Serological and Molecular characterization of Coxiella burnetii causing abortion in Livestock species in Isiolo County, Kenya.

Serological and Molecular characterization of Coxiella burnetii causing abortion in Livestock species in Isiolo County, Kenya. | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Serological and Molecular characterization of Coxiella burnetii causing abortion in Livestock species in Isiolo County, Kenya. Enock Kiprono, Hussein Abkallo, Richard Nyamota, Lynn Kirwa, Reuben Mwangi, and 6 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6205861/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Coxiella burnetii is the causative agent of Q fever, a zoonotic infection that poses serious threats to both animal and human health, particularly in the Global South. This bacterium primarily infects livestock such as cattle, sheep and goats and can be transmitted to humans through inhalation of contaminated aerosols or contact with infected products like milk and urine. The disease leads to livestock losses and febrile illnesses in humans. A longitudinal study was conducted to collect blood and serum samples from livestock; 18 Cattle, 22 Sheep, and 72 goats that aborted during the study period, from March 2022 – August 2023. Serological detection of C. burnetii immunoglobulin G (IgG) antibodies was performed using enzyme-linked immunosorbent assay (ELISA) and compared with baseline ELISA results. Molecular detection and genetic diversity assessment were conducted using quantitative PCR (qPCR) and nested PCR targeting the Cox IS1111 gene. Our results showed that Goats had the higher seropositivity (49.65%;95%CI: 41.97–57.75) followed by sheep (16.67%;95%CI: 10.37–25.69) and cattle (3.25%;95%CI: 1.40–7.37). Livestock species with a history of abortion had higher seropositivity (33.89%;95%CI:27.38–41.08) compared to non-aborting animals (14.39%;95%CI: 10.34–19.93). Conditional logistic regression model identified abortion as a significant risk factor, with goats being 26.71 times more likely to abort than cattle, and sheep 3.59 times more likely than cattle. Among the 112 blood samples, 54 tested positive by qPCR and 16 of these were subjected to Sanger sequencing. Phylogenetic analysis, performed using the maximum likelihood approach, provided insights into the genetic diversity and circulation of C. burnetii strains. This study provides both serological and molecular evidence of C. burnetii as in aborted and non-aborted livestock in Isiolo County. The findings enhance understanding of the pathogen’s epidemiology and can inform targeted disease control strategies. Q fever Coxiella burnetii qPCR Abortion Zoonoses Figures Figure 1 Figure 2 Background Q fever, caused by Coxiella burnetii , is a contagious zoonotic infectious disease of public health concern [ 1 ]. Due to limited documentation, it is classified as a Neglected Zoonotic Diseases (NZDs) [ 2 ]. Q fever has been documented in over 59 countries worldwide, with the notable exceptions of New Zealand and Antarctica. [ 3 ], [ 4 ]. The disease was first identified in Montana, USA and Queensland, Australia highlighting its global reach and importance of monitoring its spread across different regions[ 5 ]. A systematic literature review by Vanderburg et al (2014) reported Q fever seroprevalence ranging from 13–24% in goats, 4–44% in cattle, 11–33% in sheep and 1–32% in humans [ 6 ]. Molecular detection studies in Africa indicate a prevalence of 9% in cattle, 16% in sheep, 23% in goats and 3% in humans across 24 out of 54 surveyed countries[ 7 ]. In Kenya, most of the studies have focused on seroprevalence, estimating it at 28.2–57.1% in livestock population in pastoral communities, with limited data on molecular prevalence of the pathogen [ 8 ]. Although C. burnetii infects a variety of animal species, livestock such as goats, cattle, sheep are believed to be the primary source of Q fever infections in humans[ 9 ] due to close human-livestock interactions, especially in pastoral communities. The pathogen is shed through birth products, semen, milk, urine, faeces and vaginal mucus [ 5 ], [ 10 ], [ 11 ]. While Q fever has the potential to affect domestic animals, it is more prone to causing abortion in small ruminants. Furthermore, abortion and the subsequent discharge of fluids serve as a primary means of environmental contamination, increasing the risk of widespread infection among both animal and human population [ 12 ], [ 13 ], [ 14 ]. Abortion in livestock is associated with a huge socioeconomic burden, which is felt directly through loss of herds. This results in heightened poverty levels in the global south especially in livestock-dependent livelihoods. Limited access to sensitive diagnostic tools contributes to frequent misdiagnosis of Q fever. In humans, Q fever infections manifest itself as febrile illness, which is treated as presumptive malaria resulting in missed opportunities to accord, identify and treat other causes of the fever. Molecular diagnostics such as Quantitative real-time PCR (qPCR) has shown its sensitivity in identification of C. burnetii when used to target the IS1111 insertion element, which is present in numerous copies [ 15 ], [ 16 ], [ 17 ], [ 18 ]. Despite the reliability of molecular techniques in the detection of C. burnetii , it is not frequently used due to limited expertise and its associated cost [ 19 ]. The unavailability of data on phylogeny of C. burnetii in Kenya makes it difficult to understand its epidemiology. This study aims to determine the seropositivity in the aborted and non-aborting livestock species and assess the genetic diversity of C. burnetii in livestock population in Isiolo, northern Kenya. Detection and characterization of C. burnetii will contribute to understanding the epidemiology and provide more insight on Q fever which is crucial in informing interventions that will enable appropriate measures to be put in place to prevent emergence and re-emergence which is the biggest threat in the fight against zoonoses. To date, this is the first study investigating the genetic diversity of C. burnetii in Kenya. Materials and methods Ethical approval Ethical clearance for this study was obtained from the International Livestock Research Ethics Committee; reference number ILRI-IREC2020-07. Informed consent was obtained from farmers before sample collection, and a structured questionnaire was administered. Study site Samples were collected from Kinna, Garbatula sub-County, Isiolo County, located in the northeastern part of Isiolo County, Kenya (Figure 1). The area receives an average annual rainfall of 580mm, ranging from 350mm to 600mm. Short rains occur between November and December while long rains fall between March and May. The annual temperature ranges from 24°C to 30°C. The predominant economic and cultural activity in the region is pastoral livestock production system. Three livestock species; goats, sheep and cattle are raised by over 80 % of the population with goats and sheep having a higher population. This study site was chosen due to its proximity to Meru National Park, where wild and domestic animals interact, creating potential zoonotic transmission hotspots. Kinna also has a more stable animal population because of less migration and the area is also characterized by good accessibility. Sample size determination The sample size (n) used was determined using the equation 1 below [20] : Where: p represents prevalence and d is the allowable error of 0.05 A previous study conducted in a similar setting reported an average prevalence of C. burnetii in cattle, goats and sheep was 10.81% [21]. Therefore, using the average prevalence of 10.81% reported in this published study and the aforementioned equation 1, the minimum sample size for serological analysis was estimated at 148. A total of 112 livestock species that experienced abortions during the study period constituted the total number of samples that were analyzed in this study. Blood samples were screened for C. burnetii infections using both serological and molecular assays. To compare aborting and non-aborting livestock population, we used Epitool [22] to compute the minimum required sample size, which was 95. While a higher ration is preferred, our target comparison ration for aborted and non-aborted livestock was 1:4; however, we worked with a 1:2 ratio due to limited controls. Sampling design A longitudinal study was conducted between May 2022 to August 2023. Purposive sampling technique was employed whereby every livestock species that aborted was equally sampled. When abortion cases were reported, two field veterinarians were alerted by the farmers who then responded by visiting the affected households to collect serum and whole blood samples. A brief history of the aborted animal was also captured in a questionnaire. Serological results of this longitudinal study were analyzed against the results of the baseline study which was done at the beginning of the study to compare aborting and non-aborting livestock population. Sample collection Three livestock species; cattle, goats, and sheep - were sampled between March 2022 and August 2023. Two qualified veterinarians from ILRI, assisted by trained community field workers, collected blood samples while ensuring aseptic techniques. Blood was drawn from the jugular vein into two 10 mL plain vacutainers pre-labeled with unique barcodes. The red-top vacutainer was used to collect blood for serum extraction, while purple-top vacutainer was used to collect whole blood. Samples were stored at 4–8ºC in cool boxes with ice packs and transported to a field laboratory, where serum was extracted by centrifugation at 2500 ×g for 10 minutes. Both serum and EDTA blood samples were transferred to International Livestock Research Institute (ILRI) laboratories in Nairobi, Kenya, in a motorized freezer maintained at −20ºC for further analysis. Data collection Trained community field assistants administered structured questionnaires using Open Data kit (ODK) to collect house-hold level data. The recorded information included animals with recent history of abortion, the number of previous cases of abortion or stillbirths, and incidences of delivery of weak calf births. A total of 112 livestock samples were collected, corresponding to recorded abortion cases. Households were randomly selected based on the occurrence of an abortion events. Serological assays Serum samples were tested for Coxiella burnetii antibodies using the ID Screen Q Fever Indirect Multi-Species Kit (ID VET, Montpellier, France) per the manufacturer‘s instructions [23], [24]. Briefly, 5 µL of the serum samples were diluted in 245 µL of the dilution buffer and loaded on a pre-coated plate. The plate was then incubated at 21ºC for 45 minutes and then washed three times with washing solution. Briefly, 100 µL of 1× conjugate was added to each well an incubated at 21ºC for 30 minutes. It was washed three times before adding 100 µL of substrate to each well. It was then incubated for 15 minutes at 21°C and thereafter 100 µL of stop solution was added to each well. Optical density measurements were recorded at a wavelength of 450nm using a syn-energy BioTek microplate ELISA reader (Synergy, BioTek, Winooski, VT, USA). Statistical analysis The laboratory serological results were first cleaned before being combined with their respective metadata into a comma-delimited value (.CSV) file. All analyses were done using the R statistical package version 4.3.0 [25] whereby Chi-square (χ2) test was used to assess the relationship between categorical variables for independence. Seropositivity was first determined from all the samples within a confidence interval of 95%. For case control analysis, three livestock species; Goats, Cattle and Sheep were analyzed where cases refer to the number of abortions recorded during the study period and the controls were drawn from the initial screening of C. burnetii antibodies in the three livestock species before the onset of the project. Conditional logistic regression model was used to fit the cases and control using clogit model [26] in R statistical package. Odds ratios were calculated to determine the magnitude of the risk factors. DNA extraction and detection of Coxiella using qPCR Total DNA was extracted from 112 blood samples using Qiagen DNeasy blood extraction kit (Qiagen, Hilden, Germany) following the manufacturer‘s instructions. Briefly, 20 µL of proteinase K was added to 50 µL of blood samples and the volume was adjusted to 220 µL using phosphate-buffered saline (PBS). First, 200 µL Buffer AL was added and vortexed before incubating at 56°C for 10 minutes. Following incubation, 200 µL of 100% ethanol was mixed in. The resulting solution was transferred to a DNeasy Minispin column and centrifuged at 600 x g for 1 minute and 500 µL if Buffer AW1 was added, followed by another centrifugation at 6000 x g with the filtrate discarded. Next, the column was transferred to a clean tube, where 50 µL of buffer AE was used to elute the DNA from the column. C. burnetii was detected using quantitative Real-Time PCR (qPCR), targeting the Coxiella burnetii IS1111 gene with primers; Cox IS1111 F5‘ CATCACATTGCCGCGTTTAC 3‘ and Cox IS1111 R 5‘GGTTGGTCCCTCGACAACAT3‘ and probe FAM 5‘ AATCCCCAACAACACCTCCTTATTCCCAC 3‘ TAMRA [27] . The 20 µL final reaction volume comprised of 10 µL Luna Universal Probe qPCR Master Mix (New England Biolabs, Ipswich), 0.8 µL of each 10 µM forward and reverse primers and 0.4 µL of Cox IS111 probe. Thermocycling conditions were 95°C for 1 minute, 95°C for 15s for 45 cycles and 60°C for 30s. To validate the assay, limit of detection (LOD) was ascertained by generating a standard curve of positive control dilutions (from 18,000 copies/µL to 1 copy/µL). The limit of detection was established at Ct value of 35.113, meaning samples with Ct < 35.113 were considered positive. The qPCR reactions were performed using a Quant Studio 5 real time PCR systems thermocycler (Thermo Fischer scientific, United states). Conventional nested-PCR amplification of Coxiella burnetii IS1111 gene was done on the qPCR positive samples using Cox F1 5-TATGTATCCACCGTAGCCAGTC-3’ and Cox R15-CCCAACAACAACCTCCTTATTC-3‘ primers for the primary reaction generating a 685 bp fragment. The primary PCR products were used as the PCR template for the secondary reaction using Cox F2 5-GAGCGA ACCATTGGTATCG-3 and Cox R2 5-CTTTAACAGCGCTTGAACGT-3‘ primers for the secondary reaction that generated 201 bp fragment [28]. The reaction was set up in a 25 µL reaction volume consisting of 12.5 µL of Q5 high fidelity 2× master mix (New England Biolabs), 1.25 µL of both forward and reverse primer, 8 µL of Nuclease free PCR water and 2 µL of the template DNA. Amplification conditions: 98°C initial denaturation for 30s followed by 35 cycles of 98°C for 10s, 54°C for 30s, 72°C for 20s and final extension at 72°C for 2 minutes. Gel electrophoresis was done on nested PCR amplicons to visualize the amplified fragment. Bi-directional Sanger sequencing was done on the purified secondary PCR products that generated the expected band size. Sequences obtained from this study were deposited in GenBank. The sequences from this study have been deposited in GenBank under accession numbers OR684935 to OR864950. Phylogenetic analysis Raw forward and reverse sequences for each sample were assembled using SeqTrace version 0.9.0 to generate consensus sequences [29]. The identification of similar sequences was conducted using a BLASTn search, employing the Basic Local Alignment Search Tool to compare the nucleotide sequence against a comprehensive database (BLASTwww.ncbi.nlm.nih.gov/BLAST/) [30]. Similar sequences were retrieved based on the percentage identity and the E-value. Sequences that had a higher percentage identity and lower E-value were preferred as it indicated a more reliable and significant alignment between the query and target sequence [31] and 3 samples were retrieved per individually sequenced sample. The retrieved sequence together with the sequenced samples were aligned using MUSCLE [32] embedded in MEGA [32]. Bayesian Information Criterion (BIC) was used to evaluate the best model to be used to generate a phylogenetic tree [33]. Kimura-2 model was used because it had the lowest BIC [34] and phylogenetic reconstruction was done using Maximum Likelihood approach. The robustness of the phylogenetic tree was ascertained by performing bootstrap analysis of 1,000 replicates. Phylogenetic tree was visualized using fig tree [35] Results A total number 387 samples were analyzed for serology consisting of 112 from the longitudinal study and 275 from the baseline study to determine the presence of C. burnetii antibodies. Goats recorded the highest seropositivity for Coxiella burnetii at 49.65% (95%CI: 41.97-57.75), comprising 71 positive goats and 72 negative cases. Sheep followed with a seropositivity of 16.67% (95%CI: 10.37-25.69),with 15 positives and 75 negative cases. Among the 154 cattle, 5 tested positive while 149 were negative, resulting in a seropositivity of 3.25% (95%CI: 1.40-7.37). Livestock species with a history of abortion had a seropositivity of 33.89% (95%CI:27.38-41.08) compared to 14.39% (95%CI: 10.34-19.93) in the non-aborting population. The p-values for both categories; livestock species and abortion were found to be significant as shown in Table 1 below. Table 1: Seropositivity estimates of Coxiella burnetii with 95% confidence interval in three livestock species. `Descriptive Positive Negative %Seropositivity 95% CI P-value Livestock spp Cattle 5 149 3.25 1.40-7.37 <0.001 Sheep 15 75 16.67 10.37-25.69 Goats 71 72 49.65 41.97-57.75 Abortion No 30 177 14.49 10.34-19.93 <0.001 Yes 61 119 33.89 27.38-41.08 We analyzed the three livestock species where cases refer to the number of abortions recorded during the study period and the controls were drawn from the baseline study. Table 2 below represents the comparison between aborting and non-aborting livestock population. Abortion was identified as a potential risk factor, with an odds ratio (OD) of 1.14 (95%CI;0.53-2.28). However, the association was not statistically significant (P = 0.735). Among the three livestock species sampled, goats had 26.71 times higher odds of experiencing abortions compared to cattle (95%CI;8.49-84.10, P = 0.001) indicating a statistically significant association. Sheep were 3.59 times more likely to abort than cattle 95%CI (0.95-13.63), but this association was not statistically significant (P = 0.06). Table 2: Conditional logistic regression model for aborting and non-aborting livestock population. Descriptives Control Cases Coef 95%CI P-Value No abortion 387 1(ref) Abortion 387 182 1.14 0.53-2.48 0.735 Cattle 154 27 1(ref) Sheep 90 45 3.59 0.95 – 13.63 0.06 Goats 143 108 26.71 8.49 – 84.10 <0.001 Molecular detection and phylogenetic analysis Out of 112 extracted DNA samples, Coxiella burnetii DNA was detected in 54/112 (48.2%) by qPCR with the highest detection in goats (30) followed by cattle (13) and finally sheep (11). Among the qPCR-positive samples, 16 samples were successfully amplified using nested PCR. BLAST analysis showed sequence similarity ranging from 86.52 to 100% with existing Coxiella burnetii sequences in GenBank, with the Expect value (E-value) of zero and below indicating that the match was significant. A phylogenetic tree was constructed using both Isiolo and global isolates (shown in Figure 2). The analysis revealed that our isolates were distributed across the entire tree, falling into different clades. Three major clades were identified, with members of the same clade exhibiting varying branch lengths. Notably, isolates from the same species in Isiolo did not cluster together, but clustered with other isolates from different hosts in different geographical locations. Discussion Livestock species: goats, cattle and sheep serve as the primary reservoirs for C. burnetii . The presence of C. burnetii antibodies in blood indicates the possibility of prior exposure to the pathogen. Frequent contact between infected and susceptible livestock at the grazing field could enhance its transmission as the pathogen is extremely infectious. This risk is exacerbated in livestock managed under extensive feeding management systems as they are more prone to acquiring infections compared to those under intensive systems, likely due to increased movements, herd interactions and exposure to contaminated water sources [36], [37]. Our study recorded a lower C. burnetii seropositivity in cattle compared to goats and sheep. This is in line with previous study done in Kenya that categorized goats and sheep to have a higher prevalence as compared to cattle [38]. A study done by Sadiki et al 2023 showed that herd size was directly proportional to the seropositive of C. burnetii in cattle [39]. Shedding patterns play a role in the prevalence of C. burnetii with cattle mainly shedding it through milk and to a lesser extent through virginal mucus [39], [40]. Conversely, Goats and sheep have multiple shedding routes; Vaginal mucus, faces and milk which enhance its prevalence and reinforce its transmission to the environment [39], [41]. Our study found higher seropositivity in goats than in sheep and cattle, consistent with previous study in almost similar settings [42]. Since C. burnetii contaminates environment, especially soil and dust, animals that graze or browse closer to the ground are more likely to come in contact with the pathogen [43]. Goats being browsers are closer to the ground stands at high chances of contact with the contaminated environment [1], [42]. Studies have shown that, compared to cattle, the extended presence of adult goats on the farm leads to prolonged exposure which might be a factor contributing to the higher prevalence of C. burnetii antibodies in this species as goats can maintain seropositivity for prolonged period [44]. Abortion history play a role in the presence of C. burnetii antibodies in goats. Studies have shown that goats with previous history of abortion are likely to be seropositive [41]. While this study on C. burnetii seropositivity in goats cannot directly link the recorded abortion cases, we can hypothesize that the seronegative cases among aborted goats were caused by other abortifacient pathogens. Our conditional logistic regression model indicated a higher frequency of abortion among the sampled three livestock. While C. burnetii was the focus of this study, some abortion cases may be attributed to other non-infections agents such as selenium deficiency [45], [46]. Non-infectious agents were not investigated in this study. However, from the literature, these agents contribute approximately 10% of the abortion events [47]. On the other hand, in most cases, presence of broad spectrum of abortifacient pathogens increases the chances of abortion to happen [47], [48]. Other factors that have been documented to cause abortion in livestock include genetic factors, toxic chemicals, nutritional and metabolic problems [48]. An experimental study has shown that C. burnetii infection does not always cause abortion, though it may occur under infection extreme conditions [49]. Our study identified goats as the most affected by abortion, reaffirming findings that cattle are less susceptible to abortions caused by C. burnetii [50]. Some studies have documented abortion rates in goats to be about 90% implying that this livestock species is prone to infections and abortion [48], [51]. Goats are particularly susceptible to C. burnetii during pregnancy [49] due to weak immune responses, which may explain why C. burnetii is responsible for abortion in goats in this study. A study conducted by Azeb et al 2019 depicted the prevalence of C. burnetii was 50% in the population of sheep aborted implying that C. burnetii is not always associated with abortion in sheep [10], [52]. Studies in aborted sheep showed the presence of C. burnetii [53] although some studies have described the presence of C. burnetii in sheep as complicated because of the excretion of the abortifacient pathogen by healthy sheep [10]. With the use of molecular technique, qPCR, we detected C. burnetii in 54 of 112 (48.21%) blood samples. While no similar study has been conducted in Kenya, a study in Tanzania detected C. burnetii using the Cox IS1111 gene, a well-established rapid and reliable molecular diagnostic marker [18]. PCR detection using Cox IS1111 gene has proven its sensitivity and specificity for detection of C. burnetii [54],[24]. Our sequence analysis demonstrated similarity between the Isiolo C. burnetii strains with the sequences retrieved from NCBI. To the best of our knowledge, our study is the first in Kenya to carry out molecular detection of C. burnetii in an aborting livestock population. The phylogenetic tree generated from our data illustrates close evolutionary relationships between global and Isiolo isolates giving insights on the nature of pathogenic C. burnetii circulating in different regions globally. This is in line with finding from Durga et al [54]that showed similarities between isolates from India and Isolates from the global isolates based on the Cox IS1111 gene. As per our findings, close evolutionary relationship was evident in all the isolated sequences with all the sequences falling into different clades sharing a common ancestor. The difference in members of the same clade could be pointed out by the branch length which signifies the level of evolutionary divergence from member of the same clade. This was evident in OR684941, OR684942, OR684935, OR684936OR684940, OR684945 OR684946, OR684947 and OR684950 as shown in figure 2. Studies have shown that different content in the insertion sequence is responsible for disparity in evolutionary change of C. burnetii [55],[56]. This occurs as a result of genetic changes such as deletion occurring in various divisions of IS1111 insertion element leading to the emergence of diverse strains [57]. Therefore, we hypothesized that the emergence of diverse strains emanated from genetic changes occurring in IS1111 insertion element contributing to development of more divergent C. burnetii strains which is evident in OR684941 and OR682942. Additionally, variations in livestock species and regional factors likely influence C. burnetii strain distribution. Conclusion and recommendations Although pathogenicity of C. burnetii was not assessed, this study showed genetic similarity between Isolated sequence and other known pathogenic C. burnetii. From our findings, it is quite evident that C. burnetii is one of the pathogens responsible for abortion in livestock, although we are not ruling out co-infections with other abortifacient pathogens. Therefore, future studies could be focused on metagenomic analysis to determine other pathogens that could be linked to abortion in livestock species. In addition, it is worth screening the samples used in this study for other pathogens, Toxoplasma gondii, Rift Valley Fever Virus, Brucella and other pathogens that are known to cause abortion in livestock species. There is a need to carry out community sensitization, particularly among pastoralists, on proper handling and disposal of aborted materials. This will be of great essence in reducing and curtailing the spread of infection to humans. C. burnetii circulating in Isiolo is of public health concern and therefore managing Q fever in both animals and humans is crucial. Furthermore, zoonotic control methods and continuous monitoring programs should be upscaled. Declarations Data Availability All the data are included in this article. Funding Our study was anchored on Coinfection with Rift Valley Fever virus, Brucella spp and Coxiella burnetii project‖ funded by DTRA grant number HDTRA11910031. Authors’ contribution: Conceptualization (BB,HA,EK,JA,RN), Methodology ( HA,BB,RN, EK,RM,JB), Data collection (AM,MM,JA),Analysis (JA,HA,BB,RN,EM,LK,EK), Writing (EK), Supervision( BB,HA,JA,JB) All the authors have read and approved the manuscript. Competing interest No known competing interests. There was no influence from the funders on the design, conduct, and reporting of research findings. Consent for publication Not applicable. Acknowledgement We appreciate the pastoral community in Kinna ward, Garbatula subcounty for the decision to partake in our study. We also extend our appreciation to Isiolo County, department of Livestock Production for their approval to implement our study in their area. We acknowledge all the research participants who took part in our study. Ethical approval and Consent to participate Ethical clearance for this study was obtained from the International Livestock Research Ethics Committee; reference number ILRI-IREC2020-07. Informed consent was obtained from farmers before sample collection, and a structured questionnaire was administered. References Muema J, et al. Seroprevalence and Factors Associated with Coxiella burnetii Infection in Small Ruminants in Baringo County, Kenya. Zoonoses Public Health. Nov. 2017;64(7):e31–43. 10.1111/zph.12342 . Gakuya F, et al. Evidence of co-exposure with Brucella spp, Coxiella burnetii , and Rift Valley fever virus among various species of wildlife in Kenya. PLoS Negl Trop Dis. Aug. 2022;16(8). 10.1371/JOURNAL.PNTD.0010596 . Ullah Q, Jamil T, Saqib M, Iqbal M, Neubauer H. 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PLoS Negl Trop Dis. Mar. 2022;16(3). 10.1371/journal.pntd.0010214 . Epitool. Joulié A, et al. Circulation of Coxiella burnetii in a naturally infected flock of dairy sheep: Shedding dynamics, environmental contamination, and genotype diversity. Appl Environ Microbiol. 2015;81(20):7253–60. 10.1128/AEM.02180-15 . Ni H-B, Liu S-G, Jiang H-F, Wang C-R, Qian A-D. Seroprevalence of Q fever in dairy cows in northeastern China, 2017. [Online]. Available: www.internationalscholarsjournals.org. The R. Project for Statistical Computing. Conditional. Logistic Regression for Matched Pairs. Roest HIJ, Dinkla A, Koets AP, Post J, Van Keulen L. Experimental Coxiella burnetii infection in non-pregnant goats and the effect of breeding. Vet Res. May 2020;51(1). 10.1186/s13567-020-00797-7 . E.-M. K. SH, Y.-J. PS-US, D.-H. J.. J.-S. C. and K.-S. C. kschoi3@knu. ac. kr Hyung-Chul Cho, Prevalence and Molecular Characterization of C oxiella burnetii in Cattle, Goats, and Horses in the Republic of Korea, 2021. Stucky BJ. Seqtrace: A graphical tool for rapidly processing DNA sequencing chromatograms. J Biomol Techniques. Sep. 2012;23(3):90–3. 10.7171/jbt.12-2303-004 . R. IZY, Y.. M. S. M. T. L. M. Mark Johnson, NCBI BLAST: a better web interface, 2008. Help interpreting BLAST results? (Max score/Percent. Identity/E-values). Aligning Sequences. What is Bayesian Information Criterion (BIC)?. An Extension of the Kimura. Two-Parameter Model to the Natural Evolutionary Process. FIGTREE. Temple D, Manteca X. Animal Welfare in Extensive Production Systems Is Still an Area of Concern. Sep 22 2020 Front Media S A 10.3389/fsufs.2020.545902 Munyua P, et al. Prioritization of zoonotic diseases in Kenya, 2015. PLoS ONE. Aug. 2016;11(8). 10.1371/journal.pone.0161576 . Kiptanui J, Gathura PB, Kitala PM, Bett B. Seroprevalence Estimates of Q Fever and the Predictors for the Infection in Cattle, Sheep, and Goats in Nandi County, Kenya, Vet Med Int , vol. 2022, 2022. 10.1155/2022/3741285 Sadiki V, Gcebe N, Mangena ML, Ngoshe YB, Adesiyun AA. Prevalence and risk factors of Q fever (Coxiella burnetii) in cattle on farms of Limpopo province, South Africa. Front Vet Sci. 2023;10. 10.3389/fvets.2023.1101988 . Ebani VV, Coelho AC, Celina SS, Jiří J, Jiříčerný J. Coxiella burnetii in ticks, livestock, pets and wildlife: A mini-review. Magadu R, Thompson PN. Seroprevalence and factors associated with Coxiella burnetii exposure in goats in Moretele. Onderstepoort J Vet Res. 2023;90(1). 10.4102/ojvr.v90i1.2071 . Muema J, et al. Endemicity of Coxiella burnetii infection among people and their livestock in pastoral communities in northern Kenya. Heliyon. Oct. 2022;8(10). 10.1016/j.heliyon.2022.e11133 . Kersh GJ et al. Mar., Presence and Persistence of Coxiella burnetii in the environments of goat farms associated with a Q fever outbreak, Appl Environ Microbiol , vol. 79, no. 5, pp. 1697–1703, 2013, 10.1128/AEM.03472-12 ; Rungano Magadu, Peter N, Thompson. Seroprevalence and factors associated with Coxiella burnetii exposure in goats in Moretele, 2023. Rungano Magadu. Animal Health Diagnostic Center. Abortion in Goats. Yadav R, Yadav P, Singh G, Kumar S. Non Infectious Causes of Abortion in Livestock Animals-A Review. Article Int J Livest Res. 2021. 10.5455/ijlr.20201031 . de los Ramo M, Benito AA, Quílez J, Monteagudo LV, Baselga C, Tejedor MT. Coxiella burnetii and Co-Infections with Other Major Pathogens Causing Abortion in Small Ruminant Flocks in the Iberian Peninsula, Animals , vol. 12, no. 24, Dec. 2022, 10.3390/ani12243454 Roest HIJ, Dinkla A, Koets AP, Post J, Van Keulen L. Experimental Coxiella burnetii infection in non-pregnant goats and the effect of breeding. Vet Res. May 2020;51(1). 10.1186/s13567-020-00797-7 . Muskens J, Wouda W, Von Bannisseht-Wijsmuller T, Van Maanen C. Prevalence of Coxiella burnetii infections in aborted fetuses and stillborn calves, Veterinary Record , vol. 170, no. 10, p. 260, Mar. 2012, 10.1136/vr.100378 Eibach R, Bothe F, Runge M, Fischer SF, Philipp W, Ganter M. Q fever: Baseline monitoring of a sheep and a goat flock associated with human infections, Epidemiol Infect , vol. 140, no. 11, pp. 1939–1949, Nov. 2012, 10.1017/S0950268811002846 Gebretensay A, et al. Risk factors for reproductive disorders and major infectious causes of abortion in sheep in the highlands of Ethiopia. Small Ruminant Res. Aug. 2019;177:1–9. 10.1016/j.smallrumres.2019.05.019 . Zeeshan MA, et al. Sero-epidemiological study of zoonotic bacterial abortifacient agents in small ruminants. Front Vet Sci. 2023;10. 10.3389/fvets.2023.1195274 . a SVSM, a DBR, a SD, a SS, b RKG, b SS, c RS. S. P. D. d, S. B. B. d Durga Prasad Das a, Isolation of Coxiella burnetii from bovines with history of reproductive disorders in India and phylogenetic inference based on the partial sequencing of IS1111 elemen. Moses AS, Millar JA, Bonazzi M, Beare PA, Raghavan R. Horizontally acquired biosynthesis genes boost Coxiella burnetii’s physiology, Front Cell Infect Microbiol , vol. 7, no. MAY, May 2017, 10.3389/fcimb.2017.00174 Beare PA et al. Feb., Comparative genomics reveal extensive transposon-mediated genomic plasticity and diversity among potential effector proteins within the genus coxiella , Infect Immun , vol. 77, no. 2, pp. 642–656, 2009, 10.1128/IAI.01141-08 Denison AM, Thompson HA, Massung RF. IS1111 insertion sequences of Coxiella burnetii : Characterization and use for repetitive element PCR-based differentiation of Coxiella burnetii isolates. BMC Microbiol. 2007;7. 10.1186/1471-2180-7-91 . Additional Declarations No competing interests reported. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-6205861","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":434843042,"identity":"8bb459df-e137-4550-a57e-4a09f4727bef","order_by":0,"name":"Enock Kiprono","email":"data:image/png;base64,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","orcid":"","institution":"Jomo Kenyatta University of Agriculture and Technology","correspondingAuthor":true,"prefix":"","firstName":"Enock","middleName":"","lastName":"Kiprono","suffix":""},{"id":434843043,"identity":"fbf51b7f-81c0-43ab-beef-ce6ee3fdc6ee","order_by":1,"name":"Hussein Abkallo","email":"","orcid":"","institution":"International Livestock Research Institute (ILRI)","correspondingAuthor":false,"prefix":"","firstName":"Hussein","middleName":"","lastName":"Abkallo","suffix":""},{"id":434843044,"identity":"e18cd803-ee49-4df9-bd31-875a2003a13c","order_by":2,"name":"Richard Nyamota","email":"","orcid":"","institution":"International Livestock Research Institute (ILRI)","correspondingAuthor":false,"prefix":"","firstName":"Richard","middleName":"","lastName":"Nyamota","suffix":""},{"id":434843045,"identity":"f92ca1df-63e7-4aab-b7f4-36219a65f723","order_by":3,"name":"Lynn Kirwa","email":"","orcid":"","institution":"International Livestock Research Institute (ILRI)","correspondingAuthor":false,"prefix":"","firstName":"Lynn","middleName":"","lastName":"Kirwa","suffix":""},{"id":434843046,"identity":"677ab77f-bead-4d84-a0c7-520973058bab","order_by":4,"name":"Reuben Mwangi","email":"","orcid":"","institution":"International Livestock Research Institute 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(ILRI)","correspondingAuthor":false,"prefix":"","firstName":"Mathew","middleName":"","lastName":"Muturi","suffix":""},{"id":434843050,"identity":"37d818e7-053d-4ff9-bafc-57885af693b1","order_by":8,"name":"Joel Bargul","email":"","orcid":"","institution":"Jomo Kenyatta University of Agriculture and Technology","correspondingAuthor":false,"prefix":"","firstName":"Joel","middleName":"","lastName":"Bargul","suffix":""},{"id":434843051,"identity":"b3fe22ea-77cc-49c2-abe1-31b699c9bd0b","order_by":9,"name":"James Akoko","email":"","orcid":"","institution":"International Livestock Research Institute (ILRI)","correspondingAuthor":false,"prefix":"","firstName":"James","middleName":"","lastName":"Akoko","suffix":""},{"id":434843052,"identity":"e91695a9-a5ac-43d3-b987-136e3200c5f9","order_by":10,"name":"Bernard Bett","email":"","orcid":"","institution":"International Livestock Research Institute (ILRI)","correspondingAuthor":false,"prefix":"","firstName":"Bernard","middleName":"","lastName":"Bett","suffix":""}],"badges":[],"createdAt":"2025-03-11 18:23:09","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6205861/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6205861/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":79451300,"identity":"474f607c-4d22-42c9-8188-c1cc49af8e85","added_by":"auto","created_at":"2025-03-28 15:00:36","extension":"jpeg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":192366,"visible":true,"origin":"","legend":"\u003cp\u003eMap of Kenya showing the sampling site Kinna ward, Garbatula sub county within Isiolo County\u003c/p\u003e","description":"","filename":"floatimage1.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-6205861/v1/6f1bd6dc53fe82be3c989614.jpeg"},{"id":79450484,"identity":"a633bc5e-575d-46d8-9514-79715b6d91ae","added_by":"auto","created_at":"2025-03-28 14:52:36","extension":"jpeg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":1009289,"visible":true,"origin":"","legend":"\u003cp\u003ePhylogenetic tree showing evolutionary relationship of our isolates andsequences retrieved from NCBI. This tree was constructed using Maximum likelihood approach, Kimura 2 model based on COX IS 1111 gene. The scale bar of 0.08 represents substitutions per nucleotide site. The numbers at the noderepresents the support of each node. These nodes labels range from 0 to 1 with 1 representing maximal support.\u003c/p\u003e","description":"","filename":"floatimage2.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-6205861/v1/40271890751176e0c9a96ca1.jpeg"},{"id":84043210,"identity":"19ef3fbb-84da-4683-b2f2-b7b3df63f1e1","added_by":"auto","created_at":"2025-06-06 06:39:01","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2020228,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6205861/v1/5f76fd70-c588-4aae-9234-0b02bf64c2c6.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Serological and Molecular characterization of Coxiella burnetii causing abortion in Livestock species in Isiolo County, Kenya.","fulltext":[{"header":"Background","content":"\u003cp\u003eQ fever, caused by \u003cem\u003eCoxiella burnetii\u003c/em\u003e, is a contagious zoonotic infectious disease of public health concern [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. Due to limited documentation, it is classified as a Neglected Zoonotic Diseases (NZDs) [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. Q fever has been documented in over 59 countries worldwide, with the notable exceptions of New Zealand and Antarctica. [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e], [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. The disease was first identified in Montana, USA and Queensland, Australia highlighting its global reach and importance of monitoring its spread across different regions[\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. A systematic literature review by Vanderburg \u003cem\u003eet al\u003c/em\u003e (2014) reported Q fever seroprevalence ranging from 13\u0026ndash;24% in goats, 4\u0026ndash;44% in cattle, 11\u0026ndash;33% in sheep and 1\u0026ndash;32% in humans [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. Molecular detection studies in Africa indicate a prevalence of 9% in cattle, 16% in sheep, 23% in goats and 3% in humans across 24 out of 54 surveyed countries[\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. In Kenya, most of the studies have focused on seroprevalence, estimating it at 28.2\u0026ndash;57.1% in livestock population in pastoral communities, with limited data on molecular prevalence of the pathogen [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eAlthough \u003cem\u003eC. burnetii\u003c/em\u003e infects a variety of animal species, livestock such as goats, cattle, sheep are believed to be the primary source of Q fever infections in humans[\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e] due to close human-livestock interactions, especially in pastoral communities. The pathogen is shed through birth products, semen, milk, urine, faeces and vaginal mucus [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e], [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e], [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. While Q fever has the potential to affect domestic animals, it is more prone to causing abortion in small ruminants. Furthermore, abortion and the subsequent discharge of fluids serve as a primary means of environmental contamination, increasing the risk of widespread infection among both animal and human population [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e], [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e], [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. Abortion in livestock is associated with a huge socioeconomic burden, which is felt directly through loss of herds. This results in heightened poverty levels in the global south especially in livestock-dependent livelihoods.\u003c/p\u003e \u003cp\u003eLimited access to sensitive diagnostic tools contributes to frequent misdiagnosis of Q fever. In humans, Q fever infections manifest itself as febrile illness, which is treated as presumptive malaria resulting in missed opportunities to accord, identify and treat other causes of the fever. Molecular diagnostics such as Quantitative real-time PCR (qPCR) has shown its sensitivity in identification of \u003cem\u003eC. burnetii\u003c/em\u003e when used to target the IS1111 insertion element, which is present in numerous copies [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e], [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e], [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e], [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]. Despite the reliability of molecular techniques in the detection of \u003cem\u003eC. burnetii\u003c/em\u003e, it is not frequently used due to limited expertise and its associated cost [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThe unavailability of data on phylogeny of \u003cem\u003eC. burnetii\u003c/em\u003e in Kenya makes it difficult to understand its epidemiology. This study aims to determine the seropositivity in the aborted and non-aborting livestock species and assess the genetic diversity of \u003cem\u003eC. burnetii\u003c/em\u003e in livestock population in Isiolo, northern Kenya. Detection and characterization of \u003cem\u003eC. burnetii\u003c/em\u003e will contribute to understanding the epidemiology and provide more insight on Q fever which is crucial in informing interventions that will enable appropriate measures to be put in place to prevent emergence and re-emergence which is the biggest threat in the fight against zoonoses. To date, this is the first study investigating the genetic diversity of \u003cem\u003eC. burnetii\u003c/em\u003e in Kenya.\u003c/p\u003e"},{"header":"Materials and methods","content":"\u003cp\u003e\u003cstrong\u003eEthical\u0026nbsp;approval\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eEthical clearance for this study was obtained from the International Livestock Research Ethics Committee; reference number\u0026nbsp;ILRI-IREC2020-07. Informed consent was obtained from farmers before sample collection, and a structured questionnaire was administered.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eStudy site\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eSamples were collected from Kinna, Garbatula sub-County, Isiolo County, located in the northeastern part of Isiolo County, Kenya (Figure 1). The area receives an average annual rainfall of 580mm, ranging from 350mm to 600mm. Short rains occur between November and December while long rains fall between March and May. The annual temperature ranges from 24\u0026deg;C to 30\u0026deg;C. The predominant economic and cultural activity in the region is pastoral livestock production system. Three livestock species; goats, sheep and cattle are raised by over 80 % of the population with goats and sheep having a higher population. This study site was chosen due to its proximity to Meru National Park, where wild and domestic animals interact, creating potential zoonotic transmission hotspots. Kinna also has a more stable animal population because of less migration and the area is also characterized by good accessibility.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eSample\u0026nbsp;size determination\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe\u0026nbsp;sample\u0026nbsp;size (n)\u0026nbsp;used\u0026nbsp;was\u0026nbsp;determined\u0026nbsp;using\u0026nbsp;the\u0026nbsp;equation\u0026nbsp;1\u0026nbsp;below [20] :\u003cimg src=\"data:image/png;base64,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\"\u003e\u003c/p\u003e\n\u003cp\u003eWhere: \u003cstrong\u003ep\u0026nbsp;\u003c/strong\u003erepresents prevalence and \u003cstrong\u003ed\u0026nbsp;\u003c/strong\u003eis\u0026nbsp;the\u0026nbsp;allowable\u0026nbsp;error\u0026nbsp;of 0.05\u003c/p\u003e\n\u003cp\u003eA previous study conducted in a similar setting reported an average prevalence of \u003cem\u003eC. burnetii\u0026nbsp;\u003c/em\u003ein cattle, goats and sheep was 10.81% [21]. Therefore, using the average prevalence of 10.81% reported in this published study and the aforementioned equation 1, the minimum sample size for serological analysis was estimated at 148. A total of 112 livestock species that experienced abortions during the study period constituted the total number of samples that were analyzed in this study. Blood samples were screened for \u003cem\u003eC. burnetii\u0026nbsp;\u003c/em\u003einfections using\u0026nbsp;both\u0026nbsp;serological\u0026nbsp;and\u0026nbsp;molecular assays. To compare aborting and non-aborting livestock population, we used Epitool [22] to compute the minimum required sample size, which was 95. While a higher ration is preferred, our target comparison ration for aborted and non-aborted livestock was 1:4; however, we worked with a 1:2 ratio due to limited controls.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eSampling design\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eA longitudinal study was conducted between May 2022 to August 2023. Purposive sampling technique was employed whereby every livestock species that aborted was equally sampled. When abortion cases were reported, two field veterinarians were alerted by the farmers who then responded by visiting the affected households to collect serum and whole blood samples. A brief history of the aborted animal was also captured in a questionnaire. Serological results of this longitudinal study were analyzed against the results of the baseline study which was done at the beginning of the study to compare aborting and non-aborting livestock population.\u003c/p\u003e\n\u003ch3\u003eSample collection\u003c/h3\u003e\n\u003cp\u003eThree livestock species; cattle, goats, and sheep - were sampled between March 2022 and August 2023. Two qualified veterinarians from ILRI, assisted by trained community field workers, collected blood samples while ensuring aseptic techniques. Blood was drawn from the jugular vein into two 10 mL plain vacutainers pre-labeled with unique barcodes. The red-top vacutainer was used to collect blood for serum extraction, while purple-top vacutainer was used to collect whole blood. Samples were stored at 4\u0026ndash;8\u0026ordm;C in cool boxes with ice packs and transported to a field laboratory, where serum was extracted by centrifugation at 2500 \u0026times;g for 10 minutes. Both serum and EDTA blood samples were transferred to International Livestock Research Institute (ILRI) laboratories in Nairobi, Kenya, in a motorized freezer maintained at \u0026minus;20\u0026ordm;C for further analysis.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData collection\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTrained community field assistants administered structured questionnaires using Open Data kit (ODK) to collect house-hold level data. The recorded information included animals with recent history of abortion, the number of previous cases of abortion or stillbirths, and incidences of delivery of weak calf births. A total of 112 livestock samples were collected, corresponding to recorded abortion cases. Households were randomly selected based on the occurrence of an abortion events.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eSerological assays\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eSerum samples were tested for \u003cem\u003eCoxiella burnetii\u0026nbsp;\u003c/em\u003eantibodies using the ID Screen Q Fever Indirect Multi-Species Kit (ID VET, Montpellier, France) per the manufacturer\u0026lsquo;s instructions [23], [24]. Briefly, 5 \u0026micro;L of the serum samples were diluted in 245 \u0026micro;L of the dilution buffer and loaded on a pre-coated plate. The plate was then incubated at 21\u0026ordm;C for 45 minutes and then washed three times with washing solution. Briefly, 100 \u0026micro;L of 1\u0026times; conjugate was added to each well an incubated at 21\u0026ordm;C for 30 minutes. It was washed three times before adding 100 \u0026micro;L of substrate to each well. It was then incubated for 15 minutes at 21\u0026deg;C and thereafter 100 \u0026micro;L of stop solution was added to each well. Optical density measurements were recorded at a wavelength of 450nm using a syn-energy BioTek microplate \u0026nbsp;ELISA \u0026nbsp;reader (Synergy, BioTek, Winooski, VT, USA).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eStatistical analysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe laboratory serological results were first cleaned before being combined with their respective metadata into a comma-delimited value (.CSV) file. All analyses were done using the R statistical package version 4.3.0 [25] whereby Chi-square (\u0026chi;2) test was used to assess the relationship between categorical variables for independence. Seropositivity was first determined from all the samples within a confidence interval of 95%. For case control analysis, three livestock species; Goats, Cattle and Sheep were analyzed where cases refer to the number of abortions recorded during the study period and the controls were drawn from the initial screening of \u003cem\u003eC. burnetii\u003c/em\u003e antibodies in the three livestock species before the onset of the project. Conditional logistic regression model was used to fit the cases and control using clogit model [26] in R statistical package. Odds ratios were calculated to determine the magnitude of the risk factors.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDNA extraction and detection of \u003cem\u003eCoxiella\u0026nbsp;\u003c/em\u003eusing qPCR\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTotal DNA was extracted from 112 blood samples using Qiagen DNeasy blood extraction kit (Qiagen, Hilden, Germany) following the manufacturer\u0026lsquo;s instructions. Briefly, 20 \u0026micro;L of proteinase K was added to 50 \u0026micro;L of blood samples and the volume was adjusted to 220 \u0026micro;L using phosphate-buffered saline (PBS). First, 200 \u0026micro;L Buffer AL was added and vortexed before incubating at 56\u0026deg;C for 10 minutes. Following incubation, 200 \u0026micro;L of 100% ethanol was mixed in. The resulting solution was transferred to a DNeasy Minispin column and centrifuged at 600 x g for 1 minute and 500 \u0026micro;L if Buffer AW1 was added, followed by another centrifugation at 6000 x g with the filtrate discarded. Next, the column was transferred to a clean tube, where 50 \u0026micro;L of buffer AE was used to elute the DNA from the column. \u003cem\u003eC. burnetii\u0026nbsp;\u003c/em\u003ewas detected using quantitative Real-Time PCR (qPCR), targeting the \u003cem\u003eCoxiella burnetii\u003c/em\u003e IS1111 gene with primers; Cox IS1111 F5\u0026lsquo; CATCACATTGCCGCGTTTAC 3\u0026lsquo; and Cox IS1111 R 5\u0026lsquo;GGTTGGTCCCTCGACAACAT3\u0026lsquo; and probe FAM 5\u0026lsquo; AATCCCCAACAACACCTCCTTATTCCCAC 3\u0026lsquo; TAMRA [27] . The 20 \u0026micro;L final reaction volume comprised of 10 \u0026micro;L Luna Universal Probe qPCR Master Mix (New England Biolabs, Ipswich), 0.8 \u0026micro;L of each 10 \u0026micro;M forward and reverse primers and 0.4 \u0026micro;L of Cox IS111 probe. Thermocycling conditions were 95\u0026deg;C for 1 minute, 95\u0026deg;C for 15s for 45 cycles and 60\u0026deg;C for 30s. To validate the assay, limit of detection (LOD) was ascertained by generating a standard curve of positive control dilutions (from 18,000 copies/\u0026micro;L to 1 copy/\u0026micro;L). The limit of detection was established at Ct value of 35.113, meaning samples with Ct \u0026lt; 35.113 were considered positive. The qPCR reactions were performed using a Quant Studio 5 real time PCR systems thermocycler (Thermo Fischer scientific, United states).\u003c/p\u003e\n\u003cp\u003eConventional nested-PCR amplification of \u003cem\u003eCoxiella burnetii\u0026nbsp;\u003c/em\u003eIS1111 gene was done on the qPCR positive samples using Cox F1 5-TATGTATCCACCGTAGCCAGTC-3\u0026rsquo; and Cox R15-CCCAACAACAACCTCCTTATTC-3\u0026lsquo; primers for the primary reaction generating a 685 bp fragment. The primary PCR products were used as the PCR template for the secondary reaction using Cox F2 5-GAGCGA ACCATTGGTATCG-3 and Cox R2 5-CTTTAACAGCGCTTGAACGT-3\u0026lsquo; primers for the secondary reaction that generated 201 bp fragment [28]. The reaction was set up in a 25 \u0026micro;L reaction volume consisting of 12.5 \u0026micro;L of Q5 high fidelity 2\u0026times; master mix (New England Biolabs), 1.25 \u0026micro;L of both forward and reverse primer, 8 \u0026micro;L of Nuclease free PCR water and 2 \u0026micro;L of the template DNA. Amplification conditions: 98\u0026deg;C initial denaturation for 30s followed by 35 cycles of 98\u0026deg;C for 10s, 54\u0026deg;C for 30s, 72\u0026deg;C for 20s and final extension at 72\u0026deg;C for 2 minutes. Gel electrophoresis was done on nested PCR amplicons to visualize the amplified fragment. Bi-directional Sanger sequencing was done on the purified secondary PCR products that generated the expected band size. Sequences obtained from this study were deposited in GenBank. The sequences from this study have been deposited in GenBank under accession numbers OR684935 to OR864950.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ePhylogenetic analysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eRaw forward and reverse sequences for each sample were assembled using SeqTrace version 0.9.0 to generate consensus sequences [29]. The identification of similar sequences was conducted using a BLASTn search, employing the Basic Local Alignment Search Tool to compare the nucleotide sequence against a comprehensive database (BLASTwww.ncbi.nlm.nih.gov/BLAST/) [30]. Similar sequences were retrieved based on the percentage identity and the E-value. Sequences that had a higher percentage identity and lower E-value were preferred as it indicated a more reliable and significant alignment between the query and target sequence [31] and 3 samples were retrieved per individually sequenced sample. The retrieved sequence together with the sequenced samples were aligned using MUSCLE [32] \u0026nbsp;embedded in MEGA [32]. Bayesian Information Criterion (BIC) was used to evaluate the best model to be used to generate a phylogenetic tree [33]. Kimura-2 model was used because it had the lowest BIC [34] and phylogenetic reconstruction was done using Maximum Likelihood approach. The robustness of the phylogenetic tree was ascertained by performing bootstrap analysis of 1,000 replicates. Phylogenetic tree was visualized using fig tree [35]\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003eA total number 387 samples were analyzed for serology consisting of 112 from the longitudinal study and 275 from the baseline study to determine the presence of \u003cem\u003eC. burnetii\u003c/em\u003e antibodies. Goats recorded the highest seropositivity for \u003cem\u003eCoxiella burnetii\u003c/em\u003e at 49.65% (95%CI: 41.97-57.75), comprising 71 positive goats and 72 negative cases. Sheep followed with a seropositivity of 16.67% (95%CI: 10.37-25.69),with 15 positives and 75 negative cases. Among the 154 cattle, 5 tested positive while 149 were negative, resulting in a seropositivity of 3.25% (95%CI: 1.40-7.37). Livestock species with a history of abortion had a seropositivity of 33.89% (95%CI:27.38-41.08) compared to 14.39% (95%CI: 10.34-19.93) in the non-aborting population. The p-values for both categories; livestock species and abortion were found to be significant as shown in Table 1 below.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 1: Seropositivity estimates of \u003cem\u003eCoxiella burnetii\u003c/em\u003e with 95% confidence interval in three livestock species.\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"658\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 103px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e`Descriptive\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 68px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 74px;\"\u003e\n \u003cp\u003e\u003cstrong\u003ePositive\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 79px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eNegative\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 148px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e%Seropositivity\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 108px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e95% CI\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 78px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eP-value\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"3\" valign=\"top\" style=\"width: 103px;\"\u003e\n \u003cp\u003eLivestock spp\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 68px;\"\u003e\n \u003cp\u003eCattle\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 74px;\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 79px;\"\u003e\n \u003cp\u003e149\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 148px;\"\u003e\n \u003cp\u003e3.25\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 108px;\"\u003e\n \u003cp\u003e1.40-7.37\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"3\" valign=\"top\" style=\"width: 78px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 68px;\"\u003e\n \u003cp\u003eSheep\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 74px;\"\u003e\n \u003cp\u003e15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 79px;\"\u003e\n \u003cp\u003e75\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 148px;\"\u003e\n \u003cp\u003e16.67\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 108px;\"\u003e\n \u003cp\u003e10.37-25.69\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 68px;\"\u003e\n \u003cp\u003eGoats\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 74px;\"\u003e\n \u003cp\u003e71\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 79px;\"\u003e\n \u003cp\u003e72\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 148px;\"\u003e\n \u003cp\u003e49.65\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 108px;\"\u003e\n \u003cp\u003e41.97-57.75\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" valign=\"top\" style=\"width: 103px;\"\u003e\n \u003cp\u003eAbortion\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 68px;\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 74px;\"\u003e\n \u003cp\u003e30\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 79px;\"\u003e\n \u003cp\u003e177\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 148px;\"\u003e\n \u003cp\u003e14.49\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 108px;\"\u003e\n \u003cp\u003e10.34-19.93\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" valign=\"top\" style=\"width: 78px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 68px;\"\u003e\n \u003cp\u003eYes\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 74px;\"\u003e\n \u003cp\u003e61\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 79px;\"\u003e\n \u003cp\u003e119\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 148px;\"\u003e\n \u003cp\u003e33.89\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 108px;\"\u003e\n \u003cp\u003e27.38-41.08\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 103px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 68px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 74px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 79px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 148px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 108px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 78px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eWe analyzed the three livestock species where cases refer to the number of abortions recorded during the study period and the controls were drawn from the baseline study. Table 2 below represents the comparison between aborting and non-aborting livestock population. Abortion was identified as a potential risk factor, with an odds ratio (OD) of 1.14 (95%CI;0.53-2.28). However, the association was not statistically significant (P = 0.735). Among the three livestock species sampled, goats had 26.71 times higher odds of experiencing abortions compared to cattle (95%CI;8.49-84.10, P = 0.001) indicating a statistically significant association. Sheep were 3.59 times more likely to abort than cattle 95%CI (0.95-13.63), but this association was not statistically significant (P = 0.06).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 2: Conditional logistic regression model for aborting and non-aborting\u003c/strong\u003e\u003cstrong\u003e\u0026nbsp;livestock population.\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"620\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 100px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eDescriptives\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 87px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eControl\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 87px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eCases\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 87px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eCoef\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 140px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e95%CI\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 120px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eP-Value\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 100px;\"\u003e\n \u003cp\u003eNo abortion\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 87px;\"\u003e\n \u003cp\u003e387\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 87px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 87px;\"\u003e\n \u003cp\u003e1(ref)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 140px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 120px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 100px;\"\u003e\n \u003cp\u003eAbortion\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 87px;\"\u003e\n \u003cp\u003e387\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 87px;\"\u003e\n \u003cp\u003e182\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 87px;\"\u003e\n \u003cp\u003e1.14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 140px;\"\u003e\n \u003cp\u003e0.53-2.48\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 120px;\"\u003e\n \u003cp\u003e0.735\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 100px;\"\u003e\n \u003cp\u003eCattle\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 87px;\"\u003e\n \u003cp\u003e154\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 87px;\"\u003e\n \u003cp\u003e27\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 87px;\"\u003e\n \u003cp\u003e1(ref)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 140px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 120px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 100px;\"\u003e\n \u003cp\u003eSheep\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 87px;\"\u003e\n \u003cp\u003e90\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 87px;\"\u003e\n \u003cp\u003e45\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 87px;\"\u003e\n \u003cp\u003e3.59\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 140px;\"\u003e\n \u003cp\u003e0.95 \u0026ndash; 13.63\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 120px;\"\u003e\n \u003cp\u003e0.06\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 100px;\"\u003e\n \u003cp\u003eGoats\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 87px;\"\u003e\n \u003cp\u003e143\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 87px;\"\u003e\n \u003cp\u003e108\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 87px;\"\u003e\n \u003cp\u003e26.71\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 140px;\"\u003e\n \u003cp\u003e8.49 \u0026ndash; 84.10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 120px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003ch3\u003eMolecular detection and phylogenetic analysis\u003c/h3\u003e\n\u003cp\u003eOut of 112 extracted DNA samples, \u003cem\u003eCoxiella burnetii\u0026nbsp;\u003c/em\u003eDNA was detected in 54/112 (48.2%) by qPCR with the highest detection in goats (30) followed by cattle (13) and finally sheep (11). Among the qPCR-positive samples, 16 samples were successfully amplified using nested PCR. BLAST analysis showed sequence similarity ranging from 86.52 to 100% with existing \u003cem\u003eCoxiella burnetii\u003c/em\u003e sequences in GenBank, with the Expect value (E-value) of zero and below indicating that the match was significant. A phylogenetic tree was constructed using both Isiolo and global isolates (shown in Figure 2). The analysis revealed that our isolates were distributed across the entire tree, falling into different clades. Three major clades were identified, with members of the same clade exhibiting varying branch lengths. Notably, isolates from the same species in Isiolo did not cluster together, but clustered with other isolates from different hosts in different geographical locations.\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eLivestock species: goats, cattle and sheep serve as the primary reservoirs for \u003cem\u003eC. burnetii\u003c/em\u003e. The presence of \u003cem\u003eC. burnetii\u003c/em\u003e antibodies in blood indicates the possibility of prior exposure to the pathogen. Frequent contact between infected and susceptible livestock at the grazing field could enhance its transmission as the pathogen is extremely infectious. This risk is exacerbated in livestock managed under extensive feeding management systems as they are more prone to acquiring infections compared to those under intensive systems, likely due to increased movements, herd interactions and exposure to contaminated water sources [36], [37]. Our study recorded a lower \u003cem\u003eC. burnetii\u003c/em\u003e seropositivity in cattle compared to goats and sheep. This is in line with previous study done in Kenya that categorized goats and sheep to have a higher prevalence as compared to cattle [38]. A study done by Sadiki \u003cem\u003eet al\u003c/em\u003e 2023 showed that herd size was directly proportional to the seropositive of \u003cem\u003eC. burnetii\u003c/em\u003e in cattle [39]. Shedding patterns play a role in the prevalence of \u003cem\u003eC. burnetii\u003c/em\u003e with cattle mainly shedding it through milk and to a lesser extent through virginal mucus [39], [40]. Conversely, Goats and sheep have multiple shedding routes; Vaginal mucus, faces and milk which enhance its prevalence and reinforce its transmission to the environment [39], [41].\u003c/p\u003e\n\u003cp\u003eOur study found higher seropositivity in goats than in sheep and cattle, consistent with previous study in almost similar settings [42]. Since \u003cem\u003eC. burnetii\u003c/em\u003e contaminates environment, especially soil and dust, animals that graze or browse closer to the ground are more likely to come in contact with the pathogen [43]. Goats being browsers are closer to the ground stands at high chances of contact with the contaminated environment [1], [42]. Studies have shown that, compared to cattle, the extended presence of adult goats on the farm leads to prolonged exposure which might be a factor contributing to the higher prevalence of \u003cem\u003eC. burnetii\u003c/em\u003e antibodies in this species as goats can maintain seropositivity for prolonged period [44]. Abortion history play a role in the presence of \u003cem\u003eC. burnetii\u003c/em\u003e antibodies in goats. Studies have shown that goats with previous history of abortion are likely to be seropositive [41]. While this study on \u003cem\u003eC. burnetii\u003c/em\u003e seropositivity in goats cannot directly link the recorded abortion cases, we can hypothesize that the seronegative cases among aborted goats were caused by other abortifacient pathogens.\u003c/p\u003e\n\u003cp\u003eOur conditional logistic regression model indicated a higher frequency of abortion among the sampled three livestock. While \u003cem\u003eC. burnetii\u0026nbsp;\u003c/em\u003ewas the focus of this study,\u003cem\u003e\u0026nbsp;\u003c/em\u003esome abortion cases may be attributed to other non-infections agents such as selenium deficiency [45], [46]. Non-infectious agents were not investigated in this study. However, from the literature, these agents contribute approximately 10% of the abortion events [47]. On the other hand, in most cases, presence of broad spectrum of abortifacient pathogens increases the chances of abortion to happen [47], [48]. Other factors that have been documented to cause abortion in livestock include genetic factors, toxic chemicals, nutritional and metabolic problems [48].\u0026nbsp;An experimental study has\u003cem\u003e\u0026nbsp;\u003c/em\u003eshown that \u003cem\u003eC. burnetii \u0026nbsp;\u003c/em\u003einfection does not always cause abortion, though it may occur under infection extreme conditions [49].\u003c/p\u003e\n\u003cp\u003eOur study identified goats as the most affected by abortion, reaffirming findings that cattle are less susceptible to abortions caused by \u003cem\u003eC. burnetii\u003c/em\u003e [50]. Some studies have documented abortion rates in goats to be about 90% implying that this livestock species is prone to infections and abortion [48], [51]. Goats are particularly susceptible to \u003cem\u003eC. burnetii\u003c/em\u003e during pregnancy [49] due to weak immune responses, which may explain why \u003cem\u003eC. burnetii\u003c/em\u003e is responsible for abortion in goats in this study. A study conducted by Azeb \u003cem\u003eet al\u003c/em\u003e 2019 depicted the prevalence of \u003cem\u003eC. burnetii\u003c/em\u003e was 50% in the population of sheep aborted implying that \u003cem\u003eC. burnetii\u003c/em\u003e is not always associated with abortion in sheep [10], [52]. Studies in aborted sheep showed the presence of \u003cem\u003eC. burnetii\u003c/em\u003e [53] although some studies have described the presence of \u003cem\u003eC. burnetii\u003c/em\u003e in sheep as complicated because of the excretion of the abortifacient pathogen by healthy sheep [10].\u003c/p\u003e\n\u003cp\u003eWith the use of molecular technique, qPCR, we detected \u003cem\u003eC. burnetii\u0026nbsp;\u003c/em\u003ein 54 of 112 (48.21%) blood samples. While no similar study has been conducted in Kenya, a study in Tanzania detected \u003cem\u003eC. burnetii\u0026nbsp;\u003c/em\u003eusing the Cox IS1111 gene, a well-established rapid and reliable molecular diagnostic marker [18]. PCR detection using Cox IS1111 gene has proven its sensitivity and specificity for detection of \u003cem\u003eC. burnetii\u0026nbsp;\u003c/em\u003e [54],[24]. Our sequence analysis demonstrated similarity between the Isiolo \u003cem\u003eC. burnetii\u0026nbsp;\u003c/em\u003estrains with the sequences retrieved from NCBI. To the best of our knowledge, our study is the first in Kenya to carry out molecular detection of \u003cem\u003eC. burnetii\u0026nbsp;\u003c/em\u003ein an aborting livestock population.\u003c/p\u003e\n\u003cp\u003eThe phylogenetic tree generated from our data illustrates close evolutionary relationships between global and Isiolo isolates giving insights on the nature of pathogenic \u003cem\u003eC. burnetii\u0026nbsp;\u003c/em\u003ecirculating in different regions globally. This is in line with finding from Durga \u003cem\u003eet al\u003c/em\u003e [54]that showed similarities between isolates from India and Isolates from the global isolates based on the Cox IS1111 gene. As per our findings, close evolutionary relationship was evident in all the isolated sequences with all the sequences falling into different clades sharing a common ancestor. The difference in members of the same clade could be pointed out by the branch length which signifies the level of evolutionary divergence from member of the same clade. This was evident in OR684941, OR684942, OR684935, OR684936OR684940, OR684945 OR684946, OR684947 and OR684950 as shown in figure 2. Studies have shown that different content in the insertion sequence is responsible for disparity in evolutionary change of \u003cem\u003eC. burnetii\u0026nbsp;\u003c/em\u003e[55],[56]. This occurs as a result of genetic changes such as deletion occurring in various divisions of IS1111 insertion element leading to the emergence of diverse strains [57]. Therefore, we hypothesized that the emergence of diverse strains emanated from genetic changes occurring in IS1111 insertion element contributing to development of more divergent \u003cem\u003eC. burnetii\u0026nbsp;\u003c/em\u003estrains which is evident in OR684941 and OR682942. Additionally, variations in livestock species and regional factors likely influence \u003cem\u003eC. burnetii\u003c/em\u003e strain distribution.\u003c/p\u003e"},{"header":"Conclusion and recommendations","content":"\u003cp\u003eAlthough pathogenicity of \u003cem\u003eC. burnetii\u0026nbsp;\u003c/em\u003ewas not assessed, this study showed genetic similarity between Isolated sequence and other known pathogenic \u003cem\u003eC. burnetii.\u0026nbsp;\u003c/em\u003eFrom our findings, it is quite evident that \u003cem\u003eC. burnetii\u0026nbsp;\u003c/em\u003eis one of the pathogens responsible for abortion in livestock, although we are not ruling out co-infections with other abortifacient pathogens. Therefore, future studies could be focused on metagenomic analysis to determine other pathogens that could be linked to abortion in livestock species. In addition, it is worth screening the samples used in this study for other pathogens, Toxoplasma gondii, Rift Valley Fever Virus, \u003cem\u003eBrucella\u003c/em\u003e and other pathogens that are known to cause abortion in livestock species.\u003c/p\u003e\n\u003cp\u003eThere is a need to carry out community sensitization, particularly among pastoralists, on proper handling and disposal of aborted materials. This will be of great essence in reducing and curtailing the spread of infection to humans. \u003cem\u003eC. burnetii\u003c/em\u003e circulating in Isiolo is of public health concern and therefore managing Q fever in both animals and humans is crucial. Furthermore, zoonotic control methods and continuous monitoring programs should be upscaled.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eData Availability\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll the data are included in this article.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eOur\u0026nbsp;study was anchored on Coinfection with Rift Valley Fever virus, \u003cem\u003eBrucella spp\u0026nbsp;\u003c/em\u003eand \u003cem\u003eCoxiella burnetii\u0026nbsp;\u003c/em\u003eproject‖\u0026nbsp;funded\u0026nbsp;by\u0026nbsp;DTRA\u0026nbsp;grant\u0026nbsp;number\u0026nbsp;HDTRA11910031.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors\u0026rsquo; contribution:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eConceptualization (BB,HA,EK,JA,RN), Methodology ( HA,BB,RN, EK,RM,JB), Data collection (AM,MM,JA),Analysis (JA,HA,BB,RN,EM,LK,EK), Writing (EK), Supervision( BB,HA,JA,JB)\u003c/p\u003e\n\u003cp\u003eAll the authors have read and approved the manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interest\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNo known competing interests. There was no influence from the funders on the design, conduct, and reporting of research findings.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe appreciate the pastoral community in Kinna ward, Garbatula subcounty for the decision to partake in our study. We also extend our appreciation to Isiolo County, department of Livestock Production for their approval to implement our study in their area. We acknowledge all the research participants who took part in our study.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthical approval and Consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eEthical clearance for this study was obtained from the International Livestock Research Ethics Committee; reference number ILRI-IREC2020-07. Informed consent was obtained from farmers before sample collection, and a structured questionnaire was administered.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eMuema J, et al. Seroprevalence and Factors Associated with \u003cem\u003eCoxiella burnetii\u003c/em\u003e Infection in Small Ruminants in Baringo County, Kenya. Zoonoses Public Health. Nov. 2017;64(7):e31\u0026ndash;43. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1111/zph.12342\u003c/span\u003e\u003cspan address=\"10.1111/zph.12342\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGakuya F, et al. Evidence of co-exposure with Brucella spp, \u003cem\u003eCoxiella burnetii\u003c/em\u003e, and Rift Valley fever virus among various species of wildlife in Kenya. PLoS Negl Trop Dis. 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Feb., Comparative genomics reveal extensive transposon-mediated genomic plasticity and diversity among potential effector proteins within the genus \u003cem\u003ecoxiella\u003c/em\u003e, \u003cem\u003eInfect Immun\u003c/em\u003e, vol. 77, no. 2, pp. 642\u0026ndash;656, 2009, \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1128/IAI.01141-08\u003c/span\u003e\u003cspan address=\"10.1128/IAI.01141-08\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDenison AM, Thompson HA, Massung RF. IS1111 insertion sequences of \u003cem\u003eCoxiella burnetii\u003c/em\u003e: Characterization and use for repetitive element PCR-based differentiation of \u003cem\u003eCoxiella burnetii\u003c/em\u003e isolates. BMC Microbiol. 2007;7. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1186/1471-2180-7-91\u003c/span\u003e\u003cspan address=\"10.1186/1471-2180-7-91\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Q fever, Coxiella burnetii, qPCR, Abortion, Zoonoses","lastPublishedDoi":"10.21203/rs.3.rs-6205861/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6205861/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e \u003cem\u003eCoxiella burnetii\u003c/em\u003e is the causative agent of Q fever, a zoonotic infection that poses serious threats to both animal and human health, particularly in the Global South. This bacterium primarily infects livestock such as cattle, sheep and goats and can be transmitted to humans through inhalation of contaminated aerosols or contact with infected products like milk and urine. The disease leads to livestock losses and febrile illnesses in humans. A longitudinal study was conducted to collect blood and serum samples from livestock; 18 Cattle, 22 Sheep, and 72 goats that aborted during the study period, from March 2022 \u0026ndash; August 2023. Serological detection of \u003cem\u003eC. burnetii\u003c/em\u003e immunoglobulin G (IgG) antibodies was performed using enzyme-linked immunosorbent assay (ELISA) and compared with baseline ELISA results. Molecular detection and genetic diversity assessment were conducted using quantitative PCR (qPCR) and nested PCR targeting the Cox IS1111 gene.\u003c/p\u003e \u003cp\u003eOur results showed that Goats had the higher seropositivity (49.65%;95%CI: 41.97\u0026ndash;57.75) followed by sheep (16.67%;95%CI: 10.37\u0026ndash;25.69) and cattle (3.25%;95%CI: 1.40\u0026ndash;7.37). Livestock species with a history of abortion had higher seropositivity (33.89%;95%CI:27.38\u0026ndash;41.08) compared to non-aborting animals (14.39%;95%CI: 10.34\u0026ndash;19.93). Conditional logistic regression model identified abortion as a significant risk factor, with goats being 26.71 times more likely to abort than cattle, and sheep 3.59 times more likely than cattle. Among the 112 blood samples, 54 tested positive by qPCR and 16 of these were subjected to Sanger sequencing. Phylogenetic analysis, performed using the maximum likelihood approach, provided insights into the genetic diversity and circulation of \u003cem\u003eC. burnetii\u003c/em\u003e strains.\u003c/p\u003e \u003cp\u003eThis study provides both serological and molecular evidence of \u003cem\u003eC. burnetii\u003c/em\u003e as in aborted and non-aborted livestock in Isiolo County. The findings enhance understanding of the pathogen\u0026rsquo;s epidemiology and can inform targeted disease control strategies.\u003c/p\u003e","manuscriptTitle":"Serological and Molecular characterization of Coxiella burnetii causing abortion in Livestock species in Isiolo County, Kenya.","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-03-28 14:52:31","doi":"10.21203/rs.3.rs-6205861/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"584d4372-5e31-453f-ae5f-b6180a4324bb","owner":[],"postedDate":"March 28th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2025-06-06T06:38:20+00:00","versionOfRecord":[],"versionCreatedAt":"2025-03-28 14:52:31","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-6205861","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6205861","identity":"rs-6205861","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}