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. Author manuscript; available in PMC: 2024 Jun 1.
Published in final edited form as: Med Vet Entomol. 2022 Dec 5;37(2):275–285. doi: 10.1111/mve.12629

Low genetic diversity and population structuring of Amblyomma hebraeum and Rickettsia africae from coastal and inland regions in the Eastern Cape Province of South Africa

Alicia Pillay a,*, Nkululeko Nyangiwe b,c, Samson Mukaratirwa a,d
PMCID: PMC10191886  NIHMSID: NIHMS1852990  PMID: 36468449

Abstract

Amblyomma hebraeum is the main vector of Rickettsia africae, the causative agent of African tick bite fever in southern Africa. Because pathogen dispersal is known to be influenced by tick adaptations to climate or host species, this study aimed to analyze the genetic diversity of A. hebraeum and R. africae infection of ticks collected from cattle in the Eastern Cape province of South Africa. DNA was extracted, amplified, and sequenced for the COI and ITS2 markers from A. hebraeum samples and the 17kDa and ompA genes for rickettsial detection. Between six and ten haplotypes were identified from 40 COI and 31 ITS2 sequences; however, no population structuring was observed among sites (ΦST = 0.22, p < 0.05). All A. hebraeum isolates clustered with southern Africa GenBank isolates. Rickettsia africae was detected in 46.92% (95% CI= 41–53%, n=260) of ticks. All R. africae isolates clustered with strain PELE and Chucks, which were reported previously from South Africa. These results confirm that A. hebraeum populations are undergoing a recent population expansion driven by cattle movement, facilitating local and long dispersal events across the Eastern Cape province.

Keywords: Amblyomma hebraeum, Rickettsia africae, mitochondrial marker, nuclear marker, population genetics, South Africa, tick-borne disease

Graphical Abstract

graphic file with name nihms-1852990-f0001.jpg

Introduction

Amblyomma hebraeum is a three-host tick distributed across southern Africa’s savanna and thicket biomes (Estrada-Peña et al., 2008). In South Africa, A. hebraeum is found throughout the northern and eastern provinces, mainly distributed along the south-eastern coastal regions in the savanna and grassland biomes. The coastal belt environment in South Africa is most favorable to A. hebraeum due to the abundance of grasslands, more stable temperature ranges, and higher annual rainfall compared to inland regions (Marufu et al., 2011; Nyangiwe et al., 2011). However, its distribution has been reported to spread into the northeastern Northern Cape regions (Spickett et al., 2013; Yawa et al., 2018). Amblyomma hebraeum parasitizes mainly cattle, other domestic animals, and wildlife, creating an interface for human disease emergence (Horak et al., 2018; Iweriebor et al., 2020).

Rickettsia africae is an obligate, intracellular bacteria that is the etiological agent responsible for African tick bite fever (ATBF) (Raoult and Roux, 1997; Williams et al., 2007). Livestock husbandry practices play a significant role in the transmission dynamics of R. africae to humans, with a prevalence of up to 92.6% in cattle (Mutai et al., 2013; Yssouf et al., 2014). The high infection of R. africae in A. hebraeum is maintained by transovarial and transstadial transmission resulting in the larval, nymph, and adult life stages being able to infect humans (Kelly and Mason, 1991; Mutai et al., 2013; Yssouf et al., 2014). Amblyomma hebraeum is the principal vector of ATBF in southern Africa (Jensenius et al., 2003).

The rapid growth in tourism to South Africa from the diverse product offered by game reserves, heritage sites, and beaches has led to an increase in international ATBF case reports (Mxunyelwa and Lloyd, 2019; Silva-Ramos and Faccinni-Martinez, 2021). However, autochthonous case reports of ATBF are scarce due to the lack of awareness of the disease or the presentation of mild febrile symptoms that are often misdiagnosed (Tomassone et al., 2016). Molecular analysis of R. africae isolates have revealed high interspecies homogeneity, making identification and analysis difficult (Fournier et al., 2003; Kimita et al., 2016). Therefore, investigating intraspecific variation among R. africae isolates may help resolve discordance amongst the different gene fragments used to classify R. africae. Consequently, this may help diagnose the acute phase of the disease, which is often missed by serological diagnosis (Fournier et al., 2002).

Previous studies examined a single mtDNA marker and did not find any clear A. hebraeum population structuring between different coastal and a few inland regions in South Africa (Cangi et al., 2013; Matthee, 2020). Exploring the genetic diversity between inland and coastal A. hebraeum populations may help identify sites for evolutionary divergence. Thus, an in-depth and extensive study into the genetic diversity of A. hebraeum populations with more variable markers may clarify the factors driving the dispersal of the A. hebraeum-R. africae system. This study aimed to determine the population genetic structure of coastal and inland A. hebraeum ticks and R. africae isolated from the ticks using the COI and ITS-2 markers for A. hebraeum populations and the ompA and 17kDa genes for R. africae.

Methods

Study sites, collecting, sampling and taxonomy

Two-hundred-and-sixty adult A. hebraeum specimens were collected randomly from multiple cattle of mixed breeds grazing on communal pastures in rural areas of the Eastern Cape province of South Africa. Populations collected included twenty ticks each from seven coastal and six inland sites (Fig. 1). Ticks were collected from predilection sites and preserved in 70% ethanol until morphological and molecular analyses. Morphological identification of ticks was conducted at the Döhne Agricultural Research Institute in Stutterheim, South Africa, using Walker et al. (2003). Individual ticks were surface sterilized with 70% ethanol and rehydrated in distilled water before DNA extraction.

Fig. 1.

Fig. 1

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Location of study sites in the Eastern Cape Province in South Africa.

DNA extraction, amplification, and sequencing

Individual whole A. hebraeum ticks were bisected, and one half was placed into a 1.5mL microcentrifuge tube, which was utilized for DNA extraction. The other half was frozen at −80° C until further use. Tick extracts were homogenized using glass microbeads in a Disruptor Gene bead-beater (Scientific Industries, US) to facilitate the release of pathogens and protein digestion for nucleic acid extractions. Genomic DNA was extracted from individual ticks using the standard phenol-chloroform protocol (Sambrook and Russell, 2006). The standard protocol was modified by incubating ticks overnight at 56 °C to complete deproteinization. DNA was stored at 4 °C until further use.

Amblyomma hebraeum COI and ITS2 gene amplification

An ~800bp fragment of the cytochrome oxidase subunit I (COI) was amplified in a single-step PCR using forward primer AR-U-COIa (5’- AAACTRTKTRCCTTCAAAG-3’) and reverse primer AR-LCOIa (5’-GTRTTAAARTTTCGATCSGTTA-3’) (Cangi et al., 2013). Reactions were performed with a 3 min initial denaturation step at 95 °C, followed by 34 cycles at 95 °C for 30 sec, 45 sec at the annealing temperature of 40 °C, and 1 min at 68 °C with a final extension for 5 min at 72 °C. A 760 bp fragment of the internal transcribed spacer (ITS2) was amplified using primers TITS2F1 (5′-CGAGACTTGGTGTGAATTGCA-3′) and TITS2R1 (5′-TCCCATACACCACATTTCCCG-3′) (Chitimia et al., 2009). The ITS2 PCR protocol included initial denaturation at 95°C for 5 min, followed by 40 cycles of 95°C, 45 sec at 55°C, extension at 72°C for 90 sec, and a final extension of 72°C for 5 min. PCR reactions contained 25 μL of 12.5 μL OnetaqMastermix with standard buffer (NEB, UK), 1.5 μL (10μM) of each primer, and 3.5 μL (20–50 ng) of genomic DNA. COI and 17kDa isolates from a previous study were used as positive controls (MT150905.1 and MT009354) (Pillay and Mukaratirwa, 2020).

Rickettsia africae 17kDa and ompA gene amplification

A 450 bp fragment of the 17kDa gene targeting rickettsiae was amplified in a semi-nested PCR using forward primer Rp17kFw (5’-AATGAGTTTTATACTTTACAAAATTCTAAAAACCA-3’), reverse primer Rr2608Rv (5’- CATTGTTCGTCAGGTTGGCG −3’) and secondary forward primer Rr1175Fw (GCTCTTGCAACTTCTATGTT) (Jiang et al., 2005). A 530 bp fragment of the outer membrane protein A (ompA) targeting spotted fever group rickettsiae were amplified in a nested PCR using forward primer OmpAM50Fw (5’-TTGCGTTATAACACTTTTTAAGTGA-3’) and reverse primer OmpA642Rv (5’-ATTACCTATTGTTCCGTTAATGGCA-3’) followed by secondary forward primer 190-70Fw (5’-ATGGCGAATATTTCTCCAAAA-3’) and secondary reverse primer 190-702Rv (5’-GTTCCGTTAATGGCAGCATCT −3’) (Jiang et al. 2005). The 17kDa PCR protocol included initial denaturation at 94°C for five min, followed by 35 cycles of 94°C, 1 min at 50°C, extension at 68°C for 1 min, and a final extension of 72°C for 5 min. The ompA PCR protocol was performed with a 3 min initial denaturation step at 94 °C, followed by 40 cycles at 94 °C for 30 sec, 30 sec at the annealing temperature of 53 °C, and 1 min at 68 °C with a final extension for 5 min at 72 °C. PCR reactions contained 25 μL of 12.5 μL OnetaqMastermix with standard buffer (NEB, UK), 1.5 μL (10μM) of each primer, and 3.5 μL (20–50 ng) of genomic DNA.

Amplified products were separated by electrophoresis on a 1.5% agarose gel. Purification of amplicons and sequencing reactions were performed at Inqaba Biotech (Pretoria, South Africa). COI, 17kDa, ITS2, and ompA sequences were translated to amino acids in MEGAX to check for the presence of stop codons (Kumar et al., 2018). Individual COI and ITS2 mitochondrial haplotypes were identified using DNAsp v6 (Rozas et al., 2017). A BLASTN search was conducted on retrieved sequences to draw comparisons with similar published sequences (Altschul et al., 1997). Sequences were aligned using Multiple Sequence Comparison by Log - Expectation (MUSCLE), and consensus sequences were constructed using MEGAX (Kumar et al., 2018).

Quantitative analysis

Prevalence of R. africae

Prevalence 100 (%) of R. africae was calculated using an adapted formula (Thrusfield, 1995):

P=dn×100%

Where:

P= prevalence of R. africae,

d= total no. A. hebraeum infected with R. africae

n= total no. A. hebraeum screened for R. africae

The 95% confidence intervals (CIs) were computed using the MetaXL add-in for Microsoft Excel (www.epigear.com). The effect of tick sex and sampling location (inland or coastal) on prevalence was tested using Fisher’s exact test, and a p-value <0.05 was considered statistically significant. Statistical analyses were conducted using IBM SPSS V26.

Amblyomma hebraeum haplotype variation

Haplotype accumulation curves were constructed in R (R Core Development Team, 2010) to assess whether our sample sizes provided total coverage of the haplotypes present in the populations sampled using the HaploAccum function in the package Spider with 1,000 permutations (Brown et al., 2012). The Chao 1 richness estimator of total haplotype diversity and an abundance-based coverage estimator (ACE) was calculated using the Spider package’s chaohaplo function.

Phylogenetic analysis

The jModelTest (Posada, 2008) was used to determine the most suitable nucleotide substitution model using the Akaike information criteria (AIC) for Maximum Likelihood (ML) and Bayesian Inference (BI) analyses. The ML trees for COI and ITS2 haplotypes were constructed using the Tamura 3-parameter model in MEGA X. A BI topology was inferred under the Generalized time-reversible model + Gamma in MrBayes 3.2.7 (Huelsenbeck and Ronquist, 2001) for the CO1 and ITS2 haplotype datasets.

The ML trees for the 17kDa and ompA sequences were constructed using the Jukes-Cantor and Tamura 3-parameter model, respectively, in MEGAX. A BI topology was inferred under the Generalized time-reversible model in MrBayes 3.2.7 for the ompA and 17kDa haplotype datasets. GenBank sequences were added to the analyses. Trees were annotated using Figtree (http://tree.bio.ed.ac.uk/software/figtree/).

Population structure and diversity

Haplotype networks were constructed using the haploNet function in the R package pegas to visualize population structure (Paradis, 2010). Haplotype diversity, nucleotide diversity, and haplotype network branch diversity (HBd) were calculated in R version 4.1.2 (R Development Core Team, 2010). An analysis of molecular variance (AMOVA) was conducted in Arlequin v 3.5 (Excoffier and Lischer, 2010) to determine genetic structure within and among population groups. We assessed population structure by generating pairwise ΦST values among sites.

Demographic history

Tajima’s D statistic was tested against selective neutrality. Population equilibrium and Fu’s F statistic were calculated to detect population expansions by estimating departures from neutrality in Arlequin v 3.5. Mismatch distributions were constructed to visualize potential demographic expansion for the entire population and test the null hypothesis of population growth (Rogers and Harpending, 1992). The raggedness index (r) was calculated to test if the frequency of pairwise nucleotide differences followed a smooth unimodal curve expected from a growing population.

Results

Genetic diversity of A. hebraeum

Forty COI and 31 ITS2 sequences of A. hebraeum were obtained across 13 sites in the Eastern Cape province of South Africa. The COI and ITS2 sequence fragments were 581 bp and 596 bp, respectively. A total number of four COI and three ITS2 variable sites constituting one COI and three ITS2 parsimony informative sites were determined. The homology between GenBank and study sequences varied from 98–100% (Supplementary Table 1).

Haplotype diversity (Hd) was higher in ITS2 sequences (0.694) than in the COI sequences (0.535) (Table 1). The COI Hd increased substantially when GenBank sequences were included (0.756). Conversely, the ITS2 Hd (0.675) decreased slightly with the inclusion of GenBank sequences (Table 1). Nucleotide diversity was higher among ITS2 sequences (0.0001) when compared to COI sequences (0.0008). The nucleotide diversity increased when COI GenBank sequences were included (0.0017) and remained the same when ITS2 GenBank sequences were included. Branch (0.522) and HBd (0.271) were higher in the COI network (Table 1).

Table 1.

Genetic diversity parameters for Amblyomma hebraeum populations from the Eastern Cape province of South Africa

COI COI with Genbank ITS2 ITS2 with Genbank
Number of Sequences 40 47 31 37
Number of haplotypes 6 19 10 10
Haplotype diversity (Hd) 0.535 0.756 0.694 0.675
Branch diversity (Bd) 0.522 0.583 0.396 0.35
Haplotype network branch diversity (HBd) 0.271 0.433 0.250 0.229
Nucleotide diversity 0.0008 0.0017 0.001 0.001
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Haplotype rarefaction

The population haplotype rarefaction curves (Additional File 1: Fig. S1) did not reach an asymptote for both genes, indicating that only part of the actual haplotype diversity had been sampled. The Chao 1 richness method estimated that the total diversity for the COI gene was potentially at 59 haplotypes and 46.87 haplotypes using the ACE method. For the ITS2 gene, 15 haplotypes were estimated for Chao 1 and 18.51 haplotypes for the ACE method, meaning that only 10 to 66.67% of haplotypes were found in the sample size used.

Haplotype networks

Both genes had a single dominant haplotype, I2 for ITS2 and C2 for COI, where most individuals belonged (Fig. 2a, b). With the inclusion of GenBank isolates, the COI network reflected star-shaped radiations from the dominant haplotype C2 to other haplotypes, separated by one mutational difference (Fig. 2a). Mozambique (C2, C9, C10), Mpumalanga (C2, C11 – C16), and KwaZulu-Natal (C2, C7, C8) GenBank sequences had isolates belonging to the dominant haplotype C2 (Fig. 2a). Eastern Cape GenBank isolates shared a haplotype with C2. There was no clear distinction between inland and coastal haplotypes except for Caquba (C13) and Dowu (C1) in the COI network, which did not form part of the dominant haplotype C2 (Additional File 1: Fig. S3). In the ITS2 network, GenBank isolates from Eswatini and Mozambique shared a haplotype with I2 (Fig. 2b). Radiation was observed from I2, from the second most common haplotype to coastal and inland haplotypes.

Fig. 2.

Fig. 2

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Haplotype networks from Amblyomma hebraeum populations in the Eastern Cape province of South Africa. (a) mtDNA haplotypes based on 581bp COI sequences. (b) rDNA haplotypes based on 596bp ITS2 sequences.

Phylogenetic relationships

The COI BI/ML trees (Fig. 3, 4) were based on unique haplotypes, with added outgroup sister taxa (Hyalomma marginatum for COI and Amblyomma marmoreum for ITS2 and closest GenBank matches. The low haplotype diversity was reflected by the formation of two clades in the COI tree, one consisting of haplotype C1 and Eastern Cape GB isolate (JX049251.1) supported by BI/ML (0.79/70) (Fig. 3). The ITS2 tree formed a BI-supported clade (0.93) consisting of haplotype I2 (Fig. 4). A cluster composed of BI-supported (0.97 and 0.96) haplotypes I3, I5, and I6 was found within this clade.

Fig. 3.

Fig. 3

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Phylogenetic tree showing the relationships between the 40 Amblyomma hebraeum COI sequences collapsed to six haplotypes (C1 to C6) with reference sequences from GenBank. Bayesian posterior probabilities/maximum likelihood bootstrap values (1000 replications) are shown. Hyalomma marginatum (KU130611.1) was included as the outgroup. Missing information from taxa labels are represented with a (−). *Represents haplotypes from the current study. GenBank sequences included are shown with the relevant accession numbers.

Fig. 4.

Fig. 4

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Phylogenetic tree showing the relationships between the 31 Amblyomma hebraeum ITS2 sequences collapsed to 10 haplotypes (I1 to I10) with reference sequences from GenBank. Bayesian posterior probabilities/maximum likelihood bootstrap values (1000 replications) are shown. Amblyomma marmoreum (KY457491.1) was included as the outgroup. Posterior probabilities less than 1.0 at nodes are represented by a (−). Missing information from taxa labels are represented with a (−). *Represents haplotypes from the current study. GenBank sequences included are shown with the relevant accession numbers.

Population structure

Global structure within (77.96% of variation) and among groups (6.59% of variation) was detected among the 13 COI populations (ΦST = 0.22, df = 39, p < 0.05). The significant differentiation was contained by a high level of differentiation between isolates from site 2 and 13 (ΦST = 0.743, p = 0.036); site 5 and 7 (ΦST = 0.764, p = 0.018) for the COI gene. For the ITS2 gene, global structure within (77.96% of variation) and among groups (6.59% of variation) was not significantly different (ΦST = 0.22, df = 30, p > 0.05) (Additional File 1: Table S3).

Demographic history

Evidence for population expansion was analyzed using Tajima’s D and Fu’s test neutrality tests. Tajima’s D statistic was negative but not significant (COI = −1.25, P>0.10; ITS2= 0.22, P>0.10), accepting a scenario of selective neutrality and population equilibrium or indicating an excess of rare nucleotide site variants. However, Fu’s F statistic (COI =- 2,17, P<0.05; ITS2 = −1.11, P<0.05) rejected the hypothesis of constant population size, suggesting evidence of recent population expansion. Likewise, the mismatch distribution plots, including all-region populations, were smooth and unimodal, indicating potential population expansion (Additional file 1: Fig. S2).

Detection and prevalence of Rickettsia spp.

A total of 46.92% (95% CI= 41–53%, n=260) A. hebraeum specimens were positive for Rickettsia spp. by analysing the 17kDa and ompA genes (Table 2). Overall, Caquba had the highest prevalence of Rickettsia spp. (70%; 95% CI= 48–88%, n=20), and Dontsa (30%; 95% CI= 12–52%, n=20) had the lowest prevalence of Rickettsia spp. From the six inland sites, 44.17% (95% CI=36–53%, n=120) were positive for Rickettsia spp., and from the seven coastal sites, 49.29% (95% CI= 41–58%, n=140) were positive (Table 2). Female ticks had a higher prevalence of Rickettsia spp. (25%; 65/240) compared to males (21.9%, 57/260). The prevalence between males and females regarding inland and coastal sites was not statistically different (p > 0.05, Fisher’s exact test). Female ticks that were partially engorged accounted for 29.2% (38/130).

Table 2.

Number of Ambyomma hebraeum adult samples screened and infected by PCR using the 17kDa and ompA genes for Rickettsia africae.

Site ID Sites Number positive Total screened Percent infection of R. africae (%)
Inland 1 Pikoli 8 20 40
2 Dontsa 6 20 30
3 Madubela 9 20 45
4 Adelaide 10 20 50
5 Bathurst 8 20 40
6 Lusasa 12 20 60
53 120 44.17
Coastal 7 Dowu 9 20 45
8 Mazikhanye 8 20 40
9 Pozi 11 20 55
10 Thusha 10 20 50
11 Sotho 7 20 35
12 Bhola 10 20 50
13 Caquba 14 20 70
69 140 49.29
122 260 46.92
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Genetic diversity of Rickettsia africae

Forty-two partial ompA and 34 17kDa sequences of 869 bp and 358 bp, respectively, were retrieved from sequencing. BLASTn analysis of the ompA sequences showed 100% homology with various R. africae isolates from southern Africa (MK405445, MT009350, MT009355, MN972462, and MT009350). BLASTn analysis of the 17kDa sequences showed 100% homology with R. africae isolates from Ethiopia (MG515013.1) and Kenya (CP001612.1). As both ompA and 17kDa sequences showed degrees of similarities with R. africae (Supplementary Table S1 and S2) above the cut-off values (≥99.3% for the ompA and 17kDa genes) proposed by Fournier et al. (2003), they were considered a strain of R. africae.

The maximum likelihood tree based on the 17kDa sequences was rooted to outgroup Rickettsia japonica (KY484162.1) (Fig. 5). The ingroup formed an ML/BI-supported clade (89%/1.0) with the outgroup. Study samples formed an ML and BI. supported group (100%/1.0) with R. africae GenBank isolates PELE (MG515013.1) and ESF-5 (CP001612.1) from Kenya and Ethiopia, respectively. This group also contained isolates from a previous study in the Eastern Cape, of which isolate C3 (MT150905.1) formed an ML-supported clade (84%) with GenBank isolate PELE (MG515013.1).

Fig. 5.

Fig. 5

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Maximum likelihood phylogeny from the 17kDa gene of Rickettsia africae study samples from the Eastern Cape, South Africa. The blue group indicates a ML/BI supported clade where study isolates (C1, C3 and C9) cluster with GenBank isolates (MG515013.1 and CP001612.1). Posterior probabilities less than 1.0 at nodes are represented by a (−). *Represents samples from the current study. GenBank sequences included are shown with the relevant accession numbers.

The maximum likelihood tree based on the ompA sequences was rooted to outgroup Rickettsia aeshlimannii (MG920561.1). The ingroup, which consisted of only R. africae isolates, formed an ML/BI-supported monophyletic clade (100%/1.0) with the outgroup. A distinct southern African clade (Fig. 6, grey group) was observed within the ingroup, which was well supported in the ML analysis (100%). This clade contained sequences from Eswatini and provinces in South Africa (Limpopo, Kwazulu-Natal, and the Eastern Cape). Within the southern African clade, an Eastern Cape clade (red group) consisting of study sequences and GenBank sequences from the Eastern Cape (MK405445, MT009350, MT009355, MN972462, and MT009350) was observed. Rickettsia africae isolates from Lebanon and Egypt (purple group) formed an ML and BI well-supported clade (100%/1.0).

Fig. 6.

Fig. 6

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Maximum likelihood phylogeny from the ompA gene of Rickettsia africae samples (all sites) from the Eastern Cape, South Africa. Maximum likelihood bootstrap values (1000 replications)/ Bayesian posterior probabilities are shown at adjacent nodes. Rickettsia aeshlimanii (MG920561.1) was included as the outgroup. The grey highlighted group represents isolates from southern Africa, the red group indicates isolates from the current study clustering with A. hebraeum isolates from the Eastern Cape. The yellow group indicates isolates from Amblyomma variegatum and the purple group indicates isolates from Hyalomma spp. GenBank sequences included are shown with the relevant accession numbers. The scale bar shows the number of substitutions per site. Posterior probabilities less than 1.0 at nodes are represented by a (−). *Represents samples from the current study.

Discussion

This study aimed to determine the population genetic structure of A. hebraeum and R. africae across coastal and inland regions in the Eastern Cape. Haplotype analysis identified a single dominant haplotype (I1= 58% and C2 = 67.5%) widespread across all sampling sites. This finding is supported by a previous report in which 57% of A. hebraeum ticks belonged to the same haplotype (Cangi et al., 2013). Shared haplotypes throughout southern Africa may be associated with local and long dispersal events of A. hebraeum. The C2/I1 haplotype was also shared with GenBank isolates from Mozambique and the Eastern Cape, Free State, Limpopo, and Mpumalanga Province of South Africa. The demographic estimators of Fu’s F, mismatch distributions, a large proportion of shared haplotypes, and low nucleotide diversity all provide evidence for a recent population expansion of A. hebraeum in Eastern Cape populations in South Africa.

This population expansion scenario has been observed previously in A. hebraeum (Cangi et al., 2013). Local and long dispersal events may be attributed to the high number of domestic cattle in the Eastern Cape (Horak et al., 2017). Communal grazing practices that allow cattle to share pastures with neighboring herds are likely to support the local dispersal of ticks. However, domestic cattle are also frequently translocated for anthropogenic reasons, facilitating long dispersal events. Amblyomma hebraeum was more abundant at cattle and wildlife interfaces than at pastures grazed by cattle in the Eastern Cape (Smith and Parker, 2010). Previous studies did not find A. hebraeum on rodents and passerine birds, suggesting low dispersal capabilities of A. hebraeum on these animals (Hasle et al., 2009; Horak et al., 2017). Introducing A. hebraeum onto novel hosts, especially wildlife, can have damaging effects such as increased host mortality and decreased reproductive output, causing severe population reductions (Portillo et al., 2007; Halajian et al., 2016).

The low global ΦST value of 0.22 (p<0.05) indicates very shallow but significant structuring between populations. Most of the variation was contained within populations (77.96%, Supplementary Table 3). A similar result (ΦST = 0.196, p<0.05) was observed previously (Cangi et al., 2013). This shallow, small-scale genetic differentiation may reflect some biological relevance due to the considerable distance between Dontsa and Caquba (~102 km), which may present a barrier to gene flow. Alternatively, this subtle differentiation might be a mere effect of sampling bias. The ΦST values suggest genetic homogeneity over the geographic range sampled except for four sites where significant population differentiation was observed. The climate in the Eastern Cape is stable for the maintenance and proliferation of A. hebraeum on various vegetation throughout the year (Yawa et al., 2018). Additionally, cattle dipping could play a role in reducing the diversity of A. hebraeum as seen in Rhipicephalus microplus, where resistant populations would be selected over generations (Abbas et al., 2014).

The phylogenetic trees provided poor resolution and were comparatively homogenous, except for one ITS2 clade (Fig 4), where a recent split was observed between haplotypes I1 and I2. The ITS2 split was not observed in the COI phylogeny, which may be due to the differences in nuclear and mitochondrial DNA inheritance, which can affect estimates of gene flow (Presa et al., 2002).

PCR and sequencing results showed that only R. africae was detected and was present in 46.92% (122/260) of A. hebraeum. Previous studies in southern Africa support these findings, with a 30–80% prevalence from A. hebraeum collected from large ruminants (Halajian et al., 2016; Mtshali et al., 2016; Magaia et al., 2020; Pillay and Mukaratirwa, 2020). The higher prevalence of R. africae observed in both males and females could result from feeding on bacteremic cattle hosts. (Parola and Raoult, 2001). The higher prevalence of R. africae observed in females than in males could be attributed to a large number of partially engorged females, 29.2% (38/130) collected. The A. hebraeum female scutum only covers a small portion of the dorsal surface, allowing the females to ingest more blood than males and, therefore, more R. africae (Walker et al. 2003).

The prevalence of R. africae in cattle blood from the Eastern Cape was (22.22%; 20/90), and the prevalence was much lower (10.9–15.7%) in ticks collected from smaller ruminants (Iweriebor et al., 2020; Jongejan et al., 2020). Our results support previous studies on the adaptability of R. africae to A. hebraeum as a significant reservoir for the pathogen (Fournier et al., 2009). The observed differences in prevalence estimates between cattle-collected ticks and those collected from small ruminants are likely due to rickettsemia in cattle, which may be a source of infection for ticks (Adjou Moumouni et al., 2016).

The 100% similarity among study R. africae isolates and GenBank isolates at two partial gene sequences suggests a single southern Africa genotype. Low genetic diversity was observed previously in R. africae, even with the most discriminatory genotyping method for Rickettsia spp. (Fournier et al., 2009; Mediannikov et al., 2010). The phylogeny supports this result as a clear separation between clades containing R. africae isolates from southern Africa and Sub-Saharan Africa was observed. This could explain the homogeneity observed in our study, that A. hebraeum is highly adapted to R. africae isolates from southern Africa as R. africae isolates from sub-Saharan Africa are more genetically diverse (Mediannikov et al., 2012; Kimita et al., 2016). Additionally, the more extensive geographic range of A. variegatum has been shown to restrict A. hebraeum to southern Africa due to interspecific competition resulting in barriers to gene flow (Bournez et al., 2015).

The phylogenetic tree showed that all study R. africae isolates exhibit a monophyletic relationship with GenBank R. africae isolates. The lack of association between haplotypes and the phylogeny of R. africae positive and negative ticks agreed with a previous study (Kisten et al., 2021), which showed no significant variation in microbial activity diversity between R. africae positive and negative A. hebraeum. These results were attributed to the proximity between geographic sites and similar environmental conditions. However, our study sites were geographically separated; as such, the observed results were likely due to the lack of genetic structuring observed in A. hebraeum.

Population genetic observations in this study help gain more significant insights into the factors influencing the dispersal of the A. hebraeum-R. africae system, especially in the ongoing expansion from coastal to inland regions of South Africa. Comparing the diversity of questing field ticks with wildlife and domestic host-collected ticks will provide a clearer understanding of the roles played by various hosts in the dispersal of A. hebraeum. The development of microsatellite markers specific for A. hebraeum may improve the genetic characterization of A. hebraeum populations compared to single gene markers.

The genetic structure of A. hebraeum in the Eastern Cape was characterized by a recent demographic population expansion in this study. Recent long-distance dispersal events were detected based on haplotypes shared between geographically distant localities. The expansion of the A. hebraeum-R. africae system into new habitats might have been accelerated by human activities such as the movement of host species and habitat alterations (Horak et al., 2017; Torabpour et al., 2019). Introducing new domestic hosts into new habitats can disrupt natural tick-pathogen segregations resulting in super-spreader events that would be unreachable under natural conditions (Estrada-Peña et al., 2015).

The movement of cattle with ticks might result in geographic or genetic isolation of ticks, which might influence the transmission dynamics of R. africae. This is of great public health importance and requires further long-term surveillance of ATBF patients, and R. africae-infected ticks in these regions. The significant degree of shared environment between domestic and wild animal hosts increases the circulation of R. africae, which may affect travellers such as tourists visiting these regions. The high infection observed in this study in tick populations from the coastal and inland sites suggests a high probability of infections within rural communities, where the problem is generally underestimated (Katswara and Mukaratirwa, 2021).

Supplementary Material

supinfo

Additional Files:

Table S1. COI haplotypes of Amblyomma hebraeum populations from the Eastern Cape province of South Africa with closest GenBank matches

Table S2. ITS2 haplotypes of Amblyomma hebraeum populations from the Eastern Cape province of South Africa with closest GenBank matches

Table S3. Analysis of molecular variance population statistics in Amblyomma hebraeum in the Eastern cape, South Africa.

Fig. S1. Haplotype rarefaction curves of Amblyomma hebraeum populations from the Eastern Cape province of South Africa for A. COI and B. ITS2. The Chao 1/ACE estimated the total haplotypes from 1,000 permutations.

Fig. S2. Observed and expected mismatch distributions for Amblyomma hebraeum populations from the Eastern Cape province of South Africa based on A. COI and B. ITS2 gene sequences.

Fig. S3. Haplotype networks of A. 18 COI haplotypes and B. 10 ITS2 haplotypes from Amblyomma hebraeum populations in the Eastern Cape province of South Africa.

Acknowledgements

The authors would like to thank the farmers at sampling sites in the Eastern Cape for allowing the collection of samples from their cattle. The National Research Foundation (NRF) of South Africa and the NIH grant 1R01AI136035 as part of the joint NIH-NSF-USDA Ecology and Evolution of Infectious Diseases program for financial support. The funding body had no role in the study’s design and interpretation of data and writing the manuscript.

Footnotes

Ethical approval

Ethical approval for the study was obtained from the Animal Research Ethics Committee of the University of KwaZulu-Natal (AREC/056/017).

Conflicts of interest

The authors declare there are no competing interests

Data availability statement

The dataset(s) supporting the conclusions of this article are available in the GenBank repository (http://www.ncbi.nlm.nih.gov/genbank/). The ITS2 sequences were deposited under the accession numbers OK635793-OK635818 and COI accession numbers OM212676- OM212713. The ompA sequences were deposited under the accession OM249800-OM249865 and 17kDa accession numbers OM249832-OM249865.

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Associated Data

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

Supplementary Materials

supinfo

Additional Files:

Table S1. COI haplotypes of Amblyomma hebraeum populations from the Eastern Cape province of South Africa with closest GenBank matches

Table S2. ITS2 haplotypes of Amblyomma hebraeum populations from the Eastern Cape province of South Africa with closest GenBank matches

Table S3. Analysis of molecular variance population statistics in Amblyomma hebraeum in the Eastern cape, South Africa.

Fig. S1. Haplotype rarefaction curves of Amblyomma hebraeum populations from the Eastern Cape province of South Africa for A. COI and B. ITS2. The Chao 1/ACE estimated the total haplotypes from 1,000 permutations.

Fig. S2. Observed and expected mismatch distributions for Amblyomma hebraeum populations from the Eastern Cape province of South Africa based on A. COI and B. ITS2 gene sequences.

Fig. S3. Haplotype networks of A. 18 COI haplotypes and B. 10 ITS2 haplotypes from Amblyomma hebraeum populations in the Eastern Cape province of South Africa.

NIHMS1852990-supplement-supinfo.docx (417.8KB, docx)

Data Availability Statement

The dataset(s) supporting the conclusions of this article are available in the GenBank repository (http://www.ncbi.nlm.nih.gov/genbank/). The ITS2 sequences were deposited under the accession numbers OK635793-OK635818 and COI accession numbers OM212676- OM212713. The ompA sequences were deposited under the accession OM249800-OM249865 and 17kDa accession numbers OM249832-OM249865.

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