Early signs of disease in type 1 diabetes

As a severe chronic disease with long-term complications, type 1 diabetes (T1D) is a burden to the patients and their families as well as a challenge to the health care system. T1D is a heterogeneous disease with a variety of etiologies and a wide range in the rate of progression to the disease. In order to prevent and treat T1D it would be important to identify measures that could be used to predict and monitor disease progression, as well as to further understand the molecular mechanisms involved. During the past 20 yr since its initiation, the Finnish Diabetes Prediction and Prevention Project (DIPP) has collected longitudinal biological samples from children with a human leukocyte antigen gene-conferred risk of T1D. This large sample collection has provided detailed sample series that enable studies to map the progression from health to disease, as well as the healthy maturation of risk-matched children. The DIPP samples have been used in a large body of research to elucidate the factors involved in the development of T1D. Interestingly, results from recent studies exploiting omics platforms have revealed that signs of the disease process can be detected very early on, even prior to appearance of the first T1D-associated antibodies, which are currently considered the earliest indications of the emerging disease. Identification and validation of multi-modal molecular markers will we hope provide a means to subgroup the heterogeneous group of T1D patients and enable prediction, diagnosis, and monitoring of T1D. Discovery of such markers is important in the design and testing of prevention and therapies for T1D</p

Tyypin 1 diabetes ja autoimmuniteetin yhteys ympäristöömme

Teema : autoimmuunitaudit. English summar

Integrating probe-level expression changes across generations of Affymetrix arrays

There is an urgent need for bioinformatic methods that allow integrative analysis of multiple microarray data sets. While previous studies have mainly concentrated on reproducibility of gene expression levels within or between different platforms, we propose a novel meta-analytic method that takes into account the vast amount of available probe-level information to combine the expression changes across different studies. We first show that the comparability of relative expression changes and the consistency of differentially expressed genes between different Affymetrix array generations can be considerably improved by determining the expression changes at the probe-level and by considering the latest information on probe-level sequence matching instead of the probe annotations provided by the manufacturer. With the improved probe-level expression change estimates, data from different generations of Affymetrix arrays can be combined more effectively. This will allow for the full exploitation of existing results when designing and analyzing new experiments

A practical comparison of methods for detecting transcription factor binding sites in ChIP-seq experiments

<p>Abstract</p> <p>Background</p> <p>Chromatin immunoprecipitation coupled with massively parallel sequencing (ChIP-seq) is increasingly being applied to study transcriptional regulation on a genome-wide scale. While numerous algorithms have recently been proposed for analysing the large ChIP-seq datasets, their relative merits and potential limitations remain unclear in practical applications.</p> <p>Results</p> <p>The present study compares the state-of-the-art algorithms for detecting transcription factor binding sites in four diverse ChIP-seq datasets under a variety of practical research settings. First, we demonstrate how the biological conclusions may change dramatically when the different algorithms are applied. The reproducibility across biological replicates is then investigated as an internal validation of the detections. Finally, the predicted binding sites with each method are compared to high-scoring binding motifs as well as binding regions confirmed in independent qPCR experiments.</p> <p>Conclusions</p> <p>In general, our results indicate that the optimal choice of the computational approach depends heavily on the dataset under analysis. In addition to revealing valuable information to the users of this technology about the characteristics of the binding site detection approaches, the systematic evaluation framework provides also a useful reference to the developers of improved algorithms for ChIP-seq data.</p

Circulating metabolic signatures of rapid and slow progression to type 1 diabetes in islet autoantibody-positive children

Aims/hypothesisAppearance of multiple islet cell autoantibodies in early life is indicative of future progression to overt type 1 diabetes, however, at varying rates. Here, we aimed to study whether distinct metabolic patterns could be identified in rapid progressors (RP, disease manifestation within 18 months after the initial seroconversion to autoantibody positivity) vs. slow progressors (SP, disease manifestation at 60 months or later from the appearance of the first autoantibody).MethodsLongitudinal samples were collected from RP (n=25) and SP (n=41) groups at the ages of 3, 6, 12, 18, 24, or ≥ 36 months. We performed a comprehensive metabolomics study, analyzing both polar metabolites and lipids. The sample series included a total of 239 samples for lipidomics and 213 for polar metabolites.ResultsWe observed that metabolites mediated by gut microbiome, such as those involved in tryptophan metabolism, were the main discriminators between RP and SP. The study identified specific circulating molecules and pathways, including amino acid (threonine), sugar derivatives (hexose), and quinic acid that may define rapid vs. slow progression to type 1 diabetes. However, the circulating lipidome did not appear to play a major role in differentiating between RP and SP.Conclusion/interpretationOur study suggests that a distinct metabolic profile is linked with the type 1 diabetes progression. The identification of specific metabolites and pathways that differentiate RP from SP may have implications for early intervention strategies to delay the development of type 1 diabetes

Kohti yksilöllistä hoitoa - proteomiikan näkymät diagnostiikassa

Sairaudet aiheuttavat muutoksia proteiinien ilmentymisessä, ja monet diagnostiset testit perustuvat proteiinien mittaamiseen näytteestä. Valtaosalla testeistä mitataan yksittäisiä proteiineja, vaikka sairauksien aiheuttamat muutokset elimistössä ovat usein moninaisia ja vaikuttavat useiden proteiinien ilmentymiseen. Proteomiikan hyödyntäminen diagnostiikassa mahdollistaisi lukuisien eri proteiinien, esimerkiksi tiettyyn signalointireittiin kuuluvien proteiinien, samanaikaisen mittaamisen näytteestä. Kattavammat testit voisivat tarjota tarkemman diagnoosin sekä yksilöllistä tietoa esimerkiksi sairauden vaiheesta ja mahdollisista liitännäissairauksista. Proteomiikassa käytetään nykyään yleisimmin massaspektrometriaan perustuvia menetelmiä, jotka mahdollistavat tuhansien proteiinien samanaikaisen mittaamisen näytteestä. Menetelmiä on käytetty menestyksekkäästi erilaisissa biomarkkeritutkimuksissa, ja ensimmäiset massaspektrometriaan perustuvat kliiniset proteomiikkatestit on otettu käyttöön. Proteomiikan menetelmät tarjoavat lukuisia ratkaisuja ja mahdollisuuksia käytännön lääketieteeseen