Arnald Alonso

Analytical Methods in Untargeted Metabolomics: State of the Art in 2015

Metabolomics comprises the methods and techniques that are used to measure the small molecule composition of biofluids and tissues, and is actually one of the most rapidly evolving research fields. The determination of the metabolomic profile – the metabolome – has multiple applications in many biological sciences, including the developing of new diagnostic tools in medicine. Recent technological advances in nuclear magnetic resonance and mass spectrometry are significantly improving our capacity to obtain more data from each biological sample. Consequently, there is a need for fast and accurate statistical and bioinformatic tools that can deal with the complexity and volume of the data generated in metabolomic studies. In this review, we provide an update of the most commonly used analytical methods in metabolomics, starting from raw data processing and ending with pathway analysis and biomarker identification. Finally, the integration of metabolomic profiles with molecular data from other high-throughput biotechnologies is also reviewed.

GWAS replication study confirms the association of PDE3A–SLCO1C1 with anti-TNF therapy response in rheumatoid arthritis

AIM The present study was undertaken to replicate the association of candidate genes for anti-TNF response in rheumatoid arthritis. Candidate genes were selected from a recent genome-wide association study on anti-TNF response performed in a population from Denmark. MATERIALS & METHODS Genomic DNA was obtained from 315 Spanish rheumatoid arthritis patients having received an anti-TNF agent as their first biological therapy. SNPs from NR2FR2, MAP2K6, CBLN2 and PDE3A-SLCO1C1 candidate loci were genotyped. RESULTS The PDE3A-SLCO1C1 locus rs3794271 SNP showed a highly significant association with anti-TNF treatment response (p = 1.74 × 10⁻⁵). Combining the statistical evidence from the Spanish and Danish rheumatoid arthritis cohorts, the associated rs3794271 SNP reached a genome-wide significance level of association (p = 3.3 × 10⁻¹⁰). CONCLUSION The present findings establish the PDE3A-SLCO1C1 locus as a strong genetic marker of anti-TNF therapy response.

Use of antidepressants and the risk of Parkinson's disease

Background: Individuals with depression have a higher risk of Parkinson’s disease (PD) but the timing of the association is unknown. Therefore, the relationship between initiation of antidepressant therapy and PD risk was assessed in a large population based database from the UK and the timing of this association was explored. Methods: A case control study nested in the General Practice Research Database cohort, a large computerised database with clinical information from more than 3 million individuals in the UK, was conducted. Cases of PD were identified from the computer records from 1995 to 2001 and matched with up to 10 controls by age, sex and practice. Use of antidepressants was obtained from the computer records. Results: 999 PD cases and 6261 controls were included. The rate ratio (RR) and 95% CI of PD in initiators of antidepressant therapy compared with non-initiators was 1.85 (1.25 to 2.75). The association was stronger during the first 2 years after initiation of medication use (RR 2.19; 95% CI 1.38 to 3.46) than later (RR 1.23; 95% CI 0.57 to 2.67). Results were similar for selective serotonin reuptake inhibitors and tricyclic antidepressants separately. Conclusion: Initiation of any antidepressant therapy was associated with a higher risk of PD in the 2 years after the start of treatment, which suggests that depressive symptoms could be an early manifestation of PD, preceding motor dysfunction.

AStream: an R package for annotating LC/MS metabolomic data

UNLABELLED AStream, an R-statistical software package for the curation and identification of feature peaks extracted from liquid chromatography mass spectrometry (LC/MS) metabolomics data, is described. AStream detects isotopic, fragment and adduct patterns by identifying feature pairs that fulfill expected relational patterns. Data reduction by AStream allows compounds to be identified reliably and subsequently linked to metabolite databases. AStream provides researchers with a fast, reliable tool for summarizing metabolomic data, notably reducing curation time and increasing consistency of results. AVAILABILITY The AStream R package and a study example can be freely accessed at http://www.urr.cat/AStream/AStream.html.

A genome-wide association study on a southern European population identifies a new Crohn's disease susceptibility locus at RBX1-EP300

Objective Genome-wide association studies (GWAS) have identified multiple risk loci for Crohns disease (CD). However, the cumulative risk exerted by these loci is low, and the likelihood that additional, as-yet undiscovered loci contribute to the risk of CD is very high. We performed a GWAS on a southern European population to identify new CD risk loci. Design We genotyped 620 901 genome markers on 1341 CD patients and 1518 controls from Spain. The top association signals representing new candidate risk loci were subsequently analysed in an independent replication cohort of 1365 CD patients and 1396 controls. Results We identified a genome-wide significant association on chromosome 22q13.2 in the intergenic region between the RBX1 and EP300 genes (single nucleotide polymorphism rs4820425, OR 1.27, 95% CI 1.17 to 1.38, p=3.42E-8). We also found suggestive evidence for the association of the IFNGR2 (21q22.11), FOXP2 (7q31), MACROD2 (20p12.1) and AIF1 (6p21.3) loci with CD risk. Conclusions In this GWAS performed on a southern European cohort, we have identified a new risk locus for CD between RBX1 and EP300. This study demonstrates that using populations of different ancestry is a useful strategy to identify new risk loci for CD.

Focus: A Robust Workflow for One-Dimensional NMR Spectral Analysis

One-dimensional (1)H NMR represents one of the most commonly used analytical techniques in metabolomic studies. The increase in the number of samples analyzed as well as the technical improvements involving instrumentation and spectral acquisition demand increasingly accurate and efficient high-throughput data processing workflows. We present FOCUS, an integrated and innovative methodology that provides a complete data analysis workflow for one-dimensional NMR-based metabolomics. This tool will allow users to easily obtain a NMR peak feature matrix ready for chemometric analysis as well as metabolite identification scores for each peak that greatly simplify the biological interpretation of the results. The algorithm development has been focused on solving the critical difficulties that appear at each data processing step and that can dramatically affect the quality of the results. As well as method integration, simplicity has been one of the main objectives in FOCUS development, requiring very little user input to perform accurate peak alignment, peak picking, and metabolite identification. The new spectral alignment algorithm, RUNAS, allows peak alignment with no need of a reference spectrum, and therefore, it reduces the bias introduced by other alignment approaches. Spectral alignment has been tested against previous methodologies obtaining substantial improvements in the case of moderate or highly unaligned spectra. Metabolite identification has also been significantly improved, using the positional and correlation peak patterns in contrast to a reference metabolite panel. Furthermore, the complete workflow has been tested using NMR data sets from 60 human urine samples and 120 aqueous liver extracts, reaching a successful identification of 42 metabolites from the two data sets. The open-source software implementation of this methodology is available at http://www.urr.cat/FOCUS.

Identification of risk loci for Crohn's disease phenotypes using a genome-wide association study.

BACKGROUND & AIMS Crohns disease is a highly heterogeneous inflammatory bowel disease comprising multiple clinical phenotypes. Genome-wide association studies (GWASs) have associated a large number of loci with disease risk but have not associated any specific genetic variants with clinical phenotypes. We performed a GWAS of clinical phenotypes in Crohns disease. METHODS We genotyped 576,818 single-nucleotide polymorphisms in a well-characterized cohort of 1090 Crohns disease patients of European ancestry. We assessed their association with 17 phenotypes of Crohns disease (based on disease location, disease behavior, disease course, age at onset, and extraintestinal manifestations). A total of 57 markers with strong associations to Crohns disease phenotypes (P < 2 × 10(-4)) were subsequently analyzed in an independent replication cohort of 1296 patients of European ancestry. RESULTS We replicated the association of 4 loci with different Crohns disease phenotypes. Variants in MAGI1, CLCA2, 2q24.1, and LY75 loci were associated with a complicated stricturing disease course (Pcombined = 2.01 × 10(-8)), disease location (Pcombined = 1.3 × 10(-6)), mild disease course (Pcombined = 5.94 × 10(-7)), and erythema nodosum (Pcombined = 2.27 × 10(-6)), respectively. CONCLUSIONS In a GWAS, we associated 4 loci with clinical phenotypes of Crohns disease. These findings indicate a genetic basis for the clinical heterogeneity observed for this inflammatory bowel disease.

A genome-wide association study identifies a novel locus at 6q22.1 associated with ulcerative colitis

The genetic analysis of ulcerative colitis (UC) has provided new insights into the etiology of this prevalent inflammatory bowel disease. However, most of the heritability of UC (>70%) has still not been characterized. To identify new risk loci for UC we have performed the first genome-wide association study (GWAS) in a Southern European population and undertaken a meta-analysis study combining the newly genotyped 825 UC patients and 1525 healthy controls from Spain with the six previously published GWAS comprising 6687 cases and 19 718 controls from Northern-European ancestry. We identified a novel locus with genome-wide significance at 6q22.1 [rs2858829, P = 8.97 × 10(-9), odds ratio (OR) (95% confidence interval, CI] = 1.12 (1.08-1.16)] that was validated with genotype data from a replication cohort of the same Southern European ancestry consisting in 1073 cases and 1279 controls [combined P = 7.59 × 10(-10), OR (95% CI) = 1.12 (1.08-1.16)]. Furthermore, we confirmed the association of 33 reported associations with UC and we nominally validated the GWAS results of nine new risk loci (P < 0.05, same direction of effect). SNP rs2858829 lies in an intergenic region and is a strong cis-eQTL for FAM26F gene, a gene that is shown to be selectively upregulated in UC colonic mucosa with active inflammation. Our results provide new insight into the genetic risk background of UC, confirming that there is a genetic risk component that differentiates from Crohns Disease, the other major form of inflammatory bowel disease.

CNstream: A method for the identification and genotyping of copy number polymorphisms using Illumina microarrays

BackgroundUnderstanding the genetic basis of disease risk in depth requires an exhaustive knowledge of the types of genetic variation. Very recently, Copy Number Variants (CNVs) have received much attention because of their potential implication in common disease susceptibility. Copy Number Polymorphisms (CNPs) are of interest as they segregate at an appreciable frequency in the general population (i.e. > 1%) and are potentially implicated in the genetic basis of common diseases.ResultsThis paper concerns CNstream, a method for whole-genome CNV discovery and genotyping, using Illumina Beadchip arrays. Compared with other methods, a high level of accuracy was achieved by analyzing the measures of each intensity channel separately and combining information from multiple samples. The CNstream method uses heuristics and parametrical statistics to assign a confidence score to each sample at each probe; the sensitivity of the analysis is increased by jointly calling the copy number state over a set of nearby and consecutive probes. The present method has been tested on a real dataset of 575 samples genotyped using Illumina HumanHap 300 Beadchip, and demonstrates a high correlation with the Database of Genomic Variants (DGV). The same set of samples was analyzed with PennCNV, one of the most frequently used copy number inference methods for Illumina platforms. CNstream was able to identify CNP loci that are not detected by PennCNV and it increased the sensitivity over multiple other loci in the genome.ConclusionsCNstream is a useful method for the identification and characterization of CNPs using Illumina genotyping microarrays. Compared to the PennCNV method, it has greater sensitivity over multiple CNP loci and allows more powerful statistical analysis in these regions. Therefore, CNstream is a robust CNP analysis tool of use to researchers performing genome-wide association studies (GWAS) on Illumina platforms and aiming to identify CNVs associated with the variables of interest. CNstream has been implemented as an R statistical software package that can work directly from raw intensity files generated from Illumina GWAS projects. The method is available at http://www.urr.cat/cnv/cnstream.html.

Urine metabolome profiling of immune-mediated inflammatory diseases

BackgroundImmune-mediated inflammatory diseases (IMIDs) are a group of complex and prevalent diseases where disease diagnostic and activity monitoring is highly challenging. The determination of the metabolite profiles of biological samples is becoming a powerful approach to identify new biomarkers of clinical utility. In order to identify new metabolite biomarkers of diagnosis and disease activity, we have performed the first large-scale profiling of the urine metabolome of the six most prevalent IMIDs: rheumatoid arthritis, psoriatic arthritis, psoriasis, systemic lupus erythematosus, Crohn’s disease, and ulcerative colitis.MethodsUsing nuclear magnetic resonance, we analyzed the urine metabolome in a discovery cohort of 1210 patients and 100 controls. Within each IMID, two patient subgroups were recruited representing extreme disease activity (very high vs. very low). Metabolite association analysis with disease diagnosis and disease activity was performed using multivariate linear regression in order to control for the effects of clinical, epidemiological, or technical variability. After multiple test correction, the most significant metabolite biomarkers were validated in an independent cohort of 1200 patients and 200 controls.ResultsIn the discovery cohort, we identified 28 significant associations between urine metabolite levels and disease diagnosis and three significant metabolite associations with disease activity (PFDR < 0.05). Using the validation cohort, we validated 26 of the diagnostic associations and all three metabolite associations with disease activity (PFDR < 0.05). Combining all diagnostic biomarkers using multivariate classifiers we obtained a good disease prediction accuracy in all IMIDs and particularly high in inflammatory bowel diseases. Several of the associated metabolites were found to be commonly altered in multiple IMIDs, some of which can be considered as hub biomarkers. The analysis of the metabolic reactions connecting the IMID-associated metabolites showed an over-representation of citric acid cycle, phenylalanine, and glycine-serine metabolism pathways.ConclusionsThis study shows that urine is a source of biomarkers of clinical utility in IMIDs. We have found that IMIDs show similar metabolic changes, particularly between clinically similar diseases and we have found, for the first time, the presence of hub metabolites. These findings represent an important step in the development of more efficient and less invasive diagnostic and disease monitoring methods in IMIDs.