Investigation of Cryptosporidium clinical isolates and ...Investigation of Cryptosporidium clinical...

Investigation of Cryptosporidium clinical isolates and analysis with epidemiological data


Research Contract DWI 70/2/125-II

FINAL REPORT to Drinking Water Inspectorate

March 2005

Rachel Chalmers1, Stephen Hadfield1,

Paul Hunter2, Dawn Wilkinson2 and Bill Reilly3

1. Cryptosporidium Reference Unit NPHS Microbiology Swansea, Singleton Hospital, Swansea, SA2 8QA

2. School of Medicine, Health Policy and Practice University of East Anglia, Norwich, NR4 7TJ

3. Scottish Centre for Infection and Environmental Health

Clifton House, Clifton Place, Glasgow, G3 7LN

Scottish Centre for Infection and Environmental Health

Cryptosporidium Reference Unit

University of East Anglia School of Medicine, Health Policy and Practice


SCIEH, CRU,UEA i March 2005

Executive summary

1. The purpose of this project was to investigate Cryptosporidium hominis and Cryptosporidium parvum subtypes present in two different sample sets: (i) strains from cases implicated in a drinking waterborne outbreak associated with water sourced from Thirlmere and distributed via the Thirlmere aqueduct; (ii) strains from a DWI-funded case-control study of sporadic cryptosporidiosis undertaken in Wales and the North West of England to identify risk factors for sporadic cryptosporidiosis. Analysis with the epidemiological data was performed to identify trends in prevalence of subtypes, clusters of cases, risk factors and sources of infection, and measures for control.

2. Multi-locus microsatellite fragment analysis was chosen on the basis of in-house

and international evaluative studies and consultation with funders and the scientific community as the most suitable method for subtyping the required number of samples to a high degree of discrimination.

3. Three microsatellite markers were analysed: microsatellite locus (ML) 1, ML2

and the gp15 surface glycoprotein gene (synonymous with gp60). Microsatellite regions were amplified by PCR and fragment lengths determined using the CEQTM 8000 Genetic Analysis System. Fragment sizes for each locus were combined to produce a multi-locus fragment type for each strain.

4. C. parvum was found to be significantly more variable than C. hominis in both the

Thirlmere and case-control study sample sets. Therefore, subtyping using multi-locus microsatellite fragment analysis is therefore more suitable for studies of C. parvum variation than C. hominis variation.

5. Multi-locus microsatellite fragment analysis of 190 strains from the case-control

study group identified nine subtypes of C. hominis and 30 subtypes of C. parvum. 90% of C. hominis strains were of the same subtype. Three distinct clusters of the C. parvum strains were identified (SC1, SC2, SC3). Analysis with the epidemiological data revealed a significant association between C. parvum SC1 and animal contact. Associations were also found between ML1, ML2 and gp15 microsatellite sizes and animal contact, ML1 size being the most dramatic and having the potential to be a useful marker for zoonotic transmission. The results support the hypothesis that there may be specific clones of C. parvum that are adapted to a human-only life cycle. Phenographic analysis revealed significant associations between C. parvum SC1 cases and living in a rural area; this was also the case for the four most common C. parvum multi-locus fragment types, which were within SC1.

6. Analysis of 99 Thirlmere outbreak strains identified one subtype of C. hominis

and 17 subtypes of C. parvum. Three clusters of C. parvum cases were identified (C1a, C1b, C1c), corresponding to sub-clusters of SC1 from the case-control


SCIEH, CRU, UEA March 2005 ii

study group. Analysis with the epidemiological data revealed significant associations between species (C. hominis and C. parvum) and the frequency of consuming undercooked meat. The temporal incidence of C. hominis and C. parvum during the outbreak indicated that C. hominis strains were not part of this general outbreak. There was a significant relationship between living in a rural area and cases of C. parvum cluster 1a. Clustering of C. parvum subtype P36 was observed around Preston and Chorley, whereas there were no cases of P36 strains on the Fylde and only one in the Morecambe Bay area, supporting the epidemiological information which suggested that cases on the Fylde were not related to the Thirlmere supply and the cases in the Morecambe Bay area were probably not waterborne.

7. As frequency of consumption of undercooked meat was identified as a risk factor

for C. hominis and C. parvum infection, control measures should include recommendations to caterers and vulnerable individuals to cook meat thoroughly and adhere to food handling advice from the Food Standards Agency.

8. As animal contact was identified as a risk factor for infection with certain

subtypes of C. parvum, control measures should include recommendation of comprehensive hygiene and hand washing procedures for implementation during and after animal handling to minimise risk of hand-to-mouth transmission.


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CONTENTS EXECUTIVE SUMMARY i CONTENTS iii List of Tables v List of Figures v 1. INTRODUCTION 1

1.1 Introduction 1.2 Background 1 1.3 Aim 3

1.3.1 Objectives 3 2. METHODS 4 2.1 Maintenance of the National Collection of Oocysts and collection of

patient data 4 2.2 Identification of Cryptosporidium species 4

2.2.1 PCR-RFLP analysis 4 2.2.2 Sensitivity statement for PCR-based detection 5 2.2.3 SSU rRNA gene sequence analysis 5

2.3 Analysis of C. hominis and C. parvum subtypes 6 2.4 Statistical analysis 6 2.5 Geographical Information System 7 2.6 Quality Control 8 3. RESULTS 8 3.1 Cryptosporidium species identification 8 3.2 Investigation and selection of subtyping techniques 9 3.3 Investigation of C. hominis and C. parvum subtypes in the Thirlmere

and case-control study groups 9 3.4 Epidemiological analysis of subtypes present in the case-control

study group 10 3.4.1 Clustering analysis 10 3.4.2 Geographical Information System analysis 15 3.5 Epidemiological analysis of subtypes present in the Thirlmere

study group 27 4. DISCUSSION 37 5. CONCLUSIONS 38 6. OUTCOMES 39 7. RECOMMENDED FURTHER RESEARCH 40


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8. QUALITY ASSURANCE 40 9. ACKNOWLEDGEMENTS 40 10. REFERENCES 41 APPENDICES 44 Appendix I: Review of literature describing gp15/60 sequence and microsatellite analysis-based methods of Cryptosporidium subtyping 45 Appendix II: Key points from an ad hoc meeting between DWI and Cryptosporidium researchers to discuss progress on subtyping methods 49 Appendix III: In-house evaluation of Cryptosporidium subtyping methods 51 Appendix IV: Cryptosporidium Reference Unit in-house multilocus fragment typing scheme for C. parvum and C. hominis 57 Appendix V: C. parvum hierarchical cluster analysis dendrogram for case-control group 58 Appendix VI: Cluster analysis of strains of C. parvum isolated from cases at the time of the 2000 Thirlemere outbreak 60 Appendix VII: Single variable analysis (х2 test or Fisher’s exact test) of C. parvum clusters 63 Appendix VIII: Single variable analysis (Mann Whitney U Test or Fisher’s exact test) of C. hominis and C. parvum for Thirlemere Outbreak strains 66 Appendix IX: Single variable analysis (Mann Whitney U Test or Fisher’s Exact Test) of C. parvum clusters 70


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List of tables Table 1. Land use categories on the Urban / Rural classification 2001 7 Table 2. Cryptosporidium strains identified at the CRU from the case-control study and Thirlmere outbreak 8 Table 3. Distribution of MLFTs in the case-control study group 11 Table 4. Mean product size (bp) at each locus for C. hominis and C. parvum clusters 12 Table 5. Final multivariable model for all C. parvum strains 15 Table 6. Final multivariable model for strains with the ML1-242 allele 15 Table 7. The number of cases per land use category 21 Table 8. The expected number of cases per land use category 21 Table 9. The number of positive samples per land use category 26 Table 10. The expected number of positive samples per land use category 26 Table 11. Distribution of MLFTs in the Thirlmere study group 27 Table 12. Mean product size of microsatellite loci within C. parvum clusters 1a, 1b and 1c 28 Table 13. The number of cases in each land use category 33 Table 14. The expected number of cases in each land use category 33 List of figures Fig. 1. Standardised values of product sizes at three microsatellite loci plotted for each case. 12 Fig. 2. Product size at microsatellite locus ML1 with number of C. parvum cases that touched/handled farm animals prior to onset of illness 13 Fig. 3. Product size at microsatellite locus ML2 with number of C. parvum cases that touched/handled farm animals prior to onset of illness 14 Fig. 4. Product size at microsatellite locus gp15 with number of C. parvum cases that touched/handled farm animals prior to onset of illness 14 Fig. 5. Geographical distribution of the C. hominis and C. parvum cases in the case -control study group 16 Fig. 6. Geographical distribution of the C. parvum Cluster 1 cases in the case-control study group 17 Fig. 7. Geographical distribution of the C. parvum Cluster 2 cases in the case-control study group 18


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Fig. 8 Geographical distribution of the C. parvum Cluster 3 cases in the case-control study group 19 Fig. 9. The relationship between land use and C. hominis and the C. parvum genetic clusters 20 Fig. 10. Geographical distribution of the C. parvum P1 cases in the case-control study group 22 Fig. 11. Geographical distribution of the C. parvum P2 cases in the case-control study group 23 Fig. 12. Geographical distribution of the C. parvum P5 cases in the case-control study group 24 Fig. 13. Geographical distribution of the C. parvum P7 cases in the case-control study group 25 Fig. 14. Standardised product sizes at three microsatellite loci plotted for each case 28 Fig. 15. Weekly number of cases of C. hominis and C. parvum detected in outbreak 30 Fig. 16. Weekly number of cases of C. parvum subtypes/clusters detected in outbreak 31 Fig. 17. Number of C. parvum cases per week in outbreak with the most common MLFTs 32 Fig. 18. Weekly number of cases of P36 and other C. parvum MLFTs 32 Fig. 19. Geographical distribution of the three clusters 1a, 1b and 1c of C. parvum during an outbreak of cryptosporidiosis associated with Thirlmere reservoir 34 Fig. 20. Geographical distribution of the P36 MLFT of C. parvum during an outbreak of cryptosporidiosis associated with Thirlmere reservoir 35 Fig. 21. Geographical distribution of the P5 MLFT of C. parvum during an outbreak of cryptosporidiosis associated with Thirlmere reservoir 36


SCIEH, CRU,UEA 1 March 2005

1. INTRODUCTION

1.1 Introduction Cryptosporidium species are intestinal parasites which infect a range of animals; Cryptosporidium hominis (syn. C. parvum genotype 1) and C. parvum (syn. C. parvum genotype 2) being the two most commonly identified species causing disease (cryptosporidiosis) in humans (McLauchlin et al., 2000; Anon, 2002; Chalmers et al., in preparation). The main symptom of cryptosporidiosis is diarrhoea, which may be accompanied by dehydration, weight loss, abdominal pain, fever, nausea and vomiting (Chen et al., 2002). In England and Wales, in the region of 5 000 cases are reported annually (Anon, 2004). Disease, although lasting for up to two weeks, is usually self-limiting in immunocompetent individuals but may be chronic and more severe in immunocompromised patients. C. hominis is largely restricted to infection of humans, but has recently been reported from a dugong and a lamb, and other animals have been infected experimentally (Xiao et al., 2004). C. parvum also infects several animal species including cattle, sheep, goats and deer which serve as reservoirs for zoonotic infection (Xiao et al., 2004). Studies of Cryptosporidium strains using molecular methods have been important for identification of outbreak sources and of the epidemiology of cryptosporidiosis. While genotyping data, generated by polymerase chain reaction restriction fragment length polymorphism (PCR-RFLP) analysis, has been employed at the Cryptosporidium Reference Unit (CRU), Swansea to provide some level of characterisation of Cryptosporidium strains, mainly to species level, further investigation of subtypes is necessary to understand the relationship between strains causing human disease, and also for investigation of potential sources and routes of transmission of the parasite. Several subtyping methods have been described by different research groups including microsatellite sequence analysis (Aeillo et al., 1999; Cacciò et al., 2000; 2001; Enemark et al., 2002); microsatellite PCR fragment length analysis (Mallon et al., 2003a; Mallon et al., 2003b); single strand conformation polymorphism analysis (Gasser et al., 2004); gp60 sequence analysis (Strong et al., 2000; Alves et al., 2003); and tellomere sequence analysis (Blasdall et al., 2001a; Blasdall et al., 2001b). As yet there is no “gold-standard” method for Cryptosporidium subtyping; therefore, in-house evaluation of different subtyping methods was performed to assess different methods for their applicability to the project. This was supported by a recent international trial of subtyping methods (Chalmers et al., submitted). 1.2 Background Since February 2000, publicly-funded primary diagnostic clinical laboratories throughout England and Wales have been sending Cryptosporidium-positive faeces to the CRU to support the investigation of more discriminatory typing methods, principally microsatellite DNA markers, for application to epidemiological investigations. This work was originally in collaboration with the University of Glasgow and Scottish Parasite


SCIEH, CRU, UEA March 2005 2

Diagnostic Laboratory and co-ordinated by the Scottish Centre for Infection and Environmental Health. The research was funded by the Chief Scientist in Scotland and the Department for Food, Environment and Rural Affairs (Anon, 2002). Species data has been generated for over 10 000 human strains in England and Wales and selected sample sets are being investigated for the detection of genetic variation (subtypes) within the two predominant species causing human disease, Cryptosporidium parvum and Cryptosporidium hominis. Polymerase chain reaction (PCR)-based subtyping methods are being explored in collaborating laboratories, particularly sequence based analysis of the gp60 gene (Liua Xiao, CDC, Atlanta), multi-locus microsatellites (Simone Cacciò, Istituto Superiore di Sanità, Rome), microsatellite-telomeres (Nick Ashbolt, University of New South Wales), and single strand conformation polymorphisms (SSCP) (Robin Gasser, University of Melbourne). Prior to 2001, a rise in the number of reported human cases of cryptosporidiosis has been detected consistently during the late spring in England and Wales and most specifically in the areas receiving drinking water sourced from Thirlmere and distributed via the Thirlmere aqueduct (Hunter, Syed and Naumova, 2001). During 1999 this was identified epidemiologically as a drinking waterborne outbreak. The seasonality of the cases suggested a source associated with increased animal activity (e.g. lambing, cattle including calves being turned out to pasture after the winter, slurry spreading) and the distribution of C. parvum and C. hominis among human cases during 1999 and 2000 suggests an animal source of infection, since C. parvum (which has a wide host range) has predominated among the relevant cases. However, it is not known whether the repeat outbreaks are caused by the same subtypes from a common source. Interestingly, no rise in human cases was detected nationally during 2001, particularly during the late spring (Smerdon et al., 2003). This coincided with the Foot and Mouth Disease (FMD) epidemic and while no cases of Foot and Mouth Disease were reported in the Thirlmere catchment and no animals were culled, restriction orders applied to animal movement and public access restrictions were vigorously applied in the area. While species identification of Cryptosporidium strains has provided some level of characterisation and indication of sources of infection (Hunter et al., 2003), further investigation of subtypes within those species is necessary to understand the relationship between strains causing human disease, for example in subsequent years associated with this drinking water supply. Additionally, a case-control study to identify risk factors for sporadic cryptosporidiosis (funded by the DWI) has been undertaken in Wales and the North West of England in collaboration with the University of East Anglia (UEA). This has already identified differences in the risk factors for disease (Hunter et al., 2004a) and health sequelae (Hunter et al., 2004b) between C. parvum and C. hominis. Further characterisation of the strains from the cases involved in the study, which have very detailed exposure data will provide new information about trends in prevalence of subtypes, clusters of cases, risk factors and sources of infection, and measures for control. While current methods for the identification of Cryptosporidium species have supplied important information about the epidemiology of cryptosporidiosis, they have been unable to discriminate clusters of strains causing disease. Application of new subtyping tools will permit cluster identification and provide new information for the control of disease. This project has utilised existing data and collections of strains as well as



enhancing the database by prospective collection of further strains and subspecific characterisation. 1.3 Aim Maintain the National Collection of oocysts and use new molecular tools to compare the prevalence, variation and clustering of Cryptosporidium species, and use exposure data gathered during the case-control study in Wales and the North West of England to calculate odds ratios to identify risk factors for infection with subtypes identified. 1.3.1 Objectives Objective 1 Prepare and analyse, by sequencing and other PCR-based tools, control material from characterised Cryptosporidium strains for phylogenetic studies. Objective 2 Continue to collect human Cryptosporidium isolates, with a minimum dataset, from microbiology laboratories in the North West of England and Wales during 2002 and identify species. Objective 3 Continue to collect animal Cryptosporidium isolates, with a minimum dataset, from veterinary laboratories in the North West of England and Wales during 2002 and identify species. Objective 4 Identify isolates, including those recruited into the case-control study and those associated with Thirlmere reservoir, for further investigation by PCR-based sub-typing methods. The number of isolates selected will depend on the subtyping method selected for the study. Objective 5 Investigate the prevalence and distribution of Cryptosporidium subtypes. Objective 6 Analyse the subtype data with the case-control study data to calculate odds ratios to identify subspecific risk factors for infection. Objective 7 To provide a final report on the study identifying any control measures that would be of benefit to public health.



2. METHODS 2.1 Maintenance of the National Collection of Oocysts and collection of patient

data To maintain the National collection, microbiology laboratories were asked to continue to send human Cryptosporidium-positive faeces to the CRU with a minimum patient data set (name, address, post code, date of birth, sex, specimen date, clinical details, travel details). Veterinary laboratories were similarly asked to continue to send animal strains with a minimum data set (host species, age, specimen data, location) for species identification. Confirmation of sending laboratory microscopy results was performed when required using a modified Ziehl-Neelsen method as described by Casemore et al. (1985). Oocysts were separated from faecal samples by saturated-salt-solution centrifugation as described by Elwin et al. (2001). DNA was extracted from microscopy-positive faecal samples by incubating the oocyst concentrate at 100oC for 60 min, digesting with proteinase K and lysis buffer and purified using QIAamp® DNA Mini Kit spin columns (Qiagen Ltd.) as described previously (Anon, 2002). DNA was stored at –20oC prior to species determination and subtyping, where appropriate, using PCR-based techniques as described below. Species identification was undertaken in a timely manner using PCR-RFLP analysis of Cryptosporidium genes including the Cryptosporidium oocyst wall protein (COWP) and small subunit (SSU) rRNA genes as described below and results reported back to laboratories. 2.2 Identification of Cryptosporidium species 2.2.1 PCR-RFLP analysis Cryptosporidium species was determined using single-round PCR-RFLP analysis of the COWP gene or nested PCR-RFLP analysis of the SSU rRNA gene using methods based on those described by Spano et al. (1997) and Xiao et al. (2001), respectively. For PCR-RFLP analysis of the COWP gene, briefly, PCR was carried out using the forward primer 5’-GTAGATAATGGAAGAGATTGTG-3’ and reverse primer 5’-GGA CTGAAATACAGGCATTATCTTG-3’ to produce a ~550bp amplicon. The 50μl PCR mixture contained 5µl of 10x PCR buffer (Qiagen Ltd.), 1.5mM MgCl2, 200μM of each deoxynucleotide triphosphate, 600nM of each primer, 2.5U of HotStar Taq DNA polymerase (Qiagen Ltd.) and 10μl of template DNA. The thermocycling conditions were an initial denaturation step of 15 minutes at 95˚C, a further 3 minutes at 94˚C and 40 cycles of 94˚C for 50 seconds, 60˚C for 30 seconds and 72˚C for 50 seconds, before a final extension of 10 minutes at 72˚C. The PCR products were digested at 37°C for 3 hours using the restriction enzyme RsaI to differentiate between the majority of Cryptosporidium species.



For PCR-RFLP analysis of the SSU rRNA gene, briefly, the primary PCR produced fragments of ~1,325bp using the forward primer 5’-TTCTAGAGCTAATACATGCG-3’ and the reverse primer 5’-CCCATTTCCTTCGAAACAGGA-3’. The 50μl PCR reaction mixture contained 5µl of 10x PCR buffer (Qiagen Ltd.), 4.5mM MgCl2, 200μM of each deoxynucleotide triphosphate, 200nM of each primer, 2.5U of HotStar Taq DNA polymerase (Qiagen Ltd.) and 10μl of template DNA. The secondary PCR, which produced fragments of ~830bp used the forward primer 5’-GGAAGGGTTGTATTTAT TAGATAAAG-3’ and the reverse primer 5’-AAGGAGTAAGGAACAACCTCCA-3’. The PCR mixture was identical to that in the primary PCR except that 3mM MgCl2 was used and only 5μl of primary product was added. The cycling conditions for both PCRs were an initial denaturation step of 15 minutes at 95˚C, a further 3 minutes at 94˚C and 35 cycles of 94˚C for 45 seconds, 55˚C for 45 seconds and 72˚C for 60 seconds, before a final extension of 7 minutes at 72˚C. The products of the secondary PCR were digested at 37°C for 1 hour by restriction enzymes SspI and VspI to differentiate between the majority of Cryptosporidium species. A further digestion with DdeI was performed to differentiate between C. muris and C. andersoni. SSU rRNA and COWP gene restriction fragments were separated by electrophoresis on 3% agarose gels, visualised by SYBR Green I (Sigma) staining and images recorded using a digital imaging system (Apha Imager, Kodak). 2.2.2 Sensitivity statement for PCR-based detection The relative sensitivity of the COWP and SSU rRNA gene PCR-RFLP methods for the identification of Cryptosporidium species are comparable in that both showed 100% detection of quintuplicate replicates at between 5 and 50 oocysts, and are highly likely to be in single figures as demonstrated by the dilutions from commercially available flow-cytometer-counted oocyst suspensions (data not shown). The absolute limit of detection (lowest DNA dilution from which PCR products were detected) by COWP PCR was 1.25 oocyst-equivalent for C. hominis and 5 oocyst-equivalents for C. parvum. The absolute limit of detection by SSU PCR was 2.5 oocyst-equivalents for C. hominis and 0.5 oocyst equivalent for C. parvum (data not shown). 2.2.3 SSU rRNA gene sequence analysis Following PRC-RFLP analysis, unusual species and equivocal samples were confirmed by amplifying a fragment of the SSU rRNA gene and DNA sequencing in both directions. Briefly, amplicons of ~830bp were produced from each sample using the nested primer set described above (Xiao et al., 2000) and a fragment sequenced (GRI) using the forward primer 5’-AGTGACAAGAAATAACAATACAGG-3’ and the reverse primer 5’-CCTGCTTTAAGCACTCTAATTTTC-3’ (Morgan et al., 1997). The forward and reverse sequences of these fragments were then aligned and analysed using a CEQ™ 8000 Genetic Analysis System (Beckman Coulter) to obtain a consensus sequence. This sequence was then compared with all GenBank, EMBL, DDBJ and PDB sequences using the NCBI BLAST database.



2.3 Analysis of C. hominis and C. parvum subtypes Stored DNA from selected species-identified strains from the National collection, the case-control study, and Thirlmere-associated strains were analysed for the detection of genetic variation within C. parvum and C. hominis. Subtypes were identified using a multilocus fragment size analysis approach to target three microsatellite markers (ML1, ML2 and gp15/60). The ML1 fragment was amplified using the forward primer 5’-CTA AAAATGGTGGAGAATATTC-3’ and the reverse primer 5’-CAACAAAATC TATATCCTC-3’ (Cacciò et al., 2000; Cacciò et al., 2001). The ML2 fragment was amplified using the forward primer 5’-CAATGTAAGTTTACTTATGATTAT-3’ and the reverse primer 5’-CGACTATAAAGATGAGAGAAG-3’ (Cacciò et al., 2001). The gp60 fragment (synonymous with gp15) was amplified using the forward primer 5’-GCC GTTCCACTCAGAGGAAC-3’ and the reverse primer 5’-CCACATTACAAATG AAGTGCCGC-3’ (Mallon et al., 2003a). Reverse primers were supplied labelled with Beckman Coulter WellRed D3 dye (Proligo). The 20µl PCR reaction mixture for each primer contained 2µl of 10x PCR buffer (Qiagen Ltd.), 1mM MgCl2, 200µM of each deoxynucleotide triphosphate, 500nM of each primer, 2.5U of HotStar Taq DNA polymerase (Qiagen Ltd.) and 2 µl of template DNA. The cycling conditions for each PCR were an initial denaturation step of 15 minutes at 95°C, then 40 cycles of 95°C for 50 seconds, 50°C (60°C for gp15/60) for 50 seconds and 72°C for 60 seconds before a final extension of 10 minutes at 72°C. The fragment sizes of amplified products were then analysed using a CEQ™ 8000 Genetic Analysis System (Beckman Coulter). The combined results of fragment size analysis at all three markers was used to create an in-house multilocus fragment type (MLFT) for subtypes within C. parvum and C. hominis. 2.4 Statistical analysis Results of species and subspecies characterisation were collated with an existing geographic information systems (GIS) database at UEA for analysis for trends in prevalence and distribution and, for the case-control study, risk factors for disease. Data analysis was carried out using SPSS 12.0 (SPSS Inc., Chicago, IL). Microsatellite product sizes were standardised for each case at each locus [(×-µ)/ơ]. Hierarchical cluster analysis was then carried out on the C. parvum standardised product sizes of the three microsatellite loci using between group linkage and squared Euclidean distances. In the case-control study, chi-square tests (or Fisher’s exact test when data were sparse) were used to identify significant trends between C. parvum supercluster 1 and C. parvum superclusters 2 and 3 combined, with epidemiological parameters. A final multivariable model was derived using logistic regression as described in Hunter et al. (2004) using all the different strains of C. parvum and was recalculated using only the strains that possessed the ML1-242 allele. In the Thirlmere outbreak, chi-square tests (or Fisher’s exact test) were used to identify trends between epidemiological parameters with C. parvum and C. hominis infections and similarly, among the C. parvum clusters (the two most closely related clusters 1a and 1c



were combined to increase sample size). The outbreak was investigated temporally in relation to both genotype (C. hominis and C. parvum) and subtype (C. parvum clusters 1a, 1b and 1c). 2.5 Geographical Information System The first stage in the analysis involved obtaining a grid reference for each case based upon its postcode. This was achieved by cross-referencing the postcodes with the Ordnance Survey Code-Point database which provides a grid reference for each of the 1.7 million unit postcodes in the UK. However, several postcodes did not have an entry on Code-Point the most likely reason being that they are no longer in use by the Post Office. Therefore, these cases were additionally cross-referenced with the All Field Postcode Directory which contains historical postcode data. The few remaining unmatched postcodes are most likely to be recorded incorrectly. For these the associated postcode sector was identified (e.g. L40 7) by removing the last two letters from the postcode and the average grid reference for all postcodes in this sector calculated from Code Point. Any postcodes that still could not be located were deleted from the analysis. Each of these grid references was exported into a Geographical Information System (GIS) where a point in polygon procedure was used to identify the land use present at each. The land use data was the Urban/Rural classification of 2001 Output Areas which is available from the Office of National Statistics. This classifies the predominant settlement type within each Output Area (census zones consisting of approximately 125 households) into urban, town & fringe, village or hamlet & isolated dwellings. Each of these four categories is then subdivided into sparse and less sparse depending upon the settlement density in a wider geographical area. This leads to the eight land use categories presented in Table 1. Table 1. Land use categories on the Urban / Rural classification 2001

Urban Town & Fringe Village

Less Sparse

Hamlet & Isolated Dwellings Urban Town & Fringe Village

Sparse

Hamlet & Isolated Dwellings For the analysis, the total population in each of these land use areas was required and this was obtained from the 2001 UK census. Further information on the Urban / Rural classification 2001 can be obtained from: Bibby and Shepherd: Developing a New Classification of Urban and Rural Areas for Policy Purposes – the Methodology. Available at http://www.statistics.gov.uk/geography/downloads/Rural_Urban_Methodology_Reportv2.pdf



2.6 Quality Control Control material for these phylogenetic studies was prepared from epidemiologically linked and unlinked Cryptosporidium strains from the national collection by flotation and extraction using QIAamp® DNA Mini Kit spin columns (Qiagen Ltd.) and analysed by SSU rRNA and gp60 gene sequencing and multi-locus microsatellite PCR fragment and SSCP analyses as described above. Positive and negative controls were included in each run: C. hominis sample W3009 was included as a positive PCR control and to assess the precision of fragment sizing; a “no-template” water sample was included as a PCR negative control. Three sets of reference samples were also investigated:

(i) Two internationally recognised C. parvum strains: The Iowa strain (purchased from Pat Mason, Idaho in 2002) and the Moredun strain (kindly donated by Steve Wright, Moredun Animal Health, passaged in 2002).

(ii) Fifteen strains from the panel created from the National Collection (Appendix IV): seven C. parvum and eight C. hominis strains.

(iii) Strains previously subtyped by a variety of methods, including those in an international comparative trial of Cryptosporidium subtyping methods.

3. RESULTS 3.1 Cryptosporidium species identification From the archive of Cryptosporidium oocysts and DNA held at the CRU as part of the National collection of oocysts, established under project number DWI 170/2/125, a total of 353 strains were identified as those recruited into the case-control study in Wales and the North West of England and those associated with Thirlmere reservoir (Table 2). Cryptosporidium species was determined by PCR-RFLP. Table 2. Cryptosporidium strains identified at the CRU from the case-control study and Thirlmere outbreak. Total number of

laboratory confirmed cases

Number received by CRU

Cryptosporidium species

Case-control study in Wales and the North West of England

427 214 120 C. hominis 76 C. parvum 1 Cr. spp. 12 did not amplify 5 genus not confirmed

Cases linked to the Thirlmere municipal drinking water supply, April and May 2000

>200 139 15 C. hominis 122 C. parvum 1 Cr. spp. 1 genus not confirmed

Total 353



3.2 Investigation and selection of subtyping techniques Four subtyping methods (gp60 gene sequencing, denaturing polyacrylamide gel electrophoresis (DPGE), single-stranded conformational polymorphism (SSCP) analysis and multilocus microsatellite fragment analysis have been explored at the CRU and in-house SSU rRNA gene sequencing is maintained to provide a species-specific standard against which PCR-RFLP can be evaluated. Following analysis of results from an international subtyping trial, a literature review of the use of gp60 gene sequencing and microsatellite analysis methods for Cryptosporidium subtyping (Appendix I), consultation with the scientific community, with DWI (Appendix II), and in-house evaluation (Appendix III), multilocus microsatellite fragment analysis has been selected as the subtyping method to be applied to the sample sets for this project.

The final selection of multilocus microsatellite fragment analysis method was based on:

(i) Utility (including differentiation, sensitivity, specificity, turn-around time and cost).

(ii) Consensus of scientific opinion (including information from the workshop in Boulder, 2003).

(iii) The outcome of the international trial funded by the Sydney Catchment Authority and co-ordinated by the CRU (Chalmers et al., submitted).

While studies to investigate population genetics of Cryptosporidium have used a large number of microsatellite loci (e.g. Mallon et al., 2003a), it is more appropriate for epidemiological studies to carefully select a limited number of loci for a multilocus typing scheme. In this study, three loci were selected for investigation (ML1, ML2 and gp15/60), which have been used in previous studies to identify variation within C. parvum and C. hominis (Cacciò et al., 2000; Cacciò et al., 2001; Cacciò, Spano and Pozzio, 2001; Cacciò, 2003). The combined results of fragment size analyses following PCR amplification targeting each of the three loci was used to create an in-house MLFT nomenclature scheme (Appendix IV). 3.3 Investigation of C. hominis and C. parvum subtypes in the Thirlmere and

case-control study groups DNA samples from the Thirlmere and case-control study groups were tested using the multilocus microsatellite analysis method. Results are summarised below. The C. hominis control sample (W3009) was amplified in all subtyping runs and its ML1, ML2 and gp15 fragment lengths varied by less than 0.5bp (ranges of 0.35bp (n=6), 0.44bp (n=8) and 0.31bp (n=6), respectively), demonstrating high inter-assay precision. Inter-sample variation was evaluated using five sets of repeat samples (received at CRU a mean of 9 days apart; range 1 – 24 days): ML1, ML2 and gp15/60 lengths varied by less than 0.5bp (ranges, 0.29, 0.04 and 0.13bp, respectively). None of the “no template” negative control samples amplified. Investigation of the Iowa strain confirmed C. parvum MLFT 10 and the Moredun (2002) strain C. parvum MLFT 1.



3.4 Epidemiological analysis of subtypes present in the case-control study group A total of 190 sporadic strains of Cryptosporidium were included in this analysis. Of these 190 strains, some 118 were C. hominis of which 106 were typable at all three loci. There were also 72 strains of C. parvum of which 66 were typable at all three loci. The distribution of MLFTs for those strains typable at all three loci are shown in Table 3. The diversity of C. parvum was much higher than that of C. hominis. For C. parvum the Simpson’s diversity index was 0.955579 (95% Confidence Interval 0.933243 to 0.977916). For C. hominis this was 0.196945 (95% CI 0.095633 to 0.298257). The large majority of strains (90%) of C. hominis fall within a single MLFT (H1) other C. hominis types contain very few members. For C. parvum there were many more MLFTs and the most common type (P1) contained only 13% of strains. 3.4.1 Clustering analysis Given the fact that the large majority of strains (90%) of C. hominis fall within a single MLFT (H1) it was felt that further clustering would not add to the analysis of this species. Three distinct clusters of C. parvum cases were identified by the hierarchical cluster analysis (Appendix V). Figure 1 depicts these three C. parvum clusters and also the C. hominis strains. The separation between the three clusters of C. parvum and between these and C. hominis is clear. Mean microsatellite product sizes (based on measured size) are shown in Table 4 for each supercluster. In order to test whether different clusters were associated with different epidemiological risk factors, chi-squared tests were run for the risk factors tested in the main case-control study (Hunter et al., 2004). The chi-square tests (and Fisher’s exact tests) revealed a significant association only between the C. parvum clusters and touching or handling farm animals (P=0.002) (Appendix VII). All strains from patients who reported contact with farm animals were in cluster 1. None of the cases associated with strains in the other clusters reported contact with farm animals.



Table 3. Distribution of MLFTs in the case-control study group

Species MLFT No. strains

ML1 allele number

ML2 allele number

gp15 allele number

C. hominis H1 95 233 180 371 H2 3 239 180 371 H3 2 242 180 371 H4 1 224 180 371 H5 1 233 180 407 H6 1 233 180 353 H7 1 218 180 371 H8 1 218 180 413 H9 1 233 180 341

C. parvum P1 8 242 229 341 P2 5 242 229 338 P3 1 227 193 329 P4 2 227 195 338 P5 6 242 231 341 P6 4 242 233 338 P7 6 242 231 338 P8 3 242 233 341 P9 1 242 225 341 P10 2 242 232 338 P11 1 242 229 332 P12 1 242 229 359 P13 1 242 229 347 P14 1 242 231 356 P16 1 242 231 344 P17 3 242 231 347 P18 1 242 233 347 P19 1 242 235 338 P20 1 242 237 341 P21 1 242 231 350 P22 1 227 193 320 P23 2 227 195 326 P23a 1 227 229 326 P24 1 227 223 332 P25 2 227 197 311 P26 1 227 231 341 P27 1 227 195 353 P28 1 227 193 326 P29 1 227 193 329 P30 1 227 195 332



Fig. 1. Standardised values of product sizes at three microsatellite loci plotted for

each case = C. hominis, = C. parvum supercluster 1, = C. parvum supercluster 2, = C. parvum supercluster 3. Table 4. Mean product size (bp) at each locus for C. hominis and C. parvum clusters.

Mean product size per locus (ơ) (bp) Cluster ML1 ML2 gp15

C. hominis 233.4 (2.7) 180.0 (0.1) 371.4 (6.5) C. parvum SC1 242.0 (0.2) 231.7 (2.3) 341.2 (4.8) C. parvum SC2 227.1 (0.3) 194.5 (1.4) 328.5 (11.8) C. parvum SC3 227.0 (0.2) 228.7 (4.2) 333.7 (7.0)



Given that clusters 2 and 3 had identical ML1 types, we looked at the association between alleles in the various loci and contact with animals. For this analysis, all strains were included, whether or not they were typable at all three loci. In C. parvum, two alleles were identified at ML1 (ML1-227 and ML1-242), however, ML2 and gp15 were more variable (Table 4). At ML1, 43% (22/51) of cases with a product size of 242bp (ML1b) and 0% (0/14) of cases with a product size of 227bp (ML1-227) had touched or handled farm animals (Fig. 2). Similarly at ML2, 42% of cases with product sizes between 223 and 237bp had touched or handled farm animals (Fig. 3) but no cases (0/13) with a product size of 193-197bp had contact with farm animals prior to onset of illness. Product sizes varied between 311 and 371bp at locus gp15 (Fig. 4), peaking at 340-341bp. There was a significant difference in product size (bp) between cases that had touched or handled farm animals at ML1 (Mann Whitney U test, U=1096, P=0.000), ML2 (Mann Whitney U Test, U=1263.5, P=0.000) and gp15 (Mann Whitney U Test, U=1495.5, P=0.003).

��

��

��

0

10

20

30

40

50

60

215 218 221 224 227 230 233 236 239 242 245 248 251 254Product size (bp)

No.

of c

ases ��

Farm��No farm

Fig. 2. Product size at microsatellite locus ML1 with number of C. parvum cases that touched/handled farm animals prior to onset of illness



��

��

��

��

��

��0

5

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15

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25

191

195

199

203

207

211

215

219

223

227

231

235

239

Product size (bp)

No.

of c

ases ��

FarmNo farm

Fig. 3. Product size at microsatellite locus ML2 with number of C. parvum cases that touched/handled farm animals prior to onset of illness

��

��

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��

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0

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308

314

320

326

332

338

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356

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380

Product size (bp)

No.

of c

ases ��

�� FarmNo farm

Fig. 4. Product size at microsatellite locus gp15 with number of C. parvum cases that touched/handled farm animals prior to onset of illness



These results support the hypothesis that there may be specific clones of C. parvum that are adapted to a human-only life cycle, previously made by Mallon et al. (2003). In order to test this hypothesis further, the final logistic regression model for C. parvum in our earlier paper (Hunter et al., 2004) was re-run but including only those strains that were ML1 allele size 242. Table 5 shows the model using all C. parvum and Table 6 using only ML1 242. The positive association with farm animals and the negative associations with eating tomatoes and with eating raw vegetables are all stronger in the model with just ML1-242 allele strains compared to the model containing all C. parvum strains Table 5. Final multivariable model for all C. parvum strains

From Hunter et al., 2004. OR = odds ratio, CI = confidence intervals.

OR 95% CI P Touch or handle any farm animals

2.653 1.113, 6.323 0.028

Eat tomatoes 0.317 0.140, 0.719 0.005 Eat raw vegetables 0.222 0.086, 0.572 0.001 Table 6. Final multivariable model for strains with the ML1-242 allele OR = odds ratio, CI = confidence intervals. OR 95% CI P Touch or handle any farm animals

8.249 3.628, 18.76 0.000

Eat tomatoes 0.263 0.118, 0.583 0.001 Eat raw vegetables 0.151 0.049, 0.464 0.001 3.4.2 Geographical Information System analysis The aim of the analysis was to examine whether there was a spatial coexistence between land use and:

(i) location of C. hominis and clusters of C. parvum from routine surveillance; (ii) MLFT groups of C. parvum.

The results from each of these analyses will be presented in turn. (i) Location of C. hominis and clusters of C. parvum from routine surveillance: The phenographic analysis of identified C. hominis and three clusters of C. parvum and these cases are plotted in relation to land use in Figures 5 to 8. The number of cases in each land use was divided by the population and the resulting rate plotted in Figure 9.



Fig. 5. Geographical distribution of the C. hominis and C. parvum cases in the case-control study group



Fig. 6. Geographical distribution of the C. parvum Cluster 1 cases in the case-control study group



Fig. 7. Geographical distribution of the C. parvum Cluster 2 cases in the case-control study group



Fig. 8 Geographical distribution of the C. parvum Cluster 3 cases in the case-control study group



C.parvum genetic clusters and C. hominis in urban and rural settings. Rates per 100,000 population

00.5

11.5

22.5

33.5

44.5

5

Urban Town &Fringe

Village Hamlet &Isolated

Dwellings

Town &Fringe

Village Hamlet &Isolated

Dwellings

Population7,545,739

Population663,720

Population359,072

Population209,170

Population 99,250

Population156,530

Population124,140

Less Sparse Sparse

case

s pe

r 100

,000

pop

ulat

ion

Cluster 1Cluster 2Cluster 3C. hominis

Fig. 9. The relationship between land use and C. hominis and the C. parvum genetic clusters

This figure and the four maps suggest no relation between C. hominis and land use. C. parvum cluster 1 appears to be associated with rural areas and there is a very weak suggestion that C. parvum cluster 2 is associated with more urban areas as all ten cases have occurred in urban areas or less sparse Town & Fringe areas. In order to examine the relationship between cases and land use in more detail we need to compare the numbers of cases to the numbers we would expect if there were no relationship between C. hominis and the three clusters of C. parvum. The numbers of cases falling into each land use category is presented in Table 7. If there was no relationship between C. hominis and the three clusters of C. parvum with land use the number of cases of each type falling in to each land use category would be proportional to the total population living within the land uses. For example the number of C. parvum cluster 1 cases we would expect in urban areas is equal to the proportion of the total population living in urban areas (7545739 / 9157621 = 0.824) multiplied by the total number of C. parvum cluster 1 cases (46). This equals 37.9 and this and all the expected numbers of cases are presented in Table 8.



Table 7. The number of cases per land use category

Number of positive samples Total

population Cluster 1 Cluster 2 Cluster 3 C. hominis Urban 11 8 1 95 7545739

Town & Fringe 7 2 1 5 663720 Village 7 0 0 4 359072 Less

Sparse Hamlet & Isolated Dwellings 6 0 0 1 209170 Town & Fringe 3 0 0 2 99250

Village 7 0 0 1 156530 Sparse Hamlet & Isolated Dwellings 5 0 1 1 124140

Total 46 10 3 109 9157621 Table 8. The expected number of cases per land use category Expected number of positive samples Cluster 1 Cluster 2 Cluster 3 C. hominis Urban 37.9 8.2 2.5 89.8

Town & Fringe 3.3 0.7 0.2 7.9 Village 1.8 0.4 0.1 4.3 Less

Sparse Hamlet & Isolated Dwellings 1.1 0.2 0.1 2.5

Town & Fringe 0.5 0.1 0 1.2 Village 0.8 0.2 0.1 1.9 Sparse

Hamlet & Isolated Dwellings 0.6 0.1 0 1.5 Total 46 10 3 109

A chi-squared test was performed to compare the observed to the expected frequencies. This was performed for C. parvum cluster 1 and C. hominis only as the small number of samples in clusters 2 and 3 made statistical tests inappropriate. One of the conditions of the chi-squared test is that most of the expected values (80%) are greater than five. In order to satisfy this criterion it was necessary to reduce the number of land use categories to simply urban areas and non-urban areas. The results of the chi-square test demonstrated an extremely significant (p<0.0001) relationship between land use and the number of cluster 1 cases, with more cases occurring in rural areas. There was no statistically significant relationship between C. hominis and land use. This broadly corroborates the patterns observed in Figures 5 to 9. (ii) MLFT groups of C. parvum From the analysis, 35 different MLFTs of C. parvum were identified. However, most of these consisted of a single specimen making an analysis of their associations with land use impossible. As a consequence, only MLFTs with five or more cases were selected for further analysis. These were P1, P2, P5 and P7. Each of the MLFT cases was plotted alongside land use in Figures 10 to 13.



Fig. 10. Geographical distribution of the C. parvum P1 cases in the case-control study group



The number of cases of each MLFT group falling into each land use category is presented in Table 9. Using an identical methodology as before, the predicted values are presented in Table 10. Due to the low numbers of cases in each group, statistical tests of associations are not possible. However, by comparing the observed and expected values it appears that all the MLFT groups have lower than expected frequencies in the urban land use category. This is to be expected as these four MLFT categories are all within C. parvum cluster 1. This difference is most striking in P7 where none of the cases occurred in the urban or less sparse town and fringe land uses. Table 9. The number of positive samples per land use category Number of positive Samples P1 P2 P5 P7 Urban 4 3 3 0

Town & Fringe 2 0 0 0 Village 0 0 1 1 Less

Sparse Hamlet & Isolated Dwellings 1 2 0 1 Town & Fringe 0 0 1 1

Village 0 1 0 1 Sparse Hamlet & Isolated Dwellings 1 0 1 2

Total 8 6 6 6 Table 10. The expected number of positive samples per land use category Expected number of positive Samples P1 P2 P5 P7 Urban 6.6 4.9 4.9 4.9

Town & Fringe 0.6 0.4 0.4 0.4 Village 0.3 0.2 0.2 0.2 Less

Sparse Hamlet & Isolated Dwellings 0.2 0.1 0.1 0.1

Town & Fringe 0.1 0.1 0.1 0.1 Village 0.1 0.1 0.1 0.1 Sparse

Hamlet & Isolated Dwellings 0.1 0.1 0.1 0.1 Total 8 6 6 6



3.5 Epidemiological analysis of subtypes present in the Thirlmere study group A total of 99 cases of Cryptosporidium were included in the subsequent analysis. Of these strains, 11 were C. hominis of which ten were typable at all three microsatellite loci. 82 cases of C. parvum were detected and 78 of these were typable at all three microsatellite loci. The distribution of MLFTs for these typable strains are shown in Table 11. Table 11. Distribution of MLFTs in the Thirlmere study group Species MLFT No. strains ML1 allele

number ML2 allele number

gp15 allele number

C. hominis H1 10 233 180 371 C. parvum P1 6 242 229 341 P2 2 242 229 238 P5 15 242 231 341 P7 1 242 231 338 P8 4 242 233 341 P14 6 242 231 356 P19 1 242 235 338 P34 5 242 207 338 P36 29 242 229 344 P37 1 242 211 344 P38 1 229 197 368 P39 2 242 231 362 P40 1 242 221 341 P41 1 242 235 341 P42 1 245 229 338 P44 1 227 197 335 P45 1 239 231 341 All cases of C. hominis detected during the outbreak were of the same MLFT (H1). For C. parvum there were 17 different MLFTs detected in the outbreak and the most common type (P36) constituted only 37% of strains, the second most common type (P5) made up 19% of the strains. Three clusters of C. parvum cases were identified by the hierarchical cluster analysis (Appendix VI). Figure 14 depicts these three C. parvum clusters and also the C. hominis strains. Mean microsatellite product sizes (based on measured size) are shown in Table 12 for each of these clusters. All but two cases (of MLFTs P44 and P38) were of similar sizes to cases in Cluster 1 identified in the sporadic infections study. These two strains were excluded from further analysis.



Fig. 14. Standardised product sizes at three microsatellite loci plotted for each case = C. hominis (n=10), C. parvum cluster 1a, = C. parvum cluster 1b and = C. parvum cluster 1c. Table 12. Mean product size of microsatellite loci within C. parvum clusters 1a, 1b

and 1c

Mean product size (ơ) (bp) Supercluster ML1 ML2 gp15

C. parvum C1a 241.95 (0.49) 230.58 (2.12) 342.10 (1.99) C. parvum C1b 242.00 (0.00) 231.50 (0.53) 357.63 (2.72) C. parvum C1c 242.00 (0.00) 207.80 (1.79) 339.20 (2.68)



In order to test whether C. hominis and C. parvum cases were associated with different epidemiological risk factors, statistical tests were carried out for the risk factors previously tested in the case-control study (Hunter et al., 2004). The Mann Whitney U test (and Fisher’s exact tests) (Appendix VIII) revealed significant associations between species (C. hominis and C. parvum) and the frequency of consuming undercooked meat (p=0.008). For the analysis of the C. parvum clusters identified in the hierarchical cluster analysis, the two most closely related clusters were combined (1a and 1c) to increase sample sizes in the analysis. Chi-squared tests (and Fisher’s exact tests) found no significant associations between the C. parvum clusters and epidemiological risk factors. Figures 15 and 16 show the weekly increase in cases of C. hominis, C. parvum and the different C. parvum clusters. There were more C. parvum than C. hominis cases and the greatest increase in C. parvum cases occurred between weeks 3 and 4 of the outbreak (Fig. 15) while C. hominis increased slowly and remained at a low level. Cases of C. parvum approached an asymptote by week 7. C. parvum cluster 1a followed a similar pattern to that of the C. parvum clusters combined, with the greatest increase in cases between weeks 3 and 4 (Fig. 16). Clusters 1b and 1c increased slowly over the outbreak period but remained at a low level. Figure 17 shows the number of the four main C. parvum MLFTs present in the outbreak over time. Both P36 and P5 had the greatest number of cases in week 4 of the outbreak. There was no significant difference between the number of weekly cases of the most common MLFT (P36) compared with all other C. parvum MLFTs (Wilcoxon signed ranks test, Z = -1.552, P = 0.121) (Fig. 18). Given that it was clear from the temporal incidence of C. hominis and C. parvum that C. hominis strains were not part of this general outbreak, phenographic analysis was restricted to the three clusters of C. parvum from the outbreak data. Each of these is plotted alongside land use in Figure 19. The number of cases of each type falling within each land use is presented in Table 13. Using an identical methodology as before, the predicted values are presented in Table 14. Based upon the total population in the NW region.



0

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1 2 3 4 5 6 7Week of outbreak

Cum

ulat

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eek

Cumulative C. hominis Cumulative C. parvum

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Week of outbreak

Num

ber o

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eek

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Fig. 15. Weekly number of cases of C. hominis and C. parvum detected in outbreak



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eek

Cumulative C. parvum C1a Cumulative C. parvum C1b Cumulative C. parvum C1c

Fig. 16. Weekly number of cases of C. parvum subtypes/clusters detected in outbreak



��

��

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��

��

0

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1 2 3 4 5 6 7week of outbreak

Num

ber o

f cas

es

P1 P5 P34

��P36

Fig. 17. Number of C. parvum cases per week in outbreak with the most common MLFTs n≥ 5 per MLFT.

0

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Num

ber o

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P36 Other C. parvum MLFTs Fig. 18. Weekly number of cases of P36 and other C. parvum MLFTs



Table 13. The number of cases in each land use category Number of positive Samples

C. parvum cluster 1a

C. parvum cluster 1b

C. parvum cluster 1c

Total Population

Urban 61 7 4 5915317 Town & Fringe 7 1 0 362888

Village 4 0 0 209215 Less Sparse

Hamlet & Isolated Dwellings 3 0 0 136965 Town & Fringe 0 0 0 36035

Village 0 0 0 41074 Sparse Hamlet & Isolated Dwellings 2 0 1 29613

Total 75 8 4 6731107 Table 14. The expected number of cases in each land use category Number of expected samples

C. parvum cluster 1a

C. parvum cluster 1b

C. parvum cluster 1c

Total Population

Urban 67.7 7.0 4.4 5915317 Town & Fringe 4.2 0.4 0.3 362888

Village 2.4 0.2 0.2 209215 Less Sparse

Hamlet & Isolated Dwellings 1.6 0.2 0.1 136965 Town & Fringe 0.4 0.0 0.0 36035

Village 0.5 0.05 0.0 41074 Sparse Hamlet & Isolated Dwellings 0.3 0.04 0.0 29613

Total 75 8 4 6731107 A chi-squared test was performed to compare the observed to the expected frequencies. This was performed for C. parvum cluster 1a only, as the small number of samples in the other C. parvum clusters made statistical tests inappropriate. One of the conditions of the chi-squared test is that most of the expected values (80%) are greater than 5. In order to satisfy this criterion it was necessary to reduce the number of land use categories to simply urban areas and non-urban areas. The results of the chi-square test demonstrated a significant (p<0.05) relationship between land use and the number of C. parvum cluster 1a cases indicating that more cases were occurring in rural areas than would be expected. Figure 19 shows the geographical distribution of the three clusters of C. parvum and it is apparent that there are no obvious differences in the distribution of these same clusters. Figures 20 and 21 show the distribution of the two commonest MLFTs, P36 and P5.



Fig. 19. Geographical distribution of the three clusters 1a, 1b and 1c of C. parvum during an outbreak of cryptosporidiosis associated with Thirlmere reservoir



Fig. 20. Geographical distribution of the P36 MLFT of C. parvum during an outbreak of cryptosporidiosis associated with Thirlmere reservoir



Fig. 21. Geographical distribution of the P5 MLFT of C. parvum during an outbreak of cryptosporidiosis associated with Thirlmere reservoir



Although there is no obvious clustering of P5 strains, nearly all P36 strains occur in the area around Preston and Chorley, the main focus for the outbreak and the area most dependent on water from the Thirlmere supply. It is noticeable that there were no cases of cryptosporidiosis due to P36 on the Fylde and only one in the Morecambe Bay area. These findings support the conclusions of the outbreak team who felt that the cases on the Fylde were not related to the Thirlmere supply and the cases in the Morecambe Bay area were probably not waterborne (CDSC North West 2001). 4. DISCUSSION Sporadic cases There was much greater genetic diversity among C. parvum strains than among C. hominis strains, which was expected as C. hominis is a species-specific parasite. Hunter and Fraser (1990) argued that species adapted to single host species were likely to be less genetically diverse than those with a wider host range, as predicted by the theory of adaptive polymorphism. Greater genotypic variation was also found among C. parvum (Type 2) than C. hominis (Type 1) isolates in a previous study using mini- and micro-satellite loci (Mallon et al., 2003). This low genetic diversity amongst strains of C. hominis will make it difficult to develop discriminatory and reproducible typing methods for C. hominis (Hunter, 1990). Even using just three loci we have shown that there are three major groupings of C. parvum, in line with the similar finding by Mallon et al. (2003) who used 15 loci. Mallon et al. (2003) reported that the largest cluster contained strains isolated from both humans and animals, whilst the two smaller clusters contained strains isolated only from humans. In our study all strains isolated from people reporting contact with animals came from the larger cluster 1, supporting the suggestion of two clones of human-adapted stains of C. parvum. Further support for this suggestion comes from the observation of the geographical distribution of cases where the incidence of cluster 1 strains increase as communities become more rural. Probably the most interesting observation has been the finding of an association between strains of C. parvum that may be human-adapted or zoonotic and particular alleles of the microsatellites. Whilst there was an association with all three loci, the strongest association was with alleles at the ML1 locus. This observation was even more dramatic given the observation that there were only two alleles at this locus. None of the cases yielding a ML1-227 strain reported contact with farm animals whilst 43% of those yielding ML1-242 reported such contact. In a related study, 100% of 28 strains isolated from animals were ML1-242, further supporting this hypothesis (Chalmers et al., in preparation). Although the ML2 locus is more variable than the ML1 locus, the two loci correlate very closely. This linkage disequilibrium between the two loci has already been noted by other



workers (Cacciò et al., 2001). It would be temping to speculate that these two loci appear on the same chromosome as each other and also on the same chromosome as those genes responsible for zoonotic or exclusively human life cycles. It must be noted, however, that our results are at odds with those of Cacciò et al. (2001) who found three alleles at the ML1 locus (ML1-238, ML1-226, and ML1-220). They also reported isolations from animals amongst all three alleles. It is not clear whether our findings really do indicate human-adapted strains of C. parvum or whether all strains are potentially zoonotic, but that ML1-227 strains are zoonotic in other countries such as Italy where Cacciò et al. (2001) have done their work. In the latter case, these strains would have spread into the UK human population through imported foods or during foreign travel and then may have spread person to person. Thirlmere outbreak The main finding in the outbreak study was that 15 MLFTs were detected during the seven week outbreak. The number of C. hominis infections increased slowly over the outbreak period but are more likely to represent infections unrelated to the main outbreak. Similarly, there is a slow increase in the number of cases in clusters 1b and 1c, identified by the cluster analysis. C. parvum cluster 1a was the most prevalent in the outbreak period. The predominant MLFT (P36) during the outbreak was strongly clustered in the areas most strongly associated with the Thirlmere outbreak. There were, however, areas where P36 represented a minority of cases and there were many other MLFTs in the main outbreak area coincident with the outbreak. It is not clear from this study how these non-P36 strains were linked to the outbreak. It may be that the outbreak was a truly multi-strain outbreak or that the stability of the MLFTs is low and that these other types represent evolution from the primary P36 type. The issue of the stability of C. parvum MLFTs need to be investigated in more detail. One of the reasons why microsatellites have so many alleles is that due to slippage replication evolution is more rapid than would happen for other gene sequences that evolve by mutation. Furthermore, Mallon et al. (2003) have suggested that the major grouping of C. parvum is panmictic with regular genetic exchange between strains. Both of these factors would lead to a reduced type stability. 5. CONCLUSIONS • Given the very low genetic diversity of C. hominis as found in this and other studies,

multi-locus microsatellite typing does not seem to offer a worthwhile discriminatory typing scheme for this species.

• On the other hand, the microsatellite typing method was discriminatory for C.

parvum and able to distinguish a large number of different MLFTs. However, most type categories contained a very small proportion of total strain types, making statistical analyses of strains from sporadic infections difficult. Nevertheless the



observation of the association between certain ML1 alleles and a history of animal contact may be a very important contribution in enabling us to distinguish human-adapted from zoonotic strains of C. parvum.

• Microsatellite typing of outbreaks of C. parvum would appear to have an important

role in delineating the exact areas affected by the outbreak, especially when there are possible unrelated clusters of infection in nearby areas.

• Before microsatellite typing can be recommended widely, the issue of stability of the

types needs to be investigated in some detail. • Data from the case-control study samples identified frequency of consumption of

undercooked meat as a risk factor for C. hominis and C. parvum infection. Control measures should include recommendation for caterers and vulnerable individuals to cook meat thoroughly and adhere to food handling advice from the Food Standards Agency (http://www.food.gov.uk/).

• Microsatellite subtyping identified contact with animals as a risk factor for infection

with C. parvum subtypes with ML1, ML2 and gp15 microsatellite fragment sizes of 242, 223-237, and 311-371bp, respectively. Infection with subtypes with ML1 sizes other than 242 may indicate human-human transmission since none of the cases yielding a ML1-227 strain reported contact with farm animals whilst 43% of those yielding ML1-242 reported such contact; and in a previous study of 28 C. parvum strains isolated from animals, all were ML1-242 (Chalmers et al., in preparation). Control measures should include recommendation of comprehensive hygiene and hand washing procedures for implementation during and after animal handling to minimise risk of hand-to-mouth transmission.

6. OUTCOMES Major outcomes of the contract are: • Continuity of the national database and archive of Cryptosporidium oocysts. • Evaluation of the significance, and generation of understanding, of Cryptosporidium

spp. and subtype profiles present in human disease. • Information about the subtypes causing outbreaks of human disease associated with

Thirlmere. • Data and information about human cryptosporidiosis to support and benefit from on-

going ecological studies of Cryptosporidium in the environment in the NW of England.

• Identification of C. parvum subtypes likely to cause infection through zoonotic transmission. This has provided evidence for targeted control measures to be put in place with subsequent benefits to public health.



7. RECOMMENDED FURTHER RESEARCH • Systematic evaluation and sensitivity assessment of subtyping methods. This will

involve an international distribution C. hominis and C. parvum DNA to participating laboratories. At least 100 strains of each species will be subtyped and typability, repeatability, discriminatory power will be determined as described by Struelens et al. (1996) (DWI funding applied for by Chalmers and Hunter, May 2004: The systematic evaluation of schemes to subtype Cryptosporidium isolates).

• Further investigation of ‘cold’ SSCP for subtyping of Cryptosporidium strains. This may provide a more rapid and cost effective method for subtyping large numbers of strains, allowing closer to real-time investigation of outbreaks.

• Investigation of the background prevalence of C. parvum and C. hominis subtypes in the UK population. This will allow outbreaks to be put into context with respect to subtypes circulating in the population and lead to an increased understanding of the epidemiology of Cryptosporidium infections.

• Further investigation of the distribution and prevalence of C. parvum subtypes in animals. This will allow more thorough evaluation of the significance of the various microsatellite markers investigated in this study and will involve subtyping of approximately 200 strains received by the CRU from sentinel Veterinary Laboratory Agency laboratories.

8. QUALITY ASSURANCE STATEMENT The CRU has full accreditation with the Clinical Pathology Accreditation (UK) Ltd scheme. Standard operating procedures, COSHH and Risk Assessments exist for species and subspecies classification of strains and are being prepared for any methods in development and under ascertainment. 9. ACKNOWLEDGEMENTS This project was funded by the Department for Food, Environment and Rural Affairs and managed by the Drinking Water Inspectorate. The authors would like to thank CRU staff members Kristin Elwin, Anne Thomas, Cathy Bentley and David Gomez for maintenance of the National Collection of Oocysts and Cryptosporidium species determination, and Guy Robinson for provision of additional subtyping data; Robin Gasser of the University of Melbourne and Paul Pratt of VH Bio for help with evaluating the SSCP/DPGE methods; Simone Cacciò of Istituto Superiore di Sanità, Rome for guidance with the microsatellite analysis; Lihua Xiao, Vita Cama, Ling Zhou, Jennifer Ross and Jianlin Jiang of the Centers for Disease Control, Atlanta for help in evaluation of the gp60 sequencing method; David Drury and Tony Hallas of DWI for their input into discussions; Flo Harrison and Iain Lake of UEA for producing the maps. Stephen Hadfield would like to thank the Health Protection Agency for funding his visit to CDC, Atlanta through their Travel Fellowship award.



10. REFERENCES Aeillo AE, Xiao L, Limor JR, Liu C, Abrahmason MS, Lal AA. Microsatellite analysis of the human and bovine genotypes of Cryptosporidium parvum. Journal of Eukaryotic Microbiology 1999; 46: 46S-47S. Alves M, Xiao L, Sulaiman I, Lal AA, Matos O, Antunes F. Subgenotype analysis of Cryptosporidium isolates from humans, cattle, and zoo ruminants in Portugal. Journal of Clinical Microbiology 2003; 41: 2744-47. Anon. The development of a national collection for oocysts of Cryptosporidium. Foundation for Water Research, Marlow, Bucks, UK 2002 (http://www.fwr.org/). Anon, Health Protection Agency website 2004 (http://www.hpa.org.uk/). Bibby and Shepherd: Developing a New Classification of Urban and Rural Areas for Policy Purposes – the Methodology. Available at: http://www.statistics.gov.uk/geography/downloads/Rural_Urban_Methodology_Reportv2.pdf Blasdall SA, Ongerth JE, Ashbolt NJ. Differentiation of Cryptosporidium parvum subtypes in calves of four dairy herds by a novel microsatellite-telomere PCR with PAGE. Proceedings of Cryptosporidium from Molecules to Disease, 7-12 October 2001, Fremantle, Australia. Blasdall SA, Ongerth JE and Ashbolt NJ. Sub-species differentiation among Type 2 bovine C. parvum isolates using a RAPD microsatellite + telomere primer scheme. Proceedings of IWA World Water Congress, Berlin. 2001. Cacciò S, Homan W, Camilli R, Traldi G, Kortbeek T, Pozio E. A microsatellite marker reveals population heterogeneity within human and animal genotypes of Cryptosporidium parvum. Parasitology 2000; 120: 237-244. Cacciò S and Pozio E. Molecular identification of food-borne and water-borne protozoa. Southeast Asian Journal of Tropical Medicine & Public Health 2001; 32 (supp 2): 156-158. Cacciò S, Spano F, Pozio E. Large sequence variation at two microsatellite loci among zoonotic (genotype C) isolates of Cryptosporidium parvum. International Journal for Parasitology 2001; 31:1082-86. Cacciò SM. Molecular identification of species / genotypes of Cryptosporidium in clinical and environmental samples. Proceedings of Cryptosporidium parvum in food and water, January 2003, Malahide, Dublin.



Casemore DP, Armstrong M, Sands RL. Laboratory diagnosis of cryptosporidiosis. Journal of Clinical Pathology 1985; 38: 1337-41. CDSC North West (2001) Report of an outbreak of cryptosporidiosis in the North West Region – April and May 2000. CDSC North West, Chester. Chalmers R, Stapleton C, Robinson G, Watkins J, Francis C, Kay D. Establishing the relationship between farm re-stocking and cryptosporidia: The Caldew catchment study. Report in preparation for DWI and UK Water Industry Research Ltd for project DWI 70/2/163. Chalmers R, Elwin K, Thomas A, Guy E, Reilly B. Molecular Epidemiology of Cryptosporidium in England and Wales: information from the National Collection of Cryptosporidium oocysts. In preparation Chalmers RM, Ferguson C, Cacciò S, Gasser RB, Abs EL-Osta YG, Heijnen L, Xiao L, Elwin K, Hadfield S, Sinclair M, Stevens M. Direct comparison of selected methods for genetic categorisation within Cryptosporidium species. Submitted to the International Journal of Parasitology. Chalmers R and Hunter P. The systematic evaluation of schemes to subtype Cryptosporidium isolates. DWI funding application, May 2004. Chen X-M, Keithly JS, Paya CV, LaRusso NF. Cryptosporidiosis. New England Journal of Medicine 2002; 22:1723-31. Elwin K, Chalmers RM, Roberts R, Guy EC, Casemore DP. The modification of a rapid method for the identification of gene-specific polymorphisms in Cryptosporidium parvum, and application to clinical and epidemiological investigations. Applied and Environmental Microbiology 2001; 67: 5581-84. Enemark HL, Ahrens P, Juel CD, Petersen E, Petersen RF, Andersen JS, Lind P, Thamsborg SM. Molecular characterization of Danish Cryptosporidium parvum isolates. Parasitology 2002; 125:331-41 Feng X, Rich SM, Akiyoshi D, Tumwine JK, Kekitiinwa A, Nabukeera N, Tzipori S, Widmer G. Extensive polymorphism in Cryptosporidium parvum identified by multilocus microsatellite analysis. Applied & Environmental Microbiology 2000; 66: 3344-3349. Feng X, Rich SM, Tzipori S, Widmer G. Experimental evidence for genetic recombination in the opportunistic pathogen Cryptosporidium parvum. Molecular and Biochemical Parasitology 2002; 119: 55-62. Gasser RB, Abs EL-Osta Y, Prepens S, Chalmers RM. An improved “cold SSCP” for the genotypic and subgenotypic characterisation of Cryptosporidium. Molecular and Cellular Probes 2004; 18: 329-32.



Glaberman S, Moore JE, Lowery CJ, Chalmers RM, Sulaiman I, Elwin K, Rooney PJ, Millar BC, Dooley JS, Lal AA, Xiao L. Emerging Infectious Diseases 2002; 8: 631-633. Hunter, P.R. (1990) Reproducibility and indices of discriminatory power of microbial typing methods. Journal of Clinical Microbiology 28: 1903-1905. Hunter, P.R. and Fraser, C.A.M. (1990) Application of the theory of adaptive polymorphism to the ecology and epidemiology of pathogenic yeasts. Applied and Environmental Microbiology, 56: 2219-2222. Hunter PR, Syed Q, Naumova EN. Possible undetected outbreaks in areas of the North West of England supplied by an unfiltered surface water source. Communicable Disease and Public Health 2001; 4: 136-38 Hunter PR, Chalmers RM, Syed Q, Hughes LS, Woodhouse S, Swift L. Foot and Mouth Disease and cryptosporidiosis: possible interaction between two emerging infectious diseases. Emerging Infectious Diseases 2003; 9: 109-12 Hunter PR, Hughes LS, Woodhouse S, Syed Q, Verlander N, Chalmers RM and members of the project steering committee. Case-control study of sporadic cryptosporidiosis with genotyping. Emerging Infectious Diseases 2004a; 10: 1241-49 Hunter PR, Hughes S, Woodhouse S, Raj N, Syed Q, Chalmers RM, Verlander N, Goodacre J. Health sequelae of human cryptosporidiosis in immunocompetent patients. Clinical Infectious Diseases 2004b; 39: 504-10 Mallon M, MacLeod A, Wastling J, Smith H, Reilly B, Tait A. Population structures and the role of genetic exchange in the zoonotic pathogen Cryptosporidium parvum. Journal of Molecular Evolution 2003a; 56: 407-17. Mallon ME, MacLeod A, Wastling JM, Smith H, Tait A. Multilocus genotyping of Cryptosporidium parvum Type 2: population genetics and sub-structuring. Infection, Genetics and Evolution 2003b; 3: 207-18. McLauchlin J, Amar C, Pedraza-Díaz S, Nichols GL. Molecular epidemiological analysis of Cryptosporidium spp. in the United Kingdom: results of genotyping Cryptosporidium spp. in 1705 fecal samples from humans and 105 fecal samples from livestock animals. Journal of Clinical Microbiology 2000; 39: 3984-90. Morgan UM, Constantine CC, Forbes DA, Thompson RCA. Differentiation between human and animal isolates of Cryptosporidium parvum using rDNA sequencing and direct PCR analysis. Journal of Parasitology 1997; 83: 825-830. Smerdon WJ, Nichols T, Chalmers RM, Heine H, Reacher MR. Foot and Mouth Disease in livestock and reduced cryptosporidiosis in humans, England and Wales. Emerging Infectious Diseases 2003; 9: 22-28.



Spano F, Putignani L, McLauchlin J, Casemore DP, Crisanti A. PCR-RFLP analysis of the Cryptosporidium oocyst wall protein (COWP) gene discriminates between C. wrairi and C. parvum, and between C. parvum isolates of human and animal origin. FEMS Microbiology Letters 1997; 150: 207-17. Strong WB, Gut J, Nelson RG. Cloning and sequence analysis of a highly polymorphic Cryptosporidium parvum gene encoding a 60-kilodalton glycoprotein and characterization of its 15- and 45-kilodalton zoite surface antigen products. Infection and Immunity 2000; 68: 4117-34. Struelens and the Members of the European Study Group on Epidemiological Markers (ESGM), of the European Society for Clinical Microbiology and Infectious Diseases (ESCMID) Consesnsus guidelines for appropriate use and evaluation of microbial epidemiological typing systems. Clinical Microbiology and Infection 1996: 2: 2-11. Widmer G, Feng X, Tzipori S. Genetic fingerprinting and species characterization of Cryptosporidium using microsatellite polymorphism. Proceedings of the AWWA Water Quality Technology Conference, 2000. Xiao L, Singh A, Limor J, Graczyk TK, Gradus S, Lal A. Molecular characterisation of Cryptosporidium oocysts in samples of raw surface water and wastewater. Applied and Environmental Microbiology 2001; 67: 1097-1101. Xiao L, Rayer R, Ryan U, Upton SJ. Cryptosporidium taxonomy: recent advances and implications for public health. Clinical Microbiology Reviews 2004; 17: 72-97. Zhou L, Singh A, Jiang J, Xiao L. Molecular surveillance of Cryptosporidium spp. in raw wastewater in Milwaukee: Implications for understanding outbreak occurrence and transmission dynamics. Journal of Clinical Microbiology 2003; 41: 5254-5257.


SCIEH, CRU,UEA 45 March 2005

Appendix I Review of literature describing gp15/60 sequence and microsatellite analysis-based methods of Cryptsporidium

subtyping (completed January, 2004)

Table 1. Publications describing the use of gp15/60 analysis methods for Cryptosporidium subtyping MS = microsatellite. Gt = genotype. Hu = human. Bv= bovine. Bp = base pairs. Yr = year. OB = outbreak. Publication Method Amplicon

size (bp) No. and type of samples. Subtypes/Alleles Notes

Avles et al., 2003

PCR > seq (as Glaberman)

800-850 35 calves, 10 wild ruminants, 29 human

Ib (large group), Ic, Ie, If, IIa, IIb, IId

Cacciò et al., 2003

PCR of gp15 > AluI RFLP for allelic class/sequencing for full subtyping

as above for ML1+2 gp15 ~550bp

Not stated gp15 > 1a, 1a', 1a'', 1b, 1c, 1d (described by Strong) by RFLP, all isolates different by seq

ML1 & 2 data not discussed but said they gave good differentiation of gt 2 but poor for gt1 so used gp15

Glaberman et al., 2002

Nested PCR > seq

NS ?800-850

3 drinking water OBs: A: 34; B: 42; C: 44

Ia, Ib, Ic, Id, II

Strong et al., 2000

PCR> seq ~1000 23 (14 gt1, 9 gt2) Ia, Ib, Ic, Id, II ID >97% inter allelic group, <84 between groups for gt 1s; all gt2s >99%ID

Zhou et al., 2003

SSU PCR-RFLP > spp. Subtyping by gp60 nested PCR> seq

~700 179 raw Milwaukee watewater samples > 50 pos > 24pos for C.hom, 5 pos for C.parvum > 16 gp60 PCR pos

Ia, Ib (most common - identical to 1993 Milwaukee OB strain), Ie, IIa Some had mixed

gp60 PCR less sensitive than SSU ?Ib virulent + prevalent in population outside of OBs



Table 2. Publications describing the use of microsatellite analysis methods for Cryptosporidium subtyping MS = microsatellite. Gt = genotype. Hu = human. Bv= bovine. Bp = base pairs. Yr = year. NS = not stated. IMS = immunomagnetic separation. PAGE = polyacrylamide gel electrophoresis. Publication Loc(us/i) Method Amplicon

size (bp) No. and type of samples.

Subtypes/Alleles Notes

Aiello et al., 1999

MS 1 - 9 PCR > Sequencing

NS 8 gt1, 7 gt2, +59 gt1s for MS1 diversity experiment.

MS1 > Bv A+B, Hu A (most)+ B MS2, 3, 4, 5 > Bv, Hu MS6 > Bv A+B

MS 6-9 failed. All loci were shorter (deletions) for gt1.


G35348 (GAG MS-containing site) = ML1

PCR > Sequencing

~238 94 total 48 hu isolates (22 gt1, 26 gt2), 46 animal isolates (all gt2)

H1 (229bp; in US & Europe-stable over 5yrs), H2 (235bp; in Japanese), C1 (238bp; 62% of hu, US, Europe + Japanese animals-stable over 11yrs) C2 (226bp), C3 (223bp; Netherlands only), C4 (240bp; Italy only). H/C distinguishable by length & sequence


ML1, ML2 (in putative intron), (both GAn)

PCR > Sequencing

ML1 ~238 ML2 ~229

59, Italian (29 calves, 11 calves, 10 lambs, 9 HIV+ humans)

ML1-238 (C1), -226 (C2), -220 (C4-only in isolates from South + Central Italy). ML2-231 (only 1 lamb), -229, -227, -213 (only 1 hu), -193 (only some calves), -191, -187. Linkages found: ML1-226 - ML2-193; ML1-193 - ML-226; ML1-238 - ML2-229

No ML1-223 found. Large no. of alleles in HIV patients.



Cacciò and Pozio, 2001

ML1 + SCRP (AG repeats in a coding sequence)

PCR > Sequencing

ML1 ~238 SCRP

Don’t give nos. Hu + animal faeces, tap + waste water from Italy.

2 H subtypes, 6 C subtypes found by ML1. 8 subtypes among human + animal by SCRP.

Inhibitors problematic for water samples even if IMS used.


ML1, ML2, (both GAn) gp15 (TCA)

PCR of gp15 > AluI RFLP for allelic class, sequencing for full subtyping

ML1 ~238 ML2 ~229 gp15 ~550

NS gp15 > 1a, 1a', 1a'', 1b, 1c, 1d (described by Strong) by RFLP, all isolates different by sequencing.

ML1 & 2 data not discussed but said they gave good differentiation of gt 2 but poor for gt1 so used gp15

Enemark et al., 2002

G35348 (as Cacciò)

G35348 PCR > sequencing (as Cacciò) or > fragment analysis (FAM Rev primer; GeneScan)

~238 193 cattle samples (108 COWP pos, all gt2), 1 lamb, 4 piglets, 1 hedgehog, CPB0 calf lab strain, 72 human

Cattle: C1 (16.7% most in South DK), C2 (17.2%), C3 (73.1% random dist) by sequencing. C2, C2, C3 by fragment analysis-results same as sequencing when 2 results, mixed detected in 13 samples and had limit of detection of rare genotype of 1:30 - 1:100 (superior to sequencing clones= 1:3 - 1:10). Humans: H1, C2, C3 by sequencing + fragment analysis (no mixed).

MS sequencing inconclusive in 18.5% MS fragment analysis not possible in 3.1% (same as COWP). Hedgehog isolate gave unique MS sequence, but fragment analysis gave H1 subtype and SSU sequence gave gt2. Pig isolates weren’t amplified by MS PCR (only SSU sequence). ?C1 + H1 same size fragments. Need to speciate first.

Feng et al., 2000

Up to 12 loci amplified (non-coding)

PCR (32P-labelled R primer) > PAGE

NS 19 total (6 gt1, 12 gt 2, 1 genotype?)

Best = 5B12 locus > 2 gt1, 5 gt2 1G09 > 2 gt 1, 2 gt2 1F07 > 3 gt1, 3 gt2

Each isolate was shown to be different when using 12 loci



Feng et al., 2002

5B12, 4E12, 1F07, Cp273, 7E1C - as Feng et al. (2000). 5B12 and 1F07 are on different chromosomes.

PCR (32P-labelled R primer) > PAGE gel as 2000, or unlabelled > ethidium bromide.

NS Co-infected interferon gamma KO mice with 2 different subtypes> recombinant gt2 subtypes.

Mallon et al., 2003

TP14, MS5, MS9, MS12, ML 1, MS1, gp15

PCR > fragment analysis

TP14 = 258-233, MS5 = 262-252, MS9 = 303-528, MS12 = 544-665, ML 1 = 220-238, MS1 = 352-400, gp15 = 273-456

180 human and bovine

TP14 (5), MS5 (10), MS9 (7), MS12 (3), ML 1 (4), MS1 (5), gp15 (13)

Widmer et al., 2000

5B12 PCR (32P-labelled R primer) > PAGE

NS 19 total (6 gt1, 12 gt 2, 1 gt?)

5B12 locus > 2 gt1, 5 gt2 Showed change in genotype over serial pig passages


SCIEH, CRU, UEA March 2005

49

Appendix II Key points from an ad hoc meeting between DWI and Cryptosporidium

researchers to discuss progress on subtyping methods. Ashdown House, London

4th November 2003 Those present: David Drury and Tony Hallas, DWI; Rachel Chalmers, Kristin Elwin, Stephen Hadfield, and Guy Robinson, Cryptosporidium Reference Unit; Paul Hunter, UEA, Norwich Apologies: Tony Lloyd, DWI

• In the light of the Boulder workshop, and our own work, it is clearly evident that there remains the need to define species in isolates prior to more discriminatory analyses. CRU currently uses COWP PCR-RFLP but recognises that while this is not perfect (eg. poor definition of mixed-species infections) it is a valuable “screening tool”, with a high “hit rate” applied to huge numbers of isolates. The use and implications (time, cost) of SSU rRNA nested PCR-RFLP will be explored within the Unit. It is currently used by the CRU as a second line investigative tool.

• The group agreed that, for epidemiological purposes, subtyping to the nth degree

may not offer any more information than less discriminatory approaches. i.e. a point may be reached after which no further discriminatory value is offered and in fact greater error may be introduced.

• PH stressed the need for statistical analyses of typing methods, which allow

discriminatory power to be evaluated, but are not evident in many publications. He invited CRU staff to Norwich for an informal workshop early in the New Year.

• The SCA trial raised concern over the apparent repeatability of results using

SSCP and it was agreed that investigation of SSCP and DPGE by the CRU is put on hold pending discussion with Robin Gasser.

• It was agreed that further investigation of gp60 sequencing and Microsatellite

(MS) methods should be given high priority, with the most suitable and appropriate MS selected from the literature and discussion with other workers (Cacciò, Enemark, Widmer, Wastling).

• It would be helpful for a copy of the final report from the Glasgow group

(Jonathan Wastling / Andy Tait) on their MS work be sent to CRU. DD will look into this.

• It was agreed that, to allow satisfactory selection and application of subtyping

methods for the project DWI 70/2/125-II, RC would request a short extension to milestones 4 and 5, and minor rescheduling of milestones 6 and 7. A formal request with costings and dates would be put to DD and TH with the next update report in February 2004.



50

• The group, and wider scientific and public health community, has recognised the value in maintaining the National Collection of Cryptosporidium oocysts and the provision of knowledge of background strains present in order that genotyping (subtyping) during outbreaks and incidents be put into context. However, funding for this needs to be addressed. RC will discuss this further with DD and TL.



51

Appendix III In-house evaluation of Cryptosporidium subtyping methods

Each subtyping method has been tested against a panel of Cryptosporidium species isolates and, for initial optimisation and evaluation of the methods, a panel of unrelated C. hominis and C. parvum isolates were chosen based on their strong positive result by Cryptosporidium oocyst wall protein (COWP) PCR-RFLP (Table 1). Table 1. Cryptosporidium species and C. parvum/C. hominis panels ND = Not detectable. P = PCR product purified. NT = not typable.

Reference strain Our ref no. (W or A prefix) or source Cryptosporidium spp. panel: C. hominis (human clinical strain) W6399 C. parvum IOWA Pat Mason, Idaho C. parvum MOREDUN 2002 Moredun Research Institute Loch Katrin sheep strain A24 C. meleagridis (human clinical strain) W4732 C. muris Liua Xiao, CDC C. canis (human clinical strain) W190 C. felis (human clinical strain) W4002 C. baileyi Veterinary Laboratories Agency C. andersoni H3, from water sample C. hominis/ C. parvum panel (human clinical strains): C. hominis W 6828 W 6829 W 7186 W 7387 W 7403 W 7439 W 7511 W 7552 C. parvum W 6826 W 6840 W 7215 W 7363 W 7434 W 7466 W 7545

(i) SSU rRNA gene sequence analysis The method developed employs the CEQ8000 capillary electrophoresis system (Beckman Coulter). Briefly, a ~298bp segment of the SSU rRNA gene was amplified by PCR using primers UM18SF and UM18SR (Morgan et al., 1997). PCR products were purified using QIAquick PCR purification columns (Qiagen). Forward and reverse dye-termination sequencing reactions were then performed using the PCR product. Sequencing reaction products were purified by ethanol precipitation and run on the CEQ8000. Sequence analysis of the Cryptosporidium spp. and C. hominis/C. parvum isolate panels was completed except for C. muris and C. baileyi for which it was not possible to generate satisfactory sequence. Sequences were compared using BioNumerics software



52

(Applied Maths) and the dendrograms produced (Fig. 1 and 2) illustrate the low degree of intra-species variability for C. hominis (89-100% similarity) and C. parvum (88 - 99% similarity). (ii) Gp60 gene sequence analysis Sequencing of the gp60 gene, as described by Strong et al. (2000) and other groups reviewed in Appendix I, was investigated. The gp60 gene has been found to be highly polymorphic, suggesting that it is a useful target for subtyping. Briefly, gp60 gene sequences were amplified by PCR using primers Gp15ATG and Gp15Stop (Strong et al., 2000). PCR products (~980bp) were purified and forward and reverse sequencing reactions performed and analysed as for SSU rRNA sequencing reactions. C. parvum and C. hominis panel strains were analysed and compared using BioNumerics software (Applied Maths). Sequencing was successful for all strains except for sample W7552. The dendrogram produced (Fig. 3) illustrates the higher level of sequence variation within C. parvum (61-99% similarity) and C. hominis (60-100% similarity) than seen for the SSU rRNA gene. Work was also undertaken in collaboration with Dr Lihua Xiao at CDC, Atlanta during June, 2004 using nested PCR primers (data not shown). Issues regarding Cryptosporidium subtyping were discussed. Conclusions can be drawn from the work done and from the literature. Gp60 gene sequencing has the following advantages over SSU rRNA sequencing: (i) provision of good quality sequence data over a longer region; (ii) significant variability reported within C. hominis and C. parvum; (iii) sequence variants have been assigned to a limited number of allelic classes allowing good epidemiological analyses. Disadvantages of gp60 include: (i) the limited number of sequences on the international databases; (ii) the lower sensitivity of the PCR; (iii) amplifies only C. hominis, C. parvum and C. meleagridis. Overall, gp60 gene sequencing is more suitable for subtyping of strains due to its higher sequence variability, probably due to immune selective pressure. Sequencing of SSU rRNA is more suitable for species identification and could be used for species confirmation prior to subtyping and for identification of strains giving unusual PCR RFLP results.



53

100

99989796959493

W6826W7215W7363W6840W7466W7545W7434W6828W7387W7403W7511W6829W7186W7439W7552

Fig. 1. Dendrogram showing the results of analysis of the SSU rRNA gene of the C. hominis and C. parvum panel UPGMA cluster analysis was performed; scale represents percentage similarity.

100

989694929088868482

W4732W190IowaMordun 2002W6399A24W4002H3

Fig. 2. Dendrogram showing the results of analysis of the SSU rRNA gene of the Cryptosporidium spp. panel UPGMA cluster analysis was performed; scale represents percentage similarity.

C. meleagridis

C. canis

C. parvum

C. parvum

C. hominis

Loch Katrin sheep isolate

C. felis

C. andersoni

C. parvum

C. hominis



54

100

95

90858075

W 6 828

W 7 403

W 6 829

W 7 511

W 7 439

W 7 186

W 7 387

W 7 434

W 7 545

W 7 215

W 7 466

W 6 840

W 6 826

W 7 363

Fig. 3. Dendrogram showing the results of analysis of the gp60 gene of the C. hominis and C. parvum panel UPGMA cluster analysis was performed; scale represents percentage similarity. (iii) DPGE analysis A method developed by Gasser et al. (2003) gave good inter- and intra-species discrimination of C. hominis and C. parvum strains based on sequence differences within the SSU rRNA gene and internal transcribed spacer-2 (ITS-2) region. The method involved analysing PCR products on slab-format polyacrylamide gels and utilised radiolabelled primers. To improve safety, work at the CRU involved analysis of PCR products using the automated CEQ8000 capillary electrophoresis system and non-radioactive primers. The primers described by Gasser et al. (2003) were modified and synthesised labelled with Beckman Coulter fluorescent dyes for analysis using the CEQ8000. ITS-2 and SSU rDNA primers were investigated and fragment analysis using the CEQ8000 undertaken. During investigation of a selection of strains from the panels, the SSU rDNA PCR was found to be efficient for detection of C. hominis and C. parvum strains as well as C. meleagridis, C. felis and Loch Katrin sheep strains. SSU rRNA gene and ITS-2 region amplicon sizing showed that SSU rRNA gene PCR products run on agarose gels had amplicon sizes ranging from 299 - 309bp (SD = 3.1bp) for C. hominis and from 284 - 307bp (SD = 5.9bp) for C. parvum. ITS-2 amplicon sizes ranged from 246 - 253bp (SD = 3.1bp) for C. hominis and from 230 - 242bp (SD = 5.4bp) for C. parvum. Some analysis of PCR products using the CEQ8000 was undertaken to improve the accuracy of amplicon sizing over agarose gels. The accuracy of CEQ fragment analysis method was assessed using standard fragments (Beckman Coulter) and sizes were within 0.01 - 0.76% of the manufacturer’s values. The mean difference between the

C. hominis

C. parvum



55

Cryptosporidium spp. fragment sizes derived by agarose gel analysis and the CEQ was 3.4%. This was probably due to the lower resolution and greater subjectivity of size estimation using agarose gels compared with using the capillary array system. While the capillary electrophoresis system theoretically provides greater accuracy, agarose gels remain an economical way of confirming PCR products prior to this. The ITS-2 region has been reported to be more variable than the SSU rRNA gene (Gasser et al., 2003) and may therefore prove to be a more discriminatory subtyping target. However, Gasser et al. (2003) also found that the differentiation of Cryptosporidium strains was greater using an SSCP method than with DPGE. This, along with the recent development of a non-radioactive SSCP protocol, led to the decision not to explore DPGE any further in order to allocate resources to the further exploration of SSCP. (iv) SSCP analysis Gasser et al. (2003) compared their DPGE method with an SSCP-based method. They found that the differentiation of Cryptosporidium strains was greater using SSCP than with DPGE. Although the published method used radioactive labelled primers, we planned to transfer the SSCP method to the CEQ8000 platform using fluorescent dye-labelled primers. However, through our experience with DPGE, it became clear that optimisation of SSCP on the CEQ8000 would be limited. A non-radioactive, gel-based method was been developed (Gasser et al., 2004), capable of analysing large numbers of strains per gel. Dr Gasser visited the CRU in February 2004 to demonstrate the ‘cold’ SSCP method, allowing its applicability to the study to be evaluated. During Dr Gasser’s visit we investigated the issues of (i) reproducibility and (ii) strain differentiation. The apparent problems of reproducibility in the international trial were resolved by linkage analysis of profiles and is no longer an issue reffered to in Appendix II. The method gave good discrimination of C. hominis and C. parvum strains and demonstrated different banding patterns within these species, indicating the presence of subtypes. The method may be more suitable than sequence-based methods for screening large numbers of samples due to its high throughput. An in-house trial was undertaken at the CRU during March 2004 using foreign traveller and international subtyping trial samples previously tested by Dr Gasser. Unfortunately the equipment was on loan for a limited period and so full optimisation and investigation was not possible at the time. However, further investigation is planned for the future. Intra- and inter-run reproducibility was demonstrated for SSU PCR products but could not be assessed for ITS-2 PCR products due to the poor performance of the ITS-2 PCR. SSU profiles for C. parvum and C. hominis were easily distinguished. There was however no significant intra-species profile variation, consistent with Gasser et al. (2003) who found that variation was more limited for SSU PCR products than ITS-2 products. ITS-2 pattern variation could not be fully evaluated due to poor PCR efficiency. However, significant profile variation was demonstrated (15/20 positive, 15 different profiles) between strains previously shown to be different by ‘hot’-SSCP. To fully evaluate ITS-2 SSCP, ITS-2 PCR efficiency must be optimised further to i) reduce primer-dimer formation and ii) increase sensitivity and yield. Sensitivity may also be improved by optimisation of the staining conditions. Staining sensitivity and time should be improved by using SYBR Gold. Profiles were easily imported and processed by



56

BioNumerics, removing the subjectivity of assessment by eye and permitting the archiving of standard profiles for comparison. (v) Microsatellite analysis Several reports have been published describing subtyping methods based on amplification followed by sequencing, RFLP or fragment analysis of different microsatellite loci and these have been reviewed and summarised in Appendix I. Recently, the combined analysis of three microsatellite loci (ML1, ML2 and the gp15/60 gene) has been shown to provide a high degree of discrimination (Cacciò, 2003; Mallon et al., 2003) and good accordance with gp60 DNA sequence analysis (Chalmers et al., submitted). The multilocus microsatellite typing method takes advantage of the high differentiation of C. hominis strains given by the gp60 locus (Strong et al., 2000) and of C. parvum strains using the ML1 and ML2 microsatellites (Cacciò et al., 2001). One disadvantage of microsatellite analysis is that the fragment sizes are not species-specific; therefore species must be determined independently. PCR fragment size analysis of these three microsatellite loci using the CEQ8000 was investigated (Table 2). ML1 and ML2 primers were as described by Cacciò, 2003; gp15/60 primers are as described by Mallon et al. (2003a). Table 2. Microsatellite PCR fragment lengths for Cryptosporidium panel strains ND = Not detectable.

Reference strain CRU ref no. (W or A prefix) or source

Microsatellite locus PCR fragment length (bp)

ML1 ML2 gp15/60 Cryptosporidium spp. panel: C. hominis (human clinical strain) W6399 233 180 371 C. parvum IOWA Pat Mason, Idaho 242 228 338 C. parvum MOREDUN Moredun Research Institute 242 230 341 C. meleagridis (human clinical strain) W4732 ND 166 ND C. canis (human clinical strain) W190 323 ND ND C. felis (human clinical strain) W4002 ND ND ND C. baileyi Veterinary Laboratories

Agency ND ND ND

C. hominis/C. parvum panel (human clinical strains): C. hominis W 6828 234 180 371 W 6829 234 180 371 W 7186 234 180 371 W 7387 234 180 421 W 7403 234 180 371 W 7439 234 180 371 W 7511 234 180 371 W 7552 234 180 371 C. parvum W 6826 242 230 341 W 6840 242 228 339 W 7215 230 177 ND W 7363 242 230 342 W 7434 230 177 311 W 7466 230 177 ND W 7545 230 177 ND



57

Appendix IV Cryptosporidium Reference Unit in-house multilocus fragment typing

scheme for C. parvum and C. hominis

Microsatellite locus PCR fragment length (bp)

Multilocus fragment type

ML1 ML2 gp15 (MLFT) C. hominis 234 180 371 1

240 180 372 2 242 180 371 3 223 180 371 4 234 180 408 5 234 180 353 6 218 180 371 7 219 180 414 8 233 180 341 9 234 180 421 10

C. parvum 242 230 341 1 242 230 338 2 228 193 329 3 227 195 338 4 242 232 341 5 242 234 338 6 242 232 338 7 242 234 341 8 242 225 341 9 242 228 338 10 242 230 332 11 242 230 359 12 242 230 347 13 242 232 356 14 242 232 338 15 242 232 344 16 242 232 347 17 242 234 347 18 242 236 338 19 242 238 341 20 243 232 349 21 227 193 320 22 227 194 327 23 227 224 333 24 227 197 310 25 227 232 341 26 227 195 353 27 227 193 327 28 227 193 330 29 227 195 333 30 241 238 338 31 242 215 344 32 242 230 350 33 242 207 338 34 242 203 338 35 242 230 344 36 242 211 344 37 227 197 368 38 242 231 362 39 242 221 341 40 242 236 341 41 244 230 338 42 242 221 350 43 227 196 335 44 239 232 341 45



58

Appendix V C. parvum hierarchical cluster analysis dendrogram for case-control group Blue = supercluster 1, green = supercluster 2 and red = supercluster 3.



59



60

Appendix VI Cluster analysis of strains of C. parvum isolated from cases at the time of

the 2000 Thirlemere outbreak

Blue = C. parvum cluster 1a, green = C. parvum cluster 1b and red = C. parvum cluster 1c.



61



62



63

Appendix VII Single variable analysis (х2 test or Fisher’s exact test) of

C. parvum clusters 1Chi-square test for trend.

C. parvum supercluster p-value SC1 SC2 SC3 SC1 v SC2+SC3

Take regular medication known to affect immunity

Y N

0 45

0 11

0 3

-

Anyone else in previous 2 weeks in house ill with diarrhoea

Y N

9 37

3 8

0 3

1.000

Number of people 5-15 years living with you

0 1 2 3 4 5 or more

20 11 4 0 0 0

3 2 0 0 0 0

0 0 2 0 0 0

0.4891

Travel outside UK Y N

1 45

1 11

0 3

0.434

Travel within UK Y N

11 32

1 9

1 2

0.710

Gardening other than watering

Y N

10 33

2 10

0 3

0.712

Number of times swum in swimming pool

0 1 2 3 4 5 or more

32 6 6 1 1 1

7 1 2 0 0 2

2 0 1 0 0 0

0.4821

Use a toddler pool Y N

3 12

2 3

1 0

0.291

Number of times swum in a toddler pool

0 1 2 3 4 5 or more

0 1 2 0 0 0

0 0 1 0 0 1

0 0 1 0 0 0

0.3681

Swallow water while swimming in sea

Y N

1 46

1 11

0 3

0.428

Swallow water while swimming in river

Y N

1 46

0 12

0 3

1.000

Spend time sitting or sleeping outside on ground

Y N

5 38

1 10

0 3

1.000



64

Touch or handle other peoples pets

Y N

8 14

3 2

0 0

0.370

Touch or handle any farm animals

Y N

20 26

0 11

0 2

0.002

Toileting contact with a child under 5 years of age

Y N

4 43

2 10

0 3

0.626

Nappy changing contact with a child under 5 years of age

Y N

8 39

1 11

0 3

0.434

Contact with another person will with diarrhoea

Y N

6 40

1 11

1 2

1.000

Number of glasses of tap water drunk a day

0 1 2 3 4 5 or more

0 6 7 5 9 11

0 0 1 1 4 4

0 1 0 0 2 0

0.1191

Any altered taste to water supply at home

Y N

0 47

0 12

0 3

-

Drinks bottled water Y N

13 32

4 7

1 2

0.6805

Number of glasses of bottled water a day

0 1 2 3 4 5 or more

32 5 0 0 0 1

7 0 2 0 0 0

2 0 0 0 0 0

0.72491

Eat lettuce (per week) Not at all 1-2 times 3-7 times Most days

29 6 3 2

7 4 0 1

1 2 0 0

0.1791

Eat other green salad (per week)

Not at all 1-2 times 3-7 times Most days

27 10 3 0

6 3 0 1

1 2 0 0

0.1741

Eat tomatoes (per week)


26 8 4 4

7 2 1 1

2 1 0 0

0.8921

Eat coleslaw (per week)


30 8 0 0

7 2 0 0

3 0 0 0

0.8091

Eat raw vegetables (per week)


34 3 0 1

7 0 1 1

2 1 0 0

0.2481



65

Eat fresh fruit (per week)


6 10 12 15

1 5 2 3

0 2 0 1

0.2771

Eat uncooked soft cheese (per week)


30 7 2 0

8 1 0 0

1 2 0 0

0.6531

Eat uncooked hard cheese (per week)


13 10 11 6

4 3 2 2

1 1 0 1

0.7791

Eat ice cream (per week)


13 7 8 5

3 4 0 1

1 2 0 0

0.4461

Eat cream (per week) Not at all 1-2 times 3-7 times Most days

30 8 0 1

7 2 0 0

3 0 0 0

0.8091

Consume freshly pressed apple juice (per week)


34 3 0 0

7 1 1 0

3 0 0 0

0.2061

Eat any new or unusual foods

Y N

3 42

0 12

1 2

1.000

Drink pasteurised milk

Y N

37 8

10 2

3 0

1.000

Eat cereal with pasteurised milk

Y N

37 9

10 2

3 0

1.000

Number of glasses of unboiled tap water at home

0 1 2 3 4 5 or more

5 8 9 7 7 11

0 0 3 1 3 5

0 1 0 1 1 0

0.6361

If eat raw fruit and vegetables, are they normally washed before eating

Always Usually Sometimes Never

17 11 10 5

2 5 3 2

2 0 1 0

0.8421

Touch any cattle Y N

7 40

0 12

0 3

0.180



66

Appendix VIII Single variable analysis (Mann Whitney U Test or Fisher’s exact test) of C.

hominis and C. parvum for Thirlemere Outbreak strains 1Mann Whitney U Test

C. hominis C. parvum p-value

Sex M F

4 7

28 54

1.000

Attend school Y N

4 1

23 24

0.352

Attend nursery/playgroup

Y N

1 1

19 27

1.000

Number over 16s in household

0 1 2 3 4

5 or more

0 0 9 0 0 1

0 7 35 11 1 4

0.7831

Number 5-16 year olds in household

0 1 2 3 4

5 or more

2 3 2 0 0 0

8 14 11 3 0 0

0.5321

Number under 5s in household

0 1 2 3 4

5 or more

3 4 0 0 0 0

16 13 5 0 0 0

0.8491

Had diarrhoea Y N

9 1

58 2

0.375

Travel outside UK

Y N

1 10

1 79

0.228

Other person had diarrhoea

Y N

0 8

0 64

-

Fever Y N

3 5

27 34

1.000

Abdominal cramp Y N

8 0

62 4

1.000

Vomiting Y N

6 2

30 39

0.137

Blood in stool Y N

1 4

6 54

0.445

Travel within UK Local Other UK

6 0

67 4

1.000



67

Drink cold, unboiled tap

water

Y N

8 1

67 6

0.571

No. glasses per day at home

0 1 2 3 4

5 or more

1 1 2 2 0 4

6 5 16 8 5 28

0.8451

No. glasses per day at work

0 1 2 3 4

5 or more

5 1 0 0 0 0

23 6 3 4 1 5

0.1901

Icecubes Y N

2 7

23 42

0.709

Bottled water Y N

4 6

17 51

0.445

Water filter Y N

0 1

9 61

1.000

Untreated water Y N

0 9

2 66

1.000

Drinks dispenser Y N

2 6

5 65

0.149

Water fountain Y N

0 7

6 59

1.000

Home water supply

Mains Other

9 0

73 2

1.000

Disruption to water supply

Y N

1 8

6 59

1.000

Swimming pool Y N

4 4

18 58

0.197

No. times swimming

0 1 2 3 4

5 or more

4 3 0 2 0 0

58 11 4 2 2 0

0.0531

No. times immersed head

None 1-2 3-7

over 7

5 0 0 4

58 2 8 6

0.0601

Swallow water while swimming

Y N

3 6

12 60

0.356

Swim in river/lake

Y N

1 8

1 75

0.202

Swim in freshwater

Y N

0 9

1 77

1.000



68

Lettuce Not at all 1-2 3-7

Most days

4 2 2 1

27 24 12 5

0.9261

Other green salad Not at all 1-2 3-7

Most days

3 2 3 1

35 15 10 7

0.2631

Tomatoes Not at all 1-2 3-7

Most days

3 2 3 1

36 16 11 6

0.3391

Coleslaw Not at all 1-2 3-7

Most days

7 1 1 0

53 12 4 1

0.9421

Raw vegetables Not at all 1-2 3-7

Most days

5 1 2 0

48 14 4 3

0.5811

Fresh fruit Not at all 1-2 3-7

Most days

0 1 4 4

21 11 16 22

0.1001

Undercooked burgers

Not at all 1-2 3-7

Most days

9 0 0 0

66 3 0 0

0.5261

Undercooked meat

Not at all 1-2 3-7

Most days

6 3 0 0

63 4 0 0

0.0081

Raw shellfish Not at all 1-2 3-7

Most days

9 0 0 0

68 1 1 0

0.6101

Soft cheese Not at all 1-2 3-7

Most days

5 1 2 1

55 5 8 2

0.1121

Hard cheese Not at all 1-2 3-7

Most days

3 4 1 1

22 21 23 4

0.6331

Yoghurt Not at all 1-2 3-7

Most days

2 2 2 3

29 11 13 18

0.3601



69

Icecream Not at all

1-2 3-7

Most days

3 5 1 0

28 33 6 2

0.7561

Cream Not at all 1-2 3-7

Most days

8 0 0 1

58 9 2 0

0.8151

Wash lettuce Y N

8 1

51 3

0.469

Wash vegetables Y N

7 2

44 3

0.178

Wash fruit Y N

4 5

48 17

0.115

New/unusual food

Y N

1 8

6 62

0.9131

Pasteurised milk Y N

6 3

55 19

0.693

Unpasteurised milk

Y N

1 7

2 74

0.262

Pets at home Y N

2 7

45 32

0.073

Pets with diarrhoea

Y N

0 2

4 39

1.000

Visit zoo Y N

0 9

2 75

1.000

Touch farm animals

Y N

0 9

14 60

0.345

Visit farm Y N

0 9

10 59

0.595

Touch other peoples pets

Y N

2 8

22 51

0.716



70

Appendix IX Single variable analysis (Mann Whitney U Test or Fisher’s

Exact Test) of C. parvum clusters 1Mann Whitney U Test.

C. parvum cluster p-value 1a 1b 1c C1a+C1c v C1b

Sex M F

21 41

3 5

1 4

1.000

Attend school Y N

18 17

2 2

0 3

1.000

Attend nursery/playgroup

Y N

14 21

1 2

2 1

1.000

Number over 16s in household

0 1 2 3 4

5 or more

0 4 26 8 1 4

0 1 2 1 0 0

0 1 2 2 0 0

0.5011

Number 5-16 year olds in household

0 1 2 3 4

5 or more

7 11 9 3 0 0

0 1 0 0 0 0

1 1 0 0 0 0

0.8251

Number under 5s in household

0 1 2 3 4

5 or more

15 9 3 0 0 0

0 0 1 0 0 0

0 3 9 0 0 0

0.0971

Had diarrhoea Y N

44 2

6 0

5 0

1.000

Fever Y N

21 27

3 2

2 2

1.000

Abdominal cramp Y N

47 3

5 1

5 0

0.346

Vomiting Y N

23 31

1 4

3 2

0.387

Blood in stool Y N

6 42

0 4

0 4

1.000

Travel within UK Local Other UK

53 2

6 1

5 0

0.286

Drink cold, unboiled tap water

Y N

52 4

6 1

4 1

0.493



71



0 1 2 3 4

5 or more

21 5 3 3 1 5

0 1 0 0 0 0

1 0 0 1 0 0

0.8921

Icecubes Y N

17 33

2 4

2 2

1.000

Bottled water Y N

13 39

1 6

2 2

0.424

Water filter Y N

6 47

1 6

1 4

1.000

Untreated water Y N

0 52

0 7

1 3

1.000


3 51

2 5

0 4

0.086

Water fountain Y N

5 45

0 7

1 3

1.000

Home water supply Mains Other

58 0

7 0

4 1

1.000


Y N

6 44

0 7

0 4

1.000

Swimming pool Y N

12 48

2 5

1 4

0.630

No. times swimming 0 1 2 3 4

5 or more

48 9 1 1 2 0

5 1 0 0 0 0

4 0 1 0 0 0

0.7411


None 1-2 3-7

over 7

48 2 4 4

5 0 2 0

4 0 0 1

0.5341


Y N

7 49

1 6

1 4

1.000

Swim in river/lake Y N

1 59

0 6

0 5

1.000


Most days

21 20 9 3

2 3 0 2

2 1 1 0

0.4231


Most days

28 11 9 4

3 2 0 2

2 2 0 0

0.4741



72

C. parvum cluster 1b 1c

p-value

Tomatoes Not at all 1-2 3-7

Most days

27 16 8 3

4 0 1 2

3 0 1 0

0.6361


Most days

42 8 3 1

5 2 0 0

4 1 0 0

0.7871


Most days

35 12 3 3

6 1 0 0

4 1 0 0

0.2911


Most days

7 16 15 16

1 4 0 2

3 1 0 1

0.5671

Undercooked burgers Not at all 1-2 3-7

Most days

50 3 0 0

7 0 0 0

5 0 0 0

0.5411

Undercooked meat Not at all 1-2 3-7

Most days

48 3 0 0

6 1 0 0

5 0 0 0

0.3651


Most days

52 1 0 0

7 0 0 0

5 0 0 0

0.6241


Most days

40 5 7 2

6 0 1 0

5 0 0 0

0.6091


Most days

16 19 16 3

3 1 2 1

3 1 1 0

0.9391


Most days

23 10 10 12

4 0 0 3

1 0 0 2

0.9481

Icecream Not at all 1-2 3-7

Most days

21 25 5 2

4 3 0 0

2 3 0 0

0.7191



73


Cream Not at all 1-2 3-7

Most days

45 8 1 0

6 1 0 0

4 0 0 0

0.9191

Wash lettuce Y N

36 3

6 0

5 0

1.000

Wash vegetables Y N

32 3

4 0

4 0

1.000

Wash fruit Y N

35 14

4 2

5 0

0.653

New/unusual food Y N

5 48

1 6

0 5

0.510


43 15

5 2

3 2

1.000

Unpasteurised milk Y N

0 59

0 7

1 4

1.000

Pets at home Y N

35 25

5 2

4 1

0.698

Pets with diarrhoea Y N

2 31

0 5

1 3

1.000

Visit zoo Y N

2 58

0 7

0 5

1.000

Touch farm animals Y N

9 49

3 3

1 4

0.076

Visit farm Y N

7 46

2 5

1 4

0.292


Y N

20 37

1 6

0 5

0.427



74



0 1 2 3 4

5 or more

21 5 3 3 1 5

0 1 0 0 0 0

1 0 0 1 0 0

0.3081

Icecubes Y N

17 33

2 4

2 2

1.000

Bottled water Y N

13 39

1 6

2 2

0.424

Water filter Y N

6 47

1 6

1 4

1.000

Untreated water Y N

0 52

0 7

1 3

1.000


3 51

2 5

0 4

0.086

Water fountain Y N

5 45

0 7

1 3

1.000

Home water supply Mains Other

58 0

7 0

4 1

1.000


Y N

6 44

0 7

0 4

1.000

Swimming pool Y N

12 48

2 5

1 4

0.630

No. times swimming 0 1 2 3 4

5 or more

48 9 1 1 2 0

5 1 0 0 0 0

4 0 1 0 0 0

0.9731


None 1-2 3-7

over 7

48 2 4 4

5 0 2 0

4 0 0 1

0.2121


Y N

7 49

1 6

1 4

1.000

Swim in river/lake Y N

1 59

0 6

0 5

1.000


Most days

21 20 9 3

2 3 0 2

2 1 1 0

0.1191


Most days

28 11 9 4

3 2 0 2

2 2 0 0

0.2271



75



Most days

42 8 3 1

5 2 0 0

4 1 0 0

0.7591


Most days

35 12 3 3

6 1 0 0

4 1 0 0

0.7471


Most days

7 16 15 16

1 4 0 2

3 1 0 1

0.3361

Undercooked burgers Not at all 1-2 3-7

Most days

50 3 0 0

7 0 0 0

5 0 0 0

1.000

Undercooked meat Not at all 1-2 3-7

Most days

48 3 0 0

6 1 0 0

5 0 0 0

0.383


Most days

52 1 0 0

7 0 0 0

5 0 0 0

0.8851


Most days

40 5 7 2

6 0 1 0

5 0 0 0

0.8171


Most days

16 19 16 3

3 1 2 1

3 1 1 0

0.6051


Most days

23 10 10 12

4 0 0 3

1 0 0 2

0.2671

Icecream Not at all 1-2 3-7

Most days

21 25 5 2

4 3 0 0

2 3 0 0

0.7191



76

C. parvum cluster p-value

1a 1b 1c C1a+C1c v C1b Wash lettuce Y

N 36 3

6 0

5 0

1.000

Wash vegetables Y N

32 3

4 0

4 0

1.000

Wash fruit Y N

35 14

4 2

5 0

0.653

New/unusual food Y N

5 48

1 6

0 5

0.510


43 15

5 2

3 2

1.000

Unpasteurised milk Y N

0 59

0 7

1 4

1.000

Pets at home Y N

35 25

5 2

4 1

0.698

Pets with diarrhoea Y N

2 31

0 5

1 3

1.000

Visit zoo Y N

2 58

0 7

0 5

1.000

Touch farm animals Y N

9 49

3 3

1 4

0.076

Visit farm Y N

7 46

2 5

1 4

0.292


Y N

20 37

1 6

0 5

0.427

Investigation of Cryptosporidium clinical isolates and ...Investigation of Cryptosporidium clinical...

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