Quantitative Microbial Risk Assessment Based on Whole Genome Sequencing Data: Case of Listeria monocytogenes

被引:16
作者
Njage, Patrick Murigu Kamau [1 ]
Leekitcharoenphon, Pimlapas [1 ]
Hansen, Lisbeth Truelstrup [2 ]
Hendriksen, Rene S. [1 ]
Faes, Christel [3 ]
Aerts, Marc [3 ]
Hald, Tine [1 ]
机构
[1] Tech Univ Denmark, Natl Food Inst, Div Global Surveillance, Res Grp Genom Epidemiol, DK-2800 Lyngby, Denmark
[2] Tech Univ Denmark, Natl Food Inst, Res Grp Microbiol & Hyg, DK-2800 Lyngby, Denmark
[3] Katholieke Univ Leuven, Hasselt Univ, Interuniv Inst Biostat & Stat Bioinformat, B-3590 Diepenbeek, Belgium
关键词
quantitative microbial risk assessment; whole genome sequencing; exposure assessment; predictive modeling; machine learning; finite mixture models; Listeria monocytogenes; GROWTH; HETEROGENEITY; MODELS;
D O I
10.3390/microorganisms8111772
中图分类号
Q93 [微生物学];
学科分类号
071005 ; 100705 ;
摘要
The application of high-throughput DNA sequencing technologies (WGS) data remain an increasingly discussed but vastly unexplored resource in the public health domain of quantitative microbial risk assessment (QMRA). This is due to challenges including high dimensionality of WGS data and heterogeneity of microbial growth phenotype data. This study provides an innovative approach for modeling the impact of population heterogeneity in microbial phenotypic stress response and integrates this into predictive models inputting a high-dimensional WGS data for increased precision exposure assessment using an example of Listeria monocytogenes. Finite mixture models were used to distinguish the number of sub-populations for each of the stress phenotypes, acid, cold, salt and desiccation. Machine learning predictive models were selected from six algorithms by inputting WGS data to predict the sub-population membership of new strains with unknown stress response data. An example QMRA was conducted for cultured milk products using the strains of unknown stress phenotype to illustrate the significance of the findings of this study. Increased resistance to stress conditions leads to increased growth, the likelihood of higher exposure and probability of illness. Neglecting within-species genetic and phenotypic heterogeneity in microbial stress response may over or underestimate microbial exposure and eventual risk during QMRA.
引用
收藏
页码:1 / 24
页数:24
相关论文
共 58 条
  • [11] CAC/GL. Codex Alimentarius Commission, 2009, COD AL FOOD HYG BAS, P43
  • [12] BLAST plus : architecture and applications
    Camacho, Christiam
    Coulouris, George
    Avagyan, Vahram
    Ma, Ning
    Papadopoulos, Jason
    Bealer, Kevin
    Madden, Thomas L.
    [J]. BMC BIOINFORMATICS, 2009, 10
  • [13] Variation of cardinal growth parameters and growth limits according to phylogenetic affiliation in the Bacillus cereus Group. Consequences for risk assessment
    Carlin, Frederic
    Albagnac, Christine
    Rida, Ammar
    Guinebretiere, Marie-Helene
    Couvert, Olivier
    Christophe Nguyen-the
    [J]. FOOD MICROBIOLOGY, 2013, 33 (01) : 69 - 76
  • [14] SUPPORT-VECTOR NETWORKS
    CORTES, C
    VAPNIK, V
    [J]. MACHINE LEARNING, 1995, 20 (03) : 273 - 297
  • [15] Next generation of microbiological risk assessment: Potential of omics data for exposure assessment
    den Besten, Heidy M. W.
    Amezquita, Alejandro
    Bover-Cid, Sara
    Dagnas, Stephane
    Ellouze, Mariem
    Guillou, Sandrine
    Nychas, George
    O'Mahony, Cian
    Perez-Rodriguez, Fernando
    Membre, Jeanne-Marie
    [J]. INTERNATIONAL JOURNAL OF FOOD MICROBIOLOGY, 2018, 287 : 18 - 27
  • [16] Efron B., 1993, MONOGRAPHS STAT APPL, V57, DOI [10.1186/2045-4015-2-43, DOI 10.1186/2045-4015-2-43]
  • [17] Fennema O. R., 1996, Food Chemistry, V3th
  • [18] Fleiss J L., 2003, Statistical methods for rates and proportions, V3, P760, DOI DOI 10.1198/TECH.2004.S812
  • [19] Significance of whole genome sequencing for surveillance, source attribution and microbial risk assessment of foodborne pathogens
    Franz, Eelco
    Gras, Lapo Mughini
    Dallman, Tim
    [J]. CURRENT OPINION IN FOOD SCIENCE, 2016, 8 : 74 - 79
  • [20] BOOSTING A WEAK LEARNING ALGORITHM BY MAJORITY
    FREUND, Y
    [J]. INFORMATION AND COMPUTATION, 1995, 121 (02) : 256 - 285