Literature DB >> 15542156

Impact of genomics on microbial food safety.

Tjakko Abee1, Willem van Schaik, Roland J Siezen.   

Abstract

Genome sequences are now available for many of the microbes that cause food-borne diseases. The information contained in pathogen genome sequences, together with the development of themed and whole-genome DNA microarrays and improved proteomics techniques, might provide tools for the rapid detection and identification of such organisms, for assessing their biological diversity and for understanding their ability to respond to stress. The genomic information also provides insight into the metabolic capacity and versatility of microbes; for example, specific metabolic pathways might contribute to the growth and survival of pathogens in a range of niches, such as food-processing environments and the human host. New concepts are emerging about how pathogens function, both within foods and in interactions with the host. The future should bring the first practical benefits of genome sequencing to the field of microbial food safety, including strategies and tools for the identification and control of emerging pathogens.

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Year:  2004        PMID: 15542156     DOI: 10.1016/j.tibtech.2004.10.007

Source DB:  PubMed          Journal:  Trends Biotechnol        ISSN: 0167-7799            Impact factor:   19.536


  5 in total

1.  Gene expression profiling of Listeria monocytogenes strain F2365 during growth in ultrahigh-temperature-processed skim milk.

Authors:  Yanhong Liu; Amy Ream
Journal:  Appl Environ Microbiol       Date:  2008-09-19       Impact factor: 4.792

2.  Development of rapid detection and genetic characterization of salmonella in poultry breeder feeds.

Authors:  Robin Jarquin; Irene Hanning; Soohyoun Ahn; Steven C Ricke
Journal:  Sensors (Basel)       Date:  2009-07-06       Impact factor: 3.576

Review 3.  Culture-independent approaches to chlamydial genomics.

Authors:  Alyce Taylor-Brown; Danielle Madden; Adam Polkinghorne
Journal:  Microb Genom       Date:  2018-01-03

4.  Machine Learning Methods as a Tool for Predicting Risk of Illness Applying Next-Generation Sequencing Data.

Authors:  Patrick Murigu Kamau Njage; Clementine Henri; Pimlapas Leekitcharoenphon; Michel-Yves Mistou; Rene S Hendriksen; Tine Hald
Journal:  Risk Anal       Date:  2018-11-21       Impact factor: 4.000

5.  First step in using molecular data for microbial food safety risk assessment; hazard identification of Escherichia coli O157:H7 by coupling genomic data with in vitro adherence to human epithelial cells.

Authors:  Annemarie Pielaat; Martin P Boer; Lucas M Wijnands; Angela H A M van Hoek; El Bouw; Gary C Barker; Peter F M Teunis; Henk J M Aarts; Eelco Franz
Journal:  Int J Food Microbiol       Date:  2015-04-10       Impact factor: 5.277

  5 in total

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