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. 2024 Nov 14;14:27988. doi: 10.1038/s41598-024-78670-2

Polymorphisms in the Pfcrt, Pfmdr1, and Pfk13 genes of Plasmodium falciparum isolates from southern Brazzaville, Republic of Congo

Marcel Tapsou Baina 1,2, Jean Claude Djontu 1,3,✉, Jacques Dollon Mbama Ntabi 1,2, Claujens Chastel Mfoutou Mapanguy 1,2, Abel Lissom 1,4, Christevy Jeannhey Vouvoungui 1,2, Reauchelvy Kamal Boumpoutou 1, Alain Maxime Mouanga 1,5, Etienne Nguimbi 2, Francine Ntoumi 1,6,✉
PMCID: PMC11564878  PMID: 39543235

Abstract

This study aimed to analyze polymorphisms in Pfcrt, Pfmdr1, and Pfk13 genes’ markers of resistance to Artemisinin-based combination therapy (ACT), in Plasmodium falciparum isolates from southern Brazzaville, 15 years after the adoption of ACT in the Republic of Congo. A total of 369 microscopy-confirmed malaria-infected individuals were enrolled from March to October 2021 in the community and in health facilities during a cross-sectional study. The K76T mutation in the Pfcrt gene, N86Y and Y184F mutations in the Pfmdr1 gene were investigated using the polymerase chain reaction-restriction fragment length polymorphism (PCR-RFLP) while the codons region (1005–1300) of the Pfmdr1gene, and Pfk13 gene were sequenced. The prevalences of K76T, N86Y, Y184F mutations were 26.0%, 6.8%, and 27.7%, respectively. However, no mutations were detected in codons 1034, 1042, and 1246 of the Pfmdr1 gene. None of the mutations previously associated with artemisinin-based resistance were detected in the Pfk13 gene. The results reveal a significant decrease in the prevalence of K76T, N86Y, Y184F mutations, in Plasmodium falciparum isolates following the change of therapeutic policy. As artemisinin resistance is emerging throughout Africa, continued surveillance for early detection of these mutations and relevant partner markers of drug resistance are recommended in the Republic of Congo.

Supplementary Information

The online version contains supplementary material available at 10.1038/s41598-024-78670-2.

Keywords: Plasmodium falciparum, Pfcrt, Pfmdr1, Pfk13, Polymorphism, Republic of Congo

Subject terms: Biochemistry, Genetics, Molecular biology

Background

In 2022, malaria killed 608,000 people worldwide, with 96% of these deaths occurring in sub-Saharan Africa1. In the Republic of Congo, this disease remains a major public health problem, representing 59% of medical consultations, 58% of hospital admission, and 22% in patient deaths in 2022, mainly caused by Plasmodium falciparum (P. falciparum)2. To combat malaria, the World Health Organization (WHO) recommends a range of interventions, including the use of long-lasting insecticidal treated mosquito nets (LLINs), indoor residual spraying with insecticide (IRS), the use of antimalarial vaccines (RTS, S, and R21), artemisinin-based combination therapy (ACT), seasonal malaria chemoprevention (SMC), perennial malaria chemoprevention (PMC), and intermittent preventive treatment with sulfadoxine-pyrimethamine in pregnant women (IPTp-SP) in countries where transmission is high or moderate1. However, the emergence and spread of P. falciparum strains resistant to ACT (the current cornerstone of treatment for uncomplicated P. falciparum malaria) represent the greatest threat to global malaria control efforts in Africa. These concerns are all the more real with the emergence of resistance to artemisinin3–5 and to partner drugs6.

P. falciparum resistance to chloroquine (CQ) or amodiaquine (AQ) by cross-resistance has been associated with a K76T mutation within the chloroquine resistance transporter (Pfcrt) gene7–9. In addition, five different point mutations (N86Y, Y184F, S1034C, N1042D, and D1246Y) identified in the P. falciparum multidrug resistance-1(Pfmdr1) gene have been associated with alteration in the sensitivity of P. falciparum to lumefantrine (L), artemisinin (ART), quinine (Q), mefloquine (MQ), amodiaquine (AQ), and Chloroquine (CQ)10–13. Polymorphisms in both Pfcrt and Pfmdr1 genes are also thought to affect susceptibility to ACT, especially the Pfcrt K76T mutation and the Pfmdr1 86Y-164Y-1246Y (YYY) haplotype, known to be associated with recrudescence and reinfection after treatment with Artesunate-Amodiaquine (AS-AQ)14–16. Furthermore, the wild-type alleles K76 of Pfcrt gene, N86 and D1246 of Pfmdr1gene, as well as the 184F mutation, are selected following treatment with Artemether-Lumefantrine (AL)17. Over the past decade, ART resistance has been associated mainly with mutations in the gene encoding the P. falciparum Kelch13 (Pfk13) protein18. Thus, several mutations in the Pfk13 gene have been validated as molecular markers of partial ART resistance (F446I, N458Y, M476I, Y493H, R539T, I543T, P553L, R561H, P574L, C580Y, R622I, C469Yand A675V)19, while synonymous mutations detected in this gene represent candidate molecular markers. In Africa, validated molecular markers for ART resistance have been reported over the last decade in Rwanda (1-21.9%), Uganda (2.2–13.3%), Eritrea (0.8%), Ethiopia (11.1%), Ghana (2.6–6.4%), Mali (1.5–2.3%), Malawi (0.5%), Zambia (0.3%), Somalia (1.1%), Equatorial Guinea (0.2%), Democratic Republic of Congo ( 0.1–0.5%), and Tanzania (0.2–1.4%). Different canditate mutations for ART resistance were also detected within the Pfk13 gene at a prevalence ranging from 0.2 to 23.3%, with the C469F mutation being the most prevalent (23.3%) and the R515K mutation the least prevalent (0.3%)20.

In the Republic of Congo, chloroquine-resistant P. falciparum was first reported in 198621 and spread throughout the country during the 1990s22. Sulfadoxine-pyrimethamine (SP) remained clinically effective in treating uncomplicated malaria until the early 2000s23. In fact, a randomized efficacy trial conducted from 2003 to 2005 in the southern part of the Republic of Congo reported a rapid decline in efficacy of SP- and AQ-based monotherapy24. The emergence and spread of resistance to these antimalarial drugs led to the adoption of a new therapeutic policy in the country in 2006 25. Thus, ACT (artemether-lumefantrine and artesunate-amodiaquine) were selected by the Ministry of Public Health and Population as first and second-line drugs for the treatment of uncomplicated malaria26. After 15 years of widespread use of these ACTs, resistant parasites could have been selected under continued drug pressure. Therefore, close molecular monitoring of known markers is essential as an early warning system to detect the emergence and spread of drug resistance. This study aimed to assess mutations associated with ACT resistance in the Pfcrt, Pfmdr1, and Pfk13 genes in P. falciparum isolates collected simultaneously in rural and urban communities and in health facilities in southern Brazzaville, 15 years after the adoption of ACT in the Republic of Congo.

Methods

Ethics statement

The institutional ethics committee of the Fondation Congolaise pour la Recherche Médicale reviewed and approved the study protocol (N° 031/CIE/FCRM/2020). Participants were free to take part in the study, and written informed consent was obtained from each adult or parent (guardian) for under-18-year-old participants before their enrolment in the study. All malaria patients were treated with ACT according to National Malaria Control Program recommendations27, and all methods were performed in accordance with the relevant guidelines and regulations.

Study sites

The study was carried out in two villages, Ntoula and Djoumouna, in the Goma Tsé-Tsé district, and in the urban areas of Mayanga and Massissia in the Madibou health district of Brazzaville, Republic of Congo (Fig. 1).

Fig. 1.

Fig. 1

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Map of the Republic of Congo (A), Map of the study area showing the localities surveyed: Goma Tsé-Tsé district (B) and the Madibou health district (C). ArcMap 10.8 (Esri; Rwanda) software was used to generate the map.

The village of Djoumouna is located 25 km from Brazzaville and is surrounded by four rivers (Lomba river, Kinkoue river, Loumbangala river, and Djoumouna river) that supply water to a series of fish ponds, all of which could be potential breeding grounds for malaria vectors. Agriculture is the village’s main economic activity. Ntoula is a neighboring village to Djoumouna, irrigated by two rivers. This village is characterized by the presence of market gardening sites (Agri-Congo 1 and 2). The population is mainly engaged in farming and fishing. Concerning Mayanga and Massassia, these two districts are located in an urban setting in the south of Brazzaville and are irrigated by three rivers (Djoué river, Laba river, and Matou river). They host several public and private services: hospitals, health centers, schools, and industrial and administrative facilities. All these study sites are located in an area with a constant humid climate throughout the year, with an average day time temperature of 25 °C and night time temperatures varying between 16 °C and 21 °C28. Average annual rainfall is around 1,100 mm, with a rainy season lasting 9 months29,30. In these regions, malaria transmission is perennial, with P. falciparum being the predominant plasmodial species and Anopheles gambiae being the main mosquito vector31,32. The entomological inoculation rate in the region was 47.5 infecting bites/person/year in 2021 33.

Study population

The study population include individuals of both sexes, aged at least one year, with malaria infection confirmed by microscopic examination of the thick smears, recruited during two parallel surveys, one conducted in health facilities and the other in the community. Individuals who did not live in the study areas for at least two weeks were not included.

Study design and sampling

Two parallel cross-sectional surveys (one in urban health facilities and the other in urban and rural communities), were conducted from March to October 2021, covering both the rainy and dry seasons. In health facilities, consenting individuals were consecutively recruited on a daily basis. In community settings, meetings were held with local authorities prior to the field survey, to define the appropriate dates for sample collection in the various contexts. Local people were informed of the study objectives and protocol during community awareness-raising sessions during which they were allowed to ask questions. At each study site, samples were collected during both the rainy and dry seasons in individuals from a simple random sample of households.

A standard well-structured questionnaire was used to collect demographic and clinical data such as age, sex, weight, use of mosquito bed nets, use of antimalarials, use of antipyretic drugs, fever or history of fever in the last 48 h, headache, vomiting, nausea. Fever was characterized by an axillary temperature ≥ 37.5 °C. Symptomatic malaria infection was defined by the presence of fever (T ≥ 37.5 °C) or a history of fever during the previous 48 h, accompanied or not by headache, nausea and vomiting, muscle pain, or fatigue, in an individual presenting asexual forms of the parasite on microscopic examination of the thick smear34. Using a standard aseptic technique, an intravenous blood sample was collected in EDTA tubes (2–3 mL) from individuals with a positive P. falciparum-based malaria RDT: One Step® HRP-II (SD Bioline malaria antigen P.f, Standard Diagnostics Inc, Gyonggi-do, Republic of Korea) after obtaining informed consent. One part of the blood was used to perform the thick smear, and the other to measure hemoglobin levels. The remainder was separated into plasma and whole blood that was stored at -20 °C until used for molecular analyses (Fig. 2).

Fig. 2.

Fig. 2

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Flow chart of the enrollment of the study participants and samples analyses. Hb: hemoglobin; RDT+: positive malaria rapid diagnostic test; RDT-: negative malaria rapid diagnostic test; 18 S PCR: polymerase chain reaction-restriction targeting the 18 S ribosomal ARN gene of Plasmodium falciparum; PCR-RFLP: polymerase chain reaction-restriction fragment length polymorphism; Pfcrt: Plasmodium falciparum chloroquine resistance transporter; Pfmdr1:Plamodium falciparum multidrug resistance-1; Pfk13: Plasmodium falciparum Kelch13.

Sample size calculation

The following simple formula was used for calculating the adequate sample size of the study: N = (z)2 x p(1-p)/(d)2, where N is the minimal sample size, Z (1.96) is the statistic corresponding to the level of confidence, 95% (CI), d is the significant level (0.05), and P is expected prevalence of resistance related mutation. Based on average of 86% prevalence of K76T mutation estimated in the Republic of Congo between 2010 and 2015 40, the minimal sample size for this study was 183 participants for each of the health facility based-cross sectional survey, and community based-cross-sectional survey.

Diagnosis of malaria and anemia

Thick and thin blood smears were prepared from each participant’s blood sample and read by two qualified microscopists to determine the presence or absence of malaria parasites and parasitemia as previously described35. The hemoglobin level of individual’s venous blood was determined using a hematological analyzer (Cypress Diagnostic, Hulshout, Belgium) and was used for the diagnosis of anemia as previously described37.

Molecular analyses

DNA extraction

Parasite genomic DNA was extracted from whole blood using the Qiaamp DNA Mini QIAGEN kit (Cat. N°:51306) following the manufacturer’s instructions. Eluted DNA was stored at -20 °C until used for molecular analyses.

Molecular identification of Plasmodium species

Nested PCR targeting the gene encoding 18 S rRNA was performed to identify the different Plasmodium species as previously described by Snounou et al.38. For quality control, a sample without a DNA template was used in all reactions as a negative control, and Plasmodium genomic DNA from laboratory was used as a positive control for the respective Plasmodium species.

Genotyping of P. falciparum Pfcrt, Pfmdr1and Pfk13 genes

Point mutations K76T in the Pfcrt gene and N86Y and Y184F in the Pfmdr1 gene were investigated by nested PCR coupled with restriction fragment length polymorphism (RFLP) as described previously7. However, the codon region (1005–1300) of the Pfmdr1 gene, flanking mutations S1034C, N1042D, and D1246Y, and the codon region (470–630) of the Pfk13 gene, flanking mutations T474I, M476I, A481V, Y493H, T508N, P527T, G533S, N537I, R539T, I543T, P553L, R561H, V568G, P574L, A578S, C580Y, R622I, and A675V, were amplified by nested PCR from extracted genomic DNA as described by Li et al.39 and Ariey et al.18, respectively, and the obtained amplified fragments of genomic DNA were sequenced using Oxford Nanopore technology as described below. In summary, the region of the Pfcrt, Pfmdr1, and Pfk13 genes containing the codons of interest was amplified using the respective primer pairs (Crt1 and Crt2), (A1, A3, F1 and R1), and (K13-1, K13-4) followed by nested PCR using the specific primers (Drt1 and Drt2), (A2, A4, F2 and R2), and (K13-F, K13-R), respectively (Table S1). For the first reaction, 2 µl of DNA extract was added to a reaction mixture with a total volume of 25µL containing 500 nM of each primer, 2.5 mM MgCl2, 100 µM dNTP, and 0.05 units of Taq DNA polymerase (QIAGEN). The same conditions were used for the second round of PCR reaction, except the Pfcrt gene, where 1µL of amplicon was used as a template. Amplification was performed according to the conditions described in Table S1. Genomic DNA from mutant and wild-type strains (HB3, 3D7, and Dd2) was included in each PCR reaction for quality control. After confirmation of amplification by 2% agarose gel electrophoresis, only amplified products of the Pfcrt and Pfmdr1 (86–184) genes were subjected to enzymatic digestion using the restriction enzymes in accordance with the manufacturer’s instructions. For the Pfmdr1 (1034–1246) and Pfk13 genes, PCR products (amplified DNA) were subjected to sequencing. In addition, 93 isolates carrying the N86Y or Y184F mutations were randomly selected and sequenced to confirm the profiles obtained by the PCR-RFLP technique.

Amplicon sequencing

For sequencing, amplicons from PCR were first individually purified using the magnetic beads method; the eluted DNA was then quantified with the Qubit (Qubit DNA BR, Thermo Scientific). The amount of DNA in each sample was adjusted to 100 ng within 7.5 µl of solution before adding 2.5 µl of corresponding barcodes (using the SQK-RBK110.96 kit). After incubation (30 °C–1 min; 80 °C–1 min; and 4 °C–1 min) in the thermocycler (Thermo Scientific), all samples (up to fifty per run) were pooled into the same tube and cleaned up with the magnetic bead method using Ampure XP beads. The pooled DNA eluted (11 µl) constituted the library, and 1 µl of the RAP (RAPID barcoding) was immediately added to the library. For the sequencing load, the mixture was prepared by adding into the library, 37.5 µl of sequencing buffer and 25.5 µl loading beads. The prepared mixture was used for loading into the ONT GridION flow cell according to the Oxford Nanopore Rapid Barcoding (SQK-RBK110.96) protocol.

Bioinformatics analysis

The FastQ files obtained from sequencing were analyzed using Linux system command line. Since our samples were sequenced using the Oxford Nanopore Technology platform, Nanoplot (version 1.42.0) and Nanofilt (version 2.8.0) were used for the quality control of the data. In the Galaxy platform, several tools were used to generate the consensus sequences: raw data in fastQ format (after quality control) were converted using the “FASTQ groomer” to a compatible format for the “Bowtie2” tool, used for alignment (Figure S1). Finally, the “Ivar Consensus” tool was used to generate the consensus sequence based on the reference sequences of P. falciparum 3D7 retrieved from GenBank (PF3D7_0523000) for the Pfmdr1 gene and (PF3D7_1343700) for the Pfk13 gene. The Galaxy platform operates via a graphical user interface (GUI); thus, no custom scripts were written directly. However, the script generated by Galaxy for each used tool (FASTQ Groomer, Bowtie2, Ivar consensus) is provided (supplementary S1). All sequences with no gaps, were submitted to GenBank for publication and the confirmation of Plasmodium species. The complete gene sequences obtained from the Pfmdr1, Pfk13 genes were deposited to GenBank. To search for possible point mutations in the Pfmdr1 and Pfk13 gene, sequences with a high coverage (greater than 80%) were aligned with their respective reference sequences (Figure S2-5)using MEGA 11 software.

Data analysis

The data were analyzed using GraphPad Prism software (version 8). Continuous variables were expressed as means ± standard deviations (SD) or medians with interquartile ranges. The Gaussian distribution of the data was assessed by the Shapiro-Wilk normality test, and variables that passed the normality test were analyzed using parametric tests (student’s t-test or ANOVA). Non-parametric statistical tests (Mann-Whitney or Kruskal-Wallis tests) were used to analyze variables that did not follow the normal distribution. The χ2 test or Fisher’s exact test were used to compare proportions. Statistical significance was defined by p values < 0.05.

Results

General characteristics of the study population

The socio-demographic and clinical characteristics of the participants in this study are summarized in Table 1. Of 1,384 individuals screened, 369 individuals with positive malaria thick smears were enrolled in this study. The distribution by locality was as follows: 144; 65 and 160 participants were enrolled in health facilities, urban community, and rural community settings, respectively. The median age of participants was 15 years. Patients with symptomatic infection came mainly from health facilities. The geometric mean parasite density was higher in participants enrolled in health facilities compared to those enrolled in urban and rural community settings. A total of 78 (21.1%) patients had fever (T ≥ 37.5 °C) at the time of enrolment in the study, and 116 (31.4%) received antipyretic treatment, while 126 (34.1%) participants had taken antimalarials prior to enrolment. Although the mean hemoglobin level was similar across the different participant groups, the median parasitemia was significantly higher in participants recruited in the urban health facilities (8,380 trophozoites/µL of blood) compared to those enrolled in the urban community (346 trophozoites/µL of blood) and the rural community (574 trophozoites/µL of blood) (p < 0.0001).

Table 1.

Socio-demographic and clinical characteristics of the study population.

Variables Health facilities (N:144) Community Total
(N:369)
Urban
(N:65)		 Rural
(N:160)
Sex ratio (F/M) 1.4 (83/61) 1.5 (39/26) 0.8 (70/90) 1.1(192/177)
Axillary temperature (m ± SD) 37.1 ± 0.7 36.8 ± 0.6 36.8 ± 0.6 36.8 ± 0.5
Febrile, n (%)
Yes, T ≥ 37.5 °C 37 (25.7) 5 (7.7) 36 (22.5) 78 (21.1)
No, T < 37.5 °C 107 (74.3) 60 (92.3) 124 (77.5) 291 (78.9)
Hemoglobin level, g/dL (m ± sd) 11.7 ± 1.5 11.4 ± 1.7 11.3 ± 1.8 11.5 ± 1.7
Have taken an antipyretic, n (%)
Yes 101 (70.1) 12 (18.5) 03 (1.9) 116 (31.4)
No 43 (29.9) 53 (81.5) 157 (98.1) 253 (68.6)
Have taken an antimalarial, n (%)
Yes 67 (46.5) 22 (33.8) 37 (23.1) 126 (34.1)
No 77 (53.5) 43 (66.2) 123 (76.9) 243 (65.9)
Anemia, n (%)
Yes 88 (61.1) 41 (63.1) 116 (72 0.5) 245(66.4)
No 56 (38.9) 24 (36.9) 44 (27.5) 124 (33.6)
Geometric mean, Parasites/µL (95% IC)

2647

(1542–4547)

307

(184–511)

715

(546–936)

1027

(785–1343)
Parasite density group, n (%)
Median age, (year) (IC 95%) 16 (13–19) 15 (11–19) 15 (13–16) 15 (14–17)
Age group, n (%)
< 5 years 15 (10.4) 02 (03.1) 18 (11.2) 35 (09.5)
5–15 years 57 (39.6) 34 (52.3) 95 (59.4) 186 (50.4)
> 15 years 72 (50.0) 29 (44.6) 47 (29.4) 148 (40.1)
Season, n (%)
Dry 134 (93) 28 (43.1) 75 (46.9) 237 (64.2)
Rainy 10 (07) 37 (56.9) 85 (53.1) 132 (35.8)
Clinical status, n (%)
Symptomatic 144 (100) 21 (32.3) 68 (42.5) 233 (63.2)
Asymptomatic 00 (0.0) 44 (67.7) 92 (57.5) 136 (36.8)
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m: mean; sd: standard deviation.

Prevalence of point mutations within Pfcrt, Pfmdr1, andPfk13genes

A total of 77 sequences obtained for the region (86–184) of the Pfmdr1 gene, from 93 samples randomly selected to confirm the profiles obtained by the PCR-RFLP technique, were published on GenBank, with the accession numbers (OR221071 to OR221148). In addition, mutations in the region comprising codons 1034–1246 of the Pfmdr1 gene and region 470–630 of the Pfk13 gene were successfully analyzed. In total, 327 and 302 of the genotyped sequences of Pfmdr1 (1034–1246) and Pfk13 have been published in GenBank with the following accession numbers: Pfmdr1 (1034–1246), (OR348868 to OR349195), and Pfk13 (OQ714715 to OQ714806 and OR158056 to OR158267) on the basis of the presence or absence of investigated key mutations.

Molecular analysis data showed 26% prevalence of the K76T mutation in all samples collected from the three study sites. The prevalence of N86Y and Y184F mutations in the Pfmdr1 gene was 6.8% and 27.7%, respectively. However, the S1034C, N1042D, and D1246Y mutations of the Pfmdr1 gene were not detected. Concerning Pfk13 gene, none of the mutations validated for partial resistance to ART were detected in the present study. However, 25.1% of non-synonymous mutations, including 24.8% of the K479 deletion and 0.3% of the A578S mutation, were detected (Table 2).

Table 2.

Prevalence of point mutations in Pfcrt, Pfmdr1 and Pfk13 genes.

Genes Mutations Health facilities
N:142		 Urban _com
N:58		 Rural_ com
N:157		 P. value Total
N:357
n (%) n (%) n (%) n (%)
Pfcrt K76T 40 (28,2) 11 (19.0) 42 (26.8) 0.3902 93 (26)
N86Y 7 (05.0) 8 (14,8) 9 (06.0) 0.0609 24 (06.8)
Y184F 35 (24.6) 20 (34.5) 44 (28.0) 0.3680 99 (27.7)
I185K 35/35 (100) 5/5 (100) 35/35 (100) NA 75/75 (100)
E1010Q 133 (93.7) 52 (89.7) 152 (96.8) 0.1138 337 (94.5)
Pfmdr1 S1034C 0 (00.0) 0 (00.0) 0 (00.0) NA 0 (00.0)
N1042D 0 (00.0) 0 (00.0) 0 (00.0) NA 0 (00.0)
P1139S 2 (01.4) 0 (00.0) 2 (01.3) 0.6714 4 (01.1)
M1191I 0 (00.0) 0 (00.0) 2 (01.3) 0.2777 2 (00.6)
F1194L 2 (01.4) 0 (00.0) 0 (00.0) 0.2181 2 (00.6)
D1246Y 0 (00.0) 0 (00.0) 0 (00.0) NA 0 (00.0)
Pfk13 K479- 35 (24.6) 0 (00.0) 52 (33.1) < 0.0001 88 (24.8)
A578S 0 (00.0) 0 (00.0) 1 (00.6) 0.5280 1 (00.3)
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Com: Community; n: frequency; %: percentage.

Prevalence of Pfcrtand Pfmdr1 haplotypes by study site

When grouping single-nucleotide polymorphisms into haplotypes at codons N86Y-Y184F-D1246Y of the Pfmdr1 gene, four (04) key haplotypes were identified. Of these, the most common were NYD (67.6%), followed by NFD (26.0%), YYD (3.2%), and YFD (3.2%). None of the following haplotypes (NYY, NFY, YFY, or YYY) were detected. No differences were observed between the distribution of haplotypes by study site (p > 0.05), except for the YFD haplotype, which was significantly more common in the urban community compared to health facilities and the rural community (p < 0.0001)(Table 3).

Table 3.

Frequency of haplotypes from Pfcrt, Pfmdr1, and Pfk13 genes. Com: community; n: frequency; %: percentage.

Genes Haplotypes Health facilities
N:142		 Urban _com
N:46		 Rural_ com
N:151		 P. value Total
N:339
n (%) n (%) n (%) n (%)
NYD 103 (72.5) 26 (56.5) 100 (66.2) 0.1175 229 (67.6)
NYY 0 (00.0) 0 (00.0) 0 (00.0) NA 0 (00.0)
NFY 0 (00.0) 0 (00.0) 0 (00.0) NA 0 (00.0)
NFD 34 (23.9) 13 (28.3) 41 (27.2) 0.7639 88 (26.0)
Pfmdr1 YFY 0 (00.0) 0 (00.0) 0 (00.0) NA 0 (00.0)
YFD 0 (00.0) 7 (15.2) 4 (02.6) < 0.0001 11 (03.2)
YYD 7 (04.9) 0 (00.0) 4 (02.6) 0.2234 11 (03.2)
YYY 0 (00.0) 0 (00.0) 0 (00.0) NA 0 (00.0)
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Association between haplotypes, socio-demographic, and clinical data

All haplotypes showed a similar distribution with no statistical difference in the data from dry season compared to those collected in the rainy season (p > 0.05). NFD, and YFD haplotypes were significantly more frequent in participants who had taken antimalarial drugs prior to enrolment, compared to those who had not taken the drugs (p < 0.05), conversely, the other haplotypes had a similar distribution in both groups of participants (p > 0.05). Furthermore, having a P. falciparum mono-infection or a mixed infection (P. falciparum and other species) showed no significant impact on the distribution of the different haplotypes identified in this study (p > 0.05) (Table 4).

Table 4.

Relationship between socio-demographic parameters and haplotype frequency.

Haplotypes Season Taking an antimalarial Co-infection
Dry Rainy P. value Yes No P. value Pf mono-infection Pf and other co-infections P. value
N: 210 N: 104 N:111 N: 203 N: 239 N: 75
n (%) n (%) n (%) n (%) n (%) n (%)
NYD 155 (73.8) 66 (63.5) 0.06 75 (67.6) 146 (71.9) 0.44 168 (70.3) 53 (70.6) > 0.99
NYY 0 (00.0) 0 (00.0) NA 0 (00.0) 0 (00.0) NA 0 (00.0) 0 (00.0) NA
NFY 0 (00.0) 0 (00.0) NA 0 (00.0) 0 (00.0) NA 0 (00.0) 0 (00.0) NA
NFD 43 (20.5) 31 (29.8) 0.08 44 (39.6) 30 (14.8) < 0.0001 56 (23.4) 18 (24.0) > 0.99
YFY 0 (00.0) 0 (00.0) NA 0 (00.0) 0 (00.0) NA 0 (00.0) 0 (00.0) NA
YFD 03 (1.4) 05 (4.8) 0.12 08 (7.2) 01 (0.5) 0.0013 07 (2.9) 02 (2.7) > 0.99
YYD 09 (4.5) 07 (6.9) 0.32 05 (4.5) 05 (2.5) 0.33 08 (3.3) 02 (2.7) > 0.99
YYY 0 (00.0) 0 (00.0) NA 0 (00.0) 0 (00.0) NA 0 (00.0) 0 (00.0) NA
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Discussion

The present study reports on the genetic polymorphisms of the Pfcrt, Pfmdr1, and Pfk13 genes in P. falciparum isolates from southern Brazzaville, 15 years after adoption of ACT in the Republic of Congo.

The results of the study revealed that 26.0%, 6.8%, and 27.7% of isolates carried the Pfcrt K76T, Pfmdr1 N86Y, and Y184F point mutations, respectively. These data show a declining prevalence of K76T, N86Y, and Y184F mutations compared to previous data reported in the country40,41. These observations are in line with data reported elsewhere, such as in Mali and Kenya42–44. A decrease in the prevalence of the K76T mutation from 100% 41 before CQ withdrawal to 26% as reported in the present study suggests a return of CQ-susceptible strains in these study settings of the Republic of Congo. The prevalence of the N86Y mutation has decreased from 73% to 27% in 2010 and 2015 respectively 40 to 6.8% to date as indicated by this study. The fact that chloroquine and amodiaquine monotherapy were withdrawn as treatments for uncomplicated malaria in the Republic of Congo45 could explain the decreased prevalence of the N86Y mutation. Concerning the Y184F mutation, which was widespread between 2010 and 2015 (54%-49%)40, this study reported 27.7% for the mutation. This is in line with recent data from Cameroon reporting 26.8% for this Y184F mutation46. However, the data of the present study differ from those of other studies from Central African countries, which have reported an increasing rate of the Y184F mutation47,48. The present study revealed the absence of mutations S1034C, N1042D, and D1246Y within the Pfmdr1 gene. This corroborates the results of several studies carried out in Brazzaville40,49 and elsewhere48,50,51, as these mutations rarely occur in Africa.

Several reports from Africa have suggested that the appearance of the different resistant haplotypes, including NFD and NYD, may result from AL selection, while the YYY haplotype may be more frequent in areas where parasites are exposed to AS-AQ, DHAP, and CQ52,53. In addition, in vitro lumefantrine sensitivity studies reported that parasites carrying the NFD haplotype were able to resist 15-fold higher concentration of lumefantrine than those carrying the YYY haplotype54. The data of this study showed that the NFD and NYD haplotypes were frequently found in the study population (26% and 70.7%), while no YYY haplotype was observed. These results support the hypothesis of the selective effect of AL on NFD and NYD haplotypes in our study areas, as AL has been used since 2006 in the Republic of Congo as a first-line treatment for uncomplicated malaria. Consequently, the abundance of NFD and NYD haplotypes in the study settings may suggest a potential decrease in the sensitivity of the parasites to lumefantrine rather than amodiaquine. Two other haplotypes (YFD and YYD) were observed with low prevalence (3.2% respectively). The low prevalence of these haplotypes with the N86Y mutation could be associated with the reduced pressure of CQ or AQ monotherapy after the implementation of ACT, even though AQ maintains a low pressure, as it is used in combination with ART derivatives55.

Data from the present study showed no known mutations in the Pfk13 gene previously associated with reduced sensitivity of the parasites to ART. However, 25.1% of P. falciparum isolates harbored a non-synonymous mutation in the helix domain of the Pfk13 gene, with 24.8% of K479 deletion and 0.3% A578S mutation found only in rural community settings. The low proportion of non-synonymous mutations has been reported in a recent study in the south of Brazzaville56 and in several studies conducted across sub-Saharan countries such as the Democratic Republic of Congo, Cameroon, Zambia, and Sudan57–60. We detected for the first time the K479 deletion in the Republic of Congo. According to previous studies40,49,56 this mutation has not been identified anywhere, and its involvement in ART resistance remains unknown. Thus, due to the distribution of these isolates among samples from urban health facilities, urban and rural communities in southern Brazzaville, further data are required in terms of their characterization as well as the assessment of their role for in vivo and in vitro parasite elimination. Our data showed a statistically significantly higher frequency of NFD and YFD haplotypes in participants who had taken antimalarial drugs prior to enrolment in the study, which is in line with the fact that uncontrolled use of antimalarial drugs is known to influence the selection of resistant haplotypes61.

Conclusion

Molecular analysis of the Pfcrt and Pfmdr1 genes showed a significant decrease in the K76T, N86Y, and Y184F mutations in P. falciparum isolates circulating in southern Brazzaville, in the Republic of Congo. The NFD and NYD haplotypes were predominant among the analyzed isolates, and the YYY haplotype was not detected. These results may indicate reduced susceptibility of P. falciparum parasite to AL and increased susceptibility to chloroquine or amodiaquine, which needs to be confirmed by in vivo studies. Furthermore, none of the mutations in the Pfk13 gene previously associated with reduced sensibility to ART and its derivatives were observed in this study. As mutations associated with partial ART resistance are emerging throughout Africa, ongoing molecular surveillance for early detection of these mutations and of relevant markers of resistance in partner drugs remains essential.

Electronic supplementary material

Below is the link to the electronic supplementary material.

Supplementary Material 1 (179.9KB, pdf)
Supplementary Material 2 (279.3KB, pdf)
Supplementary Material 3 (404.5KB, pdf)
Supplementary Material 4 (555.8KB, pdf)
Supplementary Material 5 (553.8KB, pdf)
Supplementary Material 6 (590.4KB, pdf)
Supplementary Material 7 (585.5KB, pdf)

Acknowledgements

We acknowledge the Madibou District’s Cabinet de Consultation et Laboratoire d’Analyses Médicales staff, as well as the study participants. FN is member of CANTAM (EDCTPRegNet2015-1045) and PANDORA-ID-Net (RAI2016E-1609) networks, which are funded by EDCTP and European member states. We express our gratitude to Mr Georges Missontsa, Mr Jolivet Mayela, and Mr Steve Diafouka-Kietela for their invaluable managerial support and to Dr Adrian J F Luty for his assistance in editing.

Author contributions

B.M.T, J.C.D and F.N developed the study design. B.M.T, F.N, J.C.D, and A.L supervised the work. M.T.B, J.C.D, J.D.M.N and C.C.M.M performed the experiments in the study. M.T.B, J.C.D, J.D.M.N, A.M.M and A.L were involved in sample collection. All authors were involved in writing and approved the final manuscript.

Funding

This research was funded by the Central Africa Clinical Research network (CANTAM) [EDCTPRegNet2015-1045]. JCD and AL are supported by the Fondation Merieux. CCMM is supported by Alexander von Humboldt foundation through Central Africa regional hub (HRH-Coca).

Data availability

Data is provided within the manuscript or supplementary information files, and sequences that support the findings of this study have been deposited in GenBank with the following accession numbers: Pfmdr1 (86;184)(OR221071 to OR221148); Pfmdr1 (1034–1246)(OR348868 to OR349195), and PfK13 (OQ714715 to OQ714806 and OR158056 to OR158267).

Declarations

Competing interests

The authors declare no competing interests.

Footnotes

Publisher’s note

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Contributor Information

Jean Claude Djontu, Email: cdjontu@yahoo.fr.

Francine Ntoumi, Email: fntoumi@fcrm-congo.com, Email: ffntoumi@hotmail.com.

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Associated Data

This section collects any data citations, data availability statements, or supplementary materials included in this article.

Supplementary Materials

Supplementary Material 1 (179.9KB, pdf)
Supplementary Material 2 (279.3KB, pdf)
Supplementary Material 3 (404.5KB, pdf)
Supplementary Material 4 (555.8KB, pdf)
Supplementary Material 5 (553.8KB, pdf)
Supplementary Material 6 (590.4KB, pdf)
Supplementary Material 7 (585.5KB, pdf)

Data Availability Statement

Data is provided within the manuscript or supplementary information files, and sequences that support the findings of this study have been deposited in GenBank with the following accession numbers: Pfmdr1 (86;184)(OR221071 to OR221148); Pfmdr1 (1034–1246)(OR348868 to OR349195), and PfK13 (OQ714715 to OQ714806 and OR158056 to OR158267).

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