Dataset Information

Serum DNA motifs predict disease and clinical status in multiple sclerosis.

ABSTRACT: Using recently available mass sequencing and assembly technologies, we have been able to identify and quantify unique cell-free DNA motifs in the blood of patients with multiple sclerosis (MS). The most common MS clinical syndrome, relapsing-remitting MS (RRMS), is accompanied by a unique fingerprint of both inter- and intragenic cell-free circulating nucleic acids as specific DNA sequences that provide significant clinical sensitivity and specificity. Coding genes that are differentially represented in MS serum encode cytoskeletal proteins, brain-expressed regulators of growth, and receptors involved in nervous system signal transduction. Although coding genes distinguish RRMS and its clinical activity, several repeat sequences, such as the L1M family of LINE elements, are consistently different in all MS patients and clinical status versus the normal database. These data demonstrate that DNA motifs observed in serum are characteristic of RRMS and disease activity and are promising as a clinical tool in monitoring patient responses to treatment modalities.

SUBMITTER: Beck J

PROVIDER: S-EPMC2860467 | biostudies-literature | 2010 May

REPOSITORIES: biostudies-literature

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Serum DNA motifs predict disease and clinical status in multiple sclerosis.

Beck Julia J Urnovitz Howard B HB Saresella Marina M Caputo Domenico D Clerici Mario M Mitchell William M WM Schütz Ekkehard E

The Journal of molecular diagnostics : JMD 20100312 3

Using recently available mass sequencing and assembly technologies, we have been able to identify and quantify unique cell-free DNA motifs in the blood of patients with multiple sclerosis (MS). The most common MS clinical syndrome, relapsing-remitting MS (RRMS), is accompanied by a unique fingerprint of both inter- and intragenic cell-free circulating nucleic acids as specific DNA sequences that provide significant clinical sensitivity and specificity. Coding genes that are differentially repres ...[more]

PMID: 20228264

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Project description:Development of effective treatments requires understanding of disease mechanisms. For diseases of the central nervous system (CNS), such as multiple sclerosis (MS), human pathology studies and animal models tend to identify candidate disease mechanisms. However, these studies cannot easily link the identified processes to clinical outcomes, such as MS severity, required for causality assessment of candidate mechanisms. Technological advances now allow the generation of thousands of biomarkers in living human subjects, derived from genes, transcripts, medical images, and proteins or metabolites in biological fluids. These biomarkers can be assembled into computational models of clinical value, provided such models are generalizable. Reproducibility of models increases with the technical rigor of the study design, such as blinding, control implementation, the use of large cohorts that encompass the entire spectrum of disease phenotypes and, most importantly, model validation in independent cohort(s). To facilitate the growth of this important research area, we performed a meta-analysis of publications (n = 302) that model MS clinical outcomes extracting effect sizes, while also scoring the technical quality of the study design using predefined criteria. Finally, we generated a Shiny-App-based website that allows dynamic exploration of the data by selective filtering. On average, the published studies fulfilled only one of the seven criteria of study design rigor. Only 15.2% of the studies used any validation strategy, and only 8% used the gold standard of independent cohort validation. Many studies also used small cohorts, e.g., for magnetic resonance imaging (MRI) and blood biomarker predictors, the median sample size was <100 subjects. We observed inverse relationships between reported effect sizes and the number of study design criteria fulfilled, expanding analogous reports from non-MS fields, that studies that fail to limit bias overestimate effect sizes. In conclusion, the presented meta-analysis represents a useful tool for researchers, reviewers, and funders to improve the design of future modeling studies in MS and to easily compare new studies with the published literature. We expect that this will accelerate research in this important area, leading to the development of robust models with proven clinical value.

Dataset Information

Serum DNA motifs predict disease and clinical status in multiple sclerosis.

Publications

Serum DNA motifs predict disease and clinical status in multiple sclerosis.

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