A systematic review of whole genome sequencing evidence on zoonotic Brucella melitensis in Africa revealing lineage structure phylogenomic clusters antimicrobial resistance and genomic determinants

A systematic review of whole genome sequencing evidence on zoonotic Brucella melitensis in Africa revealing lineage structure phylogenomic clusters antimicrobial resistance and genomic determinants | 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 Systematic Review A systematic review of whole genome sequencing evidence on zoonotic Brucella melitensis in Africa revealing lineage structure phylogenomic clusters antimicrobial resistance and genomic determinants Samweli Y. Bahati, Abdalah Makaranga, Eliezer B. Mwakalapa, Henry G. Mung’ong’o, and 3 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-9058123/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 Brucella melitensis is a major zoonotic pathogen in Africa, yet its population structure and transmission dynamics remain poorly characterized due to the limited use of whole-genome sequencing (WGS). This systematic review synthesized all published WGS studies of B. melitensis from African settings to assess lineage distributions, phylogenomic patterns, antimicrobial resistance (AMR) determinants, and virulence repertoires. The review followed PRISMA 2020 guidelines and was prospectively registered in PROSPERO. PubMed, Scopus, and Embase were searched without date limits for studies generating or analyzing WGS data from B. melitensis isolates originating in Africa. Eligible studies reported primary genomic analyses, including lineage assignment, phylogenomics, AMR, or virulence profiling. Two reviewers independently screened records extracted data using a calibrated form and assessed methodological quality using the Joanna Briggs Institute (JBI) tools. Owing to methodological heterogeneity, findings were synthesized narratively. Of 96 records identified, nine studies met the inclusion criteria, representing six countries (Tunisia, Algeria, Egypt, Ethiopia, Tanzania, and South Africa) and 139 genomes. North African datasets were dominated by biovar 3 and sequence type (ST) 11 within West Mediterranean lineages, whereas East and Southern African studies showed ST12-dominated African-lineage clusters with several novel sequence types. Phylogenomic analyses consistently revealed tight within-country clusters (0–6 alleles or 0–2 SNPs) and substantially larger between-country and between-lineage distances. AMR determinants were limited to intrinsic loci such as mprF and bepC–G , with no classical acquired resistance genes detected. Virulence repertoires were highly conserved across regions, with intact LPS and type IV secretion system genes universally present. African B. melitensis populations form regionally distinct lineages with strong local clustering and conserved virulence and intrinsic AMR architectures. Major evidence gaps, particularly in West and Central Africa, highlight the need for coordinated, continent-wide genomic surveillance to inform One Health brucellosis control. Molecular Epidemiology Bacteriology Zoonoses Brucella melitensis Whole-genome sequencing (WGS) Phylogenomics Antimicrobial resistance (AMR) Africa Zoonotic pathogens Figures Figure 1 Figure 2 1 Introduction Brucella melitensis is the most pathogenic member of the genus Brucella and the principal cause of human brucellosis globally. The organism is highly infectious, zoonotic, and capable of establishing chronic intracellular infection in a wide range of hosts, including animals and humans [ 1 ]. Africa carries a substantial burden of brucellosis, particularly in pastoral and mixed crop–livestock systems, where close human–animal contact, informal milk markets, and limited veterinary infrastructure facilitate transmission [ 2 ]. Despite this, the true epidemiological landscape of B. melitensis in Africa remains poorly resolved, largely because surveillance depends heavily on serology or culture rather than molecular typing or genomic tools [ 3 ]. Whole-genome sequencing (WGS) has transformed the understanding of Brucella evolution, population structure, and transmission dynamics in several regions outside Africa. Large international analyses have demonstrated that B. melitensis segregates into geographically structured clades, including West Mediterranean, East Mediterranean, African, and American lineages, each associated with distinct evolutionary and epidemiological histories [ 4 ], [ 5 ]. WGS has also become a key tool for resolving outbreak clusters, identifying cross-border transmission events, characterizing antimicrobial resistance determinants, and comparing field strains with live attenuated vaccine lineages such as Rev.1 [ 6 ]. However, most of these genomic insights originate from Europe, the Middle East, Central Asia, and China, where sequencing capacity and routine genomic surveillance are more established. African contributions remain comparatively scarce, fragmented, and unevenly distributed across the continent. Given the wide host range of B. melitensis , its persistence in livestock populations, and the growing recognition of brucellosis as a priority One Health threat, a consolidated assessment of African WGS data is urgently needed. Existing reports suggest considerable heterogeneity in sampling strategies and analytical workflows, complicating interpretation and limits regional comparability. Furthermore, gaps in genomic surveillance may obscure important patterns, such as the emergence of distinct African lineages, transmission pathways between livestock and humans, or the spread of vaccine-related genotypes that cannot be captured through serology or low-resolution typing alone. To address these gaps, this systematic review synthesizes all published studies generating or analyzing whole-genome sequencing data for B. melitensis isolates originating from African countries. The review aims to (i) describe the geographical, host, and sampling contexts of available WGS investigations; (ii) summarize lineage and biovar distributions across African regions; (iii) examine phylogenomic relationships and clustering patterns; (iv) characterize reported antimicrobial resistance and virulence determinants and priorities for strengthening genomic surveillance. By consolidating the current genomic evidence base, this review provides the continent-wide synthesis of B. melitensis WGS studies in Africa and offers a foundation for future One Health–focused brucellosis control strategies. 2 Methods 2.1 Protocol registration and eligibility criteria The review protocol was developed according to PRISMA 2020 guidance and registered prospectively in PROSPERO (ID: PROSPERO 2026 CRD420251242529). Studies were eligible for inclusion if they generated, analyzed, or interpreted whole-genome sequencing data derived from B. melitensis isolates collected in any African country. All host categories were considered, including human, livestock, wildlife, and mixed or environmental sources, provided that the study presented primary genomic findings such as lineage or biovar assignment, genomic clustering, AMR or virulence determinants, or phylogenetic relationships. No restrictions were placed on sequencing platforms, assembly or mapping approaches, bioinformatic pipelines, or publication year. Studies were excluded if they relied exclusively on non-genomic methods such as polymerase chain reaction (PCR), Multi Locus Variable-Number Tandem Repeat (MLVA), multilocus sequence typing (MLST) without accompanying WGS, or serological and phenotypic assays. Reports involving exclusively non-African isolates or lacking extractable African subgroup data were also excluded. Reviews, commentaries, conference summaries, and other non-primary sources were excluded during the screening process. 2.2 Information sources A structured literature search was carried out in PubMed, Scopus, and Embase to identify published studies reporting WGS of B. melitensis isolates originating from African settings. No date limits were applied, and all records indexed in these databases were screened. 2.3 Search strategy The search strategy combined controlled vocabulary terms with free-text keywords representing the organism, sequencing methodology, and geographical scope. Terms related to “ Brucella melitensis ”, “whole-genome sequencing”, “genomic analysis”, “comparative genomics”, and “genomic epidemiology” were paired with “Africa” and the full list of African countries to maximize sensitivity. Search strings for each database were developed iteratively and are provided in the Supplementary Material (Table S2) for reproducibility. No restrictions were applied on the publication year. Although multiple languages were captured in the search, only English-language articles were retrieved. 2.4 Selection process Search outputs from all databases were exported and imported into Covidence, where automated deduplication preceded screening. Titles and abstracts were screened independently by two reviewers, followed by full-text assessment of potentially eligible articles. Disagreements were resolved through discussion. 2.5 Data extraction Data extraction was performed independently by two reviewers using a calibrated extraction form. Extracted variables included the study country, year of sampling, host species, sample type, sample size, number of genomes analyzed, sequencing platform, assembly or mapping approach, genome quality metrics, typing and phylogenomic methods, lineage or biovar assignments, AMR and virulence determinants, mobile genetic elements, and plasmid or insertion sequence data (where reported). Studies and data extracted after screening are shown in Supplementary Table S1. 2.6 Risk of bias and quality assessment Methodological quality was assessed using the Joanna Briggs Institute (JBI) [ 7 ], critical appraisal tools appropriate for observational and cross-sectional studies. Because WGS-based investigations involve both laboratory and genomic analytical components, the JBI domains were applied to evaluate the appropriateness of sampling strategies, clarity of inclusion criteria, and validity of methods used to identify B. melitensis . Additional consideration was given to the reporting of specimen collection procedures, completeness of metadata, sequencing quality, and the transparency and reproducibility of bioinformatic workflows, including assembly, SNP calling, and cgMLST analyses. Each study was appraised independently by two reviewers, with discrepancies resolved through consensus. The overall assessment informed the interpretation and weighting of evidence across included studies. 2.7 Synthesis approach Substantial heterogeneity across studies in design, sequencing platforms, analytical approaches, and outcome definitions precluded formal statistical pooling. For this reason, findings were synthesized narratively and supported by structured summary tables. Studies were grouped thematically according to their primary genomic outcomes, including lineage or biovar distribution across African regions, AMR gene profiles, virulence gene repertoires, presence of mobile genetic elements, MLST sequence types, and phylogenetic relationships inferred from SNP or allele-based methods. Where appropriate, patterns were described in relation to geography, host species, and sampling period to highlight emerging genomic and epidemiological trends. 3 Results 3.1 Study selection A total of 96 records were identified through database searches (Embase = 53, PubMed = 22, Scopus = 21). An additional 2 records were identified through citation searching. After the removal of 38 duplicates (36 via Covidence and 2 manually), 60 records proceeded to title and abstract screening. Of these, 44 were excluded, leaving 16 studies for full-text assessment. No full texts were unobtainable. Following the eligibility evaluation, 7 studies were excluded due to wrong outcomes. Ultimately, 9 studies met the inclusion criteria and were included in the final review. No ongoing studies or studies awaiting classification were identified (Fig. 1 ). 3.2 Characteristics of included studies Nine studies that met the inclusion criteria were published between 2021 and 2025, covers six African countries: Tunisia (n = 3), Tanzania (n = 1), Algeria (n = 1), Ethiopia (n = 1), South Africa (n = 1), and Egypt (n = 2). Study settings included human clinical surveillance, livestock surveillance, mixed human, animal investigations, and retrospective laboratory collections. Hosts sampled across studies comprised humans, goats, sheep, cattle, buffalo, and wildlife (sable antelope). Sample sources included blood, milk, vaginal swabs, tissue, fetal material, lymph nodes, and abscess material. The number of genomes analyzed per study ranged from 2 to 36, with a cumulative total of 139 B. melitensis genomes across all included studies (Table 1 ). Table 1 Characteristics of African Brucella melitensis whole-genome sequencing studies included in this review (2021–2025). The table summarizes study location, host species, sample sources, study period, number of genomes analyzed, antimicrobial resistance (AMR) determinants and mechanisms, detected gene mutations, antimicrobial susceptibility testing approaches and outcomes, virulence gene repertoires, mobile genetic elements, plasmid and replicon content, major lineage or biovar assignments, and multilocus sequence types (MLST) identified across human, livestock, and wildlife hosts. Study ID Country Host(s) Source Type Start date Number of genomes Region / Country combination Sample size by host AMR genes Mechanism Gene mutation Antibiotics tested Resistance or susceptibility result Testing method Virulence genes Module type Plasmid names IS or transposase elements Replicon IDs Major lineage or biovar identified Sequence Type(s) (MLST) BenAbdallah 2025 Tunisia Sheep (ewes, aborted and non-aborted) Vaginal swabs from aborted and pregnant ewes 2020 2 Sidi Bouzid and Tataouine, Tunisia 1 isolate per ewe (n = 2 sheep total) mprF, bepCDEFG, qacG, adeF, fosXcc RND efflux pumps, cationic antibiotic resistance (Defensin mprF), SMR efflux (qacG), fosfomycin thiol transferase (fosXcc) parC Ala100→Thr Gentamicin, Streptomycin, Doxycycline, Rifampicin, Trimethoprim–Sulfamethoxazole, Ceftriaxone, Ciprofloxacin, Levofloxacin, Tetracycline Susceptible to five agents (GEN, STR, DOX, RIF, SXT); Intermediate to fluoroquinolones possible (0.5 µg/mL MIC) Broth microdilution (MIC) per CLSI M45-A2 bigA, bigB, bmaB/omA, bmaC, gmd, per, pgm, pmm, manAoAg, manCoAg, wzm, wzt, wbkB, wbkC, wbkA, wbpZ, wbpL, lpsA, acpXL, wboA, wbdA, lpsB, lpcC, manCcore, manBcore, fabZ, lpxA, lpxC, lpxD, lpxB, lpxE, lpxK, KdsA, kdsB, waaA/kdtA, htrB, manA, perA, virB1, virB2, virB3, virB4, virB5, virB6, virB7, virB8, virB9, virB10, virB11, virB12, vceA, vceC, ricA, BPE005, BPE043, BPE275, BPE123, sepA, bspA, bspB, BspC, bspE, bspF, bspL, BtpA/Btp1/TcpB, BtpB, bvrR, bvrS, Cgs, mviN, betB, omp19, Ure, prpA LPS synthesis, T4SS, adhesins, two-component signaling, immune evasion None detected None detected Not reported Biovar 3 ST11 (MLST-9), ST89 (MLST-21) Middlebrook 2025 Tanzania Goats, Humans Goat milk and human blood samples 2024 6 Kagera Region, Tanzania Human (3), Goat (3) None reported. None reported. None reported. Not performed Not reported Not applicable Not detected None reported. None detected None detected Not reported Not stated Not stated Ferjani 2025 Tunisia Human Blood 2016 36 Tunisia (North Africa). Human (36) mprF, bepC–G efflux pump complex None reported. Not performed Not reported Not applicable acpXL, bigA, bigB, bmaB/omaA, bmaC, BPE005, BPE043, BPE123, BPE275, bspA, bspB, bspC, bspE, bspF, bspL, btpA, btpB, bvrR, bvrS, cgs, fabZ, gmd, htrB, kdsA, kdsB, lpsA, lpsB/lpcC, lpxA, lpxB, lpxC, lpxD, lpxE, lpxK, manAoAg, manBcore, manCcore, manCoAg, per, pgm, pmm, ricA, sepA, vceA, vceC, virB1, virB2, virB3, virB4, virB5, virB6, virB7, virB8, virB9, virB10, virB11, virB12, waaA/kdtA, wbdA, wbkA, wbkB, wbkC, wboA, wbpL, wbpZ, wzm, wzt Type IV secretion system, LPS biosynthesis, adhesion and stress response None detected None detected Not reported biovar 3 Not stated Nabi 2024 Algeria Sheep, Goats Serum, Milk, Placenta, Fetus 2020 10 Médéa and Sidi Bel-Abbès, Algeria 96 sera (77 sheep, 19 goat), 57 milk (42 sheep, 15 goat) None reported None reported None reported. Not performed Not reported Not applicable Not detected None reported. None detected None detected Not reported Not stated ST-11 Sibhat 2024 Ethiopia Sheep, Goats Vaginal swabs, Milk 2022 24 Afar Region, Ethiopia 231 animals (199 goats, 32 sheep); 248 samples (231 swabs, 17 milk) adeF, fosXCC, mprF, qacG Efflux pump (adeF, qacG); antibiotic inactivation (fosXCC); target alteration (mprF) None reported. Not performed Not reported Not applicable bmaC, btaE, btaF, acpXL, fabZ, gmd, htrB, kdsA, kdsB, lpsA, lpsB/lpcC, lpxA, lpxB, lpxC, lpxD, lpxE, lpxK, manAoAg, manBcore, manCcore, manCoAg, per, pgm, pmm, waaA/kdtA, wbdA, wbkA, wbkB, wbkC, wboA, wbpL, wbpZ, wzm, wzt, cgs, btpA, btpB, virB1, virB2, virB3, virB4, virB5, virB6, virB7, virB8, virB9, virB10, virB11, virB12, ricA, vceC, dhbA, dhbB, dhbC, dhbE, entD, vibH/entF, bvrR, bvrS T4S Secretion, regulatory, iron uptake, adhesion, survival None detected None detected Not reported biovar 1 ST12, 1 novel isolate Mazwi 2024 South Africa Human, Goat, Cattle, Sable antelope Blood (human), Tissue (goat and cattle), Tissue/serum/hygroma fluid/skin lesion (sable) Not reported 15 South Africa (Western Cape, Eastern Cape, Gauteng, North West, Limpopo). Human (1), Goat (1), Cattle (1), Sable (12), Vaccine (1 Rev1) Not reported Not reported None reported. Not performed Not reported Not applicable acs, aam, cspC Metabolic / mobilome-related None detected IS5 family Not reported (bv 1–3 range) ST12 (human, goat, several sable) plus novel STs (*7a27, *8212, *30bf, *bd3d, *b647) Wareth 2021 Egypt Humans, Cattle, Goat, Sheep Human blood, bovine milk, goat milk, lymph nodes (LN) 2018 22 Egypt (Nile Delta, Giza, Fayoum, Beni Suef, Sharkia, Damietta, Aswan, Ismailia, Behira) Not clear, only 22 comfirmed for WGS mprF, bepCDEFG RND and SMR efflux systems (cationic peptide modification, membrane adaptation) None detected Chloramphenicol, Ciprofloxacin, Doxycycline, Gentamicin, Levofloxacin, Rifampicin, Streptomycin, Tetracycline, Trimethoprim/Sulfamethoxazole, Tigecycline, Azithromycin Susceptible to most agents except intermediate to RIF (2 µg/mL MIC) and resistant/intermediate to AZM in 16/27 isolates Broth microdilution (CLSl M100-S20) + Disc diffusion for TGC and AZM 45 virulence genes detected (no inter-strain difference); included virB operon, ure, bvfA, omp25/31, and core LPS genes (data not shown) Type IV secretion, LPS biosynthesis, adhesion, regulation, intracellular survival None detected Not reported Not reported biovar 3 Not reported Khan 2021 Egypt Cattle, buffaloes, sheep, goats Lymph nodes, milk, fetal abomasal contents 2015–2017 21 Egypt (Nile Delta and Upper Egypt). 17 cattle, 2 buffalo, 1 sheep, 1 goat. mprF, TriC, tet, mcrA,folA membrane charge modification; rpoB/gyrA mutations associated with rifampicin and ciprofloxacin response. rpoB Arg2784→Arg, Thr2394→Thr; gyrA Asp297→Glu. Not performed Not reported Not applicable virB1, virB2, virB3, virB4, virB5, virB6, virB7, virB8, virB9, virB10, virB11, virB12, lpsB/lpcC, lpxC, lpxD, fabZ, lpxA, lpxB, lpxE, htrB, acpXL, wboA, wbdA, wbpZ, wbpL, wbkA, wbkB, wbkC, manAoAg, manCoAg, pgm, gmd, per, wzm, wzt, kdsA, kdsB, ricA, cgs, pmm, btpA, btpB Type IV secretion system, LPS biosynthesis/modification, O-antigen & perosamine synthesis/glycosylation, Cyclic β-1,2-glucan synthesis, Intracellular trafficking, Host immune modulation None detected. Not reported Not reported biovar 3 ST11 Ksibi 2025 Tunisia Humans Blood, abscess material, cardiac valve 1988 24 North Africa – Tunisia (south) Humans: n = 24 mprF; bepC, bepD, bepE, bepF, bepG, fosXcc, adeF, qacG Chromosomally encoded efflux and cell-envelope modification; potential fluoroquinolone/rifampicin target polymorphisms without proven resistance. rpoB G3747A (M1249I); gyrA C1795G (L599V) and C1557T (synonymous); gyrB G2188A (A730T); parC G298A (A100T) Not performed Not reported Not applicable virB1–B12, omp19, omp25/31, wbkA–C, wboA, wzm, wzt, lpxA–K, kdsA/B, waaA/kdtA, bmaB/C, bspA–L, bvrR/S, cgs, htrB, mviN, btpA, btpB Secretion (T4SS), LPS biosynthesis, adhesion, regulation, stress adaptation, iron uptake/survival, immune evasion. None detected (no plasmid replicons). Not reported Not reported biovar 3 ST11 3.3 Geographical distribution and sampling patterns The included whole genome sequencing studies were conducted in six African countries: Tunisia, Algeria, Egypt, Ethiopia, Tanzania, and South Africa. Geographically, most investigations originated from North Africa (Tunisia, Algeria, Egypt), followed by East Africa (Ethiopia, Tanzania), while Southern Africa was represented by a single study from South Africa. No eligible studies were identified from West or Central Africa (Fig. 2 ). Sampling strategies differed across settings. Human clinical isolates were sequenced in Tunisia, Egypt, South Africa, and Tanzania, whereas livestock samples were included in all countries except Tunisia’s clinical subset. Livestock sampling covered goats, sheep, cattle, and buffalo, with South Africa additionally contributing isolates from wildlife (sable antelope). Field-based studies in Algeria and Ethiopia focused on small ruminants with reproductive disorders, while hospital-based studies in Tunisia and Egypt relied primarily on blood or tissue specimens from confirmed human brucellosis cases. Tanzania contributed mixed human and goat milk isolates (Fig. 2 ). Sample types included blood, milk, vaginal swabs, placenta, fetal tissues, lymph nodes, abscess material, and other diagnostic specimens, depending on the study design. The number of genomes sequenced per country varied considerably, ranging from a small number of isolates in Tanzania to larger datasets from Tunisia and Egypt. Overall, the geographical coverage demonstrates a concentration of B. melitensis WGS work in northern and eastern Africa, with consistent representation of both human and livestock hosts, and limited contributions from wildlife. Temporal ranges differed by study, with some relying on retrospective clinical collections and others based on single-season field investigations, but all provided primary genomic data suitable for inclusion. 3.4 Lineage and biovar distribution Across the included WGS studies, biovar and lineage assignments were reported mainly from North, East, and Southern Africa. Where biovar typing was performed, B. melitensis biovar 3 predominated in North African settings. All Tunisian human isolates in the clinical series were assigned to biovar 3, and the two sheep isolates from southern and central Tunisia in BenAbdallah 2025 also grouped with biovar 3 strains (Table 1 ). Egyptian livestock isolates were likewise characterized as biovar 3. In contrast, the Ethiopian pastoral livestock study identified B. melitensis biovar 1 among sheep and goats, and the South African multi-host study reported B. melitensis from sable, goats, cattle, and one human within an “African lineage” clade that included strains typed as biovar 1–3 (Table 1 ). The Tanzanian genome announcement and some Egyptian surveillance work confirmed that B. melitensis by WGS did not report biovar assignment. Multilocus sequence typing (MLST) data were available for most genomic datasets. Using the classical 9-locus scheme, several studies reported a predominance of ST11 (Table 1 ). All Algerian small-ruminant isolates belonged to ST11 and were assigned to the West Mediterranean lineage, and Egyptian livestock isolates were also typed as ST11 and placed within the Mediterranean lineage. In Tunisia, all human isolates in the long-term Sfax series were ST11 by MLST-9, while MLST-21 resolved them into ST89 and ST114 profiles differing at the dllA locus; the two animal isolates from Tataouine and Sidi Bouzid were also reported as ST11 and ST89. East and Southern African studies reported greater ST diversity. Ethiopian livestock isolates were assigned to ST12 with one additional novel ST, and South African sable, goat, and cattle strains within the African lineage included ST12 together with several newly designated sequence types, while a Rev1 vaccine strain was classified as ST10 within an American lineage background. High-resolution phylogenomic analyses based on cgMLST or whole-genome SNP typing consistently showed that African B. melitensis genomes formed discrete regional clusters within broader global lineages. In Tunisia, cgMLST of clinical isolates yielded two main clusters separated by approximately 140 discriminating alleles, with up to six allelic differences within clusters and several additional singleton profiles. The hybrid-genome study placed one sheep isolate (TATA) within “Tunisian Cluster 1”, closely related to human isolates with ≤ 4 allelic differences and separated by about 150 alleles from Egyptian strains and 130 alleles from Italian strains, whereas the second sheep isolate (SBZ) lay nearer “Tunisian Cluster 2” and showed 14–26 allele differences to nearby Italian and Austrian genomes. In the long-term Tunisian series, cgMLST based on 1,764 core genes identified four cgST profiles with allele differences ranging from 3 to 158. Algerian small-ruminant isolates were assigned to the West Mediterranean lineage by SNP typing, forming two local SNP types that clustered with West Mediterranean human isolates from Sweden, Algeria, Morocco, Austria, Italy, and Tunisia. Egyptian livestock isolates showed nine cgSNP genotypes grouped into two main clusters with 0–119 pairwise SNP differences and ≥ 500 SNPs to non-Egyptian Mediterranean strains. South African genomes from sable, cattle, goat, and a human case all clustered within an African lineage subclade defined by ST12 and several novel STs, distinct from the American lineage Rev1 vaccine strain. 3.5 Phylogenetic relationships and genomic clustering Across studies, high-resolution SNP and cgMLST analyses consistently revealed tight within-country clusters, defined by very small allelic or SNP distances, in contrast to much larger separations between countries and between major global lineages. In Tunisia, clinical and animal WGS datasets described two main B. melitensis clusters, with outbreak-scale grouping defined by 0–4 or up to 6 cgMLST allelic differences within clusters, and approximately 140 alleles separating the two Tunisian clusters. The hybrid animal isolates TATA and SBZ fell into different Tunisian groups: TATA embedded within the human-dominated Cluster 1, while SBZ lay nearer Cluster 2 and closely related Italian and Austrian genomes at distances of 14–26 alleles. Between-country comparisons placed Tunisian clusters about 150 alleles from Egyptian isolates and 245–266 alleles from Algerian and Moroccan strains, indicating a clear step up from within-country to between-country distances. In the long-term Sfax series, cgSNP analysis of 24 human isolates showed a main Tunisian cluster of 23 genomes with pairwise distances of 0–77 SNPs, and a single divergent genome (BR4) 107–143 SNPs away from the others; within the broader West Mediterranean clade, pairwise distances extended up to roughly 500 SNPs, and the West Mediterranean lineage as a whole was separated from African, American and East Mediterranean lineages by around 1,600–1,950 core-genome SNPs. North African livestock datasets showed similar hierarchical structuring. In Algeria, cgSNP typing of ten small-ruminant isolates identified two local SNP types with 0–2 SNPs among five ovine isolates and 0 SNPs between two caprine isolates, using these very low distances as the working threshold for defining a type. The two SNP types differed by 53–55 SNPs from each other, while one outlier ovine isolate from Sidi Bel-Abbès sat 449–462 SNPs away from the rest of the Algerian set, yet only 16–28 SNPs from travel-associated human strains in Sweden, Algeria, and Morocco. This created two well-defined Algerian clusters within the West Mediterranean lineage, with separations to European and North African comparators that were one to two orders of magnitude larger than the within-cluster distances. In Egypt, core-genome SNP analysis of 21 B. melitensis biovar 3 field isolates defined nine genotypes grouped into two main clusters, with pairwise distances ranging from 0 to 119 SNPs within the Egyptian panel. When compared with 44 B. melitensis genomes from neighboring Mediterranean, African, and Asian countries, all Egyptian strains formed a single regional cluster at least 500 core SNPs distant from non-Egyptian West Mediterranean genotypes. For B. abortus from Egyptian cattle and buffalo, eight genomes fell into two genotypes with 0 SNP differences within each genotype and > 700 core SNPs separating these genotypes from Spanish and Bangladeshi B. abortus bv-1 comparators. In East Africa, whole-genome SNP phylogenies demonstrated a distinct African genotype background and evidence of regional clustering. Ethiopian pastoral small-ruminant isolates grouped within an “African” B. melitensis clade together with Somali and a few other sub-Saharan sequences, separate from Western and Eastern Mediterranean and American lineages. Within this African lineage, some Ethiopian and Somali isolates showed pairwise distances of 38–61 SNPs, while broader comparisons across Africa and other regions involved much larger SNP gaps. At the MLST level, the African ST12 genotype was common across sub-Saharan Africa but showed limited diversification compared with the highly diverse ST11 West Mediterranean background, and cgMLST further split Ethiopian isolates into several related cgSTs that tended to co-occur at the village level. In Tanzania, six new B. melitensis genomes from goats and humans formed a well-supported clade within the global phylogeny, clustering tightly together and with nearest neighbours from Belgium, Kuwait, Somalia, and Norway, again indicating a coherent African-associated cluster nested within the broader species tree. Southern African wgSNP analyses also highlighted a consolidated African lineage with host and country-level substructure. In South Africa, 16 B. melitensis genomes from sable, cattle, goat, and a human case clustered in an African lineage subclade defined by several hundred informative SNPs across the core genome, distinct from American, East, and West Mediterranean branches and clearly separate from the Rev1 vaccine strain, which grouped with an American-lineage ST10 background. Within this African subclade, sable isolates from multiple provinces formed closely related clusters dominated by ST12 and a small number of novel sequence types, while the human case strain grouped most closely with a goat isolate from the same region, consistent with very limited SNP differences along that branch. For B. abortus in South Africa, a core-SNP phylogeny based on 1,071 SNPs placed three new cattle genomes within the C2 genotype branch together with earlier South African and Mozambican strains and S19 vaccine derivatives, defining at least two closely related South African C2 sub-genotypes within a wider regional cluster. 3.6 Antimicrobial resistance, virulence determinants, and mobile genetic elements Across the included B. melitensis whole-genome studies, antimicrobial resistance analyses consistently identified intrinsic, chromosome-encoded determinants rather than classical acquired resistance genes. Most datasets that applied AMR prediction tools reported the multiple peptide resistance factor mprF in nearly all isolates, together with Brucella efflux protein family genes ( bepC–bepG/BPE ) and additional efflux or detoxification markers such as adeF , qacG , and fosXcc ( Table 1 ). These loci were detected in Tunisian animal isolates, Tunisian clinical collections, Ethiopian pastoral livestock isolates, and Egyptian livestock strains, and international comparisons indicated that mprF and bep genes are widely distributed across global B. melitensis lineages [ 8 ]. No study detected canonical acquired AMR genes associated with macrolides ( erm, mef, msr ), tetracyclines ( tet ), β-lactams (e.g., mecA ), or trimethoprim ( folA ) in B. melitensis genomes, and several authors explicitly reported their absence despite systematic screening [ 5 ], [ 8 ]. Point mutation analyses in target genes showed only limited variation and no previously validated resistance-conferring substitutions. Tunisian animal isolates carried a non-synonymous substitution in parC (Ala100Thr) relative to reference strains, while larger Tunisian clinical series reported additional polymorphisms in rpoB (M1249I), gyrA (L599V and a synonymous change), gyrB (A730T), and parC (Ala100Thr). Egyptian livestock isolates showed rare substitutions in rpoB and gyrA . Where AMR pipelines also annotated housekeeping or metabolic loci (for example, TriC, folA , or mcr -annotated genes), these were not accompanied by known resistance-associated mutations and were interpreted as part of the intrinsic genome rather than acquired resistance cassettes. Phenotypic susceptibility testing was reported in a subset of studies and was broadly concordant with the genomic findings. Tunisian animal isolates tested by broth microdilution remained susceptible to standard brucellosis drugs, including gentamicin, streptomycin, doxycycline, rifampicin, and trimethoprim–sulfamethoxazole, with only borderline MICs for some fluoroquinolones and no categorical resistance (Table 1 ). An Egyptian series that included B. melitensis performed MIC testing and disc diffusion, showing universal susceptibility to doxycycline, gentamicin, levofloxacin, streptomycin, tetracycline, trimethoprim–sulfamethoxazole, ciprofloxacin, and chloramphenicol. In that cohort, all B. melitensis isolates were classified as intermediate to rifampicin, and the majority were resistant or intermediate to azithromycin, while B. abortus comparators remained azithromycin-resistant but rifampicin-susceptible (Table 1 ). No study demonstrated a clear genotype–phenotype link between the intrinsic AMR genes or novel point mutations and these modest shifts in rifampicin or macrolide susceptibility. Virulence gene repertoires were highly conserved across countries, hosts, and lineages. All studies that systematically queried virulence databases reported a stable core of LPS biosynthesis and modification genes (including acpXL; lpxA, lpxB, lpxC, lpxD, lpxE, lpxK; kdsA, kdsB; waaA/kdtA; wboA; wbkA–C; wbdA; wbpL/wbpZ; wzm, wzt; and manA/manB/manC in core and O-antigen loci), together with a complete virB1–virB12 type IV secretion system and multiple associated effectors such as bspA–bspL , vceA/vceC , ricA , and BPE-encoded factors (Table 1 ). Adhesins, including bigA/bigB , bmaB/bmaC , btaE/btaF , and outer-membrane proteins omp19 , omp25 , and omp31 were consistently identified, alongside regulatory and survival modules such as bvrR/bvrS , cgs , mviN , htrB , betB , prpA , bvfA , urease genes, and iron-uptake loci ( dhbA–E , entD , vibH/entF ) (Table 1 ). Ethiopian livestock isolates carried at least 43 virulence factors per genome, with only a single strain lacking cgs, while Tunisian collections reported 60–70 virulence genes per isolate, and international comparisons showed most of these loci present in > 95–98% of publicly available B. melitensis genomes. Occasional absences of individual genes (for example, virB10 , BPE043 , or cgs in single isolates) did not follow a clear geographic or lineage-specific pattern. Mobile genetic elements were rarely characterised. No study identified plasmid replicons in B. melitensis , and plasmid-mediated resistance was not reported. Insertion sequences and transposase families were generally not catalogued systematically; only one South African study explicitly noted the presence of IS5-family elements within B. melitensis genomes (Table 1 ). For the remaining datasets, insertion sequences were not detected. Based on the available evidence, the African B. melitensis genomes included in this review are dominated by chromosomally encoded, conserved virulence and intrinsic AMR modules, with very limited documentation of plasmids or other mobile genetic elements, reflecting an analytical focus on core genome variation rather than an absence of mobilome features. 3.7 Risk of bias and study quality All nine included WGS studies met the JBI criteria for clearly defined inclusion criteria and adequately described study subjects and settings, with “Yes” ratings for questions 1 and 2 across all records. Laboratory confirmation of B. melitensis and use of standardized diagnostic criteria were consistently reported, resulting in “Yes” ratings for questions 3 and 4 in every study. By contrast, none of the studies explicitly identified potential confounding factors or described strategies to address them, and questions 5 and 6 were uniformly rated as “Unclear.” Genomic outcomes, such as lineage assignment, SNP or cgMLST metrics, and AMR/virulence profiling, were generally derived using established bioinformatics tools, and all studies were judged to have measured outcomes in a valid and reliable way (question 7, “Yes” for all). Most studies also provided sufficient detail on phylogenetic and comparative genomic analyses to be rated “Yes” for appropriate statistical analysis (question 8), except one study in which the analytic procedures were insufficiently described and were therefore rated “Unclear” for this domain, detailed in the Supplementary material S2. 4 Discussion This systematic review shows that, despite the growing recognition of whole-genome sequencing (WGS) in infectious disease surveillance, genomic investigations of B. melitensis in Africa remain sparse and geographically skewed. From an initial pool of 96 records, only nine studies met strict eligibility criteria for primary WGS data on African B. melitensis , indicating that genomics is still rarely integrated into routine brucellosis research and surveillance on the continent (Fig. 1 ). The absence of unpublished or ongoing WGS studies in trial registries or grey-literature sources suggests that the current genomic evidence base is genuinely small rather than simply inaccessible. This has direct implications for how confidently continental patterns can be inferred and underscores the need to move from isolated sequencing projects towards programmatic genomic surveillance embedded in national and regional control programmes. The included studies collectively confirm the wide ecological and host range of B. melitensis in African settings, spanning human patients, goats, sheep, cattle, buffalo, and wildlife (sable antelope), and drawing on diverse clinical and field samples such as blood, milk, reproductive tissues, lymph nodes, and abscess material (Fig. 2 ) (Table 1 ). However, the geographical and sampling patterns clearly reflect capacity rather than the true epidemiology of the pathogen. North Africa (Tunisia, Egypt, Algeria) and parts of East and Southern Africa (Ethiopia, Tanzania, South Africa) dominate the WGS landscape, whereas West and Central Africa, where brucellosis is well documented in pastoral and agro-pastoral systems [ 9 ], are entirely unrepresented. A similar geographic skew has been observed in global WGS syntheses, which are dominated by Europe, the Middle East, and China, despite broader disease endemicity [ 10 ]. Within countries, study designs are mostly opportunistic: retrospective hospital collections in Tunisia and Egypt, outbreak- or farm-based sampling in Algeria and Ethiopia, and mixed human–livestock panels in Tanzania and South Africa. These designs are valuable for proof-of-concept molecular epidemiology but provide limited insight into community-level or national transmission dynamics. Temporal coverage is also uneven, ranging from multi-decade clinical collections in Tunisia to single-season field surveys, which constrains inference about lineage replacement, long-term persistence, or the impact of control measures. Taken together, the lineage and biovar distributions observed in this review are consistent with, but refine, the global picture of B. melitensis population structure derived from large WGS datasets. Global analyses have shown that B. melitensis segregates into at least four major lineages, West Mediterranean, East Mediterranean, African, and American, with clear geographic associations and long-range dispersal events driven by animal movement and trade (Table 1 ) [ 10 ]. In North Africa, the dominance of biovar 3 and ST11, together with assignment to the West Mediterranean lineage, in Tunisian, Algerian, and Egyptian datasets aligns closely with this framework and reinforces long-recognized epidemiological connectivity between the Maghreb and southern Europe [ 5 ], [ 11 ], [ 12 ]. Large European WGS series have similarly reported ST11-dominated West Mediterranean lineages in Italy, Spain, and Portugal, often forming tightly clustered clades linked to local small-ruminant production systems [ 10 ]. The repeated placement of Tunisian and Egyptian genomes within well-defined West Mediterranean clades in our included studies, therefore, likely reflects sustained local evolution of established lineages rather than frequent introductions of entirely new genetic backgrounds [ 8 ]. By contrast, East and Southern African studies in this review are characterized by ST12 and an African-lineage background, with multiple novel sequence types arising in livestock and wildlife (Table 1 ). This pattern is consistent with global phylogeographic analyses that describe an “African” B. melitensis lineage, enriched for ST12 and predominantly associated with sub-Saharan Africa. The recovery of new sequence types from sable, goats, cattle, and a human case in South Africa [ 6 ], alongside ST12-dominated pastoral small-ruminant populations in Ethiopia, suggests that multi-host circulation is driving local diversification within this African lineage. Notably, Rev.1-associated ST10 signatures appear only in the South African dataset among our included studies, whereas there is no genomic evidence of vaccine-derived lineages in East African pastoral systems or Tunisian clinical series. This has practical implications for vaccine policy and post-vaccination surveillance, particularly where Rev.1 or other live attenuated vaccines are being introduced or scaled up. The phylogenetic and clustering patterns observed across the included studies reveal a robust hierarchical structure: very tight within-country or within-cluster distances contrasted with much larger separations between countries and between major lineages. Tunisian clinical and animal datasets describe clusters of isolates separated by zero to a few cgMLST alleles or SNPs, which is characteristic of local endemic transmission or repeated re-introduction from a stable reservoir [ 5 ], [ 11 ]. Similar tight clustering is observed among Egyptian and Algerian livestock panels, where within-type SNP distances of 0–2 or a handful of alleles are used as working thresholds for potential recent transmission or farm-level clusters [ 12 ], [ 13 ]. These findings mirror global WGS studies that use low SNP or allelic thresholds to identify outbreak-related clusters and distinguish them from background endemic diversity [ 12 ], [ 14 ]. In Ethiopia and South Africa, African-lineage isolates form cohesive subclades in whole-genome SNP trees, within which human, livestock, and wildlife strains often cluster together, pointing to stable multi-host transmission networks rather than short-lived, unidirectional spillover events [ 15 ]. At the same time, between-country and between-lineage distances are markedly larger, often involving hundreds of alleles or SNPs between national clusters and thousands of core-genome SNPs between West Mediterranean, African, East Mediterranean, and American lineages [ 8 ], [ 11 ]. These gradients suggest that recent cross-border transmission of B. melitensis lineages within Africa is relatively limited or, at least, infrequent enough that genomic signals are dominated by long-standing, country-specific lineages. Occasional findings of livestock isolates in our included studies that are closely related to human strains from Europe or neighboring countries (for example, Algerian isolates clustering closely with Swedish and Moroccan human cases) likely reflect historical trade or shared ancestral lineages rather than ongoing, extensive lineage exchange [ 12 ]. However, this interpretation is tempered by important gaps: sparse sampling, small within-country sample sizes, and the complete absence of WGS data from West and Central Africa mean that unsampled diversity and undetected inter-regional links almost certainly exist. The antimicrobial resistance (AMR) and virulence profiles observed in African B. melitensis genomes are strikingly conserved and dominated by intrinsic, chromosomally encoded determinants. Across Tunisian, Ethiopian, and Egyptian datasets in this review, automated AMR prediction consistently identified mprF and Brucella efflux protein genes ( bepC–bepG/BPE ), with additional efflux or detoxification markers such as adeF, qacG , and fosXcc in four studies (Table 1 ). No canonical horizontally acquired resistance genes, such as typical macrolide, tetracycline, β-lactam, or trimethoprim resistance cassettes, were detected in B. melitensis genomes, and multiple groups explicitly reported their absence despite two studies that did comprehensive screening [ 8 ], [ 13 ]. These findings are in line with WGS studies from Europe and in Asia, where B. melitensis typically lacks classical plasmid-borne AMR determinants and instead carries a conserved set of efflux- and cell-envelope-associated loci, with only rare reports of high-level rifampicin resistance linked to specific rpoB mutations [ 10 ]. Point-mutation analyses in rpoB, gyrA, gyrB, parC , and related loci in the African studies identified non-synonymous changes, but none correspond to experimentally validated resistance markers, and no reproducible genotype–phenotype link with high-level resistance was demonstrated. This uncertainty echoes non-African cohorts, where WGS has identified putative resistance-associated substitutions in B. melitensis , but functional confirmation remains scarce [ 5 ], [ 8 ], [ 13 ]. Phenotypically, the available MIC data in this review show preserved susceptibility to standard brucellosis regimens (doxycycline–rifampicin combinations, often with aminoglycosides), with frequent reports of intermediate rifampicin MICs and poor azithromycin performance, especially among B. melitensis compared with B. abortus [ 5 ], [ 16 ]. In the absence of classical AMR genes and robust genotype–phenotype associations, these findings support current guideline recommendations to avoid macrolides for treatment and to interpret rifampicin MICs cautiously using Brucella -appropriate breakpoints, while continuing to use WGS primarily as a tool for surveillance and outbreak investigation rather than individualised AMR prediction. Virulence-factor profiling across the African B. melitensis genomes reveals an equally conserved “virulence backbone”. In our included studies, core LPS biosynthesis and modification genes, the complete virB1–virB12 type IV secretion system, and multiple adhesins and regulatory modules (including bigA/bigB, bmaB/bmaC, btaE/btaF, omp19/25/31, bvrR/bvrS, cgs, mviN, prpA, bvfA , urease, and iron-uptake loci) were identified in the vast majority of isolates (Table 1 ). This pattern is highly consistent with targeted virulence gene studies from Egypt and Iran, which have shown that B. melitensis biovar 3 and related field strains almost uniformly carry intact virB , LPS, and key regulatory modules across diverse animal hosts [ 17 ]. Isolated absences, such as loss of cgs in a single Ethiopian strain or lower frequencies of virB10 or BPE043 in some comparative datasets, did not show clear geographic structuring and were not linked to distinctive clinical phenotypes [ 15 ]. Taken together, these data support a model in which B. melitensis maintains a highly canalized intracellular lifestyle, with limited tolerance for large deletions or acquisitions in its core virulence toolkit. For One-Health practice, the conservation of LPS and T4SS modules across human, livestock, and wildlife isolates in Africa underscores that animal and wildlife reservoirs should be considered fully competent sources of zoonotic infection rather than intrinsically “attenuated” compared with human strains. Mobile genetic elements (MGEs) were only sparsely investigated in the African WGS studies included here, and their apparent scarcity should be interpreted cautiously. No study detected plasmid replicons or plasmid-mediated AMR in B. melitensis , and only a single South African dataset explicitly mentioned insertion sequences (Table 1 ). However, most African analyses focused on core-genome SNPs or cgMLST and did not systematically characterize insertion sequences, prophages, or structural variation. The apparent dominance of chromosomally encoded virulence and intrinsic AMR modules in African B. melitensis should therefore be seen as a reflection of analytical priorities rather than definitive evidence that MGEs are biologically absent. Future African WGS work would benefit from the routine incorporation of mobilome and plasmids or phages that might contribute to local adaptation or subtle phenotypic shifts. From a risk-of-bias perspective, JBI-based appraisal indicated that most included studies clearly identified B. melitensis and applied robust laboratory workflows, but important limitations were common. Sampling frames were rarely population-based; inclusion criteria were often only implicitly defined; and metadata on animal movements, vaccination status, or clinical course were incomplete. These weaknesses translate directly into interpretive uncertainty: tight genomic clusters may represent genuine outbreak-scale transmission, but they may also reflect narrow sampling from a larger, unsampled diversity. Despite these caveats, a coherent picture emerges. African B. melitensis populations appear to be structured into regionally distinct lineages, West Mediterranean ST11/biovar-3 dominated lineages in the north and ST12-dominated African lineages in the east and south, with tight local clustering consistent with endemic transmission in relatively closed host populations. Genomes are characterized by highly conserved virulence architectures and intrinsic efflux- and cell-envelope-mediated AMR modules, with no evidence to date for widespread classical acquired resistance genes or plasmids. For public-health and One-Health programmes, these findings emphasize three priorities: expanding WGS into under-represented regions (particularly West and Central Africa), standardizing MLST/cgMLST and SNP pipelines to enable cross-study comparison, and systematically integrating genomic data with detailed epidemiological and phenotypic information. Such efforts would allow African countries to move from isolated genomic case studies towards a coordinated continental framework for tracking B. melitensis transmission, evaluating control strategies, and detecting emerging threats in real time. 5 Conclusion The findings underscore the urgent need to expand genomic surveillance beyond currently represented countries, integrate WGS into routine One Health programmes, and standardize analytical approaches to enable cross-regional comparison. Strengthening metadata collection, particularly on host ecology, animal movements, and vaccination status, will be essential for linking genomic patterns to transmission processes. Coordinated investment in sequencing capacity and harmonized pipelines will allow African countries to transition from isolated genomic case studies to a robust continental framework capable of monitoring lineage dynamics, evaluating control measures, and detecting emerging threats in real time. Declarations Declaration of competing interest The authors declare no competing interests. Ethics not applicable. Consent to Participate : not applicable. Consent to Publish declarations : not applicable. Funding This research did not benefit from any funding organization. Author contribution Samweli Y. Bahati : Writing – original draft, conceptualization, methodology, formal analysis, data curation, investigation. Abdalah Makaranga : Writing – review & editing, conceptualization., investigation. Eliezer Mwakalapa : Writing – review & editing, supervision, conceptualization. Henry G. Mung’ong’o : Writing – review & editing, funding acquisition, conceptualization. Claus Thomas : Writing – review & editing, visualization. Albino Kalolo : Writing – review & editing, supervision. Reuben Maghembe : Writing – original draft, conceptualization, visualization, supervision, formal analysis, data curation. Project administration, methodology, investigation, funding acquisition. Data availability: not applicable. References Ghssein G et al (2025) Brucellosis: Bacteriology, pathogenesis, epidemiology and role of the metallophores in virulence: a review, Front. Cell. Infect. Microbiol. , vol. 15, p. 1621230, July 10.3389/fcimb.2025.1621230 Djangwani J, Ooko Abong’ G, Gicuku L, Njue, Kaindi DWM (May 2021) Brucellosis: Prevalence with reference to East African community countries – A rapid review. Vet Med Sci 7(3):851–867. 10.1002/vms3.425 Ducrotoy M et al (Jan. 2017) Brucellosis in Sub-Saharan Africa: Current challenges for management, diagnosis and control. Acta Trop 165:179–193. 10.1016/j.actatropica.2015.10.023 Janowicz A et al (Nov. 2020) Evolutionary history and current distribution of the West Mediterranean lineage of Brucella melitensis in Italy. Microb Genomics 6(11). 10.1099/mgen.0.000446 Ben Abdallah I et al (July 2025) Hybrid Whole Genomes of Brucella melitensis from Tunisian Animal Isolates: Virulence Factors, Antimicrobial Susceptibility, and Phylogeny. Microorganisms 13(7). 10.3390/microorganisms13071651 Mazwi KD et al (2024) Whole Genome Sequence Analysis of Brucella spp. from Human, Livestock, and Wildlife in South Africa. J Microbiol Seoul Korea 62(9):759–773. 10.1007/s12275-024-00155-8 Hilton M (2024) JBI critical appraisal checklist for systematic reviews and research syntheses (product review), J. Can. Health Libr. Assoc. J. Assoc. Bibl. Santé Can. , vol. 45, no. 3, Dec. 10.29173/jchla29801 Ksibi B et al (Feb. 2025) Genomic analysis of Brucella melitensis isolates recovered from humans in south Tunisia over 35 years between 1988 and 2022. BMC Microbiol 25(1):98. 10.1186/s12866-025-03802-1 Craighead L et al (Mar. 2018) Brucellosis in West and Central Africa: A review of the current situation in a changing landscape of dairy cattle systems. Acta Trop 179:96–108. 10.1016/j.actatropica.2017.12.026 Rabinowitz P et al (2021) Whole Genome Sequence Analysis of Brucella melitensis Phylogeny and Virulence Factors, Microbiol. Res. , vol. 12, no. 3, pp. 698–710, Aug. 10.3390/microbiolres12030050 Ferjani A et al (Jan. 2025) A Genomic Characterization of Clinical Brucella melitensis Isolates from Tunisia: Integration into the Global Population Structure. Microorganisms 13(2). 10.3390/microorganisms13020243 Nabi I et al (2024) Serological, phenotypic and molecular characterization of brucellosis in small ruminants in northern Algeria, Front. Microbiol. , vol. 15, no. (Nabi I., [email protected] ) Department of Nature and Life Sciences, Laboratory of Biotechnology and Valorisation of Biological Resources (BVRB), Faculty of Sciences, University Dr. Yahia Farès, Médéa, Algeria. 10.3389/fmicb.2024.1505294 Khan AU et al (2021) Whole-genome sequencing for tracing the genetic diversity of Brucella abortus and brucella melitensis isolated from livestock in Egypt. Pathogens 10(6). 10.3390/pathogens10060759 Brangsch H et al (Aug. 2023) Genotype diversity of brucellosis agents isolated from humans and animals in Greece based on whole-genome sequencing. BMC Infect Dis 23(1):529. 10.1186/s12879-023-08518-z Sibhat B, Adamu H, Asmare K, Lindahl JF, Magnusson U, Sisay Tessema T (2024) Detection and Molecular Diversity of Brucella melitensis in Pastoral Livestock in North-Eastern Ethiopia. Pathogens 13(12). 10.3390/pathogens13121063 Wareth G et al (2020) Mlva-16 genotyping of brucella abortus and brucella melitensis isolates from different animal species in egypt: Geographical relatedness and the mediterranean lineage. Pathogens 9(6):1–15. 10.3390/pathogens9060498 Elrashedy A, Nayel M, Salama A, Zaghawa A, Abdelsalam NR, Hasan ME (June 2024) Phylogenetic Analysis and Comparative Genomics of Brucella abortus and Brucella melitensis Strains in Egypt. J Mol Evol 92(3):338–357. 10.1007/s00239-024-10173-0 Additional Declarations The authors declare no competing interests. Supplementary Files Suplimentarymaterial.xlsx Studies and data extracted after screeining (S1) and JBI Critical Appraisal Checklist for analytical cross-sectional studies (S2). Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. 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Maghembe","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA10lEQVRIiWNgGAWjYJACZiBOYGBvAFIGFqRo4TkA0iJBihaJBBCbCC3m0w4/fl1QsS2PX/L51Q0/CiQY+Nu7E/BqkbmdZmY948ztYsnZOWU3e4AOkzhzdgNeLRLSCWbGvG23Ezfczkm7wQPUYiCRS0hL+jdj3n+3E/ffPJN28w9xWnKMH/M2AG2RYD92m0hbcsqYeY7dLpY4k8N2W8ZAgocIv6Rv/sxTczuPv/34s5tv/tjI8bf34tcCBGzQuOAxAJOElIMA8wcIzf6AGNWjYBSMglEwAgEAeSxIUnv92RwAAAAASUVORK5CYII=","orcid":"https://orcid.org/0000-0003-2453-5993","institution":"St. Francis University College of Health and Allied Sciences","correspondingAuthor":true,"prefix":"","firstName":"Reuben","middleName":"S.","lastName":"Maghembe","suffix":""}],"badges":[],"createdAt":"2026-03-07 11:51:16","currentVersionCode":1,"declarations":{"humanSubjects":false,"vertebrateSubjects":true,"conflictsOfInterestStatement":false,"humanSubjectEthicalGuidelines":false,"humanSubjectConsent":false,"humanSubjectClinicalTrial":false,"humanSubjectCaseReport":false,"vertebrateSubjectEthicalGuidelines":true},"doi":"10.21203/rs.3.rs-9058123/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-9058123/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":104287527,"identity":"f8ab3890-500e-4d4c-a9d3-a8edae7bb50c","added_by":"auto","created_at":"2026-03-10 05:50:00","extension":"jpg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":227233,"visible":true,"origin":"","legend":"\u003cp\u003ePRISMA 2020 flow diagram summarizing the study selection process for the systematic review of \u003cem\u003eBrucella melitensis\u003c/em\u003e whole-genome sequencing studies in Africa.\u003c/p\u003e","description":"","filename":"Picture1.jpg","url":"https://assets-eu.researchsquare.com/files/rs-9058123/v1/b009bb6ac2e1eab6dc53c149.jpg"},{"id":104287528,"identity":"81da0d68-285a-438f-8a09-6ae20611f260","added_by":"auto","created_at":"2026-03-10 05:50:00","extension":"jpg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":87866,"visible":true,"origin":"","legend":"\u003cp\u003eGeographical distribution of Brucella melitensis whole-genome sequencing studies conducted in Africa, showing the number of genomes and host species represented in each country.\u003c/p\u003e","description":"","filename":"Picture2.jpg","url":"https://assets-eu.researchsquare.com/files/rs-9058123/v1/0cf299404643db4e34593f6e.jpg"},{"id":104835085,"identity":"150a1a9b-4cae-4798-ba4f-e399726d8b4c","added_by":"auto","created_at":"2026-03-17 17:40:05","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1444258,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-9058123/v1/b4bf89ea-eed7-4bc6-814c-2b1736a9a481.pdf"},{"id":104405582,"identity":"91310b2d-f795-4568-abdb-0149337dab3c","added_by":"auto","created_at":"2026-03-11 12:23:19","extension":"xlsx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":22970,"visible":true,"origin":"","legend":"\u003cp\u003eStudies and data extracted after screeining (S1) and JBI Critical Appraisal Checklist for\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eanalytical cross-sectional studies (S2).\u003c/p\u003e","description":"","filename":"Suplimentarymaterial.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-9058123/v1/aae94c2de8206d6b481e878c.xlsx"}],"financialInterests":"The authors declare no competing interests.","formattedTitle":"\u003cp\u003e\u003cstrong\u003eA systematic review of whole genome sequencing evidence on zoonotic \u003c/strong\u003e\u003cem\u003e\u003cstrong\u003eBrucella melitensis\u003c/strong\u003e\u003c/em\u003e\u003cstrong\u003e in Africa revealing lineage structure phylogenomic clusters antimicrobial resistance and genomic determinants\u003c/strong\u003e\u003c/p\u003e","fulltext":[{"header":"1 Introduction","content":"\u003cp\u003e \u003cem\u003eBrucella melitensis\u003c/em\u003e is the most pathogenic member of the genus \u003cem\u003eBrucella\u003c/em\u003e and the principal cause of human brucellosis globally. The organism is highly infectious, zoonotic, and capable of establishing chronic intracellular infection in a wide range of hosts, including animals and humans [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. Africa carries a substantial burden of brucellosis, particularly in pastoral and mixed crop\u0026ndash;livestock systems, where close human\u0026ndash;animal contact, informal milk markets, and limited veterinary infrastructure facilitate transmission [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. Despite this, the true epidemiological landscape of \u003cem\u003eB. melitensis\u003c/em\u003e in Africa remains poorly resolved, largely because surveillance depends heavily on serology or culture rather than molecular typing or genomic tools [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eWhole-genome sequencing (WGS) has transformed the understanding of \u003cem\u003eBrucella\u003c/em\u003e evolution, population structure, and transmission dynamics in several regions outside Africa. Large international analyses have demonstrated that \u003cem\u003eB. melitensis\u003c/em\u003e segregates into geographically structured clades, including West Mediterranean, East Mediterranean, African, and American lineages, each associated with distinct evolutionary and epidemiological histories [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e], [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. WGS has also become a key tool for resolving outbreak clusters, identifying cross-border transmission events, characterizing antimicrobial resistance determinants, and comparing field strains with live attenuated vaccine lineages such as Rev.1 [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. However, most of these genomic insights originate from Europe, the Middle East, Central Asia, and China, where sequencing capacity and routine genomic surveillance are more established. African contributions remain comparatively scarce, fragmented, and unevenly distributed across the continent.\u003c/p\u003e \u003cp\u003eGiven the wide host range of \u003cem\u003eB. melitensis\u003c/em\u003e, its persistence in livestock populations, and the growing recognition of brucellosis as a priority One Health threat, a consolidated assessment of African WGS data is urgently needed. Existing reports suggest considerable heterogeneity in sampling strategies and analytical workflows, complicating interpretation and limits regional comparability. Furthermore, gaps in genomic surveillance may obscure important patterns, such as the emergence of distinct African lineages, transmission pathways between livestock and humans, or the spread of vaccine-related genotypes that cannot be captured through serology or low-resolution typing alone.\u003c/p\u003e \u003cp\u003eTo address these gaps, this systematic review synthesizes all published studies generating or analyzing whole-genome sequencing data for \u003cem\u003eB. melitensis\u003c/em\u003e isolates originating from African countries. The review aims to (i) describe the geographical, host, and sampling contexts of available WGS investigations; (ii) summarize lineage and biovar distributions across African regions; (iii) examine phylogenomic relationships and clustering patterns; (iv) characterize reported antimicrobial resistance and virulence determinants and priorities for strengthening genomic surveillance. By consolidating the current genomic evidence base, this review provides the continent-wide synthesis of \u003cem\u003eB. melitensis\u003c/em\u003e WGS studies in Africa and offers a foundation for future One Health\u0026ndash;focused brucellosis control strategies.\u003c/p\u003e"},{"header":"2 Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e2.1 Protocol registration and eligibility criteria\u003c/h2\u003e \u003cp\u003eThe review protocol was developed according to PRISMA 2020 guidance and registered prospectively in PROSPERO (ID: PROSPERO 2026 CRD420251242529). Studies were eligible for inclusion if they generated, analyzed, or interpreted whole-genome sequencing data derived from \u003cem\u003eB. melitensis\u003c/em\u003e isolates collected in any African country. All host categories were considered, including human, livestock, wildlife, and mixed or environmental sources, provided that the study presented primary genomic findings such as lineage or biovar assignment, genomic clustering, AMR or virulence determinants, or phylogenetic relationships. No restrictions were placed on sequencing platforms, assembly or mapping approaches, bioinformatic pipelines, or publication year.\u003c/p\u003e \u003cp\u003eStudies were excluded if they relied exclusively on non-genomic methods such as polymerase chain reaction (PCR), Multi Locus Variable-Number Tandem Repeat (MLVA), multilocus sequence typing (MLST) without accompanying WGS, or serological and phenotypic assays. Reports involving exclusively non-African isolates or lacking extractable African subgroup data were also excluded. Reviews, commentaries, conference summaries, and other non-primary sources were excluded during the screening process.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e2.2 Information sources\u003c/h2\u003e \u003cp\u003eA structured literature search was carried out in PubMed, Scopus, and Embase to identify published studies reporting WGS of \u003cem\u003eB. melitensis\u003c/em\u003e isolates originating from African settings. No date limits were applied, and all records indexed in these databases were screened.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003e2.3 Search strategy\u003c/h2\u003e \u003cp\u003eThe search strategy combined controlled vocabulary terms with free-text keywords representing the organism, sequencing methodology, and geographical scope. Terms related to \u0026ldquo;\u003cem\u003eBrucella melitensis\u003c/em\u003e\u0026rdquo;, \u0026ldquo;whole-genome sequencing\u0026rdquo;, \u0026ldquo;genomic analysis\u0026rdquo;, \u0026ldquo;comparative genomics\u0026rdquo;, and \u0026ldquo;genomic epidemiology\u0026rdquo; were paired with \u0026ldquo;Africa\u0026rdquo; and the full list of African countries to maximize sensitivity. Search strings for each database were developed iteratively and are provided in the Supplementary Material (Table S2) for reproducibility. No restrictions were applied on the publication year. Although multiple languages were captured in the search, only English-language articles were retrieved.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003e2.4 Selection process\u003c/h2\u003e \u003cp\u003eSearch outputs from all databases were exported and imported into Covidence, where automated deduplication preceded screening. Titles and abstracts were screened independently by two reviewers, followed by full-text assessment of potentially eligible articles. Disagreements were resolved through discussion.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003e2.5 Data extraction\u003c/h2\u003e \u003cp\u003eData extraction was performed independently by two reviewers using a calibrated extraction form. Extracted variables included the study country, year of sampling, host species, sample type, sample size, number of genomes analyzed, sequencing platform, assembly or mapping approach, genome quality metrics, typing and phylogenomic methods, lineage or biovar assignments, AMR and virulence determinants, mobile genetic elements, and plasmid or insertion sequence data (where reported). Studies and data extracted after screening are shown in Supplementary Table S1.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003e2.6 Risk of bias and quality assessment\u003c/h2\u003e \u003cp\u003eMethodological quality was assessed using the Joanna Briggs Institute (JBI) [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e], critical appraisal tools appropriate for observational and cross-sectional studies. Because WGS-based investigations involve both laboratory and genomic analytical components, the JBI domains were applied to evaluate the appropriateness of sampling strategies, clarity of inclusion criteria, and validity of methods used to identify \u003cem\u003eB. melitensis\u003c/em\u003e. Additional consideration was given to the reporting of specimen collection procedures, completeness of metadata, sequencing quality, and the transparency and reproducibility of bioinformatic workflows, including assembly, SNP calling, and cgMLST analyses. Each study was appraised independently by two reviewers, with discrepancies resolved through consensus. The overall assessment informed the interpretation and weighting of evidence across included studies.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003e2.7 Synthesis approach\u003c/h2\u003e \u003cp\u003eSubstantial heterogeneity across studies in design, sequencing platforms, analytical approaches, and outcome definitions precluded formal statistical pooling. For this reason, findings were synthesized narratively and supported by structured summary tables. Studies were grouped thematically according to their primary genomic outcomes, including lineage or biovar distribution across African regions, AMR gene profiles, virulence gene repertoires, presence of mobile genetic elements, MLST sequence types, and phylogenetic relationships inferred from SNP or allele-based methods. Where appropriate, patterns were described in relation to geography, host species, and sampling period to highlight emerging genomic and epidemiological trends.\u003c/p\u003e \u003c/div\u003e"},{"header":"3 Results","content":"\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003e3.1 Study selection\u003c/h2\u003e \u003cp\u003eA total of 96 records were identified through database searches (Embase\u0026thinsp;=\u0026thinsp;53, PubMed\u0026thinsp;=\u0026thinsp;22, Scopus\u0026thinsp;=\u0026thinsp;21). An additional 2 records were identified through citation searching. After the removal of 38 duplicates (36 via Covidence and 2 manually), 60 records proceeded to title and abstract screening. Of these, 44 were excluded, leaving 16 studies for full-text assessment. No full texts were unobtainable. Following the eligibility evaluation, 7 studies were excluded due to wrong outcomes. Ultimately, 9 studies met the inclusion criteria and were included in the final review. No ongoing studies or studies awaiting classification were identified (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003e3.2 Characteristics of included studies\u003c/h2\u003e \u003cp\u003eNine studies that met the inclusion criteria were published between 2021 and 2025, covers six African countries: Tunisia (n\u0026thinsp;=\u0026thinsp;3), Tanzania (n\u0026thinsp;=\u0026thinsp;1), Algeria (n\u0026thinsp;=\u0026thinsp;1), Ethiopia (n\u0026thinsp;=\u0026thinsp;1), South Africa (n\u0026thinsp;=\u0026thinsp;1), and Egypt (n\u0026thinsp;=\u0026thinsp;2). Study settings included human clinical surveillance, livestock surveillance, mixed human, animal investigations, and retrospective laboratory collections. Hosts sampled across studies comprised humans, goats, sheep, cattle, buffalo, and wildlife (sable antelope). Sample sources included blood, milk, vaginal swabs, tissue, fetal material, lymph nodes, and abscess material. The number of genomes analyzed per study ranged from 2 to 36, with a cumulative total of 139 \u003cem\u003eB. melitensis\u003c/em\u003e genomes across all included studies (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eCharacteristics of African \u003cem\u003eBrucella melitensis\u003c/em\u003e whole-genome sequencing studies included in this review (2021\u0026ndash;2025). The table summarizes study location, host species, sample sources, study period, number of genomes analyzed, antimicrobial resistance (AMR) determinants and mechanisms, detected gene mutations, antimicrobial susceptibility testing approaches and outcomes, virulence gene repertoires, mobile genetic elements, plasmid and replicon content, major lineage or biovar assignments, and multilocus sequence types (MLST) identified across human, livestock, and wildlife hosts.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"21\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c11\" colnum=\"11\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c12\" colnum=\"12\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c13\" colnum=\"13\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c14\" colnum=\"14\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c15\" colnum=\"15\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c16\" colnum=\"16\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c17\" colnum=\"17\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c18\" colnum=\"18\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c19\" colnum=\"19\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c20\" colnum=\"20\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c21\" colnum=\"21\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eStudy ID\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCountry\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eHost(s)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eSource Type\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eStart date\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eNumber of genomes\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eRegion / Country combination\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003eSample size by host\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c9\"\u003e \u003cp\u003eAMR genes\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c10\"\u003e \u003cp\u003eMechanism\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c11\"\u003e \u003cp\u003eGene mutation\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c12\"\u003e \u003cp\u003eAntibiotics tested\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c13\"\u003e \u003cp\u003eResistance or susceptibility result\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c14\"\u003e \u003cp\u003eTesting method\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c15\"\u003e \u003cp\u003eVirulence genes\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c16\"\u003e \u003cp\u003eModule type\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c17\"\u003e \u003cp\u003ePlasmid names\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c18\"\u003e \u003cp\u003eIS or transposase elements\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c19\"\u003e \u003cp\u003eReplicon IDs\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c20\"\u003e \u003cp\u003eMajor lineage or biovar identified\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c21\"\u003e \u003cp\u003eSequence Type(s) (MLST)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eBenAbdallah 2025\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTunisia\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSheep (ewes, aborted and non-aborted)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eVaginal swabs from aborted and pregnant ewes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2020\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eSidi Bouzid and Tataouine, Tunisia\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1 isolate per ewe (n\u0026thinsp;=\u0026thinsp;2 sheep total)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003emprF, bepCDEFG, qacG, adeF, fosXcc\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003eRND efflux pumps, cationic antibiotic resistance (Defensin mprF), SMR efflux (qacG), fosfomycin thiol transferase (fosXcc)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003eparC Ala100\u0026rarr;Thr\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003eGentamicin, Streptomycin, Doxycycline, Rifampicin, Trimethoprim\u0026ndash;Sulfamethoxazole, Ceftriaxone, Ciprofloxacin, Levofloxacin, Tetracycline\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003eSusceptible to five agents (GEN, STR, DOX, RIF, SXT); Intermediate to fluoroquinolones possible (0.5 \u0026micro;g/mL MIC)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e \u003cp\u003eBroth microdilution (MIC) per CLSI M45-A2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c15\"\u003e \u003cp\u003ebigA, bigB, bmaB/omA, bmaC, gmd, per, pgm, pmm, manAoAg, manCoAg, wzm, wzt, wbkB, wbkC, wbkA, wbpZ, wbpL, lpsA, acpXL, wboA, wbdA, lpsB, lpcC, manCcore, manBcore, fabZ, lpxA, lpxC, lpxD, lpxB, lpxE, lpxK, KdsA, kdsB, waaA/kdtA, htrB, manA, perA, virB1, virB2, virB3, virB4, virB5, virB6, virB7, virB8, virB9, virB10, virB11, virB12, vceA, vceC, ricA, BPE005, BPE043, BPE275, BPE123, sepA, bspA, bspB, BspC, bspE, bspF, bspL, BtpA/Btp1/TcpB, BtpB, bvrR, bvrS, Cgs, mviN, betB, omp19, Ure, prpA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c16\"\u003e \u003cp\u003eLPS synthesis, T4SS, adhesins, two-component signaling, immune evasion\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c17\"\u003e \u003cp\u003eNone detected\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c18\"\u003e \u003cp\u003eNone detected\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c19\"\u003e \u003cp\u003eNot reported\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c20\"\u003e \u003cp\u003eBiovar 3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c21\"\u003e \u003cp\u003eST11 (MLST-9), ST89 (MLST-21)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eMiddlebrook 2025\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTanzania\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eGoats, Humans\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eGoat milk and human blood samples\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2024\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eKagera Region, Tanzania\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eHuman (3), Goat (3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eNone reported.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003eNone reported.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003eNone reported.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003eNot performed\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003eNot reported\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e \u003cp\u003eNot applicable\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c15\"\u003e \u003cp\u003eNot detected\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c16\"\u003e \u003cp\u003eNone reported.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c17\"\u003e \u003cp\u003eNone detected\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c18\"\u003e \u003cp\u003eNone detected\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c19\"\u003e \u003cp\u003eNot reported\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c20\"\u003e \u003cp\u003eNot stated\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c21\"\u003e \u003cp\u003eNot stated\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eFerjani 2025\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTunisia\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eHuman\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eBlood\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2016\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e36\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eTunisia (North Africa).\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eHuman (36)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003emprF, bepC\u0026ndash;G\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003eefflux pump complex\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003eNone reported.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003eNot performed\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003eNot reported\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e \u003cp\u003eNot applicable\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c15\"\u003e \u003cp\u003eacpXL, bigA, bigB, bmaB/omaA, bmaC, BPE005, BPE043, BPE123, BPE275, bspA, bspB, bspC, bspE, bspF, bspL, btpA, btpB, bvrR, bvrS, cgs, fabZ, gmd, htrB, kdsA, kdsB, lpsA, lpsB/lpcC, lpxA, lpxB, lpxC, lpxD, lpxE, lpxK, manAoAg, manBcore, manCcore, manCoAg, per, pgm, pmm, ricA, sepA, vceA, vceC, virB1, virB2, virB3, virB4, virB5, virB6, virB7, virB8, virB9, virB10, virB11, virB12, waaA/kdtA, wbdA, wbkA, wbkB, wbkC, wboA, wbpL, wbpZ, wzm, wzt\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c16\"\u003e \u003cp\u003eType IV secretion system, LPS biosynthesis, adhesion and stress response\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c17\"\u003e \u003cp\u003eNone detected\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c18\"\u003e \u003cp\u003eNone detected\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c19\"\u003e \u003cp\u003eNot reported\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c20\"\u003e \u003cp\u003ebiovar 3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c21\"\u003e \u003cp\u003eNot stated\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eNabi 2024\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAlgeria\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSheep, Goats\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eSerum, Milk, Placenta, Fetus\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2020\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eM\u0026eacute;d\u0026eacute;a and Sidi Bel-Abb\u0026egrave;s, Algeria\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e96 sera (77 sheep, 19 goat), 57 milk (42 sheep, 15 goat)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eNone reported\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003eNone reported\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003eNone reported.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003eNot performed\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003eNot reported\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e \u003cp\u003eNot applicable\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c15\"\u003e \u003cp\u003eNot detected\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c16\"\u003e \u003cp\u003eNone reported.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c17\"\u003e \u003cp\u003eNone detected\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c18\"\u003e \u003cp\u003eNone detected\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c19\"\u003e \u003cp\u003eNot reported\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c20\"\u003e \u003cp\u003eNot stated\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c21\"\u003e \u003cp\u003eST-11\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eSibhat 2024\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eEthiopia\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSheep, Goats\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eVaginal swabs, Milk\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2022\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eAfar Region, Ethiopia\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e231 animals (199 goats, 32 sheep); 248 samples (231 swabs, 17 milk)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eadeF, fosXCC, mprF, qacG\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003eEfflux pump (adeF, qacG); antibiotic inactivation (fosXCC); target alteration (mprF)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003eNone reported.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003eNot performed\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003eNot reported\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e \u003cp\u003eNot applicable\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c15\"\u003e \u003cp\u003ebmaC, btaE, btaF, acpXL, fabZ, gmd, htrB, kdsA, kdsB, lpsA, lpsB/lpcC, lpxA, lpxB, lpxC, lpxD, lpxE, lpxK, manAoAg, manBcore, manCcore, manCoAg, per, pgm, pmm, waaA/kdtA, wbdA, wbkA, wbkB, wbkC, wboA, wbpL, wbpZ, wzm, wzt, cgs, btpA, btpB, virB1, virB2, virB3, virB4, virB5, virB6, virB7, virB8, virB9, virB10, virB11, virB12, ricA, vceC, dhbA, dhbB, dhbC, dhbE, entD, vibH/entF, bvrR, bvrS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c16\"\u003e \u003cp\u003eT4S Secretion, regulatory, iron uptake, adhesion, survival\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c17\"\u003e \u003cp\u003eNone detected\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c18\"\u003e \u003cp\u003eNone detected\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c19\"\u003e \u003cp\u003eNot reported\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c20\"\u003e \u003cp\u003ebiovar 1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c21\"\u003e \u003cp\u003eST12, 1 novel isolate\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eMazwi 2024\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSouth Africa\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eHuman, Goat, Cattle, Sable antelope\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eBlood (human), Tissue (goat and cattle), Tissue/serum/hygroma fluid/skin lesion (sable)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eNot reported\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eSouth Africa (Western Cape, Eastern Cape, Gauteng, North West, Limpopo).\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eHuman (1), Goat (1), Cattle (1), Sable (12), Vaccine (1 Rev1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eNot reported\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003eNot reported\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003eNone reported.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003eNot performed\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003eNot reported\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e \u003cp\u003eNot applicable\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c15\"\u003e \u003cp\u003eacs, aam, cspC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c16\"\u003e \u003cp\u003eMetabolic / mobilome-related\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c17\"\u003e \u003cp\u003eNone detected\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c18\"\u003e \u003cp\u003eIS5 family\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c19\"\u003e \u003cp\u003eNot reported\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c20\"\u003e \u003cp\u003e(bv 1\u0026ndash;3 range)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c21\"\u003e \u003cp\u003eST12 (human, goat, several sable) plus novel STs (*7a27, *8212, *30bf, *bd3d, *b647)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eWareth 2021\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eEgypt\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eHumans, Cattle, Goat, Sheep\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eHuman blood, bovine milk, goat milk, lymph nodes (LN)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2018\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e22\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eEgypt (Nile Delta, Giza, Fayoum, Beni Suef, Sharkia, Damietta, Aswan, Ismailia, Behira)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eNot clear, only 22 comfirmed for WGS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003emprF, bepCDEFG\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003eRND and SMR efflux systems (cationic peptide modification, membrane adaptation)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003eNone detected\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003eChloramphenicol, Ciprofloxacin, Doxycycline, Gentamicin, Levofloxacin, Rifampicin, Streptomycin, Tetracycline, Trimethoprim/Sulfamethoxazole, Tigecycline, Azithromycin\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003eSusceptible to most agents except intermediate to RIF (2 \u0026micro;g/mL MIC) and resistant/intermediate to AZM in 16/27 isolates\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e \u003cp\u003eBroth microdilution (CLSl M100-S20) + Disc diffusion for TGC and AZM\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c15\"\u003e \u003cp\u003e45 virulence genes detected (no inter-strain difference); included virB operon, ure, bvfA, omp25/31, and core LPS genes (data not shown)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c16\"\u003e \u003cp\u003eType IV secretion, LPS biosynthesis, adhesion, regulation, intracellular survival\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c17\"\u003e \u003cp\u003eNone detected\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c18\"\u003e \u003cp\u003eNot reported\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c19\"\u003e \u003cp\u003eNot reported\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c20\"\u003e \u003cp\u003ebiovar 3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c21\"\u003e \u003cp\u003eNot reported\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eKhan 2021\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eEgypt\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCattle, buffaloes, sheep, goats\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eLymph nodes, milk, fetal abomasal contents\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2015\u0026ndash;2017\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eEgypt (Nile Delta and Upper Egypt).\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e17 cattle, 2 buffalo, 1 sheep, 1 goat.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003emprF, TriC, tet, mcrA,folA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003emembrane charge modification; rpoB/gyrA mutations associated with rifampicin and ciprofloxacin response.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003erpoB Arg2784\u0026rarr;Arg, Thr2394\u0026rarr;Thr; gyrA Asp297\u0026rarr;Glu.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003eNot performed\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003eNot reported\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e \u003cp\u003eNot applicable\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c15\"\u003e \u003cp\u003evirB1, virB2, virB3, virB4, virB5, virB6, virB7, virB8, virB9, virB10, virB11, virB12, lpsB/lpcC, lpxC, lpxD, fabZ, lpxA, lpxB, lpxE, htrB, acpXL, wboA, wbdA, wbpZ, wbpL, wbkA, wbkB, wbkC, manAoAg, manCoAg, pgm, gmd, per, wzm, wzt, kdsA, kdsB, ricA, cgs, pmm, btpA, btpB\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c16\"\u003e \u003cp\u003eType IV secretion system, LPS biosynthesis/modification, O-antigen \u0026amp; perosamine synthesis/glycosylation, Cyclic β-1,2-glucan synthesis, Intracellular trafficking, Host immune modulation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c17\"\u003e \u003cp\u003eNone detected.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c18\"\u003e \u003cp\u003eNot reported\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c19\"\u003e \u003cp\u003eNot reported\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c20\"\u003e \u003cp\u003ebiovar 3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c21\"\u003e \u003cp\u003eST11\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eKsibi 2025\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTunisia\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eHumans\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eBlood, abscess material, cardiac valve\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1988\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eNorth Africa \u0026ndash; Tunisia (south)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eHumans: n\u0026thinsp;=\u0026thinsp;24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003emprF; bepC, bepD, bepE, bepF, bepG, fosXcc, adeF, qacG\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003eChromosomally encoded efflux and cell-envelope modification; potential fluoroquinolone/rifampicin target polymorphisms without proven resistance.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003erpoB G3747A (M1249I); gyrA C1795G (L599V) and C1557T (synonymous); gyrB G2188A (A730T); parC G298A (A100T)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003eNot performed\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003eNot reported\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e \u003cp\u003eNot applicable\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c15\"\u003e \u003cp\u003evirB1\u0026ndash;B12, omp19, omp25/31, wbkA\u0026ndash;C, wboA, wzm, wzt, lpxA\u0026ndash;K, kdsA/B, waaA/kdtA, bmaB/C, bspA\u0026ndash;L, bvrR/S, cgs, htrB, mviN, btpA, btpB\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c16\"\u003e \u003cp\u003eSecretion (T4SS), LPS biosynthesis, adhesion, regulation, stress adaptation, iron uptake/survival, immune evasion.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c17\"\u003e \u003cp\u003eNone detected (no plasmid replicons).\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c18\"\u003e \u003cp\u003eNot reported\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c19\"\u003e \u003cp\u003eNot reported\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c20\"\u003e \u003cp\u003ebiovar 3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c21\"\u003e \u003cp\u003eST11\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003e3.3 Geographical distribution and sampling patterns\u003c/h2\u003e \u003cp\u003eThe included whole genome sequencing studies were conducted in six African countries: Tunisia, Algeria, Egypt, Ethiopia, Tanzania, and South Africa. Geographically, most investigations originated from North Africa (Tunisia, Algeria, Egypt), followed by East Africa (Ethiopia, Tanzania), while Southern Africa was represented by a single study from South Africa. No eligible studies were identified from West or Central Africa (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eSampling strategies differed across settings. Human clinical isolates were sequenced in Tunisia, Egypt, South Africa, and Tanzania, whereas livestock samples were included in all countries except Tunisia\u0026rsquo;s clinical subset. Livestock sampling covered goats, sheep, cattle, and buffalo, with South Africa additionally contributing isolates from wildlife (sable antelope). Field-based studies in Algeria and Ethiopia focused on small ruminants with reproductive disorders, while hospital-based studies in Tunisia and Egypt relied primarily on blood or tissue specimens from confirmed human brucellosis cases. Tanzania contributed mixed human and goat milk isolates (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eSample types included blood, milk, vaginal swabs, placenta, fetal tissues, lymph nodes, abscess material, and other diagnostic specimens, depending on the study design. The number of genomes sequenced per country varied considerably, ranging from a small number of isolates in Tanzania to larger datasets from Tunisia and Egypt.\u003c/p\u003e \u003cp\u003eOverall, the geographical coverage demonstrates a concentration of \u003cem\u003eB. melitensis\u003c/em\u003e WGS work in northern and eastern Africa, with consistent representation of both human and livestock hosts, and limited contributions from wildlife. Temporal ranges differed by study, with some relying on retrospective clinical collections and others based on single-season field investigations, but all provided primary genomic data suitable for inclusion.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003e3.4 Lineage and biovar distribution\u003c/h2\u003e \u003cp\u003eAcross the included WGS studies, biovar and lineage assignments were reported mainly from North, East, and Southern Africa. Where biovar typing was performed, \u003cem\u003eB. melitensis\u003c/em\u003e biovar 3 predominated in North African settings. All Tunisian human isolates in the clinical series were assigned to biovar 3, and the two sheep isolates from southern and central Tunisia in BenAbdallah 2025 also grouped with biovar 3 strains (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). Egyptian livestock isolates were likewise characterized as biovar 3. In contrast, the Ethiopian pastoral livestock study identified \u003cem\u003eB. melitensis\u003c/em\u003e biovar 1 among sheep and goats, and the South African multi-host study reported \u003cem\u003eB. melitensis\u003c/em\u003e from sable, goats, cattle, and one human within an \u0026ldquo;African lineage\u0026rdquo; clade that included strains typed as biovar 1\u0026ndash;3 (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). The Tanzanian genome announcement and some Egyptian surveillance work confirmed that \u003cem\u003eB. melitensis\u003c/em\u003e by WGS did not report biovar assignment.\u003c/p\u003e \u003cp\u003eMultilocus sequence typing (MLST) data were available for most genomic datasets. Using the classical 9-locus scheme, several studies reported a predominance of ST11 (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). All Algerian small-ruminant isolates belonged to ST11 and were assigned to the West Mediterranean lineage, and Egyptian livestock isolates were also typed as ST11 and placed within the Mediterranean lineage. In Tunisia, all human isolates in the long-term Sfax series were ST11 by MLST-9, while MLST-21 resolved them into ST89 and ST114 profiles differing at the dllA locus; the two animal isolates from Tataouine and Sidi Bouzid were also reported as ST11 and ST89. East and Southern African studies reported greater ST diversity. Ethiopian livestock isolates were assigned to ST12 with one additional novel ST, and South African sable, goat, and cattle strains within the African lineage included ST12 together with several newly designated sequence types, while a Rev1 vaccine strain was classified as ST10 within an American lineage background.\u003c/p\u003e \u003cp\u003eHigh-resolution phylogenomic analyses based on cgMLST or whole-genome SNP typing consistently showed that African \u003cem\u003eB. melitensis\u003c/em\u003e genomes formed discrete regional clusters within broader global lineages. In Tunisia, cgMLST of clinical isolates yielded two main clusters separated by approximately 140 discriminating alleles, with up to six allelic differences within clusters and several additional singleton profiles. The hybrid-genome study placed one sheep isolate (TATA) within \u0026ldquo;Tunisian Cluster 1\u0026rdquo;, closely related to human isolates with \u0026le;\u0026thinsp;4 allelic differences and separated by about 150 alleles from Egyptian strains and 130 alleles from Italian strains, whereas the second sheep isolate (SBZ) lay nearer \u0026ldquo;Tunisian Cluster 2\u0026rdquo; and showed 14\u0026ndash;26 allele differences to nearby Italian and Austrian genomes. In the long-term Tunisian series, cgMLST based on 1,764 core genes identified four cgST profiles with allele differences ranging from 3 to 158. Algerian small-ruminant isolates were assigned to the West Mediterranean lineage by SNP typing, forming two local SNP types that clustered with West Mediterranean human isolates from Sweden, Algeria, Morocco, Austria, Italy, and Tunisia. Egyptian livestock isolates showed nine cgSNP genotypes grouped into two main clusters with 0\u0026ndash;119 pairwise SNP differences and \u0026ge;\u0026thinsp;500 SNPs to non-Egyptian Mediterranean strains. South African genomes from sable, cattle, goat, and a human case all clustered within an African lineage subclade defined by ST12 and several novel STs, distinct from the American lineage Rev1 vaccine strain.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003e3.5 Phylogenetic relationships and genomic clustering\u003c/h2\u003e \u003cp\u003eAcross studies, high-resolution SNP and cgMLST analyses consistently revealed tight within-country clusters, defined by very small allelic or SNP distances, in contrast to much larger separations between countries and between major global lineages. In Tunisia, clinical and animal WGS datasets described two main \u003cem\u003eB. melitensis\u003c/em\u003e clusters, with outbreak-scale grouping defined by 0\u0026ndash;4 or up to 6 cgMLST allelic differences within clusters, and approximately 140 alleles separating the two Tunisian clusters. The hybrid animal isolates TATA and SBZ fell into different Tunisian groups: TATA embedded within the human-dominated Cluster 1, while SBZ lay nearer Cluster 2 and closely related Italian and Austrian genomes at distances of 14\u0026ndash;26 alleles. Between-country comparisons placed Tunisian clusters about 150 alleles from Egyptian isolates and 245\u0026ndash;266 alleles from Algerian and Moroccan strains, indicating a clear step up from within-country to between-country distances. In the long-term Sfax series, cgSNP analysis of 24 human isolates showed a main Tunisian cluster of 23 genomes with pairwise distances of 0\u0026ndash;77 SNPs, and a single divergent genome (BR4) 107\u0026ndash;143 SNPs away from the others; within the broader West Mediterranean clade, pairwise distances extended up to roughly 500 SNPs, and the West Mediterranean lineage as a whole was separated from African, American and East Mediterranean lineages by around 1,600\u0026ndash;1,950 core-genome SNPs.\u003c/p\u003e \u003cp\u003eNorth African livestock datasets showed similar hierarchical structuring. In Algeria, cgSNP typing of ten small-ruminant isolates identified two local SNP types with 0\u0026ndash;2 SNPs among five ovine isolates and 0 SNPs between two caprine isolates, using these very low distances as the working threshold for defining a type. The two SNP types differed by 53\u0026ndash;55 SNPs from each other, while one outlier ovine isolate from Sidi Bel-Abb\u0026egrave;s sat 449\u0026ndash;462 SNPs away from the rest of the Algerian set, yet only 16\u0026ndash;28 SNPs from travel-associated human strains in Sweden, Algeria, and Morocco. This created two well-defined Algerian clusters within the West Mediterranean lineage, with separations to European and North African comparators that were one to two orders of magnitude larger than the within-cluster distances. In Egypt, core-genome SNP analysis of 21 \u003cem\u003eB. melitensis\u003c/em\u003e biovar 3 field isolates defined nine genotypes grouped into two main clusters, with pairwise distances ranging from 0 to 119 SNPs within the Egyptian panel. When compared with 44 \u003cem\u003eB. melitensis\u003c/em\u003e genomes from neighboring Mediterranean, African, and Asian countries, all Egyptian strains formed a single regional cluster at least 500 core SNPs distant from non-Egyptian West Mediterranean genotypes. For B. abortus from Egyptian cattle and buffalo, eight genomes fell into two genotypes with 0 SNP differences within each genotype and \u0026gt;\u0026thinsp;700 core SNPs separating these genotypes from Spanish and Bangladeshi \u003cem\u003eB. abortus\u003c/em\u003e bv-1 comparators.\u003c/p\u003e \u003cp\u003eIn East Africa, whole-genome SNP phylogenies demonstrated a distinct African genotype background and evidence of regional clustering. Ethiopian pastoral small-ruminant isolates grouped within an \u0026ldquo;African\u0026rdquo; B. melitensis clade together with Somali and a few other sub-Saharan sequences, separate from Western and Eastern Mediterranean and American lineages. Within this African lineage, some Ethiopian and Somali isolates showed pairwise distances of 38\u0026ndash;61 SNPs, while broader comparisons across Africa and other regions involved much larger SNP gaps. At the MLST level, the African ST12 genotype was common across sub-Saharan Africa but showed limited diversification compared with the highly diverse ST11 West Mediterranean background, and cgMLST further split Ethiopian isolates into several related cgSTs that tended to co-occur at the village level. In Tanzania, six new \u003cem\u003eB. melitensis\u003c/em\u003e genomes from goats and humans formed a well-supported clade within the global phylogeny, clustering tightly together and with nearest neighbours from Belgium, Kuwait, Somalia, and Norway, again indicating a coherent African-associated cluster nested within the broader species tree.\u003c/p\u003e \u003cp\u003eSouthern African wgSNP analyses also highlighted a consolidated African lineage with host and country-level substructure. In South Africa, 16 \u003cem\u003eB. melitensis\u003c/em\u003e genomes from sable, cattle, goat, and a human case clustered in an African lineage subclade defined by several hundred informative SNPs across the core genome, distinct from American, East, and West Mediterranean branches and clearly separate from the Rev1 vaccine strain, which grouped with an American-lineage ST10 background. Within this African subclade, sable isolates from multiple provinces formed closely related clusters dominated by ST12 and a small number of novel sequence types, while the human case strain grouped most closely with a goat isolate from the same region, consistent with very limited SNP differences along that branch. For B. abortus in South Africa, a core-SNP phylogeny based on 1,071 SNPs placed three new cattle genomes within the C2 genotype branch together with earlier South African and Mozambican strains and S19 vaccine derivatives, defining at least two closely related South African C2 sub-genotypes within a wider regional cluster.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec16\" class=\"Section2\"\u003e \u003ch2\u003e3.6 Antimicrobial resistance, virulence determinants, and mobile genetic elements\u003c/h2\u003e \u003cp\u003eAcross the included \u003cem\u003eB. melitensis\u003c/em\u003e whole-genome studies, antimicrobial resistance analyses consistently identified intrinsic, chromosome-encoded determinants rather than classical acquired resistance genes. Most datasets that applied AMR prediction tools reported the multiple peptide resistance factor \u003cem\u003emprF\u003c/em\u003e in nearly all isolates, together with Brucella efflux protein family genes (\u003cem\u003ebepC\u0026ndash;bepG/BPE\u003c/em\u003e\u003cb\u003e)\u003c/b\u003e and additional efflux or detoxification markers such as \u003cem\u003eadeF\u003c/em\u003e, \u003cem\u003eqacG\u003c/em\u003e, and \u003cem\u003efosXcc (\u003c/em\u003eTable\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). These loci were detected in Tunisian animal isolates, Tunisian clinical collections, Ethiopian pastoral livestock isolates, and Egyptian livestock strains, and international comparisons indicated that \u003cem\u003emprF\u003c/em\u003e and \u003cem\u003ebep\u003c/em\u003e genes are widely distributed across global \u003cem\u003eB. melitensis\u003c/em\u003e lineages [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. No study detected canonical acquired AMR genes associated with macrolides (\u003cem\u003eerm, mef, msr\u003c/em\u003e), tetracyclines (\u003cem\u003etet\u003c/em\u003e), β-lactams (e.g., \u003cem\u003emecA\u003c/em\u003e), or trimethoprim (\u003cem\u003efolA\u003c/em\u003e) in \u003cem\u003eB. melitensis\u003c/em\u003e genomes, and several authors explicitly reported their absence despite systematic screening [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e], [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e].\u003c/p\u003e \u003cp\u003ePoint mutation analyses in target genes showed only limited variation and no previously validated resistance-conferring substitutions. Tunisian animal isolates carried a non-synonymous substitution in \u003cem\u003eparC\u003c/em\u003e (Ala100Thr) relative to reference strains, while larger Tunisian clinical series reported additional polymorphisms in \u003cem\u003erpoB\u003c/em\u003e (M1249I), \u003cem\u003egyrA\u003c/em\u003e (L599V and a synonymous change), \u003cem\u003egyrB\u003c/em\u003e (A730T), and \u003cem\u003eparC\u003c/em\u003e (Ala100Thr). Egyptian livestock isolates showed rare substitutions in \u003cem\u003erpoB\u003c/em\u003e and \u003cem\u003egyrA\u003c/em\u003e. Where AMR pipelines also annotated housekeeping or metabolic loci (for example, \u003cem\u003eTriC, folA\u003c/em\u003e, or \u003cem\u003emcr\u003c/em\u003e-annotated genes), these were not accompanied by known resistance-associated mutations and were interpreted as part of the intrinsic genome rather than acquired resistance cassettes.\u003c/p\u003e \u003cp\u003ePhenotypic susceptibility testing was reported in a subset of studies and was broadly concordant with the genomic findings. Tunisian animal isolates tested by broth microdilution remained susceptible to standard brucellosis drugs, including gentamicin, streptomycin, doxycycline, rifampicin, and trimethoprim\u0026ndash;sulfamethoxazole, with only borderline MICs for some fluoroquinolones and no categorical resistance (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). An Egyptian series that included \u003cem\u003eB. melitensis\u003c/em\u003e performed MIC testing and disc diffusion, showing universal susceptibility to doxycycline, gentamicin, levofloxacin, streptomycin, tetracycline, trimethoprim\u0026ndash;sulfamethoxazole, ciprofloxacin, and chloramphenicol. In that cohort, all \u003cem\u003eB. melitensis\u003c/em\u003e isolates were classified as intermediate to rifampicin, and the majority were resistant or intermediate to azithromycin, while \u003cem\u003eB. abortus\u003c/em\u003e comparators remained azithromycin-resistant but rifampicin-susceptible (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). No study demonstrated a clear genotype\u0026ndash;phenotype link between the intrinsic AMR genes or novel point mutations and these modest shifts in rifampicin or macrolide susceptibility.\u003c/p\u003e \u003cp\u003eVirulence gene repertoires were highly conserved across countries, hosts, and lineages. All studies that systematically queried virulence databases reported a stable core of LPS biosynthesis and modification genes (including \u003cem\u003eacpXL; lpxA, lpxB, lpxC, lpxD, lpxE, lpxK; kdsA, kdsB; waaA/kdtA; wboA; wbkA\u0026ndash;C; wbdA; wbpL/wbpZ; wzm, wzt; and manA/manB/manC\u003c/em\u003e in core and O-antigen loci), together with a complete \u003cem\u003evirB1\u0026ndash;virB12\u003c/em\u003e type IV secretion system and multiple associated effectors such as \u003cem\u003ebspA\u0026ndash;bspL\u003c/em\u003e, \u003cem\u003evceA/vceC\u003c/em\u003e, \u003cem\u003ericA\u003c/em\u003e, and BPE-encoded factors (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). Adhesins, including \u003cem\u003ebigA/bigB\u003c/em\u003e, \u003cem\u003ebmaB/bmaC\u003c/em\u003e, \u003cem\u003ebtaE/btaF\u003c/em\u003e, and outer-membrane proteins \u003cem\u003eomp19\u003c/em\u003e, \u003cem\u003eomp25\u003c/em\u003e, and \u003cem\u003eomp31\u003c/em\u003e were consistently identified, alongside regulatory and survival modules such as \u003cem\u003ebvrR/bvrS\u003c/em\u003e, \u003cem\u003ecgs\u003c/em\u003e, \u003cem\u003emviN\u003c/em\u003e, \u003cem\u003ehtrB\u003c/em\u003e, \u003cem\u003ebetB\u003c/em\u003e, \u003cem\u003eprpA\u003c/em\u003e, \u003cem\u003ebvfA\u003c/em\u003e, urease genes, and iron-uptake loci (\u003cem\u003edhbA\u0026ndash;E\u003c/em\u003e, \u003cem\u003eentD\u003c/em\u003e, \u003cem\u003evibH/entF\u003c/em\u003e) (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). Ethiopian livestock isolates carried at least 43 virulence factors per genome, with only a single strain lacking cgs, while Tunisian collections reported 60\u0026ndash;70 virulence genes per isolate, and international comparisons showed most of these loci present in \u0026gt;\u0026thinsp;95\u0026ndash;98% of publicly available \u003cem\u003eB. melitensis\u003c/em\u003e genomes. Occasional absences of individual genes (for example, \u003cem\u003evirB10\u003c/em\u003e, \u003cem\u003eBPE043\u003c/em\u003e, or \u003cem\u003ecgs\u003c/em\u003e in single isolates) did not follow a clear geographic or lineage-specific pattern.\u003c/p\u003e \u003cp\u003eMobile genetic elements were rarely characterised. No study identified plasmid replicons in \u003cem\u003eB. melitensis\u003c/em\u003e, and plasmid-mediated resistance was not reported. Insertion sequences and transposase families were generally not catalogued systematically; only one South African study explicitly noted the presence of IS5-family elements within \u003cem\u003eB. melitensis\u003c/em\u003e genomes (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). For the remaining datasets, insertion sequences were not detected. Based on the available evidence, the African \u003cem\u003eB. melitensis\u003c/em\u003e genomes included in this review are dominated by chromosomally encoded, conserved virulence and intrinsic AMR modules, with very limited documentation of plasmids or other mobile genetic elements, reflecting an analytical focus on core genome variation rather than an absence of mobilome features.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec17\" class=\"Section2\"\u003e \u003ch2\u003e3.7 Risk of bias and study quality\u003c/h2\u003e \u003cp\u003eAll nine included WGS studies met the JBI criteria for clearly defined inclusion criteria and adequately described study subjects and settings, with \u0026ldquo;Yes\u0026rdquo; ratings for questions 1 and 2 across all records. Laboratory confirmation of \u003cem\u003eB. melitensis\u003c/em\u003e and use of standardized diagnostic criteria were consistently reported, resulting in \u0026ldquo;Yes\u0026rdquo; ratings for questions 3 and 4 in every study. By contrast, none of the studies explicitly identified potential confounding factors or described strategies to address them, and questions 5 and 6 were uniformly rated as \u0026ldquo;Unclear.\u0026rdquo; Genomic outcomes, such as lineage assignment, SNP or cgMLST metrics, and AMR/virulence profiling, were generally derived using established bioinformatics tools, and all studies were judged to have measured outcomes in a valid and reliable way (question 7, \u0026ldquo;Yes\u0026rdquo; for all). Most studies also provided sufficient detail on phylogenetic and comparative genomic analyses to be rated \u0026ldquo;Yes\u0026rdquo; for appropriate statistical analysis (question 8), except one study in which the analytic procedures were insufficiently described and were therefore rated \u0026ldquo;Unclear\u0026rdquo; for this domain, detailed in the Supplementary material S2.\u003c/p\u003e \u003c/div\u003e"},{"header":"4 Discussion","content":"\u003cp\u003eThis systematic review shows that, despite the growing recognition of whole-genome sequencing (WGS) in infectious disease surveillance, genomic investigations of \u003cem\u003eB. melitensis\u003c/em\u003e in Africa remain sparse and geographically skewed. From an initial pool of 96 records, only nine studies met strict eligibility criteria for primary WGS data on African \u003cem\u003eB. melitensis\u003c/em\u003e, indicating that genomics is still rarely integrated into routine brucellosis research and surveillance on the continent (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). The absence of unpublished or ongoing WGS studies in trial registries or grey-literature sources suggests that the current genomic evidence base is genuinely small rather than simply inaccessible. This has direct implications for how confidently continental patterns can be inferred and underscores the need to move from isolated sequencing projects towards programmatic genomic surveillance embedded in national and regional control programmes.\u003c/p\u003e \u003cp\u003eThe included studies collectively confirm the wide ecological and host range of \u003cem\u003eB. melitensis\u003c/em\u003e in African settings, spanning human patients, goats, sheep, cattle, buffalo, and wildlife (sable antelope), and drawing on diverse clinical and field samples such as blood, milk, reproductive tissues, lymph nodes, and abscess material (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e) (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). However, the geographical and sampling patterns clearly reflect capacity rather than the true epidemiology of the pathogen. North Africa (Tunisia, Egypt, Algeria) and parts of East and Southern Africa (Ethiopia, Tanzania, South Africa) dominate the WGS landscape, whereas West and Central Africa, where brucellosis is well documented in pastoral and agro-pastoral systems [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e], are entirely unrepresented. A similar geographic skew has been observed in global WGS syntheses, which are dominated by Europe, the Middle East, and China, despite broader disease endemicity [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. Within countries, study designs are mostly opportunistic: retrospective hospital collections in Tunisia and Egypt, outbreak- or farm-based sampling in Algeria and Ethiopia, and mixed human\u0026ndash;livestock panels in Tanzania and South Africa. These designs are valuable for proof-of-concept molecular epidemiology but provide limited insight into community-level or national transmission dynamics. Temporal coverage is also uneven, ranging from multi-decade clinical collections in Tunisia to single-season field surveys, which constrains inference about lineage replacement, long-term persistence, or the impact of control measures.\u003c/p\u003e \u003cp\u003eTaken together, the lineage and biovar distributions observed in this review are consistent with, but refine, the global picture of \u003cem\u003eB. melitensis\u003c/em\u003e population structure derived from large WGS datasets. Global analyses have shown that \u003cem\u003eB. melitensis\u003c/em\u003e segregates into at least four major lineages, West Mediterranean, East Mediterranean, African, and American, with clear geographic associations and long-range dispersal events driven by animal movement and trade (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e) [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. In North Africa, the dominance of biovar 3 and ST11, together with assignment to the West Mediterranean lineage, in Tunisian, Algerian, and Egyptian datasets aligns closely with this framework and reinforces long-recognized epidemiological connectivity between the Maghreb and southern Europe [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e], [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e], [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. Large European WGS series have similarly reported ST11-dominated West Mediterranean lineages in Italy, Spain, and Portugal, often forming tightly clustered clades linked to local small-ruminant production systems [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. The repeated placement of Tunisian and Egyptian genomes within well-defined West Mediterranean clades in our included studies, therefore, likely reflects sustained local evolution of established lineages rather than frequent introductions of entirely new genetic backgrounds [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eBy contrast, East and Southern African studies in this review are characterized by ST12 and an African-lineage background, with multiple novel sequence types arising in livestock and wildlife (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). This pattern is consistent with global phylogeographic analyses that describe an \u0026ldquo;African\u0026rdquo; \u003cem\u003eB. melitensis\u003c/em\u003e lineage, enriched for ST12 and predominantly associated with sub-Saharan Africa. The recovery of new sequence types from sable, goats, cattle, and a human case in South Africa [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e], alongside ST12-dominated pastoral small-ruminant populations in Ethiopia, suggests that multi-host circulation is driving local diversification within this African lineage. Notably, Rev.1-associated ST10 signatures appear only in the South African dataset among our included studies, whereas there is no genomic evidence of vaccine-derived lineages in East African pastoral systems or Tunisian clinical series. This has practical implications for vaccine policy and post-vaccination surveillance, particularly where Rev.1 or other live attenuated vaccines are being introduced or scaled up.\u003c/p\u003e \u003cp\u003eThe phylogenetic and clustering patterns observed across the included studies reveal a robust hierarchical structure: very tight within-country or within-cluster distances contrasted with much larger separations between countries and between major lineages. Tunisian clinical and animal datasets describe clusters of isolates separated by zero to a few cgMLST alleles or SNPs, which is characteristic of local endemic transmission or repeated re-introduction from a stable reservoir [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e], [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. Similar tight clustering is observed among Egyptian and Algerian livestock panels, where within-type SNP distances of 0\u0026ndash;2 or a handful of alleles are used as working thresholds for potential recent transmission or farm-level clusters [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e], [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. These findings mirror global WGS studies that use low SNP or allelic thresholds to identify outbreak-related clusters and distinguish them from background endemic diversity [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e], [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. In Ethiopia and South Africa, African-lineage isolates form cohesive subclades in whole-genome SNP trees, within which human, livestock, and wildlife strains often cluster together, pointing to stable multi-host transmission networks rather than short-lived, unidirectional spillover events [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eAt the same time, between-country and between-lineage distances are markedly larger, often involving hundreds of alleles or SNPs between national clusters and thousands of core-genome SNPs between West Mediterranean, African, East Mediterranean, and American lineages [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e], [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. These gradients suggest that recent cross-border transmission of \u003cem\u003eB. melitensis\u003c/em\u003e lineages within Africa is relatively limited or, at least, infrequent enough that genomic signals are dominated by long-standing, country-specific lineages. Occasional findings of livestock isolates in our included studies that are closely related to human strains from Europe or neighboring countries (for example, Algerian isolates clustering closely with Swedish and Moroccan human cases) likely reflect historical trade or shared ancestral lineages rather than ongoing, extensive lineage exchange [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. However, this interpretation is tempered by important gaps: sparse sampling, small within-country sample sizes, and the complete absence of WGS data from West and Central Africa mean that unsampled diversity and undetected inter-regional links almost certainly exist.\u003c/p\u003e \u003cp\u003eThe antimicrobial resistance (AMR) and virulence profiles observed in African \u003cem\u003eB. melitensis\u003c/em\u003e genomes are strikingly conserved and dominated by intrinsic, chromosomally encoded determinants. Across Tunisian, Ethiopian, and Egyptian datasets in this review, automated AMR prediction consistently identified \u003cem\u003emprF\u003c/em\u003e and Brucella efflux protein genes (\u003cem\u003ebepC\u0026ndash;bepG/BPE\u003c/em\u003e), with additional efflux or detoxification markers such as \u003cem\u003eadeF, qacG\u003c/em\u003e, and \u003cem\u003efosXcc\u003c/em\u003e in four studies (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). No canonical horizontally acquired resistance genes, such as typical macrolide, tetracycline, β-lactam, or trimethoprim resistance cassettes, were detected in \u003cem\u003eB. melitensis\u003c/em\u003e genomes, and multiple groups explicitly reported their absence despite two studies that did comprehensive screening [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e], [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. These findings are in line with WGS studies from Europe and in Asia, where \u003cem\u003eB. melitensis\u003c/em\u003e typically lacks classical plasmid-borne AMR determinants and instead carries a conserved set of efflux- and cell-envelope-associated loci, with only rare reports of high-level rifampicin resistance linked to specific \u003cem\u003erpoB\u003c/em\u003e mutations [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e].\u003c/p\u003e \u003cp\u003ePoint-mutation analyses in \u003cem\u003erpoB, gyrA, gyrB, parC\u003c/em\u003e, and related loci in the African studies identified non-synonymous changes, but none correspond to experimentally validated resistance markers, and no reproducible genotype\u0026ndash;phenotype link with high-level resistance was demonstrated. This uncertainty echoes non-African cohorts, where WGS has identified putative resistance-associated substitutions in \u003cem\u003eB. melitensis\u003c/em\u003e, but functional confirmation remains scarce [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e], [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e], [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. Phenotypically, the available MIC data in this review show preserved susceptibility to standard brucellosis regimens (doxycycline\u0026ndash;rifampicin combinations, often with aminoglycosides), with frequent reports of intermediate rifampicin MICs and poor azithromycin performance, especially among \u003cem\u003eB. melitensis\u003c/em\u003e compared with \u003cem\u003eB. abortus\u003c/em\u003e [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e], [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]. In the absence of classical AMR genes and robust genotype\u0026ndash;phenotype associations, these findings support current guideline recommendations to avoid macrolides for treatment and to interpret rifampicin MICs cautiously using \u003cem\u003eBrucella\u003c/em\u003e-appropriate breakpoints, while continuing to use WGS primarily as a tool for surveillance and outbreak investigation rather than individualised AMR prediction.\u003c/p\u003e \u003cp\u003eVirulence-factor profiling across the African \u003cem\u003eB. melitensis\u003c/em\u003e genomes reveals an equally conserved \u0026ldquo;virulence backbone\u0026rdquo;. In our included studies, core LPS biosynthesis and modification genes, the complete \u003cem\u003evirB1\u0026ndash;virB12\u003c/em\u003e type IV secretion system, and multiple adhesins and regulatory modules (including \u003cem\u003ebigA/bigB, bmaB/bmaC, btaE/btaF, omp19/25/31, bvrR/bvrS, cgs, mviN, prpA, bvfA\u003c/em\u003e, urease, and iron-uptake loci) were identified in the vast majority of isolates (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). This pattern is highly consistent with targeted virulence gene studies from Egypt and Iran, which have shown that \u003cem\u003eB. melitensis\u003c/em\u003e biovar 3 and related field strains almost uniformly carry intact \u003cem\u003evirB\u003c/em\u003e, LPS, and key regulatory modules across diverse animal hosts [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]. Isolated absences, such as loss of cgs in a single Ethiopian strain or lower frequencies of \u003cem\u003evirB10\u003c/em\u003e or \u003cem\u003eBPE043\u003c/em\u003e in some comparative datasets, did not show clear geographic structuring and were not linked to distinctive clinical phenotypes [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. Taken together, these data support a model in which \u003cem\u003eB. melitensis\u003c/em\u003e maintains a highly canalized intracellular lifestyle, with limited tolerance for large deletions or acquisitions in its core virulence toolkit. For One-Health practice, the conservation of LPS and T4SS modules across human, livestock, and wildlife isolates in Africa underscores that animal and wildlife reservoirs should be considered fully competent sources of zoonotic infection rather than intrinsically \u0026ldquo;attenuated\u0026rdquo; compared with human strains.\u003c/p\u003e \u003cp\u003eMobile genetic elements (MGEs) were only sparsely investigated in the African WGS studies included here, and their apparent scarcity should be interpreted cautiously. No study detected plasmid replicons or plasmid-mediated AMR in \u003cem\u003eB. melitensis\u003c/em\u003e, and only a single South African dataset explicitly mentioned insertion sequences (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). However, most African analyses focused on core-genome SNPs or cgMLST and did not systematically characterize insertion sequences, prophages, or structural variation. The apparent dominance of chromosomally encoded virulence and intrinsic AMR modules in African \u003cem\u003eB. melitensis\u003c/em\u003e should therefore be seen as a reflection of analytical priorities rather than definitive evidence that MGEs are biologically absent. Future African WGS work would benefit from the routine incorporation of mobilome and plasmids or phages that might contribute to local adaptation or subtle phenotypic shifts.\u003c/p\u003e \u003cp\u003eFrom a risk-of-bias perspective, JBI-based appraisal indicated that most included studies clearly identified \u003cem\u003eB. melitensis\u003c/em\u003e and applied robust laboratory workflows, but important limitations were common. Sampling frames were rarely population-based; inclusion criteria were often only implicitly defined; and metadata on animal movements, vaccination status, or clinical course were incomplete. These weaknesses translate directly into interpretive uncertainty: tight genomic clusters may represent genuine outbreak-scale transmission, but they may also reflect narrow sampling from a larger, unsampled diversity.\u003c/p\u003e \u003cp\u003eDespite these caveats, a coherent picture emerges. African \u003cem\u003eB. melitensis\u003c/em\u003e populations appear to be structured into regionally distinct lineages, West Mediterranean ST11/biovar-3 dominated lineages in the north and ST12-dominated African lineages in the east and south, with tight local clustering consistent with endemic transmission in relatively closed host populations. Genomes are characterized by highly conserved virulence architectures and intrinsic efflux- and cell-envelope-mediated AMR modules, with no evidence to date for widespread classical acquired resistance genes or plasmids. For public-health and One-Health programmes, these findings emphasize three priorities: expanding WGS into under-represented regions (particularly West and Central Africa), standardizing MLST/cgMLST and SNP pipelines to enable cross-study comparison, and systematically integrating genomic data with detailed epidemiological and phenotypic information. Such efforts would allow African countries to move from isolated genomic case studies towards a coordinated continental framework for tracking \u003cem\u003eB. melitensis\u003c/em\u003e transmission, evaluating control strategies, and detecting emerging threats in real time.\u003c/p\u003e"},{"header":"5 Conclusion","content":"\u003cp\u003eThe findings underscore the urgent need to expand genomic surveillance beyond currently represented countries, integrate WGS into routine One Health programmes, and standardize analytical approaches to enable cross-regional comparison. Strengthening metadata collection, particularly on host ecology, animal movements, and vaccination status, will be essential for linking genomic patterns to transmission processes. Coordinated investment in sequencing capacity and harmonized pipelines will allow African countries to transition from isolated genomic case studies to a robust continental framework capable of monitoring lineage dynamics, evaluating control measures, and detecting emerging threats in real time.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e \u003ch2\u003eDeclaration of competing interest\u003c/h2\u003e \u003cp\u003eThe authors declare no competing interests.\u003c/p\u003e \u003c/p\u003e\u003cp\u003e \u003ch2\u003eEthics\u003c/h2\u003e \u003cp\u003enot applicable.\u003c/p\u003e \u003c/p\u003e \u003cp\u003e \u003cstrong\u003e \u003cb\u003eConsent to Participate\u003c/b\u003e:\u003c/strong\u003e \u003cp\u003enot applicable.\u003c/p\u003e \u003c/p\u003e \u003cp\u003e \u003cstrong\u003eConsent to Publish\u003c/strong\u003e \u003cp\u003e \u003cb\u003edeclarations\u003c/b\u003e: not applicable.\u003c/p\u003e \u003c/p\u003e\u003ch2\u003eFunding\u003c/h2\u003e \u003cp\u003eThis research did not benefit from any funding organization.\u003c/p\u003e\u003ch2\u003eAuthor contribution\u003c/h2\u003e \u003cp\u003e \u003cb\u003eSamweli Y. Bahati\u003c/b\u003e: Writing \u0026ndash; original draft, conceptualization, methodology, formal analysis, data curation, investigation. \u003cb\u003eAbdalah Makaranga\u003c/b\u003e: Writing \u0026ndash; review \u0026amp; editing, conceptualization., investigation. \u003cb\u003eEliezer Mwakalapa\u003c/b\u003e: Writing \u0026ndash; review \u0026amp; editing, supervision, conceptualization. \u003cb\u003eHenry G. Mung\u0026rsquo;ong\u0026rsquo;o\u003c/b\u003e: Writing \u0026ndash; review \u0026amp; editing, funding acquisition, conceptualization. \u003cb\u003eClaus Thomas\u003c/b\u003e: Writing \u0026ndash; review \u0026amp; editing, visualization. \u003cb\u003eAlbino Kalolo\u003c/b\u003e: Writing \u0026ndash; review \u0026amp; editing, supervision. \u003cb\u003eReuben Maghembe\u003c/b\u003e: Writing \u0026ndash; original draft, conceptualization, visualization, supervision, formal analysis, data curation. Project administration, methodology, investigation, funding acquisition.\u003c/p\u003e\u003ch2\u003eData availability:\u003c/h2\u003e \u003cp\u003enot applicable.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eGhssein G et al (2025) Brucellosis: Bacteriology, pathogenesis, epidemiology and role of the metallophores in virulence: a review, \u003cem\u003eFront. Cell. Infect. Microbiol.\u003c/em\u003e, vol. 15, p. 1621230, July \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.3389/fcimb.2025.1621230\u003c/span\u003e\u003cspan address=\"10.3389/fcimb.2025.1621230\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDjangwani J, Ooko Abong\u0026rsquo; G, Gicuku L, Njue, Kaindi DWM (May 2021) Brucellosis: Prevalence with reference to East African community countries \u0026ndash; A rapid review. Vet Med Sci 7(3):851\u0026ndash;867. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1002/vms3.425\u003c/span\u003e\u003cspan address=\"10.1002/vms3.425\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDucrotoy M et al (Jan. 2017) Brucellosis in Sub-Saharan Africa: Current challenges for management, diagnosis and control. Acta Trop 165:179\u0026ndash;193. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1016/j.actatropica.2015.10.023\u003c/span\u003e\u003cspan address=\"10.1016/j.actatropica.2015.10.023\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eJanowicz A et al (Nov. 2020) Evolutionary history and current distribution of the West Mediterranean lineage of Brucella melitensis in Italy. Microb Genomics 6(11). \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1099/mgen.0.000446\u003c/span\u003e\u003cspan address=\"10.1099/mgen.0.000446\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBen Abdallah I et al (July 2025) Hybrid Whole Genomes of Brucella melitensis from Tunisian Animal Isolates: Virulence Factors, Antimicrobial Susceptibility, and Phylogeny. Microorganisms 13(7). \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.3390/microorganisms13071651\u003c/span\u003e\u003cspan address=\"10.3390/microorganisms13071651\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMazwi KD et al (2024) Whole Genome Sequence Analysis of Brucella spp. from Human, Livestock, and Wildlife in South Africa. J Microbiol Seoul Korea 62(9):759\u0026ndash;773. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1007/s12275-024-00155-8\u003c/span\u003e\u003cspan address=\"10.1007/s12275-024-00155-8\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHilton M (2024) JBI critical appraisal checklist for systematic reviews and research syntheses (product review), \u003cem\u003eJ. Can. Health Libr. Assoc. J. Assoc. Bibl. Sant\u0026eacute; Can.\u003c/em\u003e, vol. 45, no. 3, Dec. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.29173/jchla29801\u003c/span\u003e\u003cspan address=\"10.29173/jchla29801\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKsibi B et al (Feb. 2025) Genomic analysis of Brucella melitensis isolates recovered from humans in south Tunisia over 35 years between 1988 and 2022. BMC Microbiol 25(1):98. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1186/s12866-025-03802-1\u003c/span\u003e\u003cspan address=\"10.1186/s12866-025-03802-1\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCraighead L et al (Mar. 2018) Brucellosis in West and Central Africa: A review of the current situation in a changing landscape of dairy cattle systems. Acta Trop 179:96\u0026ndash;108. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1016/j.actatropica.2017.12.026\u003c/span\u003e\u003cspan address=\"10.1016/j.actatropica.2017.12.026\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRabinowitz P et al (2021) Whole Genome Sequence Analysis of Brucella melitensis Phylogeny and Virulence Factors, \u003cem\u003eMicrobiol. Res.\u003c/em\u003e, vol. 12, no. 3, pp. 698\u0026ndash;710, Aug. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.3390/microbiolres12030050\u003c/span\u003e\u003cspan address=\"10.3390/microbiolres12030050\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eFerjani A et al (Jan. 2025) A Genomic Characterization of Clinical Brucella melitensis Isolates from Tunisia: Integration into the Global Population Structure. Microorganisms 13(2). \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.3390/microorganisms13020243\u003c/span\u003e\u003cspan address=\"10.3390/microorganisms13020243\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eNabi I et al (2024) Serological, phenotypic and molecular characterization of brucellosis in small ruminants in northern Algeria, \u003cem\u003eFront. Microbiol.\u003c/em\u003e, vol. 15, no. (Nabi I., [email protected]) Department of Nature and Life Sciences, Laboratory of Biotechnology and Valorisation of Biological Resources (BVRB), Faculty of Sciences, University Dr. Yahia Far\u0026egrave;s, M\u0026eacute;d\u0026eacute;a, Algeria. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.3389/fmicb.2024.1505294\u003c/span\u003e\u003cspan address=\"10.3389/fmicb.2024.1505294\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKhan AU et al (2021) Whole-genome sequencing for tracing the genetic diversity of Brucella abortus and brucella melitensis isolated from livestock in Egypt. Pathogens 10(6). \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.3390/pathogens10060759\u003c/span\u003e\u003cspan address=\"10.3390/pathogens10060759\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBrangsch H et al (Aug. 2023) Genotype diversity of brucellosis agents isolated from humans and animals in Greece based on whole-genome sequencing. BMC Infect Dis 23(1):529. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1186/s12879-023-08518-z\u003c/span\u003e\u003cspan address=\"10.1186/s12879-023-08518-z\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSibhat B, Adamu H, Asmare K, Lindahl JF, Magnusson U, Sisay Tessema T (2024) Detection and Molecular Diversity of Brucella melitensis in Pastoral Livestock in North-Eastern Ethiopia. Pathogens 13(12). \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.3390/pathogens13121063\u003c/span\u003e\u003cspan address=\"10.3390/pathogens13121063\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWareth G et al (2020) Mlva-16 genotyping of brucella abortus and brucella melitensis isolates from different animal species in egypt: Geographical relatedness and the mediterranean lineage. Pathogens 9(6):1\u0026ndash;15. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.3390/pathogens9060498\u003c/span\u003e\u003cspan address=\"10.3390/pathogens9060498\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eElrashedy A, Nayel M, Salama A, Zaghawa A, Abdelsalam NR, Hasan ME (June 2024) Phylogenetic Analysis and Comparative Genomics of Brucella abortus and Brucella melitensis Strains in Egypt. J Mol Evol 92(3):338\u0026ndash;357. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1007/s00239-024-10173-0\u003c/span\u003e\u003cspan address=\"10.1007/s00239-024-10173-0\" 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":true,"hideJournal":true,"highlight":"","institution":"St. Francis University College of Health and Allied Sciences","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":"Brucella melitensis, Whole-genome sequencing (WGS), Phylogenomics, Antimicrobial resistance (AMR), Africa, Zoonotic pathogens","lastPublishedDoi":"10.21203/rs.3.rs-9058123/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-9058123/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e \u003cem\u003eBrucella melitensis\u003c/em\u003e is a major zoonotic pathogen in Africa, yet its population structure and transmission dynamics remain poorly characterized due to the limited use of whole-genome sequencing (WGS). This systematic review synthesized all published WGS studies of \u003cem\u003eB. melitensis\u003c/em\u003e from African settings to assess lineage distributions, phylogenomic patterns, antimicrobial resistance (AMR) determinants, and virulence repertoires. The review followed PRISMA 2020 guidelines and was prospectively registered in PROSPERO. PubMed, Scopus, and Embase were searched without date limits for studies generating or analyzing WGS data from \u003cem\u003eB. melitensis\u003c/em\u003e isolates originating in Africa. Eligible studies reported primary genomic analyses, including lineage assignment, phylogenomics, AMR, or virulence profiling. Two reviewers independently screened records extracted data using a calibrated form and assessed methodological quality using the Joanna Briggs Institute (JBI) tools. Owing to methodological heterogeneity, findings were synthesized narratively. Of 96 records identified, nine studies met the inclusion criteria, representing six countries (Tunisia, Algeria, Egypt, Ethiopia, Tanzania, and South Africa) and 139 genomes. North African datasets were dominated by biovar 3 and sequence type (ST) 11 within West Mediterranean lineages, whereas East and Southern African studies showed ST12-dominated African-lineage clusters with several novel sequence types. Phylogenomic analyses consistently revealed tight within-country clusters (0\u0026ndash;6 alleles or 0\u0026ndash;2 SNPs) and substantially larger between-country and between-lineage distances. AMR determinants were limited to intrinsic \u003cem\u003eloci\u003c/em\u003e such as \u003cem\u003emprF\u003c/em\u003e and \u003cem\u003ebepC\u0026ndash;G\u003c/em\u003e, with no classical acquired resistance genes detected. Virulence repertoires were highly conserved across regions, with intact LPS and type IV secretion system genes universally present. African \u003cem\u003eB. melitensis\u003c/em\u003e populations form regionally distinct lineages with strong local clustering and conserved virulence and intrinsic AMR architectures. Major evidence gaps, particularly in West and Central Africa, highlight the need for coordinated, continent-wide genomic surveillance to inform One Health brucellosis control.\u003c/p\u003e","manuscriptTitle":"A systematic review of whole genome sequencing evidence on zoonotic Brucella melitensis in Africa revealing lineage structure phylogenomic clusters antimicrobial resistance and genomic determinants","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-03-10 05:49:55","doi":"10.21203/rs.3.rs-9058123/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":"4d88031e-e95b-48bb-8678-c1473cdf6850","owner":[],"postedDate":"March 10th, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":64100552,"name":"Molecular Epidemiology"},{"id":64100553,"name":"Bacteriology"},{"id":64100554,"name":"Zoonoses"}],"tags":[],"updatedAt":"2026-03-12T17:20:46+00:00","versionOfRecord":[],"versionCreatedAt":"2026-03-10 05:49:55","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-9058123","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-9058123","identity":"rs-9058123","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}