Intramolecular homolytic substitution in selenoxides and selenones

G3(MP2)-RAD calculations provide activation energies for intramolecular homolytic substitution in the 4-(alkylselenoxo)butyl and 4-(alkylselendioxo)butyl radicals ranging from 21–39 kJ mol−1, and 143–170 kJ mol−1 for the selenoxide and selenone, respectively. Arrhenius data translate into rate constants for ring-closure of 1.5×105−2.5×108 s−1 (80°) for the selenoxides, and 5.4×10−14−5.1×10−11 s−1 (80°) for the corresponding selenones. NBO analyses show alkyl radicals are electrophilic during homolytic substitution at selenoxide selenium. The dominant orbital interaction in the transition state is worth 2413 kJ mol−1 and involves the SOMO and the lone-pair of electrons on selenium. The corresponding selenones are calculated to ring-close through transition states in which alkyl radicals are nucleophilic, but involve weak (SOMO--> σ* and SOMO--> π*) interactions. Consequently, this chemistry is not viable for selenones because of the lack of lone-pairs of electrons on the chalcogen

EuroPhenome: a repository for high-throughput mouse phenotyping data.

The broad aim of biomedical science in the postgenomic era is to link genomic and phenotype information to allow deeper understanding of the processes leading from genomic changes to altered phenotype and disease. The EuroPhenome project (http://www.EuroPhenome.org) is a comprehensive resource for raw and annotated high-throughput phenotyping data arising from projects such as EUMODIC. EUMODIC is gathering data from the EMPReSSslim pipeline (http://www.empress.har.mrc.ac.uk/) which is performed on inbred mouse strains and knock-out lines arising from the EUCOMM project. The EuroPhenome interface allows the user to access the data via the phenotype or genotype. It also allows the user to access the data in a variety of ways, including graphical display, statistical analysis and access to the raw data via web services. The raw phenotyping data captured in EuroPhenome is annotated by an annotation pipeline which automatically identifies statistically different mutants from the appropriate baseline and assigns ontology terms for that specific test. Mutant phenotypes can be quickly identified using two EuroPhenome tools: PhenoMap, a graphical representation of statistically relevant phenotypes, and mining for a mutant using ontology terms. To assist with data definition and cross-database comparisons, phenotype data is annotated using combinations of terms from biological ontologies

Sap Transporter Mediated Import and Subsequent Degradation of Antimicrobial Peptides in Haemophilus

Antimicrobial peptides (AMPs) contribute to host innate immune defense and are a critical component to control bacterial infection. Nontypeable Haemophilus influenzae (NTHI) is a commensal inhabitant of the human nasopharyngeal mucosa, yet is commonly associated with opportunistic infections of the upper and lower respiratory tracts. An important aspect of NTHI virulence is the ability to avert bactericidal effects of host-derived antimicrobial peptides (AMPs). The Sap (sensitivity to antimicrobial peptides) ABC transporter equips NTHI to resist AMPs, although the mechanism of this resistance has remained undefined. We previously determined that the periplasmic binding protein SapA bound AMPs and was required for NTHI virulence in vivo. We now demonstrate, by antibody-mediated neutralization of AMP in vivo, that SapA functions to directly counter AMP lethality during NTHI infection. We hypothesized that SapA would deliver AMPs to the Sap inner membrane complex for transport into the bacterial cytoplasm. We observed that AMPs localize to the bacterial cytoplasm of the parental NTHI strain and were susceptible to cytoplasmic peptidase activity. In striking contrast, AMPs accumulated in the periplasm of bacteria lacking a functional Sap permease complex. These data support a mechanism of Sap mediated import of AMPs, a novel strategy to reduce periplasmic and inner membrane accumulation of these host defense peptides

AA9int: SNP interaction pattern search using non-hierarchical additive model set.

MOTIVATION: The use of single nucleotide polymorphism (SNP) interactions to predict complex diseases is getting more attention during the past decade, but related statistical methods are still immature. We previously proposed the SNP Interaction Pattern Identifier (SIPI) approach to evaluate 45 SNP interaction patterns/patterns. SIPI is statistically powerful but suffers from a large computation burden. For large-scale studies, it is necessary to use a powerful and computation-efficient method. The objective of this study is to develop an evidence-based mini-version of SIPI as the screening tool or solitary use and to evaluate the impact of inheritance mode and model structure on detecting SNP-SNP interactions. RESULTS: We tested two candidate approaches: the 'Five-Full' and 'AA9int' method. The Five-Full approach is composed of the five full interaction models considering three inheritance modes (additive, dominant and recessive). The AA9int approach is composed of nine interaction models by considering non-hierarchical model structure and the additive mode. Our simulation results show that AA9int has similar statistical power compared to SIPI and is superior to the Five-Full approach, and the impact of the non-hierarchical model structure is greater than that of the inheritance mode in detecting SNP-SNP interactions. In summary, it is recommended that AA9int is a powerful tool to be used either alone or as the screening stage of a two-stage approach (AA9int+SIPI) for detecting SNP-SNP interactions in large-scale studies. AVAILABILITY AND IMPLEMENTATION: The 'AA9int' and 'parAA9int' functions (standard and parallel computing version) are added in the SIPI R package, which is freely available at https://linhuiyi.github.io/LinHY_Software/. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online

Large-scale association analysis identifies new lung cancer susceptibility loci and heterogeneity in genetic susceptibility across histological subtypes.

Although several lung cancer susceptibility loci have been identified, much of the heritability for lung cancer remains unexplained. Here 14,803 cases and 12,262 controls of European descent were genotyped on the OncoArray and combined with existing data for an aggregated genome-wide association study (GWAS) analysis of lung cancer in 29,266 cases and 56,450 controls. We identified 18 susceptibility loci achieving genome-wide significance, including 10 new loci. The new loci highlight the striking heterogeneity in genetic susceptibility across the histological subtypes of lung cancer, with four loci associated with lung cancer overall and six loci associated with lung adenocarcinoma. Gene expression quantitative trait locus (eQTL) analysis in 1,425 normal lung tissue samples highlights RNASET2, SECISBP2L and NRG1 as candidate genes. Other loci include genes such as a cholinergic nicotinic receptor, CHRNA2, and the telomere-related genes OFBC1 and RTEL1. Further exploration of the target genes will continue to provide new insights into the etiology of lung cancer

Shared genetic risk between eating disorder- and substance-use-related phenotypes:Evidence from genome-wide association studies

First published: 16 February 202