Polymorphisms in DNA-repair genes in a cohort of prostate cancer patients from different areas in Spain: heterogeneity between populations as a confounding factor in association studies

Background: Differences in the distribution of genotypes between individuals of the same ethnicity are an important confounder factor commonly undervalued in typical association studies conducted in radiogenomics. Objective: To evaluate the genotypic distribution of SNPs in a wide set of Spanish prostate cancer patients for determine the homogeneity of the population and to disclose potential bias. Design, Setting, and Participants: A total of 601 prostate cancer patients from Andalusia, Basque Country, Canary and Catalonia were genotyped for 10 SNPs located in 6 different genes associated to DNA repair: XRCC1 (rs25487, rs25489, rs1799782), ERCC2 (rs13181), ERCC1 (rs11615), LIG4 (rs1805388, rs1805386), ATM (rs17503908, rs1800057) and P53 (rs1042522). The SNP genotyping was made in a Biotrove OpenArrayH NT Cycler. Outcome Measurements and Statistical Analysis: Comparisons of genotypic and allelic frequencies among populations, as well as haplotype analyses were determined using the web-based environment SNPator. Principal component analysis was made using the SnpMatrix and XSnpMatrix classes and methods implemented as an R package. Non-supervised hierarchical cluster of SNP was made using MultiExperiment Viewer. Results and Limitations: We observed that genotype distribution of 4 out 10 SNPs was statistically different among the studied populations, showing the greatest differences between Andalusia and Catalonia. These observations were confirmed in cluster analysis, principal component analysis and in the differential distribution of haplotypes among the populations. Because tumor characteristics have not been taken into account, it is possible that some polymorphisms may influence tumor characteristics in the same way that it may pose a risk factor for other disease characteristics. Conclusion: Differences in distribution of genotypes within different populations of the same ethnicity could be an important confounding factor responsible for the lack of validation of SNPs associated with radiation-induced toxicity, especially when extensive meta-analysis with subjects from different countries are carried out.

Polymorphisms in DNA-repair genes in a cohort of prostate cancer patients from different areas in Spain: heterogeneity between populations as a confounding factor in association studies

Background: Differences in the distribution of genotypes between individuals of the same ethnicity are an important confounder factor commonly undervalued in typical association studies conducted in radiogenomics. Objective: To evaluate the genotypic distribution of SNPs in a wide set of Spanish prostate cancer patients for determine the homogeneity of the population and to disclose potential bias. Design, Setting, and Participants: A total of 601 prostate cancer patients from Andalusia, Basque Country, Canary and Catalonia were genotyped for 10 SNPs located in 6 different genes associated to DNA repair: XRCC1 (rs25487, rs25489, rs1799782), ERCC2 (rs13181), ERCC1 (rs11615), LIG4 (rs1805388, rs1805386), ATM (rs17503908, rs1800057) and P53 (rs1042522). The SNP genotyping was made in a Biotrove OpenArrayH NT Cycler. Outcome Measurements and Statistical Analysis: Comparisons of genotypic and allelic frequencies among populations, as well as haplotype analyses were determined using the web-based environment SNPator. Principal component analysis was made using the SnpMatrix and XSnpMatrix classes and methods implemented as an R package. Non-supervised hierarchical cluster of SNP was made using MultiExperiment Viewer. Results and Limitations: We observed that genotype distribution of 4 out 10 SNPs was statistically different among the studied populations, showing the greatest differences between Andalusia and Catalonia. These observations were confirmed in cluster analysis, principal component analysis and in the differential distribution of haplotypes among the populations. Because tumor characteristics have not been taken into account, it is possible that some polymorphisms may influence tumor characteristics in the same way that it may pose a risk factor for other disease characteristics. Conclusion: Differences in distribution of genotypes within different populations of the same ethnicity could be an important confounding factor responsible for the lack of validation of SNPs associated with radiation-induced toxicity, especially when extensive meta-analysis with subjects from different countries are carried out.

Antibiotic resistance genes in the bacteriophage DNA fraction of environmental samples

Antibiotic resistance is an increasing global problem resulting from the pressure of antibiotic usage, greater mobility of the population, and industrialization. Many antibiotic resistance genes are believed to have originated in microorganisms in the environment, and to have been transferred to other bacteria through mobile genetic elements. Among others, ß-lactam antibiotics show clinical efficacy and low toxicity, and they are thus widely used as antimicrobials. Resistance to ß-lactam antibiotics is conferred by ß-lactamase genes and penicillin-binding proteins, which are chromosomal- or plasmid-encoded, although there is little information available on the contribution of other mobile genetic elements, such as phages. This study is focused on three genes that confer resistance to ß-lactam antibiotics, namely two ß-lactamase genes (blaTEM and blaCTX-M9) and one encoding a penicillin-binding protein (mecA) in bacteriophage DNA isolated from environmental water samples. The three genes were quantified in the DNA isolated from bacteriophages collected from 30 urban sewage and river water samples, using quantitative PCR amplification. All three genes were detected in the DNA of phages from all the samples tested, in some cases reaching 104 gene copies (GC) of blaTEM or 102 GC of blaCTX-M and mecA. These values are consistent with the amount of fecal pollution in the sample, except for mecA, which showed a higher number of copies in river water samples than in urban sewage. The bla genes from phage DNA were transferred by electroporation to sensitive host bacteria, which became resistant to ampicillin. blaTEM and blaCTX were detected in the DNA of the resistant clones after transfection. This study indicates that phages are reservoirs of resistance genes in the environment.

Interfaz web para mostrar los genes según las relaciones que mantienen sus expresiones

La investigació entre les relacions dels nivells d’expressió dels gens aporta molta informació sobre els processos biològics i patològics. Mitjançant la tècnica de les microarrays es possibilita la investigació de les relacions d’expressió de milers de gens a la vegada. La finalitat d’aquest projecte es fent ús de l’aplicatiu web PCOPGene-Net, permetre la identificació dels gens per les relacions d’expressió no lineals que tenen amb la resta de gens i permetre també la identificació de les relacions d’expressió no lineals entre els gens d’una microarray.

I. Identification and characterisation of epigenetically altered tumor supressor genes by proteomic and genomic approches / II. Studies of genes regualted by MeCP2 union to its promoter regions by proteomic and genmomic approches

DNA methylation has an important impact on normal cell physiology, thus any defects in this mechanism may be related to the development of various diseases In this project we are interested in identifying epigeneticaliy modified genes, in general controlled by processes related to the DNA methylation, by means of a new strategy combining protomic and genomic analyses. First, the two Dimensional-Difference Gel Electrophoresis (2-DIGE) protein analyses of extracts obtained from HCT-116 wt and double knockout for DNMT1 and DNMT3b (DKO) cells revealed 34 proteins overexpressed in the condition of DNMTs depletion. From five genes with higher transcript lavels in DKO cells, comparing with HCT-116 wt. oniy AKR1B1, UCHLl and VIM are melhylated in HCT-116. As expected. the DNA methvlation 1s lost in DKO cells. The rneth,vl ation of VIM and UCHLl promoters in some cancer samples has already been repaired, thus further studies has been focused on AKRlBI. AKR1B1 expression due lo DNA methyiaton of promoter region seems to occur specilfically in the colon cancer cell Iines. which was confirmed in the DNA rnethylation status and expression analyses. performed on 32 different cancer cell lines (including colon, breast, lymphoma, leukemia, neuroblastoma, glioma and lung cancer cell Iines) as well as normal colon and normal lymphocytes samples. AKRIBI expression after treatments with DNA demethvlating agent (AZA) was rescued in 5 coloncancer cell lines (including genetic regulation of the candidate gene. The methylation status of the rest of the genes identified in proteomic analysis was checked by methylation specific PCR (MSP) experiment and all appeared to be unmethylated. The similar research has been done also bv means of Mecp2-null mouse model For 14 selected candidate genes the analyses of expression leveis, methylation Status and MeCP2 interaction with promoters are currently being performed.

SGP-1: prediction and validation of homologous genes based on sequence alignments

Conventional methods of gene prediction rely on the recognition of DNA-sequence signals, the coding potential or the comparison of a genomic sequence with a cDNA, EST, or protein database. Reasons for limited accuracy in many circumstances are species-specific training and the incompleteness of reference databases. Lately, comparative genome analysis has attracted increasing attention. Several analysis tools that are based on human/mouse comparisons are already available. Here, we present a program for the prediction of protein-coding genes, termed SGP-1 (Syntenic Gene Prediction), which is based on the similarity of homologous genomic sequences. In contrast to most existing tools, the accuracy of SGP-1 depends little on species-specific properties such as codon usage or the nucleotide distribution. SGP-1 may therefore be applied to nonstandard model organisms in vertebrates as well as in plants, without the need for extensive parameter training. In addition to predicting genes in large-scale genomic sequences, the program may be useful to validate gene structure annotations from databases. To this end, SGP-1 output also contains comparisons between predicted and annotated gene structures in HTML format. The program can be accessed via a Web server at http://soft.ice.mpg.de/sgp-1. The source code, written in ANSI C, is available on request from the authors.

An assessment of gene prediction accuracy in large DNA sequences

One of the first useful products from the human genome will be a set of predicted genes. Besides its intrinsic scientific interest, the accuracy and completeness of this data set is of considerable importance for human health and medicine. Though progress has been made on computational gene identification in terms of both methods and accuracy evaluation measures, most of the sequence sets in which the programs are tested are short genomic sequences, and there is concern that these accuracy measures may not extrapolate well to larger, more challenging data sets. Given the absence of experimentally verified large genomic data sets, we constructed a semiartificial test set comprising a number of short single-gene genomic sequences with randomly generated intergenic regions. This test set, which should still present an easier problem than real human genomic sequence, mimics the approximately 200kb long BACs being sequenced. In our experiments with these longer genomic sequences, the accuracy of GENSCAN, one of the most accurate ab initio gene prediction programs, dropped significantly, although its sensitivity remained high. Conversely, the accuracy of similarity-based programs, such as GENEWISE, PROCRUSTES, and BLASTX was not affected significantly by the presence of random intergenic sequence, but depended on the strength of the similarity to the protein homolog. As expected, the accuracy dropped if the models were built using more distant homologs, and we were able to quantitatively estimate this decline. However, the specificities of these techniques are still rather good even when the similarity is weak, which is a desirable characteristic for driving expensive follow-up experiments. Our experiments suggest that though gene prediction will improve with every new protein that is discovered and through improvements in the current set of tools, we still have a long way to go before we can decipher the precise exonic structure of every gene in the human genome using purely computational methodology.

Assembling genes from predicted exons in linear time with dynamic programming

In a number of programs for gene structure prediction in higher eukaryotic genomic sequences, exon prediction is decoupled from gene assembly: a large pool of candidate exons is predicted and scored from features located in the query DNA sequence, and candidate genes are assembled from such a pool as sequences of nonoverlapping frame-compatible exons. Genes are scored as a function of the scores of the assembled exons, and the highest scoring candidate gene is assumed to be the most likely gene encoded by the query DNA sequence. Considering additive gene scoring functions, currently available algorithms to determine such a highest scoring candidate gene run in time proportional to the square of the number of predicted exons. Here, we present an algorithm whose running time grows only linearly with the size of the set of predicted exons. Polynomial algorithms rely on the fact that, while scanning the set of predicted exons, the highest scoring gene ending in a given exon can be obtained by appending the exon to the highest scoring among the highest scoring genes ending at each compatible preceding exon. The algorithm here relies on the simple fact that such highest scoring gene can be stored and updated. This requires scanning the set of predicted exons simultaneously by increasing acceptor and donor position. On the other hand, the algorithm described here does not assume an underlying gene structure model. Indeed, the definition of valid gene structures is externally defined in the so-called Gene Model. The Gene Model specifies simply which gene features are allowed immediately upstream which other gene features in valid gene structures. This allows for great flexibility in formulating the gene identification problem. In particular it allows for multiple-gene two-strand predictions and for considering gene features other than coding exons (such as promoter elements) in valid gene structures.

Estimation of protein coding density in a corpus of DNA sequence data

A number of experimental methods have been reported for estimating the number of genes in a genome, or the closely related coding density of a genome, defined as the fraction of base pairs in codons. Recently, DNA sequence data representative of the genome as a whole have become available for several organisms, making the problem of estimating coding density amenable to sequence analytic methods. Estimates of coding density for a single genome vary widely, so that methods with characterized error bounds have become increasingly desirable. We present a method to estimate the protein coding density in a corpus of DNA sequence data, in which a ‘coding statistic’ is calculated for a large number of windows of the sequence under study, and the distribution of the statistic is decomposed into two normal distributions, assumed to be the distributions of the coding statistic in the coding and noncoding fractions of the sequence windows. The accuracy of the method is evaluated using known data and application is made to the yeast chromosome III sequence and to C.elegans cosmid sequences. It can also be applied to fragmentary data, for example a collection of short sequences determined in the course of STS mapping.

Identifying protein-coding genes in genomic sequences

The vast majority of the biology of a newly sequenced genome is inferred from the set of encoded proteins. Predicting this set is therefore invariably the first step after the completion of the genome DNA sequence. Here we review the main computational pipelines used to generate the human reference protein-coding gene sets.

Conserved chromosomal clustering of genes governed by chromatin regulators in Drosophila

BACKGROUND: The trithorax group (trxG) and Polycomb group (PcG) proteins are responsible for the maintenance of stable transcriptional patterns of many developmental regulators. They bind to specific regions of DNA and direct the post-translational modifications of histones, playing a role in the dynamics of chromatin structure. RESULTS: We have performed genome-wide expression studies of trx and ash2 mutants in Drosophila melanogaster. Using computational analysis of our microarray data, we have identified 25 clusters of genes potentially regulated by TRX. Most of these clusters consist of genes that encode structural proteins involved in cuticle formation. This organization appears to be a distinctive feature of the regulatory networks of TRX and other chromatin regulators, since we have observed the same arrangement in clusters after experiments performed with ASH2, as well as in experiments performed by others with NURF, dMyc, and ASH1. We have also found many of these clusters to be significantly conserved in D. simulans, D. yakuba, D. pseudoobscura and partially in Anopheles gambiae. CONCLUSION: The analysis of genes governed by chromatin regulators has led to the identification of clusters of functionally related genes conserved in other insect species, suggesting this chromosomal organization is biologically important. Moreover, our results indicate that TRX and other chromatin regulators may act globally on chromatin domains that contain transcriptionally co-regulated genes.

Conserved chromosomal clustering of genes governed by chromatin regulators in Drosophila

Background: The trithorax group (trxG) and Polycomb group (PcG) proteins are responsible for the maintenance of stable transcriptional patterns of many developmental regulators. They bind to specific regions of DNA and direct the post-translational modifications of histones, playing a role in the dynamics of chromatin structure.Results: We have performed genome-wide expression studies of trx and ash2 mutants in Drosophila melanogaster. Using computational analysis of our microarray data, we have identified 25 clusters of genes potentially regulated by TRX. Most of these clusters consist of genes that encode structural proteins involved in cuticle formation. This organization appears to be a distinctive feature of the regulatory networks of TRX and other chromatin regulators, since we have observed the same arrangement in clusters after experiments performed with ASH2, as well as in experiments performed by others with NURF, dMyc, and ASH1. We have also found many of these clusters to be significantly conserved in D. simulans, D. yakuba, D. pseudoobscura and partially in Anopheles gambiae.Conclusion: The analysis of genes governed by chromatin regulators has led to the identification of clusters of functionally related genes conserved in other insect species, suggesting this chromosomal organization is biologically important. Moreover, our results indicate that TRX and other chromatin regulators may act globally on chromatin domains that contain transcriptionally co-regulated genes.

Comparative effects of oleoyl-estrone and a specific b3-adrenergic agonist (CL316, 243) on the expression of genes involved in energy metabolism of rat white adipose tissue

Background: The combination of oleoyl-estrone (OE) and a selective b3-adrenergic agonist (B3A; CL316,243) treatment in rats results in a profound and rapid wasting of body reserves (lipid). Methods: In the present study we investigated the effect of OE (oral gavage) and/or B3A (subcutaneous constant infusion) administration for 10 days to overweight male rats, compared with controls, on three distinct white adipose tissue (WAT) sites: subcutaneous inguinal, retroperitoneal and epididymal. Tissue weight, DNA (and, from these values cellularity), cAMP content and the expression of several key energy handling metabolism and control genes were analyzed and computed in relation to the whole site mass. Results: Both OE and B3A significantly decreased WAT mass, with no loss of DNA (cell numbers). OE decreased and B3A increased cAMP. Gene expression patterns were markedly different for OE and B3A. OE tended to decrease expression of most genes studied, with no changes (versus controls) of lipolytic but decrease of lipogenic enzyme genes. The effects of B3A were widely different, with a generalized increase in the expression of most genes, including the adrenergic receptors, and, especially the uncoupling protein UCP1. Discussion: OE and B3A, elicit widely different responses in WAT gene expression, end producing similar effects, such as shrinking of WAT, loss of fat, maintenance of cell numbers. OE acted essentially on the balance of lipolysislipogenesis and the blocking of the uptake of substrates; its decrease of synthesis favouring lipolysis. B3A induced a shotgun increase in the expression of most regulatory systems in the adipocyte, an effect that in the end favoured again the loss of lipid; this barely selective increase probably produces inefficiency, which coupled with the increase in UCP1 expression may help WAT to waste energy through thermogenesis. Conclusions: There were considerable differences in the responses of the three WAT sites. OE in general lowered gene expression and stealthily induced a substrate imbalance. B3A increasing the expression of most genes enhanced energy waste through inefficiency rather than through specific pathway activation. There was not a synergistic effect between OE and B3A in WAT, but their combined action increased WAT energy waste.

Essential residues in the H-NS binding site of Hha, a co-regulator of horizontally acquired genes in Enterobacteria

Proteins of the Hha/YmoA family co-regulate with H-NS the expression of horizontally acquired genes in Enterobacteria. Systematic mutations of conserved acidic residues in Hha have allowed the identification of D48 as an essential residue for H-NS binding and the involvement of E25. Mutations of these residues resulted in deregulation of sensitive genes in vivo. D48 is only partially solvent accessible, yet it defines the functional binding interface between Hha and H-NS confirming that Hha has to undergo a conformational change to bind H-NS. Exposed acidic residues, such as E25, may electrostatically facilitate and direct the approach of Hha to the positively charged region of H-NS enabling the formation of the final complex when D48 becomes accessible by a conformational change of Hha.

Differences in the evolutionary history of disease genes affected by dominant or recessive mutations

Background: Global analyses of human disease genes by computational methods have yielded important advances in the understanding of human diseases. Generally these studies have treated the group of disease genes uniformly, thus ignoring the type of disease-causing mutations (dominant or recessive). In this report we present a comprehensive study of the evolutionary history of autosomal disease genes separated by mode of inheritance.Results: We examine differences in protein and coding sequence conservation between dominant and recessive human disease genes. Our analysis shows that disease genes affected by dominant mutations are more conserved than those affected by recessive mutations. This could be a consequence of the fact that recessive mutations remain hidden from selection while heterozygous. Furthermore, we employ functional annotation analysis and investigations into disease severity to support this hypothesis. Conclusion: This study elucidates important differences between dominantly- and recessively-acting disease genes in terms of protein and DNA sequence conservation, paralogy and essentiality. We propose that the division of disease genes by mode of inheritance will enhance both understanding of the disease process and prediction of candidate disease genes in the future.