Hesteetako hanturazko gaixotasuna: patogenia, tratamenduak eta mikrobiotan oinarritutako biomarkatzaileak

Inflammatory bowel disease (IBD) encompasses two types of idiopathic intestinal diseases, ulcerative colitis (UC) and Crohn’s disease (CD). Both are chronic, heterogeneous, and severe inflammatory disorders that primarily affect the intestine. IBD has emerged as a global disease with a sharp increase in worldwide incidence and prevalence. Although the specific underlying cause of UC is unknown, it is considered the result of a complex interaction between the microbiota, immune system, host genetics and environmental factors. The advent of new technologies has enabled the identification of disturbed microbiota composition and function and reduced microbial diversity in IBD pa-tients, termed dysbiosis. Obtained clinical and experimental data points dysbiosis as a key player in IBD pathogenesis, but it is still unclear whether it is the cause of consequence. Although a wide range of therapies are approved for use as treatment for IBD, there is no cure. TNF inhibitors are frequently used to induce clinical remission in severe patients, however a roughly one third of the patients may not respond, and another third may lose response over time. In addition, the cost associated with IBD treatments is increasing over time, mainly due the costs associated with the biologic treatment. Emerging evidence has pointed towards gut microbiota to find a set of biomarkers for diagnosis, and for prediction of disease severity and infliximab treatment response in IBD patients. The human fecal microbiota harbors promising and non-invasive biomarkers, which emphasizes its potential ability to stratify IBD patients and apply personalized therapy for optimal outcomes.; Hesteetako hanturazko gaixotasunak (HHG, ingelesez IBD) bi gaixotasun idiopatiko ezberdin biltzen ditu, ultzeradun kolitisa (UK) eta Crohn-en gaixotasuna (CG). Biak batez ere hesteari eragiten dioten gaixotasun kronikoak, heterogeneoak eta larriak dira. HHGa handitzen ari den gaixotasun globala da, eta haren intzidentzia eta prebalentzia areagotuz doa mundu osoan. UKaren kausa zehatza ezezaguna bada ere, uste da mikrobiotaren, sistema immunearen, ostalariaren genetikaren eta ingurumen-faktoreen arteko elkarrekintza konplexuaren ondorioz sortzen dela. Genomikako teknologia berriek HHG-pazienteetan mikrobiotaren osaeran eta funtzioan aldaketak identifikatzea ahalbidetu dute. Aldaketa horiek disbiosia bezala ezagutzen dira. Lortutako datu kliniko eta esperimentalak, disbiosia, HHGen funtsezko eragile gisa finkatzen dute, nahiz eta oraindik argi egon ez kausa edo ondorioa den. Gaur egun H HGaren aurkako tratamendu ugari dauden arren, ez dago sendabide-H HGaren aurkako tratamendu ugari dauden arren, ez dago sendabide- aurkako tratamendu ugari dauden arren, ez dago sendabide-rik. Paziente larrietan sintomatologia arintzeko, TNF inhibitzaileak dira gehien erabiltzen den tratamendua. Hala ere, pazienteen heren batek ez dio tratamenduari erantzuten eta beste heren batek denborarekin erantzuna galtzen du. Gainera, tratamenduei lotutako kostu ekonomikoa eten-gabe handitzen ari da, batez ere tratamendu biologikoen kostuengatik. HHGa duten gaixoen hesteetako mikrobiota baliagarri bilakatzen ari da gaixotasuna diagnostikatzeko, gaixotasunaren larritasuna zehazteko eta tratamenduarekiko erantzuna iragartzeko, hau da, mikrobiotaren osaera biomarkatzaile gisa erabil daiteke. Izan ere, giza gorotzetako mikrobiota biomarkatzaile esperantzagarri eta ez-inbaditzaile bihurtu da. Mikro-biotaren azterketak HHGa duten gaixoak sailkatzeko aukerak gehitzen ditu, eta tratamendu pertsonalizatuen aukera zabaldu

Bayesian classifiers with consensus gene selection: a case study in the systemic lupus erythematosus

Within the wide field of classification on the Machine Learning discipline, Bayesian classifiers are very well established paradigms. They allow the user to work with probabilistic processes, as well as, with graphical representations of the relationships among the variables of a problem. Bayesian classifiers assign the corresponding predicted class of a certain pattern as the one that has the highest a posteriori probability. This a posteriori probability is computed by means of the Bayes theorem in conjunction with assumptions about the density of the patterns conditioned to the class. In this work three of these classification paradigms are applied to a DNA microarray database of control, systemic lupus erythematosus and antiphospholipid syndrome samples. The number of genes from which the models are induced is considerably reduced by means of a novel consensus filter gene selection technique. Combining a nonparametric bootstrap resampling technique and the k dependence Bayesian classifier paradigm, we propose a new method to obtain gene interaction networks of high reliability. These gene networks can be seen as a tool to study the relationships among the genes of the domain. In fact, some of the previous knowledge about both pathologies is confirmed by the new approach