Next generation microbiological risk assessment meta-omics: The next need for integration.

The development of a multi-omics approach has provided a new approach to the investigation of microbial communities allowing an integration of data, which can be used to better understand the behaviour of and interactions between community members. Metagenomics, metatranscriptomics, metaproteomics and metabolomics have the potential of producing a large amount of data in a very short time, however an important challenge is how to exploit and interpret these data to assist risk managers in food safety and quality decisions. This can be achieved by integrating multi-omics data in microbiological risk assessment. In this paper we identify limitations and challenges of the multi-omics approach, underlining promising potentials, but also identifying gaps, which should be addressed for its full exploitation. A view on how this new way of investigation will impact the traditional microbiology schemes in the food industry is also presented.

[1]  Bas Teusink,et al.  Modelling strategies for the industrial exploitation of lactic acid bacteria , 2006, Nature Reviews Microbiology.

[2]  Peter S Evans,et al.  Tracing Origins of the Salmonella Bareilly Strain Causing a Food-borne Outbreak in the United States. , 2016, The Journal of infectious diseases.

[3]  P. Ashton,et al.  Evaluating techniques for metagenome annotation using simulated sequence data , 2016, FEMS microbiology ecology.

[4]  Jeanne-Marie Membré,et al.  Potential application of quantitative microbiological risk assessment techniques to an aseptic-UHT process in the food industry. , 2013, International journal of food microbiology.

[5]  Jens Nielsen,et al.  New insight into the gut microbiome through metagenomics , 2015 .

[6]  Y. Hahn,et al.  Metagenomic Analysis of Kimchi, a Traditional Korean Fermented Food , 2011, Applied and Environmental Microbiology.

[7]  F. Klawonn,et al.  Transcriptome Profiling of Antimicrobial Resistance in Pseudomonas aeruginosa , 2016, Antimicrobial Agents and Chemotherapy.

[8]  Doron Betel,et al.  Identification of low abundance microbiome in clinical samples using whole genome sequencing , 2015, Genome Biology.

[9]  F. Devlieghere,et al.  Psychrotrophic lactic acid bacteria associated with production batch recalls and sporadic cases of early spoilage in Belgium between 2010 and 2014. , 2014, International journal of food microbiology.

[10]  T. Sérot,et al.  Sensory characteristics of spoilage and volatile compounds associated with bacteria isolated from cooked and peeled tropical shrimps using SPME-GC-MS analysis. , 2011, International journal of food microbiology.

[11]  Mihai Pop,et al.  Microbiome Metagenomic Analysis of the Human Distal Gut , 2009 .

[12]  The effect of DNA extraction methodology on gut microbiota research applications , 2016, BMC Research Notes.

[13]  Nicholas A. Bokulich,et al.  Mapping microbial ecosystems and spoilage-gene flow in breweries highlights patterns of contamination and resistance , 2015, eLife.

[14]  F. De Filippis,et al.  Exploring the Sources of Bacterial Spoilers in Beefsteaks by Culture-Independent High-Throughput Sequencing , 2013, PloS one.

[15]  Edward M. Fox,et al.  Phylogenetic Profiles of In-House Microflora in Drains at a Food Production Facility: Comparison and Biocontrol Implications of Listeria-Positive and -Negative Bacterial Populations , 2014, Applied and Environmental Microbiology.

[16]  M. Pop,et al.  Metagenomic Analysis of the Human Distal Gut Microbiome , 2006, Science.

[17]  Christian Solem,et al.  Experimental determination of control of glycolysis in Lactococcus lactis , 2002, Antonie van Leeuwenhoek.

[18]  D. Ercolini,et al.  Zooming into food-associated microbial consortia: a ‘cultural’ evolution , 2015 .

[19]  M. Marco,et al.  The Core and Seasonal Microbiota of Raw Bovine Milk in Tanker Trucks and the Impact of Transfer to a Milk Processing Facility , 2016, mBio.

[20]  Jean Pierre Nshimyimana,et al.  Next-generation sequencing (NGS) for assessment of microbial water quality: current progress, challenges, and future opportunities , 2015, Front. Microbiol..

[21]  L. Cocolin,et al.  Differential gene expression profiling of Listeria monocytogenes in Cacciatore and Felino salami to reveal potential stress resistance biomarkers. , 2015, Food microbiology.

[22]  Royston Goodacre,et al.  A flavour of omics approaches for the detection of food fraud , 2016 .

[23]  Lapo Mughini Gras,et al.  Significance of whole genome sequencing for surveillance, source attribution and microbial risk assessment of foodborne pathogens , 2016 .

[24]  María Cruz Martín,et al.  A PCR-DGGE method for the identification of histamine-producing bacteria in cheese , 2016 .

[25]  F. Sanger,et al.  A rapid method for determining sequences in DNA by primed synthesis with DNA polymerase. , 1975, Journal of molecular biology.

[26]  B. Palsson,et al.  Insight into human alveolar macrophage and M. tuberculosis interactions via metabolic reconstructions , 2010, Molecular systems biology.

[27]  Stanley Brul,et al.  Microbial systems biology: new frontiers open to predictive microbiology. , 2008, International journal of food microbiology.

[28]  J. Raes,et al.  Microbial interactions: from networks to models , 2012, Nature Reviews Microbiology.

[29]  Christina Boucher,et al.  Use of Metagenomic Shotgun Sequencing Technology To Detect Foodborne Pathogens within the Microbiome of the Beef Production Chain , 2016, Applied and Environmental Microbiology.

[30]  Carole Feurer,et al.  Origin and ecological selection of core and food-specific bacterial communities associated with meat and seafood spoilage , 2014, The ISME Journal.

[31]  Jozsef Baranyi,et al.  In vivo and in silico determination of essential genes of Campylobacter jejuni , 2011, BMC Genomics.

[32]  Stanley Brul,et al.  ‘Omics’ technologies in quantitative microbial risk assessment , 2012 .

[33]  J. Nielsen,et al.  Uncovering transcriptional regulation of metabolism by using metabolic network topology. , 2005, Proceedings of the National Academy of Sciences of the United States of America.

[34]  Edward J. O'Brien,et al.  Genome-scale models of metabolism and gene expression extend and refine growth phenotype prediction , 2013, Molecular systems biology.

[35]  Yi Chen,et al.  Listeria monocytogenes in Stone Fruits Linked to a Multistate Outbreak: Enumeration of Cells and Whole-Genome Sequencing , 2016, Applied and Environmental Microbiology.

[36]  Bas Teusink,et al.  Understanding the Adaptive Growth Strategy of Lactobacillus plantarum by In Silico Optimisation , 2009, PLoS Comput. Biol..

[37]  Danilo Ercolini,et al.  Microbiota of an Italian Grana-Like Cheese during Manufacture and Ripening, Unraveled by 16S rRNA-Based Approaches , 2016, Applied and Environmental Microbiology.

[38]  Nicholas Chia,et al.  Comparison of microbial DNA enrichment tools for metagenomic whole genome sequencing. , 2016, Journal of microbiological methods.

[39]  Xu-xiang Zhang,et al.  Metagenomic insights into ultraviolet disinfection effects on antibiotic resistome in biologically treated wastewater. , 2016, Water research.

[40]  Katherine H. Huang,et al.  Identifying personal microbiomes using metagenomic codes , 2015, Proceedings of the National Academy of Sciences.

[41]  Jason A. Papin,et al.  Inference of Network Dynamics and Metabolic Interactions in the Gut Microbiome , 2015, PLoS Comput. Biol..

[42]  Nicholas A. Bokulich,et al.  A new perspective on microbial landscapes within food production. , 2016, Current opinion in biotechnology.

[43]  Kieran Jordan,et al.  A Review on the Applications of Next Generation Sequencing Technologies as Applied to Food-Related Microbiome Studies , 2017, Front. Microbiol..

[44]  P. R. Jensen,et al.  Lactate dehydrogenase has no control on lactate production but has a strong negative control on formate production in Lactococcus lactis. , 2001, European journal of biochemistry.

[45]  P. Auvinen,et al.  Comparison of microbial communities in marinated and unmarinated broiler meat by metagenomics. , 2012, International journal of food microbiology.

[46]  Maya Gokhale,et al.  Scalable metagenomic taxonomy classification using a reference genome database , 2013, Bioinform..

[47]  F. Postollec,et al.  An integrative approach to identify Bacillus weihenstephanensis resistance biomarkers using gene expression quantification throughout acid inactivation. , 2012, Food microbiology.

[48]  Duy Tin Truong,et al.  Strain-level microbial epidemiology and population genomics from shotgun metagenomics , 2016, Nature Methods.

[49]  Zachary A. King,et al.  Constraint-based models predict metabolic and associated cellular functions , 2014, Nature Reviews Genetics.

[50]  L. Cocolin,et al.  Culture independent methods to assess the diversity and dynamics of microbiota during food fermentation. , 2013, International journal of food microbiology.

[51]  Yoonsoo Hahn,et al.  Metatranscriptomic analysis of lactic acid bacterial gene expression during kimchi fermentation. , 2013, International journal of food microbiology.

[52]  Rob Knight,et al.  Co-Enriching Microflora Associated with Culture Based Methods to Detect Salmonella from Tomato Phyllosphere , 2013, PloS one.

[53]  G. Miotto,et al.  A genomic and transcriptomic approach to investigate the blue pigment phenotype in Pseudomonas fluorescens. , 2015, International journal of food microbiology.

[54]  F. Aarestrup,et al.  Impact of Sample Type and DNA Isolation Procedure on Genomic Inference of Microbiome Composition , 2016, mSystems.

[55]  T. Abee,et al.  Prediction of Bacillus weihenstephanensis acid resistance: the use of gene expression patterns to select potential biomarkers. , 2013, International journal of food microbiology.

[56]  T. Sérot,et al.  Evaluation of the spoilage potential of bacteria isolated from spoiled raw salmon (Salmo salar) fillets stored under modified atmosphere packaging. , 2013, International journal of food microbiology.

[57]  D. Porcellato,et al.  Bacterial dynamics and functional analysis of microbial metagenomes during ripening of Dutch-type cheese , 2016 .

[58]  Karen Jarvis,et al.  Whole Genome DNA Sequence Analysis of Salmonella subspecies enterica serotype Tennessee obtained from related peanut butter foodborne outbreaks. , 2016, PloS one.

[59]  Alejandro Sanchez-Flores,et al.  Metagenomic analysis of a Mexican ripened cheese reveals a unique complex microbiota. , 2016, Food microbiology.

[60]  Jack A. Gilbert,et al.  Metatranscriptomics reveals temperature-driven functional changes in microbiome impacting cheese maturation rate , 2016, Scientific Reports.

[61]  B. Taminiau,et al.  Metagenomic insights into the dynamics of microbial communities in food. , 2015, International journal of food microbiology.

[62]  Tomer Shlomi,et al.  Prediction of Microbial Growth Rate versus Biomass Yield by a Metabolic Network with Kinetic Parameters , 2012, PLoS Comput. Biol..

[63]  K. Nelson,et al.  Microbiomes, metagenomics, and primate conservation: New strategies, tools, and applications , 2016 .

[64]  Danilo Ercolini,et al.  High-Throughput Sequencing and Metagenomics: Moving Forward in the Culture-Independent Analysis of Food Microbial Ecology , 2013, Applied and Environmental Microbiology.