Advances in methods to detect, isolate and quantify foodborne pathogens

Foodborne diseases impact human health and economies worldwide in terms of health care and productivity loss. Prevention is necessary and methods to detect, isolate and quantify foodborne pathogens play a fundamental role, changing continuously to face microorganisms and food production evolution. Official methods are mainly based on microorganisms growth in different media and their isolation on selective agars followed by confirmation of presumptive colonies through biochemical and serological test. A complete identification requires form 7 to 10 days. Over the last decades, new molecular techniques based on antibodies and nucleic acids allow a more accurate typing and a faster detection and quantification. The present thesis aims to apply molecular techniques to improve official methods performances regarding two pathogens: Shiga-like Toxin-producing Escherichia coli (STEC) and Listeria monocytogenes. In 2011, a new strain of STEC belonging to the serogroup O104 provoked a large outbreak. Therefore, the development of a method to detect and isolate STEC O104 is demanded. The first objective of this work is the detection, isolation and identification of STEC O104 in sprouts artificially contaminated. Multiplex PCR assays and antibodies anti-O104 incorporated in reagents for immunomagnetic separation and latex agglutination were employed. Contamination levels of less than 1 CFU/g were detected. Multiplex PCR assays permitted a rapid screening of enriched food samples and identification of isolated colonies. Immunomagnetic separation and latex agglutination allowed a high sensitivity and rapid identification of O104 antigen, respectively. The development of a rapid method to detect and quantify Listeria monocytogenes, a high-risk pathogen, is the second objective. Detection of 1 CFU/ml and quantification of 10–1,000 CFU/ml in raw milk were achieved by a sample pretreatment step and quantitative PCR in about 3h. L. monocytogenes growth in raw milk was also evaluated.

Early Warning For Large Earthquakes: Observations, Models and Real-Time Data Analysis

This thesis is a collection of works focused on the topic of Earthquake Early Warning, with a special attention to large magnitude events. The topic is addressed from different points of view and the structure of the thesis reflects the variety of the aspects which have been analyzed. The first part is dedicated to the giant, 2011 Tohoku-Oki earthquake. The main features of the rupture process are first discussed. The earthquake is then used as a case study to test the feasibility Early Warning methodologies for very large events. Limitations of the standard approaches for large events arise in this chapter. The difficulties are related to the real-time magnitude estimate from the first few seconds of recorded signal. An evolutionary strategy for the real-time magnitude estimate is proposed and applied to the single Tohoku-Oki earthquake. In the second part of the thesis a larger number of earthquakes is analyzed, including small, moderate and large events. Starting from the measurement of two Early Warning parameters, the behavior of small and large earthquakes in the initial portion of recorded signals is investigated. The aim is to understand whether small and large earthquakes can be distinguished from the initial stage of their rupture process. A physical model and a plausible interpretation to justify the observations are proposed. The third part of the thesis is focused on practical, real-time approaches for the rapid identification of the potentially damaged zone during a seismic event. Two different approaches for the rapid prediction of the damage area are proposed and tested. The first one is a threshold-based method which uses traditional seismic data. Then an innovative approach using continuous, GPS data is explored. Both strategies improve the prediction of large scale effects of strong earthquakes.

Identification and characterization of the sRNA network in Neisseria meningitidis

Bacterial small regulatory RNAs (sRNAs) are posttranscriptional regulators involved in stress responses. These short non-coding transcripts are synthesised in response to a signal, and control gene expression of their regulons by modulating the translation or stability of the target mRNAs, often in concert with the RNA chaperone Hfq. Characterization of a Hfq knock out mutant in Neisseria meningitidis revealed that it has a pleiotropic phenotype, suggesting a major role for Hfq in adaptation to stresses and virulence and the presence of Hfq-dependent sRNA activity. Global gene expression analysis of regulated transcripts in the Hfq mutant revealed the presence of a regulated sRNA, incorrectly annotated as an open reading frame, which we renamed AniS. The synthesis of this novel sRNA is anaerobically induced through activation of its promoter by the FNR global regulator and through global gene expression analyses we identified at least two predicted mRNA targets of AniS. We also performed a detailed molecular analysis of the action of the sRNA NrrF,. We demonstrated that NrrF regulates succinate dehydrogenase by forming a duplex with a region of complementarity within the sdhDA region of the succinate dehydrogenase transcript, and Hfq enhances the binding of this sRNA to the identified target in the sdhCDAB mRNA; this is likely to result in rapid turnover of the transcript in vivo. In addition, in order to globally investigate other possible sRNAs of N. meningitdis we Deep-sequenced the transcriptome of this bacterium under both standard in vitro and iron-depleted conditions. This analysis revealed genes that were actively transcribed under the two conditions. We focused our attention on the transcribed non-coding regions of the genome and, along with 5’ and 3’ untranslated regions, 19 novel candidate sRNAs were identified. Further studies will be focused on the identification of the regulatory networks of these sRNAs, and their targets.