Rev1 deficiency induces a metabolic shift in MEFs that can be manipulated by the NAD plus precursor nicotinamide riboside

Replication stress, caused by Rev1 deficiency, is associated with mitochondrial dysfunction, and metabolic stress. However, the overall metabolic alterations and possible interventions to rescue the deficits due to Rev1 loss remain unclear. Here, we report that loss of Rev1 leads to intense changes in metabolites and that this can be manipulated by NAD + supplementation. Autophagy decreases in Rev1-/- mouse embryonic fibroblasts (MEFs) and can be restored by supplementing the NAD+ precursor nicotinamide riboside (NR). The abnormal mitochondrial morphology in Rev1-/-MEFs can be partially reversed by NR supplementation, which also protects the mito-chondrial cristae from rotenone-induced degeneration. In nematodes rev-1 deficiency causes sensitivity to oxidative stress but this cannot be rescued by NR supplementation. In conclusion, Rev1 deficiency leads to metabolic dysregulation of especially lipid and nucleotide metabolism, impaired autophagy, and mitochondrial anomalies, and all of these phenotypes can be improved by NR replenishment in MEFs.Genome Instability and Cance

Two integrated and highly predictive functional analysis-based procedures for the classification of MSH6 variants in Lynch syndrome

Purpose: Variants in the DNA mismatch repair (MMR) gene MSH6, identified in individuals suspected of Lynch syndrome, are difficult to classify owing to the low cancer penetrance of defects in that gene. This not only obfuscates personalized health care but also the development of a rapid and reliable classification procedure that does not require clinical data. Methods: The complete in vitro MMR activity (CIMRA) assay was calibrated against clinically classified MSH6 variants and, employing Bayes’ rule, integrated with computational predictions of pathogenicity. To enable the validation of this two-component classification procedure we have employed a genetic screen to generate a large set of inactivating Msh6 variants, as proxies for pathogenic variants. Results: The genetic screen-derived variants established that the two-component classification procedure displays high sensitivities and specificities. Moreover, these inactivating variants enabled the direct reclassification of human variants of uncertain significance (VUS) as (likely) pathogenic. Conclusion: The two-component classification procedure and the genetic screens provide complementary approaches to rapidly and cost-effectively classify the large majority of human MSH6 variants. The approach followed here provides a template for the classification of variants in other disease-predisposing genes, facilitating the translation of personalized genomics into personalized health care

Nanotechnology applied to European food production – A review of ethical and regulatory issues

Various ethical issues are associated with agrifood nanotechnology, linked to the ethical concepts of autonomy, beneficence, non-malfeasance and justice (ensuring safety, effective risk assessment, transparency, consumer benefits and choice, animal welfare and environmental protection). Nanotechnology applications are currently covered by legislative instruments originally designed for other purposes. Risk assessment procedures are in most cases not specific to (agrifood) nano-materials, resulting in uncertainty regarding the nature and extent of potential risks. There are currently no requirements for nano-materials used in agrifood production to be labelled. Ethical principles, and societal acceptance require labelling of food products that are produced using nanotechnology

Two-Point Functions and Boundary States in Boundary Logarithmic Conformal Field Theories

Our main aim in this thesis is to address the results and prospects of boundary logarithmic conformal field theories: theories with boundaries that contain the above Jordan cell structure. We have investigated c_{p,q} boundary theory in search of logarithmic theories and have found logarithmic solutions of two-point functions in the context of the Coulomb gas picture. Other two-point functions have also been studied in the free boson construction of BCFT with SU(2)_k symmetry. In addition, we have analyzed and obtained the boundary Ishibashi state for a rank-2 Jordan cell structure [hep-th/0103064]. We have also examined the (generalised) Ishibashi state construction and the symplectic fermion construction at c=-2 for boundary states in the context of the c=-2 triplet model. The differences between two constructions are interpreted, resolved and extended beyond each case.Comment: Ph.D. Thesis (University of Oxford), 96 pages, the layout is modified from the origina

Genome-wide association study of serum fructosamine and glycated albumin in adults without diagnosed diabetes: Results from the atherosclerosis risk in communities study

Fructosamine and glycated albumin are potentially useful alternatives to hemoglobin A1c (HbA1c) as diabetes biomarkers. The genetic determinants of fructosamine and glycated albumin, however, are unknown. We performed genome-wide association studies of fructosamine and glycated albumin among 2,104 black and 7,647 white participants without diabetes in the Atherosclerosis Risk in Communities (ARIC) Study and replicated findings in the Coronary Artery Risk Development in Young Adults (CARDIA) study. Among whites, rs34459162, a novel missense single nucleotide polymorphism (SNP) in RCN3, was associated with fructosamine (P = 5.3 3 1029) and rs1260236, a known diabetes-related missense mutation in GCKR, was associated with percent glycated albumin (P = 5.9 3 1029) and replicated in CARDIA. We also found two novel associations among blacks: an intergenic SNP, rs2438321, associated with fructosamine (P = 6.2 3 1029), and an intronic variant in PRKCA, rs59443763, associated with percent glycated albumin (P = 4.1 3 1029), but these results did not replicate. Few established fasting glucose or HbA1c SNPs were also associated with fructosamine or glycated albumin. Overall, we found genetic variants associated with the glycemic information captured by fructosamine and glycated albumin as well as with their nonglycemic component. This highlights the importance of examining the genetics of hyperglycemia biomarkers to understand the information they capture, including potential glucose-independent factors

Dimensionality reduction and prediction of the protein macromolecule dissolution profile

A suitable regression model for predicting the dissolution profile of Poly (lactic-co-glycolic acid) (PLGA) micro-and nanoparticles can play a significant role in pharmaceutical/medical applications. The rate of dissolution of proteins is influenced by several factors and taking all such influencing factors into account; we have a dataset in hand with three hundred input features. Therefore, a primary approach before identifying a regression model is to reduce the dimensionality of the dataset at hand. On the one hand, we have adopted Backward Elimination Feature selection techniques for an exhaustive analysis of the predictability of each combination of features. On the other hand, several linear and non-linear feature extraction methods are used in order to extract a new set of features out of the available dataset. A comprehensive experimental analysis for the selection or extraction of features and identification of the corresponding prediction model is offered. The designed experiment and prediction models offer substantially better performance over the earlier proposed prediction models in literature for the said problem

Clustering Algorithms: Their Application to Gene Expression Data

Gene expression data hide vital information required to understand the biological process that takes place in a particular organism in relation to its environment. Deciphering the hidden patterns in gene expression data proffers a prodigious preference to strengthen the understanding of functional genomics. The complexity of biological networks and the volume of genes present increase the challenges of comprehending and interpretation of the resulting mass of data, which consists of millions of measurements; these data also inhibit vagueness, imprecision, and noise. Therefore, the use of clustering techniques is a first step toward addressing these challenges, which is essential in the data mining process to reveal natural structures and iden-tify interesting patterns in the underlying data. The clustering of gene expression data has been proven to be useful in making known the natural structure inherent in gene expression data, understanding gene functions, cellular processes, and subtypes of cells, mining useful information from noisy data, and understanding gene regulation. The other benefit of clustering gene expression data is the identification of homology, which is very important in vaccine design. This review examines the various clustering algorithms applicable to the gene expression data in order to discover and provide useful knowledge of the appropriate clustering technique that will guarantee stability and high degree of accuracy in its analysis procedure

Causal effects on complex traits are similar for common variants across segments of different continental ancestries within admixed individuals

Individuals of admixed ancestries (for example, African Americans) inherit a mosaic of ancestry segments (local ancestry) originating from multiple continental ancestral populations. This offers the unique opportunity of investigating the similarity of genetic effects on traits across ancestries within the same population. Here we introduce an approach to estimate correlation of causal genetic effects (radmix) across local ancestries and analyze 38 complex traits in African-European admixed individuals (N = 53,001) to observe very high correlations (meta-analysis radmix = 0.95, 95% credible interval 0.93–0.97), much higher than correlation of causal effects across continental ancestries. We replicate our results using regression-based methods from marginal genome-wide association study summary statistics. We also report realistic scenarios where regression-based methods yield inflated heterogeneity-by-ancestry due to ancestry-specific tagging of causal effects, and/or polygenicity. Our results motivate genetic analyses that assume minimal heterogeneity in causal effects by ancestry, with implications for the inclusion of ancestry-diverse individuals in studies