Two rare variants that affect the same amino acid in CFTR have distinct responses to ivacaftor

Some residues in the cystic fibrosis transmembrane conductance regulator (CFTR) channel are the site of more than one CFTR variant that cause cystic fibrosis. Here, we investigated the function of S1159F and S1159P, two variants associated with different clinical phenotypes, which affect the same pore-lining residue in transmembrane segment 12 that are both strongly potentiated by ivacaftor when expressed in CFBE41o− bronchial epithelial cells. To study the single-channel behaviour of CFTR, we applied the patch-clamp technique to Chinese hamster ovary cells heterologously expressing CFTR variants incubated at 27°C to enhance channel residence at the plasma membrane. S1159F- and S1159P-CFTR formed Cl− channels activated by cAMP-dependent phosphorylation and gated by ATP that exhibited thermostability at 37°C. Both variants modestly reduced the single-channel conductance of CFTR. By severely attenuating channel gating, S1159F- and S1159P-CFTR reduced the open probability (Po) of wild-type CFTR by ≥75% at ATP (1 mM); S1159F-CFTR caused the greater decrease in Po consistent with its more severe clinical phenotype. Ivacaftor (10–100 nM) doubled the Po of both CFTR variants without restoring Po values to wild-type levels, but concomitantly, ivacaftor decreased current flow through open channels. For S1159F-CFTR, the reduction of current flow was marked at high (supersaturated) ivacaftor concentrations (0.5–1 μM) and voltage-independent, identifying an additional detrimental action of elevated ivacaftor concentrations. In conclusion, S1159F and S1159P are gating variants, which also affect CFTR processing and conduction, but not stability, necessitating the use of combinations of CFTR modulators to optimally restore their channel activity

Pulmonary Nontuberculous Mycobacterial Infection. A Multisystem, Multigenic Disease

Rationale: The clinical features of patients infected with pulmonary nontuberculous mycobacteria (PNTM) are well described, but the genetic components of infection susceptibility are not

A framework for human microbiome research

A variety of microbial communities and their genes (the microbiome) exist throughout the human body, with fundamental roles in human health and disease. The National Institutes of Health (NIH)-funded Human Microbiome Project Consortium has established a population-scale framework to develop metagenomic protocols, resulting in a broad range of quality-controlled resources and data including standardized methods for creating, processing and interpreting distinct types of high-throughput metagenomic data available to the scientific community. Here we present resources from a population of 242 healthy adults sampled at 15 or 18 body sites up to three times, which have generated 5,177 microbial taxonomic profiles from 16S ribosomal RNA genes and over 3.5 terabases of metagenomic sequence so far. In parallel, approximately 800 reference strains isolated from the human body have been sequenced. Collectively, these data represent the largest resource describing the abundance and variety of the human microbiome, while providing a framework for current and future studies

Structure, function and diversity of the healthy human microbiome

Author Posting. © The Authors, 2012. This article is posted here by permission of Nature Publishing Group. The definitive version was published in Nature 486 (2012): 207-214, doi:10.1038/nature11234.Studies of the human microbiome have revealed that even healthy individuals differ remarkably in the microbes that occupy habitats such as the gut, skin and vagina. Much of this diversity remains unexplained, although diet, environment, host genetics and early microbial exposure have all been implicated. Accordingly, to characterize the ecology of human-associated microbial communities, the Human Microbiome Project has analysed the largest cohort and set of distinct, clinically relevant body habitats so far. We found the diversity and abundance of each habitat’s signature microbes to vary widely even among healthy subjects, with strong niche specialization both within and among individuals. The project encountered an estimated 81–99% of the genera, enzyme families and community configurations occupied by the healthy Western microbiome. Metagenomic carriage of metabolic pathways was stable among individuals despite variation in community structure, and ethnic/racial background proved to be one of the strongest associations of both pathways and microbes with clinical metadata. These results thus delineate the range of structural and functional configurations normal in the microbial communities of a healthy population, enabling future characterization of the epidemiology, ecology and translational applications of the human microbiome.This research was supported in part by National Institutes of Health grants U54HG004969 to B.W.B.; U54HG003273 to R.A.G.; U54HG004973 to R.A.G., S.K.H. and J.F.P.; U54HG003067 to E.S.Lander; U54AI084844 to K.E.N.; N01AI30071 to R.L.Strausberg; U54HG004968 to G.M.W.; U01HG004866 to O.R.W.; U54HG003079 to R.K.W.; R01HG005969 to C.H.; R01HG004872 to R.K.; R01HG004885 to M.P.; R01HG005975 to P.D.S.; R01HG004908 to Y.Y.; R01HG004900 to M.K.Cho and P. Sankar; R01HG005171 to D.E.H.; R01HG004853 to A.L.M.; R01HG004856 to R.R.; R01HG004877 to R.R.S. and R.F.; R01HG005172 to P. Spicer.; R01HG004857 to M.P.; R01HG004906 to T.M.S.; R21HG005811 to E.A.V.; M.J.B. was supported by UH2AR057506; G.A.B. was supported by UH2AI083263 and UH3AI083263 (G.A.B., C. N. Cornelissen, L. K. Eaves and J. F. Strauss); S.M.H. was supported by UH3DK083993 (V. B. Young, E. B. Chang, F. Meyer, T. M. S., M. L. Sogin, J. M. Tiedje); K.P.R. was supported by UH2DK083990 (J. V.); J.A.S. and H.H.K. were supported by UH2AR057504 and UH3AR057504 (J.A.S.); DP2OD001500 to K.M.A.; N01HG62088 to the Coriell Institute for Medical Research; U01DE016937 to F.E.D.; S.K.H. was supported by RC1DE0202098 and R01DE021574 (S.K.H. and H. Li); J.I. was supported by R21CA139193 (J.I. and D. S. Michaud); K.P.L. was supported by P30DE020751 (D. J. Smith); Army Research Office grant W911NF-11-1-0473 to C.H.; National Science Foundation grants NSF DBI-1053486 to C.H. and NSF IIS-0812111 to M.P.; The Office of Science of the US Department of Energy under Contract No. DE-AC02-05CH11231 for P.S. C.; LANL Laboratory-Directed Research and Development grant 20100034DR and the US Defense Threat Reduction Agency grants B104153I and B084531I to P.S.C.; Research Foundation - Flanders (FWO) grant to K.F. and J.Raes; R.K. is an HHMI Early Career Scientist; Gordon&BettyMoore Foundation funding and institutional funding fromthe J. David Gladstone Institutes to K.S.P.; A.M.S. was supported by fellowships provided by the Rackham Graduate School and the NIH Molecular Mechanisms in Microbial Pathogenesis Training Grant T32AI007528; a Crohn’s and Colitis Foundation of Canada Grant in Aid of Research to E.A.V.; 2010 IBM Faculty Award to K.C.W.; analysis of the HMPdata was performed using National Energy Research Scientific Computing resources, the BluBioU Computational Resource at Rice University

Direct asymmetric catalytic syntheses of α,β-difunctional amino and hydroxy carbonyls via the bifunctional catalytic in-situ generation of chiral enolates

EThOS - Electronic Theses Online ServiceGBUnited Kingdo

Method of and system for producing electrical power

A method and system for converting the chemical energy of methane to electrical energy. Methane is thermally decomposed to hydrogen and carbon in a decomposing unit at a temperature not less than about 1200{degree}K and at a pressure at least slightly above atmospheric pressure. Carbon and substantially pure oxygen and a cesium or potassium seed material is transmitted to a combustor which is maintained at a pressure of at least about 50 atmospheres to combust the carbon and oxygen and provide an ionized plasma having a temperature not less than about 2900{degree}K. The ionized plasma is accelerated to a velocity not less than about 1000 m/sec and transported through an MHD generator having a magnetic field in the range of from about 4 to about 6 Tesla to generate dc power. The ionized plasma is deaccelerated and passed from the MHD generator in heat exchange relationship with the methane to heat same for decomposition, and thereafter any cesium or potassium seed material is recovered and transported to the combustor, and the dc power from the MHD generator is converted to ac power

Fine Mapping on Chromosome 10q22-q23 Implicates Neuregulin 3 in Schizophrenia

Linkage studies have implicated 10q22-q23 as a schizophrenia (SZ) susceptibility locus in Ashkenazi Jewish (AJ) and Han Chinese from Taiwan populations. To further explore our previous linkage signal in the AJ population (NPL score: 4.27, empirical p = 2 × 10−5), we performed a peakwide association fine mapping study by using 1414 SNPs across ∼12.5 Mb in 10q22-q23. We genotyped 1515 AJ individuals, including 285 parent-child trios, 173 unrelated cases, and 487 unrelated controls. We analyzed the binary diagnostic phenotype of SZ and 9 heritable quantitative traits derived from a principal components factor analysis of 73 items from our consensus diagnostic ratings and direct assessment interviews. Although no marker withstood multiple test correction for association with the binary SZ phenotype, we found strong evidence of association by using the “delusion” factor as the quantitative trait at three SNPs (rs10883866, rs10748842, and rs6584400) located in a 13 kb interval in intron 1 of Neuregulin 3 (NRG3). Our best p value from family-based association analysis was 7.26 × 10−7. We replicated this association in the collection of 173 unrelated AJ cases (p = 1.55 × 10−2), with a combined p value of 2.30 × 10−7. After performing 10,000 permutations of each of the phenotypes, we estimated the empirical study-wide significance across all 9 factors (90,000 permutations) to be p = 2.7 × 10−3. NRG3 is primarily expressed in the central nervous system and is one of three paralogs of NRG1, a gene strongly implicated in SZ. These biological properties together with our linkage and association results strongly support NRG3 as a gene involved in SZ

A deep learning approach to identify gene targets of a therapeutic for human splicing disorders

Drugs that modify RNA splicing are promising treatments for many genetic diseases. Here the authors show that deep learning strategies can predict drug targets, strongly supporting the use of in silico approaches to expand the therapeutic potential of drugs that modulate RNA splicing