A Comprehensive Evaluation of the Performance of Prediction Algorithms on Clinically Relevant Missense Variants.

The rapid integration of genomic technologies in clinical diagnostics has resulted in the detection of a multitude of missense variants whose clinical significance is often unknown. As a result, a plethora of computational tools have been developed to facilitate variant interpretation. However, choosing an appropriate software from such a broad range of tools can be challenging; therefore, systematic benchmarking with high-quality, independent datasets is critical. Using three independent benchmarking datasets compiled from the ClinVar database, we evaluated the performance of ten widely used prediction algorithms with missense variants from 21 clinically relevant genes, including BRCA1 and BRCA2. A fourth dataset consisting of 1053 missense variants was also used to investigate the impact of type 1 circularity on their performance. The performance of the prediction algorithms varied widely across datasets. Based on Matthews Correlation Coefficient and Area Under the Curve, SNPs&GO and PMut consistently displayed an overall above-average performance across the datasets. Most of the tools demonstrated greater sensitivity and negative predictive values at the expense of lower specificity and positive predictive values. We also demonstrated that type 1 circularity significantly impacts the performance of these tools and, if not accounted for, may confound the selection of the best performing algorithms.

Shotgun Analysis of Gut Microbiota with Body Composition and Lipid Characteristics in Crohn's Disease.

Alterations to intestinal microbiota are assumed to occur in the pathogenesis of inflammatory bowel disease (IBD). This study aims to analyze the association of fecal microbiota composition, body composition, and lipid characteristics in patients with Crohn's disease (CD). In our cross-sectional study, patients with CD were enrolled and blood and fecal samples were collected. Clinical and endoscopic disease activity and body composition were assessed and laboratory tests were made. Fecal bacterial composition was analyzed using the shotgun method. Microbiota alterations based on obesity, lipid parameters, and disease characteristics were analyzed. In this study, 27 patients with CD were analyzed, of which 37.0% were obese based on visceral fat area (VFA). Beta diversities were higher in non-obese patients (p < 0.001), but relative abundances did not differ. C. innocuum had a higher abundance at a high cholesterol level than Bacillota (p = 0.001, p = 0.0034). Adlercreutzia, B. longum, and Blautia alterations were correlated with triglyceride levels. Higher Clostridia (p = 0.009) and B. schinkii (p = 0.032) and lower Lactobacillus (p = 0.035) were connected to high VFA. Disease activity was coupled with dysbiotic elements. Microbiota alterations in obesity highlight the importance of gut microbiota in diseases with a similar inflammatory background and project therapeutic options.

Prolonged activity of the transposase helper may raise safety concerns during DNA transposon-based gene therapy.

DNA transposon-based gene delivery vectors represent a promising new branch of randomly integrating vector development for gene therapy. For the side-by-side evaluation of the piggyBac and Sleeping Beauty systems-the only DNA transposons currently employed in clinical trials-during therapeutic intervention, we treated the mouse model of tyrosinemia type I with liver-targeted gene delivery using both transposon vectors. For genome-wide mapping of transposon insertion sites we developed a new next-generation sequencing procedure called streptavidin-based enrichment sequencing, which allowed us to identify approximately one million integration sites for both systems. We revealed that a high proportion of piggyBac integrations are clustered in hot regions and found that they are frequently recurring at the same genomic positions among treated animals, indicating that the genome-wide distribution of Sleeping Beauty-generated integrations is closer to random. We also revealed that the piggyBac transposase protein exhibits prolonged activity, which predicts the risk of oncogenesis by generating chromosomal double-strand breaks. Safety concerns associated with prolonged transpositional activity draw attention to the importance of squeezing the active state of the transposase enzymes into a narrower time window.