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

• 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

• 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.

• 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.