Diversity of CFTR variants across ancestries characterized using 454,727 UK biobank whole exome sequences.

BACKGROUND: Limited understanding of the diversity of variants in the cystic fibrosis transmembrane conductance regulator (CFTR) gene across ancestries hampers efforts to advance molecular diagnosis of cystic fibrosis (CF). The consequences pose a risk of delayed diagnoses and subsequently worsened health outcomes for patients. Therefore, characterizing the spectrum of CFTR variants across ancestries is critical for revolutionizing molecular diagnoses of CF. METHODS: We analyzed 454,727 UK Biobank (UKBB) whole-exome sequences to characterize the diversity of CFTR variants across ancestries. Using the PanUKBB classification, the participants were assigned into six major groups: African (AFR), American/American Admixed (AMR), Central South Asia (CSA), East Asian (EAS), European (EUR), and Middle East (MID). We segregated ancestry-specific CFTR variants, including those that are CF-causing or clinically relevant. The ages of certain CF-causing variants were determined and analyzed for selective pressure effects, and curated phenotype analysis was performed for participants with clinically relevant CFTR genotypes. RESULTS: We detected over 4000 CFTR variants, including novel ancestry-specific variants, across six ancestries. Europeans had the most unique CFTR variants [n = 2212], while the American group had the least unique variants [n = 23]. F508del was the most prevalent CF-causing variant found in all ancestries, except in EAS, where V520F was the most prevalent. Common EAS variants such as 3600G > A, V456A, and V520, which appeared approximately 270, 215, and 338 generations ago, respectively, did not show evidence of selective pressure. Sixteen participants had two CF-causing variants, with two being diagnosed with CF. We found 154 participants harboring a CF-causing and varying clinical consequences (VCC) variant. Phenotype analysis performed for participants with multiple clinically relevant variants returned significant associations with CF and its pulmonary phenotypes [Bonferroni-adjusted p < 0.05]. CONCLUSIONS: We leveraged the UKBB database to comprehensively characterize the broad spectrum of CFTR variants across ancestries. The detection of over 4000 CFTR variants, including several ancestry-specific and uncharacterized CFTR variants, warrants the need for further characterization of their functional and clinical relevance. Overall, the presentation of classical CF phenotypes seen in non-CF diagnosed participants with more than one CF-causing variant indicates that they may benefit from current CFTR modulator therapies.

A Comprehensive Review of Self-Healing Polymer, Metal, and Ceramic Matrix Composites and Their Modeling Aspects for Aerospace Applications.

Composites can be divided into three groups based on their matrix materials, namely polymer, metal and ceramic. Composite materials fail due to micro cracks. Repairing is complex and almost impossible if cracks appear on the surface and interior, which minimizes reliability and material life. In order to save the material from failure and prolong its lifetime without compromising mechanical properties, self-healing is one of the emerging and best techniques. The studies to address the advantages and challenges of self-healing properties of different matrix materials are very limited; however, this review addresses all three different groups of composites. Self-healing composites are fabricated to heal cracks, prevent any obstructed failure, and improve the lifetime of structures. They can self-diagnose their structure after being affected by external forces and repair damages and cracks to a certain degree. This review aims to provide information on the recent developments and prospects of self-healing composites and their applications in various fields such as aerospace, automobiles etc. Fabrication and characterization techniques as well as intrinsic and extrinsic self-healing techniques are discussed based on the latest achievements, including microcapsule embedment, fibers embedment, and vascular networks self-healing.

Epitachophoresis is a novel versatile total nucleic acid extraction method.

Epitachophoresis is a novel next generation extraction system capable of isolating DNA and RNA simultaneously from clinically relevant samples. Here we build on the versatility of Epitachophoresis by extracting diverse nucleic acids ranging in lengths (20 nt-290 Kbp). The quality of extracted miRNA, mRNA and gDNA was assessed by downstream Next-Generation Sequencing.

Ultra-high throughput single-cell analysis of proteins and RNAs by split-pool synthesis.

Single-cell omics provide insight into cellular heterogeneity and function. Recent technological advances have accelerated single-cell analyses, but workflows remain expensive and complex. We present a method enabling simultaneous, ultra-high throughput single-cell barcoding of millions of cells for targeted analysis of proteins and RNAs. Quantum barcoding (QBC) avoids isolation of single cells by building cell-specific oligo barcodes dynamically within each cell. With minimal instrumentation (four 96-well plates and a multichannel pipette), cell-specific codes are added to each tagged molecule within cells through sequential rounds of classical split-pool synthesis. Here we show the utility of this technology in mouse and human model systems for as many as 50 antibodies to targeted proteins and, separately, >70 targeted RNA regions. We demonstrate that this method can be applied to multi-modal protein and RNA analyses. It can be scaled by expansion of the split-pool process and effectively renders sequencing instruments as versatile multi-parameter flow cytometers.

Author Correction: Ultra-high throughput single-cell analysis of proteins and RNAs by split-pool synthesis.

An amendment to this paper has been published and can be accessed via a link at the top of the paper.

Mining protein networks for synthetic genetic interactions.

BACKGROUND: The local connectivity and global position of a protein in a protein interaction network are known to correlate with some of its functional properties, including its essentiality or dispensability. It is therefore of interest to extend this observation and examine whether network properties of two proteins considered simultaneously can determine their joint dispensability, i.e., their propensity for synthetic sick/lethal interaction. Accordingly, we examine the predictive power of protein interaction networks for synthetic genetic interaction in Saccharomyces cerevisiae, an organism in which high confidence protein interaction networks are available and synthetic sick/lethal gene pairs have been extensively identified. RESULTS: We design a support vector machine system that uses graph-theoretic properties of two proteins in a protein interaction network as input features for prediction of synthetic sick/lethal interactions. The system is trained on interacting and non-interacting gene pairs culled from large scale genetic screens as well as literature-curated data. We find that the method is capable of predicting synthetic genetic interactions with sensitivity and specificity both exceeding 85%. We further find that the prediction performance is reasonably robust with respect to errors in the protein interaction network and with respect to changes in the features of test datasets. Using the prediction system, we carried out novel predictions of synthetic sick/lethal gene pairs at a genome-wide scale. These pairs appear to have functional properties that are similar to those that characterize the known synthetic lethal gene pairs. CONCLUSION: Our analysis shows that protein interaction networks can be used to predict synthetic lethal interactions with accuracies on par with or exceeding that of other computational methods that use a variety of input features, including functional annotations. This indicates that protein interaction networks could plausibly be rich sources of information about epistatic effects among genes.

Bifurcation discovery tool.

MOTIVATION: Biochemical networks often yield interesting behavior such as switching, oscillation and chaotic dynamics. This article describes a tool that is capable of searching for bifurcation points in arbitrary ODE-based reaction networks by directing the user to regions in the parameter space, where such interesting dynamical behavior can be observed. RESULTS: We have implemented a genetic algorithm that searches for Hopf bifurcations, turning points and bistable switches. The software is implemented as a Systems Biology Workbench (SBW) enabled module and accepts the standard SBML model format. The interface permits a user to choose the parameters to be searched, admissible parameter ranges, and the nature of the bifurcation to be sought. The tool will return the parameter values for the model for which the particular behavior is observed. AVAILABILITY: The software, tutorial manual and test models are available for download at the following website: http:/www.sys-bio.org/ under the bifurcation link. The software is an open source and licensed under BSD.