors. Hierarchical clustering classified hereditary breast tumors in four major, groups according to the type and amount of genomic alterations, showing one group with a significantly poor overall survival (p = 0.0221). Within this cluster, deletion of PLEKHO1, GDF11, DARC, DAG1 and CD63 may be associated to the worse outcome of the patients.ConclusionsThese results support the fact that BRCA1 lack of expression in tumors should be used as a marker for BRCAness and to select these patients for synthetic lethality approaches such as treatment with PARP inhibitors. In addition, the identification of specific alterations in breast tumors associated with poor survival, immune response or with a BRCAness phenotype will allow the use of a more personalized treatment in these patients.

Modeling Parsimonious Putative Regulatory Networks: Complexity and Heuristic Approach

A relevant problem in systems biology is the description of the regulatory interactions between genes. It is observed that pairs of genes have significant correlation through several experimental conditions. The question is to find causal relationships that can explain this experimental evidence. A putative regulatory network can be represented by an oriented weighted graph, where vertices represent genes, arcs represent predicted regulatory interactions and the arc weights represent the p-value of the prediction. Given such graph, and experimental evidence of correlation between pairs of vertices, we propose an abstraction and a method to enumerate all parsimonious subgraphs that assign causality relationships compatible with the experimental evidence. When the problem is modeled as the minimization of a global weight function, we show that the enumeration of scenarios is a hard problem. As an heuristic, we model the problem as a set of independent minimization problems, each solvable in polynomial time, which can be combined to explore a relevant subset of the solution space. We present a logic-programming formalization of the model implemented using Answer Set Programming. We show that, when the graph follows patterns that can be found in real organisms, our heuristic finds solutions that are good approximations to the full model. We encoded these approach using Answer Set Programming, applied this to a specific case in the organism E. coli and compared the execution time of each approach.

Abstract 5077: Array CGH genomic profile of hereditary breast cancer tumors: Identification of tumor suppressor genes in deleted regions, determination of promoter hypermethylation and their protein expression in tumor biopsies

Proceedings: AACR 103rd Annual Meeting 2012‐‐ Mar 31‐Apr 4, 2012; Chicago, IL Breast cancer, as other cancer types, is a genetic disease caused by sequential accumulation of mutations and genomic alterations involving tumor suppressor genes and oncogenes. In this matter, inactivation of tumor suppressor genes may occur by deletion, promoter hypermethylation or point mutations. We analyzed by array-CGH tumor DNA from 52 patients with hereditary breast cancer: 3 BRCA1, 4 BRCA2 and 45 from patients with no identified mutations (BRCAX). Our analysis revealed that BRCA1 mutated tumors have different genomic alterations compared to BRCA2 tumors. BRCAX tumors showed frequent deletions at: 1q21.3 (20%), 1p31.1 and 9q33.1 (18%); and frequent amplifications at 1q23.1 (20%), followed by 1q21.1 and several gains in chromosome 19 (18%). All genes involved in the deleted or amplified regions were compared with gene expression data in the ONCOMINE database finding correspondence among genomic alterations and gene expression. Based on these results, we selected 3 tumor suppressor genes for promoter methylation and protein expression analyses: RASSF1A, SLIT2 and WIF1. Methylation analysis through MS-PCR revealed that RASSF1A promoter was hypermethylated in 67% of hereditary tumors, and significantly associated to loss of expression (p=0.007, OR 9.6, CI 1.77-52.19). WIF1 promoter was hypermethylated in 68% of tumors with a significant association to loss of expression (p=0.042, OR 6.4, CI 1.155-35.45). Finally, SLIT2 was found as the most frequently hypermethylated promoter among tumors (80%) showing loss of expression in 90% of these tumors. Our results indicate that silencing of RASSF1A, SLIT2 and WIF1 through promoter hypermethylation may have an important role in hereditary breast tumor development. Fondecyt 1040779 y 1080595. Citation Format: {Authors}. {Abstract title} [abstract]. In: Proceedings of the 103rd Annual Meeting of the American Association for Cancer Research; 2012 Mar 31-Apr 4; Chicago, IL. Philadelphia (PA): AACR; Cancer Res 2012;72(8 Suppl):Abstract nr 5077. doi:1538-7445.AM2012-5077

Design and Use of Oligonucleotide Microarrays for Identification of Biomining Microorganisms

Results from a high density microarray having 32,392 50-mer oligonucleotides, termed BMS2.1, were analyzed and used for the design of a new slide, called BMS 3.0, with 560 specific oligonucleotides manufactured in standard slides to be used in any open platform. Hybridizations of several samples, either known microorganisms or environmental samples, were performed. Automatic microarray analysis software was built in order to handle these data in a quick an efficient way. While oligonucleotides designed for microorganisms with known genome sequence showed a very good behavior, according to predicted design, variations were detected when different strains were hybridized, probably due to inadequate specificity of probes. Appropriate parameterization of the analysis software will improve prediction of presence for most of the microorganisms.

Deciphering transcriptional regulations coordinating the response to environmental changes.

BackgroundGene co-expression evidenced as a response to environmental changes has shown that transcriptional activity is coordinated, which pinpoints the role of transcriptional regulatory networks (TRNs). Nevertheless, the prediction of TRNs based on the affinity of transcription factors (TFs) with binding sites (BSs) generally produces an over-estimation of the observable TF/BS relations within the network and therefore many of the predicted relations are spurious.ResultsWe present Lombarde, a bioinformatics method that extracts from a TRN determined from a set of predicted TF/BS affinities a subnetwork explaining a given set of observed co-expressions by choosing the TFs and BSs most likely to be involved in the co-regulation. Lombarde solves an optimization problem which selects confident paths within a given TRN that join a putative common regulator with two co-expressed genes via regulatory cascades. To evaluate the method, we used public data of Escherichia coli to produce a regulatory network that explained almost all observed co-expressions while using only 19 % of the input TF/BS affinities but including about 66 % of the independent experimentally validated regulations in the input data. When all known validated TF/BS affinities were integrated into the input data the precision of Lombarde increased significantly. The topological characteristics of the subnetwork that was obtained were similar to the characteristics described for known validated TRNs.ConclusionsLombarde provides a useful modeling scheme for deciphering the regulatory mechanisms that underlie the phenotypic responses of an organism to environmental challenges. The method can become a reliable tool for further research on genome-scale transcriptional regulation studies.