Use of Galleria mellonella as a Model Organism to Study Legionella pneumophila Infection

Legionella pneumophila, the causative agent of a severe pneumonia named Legionnaires' disease, is an important human pathogen that infects and replicates within alveolar macrophages. Its virulence depends on the Dot/Icm type IV secretion system (T4SS), which is essential to establish a replication permissive vacuole known as the Legionella containing vacuole (LCV). L. pneumophila infection can be modeled in mice however most mouse strains are not permissive, leading to the search for novel infection models. We have recently shown that the larvae of the wax moth Galleria mellonella are suitable for investigation of L. pneumophila infection. G. mellonella is increasingly used as an infection model for human pathogens and a good correlation exists between virulence of several bacterial species in the insect and in mammalian models. A key component of the larvae's immune defenses are hemocytes, professional phagocytes, which take up and destroy invaders. L. pneumophila is able to infect, form a LCV and replicate within these cells. Here we demonstrate protocols for analyzing L. pneumophila virulence in the G. mellonella model, including how to grow infectious L. pneumophila, pretreat the larvae with inhibitors, infect the larvae and how to extract infected cells for quantification and immunofluorescence microscopy. We also describe how to quantify bacterial replication and fitness in competition assays. These approaches allow for the rapid screening of mutants to determine factors important in L. pneumophila virulence, describing a new tool to aid our understanding of this complex pathogen

Spud 1.0: generalising and automating the user interfaces of scientific computer models

The interfaces by which users specify the scenarios to be simulated by scientific computer models are frequently primitive, under-documented and ad-hoc text files which make using the model in question difficult and error-prone and significantly increase the development cost of the model. In this paper, we present a model-independent system, Spud, which formalises the specification of model input formats in terms of formal grammars. This is combined with an automated graphical user interface which guides users to create valid model inputs based on the grammar provided, and a generic options reading module, libspud, which minimises the development cost of adding model options. <br><br> Together, this provides a user friendly, well documented, self validating user interface which is applicable to a wide range of scientific models and which minimises the developer input required to maintain and extend the model interface

A multistage antimalarial targets the plasmepsins IX and X essential for invasion and egress.

: Regulated exocytosis by secretory organelles is important for malaria parasite invasion and egress. Many parasite effector proteins, including perforins, adhesins, and proteases, are extensively proteolytically processed both pre- and postexocytosis. Here we report the multistage antiplasmodial activity of the aspartic protease inhibitor hydroxyl-ethyl-amine-based scaffold compound 49c. This scaffold inhibits the preexocytosis processing of several secreted rhoptry and microneme proteins by targeting the corresponding maturases plasmepsins IX (PMIX) and X (PMX), respectively. Conditional excision of PMIX revealed its crucial role in invasion, and recombinantly active PMIX and PMX cleave egress and invasion factors in a 49c-sensitive manner.<br/

Structural and functional relationships in the virulence-associated cathepsin L proteases of the parasitic liver fluke, Fasciola hepatica

The helminth parasite Fasciola hepatica secretes cysteine proteases to facilitate tissue invasion, migration, and development within the mammalian host. The major proteases cathepsin L1 (FheCL1) and cathepsin L2 (FheCL2) were recombinantly produced and biochemically characterized. By using site-directed mutagenesis, we show that residues at position 67 and 205, which lie within the S2 pocket of the active site, are critical in determining the substrate and inhibitor specificity. FheCL1 exhibits a broader specificity and a higher substrate turnover rate compared with FheCL2. However, FheCL2 can efficiently cleave substrates with a Pro in the P2 position and degrade collagen within the triple helices at physiological pH, an activity that among cysteine proteases has only been reported forhuman cathepsin K. The 1.4-Å three-dimensional structure of the FheCL1 was determined by x-ray crystallography, and the three-dimensional structure of FheCL2 was constructed via homology-based modeling. Analysis and comparison of these structures and our biochemical data with those of human cathepsins L and Kprovided an interpretation of the substrate-recognition mechanisms of these major parasite proteases. Furthermore, our studies suggest that a configuration involving residue 67 and the "gatekeeper" residues 157 and 158 situated at the entrance of the active site pocket create a topology that endows FheCL2 with its unusual collagenolytic activity. The emergence of a specialized collagenolytic function in Fasciola likely contributes to the success of this tissue-invasive parasite. © 2008 by The American Society for Biochemistry and Molecular Biology, Inc

Ordering variable for parton showers

The parton splittings in a parton shower are ordered according to an ordering variable, for example the transverse momentum of the daughter partons relative to the direction of the mother, the virtuality of the splitting, or the angle between the daughter partons. We analyze the choice of the ordering variable and conclude that one particular choice has the advantage of factoring softer splittings from harder splittings graph by graph in a physical gauge.Comment: 28 pages, 5 figure

Risk factors for exacerbations and pneumonia in patients with chronic obstructive pulmonary disease: a pooled analysis.

BACKGROUND: Patients with chronic obstructive pulmonary disease (COPD) are at risk of exacerbations and pneumonia; how the risk factors interact is unclear. METHODS: This post-hoc, pooled analysis included studies of COPD patients treated with inhaled corticosteroid (ICS)/long-acting β2 agonist (LABA) combinations and comparator arms of ICS, LABA, and/or placebo. Backward elimination via Cox's proportional hazards regression modelling evaluated which combination of risk factors best predicts time to first (a) pneumonia, and (b) moderate/severe COPD exacerbation. RESULTS: Five studies contributed: NCT01009463, NCT01017952, NCT00144911, NCT00115492, and NCT00268216. Low body mass index (BMI), exacerbation history, worsening lung function (Global Initiative for Chronic Obstructive Lung Disease [GOLD] stage), and ICS treatment were identified as factors increasing pneumonia risk. BMI was the only pneumonia risk factor influenced by ICS treatment, with ICS further increasing risk for those with BMI <25 kg/m2. The modelled probability of pneumonia varied between 3 and 12% during the first year. Higher exacerbation risk was associated with a history of exacerbations, poorer lung function (GOLD stage), female sex and absence of ICS treatment. The influence of the other exacerbation risk factors was not modified by ICS treatment. Modelled probabilities of an exacerbation varied between 31 and 82% during the first year. CONCLUSIONS: The probability of an exacerbation was considerably higher than for pneumonia. ICS reduced exacerbations but did not influence the effect of risks associated with prior exacerbation history, GOLD stage, or female sex. The only identified risk factor for ICS-induced pneumonia was BMI <25 kg/m2. Analyses of this type may help the development of COPD risk equations

Genetic Polymorphisms Related to VO2max Adaptation Are Associated With Elite Rugby Union Status and Competitive Marathon Performance

PURPOSE: Genetic polymorphisms have been associated with the adaptation to training in maximal oxygen uptake (V˙O2max). However, the genotype distribution of selected polymorphisms in athletic cohorts is unknown, with their influence on performance characteristics also undetermined. This study investigated whether the genotype distributions of 3 polymorphisms previously associated with V˙O2max training adaptation are associated with elite athlete status and performance characteristics in runners and rugby athletes, competitors for whom aerobic metabolism is important. METHODS: Genomic DNA was collected from 732 men including 165 long-distance runners, 212 elite rugby union athletes, and 355 nonathletes. Genotype and allele frequencies of PRDM1 rs10499043 C/T, GRIN3A rs1535628 G/A, and KCNH8 rs4973706 T/C were compared between athletes and nonathletes. Personal-best marathon times in runners, as well as in-game performance variables and playing position, of rugby athletes were analyzed according to genotype. RESULTS: Runners with PRDM1 T alleles recorded marathon times ∼3 minutes faster than CC homozygotes (02:27:55 [00:07:32] h vs 02:31:03 [00:08:24] h, P = .023). Rugby athletes had 1.57 times greater odds of possessing the KCNH8 TT genotype than nonathletes (65.5% vs 54.7%, χ2 = 6.494, P = .013). No other associations were identified. CONCLUSIONS: This study is the first to demonstrate that polymorphisms previously associated with V˙O2max training adaptations in nonathletes are also associated with marathon performance (PRDM1) and elite rugby union status (KCNH8). The genotypes and alleles previously associated with superior endurance-training adaptation appear to be advantageous in long-distance running and achieving elite status in rugby union

A Sensitive Branched DNA HIV-1 Signal Amplification Viral Load Assay with Single Day Turnaround

Branched DNA (bDNA) is a signal amplification technology used in clinical and research laboratories to quantitatively detect nucleic acids. An overnight incubation is a significant drawback of highly sensitive bDNA assays. The VERSANT® HIV-1 RNA 3.0 Assay (bDNA) (“Versant Assay”) currently used in clinical laboratories was modified to allow shorter target incubation, enabling the viral load assay to be run in a single day. To dramatically reduce the target incubation from 16–18 h to 2.5 h, composition of only the “Lysis Diluent” solution was modified. Nucleic acid probes in the assay were unchanged. Performance of the modified assay (assay in development; not commercially available) was evaluated and compared to the Versant Assay. Dilution series replicates (>950 results) were used to demonstrate that analytical sensitivity, linearity, accuracy, and precision for the shorter modified assay are comparable to the Versant Assay. HIV RNA-positive clinical specimens (n = 135) showed no significant difference in quantification between the modified assay and the Versant Assay. Equivalent relative quantification of samples of eight genotypes was demonstrated for the two assays. Elevated levels of several potentially interfering endogenous substances had no effect on quantification or specificity of the modified assay. The modified assay with drastically improved turnaround time demonstrates the viability of signal-amplifying technology, such as bDNA, as an alternative to the PCR-based assays dominating viral load monitoring in clinical laboratories. Highly sensitive bDNA assays with a single day turnaround may be ideal for laboratories with especially stringent cost, contamination, or reliability requirements

Beyond Volume: The Impact of Complex Healthcare Data on the Machine Learning Pipeline

From medical charts to national census, healthcare has traditionally operated under a paper-based paradigm. However, the past decade has marked a long and arduous transformation bringing healthcare into the digital age. Ranging from electronic health records, to digitized imaging and laboratory reports, to public health datasets, today, healthcare now generates an incredible amount of digital information. Such a wealth of data presents an exciting opportunity for integrated machine learning solutions to address problems across multiple facets of healthcare practice and administration. Unfortunately, the ability to derive accurate and informative insights requires more than the ability to execute machine learning models. Rather, a deeper understanding of the data on which the models are run is imperative for their success. While a significant effort has been undertaken to develop models able to process the volume of data obtained during the analysis of millions of digitalized patient records, it is important to remember that volume represents only one aspect of the data. In fact, drawing on data from an increasingly diverse set of sources, healthcare data presents an incredibly complex set of attributes that must be accounted for throughout the machine learning pipeline. This chapter focuses on highlighting such challenges, and is broken down into three distinct components, each representing a phase of the pipeline. We begin with attributes of the data accounted for during preprocessing, then move to considerations during model building, and end with challenges to the interpretation of model output. For each component, we present a discussion around data as it relates to the healthcare domain and offer insight into the challenges each may impose on the efficiency of machine learning techniques.Comment: Healthcare Informatics, Machine Learning, Knowledge Discovery: 20 Pages, 1 Figur