Molecular evaluation and genetic diversity of bovine tick-borne pathogens in southern Malawi

doi:10.21203/rs.3.rs-2957010/v1

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Molecular evaluation and genetic diversity of bovine tick-borne pathogens in southern Malawi

https://doi.org/10.21203/rs.3.rs-2957010/v1

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Purpose: Tick-borne disease (TBD) prevention and control is dependent on a thorough understanding of disease vectors and pathogens. In Malawi, there is little information on molecular investigations of cattle tick-borne pathogens (TBPs). This study aimed to detect TBPs infecting cattle in southern Malawi which has the highest cattle population in Malawi.

Materials and Methods: A total of 220 apparently healthy cattle blood samples were collected in 6 southern Malawi districts. Detection of TBPs was done using polymerase chain reaction (PCR) tests with species-specific primers.

Results: A 72.3% overall infection rate (159/220) was recorded, with Anaplasma marginale (51/220, 23.2%), A. bovis (21/220, 9.6%), A. phagocytophilum (49/220, 22.3%), Babesia bigemina (76/220, 34.5%), B. bovis (16/220, 7.3%), B. naoakii(6/220, 2.7%), Theileria parva (34/220, 15.5%), T. mutans, (9/220, 4.1%), and T. taurotragi (49/220, 22.3%) as the detected pathogens.

Conclusion: The study reports B. naoakii, and B. bovis for the first time in cattle from Malawi. The study provides essential information on the distribution of TBDs and for carrying out potential control efforts in Malawi.

Tick-borne pathogens

cattle

PCR

Malawi

Tick-borne diseases (TBDs) account for a substantial number of infections in humans and animals that are transmitted by vectors. Globally, 80% of the bovine population is greatly afflicted by these TBDs through decreased livestock production and loss of resources in fighting the diseases [1]. It is a key burden on livestock farming and has a substantial economic impact on rural people, disturbing their food accessibility and agricultural undertakings [2]. In Africa, farmers are subjected to economic losses due to huge cattle deaths, body condition losses of the animals, reduced milk production, and huge amounts of costs used in dealing with the tick burden and diseases they transmit [1, 3, 4]. Anaplasmosis, babesiosis, ehrlichiosis, and theileriosis are the mainstay health and management drawbacks for cattle and small ruminant production [5, 6].

East Coast fever (ECF), caused by Theileria parva, leads to acute lymphoproliferative diseases which are characterized by feverishness, adenopathy, and anorexia, usually leading to death in most affected cattle populations [7–9], and is spread by Rhipicephalus appendiculatus [10, 11]. Similarly, T. mutans causes severe illness in bovines [12]. Some of the clinical signs of T. mutans infection that have been reported in cattle include anemia, lymphadenopathy, jaundice, and cachexia, which may be followed by death [13]. T. mutans cases and those of T. parva present almost similar clinical signs and these may lead to confusion in cattle populations [14], although T. mutans is spread by Amblyomma ticks [10]. Similarly, T. taurotragi causes cerebral theileriosis, which is also called "turning sickness" in bovines [13], and is also spread by R. appendiculatus [10].

Babesia species cause bovine babesiosis. B. bigemina and B. bovis, which are the two utmost important species in bovines, are mostly spread by Rhipicephalus ticks [10]. Bovine babesiosis is clinically manifested as pyrexia, hemoglobinuria, anemia, and jaundice [15]. Meanwhile, Anaplasma marginale, which causes bovine anaplasmosis, is marked by anemia, jaundice, pyrexia, cachexia, abortions, and stupor. In the worst scenario cases, it can lead to death [16]. A. phagocytophilum causes bovine granulocytic anaplasmosis, also called tick-borne fever. It can cause clinical disease in cattle, although cases have only been reported in Europe [17]. However, despite Ixodes ticks being responsible for the transmission, mechanical transfer of infected blood via hematophagous arthropods, placental transfer during pregnancy, and fomites have also been reported as possible transmission methods [18]. A. bovis causes monocytic anaplasmosis in bovine, a condition characterized by the organism's invasion of the monocytes [19]. The infection is asymptomatic in most cases despite reports of recognizable illness in calves [20].

The livestock subsector makes a significant contribution to Malawi’s economy and food security. The subsector contributes approximately 10.5% to the national income and roughly 37.4% to the agricultural gross domestic product in Malawi [21]. About 60% of Malawians, mostly smallholder farmers, own livestock which includes cattle, goats, sheep, pigs, poultry, and other non-conventional livestock species [22]. Malawi has a cattle population of about 1,959,101, about 92% of which (1,802,372) is composed of the indigenous Malawi zebu, while the remainder comprises dairy crossbreeds and exotic breeds [22]. Half of the cattle population is found in southern Malawi [22]. The Malawian human population is estimated at 17,563,749 as of 2018, and the country has one of the lowest cattle populations per capita in the world [22, 23]. Ticks, namely Amblyomma, Rhipicephalus, and Hyalomma have been reported in Malawi [24]. TBPs Anaplasma, Babesia, Ehrlichia, and Theileria have also been reported [23, 25, 26]. Regardless of the TBD challenges in livestock in Malawi, the country lacks sufficient knowledge about the prevalence and epidemiology of these diseases, including the molecular characteristics of the pathogens. Thus, this study was carried out to conduct a molecular survey on cattle TBP infections and determine the genetic characteristics of bovine TBPs in southern Malawi.

2.1. Location and sample collection

The survey took place in 6 districts of southern Malawi that were selected based on convenience (Fig. 1). The southern region of Malawi has 13 districts in total and is almost surrounded by Mozambique. The region has mild to hot weather conditions with temperatures between 25 ℃ and 40 ℃ and a dry season from April to November while December to March is considered the rainy season. Blood samples from randomly selected 220 local cattle and dairy crosses (apparently healthy) and communally grazed, were collected between January and March 2022 in Blantyre (n=47), Chikwawa (n=43), Chiradzulu (n=37), Mulanje (n=28), Thyolo (n=29), and Zomba (n=36) districts. Of the samples, 134 were female and 86 were male. Nearly 2–5 mL of bovine blood was acquired from each animal into a vacutainer tube containing EDTA (BD, New Jersey, USA). Young animals (<1.5 years of age) and adults (>1.5 years of age) were categorized separately among the selected animals. The age was determined by reviewing the farmers' records and analyzing the teeth. After collection, samples were briefly kept on ice while the field sampling process continued before being transferred to the laboratory for long-term storage at 4 °C pending further processing.

2.2. DNA extraction

200 mL of the collected blood was used to extract deoxyribonucleic acid (DNA) through the commercial blood extraction mini kit for DNA (Qiagen, Hilden, Germany) according to the manufacturer’s manual. After extraction, DNA was kept at –30 °C until the time for pathogen screening in the laboratory.

2.3. Ethical approval

We acquired authorization for sampling cattle in the study locations from the Malawi Agriculture and Food Security Ministry (MoAFS) through the Animal Health and Livestock Development Department, with permission ID: DAHLD 002/2022. Before sample collection, cattle owners were briefed on the significance of the activity and freely accepted to take part. Blood was obtained by licensed veterinarians by following proper standards for animal sampling ratified through Obihiro University of Agriculture and Veterinary Medicine, Japan (permission ID: 22-23).

2.4. PCR-based molecular detection of TBPs

Using PCR assays and species-specific primers, various TBPs were screened from the DNA samples (Table 1). A total reaction mastermix volume of 10 µL containing 0.2 µL of 10 µM of every primer, 2 µL of One Taq 5× buffer (New England Biolabs, Ipswich, UK), 0.2 µL of deoxynucleotide triphosphates (10mM dNTPs) mix, 0.05 µL of one Taq DNA polymerase, 1 µL of template DNA sample, and 6.35 µL of UltraPure™ water (Invitrogen, Waltham, Massachusetts, USA). A prior study’s PCR-positive samples served as the positive control [27], while UltraPure^TM water served as the negative control for quality control. A VeritiPro™ thermocycler (Applied Biosystems, Massachusetts, USA) was used to run the reaction mixtures with amplification conditions from prior research (Table 1). After gel electrophoresis and staining, the results were confirmed on a UV transilluminator.

2.5. Sequencing of the PCR-positive samples

The individual positive detected pathogen samples (n=1–5) were randomly selected for confirmation of the pathogens by sequencing. QIAquick gel extraction kit (Qiagen) was used and the DNA was extracted from the agarose gel and had its concentration determined using a spectrophotometer (NanoDrop 2000, ThermoFisher Scientific, Massachusetts, USA). Applied Biosystems' Sequencing Kit (Big Dye Terminator Cycle) and the ABI Prism 3100 Genetic Analyzer were used. CodonCode Aligner version 9 (CodonCode Corporation, Massachusetts, USA) was used to trim and combine the sequences. Nucleotide BLAST by means of NCBI was utilized to compare the newly assembled arrangements to those already in the databank.

2.6. Phylogenetic analysis

The evolutionary evaluation of sequences collected occurred using MEGA XI software [28] and the maximum likelihood method with the bootstrap value set at 1,000 replicates was used for constructing the phylogenetic trees.

2.7. Accession numbers assigned

This study's sequences were submitted to the databank of the NCBI through BankIt and GenBank for DNA sequences and ribosomal RNA sequences. The sequences of this study were assigned the following accession numbers: OP866888–OP866892 for T. parvap104; OP821414–OP821415 for T. mutans and OP824492–OP824498 for T. taurotragi 18S rRNA; OP866967–OP866971 for B. bigeminarap-1a; OP839189–OP839190 for B. bovissbp-4; OP854628–OP854629 for B. naoakiiama-1; OP868837–OP868840 for A. marginalegroEL; OP824619–OP824620 for A. phagocytophilum; and OP824766 for A. bovis 16S rRNA.

2.8. Statistical analysis

The following formula was used to get the detection rate; P=N/T*100 where P=detection rate, N=positives detected, T=samples screened. Statistical tests were executed in GraphPad Prism 8 software (GraphPad, California, United States of America) to examine the relationship between TBP detection rates and study area, age group, and animal sex, with the significance level fixed at p<0.05.

3.1. TBP infection rates

One hundred fifty-nine (72.3%) samples showed amplicons for at least 2 of the 9 TBPs. In this study, the following pathogens were identified: A. marginale (23.2%; 51/220), A. phagocytophilum (22.3%; 49/220), A. bovis (9.6%; 21/220), B. bigemina (34.5%; 76/220), B. naoakii (2.7%; 6/220) and B. bovis (7.3%; 16/220), T. parva (15.5%; 34/220), T. taurotragi (22.3%; 49/220), and T. mutans (4.1%; 9/220) (Table 2). In terms of infection rate by sex, 41 males (47.7%) and 118 females (88.1%) were infected with at least one of the examined pathogens. Females exhibited significantly higher detection rates for T. taurotragi (26.9%; p = 0.047) and B. bovis (10.4%; p = 0.031) (Table 3). Eighteen calves (48.6%) and 141 adults (77.1%) were infected with at least one pathogen. Age was strongly linked with infection rates for T. taurotragi (26.8%; p<0.001), B. bigemina (37.7%; p = 0.036), and A. bovis (11.5%; p = 0.029) (Table 3). As denoted in Table 2, substantial variances among six locations for cattle positive for T. parva (p = 0.009), B. bigemina (p=0.026), A. phagocytophilum (p=0.031) were observed. In this study, samples were also analyzed for E. ruminantium, Coxiella burnetii, A. platys, T. orientalis, and Rickettsia spp., but all samples were negative.

3.2. Co-infection of TBP-positive samples

In this study, two or more pathogen species (co-infections) were found in all positive samples (159/220; 72.3%). Up to seven pathogens were detected in one host, including two pathogens (64.2%; 102/159) in the majority of the positive samples. This was followed by three mixed infections (21.4%; 34/159), four mixed infections (8.2%; 13/159), and five mixed infections (4.6%; 7/159). Two animals had six mixed infections (1.3%; 2/159) and one animal had seven mixed infections (0.6%) (Table 4). B. bigemina-T. parva combination was the most common co-infection detected (n=16), followed by B. bigemina-A. phagocytophilum (n=11), B. bigemina-A. marginale (n=8), and B. bigemina-T. mutans (n=6) (Supplementary Table 1).

3.3. Gene sequence analysis

The groEL gene sequences of A. marginale (OP868837–OP868840) were well conserved among themselves, with high identity values of 97.2–99.6%. The identity values of these sequences ranged from 99.1–100% when compared with sequences KX685362–KX685365 of cattle from Benin and MG700525 of cattle from Tanzania. The 16S rRNA nucleotide sequences for A. phagocytophilum (OP824619 and OP824620) and A. bovis (OP824766) showed sequence identity of 99.3% with sequences reported in cattle from Poland (GQ450278) and tick from South Korea (AF470699) and 100% with MZ148472 of cattle from China.

Moreover, the percent sequence identity values of B. bigemina (OP866967–OP866971) in this study ranged from 88.2–99.8%. In addition, the sequences had similarity scores ranging from 98.4–100% with the bovine sequences KP347559 and MK481015, which were obtained from past studies in Kenya and South Africa, respectively. Sequences (n=2; OP839189–OP839190) of B. bovis sbp-4 shared identity values ranging from 94.4–99.2% and showed higher identities of up to 99.8–100% with sequences ON012668 and KF626632, which were previously isolated from cattle in Kenya and South Africa, respectively. Additionally, two B. naoakii ama-1 sequences (OP854628 and OP854629) showed identity values of 99.5–100%. The sequences were identical with sequences LC385892 and LC653109, which were previously isolated from cattle in Sri Lanka and camel in Egypt, respectively.

In contrast, T. parva sequences (OP866888–OP866892) varied between 83.4% and 99.6%, indicating genetic variability among themselves. The sequences showed 96.3% and 100% identity with sequences of Cameroonian cattle (MK568804) and Tanzanian cattle (MG700531), respectively. In addition, the percent identity arrangements of T. taurotragi (n=7; OP824492–OP824498) and T. mutans (n=2; OP821414–OP821415) ranged between 94.5–99.6% and 99.2–100%, respectively. The sequences exhibited between 97.7% and 100% identity with MN726650 of Tanzanian cattle and MH751463 of South African cattle.

3.4. Phylogenetic analyses

Assessing the evolutionary relationship of herein identified pathogens alongside those described elsewhere, phylogenetic trees were generated (Fig. 2–4 and Supplementary Fig. 1–4). Phylogenetic trees were generated using sequences from the databank of NCBI. Three of the A. marginale sequences from this study (OP868837, OP868838, and OP868840) clustered in the same subclade with available homologous sequences in the databank, whereas OP868839 formed an independent branch, a sister subclade to the first subclade (Fig 2). The alignments of A. phagocytophilum and for A. bovis grouped with other GenBank-obtained homologous sequences (Supplementary Fig 4).

Babesia bigemina rap-1a sequences OP866967 and OP866970 clustered in a different subclade, diverging from the main clade where OP866968, OP866969, and OP866971 grouped with isolates from Benin (KX685386), Tanzania (MG210824), Burkina Faso (OK323209), Kenya (KP347559), Bangladesh (MH790974), Uganda (MG426202), and Turkey (KT220512) (Figure 3). The B. naoakii ama-1 sequences (OP854628 and OP854629) from this study clustered with previously reported isolates and were most closely related to LC506533 from Mongolia and LC653109 from Egypt (Supplementary Fig 2). In addition, the B. bovis sbp-4 sequences (OP839189 and OP839190) of this study clustered with sequences LC611418 reported in ticks from Sudan, AB569302 in cattle from Mongolia, AB617641 in cattle from Syria, MZ197893 in cattle and MZ197894 in goat from Egypt, ON012668 in cattle from Kenya, KX685399 in cattle from Benin, and KF626632 in cattle from South Africa (Supplementary Fig 3).

Theileria parva p104 sequences showed some degree of diversity, with OP866888–OP866890 located in the major subclades, while OP866891 and OP866892 grouped in a well-supported and distinct clade (Fig 4). The sequences obtained in this study for T. mutans (OP821414 and OP821415) and T. taurotragi (OP824492–OP824498) were conserved and clustered together with T. mutans sequences from South Africa (MH751463), Angola (MT898574), and Mozambique (FJ869899) and T. taurotragi sequences previously obtained from South Africa (L19082), and Tanzania (OP380380) (Supplementary Fig 1).

In the present survey, relatively high rates of TBP infections among cattle populations in southern Malawi were recorded, implying a probable cause for stunted cattle population estimates in the region and the entire country. We also report the molecular detection of B. naoakii and B. bovis for the first time in cattle in Malawi. Among the TBPs that have previously been reported in Malawi, Anaplasmataceae, Babesia spp., and Theileria spp. are believed to cause high cattle mortalities [23]. There were high frequencies of co-infections in the cattle samples, with up to seven pathogens detected per animal in this study.

Anaplasma marginale is the rickettsial parasite responsible for anaplasmosis in bovines and is transmitted by R.microplus [10]. However, ticks of Dermacentor spp., in-utero, and mechanical transmissions are also possible [16,29]. This bacterium is a significant TBP of cattle, with mature exotic breeds more susceptible [30]. Herein, a prevalence of 23.1% of A. marginale is reported, which is almost the same prevalence in the previous report in Malawi (24%) [23] and higher than the 16% reported in Tanzania [31] and 7.9% from Kenya [32]. However, this prevalence is relatively lower compared to that reported in Uganda (73.8%) [33]. This is likely owing to alterations in tick vector distribution as well as transmission modes. The groEL sequences from this study grouped together, implying that the same isolates of A. marginale are prevalent in the bovine population in southern Malawi. We also report a 9.6% detection rate for A. bovis and 22.2% for A. phagocytophilum. These results are relatively higher than those previously reported in Ethiopia (2.7%) for A. phagocytophilum [34]. In contrast, A. bovis detection was previously reported in Malawi (2.6%) [23] and Kenya (3.1%) [35]. These results are on the lower side compared to the recorded rate in this study.

We report a 34.5% detection of B. bigemina which is significantly higher than earlier reports in Malawi (3.1%) [23], Tanzania (15.9%) [31], Kenya (13.8%) [36], and Uganda (5.0%) [33]. The higher detection rate observed in this study could be due to, among others, lower or moderate temperatures during and toward the period of this study. Another reason could be the frequent contact of the animals with the tick vectors endemic to the areas [24]. Cattle with inapparent infection may remain carriers for life, thereby continuously infecting other animals in places where tick vectors are abundant [37]. B. bigemina isolates from this study clustered differently in a subclade, diverting from the main clade. This finding suggests that there might be more than one genotype in the study area. We also report B. naoakii (2.7%) and B. bovis (7.3%) in this survey, the first time in Malawi. The B. bovis detection is higher as compared to the 4.5% documented in Tanzania [38]. However, the prevalence is lower than that reported in Kenya (15.8%) [32] and Zambia (32.7%) [39]. This could be related to variations in tick distribution and climate in different regions [40]. On the other hand, B. naoakii, which can cause clinical babesiosis, was detected in 2.7% of samples, which is on the lower side compared to previously reported rates from Vietnam (9.6%) [41], Mongolia (27.9%) [42], and the Philippines (11.3%) [41].

A 15.5% prevalence for T. parva is also reported herein. The documentation of T. parva in five of the six non-endemic sampling districts suggests that the pathogen has geographically spread out in Malawi. The reported prevalence is considerably lower than previously reported at one site (33%) [23], in Tanzania (34.4% and 41.2%) [27,38], and in Zambia (24.4%) [39]. This might be due to differences in tick-vector activity seasons and sample collection periods, among other things. There are more TBPs in summer and spring when tick activity is high. Additionally, prevailing climatic changes may affect tick distribution, thereby affecting TBD prevalence [43]. In assessing the evolutionary relationship, T. parva sequences clustered into several clades, indicating that multiple strains may be circulating in the bovines in the study area.

The detected 4.1% for T. mutans, is notably lower compared to the previously reported 73.8% from Malawi [23] and 68.6% from Tanzania [31]. This finding can be attributed to differences in tick activity due to the seasonality of the Amblyomma vector ticks which are implicated in transmitting T. mutans [44]. Meanwhile, T. taurotragi is a protozoan parasite of the genus Theileria that causes mild theileriosis in cattle and elands. Among other clinical signs, it has been associated with a bovine cerebral disease known as turning disease, which can be confused with T. annulata in the field setting, making T. taurotragi an economic disease of veterinary importance [13,45]. Here, T. taurotragi is found to be 22.2%, which is higher as compared to (12.6%) in Malawi [23]. This may be a result of the growth of tick vector distribution in the study area and a conducive environment for the disease and its tick vectors to proliferate in the study area [10,24]. However, the stated detection percentage is lower than in Tanzania, where it was 48% [38], and 36.6% in Zambia [39]. This finding may be a result of differences in climate conditions necessary for the maintenance of pathogens in the ticks [46].

Intriguingly, co-infections were prevalent in this survey, indicating simultaneous parasites in the same host. Concerning co-infections, pathogenicity can be enhanced or attenuated in endemic areas depending on the parasitic trend and relative relevance of the disease state [47,48]. Consequently, diverse infections may protect or contribute to diseases in cattle. Altogether, the interaction of TBPs in endemic locations in local zebu cattle hints at the existence of ecological and epidemiological strategies for surviving the hurdles posed by TBDs during co-infections [33]. In addition, the increased multiple infections in a host with the interaction between and among the pathogens influence the disease pattern as well as the diagnosis and probable treatment of the diseases [49]. Nonetheless, this finding represents a wake-up call for further efforts to control TBDs by focusing on targeting the tick vectors of pathogens.

The present study showed high frequencies of TBPs in cattle in southern Malawi. In addition, B. naoakii and B. bovis are reported for the first time in Malawi. Additionally, co-infections were highly prevalent in infected bovines. These findings provide crucial data for control initiatives against TBDs in Malawi.

Author Contributions:

Boniface Chikufenji: Data curation, conceptualization, analysis, methodology, investigation, visualization, drafting the original manuscript, writing and editing the original draft. Eloiza May Galon: Methodology, formal analysis, software, visualization, writing- preparation and review of the original draft. Elisha Chatanga: Conceptualization, methodology, and writing- preparation, review, and editing of the original draft. Nathan Kamanga: Methodology and resources. Uday Kumar Mohanta and Zhuowei Ma: Methodology and investigation. Kyoko Hayashida: Writing, reviewing and editing. Xuenan Xuan: Funding acquisition, guidance, conceptualization, writing, review and editing, methodology and validation.

Acknowledgments

We thank Dr. Tanjila Hasan, Madalitso Jessie Nkhata, Vincent Kachisi, Joe Magombo and animal owners for the various roles played in this study. This study was funded by the grant-in-Aid for Scientific Research (18KK0188), JSPS Core-to-Core program, and Strategic International Collaborative Research Project (JPJ008837) sponsored by the Ministry of Agriculture, Forestry, and Fisheries of Japan.

Data accessibility: All the data for this study has been given herein and is accessible to everyone.

Conflict of interest: All authors confirm that they hold no contesting individual or group interests whatsoever that could otherwise influence the reported work and results herein.

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Table 1

Primers used in the detection of bovine tick-borne pathogens in this study
Target gene	Primer sequences		Annealing
	Forward 5' → 3' Reverse		temp. (℃)	References
B. bigemina (BbigRAP-1a)	GAGTCTGCCAAATCCTTAC	TCCTCTACAGCTGCTTCG	55	[50]
B. bigemina (BbigRAP-1a)	AGCTTGCTTTCACAACTCGCC	TTGGTGCTTTGACCGACGACAT	50	[50]
B. bovis (BboSBP-4)	AGTTGTTGGAGGAGGCTAAT	TCCTTCTCGGCGTCCTTTTC	55	[50]
B. bovis (BboSBP-4)	GAAATCCCTGTTCCAGAG	TCGTTGATAACACTGCAA	50	[50]
T. parva (p104)	ATTTAAGGAACCTGACGTGACTGC	TAAGATGCCGACTATTAATGACACC	65	[27]
T. parva (p104)	GGCCAAGGT CTCCTTCAGATTACG	TGGGTGTGTTTCCTCGTCATCTGC	60	[27]
A. marginale (groEL)	GACTACCACATGCTCCATACTGACTG	GACGTCCACAACTACTGCATTCAAG	74 − 65	[51]
A. marginale (groEL)	GTCTGAAGATGAGATTGCACAGGTTG	CCTTTGATGCCGTCCAGAGATGCA	74 − 68	[51]
T. mutans (18S rRNA)	GACACAGGGAGGTAGTGACAAG	CTAAGAATTTCACCTCTGACAGT	60	[27]
T. mutans (18S rRNA)	GACACAGGGAGGTAGTGACAAG	AACATTCGGAGACGCAAGCGAG	68	[27]
T.orientalis (MPSP)	CTTTGCCTAGGATACTTCCT	ACGGCAAGTGGTGAGAACT	58	[52]
A. bovis (16SrRNA)	CTC GTA GCT TGC TAT GAG AAC	TCT CCC GGA CTC CAG TCT G	55	[53]
A. phagocytophilum (16SrRNA)	GCT GAA TGT GGG GAT AAT TTA T	ATGGCTGCTTCCTTTCGGTTA	54	[53]
B.naoakii (AMA-1)	TGGCGCCGACTTCCTGGAGCCCATCTCCAA	AGCTGGGGCCCTCCTTCGATGAACCGTCGG	64	[54]
Erlichia rumminantium (PSC 20)	CTT GAT GGA GGA TTA AAA GCA	GTA ATG TTT CAT GTG AAT TGA TCC	57	[55]
A. platys (groEL)	AAGGCGAAAGAAGCAGTCTTA	CATAGTCTGAAGTGGAGGAC	53	[56]
Rickettsia spp	GGG GAC CTG CTC ACG GCG G	CAT AAC CAG TGT AAA	55	[55]
Coxiella burnetiid	ACT CAA CGC ACT GGA ACC GC	TAG CTG AAG CCA ATT CGC C	57–62	[55]
T. taurotragi (18S rRNA)	GACACAGGGAGGTAGTGACAAG	CTAAGAATTTCACCTCTGACAGT	60	[27]
T. taurotragi (18S rRNA)	GACACAGGGAGGTAGTGACAAG	GAACCGTCCGAAAAAAGCCACG	68	[27]

Table 2

The detection rates of pathogens based on study sites.
Pathogen	Study site
	Chiradzulu	Thyolo	Mulanje	Blantyre	Chikwawa	Zomba	Overall (n = 220)	p value
	(n = 37)	(n = 29)	(n = 28)	(n = 47)	(n = 43)	(n = 36)
T. parva	12 (32.4%)	2 (6.9%)	5 (17.9%)	3 (6.4%)	9 (20.9%)	3 (8.3%)	34 (15.5%)	0.009**
T. mutans	3 (8.1%)	0	0	3 (6.4%)	1 (2.3%)	2 (5.6%)	9 (4.1%)	0.401
T. taurotragi	8 (21.6%)	11 (37.9%)	5 (17.9%)	12 (25.5%)	8 (18.6%)	5 (13.9%)	49 (22.3%)	0.259
B. bigemina	16 (43.2%)	8 (27.6%)	11 (39.3%)	12 (25.5%)	22 (51.2%)	7 (19.4%)	76 (34.5%)	0.026*
B. bovis	4 (10.8%)	0	1 (3.6%)	2 (4.3%)	7 (16.3%)	2 (5.6%)	16 (7.3%)	n.a.
B. naoakii	1 (2.7%)	0	0	2 (4.3%)	3 (7.0%)	0	6 (2.7%)	n.a.
A. marginale	12 (32.4%)	4 (13.8%)	7 (25.0%)	5 (10.6%)	14 (32.6%)	9 (25.0%)	51 (23.2%)	0.087
A. bovis	4 (10.8%)	6 (20.7%)	3 (10.7%)	2 (4.3%)	6 (14.0%)	0	21 (9.6%)	0.061
A. phagocytophilum	3 (8.8%)	5 (17.2%)	8 (28.6%)	7 (14.9%)	17 (39.5%)	9 (25.0%)	49 (22.3%)	0.031*
Significance levels of p values: * <0.05, ** <0.01. Abbreviations: n denotes the number of samples, n.a. not analyzed.

Table 3

Detection rates of tick-borne pathogens based on sex and age group of cattle.
Category	T. parva		T. mutans	T. taurotragi	B. bigemina	B. naoakii	B. bovis	A. bovis	A. phagocytophilum	A. marginale
Sex
Male (n = 86)	10 (11.6%)		2 (2.3%)	13 (15.1%)	29 (33.7%)	1 (1.2%)	2 (2.3%)	5 (5.8%)	14 (16.3%)	15 (17.4%)
Female (n = 134)	24 (17.9%)		7 (5.2%)	36 (26.9%)	47 (35.1%)	5 (3.7%)	14 (10.4%)	16 (11.9%)	35 (26.1%)	36 (26.9%)
p value	0.253		0.488	0.047*	0.885	0.408	0.031*	0.162	0.098	0.140
Age group
Calves < 1.5 years (n = 37)		6 (16.2%)	2 (5.4%)	0	7 (18.9%)	1 (2.7%)	2 (5.4%)	0	7 (18.9%)	12 (32.4%)
Adults > 1.5 years (n = 183)	28 (15.3%)		7 (3.8%)	49 (26.8%)	69 (37.7%)	5 (2.7%)	14 (7.7%)	21 (11.5%)	42 (23.0%)	39 (21.3%)
p value	0.808		0.649	< 0.001***	0.036*	> 0.999	> 0.999	0.029*	0.670	0.198
Significance levels of p values: * <0.05, *** <0.001. Abbreviations: n denotes the number of samples.

Table 4

Categories and frequencies of tick-borne pathogen co-infections detected.
Co-infection types	Frequency	%
Double	102	64.2
Triple	34	21.4
Quadruple	13	8.2
Quintuple	7	4.4
Hextuple	2	1.3
Septuple	1	0.6
Overall	159	100

SupplementaryFig.1.tif
Supplementary Figure 1. Phylogenetic analysis of Theileria mutans and Theileria taurotragi identified in this study is based on the 18S rRNA gene. The tree was constructed by MEGA XI using the maximum likelihood method based on Kimura 2-parameter model. The occurrence of the nodes was assessed by bootstrap in 1,000 replications. The sequences of this study are shown in red. Plasmodium falciparum(JQ627150) was used as an outgroup.
SupplementaryFig.2.tif
Supplementary Figure 2. Phylogenetic analysis of Babesia naoakii identified in this study is based on the AMA-1 gene. The tree was constructed by MEGA XI using the maximum likelihood method based on Kimura 2-parameter model. The occurrence of the nodes was assessed by bootstrap in 1,000 replications. The sequences of this study are shown in red. Babesia gibsoni (KR013010) was used as an outgroup.
SupplementaryFig.3.tif
Supplementary Figure 3. Phylogenetic analysis of Babesia bovis identified in this study is based on the SBP-4 gene. The tree was constructed by MEGA XI using the maximum likelihood method based on Kimura 2-parameter model. The occurrence of the nodes was assessed by bootstrap in 1,000 replications. The sequences of this study are shown in red. Babesia caballi (MT032180) was used as an outgroup.
SupplementaryFig.4.tif
Supplementary Figure 4. The phylogenetic analysis of A. phagocytophilum and A. bovis identified in this study is based on the 16S rRNA gene. The tree was constructed by MEGA XI using the maximum likelihood method based on Hasegawa-Kishino Yano model. The occurrence of the nodes was assessed by bootstrap in 1,000 replications. The sequences of this study are shown in red. Anaplasma centrale (EF520690) was used as an outgroup.
SupplementaryTable1Coinfectioncombinations.docx
Supplementary table 1: Pathogen combinations in co-infected cattle.

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Molecular evaluation and genetic diversity of bovine tick-borne pathogens in southern Malawi

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