REMUNERACION PARA EFECTOS DE INDEMNIZACION
2. MEDIOS ALTERNATIVOS
3.3 AUDIENCIA PRELIMINAR
Since it was determined that parasitaemia had a negative correlation with some cytokines (Figure 19), we assessed whether there was a correlation between treatment-induced parasite genetic polymorphism and cytokine profiles or corticosteroid levels.
The relationship between numbers of mutant alleles detected and the antimalarial drug resistant genes and cytokine levels showed that there was a negative correlation between number of mutant alleles and IL-10 and TGF-β (ρ ≤ -0.373 and ≤ -0.307, respectively) and a positive correlation with TNF-α indicating that its production increases with increasing number of mutant alleles (ρ ≤ 0.350) Figure 17. However, there was a negative correlation between corticosteroids, cortisol and dexamethasoneinduced protein, and number of mutant alleles (ρ ≤ 0.2849 and ≤ -0.2485, respectively) Figure 18. This indicates that corticosteroid elaboration decreases with increasing mutation
4.2.4 The correlation between parasitaemia and secretion of cytokines and corticosteroids It is known that the ratio of proinflammatory cytokines to anti-inflammatory cytokines could affect the status of the patient and predict the course of clinical infection during malaria infection (Dodoo et al., 2002; Dunst et al., 2017; Elenkov and Chrousos, 2002). Cytokines and corticosteroids are endogenously expressed stimulators of development of fever, the predominant
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clinical symptom of malaria infection. The onset of fever is known to be dependent on parasite threshold. We investigated the role of parasitaemia on cytokine and corticosteroid secretion that may drive ACT post-treatment syndrome
The relationship between cytokine and corticosteroid secretion and parasitaemia showed that there was marked negative correlation between parasitaemia and TGF-β (ρ ≤ -0.923) but minimal effect on IL-10, IL-12, IFN-γ, and TNF-α as depicted in Figure 19. Parasitaemia also has minimal effect on corticosteroid elaboration as shown in Figure 20
4.2.5 The relationship between cytokine and corticosteroid elaboration and drug treatment To investigate how treatment affects interplay between corticosteroids and cytokines during the course of malaria infection, we assessed the impact of treatment on cytokine and corticosteroids profiles. We directly assessed the effect of ACT treatment on cytokines and corticosteroid secretion to determine if our findings could represents a pattern of correlation between treatment and lack of it on parasite moderation of cytokine and corticosteroid patterns. The cytokines (IL-12p70, IFN-γ, TNF-α, TGF-β and IL-10), and corticosteroids (cortisol and dexamethasone) profiles are reported in Figure 21 and 22 respectively. The result of the cytokine assay showed that the mean IL-12p70 concentration in the treated was higher than in both the untreated group and the control but without any significant difference among the groups. The level of IFN-γ in the treated group was significantly lower than the untreated group (p=0.0048) while that of the untreated group was significantly lower than the control (p=0.001). The elaboration of TNF-α in the treated group was significantly higher than both the untreated group (p=0.042) and control (p=0.00053)
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TGF-β secretion measured in both the treated and untreated group with parasitaemia was lower than in the control group though the difference between the means of all the groups was not significant. IL-10 elaboration in the plasma was highest in the untreated group while levels in the treated and untreated group was significantly higher than the control (p=0.015) and (p=0.007) respectively.
The aggregate of these results showed that there was post treatment elaboration of the pro-inflammatory cytokines, IL-12p70 and TNF-α except IFN-γ but reduction of the anti-inflammatory cytokines IL-10 and TGF-β. In this study, there was a correlation pattern showing that in treated patients, with persistent parasites, IL-10, IFN-γ and TGF-β levels were markedly lower than in untreated patients. This suggests that treatment is impactful on the effect of parasitaemia on the profile of regulatory cytokines (IL-10 and TGF-β) in malaria patients consistent with an impaired role for IL-10 and TGF-β in the course malaria of infection. This point to enhancement of a post treatment persistence of elaboration of pro-inflammatory immune
response.The relationship between treatment and corticosteroids secretion revealed an increased level of cortisol but a decreased level of dexamethasone-induced protein in treated
compared with untreated patients
105 Table 8: Sample characteristics of study population
Microscopy positive Amukoko(n=33)
Number (%)
Agura(n=56) Number (%)
Ijede(n=30) Number (%)
Total(n=119) Number (%) Characteristics
Male Female Mean age(yrs.)
Age range Mean Temperature(0C)
Mean parasitaemia/μL
19(57) 14(42) 20.6±12.6
2 to 43 37.02±0.56 25855.8±54297
23(41) 33(60) 15.8±15.2
2 to 73 37.67±1.39 35561.77±51592
9(30) 21(70) 21.03±14.7
5 to 65 37.67±2.16 20984.32±21717
51(43) 68(57) 18.5±14.4
2 to 73 37.5±1.49 28847±46461 Clinical feature
Fever Chills Headache Body pain Other symptoms#
27(82) 15(45) 22(67) 10(30) 26(79)
55(98) 47(84) 36(64) 16(29) 33(59)
26(87) 20(67) 25(83) 14(47) 15(30)
108(91) 82(69) 83(70) 40(34) 74(62) Treatment
ACT CQ
SP Pain reliever
6(18) 2(6) 0(0) 27(82)
12(21) 2(4) 2(4) 43(77)
5(17) 0(0) 3(10) 26(87)
23(19) 4(3) 5(4) 96(81)
# Other symptoms- vomiting, weakness.
ACT: artemisinin combination therapy
CQ: chloroquine. SP: sulphadoxine/pyrimethamine
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Table 9: Distribution of mutant and wild type alleles in the samples sequenced by Sanger and NGS
Gene Mutations Sanger NGS
Mutant allele Number (%)
Wild type Number (%)
Mutant allele Number (%)
Wild type Number (%)
Pfcrt
M74I N75E K76T A220S Q271E I356T R371I
7(26.9) 7(26.9) 7(26.9) NA NA NA NA
19(73.1) 19(73.1) 19(73.1)
NA NA NA NA
9(34.6) 9(34.6) 9(34.6) 7(26.9) 6(23) 8(30.8) 7(26.9)
17(65.4) 17(65.4) 17(65.4) 19(73.1) 20(77) 18(69.2) 19(73.1)
Pfmdr1
N86Y Y184F N504K N649D F938Y S967N
2(7.7) 17(65.4)
NA NA NA NA
24(92.3) 9(34.6)
NA NA NA NA
2(7.7) 22(84.6)
4(15.4) 3(11.5) 3(11.5) 1(3.8)
24(92.3) 4(15.4) 22(84.6) 23(88.5) 23(88.5) 25(96.2)
Pfk13
Q613H K189T H136N
0 NA NA
NA NA NA
1(3.8) 17(65.4)
1(3.8)
25(96.2) 9(34.6) 25(96.2)
Table 9 Legend: The mutations detected in the Pfcrt, Pfmdr1, and Pfk13 genes that were sequenced bySanger and NGS methods are displayed. The total number of samples that carried the specific mutation is shown and per cent is given in parentheses. NA= not applicable. Unlike NGS, Sanger could only sequence fragments and not full length of the genes
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Table 10 .Prevalence of Pf-resistant genes to artemisinin, chloroquine and amodiaquine in Nigeria
PREVIOUS STUDIES THIS STUDY
Genes
Prevalence of mutant
genes
Mutant genes/year of study
References Prevalence/mutant genes Pfk13
4%
3%
0%
39%
V510V
Q613H,A578S,D464N Nil
K189T,R255K/2011-2012
Oboh et al,2018 Ugbasi et al,2017 Dokunmu et al,2018 Ashley et al,2014
73.1%(K189T,H136N, Q613H)
Pfcrt
80.6%
57%
75.9%
91.6%
77.3%
K76T/2002 K76T/2004 K76T/2007 K76T/2008 K76T/2010
Olukosi et al, 2014 Happi et al, 2006 Agomo et al, 2016 Oladosu et al, 2014
Olukosi et al, 2014
34.6%(K76T)
Pfmdr1
44.3%
35%
25%
69%
31.5%
40%
88%
28.1%
62.2%
N86Y/2002 N86Y/2004 N86Y/2007 N86Y/2008 N86/2004 N86/2006 Y184/2006 Y184F/2007 Y184F/2008
Olukosi et al, 2014 Happi et al, 2006 Agomo et al, 2016 Oladosu et al, 2014
Happi et al, 2006 Happi et al, 2009 Happi et al, 2009 Agomo et al, 2016 Oladosu et al, 2014
7.7%(N86Y)
92.3%(N86) 84.6%(Y184F)
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Figure 7: The distribution of Pfcrt halotypes detected by NGS
Legend: The Pfcrt mutant haplotypes CVIET, CVMET and wild type CVMNK are based on protein sequence at codons 72-76 while Pfcrt mutant haplotypes SEII, SETI, AEIR and wild type AQIR are based on protein sequence at codons 220,271,356 and 371
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Figure 8: The distribution of Pfcrt halotypes detected by Sanger
Legend: The ability to detect certain haplotypes by Sanger is limited because only fragments and not full length gene could be sequenced by the method. The Pfcrt mutant haplotypes CVIET, CVMET and wild type CVMNK are based on protein sequence at codons 72-76.
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Figure 9: The distribution of Pfmdr1 halotypes detected by NGS
Legend: Mutant Pfmdr1 haplotypes KNYS, KDFS, NDFS, KNFN, NNYS and wild type NNFS are based on protein sequence at codons 504,649,938 and 967 while YF, NF and wild type NY haplotypes were based on protein sequence at codons 86 and 184
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Figure 10: The distribution of Pfmdr1 halotypes detected by Sanger
Legend: The ability to detect certain haplotypes by Sanger is limited because only fragments and not full length gene could be sequenced by the method. The mutant Pfmdr1 haplotypes YF, NF and wild type NY are based on protein sequence at codons 86 and 184
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Figure 11: The total number of samples exhibiting mutations in the Pfk13 (k13), Pfcrt (CRT), and Pfmdr1 (MDR) genes as detected by the Sanger (blue) and NGS (orange) methods
113 Sample ID RESISTANT ALLELES
AG596 H136N M74I N75E K76T A220S N504K F938Y
45.80% 41%
AG579 K189T M74I N75E K76T N504K S967N
79.30% 65.50% 32.40% 26.30% 26.30% 14.50% 56.30%
AG548 K189T M74I N75E K76T A220S
12.50%
AG525 K189T N75E K76T A220S Q271E
39% 26% 14% 14%
IJ 65 K189T M74I N75E A220S I356T Q271E R371I Y184F AG491 K189T M74I N75E K76T Y184F
AG546 M74I N75E K76T A220S I356T
AK241 M74I N75E K76T I356T Q271E R371I Y184F AG560 M74I N75E K76T Y184F
AG539 K189T Y184F N504K N649D
49.20% 96.60%
AG557 K189T Y184F F938Y
80.20%
AG001 K189T N86Y Y184F AK429 K189T Y184F
AK566 K189T Y184F IJ25 K189T Y184F AG561 K189T Y184F
41.20%
AG582 Q613H Y184F
97.80%
AG584 K189T Y184F IJ145 K189T
AG592 K189T AG556 K189T AG547 K189T AK001 Y184F AG576 Y184F AG597 Y184F AG555 Y184F
33.10%
N504K
49.20%
F938Y
N649D 86%
N75E
K76T Q271E R371I
26% 23.80% 56,70.2% 10.20%
K76T R371I Y184F N504K N649D
N75E I356T Q271E R371I Y184F
32.40% 35% 26.30% 28.40% 26.30%
N75E A220S I356T Q271E R371I Y184F Y184F
N75E I356T Q271E R371I
Figure 12: The individual sample data indicating the mutations detected by NGS in the Pfk13 (blue), Pfcrt (green), and Pfmdr1 (red) genes. Variant frequency (VF) is the minimum fraction of reads that must contain a base for it to be called a variation (i.e. polymorphism). VF values of less than 100% are displayed with the specific mutation.
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Figure 13: The relationship between treatment and parasite load in the malaria positive sample population
115
Figure 14: The percentage of treated and untreated patients that exhibited at least one mutant allele in the Pfk13(K13), Pfcrt (CRT), or Pfmdr1 (MDR) genes
116
Figure 15: The average number of mutant alleles detected per patient in mutation-positive treated and untreated patient samples (p ≤ 0.342)
117
Figure 16: The effect of mutations in the malaria-positive patient samples on levels of cytokines.
Legend: Total levels of IL-10, IL-12p70, IFN-γ, and TNF-α and TGF-β cytokines detected by ELISA and expressed in pg/mL. The correlation between the number of mutant alleles and cytokine levels is depicted with significant correlation coefficients for IL-10 (ρ ≤ -0.404) and TNF-α (ρ ≤ 0.332).
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Figure 17: The effect of mutations in the malaria-positive patient samples on levels of corticosteroids.
Legend: The correlation between cortisol and dexamethasone-induced protein (D.I.P.) levels detected by ELISA and expressed in ng/mL in patients with mutant alleles in P. falciparum is depicted. Correlation coefficients: cortisol (ρ ≤ -0.2849) and D.I.P. (ρ ≤ -0.2485).
119
Figure 18: The relationship between parasitaemia and cytokines in the malaria-positive sample population.
Legend: Parasitaemia was determined by microscopy and qPCR. Levels of IL-10, IL-12, IFN-γ, TNF-α and TGF-β were determined by ELISA and expressed as pg/mL. The correlation between parasitaemia and cytokines is represented with a significant correlation noted for TGFβ (ρ ≤ -0.923)
120
Figure 19: The relationship between parasitaemia and corticosteroid elaboration in the malaria-positive sample population.
Legend: Parasitaemia was determined by microscopy and qPCR. Levels of cortisol and dexamethasone-induced protein (D.I.P.) were determined by ELISA and expressed as ng/mL.
Correlation coefficients: cortisol (ρ ≤ -0.042) and D.I.P. (ρ ≤ -0.014 0
50 100 150 200 250 300 350 400 450 500
0 20000 40000 60000 80000
C o rt ic o st er o id s( n g /m l)
Parasite/μl of blood
cortisol
D.I.P
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Figure 20- Comparisons of cytokine levels across three categories.
Legend: ANOVA was performed to compare the three categories: IL-10 (p ≤ 0.199), IL-12 (p ≤ 0.758), IFN-γ (p ≤ 0.001), TNF-α (p ≤ 0.0910), and TGF-β (p ≤ 0.287).
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Figure 21- Total cortisol and dexamethasone-induced protein levels detected by ELISA and expressed in ng/mL in treated, untreated, and control patient samples.
Legend: ANOVA was performed to compare the three categories: cortisol (p ≤ 0.400) and D.I.P.
(p ≤ 0.0102
123
Figure 22: Aligned sequences of Plasmodium falciparum multi drug resistant -1 gene with reference sequence showing mutation at codon 504
124
Figure 23: Aligned sequences of Plasmodium falciparum multi drug resistant -1 gene with reference sequence showing mutation at codon 649
125
Figure 24: Aligned sequences of Plasmodium falciparum multi drug resistant -1 gene with reference sequence showing mutation at codon 967
126
Figure 25: Aligned sequences of Plasmodium falciparum multi drug resistant -1 gene with reference sequence showing mutation at codon 938
127
CHAPTER FIVE
DISCUSSION AND CONCLUSION
The incidence of malaria in Nigeria is high with Plasmodium falciparum responsible for more than 80% of all cases with varying incidence between different regions of the country.
Consequently, there is need for continuous surveillance to monitor the dynamics of resistant parasite population using molecular methods as a guide to treatment options. This present study proactively undertook surveillance for mutations that may lead to future resistance to the ACT by generating sequencing data from clinical isolates in individual patient samples in a high malaria endemic area using Sanger and NGS methods.
5.1 SANGER AND NGS RESULTS
Studies of genetic polymorphisms associated with drug resistance are technically much simpler than in vitro studies of parasite sensitivity. It has already been well documented in past studies that the prevalence of a number of polymorphisms that impact on drugs sensitivity varies greatly around the world.There is also interest in changes that has occured in polymorphism prevalence overtime. For example, in Uganda, parasites have demonstrated marked changes in the prevalence of some key polymorphisms over the last decade, following changes in treatment practices for malaria from chloroquine to chloroquine/sulfadoxine–pyrimethamine to artemether/
lumefantrine. Most notably, the prevalence of three wild type alleles, pfcrt K76, pfmdr1 N86, and pfmdr1 D1246, have all increased markedly in recent years (Rasmussen et al, 2017). It was observed that this increase was greater in children treated with artemether/lumefantrine for all episodes of malaria than in those treated with dihydroartemisinin/ piperaquine (Rasmussen et al, 2017). This scenario contrast sharply to what obtains in the P. falciparum parasites at the China–
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Myanmar border area, where the pfcrt 76T and 220S genotypes remained almost fixed in a recent study. However, these changes do not impact negatively on the sensitivity of lumefantrine or the efficacy of artemether/lumefantrine treatment (Wang et al, 2012).
Unlike other PCR based genotyping methods which are only well suited for identification of major resistance alleles, next generation sequencing(NGS) method can accurately capture mixed infection haplotypes and detect minor resistance alleles as low as 1%(Flaherty et al., 2012;
Pulido-Tamayo et al., 2015). This study took advantage of the sensitivity of NGS method to detect the naturally circulating P. falciparum antimalarial resistant alleles in individual patient sample in a malaria endemic area. Our results showed demonstrable evidence of the effectiveness of NGS method at detecting more mutations and haplotypes that were not identified by the Sanger method; thus highlighting the efficacy of this method for surveillance of circulating antimalarial resistance-associated alleles.
This result emphasizes the importance of investment in better tools for antimalarial resistance surveillance in endemic areas to avoid missing out on important therapeutically important mutations that may compromise future effectiveness of antimalarial therapy. Polymorphism in the Pfk13, Pfcrt and Pfmdr1 genes has been used as valuable surveillance tools to monitor resistance alleles to artemisinin, chloroquine, and amodiaquine (Ashley et al, 2014, Amato et al, 2015)