Structurally Distinct Ligands Rescue Biogenesis Defects of the KATP Channel Complex via a Converging Mechanism

Small molecules that correct protein misfolding and misprocessing defects offer a potential therapy for numerous human diseases. However, mechanisms underlying pharmacological correction of such defects, especially in heteromeric complexes with structurally diverse constituent proteins, are not well understood. Here we investigate how two chemically distinct compounds, glibenclamide and carbamazepine, correct biogenesis defects in ATP-sensitive potassium (K_ATP) channels composed of sulfonylurea receptor 1 (SUR1) and Kir6.2. We present evidence that despite structural differences, carbamazepine and glibenclamide compete for binding to K_ATP channels, and both drugs share a binding pocket in SUR1 to exert their effects. Moreover, both compounds engage Kir6.2, in particular the distal N terminus of Kir6.2, which is involved in normal channel biogenesis, for their chaperoning effects on SUR1 mutants. Conversely, both drugs can correct channel biogenesis defects caused by Kir6.2 mutations in a SUR1-dependent manner. Using an unnatural, photocross-linkable amino acid, azidophenylalanine, genetically encoded in Kir6.2, we demonstrate in living cells that both drugs promote interactions between the distal N terminus of Kir6.2 and SUR1. These findings reveal a converging pharmacological chaperoning mechanism wherein glibenclamide and carbamazepine stabilize the heteromeric subunit interface critical for channel biogenesis to overcome defective biogenesis caused by mutations in individual subunits.     

Modeled Changes in Potential Grassland Productivity and in Grass-Fed Ruminant Livestock Density in Europe over 1961–2010

About 25% of European livestock intake is based on permanent and sown grasslands. To fulfill rising demand for animal products, an intensification of livestock production may lead to an increased consumption of crop and compound feeds. In order to preserve an economically and environmentally sustainable agriculture, a more forage based livestock alimentation may be an advantage. However, besides management, grassland productivity is highly vulnerable to climate (i.e., temperature, precipitation, CO₂ concentration), and spatial information about European grassland productivity in response to climate change is scarce. The process-based vegetation model ORCHIDEE-GM, containing an explicit representation of grassland management (i.e., herbage mowing and grazing), is used here to estimate changes in potential productivity and potential grass-fed ruminant livestock density across European grasslands over the period 1961–2010. Here “potential grass-fed ruminant livestock density” denotes the maximum density of livestock that can be supported by grassland productivity in each 25 km × 25 km grid cell. In reality, livestock density could be higher than potential (e.g., if additional feed is supplied to animals) or lower (e.g., in response to economic factors, pedo-climatic and biotic conditions ignored by the model, or policy decisions that can for instance reduce livestock numbers). When compared to agricultural statistics (Eurostat and FAOstat), ORCHIDEE-GM gave a good reproduction of the regional gradients of annual grassland productivity and ruminant livestock density. The model however tends to systematically overestimate the absolute values of productivity in most regions, suggesting that most grid cells remain below their potential grassland productivity due to possible nutrient and biotic limitations on plant growth. When ORCHIDEE-GM was run for the period 1961–2010 with variable climate and rising CO₂, an increase of potential annual production (over 3%) per decade was found: 97% of this increase was attributed to the rise in CO₂, -3% to climate trends and 15% to trends in nitrogen fertilization and deposition. When compared with statistical data, ORCHIDEE-GM captures well the observed phase of climate-driven interannual variability in grassland production well, whereas the magnitude of the interannual variability in modeled productivity is larger than the statistical data. Regional grass-fed livestock numbers can be reproduced by ORCHIDEE-GM based on its simple assumptions and parameterization about productivity being the only limiting factor to define the sustainable number of animals per unit area. Causes for regional model-data misfits are discussed, including uncertainties in farming practices (e.g., nitrogen fertilizer application, and mowing and grazing intensity) and in ruminant diet composition, as well as uncertainties in the statistical data and in model parameter values.     

New substitution models for rooting phylogenetic trees

The root of a phylogenetic tree is fundamental to its biological interpretation, but standard substitution models do not provide any information on its position. Here, we describe two recently developed models that relax the usual assumptions of stationarity and reversibility, thereby facilitating root inference without the need for an outgroup. We compare the performance of these models on a classic test case for phylogenetic methods, before considering two highly topical questions in evolutionary biology: the deep structure of the tree of life and the root of the archaeal radiation. We show that all three alignments contain meaningful rooting information that can be harnessed by these new models, thus complementing and extending previous work based on outgroup rooting. In particular, our analyses exclude the root of the tree of life from the eukaryotes or Archaea, placing it on the bacterial stem or within the Bacteria. They also exclude the root of the archaeal radiation from several major clades, consistent with analyses using other rooting methods. Overall, our results demonstrate the utility of non-reversible and non-stationary models for rooting phylogenetic trees, and identify areas where further progress can be made.     

Clinical Utility of a Coronary Heart Disease Risk Prediction Gene Score in UK Healthy Middle Aged Men and in the Pakistani Population

BackgroundNumerous risk prediction algorithms based on conventional risk factors for Coronary Heart Disease (CHD) are available but provide only modest discrimination. The inclusion of genetic information may improve clinical utility.MethodsWe tested the use of two gene scores (GS) in the prospective second Northwick Park Heart Study (NPHSII) of 2775 healthy UK men (284 cases), and Pakistani case-control studies from Islamabad/Rawalpindi (321 cases/228 controls) and Lahore (414 cases/219 controls). The 19-SNP GS included SNPs in loci identified by GWAS and candidate gene studies, while the 13-SNP GS only included SNPs in loci identified by the CARDIoGRAMplusC4D consortium.ResultsIn NPHSII, the mean of both gene scores was higher in those who went on to develop CHD over 13.5 years of follow-up (19-SNP p=0.01, 13-SNP p=7x10^-3). In combination with the Framingham algorithm the GSs appeared to show improvement in discrimination (increase in area under the ROC curve, 19-SNP p=0.48, 13-SNP p=0.82) and risk classification (net reclassification improvement (NRI), 19-SNP p=0.28, 13-SNP p=0.42) compared to the Framingham algorithm alone, but these were not statistically significant. When considering only individuals who moved up a risk category with inclusion of the GS, the improvement in risk classification was statistically significant (19-SNP p=0.01, 13-SNP p=0.04). In the Pakistani samples, risk allele frequencies were significantly lower compared to NPHSII for 13/19 SNPs. In the Islamabad study, the mean gene score was higher in cases than controls only for the 13-SNP GS (2.24 v 2.34, p=0.04). There was no association with CHD and either score in the Lahore study.ConclusionThe performance of both GSs showed potential clinical utility in European men but much less utility in subjects from Pakistan, suggesting that a different set of risk loci or SNPs may be required for risk prediction in the South Asian population.     

Phylogenetic Exploration of Nosocomial Transmission Chains of 2009 Influenza A/H1N1 among Children Admitted at Red Cross War Memorial Children’s Hospital,Cape Town,South Africa in 2011

Traditional modes of investigating influenza nosocomial transmission have entailed a combination of confirmatory molecular diagnostic testing and epidemiological investigation. Common hospital-acquired infections like influenza require a discerning ability to distinguish between viral isolates to accurately identify patient transmission chains. We assessed whether influenza hemagglutinin sequence phylogenies can be used to enrich epidemiological data when investigating the extent of nosocomial transmission over a four-month period within a paediatric Hospital in Cape Town South Africa. Possible transmission chains/channels were initially determined through basic patient admission data combined with Maximum likelihood and time-scaled Bayesian phylogenetic analyses. These analyses suggested that most instances of potential hospital-acquired infections resulted from multiple introductions of Influenza A into the hospital, which included instances where virus hemagglutinin sequences were identical between different patients. Furthermore, a general inability to establish epidemiological transmission linkage of patients/viral isolates implied that identified isolates could have originated from asymptomatic hospital patients, visitors or hospital staff. In contrast, a traditional epidemiological investigation that used no viral phylogenetic analyses, based on patient co-admission into specific wards during a particular time-frame, suggested that multiple hospital acquired infection instances may have stemmed from a limited number of identifiable index viral isolates/patients. This traditional epidemiological analysis by itself could incorrectly suggest linkage between unrelated cases, underestimate the number of unique infections and may overlook the possible diffuse nature of hospital transmission, which was suggested by sequencing data to be caused by multiple unique introductions of influenza A isolates into individual hospital wards. We have demonstrated a functional role for viral sequence data in nosocomial transmission investigation through its ability to enrich traditional, non-molecular observational epidemiological investigation by teasing out possible transmission pathways and working toward more accurately enumerating the number of possible transmission events.     

The influence of population structure on gene expression and flowering time variation in the ubiquitous weed Capsella bursa‐pastoris (Brassicaceae)

Population structure is a potential problem when testing for adaptive phenotypic differences among populations. The observed phenotypic differences among populations can simply be due to genetic drift, and if the genetic distance between them is not considered, the differentiation may be falsely interpreted as adaptive. Conversely, adaptive and demographic processes might have been tightly associated and correcting for the population structure may lead to false negatives. Here, we evaluated this problem in the cosmopolitan weed Capsella bursa‐pastoris. We used RNA‐Seq to analyse gene expression differences among 24 accessions, which belonged to a much larger group that had been previously characterized for flowering time and circadian rhythm and were genotyped using genotyping‐by‐sequencing (GBS) technique. We found that clustering of accessions for gene expression retrieved the same three clusters that were obtained with GBS data previously, namely Europe, the Middle East and Asia. Moreover, the three groups were also differentiated for both flowering time and circadian rhythm variation. Correction for population genetic structure when analysing differential gene expression analysis removed all differences among the three groups. This may suggest that most differences are neutral and simply reflect population history. However, geographical variation in flowering time and circadian rhythm indicated that the distribution of adaptive traits might be confounded by population structure. To bypass this confounding effect, we compared gene expression differentiation between flowering ecotypes within the genetic groups. Among the differentially expressed genes, FLOWERING LOCUS C was the strongest candidate for local adaptation in regulation of flowering time.     

The effectiveness of different planting frameworks for recruitment of tropical rainforest species on ex‐rainforest land

A long‐term rainforest restoration experiment was established on abandoned pasture in northeastern Queensland in 1993 to examine the effectiveness of five different restoration planting methods: (T1) control (no plantings); (T2) pioneer monoculture (planting seedlings of one pioneer species, Homalanthus novoguineensis, Euphorbiaceae); (T3) Homalanthus group framework method (H. novoguineensis and eight other pioneer species); (T4) Alphitonia group framework method (Alphitonia petriei, Rhamnaceae, with eight other pioneer species); and (T5) maximum diversity method (planting pioneers, middle‐phase species, and mature‐phase species). We investigated temporal patterns in the (1) fate of seedlings originally planted in 1993; (2) natural recruitment of native plant species; and (3) current habitat structure (canopy cover and ground cover of grasses and invasive plants) within each restoration treatment. A total of 97% of seedlings planted in T2 died within the first 13 years and all had died by 2014. A total of 72% of seedlings planted in T3, 55.5% of seedlings planted in T4, and 55% of seedlings planted in T5 also died by 2014. By 2014, 42 species from 21 families had recruited across the experimental site, and the abundance of recruits was almost twice that recorded in 2001 and 2006. Overall, T3, T4, and T5 had the greatest diversity and abundance of recruits. By 2014, canopy cover was greatest in T3, T4, and T5 but grass cover was least in T5. It is concluded that some restoration success measures increase with planting diversity, but overall the rate of recovery is similar in framework species and maximum diversity method.