Alex Sánchez-Pla

Aberrant epigenome in iPSC‐derived dopaminergic neurons from Parkinson's disease patients

The epigenomic landscape of Parkinsons disease (PD) remains unknown. We performed a genomewide DNA methylation and a transcriptome studies in induced pluripotent stem cell (iPSC)‐derived dopaminergic neurons (DAn) generated by cell reprogramming of somatic skin cells from patients with monogenic LRRK2‐associated PD (L2PD) or sporadic PD (sPD), and healthy subjects. We observed extensive DNA methylation changes in PD DAn, and of RNA expression, which were common in L2PD and sPD. No significant methylation differences were present in parental skin cells, undifferentiated iPSCs nor iPSC‐derived neural cultures not‐enriched‐in‐DAn. These findings suggest the presence of molecular defects in PD somatic cells which manifest only upon differentiation into the DAn cells targeted in PD. The methylation profile from PD DAn, but not from controls, resembled that of neural cultures not‐enriched‐in‐DAn indicating a failure to fully acquire the epigenetic identity own to healthy DAn in PD. The PD‐associated hypermethylation was prominent in gene regulatory regions such as enhancers and was related to the RNA and/or protein downregulation of a network of transcription factors relevant to PD (FOXA1, NR3C1, HNF4A, and FOSL2). Using a patient‐specific iPSC‐based DAn model, our study provides the first evidence that epigenetic deregulation is associated with monogenic and sporadic PD.

Transcriptomics: mRNA and alternative splicing.

Transcriptomics has emerged as a powerful approach for biomarker discovery. In the present review, the two main types of high throughput transcriptomic technologies - microarrays and next generation sequencing - that can be used to identify candidate biomarkers are briefly described. Microarrays, the mainstream technology of the last decade, have provided hundreds of valuable datasets in a wide variety of diseases including multiple sclerosis (MS), in which this approach has been used to disentangle different aspects of its complex pathogenesis. RNA-seq, the current next generation sequencing approach, is expected to provide similar power as microarrays but extending their capabilities to aspects up to now more difficult to analyse such as alternative splicing and discovery of novel transcripts.

Microarray expression analysis in idiopathic and LRRK2-associated Parkinson's disease

LRRK2 mutations are the most common genetic cause of Parkinsons disease (PD). We performed a whole-genome RNA profiling of putamen tissue from idiopathic PD (IPD), LRRK2-associated PD (G2019S mutation), neurologically healthy controls and one asymptomatic LRRK2 mutation carrier, by using the Genechip Human Exon 1.0-ST Array. The differentially expressed genes found in IPD revealed an alteration of biological pathways related to long-term potentiation (LTP), GABA receptor signalling, and calcium signalling pathways, among others. These pathways are mainly related with cell signalling cascades and synaptic plasticity processes. They were also altered in the asymptomatic LRRK2 mutation carrier but not in the LRRK2-associated PD group. The expression changes seen in IPD might be attributed to an adaptive consequence of a dysfunction in the dopamine transmission. The lack of these altered molecular pathways in LRRK2-associated PD patients suggests that these cases could show a different molecular response to dopamine transmission impairment.

Molecular mechanisms of tungstate-induced pancreatic plasticity: a transcriptomics approach

BackgroundSodium tungstate is known to be an effective anti-diabetic agent, able to increase beta cell mass in animal models of diabetes, although the molecular mechanisms of this treatment and the genes that control pancreas plasticity are yet to be identified. Using a transcriptomics approach, the aim of the study is to unravel the molecular mechanisms which participate in the recovery of exocrine and endocrine function of streptozotocin (STZ) diabetic rats treated with tungstate, determining the hyperglycemia contribution and the direct effect of tungstate.ResultsStreptozotocin (STZ)-diabetic rats were treated orally with tungstate for five weeks. Treated (STZ)-diabetic rats showed a partial recovery of exocrine and endocrine function, with lower glycemia, increased insulinemia and amylasemia, and increased beta cell mass achieved by reducing beta cell apoptosis and raising beta cell proliferation. The microarray analysis of the pancreases led to the identification of three groups of differentially expressed genes: genes altered due to diabetes, genes restored by the treatment, and genes specifically induced by tungstate in the diabetic animals. The results were corroborated by quantitative PCR. A detailed description of the pathways involved in the pancreatic effects of tungstate is provided in this paper. Hyperglycemia contribution was studied in STZ-diabetic rats treated with phloridzin, and the direct effect of tungstate was determined in INS-1E cells treated with tungstate or serum from untreated or treated STZ-rats, observing that tungstate action in the pancreas takes places via hyperglycemia-independent pathways and via a combination of tungstate direct and indirect (through the serum profile modification) effects. Finally, the MAPK pathway was evaluated, observing that it has a key role in the tungstate-induced increase of beta cell proliferation as tungstate activates the mitogen-activated protein kinase (MAPK) pathway directly by increasing p42/p44 phosphorylation and indirectly by decreasing the expression of raf kinase inhibitor protein (Rkip), a negative modulator of the pathway.ConclusionIn conclusion, tungstate improves pancreatic function through a combination of hyperglycemia-independent pathways and through its own direct and indirect effects, whereas the MAPK pathway has a key role in the tungstate-induced increase of beta cell proliferation.

PTOV1 is overexpressed in human high-grade malignant tumors

The prostate tumor overexpressed-1 (PTOV1) protein was first described overexpressed in prostate cancer but not detected in normal prostate. PTOV1 expression is associated to increased cancer proliferation in vivo and in vitro. In prostate biopsy, PTOV1 detection is helpful in the early diagnosis of cancer. The purpose of this study was to analyze the relevance of PTOV1 expression to identify aggressive tumors derived from 12 different histological tissues. Tissue microarrays (TMAs) containing 182 biopsy samples, including 168 human tumors, were analyzed for PTOV1 and Ki67 expression by immunohistochemistry. Tumors of low and high histological grade were selected from lung, breast, endometrium, pancreas liver, skin, ovary, colon, stomach, kidney, bladder, and cerebral gliomas. One TMA with representative tissues without cancer (14 samples) was used as control. PTOV1 expression was analyzed semiquantitatively for the intensity and percentage of positive cells. Ki67 was evaluated for tumors proliferative index. Results show that PTOV1 was expressed in over 95% of tumors examined. Its expression was significantly associated to high-grade tumors (p = 0.014). This association was most significant in urothelial bladder carcinomas (p = 0.026). Overall, the expression of Ki67 was associated to high-grade tumors, and it was significant in several tumor types. PTOV1 and Ki67 were significantly co-overexpressed in all tumors (p = 0.001), and this association was significant in clear cell renal carcinoma (p = 0.005). In conclusion, PTOV1 expression is associated to more aggressive human carcinomas and more significantly to bladder carcinomas suggesting that this protein is a potential new marker of aggressive disease in the latter tumors.

Brain transcriptomic profiling in idiopathic and LRRK2-associated Parkinson's disease

LRRK2 mutations are the most common genetic cause of Parkinsons disease (PD). We performed a whole-genome RNA profiling of locus coeruleus post-mortem tissue, a histopathologically affected brain tissue in PD, from idiopathic PD (IPD) and LRRK2-associated PD patients. The differentially expressed genes found in IPD and LRRK2-associated PD are involved in the gene ontology terms of synaptic transmission and neuron projection. In addition, differentially expressed genes in the IPD group are associated with immune system related pathways. Specifically, the study performed highlights the presence of differential expression of genes located in the chromosome 6p21.3 belonging to the class II HLA. Our findings support the hypothesis of a potential role of neuroinflammation and the involvement of the HLA genetic area in IPD pathogenesis. Future studies are necessary to shed light on the relation of immune system related pathways in the etiopathogenesis of PD.

Evaluation and comparison of bioinformatic tools for the enrichment analysis of metabolomics data

BackgroundBioinformatic tools for the enrichment of ‘omics’ datasets facilitate interpretation and understanding of data. To date few are suitable for metabolomics datasets. The main objective of this work is to give a critical overview, for the first time, of the performance of these tools. To that aim, datasets from metabolomic repositories were selected and enriched data were created. Both types of data were analysed with these tools and outputs were thoroughly examined.ResultsAn exploratory multivariate analysis of the most used tools for the enrichment of metabolite sets, based on a non-metric multidimensional scaling (NMDS) of Jaccard’s distances, was performed and mirrored their diversity. Codes (identifiers) of the metabolites of the datasets were searched in different metabolite databases (HMDB, KEGG, PubChem, ChEBI, BioCyc/HumanCyc, LipidMAPS, ChemSpider, METLIN and Recon2). The databases that presented more identifiers of the metabolites of the dataset were PubChem, followed by METLIN and ChEBI. However, these databases had duplicated entries and might present false positives. The performance of over-representation analysis (ORA) tools, including BioCyc/HumanCyc, ConsensusPathDB, IMPaLA, MBRole, MetaboAnalyst, Metabox, MetExplore, MPEA, PathVisio and Reactome and the mapping tool KEGGREST, was examined. Results were mostly consistent among tools and between real and enriched data despite the variability of the tools. Nevertheless, a few controversial results such as differences in the total number of metabolites were also found. Disease-based enrichment analyses were also assessed, but they were not found to be accurate probably due to the fact that metabolite disease sets are not up-to-date and the difficulty of predicting diseases from a list of metabolites.ConclusionsWe have extensively reviewed the state-of-the-art of the available range of tools for metabolomic datasets, the completeness of metabolite databases, the performance of ORA methods and disease-based analyses. Despite the variability of the tools, they provided consistent results independent of their analytic approach. However, more work on the completeness of metabolite and pathway databases is required, which strongly affects the accuracy of enrichment analyses. Improvements will be translated into more accurate and global insights of the metabolome.

Comparison of lists of genes based on functional profiles

BackgroundHow to compare studies on the basis of their biological significance is a problem of central importance in high-throughput genomics. Many methods for performing such comparisons are based on the information in databases of functional annotation, such as those that form the Gene Ontology (GO). Typically, they consist of analyzing gene annotation frequencies in some pre-specified GO classes, in a class-by-class way, followed by p-value adjustment for multiple testing. Enrichment analysis, where a list of genes is compared against a wider universe of genes, is the most common example.ResultsA new global testing procedure and a method incorporating it are presented. Instead of testing separately for each GO class, a single global test for all classes under consideration is performed. The test is based on the distance between the functional profiles, defined as the joint frequencies of annotation in a given set of GO classes. These classes may be chosen at one or more GO levels. The new global test is more powerful and accurate with respect to type I errors than the usual class-by-class approach. When applied to some real datasets, the results suggest that the method may also provide useful information that complements the tests performed using a class-by-class approach if gene counts are sparse in some classes. An R library, goProfiles, implements these methods and is available from Bioconductor, http://bioconductor.org/packages/release/bioc/html/goProfiles.html.ConclusionsThe method provides an inferential basis for deciding whether two lists are functionally different. For global comparisons it is preferable to the global chi-square test of homogeneity. Furthermore, it may provide additional information if used in conjunction with class-by-class methods.

DNA Microarrays Technology: Overview and Current Status

Abstract DNA microarray is a recent technology in which a high number of nucleic acid sequences are bound to a surface and are used to identify and quantify the DNA on a sample by letting both groups of sequences, in the sample and on the array, to hybridize, and subsequently identifying the hybridized sequences. Microarrays have been applied to all types of biological and medical problems, from cancer prognosis to the study of circadian cycles and fruit ripening. The most common types of applications are in gene expression but they have also been heavily used to quantify genetic variation, to detect aberrant numbers of copies associated with diseases, and in many other situations. Microarrays have been the technique of choice during the first decade of the twenty-first century for many applications, but with the advent of next-generation sequencing techniques it may be expected that some of their applications are adopted by this new technology, especially in those applications where microarrays show some limitations.

Hypothesis testing approaches to the exon prediction problem

MOTIVATION Many gene identification methods assign scores to gene elements prior to their assembly into predicted genes. The scoring system is often based on log-likelihood ratios. These methods usually perform well but it is difficult to interpret how significant a score is. RESULTS We have developed several tests of significance for the scores: (1) a sum-of-scores test (SST), (2) an intersection-union test (IUT), based on a multiple hypothesis testing interpretation of an exons score and (3) a meta-analytical approach (MA), which combines several P-values, corresponding to the exons parts, to yield a global P-value. We performed simulation studies, which show that the MA has better sensitivity and specificity than other methods and is easier to interpret by non-expert users. This is an improvement over other methods and is especially relevant for users who would like to predict incomplete gene sequences.