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

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For comparison of graded microscopy with ITS1-PCR, random sampling method was used, in which, from the 48 scanned and graded slides, 9 microscopically positives and 11 microscopically negatives (~1:1 ratio) were randomly selected.

Seventy five percent (45/60) of the 5mm x 5mm test squares could be scanned and graded for amastigotes microscopically. The remaining 15 could not be evaluated microscopically because of staining failure: in 12 cases stain was too thick and in three cases too thin (Table 3.2). These slides were considered negative (Figure 3.1 & 3.2). Table 3.1 shows that a parasite density of (+) in the graded microscopy was observed in 21 squares, (++) in 2 squares, (-) in 22 squares. Thus, finally 38% (23/60) were positive which can be considered as the sensitivity of microscopy taking in account all samples, and 52% (23/45) skipping the 15 wrongly stained slides.

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Of the 60 squares tested with ITS1-PCR, 52 (87%) were positive for leishmanial DNA (Table 3.3). The rate of positivity for ITS1-PCR among the 45 samples that could be screened by graded microscopy was shown to be 87% (39/45) too.

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.

3.1.1.2 Statistical comparison of sensitivities

When comparing the results obtained by ITS1-PCR with those of graded microscopy, it became obvious that the improvement in diagnostic capability is statistically very significant by using ITS1-PCR (McNemar’s test, P<0.0013). In the first group, or stratum, of the 23 microscopy positive squares 20 were ITS1-PCR positive (87%). Of the remaining three ITS1-PCR negatives, one failed in DNA-extraction, one microscopically showed only one amastigote in the whole square (more than 625 OIF), while the other was not quantified but graded as +1 (Table 3.1). In the second group (22 microscopy negative squares), 18 were ITS1-PCR positive (82%) and 4 remained negative. Out of the 60 squares scanned for amastigotes under x100 oil- immersion bright-field microscopy, 15 (25%) squares could not be evaluated due to bad staining. Of the 12 darkly-stained squares, 11 were ITS1-PCR positive and one was PCR negative. The 3 poorly–stained squares were all ITS1-PCR positive. Thus, 93% (14/15) of the slides where microscopy failed were positive by PCR. The DNA extraction control in the ITS 1-PCR negative probe was positive indicating that the Giemsa-stain did not influence the outcome of the PCR.

3.1.1.3 Diagnostic relevance of ITS1-PCR

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The 45 squares that represented 15 human negative controls were all negative by microscopy and ITS1-PCR. When merging the results of negative controls with those of the patients in a 2 x 2 contingency tables, the sensitivity of microscopy becomes 37% with a positive predictive value of 100%, a specificity of 100% and a negative predictive value of 54% (Table 3.3).

In reality one would try to prepare a new slide in case of staining failure. Thus we separately calculated the data of the sufficiently stained slides (45). Microscopy then comes out a little better: sensitivity becomes 49% with a positive predictive value of 100%, a specificity of 100% and a negative predictive value of 66% as shown in Table 3.3.

Comparing the results with ITS1-PCR, the advantage is obvious. Sensitivity increases to 87% with a positive predictive value of 100%, a specificity of 100% and a negative predictive value of 85% as shown in table 3.3.

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Table 3.1 Outcome of graded microscopy and ITS1-PCR using Giemsa-stained slides obtained from patients.

Microscopy grades

(no. amastigotes)

No. scanned squares

Microscopy results

(positive)

ITS1-PCR positive

++ (2 to 10 per OIF )

2

2 (2)

2

+ (1 per square to 1 per OIF)

21

21 (21)

18**

- (0 per square)

22

22 (0)

18

No microscopical evaluation

possible*

15*

0

14

Total

60

45 (23)

52

* Failure in staining did not allow microscopy.
** One sample is counted negative as a result of extraction failure.

Table 3.2 Comparison of graded microscopy and ITS1-PCR in the 60 square-test group.

PCR (+)

PCR (-)

Total

Microscopy (+)

20

2
1*


23*

Microscopy (-)

18
3 poorly stained**
12 darkly stained**

4

1



37

Total

52

8

60

* One sample is counted negative as a result of extraction failure.
** No microscopical examination possible because of failed staining

Table 3.3 Sensitivity and specificity of microscopy and ITS1-PCR compared to a negative control group

CL (+)

CL (-)

Total

microscopy (+)

22

0

22

microscopy (-)

38* (23)**

45

83* (68)**

Total Microscopy

60* (45)**

45

105 (90)**

PCR (+)

52

0

52

PCR (-)

8

45

53

Total PCR

60

45

105

* including the 15 staining failures
** without staining failures

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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.

Distilled H2O is the negative control; 20ng of L. turanica (known not to infect humans) is used as positive control. A 1 kb ladder is the molecular size marker. Numbers in brackets represent numbers of squares tested, while the (+) is the inhibition control (test sample plus L. turanica).

3.1.2 Clinical diagnosis of cutaneous leishmaniasis: filter paper and unstained smears as potential sampling methods for ITS1-PCR

During the study period, February 1994 and July 2004, 418 skin scrapings spotted on filter papers, 173 unstained tissue smears, 943 Giemsa-stained smears and 270 NNN cultures were collected from patients attending the ICS-Jericho Medical Laboratory. DNA was extracted from spots on filter papers and from material on unstained smears, and diagnosed using ITS1-PCR (Figure 3.3). Giemsa-stained smears were scanned and cultures were checked for promastigotes by bright field microscopy. The species identification for DNA extracted from cultures, filter papers and smears was carried out by digesting the ITS1 PCR product with restriction enzymes HaeIII and MnlI. A CL case was, according to the WHO operational definition, a person showing clinical signs (skin or mucosal lesions) with parasitological confirmation of the diagnosis (positive smear or culture) (WHO Recommended Surveillance Standards, 1999).

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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).

Table 3.4 shows the results using three different gold standards: WHO gold standard based on the operational case definition (WHO Recommended Surveillance Standards, 1999) that considers a CL cases to be the one confirmed by either microscopy or in-vitro culture or both. In the combined gold standard we considered a CL case to be the one confirmed by at least one of the four methods used, direct smear microscopy, in-vitro culture, PCR using FP or PCR using US. The third gold standard is the broad gold standard which considers CL case to be the one showing lesion(s) in an endemic area with compatible clinical history.

From the 943 patients in the database, 64 had been tested using all four methods known as matched cases. Sensitivity is the number of positive results by a certain method over the number of CL cases as defined by the selected gold standard. Using the WHO gold standard, the sensitivity of the ITS1-PCR by FP is 78% while it increases when using US to 86%. Similar sensitivities for FP (77%) and US (84%) are obtained using the combined gold standard. The sensitivity of the microscopy and in-vitro culture using the combined gold standard was 65% and 50%, respectively.

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In the broad gold standard using the 64 matched cases the sensitivity (rate of positivity) of the microscopy, in-vitro culture, PCR-FP and PCR-US were 55, 41, 66 and 72%, respectively.

A total of 119 (44%) strains were isolated by culturing dermal tissue aspirates in either rabbit blood–agar semisolid medium or NNN medium. Of the 418 clinical samples spotted on filter papers and checked by ITS1-PCR, 219 were positive, indicating a 52.4% positivity rate. Of the 173 unstained smears, 97 (56%) were positive by PCR (Table 3.4). The lowest rate of positivity was recorded by microscopy (42%).

Amplification of the 300–350 bp ITS1 amplicon and its subsequent digestion with the endonuclease HaeIII did enable detection of Leishmania parasites and identification of the infecting species. The restriction patterns obtained for L. major, L. tropica and L. infantum, another species of Leishmania present in the Middle East, were clearly different (Figure 3.4). Of the 298 cases of CL from FP, US and in-vitro culture tested by PCR-RFLP, 181(60.7%) contained DNA of L. major, 106 (35.6%) DNA of L. tropica while 11 (3.7%) of the FP remained unidentified.

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Table 3.4 Sensitivity of the four diagnostic methods using 3 types of ‘gold standard’ (n=64 cases)

WHO (n=64)

Combined (n=64)

Clinical (n=64)

(n=943)

Microscopy

(Smear)

----

65%

55% (35/64)

42.1% (397/943)

In-vitro Culture

----

50%

41% (26/64)

44% (119/270)

PCR-ITS1-FP

78%

77%

66% (42/64)

52.4%(219/418)

PCR-ITS1-US

86%

84%

72% (46/64)

56%(97/173)

Figure. 3.4 Restriction analysis patterns of the amplified ITS1 digested with HaeIII.

Restriction pattern of reference cultured strains: Inf, Tro and Maj represent L. infantum MCAN/IL/97/LRC-L717 (184, 72, 55 bp), L. tropica, MHOM/SU/80/K28 (185, 57, 53, 24 bp) and L. major, MHOM/TM/82/Lev (203, 132 bp), respectively. The next 7 numbers represent DNA samples from blood and tissue blotted on filter papers isolated fromdifferent CL patients living in the District of Jericho: L. tropica= 465, 680, 686 and 690; L. major=431, 487 and 488.

The clinical diagnostic scheme for cutaneous leishmaniasis as shown in Figure 3.5 has proved to be effective for diagnosis and species identification of CL cases in Jericho and its vicinity. Yet, as clinical samples contain all possible contaminants and inhibitors, a strong control panel (Table 3.5) was utilized.

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Table 3.5 The battery of controls used in the PCR to ensure validity of results

Control

Sample

Comment

1

Negative control

dd H2O

Check for contamination

2

Positive control

Leishmania turanica

Efficiency of amplification

3

Inhibition control

Template DNA + L. turanica

To check for inhibitors i. e hemoglobin, stain, immersion oil, etc.

4

House-keeping gene

Template DNA (β-Actin primers)

Check for DNA integrity and DNA extraction failure (true negativity test).

Figure 3.5 Clinical diagnostic flowcharts for cutaneous leishmanisis

3.2 Molecular epidemiology

3.2.1 L. major vs. L. tropica in Jericho

All classical stages of the development of leishmaniomas were seen among the cases: from small erythematous papules through nodules and to ulcerative lesions; whereas unusual clinical manifestations such as the sporotrichoid pattern, i.e., subcutaneous nodules developing along lymphatics, hyperkeratosis, i.e., thick adherent scale and leishmaniasis recidivans also known as lupoid leishmaniasis were not. It was very difficult and even impossible to discern if cases were caused by L. major or L. tropica by the clinical picture. However, three severe cases led to the suspicion that they were not caused by L. major, as they were different from the cases of CL generally seen in the vicinity of Jericho. The three cases displayed common features. They all presented a single lesion, two of which were on the nose and one on the chin. Development was slow and all three only sought medical advice 6–12 months after the first appearance of the lesion. All three lesions resisted antimony treatment and took 6 months or more to heal leaving scars. The lesion on the chin and one of those on the nose were caused by L. tropica. After this, ITS1-PCR and RFLP results were continuously spotted on maps to show the distribution pattern of L. major and L. tropica in Jericho and its vicinity which showed that L. major is predominant in the alluvial soil/sand and chenopodiaceae-rich plains such as Jericho city and A’uja village, while the L. tropica is close to rural villages surrounded by rocky areas such as Zubaidat village (Figure 3.6). Jericho City contains a majority of L. major (76%) and the rest (24%) is L. tropica (Figure 3.6). A total of 94 of the 102 patients (92%), infected by L. tropica and answered the question whether they had been travelling out of the vicinity of Jericho during the last 3 months, claimed that they had not. The other eight cases were people who came either from the hilly regions around Jerusalem or cities like Ramallah and Jenin. Eighty-six per cent (149) of the 174 patients, infected by L. major and answered the question, 149(86%) also said they had stayed in the vicinity of Jericho during the last 3 months.

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Most of the patients had single lesions on the cheek or upper and lower extremities (Table 3.6) and sought medical intervention within 3 months of the appearance of the lesion. As Jericho is a hyperendemic region, the inhabitants are well aware of CL, which is also partially owing to the education campaigns conducted by Islah Medical Center in Jericho over the last decade.

Table 3.6 Comparison of the clinical features of CL cases caused by L. major and L. tropica in the district of Jericho

L. major

L. tropica

Duration (months)

≤ 3

143 (95.3%)

75 (78.1%)

3-6

7 (4.7%)

12 (12.5%)

> 6

0 (0.0%)

9 (9.4%)

Total*

150 (100)

96 (100)

Location

Forehead

25 (10.5%)

12(8.9%)

Chin

11(4.6%)

15(11.2%)

Eye

10(4.2%)

8(5.9%)

Nose

9(3.8%)

13(9.7%)

Ear

7(2.9%)

5(3.7%)

Cheek

43(18.1%)

36(26.9%)

Arm

76(32.1%)

30(22.4%)

Leg

49(20.7%)

13(9.7%)

Neck

7(2.9%)

2(1.5%)

Total *

237 (100)

134 (100)

No. of lesions

1

74 (41.2%)

56 (56.6%)

2

51 (28.7%)

24(24.3%)

≥3

54 (30.1%)

19(19.1%)

Total *

179 (100)

99 (100)

* Figures represent the total number of patients who answered question related to each clinical feature.

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:

The red triangles represent cases caused by L. tropica and the blue ovals cases caused by L. major.

3.2.2 Sex-age group distribution

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The total number of CL cases as well as those caused by either L. major or L. tropica was plotted against demographic variables, sex and age (Figures 3.7a, b and c). Children (<14) are most prone to infection with age group 0-4 being more exposed than others. The male youth (20-24) also form another risk category. In females the age group 30-34 shows an increased incidence of L. tropica cases. L. tropica has no sex tendency (M: F- 50:54) unlike the L. major (M: F-107:70) and total CL cases (M: F- 290:221). Children (<14) who have increased incidence rate show no defined pattern for sex distribution, yet the males in their early twenties (20-24) seem to be more prone to be infected than females.

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).

3.2.3 Annual rain fall (ARF) and CL

Number of cases of CL and annual rain fall (ARF) in mm was plotted versus year. Spearman’s correlation was used to determine if there existed a significant relationship between the ARF and number of CL cases. We found a very low correlation between ARF and CL (n=11, rs= 0.300; P= 0.05) (Figure3.8-a); correlation was even lower for L. major (n= 7; rs= 0.270; P=0.05) (Figure 3.8-b). Also for L. tropica cases the correlation was insignificant (n=7; rs=-0.324; P=0.05) (Figure 3.8-c). For the total number of CL cases, which includes L. major and L.tropica as well as undetermined cases, the study period was between 1994 and 2004, while for the cases of L. major and of L. tropica the period was between 1998 and 2004 because CR diagnostics was first introduced into Jericho in 1998.

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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.

3.2.4 Seasonality of CL

Figure 3.9 shows that the seasonality of all CL cases as well as of those due to either L. major or L. tropica was comparable during this study. Cases always start to peak from October and declines in April.

One can notice that annual distribution of all cases of CL peaked three times: 1995, 2001 and 2004 (Figure 3.8a). However, the patterns of L. major and L. tropica cases starting from 1998 were different. L. major peaked in 2004 while L. tropica did so in 2001. This figure also demonstrates L. tropica to exist in Jericho area as early as 1998.

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Moving average is an indicator that shows the average value number of cases of CL over a period of time. This useful analytical tool aims at spotting trends in distribution. It is common in the stock markets. Moving average with a window period (time span) of four months (season) has been used to spot trends in the distribution of CL cases in Jericho area. Moving average (Figure 3.10) confirms the possibility of having more than one peak a year, a major (primary) and minor (secondary) peaks such as 1994 and 2002. It also shows that three major peaks took place during the study period (1994-2004): 1995, 2001 and 2004. The 2004 peak is unprecedented. However, a peak is witnessed every year except in 1997-1998 season where the number of cases was low, hence, a peak was absent. As a rule of thumb, the sensitivity for detecting trends and patterns of distribution of CL can be increased by shortening the time span. The longer the time span, the less sensitive or the more smoothed the moving average will be. Nevertheless, this largely depends on the incidence rate of the infection. With increasing incidences the time span can be reduced down to one month or even weeks, which is impossible in the case of rare infections.

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

3.3 Public health surveillance and cluster analysis

3.3.1 Descriptive data of CL cases

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This part of the study is an observational epidemiological investigation of CL incidence in Jericho-Palestine. The total population at risk was 31,089 in 1997 to 40,909 in 2004 (Palestinian Central Bureau of Statistics, 2005) (Table 3.7).

During the period under investigation (3.2.1994 and 12.8.2004) PCR-ITS1 and RFLP (restriction fragment length polymorphism) were used to genotype CL cases in Jericho area. The number of geographically mapped cases was 181 L. major, 100 L. tropica and 492 CL (L. major, L. tropica and undetermined cases). Undetermined cases are CL cases which were not typed to be L. major or L. tropica either due to failure of genotyping by RFLP or lack of samples for further testing.

Relative risk was adjusted for seasonality of CL infection, in which during the summer the number of cases is reduced to half. Although large proportion (318/479) of the total cases of CL was children below 14, age-adjustment was not conducted because this pattern was uniform in the whole study area during the study period.

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Table 3.7 Geographical and population data of the 9 study areas in the District of Jericho-Palestine

Location

Latitude

Longitude

population

1997

2004

1.

Nabi-Musa (1)

31.79604

35.43941

150

200

2

Aqbat-Jabr-RC

31.83960

35.44545

4521

5949

3

Jericho

31.85909

35.46404

14551

19147

4

Nuaimeh (2)

31.89170

35.43568

830

1192

5

A'uja

31.94818

35.46125

2858

3761

6

Fasayil

32.02624

35.44339

641

844

7

Jiftlik

32.14683

35.48001

3136

4127

8

Zubaidat

32.17590

35.53135

955

1257

9

Ein –assultan RC

31.87717

35.44692

1451

1909

(1)Includs Khal-al-ahmar, (2)includes Duke Village.

3.3.2 Purely spatial analysis, adjusted for season

Cluster analysis by SaTScan examines geographic variations in a ten-year period (1994-2004) using both purely spatial and space-time models to determine whether observed fluctuations in incidence rates are random or whether fluctuations represent statistically significant deviations from randomness.

The purely spatial scan statistic which is time independent was performed for all CL cases including cases of L. major, L. tropica and non-genotyped. Table 3.8 shows numbers of observed and expected cases, relative risks (RR) and p-values for the purely spatial analysis of cases adjusted for season. Expected case counts can be calculated by dividing the observed count by the RR. Using Poisson probability model, time precision of one year, number of Monte Carlo replications of 1000, maximum spatial cluster of ≤ 50 % of population and p-value=0.05, the purely spatial statistic revealed 4 statistically significant (p=0.001) clusters of high CL case numbers. Zubaidat village is shown to be the most likely significant cluster. Other secondary clusters of statistical significance were the villages of A’uja, Fasayil and the Bedouin encampments in Nabi-Musa and Khan-al-ahmar on the Jerusalem-Jericho highway. Nuaimeh village is another, but not statistically significant, cluster as the risk is increased there by 69.4%. Figure 3.11 summarizes the purely spatial season-adjusted analysis of CL mapped in terms of significance. Jericho being the most populous (20,000) area in this study did not form a cluster and the RR was 0.692 (data not shown).

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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.

Area

Observed

Expected

RR

p-value

CL

1. Zubaidat

61

16.48

4.084

0.0010

2. Nabi-Musa

29

3.58

8.106

0.0010

3. Fasayil

95

10.83

2.581

0.0010

3. A’uja

95

48.36

1.471

0.0010

4. Nuaimeh

23

13.85

1.694

0.1310

L. major

1. Fasayil

17

4.00

4.607

0.0010

1. A’uja

38

17.87

2.438

0.0010

2. Nuaimeh

11

4.32

2.234

0.1110

L. tropica

1. Zubaidat

34

3.35

14.866

0.0010

2. Nabi-Musa

17

0.73

27.962

0.0010

Fasayil and A’uja villages were shown to be the most likely statistically significant cluster for L.major with 4.607 and 2.438 times more cases than expected, respectively. Nuaimeh, as in CL, was a border line cluster with RR of 2.234 but a p=0.1110 (Table 3.8, Figure 3.11).

Two L. tropica clusters were found, the major cluster in Zubaidat village, in the far north, and a secondary cluster in Nabi-Musa/Khan-al-ahmar Bedouin encampment in the south.

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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.

The most likely clusters are shown in red circles while the secondary clusters are shown in blue with p-value= 0.0010. The two small blue circles in (a) and (b) have border line significance.

3.3.3 Space-time analysis, adjusted for season

Table 3.9 and Figure 3.12 show space-time, season-adjusted results of SaTScan for CL, L. major and L. tropica cases with clusters shown as circular windows. These clusters are flexible both in location and size and their exact borders remain uncertain (Kulldorff et al., 1997). Also, each of them has maximum 50% of the number of cases in that area. This maximum size circle is ideal as it detects both small and large clusters (Kulldorff et al., 1998 b). Four areas are found to be significant clusters for CL. Season-adjusted space-time analysis showed more clusters than the purely spatial did. Nuaimeh became for example a significant cluster with risk increasing by 100%. Jericho city in 1995 had less observed cases (200) than expected (245) with RR=0.686 indicating about 31% fewer cases than expected and thus did not form a significant cluster. It was only between 2000 and 2004 that Zubaidat, Fasayil, A’uja, Nabi-Musa and Nuaimeh formed apparent clusters.

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.

Area

Cluster Time

Observed

Expected

RR

p-value

CL

1. Zubaidat

2001–2004

61

16.55

4.066

0.0010

2. Fasayil

2003-2004

27

10.87

2.569

0.0010

2. A’uja

2003-2004

68

48.57

1.464

0.0010

3. Nabi-Musa

2000-2003

29

3.60

8.492

0.0010

4. Nuaimeh

2003-2004

23

11.74

2.006

0.0020

5. Jericho

1995

200

245.81

0.686

0.7620

L. major

1. Fasayil

2003-2004

17

4.00

4.587

0.0010

1. A’uja

2003-2004

38

17.87

2.426

0.0010

2. Nuaimeh

2001-2004

11

4.32

2.646

0.0030

3. Jericho

2001-2004

84

90.43

0.867

0.0140

L. tropica

1. Zubaidat

2001-2002

34

3.36

14.799

0.0010

2. Nabi-Musa

2000-2004

17

0.73

27.769

0.0010

3. Aqbat-Jabr-RC

2001-2002

16

15.63

1.029

0.015

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Of the L. major clusters, Fasayil and A’uja remained, as in purely spatial results, the most likely significant cluster. As stated above, Nuaimeh became a significant cluster with 2.646 fold increase in relative risk indicated by more cases than expected between 2001 and 2004. Within the same period, Jericho city had, for the first and only time, almost as much observed cases as expected: 84 observed cases and an RR of 0.867, indicating approximately 13% fewer cases than expected. For L. tropica, the two clusters identified in the spatial analysis remained as they are. In addition, the Aqbat-Jabr refugee camp appeared as cluster in 2001-2002 with very slight excess of observed cases (16 cases) compared to expected cases (15.63).

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.

The most likely clusters are shown in red circles while the secondary clusters are shown in blue with p-value= 0.0010. The time period shown close to each circle represents the time frame during which this geographical site was considered a statistically significant cluster.

3.4 Shewhart’s Plot: Early warning system

Shewhart Plot (Levy and Jennings, 1950; Westgard et al., 1981) is another graphical display that depicts trends and peaks of leishmaniasis, in addition to the moving average in (3. 2. 4) and SaTScan (3. 3) techniques mentioned above. Mean () and standard deviation were calculated for the annual total number of cases (14, 49, 27, 11, 39, 42, 31, 71, 46, 65 and 116). Grubb’s test was conducted to check for outliers at the significant level of p=0.05 and none were detected. The mean () was 46.25 and standard deviation was 29.65. With lower and upper limits being 1SD below or above the mean, cases in 1994 (14) and 1997 (11) were below mean whereas cases in 2004 were exceptionally above mean (116) indicating an outbreak or peak (Figure 13.3). The year 2001 was close to becoming a peak.

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Figure 3.13 Shewhart’s Chart for cases of CL in Jericho District, 1994-2004. Mean (m) is 46.25 and SD=29.65.

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

Ten microsatellite markers were isolated from a genomic library of L. major using the Basic Local Alignment Search Tool (BLAST) (http://www.sanger.ac.uk/Projects/L_major/) based on loci located on five chromosomes (Table 2.3). Even if found on the same chromosome, loci were located at long distances from each other and thus considered to be independent. Five of the markers represented trinucleotide repeats (GTG), 4 different dinucleotide stretches and one was a tetranucleotide repeat. PCR primers of 20 bp size were designed at a distance of five nucleotides from both ends of the microsatellite repeats to make insertions/deletions in the flanking regions unlikely. The ten markers were tested using 106 strains of L. major (Table 3.11) from 19 countries in Asia and Africa (Figure 2.3). To search for polymorphism, the PCR products were screened on PAGE and/or 3.5% MetaPhor agarose gel electrophoresis, and sized by capillary electrophoresis (CE) using an automated sequencer (Figure 3.14). The degree of polymorphism differed in the markers from a minimum of 3 in markers 4GTG and 1GC to maximal 10 alleles in markers 45GTG, 71AT and 1CA within the strains analyzed (Table 3.10).

Figure 3.14 Different techniques were used to allocate microsatellite variation.

(a) A PAGE run showing two markers 4GTG, 27GTG with 10 bp ladder as molecular size standard. (b) 3.5% MetaPhor agarose gel: Lanes 1-8 represent 27GTG marker with the L. major Friedlin reference strain in lane 6. Lanes 9-20 represent 39GTG marker with the Friedlin reference strain in lane 20. (c) CE run for L. major 74 from Jericho -Palestine using Beckman coulter CEQ8000. The blue peak shows the fragment size for the 1GACA marker and the green peak for the 39GTG marker. The red peaks represent the size marker

↓72

Table 3.10 shows that the number of alleles varies between loci, ranging from 3 as in 4GTG and 1GC to 10 as in 45GTG, 71AT and 1CA. Increased degree of inbreeding within loci (FIS, Fis or f) ranging from 0.874-1.00 (P<0.05) with a mean of 0.976 is witnessed.

Observed (Ho) heterozygosity among loci was in all populations extremely low compared with the heterozygosity (He) expected under assumption of Hardy–Weinberg equilibrium. The greatest difference between He and Ho is in the locus 45GTG (0.784). The greater this difference, the higher was the corresponding inbreeding coefficient (Fis) as Fis = 1-Ho/He (Table 3.10)

The mean number of alleles per locus (A) within each population ranged from 3 to 10 with a mean of 6.7. The highest (A) is noticed in the 45GTG, 71AT and 1CA indicating increased heterogeneity in these loci.

↓73

Table 3.10 Characterisation of the 10 microsatellite markers used for population studies in L. major.

Locus

No.

Alleles§

Descriptive statistics**

n

A

He

Ho

Fis

4GTG

3

105

3

0.362

0.0095

0.974

27GTG

6

105

6

0.631

0.000

1.000

36GTG

7

101

7

0.711

0.000

1.000

39GTG

7

93

7

0.737

0.011

0.986

45GTG

10

104

10

0.784

0.000

1.000

1GC

3

103

3

0.330

0.000

1.000

28AT

8

104

8

0.533

0.029

0.946

71AT

10

105

10

0.604

0.076

0.874

1GACA

3

103

3

0.548

0.009

0.982

1CA

10

102

10

0.509

0.000

1.000

All

67

102.5

6.7

0.575

0.014

0.976

§ Number of alleles per locus as shown by FSTAT software.
** (n) Average sample size, (A) average number of alleles per locus, (He) expected proportion of heterozygotes, (Ho) observed proportion of heterozygotes (under hardy Weinberg equilibrium), inbreeding coefficient (Fis), were obtained using GDA software.

3.5.2 Assignment of multilocus microsatellite profiles to the strains of L. major under study

The sizes of the 10 microsatellite markers were estimated for the 106 strains of L. major studies herein by either using the fragment analysis tool of the Beckman/Coulter sequencer by comparing with reference strains. The strain MHOM/IL/1980/Friedlin which was sequenced in the Leishmania genome project was included in all experiments and served as reference for calculating of the repeat numbers. A multilocus microsatellite profile, also called genotype later on, which summarizes the repeat numbers obtained for the 10 markers, was assigned to each of the 106 strains analysed as some of them were uniquely represented by single strains (Table 3.11). A total of 67 (including the L. tropica out group) different genotypes were obtained for these strains. Eleven isolates from Termiz-UZ were represented by 1 genotype, 2 strains from Mubarek-UZ were represented by 1 genotype and another 7 from the same area were represented by 1 genotype, 2 strains, one from UZ and the other from TM were, also, represented by 1 genotype, 2 strains from Jericho were represented by 1 genotype, 9 isolates from Jericho, Arava, and Qetziot (Negev) were represented by 1 genotype, another 4 strains from Jericho were represented by 1 genotype, 2 strains from Beersheba were represented by 1 genotype, 7 strains from UZ, TM and KZ were represented by 1 genotype and, finally,1 genotype represented 2 strains from Negev and 1 from Sinai-Egypt. Only individual profiles were found for the African strains.

Table 3.11 The multilocus microsatellite profiles of the strains of L. major analysed in this study

#

WHO code

Lab. code

Site/Country

Source

4 GTG

27 GTG

36 GTG

39 GTG

45 GTG

1 GC

28 AT

71 AT

1 GACA

1 CA

1

MHOM/SU/1973/5ASKH

MAJ-01

Ashgabad- Turkmenistan

LRC

7

8

10

2

4

7

9

10

6

14

2

IPAP/TM/1991/M-97

MAJ-26

Tezeel- Turkmenistan

MARTS

7

8

10

2

4

7

9

10

6

14

3

MHOM/TM/1987/Rod

MAJ-39

Bakharden- Turkmenistan

MARTS

7

8

10

2

4

7

9

10

6

14

4

MRHO/TM/1995/T-9537

MAJ-35

Serax- Turkmenistan

MARTS

7

8

--

2

4

7

9

10

6

14

5

MHOM/TM/1982/Lev

MAJ-36

Geok-Tepe- Turkmenistan

MARTS

7

8

10

2

4

7

8/10

10

6

14

6

MHOM/TM/1986/ER

MAJ-37

Tejen- Turkmenistan

MARTS

7

8

10

2

4

7

9

10/11

6

14

7

MHOM/UZ/1999/Nuriya

MAJ-25

Mubarek-Uzbekistan

LRC

7

8

10

2

4

7

9

10

6

14

8

MHOM/UZ/1987/BUR

MAJ-27

Karaulbasar-Uzbekistan

MARTS

7

8

10

2

4

7

9

10

6

14

9

MRHO/UZ/1987/KK29

MAJ-29

Takhtakupyr-Uzbekistan

MARTS

7

8

10

2

4

7

9

10

6

14

10

MHOM/UZ/87/Kurb

MAJ-33

Karshi Uzbekistan

MARTS

7

8

--

2

4

7

9

10

6

14

11

MHOM/UZ/2002/Isv M-22h

MAJ-79

Mubarek Uzbekistan

ISAEV

6

8

10

2

4

7

10

13

6

14

12

MHOM/UZ/2002/Isv M-17h

MAJ-80

Mubarek Uzbekistan

ISAEV

7

8

10

2

4

7

10

13

6

14

13

MHOM/UZ/2002/Isv M-12h

MAJ-81

Mubarek Uzbekistan

ISAEV

7

8

10

2

4

7

10

13

6

14

14

MHOM/UZ/2002/Isv M-10h

MAJ-82

Mubarek Uzbekistan

ISAEV

7

8

10

2

4

7

9

13/16

6

14

15

MHOM/UZ/2002/Isv M-30h

MAJ-85

Mubarek Uzbekistan

ISAEV

7

8

10

2

4

7

10

13

6

14

16

MHOM/UZ/2002/Isv M-29h

MAJ-86

Mubarek Uzbekistan

ISAEV

7

8

10

2

4

7

9

13

6

14

18

MHOM/UZ/2002/Isv M-28h

MAJ-87

Mubarek Uzbekistan

ISAEV

7

8

10

2

4

7

10

13

6

14

19

MHOM/UZ/2002/Isv M-27h

MAJ-88

Mubarek Uzbekistan

ISAEV

7

8

9

2

4

7

9/10

13

6

14

20

MHOM/UZ/2002/Isv M-26h

MAJ-89

Mubarek Uzbekistan

ISAEV

7

8

9

2

4

7

9/10

13

6/7

14

21

MHOM/UZ/2002/Isv M-25h

MAJ-90

Mubarek Uzbekistan

ISAEV

7

8

10

2

4

7

10

13

6

14

22

MHOM/UZ/2000/Isv T-03h

MAJ-102

Termez Uzbekistan

ISAEV

7

8

9

2

4

7

9

13/16

6

14

23

MHOM/UZ/1998/Isv M-09h

MAJ-103

Mubarek Uzbekistan

ISAEV

7

8

10

2

4

7

10

13

6

14

24

MHOM/UZ/1998/Isv M-08h

MAJ-104

Mubarek Uzbekistan

ISAEV

7

8

9

2

4

7

9

--

6

14

25

MHOM/UZ/1998/Isv M-04h

MAJ-105

Mubarek Uzbekistan

ISAEV

7

8

10

2

4

7

9

13

6

14

26

MHOM/UZ/1998/Isv M-01h

MAJ-107

Mubarek Uzbekistan

ISAEV

7

8

10

2

4

7

10

13

6

14

27

MHOM/UZ/1998/Isv M-02h

MAJ-115

Mubarek Uzbekistan

ISAEV

7

8

9

2

4

7

--

16

6

14

28

MHOM/UZ/03/Th21

MAJ-150

Termez Uzbekistan

ISAEV

7

8

9

2

4

7

9

13

6

14

29

MHOM/UZ/03/Th24

MAJ-151

Termez Uzbekistan

ISAEV

7

8

9

2

4

7

9

13

6

14

30

MHOM/UZ/03/Th29

MAJ-152

Termez Uzbekistan

ISAEV

7

8

9

2

4

7

9

13

6

14

31

MHOM/UZ/03/Th32

MAJ-153

Termez Uzbekistan

ISAEV

7

8

9

2

4

7

9

13

6

14

32

MHOM/UZ/03/Th35

MAJ-154

Termez Uzbekistan

ISAEV

7

8

9

2

4

7

9

13

6

14

33

MRHO/UZ/03/Th6g

MAJ-155

Termez Uzbekistan

ISAEV

7

8

9

2

4

7

9

13

6

14

34

MRHO/UZ/03/Th20g

MAJ-156

Termez Uzbekistan

ISAEV

7

8

9

2

4

7

9

13

6

14

35

MRHO/UZ/03/Th23g

MAJ-157

Termez Uzbekistan

ISAEV

7

8

9

2

4

7

9

13

6

14

36

MRHO/UZ/03/Th38g

MAJ-158

Termez Uzbekistan

ISAEV

7

8

9

2

4

7

9

13

6

14

37

MRHO/UZ/03/Th44g

MAJ-159

Termez Uzbekistan

ISAEV

7

8

9

2

4

7

9

13

6

14

38

MRHO/UZ/03/Th37g

MAJ-160

Termez Uzbekistan

ISAEV

7

8

9

2

4

7

9

13

6

14

39

MRHO/SU/59/NealP

MAJ-123

??? Central Asia

MARTS

7

8

9

--

5

7

9

11

6

14

40

MRHO/KZ/1988/Tur-27R

MAJ-38

Turkestan

MARTS

7

8

10

2

4

7

9

10

6

14

41

MHOM/WA/87/NEL2

MAJ-02

???-East and West Africa

KIT

6

9

7

3

10

8

10

9

7

23

42

MTAT/KE/????/NLB089A

MAJ-06

Marigat-Kenya

LRC

6

9

7

3

10

7

13

13

7

17

43

MTAT/KE/195?/T4

MAJ-22

Baringo-Kenya

LRC

6

9

7

3

10

7

13

7

7

17

44

MHOM/SN/????/DK99

MAJ-44

Keur Moussa-Senegal

LRC

6

10

7

10

18

8

10

11

7

17

45

MHOM/SN/????/106

MAJ-45

Podor des Fleuve- Senegal

LRC

6

10

7

10

18

8

10

13

7

17

46

MHOM/SN/2000/DK74

MAJ-58

 Senegal

LSHTM

6

9

6

2

10

7

12

13

7

18

47

MHOM/SN/1996/LEM3181

MAJ-48

Thies- Senegal

LEMPP

6

10

7

10

18

7

5

14

8

8

48

MHOM/SN/1996/DPPE23

MAJ-49

Thies- Senegal

LEMPP

6

10

7

10

14

7

10

13

8

4

49

MHOM/MA/1992/LEM2463

MAJ-46

Ain Beni Mathar-Morocco

LEMPP

6

10

--

--

18

--

8

9

6

--

50

MHOM/MA/1995/LEM2983

MAJ-47

El Rachidia- Morocco

LEMPP

6

9

9

2

10

7

10

13

7

28

51

MHOM/MA/1981/LEM265

MAJ-57

 Marocco

LSHTM

6

9

9

2

10

7

10

13

8

28

52

MHOM/BF/1996/LIPA538

MAJ-50

Ouagadougou-Burkina Faso

LEMPP

6

10

8

10

18

7

9

15

8

8

53

MHOM/BF/1998/LPN166

MAJ-55

Ouagadougou- Burkina Faso

LEMPP

6

10

7

10

19

7

11

15

8

8

54

MHOM/TN/1997/LPN162

MAJ-51

Sfax-Tunisia

LEMPP

6

10

9

--

18

7

11

12

6

14

55

MHOM/TN/1994/GLC7

MAJ-54

Gafsa-Tunisia

LEMPP

6

10

9

--

18

7

8

12

6

14

56

MHOM/DZ/1998/CRE95

MAJ-52

M'sila-Algeria

LEMPP

6

10

9

--

18

7

8

10

6

14

57

MHOM/DZ/1998/LPS13

MAJ-53

Biskra- Algeria

LEMPP

6

10

9

--

18

7

5/8

9/12

6

14

58

MHOM/TR/1993/HA

MAJ-41

Manisa-Turkey

K.P. Chang

6

10

7

10

18

8

10

15

7

22

59

MHOM/TR/1993/SY

MAJ-42

Aydin-Turkey

K.P. Chang

6

10

7

10

18

8

10

15

7

22

60

MHOM/TR/1994/HK

MAJ-43

Kars-Turkey

K.P. Chang

7, 5

9

9

9

12

7

9

13

7

14

61

MHOM/IL/1967/Jericho

MAJ-09

Jericho area

LRC

7

9

9

9

12

7

9

13

7

14

62

MHOM/IL/1986/Blum

MAJ-10

Jericho area

LRC

7

9

9

9

12

7

9

13

7

14

63

MHOM/IL/1990/LRC-L585

MAJ-12

Jericho area

LRC

7

9

9

9

12

7

9

13

7

14

64

MHOM/PS/1998/PAL1

MAJ-19

Jericho area

ISLAH

7

9

9

9

12

7

9

13

7

14

65

MHOM/IL/2000/L779

MAJ-23

Jericho area

LRC

7

9

9

9

12

7

9

13

7

14

66

MHOM/PS/2000/ISLAH503

MAJ-24

Jericho area

ISLAH

7

9

9

9

12

7

9

13

7

14

67

MHOM/PS/1998/PAL2

MAJ-20

Jericho area

ISLAH

7

9

10

9

12

7

9

13

7

14

68

MHOM/PS/1998/PAL3

MAJ-21

Jericho area

ISLAH

7

9

10

9

12

7

9

13

7

14

69

MHOM/IL/1980/Friedlin

MAJ-13

Jericho area

LRC

7

9

9

9

12

7

9

13

7

14

70

MHOM/PS/2000/ISLAH 506

MAJ-66

Jericho area

ISLAH

7

9

10

9

12

7

9

13

7

14

71

MHOM/PS/2001/ISLAH 657

MAJ-70

Jericho area

ISLAH

7

7

9

9

12

7

9

13/16

7

14

72

MHOM/PS/2001/ISLAH 658

MAJ-71

Jericho area

ISLAH

7

9

10

9

12

7

9

13

6

14

73

MHOM/PS/2001/ISLAH 659

MAJ-72

Jericho area

ISLAH

7

9

10

9

12

7

9

13

7

14

74

MHOM/PS/2002/ISLAH 662

MAJ-73

Jericho area

ISLAH

7

9

9

12

12

7

9

13

7

14

75

MHOM/PS/2002/ISLAH 697

MAJ-76

Jericho area

ISLAH

7

9

9

9

12

7

9

13/16

7

14

76

MHOM/PS/2002/ISLAH690

MAJ-77

Jericho area

ISLAH

7

9

9

9

12

7

9

13/16

7

14

77

MHOM/PS/2002/ISLAH 691

MAJ-78

Jericho area

ISLAH

7

9

9

9

12

7

10

16

7

14

78

MHOM/PS/2003/ISLAH 718

MAJ-99

Jericho area

ISLAH

7

9

10

9

9

7

9

11

7

14

79

MHOM/PS/2001/ISL600

MAJ-116

Jericho area

ISLAH

7

9

8

9

12

7

`8/9

13

7

14

80

MHOM/PS/2003/ISL720

MAJ-120

Jericho area

ISLAH

7

9

8

12

7/9

7

9

13

7

14

81

MHOM/IL/03/LRC-L962

MAJ-111

Negev???

LRC

7

9

8

9

12

7

9

13

7

14

82

MHOM/IL/2003/LRC-L949

MAJ-98

Jericho area

LRC

7

9

9

9

9

7

9

13

7

14

83

MHOM/IL/03/LRC-L964

MAJ-113

Beer Sheva

LRC

7

9

8

9

12

7

9

13

7

14

84

MHOM/IL/2003/LRC-L965

MAJ-114

Negev desert-Qeziot

LRC

7

9

9

9

12

7

9

13

7

14

85

MPSA/IL/1983/PSAM398

MAJ-16

Arava

LRC

7

9

9

9

12

7

9

13

7

14

86

IPAP/IL/1984/Uvda

MAJ-11

Negev-Uvda

LRC

7

9

9

12

9

8

9

13

7

19

87

IPAP/IL/1998/LRC-L746

MAJ-14

Negev-Uvda

LRC

7

9

9

12

9

8

9

13

7

19

88

IPAP/IL/1998/LRC-L464

MAJ-15

Negev-Uvda

LRC

7

9

9

12

9

7

9

13

7

19

89

MHOM/IL/2001/LRC-L846

MAJ-74

Jericho area

LRC

7

9

9

12/14

12

8

9

13

7

19

90

MHOM/IL/2003/LRC-L952

MAJ-94

Negev-Queziot

LRC

7

9

9

12

10

8

9

13/16

7

19

91

MHOM/IL/02/LRC-L946

MAJ-97

Negev-Yerucham

LRC

7

9

8

12

8

8

10

8

7

19

92

MHOM/IL/2002/LRC-L940

MAJ-101

Negev-Yerucham

LRC

7

8

9

12

12

8

9

16

7

19

93

MHOM/IL/2003/LRC-L958

MAJ-108

Arava-En Yahaf

LRC

7

9

5

12

9

8

9

13

7

19

94

MHOM/IL/2002/LRC-L862

MAJ-69

 Negev???

LRC

7

9

5

12

9

8

9

16

7

19

95

MHOM/IL/03/LRC-L960

MAJ-110

Negev-Revivim

LRC

7

9

8

12

9

8

9

13

7

19

96

MHOM/IL/03/LRC-L963

MAJ-112

Negev-Shifta

LRC

7

9

8

12

9

8

9

14

6

19

97

MHOM/IL/2003/LRC-L1000

MAJ-122

Qaliya, Dead Sea

LRC

7

9

8

12

9

8

9

11

7

19

98

MHOM/EG/1984/LRC-L505

MAJ-18

Sinai-Egypt

LRC

7

9

--

12

9

8

9

13

7

19

99

IPAP/EG/1989/RTC-13

MAJ-17

Sinai-Egypt

LRC

7

9

9

12

9

8

9

13

7

19

100

MRHO/IR/1976/vaccine-strain

MAJ-59

Iran

LSHTM

6

9

6

12

9

7

16

13

6

24

101

MHOM/IQ/1986/BH012

MAJ-60

Iraq

LSHTM

7

9

6

12

10

7

16

16

6

23

102

MHOM/KW/1976/P47

MAJ-61

Kuwait

LSHTM

7

9

9

9

12

6

9

13

7

14

103

MPSM/SA/1989/SABIR-1

MAJ-62

Saudi Arabia

LSHTM

7

6

5

12

9

7

9

13

6

14

104

MHOM/SA/1984/KFUH68757

MAJ-63

Saudi Arabia

LSHTM

7

6

7

12

9

7

9

13

6

14

105

MHOM/SD/2004/MW1

MAJ-166

Sudan-Gadaref

M. Mukhtar

7

9

6

8

10

8

11

11

7

22

106

MHOM/SD/2004/MW38

MAJ-167

Sudan-Gadaref

M. Mukhtar

7

9

6

8

10

8

10

13

7

22

107

MHOM/SD/2004/MW94

MAJ-168

Sudan-Gadaref

M. Mukhtar

6

9

6

8

10

8

10

12

7

23

MARTS = Martsinovsky Institute of Medical Parasitology and Tropical Medicine, Sechenov Medical Academy Moscow, Russia (M.V. Strelkova); LRC = Leishmania reference centre, Hadassah Medical School, Hebrew University Jerusalem (L. F. Schnur); ISAEV = Isaev Insitute of Medical Parasitology, Samarkand, Uzbekistan (S.A. Razakov); LSHTM = London School of Hygiene and Tropical Medicine, UK (I. Mauricio); LEMPP = Centre National de reference des Leishmaniosis, Montpellier, France (F. Pratlong); KIT = Royal Tropical Institute Amsterdam, The Netherlands (H. Schallig). ISLAH: Islah medical laboratory in Jericho-Palestine.
IPAP: from Phlebotomus papatasi, MOHM: Human orign, MPSA: from Psammomys obesus, MRHO: from Rhombomy opimus

3.5.3 Population structure of L. major using Structure

3.5.3.1 Estimation of population structure using non-predefined populations

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To estimate the optimal number of populations in our data set, a batch run was performed using values of K from 1 to 12. The runs were based on burn-in length period of 30,000 iterations and 1000000 MCMC (Markov Chain Monte Carlo) repeats. The mean values of ln likelihood, ln Pr (X\K) for K=1, 2… 12 were estimated and plotted against K (Figure 3.15). At K= 7, the curve started to form a plateau and the optimal K is, therefore, expected to be 7.

To understand the dynamics of population structure we have tested the outcome of clustering for K values from 2 to 7.

Figure 3.15. Curve of the mean value Ln likelihood, ln Pr (x\K) showing a plateau starting at K=7.

↓75

Figure 3.16 displays membership coefficient which depicts each subgroup in a different colour with each individual as a fixed-length vertical line sometimes partitioned into K coloured segments which reflect membership coefficient of the individual isolates in the various subgroups (Rosenberg et al., 2002). At K=2, the method correctly inferred that the underlying population structure consists of two subpopulations, the Central Asian (CA) and ‘the rest’. Each increase in K splits one of the clusters obtained with the previous value. At K =3, the clusters were anchored by Central Asia, Middle East - South West Asia- (ME) and Africa regions, but 4 Middle Eastern (ME) isolates, 2 Turkish (TR), 1 Iraqi (IQ) and 1 Iranian (IR), grouped with the African cluster. With further splitting at K=4, African strains were divided into two clusters, AF1 (13 genotypes: 1IL (Jericho), 1IR, 1IQ, 5EA (East Africa), 2NA (North Africa), 1WA (West Africa), 2TR) and AF2 (11 genotypes: 3NA, 1WA, 5EA, 1TR, 1IQ). One L. major from Negev desert (IL) deserted its Middle Eastern cluster in K=3 to group with AF1. However, this isolate had partial membership in both clusters with membership coefficients of 0.4 in the Middle Eastern cluster and 0.6 in the AF1 cluster; reflecting continuous variation in allele frequencies across regions or admixture of neighbouring groups. Two of the three Turkish isolates were with Africa 1 (AF1) while the third with Africa 2 (AF2) cluster.

At K=5, the Middle Eastern cluster broke down into to distinct clusters, interestingly, with strains from the Jordan valley as ME1 (27 genotypes: 20PS (Palestine) +1TR+5 IL-Negev+1KW (Kuwait)) and those from the Negev and Sinai deserts as ME2 (16 genotypes: 12 IL -Negev+2 PS+2SA). The two SA (Saudi Arabia) genotypes from ME2 at K=6, 1 IQ 1IR in addition to 3 samples from CA formed a new cluster called ME3. The Central Asian cluster, CA from UZ, TM, and KZ, remained intact and so did the two African clusters AF1 (13 genotypes: 5NA+6WA+ 2TR) and AF2 (11 genotypes: 2NA+2WA+ 5EA+ 1IR+1IQ) apart from minimal change in AF1. So far, relative correspondence between regional affiliation and genetic ancestry has been noticed. At K=6 clusters remained as they are except for Central Asian which split into two clusters, CA1 (23 genotypes: 12 Mubarak, 1Termez, 6TM (Turkmenistan), 1KZ (Kazakhstan), 3UZ (Uzbekistan) and CA2 (16 genotypes: 10Termez+5 Mubarak+1SU (USSR)) which were re-joined again at K=7. And in K=7, African strains grouped into three (Figure 3.16) different clusters. These were AF1 (6 genotypes: 2WA, 5EA, 2NA), AF2 (5 genotypes: 1WA, 4NA) and AF3 (8 genotypes: 6 WA, 2 TR). Around 75% (20/27) of the ME1 cluster come from the Jordan Valley focus while 16% (4/24) come from the Negev focus. One TR and 1 KW joined this cluster. ME2 cluster is mostly (10/14=71%) composed of genotypes from the triangle-shaped Negev desert extending from the Jordanian borders in the east to the Egyptian borders in the west. The rest come from the Jordan Valley focus.

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.

Colored segments represent the sample‘s estimated membership in each of the K inferred clusters. Individual isolates can have membership in multiple clusters, with membership coefficients summing to 1 across clusters. CA= Central Asia; ME=Middle East; AF=Africa.

↓76

The analysis of multilocus genotypes without relying on information about their geographical origin allowed inference of their genetic relationships. The application of the model-based clustering algorithm, Structure computer software, allowed to allocate seven clusters subgroups in our sample set with distinctive allele frequencies. Using Wrights F-statistic in pairs, seven clusters (K=7) were proved to be the optimally differentiated populations (Table 3. 17).

3.5.3.2 Estimation of population structure using predefined population

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.

Regions may have more than one colour. For instance, strains from ME represented by yellow colour geographically belong to ME but genetically to East Africa.

The sample collection areas in Asia and Africa were divided into 5 major regions: Central Asia, Middle East (Southwest Asia), North Africa, West Africa and East Africa. Boundaries between these regions mostly corresponded to major physical barriers (seas and Sahara). Assigning each L. major isolate to one of these areas, corresponding to its collections site, without any idea about the underlying genetic relationships was tested by Structure 2.1 using admixture model. The Central Asian cluster was shown to be ideal, genetically and geographically, with all central Asian isolates having a very high coefficient of membership of 0.999 (Figure 3.17). Two of the three Turkish and single Iraqi and Iranian strains which are geographically closer to the Middle East (Southwest Asia) belong genetically to West Africa and East Africa, respectively, while the third geographically belonged to East Africa but genetically to West Africa. Using Chi-square (Table3.12) it was proven that genetic clusters corresponded significantly to the geographically predefined regions (X2 =288.4, P<0.0001, df 16). A sample of unknown origin (MHOM/WA/87/NEL2) in which the patient caught CL while roaming between west and east Africa was shown to originate from East Africa.

↓77

Table 3.12 The number of L. major isolates in the five predefined clusters inferred by Structure v. 2.

Cluster 1

Cluster2

Cluster3

Cluster4

Cluster5

Middle East

43

0

0

0

0

Central Asia

0

39

0

0

0

North Africa

0

0

5

0

0

East Africa

2

0

2

6

1

West Africa

2

0

0

1

5

(X2 =288.4, P<0.0001, 16 df)

3.5.4 Analysis of L. major population structure using distance-based methods

For the 67 genotypes of L. major, the repeat numbers were organised in an input file in a diploid form to calculate the genetic distances. Two different measures for genetic distance both implemented in the MICROSAT software have been used: proportion of shared alleles, Dps and Delta mu squared alleles, Ddm (or Dµ2); the latter has been developed especially for the analysis of microsatellite variation (Zhivotovsky et al., 2000). NJ and UPMGA trees were constructed based on the distance matrices obtained. The statistical significance of each node on the dendrogram was assessed by bootstrap analysis generating 1000 randomly re-sampled subsets of the data. A strain of L. tropica was used as an out-group.

Both the NJ and UPMGA cladogram based on Dps (Figure 3. 18) displayed exactly the same pattern, albeit with different bootstrap support, showing six major clusters and sub-clusters. Structure at K=7 revealed seven clusters as shown in figure 3.15: CA, ME1, ME2, ME3, AF1, AF2, and AF3. The Central Asian cluster includes all the 39 L. major isolates coming from (UZ) Uzbekistan, (TM) Turkmenistan and (KZ) Kazakhstan. The strains from Middle East were assigned to two clearly separated groups with genotypes from the Jordan Valley focus forming the clade Middle East 1 (ME1) and those from the Negev, with 1 genotype from the Sinai desert, belonging to Middle East 2 (ME2). A third ME cluster, ME3 composed of 4 from ME (SA, IQ and IR) and 3 CA (UZ). The African strains formed three clusters. First, AF1 representing genotypes from North, East and West (NEW) Africa as SD, KE, MA and SN, the second is AF2 which consists of genotypes from West Africa as SN and BF and North Africa. The third is a cluster, AF3, consisting of strains from West African countries in addition to 2 TR strains. The Dps-based cladograms showed largely congruent topology compared to the results of Structure analysis at K=7. However, CA2 (Figure 3.16) was not well supported in the trees (Figure 3.18). Strains from SA, IQ, IR and central Asia that were assigned ME3 by Structure were scattered all over the trees. The two SA strains sub-clustered close to CA and the IR and IQ strains sub-clustered close to AF1 Africa, while the three strains from UZ clustered in the CA clade. Two of the three Turkish isolates are grouping with the AF3 cluster, while the third Turkish strain is found in the cluster ME 1. It was observed that bootstrap support for the African clades was better compared to the other clades in the Dps trees.

↓78

The UPGMA cladogram based on delta mu squared distance, Ddm (Dµ2) (Fig 3.18c); showed very poor congruence with the results of structure analysis. Despite this, the UPGMA-Ddm tree still highlights a well-distinct Central Asian and two Middle Eastern (Southwest Asia) clades.

Figure 3.18 (a) NJ-DPS-boot1000 genotypes

Figure 3.18 (b) UPGMA-DPS-boot1000 genotypes

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Figure 3.18 (c) UPGMA-Ddm-boot1000 genotypes

Figure 3.18 Dendrograms for the 67 L. major genotypes with L. tropica MHOM/IL/2002/LRC-L943 as out-group, calculated with 1000 bootstraps. (a) Consensus neighbour-joining tree calculated using distance of proportion of shared alleles (Dps). (b) Consensus UPGMA tree using Dps (c) consensus UPGMA using Delta mu squared (Ddm) distance. Bootstrap values above 0.5 are shown with 1 representing 100%. CA= Central Asia, ME=Middle East, AF=Africa, IQ=Iraq, IR=Iran, KW=Kuwait. End branches with arrows are genotypes representing 2 or more strains as indicated by the number in brackets.

3.5.5 Genetic isolation of the L. major populations identified in this study

Fst, as a measure for genetic differentiation between populations, was calculated in a pairwise manner using FSTAT, GENEPOP and GDA software. The calculations were based on the optimal number of populations K=7 as defined by Structure analysis. As shown in Table 3.13, all are separated by a very great genetic isolation with significant departure from zero (Fstat > 0.25).

Using predefined geographic clustering into the five major regions, the differentiation was moderate between West and North Africa (0.1459) from one side and between West and East Africa (0.1258) from the other.

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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.

(a)

CA

ME2

AF1

AF2

ME3

AF3

ME1

CA

0.7417

0.7075

0.7394

0.5500

0.7685

0.7249

ME2

0.0871

0.4585

0.7097

0.4399

0.6088

0.6026

AF1

0.1034

0.2953

0.5784

0.3931

0.3817

0.6124

AF2

0.0881

0.1023

0.1822

0.4507

0.4859

0.7800

ME3

0.2045

0.3183

0.3860

0.3047

0.5154

0.5889

AF3

0.0753

0.1608

0.4050

0.2645

0.2351

0.7419

ME1

0.0949

0.1649

0.1608

0.0705

0.1745

0.0870

CA: 36: UZ+TM+KZ; ME2: 10IL-Negev & Sinai +4 Jericho; AF1: 6 genotypes: 2WA, 5EA, 2NA; AF2: 5 genotypes: 1WA, 4NA; AF3: 8: 6WA, 2TR; ME3: 3CA, IR, IQ, 2 SA; ME3: 3CA, 1IR, 1IQ, 2SA; AF3: 6 WA, 2 TR; ME1 (27): 20PS+1TR+5 IL- Negev+1KW.

(b)

M. East

C. Asia

N. Africa

E. Africa

W. Africa

M. East

0.5112

0.4224

0.3616

0.3867

C. Asia

0.2390

 

0.6114

0.7228

0.6841

N. Africa

0.3419

0.1589

0.3544

0.1459

E. Africa

0.4414

0.0959

0.4554

0.1268

W. Africa

0.3966

0.1154

1.4962

1.7216

Fst can be used to estimate genetic flow or migration rate, Nm, as Nm = 1-FST/4 FST (Souto and Premoli, 2003). Genetic flow migration (or gene flow) refers to the movement of individuals among subpopulations and can set a limit as to how much genetic divergence can occur. At K=7 genetic flow or migration is low ranging from 0.0705 to 0.4050. At the predefined population identification, there was a clear genetic flow (migration rate) between the African regions (1.4962 and 1.7216).

3.5.6 Estimation of allele numbers in geographical groups of strains

The mean number of alleles was plotted against the three main groups of strains that were derived from Central Asia, Middle East (Southwest Asia) and Africa (Figure 3.19). It is shown that the African group has the highest number of alleles (41) followed by Middle East (25) and Central Asia (15). This comparison was based on allele numbers counted for 17 African strains, 50 from Middle East and 39 from Central Asia. To overcome the problem with different numbers of strains from the three areas, a shuffling procedure was developed. An increment window was selected based on the region having the lowest number of strains which was Africa with 17 strains. The 17-increment window was serially moved over the strains of the two other regions. Each time the window was moved, the number of alleles was counted. Based on this, it moved 22 times over the 39- strain -Central Asian region and 34 times over the 50- strain- Middle East region. Mean and standard deviation for Central Asia and Middle East were estimated, which were 15 and 25, respectively, for mean and 0.72 and 6.2, respectively, for standard deviation. The gradual decrease in allele numbers on the geographic line from Africa via Middle East to Central Asia reflects a decreased genetic variation. The mean number of African alleles was highest, despite that the number isolates available was the lowest.

↓81

Figure 3.19 The mean number of alleles in the three major geographical regions.

Standard deviation is shown to be minimal in the Central Asia, 0.7162, while as high as 6.36 in the Middle East.


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