Discovery of new intracellular pathogens by amoebal coculture and amoebal enrichment approaches.

Intracellular pathogens such as legionella, mycobacteria and Chlamydia-like organisms are difficult to isolate because they often grow poorly or not at all on selective media that are usually used to cultivate bacteria. For this reason, many of these pathogens were discovered only recently or following important outbreaks. These pathogens are often associated with amoebae, which serve as host-cell and allow the survival and growth of the bacteria. We intend here to provide a demonstration of two techniques that allow isolation and characterization of intracellular pathogens present in clinical or environmental samples: the amoebal coculture and the amoebal enrichment. Amoebal coculture allows recovery of intracellular bacteria by inoculating the investigated sample onto an amoebal lawn that can be infected and lysed by the intracellular bacteria present in the sample. Amoebal enrichment allows recovery of amoebae present in a clinical or environmental sample. This can lead to discovery of new amoebal species but also of new intracellular bacteria growing specifically in these amoebae. Together, these two techniques help to discover new intracellular bacteria able to grow in amoebae. Because of their ability to infect amoebae and resist phagocytosis, these intracellular bacteria might also escape phagocytosis by macrophages and thus, be pathogenic for higher eukaryotes.

Lausannevirus, a giant amoebal virus encoding histone doublets.

Large viruses infecting algae or amoebae belong to the NucleoCytoplasmic Large DNA Viruses (NCLDV) and present genotypic and phenotypic characteristics that have raised major interest among microbiologists. Here, we describe a new large virus discovered in Acanthamoeba castellanii co-culture of an environmental sample. The virus, referred to as Lausannevirus, has a very limited host range, infecting Acanthamoeba spp. but being unable to infect other amoebae and mammalian cell lines tested. Within A. castellanii, this icosahedral virus of about 200 nm exhibits a development cycle similar to Mimivirus, with an eclipse phase 2 h post infection and a logarithmic growth leading to amoebal lysis in less than 24 h. The 346 kb Lausannevirus genome presents similarities with the recently described Marseillevirus, sharing 89% of genes, and thus belongs to the same family as confirmed by core gene phylogeny. Interestingly, Lausannevirus and Marseillevirus genomes both encode three proteins with predicted histone folds, including two histone doublets, that present similarities to eukaryotic and archaeal histones. The discovery of Lausannevirus and the analysis of its genome provide some insight in the evolution of these large amoebae-infecting viruses.

The effect of a smoking ban introduction to environmental tobacco smoke exposure in Swiss hospitality workers : Abstracts of the 23rd Annual Conference of the International Society of Environmental Epidemiology (ISEE), September 13-16, 2011, Barcelona, Spain

Background and Aims: To protect the population from environmental tobacco smoke (ETS) Switzerland introduced a nationwide rather heterogeneous smoking ban in May 2010. The exposure situation of non-smoking hospitality workers before and after implementation of the new law is being assessed in a prospective cohort study. Methods: Exposure to ETS was measured using a novel method developed by the Institute for Work and Health in Lausanne. It is a passive sampler called MoNIC (Monitor of NICotine). The nicotine of the ETS is fixed onto a filter and transformed into salt of not volatile nicotine. Subsequently the number of passively smoked cigarettes is calculated. Badges were placed at the workplace as well as distributed to the participants for personal measuring. Additionally a salivary sample was taken to determine nicotine concentration. Results: At baseline Spearman's correlation between workplace and personal badge was 0.47. The average cigarette equivalents per day at the workplace obtained by badge significantly dropped from 5.1 (95%- CI: 2.4 to 7.9) at baseline to 0.3 (0.2 to 0.4) at first follow-up (n=29) three months later (p<0.001). For personal badges the number of passively smoked cigarettes declined from 1.5 (2.7 to 0.4) per day to 0.5 (0.3 to 0.8) (n=16).Salivary nicotine concentration in a subset of 13 participants who had worked on the day prior to the examination was 2.63 ng/ml before and 1.53 ng/ml after the ban (p=0.04). Spearman's correlation of salivary nicotine was 0.56 with workplace badge and 0.79 with personal badge concentrations. Conclusions: Workplace measurements clearly reflect the smoking regulation in a venue. The MoNIC badge proves to be a sensitive measuring device to determine personal ETS exposure and it is a demonstrative measure for communication with lay audiences and study participants as the number of passively smoked cigarettes is an easily conceivable result.

Bacterial biosensors for measuring availability of environmental pollutants

Abstract: Traditionally, pollution risk assessment is based on the measurement of a pollutant's total concentration in a sample. The toxicity of a given pollutant in the environment, however, is tightly linked to its bioavailability, which may differ significantly from the total amount. Physico-chemical and biological parameters strongly influence pollutant fate in terms of leaching, sequestration and biodegradation. Bacterial sensorreporters, which consist of living micro-organisms genetically engineered to produce specific output in response to target chemicals, offer an interesting alternative to monitoring approaches. Bacterial sensor-reporters detect bioavailable and/or bioaccessible compound fractions in samples. Currently, a variety of environmental pollutants can be targeted by specific biosensor-reporters. Although most of such strains are still confined to the lab, several recent reports have demonstrated utility of bacterial sensing-reporting in the field, with method detection limits in the nanomolar range. This review illustrates the general design principles for bacterial sensor-reporters, presents an overview of the existing biosensor-reporter strains with emphasis on organic compound detection. A specific focus throughout is on the concepts of bioavailability and bioaccessibility, and how bacteria-based sensing-reporting systems can help to improve our basic understanding of the different processes at work.

Association of work related chronic stressors and psychiatric symptoms in a Swiss sample of police officers : a cross sectional questionnaire study

Purpose (1) To identify work related stressors that are associated with psychiatric symptoms in a Swiss sample of policemen and (2) to develop a model for identifying officers at risk for developing mental health problems. Method The study design is cross sectional. A total of 354 male police officers answered a questionnaire assessing a wide spectrum of work related stressors. Psychiatric symptoms were assessed using the "TST questionnaire" (Langner in J Health Hum Behav 4, 269-276, 1962). Logistic regression with backward procedure was used to identify a set of variables collectively associated with high scores for psychiatric symptoms. Results A total of 42 (11.9%) officers had a high score for psychiatric symptoms. Nearly all potential stressors considered were significantly associated (at P < 0.05) with a high score for psychiatric symptoms. A significant model including 6 independent variables was identified: lack of support from superior and organization OR = 3.58 (1.58-8.13), self perception of bad quality work OR = 2.99 (1.35-6.59), inadequate work schedule OR = 2.84 (1.22-6.62), high mental/intellectual demand OR = 2.56 (1.12-5.86), age (in decades) OR = 1.82 (1.21-2.73), and score for physical environment complaints OR = 1.30 (1.03-1.64). Conclusions Most of work stressors considered are associated with psychiatric symptoms. Prevention should target the most frequent stressors with high association to symptoms. Complaints of police officers about stressors should receive proper consideration by the management of public administration. Such complaints might be the expression of psychiatric caseness requiring medical assistance. Particular attention should be given to police officers complaining about many stressors identified in this study's multiple model. [Authors]

Machine learning for spatial environmental data. Theory, applications and software

The book presents the state of the art in machine learning algorithms (artificial neural networks of different architectures, support vector machines, etc.) as applied to the classification and mapping of spatially distributed environmental data. Basic geostatistical algorithms are presented as well. New trends in machine learning and their application to spatial data are given, and real case studies based on environmental and pollution data are carried out. The book provides a CD-ROM with the Machine Learning Office software, including sample sets of data, that will allow both students and researchers to put the concepts rapidly to practice.

Rapid LC-MS/MS quantification of the major benzodiazepines and their metabolites on dried blood spots using a simple and cost-effective sample pretreatment.

BACKGROUND: Dried blood spots (DBS) sampling has gained popularity in the bioanalytical community as an alternative to conventional plasma sampling, as it provides numerous benefits in terms of sample collection and logistics. The aim of this work was to show that these advantages can be coupled with a simple and cost-effective sample pretreatment, with subsequent rapid LC-MS/MS analysis for quantitation of 15 benzodiazepines, six metabolites and three Z-drugs. For this purpose, a simplified offline procedure was developed that consisted of letting a 5-µl DBS infuse directly into 100 µl of MeOH, in a conventional LC vial. RESULTS: The parameters related to the DBS pretreatment, such as extraction time or internal standard addition, were investigated and optimized, demonstrating that passive infusion in a regular LC vial was sufficient to quantitatively extract the analytes of interest. The method was validated according to international criteria in the therapeutic concentration ranges of the selected compounds. CONCLUSION: The presented strategy proved to be efficient for the rapid analysis of the selected drugs. Indeed, the offline sample preparation was reduced to a minimum, using a small amount of organic solvent and consumables, without affecting the accuracy of the method. Thus, this approach enables simple and rapid DBS analysis, even when using a non-DBS-dedicated autosampler, while lowering the costs and environmental impact.

Computational analysis of the genetic and environmental contributions to disease-related human phenotypes: From predicting adverse lipid response of HIV patients receiving antiretroviral therapy to genome-wide association studies of cardiovascular and lipid related disorders

Genetic variants influence the risk to develop certain diseases or give rise to differences in drug response. Recent progresses in cost-effective, high-throughput genome-wide techniques, such as microarrays measuring Single Nucleotide Polymorphisms (SNPs), have facilitated genotyping of large clinical and population cohorts. Combining the massive genotypic data with measurements of phenotypic traits allows for the determination of genetic differences that explain, at least in part, the phenotypic variations within a population. So far, models combining the most significant variants can only explain a small fraction of the variance, indicating the limitations of current models. In particular, researchers have only begun to address the possibility of interactions between genotypes and the environment. Elucidating the contributions of such interactions is a difficult task because of the large number of genetic as well as possible environmental factors.In this thesis, I worked on several projects within this context. My first and main project was the identification of possible SNP-environment interactions, where the phenotypes were serum lipid levels of patients from the Swiss HIV Cohort Study (SHCS) treated with antiretroviral therapy. Here the genotypes consisted of a limited set of SNPs in candidate genes relevant for lipid transport and metabolism. The environmental variables were the specific combinations of drugs given to each patient over the treatment period. My work explored bioinformatic and statistical approaches to relate patients' lipid responses to these SNPs, drugs and, importantly, their interactions. The goal of this project was to improve our understanding and to explore the possibility of predicting dyslipidemia, a well-known adverse drug reaction of antiretroviral therapy. Specifically, I quantified how much of the variance in lipid profiles could be explained by the host genetic variants, the administered drugs and SNP-drug interactions and assessed the predictive power of these features on lipid responses. Using cross-validation stratified by patients, we could not validate our hypothesis that models that select a subset of SNP-drug interactions in a principled way have better predictive power than the control models using "random" subsets. Nevertheless, all models tested containing SNP and/or drug terms, exhibited significant predictive power (as compared to a random predictor) and explained a sizable proportion of variance, in the patient stratified cross-validation context. Importantly, the model containing stepwise selected SNP terms showed higher capacity to predict triglyceride levels than a model containing randomly selected SNPs. Dyslipidemia is a complex trait for which many factors remain to be discovered, thus missing from the data, and possibly explaining the limitations of our analysis. In particular, the interactions of drugs with SNPs selected from the set of candidate genes likely have small effect sizes which we were unable to detect in a sample of the present size (<800 patients).In the second part of my thesis, I performed genome-wide association studies within the Cohorte Lausannoise (CoLaus). I have been involved in several international projects to identify SNPs that are associated with various traits, such as serum calcium, body mass index, two-hour glucose levels, as well as metabolic syndrome and its components. These phenotypes are all related to major human health issues, such as cardiovascular disease. I applied statistical methods to detect new variants associated with these phenotypes, contributing to the identification of new genetic loci that may lead to new insights into the genetic basis of these traits. This kind of research will lead to a better understanding of the mechanisms underlying these pathologies, a better evaluation of disease risk, the identification of new therapeutic leads and may ultimately lead to the realization of "personalized" medicine.