Molecular Profiling of Shiga Toxin-Producing Escherichia coli and Enteropathogenic E. coli Strains Isolated from French Coastal Environments

Shiga toxin-producing Escherichia coli (STEC) and enteropathogenic E. coli (EPEC) strains may be responsible for food-borne infections in humans. Twenty-eight STEC and 75 EPEC strains previously isolated from French shellfish-harvesting areas and their watersheds and belonging to 68 distinguishable serotypes were characterized in this study. High-throughput real-time PCR was used to search for the presence of 75 E. coli virulence-associated gene targets, and genes encoding Shiga toxin (stx) and intimin (eae) were subtyped using PCR tests and DNA sequencing, respectively. The results showed a high level of diversity between strains, with 17 unique virulence gene profiles for STEC and 56 for EPEC. Seven STEC and 15 EPEC strains were found to display a large number or a particular combination of genetic markers of virulence and the presence of stx and/or eae variants, suggesting their potential pathogenicity for humans. Among these, an O26:H11 stx1a eae-β1 strain was associated with a large number of virulence-associated genes (n = 47), including genes carried on the locus of enterocyte effacement (LEE) or other pathogenicity islands, such as OI-122, OI-71, OI-43/48, OI-50, OI-57, and the high-pathogenicity island (HPI). One O91:H21 STEC strain containing 4 stx variants (stx1a, stx2a, stx2c, and stx2d) was found to possess genes associated with pathogenicity islands OI-122, OI-43/48, and OI-15. Among EPEC strains harboring a large number of virulence genes (n, 34 to 50), eight belonged to serotype O26:H11, O103:H2, O103:H25, O145:H28, O157:H7, or O153:H2.

Marine protist diversity in European coastal waters and sediments as revealed by high-throughput sequencing

Although protists are critical components of marine ecosystems, they are still poorly characterized. Here we analysed the taxonomic diversity of planktonic and benthic protist communities collected in six distant European coastal sites. Environmental deoxyribonucleic acid (DNA) and ribonucleic acid (RNA) from three size fractions (pico-, nano- and micro/mesoplankton), as well as from dissolved DNA and surface sediments were used as templates for tag pyrosequencing of the V4 region of the 18S ribosomal DNA. Beta-diversity analyses split the protist community structure into three main clusters: picoplankton-nanoplankton-dissolved DNA, micro/mesoplankton and sediments. Within each cluster, protist communities from the same site and time clustered together, while communities from the same site but different seasons were unrelated. Both DNA and RNA-based surveys provided similar relative abundances for most class-level taxonomic groups. Yet, particular groups were overrepresented in one of the two templates, such as marine alveolates (MALV)-I and MALV-II that were much more abundant in DNA surveys. Overall, the groups displaying the highest relative contribution were Dinophyceae, Diatomea, Ciliophora and Acantharia. Also, well represented were Mamiellophyceae, Cryptomonadales, marine alveolates and marine stramenopiles in the picoplankton, and Monadofilosa and basal Fungi in sediments. Our extensive and systematic sequencing of geographically separated sites provides the most comprehensive molecular description of coastal marine protist diversity to date.

Distinct immune responses of juvenile and adult oysters (Crassostrea gigas) to viral and bacterial infections

Since 2008, massive mortality events of Pacific oysters (Crassostrea gigas) have been reported worldwide and these disease events are often associated with Ostreid herpesvirus type 1 (OsHV-1). Epidemiological field studies have also reported oyster age and other pathogens of the Vibrio genus are contributing factors to this syndrome. We undertook a controlled laboratory experiment to simultaneously investigate survival and immunological response of juvenile and adult C. gigas at different time-points post-infection with OsHV-1, Vibrio tasmaniensis LGP32 and V. aestuarianus. Our data corroborates epidemiological studies that juveniles are more susceptible to OsHV-1, whereas adults are more susceptible to Vibrio. We measured the expression of 102 immune-genes by high-throughput RT-qPCR, which revealed oysters have different transcriptional responses to OsHV-1 and Vibrio. The transcriptional response in the early stages of OsHV-1 infection involved genes related to apoptosis and the interferon-pathway. Transcriptional response to Vibrio infection involved antimicrobial peptides, heat shock proteins and galectins. Interestingly, oysters in the later stages of OsHV-1 infection had a transcriptional response that resembled an antibacterial response, which is suggestive of the oyster's microbiome causing secondary infections (dysbiosis-driven pathology). This study provides molecular evidence that oysters can mount distinct immune response to viral and bacterial pathogens and these responses differ depending on the age of the host.

The transformed-stationary approach: a generic and simplified methodology for non-stationary extreme value analysis

Statistical approaches to study extreme events require, by definition, long time series of data. In many scientific disciplines, these series are often subject to variations at different temporal scales that affect the frequency and intensity of their extremes. Therefore, the assumption of stationarity is violated and alternative methods to conventional stationary extreme value analysis (EVA) must be adopted. Using the example of environmental variables subject to climate change, in this study we introduce the transformed-stationary (TS) methodology for non-stationary EVA. This approach consists of (i) transforming a non-stationary time series into a stationary one, to which the stationary EVA theory can be applied, and (ii) reverse transforming the result into a non-stationary extreme value distribution. As a transformation, we propose and discuss a simple time-varying normalization of the signal and show that it enables a comprehensive formulation of non-stationary generalized extreme value (GEV) and generalized Pareto distribution (GPD) models with a constant shape parameter. A validation of the methodology is carried out on time series of significant wave height, residual water level, and river discharge, which show varying degrees of long-term and seasonal variability. The results from the proposed approach are comparable with the results from (a) a stationary EVA on quasi-stationary slices of non-stationary series and (b) the established method for non-stationary EVA. However, the proposed technique comes with advantages in both cases. For example, in contrast to (a), the proposed technique uses the whole time horizon of the series for the estimation of the extremes, allowing for a more accurate estimation of large return levels. Furthermore, with respect to (b), it decouples the detection of non-stationary patterns from the fitting of the extreme value distribution. As a result, the steps of the analysis are simplified and intermediate diagnostics are possible. In particular, the transformation can be carried out by means of simple statistical techniques such as low-pass filters based on the running mean and the standard deviation, and the fitting procedure is a stationary one with a few degrees of freedom and is easy to implement and control. An open-source MAT-LAB toolbox has been developed to cover this methodology, which is available at https://github.com/menta78/tsEva/(Mentaschi et al., 2016).