Genome characteristics and type IV effector protein repertoire of Coxiella burnetii depend rather on Genomic Groups than on host species

Genome characteristics and type IV effector protein repertoire of Coxiella burnetii depend rather on Genomic Groups than on host species | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Genome characteristics and type IV effector protein repertoire of Coxiella burnetii depend rather on Genomic Groups than on host species Hanka Brangsch, Christian Berens, Selina Fuchs, Stephen Fitzgerald, and 3 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8306611/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 22 Apr, 2026 Read the published version in BMC Microbiology → Version 1 posted 10 You are reading this latest preprint version Abstract Background Q fever is a zoonotic disease with virtually worldwide dissemination. Its bacterial agent, Coxiella burnetii , is primarily found in cattle and small ruminants. Disease manifestation is highly variable, i.e. asymptomatic, acute or chronic in humans, and subclinical or present as reproductive disorders in ruminants. Different genomic lineages of C. burnetii have been recognized and are considered to show host preferences and influence the disease outcome. The virulence of C. burnetii is essentially determined by effector proteins that modulate host cell processes, allowing the bacterium to persist and proliferate in the host. Thus, these effectors have been suggested to play a role in the presumed host specificity and disease manifestation. Results In the present study, a comprehensive set of 140 C. burnetii genomes from ten Genomic Groups (GGs) and various hosts was studied bioinformatically to determine if there was an association between their genomic characteristics, including the effector protein repertoire, and their isolation source. The differences in genome size, IS1111 count, number of coding sequences, accessory genome and others observed could be attributed to lineage-specific traits. Likewise, the GGs showed conserved sets of effector proteins, although intra-lineage variances were high in GGIV. Several effector proteins, e.g. Cem8 (CBU_1634a) and CBU_0469, were highly conserved, while CBU_2007 showed a remarkably high number of sequence variants. Conclusions C. burnetii exhibits genomic diversity that aligns with phylotypes rather than host species, suggesting that genomic traits as well as host factors influence disease outcome rather than a host species specific adaptation. Coxiella burnetii genotyping T4BSS effector proteins pangenome SNP host preference Genomic Groups type IV secretion system Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Background Coxiella burnetii is a Gram-negative, obligate intracellular zoonotic pathogen and the etiological agent of Q (query) fever in humans or coxiellosis in animals. Q fever is distributed worldwide, except in New Zealand, and has been categorized as a priority zoonotic disease by the European Food Safety Authority (EFSA) since 2023. C. burnetii displays a broad host spectrum and infects a variety of species, including humans, domestic and wild animals, ticks and birds [ 1 ]. Disease manifestation differs between humans and animals. In humans, an infection remains often asymptomatic. About 40–50% of infected individuals develop a mild flu-like illness. However, in some patients, the infection progresses to an atypical pneumonia or hepatitis. A small percentage (2–5%) of infected individuals develop chronic Q fever, months or years after the initial infection. Chronic Q fever is mainly characterized by a potentially fatal endocarditis [ 1 ]. Ruminants, such as cattle, sheep or goats, are considered the main reservoir and source of human C. burnetii infections. Infections in sheep and goats are mostly asymptomatic. However, weak offspring and late term abortions do occur, with the abortion rate being higher in goats than in sheep. Fertility problems are common in cattle, but the symptoms are more varied [ 2 ]. Infected animals shed the pathogen through their feces and milk, but primarily through birthing products [ 1 ]. Humans are mainly infected by inhalation of contaminated dust, with less than ten bacteria being sufficient to cause disease [ 3 ]. Differences in the disease manifestations observed led to the assumption of an isolate-specific virulence and to the establishment of six Genomic Groups (GGI-VI) by restriction fragment length polymorphism (RFLP) analysis in the early 1990s [ 4 ]. These genomic groups (GGs) correlated with disease manifestations: isolates of GGI to GGIII originated mainly from patients with acute Q fever whereas GGIV and GGV isolates were associated with chronic human Q fever cases. This original genomic grouping is still valid and was extended by modern typing methods, such as multispacer sequence typing (MST) and core genome single nucleotide polymorphism (SNP) typing [ 5 , 6 ]. In MST, the allelic states of ten genomic loci are determined, while SNP analysis investigates base differences between strains in the entire DNA sequence, allowing a detailed differentiation. The initial panel of six GGs was extended by subdivision of GGII (a-d) and GGIV (a-b) [ 5 , 7 ], based on specific SNPs, and addition of GGVII and GGVIII [ 8 ]. Rodent infection models supported the hypothesis of a genomic profile-specific pathotype with GGI to GGIII isolates causing more severe clinical signs, whereas GGIV and GGV isolates caused no or only mild disease. [ 9 , 10 ]. Additionally, the increasing availability of genome sequencing data of C. burnetii isolates from various host species revealed that GGs are dominated further by isolates from certain hosts, e.g. GGIII is dominated by isolates from cattle, whereas goat isolates are more frequently found in GGII-b and human isolates in GGII-a, GGIV and GGV [ 5 ]. The genomes of C. burnetii isolates comprise a chromosome with ~ 2 million base pairs and a mean GC content of 42.6% [ 11 , 12 ]. In total, the genome contains an estimated 2,134 coding elements, the exact number of which varies between different C. burnetii isolates [ 11 ]. In a recent study, 75 isolates were analyzed and grouped into 22 MST genotypes and 13 clusters [ 12 , 13 ]. Importantly, all isolates analyzed contained genes encoding a type IVB secretion system (T4BSS) [ 12 ]. T4BSS are complex nanomachines that span the entire bacterial cell envelope and deliver DNA or effector proteins into the host cell environment [ 14 , 15 ]. Effector proteins manipulate a variety of host cell pathways to ensure bacterial propagation. Thus, the T4BSS is integral for bacterial virulence [ 16 ]. C. burnetii encodes 23 homologs of the 26 Legionella pneumophila dot / icm genes, that encode the T4BSS [ 17 , 18 ]. The T4BSSs of these two pathogens are not only structurally, but also functionally similar. C. burnetii lacking a functional T4BSS is unable to replicate intracellularly [ 19 , 20 ], demonstrating the importance of this secretion system for virulence. To date, ~ 150 C. burnetii T4BSS effector proteins have been identified, but only few have assigned functions [ 21 , 22 ]. These effectors promote biogenesis of the C. burnetii -containing vacuole (CCV), interfere with vesicular trafficking, maintain host cell survival and manipulate host immune defenses [ 22 , 23 ]. The CCV is established after uptake of C. burnetii into the host cell. The nascent CCV has a neutral pH and is decorated with early endosomal marker proteins. Maturing CCVs are phagolysosome-like compartments with an acidic pH of ~ 4–5 [ 24 – 26 ]. These acidic conditions induce the translocation of T4BSS effector proteins into the host cell [ 27 ], which in turn allows completion of CCV maturation into a large, replication-competent vacuole, and modulation of the host cell in favor of the pathogen. Several studies have demonstrated considerable heterogeneity among the C. burnetii effector protein profiles from different isolates [ 18 , 28 – 31 ]. In a study, in which the repertoire of effector proteins was compared in five isolates, only 44 out of the143 effector proteins analyzed were present and intact in all five strains [ 18 ]. Here, we analyzed the genomes of 140 C. burnetii isolates to determine whether affiliation to a GG and/or the T4BSS effector protein repertoire as well as their secretion system might allow the prediction of virulence potential or host species specificity of an isolate. The dataset comprised 102 publicly available genomes and 38 recently sequenced C. burnetii isolates from Germany. All GGs were represented, except for GGII-d, GGVII and GGVIII, as well as common host species, such as cattle, goats, sheep, humans, ticks, and rodents. They originated from acute and chronic Q fever cases or from afterbirth material and milk from ruminants. Results Selection of C. burnetii genome sequences To generate a comprehensive and high-quality dataset of C. burnetii genomes, genomic data from the NCBI Short Read Archive (SRA) (n = 110) and the RefSeq database (n = 150) was retrieved. Further, 38 isolates from the C. burnetii strain collection of the Friedrich-Loeffler-Institut were included that had been collected in Germany and the Netherlands, from small ruminants, cattle and, in one instance, from a patient between 1989 and 2021 (Additional file 1 - Table S1 ). These served to complement the publicly available genomic data. These isolates were sequenced by Illumina and Nanopore technologies. All C. burnetii datasets were assessed for their quality and completeness. Metagenomic data sets were removed because of low C. burnetii -specific read counts. Duplicates of identical strains were also excluded. Overall, 127 public data sets (SRA n = 31; RefSeq n = 96) of high quality representing individual isolates were chosen for further analyses (Additional file 1 - Table S1 ). Almost half of these (n = 62) originated from humans. All assemblies or the corresponding read data were subjected to SNP typing together with the SRA data for excluding duplicate strains. The core genome SNP alignment contained 14,589 SNPs and 0 to 5,681 nucleotide differences were observed between individual strains. Duplicates of identical strains were removed (n = 25), leaving 140 unique strains in the final dataset. For all downstream analyses, 112 unique public data and 38 new genome sequence data (n = 140) sets were used. Many of these strains were of human origin (n = 58), but strains originating from cattle (n = 34), goats (n = 22) and sheep (n = 12) were also included. Furthermore, one strain each had been isolated from a dog, a mouse, the soil and a not-specified ruminant, respectively. Six strains came from ticks and three from kangaroo rats. The majority of the strains (n = 99) had been isolated in Europe, particularly in France and Germany. Selection of C. burnetii effectors To assess if the repertoire of effector genes varied among isolates, and if any observed differences correlated with potential adaptation to specific hosts and/or disease manifestation, a comprehensive literature survey was conducted to gather data on known C. burnetii effector proteins. Overall, a total of 156 effector genes comprising 146 chromosomally encoded and ten plasmid encoded genes were identified in the reference strain Nine Mile I (Additional file 2 - Table S2 ). Of these, two could not be found in the NMI reference strain (CBU_0088, CBU_1251) and 23 have been marked as discontinued in NCBI. Additionally, homologues of effector-coding genes of strain Nine Mile I were searched in the reference strains Dugway 5J108-111, CbuG_Q212, CbuK_Q154 and RSA331, resulting in the identification of 438 homologous effector sequences across all strains (Additional file 2 - Table S2 ). Placement of the strains in the C. burnetii phylogeny The strain selection should represent a wide range of known C. burnetii phylotypes for gaining a comprehensive insight in effector protein variation. Thus, the genomic diversity of the isolate or genome data sets was first assessed by in silico MST analysis followed by linking to GGs according to Hemsley et al. [ 6 ]. In total, 18 different sequence types (STs) were identified, mostly ST61 (n = 38), ST16 (n = 28) and ST18 (n = 19). However, various novel alleles were found, so that an ST could not be assigned to 34 strains (Fig. 1 , Additional file 1 - Table S1 ). Based on the MST results, the strains were also assigned to a GG, showing that our dataset included strains from ten of the 13 known GGs. The subsequent core genome SNP (cgSNP) analysis confirmed the MST and GG results, as all strains with an identical MST ST and the same GG clustered together (Fig. 1 ). The size of the core genome SNP alignment totaled 15,343 nucleotides; more than in the previous alignment for quality control, accounting for the higher quality of the final dataset. Cattle isolates dominated GGIII, whereas sheep- and goat-associated strains were primarily found in GGII-a and GGII-b. Remarkably, almost no animal isolates were found in GGIV and GGV, i.e. these groups were dominated by human isolates. However, most GGs (GGI, GGII-a/b, GGIII, GGIV-a/b) were composed of strains that had been isolated from three to four different host species, while two GGs (GGII-c, GGV) were detected in only two host species each (human and goat or human and dog, respectively) and GGVI exclusively comprised isolates from a single host species (kangaroo rat). The human isolate Cb3506 from the United Kingdom, which was located on the same branch as GGII and GGIII, could neither be assigned to an MST ST, nor placed within a GG. Collectively, this dataset represented the majority of known C. burnetii GGs. Furthermore, GGs previously determined to be dominated by specific host species were confirmed, although most GGs were associated with three different host species. Genome characterization and pangenome analysis Genome characterization Using the cgSNP typing approach, almost all strains were assigned to a GG. Thus, in the following analyses, the genomes of the groups could be characterized collectively and differences between these groups was assessed. The genome sizes ranged from 1,955,281 bp in one human isolate, that could not be assigned to a GG, to 2,212,937 bp in the GGVI reference strain Dugway 5J108-111. In GGV, the mean genome size was lowest (appr. 1,992 kbp) (Table 1 ). The variation in genome size was highest in GGII-b, GGII-c and GGIV-a. No connection between genome size and the associated host was apparent (Additional file 3 - Figure S1 ). The number of coding sequences detected ranged from 1871 in a strain from GGIV-b to 2248 in a GGII-b strain. The lowest mean number of coding sequences (CDSs) was found in GGIV-b, while the genomes of GGII-a showed not only the highest number of CDSs, but also of pseudogenes, i.e. non-protein-coding genes. GGI genomes had the least number of pseudogenes (n = 46 ± 2), even less than the Dugway strains from GGVI (n = 58 ± 8) (Additional file 1 - Table S1 ). The number of genes without significant similarities to known genes (“hypotheticals”) was lowest in GGVI and highest in the GGII subgroups. These data showed that genome size and the number of CDS or pseudogenes differ between the GGs. No correlation between the number of pseudogenes and genome size was found, i.e. the number of pseudogenes was highest in GGs (GGII and IV-a) with the most variation in genome size, but isolates with small genomes (GGV) harbored a similarly large number of pseudogenes. Table 1 Genome characteristics of the 140 C. burnetii strains analyzed, according to their affiliation with a Genomic Group (GG). Given are the arithmetic mean and its standard deviation (SD). GG #* Genome size CDSs Pseudogenes Hypotheticals Mean SD Mean SD Mean SD Mean SD I 23 2,012,857 18,387 2018 30 46 2 305 11 II-a 23 2,044,679 23,953 2100 51 111 15 353 23 II-b 13 2,032,638 44,789 2073 92 102 22 359 46 II-c 9 2,041,355 44,428 2084 115 103 27 361 57 III 36 2,017,346 15,564 2044 26 80 9 330 10 I-III-like 1 2,018,549 - 2024 - 59 - 303 - IV-a 13 2,044,119 43,717 2013 80 108 17 318 34 IV-b 13 2,043,400 26,878 1980 43 83 8 301 32 V 5 1,992,946 15,407 2009 28 99 7 346 13 VI 3 2,198,908 9,920 2079 20 58 8 272 3 NA 1 1,955,281 - 1989 - 81 - 325 - *# number of isolates or genome data sets included Insertion elements Insertion elements (IS), especially the IS1111 element, are associated with genome rearrangements and genome plasticity in C. burnetii . Insertion events can introduce gene disruption, small indels or mutations and have been associated with a pathoadaptive evolutionary process [ 32 ]. Therefore, the number and type of IS elements were determined and compared among strains or genomic data sets of different GGs. Only genomes with a maximum of three contigs (n = 71) were analyzed, as fragmented assemblies often show breaks at repetitive elements and, thus, the number of IS elements could be overestimated. In these 71 genomes, four to 114 IS elements belonging to eight families (IS110, IS1634, IS3, IS30, ISAS1, ISNCY, IS4, IS481) were detected (Additional file 4 - Table S3 ). Dugway 5J108-111 was the only strain analyzed in which an IS4 sequence was detected. All strains had one copy of ISNCY. Elements of the IS481 family were only detected in the genomes of GGIV-a, GGV and GGVI, but not in GGIV-b, except for strain Namibia (MST30). IS1111, the only known representative of family IS110 in C. burnetii , was identified up to 103 times in one genome (goat isolate 3262 of GGII-b) (Additional file 4 - Table S3 ). However, to our surprise, ISEScan did not detect IS1111 elements in three genomes (strains DOG UTAD (GGV), CB121 (GGII-c), BRASOV (GGI)). Checking the annotation files revealed that the strains did harbor transposons, but apparently the degree of sequence identity at the nucleotide level was not high enough to be detected by the bioinformatic tool used. Additionally, only a single copy of IS1111 was found in ten strains. The distribution of the number of IS1111 elements within and between the GGs is displayed in Fig. 2 . Most GGs were consistent in their IS1111 copy number, particularly GGI, GGII-a and GGIII, where most isolates harbored around 20, 50 and 22 IS1111 copies, respectively. Two isolates from goat and sheep, respectively, of GGII-b stood out, as 102 and 103 IS1111 copies were detected. A correlation between the number of IS elements and the host species was not apparent, e.g. isolates from goats were often among the strains with the highest or the lowest number of IS1111 copies. Overall, the differences observed in the number of IS1111 elements were mostly in accordance with the phylogenetic grouping of the strains, but no connection to host species was observed. Pangenome analysis Using the complete dataset of 140 genomes, a pangenome analysis was conducted (Table 2 ). The aim was to determine if a specific set of accessory genes could differentiate strains from different GGs and/or strains from identical hosts or if differences primarily occurred in genes conserved across the C. burnetii phylogeny. Of the 2237 total genes detected, 72.6% were found in at least 99% of the strains and constituted the core genome in this study. Less than 10% of the genes were only found in less than 20 genomes each (15%). In agreement with the previous cgSNP, a phylogeny based on only core genes identified in the pangenome analysis generated the same GG clusters (Fig. 3 ). The corresponding visualization of gene presence, displayed as blue bars in Fig. 3 , indicated that each GG possessed a specific set of accessory genes. Particularly, the Dugway isolates of GGVI featured a large set of genes absent in other isolates. This Group also had the largest number of lineage-specific core genes (n = 1979) (Additional file 5 - Table S4 ), while GGIV-b harbored the lowest number of lineage-specific core genes (n = 1664). However, the percentages of core genes relative to the overall number of genes detected differed between the groups, with the fewest conserved genes in GGII-b. The presence/absence information of the accessory genes (n = 723), i.e. genes present in less than 140 genomes, was used for a Neighbor Joining analysis, to see if it correlated with GG or the source of isolation (Fig. 4 ). This analysis confirmed that the accessory genes found in the strains were determined by affiliation to a GG rather than to a host species, as clusters were formed by GG rather than by isolate origin. Remarkably, the accessory gene spectrum of the GGs was diverse and only a few coherent clusters were observed. Taken together, the accessory genes among all strains analyzed clustered according to the GGs by cgSNP and pangenome analyses. Each GG possessed a specific set of accessory genes, but with a certain variability within the group. Table 2 Result of the pangenome analysis of 140 C. burnetii genomes Fraction Definition No. % Core genes (99% <= strains < = 100%) 1624 72.6 Soft core genes (95% <= strains < 99%) 100 4.5 Shell genes (15% <= strains < 95%) 303 13.5 Cloud genes (0% <= strains < 15%) 210 9.4 Total genes (0% <= strains < = 100%) 2237 T4BSS effector protein variations Nucleotide-level analysis of effectors We next compared the effector gene and predicted protein sequences of all T4BSS effector proteins identified to assess if the effector gene repertoires of different C. burnetii isolates were associated with host species. Across the 100 effector genes of the core genome 1,213 variant positions were detected at the single nucleotide level. There was a high degree of similarity within the GGs, as all strains within the four largest groups (GGIII, GGII-a, GGII-b and GGI) differed not more than seven bases within the effector gene regions. However, the SNP differences in GGIV-a and GGIV-b were higher (Table 3 ). Direct comparison of core genome- and effector gene-based SNP typing (Fig. 5 ) revealed that the overall clustering of the GGs and the branch placement of most strains remained coherent. Only the GGIII strains and some GGI strains clustered differently within the group. This indicates a higher degree of variation regarding the SNP positions within this group. The SNP positions in the core genome and in the effector-coding genes were screened for variants that were shared by all isolates of the same GG or from the same host, but differed in other strains (Additional file 6 - Table S5 ). Unique SNPs were identified in the core genome region as well as in effector genes for all data sets with a GG assignment (Table 3 ). GGV had a high number of unique nucleotide variants and GGIV-b exhibited a single SNP (CBU_1459), but only when strain Namibia (MST ST30) was included. The latter did not cluster perfectly with other strains of GGIV-b. Only a few unique mutations were detected in the effector gene regions for GGII-a to -c. No characteristic unique nucleotide variants were found when comparing the strains by their host species, not even when considering the complete core genome region. This finding was valid even when samples with potential accidental hosts or vectors were removed and only samples from cattle, human, rodent, sheep and goat were considered. Only for the rodent isolates, which all belonged to GGVI, unique SNPs were detected, which coincided with the previous results for GGVI. Therefore, mutations in the core genome effector gene sequences coincided with the GG, allowing typing of isolates based on GG-unique SNPs. However, no association between the core genome effector gene sequences and host species was found. Table 3 Number of single nucleotide variants and predicted effector protein sequence variants unique to each Genomic Group at the core genome level (“genome”) and in gene positions corresponding to NMI effectors (“effector genes”). Numbers in brackets show results for GGIV-b when strain Namibia (MST ST30) is excluded from the group. Region Total SNPs I II-a II-b II-c III I-III-like IV-a IV-b V VI Genome 15,343 388 110 67 39 360 228 651 1 (151) 2614 1175 Effector genes 1,213 38 6 4 3 24 20 49 0 (13) 210 90 Effector proteins - 25 12 3 2 16 19 12 3 (7) 60 67 Protein-level analysis of effector proteins As differences in the nucleotide sequence do not necessarily translate to differences at the protein level, the effectors were also analyzed based on their predicted protein sequences to assess if sequence changes might impact protein functionality. Seventeen of the 156 effector genes initially identified in the literature were annotated as pseudogenes in the RefSeq record, that do not have a translation product. Further, 25 effectors were not found due to discontinuation in the new RefSeq annotation version (v2) (Additional file 2 - Table S2 ). A comparison of the genome position of the remaining 131 coding sequences to the Bakta annotation of NMI showed, that all but one (CBU_0375) of the effectors were present and represented ORFs, even if they were pseudogenes in the original annotation. Additionally, the gene sequence of all NMI effectors from the RefSeq annotation were searched for in four reference strains representing different genomic groups: Dugway 5J108-111 (GGVI), CbuG_Q212 (V), CbuK_Q154 (IV-a) and RSA331 (II-a) (Additional file 7 - Figure S2 ). The protein sequences encoded by homologous genes were downloaded and a database was created. The gene products predicted from all strains in the dataset investigated were compared to this effector protein database and potential effector proteins were extracted. By this approach, 157 genomic loci and corresponding gene products were identified as potential effectors, which were investigated further. Each complete protein sequence variant was given a number to differentiate between them. This enumeration started anew for every effector. Incomplete sequences were labelled 'truncated'. Figure 6 gives an overview over the variants observed for the effector proteins, sorted according to the pangenome phylogeny. If an effector protein was classified as truncated, it indicated that it was shorter than the longest observed sequence of this effector protein. Further, the coding region was classified as disrupted, if more than a single ORF was detected for this genomic locus in the pangenome analysis. All 130 NMI effector proteins were found to be present in all strains, regardless of their truncation or disruption status (Additional file 8 - Table S6 ). When considering only fill-length proteins, 35 NMI effectors were found in all strains and, additionally, all strains harbored three effectors of the strains Dugway (CBUD_1462) and CbuG (CbuG_0789, CbuG_1711). In general, effector protein variants correlated with their respective GG, with some variation also occurring within a GG. To confirm this, a neighbor joining analysis was conducted with the effector sequence types (Additional file 9 - Figure S3 ). As expected, the strains clustered according to their GG. No connection to the host species was observed. Further, no unique sequence type was found when looking for host-specific effector types in the dataset. However, all GGs harbored effectors with unique sequence types (Table 3 , Additional file 10 - Table S7 ). In GGII-b and II-c, only three and two effector variants were unique, respectively, which agreed with the frequent overlap of sequence types in GGII, and with the higher variance in these two sub-groups. Likewise, for GGIV-a and IV-b, a higher variance was observed, leading to low numbers of unique effector types. As observed for SNPs, excluding strain Namibia from GGIV-b increased the number of unique effector variants. In this case, strain Namibia showed 49 uniquely different effector sequences (Additional file 10 - Table S7 ). Two effectors were conserved among all strains studied: CBU_0469 and CBU_1634a (Cem8). Also, CBU_1314a was highly conserved among all GGs, except for GGV, in which it was not detected, and for MST ST30 of GGIV-b, strain Namibia, that harbored a single amino acid exchange. Likewise, CBU_1594 (MceD) and CBU_2076 were identical in all strains, except for the GGVI genomes, in which they contained the C-terminal substitutions I109V and A97S, respectively. Several effectors were not detected in all genomes. CBU_0072 (AnkA) was absent from GGII-b, GGII-c and GGVI as well as from a few GGIV-a and GGIV-b strains. The latter two groups and GGVI lacked CBU_0881 (CoxCC5), whereas CoxU1 (CBU_0814) was only intact in GGIV-a/b. Likewise, the genomes of GGVI possessed effectors that were missing or not intact in other strains: CBU_1107, CBU_1754-7 and CBU_2028. Several effector proteins (n = 23), that were detected by screening with the effector protein database (Diamond database in Additional file 7 - Figure S2 ), could not be assigned to a NMI homologue, but showed high similarity to effector proteins from the strains Dugway 5J108-111, CbuG_Q212, CbuK_Q154 and RSA331. Thus, in Fig. 6 , these proteins were named according to their respective match in the protein database. Interestingly, CBUD_0392, whose sequence was taken from the Dugway reference strain, was detected as truncated in GGVI and others, because a homologous protein was found in GGI, which was 159 aa longer at the C terminus than the Dugway reference protein. CBUD_0454, also from the Dugway reference, was disrupted in GGI, but intact in all GGII and GGIII strains, with one exception in GGII-c. Six proteins (CBUD_RS11275, CBUD_RS08635, CpeI, CpeJ, CpeK, CpeL) were identified by their annotation but were not found by screening of the database or comparison to the NMI loci. CpeI to CpeL were annotated as Dot/Icm T4BSS effectors while CBUD_RS11275 and CBUD_RS08635 were described as ankyrin repeat domain-containing proteins. While CpeIL and CBUD_RS08635 were only detected in GGVI, variants of the 627 aa long CBUD_RS11275 were found in GGII, GGIII and GGVI. The protein was truncated at the N terminus in GGII-b and GGII-c. When analyzing the effector repertoire at the protein level, several effectors were conserved in all GGs, a few effectors were present only in GGIV or absent in IV and V. Overall, a GG-specific effector sequence type pattern was observed, but a connection to the host species was not detected. However, this analysis did not assess functionality of the detected effector variants, which may impact virulence. Detailed analysis of selected effector protein variants Analyzing the genomic diversity of genes encoding putative effector proteins can help identify regions or amino acids essential for molecular activity [ 31 ]. Often, the C-terminal end (especially the last 20 amino acids) is essential for recognition and export by the T4BSS, while the N-terminal sequence may contribute to effector function or localization [ 30 , 33 , 34 ]. Thus, we analyzed the sequences of five potential T4BSS effector proteins, for which experimental data on their function and interaction with the host cell were available. These five effector proteins – CBU_0077 (MceA), CBU_0513 (CinF), CBU_0781 (AnkG), CBU_0822 (CbFic2) and CBU_2007 (Vice) – have been associated with interfering with apoptosis and host cell transcription, and/or are essential for intracellular replication and CCV biogenesis. CBU_0077 (MceA) MceA co-localizes with mitochondria but its function is unknown [ 35 ]. This effector was found in all genomes, with altogether six sequence variants (Fig. 7 ). While most GGs shared the protein sequence of NMI (GGI), GGII-a had a unique variant due to a substitution (A30S). The variability in GGIV-a and GGIV-b was higher, as both GGs showed two and three sequence variants, respectively. In GGIV-b, two variants (type 5 and type 6) had a K143E substitution and three strains an additional G55S exchange (type 6). In GGIV-a, CBU_0077 exhibited one substitution (Q113E) in two strains (type 3) and the majority of the strains (n = 11) also featured an S186N amino acid exchange (type 4). In all sequence variants, the C-terminus, which is likely required for secretion, was conserved. If the other single amino acid changes interfere with protein localization or function is unknown. CBU_0513 (CinF) Ectopically expressed CinF has cytoplasmic localization and is essential for intracellular replication [ 36 ]. Its sequence was intact and identical in GGI, GGII, GGIII, and GGI-III-like (Fig. 7 ). In GGIV, it was either intact or truncated at the C-terminal end by 19 aa residues. The later likely prevents T4BSS secretion, as the C-terminal ten amino acids contain the translocation signal [ 30 ]. In GGV, there were three substitutions (E87K, F106V, A318S) relative to the NMI reference, while the GGVI variant differed in only one position (W75R) from the majority of strains. CBU_0781 (AnkG) AnkG was one of the first C. burnetii T4BSS effector proteins identified [ 37 ]. Its task is the inhibition of host cell apoptosis and several amino acids within the N-terminal region were shown to be essential for its function [ 33 , 34 , 38 ]. In all strains of GGI, GGIII, GGII-b and GGII-c, the sequence of AnkG was intact and identical to the NMI reference protein (Fig. 7 ). However, eight sequence variants were found. In almost all strains of GGII-a, the ORF was disrupted, likely preventing function or secretion, except for strain CB180, in which the sequence was identical to the NMI reference protein. Full-length proteins of this effector showed one of two different amino acid mutations at the N-terminal end: amino acid position 11 encoded either isoleucine (variant of NMI reference) or leucine (GGV and GGVI), which could impact protein activity [ 34 ]. CBU_0822 (CbFic2) Whether CbFic2 is a T4BSS effector protein has still to be determined, as experimental validation is lacking. Two domains were identified, an HTH domain (amino acids 304–362) required for nuclear localization and DNA binding, and a predicted Fic motif (amino acids 205–216), which are both essential for protein functionality [ 39 ]. CbFic2 was identical in most strains of GGI, GGII and GGIII, with two exceptions in GGII-a and GGII-c, respectively. These harboured each a substitution at the N-terminus: P12S or L20F. In GGVI, there were two substitutions relative to NMI: T217A and S263L. In GGIV, several different substitutions were observed. Further, in GGV, an insertion of serine at position 336 was observed. As this is located within the HTH domain, it might influence nuclear localization and/or DNA binding and thus protein activity. Importantly, none of the variants analysed was mutated in the amino acids 66 and/ or 205, which might alter the enzymatic activity of CbFic2 [ 39 ]. CBU_2007 (Vice) Vice was identified as a cytoplasmic T4BSS effector protein [ 36 , 40 ]. It was shown to be important for the establishment of a large CCV and for intracellular replication [ 22 ]. This effector exhibited the highest number of sequence variants, 22, among all detected effector proteins (Fig. 7 ). Ten of these were found in only a single strain each and three were only found in two strains each. Altogether, 38 variable amino acid positions were detected. Additionally, there was a deletion of a single amino acid in both sequence types of GGV (types 20 and 21) and a deletion in strain Cb3506 extending over five amino acids (type 19). In strain CBI_2022 of GGIV-a, the protein was truncated by 98 aa at the C-terminal end (type 15), likely preventing secretion [ 30 ]. The strains of Genomic Groups GGII-c, GGIII and GGIV were the only ones that harboured a single Vice sequence variant each. It is notable that the Vice variants were always identical in strains of the same MST sequence type. To verify whether the non-synonymous mutations in the gene sequence of Vice were associated with additional silent mutations, the SNPs found in the gene locus of Vice were checked. Remarkably, the vast majority of mutations in the gene were missense variants. Synonymous base exchanges were only found in four strains: CbuG_Q212 (GGV), CB149 (GGIV-a), CB202 (GGIV-a) and Cb3506 (GG_NA). T4BSS protein sequence variation Similar to the effector proteins, there was considerable variability in the protein sequences of the T4SS in the strains investigated (Supplementary Fig. 4, Supplementary Table 8). These protein vari-ants largely coincided with the GG. Again, the intra-GG variability was highest in GGIV and GGV. IcmH was disrupted in GGIII. IcmV was truncated at C-terminal end in all GGs except for GGII-a. Only IcmT and IcmR were conserved across all GGs. Also, DotN and IcmL2 were highly conserved, as protein sequence variations were only observed in three strains each (CbuG_Q212, DOG UTAD, Scurry_Q217 and 22QC1336, CbCVIC1, CB13, respectively). Discussion Coxiella burnetii exhibits a broad host range, and the outcome of infection can vary considerably [ 2 , 41 , 42 ]. While the bacterium infects both small and large ruminants, human Q fever outbreaks are almost exclusively linked to shedding by sheep and goats. Human infections are rarely linked to cattle, even though the seroprevalence is high in cattle [ 43 ]. The differences in disease manifestation led to the hypothesis of isolate- and, later, GG-specific virulence or host adaptation [ 4 , 5 , 9 , 10 ]. However, several studies contradict this hypothesis and indicate that host factors contribute to the disease outcome [ 44 – 46 ]. The ability of C. burnetii to invade and persist in the host cell is, besides the expression of a full-length smooth lipopolysaccharide, facilitated by the production and secretion of effector proteins which interfere with or modulate host cell processes [ 47 , 48 ]. In the present study, we aimed at characterizing C. burnetii strains based on genomic features and differences in their effector protein repertoires. By adding genome data from strains of animal origin to the publicly available, human-dominated dataset, we applied a One Health approach to coxiellosis and the potential role of C. burnetii effector proteins in host specificity. Our analyses showed that several genomic traits of C. burnetii were consistent with the classification of the agent into GGs. These Groups were also congruent with sequence types determined by MST, as shown by the results presented here and by others [ 6 , 7 ]. However, not all of the known GGs were present in the dataset investigated, i.e. GGI-b and GGII-d as well as GGVII and GGVIII were missing due to the lack of good-quality sequencing data. GGs are assumed to be associated with a preferred host species and specific disease manifestations in humans, as GGI-III were predominantly found in patients with acute Q fever, whereas GGIV and GGV were mostly connected to chronic cases [ 4 ]. In agreement with previous findings [ 5 , 6 ], the results presented showed that GGIII was dominated by cattle isolates, while most goat and sheep isolates belonged to GGII-a and GGII-b. The GGIV-a, GGIV-b and GGV were human isolate-dominated groups and associated with chronic Q fever as described before. The difficulty in correlating disease phenotype with isolate sequence identity is nicely demonstrated by GGII-c which was originally associated with acute human Q fever cases, but was dominated in this study by isolates from human chronic Q fever cases. A recent study from Spain has shown that similar C. burnetii genotypes can lead to acute as well as chronic disease outcomes in humans [ 49 ], calling into question the link between GG and Q fever manifestation. Interestingly, MST ST8 (GGIV-a) had been linked to goats before, as it was detected in caprine milk in the USA [ 50 ], but only two isolates from the dataset investigated here that originated from goats fell in this cluster. Considering the fact that coxiellosis is primarily an animal disease, the over-representation of good-quality sequencing data from human chronic Q fever specimens in the publicly available databases can be considered a sampling bias. Despite C. burnetii being endemic almost worldwide, there is a lack of comprehensive sequence data, particularly from animals. By adding 36 samples of animal origin from Germany, we aimed to reduce this bias. The results displayed here show that most genomic groups are found in multiple host species, albeit with some level of dominance for certain species. This host range may become even more diverse with increasing availability of sequence data from isolates of animal and human origin, geographical source or different disease outcome. Multiple hosts in each GG suggest a certain degree of host flexibility and adaptability of C. burnetii rather than strict host specificity. The core genome comprises all genes that are present in all genomes of a species, but its composition can vary considerably depending on the strains included in the analysis. Here, we found 2237 genes in 140 C. burnetii genomes, 1624 of which constituted the core genome genes present in at least 99% of the strains, i.e. 139 isolates. In previous studies, the core genome of C. burnetii was estimated to be smaller. Hemsley et al. [ 5 ] found 1311 or 989 core genome genes among 67 isolates, depending on the bioinformatic pipeline applied, whereas Abou Abdallah et al. [ 12 ] found a considerably higher number of total genes (n = 4501) and a similar number of genes in the core genome (n = 1211) among 75 C. burnetii genomes. It has to be noted that several genomes that were included in the dataset of the latter study were excluded here as they failed quality control. This highlights the impact and importance of rigorous quality screening of genomic data prior to analysis. In contrast to other GGs, the GGVI (Dugway strains) were found to be attenuated or avirulent in most hosts [ 51 ]. In agreement with other reports, these strains had the largest genomes of all groups. However, in contrast to a previous study [ 32 ], they did not have the lowest pseudogene content. Based on the Bakta annotation, the number of pseudogenes in NMI was almost half of that reported in the initial publication of the complete NMI genome [ 11 ], which identified 83 pseudogenes. The Dugway isolates, however, had fewer hypothetical genes, i.e. genes of unknown function, than NMI. Genome degradation usually leads to the formation of pseudogenes, but annotation pipelines might also classify them as hypotheticals, which could explain the discrepancy. The comparability of genome studies can be hampered by the usage of different annotation tools and concomitant differences in CDS annotation. As demonstrated for NMI, known sequences, such as effector-coding genes, might not be found in new annotations or open reading frames can differ in their extent. The larger genome size and higher core gene content observed here, together with the observed avirulence in most hosts, could explain why GGVI is considered closer to the last common ancestor in the C. burnetii phylogeny than the other GGs [ 32 , 52 ]. These other lineages might have emerged as consequence of the pathogen’s introduction into other hosts, resulting in gene decay. Genome reduction is a common phenomenon during the evolution of obligate intracellular bacteria as the selective pressure on genes, that are not required, is reduced, ultimately leading to their loss. It is hypothesized that this process is still ongoing in C. burnetii [ 11 ] and it might even increase the pathogen’s virulence [ 53 ]. When there is a shift in a pathogen’s virulence towards a host, usually similar levels of susceptibility and virulence can be expected in phylogenetically closely related hosts, as shown for viruses [ 54 ]. This agrees with the apparent host preferences of GGs, i.e. ruminant- and human-dominated lineages. However, other hosts can also be susceptible, as the host’s health state influences the disease outcome, which could account for human infections by lineages that are dominantly found in ruminants. Among the dataset investigated, the difference of strain Namibia to other strains of the same GG, GGIV-b, was striking. Based on the findings presented, it can be assumed that MST ST30, to which strain Namibia belongs, could represent another sub-group of GGIV, as it has a characteristic set of SNPs and effector protein variants. Compared to other intracellular bacteria, C. burnetii has a high IS element content with considerable variation between individual strains within the same Genomic Group. Some elements, like ISNCY (“IS not classified yet”) and IS1111 are conserved across all lineages, even with respect to their insertion site [ 55 ], while others were only rarely detected, like IS4, which is known to be located on the QpDG plasmid of the Dugway strains [ 32 ], and IS481. Surprisingly, IS1111, which is commonly used as target for C. burnetii diagnostic PCR [ 56 ], was not detected in three strains. This could be a false-negative result caused by sequence deviations from the reference sequence of the IS1111 elements in these strains, as deletions in IS1111 copies has been noted before [ 55 ]. However, some authors also reported C. burnetii strains lacking IS1111, which originated from marine mammals, e.g. in Australia and Alaska [ 57 , 58 ]. Here, the strains that lacked IS1111 came from a dog from Canada and human patients from Switzerland and Romania, respectively. IS1111 is particularly interesting because Seshadri et al. [ 11 ] found a genomic locus resembling a pathogenicity island connected to IS1111 elements, indicating that they might be involved in pathogenicity. As the insertion and excision of insertion elements can modulate gene expression [ 59 ], combined with our finding, that the IS content varies considerably even within GGs, indicates that the role of IS elements in C. burnetii virulence warrants further in-depth investigation. Since the effector protein repertoire might impact disease manifestation and host preference, the presence and absence of known effector proteins in the 140 C. burnetii strains was investigated. In agreement with previous studies [ 18 ], the strains of GGVI harbored several unique effectors. According to a study by Metters et al. [ 60 ], who used transposon library screening for determining essential genes in C. burnetii , there are 512 genes essential for survival of the reference strain Nine Mile I in axenic medium, among which there were also 12 effector-coding genes. These 12 essential effectors were also detected in the current study, although not always in every GG. Here, we identified five effector proteins with highly conserved sequences among all strains: Cem8 (CBU_1634a), CBU_0469, CBU_1314a (not in GGV), CBU_1594 (MceD), and CBU_2076. None of these were identified as essential by Hemsley et al. [ 60 ]. However, genes essential for survival in vivo , might not always be necessary for survival in axenic medium. The high level of conservation of the five effector proteins suggests important roles for these proteins in C. burnetii virulence. However, experimental evidence is lacking. Additionally, five effector proteins were investigated in detail, CBU_0077 (MceA), CBU_0513 (CinF), CBU_0781 (AnkG), CBU_0822 (CbFic2) and CBU_2007 (Vice). These have been proven to be translocated into the host cell in a T4BSS-dependent manner and host cell targets or pathways have been identified, except for CBU_0822. They interfere with apoptosis and host cell transcription, and/or are essential for intracellular replication and CCV biogenesis. All the effector proteins analyzed in depth displayed different degrees of sequence variation, with varying severity of effect. For example, the C-terminal region of MceA was conserved among all isolates analyzed ensuring translocation into the host cell [ 61 ]. However, several amino acid substitutions were found within the N-terminal region which may affect its function. Contrary, CinF, AnkG and CbFic2 displayed low to moderate numbers of sequence variants but several of these variants featured deletions at the C-terminal or N-terminal region or a frame shift, that could impede translocation or function. Interestingly, most sequence variations in MceA, CinF, AnkG and CbFic2 were found in isolates from GGIV and/or GGV, which are associated with chronic Q fever. The effector protein Vice showed a high level of sequence variation across most Genomic Groups. Strikingly, most missense variations were found in GGI, GGII and GGIII isolates and synonymous mutations in GGIV and GGV isolates. This might indicate an ongoing adaptation process in isolates of GGIV and GGV due to a chronic or persistent phase of infection. Therefore, a persistent, low activity phase in the host for long periods of time may create selection pressure promoting genetic adaptation and diversification e.g. of the here analyzed T4BSS effector proteins [ 62 , 63 ]. However, this assumption may be biased due to the limited availability of data from acute Q fever cases and cannot be proven or disproven on the basis of our data. A functional effector protein secretion system is a prerequisite for host infection, i.e. pathogenicity. Thus, we expected the T4BSS components to be highly conserved. To our surprise, these proteins also displayed a considerable level of variability, particularly in GGVI-a and GGIV-b. Not all 24 T4BSS proteins have known functions. Some might be dispensable, e.g. IcmH, which was disrupted in GGIII. For the T4BSS proteins, the localization of mutations must be investigated in detail, as some mutations might not affect functionally important regions and therefore should not interfere with the protein’s function. In vivo studies showed that the pathogenicity and virulence of C. burnetii correlate with genomic lineages [ 9 , 10 , 64 ], and that GGs harbor specific gene inventories and nucleotide polymorphisms, e.g. deletions [ 5 , 8 ]. The results presented here were all in agreement with the hypothesis that effector protein variants are connected to genomic lineages, rather than hosts. From our analyses neither the mere presence or absence of effector genes nor the occurrence of specific SNPs can be correlated with the host species. However, only the core genome region of the strains was considered in the SNP analysis. Thus, SNPs in genes, that were absent from NMI or other genomes included in the dataset, were not analyzed. Comprehensive investigations are complicated due to the lack of sufficient genomic data as well as metadata, i.e. a human infection could be attributed to contact with an animal source or if animals were held in mixed herds. The latter was previously demonstrated, where a cattle-associated genotype caused abortion in goats [ 65 ]. As our analyses results suggest that no single effector determines host specificity, what else could determine host preference or disease manifestation? One possibility is that transcriptional regulation of effector protein-coding genes and potential effector synergetic effects might play a role. Furthermore, there could be yet undiscovered effector proteins and virulence determinants. Besides, several other factors have been hypothesized to influence disease outcome, such as plasmid presence and LPS chemotype [ 4 , 12 , 66 ]. Disease manifestation additionally depends on the individual immune status of the host. Predisposed patients with an existing heart condition, immune suppression or pregnant women are more likely to develop chronic Q fever [ 44 , 45 ]. The release of cytokines, such as tumor necrosis factor-alpha (TNF-α) and interleukin (IL)-10, has been linked to Q fever endocarditis, whereas the release of TNF-α, interferon-gamma and IL-6 was observed in human acute Q fever [ 67 – 69 ]. However, infections in ruminants have a broad spectrum of clinical outcomes, with abortion rates being higher in goats than in sheep and rare in cattle [ 70 ]. Placentation type (synepitheliochorial) of cattle, sheep and goats are very similar, with trophoblasts migrating and fusing with maternal epithelial cells (syncytium) building a stable interface [ 71 , 72 ]. In these host species, C. burnetii exhibits a tropism for the reproductive organs and replicates within the trophoblast layer [ 73 , 74 ]. This cell type is essential for immune suppression and tolerance by secretion of steroids and hormones to avoid embryonic loss. It was shown that progesterone has an inhibitory effect on C. burnetii replication in human trophoblasts (JEG-3) [ 75 ]. Thus, hormone levels or the individual immune status of the host may influence pregnancy outcome. Conclusions This study highlights the genomic diversity of C. burnetii , between and within distinct GGs which could imply preferences for certain hosts. Effector protein profiles were found to correspond to genomic lineage rather than to host origin. In-depth analyses of selected effectors (e.g. AnkG, CbFic2, CinF, MceA and Vice) demonstrated that specific amino acid substitutions and truncations may influence protein localization and activity, potentially affecting virulence. However, no single effector gene or mutation could be definitively linked to host specificity. The data emphasize the need for broader, high-quality genomic and functional datasets, particularly from animal sources and human acute Q fever cases, to resolve the multifactorial determinants of host adaptation and pathogenesis in C. burnetii . Transcriptional regulation, effector interactions, and additional virulence factors, such as plasmid type and LPS chemotype, along with host immune responses, likely play critical roles in the adaptability of C. burnetii to host species, and contribute to disease outcome. Methods Data acquisition and quality control The Short Read Archive of NCBI was browsed (accession date: 12.07.2024) for C. burnetii data with the criteria „DNA“, „Illumina“ and „paired“. The resulting data was downloaded and the quality was checked using the WGSBAC pipeline v2 ( https://gitlab.com/FLI_Bioinfo/WGSBAC/-/tree/version2 ) by determining the coverage, assembling the genomes using Shovill, assessing assembly quality with QUAST, and checking reads and assemblies for contamination using Kraken2. The following thresholds were used as exclusion criteria: coverage 2.2 Mb, total number of contigs > 120, GC% >42.8%, GC% <42.3%, Kraken2 best match for reads not Coxiella or less than 90% Coxiella and Kraken2 best match for contigs 50%). Further, RefSeq was browsed for C. burnetii genomes and the assemblies downloaded for quality control. The assembly statistics were assessed using QUAST v5.2.0 [ 76 ]. Kraken2 v2.0.7_beta [ 77 ], CheckM v1.2.3 [ 78 ] and BUSCO v5.7.1 [ 79 ] were used for inter- and intraspecific contamination detection, respectively, as well as for a completeness check. Genomes that showed less than 85% complete BUSCOs of the legionellales_odb10 were excluded. Strain cultivation and DNA isolation To complement the publicly available dataset, C. burnetii strains from the National Reference Laboratory for Q Fever, Germany, of the Friedrich-Loeffler-Institut were chosen. These were cultivated under biosafety level 3 conditions in ACCM-2 as previously described [ 80 , 81 ]. Briefly, 500 ml of ACCM-2 were inoculated with 1e + 05 bacteria/ml and incubated for 7 days at 37°C with 5% CO 2 and 2.5% O 2 . Bacteria were harvested by centrifugation at 15,000 x g and 4°C for 20 min. Bacterial pellets were resuspended in 1 ml sucrose glycerol buffer (270 mM sucrose, 10% glycerol) and stored at -80°C. DNA was extracted from 20 µl bacterial suspension using the QIAamp DNA mini Kit (QIAGEN GmbH, Hilden, Germany) as recommended by the manufacturer. Bacteria were quantified by real time PCR (qPCR) using the isocitrate dehydrogenase encoding gene ( icd ) as target as previously described [ 82 ]. Whole genome sequencing and genome assembly Bacterial biomass was resuspended and inactivated in DNA/RNA Shield buffer (Zymo Research Europe GmbH, Freiburg, Germany) and sent for DNA extraction and subsequent whole genome sequencing to MicrobesNG (Birmingham, United Kingdom). Short-read libraries were prepared with the NexteraXT kit (Illumina Inc., San Diego, USA) and sequencing was conducted on a NovaSeq6000 machine. Adapters were subsequently trimmed from the reads using Trimmomatic v0.30 [ 83 ] with a sliding window quality cutoff of Q15. Additionally, long-read Nanopore sequencing was conducted using the Rapid Barcoding Kit (SQK-RBK114.96) (Oxford Nanopore Technologies Ltd, Oxford, United Kingdom) for library preparation. The libraries were loaded on an R10.4.1 type flowcell (FLO-MIN114) and sequenced on a GridION device. Basecalling was done directly on the GridION using the high-accuracy model [email protected] . The long- and short-read data was assembled using the BONT pipeline (as of 25.7.2024) ( https://gitlab.com/FLI_Bioinfo/BONT ). As assemblers, Flye v2.9.4-b1799 [ 84 ] with the --meta option to account for coverage differences between plasmid and chromosome and Unicycler v0.5.0 [ 85 ] were chosen to cover a long-read- and a short-read-first approach. In both approaches, the assemblies were polished using Illumina reads by polyPolish v0.6.0 [ 86 ]. The quality of the assemblies was checked as described above. The assembly approach using the flye assembler yielded complete genomes comprising the chromosome and a plasmid. There was only one exception, C. burnetii strain 18QC1770, for which the coverage of the long reads was not sufficient for the long-read first approach, which is why Unicycler was used for assembly, yielding a genome with 36 contigs. The raw sequencing data were deposited with the European Nucleotide Archive under the project number PRJEB88958. Multispacer sequence typing and single nucleotide polymorphism analysis For in silico multispacer sequence typing (MST), a database with the spacer sequences was built using ABRicate v1.0.1 ( https://github.com/tseemann/abricate ) from the alleles available at https://ifr48.timone.univ-mrs.fr/mst/Coxiella_burnetii/spacers.html (accessed on 02.08.2024). The assemblies were screened and the resulting profiles were browsed in CoxBase ( https://coxbase.q-gaps.de/webapp/ ) [ 87 ] for assignment of a sequence type (ST). Single nucleotide polymorphisms in the core genome region (cgSNPs) were determined using Snippy v4.6.0 ( https://github.com/tseemann/snippy ). Where available, raw sequencing data was used. For genomes, that were only available as assemblies, the –contig option was used. The C. burnetii strain Nine Mile I (GCF_000007765.2) served as reference genome. The output was an alignment of all core genome SNP positions, which was analysed by maximum likelihood analysis using RAxML v8.2.12 [ 88 ] (raxmlHPC-PTHREADS -m ASC_GTRCAT --asc-corr = lewis -V -N autoMRE -p 12345 -x 12345 -f a). The SNP differences between strains in this alignment were counted by the script snp-dists v0.8.2 ( https://github.com/tseemann/snp-dists ) that created a distance matrix, which was used for cluster analysis with the hclust function in R [ 89 ]. Maximum likelihood trees from different approaches were compared using the tanglegram function of Dendroscope v3.5.9 [ 90 ]. The effects of SNPs at the amino acid level were checked using snpeff as implemented in Snippy. Genome characteristics and pangenome analyses The assemblies were annotated using Bakta v1.9.3 with database v5.1(full) [ 91 ] and a pangenome analysis was conducted using Panaroo v1.4.2 [ 92 ]. The resulting filtered core genome alignment was used for maximum likelihood analysis and the construction of a phylogenetic tree by RAxML v.8.2.12 (parameters: -m GTRGAMMA -p 2352890 -# 100). The tree and the pangenome presence/absence matrix was visualized by Phandango [ 93 ]. Further, a Neighbour Joining analysis was done based on the presence and absence of accessory genes with GrapeTree v1.5.0 [ 94 ]. Insertion sequences (IS elements) were detected using ISEScan v1.7.2.3 [ 95 ]. Boxplots were created in Python using the Seaborn package v0.13.2 [ 96 ]. Effector protein and T4BSS screening and comparison The positions of the coding sequences of the NMI effector proteins identified in the literature search were extracted from the RefSeq annotation files (Additional file 7 - Figure S2 ). The start and stop positions of these coding sequences were compared to the new Bakta-based ORF positions of GCF_000007765.2. Most of the effector ORFs (n = 118) of this new Bakta-based annotation of NMI were identical to the original RefSeq annotation. However, for 12 effectors, the newly annotated ORFs started or ended at a different position and for one effector (CBU_0375, formerly annotated as pseudogene) no new counterpart was identified, as neither start nor end position was identical to the original annotation. Besides the protein sequences, the gene sequences of the NMI effector proteins were also extracted from the RefSeq annotation file. The sequences were searched for in four reference strains (GCF_000017105.1, GCF_000019865.1, GCF_000019885.1, GCF_000018745.1) using the BLASTn online service [ 97 ]. Homologous genes were listed (Additional file 2 - Table S2 ) and the corresponding protein sequences downloaded from NCBI RefSeq. For screening the annotated genomes in the dataset studied, a database of all effector proteins found in the reference genomes was created that was used for screening with Diamond v2.1.8 [ 98 ] (parameters: query coverage 80%; subject coverage 40%; sequence identity 80%). The translation products of the CDSs, that were identified as potentially effector protein-coding, were extracted from the Bakta annotation files using seqkit v2.9.0 [ 99 ] and separate alignments were created for each effector by MAFFT v7.520 [ 100 ] using the –auto option. The sequences of each alignment were assigned to clusters for determining identical sequence types using the SciPy Python package v1.14.1 (scipy.cluster.hierarchy function), as well as potential truncations [ 101 ]. The alignments were visually checked to confirm the truncation classification. The result of this analysis was visualized in Microreact [ 102 ] together with the phylogenetic tree generated in the pangenome analysis. Further, the data was subjected to Neighbour Joining analysis as described before. The components of T4BSS were compared in a similar manner. For this, the genes encoding the T4BSS components (n = 24) were taken from the virulence factor database [ 103 ]. Corresponding protein sequences were extracted from the RefSeq annotation and used for creating a database, which was used for screening the Bakta annotation of NMI. Then, the corresponding lines were extracted from the pangenome gene presence/absence table and all corresponding protein sequences from all strains investigated were compared as described above. Abbreviations PCR polymerase chain reaction GG Genomic Group MST multispacer sequence typing ST sequence type NMI strain Nine Mile I cgSNP core genome single nucleotide polymorphism SNP single nucleotide polymorphism ORF open reading frame DNA deoxyribonucleic acid T4BSS type IVB secretion system CCV Coxiella –containing vacuole Declarations Ethics approval and consent to participate Not applicable. Consent for publication Not applicable. Availability of data and materials The datasets generated during the current study are available in the European Nucleotide Archive under project number PRJEB88958. Competing interests The authors declare that they have no competing interests. Funding This project is funded by the Federal Ministry of Agriculture, Food and Regional Identity (BMLEH) under project number 2823ERA30D within the framework of ERA-NETs ICRAD as part of “Improved molecular surveillance and assessment of host adaptation and virulence of Coxiella burnetii in Europe” (Q-Net-Assess) (to KM and coordinated by TNM). This work was supported by the Deutsche Forschungsgemeinschaft (DFG, German Research Foundation): project A3 (to AL) within the Research Training Group “Immunomicrotope”, (GRK 2740/447268119) and project LU 1357/5-2. Authors' contributions HB – data acquisition, analysis, visualization, writing draft CB – supervision, discussion of results, revising manuscript SFu – Coxiella burnetii isolate preparation, data preparation, discussion of results, revising manuscript SFi – data acquisition, discussion of results, revising manuscript TNM – data acquisition, discussion of results, revising manuscript AL – conceived and supervised the study, data acquisition, discussion of results, writing and revising manuscript KM – supervision, data acquisition, discussion of results, revising manuscript All authors read, edited and approved the manuscript. Acknowledgements The authors are thankful to Petra Sippach for her excellent technical support. We thank Tina Blochwitz and Nadin Engelhardt for their excellent technical assistance, especially under BSL3 conditions. References Maurin M, Raoult D. Q fever. Clin Microbiol Rev. 1999;12(4):518-53. Gisbert P, Garcia-Ispierto I, Quintela LA, Guatteo R. 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Supplementary Files Additionalfile1TableS1Strainsused.xlsx Additional file 1 - Table S1 - Strains used.xlsx List of strains used in this study with corresponding metadata and results of genome data quality analysis as well as MST analysis. Additionalfile2TableS2KnownC.burnetiieffectorproteincodinggenes.xlsx Additional file 2 - Table S2 - Known C. burnetii effector protein-coding genes.xlsx List of accession numbers of known effector protein-encoding loci in C. burnetii strain Nine Mile I. Corresponding loci in other reference strains were identified by BLAST search. Additionalfile3FigureS1BoxplotsChromosomesize.png Additional file 3 - Figure S1 - Boxplots Chromosome size.png Box plot showing the distribution of genome assembly sizes of all investigated C. burnetii genome assemblies (n = 140). Shapes and filling of the data points indicate the host of the strain. Additionalfile4TableS3DetectedISelements.xlsx Additional file 4 - Table S3 - Detected IS elements.xlsx Typed and corresponding numbers of IS elements detected in 71 C. burnetii genome assemblies that were almost closed (max. three contigs). Additionalfile5TableS4NumberofcoregenesperGenomicGroup.xlsx Additional file 5 - Table S4 - Number of core genes per Genomic Group.xlsx Total number of genes shared by all strains of each Genomic Group and relative percentage compared to the mean of detected genes in all strains of a Genomic Group. Groups II and IV have been split into subgroups and are only mentioned for backward comparison. Additionalfile6TableS5UniqueSNPsincoregenomeandeffectorgenes.xlsx Additional file 6 - Table S5 - Unique SNPs in core genome and effector genes.xlsx Location of unique base variants per Genomic Group detected in either the complete core genome or the effector genes that were present in all strains. The positions are relative to the reference strain Nine Mile I (GCF_000007765.2). Additionalfile7FigureS2Screeningforeffectorproteins.png Additional file 7 - Figure S2 - Screening for effector proteins.png Workflow used in the study for detecting homologous genes and proteins of the effector-coding genes listed in Additional file 2 - Table S2. The figure was created with BioRender (https://biorender.com/). Additionalfile8TableS6Sequencevariationsofdetectedeffectorproteins.xlsx Additional file 8 - Table S6 - Sequence variations of detected effector proteins.xlsx Sequence variants of T4BSS effector proteins detected in the investigated C. burnetii strains. A number was assigned to each variation. Additionalfile9FigureS3NJtreeeffectorproteins.png Additional file 9 - Figure S3 - NJ tree effector proteins.png Neighbor joining tree based on the results of sequence variations of effector protein sequences detected in the investigated strains (see Additional file 8 - Table S6). Additionalfile10TableS7UniqueeffectorproteinvariantperGenomicGroup.xlsx Additional file 10 - Table S7 - Unique effector protein variant per Genomic Group.xlsx Unique sequence variants of effector proteins per Genomic Group. The designation of the proteins is according to the proteins’ similarity to reference strain Nine Mile I (GCF_000007765.2) effectors. Additionalfile11FigureS4T4BSSproteinvariants.png Additional file 11 - Figure S4 - T4BSS protein variants.png Presence and status of T4BSS proteins in 140 C. burnetii strains. Block colors indicate differences in amino acid sequence, i.e. blocks with identical colors for one protein show sequence identity. Host species, Genomic Group affiliation and truncation and disruption are indicated as given on the right side of the figure. Additionalfile12TableS8T4SSproteinsequencevariants.xlsx Additional file 12 - Table S8 - T4BSS protein sequence variants.xlsx Sequence variants of T4BSS proteins detected in the investigated C. burnetii strains. A number was assigned to each sequence variation. 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1","display":"","copyAsset":false,"role":"figure","size":331686,"visible":true,"origin":"","legend":"\u003cp\u003eMaximum likelihood tree based on cgSNP alignment of 140 \u003cem\u003eC. burnetii\u003c/em\u003e strains with their sequence accessions and their strain designations for public data. Leaf colors indicate the host of isolation (circles). GGs are separated by horizontal lines. \u003cem\u003eIn silico\u003c/em\u003e MST sequence type and the country of origin are shown at the branch tips. The scale bar indicates base substitutions per alignment site.\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-8306611/v1/eb5877c79e36724cef4a34a5.png"},{"id":98586945,"identity":"b8dfbe10-c0fa-4873-bd7f-e201055b12ff","added_by":"auto","created_at":"2025-12-19 09:48:27","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":45484,"visible":true,"origin":"","legend":"\u003cp\u003eBox plot showing the distribution of IS1111 elements detected in 71 \u003cem\u003eC. burnetii\u003c/em\u003egenome assemblies that were almost closed (max. three contigs). Shapes and filling of the data points indicate the host of the strain.\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-8306611/v1/17b9cf6546fef419539b5842.png"},{"id":98627387,"identity":"612d6e16-f4d6-4757-81a7-fb08cf034e72","added_by":"auto","created_at":"2025-12-19 17:10:20","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":181314,"visible":true,"origin":"","legend":"\u003cp\u003ePangenome analysis of 140 \u003cem\u003eC. burnetii\u003c/em\u003e strains. The maximum likelihood tree was generated based on the core genes. 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The strain names are colored according to their host.\u003c/p\u003e","description":"","filename":"5.png","url":"https://assets-eu.researchsquare.com/files/rs-8306611/v1/ebc71dad1d41df09b1893e15.png"},{"id":98627942,"identity":"4f49c06f-a65a-451f-a076-c704a0dbcfc7","added_by":"auto","created_at":"2025-12-19 17:10:47","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":1212822,"visible":true,"origin":"","legend":"\u003cp\u003ePresence and status of potential effector proteins in 140 \u003cem\u003eC. burnetii\u003c/em\u003e strains. Block colors indicate differences in amino acid sequence, i.e. blocks with identical colors for one protein show sequence identity. Host species, Genomic Group affiliation and truncation and disruption are indicated as given on the right side of the figure.\u003c/p\u003e","description":"","filename":"6.png","url":"https://assets-eu.researchsquare.com/files/rs-8306611/v1/e2d5c3dedfd4621de817fedd.png"},{"id":98586957,"identity":"30c43a4d-4304-4e91-b12c-5d4a1aacfa50","added_by":"auto","created_at":"2025-12-19 09:48:27","extension":"png","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":250228,"visible":true,"origin":"","legend":"\u003cp\u003eSchematic representation of protein sequence variants for five \u003cem\u003eC. burnetii\u003c/em\u003eeffectors. Red, blue and yellow bars indicate amino acid changes relative to the consensus sequence (black bars). Dashes indicate deletions.\u003c/p\u003e","description":"","filename":"7.png","url":"https://assets-eu.researchsquare.com/files/rs-8306611/v1/163ee27fc7f8469137ca91b7.png"},{"id":107928175,"identity":"7bcbf66a-68e2-4a05-9edb-0dee2db99d44","added_by":"auto","created_at":"2026-04-27 16:08:48","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2727685,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8306611/v1/425c160b-816d-4f80-8e38-f2a2953af291.pdf"},{"id":98629152,"identity":"141ad8d6-d551-4958-a308-b02560220f3b","added_by":"auto","created_at":"2025-12-19 17:13:18","extension":"xlsx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":37248,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cu\u003eAdditional file 1 - Table S1 - Strains used.xlsx\u003c/u\u003e\u003c/p\u003e\n\u003cp\u003eList of strains used in this study with corresponding metadata and results of genome data quality analysis as well as MST analysis.\u003c/p\u003e","description":"","filename":"Additionalfile1TableS1Strainsused.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-8306611/v1/f1bc3d97fdf1b2e9a419d9f3.xlsx"},{"id":98586993,"identity":"70d7e3a3-76c1-4769-8ace-79ab96f3370a","added_by":"auto","created_at":"2025-12-19 09:48:29","extension":"xlsx","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":20266,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cu\u003eAdditional file 2 - Table S2 - Known C. burnetii effector protein-coding genes.xlsx\u003c/u\u003e\u003c/p\u003e\n\u003cp\u003eList of accession numbers of known effector protein-encoding loci in \u003cem\u003eC. burnetii\u003c/em\u003e strain Nine Mile I. Corresponding loci in other reference strains were identified by BLAST search.\u003c/p\u003e","description":"","filename":"Additionalfile2TableS2KnownC.burnetiieffectorproteincodinggenes.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-8306611/v1/daeb44b2d62888244b52b87b.xlsx"},{"id":98628522,"identity":"97b0ffbc-06b0-4114-b038-9655f4966036","added_by":"auto","created_at":"2025-12-19 17:11:41","extension":"png","order_by":3,"title":"","display":"","copyAsset":false,"role":"supplement","size":475903,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cu\u003eAdditional file 3 - Figure S1 - Boxplots Chromosome size.png\u003c/u\u003e\u003c/p\u003e\n\u003cp\u003eBox plot showing the distribution of genome assembly sizes of all investigated \u003cem\u003eC. burnetii\u003c/em\u003e genome assemblies (n = 140). Shapes and filling of the data points indicate the host of the strain.\u003c/p\u003e","description":"","filename":"Additionalfile3FigureS1BoxplotsChromosomesize.png","url":"https://assets-eu.researchsquare.com/files/rs-8306611/v1/5d5fad46c7837a186c9520a9.png"},{"id":98586951,"identity":"b7dcaf4b-c109-4fb0-95ca-538d7e28d969","added_by":"auto","created_at":"2025-12-19 09:48:27","extension":"xlsx","order_by":4,"title":"","display":"","copyAsset":false,"role":"supplement","size":12058,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cu\u003eAdditional file 4 - Table S3 - Detected IS elements.xlsx\u003c/u\u003e\u003c/p\u003e\n\u003cp\u003eTyped and corresponding numbers of IS elements detected in 71 \u003cem\u003eC. burnetii\u003c/em\u003e genome assemblies that were almost closed (max. three contigs).\u003c/p\u003e","description":"","filename":"Additionalfile4TableS3DetectedISelements.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-8306611/v1/923b12544faaa7b7c59aa3c7.xlsx"},{"id":98586959,"identity":"f0d6f6f2-dcea-4e72-ac44-1a725632e8c4","added_by":"auto","created_at":"2025-12-19 09:48:27","extension":"xlsx","order_by":5,"title":"","display":"","copyAsset":false,"role":"supplement","size":11471,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cu\u003eAdditional file 5 - Table S4 - Number of core genes per Genomic Group.xlsx\u003c/u\u003e\u003c/p\u003e\n\u003cp\u003eTotal number of genes shared by all strains of each Genomic Group and relative percentage compared to the mean of detected genes in all strains of a Genomic Group. Groups II and IV have been split into subgroups and are only mentioned for backward comparison.\u003c/p\u003e","description":"","filename":"Additionalfile5TableS4NumberofcoregenesperGenomicGroup.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-8306611/v1/603ca10c2a7a3a9cd4f5e9af.xlsx"},{"id":98586968,"identity":"beb76023-adca-4456-8b4d-d4306492a7b3","added_by":"auto","created_at":"2025-12-19 09:48:27","extension":"xlsx","order_by":6,"title":"","display":"","copyAsset":false,"role":"supplement","size":152022,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cu\u003eAdditional file 6 - Table S5 - Unique SNPs in core genome and effector genes.xlsx\u003c/u\u003e\u003c/p\u003e\n\u003cp\u003eLocation of unique base variants per Genomic Group detected in either the complete core genome or the effector genes that were present in all strains. The positions are relative to the reference strain Nine Mile I (GCF_000007765.2).\u003c/p\u003e","description":"","filename":"Additionalfile6TableS5UniqueSNPsincoregenomeandeffectorgenes.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-8306611/v1/d333d5d1034c013d4450acc5.xlsx"},{"id":98586971,"identity":"0c0b9f2e-3e79-4a65-bd5b-0815ecdcea5b","added_by":"auto","created_at":"2025-12-19 09:48:27","extension":"png","order_by":7,"title":"","display":"","copyAsset":false,"role":"supplement","size":590196,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cu\u003eAdditional file 7 - Figure S2 - Screening for effector proteins.png\u003c/u\u003e\u003c/p\u003e\n\u003cp\u003eWorkflow used in the study for detecting homologous genes and proteins of the effector-coding genes listed in Additional file 2 - Table S2. The figure was created with BioRender (https://biorender.com/).\u003c/p\u003e","description":"","filename":"Additionalfile7FigureS2Screeningforeffectorproteins.png","url":"https://assets-eu.researchsquare.com/files/rs-8306611/v1/cbe36675cd9834cf376a4780.png"},{"id":98586976,"identity":"29bdaf8a-9fa1-4881-bcdd-1dabea097e62","added_by":"auto","created_at":"2025-12-19 09:48:27","extension":"xlsx","order_by":8,"title":"","display":"","copyAsset":false,"role":"supplement","size":102552,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cu\u003eAdditional file 8 - Table S6 - Sequence variations of detected effector proteins.xlsx\u003c/u\u003e\u003c/p\u003e\n\u003cp\u003eSequence variants of T4BSS effector proteins detected in the investigated \u003cem\u003eC. burnetii\u003c/em\u003e strains. A number was assigned to each variation.\u003c/p\u003e","description":"","filename":"Additionalfile8TableS6Sequencevariationsofdetectedeffectorproteins.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-8306611/v1/db77fd3631b71d01d640f1d5.xlsx"},{"id":98627910,"identity":"5a99a5d3-a0e7-41ec-a97a-0a6feef7a38f","added_by":"auto","created_at":"2025-12-19 17:10:47","extension":"png","order_by":9,"title":"","display":"","copyAsset":false,"role":"supplement","size":980286,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cu\u003eAdditional file 9 - Figure S3 - NJ tree effector proteins.png\u003c/u\u003e\u003c/p\u003e\n\u003cp\u003eNeighbor joining tree based on the results of sequence variations of effector protein sequences detected in the investigated strains (see Additional file 8 - Table S6).\u003c/p\u003e","description":"","filename":"Additionalfile9FigureS3NJtreeeffectorproteins.png","url":"https://assets-eu.researchsquare.com/files/rs-8306611/v1/c029f00b461dd8dc5dbbf1af.png"},{"id":98628036,"identity":"9361032f-e187-4c6c-9556-598a3115ae3b","added_by":"auto","created_at":"2025-12-19 17:10:54","extension":"xlsx","order_by":10,"title":"","display":"","copyAsset":false,"role":"supplement","size":11452,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cu\u003eAdditional file 10 - Table S7 - Unique effector protein variant per Genomic Group.xlsx\u003c/u\u003e\u003c/p\u003e\n\u003cp\u003eUnique sequence variants of effector proteins per Genomic Group. The designation of the proteins is according to the proteins’ similarity to reference strain Nine Mile I (GCF_000007765.2) effectors.\u003c/p\u003e","description":"","filename":"Additionalfile10TableS7UniqueeffectorproteinvariantperGenomicGroup.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-8306611/v1/feaecd66a5a384f9396d0f0c.xlsx"},{"id":98586965,"identity":"c09826a6-2d73-415c-b63f-42a62dcdba0a","added_by":"auto","created_at":"2025-12-19 09:48:27","extension":"png","order_by":11,"title":"","display":"","copyAsset":false,"role":"supplement","size":641613,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cu\u003eAdditional file 11 - Figure S4 - T4BSS protein variants.png\u003c/u\u003e\u003c/p\u003e\n\u003cp\u003ePresence and status of T4BSS proteins in 140 \u003cem\u003eC. burnetii\u003c/em\u003estrains. Block colors indicate differences in amino acid sequence, i.e. blocks with identical colors for one protein show sequence identity. Host species, Genomic Group affiliation and truncation and disruption are indicated as given on the right side of the figure.\u003c/p\u003e","description":"","filename":"Additionalfile11FigureS4T4BSSproteinvariants.png","url":"https://assets-eu.researchsquare.com/files/rs-8306611/v1/01bf61e2e20652d83542118c.png"},{"id":98628524,"identity":"e3de8334-8176-455e-a863-8c0b9a1f3abb","added_by":"auto","created_at":"2025-12-19 17:11:41","extension":"xlsx","order_by":12,"title":"","display":"","copyAsset":false,"role":"supplement","size":21594,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cu\u003eAdditional file 12 - Table S8 - T4BSS protein sequence variants.xlsx\u003c/u\u003e\u003c/p\u003e\n\u003cp\u003eSequence variants of T4BSS proteins detected in the investigated \u003cem\u003eC. burnetii\u003c/em\u003e strains. A number was assigned to each sequence variation.\u003c/p\u003e","description":"","filename":"Additionalfile12TableS8T4SSproteinsequencevariants.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-8306611/v1/adf9143cf2b6e28ad97d5a83.xlsx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Genome characteristics and type IV effector protein repertoire of Coxiella burnetii depend rather on Genomic Groups than on host species","fulltext":[{"header":"Background","content":"\u003cp\u003e \u003cem\u003eCoxiella burnetii\u003c/em\u003e is a Gram-negative, obligate intracellular zoonotic pathogen and the etiological agent of Q (query) fever in humans or coxiellosis in animals. Q fever is distributed worldwide, except in New Zealand, and has been categorized as a priority zoonotic disease by the European Food Safety Authority (EFSA) since 2023.\u003c/p\u003e \u003cp\u003e \u003cem\u003eC. burnetii\u003c/em\u003e displays a broad host spectrum and infects a variety of species, including humans, domestic and wild animals, ticks and birds [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. Disease manifestation differs between humans and animals. In humans, an infection remains often asymptomatic. About 40\u0026ndash;50% of infected individuals develop a mild flu-like illness. However, in some patients, the infection progresses to an atypical pneumonia or hepatitis. A small percentage (2\u0026ndash;5%) of infected individuals develop chronic Q fever, months or years after the initial infection. Chronic Q fever is mainly characterized by a potentially fatal endocarditis [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. Ruminants, such as cattle, sheep or goats, are considered the main reservoir and source of human \u003cem\u003eC. burnetii\u003c/em\u003e infections. Infections in sheep and goats are mostly asymptomatic. However, weak offspring and late term abortions do occur, with the abortion rate being higher in goats than in sheep. Fertility problems are common in cattle, but the symptoms are more varied [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. Infected animals shed the pathogen through their feces and milk, but primarily through birthing products [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. Humans are mainly infected by inhalation of contaminated dust, with less than ten bacteria being sufficient to cause disease [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eDifferences in the disease manifestations observed led to the assumption of an isolate-specific virulence and to the establishment of six Genomic Groups (GGI-VI) by restriction fragment length polymorphism (RFLP) analysis in the early 1990s [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. These genomic groups (GGs) correlated with disease manifestations: isolates of GGI to GGIII originated mainly from patients with acute Q fever whereas GGIV and GGV isolates were associated with chronic human Q fever cases.\u003c/p\u003e \u003cp\u003eThis original genomic grouping is still valid and was extended by modern typing methods, such as multispacer sequence typing (MST) and core genome single nucleotide polymorphism (SNP) typing [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. In MST, the allelic states of ten genomic loci are determined, while SNP analysis investigates base differences between strains in the entire DNA sequence, allowing a detailed differentiation. The initial panel of six GGs was extended by subdivision of GGII (a-d) and GGIV (a-b) [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e, \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e], based on specific SNPs, and addition of GGVII and GGVIII [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. Rodent infection models supported the hypothesis of a genomic profile-specific pathotype with GGI to GGIII isolates causing more severe clinical signs, whereas GGIV and GGV isolates caused no or only mild disease. [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. Additionally, the increasing availability of genome sequencing data of \u003cem\u003eC. burnetii\u003c/em\u003e isolates from various host species revealed that GGs are dominated further by isolates from certain hosts, e.g. GGIII is dominated by isolates from cattle, whereas goat isolates are more frequently found in GGII-b and human isolates in GGII-a, GGIV and GGV [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThe genomes of \u003cem\u003eC. burnetii\u003c/em\u003e isolates comprise a chromosome with ~\u0026thinsp;2\u0026nbsp;million base pairs and a mean GC content of 42.6% [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e, \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. In total, the genome contains an estimated 2,134 coding elements, the exact number of which varies between different \u003cem\u003eC. burnetii\u003c/em\u003e isolates [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. In a recent study, 75 isolates were analyzed and grouped into 22 MST genotypes and 13 clusters [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. Importantly, all isolates analyzed contained genes encoding a type IVB secretion system (T4BSS) [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. T4BSS are complex nanomachines that span the entire bacterial cell envelope and deliver DNA or effector proteins into the host cell environment [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. Effector proteins manipulate a variety of host cell pathways to ensure bacterial propagation. Thus, the T4BSS is integral for bacterial virulence [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]. \u003cem\u003eC. burnetii\u003c/em\u003e encodes 23 homologs of the 26 \u003cem\u003eLegionella pneumophila dot\u003c/em\u003e/\u003cem\u003eicm\u003c/em\u003e genes, that encode the T4BSS [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e, \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]. The T4BSSs of these two pathogens are not only structurally, but also functionally similar. \u003cem\u003eC. burnetii\u003c/em\u003e lacking a functional T4BSS is unable to replicate intracellularly [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e, \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e], demonstrating the importance of this secretion system for virulence. To date, ~\u0026thinsp;150 \u003cem\u003eC. burnetii\u003c/em\u003e T4BSS effector proteins have been identified, but only few have assigned functions [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e, \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]. These effectors promote biogenesis of the \u003cem\u003eC. burnetii\u003c/em\u003e-containing vacuole (CCV), interfere with vesicular trafficking, maintain host cell survival and manipulate host immune defenses [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e, \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]. The CCV is established after uptake of \u003cem\u003eC. burnetii\u003c/em\u003e into the host cell. The nascent CCV has a neutral pH and is decorated with early endosomal marker proteins. Maturing CCVs are phagolysosome-like compartments with an acidic pH of ~\u0026thinsp;4\u0026ndash;5 [\u003cspan additionalcitationids=\"CR25\" citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]. These acidic conditions induce the translocation of T4BSS effector proteins into the host cell [\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e], which in turn allows completion of CCV maturation into a large, replication-competent vacuole, and modulation of the host cell in favor of the pathogen.\u003c/p\u003e \u003cp\u003eSeveral studies have demonstrated considerable heterogeneity among the \u003cem\u003eC. burnetii\u003c/em\u003e effector protein profiles from different isolates [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e, \u003cspan additionalcitationids=\"CR29 CR30\" citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e]. In a study, in which the repertoire of effector proteins was compared in five isolates, only 44 out of the143 effector proteins analyzed were present and intact in all five strains [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eHere, we analyzed the genomes of 140 \u003cem\u003eC. burnetii\u003c/em\u003e isolates to determine whether affiliation to a GG and/or the T4BSS effector protein repertoire as well as their secretion system might allow the prediction of virulence potential or host species specificity of an isolate. The dataset comprised 102 publicly available genomes and 38 recently sequenced \u003cem\u003eC. burnetii\u003c/em\u003e isolates from Germany. All GGs were represented, except for GGII-d, GGVII and GGVIII, as well as common host species, such as cattle, goats, sheep, humans, ticks, and rodents. They originated from acute and chronic Q fever cases or from afterbirth material and milk from ruminants.\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003e \u003cb\u003eSelection of\u003c/b\u003e \u003cb\u003eC. burnetii\u003c/b\u003e \u003cb\u003egenome sequences\u003c/b\u003e\u003c/p\u003e \u003cp\u003eTo generate a comprehensive and high-quality dataset of \u003cem\u003eC. burnetii\u003c/em\u003e genomes, genomic data from the NCBI Short Read Archive (SRA) (n\u0026thinsp;=\u0026thinsp;110) and the RefSeq database (n\u0026thinsp;=\u0026thinsp;150) was retrieved. Further, 38 isolates from the \u003cem\u003eC. burnetii\u003c/em\u003e strain collection of the Friedrich-Loeffler-Institut were included that had been collected in Germany and the Netherlands, from small ruminants, cattle and, in one instance, from a patient between 1989 and 2021 (Additional file 1 - Table \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e). These served to complement the publicly available genomic data. These isolates were sequenced by Illumina and Nanopore technologies.\u003c/p\u003e \u003cp\u003eAll \u003cem\u003eC. burnetii\u003c/em\u003e datasets were assessed for their quality and completeness. Metagenomic data sets were removed because of low \u003cem\u003eC. burnetii\u003c/em\u003e-specific read counts. Duplicates of identical strains were also excluded. Overall, 127 public data sets (SRA n\u0026thinsp;=\u0026thinsp;31; RefSeq n\u0026thinsp;=\u0026thinsp;96) of high quality representing individual isolates were chosen for further analyses (Additional file 1 - Table \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e). Almost half of these (n\u0026thinsp;=\u0026thinsp;62) originated from humans.\u003c/p\u003e \u003cp\u003eAll assemblies or the corresponding read data were subjected to SNP typing together with the SRA data for excluding duplicate strains. The core genome SNP alignment contained 14,589 SNPs and 0 to 5,681 nucleotide differences were observed between individual strains. Duplicates of identical strains were removed (n\u0026thinsp;=\u0026thinsp;25), leaving 140 unique strains in the final dataset.\u003c/p\u003e \u003cp\u003eFor all downstream analyses, 112 unique public data and 38 new genome sequence data (n\u0026thinsp;=\u0026thinsp;140) sets were used. Many of these strains were of human origin (n\u0026thinsp;=\u0026thinsp;58), but strains originating from cattle (n\u0026thinsp;=\u0026thinsp;34), goats (n\u0026thinsp;=\u0026thinsp;22) and sheep (n\u0026thinsp;=\u0026thinsp;12) were also included. Furthermore, one strain each had been isolated from a dog, a mouse, the soil and a not-specified ruminant, respectively. Six strains came from ticks and three from kangaroo rats. The majority of the strains (n\u0026thinsp;=\u0026thinsp;99) had been isolated in Europe, particularly in France and Germany.\u003c/p\u003e \u003cp\u003e \u003cb\u003eSelection of\u003c/b\u003e \u003cb\u003eC. burnetii\u003c/b\u003e \u003cb\u003eeffectors\u003c/b\u003e\u003c/p\u003e \u003cp\u003eTo assess if the repertoire of effector genes varied among isolates, and if any observed differences correlated with potential adaptation to specific hosts and/or disease manifestation, a comprehensive literature survey was conducted to gather data on known \u003cem\u003eC. burnetii\u003c/em\u003e effector proteins. Overall, a total of 156 effector genes comprising 146 chromosomally encoded and ten plasmid encoded genes were identified in the reference strain Nine Mile I (Additional file 2 - Table \u003cspan refid=\"MOESM2\" class=\"InternalRef\"\u003eS2\u003c/span\u003e). Of these, two could not be found in the NMI reference strain (CBU_0088, CBU_1251) and 23 have been marked as discontinued in NCBI. Additionally, homologues of effector-coding genes of strain Nine Mile I were searched in the reference strains Dugway 5J108-111, CbuG_Q212, CbuK_Q154 and RSA331, resulting in the identification of 438 homologous effector sequences across all strains (Additional file 2 - Table \u003cspan refid=\"MOESM2\" class=\"InternalRef\"\u003eS2\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cb\u003ePlacement of the strains in the\u003c/b\u003e \u003cb\u003eC. burnetii\u003c/b\u003e \u003cb\u003ephylogeny\u003c/b\u003e\u003c/p\u003e \u003cp\u003eThe strain selection should represent a wide range of known \u003cem\u003eC. burnetii\u003c/em\u003e phylotypes for gaining a comprehensive insight in effector protein variation. Thus, the genomic diversity of the isolate or genome data sets was first assessed by \u003cem\u003ein silico\u003c/em\u003e MST analysis followed by linking to GGs according to Hemsley et al. [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. In total, 18 different sequence types (STs) were identified, mostly ST61 (n\u0026thinsp;=\u0026thinsp;38), ST16 (n\u0026thinsp;=\u0026thinsp;28) and ST18 (n\u0026thinsp;=\u0026thinsp;19). However, various novel alleles were found, so that an ST could not be assigned to 34 strains (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e, Additional file 1 - Table \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e). Based on the MST results, the strains were also assigned to a GG, showing that our dataset included strains from ten of the 13 known GGs.\u003c/p\u003e \u003cp\u003eThe subsequent core genome SNP (cgSNP) analysis confirmed the MST and GG results, as all strains with an identical MST ST and the same GG clustered together (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). The size of the core genome SNP alignment totaled 15,343 nucleotides; more than in the previous alignment for quality control, accounting for the higher quality of the final dataset.\u003c/p\u003e \u003cp\u003eCattle isolates dominated GGIII, whereas sheep- and goat-associated strains were primarily found in GGII-a and GGII-b. Remarkably, almost no animal isolates were found in GGIV and GGV, i.e. these groups were dominated by human isolates. However, most GGs (GGI, GGII-a/b, GGIII, GGIV-a/b) were composed of strains that had been isolated from three to four different host species, while two GGs (GGII-c, GGV) were detected in only two host species each (human and goat or human and dog, respectively) and GGVI exclusively comprised isolates from a single host species (kangaroo rat). The human isolate Cb3506 from the United Kingdom, which was located on the same branch as GGII and GGIII, could neither be assigned to an MST ST, nor placed within a GG.\u003c/p\u003e \u003cp\u003eCollectively, this dataset represented the majority of known \u003cem\u003eC. burnetii\u003c/em\u003e GGs. Furthermore, GGs previously determined to be dominated by specific host species were confirmed, although most GGs were associated with three different host species.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eGenome characterization and pangenome analysis\u003c/h2\u003e \u003cdiv id=\"Sec4\" class=\"Section3\"\u003e \u003ch2\u003eGenome characterization\u003c/h2\u003e \u003cp\u003eUsing the cgSNP typing approach, almost all strains were assigned to a GG. Thus, in the following analyses, the genomes of the groups could be characterized collectively and differences between these groups was assessed.\u003c/p\u003e \u003cp\u003eThe genome sizes ranged from 1,955,281 bp in one human isolate, that could not be assigned to a GG, to 2,212,937 bp in the GGVI reference strain Dugway 5J108-111. In GGV, the mean genome size was lowest (appr. 1,992 kbp) (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). The variation in genome size was highest in GGII-b, GGII-c and GGIV-a. No connection between genome size and the associated host was apparent (Additional file 3 - Figure \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e). The number of coding sequences detected ranged from 1871 in a strain from GGIV-b to 2248 in a GGII-b strain. The lowest mean number of coding sequences (CDSs) was found in GGIV-b, while the genomes of GGII-a showed not only the highest number of CDSs, but also of pseudogenes, i.e. non-protein-coding genes. GGI genomes had the least number of pseudogenes (n\u0026thinsp;=\u0026thinsp;46\u0026thinsp;\u0026plusmn;\u0026thinsp;2), even less than the Dugway strains from GGVI (n\u0026thinsp;=\u0026thinsp;58\u0026thinsp;\u0026plusmn;\u0026thinsp;8) (Additional file 1 - Table \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e). The number of genes without significant similarities to known genes (\u0026ldquo;hypotheticals\u0026rdquo;) was lowest in GGVI and highest in the GGII subgroups.\u003c/p\u003e \u003cp\u003eThese data showed that genome size and the number of CDS or pseudogenes differ between the GGs. No correlation between the number of pseudogenes and genome size was found, i.e. the number of pseudogenes was highest in GGs (GGII and IV-a) with the most variation in genome size, but isolates with small genomes (GGV) harbored a similarly large number of pseudogenes.\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\u003eGenome characteristics of the 140 \u003cem\u003eC. burnetii\u003c/em\u003e strains analyzed, according to their affiliation with a Genomic Group (GG). Given are the arithmetic mean and its standard deviation (SD).\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"10\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" 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=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" 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=\"char\" char=\".\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGG\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003e#*\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003eGenome size\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003eCDSs\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c8\" namest=\"c7\"\u003e \u003cp\u003ePseudogenes\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c10\" namest=\"c9\"\u003e \u003cp\u003eHypotheticals\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMean\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eSD\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eMean\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eSD\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eMean\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003eSD\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c9\"\u003e \u003cp\u003eMean\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c10\"\u003e \u003cp\u003eSD\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2,012,857\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e18,387\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e2018\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e46\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e305\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e11\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eII-a\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2,044,679\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e23,953\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e2100\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e51\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e111\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e353\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e23\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eII-b\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2,032,638\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e44,789\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e2073\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e92\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e102\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e22\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e359\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e46\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eII-c\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2,041,355\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e44,428\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e2084\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e115\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e103\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e27\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e361\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e57\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIII\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e36\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2,017,346\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e15,564\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e2044\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e26\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e80\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e330\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e10\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eI-III-like\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2,018,549\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e2024\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e59\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e303\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIV-a\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2,044,119\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e43,717\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e2013\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e80\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e108\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e318\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e34\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIV-b\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2,043,400\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e26,878\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1980\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e43\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e83\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e301\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e32\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eV\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1,992,946\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e15,407\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e2009\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e28\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e99\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e346\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e13\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2,198,908\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e9,920\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e2079\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e58\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e272\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1,955,281\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1989\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e81\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e325\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"10\"\u003e*# number of isolates or genome data sets included\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003c/div\u003e\n\u003ch3\u003eInsertion elements\u003c/h3\u003e\n\u003cp\u003eInsertion elements (IS), especially the IS1111 element, are associated with genome rearrangements and genome plasticity in \u003cem\u003eC. burnetii\u003c/em\u003e. Insertion events can introduce gene disruption, small indels or mutations and have been associated with a pathoadaptive evolutionary process [\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e]. Therefore, the number and type of IS elements were determined and compared among strains or genomic data sets of different GGs. Only genomes with a maximum of three contigs (n\u0026thinsp;=\u0026thinsp;71) were analyzed, as fragmented assemblies often show breaks at repetitive elements and, thus, the number of IS elements could be overestimated. In these 71 genomes, four to 114 IS elements belonging to eight families (IS110, IS1634, IS3, IS30, ISAS1, ISNCY, IS4, IS481) were detected (Additional file 4 - Table \u003cspan refid=\"MOESM3\" class=\"InternalRef\"\u003eS3\u003c/span\u003e). Dugway 5J108-111 was the only strain analyzed in which an IS4 sequence was detected. All strains had one copy of ISNCY. Elements of the IS481 family were only detected in the genomes of GGIV-a, GGV and GGVI, but not in GGIV-b, except for strain Namibia (MST30). IS1111, the only known representative of family IS110 in \u003cem\u003eC. burnetii\u003c/em\u003e, was identified up to 103 times in one genome (goat isolate 3262 of GGII-b) (Additional file 4 - Table \u003cspan refid=\"MOESM3\" class=\"InternalRef\"\u003eS3\u003c/span\u003e). However, to our surprise, ISEScan did not detect IS1111 elements in three genomes (strains DOG UTAD (GGV), CB121 (GGII-c), BRASOV (GGI)). Checking the annotation files revealed that the strains did harbor transposons, but apparently the degree of sequence identity at the nucleotide level was not high enough to be detected by the bioinformatic tool used. Additionally, only a single copy of IS1111 was found in ten strains. The distribution of the number of IS1111 elements within and between the GGs is displayed in Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e. Most GGs were consistent in their IS1111 copy number, particularly GGI, GGII-a and GGIII, where most isolates harbored around 20, 50 and 22 IS1111 copies, respectively. Two isolates from goat and sheep, respectively, of GGII-b stood out, as 102 and 103 IS1111 copies were detected. A correlation between the number of IS elements and the host species was not apparent, e.g. isolates from goats were often among the strains with the highest or the lowest number of IS1111 copies.\u003c/p\u003e \u003cp\u003eOverall, the differences observed in the number of IS1111 elements were mostly in accordance with the phylogenetic grouping of the strains, but no connection to host species was observed.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e\n\u003ch3\u003ePangenome analysis\u003c/h3\u003e\n\u003cp\u003eUsing the complete dataset of 140 genomes, a pangenome analysis was conducted (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). The aim was to determine if a specific set of accessory genes could differentiate strains from different GGs and/or strains from identical hosts or if differences primarily occurred in genes conserved across the \u003cem\u003eC. burnetii\u003c/em\u003e phylogeny. Of the 2237 total genes detected, 72.6% were found in at least 99% of the strains and constituted the core genome in this study. Less than 10% of the genes were only found in less than 20 genomes each (15%). In agreement with the previous cgSNP, a phylogeny based on only core genes identified in the pangenome analysis generated the same GG clusters (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). The corresponding visualization of gene presence, displayed as blue bars in Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e, indicated that each GG possessed a specific set of accessory genes. Particularly, the Dugway isolates of GGVI featured a large set of genes absent in other isolates. This Group also had the largest number of lineage-specific core genes (n\u0026thinsp;=\u0026thinsp;1979) (Additional file 5 - Table \u003cspan refid=\"MOESM4\" class=\"InternalRef\"\u003eS4\u003c/span\u003e), while GGIV-b harbored the lowest number of lineage-specific core genes (n\u0026thinsp;=\u0026thinsp;1664). However, the percentages of core genes relative to the overall number of genes detected differed between the groups, with the fewest conserved genes in GGII-b.\u003c/p\u003e \u003cp\u003eThe presence/absence information of the accessory genes (n\u0026thinsp;=\u0026thinsp;723), i.e. genes present in less than 140 genomes, was used for a Neighbor Joining analysis, to see if it correlated with GG or the source of isolation (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e). This analysis confirmed that the accessory genes found in the strains were determined by affiliation to a GG rather than to a host species, as clusters were formed by GG rather than by isolate origin. Remarkably, the accessory gene spectrum of the GGs was diverse and only a few coherent clusters were observed.\u003c/p\u003e \u003cp\u003eTaken together, the accessory genes among all strains analyzed clustered according to the GGs by cgSNP and pangenome analyses. Each GG possessed a specific set of accessory genes, but with a certain variability within the group.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eResult of the pangenome analysis of 140 \u003cem\u003eC. burnetii\u003c/em\u003e genomes\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\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=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFraction\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDefinition\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNo.\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e%\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCore genes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(99% \u0026lt;= strains\u0026thinsp;\u0026lt;\u0026thinsp;=\u0026thinsp;100%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1624\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e72.6\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSoft core genes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(95% \u0026lt;= strains\u0026thinsp;\u0026lt;\u0026thinsp;99%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e100\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e4.5\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eShell genes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(15% \u0026lt;= strains\u0026thinsp;\u0026lt;\u0026thinsp;95%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e303\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e13.5\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCloud genes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(0% \u0026lt;= strains\u0026thinsp;\u0026lt;\u0026thinsp;15%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e210\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e9.4\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eTotal genes\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e(0% \u0026lt;= strains\u0026thinsp;\u0026lt;\u0026thinsp;=\u0026thinsp;100%)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2237\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e\n\u003ch3\u003eT4BSS effector protein variations\u003c/h3\u003e\n\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eNucleotide-level analysis of effectors\u003c/h2\u003e \u003cp\u003eWe next compared the effector gene and predicted protein sequences of all T4BSS effector proteins identified to assess if the effector gene repertoires of different \u003cem\u003eC. burnetii\u003c/em\u003e isolates were associated with host species.\u003c/p\u003e \u003cp\u003eAcross the 100 effector genes of the core genome 1,213 variant positions were detected at the single nucleotide level. There was a high degree of similarity within the GGs, as all strains within the four largest groups (GGIII, GGII-a, GGII-b and GGI) differed not more than seven bases within the effector gene regions. However, the SNP differences in GGIV-a and GGIV-b were higher (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eDirect comparison of core genome- and effector gene-based SNP typing (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e) revealed that the overall clustering of the GGs and the branch placement of most strains remained coherent. Only the GGIII strains and some GGI strains clustered differently within the group. This indicates a higher degree of variation regarding the SNP positions within this group.\u003c/p\u003e \u003cp\u003eThe SNP positions in the core genome and in the effector-coding genes were screened for variants that were shared by all isolates of the same GG or from the same host, but differed in other strains (Additional file 6 - Table \u003cspan refid=\"MOESM5\" class=\"InternalRef\"\u003eS5\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eUnique SNPs were identified in the core genome region as well as in effector genes for all data sets with a GG assignment (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). GGV had a high number of unique nucleotide variants and GGIV-b exhibited a single SNP (CBU_1459), but only when strain Namibia (MST ST30) was included. The latter did not cluster perfectly with other strains of GGIV-b. Only a few unique mutations were detected in the effector gene regions for GGII-a to -c.\u003c/p\u003e \u003cp\u003eNo characteristic unique nucleotide variants were found when comparing the strains by their host species, not even when considering the complete core genome region. This finding was valid even when samples with potential accidental hosts or vectors were removed and only samples from cattle, human, rodent, sheep and goat were considered. Only for the rodent isolates, which all belonged to GGVI, unique SNPs were detected, which coincided with the previous results for GGVI.\u003c/p\u003e \u003cp\u003eTherefore, mutations in the core genome effector gene sequences coincided with the GG, allowing typing of isolates based on GG-unique SNPs. However, no association between the core genome effector gene sequences and host species was found.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eNumber of single nucleotide variants and predicted effector protein sequence variants unique to each Genomic Group at the core genome level (\u0026ldquo;genome\u0026rdquo;) and in gene positions corresponding to NMI effectors (\u0026ldquo;effector genes\u0026rdquo;). Numbers in brackets show results for GGIV-b when strain Namibia (MST ST30) is excluded from the group.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"12\"\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=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" 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=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" 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=\"char\" char=\".\" class=\"colspec\" colname=\"c11\" colnum=\"11\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c12\" colnum=\"12\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRegion\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTotal SNPs\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eI\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eII-a\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eII-b\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eII-c\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eIII\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003eI-III-like\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c9\"\u003e \u003cp\u003eIV-a\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c10\"\u003e \u003cp\u003eIV-b\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c11\"\u003e \u003cp\u003eV\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c12\"\u003e \u003cp\u003eVI\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGenome\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e15,343\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e388\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e110\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e67\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e39\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e360\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e228\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e651\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e1 (151)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e2614\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c12\"\u003e \u003cp\u003e1175\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEffector genes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1,213\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e38\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e49\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0 (13)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e210\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c12\"\u003e \u003cp\u003e90\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEffector proteins\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e3 (7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e60\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c12\"\u003e \u003cp\u003e67\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eProtein-level analysis of effector proteins\u003c/h3\u003e\n\u003cp\u003eAs differences in the nucleotide sequence do not necessarily translate to differences at the protein level, the effectors were also analyzed based on their predicted protein sequences to assess if sequence changes might impact protein functionality.\u003c/p\u003e \u003cp\u003eSeventeen of the 156 effector genes initially identified in the literature were annotated as pseudogenes in the RefSeq record, that do not have a translation product. Further, 25 effectors were not found due to discontinuation in the new RefSeq annotation version (v2) (Additional file 2 - Table \u003cspan refid=\"MOESM2\" class=\"InternalRef\"\u003eS2\u003c/span\u003e). A comparison of the genome position of the remaining 131 coding sequences to the Bakta annotation of NMI showed, that all but one (CBU_0375) of the effectors were present and represented ORFs, even if they were pseudogenes in the original annotation. Additionally, the gene sequence of all NMI effectors from the RefSeq annotation were searched for in four reference strains representing different genomic groups: Dugway 5J108-111 (GGVI), CbuG_Q212 (V), CbuK_Q154 (IV-a) and RSA331 (II-a) (Additional file 7 - Figure \u003cspan refid=\"MOESM2\" class=\"InternalRef\"\u003eS2\u003c/span\u003e). The protein sequences encoded by homologous genes were downloaded and a database was created. The gene products predicted from all strains in the dataset investigated were compared to this effector protein database and potential effector proteins were extracted.\u003c/p\u003e \u003cp\u003eBy this approach, 157 genomic loci and corresponding gene products were identified as potential effectors, which were investigated further. Each complete protein sequence variant was given a number to differentiate between them. This enumeration started anew for every effector. Incomplete sequences were labelled 'truncated'. Figure\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003e gives an overview over the variants observed for the effector proteins, sorted according to the pangenome phylogeny. If an effector protein was classified as truncated, it indicated that it was shorter than the longest observed sequence of this effector protein. Further, the coding region was classified as disrupted, if more than a single ORF was detected for this genomic locus in the pangenome analysis.\u003c/p\u003e \u003cp\u003eAll 130 NMI effector proteins were found to be present in all strains, regardless of their truncation or disruption status (Additional file 8 - Table \u003cspan refid=\"MOESM6\" class=\"InternalRef\"\u003eS6\u003c/span\u003e). When considering only fill-length proteins, 35 NMI effectors were found in all strains and, additionally, all strains harbored three effectors of the strains Dugway (CBUD_1462) and CbuG (CbuG_0789, CbuG_1711).\u003c/p\u003e \u003cp\u003eIn general, effector protein variants correlated with their respective GG, with some variation also occurring within a GG. To confirm this, a neighbor joining analysis was conducted with the effector sequence types (Additional file 9 - Figure \u003cspan refid=\"MOESM3\" class=\"InternalRef\"\u003eS3\u003c/span\u003e). As expected, the strains clustered according to their GG. No connection to the host species was observed. Further, no unique sequence type was found when looking for host-specific effector types in the dataset. However, all GGs harbored effectors with unique sequence types (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e, Additional file 10 - Table \u003cspan refid=\"MOESM7\" class=\"InternalRef\"\u003eS7\u003c/span\u003e). In GGII-b and II-c, only three and two effector variants were unique, respectively, which agreed with the frequent overlap of sequence types in GGII, and with the higher variance in these two sub-groups. Likewise, for GGIV-a and IV-b, a higher variance was observed, leading to low numbers of unique effector types. As observed for SNPs, excluding strain Namibia from GGIV-b increased the number of unique effector variants. In this case, strain Namibia showed 49 uniquely different effector sequences (Additional file 10 - Table \u003cspan refid=\"MOESM7\" class=\"InternalRef\"\u003eS7\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eTwo effectors were conserved among all strains studied: CBU_0469 and CBU_1634a (Cem8). Also, CBU_1314a was highly conserved among all GGs, except for GGV, in which it was not detected, and for MST ST30 of GGIV-b, strain Namibia, that harbored a single amino acid exchange. Likewise, CBU_1594 (MceD) and CBU_2076 were identical in all strains, except for the GGVI genomes, in which they contained the C-terminal substitutions I109V and A97S, respectively.\u003c/p\u003e \u003cp\u003eSeveral effectors were not detected in all genomes. CBU_0072 (AnkA) was absent from GGII-b, GGII-c and GGVI as well as from a few GGIV-a and GGIV-b strains. The latter two groups and GGVI lacked CBU_0881 (CoxCC5), whereas CoxU1 (CBU_0814) was only intact in GGIV-a/b. Likewise, the genomes of GGVI possessed effectors that were missing or not intact in other strains: CBU_1107, CBU_1754-7 and CBU_2028.\u003c/p\u003e \u003cp\u003eSeveral effector proteins (n\u0026thinsp;=\u0026thinsp;23), that were detected by screening with the effector protein database (Diamond database in Additional file 7 - Figure \u003cspan refid=\"MOESM2\" class=\"InternalRef\"\u003eS2\u003c/span\u003e), could not be assigned to a NMI homologue, but showed high similarity to effector proteins from the strains Dugway 5J108-111, CbuG_Q212, CbuK_Q154 and RSA331. Thus, in Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003e, these proteins were named according to their respective match in the protein database. Interestingly, CBUD_0392, whose sequence was taken from the Dugway reference strain, was detected as truncated in GGVI and others, because a homologous protein was found in GGI, which was 159 aa longer at the C terminus than the Dugway reference protein. CBUD_0454, also from the Dugway reference, was disrupted in GGI, but intact in all GGII and GGIII strains, with one exception in GGII-c.\u003c/p\u003e \u003cp\u003eSix proteins (CBUD_RS11275, CBUD_RS08635, CpeI, CpeJ, CpeK, CpeL) were identified by their annotation but were not found by screening of the database or comparison to the NMI loci. CpeI to CpeL were annotated as Dot/Icm T4BSS effectors while CBUD_RS11275 and CBUD_RS08635 were described as ankyrin repeat domain-containing proteins. While CpeIL and CBUD_RS08635 were only detected in GGVI, variants of the 627 aa long CBUD_RS11275 were found in GGII, GGIII and GGVI. The protein was truncated at the N terminus in GGII-b and GGII-c.\u003c/p\u003e \u003cp\u003eWhen analyzing the effector repertoire at the protein level, several effectors were conserved in all GGs, a few effectors were present only in GGIV or absent in IV and V. Overall, a GG-specific effector sequence type pattern was observed, but a connection to the host species was not detected. However, this analysis did not assess functionality of the detected effector variants, which may impact virulence.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e\n\u003ch3\u003eDetailed analysis of selected effector protein variants\u003c/h3\u003e\n\u003cp\u003eAnalyzing the genomic diversity of genes encoding putative effector proteins can help identify regions or amino acids essential for molecular activity [\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e]. Often, the C-terminal end (especially the last 20 amino acids) is essential for recognition and export by the T4BSS, while the N-terminal sequence may contribute to effector function or localization [\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e, \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e, \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e]. Thus, we analyzed the sequences of five potential T4BSS effector proteins, for which experimental data on their function and interaction with the host cell were available. These five effector proteins \u0026ndash; CBU_0077 (MceA), CBU_0513 (CinF), CBU_0781 (AnkG), CBU_0822 (CbFic2) and CBU_2007 (Vice) \u0026ndash; have been associated with interfering with apoptosis and host cell transcription, and/or are essential for intracellular replication and CCV biogenesis.\u003c/p\u003e \u003cp\u003eCBU_0077 (MceA)\u003c/p\u003e \u003cp\u003eMceA co-localizes with mitochondria but its function is unknown [\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e]. This effector was found in all genomes, with altogether six sequence variants (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003e). While most GGs shared the protein sequence of NMI (GGI), GGII-a had a unique variant due to a substitution (A30S). The variability in GGIV-a and GGIV-b was higher, as both GGs showed two and three sequence variants, respectively. In GGIV-b, two variants (type 5 and type 6) had a K143E substitution and three strains an additional G55S exchange (type 6). In GGIV-a, CBU_0077 exhibited one substitution (Q113E) in two strains (type 3) and the majority of the strains (n\u0026thinsp;=\u0026thinsp;11) also featured an S186N amino acid exchange (type 4). In all sequence variants, the C-terminus, which is likely required for secretion, was conserved. If the other single amino acid changes interfere with protein localization or function is unknown.\u003c/p\u003e \u003cp\u003eCBU_0513 (CinF)\u003c/p\u003e \u003cp\u003eEctopically expressed CinF has cytoplasmic localization and is essential for intracellular replication [\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e]. Its sequence was intact and identical in GGI, GGII, GGIII, and GGI-III-like (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003e). In GGIV, it was either intact or truncated at the C-terminal end by 19 aa residues. The later likely prevents T4BSS secretion, as the C-terminal ten amino acids contain the translocation signal [\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e]. In GGV, there were three substitutions (E87K, F106V, A318S) relative to the NMI reference, while the GGVI variant differed in only one position (W75R) from the majority of strains.\u003c/p\u003e \u003cp\u003eCBU_0781 (AnkG)\u003c/p\u003e \u003cp\u003eAnkG was one of the first \u003cem\u003eC. burnetii\u003c/em\u003e T4BSS effector proteins identified [\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e]. Its task is the inhibition of host cell apoptosis and several amino acids within the N-terminal region were shown to be essential for its function [\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e, \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e, \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e]. In all strains of GGI, GGIII, GGII-b and GGII-c, the sequence of AnkG was intact and identical to the NMI reference protein (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003e). However, eight sequence variants were found. In almost all strains of GGII-a, the ORF was disrupted, likely preventing function or secretion, except for strain CB180, in which the sequence was identical to the NMI reference protein. Full-length proteins of this effector showed one of two different amino acid mutations at the N-terminal end: amino acid position 11 encoded either isoleucine (variant of NMI reference) or leucine (GGV and GGVI), which could impact protein activity [\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eCBU_0822 (CbFic2)\u003c/p\u003e \u003cp\u003eWhether CbFic2 is a T4BSS effector protein has still to be determined, as experimental validation is lacking. Two domains were identified, an HTH domain (amino acids 304\u0026ndash;362) required for nuclear localization and DNA binding, and a predicted Fic motif (amino acids 205\u0026ndash;216), which are both essential for protein functionality [\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e]. CbFic2 was identical in most strains of GGI, GGII and GGIII, with two exceptions in GGII-a and GGII-c, respectively. These harboured each a substitution at the N-terminus: P12S or L20F. In GGVI, there were two substitutions relative to NMI: T217A and S263L. In GGIV, several different substitutions were observed. Further, in GGV, an insertion of serine at position 336 was observed. As this is located within the HTH domain, it might influence nuclear localization and/or DNA binding and thus protein activity. Importantly, none of the variants analysed was mutated in the amino acids 66 and/ or 205, which might alter the enzymatic activity of CbFic2 [\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eCBU_2007 (Vice)\u003c/p\u003e \u003cp\u003eVice was identified as a cytoplasmic T4BSS effector protein [\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e, \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e]. It was shown to be important for the establishment of a large CCV and for intracellular replication [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]. This effector exhibited the highest number of sequence variants, 22, among all detected effector proteins (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003e). Ten of these were found in only a single strain each and three were only found in two strains each. Altogether, 38 variable amino acid positions were detected. Additionally, there was a deletion of a single amino acid in both sequence types of GGV (types 20 and 21) and a deletion in strain Cb3506 extending over five amino acids (type 19). In strain CBI_2022 of GGIV-a, the protein was truncated by 98 aa at the C-terminal end (type 15), likely preventing secretion [\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e]. The strains of Genomic Groups GGII-c, GGIII and GGIV were the only ones that harboured a single Vice sequence variant each. It is notable that the Vice variants were always identical in strains of the same MST sequence type. To verify whether the non-synonymous mutations in the gene sequence of Vice were associated with additional silent mutations, the SNPs found in the gene locus of Vice were checked. Remarkably, the vast majority of mutations in the gene were missense variants. Synonymous base exchanges were only found in four strains: CbuG_Q212 (GGV), CB149 (GGIV-a), CB202 (GGIV-a) and Cb3506 (GG_NA).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eT4BSS protein sequence variation\u003c/h2\u003e \u003cp\u003eSimilar to the effector proteins, there was considerable variability in the protein sequences of the T4SS in the strains investigated (Supplementary Fig.\u0026nbsp;4, Supplementary Table\u0026nbsp;8). These protein vari-ants largely coincided with the GG. Again, the intra-GG variability was highest in GGIV and GGV. IcmH was disrupted in GGIII. IcmV was truncated at C-terminal end in all GGs except for GGII-a. Only IcmT and IcmR were conserved across all GGs. Also, DotN and IcmL2 were highly conserved, as protein sequence variations were only observed in three strains each (CbuG_Q212, DOG UTAD, Scurry_Q217 and 22QC1336, CbCVIC1, CB13, respectively).\u003c/p\u003e \u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003e \u003cem\u003eCoxiella burnetii\u003c/em\u003e exhibits a broad host range, and the outcome of infection can vary considerably [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e, \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e, \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e]. While the bacterium infects both small and large ruminants, human Q fever outbreaks are almost exclusively linked to shedding by sheep and goats. Human infections are rarely linked to cattle, even though the seroprevalence is high in cattle [\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e]. The differences in disease manifestation led to the hypothesis of isolate- and, later, GG-specific virulence or host adaptation [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e, \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. However, several studies contradict this hypothesis and indicate that host factors contribute to the disease outcome [\u003cspan additionalcitationids=\"CR45\" citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e]. The ability of \u003cem\u003eC. burnetii\u003c/em\u003e to invade and persist in the host cell is, besides the expression of a full-length smooth lipopolysaccharide, facilitated by the production and secretion of effector proteins which interfere with or modulate host cell processes [\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e, \u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e]. In the present study, we aimed at characterizing \u003cem\u003eC. burnetii\u003c/em\u003e strains based on genomic features and differences in their effector protein repertoires. By adding genome data from strains of animal origin to the publicly available, human-dominated dataset, we applied a One Health approach to coxiellosis and the potential role of \u003cem\u003eC. burnetii\u003c/em\u003e effector proteins in host specificity.\u003c/p\u003e \u003cp\u003eOur analyses showed that several genomic traits of \u003cem\u003eC. burnetii\u003c/em\u003e were consistent with the classification of the agent into GGs. These Groups were also congruent with sequence types determined by MST, as shown by the results presented here and by others [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e, \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. However, not all of the known GGs were present in the dataset investigated, i.e. GGI-b and GGII-d as well as GGVII and GGVIII were missing due to the lack of good-quality sequencing data.\u003c/p\u003e \u003cp\u003eGGs are assumed to be associated with a preferred host species and specific disease manifestations in humans, as GGI-III were predominantly found in patients with acute Q fever, whereas GGIV and GGV were mostly connected to chronic cases [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. In agreement with previous findings [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e], the results presented showed that GGIII was dominated by cattle isolates, while most goat and sheep isolates belonged to GGII-a and GGII-b. The GGIV-a, GGIV-b and GGV were human isolate-dominated groups and associated with chronic Q fever as described before. The difficulty in correlating disease phenotype with isolate sequence identity is nicely demonstrated by GGII-c which was originally associated with acute human Q fever cases, but was dominated in this study by isolates from human chronic Q fever cases. A recent study from Spain has shown that similar \u003cem\u003eC. burnetii\u003c/em\u003e genotypes can lead to acute as well as chronic disease outcomes in humans [\u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e], calling into question the link between GG and Q fever manifestation. Interestingly, MST ST8 (GGIV-a) had been linked to goats before, as it was detected in caprine milk in the USA [\u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e], but only two isolates from the dataset investigated here that originated from goats fell in this cluster. Considering the fact that coxiellosis is primarily an animal disease, the over-representation of good-quality sequencing data from human chronic Q fever specimens in the publicly available databases can be considered a sampling bias. Despite \u003cem\u003eC. burnetii\u003c/em\u003e being endemic almost worldwide, there is a lack of comprehensive sequence data, particularly from animals. By adding 36 samples of animal origin from Germany, we aimed to reduce this bias. The results displayed here show that most genomic groups are found in multiple host species, albeit with some level of dominance for certain species. This host range may become even more diverse with increasing availability of sequence data from isolates of animal and human origin, geographical source or different disease outcome. Multiple hosts in each GG suggest a certain degree of host flexibility and adaptability of \u003cem\u003eC. burnetii\u003c/em\u003e rather than strict host specificity.\u003c/p\u003e \u003cp\u003eThe core genome comprises all genes that are present in all genomes of a species, but its composition can vary considerably depending on the strains included in the analysis. Here, we found 2237 genes in 140 \u003cem\u003eC. burnetii\u003c/em\u003e genomes, 1624 of which constituted the core genome genes present in at least 99% of the strains, i.e. 139 isolates. In previous studies, the core genome of \u003cem\u003eC. burnetii\u003c/em\u003e was estimated to be smaller. Hemsley et al. [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e] found 1311 or 989 core genome genes among 67 isolates, depending on the bioinformatic pipeline applied, whereas Abou Abdallah et al. [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e] found a considerably higher number of total genes (n\u0026thinsp;=\u0026thinsp;4501) and a similar number of genes in the core genome (n\u0026thinsp;=\u0026thinsp;1211) among 75 \u003cem\u003eC. burnetii\u003c/em\u003e genomes. It has to be noted that several genomes that were included in the dataset of the latter study were excluded here as they failed quality control. This highlights the impact and importance of rigorous quality screening of genomic data prior to analysis.\u003c/p\u003e \u003cp\u003eIn contrast to other GGs, the GGVI (Dugway strains) were found to be attenuated or avirulent in most hosts [\u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e51\u003c/span\u003e]. In agreement with other reports, these strains had the largest genomes of all groups. However, in contrast to a previous study [\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e], they did not have the lowest pseudogene content. Based on the Bakta annotation, the number of pseudogenes in NMI was almost half of that reported in the initial publication of the complete NMI genome [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e], which identified 83 pseudogenes. The Dugway isolates, however, had fewer hypothetical genes, i.e. genes of unknown function, than NMI. Genome degradation usually leads to the formation of pseudogenes, but annotation pipelines might also classify them as hypotheticals, which could explain the discrepancy. The comparability of genome studies can be hampered by the usage of different annotation tools and concomitant differences in CDS annotation. As demonstrated for NMI, known sequences, such as effector-coding genes, might not be found in new annotations or open reading frames can differ in their extent.\u003c/p\u003e \u003cp\u003eThe larger genome size and higher core gene content observed here, together with the observed avirulence in most hosts, could explain why GGVI is considered closer to the last common ancestor in the \u003cem\u003eC. burnetii\u003c/em\u003e phylogeny than the other GGs [\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e, \u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e52\u003c/span\u003e]. These other lineages might have emerged as consequence of the pathogen\u0026rsquo;s introduction into other hosts, resulting in gene decay. Genome reduction is a common phenomenon during the evolution of obligate intracellular bacteria as the selective pressure on genes, that are not required, is reduced, ultimately leading to their loss. It is hypothesized that this process is still ongoing in \u003cem\u003eC. burnetii\u003c/em\u003e [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e] and it might even increase the pathogen\u0026rsquo;s virulence [\u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e53\u003c/span\u003e]. When there is a shift in a pathogen\u0026rsquo;s virulence towards a host, usually similar levels of susceptibility and virulence can be expected in phylogenetically closely related hosts, as shown for viruses [\u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e54\u003c/span\u003e]. This agrees with the apparent host preferences of GGs, i.e. ruminant- and human-dominated lineages. However, other hosts can also be susceptible, as the host\u0026rsquo;s health state influences the disease outcome, which could account for human infections by lineages that are dominantly found in ruminants.\u003c/p\u003e \u003cp\u003eAmong the dataset investigated, the difference of strain Namibia to other strains of the same GG, GGIV-b, was striking. Based on the findings presented, it can be assumed that MST ST30, to which strain Namibia belongs, could represent another sub-group of GGIV, as it has a characteristic set of SNPs and effector protein variants.\u003c/p\u003e \u003cp\u003eCompared to other intracellular bacteria, \u003cem\u003eC. burnetii\u003c/em\u003e has a high IS element content with considerable variation between individual strains within the same Genomic Group. Some elements, like ISNCY (\u0026ldquo;IS not classified yet\u0026rdquo;) and IS1111 are conserved across all lineages, even with respect to their insertion site [\u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e55\u003c/span\u003e], while others were only rarely detected, like IS4, which is known to be located on the QpDG plasmid of the Dugway strains [\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e], and IS481. Surprisingly, IS1111, which is commonly used as target for \u003cem\u003eC. burnetii\u003c/em\u003e diagnostic PCR [\u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e56\u003c/span\u003e], was not detected in three strains. This could be a false-negative result caused by sequence deviations from the reference sequence of the IS1111 elements in these strains, as deletions in IS1111 copies has been noted before [\u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e55\u003c/span\u003e]. However, some authors also reported \u003cem\u003eC. burnetii\u003c/em\u003e strains lacking IS1111, which originated from marine mammals, e.g. in Australia and Alaska [\u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e57\u003c/span\u003e, \u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e58\u003c/span\u003e]. Here, the strains that lacked IS1111 came from a dog from Canada and human patients from Switzerland and Romania, respectively. IS1111 is particularly interesting because Seshadri et al. [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e] found a genomic locus resembling a pathogenicity island connected to IS1111 elements, indicating that they might be involved in pathogenicity. As the insertion and excision of insertion elements can modulate gene expression [\u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e59\u003c/span\u003e], combined with our finding, that the IS content varies considerably even within GGs, indicates that the role of IS elements in \u003cem\u003eC. burnetii\u003c/em\u003e virulence warrants further in-depth investigation.\u003c/p\u003e \u003cp\u003eSince the effector protein repertoire might impact disease manifestation and host preference, the presence and absence of known effector proteins in the 140 \u003cem\u003eC. burnetii\u003c/em\u003e strains was investigated. In agreement with previous studies [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e], the strains of GGVI harbored several unique effectors. According to a study by Metters et al. [\u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e60\u003c/span\u003e], who used transposon library screening for determining essential genes in \u003cem\u003eC. burnetii\u003c/em\u003e, there are 512 genes essential for survival of the reference strain Nine Mile I in axenic medium, among which there were also 12 effector-coding genes. These 12 essential effectors were also detected in the current study, although not always in every GG. Here, we identified five effector proteins with highly conserved sequences among all strains: Cem8 (CBU_1634a), CBU_0469, CBU_1314a (not in GGV), CBU_1594 (MceD), and CBU_2076. None of these were identified as essential by Hemsley et al. [\u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e60\u003c/span\u003e]. However, genes essential for survival \u003cem\u003ein vivo\u003c/em\u003e, might not always be necessary for survival in axenic medium. The high level of conservation of the five effector proteins suggests important roles for these proteins in \u003cem\u003eC. burnetii\u003c/em\u003e virulence. However, experimental evidence is lacking.\u003c/p\u003e \u003cp\u003eAdditionally, five effector proteins were investigated in detail, CBU_0077 (MceA), CBU_0513 (CinF), CBU_0781 (AnkG), CBU_0822 (CbFic2) and CBU_2007 (Vice). These have been proven to be translocated into the host cell in a T4BSS-dependent manner and host cell targets or pathways have been identified, except for CBU_0822. They interfere with apoptosis and host cell transcription, and/or are essential for intracellular replication and CCV biogenesis. All the effector proteins analyzed in depth displayed different degrees of sequence variation, with varying severity of effect. For example, the C-terminal region of MceA was conserved among all isolates analyzed ensuring translocation into the host cell [\u003cspan citationid=\"CR61\" class=\"CitationRef\"\u003e61\u003c/span\u003e]. However, several amino acid substitutions were found within the N-terminal region which may affect its function. Contrary, CinF, AnkG and CbFic2 displayed low to moderate numbers of sequence variants but several of these variants featured deletions at the C-terminal or N-terminal region or a frame shift, that could impede translocation or function. Interestingly, most sequence variations in MceA, CinF, AnkG and CbFic2 were found in isolates from GGIV and/or GGV, which are associated with chronic Q fever. The effector protein Vice showed a high level of sequence variation across most Genomic Groups. Strikingly, most missense variations were found in GGI, GGII and GGIII isolates and synonymous mutations in GGIV and GGV isolates. This might indicate an ongoing adaptation process in isolates of GGIV and GGV due to a chronic or persistent phase of infection. Therefore, a persistent, low activity phase in the host for long periods of time may create selection pressure promoting genetic adaptation and diversification e.g. of the here analyzed T4BSS effector proteins [\u003cspan citationid=\"CR62\" class=\"CitationRef\"\u003e62\u003c/span\u003e, \u003cspan citationid=\"CR63\" class=\"CitationRef\"\u003e63\u003c/span\u003e]. However, this assumption may be biased due to the limited availability of data from acute Q fever cases and cannot be proven or disproven on the basis of our data.\u003c/p\u003e \u003cp\u003eA functional effector protein secretion system is a prerequisite for host infection, i.e. pathogenicity. Thus, we expected the T4BSS components to be highly conserved. To our surprise, these proteins also displayed a considerable level of variability, particularly in GGVI-a and GGIV-b. Not all 24 T4BSS proteins have known functions. Some might be dispensable, e.g. IcmH, which was disrupted in GGIII. For the T4BSS proteins, the localization of mutations must be investigated in detail, as some mutations might not affect functionally important regions and therefore should not interfere with the protein\u0026rsquo;s function.\u003c/p\u003e \u003cp\u003e \u003cem\u003eIn vivo\u003c/em\u003e studies showed that the pathogenicity and virulence of \u003cem\u003eC. burnetii\u003c/em\u003e correlate with genomic lineages [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e, \u003cspan citationid=\"CR64\" class=\"CitationRef\"\u003e64\u003c/span\u003e], and that GGs harbor specific gene inventories and nucleotide polymorphisms, e.g. deletions [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. The results presented here were all in agreement with the hypothesis that effector protein variants are connected to genomic lineages, rather than hosts. From our analyses neither the mere presence or absence of effector genes nor the occurrence of specific SNPs can be correlated with the host species. However, only the core genome region of the strains was considered in the SNP analysis. Thus, SNPs in genes, that were absent from NMI or other genomes included in the dataset, were not analyzed. Comprehensive investigations are complicated due to the lack of sufficient genomic data as well as metadata, i.e. a human infection could be attributed to contact with an animal source or if animals were held in mixed herds. The latter was previously demonstrated, where a cattle-associated genotype caused abortion in goats [\u003cspan citationid=\"CR65\" class=\"CitationRef\"\u003e65\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eAs our analyses results suggest that no single effector determines host specificity, what else could determine host preference or disease manifestation? One possibility is that transcriptional regulation of effector protein-coding genes and potential effector synergetic effects might play a role. Furthermore, there could be yet undiscovered effector proteins and virulence determinants. Besides, several other factors have been hypothesized to influence disease outcome, such as plasmid presence and LPS chemotype [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e, \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e, \u003cspan citationid=\"CR66\" class=\"CitationRef\"\u003e66\u003c/span\u003e]. Disease manifestation additionally depends on the individual immune status of the host. Predisposed patients with an existing heart condition, immune suppression or pregnant women are more likely to develop chronic Q fever [\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e, \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e]. The release of cytokines, such as tumor necrosis factor-alpha (TNF-α) and interleukin (IL)-10, has been linked to Q fever endocarditis, whereas the release of TNF-α, interferon-gamma and IL-6 was observed in human acute Q fever [\u003cspan additionalcitationids=\"CR68\" citationid=\"CR67\" class=\"CitationRef\"\u003e67\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR69\" class=\"CitationRef\"\u003e69\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eHowever, infections in ruminants have a broad spectrum of clinical outcomes, with abortion rates being higher in goats than in sheep and rare in cattle [\u003cspan citationid=\"CR70\" class=\"CitationRef\"\u003e70\u003c/span\u003e]. Placentation type (synepitheliochorial) of cattle, sheep and goats are very similar, with trophoblasts migrating and fusing with maternal epithelial cells (syncytium) building a stable interface [\u003cspan citationid=\"CR71\" class=\"CitationRef\"\u003e71\u003c/span\u003e, \u003cspan citationid=\"CR72\" class=\"CitationRef\"\u003e72\u003c/span\u003e]. In these host species, \u003cem\u003eC. burnetii\u003c/em\u003e exhibits a tropism for the reproductive organs and replicates within the trophoblast layer [\u003cspan citationid=\"CR73\" class=\"CitationRef\"\u003e73\u003c/span\u003e, \u003cspan citationid=\"CR74\" class=\"CitationRef\"\u003e74\u003c/span\u003e]. This cell type is essential for immune suppression and tolerance by secretion of steroids and hormones to avoid embryonic loss. It was shown that progesterone has an inhibitory effect on \u003cem\u003eC. burnetii\u003c/em\u003e replication in human trophoblasts (JEG-3) [\u003cspan citationid=\"CR75\" class=\"CitationRef\"\u003e75\u003c/span\u003e]. Thus, hormone levels or the individual immune status of the host may influence pregnancy outcome.\u003c/p\u003e"},{"header":"Conclusions","content":"\u003cp\u003eThis study highlights the genomic diversity of \u003cem\u003eC. burnetii\u003c/em\u003e, between and within distinct GGs which could imply preferences for certain hosts. Effector protein profiles were found to correspond to genomic lineage rather than to host origin. In-depth analyses of selected effectors (e.g. AnkG, CbFic2, CinF, MceA and Vice) demonstrated that specific amino acid substitutions and truncations may influence protein localization and activity, potentially affecting virulence. However, no single effector gene or mutation could be definitively linked to host specificity. The data emphasize the need for broader, high-quality genomic and functional datasets, particularly from animal sources and human acute Q fever cases, to resolve the multifactorial determinants of host adaptation and pathogenesis in \u003cem\u003eC. burnetii\u003c/em\u003e. Transcriptional regulation, effector interactions, and additional virulence factors, such as plasmid type and LPS chemotype, along with host immune responses, likely play critical roles in the adaptability of \u003cem\u003eC. burnetii\u003c/em\u003e to host species, and contribute to disease outcome.\u003c/p\u003e"},{"header":"Methods","content":"\u003ch2\u003eData acquisition and quality control\u003c/h2\u003e\u003cp\u003eThe Short Read Archive of NCBI was browsed (accession date: 12.07.2024) for \u003cem\u003eC. burnetii\u003c/em\u003e data with the criteria „DNA“, „Illumina“ and „paired“. The resulting data was downloaded and the quality was checked using the WGSBAC pipeline v2 (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://gitlab.com/FLI_Bioinfo/WGSBAC/-/tree/version2\u003c/span\u003e\u003cspan address=\"https://gitlab.com/FLI_Bioinfo/WGSBAC/-/tree/version2\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) by determining the coverage, assembling the genomes using Shovill, assessing assembly quality with QUAST, and checking reads and assemblies for contamination using Kraken2. The following thresholds were used as exclusion criteria: coverage \u0026lt; 30X, assembly size \u0026gt; 2.2 Mb, total number of contigs \u0026gt; 120, GC% \u0026gt;42.8%, GC% \u0026lt;42.3%, Kraken2 best match for reads not \u003cem\u003eCoxiella\u003c/em\u003e or less than 90% \u003cem\u003eCoxiella\u003c/em\u003e and Kraken2 best match for contigs \u0026lt; 90% \u003cem\u003eCoxiella\u003c/em\u003e. Contamination with human DNA was considered tolerable, if it was not dominant (\u0026gt; 50%).\u003c/p\u003e\u003cp\u003eFurther, RefSeq was browsed for \u003cem\u003eC. burnetii\u003c/em\u003e genomes and the assemblies downloaded for quality control. The assembly statistics were assessed using QUAST v5.2.0 [\u003cspan citationid=\"CR76\" class=\"CitationRef\"\u003e76\u003c/span\u003e]. Kraken2 v2.0.7_beta [\u003cspan citationid=\"CR77\" class=\"CitationRef\"\u003e77\u003c/span\u003e], CheckM v1.2.3 [\u003cspan citationid=\"CR78\" class=\"CitationRef\"\u003e78\u003c/span\u003e] and BUSCO v5.7.1 [\u003cspan citationid=\"CR79\" class=\"CitationRef\"\u003e79\u003c/span\u003e] were used for inter- and intraspecific contamination detection, respectively, as well as for a completeness check. Genomes that showed less than 85% complete BUSCOs of the legionellales_odb10 were excluded.\u003c/p\u003e\u003ch2\u003eStrain cultivation and DNA isolation\u003c/h2\u003e\u003cp\u003eTo complement the publicly available dataset, \u003cem\u003eC. burnetii\u003c/em\u003e strains from the National Reference Laboratory for Q Fever, Germany, of the Friedrich-Loeffler-Institut were chosen. These were cultivated under biosafety level 3 conditions in ACCM-2 as previously described [\u003cspan citationid=\"CR80\" class=\"CitationRef\"\u003e80\u003c/span\u003e, \u003cspan citationid=\"CR81\" class=\"CitationRef\"\u003e81\u003c/span\u003e]. Briefly, 500 ml of ACCM-2 were inoculated with 1e + 05 bacteria/ml and incubated for 7 days at 37°C with 5% CO\u003csub\u003e2\u003c/sub\u003e and 2.5% O\u003csub\u003e2\u003c/sub\u003e. Bacteria were harvested by centrifugation at 15,000 x g and 4°C for 20 min. Bacterial pellets were resuspended in 1 ml sucrose glycerol buffer (270 mM sucrose, 10% glycerol) and stored at -80°C. DNA was extracted from 20 µl bacterial suspension using the QIAamp DNA mini Kit (QIAGEN GmbH, Hilden, Germany) as recommended by the manufacturer. Bacteria were quantified by real time PCR (qPCR) using the isocitrate dehydrogenase encoding gene (\u003cem\u003eicd\u003c/em\u003e) as target as previously described [\u003cspan citationid=\"CR82\" class=\"CitationRef\"\u003e82\u003c/span\u003e].\u003c/p\u003e\u003ch2\u003eWhole genome sequencing and genome assembly\u003c/h2\u003e\u003cp\u003eBacterial biomass was resuspended and inactivated in DNA/RNA Shield buffer (Zymo Research Europe GmbH, Freiburg, Germany) and sent for DNA extraction and subsequent whole genome sequencing to MicrobesNG (Birmingham, United Kingdom). Short-read libraries were prepared with the NexteraXT kit (Illumina Inc., San Diego, USA) and sequencing was conducted on a NovaSeq6000 machine. Adapters were subsequently trimmed from the reads using Trimmomatic v0.30 [\u003cspan citationid=\"CR83\" class=\"CitationRef\"\u003e83\u003c/span\u003e] with a sliding window quality cutoff of Q15. Additionally, long-read Nanopore sequencing was conducted using the Rapid Barcoding Kit (SQK-RBK114.96) (Oxford Nanopore Technologies Ltd, Oxford, United Kingdom) for library preparation. The libraries were loaded on an R10.4.1 type flowcell (FLO-MIN114) and sequenced on a GridION device. Basecalling was done directly on the GridION using the high-accuracy model [email protected].\u003c/p\u003e\u003cp\u003eThe long- and short-read data was assembled using the BONT pipeline (as of 25.7.2024) (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://gitlab.com/FLI_Bioinfo/BONT\u003c/span\u003e\u003cspan address=\"https://gitlab.com/FLI_Bioinfo/BONT\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e). As assemblers, Flye v2.9.4-b1799 [\u003cspan citationid=\"CR84\" class=\"CitationRef\"\u003e84\u003c/span\u003e] with the --meta option to account for coverage differences between plasmid and chromosome and Unicycler v0.5.0 [\u003cspan citationid=\"CR85\" class=\"CitationRef\"\u003e85\u003c/span\u003e] were chosen to cover a long-read- and a short-read-first approach. In both approaches, the assemblies were polished using Illumina reads by polyPolish v0.6.0 [\u003cspan citationid=\"CR86\" class=\"CitationRef\"\u003e86\u003c/span\u003e]. The quality of the assemblies was checked as described above. The assembly approach using the flye assembler yielded complete genomes comprising the chromosome and a plasmid. There was only one exception, \u003cem\u003eC. burnetii\u003c/em\u003e strain 18QC1770, for which the coverage of the long reads was not sufficient for the long-read first approach, which is why Unicycler was used for assembly, yielding a genome with 36 contigs.\u003c/p\u003e\u003cp\u003eThe raw sequencing data were deposited with the European Nucleotide Archive under the project number PRJEB88958.\u003c/p\u003e\u003ch2\u003eMultispacer sequence typing and single nucleotide polymorphism analysis\u003c/h2\u003e\u003cp\u003eFor \u003cem\u003ein silico\u003c/em\u003e multispacer sequence typing (MST), a database with the spacer sequences was built using ABRicate v1.0.1 (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://github.com/tseemann/abricate\u003c/span\u003e\u003cspan address=\"https://github.com/tseemann/abricate\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) from the alleles available at \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://ifr48.timone.univ-mrs.fr/mst/Coxiella_burnetii/spacers.html\u003c/span\u003e\u003cspan address=\"https://ifr48.timone.univ-mrs.fr/mst/Coxiella_burnetii/spacers.html\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (accessed on 02.08.2024). The assemblies were screened and the resulting profiles were browsed in CoxBase (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://coxbase.q-gaps.de/webapp/\u003c/span\u003e\u003cspan address=\"https://coxbase.q-gaps.de/webapp/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) [\u003cspan citationid=\"CR87\" class=\"CitationRef\"\u003e87\u003c/span\u003e] for assignment of a sequence type (ST).\u003c/p\u003e\u003cp\u003eSingle nucleotide polymorphisms in the core genome region (cgSNPs) were determined using Snippy v4.6.0 (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://github.com/tseemann/snippy\u003c/span\u003e\u003cspan address=\"https://github.com/tseemann/snippy\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e). Where available, raw sequencing data was used. For genomes, that were only available as assemblies, the –contig option was used. The \u003cem\u003eC. burnetii\u003c/em\u003e strain Nine Mile I (GCF_000007765.2) served as reference genome. The output was an alignment of all core genome SNP positions, which was analysed by maximum likelihood analysis using RAxML v8.2.12 [\u003cspan citationid=\"CR88\" class=\"CitationRef\"\u003e88\u003c/span\u003e] (raxmlHPC-PTHREADS -m ASC_GTRCAT --asc-corr = lewis -V -N autoMRE -p 12345 -x 12345 -f a). The SNP differences between strains in this alignment were counted by the script snp-dists v0.8.2 (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://github.com/tseemann/snp-dists\u003c/span\u003e\u003cspan address=\"https://github.com/tseemann/snp-dists\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) that created a distance matrix, which was used for cluster analysis with the hclust function in R [\u003cspan citationid=\"CR89\" class=\"CitationRef\"\u003e89\u003c/span\u003e]. Maximum likelihood trees from different approaches were compared using the tanglegram function of Dendroscope v3.5.9 [\u003cspan citationid=\"CR90\" class=\"CitationRef\"\u003e90\u003c/span\u003e]. The effects of SNPs at the amino acid level were checked using snpeff as implemented in Snippy.\u003c/p\u003e\u003ch2\u003eGenome characteristics and pangenome analyses\u003c/h2\u003e\u003cp\u003eThe assemblies were annotated using Bakta v1.9.3 with database v5.1(full) [\u003cspan citationid=\"CR91\" class=\"CitationRef\"\u003e91\u003c/span\u003e] and a pangenome analysis was conducted using Panaroo v1.4.2 [\u003cspan citationid=\"CR92\" class=\"CitationRef\"\u003e92\u003c/span\u003e]. The resulting filtered core genome alignment was used for maximum likelihood analysis and the construction of a phylogenetic tree by RAxML v.8.2.12 (parameters: -m GTRGAMMA -p 2352890 -# 100). The tree and the pangenome presence/absence matrix was visualized by Phandango [\u003cspan citationid=\"CR93\" class=\"CitationRef\"\u003e93\u003c/span\u003e]. Further, a Neighbour Joining analysis was done based on the presence and absence of accessory genes with GrapeTree v1.5.0 [\u003cspan citationid=\"CR94\" class=\"CitationRef\"\u003e94\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eInsertion sequences (IS elements) were detected using ISEScan v1.7.2.3 [\u003cspan citationid=\"CR95\" class=\"CitationRef\"\u003e95\u003c/span\u003e]. Boxplots were created in Python using the Seaborn package v0.13.2 [\u003cspan citationid=\"CR96\" class=\"CitationRef\"\u003e96\u003c/span\u003e].\u003c/p\u003e\u003ch2\u003eEffector protein and T4BSS screening and comparison\u003c/h2\u003e\u003cp\u003eThe positions of the coding sequences of the NMI effector proteins identified in the literature search were extracted from the RefSeq annotation files (Additional file 7 - Figure \u003cspan refid=\"MOESM2\" class=\"InternalRef\"\u003eS2\u003c/span\u003e). The start and stop positions of these coding sequences were compared to the new Bakta-based ORF positions of GCF_000007765.2. Most of the effector ORFs (n = 118) of this new Bakta-based annotation of NMI were identical to the original RefSeq annotation. However, for 12 effectors, the newly annotated ORFs started or ended at a different position and for one effector (CBU_0375, formerly annotated as pseudogene) no new counterpart was identified, as neither start nor end position was identical to the original annotation.\u003c/p\u003e\u003cp\u003eBesides the protein sequences, the gene sequences of the NMI effector proteins were also extracted from the RefSeq annotation file. The sequences were searched for in four reference strains (GCF_000017105.1, GCF_000019865.1, GCF_000019885.1, GCF_000018745.1) using the BLASTn online service [\u003cspan citationid=\"CR97\" class=\"CitationRef\"\u003e97\u003c/span\u003e]. Homologous genes were listed (Additional file 2 - Table \u003cspan refid=\"MOESM2\" class=\"InternalRef\"\u003eS2\u003c/span\u003e) and the corresponding protein sequences downloaded from NCBI RefSeq.\u003c/p\u003e\u003cp\u003eFor screening the annotated genomes in the dataset studied, a database of all effector proteins found in the reference genomes was created that was used for screening with Diamond v2.1.8 [\u003cspan citationid=\"CR98\" class=\"CitationRef\"\u003e98\u003c/span\u003e] (parameters: query coverage 80%; subject coverage 40%; sequence identity 80%). The translation products of the CDSs, that were identified as potentially effector protein-coding, were extracted from the Bakta annotation files using seqkit v2.9.0 [\u003cspan citationid=\"CR99\" class=\"CitationRef\"\u003e99\u003c/span\u003e] and separate alignments were created for each effector by MAFFT v7.520 [\u003cspan citationid=\"CR100\" class=\"CitationRef\"\u003e100\u003c/span\u003e] using the –auto option. The sequences of each alignment were assigned to clusters for determining identical sequence types using the SciPy Python package v1.14.1 (scipy.cluster.hierarchy function), as well as potential truncations [\u003cspan citationid=\"CR101\" class=\"CitationRef\"\u003e101\u003c/span\u003e]. The alignments were visually checked to confirm the truncation classification.\u003c/p\u003e\u003cp\u003eThe result of this analysis was visualized in Microreact [\u003cspan citationid=\"CR102\" class=\"CitationRef\"\u003e102\u003c/span\u003e] together with the phylogenetic tree generated in the pangenome analysis. Further, the data was subjected to Neighbour Joining analysis as described before.\u003c/p\u003e\u003cp\u003eThe components of T4BSS were compared in a similar manner. For this, the genes encoding the T4BSS components (n = 24) were taken from the virulence factor database [\u003cspan citationid=\"CR103\" class=\"CitationRef\"\u003e103\u003c/span\u003e]. Corresponding protein sequences were extracted from the RefSeq annotation and used for creating a database, which was used for screening the Bakta annotation of NMI. Then, the corresponding lines were extracted from the pangenome gene presence/absence table and all corresponding protein sequences from all strains investigated were compared as described above.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cdiv class=\"DefinitionList\"\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003ePCR\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003epolymerase chain reaction\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eGG\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eGenomic Group\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eMST\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003emultispacer sequence typing\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eST\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003esequence type\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eNMI\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003estrain Nine Mile I\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003ecgSNP\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003ecore genome single nucleotide polymorphism\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eSNP\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003esingle nucleotide polymorphism\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eORF\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eopen reading frame\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eDNA\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003edeoxyribonucleic acid\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eT4BSS\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003etype IVB secretion system\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eCCV\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003e \u003cem\u003eCoxiella\u003c/em\u003e\u0026ndash;containing vacuole\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003c/div\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and materials\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe datasets generated during the current study are available in the European Nucleotide Archive under project number PRJEB88958.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that they have no competing interests.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis project is funded by the Federal Ministry of Agriculture, Food and Regional Identity (BMLEH) under project number 2823ERA30D within the framework of ERA-NETs ICRAD as part of “Improved molecular surveillance and assessment of host adaptation and virulence of \u003cem\u003eCoxiella burnetii\u0026nbsp;\u003c/em\u003ein Europe” (Q-Net-Assess) (to KM and coordinated by TNM). This work was supported by the Deutsche Forschungsgemeinschaft (DFG, German Research Foundation): project A3 (to AL) within the Research Training Group “Immunomicrotope”, (GRK 2740/447268119) and project LU 1357/5-2.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors' contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eHB – data acquisition, analysis, visualization, writing draft\u003c/p\u003e\n\u003cp\u003eCB – supervision, discussion of results, revising manuscript\u003c/p\u003e\n\u003cp\u003eSFu – \u003cem\u003eCoxiella burnetii\u003c/em\u003e isolate preparation, data preparation, discussion of results, revising manuscript\u003c/p\u003e\n\u003cp\u003eSFi – data acquisition, discussion of results, revising manuscript\u003c/p\u003e\n\u003cp\u003eTNM – data acquisition, discussion of results, revising manuscript\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eAL – conceived and supervised the study, data acquisition, discussion of results, writing and revising manuscript\u003c/p\u003e\n\u003cp\u003eKM – supervision, data acquisition, discussion of results, revising manuscript\u003c/p\u003e\n\u003cp\u003eAll authors read, edited and approved the manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors are thankful to Petra Sippach for her excellent technical support. We thank Tina Blochwitz and Nadin Engelhardt for their excellent technical assistance, especially under BSL3 conditions.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eMaurin M, Raoult D. Q fever. Clin Microbiol Rev. 1999;12(4):518-53.\u003c/li\u003e\n\u003cli\u003eGisbert P, Garcia-Ispierto I, Quintela LA, Guatteo R. Coxiella burnetii and Reproductive Disorders in Cattle: A Systematic Review. Animals (Basel). 2024;14(9).\u003c/li\u003e\n\u003cli\u003eBaca OG, Paretsky D. Q fever and Coxiella burnetii: a model for host-parasite interactions. Microbiol Rev. 1983;47(2):127-49.\u003c/li\u003e\n\u003cli\u003eHendrix LR, Samuel JE, Mallavia LP. Differentiation of Coxiella burnetii isolates by analysis of restriction-endonuclease-digested DNA separated by SDS-PAGE. J Gen Microbiol. 1991;137(2):269-76.\u003c/li\u003e\n\u003cli\u003eHemsley CM, O\u0026apos;Neill PA, Essex-Lopresti A, Norville IH, Atkins TP, Titball RW. 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Int J Med Microbiol. 2023;313(6):151590.\u003c/li\u003e\n\u003cli\u003eSchulze-Luehrmann J, Eckart RA, Olke M, Saftig P, Liebler-Tenorio E, L\u0026uuml;hrmann A. LAMP proteins account for the maturation delay during the establishment of the Coxiella burnetii-containing vacuole. Cell Microbiol. 2016;18(2):181-94.\u003c/li\u003e\n\u003cli\u003eSamanta D, Clemente TM, Schuler BE, Gilk SD. Coxiella burnetii Type 4B Secretion System-dependent manipulation of endolysosomal maturation is required for bacterial growth. PLoS Pathog. 2019;15(12):e1007855.\u003c/li\u003e\n\u003cli\u003eHall BA, Senior KE, Ocampo NT, Samanta D. Coxiella burnetii-containing vacuoles interact with host recycling endosomal proteins Rab11a and Rab35 for vacuolar expansion and bacterial growth. Front Cell Infect Microbiol. 2024;14:1394019.\u003c/li\u003e\n\u003cli\u003eNewton HJ, McDonough JA, Roy CR. 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Nucleic Acids Res. 2022;50(D1):D912-d7.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"bmc-microbiology","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"mcro","sideBox":"Learn more about [BMC Microbiology](http://bmcmicrobiol.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/mcro","title":"BMC Microbiology","twitterHandle":"#bmcmicrobiology","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Coxiella burnetii, genotyping, T4BSS effector proteins, pangenome, SNP, host preference, Genomic Groups, type IV secretion system","lastPublishedDoi":"10.21203/rs.3.rs-8306611/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8306611/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e \u003cp\u003eQ fever is a zoonotic disease with virtually worldwide dissemination. Its bacterial agent, \u003cem\u003eCoxiella burnetii\u003c/em\u003e, is primarily found in cattle and small ruminants. Disease manifestation is highly variable, i.e. asymptomatic, acute or chronic in humans, and subclinical or present as reproductive disorders in ruminants. Different genomic lineages of \u003cem\u003eC. burnetii\u003c/em\u003e have been recognized and are considered to show host preferences and influence the disease outcome. The virulence of \u003cem\u003eC. burnetii\u003c/em\u003e is essentially determined by effector proteins that modulate host cell processes, allowing the bacterium to persist and proliferate in the host. Thus, these effectors have been suggested to play a role in the presumed host specificity and disease manifestation.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eIn the present study, a comprehensive set of 140 \u003cem\u003eC. burnetii\u003c/em\u003e genomes from ten Genomic Groups (GGs) and various hosts was studied bioinformatically to determine if there was an association between their genomic characteristics, including the effector protein repertoire, and their isolation source. The differences in genome size, IS1111 count, number of coding sequences, accessory genome and others observed could be attributed to lineage-specific traits. Likewise, the GGs showed conserved sets of effector proteins, although intra-lineage variances were high in GGIV. Several effector proteins, e.g. Cem8 (CBU_1634a) and CBU_0469, were highly conserved, while CBU_2007 showed a remarkably high number of sequence variants.\u003c/p\u003e\u003ch2\u003eConclusions\u003c/h2\u003e \u003cp\u003e \u003cem\u003eC. burnetii\u003c/em\u003e exhibits genomic diversity that aligns with phylotypes rather than host species, suggesting that genomic traits as well as host factors influence disease outcome rather than a host species specific adaptation.\u003c/p\u003e","manuscriptTitle":"Genome characteristics and type IV effector protein repertoire of Coxiella burnetii depend rather on Genomic Groups than on host species","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-12-19 09:48:22","doi":"10.21203/rs.3.rs-8306611/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2026-01-05T17:30:54+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-01-04T20:38:42+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"221547676121966796348439802940952886162","date":"2025-12-14T17:58:52+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-12-12T20:41:38+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"58017495700795013483931438570456855361","date":"2025-12-12T15:25:47+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-12-12T15:15:32+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2025-12-10T03:17:00+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-12-09T11:49:24+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-12-09T11:49:18+00:00","index":"","fulltext":""},{"type":"submitted","content":"BMC Microbiology","date":"2025-12-08T10:20:10+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"bmc-microbiology","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"mcro","sideBox":"Learn more about [BMC Microbiology](http://bmcmicrobiol.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/mcro","title":"BMC Microbiology","twitterHandle":"#bmcmicrobiology","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"1d3de613-af35-4e7b-9999-e2e3176403fa","owner":[],"postedDate":"December 19th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2026-04-27T16:06:35+00:00","versionOfRecord":{"articleIdentity":"rs-8306611","link":"https://doi.org/10.1186/s12866-026-04897-w","journal":{"identity":"bmc-microbiology","isVorOnly":false,"title":"BMC Microbiology"},"publishedOn":"2026-04-22 15:59:29","publishedOnDateReadable":"April 22nd, 2026"},"versionCreatedAt":"2025-12-19 09:48:22","video":"","vorDoi":"10.1186/s12866-026-04897-w","vorDoiUrl":"https://doi.org/10.1186/s12866-026-04897-w","workflowStages":[]},"version":"v1","identity":"rs-8306611","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8306611","identity":"rs-8306611","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}