Pedro Carmona-Saez

Integrated analysis of gene expression by association rules discovery

BackgroundMicroarray technology is generating huge amounts of data about the expression level of thousands of genes, or even whole genomes, across different experimental conditions. To extract biological knowledge, and to fully understand such datasets, it is essential to include external biological information about genes and gene products to the analysis of expression data. However, most of the current approaches to analyze microarray datasets are mainly focused on the analysis of experimental data, and external biological information is incorporated as a posterior process.ResultsIn this study we present a method for the integrative analysis of microarray data based on the Association Rules Discovery data mining technique. The approach integrates gene annotations and expression data to discover intrinsic associations among both data sources based on co-occurrence patterns. We applied the proposed methodology to the analysis of gene expression datasets in which genes were annotated with metabolic pathways, transcriptional regulators and Gene Ontology categories. Automatically extracted associations revealed significant relationships among these gene attributes and expression patterns, where many of them are clearly supported by recently reported work.ConclusionThe integration of external biological information and gene expression data can provide insights about the biological processes associated to gene expression programs. In this paper we show that the proposed methodology is able to integrate multiple gene annotations and expression data in the same analytic framework and extract meaningful associations among heterogeneous sources of data. An implementation of the method is included in the Enge ne software package.

Discovering semantic features in the literature: a foundation for building functional associations

BackgroundExperimental techniques such as DNA microarray, serial analysis of gene expression (SAGE) and mass spectrometry proteomics, among others, are generating large amounts of data related to genes and proteins at different levels. As in any other experimental approach, it is necessary to analyze these data in the context of previously known information about the biological entities under study. The literature is a particularly valuable source of information for experiment validation and interpretation. Therefore, the development of automated text mining tools to assist in such interpretation is one of the main challenges in current bioinformatics research.ResultsWe present a method to create literature profiles for large sets of genes or proteins based on common semantic features extracted from a corpus of relevant documents. These profiles can be used to establish pair-wise similarities among genes, utilized in gene/protein classification or can be even combined with experimental measurements. Semantic features can be used by researchers to facilitate the understanding of the commonalities indicated by experimental results. Our approach is based on non-negative matrix factorization (NMF), a machine-learning algorithm for data analysis, capable of identifying local patterns that characterize a subset of the data. The literature is thus used to establish putative relationships among subsets of genes or proteins and to provide coherent justification for this clustering into subsets. We demonstrate the utility of the method by applying it to two independent and vastly different sets of genes.ConclusionThe presented method can create literature profiles from documents relevant to sets of genes. The representation of genes as additive linear combinations of semantic features allows for the exploration of functional associations as well as for clustering, suggesting a valuable methodology for the validation and interpretation of high-throughput experimental data.

SPACE: an algorithm to predict and quantify alternatively spliced isoforms using microarrays

Exon and exon+junction microarrays are promising tools for studying alternative splicing. Current analytical tools applied to these arrays lack two relevant features: the ability to predict unknown spliced forms and the ability to quantify the concentration of known and unknown isoforms. SPACE is an algorithm that has been developed to (1) estimate the number of different transcripts expressed under several conditions, (2) predict the precursor mRNA splicing structure and (3) quantify the transcript concentrations including unknown forms. The results presented here show its robustness and accuracy for real and simulated data.

Shared signatures between rheumatoid arthritis, systemic lupus erythematosus and Sjögren’s syndrome uncovered through gene expression meta-analysis

IntroductionSystemic lupus erythematosus (SLE), rheumatoid arthritis (RA) and Sjögren’s syndrome (SjS) are inflammatory systemic autoimmune diseases (SADs) that share several clinical and pathological features. The shared biological mechanisms are not yet fully characterized. The objective of this study was to perform a meta-analysis using publicly available gene expression data about the three diseases to identify shared gene expression signatures and overlapping biological processes.MethodsPreviously reported gene expression datasets were selected and downloaded from the Gene Expression Omnibus database. Normalization and initial preprocessing were performed using the statistical programming language R and random effects model–based meta-analysis was carried out using INMEX software. Functional analysis of over- and underexpressed genes was done using the GeneCodis tool.ResultsThe gene expression meta-analysis revealed a SAD signature composed of 371 differentially expressed genes in patients and healthy controls, 187 of which were underexpressed and 184 overexpressed. Many of these genes have previously been reported as significant biomarkers for individual diseases, but others provide new clues to the shared pathological state. Functional analysis showed that overexpressed genes were involved mainly in immune and inflammatory responses, mitotic cell cycles, cytokine-mediated signaling pathways, apoptotic processes, type I interferon–mediated signaling pathways and responses to viruses. Underexpressed genes were involved primarily in inhibition of protein synthesis.ConclusionsWe define a common gene expression signature for SLE, RA and SjS. The analysis of this signature revealed relevant biological processes that may play important roles in the shared development of these pathologies.

A literature-based similarity metric for biological processes

BackgroundRecent analyses in systems biology pursue the discovery of functional modules within the cell. Recognition of such modules requires the integrative analysis of genome-wide experimental data together with available functional schemes. In this line, methods to bridge the gap between the abstract definitions of cellular processes in current schemes and the interlinked nature of biological networks are required.ResultsThis work explores the use of the scientific literature to establish potential relationships among cellular processes. To this end we haveused a document based similarity method to compute pair-wise similarities of the biological processes described in the Gene Ontology (GO). The method has been applied to the biological processes annotated for the Saccharomyces cerevisiae genome. We compared our results with similarities obtained with two ontology-based metrics, as well as with gene product annotation relationships. We show that the literature-based metric conserves most direct ontological relationships, while reveals biologically sounded similarities that are not obtained using ontology-based metrics and/or genome annotation.ConclusionThe scientific literature is a valuable source of information from which to compute similarities among biological processes. The associations discovered by literature analysis are a valuable complement to those encoded in existing functional schemes, and those that arise by genome annotation. These similarities can be used to conveniently map the interlinked structure of cellular processes in a particular organism.

Prognostic factor analysis of circulating tumor cells in peripheral blood of patients with peritoneal carcinomatosis of colon cancer origin treated with cytoreductive surgery plus an intraoperative hyperthermic intraperitoneal chemotherapy procedure (CRS + HIPEC)

PURPOSE Complete cytoreductive surgery (CRS) with hyperthermic intraperitoneal chemotherapy (HIPEC) has changed the therapeutic landscape, improving overall survival in patients with peritoneal carcinomatosis with a colonic origin. The main limitation of this aggressive locoregional procedure, however, is extra-abdominal or distant spread. The objective of this study was to identify the prognostic value of circulating tumor cells (CTCs) in patients with peritoneal carcinomatosis of colonic origin undergoing CRS + HIPEC. PATIENTS AND METHODS Fourteen patients diagnosed with peritoneal carcinomatosis from colon cancer and suitable for potentially curative treatment with CRS + HIPEC were included in this study. CTCs were isolated from the peripheral blood by immunomagnetic techniques by the use of a multi-cytokeratin-specific antibody and detected via immunocytochemical methods. The phenotypic characterization of EGFR on CTCs was analyzed by immunofluorescence. RESULTS At baseline, 50% of the patients were positive for CTCs, with a mean value of 5.5 CTCs per 10 mL of peripheral blood. After surgery, 28.57% of the patients presented CTCs, with a mean value of 6.75 CTCs per 10 mL. A positive correlation was found between the presence of CTC-negative, epidermal growth factor receptor-positive at baseline and the patients who had symptoms of intestinal obstruction (21.4%). In addition, the presence of CTCs identified patients with distant dissemination and was also significantly correlated with progression-free survival (P = .0024). CONCLUSION The detection and characterization of CTCs are good prognostic and predictive markers in patients with peritoneal carcinomatosis resulting from colon cancer. These analyses could be used as a new tool to identify subpopulations of patients who could benefit from CRS + HIPEC treatment.

Prognostic role of genetic biomarkers in clinical progression of prostate cancer

The aim of this study was to analyze the use of 12 single-nucleotide polymorphisms in genes ELAC2, RNASEL and MSR1 as biomarkers for prostate cancer (PCa) detection and progression, as well as perform a genetic classification of high-risk patients. A cohort of 451 men (235 patients and 216 controls) was studied. We calculated means of regression analysis using clinical values (stage, prostate-specific antigen, Gleason score and progression) in patients and controls at the basal stage and after a follow-up of 72 months. Significantly different allele frequencies between patients and controls were observed for rs1904577 and rs918 (MSR1 gene) and for rs17552022 and rs5030739 (ELAC2). We found evidence of increased risk for PCa in rs486907 and rs2127565 in variants AA and CC, respectively. In addition, rs627928 (TT–GT), rs486907 (AG) and rs3747531 (CG–CC) were associated with low tumor aggressiveness. Some had a weak linkage, such as rs1904577 and rs2127565, rs4792311 and rs17552022, and rs1904577 and rs918. Our study provides the proof-of-principle that some of the genetic variants (such as rs486907, rs627928 and rs2127565) in genes RNASEL, MSR1 and ELAC2 can be used as predictors of aggressiveness and progression of PCa. In the future, clinical use of these biomarkers, in combination with current ones, could potentially reduce the rate of unnecessary biopsies and specific treatments.

Support for phosphoinositol 3 kinase and mTOR inhibitors as treatment for lupus using in-silico drug-repurposing analysis

BackgroundSystemic lupus erythematosus (SLE) is an autoimmune disease with few treatment options. Current therapies are not fully effective and show highly variable responses. In this regard, large efforts have focused on developing more effective therapeutic strategies. Drug repurposing based on the comparison of gene expression signatures is an effective technique for the identification of new therapeutic approaches. Here we present a drug-repurposing exploratory analysis using gene expression signatures from SLE patients to discover potential new drug candidates and target genes.MethodsWe collected a compendium of gene expression signatures comprising peripheral blood cells and different separate blood cell types from SLE patients. The Lincscloud database was mined to link SLE signatures with drugs, gene knock-down, and knock-in expression signatures. The derived dataset was analyzed in order to identify compounds, genes, and pathways that were significantly correlated with SLE gene expression signatures.ResultsWe obtained a list of drugs that showed an inverse correlation with SLE gene expression signatures as well as a set of potential target genes and their associated biological pathways. The list includes drugs never or little studied in the context of SLE treatment, as well as recently studied compounds.ConclusionOur exploratory analysis provides evidence that phosphoinositol 3 kinase and mammalian target of rapamycin (mTOR) inhibitors could be potential therapeutic options in SLE worth further future testing.

Metagene projection characterizes GEN2.2 and CAL-1 as relevant human plasmacytoid dendritic cell models

Motivation Plasmacytoid dendritic cells (pDC) play a major role in the regulation of adaptive and innate immunity. Human pDC are difficult to isolate from peripheral blood and do not survive in culture making the study of their biology challenging. Recently, two leukemic counterparts of pDC, CAL-1 and GEN2.2, have been proposed as representative models of human pDC. Nevertheless, their relationship with pDC has been established only by means of particular functional and phenotypic similarities. With the aim of characterizing GEN2.2 and CAL-1 in the context of the main circulating immune cell populations we have performed microarray gene expression profiling of GEN2.2 and carried out an integrated analysis using publicly available gene expression datasets of CAL-1 and the main circulating primary leukocyte lineages. Results Our results show that GEN2.2 and CAL-1 share common gene expression programs with primary pDC, clustering apart from the rest of circulating hematopoietic lineages. We have also identified common differentially expressed genes that can be relevant in pDC biology. In addition, we have revealed the common and differential pathways activated in primary pDC and cell lines upon CpG stimulatio. Availability and implementation R code and data are available in the supplementary material. Contact pedro.carmona@genyo.es or concepcion.maranon@genyo.es. Supplementary information Supplementary data are available at Bioinformatics online.

Genetic polymorphisms influence on the response to clopidogrel in peripheral artery disease patients following percutaneous transluminal angioplasty.

AIM To study the association of ABCB1 and CYP2C19 polymorphisms and the clopidogrel response in Spanish peripheral artery disease patients following percutaneous transluminal angioplasty (PTA) and to perform a meta-analysis. MATERIALS & METHODS 72 patients were recruited and 122 patients included in the meta-analysis. We evaluated the effect of ABCB1 3435 C>T, CYP2C19*2 and CYP2C19*3 and primary end point (restenosis/occlusion of the treated lesions) during 12 months after PTA. RESULTS CYP2C19*2 and/or ABCB1 TT patients were associated with primary end point (OR: 5.00; 95% CI: 1.75-14.27). The meta-analysis confirmed the association of CYP2C19*2 and new atherothrombotic ischemic events (OR: 5.40; 95% CI: 2.30-12.70). CONCLUSION The CYP2C19 and ABCB1 polymorphisms could be genetic markers of cardiovascular events in peripheral artery disease patients following PTA treated with clopidogrel.