Survival of Mycobacterium avium subspecies paratuberculosis in retail pasteurised milk

Zara E Gerrard1, Benjamin M C Swift2, George Botsaris3

  • 1University of Nottingham, Sutton Bonington Campus, College Road, Leicestershire LE12 5RD, England; SRUC, West Mains Road, Edinburgh EH9 3JG, Scotland.

Food Microbiology
|May 1, 2018
PubMed

Insights

Pasteurised milk can still contain viable Mycobacterium avium subspecies paratuberculosis (MAP) cells, posing a risk of human exposure. This study highlights MAP

Area of Science:

  • Food safety and microbiology
  • Veterinary public health
  • Bacteriology

Background:

  • Mycobacterium avium subspecies paratuberculosis (MAP) is a pathogen of concern in milk.
  • Pasteurisation is a key process for eliminating milkborne pathogens.
  • Previous surveys have investigated MAP in milk with varying detection methods.

Purpose of the Study:

  • To survey retail pasteurised milk for viable MAP using the Phage-PCR assay.
  • To compare the efficacy of Phage-PCR with other detection methods for MAP.
  • To investigate the source and distribution of MAP in milk.

Main Methods:

  • Survey of 368 retail semi-skimmed pasteurised milk samples in England (May 2014 - June 2015).
  • Utilisation of the Phage-PCR assay for viable MAP detection.
  • Comparison of Phage-PCR results with culture and direct PCR methods.

Main Results:

  • 10.3% of surveyed milk samples contained viable MAP cells.
  • Phage-PCR detected lower cell numbers and increased MAP-positive samples compared to other methods.
  • MAP appears to be shed directly into milk within the udder, not solely from faecal contamination.
  • MAP cells were found to be clustered and potentially located within somatic cells prior to lysis.

Conclusions:

  • Pasteurisation does not fully eliminate viable MAP in milk.
  • The Phage-PCR assay offers enhanced sensitivity for MAP detection in milk.
  • MAP's presence within somatic cells may confer protection against heat inactivation during pasteurisation.
  • Intracellular MAP within somatic cells is a potential explanation for viable MAP in retail milk.

Related Concept Videos

Survival Curves01:18

Survival Curves

Survival curves are graphical representations that depict the survival experience of a population over time, offering an intuitive way to track the proportion of individuals who remain event-free at each time point. These curves are widely used in fields such as medicine, public health, and reliability engineering to visualize and compare survival probabilities across different groups or conditions.
The Kaplan-Meier estimator is the most common method for constructing survival curves. This...
727
Survival Tree01:19

Survival Tree

Survival trees are a non-parametric method used in survival analysis to model the relationship between a set of covariates and the time until an event of interest occurs, often referred to as the "time-to-event" or "survival time." This method is particularly useful when dealing with censored data, where the event has not occurred for some individuals by the end of the study period, or when the exact time of the event is unknown.
 Building a Survival Tree
Constructing a...
434
Introduction To Survival Analysis01:18

Introduction To Survival Analysis

Survival analysis is a statistical method used to study time-to-event data, where the "event" might represent outcomes like death, disease relapse, system failure, or recovery. A unique feature of survival data is censoring, which occurs when the event of interest has not been observed for some individuals during the study period. This requires specialized techniques to handle incomplete data effectively.
The primary goal of survival analysis is to estimate survival time—the time...
830
Comparing the Survival Analysis of Two or More Groups01:20

Comparing the Survival Analysis of Two or More Groups

Survival analysis is a cornerstone of medical research, used to evaluate the time until an event of interest occurs, such as death, disease recurrence, or recovery. Unlike standard statistical methods, survival analysis is particularly adept at handling censored data—instances where the event has not occurred for some participants by the end of the study or remains unobserved. To address these unique challenges, specialized techniques like the Kaplan-Meier estimator, log-rank test, and...
613
Truncation in Survival Analysis01:09

Truncation in Survival Analysis

Truncation in survival analysis refers to the exclusion of individuals or events from the dataset based on specific criteria related to the time of the event. This exclusion can happen in two primary forms: left truncation and right truncation.
Left truncation occurs when individuals who experienced the event of interest before a certain time are not included in the study. This is often due to a "delayed entry" into the study where only those who survive until a certain entry point are...
631
Assumptions of Survival Analysis01:15

Assumptions of Survival Analysis

Survival models analyze the time until one or more events occur, such as death in biological organisms or failure in mechanical systems. These models are widely used across fields like medicine, biology, engineering, and public health to study time-to-event phenomena. To ensure accurate results, survival analysis relies on key assumptions and careful study design.
433