DNA Sequence Analysis in Clinical Medicine, Proceeding Cautiously

Overview of frequent pattern mining

Various methods of frequent pattern mining have been applied to genetic problems, specifically, to the combined association of two genotypes (a genotype pattern, or diplotype) at different DNA variants with disease. These methods have the ability to come up with a selection of genotype patterns that are more common in affected than unaffected individuals, and the assessment of statistical significance for these selected patterns poses some unique problems, which are briefly outlined here.

The Resolved Mutual Information Function as a Structural Fingerprint of Biomolecular Sequences for Interpretable Machine Learning Classifiers

In the present article we propose the application of variants of the mutual information function as characteristic fingerprints of biomolecular sequences for classification analysis. In particular, we consider the resolved mutual information functions based on Shannon-, Rényi-, and Tsallis-entropy. In combination with interpretable machine learning classifier models based on generalized learning vector quantization, a powerful methodology for sequence classification is achieved which allows substantial knowledge extraction in addition to the high classification ability due to the model-inherent robustness. Any potential (slightly) inferior performance of the used classifier is compensated by the additional knowledge provided by interpretable models. This knowledge may assist the user in the analysis and understanding of the used data and considered task. After theoretical justification of the concepts, we demonstrate the approach for various example data sets covering different areas in biomolecular sequence analysis.

Rat models of human diseases and related phenotypes: a systematic inventory of the causative genes

The laboratory rat has been used for a long time as the model of choice in several biomedical disciplines. Numerous inbred strains have been isolated, displaying a wide range of phenotypes and providing many models of human traits and diseases. Rat genome mapping and genomics was considerably developed in the last decades. The availability of these resources has stimulated numerous studies aimed at discovering causal disease genes by positional identification. Numerous rat genes have now been identified that underlie monogenic or complex diseases and remarkably, these results have been translated to the human in a significant proportion of cases, leading to the identification of novel human disease susceptibility genes, helping in studying the mechanisms underlying the pathological abnormalities and also suggesting new therapeutic approaches. In addition, reverse genetic tools have been developed. Several genome-editing methods were introduced to generate targeted mutations in genes the function of which could be clarified in this manner [generally these are knockout mutations]. Furthermore, even when the human gene causing a disease had been identified without resorting to a rat model, mutated rat strains (in particular KO strains) were created to analyze the gene function and the disease pathogenesis. Today, over 350 rat genes have been identified as underlying diseases or playing a key role in critical biological processes that are altered in diseases, thereby providing a rich resource of disease models. This article is an update of the progress made in this research and provides the reader with an inventory of these disease genes, a significant number of which have similar effects in rat and humans.

Roles for selenium and selenoprotein P in the development, progression, and prevention of intestinal disease

Selenium (Se) is a micronutrient essential to human health, the function of which is mediated in part by incorporation into a class of proteins known as selenoproteins (SePs). As many SePs serve antioxidant functions, Se has long been postulated to protect against inflammation and cancer development in the gut by attenuating oxidative stress. Indeed, numerous studies over the years have correlated Se levels with incidence and severity of intestinal diseases such as inflammatory bowel disease (IBD) and colorectal cancer (CRC). Similar results have been obtained with the Se transport protein, selenoprotein P (SELENOP), which is decreased in the plasma of both IBD and CRC patients. While animal models further suggest that decreases in Se or SELENOP augment colitis and intestinal tumorigenesis, large-scale clinical trials have yet to show a protective effect in patient populations. In this review, we discuss the function of Se and SELENOP in intestinal diseases and how research into these mechanisms may impact patient treatment.