Flexible origin of hydrocarbon/pheromone precursors in Drosophila melanogaster

In terrestrial insects, cuticular hydrocarbons (CHCs) provide protection from desiccation. Specific CHCs can also act as pheromones, which are important for successful mating. Oenocytes are abdominal cells thought to act as specialized units for CHC biogenesis that consists of long-chain fatty acid (LCFA) synthesis, optional desaturation(s), elongation to very long-chain fatty acids (VLCFAs), and removal of the carboxyl group. By investigating CHC biogenesis in Drosophila melanogaster, we showed that VLCFA synthesis takes place only within the oenocytes. Conversely, several pathways, which may compensate for one another, can feed the oenocyte pool of LCFAs, suggesting that this step is a critical node for regulating CHC synthesis. Importantly, flies deficient in LCFA synthesis sacrificed their triacylglycerol stores while maintaining some CHC production. Moreover, pheromone production was lower in adult flies that emerged from larvae that were fed excess dietary lipids, and their mating success was lower. Further, we showed that pheromone production in the oenocytes depends on lipid metabolism in the fat tissue and that fatty acid transport protein, a bipartite acyl-CoA synthase (ACS)/FA transporter, likely acts through its ACS domain in the oenocyte pathway of CHC biogenesis. Our study highlights the importance of environmental and physiological inputs in regulating LCFA synthesis to eventually control sexual communication in a polyphagous animal.     

Deletion of the ORF9p Acidic Cluster Impairs the Nuclear Egress of Varicella-Zoster Virus Capsids

The protein encoded by ORF9 is essential for varicella-zoster virus (VZV) replication. Previous studies documented its presence in the trans-Golgi network and its involvement in secondary envelopment. In this work, we deleted the ORF9p acidic cluster, destroying its interaction with ORF47p, and this resulted in a nuclear accumulation of both proteins. This phenotype results in an accumulation of primary enveloped capsids in the perinuclear space, reflecting a capsid de-envelopment defect.     

High-Throughput Microfluidic Method To Study Biofilm Formation and Host-Pathogen Interactions in Pathogenic Escherichia coli

Biofilm formation and host-pathogen interactions are frequently studied using multiwell plates; however, these closed systems lack shear force, which is present at several sites in the host, such as the intestinal and urinary tracts. Recently, microfluidic systems that incorporate shear force and very small volumes have been developed to provide cell biology models that resemble in vivo conditions. Therefore, the objective of this study was to determine if the BioFlux 200 microfluidic system could be used to study host-pathogen interactions and biofilm formation by pathogenic Escherichia coli. Strains of various pathotypes were selected to establish the growth conditions for the formation of biofilms in the BioFlux 200 system on abiotic (glass) or biotic (eukaryotic-cell) surfaces. Biofilm formation on glass was observed for the majority of strains when they were grown in M9 medium at 30°C but not in RPMI medium at 37°C. In contrast, HRT-18 cell monolayers enhanced binding and, in most cases, biofilm formation by pathogenic E. coli in RPMI medium at 37°C. As a proof of principle, the biofilm-forming ability of a diffusely adherent E. coli mutant strain lacking AIDA-I, a known mediator of attachment, was assessed in our models. In contrast to the parental strain, which formed a strong biofilm, the mutant formed a thin biofilm on glass or isolated clusters on HRT-18 monolayers. In conclusion, we describe a microfluidic method for high-throughput screening that could be used to identify novel factors involved in E. coli biofilm formation and host-pathogen interactions under shear force.     

A new positive relationship between pCO2 and stomatal frequency in Quercus guyavifolia (Fagaceae): a potential proxy for palaeo-CO2 levels

Background and Aims The inverse relationship between atmospheric CO₂ partial pressure (pCO₂) and stomatal frequency in many species of plants has been widely used to estimate palaeoatmospheric CO₂ (palaeo-CO₂) levels; however, the results obtained have been quite variable. This study attempts to find a potential new proxy for palaeo-CO₂ levels by analysing stomatal frequency in Quercus guyavifolia (Q. guajavifolia, Fagaceae), an extant dominant species of sclerophyllous forests in the Himalayas with abundant fossil relatives.Methods Stomatal frequency was analysed for extant samples of Q. guyavifolia collected from17 field sites at altitudes ranging between 2493 and 4497 m. Herbarium specimens collected between 1926 and 2011 were also examined. Correlations of pCO₂–stomatal frequency were determined using samples from both sources, and these were then applied to Q. preguyavaefolia fossils in order to estimate palaeo-CO₂ concentrations for two late-Pliocene floras in south-western China.Key Results In contrast to the negative correlations detected for most other species that have been studied, a positive correlation between pCO₂ and stomatal frequency was determined in Q. guyavifolia sampled from both extant field collections and historical herbarium specimens. Palaeo-CO₂ concentrations were estimated to be approx. 180–240 ppm in the late Pliocene, which is consistent with most other previous estimates.Conclusions A new positive relationship between pCO₂ and stomatal frequency in Q. guyavifolia is presented, which can be applied to the fossils closely related to this species that are widely distributed in the late-Cenozoic strata in order to estimate palaeo-CO₂ concentrations. The results show that it is valid to use a positive relationship to estimate palaeo-CO₂ concentrations, and the study adds to the variety of stomatal density/index relationships that available for estimating pCO₂. The physiological mechanisms underlying this positive response are unclear, however, and require further research.     

External validation of the Hospital-patient One-year Mortality Risk (HOMR) model for predicting death within 1 year after hospital admission

Background:

Predicting long-term survival after admission to hospital is helpful for clinical, administrative and research purposes. The Hospital-patient One-year Mortality Risk (HOMR) model was derived and internally validated to predict the risk of death within 1 year after admission. We conducted an external validation of the model in a large multicentre study.

Methods:

We used administrative data for all nonpsychiatric admissions of adult patients to hospitals in the provinces of Ontario (2003–2010) and Alberta (2011–2012), and to the Brigham and Women’s Hospital in Boston (2010–2012) to calculate each patient’s HOMR score at admission. The HOMR score is based on a set of parameters that captures patient demographics, health burden and severity of acute illness. We determined patient status (alive or dead) 1 year after admission using population-based registries.

Results:

The 3 validation cohorts (n = 2 862 996 in Ontario, 210 595 in Alberta and 66 683 in Boston) were distinct from each other and from the derivation cohort. The overall risk of death within 1 year after admission was 8.7% (95% confidence interval [CI] 8.7% to 8.8%). The HOMR score was strongly and significantly associated with risk of death in all populations and was highly discriminative, with a C statistic ranging from 0.89 (95% CI 0.87 to 0.91) to 0.92 (95% CI 0.91 to 0.92). Observed and expected outcome risks were similar (median absolute difference in percent dying in 1 yr 0.3%, interquartile range 0.05%–2.5%).

Interpretation:

The HOMR score, calculated using routinely collected administrative data, accurately predicted the risk of death among adult patients within 1 year after admission to hospital for nonpsychiatric indications. Similar performance was seen when the score was used in geographically and temporally diverse populations. The HOMR model can be used for risk adjustment in analyses of health administrative data to predict long-term survival among hospital patients.The life expectancy of individual patients can be important for both medical decision-making and research. Patients with a short life expectancy may choose to defer preventive treatments, screening interventions or interventional procedures for conditions that are currently asymptomatic. An accurate assessment of risk of death, particularly if that risk is high, could motivate and inform discussions between patients and physicians regarding goals of care. In addition, accurate prognostications are essential for adjusting statistical models that have death as an outcome (or as a competing risk for other outcomes) in both research and administration.We recently derived and internally validated a model that predicts the risk of death from any cause at 1 year after admission to hospital.¹ The Hospital-patient One-year Mortality Risk (HOMR) model consists of covariates whose values are determined at admission using routinely collected health administrative data (Figure 1). These covariates include patient demographics (age, sex and living status); health burden (measured using the Charlson Comorbidity Index score, home oxygen status and the number of visits to emergency departments and admissions to hospital by ambulance in the previous year); and acuity of illness (admission urgency and hospital service, direct admission to an intensive care unit and whether the admission was an urgent readmission to hospital). The latter category was also gauged using the Diagnostic Risk Score, which quantifies risk of death for particular diagnoses beyond that explained by the other covariates (Appendix 1, available at www.cmaj.ca/lookup/suppl/doi:10.1503/cmaj.150209/-/DC1).Open in a separate windowFigure 1:Covariates used to calculate a patient’s Hospital-patient One-year Mortality Risk (HOMR) score at the time of admission to hospital. The Diagnostic Risk Score (Appendix 1) quantifies risk of death for diagnostic groups beyond that explained by the other covariates. Points for the interacting covariates of age and Charlson Comorbidity Index score include the risk of patient age, comorbidity score and their interaction. In contrast, points for living status and admission urgency include the risk of these covariates and their interaction with admissions by ambulance in the previous year; points for the latter covariate are considered separately. See www.cmaj.ca/lookup/suppl/doi:10.1503/cmaj.150209/-/DC1)Discrete values for each covariate are given specific points, which are summed to create the HOMR score (Figure 1). In an internal validation population, the HOMR score accurately predicted the risk of death from any cause within 1 year after admission, with a C statistic of 0.92 and excellent calibration among adult residents of Ontario admitted to hospital for nonpsychiatric indications in 2011.¹Although these statistics are impressive, external validation is required to determine the true usefulness of any statistical model. External validation is necessary to prove that the model’s performance is not idiosyncratic to the patients, physicians, institutions or data systems used to derive and internally test it.^2,3 A prognostic model should remain accurate when retested with different patients (reproducibility), during different periods (historical transportability) and in different locations (geographic transportability).⁴ We conducted an external validation of the HOMR model in a multicentre study that included Canadian and American hospitals.