Spatial and genetic clustering of Plasmodium falciparum and Plasmodium vivax infections in a low-transmission area of Ethiopia

The distribution of malaria infections is heterogeneous in space and time, especially in low transmission settings. Understanding this clustering may allow identification and targeting of pockets of transmission. In Adama district, Ethiopia, Plasmodium falciparum and P. vivax malaria patients and controls were examined, together with household members and immediate neighbors. Rapid diagnostic test and quantitative PCR (qPCR) were used for the detection of infections that were genetically characterized by a panel of microsatellite loci for P. falciparum (26) and P. vivax (11), respectively. Individuals living in households of clinical P. falciparum patients were more likely to have qPCR detected P. falciparum infections (22.0%, 9/41) compared to individuals in control households (8.7%, 37/426; odds ratio, 2.9; 95% confidence interval, 1.3–6.4; P = .007). Genetically related P. falciparum, but not P. vivax infections showed strong clustering within households. Genotyping revealed a marked temporal cluster of P. falciparum infections, almost exclusively comprised of clinical cases. These findings uncover previously unappreciated transmission dynamics and support a rational approach to reactive case detection strategies for P. falciparum in Ethiopia.


Scientific Reports
| (2020) 10:19975 | https://doi.org/10.1038/s41598-020-77031-z www.nature.com/scientificreports/ the genetic relatedness of infections. Such studies can provide a better understanding of fine-scale transmission patterns and estimate the contribution to transmission of infections that are not detected by routine interventions.
In this study, the prevalence and distribution of asymptomatic P. falciparum and P. vivax infections surrounding passively detected RDT-confirmed clinical malaria patients was assessed using RDT and quantitative PCR (qPCR) in Batu Degaga, Ethiopia. Spatial and temporal relatedness and clustering of infections within and between households of passively detected malaria infections and controls were investigated by genotyping 26 and 11 microsatellite loci for P. falciparum and P. vivax, respectively, which had been shown previously to be useful to describe population structure 12,[19][20][21][22][23][24][25] .
Spatial and temporal relatedness of P. falciparum and P. vivax infections. Pairwise genetic relatedness in 70 P. falciparum samples from 57 households (with allele calls from at least 15 loci) and 91 P. vivax samples from 79 households (with allele calls from at least 6 loci) were compared using the IBS metric. These data were used to determine how genetic relatedness of infections varied in space and time. P. falciparum infections sampled from the same household were more strongly related than infections from different households (median genetic relatedness: 0.81 vs. 0.37, P < 0.001; Fig. 4A). The degree of relatedness did not differ depending on household distance. P. falciparum infections that were genetically related (IBS > 0.5) and sampled from different households (Fig. 4B), were sampled within a shorter time frame compared to unrelated infections (median number of days: 17 vs. 25, P < 0.001; Fig. 4C), suggesting temporal clustering of genetically related P. falciparum infections. In contrast, overall pairwise genetic relatedness between P. vivax infections was low, with no spatial (Fig. 4D,E) or temporal clustering (Fig. 4F).
Fine-scale population structure of P. falciparum but not P. vivax infections. Analysis of dominant alleles at each locus using MavericK identified three sub-populations (K = 3), with the highest model evidence for both P. falciparum and P. vivax. Most parasites had their origin clearly assigned to a single cluster; few of them were admixed as shown by bars partitioned into K colored segments (Fig. 5). P. falciparum infections detected around index cases vs. controls were comprised of different populations (Fig. 5A), with infections belonging to group-3 almost exclusively detected in and around index cases households (23% vs. 4%, P = 0.010) and group-1 overrepresented in controls (72% vs. 42%, P = 0.003). Infections assigned to group-3 were identified in five independent RCD investigations and included one individual in the control group whose infection was genetically related to one of the index cases (IBS = 0.7). Analyses of the elapsed time between samples in each group showed that infections assigned to group-3 were clustered temporally (median number of days = 16, IQR: 0-30) compared to group-1 (23, IQR: 12-42; P = 0.007) and group-2 (25, IQR: 7-41; P = 0.004). These observations support the hypothesis that infections identified through investigation around index cases are linked possibly due to recent, local spread of group-3 haplotypes during the study period. No P. vivax genetic groups were overrepresented either to the control or index cases (Fig. 5B).

Figure 2.
Comparison of the odds of detecting infections around households. The forest plot shows the odds ratio and 95% confidence interval for detecting asymptomatic malaria infections by 18S based qPCR in households of index cases (Index HH) and neighbors of index cases compared to control households (community control) for P. falciparum and P. vivax infections separately after excluding the index cases. Indicated on the X-axis the Log 10 transformed odds ratio and on Y-axis are the different comparison groups. The prevalence of RDT and qPCR detectable asymptomatic infections for each household category and species are also indicted for each group to the left of the figure.

Discussion
In the current study, we examined spatial clustering of Plasmodium infections in a low endemic area in Ethiopia with a focus on asymptomatic infections in the vicinity of passively detected clinical index cases. Asymptomatic P. falciparum infections were clustered in index case households whilst there was no such evidence for clustering of P. vivax infections. P. vivax infections were more genetically complex and diverse than P. falciparum infections, with no detectable spatial or temporal clustering. We identified fine-scale focal transmission and a genetic cluster around index cases that may represent a clonal P. falciparum expansion, highlighting the added value of genotyping to understand the contrasting transmission epidemiology of the two Plasmodium species. Spatial and temporal heterogeneity in the occurrence of infections is widely acknowledged in malaria 26,27 and other infectious diseases [28][29][30] . Several countries have adopted RCD approaches that take advantage of infection clustering and assume that transmission chains, or at least transmission sources, can be interrupted by targeting infected household members. In some settings where symptomatic and asymptomatic malaria cases do not overlap spatially, clusters of infections can be missed by RCD 31 . This highlights the need for detailed studies on Plasmodium transmission patterns in settings aiming for elimination 32 . In the current study, we moved beyond spatial patterns in infection prevalence and examined small-scale transmission patterns by genotyping microsatellite markers. The overall genetic diversity (i.e. the mean expected heterozygosity) in P. falciparum infections  We observed a clear overlap of symptomatic and asymptomatic P. falciparum infections; the odds of detecting an infection within the household of clinical patients was significantly higher than in neighboring households 8,18,34,35 . MOI and genetic diversity of infections were higher around passively detected P. falciparum compared to controls, indicative of higher transmission. We observed that the very strong household-level clustering for P. falciparum was matched with strong genetic relatedness of infections within a household. Density of asymptomatic P. falciparum infections surrounding clinical cases was higher than in infections detected in control households. This might indicate that the former infections were acquired more recently compared to older, very low density infections detected in controls. All of these findings corroborate that RCD is useful to detect clusters of recently acquired P. falciparum infections. Results from control households, however, show that a substantial number of infections is not linked to index cases. As found in other studies 36,37 , RCD alone is thus unlikely to achieve elimination.
We detected a genetic cluster of P. falciparum infections, present in multiple households, that was overrepresented in and around index cases. These findings suggest a possible population expansion of a single parasite genotype. We can hypothesize that possible expansion, occurring against the background of wide-spread asymptomatic infections, could indicate introduction of a clone from another site. Its association with clinical cases suggests that some infections disproportionally give rise to symptoms 18 .
As in other sites where P. falciparum and P. vivax are co-endemic, P. vivax was more diverse, showed high MOI, and exhibited lower levels of population structure [38][39][40][41] 42 . There was no significant difference in the odds to detect a asymptomatic P. vivax infection in the household or neighbors of an index case compared to a control household. There was no difference in the MOI and heterogeneity of infections around passively detected P. vivax infections and controls. No spatial clustering was observed for P. vivax. The loss of relatedness and household level clustering may reflect differences in the biology of these two Plasmodium species where the majority of clinical P. vivax cases are attributable to relapsing episodes and do not necessarily reflect an active circulating infection that involves  www.nature.com/scientificreports/ mosquito bites 43,44 . This would contribute to considerable variation in the time between the original inoculation by mosquito bite and parasite detection in the bloodstream, and thus diffuse any genomic signal. In summary, RCD is not more effective than random screening to identify asymptomatic P. vivax infections in the study site. Our findings highlight the value of genotyping approaches to infer spatial and temporal clustering of P. falciparum infections. Whilst infections are prevalent outside households of index cases, and thus may be missed by RCD approaches, a disproportionate number of infections may be targeted by RCD 45 . Our finding of a possible population expansion of a single parasite genotype arising from a clone that was frequently detected in clinical cases further supports RCD approaches that may interrupt clinically relevant transmission chains. The lack of spatial (genetic) clustering in P. vivax may be attributable to the limitations of the methods used in this study, in addition to its unique biology discussed above, calling for more robust approaches. The biology of P. vivax; existence of hypnozoites 43,46,47 , generation of gametocytes at the very early stage 48 , lower parasite densities 49,50 , and higher vectoral capacity [51][52][53] , makes it a harder species to control and one that may require a different strategy to that of P. falciparum 54,55 . Future studies may benefit from more sample size and including genotyping of mosquitoes paired with the human sampling in study households 56 . In summary, for P. vivax RCD may not be more efficient than untargeted screening to identify asymptomatic infections in the study site. Control strategies against P. vivax need to target hypnozoites using radical cure, as the majority of infections detected are attributable to relapsing episodes rather than active infections 57 . Justified by the very low prevalence of glucose-6-phosphate dehydrogenase deficiency [58][59][60][61][62][63][64] . Ethiopia is currently implementing a 14-day dose primaquine for radical cure in P. vivax [65][66][67] .

Study site, population and ethics statement. This study was conducted between October and
December 2016 in Batu Degaga within Adama district, Ethiopia. Adama district is located at an altitude range of 1400-2300 m above sea level, and has an estimated population of 183,502 within 38,230 households (District Health Office Report). P. falciparum and P. vivax are co-endemic in the district 5,68 with 60% of the infections attributed to P. vivax 5 . The area is characterized by unstable seasonal transmission that peaks following the two rainy seasons: September to November (major) and April to May (minor). Two health posts, the lowest level governmental heath facilities, serve the study area. Only one of the two health posts (the southern health post, Fig. 6) was providing service during the entire study period. The northern health post was opened only for 3 days during the study period.
The study was approved by the ethics review boards of the Department of Medical Biochemistry at Addis Ababa University (DRERC 19/16), Armauer Hansen Research Institute (PO52/14), the National Research Ethics Review Committee (310/109/2016), and the London School of Hygiene and Tropical Medicine (10628). Written informed consent was obtained from all participants and/or parents/guardians. All experiments were performed in accordance with permission obtained from participants on the consent forms, following relevant guidelines and regulations. If participant was found RDT positive while being febrile during household level sampling, he/she was referred to the nearest health post to be treated with current first-line antimalarial drugs according to the Ethiopian national malaria treatment guidelines, artemether-lumefantrine for P. falciparum and chloroquine for P. vivax.

Controls
Index cases

P. vivax, K=3
Group-1 Group-2 Group-3  Patients that were suspected of malaria but tested RDT negative on the same day were included as controls. Individuals who attended the health post for vaccination, trauma, pregnancy test, antenatal care, and family planning were not considered. Index cases were independent of one another (i.e. they wouldn't be found in the same cluster of households assessed for another index case). A total of 147 patients attended the clinic during the study period of whom 52 presented with main complaint of fever. Fever was confirmed in 44 of them of whom 24 were RDT positive for malaria. Patients who were referred to the nearby health center for better treatment (n = 4), were not residents of the study area (n = 2), or did not have completed information (n = 2) were excluded from the study. Controls were recruited among patients that presented to the same health post where the index case was recruited. During the study period, 0-6 patients attended the clinic in a day and the health extension workers were available for 2-3 days a week from 9:00-12:00 a.m. The northern health post was opened occasionally and the same health extension workers were responsible and two index cases and two controls were recruited. Household members and members of the six nearest neighboring households of index cases and controls were tested for malaria by RDT within 2 days after the index or control cases were identified 8 and had their household geo-located with a handheld GPS receiver (GPSMAP 62 s; Garmin International). Neighboring households were selected based on proximity to the respective index case or control household upon obtaining permission from the head of the house. Socio-demographic and epidemiological data were collected, together with finger prick blood samples to diagnose malaria using RDT and to prepare dried blood spots (DBS) on 3MM Whatman papers (Whatman, Maidstone, UK) from all study participants including index cases, controls, neighbors and household members.
DNA was extracted from a 6 mm diameter punch, using MagNa Pure LC 2.0 Instrument and Total Nucleic Acid Kit-High Performance (Roche Life Sciences) with prior treatment using Buffer ATL (QIAGEN) and Proteinase K (QIAGEN) 5 . Parasites were quantified using species-specific qPCR that targeted the 18S small subunit rRNA gene 69 . qPCR positive samples were genotyped using 26 microsatellite markers for P. falciparum and 11 markers for P. vivax using previously established laboratory and data analyses protocols 12,[19][20][21][22][23] . Mixed species infections were genotyped with both P. falciparum and P. vivax markers. Genotyping was successful in 92/93 P. falciparum and 99/123 P. vivax positive samples. For haplotype reconstruction in multiclonal infections, the predominant peak was included. Figure 6. Map of study area and distribution of study households. Shown in the right bottom corner is a map of Ethiopia with boundaries indicating administrative regions (asterisk indicating the study district, Adama, Oromia region). The location of index households (thick circles; P. falciparum, dark blue; P. vivax, blue; mixed species, light blue) and neighbors of index households (thin circles) surveyed during the study are indicated together with control households (thick triangles) and neighbor households of controls (think triangles). The two health posts are indicated in black and bold letter 'H' . The entire study area was 0.031 km 2 . The QGIS software version 3.14.16 (QGIS developer team, Open Source Geospatial Foundation Project) was used to map study households (https ://issue s.qgis.org/proje cts/qgis/). Coordinates of study household were geo-located with a handheld GPS receiver (GPSMAP 62 s; Garmin International). Odds ratios (OR) with 95% confidence intervals (95% CI) were calculated using Generalized Estimating Equations to determine the association between being parasite positive and living in households of index cases or surrounding houses. These estimates were adjusted for clustering of species-specific observations from the same household with robust standard errors and used community members not belonging to index cases or neighboring households as a reference. Multiplicity of Infection (MOI) was defined as the highest number of alleles detected by at least two loci, reducing the impact of false positive allele calls from a single, outlier locus 12,22 . Population-level genetic diversity was determined by the expected heterozygosity (H E ) of each locus. H E was calculated for each locus using the formula H E = n n−1 [1 − p i 2 ] , where n = the number of isolates analyzed and p i = the allele frequency of the ith allele in the population. Mean H E was calculated by taking the average of H E across all loci. To assess the genetic relatedness and clustering of P. falciparum and P. vivax infections, pairwise genetic relatedness was determined for each pair of samples using a modified identity by state (IBS) metric 22 . Briefly, pairwise IBS was calculated based on the number of shared alleles between isolates, allowing estimation of genetic relatedness from monoclonal as well as polyclonal samples. Samples were considered related if the pairwise genetic relatedness was greater than or equal to 0.5 (i.e. at least 50% of microsatellite markers shared the same alleles). This threshold reflects the degree of relatedness of meiotic siblings, which are the result of sexual recombination of different clones in the mosquito. Population structure was assessed using the program MavericK 70 , under the admixture model with 500 burn-in iterations, 5000 sampling iterations and 20 thermodynamic rungs.

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