Trustworthy Identification of Resistance Biomarkers of Bacillus weihenstephanensis: Workflow of the Quality Assurance Procedure Noémie Desriac 1 & Florence Postollec 2 & Louis Coroller 1 & Sonia Pavan 2 & Jérôme Combrisson 3 & Sylvie Hallier-Soulier 4 & Danièle Sohier 5 Received: 26 July 2017 /Accepted: 5 October 2017 # Springer Science+Business Media, LLC 2017 Abstract Omics databases have exploded, opening the ave- nue to take strain diversity or physiological variability into consideration in microbiological risk assessment (MRA). However, one obstacle to the integration of omics data in MRA is the production of quantitative data that may be used to build mathematical models. Gene expression is recognized as relevant biomarker to describe bacterial behavior and re- verse transcription quantitative PCR (RT-qPCR) is considered as the gold standard for accurate, sensitive, and fast measure- ment of gene expression. However, numerous critical points may arise throughout the entire workflow of RT-qPCR data acquisition influencing accuracy of the results and reliability of the conclusions. Although recommendations about the minimum information that should be found in publications about quantitative real-time PCR experiments, heterogeneity in the reporting of RT-qPCR quality controls in publications remains. Herein, the step-by step RT-qPCR quality controls established for the selection of Bacillus weihenstephanensis resistance biomarkers were described. Throughout this exam- ple, appropriate quality procedures and quality controls that shall be set up and carefully assessed to ensure reliable inter- pretations in RT-qPCR were depicted. Keywords RT-qPCR . Quality control . Quality procedure . Bacillus weihenstephanensis Introduction Predictive microbiology is one of the microbiological risk assessment (MRA) components as described in the European Regulation (European Commission 2005). MRA and related modeling approaches are usually considered as particularly appropriate by governmental food safety organi- zations (Codex Alimentarius 1999) and for integration into the decision-making tasks and tools. But this is also of relevance to numerous industrial applications, such as shelf-life determi- nation, heat treatment optimisation, raw material selection, assessment of non-thermal inactivation processes and new formulation development. Food predictive microbiology models were for many years mainly developed using culture based methods and worst case scenari to ensure food safety (McMeekin et al. 2002). It is now commonly recognized that this approach leads to over- or underestimations in microbial quantitative risk and could be improved by integrating the bacterial and cellular adaptive response to various stresses (Brul et al. 2012). In the meantime, genomics, transcripto- mics, proteomics, and more generally omics databases have blown up, offering wonderful opportunities to develop new methodologies to qualify cellular physiology and improve risk assessment (Brul et al. 2012; Havelaar et al. 2010; McMeekin et al. 2008; Rantsiou et al. 2011). These technologies will Electronic supplementary material The online version of this article (https://doi.org/10.1007/s12161-017-1058-0) contains supplementary material, which is available to authorized users. * Noémie Desriac noemie.desriac@univ-brest.fr 1 Université de Brest, EA3882, Laboratoire Universitaire de Biodiversité et Ecologie Microbienne, UMT 14.01 SPORE-RISK, ISBAM, 6 rue de l’Université, F-29334 Quimper, France 2 ADRIA Développement, UMT 14.01 SPORE-RISK, Z.A. de Creac’h Gwen, F-29196 Quimper Cedex, France 3 NGS Industry Europe, Biofortis Mérieux NutriSciences Biofortis SAS, 3 route de la Chatterie, 44800 Saint Herblain, France 4 Pall GeneDisc Technologies, Centre d’affaire CICEA, 1 rue du Courtil, bâtiment 4, F-35170, Bruz, France 5 Bremen, Germany Food Anal. Methods https://doi.org/10.1007/s12161-017-1058-0