Predictive markers of transmission in areas with different malaria endemicity in north-eastern Tanzania based on seroprevalence of antibodies against Plasmodium falciparum

Objective A community-based cross-sectional study was done to assess Plasmodium falciparum exposure in areas with different malaria endemicity in north-eastern Tanzania using serological markers; PfAMA-1 and PfMSP-119. Results Bondo had a higher seroprevalence 36.6% (188) for PfAMA-1 as compared to Hai 13.8% (33), χ2 = 34.66, p < 0.01. Likewise, Bondo had a higher seroprevalence 201(36.6%) for PfMSP-1 as compared to Hai 41 (17.2%), χ2 = 29.62, p < 0.01. Anti-PfAMA-1 titters were higher in malaria positive individuals (n = 47) than in malaria negative individuals (n = 741) (p = 0.07). Anti-PfMSP-1 antibody concentrations were significantly higher in malaria-positive individuals (n = 47) than in malaria-negative individuals (n = 741) (p = 0.003). Antibody response against PfAMA-1 was significantly different between the three age groups; < 5 years, 5 to 15 years and > 15 years in both sites of Bondo and Hai. Likewise, antibody response against PfMSP-119 was significantly different between the three age groups in the two sites (p < 0.001). We also found significant differences in the anti-PfAMA-1and anti-PfMSP-119 antibody concentrations among the three age groups in the two sites (p = 0.004 and 0.005) respectively. Immunological indicators of P. falciparum exposure have proven to be useful in explaining long-term changes in the transmission dynamics, especially in low transmission settings. Supplementary Information The online version contains supplementary material available at 10.1186/s13104-021-05818-y.


Introduction
Africa carries the highest burden of malaria with more than 70% of all malaria cases and deaths [1]. Each year, 10 to 12 million people contract malaria and more than 80,000 dies [2,3]. Plasmodium falciparum is mainly responsible for 99.7% of estimated malaria cases [4].
In many countries, local malaria transmission has decreased due to the extensive efforts being devoted to malaria control and elimination [5]. P. falciparum accounts for 96 percent of cases [6], malaria prevalence varies from < 1 percent in the highlands of Arusha to as high as 15 percent in the Southern Zone and 24 percent along the Lake and Western Zones. Immunity to P. falciparum malaria is poorly understood, however, evidence shows that antibody-dependent cellular mechanisms play a key role in immunity against P. falciparum malaria Open Access BMC Research Notes *Correspondence: robert.kaaya@kcmuco.ac.tz 1 Departmentof Parasitology and Entomology, Faculty of Medicine, Kilimanjaro Christian Medical University College, Moshi, Tanzania Full list of author information is available at the end of the article parasite [7,8]. The rate of its development is believed to be associated with transmission intensity which is stagespecific and is rarely sterile [6]. In many epidemiological studies, the determination of malaria transmission has been based on the antibody levels against P. falciparum antigens [9]. Recent immunological studies revealed that antibodies against merozoite antigens act as biomarkers of malaria exposure and that, with increasing exposure and responses of higher levels, antibodies may act as biomarkers of protective immunity [10].
Apical membrane antigen 1 (AMA-1) is expressed on merozoites and sporozoites of P. falciparum as a type I integral membrane protein [11] while Merozoite surface protein1 (MSP-1), is a highly conserved protein among Plasmodium species as well as the most abundant protein expressed on the surface of merozoites [12]. Antibodies against MSP-1 and AMA-1 antigens are potential markers of both exposure to P. falciparum and protection against the disease [7,13] and have proven to be informative, in areas where transmission has dropped to low sustained levels, for monitoring the timing and magnitude of transmission reduction [13] as well as in obtaining epidemiological information in malaria control programmes [14].
In areas with low malaria transmission, it has become extremely difficult to detect changes in transmission intensity using conventional methods such as the entomologic inoculation rate (EIR) or malaria prevalence rates. Low transmission areas (low endemicity) sometimes have low mosquito density, below the detection limits of common mosquito trapping methods [15,16] and the parasite prevalence also becomes less reliable [17][18][19]. Malaria serological markers may aid in estimating malaria transmission intensity [20][21][22]. Seroconversion rates may provide insight into recent changes in malaria transmission [23]. Due to the fact antibodies can persist for months or years after infection, seroconversion rates are less affected by the effects of unstable or seasonal transmission [20,21]. We investigated the antibody response to recombinant AMA-1 and MSP-1 in individuals living in two regionally distinct malariaendemic zones.

Study area
The study was conducted during April and December 2014 in two different areas of the Tanzanian mainland. The first site was Bondo in the Tanga region, inhabited by 7970 people [24]. The second study site was Hai in Kilimanjaro region [14]. Participant recruitment procedures and study design have been previously described [25], (Additional file 1: Fig. S1).

Participant enrolment and sample collection
Participants 2 years of age and above who reside in the study areas were enrolled in the study. A blood sample was obtained by finger prick, a portion of blood was used for malaria rapid test, which was performed onsite. A blood spot was prepared for each participant, then dried and stored for further analysis. A 3.0 mm diameter circle of dried blood spot (equivalent to 2 µl whole blood/1 µl serum) was reconstituted in 200 µl of sodium azide-phosphate buffered saline-tween (0.05%) (PBST/0.1% Azide).

Malaria parasite detection by polymerase chain reaction (PCR)
Parasite DNA was extracted using the simple Chelex-Saponin method, Plasmodium nucleic acid amplification was conducted using genus-specific reverse and forward primers (rPLU6-5′TTA AAA TTG TTG CAG TTA AAACG3′ and rPLU5-5′CCT GTT GTT GCC TTA AAC TCC3′) targeting small sub-unit ribosomal RNA (ssurRNA) of the parasite. A reaction mix of 20 µl per sample was used, 5 µl of template DNA extracted from participants whole blood plus 15 µl of nuclease-free water, dNTPs, Taq enzyme, buffers and salts. Amplification conditions were, 95 °C for 5 min followed by 30 cycles of 94 °C for 1 min, 58 °C for 2 min and 72 °C for 5 min then one final extension cycle at 72 °C for 10 min. Amplification products were run in Ethidium bromide agarose gel (2%) electrophoresis at 120 V, 50 watts and 120 mA. The amplified bands were visualized under ultra-violet light trans-illuminator [26,27].

Data analysis
All data were analyzed using SPSS 20.0 software (SPSS Inc., Chicago, IL, USA) and GraphPad Prism8 software (San Diego, CA). After verifying that Optical density (OD) values were not normally distributed (p < 0.0001; Anderson-Darling test), non-parametric tests were performed to compare the OD. The Mann-Whitney test was used for the comparison of Antibody levels of two independent groups. The non-parametric Kruskal-Wallis test was used for the comparison of more than two groups. Pearson's Chi-squared (χ 2 ) test was used to compare two proportions.
In assessing whether these differences were influenced by age, we calculated the differences among < 5 years, 5 to 15 years and > 15 years per site. Antibody response against PfAMA-1 was significantly different between the three age groups in both sites. (Kruskal-Wallis test, p < 0.001) ( Table 1). Likewise, antibody response against PfMSP-1 19 was significantly different between the three-age groups in the two sites (Kruskal-Wallis test, p < 0.001) ( Table 1). We also found significant differences in the anti-PfAMA-1antibody concentrations among the groups (Kruskal-Wallis test, p = 0.004), as indicated in Fig. 2A, B. Lastly, we also noted significant differences in the anti-PfMSP-1 19 antibody concentrations among the age groups (Kruskal-Wallis test, p = 0.005) (Fig. 2C, D).

Discussion
The purpose of this study was to use immunological markers to investigate malaria transmission patterns in areas with diverse malaria endemicities.
In this study, malaria prevalence by PCR in Bondo was 28.1%. Since Bondo is a malaria-endemic area, malaria transmission occurs nearly all year long with a peak period from April to June. No significant difference was observed in malaria prevalence among age groups in the present study. This is contrary to the study conducted in 2011 which suggested a widening of the age group at risk for malaria infection to older children of 5-15 years [28]. A previous study conducted in two villages in the same region about 70 km from the current study found a re-emergence of malaria despite previous reports of a decline in malaria [29]. It is estimated that parasite prevalence at that time was 25% and it stayed there throughout 2016 [30]. Hai site had a very low malaria prevalence, thus remaining an area of low transmission and The mRDT tests did not detect any active infections, which suggests low-density parasite circulating in the population, similar to earlier findings [31]. There is, however, some evidence that individuals harbouring submicroscopic parasites could be sources of new infections since mosquitoes can carry parasites with very low density (< 5 parasites/µl) [26,32,33], and hence, the use of a more sensitive diagnostic tool like PCR in clinical malaria diagnosis is necessary. Consequently, scientific evidence from these findings is consistent with the notion of mass drug therapy for individuals with microscopic parasites considering efforts to eliminate malaria. In our study, Interestingly, when the age-dependent analysis was done, older children (5-15 years) had a relatively low seroprevalence to PfAMA-1 antigens as compared to younger children and Adults. Antibodies to malaria antigens can explain long-term changes in malaria transmission dynamics [21]. In 2009 a survey conducted in Moshi district, an area bordering Hai found a low seroprevalence in younger children suggesting very low exposure to malaria parasites [34]. In populations with low immunity, such as young children, antibodies to MSP-1 act as a significant biomarker of malaria exposure and with increasing exposure the antibodies may contribute to protective immunity [10].
Seroprevalence in moderate malaria transmission setting such as Bondo can play a small role in determining malaria transmission patterns although seroprevalence is almost two folds higher than Hai. A slight decline in seroprevalence was observed in the study area when compared with previous studies [21,31], indicating a long-term reduction in malaria parasite exposure, which may be attributed to intense malaria interventions in Tanzania [35,36].
Study results showed that the overall concentration of PfMSP-1 19 was significantly higher in participants with positive malaria tests than in non-positive participants. As expected Bondo had higher antibody concentrations against both antigens as compared to Hai. Children with < 5 years present with low antibody titters suggesting a lack of recent malaria exposure and this makes the group vulnerable to the symptomatic manifestation of the disease. Earlier findings revealed that more than half of the participants reported being symptomatic and were malaria positive by mRDT [21]. There is evidence of malaria transmission in low malaria-endemic areas, where traditional malaria indicators like prevalence and sporozoite levels may underestimate the burden of the disease.

Conclusion
In this study, immunological markers were found to be a useful indicator of ongoing malaria transmission, especially in low-endemic areas. Routine malaria surveillance can be made more effective by using these immunological markers to highlight the importance of customized and targeted control interventions.

Study limitation
This study might not explain the recent changes in malaria transmission since it was a cross-sectional survey. A longitudinal study would have been appropriate in explaining seasonal variations in malaria infection rates across the study areas.