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

被引:15
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
  • [1] Cellular Heterogeneity: Do Differences Make a Difference?
    Altschuler, Steven J.
    Wu, Lani F.
    [J]. CELL, 2010, 141 (04) : 559 - 563
  • [2] [Anonymous], 2003, QUANT ASS REL RISK P
  • [3] CONTROLLING THE FALSE DISCOVERY RATE - A PRACTICAL AND POWERFUL APPROACH TO MULTIPLE TESTING
    BENJAMINI, Y
    HOCHBERG, Y
    [J]. JOURNAL OF THE ROYAL STATISTICAL SOCIETY SERIES B-STATISTICAL METHODOLOGY, 1995, 57 (01) : 289 - 300
  • [4] A mobile genetic element profoundly increases heat resistance of bacterial spores
    Berendsen, Erwin M.
    Boekhorst, Jos
    Kuipers, Oscar P.
    Wells-Bennik, Marjon H. J.
    [J]. ISME JOURNAL, 2016, 10 (11) : 2633 - 2642
  • [5] Bishop C.M., 2007, Technometrics, Vfirst
  • [6] Statistical modeling: The two cultures
    Breiman, L
    [J]. STATISTICAL SCIENCE, 2001, 16 (03) : 199 - 215
  • [7] Brodersen Kay H., 2010, Proceedings of the 2010 20th International Conference on Pattern Recognition (ICPR 2010), P3121, DOI 10.1109/ICPR.2010.764
  • [8] Knowledge-based analysis of microarray gene expression data by using support vector machines
    Brown, MPS
    Grundy, WN
    Lin, D
    Cristianini, N
    Sugnet, CW
    Furey, TS
    Ares, M
    Haussler, D
    [J]. PROCEEDINGS OF THE NATIONAL ACADEMY OF SCIENCES OF THE UNITED STATES OF AMERICA, 2000, 97 (01) : 262 - 267
  • [9] When is simple good enough: A comparison of the Gompertz, Baranyi, and three-phase linear models for fitting bacterial growth curves
    Buchanan, RL
    Whiting, RC
    Damert, WC
    [J]. FOOD MICROBIOLOGY, 1997, 14 (04) : 313 - 326
  • [10] Identifying SNPs predictive of phenotype using random forests
    Bureau, A
    Dupuis, J
    Falls, K
    Lunetta, KL
    Hayward, B
    Keith, TP
    Van Eerdewegh, P
    [J]. GENETIC EPIDEMIOLOGY, 2005, 28 (02) : 171 - 182