David Arteta

Genetic factors conferring an increased susceptibility to develop Crohn's disease also influence disease phenotype: results from the IBDchip European Project

Objective Through genome-wide association scans and meta-analyses thereof, over 70 genetic loci (Crohns disease (CD) single nucleotide polymorphisms (SNPs)) are significantly associated with CD. We aimed to investigate the influence of CD-SNPs and basic patient characteristics on CD clinical course, and develop statistical models to predict CD clinical course. Design This retrospective study included 1528 patients with CD with more than 10 years of follow-up from eight European referral hospitals. CD outcomes of interest were ileal (L1), colonic (L2) and ileocolonic disease location (L3); stenosing (B2) or penetrating behaviour (B3); perianal disease; extraintestinal manifestations; and bowel resection. A complicated disease course was defined as stenosing or penetrating behaviour, perianal disease and/or bowel resection. Association between CD-SNPs or patient characteristics and specified outcomes was studied. Results Several CD-SNPs and clinical characteristics were statistically associated with outcomes of interest. The NOD2 gene was the most important genetic factor, being an independent predictive factor for ileal location (p=2.02×10-06, OR=1.90), stenosing (p=3.16×10-06, OR=1.82) and penetrating (p=1.26×10-02, OR=1.25) CD behaviours, and need for surgery (p=2.28×e-05, OR=1.73), and as such was also the strongest factor associated with a complicated disease course (p=6.86×10-06, OR=2.96). Immunomodulator (azathioprine/6-mercaptopurine and methotrexate) use within 3 years after diagnosis led to a reduction in bowel stenoses (p=1.48×10-06, OR=0.35) and surgical rate (p=1.71×10-07, OR=0.34). Association between each outcome and genetic scores, created using significant SNPs in the univariate analysis, revealed large differences in the probability of developing fistulising disease (IL23R, LOC441108, PRDM1, NOD2; p=9.64e-4, HR=1.43), need for surgery (IRGM, TNFSF15, C13ORF31, NOD2; p=7.12×10-03, HR=1.35), and stenosing disease (NOD2, JAK2, ATG16L1; p=3.01×10-02, HR=1.29) among patients with low and high score. Conclusions This large multicentre cohort study has found several genetic and clinical factors influencing the clinical course of CD. NOD2 and early immunomodulator use are the clinically most meaningful predictors for its clinical course.

Can we predict top‐level sports performance in power vs endurance events? A genetic approach

The goal of our study was to discriminate potential genetic differences between humans who are in both endpoints of the sports performance continuum (i.e. world‐class endurance vs power athletes). We used DNA‐microarray technology that included 36 genetic variants (within 20 different genes) to compare the genetic profile obtained in two cohorts of world‐class endurance (N=100) and power male athletes (N=53) of the same ethnic origin. Stepwise multivariate logistic regression showed that the rs1800795 (IL6−174 G/C), rs1208 (NAT2 K268R) and rs2070744 (NOS3−786 T/C) polymorphisms significantly predicted sport performance (model χ2=25.3, df=3, P‐value <0.001). Receiver–operating characteristic (ROC) curve analysis showed a significant discriminating accuracy of the model, with an area under the ROC curve of 0.72 (95% confidence interval: 0.66–0.81). The contribution of the studied genetic factors to sports performance was 21.4%. In summary, although an individuals potential for excelling in endurance or power sports can be partly predicted based on specific genetic variants (many of which remain to be identified), the contribution of complex gene–gene interactions, environmental factors and epigenetic mechanisms are also important contributors to the “complex trait” of being an athletic champion. Such trait is likely not reducible to defined genetic polymorphisms.

The-174 G/C polymorphism of the IL6 gene is associated with elite power performance

The -174 G/C polymorphism [rs1800795] of the IL6 gene is a candidate to explain individual variations in health and exercise related phenotypes. We compared -174 G/C genotypic and allelic frequencies in three groups of men of the same Caucasian (Spanish) descent: elite endurance athletes (cyclists, runners; n=100); elite power athletes (jumpers, throwers, sprinters; n=53) and non-athletic controls (n=100). The frequency of the GG genotype (P=0.030) and G allele (P=0.026) was higher in the power athletes group compared with the control group. The frequency of the GG genotype (P=0.033) and G allele (P=0.013) was also higher in the power athletes group compared with the endurance athletes group. The odds ratio of being a power athlete if the subject had the GG genotype (dominant model) was 2.471 (95% confidence interval: 1.242-4.915) compared to the control group or the endurance athlete group. We did not find differences between the control and endurance athlete groups. In summary, our findings suggest that the G allele of the IL6 -174 G/C polymorphism might favour sprint/power sports performance.

De Novo Transcriptome Sequencing of the Octopus vulgaris Hemocytes Using Illumina RNA-Seq Technology: Response to the Infection by the Gastrointestinal Parasite Aggregata octopiana

Background Octopus vulgaris is a highly valuable species of great commercial interest and excellent candidate for aquaculture diversification; however, the octopus’ well-being is impaired by pathogens, of which the gastrointestinal coccidian parasite Aggregata octopiana is one of the most important. The knowledge of the molecular mechanisms of the immune response in cephalopods, especially in octopus is scarce. The transcriptome of the hemocytes of O. vulgaris was de novo sequenced using the high-throughput paired-end Illumina technology to identify genes involved in immune defense and to understand the molecular basis of octopus tolerance/resistance to coccidiosis. Results A bi-directional mRNA library was constructed from hemocytes of two groups of octopus according to the infection by A. octopiana, sick octopus, suffering coccidiosis, and healthy octopus, and reads were de novo assembled together. The differential expression of transcripts was analysed using the general assembly as a reference for mapping the reads from each condition. After sequencing, a total of 75,571,280 high quality reads were obtained from the sick octopus group and 74,731,646 from the healthy group. The general transcriptome of the O. vulgaris hemocytes was assembled in 254,506 contigs. A total of 48,225 contigs were successfully identified, and 538 transcripts exhibited differential expression between groups of infection. The general transcriptome revealed genes involved in pathways like NF-kB, TLR and Complement. Differential expression of TLR-2, PGRP, C1q and PRDX genes due to infection was validated using RT-qPCR. In sick octopuses, only TLR-2 was up-regulated in hemocytes, but all of them were up-regulated in caecum and gills. Conclusion The transcriptome reported here de novo establishes the first molecular clues to understand how the octopus immune system works and interacts with a highly pathogenic coccidian. The data provided here will contribute to identification of biomarkers for octopus resistance against pathogens, which could improve octopus farming in the near future.

The Expanded mtDNA Phylogeny of the Franco-Cantabrian Region Upholds the Pre-Neolithic Genetic Substrate of Basques

The European genetic landscape has been shaped by several human migrations occurred since Paleolithic times. The accumulation of archaeological records and the concordance of different lines of genetic evidence during the last two decades have triggered an interesting debate concerning the role of ancient settlers from the Franco-Cantabrian region in the postglacial resettlement of Europe. Among the Franco-Cantabrian populations, Basques are regarded as one of the oldest and more intriguing human groups of Europe. Recent data on complete mitochondrial DNA genomes focused on macrohaplogroup R0 revealed that Basques harbor some autochthonous lineages, suggesting a genetic continuity since pre-Neolithic times. However, excluding haplogroup H, the most representative lineage of macrohaplogroup R0, the majority of maternal lineages of this area remains virtually unexplored, so that further refinement of the mtDNA phylogeny based on analyses at the highest level of resolution is crucial for a better understanding of the European prehistory. We thus explored the maternal ancestry of 548 autochthonous individuals from various Franco-Cantabrian populations and sequenced 76 mitogenomes of the most representative lineages. Interestingly, we identified three mtDNA haplogroups, U5b1f, J1c5c1 and V22, that proved to be representative of Franco-Cantabria, notably of the Basque population. The seclusion and diversity of these female genetic lineages support a local origin in the Franco-Cantabrian area during the Mesolithic of southwestern Europe, ∼10,000 years before present (YBP), with signals of expansions at ∼3,500 YBP. These findings provide robust evidence of a partial genetic continuity between contemporary autochthonous populations from the Franco-Cantabrian region, specifically the Basques, and Paleolithic/Mesolithic hunter-gatherer groups. Furthermore, our results raise the current proportion (≈15%) of the Franco-Cantabrian maternal gene pool with a putative pre-Neolithic origin to ≈35%, further supporting the notion of a predominant Paleolithic genetic substrate in extant European populations.

Are elite endurance athletes genetically predisposed to lower disease risk

We compared a polygenic profile that combined 33 disease risk-related mutations and polymorphisms among nonathletic healthy control subjects and elite endurance athletes. The study sample comprised 100 healthy Spanish male nonathletic (sedentary) control subjects and 100 male elite endurance athletes. We analyzed 33 disease risk-related mutations and polymorphisms. We computed a health-related total genotype score (TGS, 0-100) from the accumulated combination of the 33 variants. We did not observe significant differences in genotype or allele distributions among groups, except for the rs4994 polymorphism (P < 0.001). The computed health-related TGS was similar among groups (23.8 +/- 1.0 vs. 24.2 +/- 0.8 in control subjects and athletes, respectively; P = 0.553). Similar results were obtained when computing specific TGSs for each main disease category (cardiovascular disease and cancer). We observed no evidence that male elite endurance athletes are genetically predisposed to have lower disease risk than matched nonathletic control subjects.

Analysis of multiple candidate genes in association with phenotypes of multiple sclerosis

Multiple sclerosis is a heterogeneous neurological disease with varying degrees of severity. The common hypothesis is that susceptibility to multiple sclerosis and its phenotype are caused by a combination of environmental and genetic factors. The genetic part exerts its effect through several genes, each having modest effects. We evaluated whether disease severity could be predicted by a model based on clinical data and data from a DNA chip. The DNA chip was designed containing several single nucleotide polymorphisms in 44 genes, previously described to be associated with multiple sclerosis. A total of 605 patients with multiple sclerosis were included in this analysis, using gender, onset type and age at onset as clinical covariates. We correlated 80 single nucleotide polymorphisms to the degree of disease severity using the following three outcome measures: linear Multiple Sclerosis Severity Score, dichotomous Multiple Sclerosis Severity Score (using a cut-off point of 2.5) and time to reach Expanded Disability Status Scale score 6. Sixty-nine single nucleotide polymorphisms were included in the analysis. No individual single nucleotide polymorphism showed a significant association; however, a combination of single nucleotide polymorphisms significantly improved the prediction of disease severity in addition to the clinical variables. In all three models the Interleukin 2 gene was included, confirming a previously reported modest effect on disease severity. The highest power was obtained using the dichotomized Multiple Sclerosis Severity Score as outcome. Several single nucleotide polymorphisms showed their added predictive value over the clinical data in the predictive models. These results support our hypothesis that disease severity is determined by clinical variables and genetic influences (through several genes with small effects) in concert.

The use of gene array technology and proteomics in the search of new targets of diseases for therapeutics

The advent of functional genomics has been greatly broadening our view and accelerating our way in numerous medical research fields. The complete genomic data acquired from the human genome project and the desperate clinical need of comprehensive analytical tools to study complex diseases, has allowed rapid evolution of genomic and proteomic technologies, speeding the rate and number of discoveries in new biomarkers. By jointly using genomics, proteomics and bioinformatics there is a great potential to make considerable contribution to biomarker identification and to revolutionize both the development of new therapies and drug development process.

Using Multidimensional Bayesian Network Classifiers to Assist the Treatment of Multiple Sclerosis

Multiple sclerosis is an autoimmune disorder of the central nervous system and potentially the most common cause of neurological disability in young adults. The clinical disease course is highly variable and different multiple sclerosis subtypes can be defined depending on the progression of the severity of the disease. In the early stages, the disease subtype is unknown, and there is no information about how the severity is going to evolve. As there are different treatment options available depending on the progression of the disease, early identification has become highly relevant. Thus, given a new patient, it is important to diagnose the disease subtype. Another relevant information to predict is the expected time to reach a severity level indicating that assistance for walking is required. Given that we have to predict two correlated class variables: disease subtype and time to reach certain severity level, we use multidimensional Bayesian network classifiers because they can model and exploit the relations among both variables. Besides, the obtained models can be validated by the physicians using their expert knowledge due to the interpretability of Bayesian networks. The learning of the classifiers is made by means of a novel multiobjective approach which tries to maximize the accuracy of both class variables simultaneously. The application of the methodology proposed in this paper can help a physician to identify the expected progression of the disease and to plan the most suitable treatment.