Aus dem Institut für Mikrobiologie und Hygiene
der Medizinischen Fakultät der Charité – Universitätsmedizin Berlin

DISSERTATION

Molecular Epidemiology, Clinical Molecular Diagnosis and Genetic Diversity of Cutaneous Leishmaniasis
in Jericho, Palestine

zur Erlangung des akademischen Grades
Doctor rerum medicarum
(Dr. rer. medic)

vorgelegt der Medizinischen Fakultät Charité
der Universitätsmedizin- Charité

Von
Amer Al-Jawabreh
Aus Palästina

Gutachter:
1. Prof. Dr. med. W. Presber
2. Prof. Dr. W. Solbach
3. Prof. Dr. Ch. Jaffe

Datum der Promotion: 25th November, 2005

This research has been part of the scholarship granted to A. Al-Jawabreh by Deutsche Akademische Austauschdienst (DAAD).
The molecular work has been carried out at the laboratory of the Institut für Mikrobiologie und Hygiene- Universitätsmedizin- Charité, Berlin Germany under close supervision of Dr. G. Schoenian and the isolation and the diagnosis at ICS-Jericho Medical laboratory in Jericho, Palestine

Abstract

Parasitological diagnosis of cutaneous leishmaniasis is imperative before treatment. In this study we compared the sensitivity of the diagnosis of Giemsa-stained skin scrapings by standardized graded direct microscopy with that of ITS1-PCR. Out of the 60 squares scanned for amastigotes using x100 oil- immersion light microscopy, 45 (75%) gave usable results. Fifteen (25%) squares could not be microscopically scanned. Of the 23 microscopy-positive squares, 20 (87%) were positive by PCR. Of the 22 squares negative for microscopy, 18 (82%) were ITS1-PCR positive. ITS1-PCR showed a sensitivity of 87% with positive predictive value of 100% and a specificity of 100% with negative predictive value of 85%.

In-vitro cultivation using NNN medium and direct smear microscopy of Giemsa-stained slides, PCR amplifying region 1 of internal transcribed spacer (ITS1) using skin scrapings spotted on filter papers (FP) and unstained tissue smears (US) were compared on the basis of three gold standards: WHO case definition, combined and clinical gold standard. PCR using US was more sensitive than all other methods using the 3 gold standards. Of the 298 cases of CL from FP, US and in-vitro culture tested by PCR-RFLP using HaeIII and MnlI, 181(60.7%) contained DNA of L. major, 106 (35.6%) DNA of L. tropica, while 11 (3.7%) of the FP remained unidentified.

Molecular epidemiology was used to study the distribution of Leishmania species in Jericho. Spatial analysis showed three statistically significant clusters of CL, one cluster for L. major (Auja-Fasayil villages) and two clusters for L. tropica, (Zubaidat village and Nabi-Musa Bedouin encampment) were recorded. In the case of space-time, four clusters were detected: Zubaidat village for four years, A’uja-Fasayil villages for one year, Nabi-Musa for three years and Nuaimeh village for one year. Clusters for L. major were noted in A’uja-Fasayil villages for one year and Nuaimeh village for three years. L. tropica clusters were in Zubaidat village for one year, Nabi-Musa for four years and Aqbat-Jaber refugee camp for one year.

Microsatellites, or simple sequence repeats (SSR), are very useful genetic markers for population genetic studies. Ten pairs of PCR primers, annealing to the unique flanking regions, were designed to amplify microsatellite loci identified in the genome sequence of L. major on chromosomes 1, 3, 5, 21 and 35. The microsatellite repeats are (CA)n, (AT)n, (GTG)n, and (GACA)n with varying lengths. A total of 106 strains isolated in different endemic regions of Central Asia, Middle East and Africa were analysed in this study. Analysis of genetic distance revealed the existence of 7 discrete populations of L. major including two genetically isolated populations in the Middle East. This was confirmed by a Bayesian model-based clustering approach that assigned the individual strains to the same number of populations.

Keywords: Cutaneous leishmaniasis, L. major, L. tropica, ITS1-PCR, Genetic diversity, Molecular epidemiology, Jericho-Palestine, Microsatellites

Zusammenfassung

In der vorliegenden Arbeit wurde die Sensitivität des Nachweises von Leishmanien in Giemsa-gefärbten Bioptaten aus Hautulzerationen mittels direkter Mikroskopie mit der Sensitivität der ITS1-PCR verglichen. Bei der ITS1-PCR wurde eine Sensitivität von 87 % mit einem positiven predictive value von 100 %, sowie eine Spezifität von 100 % mit einem negativen predictive value von 85 % nachgewiesen.

Weiterhin wurden vier verschiedene Nachweismethoden miteinander verglichen: die in vitro Kultivierung in NNN Medium, die direkte Mikroskopie von Giemsa gefärbten Hautbioptaten, die PCR Amplifizierung der ITS1 Region aus auf Filterpapier aufgetragenen Hautbioptaten (FP) sowie die ITS1-PCR von ungefärbten Hautbioptaten (US). Die PCR der US erwies sich als die sensitivste Methode.

Die Verbreitung von Leishmanien Arten in Jericho wurde mittels molekularer Epidemiologie untersucht. Die räumliche (Spatial) Analyse zeigte drei statistisch relevante Cluster innerhalb der kutanen Leishmaniose (CL): ein Cluster mit L. major und zwei L. tropica Cluster. Bei der Raum-Zeit–Analyse wurden vier Cluster von Kutanen Leishmaniose, zwei L. major und drei L. tropica Cluster nachgewiesen.

Insgesamt 106 Stämme, die aus verschiedenen endemischen Regionen in Zentralasien, im Nahen Osten und Afrika stammen, wurden mit 10 Mikrosatellitenmarkern untersucht. Die Auswertung erfolgte über zwei Analysemethoden: die Distanz-basierte und die Modell-basierte Methode. Anhand der L. major Genomsequenz wurden PCR-Primer zur Amplifizierung von Mikrosatellitenloci von L. major entwickelt, die auf den Chromosomen 1, 3, 5, 21 und 35 liegen. Sieben unterschiedliche L. major Populationen einschließlich zweier genetisch isolierter Populationen im Nahen Osten wurden mit diesen Markern nachgewiesen.

Eigene Schlagworte: Kutane Leishmaniose, L. major, L. tropica, ITS1-PCR, genetische Diversität, molekulare Epidemiologie, Jericho-Palästina, Mikrosatelliten

Table of contents

• 1 INTRODUCTION
  • 1.1 Historical background
  • 1.2 Clinical symptoms of leishmaniases
  • 1.3  Epidemiology of leishmaniasis:
    • 1.3.1  Global view
    • 1.3.2 Local view
  • 1.4 Leishmaniasis: Public Health Surveillance
  • 1.5 Clinical diagnosis and identification
  • 1.6 Multilocus enzyme electrophoresis (MLEE)
  • 1.7 Microsatellites
    • 1.7.1 Mutation mechanism
    • 1.7.2 Functions of microsatellites
    • 1.7.3 Application of microsatellites for Leishmania strain typing
  • 1.8 Two models for microsatellite evolution
  • 1.9 Genetic distance measures used in microsatellite analyses
  • 1.10 L. major and L. tropica
    • 1.10.1 Distribution
    • 1.10.2 Clinical features
    • 1.10.3 Clinical diagnosis and characterization
    • 1.10.4 Vector
    • 1.10.5 Reservoir
    • 1.10.6 Control
    • 1.10.7 Treatment
  • 1.11 Population genetics of Leishmania parasites
  • 1.12  Genetic diversity and bottleneck theory
  • 1.13 Objectives (Figure 1.7)
• 2  MATERIALS AND METHODS
  • 2.1  Clinical molecular diagnosis and local molecular epidemiology
    • 2.1.1 Patients and study area:
  • 2.2 Sample collection
    • 2.2.1 Patient data sheets
    • 2.2.2 Collection and Giemsa staining of skin scrapings
    • 2.2.3 Sampling using filter papers and unstained smears
    • 2.2.4 Cultivation of parasites from dermal tissue aspirates
  • 2.3 DNA extraction
    • 2.3.1 DNA extraction of cultured parasites
    • 2.3.2 DNA extraction of Clinical samples
  • 2.4 PCR amplification: Internal transcribed spacer (ITS1)
  • 2.5 Panel of controls used in diagnostic PCR
    • 2.5.1 DNA extraction control
    • 2.5.2 Positive and negative controls
    • 2.5.3 Inhibition control
  • 2.6 RFLP analysis of ITS1 amplicons
  • 2.7 Evaluation studies
    • 2.7.1 Graded microscopy vs ITS1-PCR
      • 2.7.1.1 Patients and study area
      • 2.7.1.2 Sample collection and preparation:
      • 2.7.1.3 Standardized graded microscopy:
    • 2.7.2 Filter Paper vs Unstained smears and conventional methods vs PCR
  • 2.8 Genetic microsatellite variation and global molecular epidemiology
    • 2.8.1  Study area
  • 2.9 Microsatellite markers
    • 2.9.1 Amplification of microsatellite markers by PCR
    • 2.9.2 Polyacrylamide gel electrophoresis (PAGE)
    • 2.9.3 Silver staining
    • 2.9.4 Capillary electrophoresis (CE) using CEQ^TM 8000 Beckman coulter
  • 2.10 Data analysis: clustering methods and presentation of genetic data
    • 2.10.1 Calculating a distance matrix using MICROSAT
    • 2.10.2 Drawing of NJ and UPGMA consensus trees using PHYLIP/ PAUP
    • 2.10.3 Structuring populations with Structure 2.0
    • 2.10.4 F-Statistic by F-STAT and GENEPOP
    • 2.10.5 Descriptive statistics for markers by GDA
  • 2.11 Epidemiological data banking and analysis: Epi Info™
  • 2.12 Geographical clustering and public health surveillance
    • 2.12.1  Spatial scan statistics
    • 2.12.2 Space-time scan statistics
    • 2.12.3 Adjustment for season relative risk
    • 2.12.4 Adjusting for covariates
  • 2.13 Shewhart’s Chart
• 3 RESULTS
  • 3.1  Method comparison
    • 3.1.1 Graded microscopy and ITS1-PCR
      • 3.1.1.1 Positivity rates and sensitivities of ITS1-PCR and graded microscopy
      • 3.1.1.2 Statistical comparison of sensitivities
      • 3.1.1.3 Diagnostic relevance of ITS1-PCR
    • 3.1.2 Clinical diagnosis of cutaneous leishmaniasis: filter paper and unstained smears as potential sampling methods for ITS1-PCR
  • 3.2 Molecular epidemiology
    • 3.2.1 L. major vs. L. tropica in Jericho
    • 3.2.2 Sex-age group distribution
    • 3.2.3 Annual rain fall (ARF) and CL
    • 3.2.4 Seasonality of CL
  • 3.3 Public health surveillance and cluster analysis
    • 3.3.1 Descriptive data of CL cases
    • 3.3.2 Purely spatial analysis, adjusted for season
    • 3.3.3 Space-time analysis, adjusted for season
  • 3.4 Shewhart’s Plot: Early warning system
  • 3.5 Genetic variability within L. major as revealed by Multilocus Microsatellite Analysis (MLMT)
    • 3.5.1 Description of the microsatellite markers used in this study
    • 3.5.2 Assignment of multilocus microsatellite profiles to the strains of L. major under study
    • 3.5.3 Population structure of L. major using Structure
      • 3.5.3.1 Estimation of population structure using non-predefined populations
      • 3.5.3.2 Estimation of population structure using predefined population
    • 3.5.4 Analysis of L. major population structure using distance-based methods
    • 3.5.5 Genetic isolation of the L. major populations identified in this study
    • 3.5.6 Estimation of allele numbers in geographical groups of strains
• 4 DISCUSSION
  • 4.1 Method comparison
    • 4.1.1 Graded microscopy and ITS1-PCR
    • 4.1.2 Clinical diagnosis of cutaneous leishmaniasis
  • 4.2 Molecular epidemiology
    • 4.2.1  Seasonality of CL
  • 4.3 Public health surveillance and cluster analysis
  • 4.4 Multilocus microsatellite analysis and population structure
    • 4.4.1 Optimal number of markers and isolates
    • 4.4.2 Distance and model-based methods: congruence and contradiction
    • 4.4.3 Origin of L. major and bottleneck theory
  • 4.5 Recommendations
• List of Abbreviations
• References
• Acknowledgements
• Erklärung

Tables

• Table 1.1 Selected studies that compared methods of diagnosis for CL
• Table 1.2 Summary of epidemiological and clinical features of L. major and L. tropica
• Table 2.1 Components and quantities for the Master Mix (MM) for one sample
• Table 2.2 Grading of parasites in Giemsa-stained smears from skin lesions.
• Table 2.3 The 10 microsatellites markers used for the analysis of populations of L. major.
• Table 2.4 Software packages used in the study
• Table 3.1 Outcome of graded microscopy and ITS1-PCR using Giemsa-stained slides obtained from patients.
• Table 3.2 Comparison of graded microscopy and ITS1-PCR in the 60 square-test group.
• Table 3.3 Sensitivity and specificity of microscopy and ITS1-PCR compared to a negative control group
• Table 3.4 Sensitivity of the four diagnostic methods using 3 types of ‘gold standard’ (n=64 cases)
• Table 3.5 The battery of controls used in the PCR to ensure validity of results
• Table 3.6 Comparison of the clinical features of CL cases caused by L. major and L. tropica in the district of Jericho
• Table 3.7 Geographical and population data of the 9 study areas in the District of Jericho-Palestine
• Table 3.8 Purely spatial analyses: season-adjusted statistics of CL, L. major and L. tropica cases, District of Jericho, 1994-2004, RR: relative risk.
• Table 3.9 Space-time analyses: season-adjusted statistic of CL, L. major and L. tropica cases, District of Jericho, 1994-2004. RR: relative risk.
• Table 3.10 Characterisation of the 10 microsatellite markers used for population studies in L. major.
• Table 3.11 The multilocus microsatellite profiles of the strains of L. major analysed in this study
• Table 3.12 The number of L. major isolates in the five predefined clusters inferred by Structure v. 2.
• Table 3.13 Estimates for Fst, measures of genetic differentiation (above diagonal), for all loci between populations of L. major as measured by FSTAT. Below diagonal is the corresponding calculated migration rate, Nm. (a) optimal population at K=7. (b) Predefined population according to the five major geographic regions.

Images

• Figure 1.1 Replication slippage caused by dissociation of the two strands, re-aligning of the nascent strand out of the register (left), and then continued replication as part of the mismatch repair.
• Figure 1.2 Possible functions of microsatellites (You et al., 2002)
• Figure 1.3 Two cases of CL from Palestinian patients in Jericho. The disfigured chin case was caused by L. tropica. A case of L. major shows 3 lesions on the leg of a Chinese worker who lived in Jericho for one year.
• Figure 1.4 Promastigotes: flagellated motile forms found in the vector and in culture (x1000).
• Figure 1.5 Phlebotomus species are the vectors for the Old World Leishmaniasis.
• Figure 1.6 (a) Psamommys obesus, fat sand rat, the established reservoir for L. major. (b) Rock hyrax from which L. tropica has been isolated
• Figure 1.7 Study plan
• Figure 2.1 (A) Satellite image of the District of Jericho in the Eastern Mediterranean region. (B) District of Jericho. (C) Map of the Palestinian governorates according to Palestinian classification.
• Figure 2.2 Schematic representation of the internal transcribed spacer (ITS) in the ribosomal operon with primers amplifying different parts of the spacer.
• Figure 2.3 Map showing the countries of origin for microsatellite analysis. One strain is not shown on the map as the origin was an unknown country in Africa.
• Figure 2.4 Schematic representation of the45GTG marker in which the sequence consist of the repeat region in the middle, (GTG) 12, flanking regions of 5-6 bp on each side, and primer sequence of 20 bp on each side of the flanking regions.
• Figure 3.1 Standardized grading microscopy: Giemsa-stained smears showing the three labeled 5 mm x 5 mm squares. Slide 777 prepared for microscopy with square 3 being purposefully selected to be a darkly stained area. Slide 738 shows the slide after the material is scraped off and DNA-extracted.
• Figure 3.2 Intracellular (a) and extracellular (b) leishmanial amastigotes in a Giemsa-stained smear made from scrapings of cutaneous lesions (bright-field microscopy, x 1000). (c) PCR amplification of the 350 bp ITS1 region represented on 1.5% agarose gel.
• Figure 3.3 Unstained direct tissue smears shown before and after scraping of material for DNA extraction (a) and tissue and blood spotted on filter papers shown before and after punching (b).
• Figure. 3.4 Restriction analysis patterns of the amplified ITS1 digested with HaeIII.
• Figure 3.5 Clinical diagnostic flowcharts for cutaneous leishmanisis
• Figure 3.6 Map 1 shows the distribution of L. major (the first number in blue) and L. tropica (the second number in red) in the District of Jericho, 1997-2004. Map 2 shows the distribution in the City of Jericho and its immediate vicinity, 1997-2002:
• Figure 3.7 (a) Distribution of CL by age and sex in Jericho district, 1994-2004, (b) Distribution of L. major (c) Distribution of L. tropica. The bars represent the age groups arranged in the order shown in (a).
• Figure 3.8 (a) shows the annual distribution of total number of CL cases versus annual rain fall (ARF) (b) L. major cases only (c) L. tropica cases only.
• Figure 3.9 Line graph comparing seasonality for CL, L. major and L. tropica.
• Figure 3.10 Moving average for CL cases in Jericho with a window period of four months, 1994-2004
• Figure 3.11 Spatial distributions of the CL (a), L. major (b) and L. tropica (c) in the district of Jericho in Palestine between 1994 and 2004.
• Figure 3.12. Space-time distribution of the CL (a), L. major (b) and L. tropica (c) cases in the district of Jericho in Palestine between 1994 and 2004.
• Figure 3.13 Shewhart’s Chart for cases of CL in Jericho District, 1994-2004. Mean (m) is 46.25 and SD=29.65.
• Figure 3.14 Different techniques were used to allocate microsatellite variation.
• Figure 3.15. Curve of the mean value Ln likelihood, ln Pr (x\K) showing a plateau starting at K=7.
• Figure 3.16. Estimated population structure shown as plots of Q (estimated membership coefficient for each sample) at K 2 to 7 which is represented by a single vertical line.
• Figure 3.17 Population structure as shown by plots of Q (the estimated membership coefficient for each sample) using five populations predefined according to their geographical origin.
• Figure 3.18 (a) NJ-DPS-boot1000 genotypes
• Figure 3.18 (b) UPGMA-DPS-boot1000 genotypes
• Figure 3.18 (c) UPGMA-Ddm-boot1000 genotypes
• Figure 3.19 The mean number of alleles in the three major geographical regions.