critical evaluation of the accuracy of the enumeration methodology of coliforms and e. coli in water

306 Pages·2012·9.75 MB·English

CRITICAL EVALUATION OF THE ACCURACY OF THE ENUMERATION METHODOLOGY OF
COLIFORMS AND E. COLI IN WATER FROM RIVERS USED FOR THE IRRIGATION OF
FRESH PRODUCE

AMANDA SALOMÉ BRAND

Dissertation presented for the degree of Doctor of Philosophy in Food Science
in the Department of Food Science
Faculty of AgriSciences
at
Stellenbosch University

Supervisor: Prof. Trevor J. Britz
Co-supervisor: Dr. Gunnar O. Sigge

December 2012
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Declaration
By submitting this dissertation electronically, I declare that the entirety of the work contained
therein is my own, original work, that I am the sole author thereof (save to the extent explicitly
otherwise stated), that reproduction and publication thereof by Stellenbosch University will not
infringe any third party rights and that I have not previously in its entirety or in part submitted it for
obtaining any qualification.

Signature: ___________________ Date: 12 October 2012

Copyright © 2012 Stellenbosch University
All rights reserved
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Abstract

The accuracy of methods for the enumeration of coliforms and Escherichia coli present in river
water intended for the irrigation of fresh produce has been critically evaluated to determine whether
the results of the traditional method were reliable in indicating faecal pollution. The potential of
rapid alternative methods were also explored.
Baseline monitoring of the Berg River showed the presence of potential pathogens such as
Salmonella, and also that E. coli levels exceeded international guidelines for the safe irrigation of
minimally processed foods (MPFs) in 20.5% of cases, which indicated faecal intrusion. An
exploratory study into the use of microbiological and physico-chemical parameters in predicting E.
coli numbers, as a rapid alternative to direct enumeration, was conducted. These measurements,
neither individually nor in combination, could accurately predict the E. coli numbers.
The rapid method Colilert-18 was compared against multiple tube fermentation (MTF) for
the enumeration of coliforms and E. coli. Spearman rank correlation coefficients showed that
Colilert-18 had acceptable (r2=0.69) and fair (r2=0.74) correlations with MTF for coliform and E. coli
enumeration, respectively. Bland and Altman statistics were used to determine pollution influence,
and Colilert-18 showed increasing disagreement with MTF at very high concentrations of coliforms
and E. coli.
Bacterial isolates obtained from MTF reactions were identified using biochemical and mass
spectrometry methods. These identifications revealed that the greatest contributors to inaccurate
coliform enumeration by MTF were false negative coliforms which fail to produce gas from lactose.
Numerical biochemical data suggested that these isolates may be able to use other carbohydrates
preferentially over lactose. Inaccurate E. coli enumeration was caused by E. coli strains which
could not utilise lactose or 4-methylumbelliferyl-β-D-glucuronide (MUG), as well as non-E. coli
isolates which were able to hydrolyse MUG. The method of transfer of bacteria between MTF
media was also identified as problematic for accuracy.
Monoplex polymerase chain reaction (PCR) differentiation of MTF isolates showed that
detection of the uidA gene showed the greatest accuracy in the detection of E. coli, while the
multiplex PCR protocol for detecting diarrheagenic E. coli pathotypes identified one strain of
enteroaggregative E. coli (EAEC).
A qualitative methodological risk classification was used, in combination with the individual
reactions of MTF isolates, to elucidate their contribution to enumeration inaccuracy and to evaluate
the effect of MUG and Levine-eosin methylene blue (L-EMB) agar. The classification indicated that
inaccurate enumeration of E. coli was more problematic than that of coliforms, but revealed that
the exclusion of MUG from MTF may increase the accuracy of E. coli enumeration. The omission
of L-EMB would have very little effect on E. coli enumeration accuracy.
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This work confirmed that MTF is fairly reliable in the enumeration of coliforms and E. coli.
Inaccuracies are primarily attributable to atypical organisms which are considered to make up a
small proportion of the total bacterial population. Colilert-18 was shown to be an acceptably
accurate alternative, and its rapid production of results can be highly advantageous in the
monitoring of irrigation water used for MPFs.
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Opsomming

Die akkuraatheid van metodes vir die telling van kolivorme en Escherichia coli in rivier water,
gebruik vir besproeiing van vars produkte, is krities geëvalueer om vas te stel of die resultate van
tradisionele metodes betroubaar was in die aanduiding van fekale besoedeling. Die potensiaal van
snelle alternatiewe metodes is ook ondersoek.
Basislyn monitering van die Berg River het aangedui dat potensiële patogene soos
Salmonella teenwoordig is, en dat E. coli vlakke internasionale riglyne vir die veilige besproeiing
van minimaal geprosesseerde voedsels (MGVs) oorskry het in 20.5% van gevalle, wat dui op
fekale besmetting. ‘n Ondersoekende studie van die bruikbaarheid van mikrobiologiese en fisies-
chemiese parameters in die voorspelling van E. coli getalle, as snelle alternatief tot direkte telling,
is uitgevoer. Hierdie metings kon nie, individueel of in kombinasie, akkurate voorspellings van E.
coli getalle maak nie.
Die snelle metode Colilert-18 is vergelyk met veelvoudige buis fermentasie (VBF) in die
telling van kolivorme en E. coli. Spearman rang korrelasie koëffisiënte het aangetoon dat Colilert-
18 aanvaarbare (r2=0.69) en goeie (r2=0.74) korrelasies met VBF gehad het vir kolivorm en E. coli
tellings, respektiewelik. Bland en Altman statistiek is gebruik om die invloed van besoedeling te
bepaal, en Colilert-18 het afnemende ooreenstemming met VBF getoon by baie hoë kolivorm en E.
coli konsentrasies.
Bakteriële isolate verkry vanaf VBF reaksies is geïdentifiseer met behulp van biochemiese
en massa spektrometrie metodes. Hierdie identifikasies het getoon dat vals-negatiewe kolivorme,
wat nie gas vanaf laktose kan produseer nie, die grootste bydraende faktor is in onakkurate
kolivorm telling deur VBF. Biochemiese data het voorgestel dat hierdie isolate moontlik ander
koolhidrate by voorkeur bo laktose gebruik. Onakkurate E. coli tellings is veroorsaak deur E. coli
isolate wat nie laktose of 4-metielumbelliferiel-β-D-glukuronied (MUG) kon verbruik nie, sowel as
nie-E. coli isolate wat wel MUG kon hidroliseer. Die oordrag-metode van bakterieë tussen VBF
media is ook geïdentifiseer as problematies.
Monopleks polimerase ketting reaksie (PKR) onderskeiding van VBF isolate het aangedui
dat opsporing van die uidA geen die grootste akkuraatheid vir die opsporing van E. coli het, terwyl
die multipleks PKR protokol vir die opsporing van diarree-veroorsakende E. coli patotipes een stam
van entero-aggregerende E. coli (EAEC) geïdentifiseer het.
‘n Kwalitatiewe metodologiese risiko klassifikasie is gebruik, in kombinasie met die
individuele reaksies van VBF isolate, om hul bydrae tot telling onakkuraatheid vas te stel. Die
effek van MUG en Levine-eosien metileenblou (L-EMB) agar is ook geëvalueer. Daar is bevind dat
die onakkurate telling van E. coli meer problematies is as dié van kolivorme, maar ook dat die
uitlating van MUG by VBF die akkuraatheid van E. coli tellings kan verhoog. Die uitlating van L-
EMB agar sal ‘n ignoreerbare effek hê op E. coli telling akkuraatheid.
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Hierdie werk het bevestig dat VBF akkuraat is in die telling van kolivorme en E. coli.
Onakkuraathede word primêr toegeskryf aan atipiese organismes, wat beskou word as ‘n klein
proporsie van die totale bakteriële bevolking. Colilert-18 is ‘n aanvaarbaar akkurate alternatief, en
die metode se produksie van snelle resultate kan hoogs voordelig wees in die monitering van
MGVs besproeiingswater.
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Acknowledgements

First and foremost, I would like to thank my Creator for giving me the opportunities, the abilities and
the strength to complete this work. Without His auspices none of this would have been possible.
Deep gratitude is also due to several individuals and institutions that have contributed to this study
financially, scientifically or personally. I would like to express sincere thanks to the following
people:

The Water Research Commission (WRC) for financial assistance. This study was part of an
ongoing solicited research project (K5/1773) (A quantitative investigation into the link between
irrigation water quality and food safety), funded and managed by the Water Research Commission
and co-funded with the Department of Agriculture;

The financial assistance of the National Research Foundation (NRF) towards this research is
hereby acknowledged. Opinions expressed and conclusions arrived at, are those of the author
and are not necessarily to be attributed to the NRF;

Stellenbosch University for financial assistance;

The Ernst and Ethel Eriksen Trust for financial assistance;

My supervisors, Prof T.J. Britz and Dr G.O. Sigge, for their contributions to the study and for giving
me the opportunity to travel abroad;

Dr J.M. Barnes for her seemingly boundless willingness to help, support and encourage me. I will
never forget the knowledge that you have so selflessly shared with me and hold you in the very
highest regard as a friend and mentor;

Dr C. Lamprecht for her patience and immense help in conducting the PCR analyses. Thank you
also for sharing your scientific expertise with me from the beginning of my university career;

Bruker South Africa and, in particular, Liezel Raubenheimer for arranging the loan of the Microflex
LT MALDI Biotyper and Helmut Flinspach for the instrument installation;

Monika Schneider of Bruker Germany who travelled to South Africa to provide training in the use of
the MALDI Biotyper, and who did so with great patience and encouragement. Danke schön!;

Prof Martin Kidd at the Center for Statistical Consultation for conducting the statistical analyses;
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The Genetics Department, particularly Prof L. Warnich, for use of their gel documentation system;

Dr T.G. Barnard of the Water and Health Research Unit at the University of Johannesburg for
generously providing E. coli pathotype strains;

Nadia Niemann and Tshepo Kikine for the use of some of their river data for statistical
comparisons in Chapters 3 and 4;

The academic, administrative and technical staff at the Food Science Department for support, both
in their professional and personal capacities;

My fellow postgraduate students in the Food Microbiology laboratory: Alison, Marijke, Nicola,
Nadia, Tshepo, Anneri, Nika and Marlize. In your own ways you have all contributed to this study
through your help, support and unique understanding of the difficulties of this process. Thank you
for all the conversations, laughs, coffee runs, memories and your continued friendship;

Nadia Niemann for not only being a great colleague to work with in the laboratory, but also an
amazing friend. I am very grateful that I have had the opportunity to gain you as a friend through
this experience, and I thank you deeply for all your support and understanding;

Thashlin Govender and Armelle Ntsame Affane for all the support, advice and words of
encouragement especially during the writing of this dissertation;

All my fellow postgraduate students from 2008 to 2012;

My mother and sister for their unwavering support during this process, and for putting up with me
even though they did not always understand what I was going through. I know that I haven’t been
the easiest person to live with the past year. Please know that I love you both and accept my
deepest and sincerest thanks from the very bottom of my heart;

I would like to thank everyone, some mentioned here and others not, who have seen potential in
me throughout my life. You all believed that I could do it long before I believed it myself and for
that I owe you my lifelong gratitude; and finally

I would like to thank my father for showing me that anything is possible through hard work, but
teaching me that I should always choose to do something that will benefit others too. I love and
miss you always.
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Contents

Declaration ii
Abstract iii
Opsomming v
Acknowledgements vii
List of Tables x
List of Figures xv
List of Abbreviations xvii

CHAPTER 1 Introduction 1

CHAPTER 2 Literature review 7

CHAPTER 3 Baseline study of microbiological and physico-chemical parameters of 60
the upper Berg River and their predictive ability for E. coli counts

CHAPTER 4 Evaluation of Colilert-18 as alternative method for multiple tube 93
fermentation in the enumeration of coliforms and E. coli from South
African river water

CHAPTER 5 Phenotypic characterisation and identification of atypical and typical 124
organisms isolated from the multiple tube fermentation method

CHAPTER 6 Monoplex and multiplex PCR of typical and atypical coliforms and 168
E. coli isolated during multiple tube fermentation of river water

CHAPTER 7 Numerical analysis of atypical and typical multiple tube fermentation 194
isolates based on biochemical reactions and ribosomal proteins

CHAPTER 8 Contribution of organisms isolated from the multiple tube fermentation 223
method and classification of methodological risk for under- and over-
estimation of coliforms and E. coli

CHAPTER 9 General discussion and conclusion 280
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List of Tables

Table 2.1 Guidelines for assessing the potential health risk for the four water uses 11
Table 2.2 Level of faecal coliforms detected in the Plankenburg at point above 11
and below Kayamandi (cfu.100 mL-1)
Table 2.3 Microbiological data obtained from the Plankenburg River below 13
Kayamandi (September 2007-April 2008)
Table 2.4 Orally transmitted waterborne pathogens and their significance in water 14
supplies
Table 2.5 Infectious diseases associated with water 15
Table 2.6 Major steps and hurdles in the multiple tube fermentation (MTF) method 32
Table 2.7 Organisms with the ability to produce β-D-glucuronidase 34
Table 2.8 Diarrheagenic E. coli pathotypes and associated target genes used in 45
PCR detection
Table 3.1 Aerobic colony counts for river water samples from three Berg River 68
sampling points
Table 3.2 Coliform counts for the river water samples from three sampling points 70
Table 3.3 E. coli counts for the river water samples from three sampling points 71
Table 3.4 Intestinal enterococci count for river water samples from three sampling 73
points
Table 3.5 Results for L. monocytogenes analyses from three sampling sites after 74
primary enrichment
Table 3.6 Results for L. monocytogenes analyses from three sampling sites after 75
secondary enrichment
Table 3.7 Results for Salmonella spp. analyses in river water from three sampling 77
sites after secondary enrichment in RV broth
Table 3.8 Results for Salmonella spp. analyses in river water from three sampling 77
sites after secondary enrichment in SC broth
Table 3.9 Alkalinity, conductivity and pH values for river water samples from three 81
sites
Table 3.10 COD values for river water samples from three sites 82
Table 4.1 Distribution of coliform data across four rivers 99
Table 4.2 Coliform values (MPN.100 mL-1) for the Berg River sites, enumerated 100
by Colilert and MTF
Table 4.3 Coliform values (MPN.100 mL-1) for the Plankenburg River sites, 101
enumerated by Colilert and MTF
Table 4.4 Coliform values (MPN.100 mL-1) for sites in the Eerste and Lourens Rivers, 102
enumerated by Colilert and MTF