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Evolutionarily stable gene clusters shed light on the common grounds of pathogenicity in the Acinetobacter calcoaceticus-baumannii complex [1]

['Bardya Djahanschiri', 'Applied Bioinformatics Group', 'Inst. Of Cell Biology', 'Neuroscience', 'Goethe University Frankfurt', 'Frankfurt Am Main', 'Gisela Di Venanzio', 'Department Of Molecular Microbiology', 'Washington University School Of Medicine', 'St Louis']

Date: 2022-08 

Nosocomial pathogens of the Acinetobacter calcoaceticus-baumannii (ACB) complex are a cautionary example for the world-wide spread of multi- and pan-drug resistant bacteria. Aiding the urgent demand for novel therapeutic targets, comparative genomics studies between pathogens and their apathogenic relatives shed light on the genetic basis of human-pathogen interaction. Yet, existing studies are limited in taxonomic scope, sensing of the phylogenetic signal, and resolution by largely analyzing genes independent of their organization in functional gene clusters. Here, we explored more than 3,000 Acinetobacter genomes in a phylogenomic framework integrating orthology-based phylogenetic profiling and microsynteny conservation analyses. We delineate gene clusters in the type strain A. baumannii ATCC 19606 whose evolutionary conservation indicates a functional integration of the subsumed genes. These evolutionarily stable gene clusters (ESGCs) reveal metabolic pathways, transcriptional regulators residing next to their targets but also tie together sub-clusters with distinct functions to form higher-order functional modules. We shortlisted 150 ESGCs that either co-emerged with the pathogenic ACB clade or are preferentially found therein. They provide a high-resolution picture of genetic and functional changes that coincide with the manifestation of the pathogenic phenotype in the ACB clade. Key innovations are the remodeling of the regulatory-effector cascade connecting LuxR/LuxI quorum sensing via an intermediate messenger to biofilm formation, the extension of micronutrient scavenging systems, and the increase of metabolic flexibility by exploiting carbon sources that are provided by the human host. We could show experimentally that only members of the ACB clade use kynurenine as a sole carbon and energy source, a substance produced by humans to fine-tune the antimicrobial innate immune response. In summary, this study provides a rich and unbiased set of novel testable hypotheses on how pathogenic Acinetobacter interact with and ultimately infect their human host. It is a comprehensive resource for future research into novel therapeutic strategies.

The spread of multi- and pan-drug resistant bacterial pathogens is a worldwide threat to human health. Understanding the genetics of host colonization and infection can substantially help in devising novel ways of treatment. Acinetobacter baumannii, a nosocomial pathogen ranked top by the World Health Organization in the list of bacteria for which novel therapeutic approaches are needed, is a prime example. Here, we have carved out the genetic make-up that distinguishes A. baumannii and its pathogenic next relatives from other and mostly apathogenic Acinetobacter species. We found a rich spectrum of pathways and regulatory modules that reveal how the pathogens have modified biofilm formation, iron scavenging, and their carbohydrate metabolism to adapt to their human host. Among these, the capability to metabolize kynurenine is particularly intriguing. Humans produce this substance to contain bacterial invaders and to fine-tune the innate immune response. But A. baumannii and closely related pathogens found a way to feed on kynurenine. This suggests that the pathogens might be able to dysregulate the human immune response. In summary, our study substantially deepens the understanding of how a highly critical pathogen interacts with its host, which substantially eases the identification of novel targets for innovative therapeutic strategies.

Funding: This study was supported by a grant by the German Research Foundation (DFG) in the scope of the Research Group FOR2251 “Adaptation and persistence of A. baumannii.” Grant ID EB-285-2/2 to IE, AV 9/7-2 to BA, GO 2491/1-2 to SG, and WI 3272/3-2 to GW. MFF was supported by grants from the National Institute of Allergy and Infectious Diseases (grant R01AI144120). Cloud computational resources through the de.NBI cloud are granted by the German Bundesministerium für Bildung und Forschung grant FKZ 031A533B to AG. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.

Data Availability: The authors confirm that all data underlying the findings are fully available without restriction. All numerical data that underlies figures and/or summary statistics is within the manuscript and its Supporting Information files. The large-scale dataset representing the sample of Proteobacteria obtained from NCBI RefSeq as well as the underlying raw Gower distance matrix are provided via figshare: https://doi.org/10.6084/m9.figshare.16910974.v1 . Genomic data analyzed in this study are publicly available in the NCBI Reference Sequence Database (RefSeq) versions 87 and 204. For each selected genome, the assembly accessions are listed in S1 and S9 Tables, respectively. We obtained genome annotation files (*.gff and *_feature_table.txt), protein sequences and coding sequences (*.faa and *cds_from_genomic.fna), and genome sequence (*_genomic.fna) files from the database [ ftp://ftp.ncbi.nlm.nih.gov/refseq/release/release-catalog/archive/ ]. The source code to our custom tool Vicinator is available under the GPL license 3.0 on Github ( https://github.com/BIONF/Vicinator ).

Here, we exploit the availability of thousands of Acinetobacter spp. genomes in the public databases (NCBI RefSeq; [ 47 ]) to shed light on the evolution of the pathogenic ACB complex at a resolution that extends from a genus-wide overview to the level of individual clonal lineages within A. baumannii. For the first time, we integrate genus-wide ortholog searches with analyses of gene order conservation providing a highly-resolved view on the joint evolutionary fate of neighboring genes using the type strain ATCC 19606 as a reference. This revealed 150 evolutionarily stable gene clusters (ESGC ACB ) that are prevalent in the ACB complex and rare or absent in the other members of the genus. The functional annotations of these ESGCs provide insights into the genetic and functional specifics of a clade comprising mostly pathogens, and thus direct the focus to key processes likely relevant for the adaptation of the bacterium to the human host. We find that the ACB complex acquired novel genetic modules for the regulation and formation of biofilms, for the scavenging of micronutrients, and have substantially extended their capabilities to exploit a diverse set of carbon sources.

A systemic understanding of how A. baumannii interacts with and infects their human host can lead to novel paths for antimicrobial treatments [ 20 – 22 ]. Three main approaches have been used to elucidate the molecular basis of Acinetobacter virulence. Candidate approaches have scanned for virulence factors previously characterized in other bacterial pathogens [ 4 , 7 , 23 – 27 ]. To extend the scope beyond pre-compiled virulence factor catalogs, a diverse set of genome-wide experimental approaches have been pursued. Among others, they assessed the effect of gene knockouts on the infection process (e.g. [ 14 , 28 ]), investigated transcriptional changes under conditions the bacterium encounters in the human host (e. g. [ 25 , 29 , 30 ]), studied adaptation evolution of bacteria inside the human host [ 31 ], and reconstructed the protein interaction network contributing to the understanding of bacterial antibiotic resistance mechanisms [ 32 ]. However, experiments are usually performed only on a small set of model strains (e.g. [ 33 ]), and the limited set of tested conditions cannot reflect the diversity of infection sites in the human body. Moreover, factors contributing only indirectly to virulence, such as metabolic pathways that facilitate the tapping of host resources [ 34 ], are hard to capture. Comparative genomics provide complementary evidences in the search of virulence related traits. The genus Acinetobacter encompasses to date 72 (validly) named and mostly nonpathogenic species [ 35 ] isolated from habitats that range from floral nectar to animals [ 36 ]. This diversity represents a perfect setup to identify genomic changes that correlate with the evolutionary emergence of the pathogenic potential [ 37 ]. Thus far, comparative genomics studies have begun to shed light on the general evolution of the genus [ 11 ] and the clonal epidemiology [ 38 ] of A. baumannii. They indicated that a major driver of A. baumannii’s success as a pathogen is its remarkably flexible genome [ 11 , 39 ] which is characterized by high mutation rates [ 40 ] paired with the ability to acquire new, or alter the structure or expression of existing genes [ 13 – 15 ]. This promotes a rapid adaptation to novel and adverse environmental conditions, as well as the spread of antimicrobial resistance determinants [ 41 , 42 ]. However, against intuition, most of the known virulence determinants were found also in the nonpathogenic members of the genus (e.g. [ 4 , 43 – 45 ]). Thus, it is still largely unknown which genetic changes correlate with the emergence of the ACB complex as opportunistic human pathogens, and the genetic basis underlying the adaptation of A. baumannii to the human host largely remains to be understood [ 46 ].

Acinetobacter is a physiologically and biochemically diverse genus of Gram-negative coccobacilli and most of its species are considered benign. But the genus also harbors the Acinetobacter calcoaceticus-baumannii (ACB) complex, a group of closely related human opportunistic pathogens [ 1 , 2 ] that account for the vast majority of severe hospital-acquired Acinetobacter spp. infections [ 3 – 7 ]. Acinetobacter baumannii is the most critical member of the ACB complex. On a global scale, this species alone signs responsible for up to 5% of the total bacterial infections in hospitals [ 8 ]. Many outbreaks worldwide can be attributed to one of eight genetically well distinguishable clonal complexes within the population of A. baumannii, all sharing the resistance against carbapenem [ 9 , 10 ]. By now, antibiotic resistance determinants against virtually all available antibiotics drugs are present in A. baumannii [ 11 ], and multi- or even pan-drug resistant strains are isolated from 44% of all patients with an A. baumannii induced infection [ 12 ]. At the same time, both the frequency and severity of infections have increased. Recent case studies report mortality rates of up to 70% [ 13 – 15 ] as well as growing numbers of epidemic outbreaks [ 16 ]. In recent years, significant advancements in the molecular characterization of drug resistance mechanisms have led to more informed drug administration schemes for hospitalized patients [ 6 ]. Still, the ease with which A. baumannii acquires resistance to novel antibiotics [ 12 ] makes it likely that resistant strains and their resistance determinants are going to spread at a faster pace than novel antimicrobials become available [ 17 ]. Moreover, community-acquired infections by members of the ACB complex begin to rise [ 6 , 18 ]. As a consequence, A. baumannii ranks top in the WHO charts of pathogens for which drug development is most urgent [ 19 ].

Results

The ability to infect humans emerged in the course of Acinetobacter spp. evolution and is a hallmark of the ACB complex. Here, we exploited the full diversity of Acinetobacter genomes available in the public databases that were available at the onset of the study (S1 Table). We use this resource to trace changes in the Acinetobacter pan-genome that correlate with the manifestation of pathogenicity in the ACB complex. To make the analyses computationally tractable we devised a two-stage strategy.

In the priming stage, we determined the evolutionary relationships within the Acinetobacter pan-genome with the orthology inference tool OMA [48]. Because the computational complexity of the OMA ortholog search scales exponentially with the numbers of genes in the pan-genome, we compiled a representative set of strains (Set-R) for this analysis. In brief, we considered all available type, reference and representative genomes, as well as all validly named species for which a genome sequence was available at the study onset. This set was filled to a total number of 232 strains by adding further genomes to maximize the phylogenetic diversity of the taxon set (see Methods and Fig A in S1 Text). The corresponding strains together with genome assembly statistics, and, where available, the origins of the isolates are summarized in S2 Table. Members of the ACB complex harbor, on average, 14% more genes than other members of this genus (students t-test—p<0.001; S1A Fig). Gene counts were highly correlated with genome lengths (spearman, ρ = 0.98), and neither the difference in genome length nor the number of encoded genes was significantly correlated with the assembly status (Completeness status “Complete” vs. Others, Kruskal-Wallis, p = 0.311) (see S1B Fig for further information). The Set-R pan-genome comprises 22,350 orthologous groups harboring 783,306 proteins; 16,000 proteins remained singletons (see S1C Fig for a graphical representation). Rarefaction analyses revealed that the pan-genomes of the entire genus, the ACB complex, and A. baumannii are open (Fig B in S1 Text). 889 genes represent the core genome of Acinetobacter (S3 Table and S1 Text: Section Core-genome reconstruction). Eventually, we tentatively annotated gene function in the Set-R pan-genome by linking the individual genes to COGs [49], to KEGG KOs [50], to entries in the virulence factor databases PATRIC [23] and VFDB [51], and by predicting their subcellular localization.

In the extension stage, we used a targeted ortholog search to complement the orthologous groups from the SET-R analysis with sequences from the remaining 2,820 Acinetobacter genomes (Set-F).

The Acinetobacter-Dashboard Acinetobacter research worldwide benefits from the FAIR principle where scientific data is findable, accessible, interoperable and reproducible [52]. As a first step in this direction, we have developed the web application Aci-Dash (https://aci-dash.ingress.rancher.computational.bio/; Fig 1). For each strain, the user can obtain information about the sample origin and get access to all genes annotated in the respective genome together with an overview of its abundance in the other 231 strains. For example, this allows the rapid identification of genes that are specific to a strain, a clade, or that are part of the core genome. Moreover, genes can be sub-selected based on their genomic position, which allows to explore the phylogenetic profiles of neighboring genes. Interactive plots make it straightforward to retrieve further information about individual or groups of genes, such as their assignment to COG or KO groups, or their representation in virulence databases (see above). Thus, Aci-Dash is the first web-based platform to interactively browse and explore the Acinetobacter pan-genome that is spanned by the 232 strains of Set-R. PPT PowerPoint slide

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TIFF original image Download: Fig 1. Aci-Dash–Interactive exploration of phylogenetic abundance patterns and accessory annotations for the Set-R pan-genome. For each of the 232 strains, Aci-Dash provides further details about year and site of sampling. The map was generated with Plotly for Python with a basemap from Natural Earth (https://www.naturalearthdata.com). The interactive scatter plot reveals, for each gene of the selected strain, the abundance of orthologs in a user-defined in- and outgroup. For each gene individually, the ortholog abundance can be resolved on a clade level (cf. Fig 2), and further information including functional annotation transfer from KEGG and COG as well as known virulence factors is displayed. https://doi.org/10.1371/journal.pgen.1010020.g001

Consistency-based phylogeny of the genus Acinetobacter To establish a stable phylogenetic backbone for our analysis, we reconstructed the maximum likelihood evolutionary relationships of the taxa in Set-R and Set-F, respectively, from three non-overlapping partitions of the 889 core genes. The majority-rule consensus phylogenies from the three trees each (Set-R–Fig 2A; Set-F–Fig 2B; for higher resolution versions see S2 and S3 Figs, respectively) reveal that all named species (at the time of download) as well as the members of the ACB complex are consistently placed into monophyletic clades. Incongruencies between the three partition trees are confined to the branching order within individual species, and here mainly within the densely sampled A. baumannii and A. pittii. This indicates that genetic recombination, which is most likely the source of the incongruent phylogenetic signal [53], is common enough only within species to interfere with phylogenomic reconstructions based on hundreds of genes [54]. Across the genus, we detected and corrected individual taxonomic assignments that are at odds with the phylogenetic placement of the taxa, and most likely indicate mislabeled strains ([55,56]). Specifically, we corrected 16 of such instances within the ACB clade, of which ten were wrongly classified as A. baumannii according to NCBI RefSeq. In turn, 60 out of 182 genomes with an unknown taxonomic assignment were placed within the ACB clade (see S4 Table for species and clade assignments including the corrections). Interestingly, a comparison of the average pair-wise nucleotide identity (ANI) across all genomes within Set-R revealed that at least two genomes placed into the ACB clade cannot be associated with any known species (ANI <95%, cf. S4 Fig). This indicates that the full species diversity of the ACB complex is not yet fully charted. PPT PowerPoint slide

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TIFF original image Download: Fig 2. The phylogeny of the genus Acinetobacter. (A) Majority-rule consensus phylogeny of 232 Acinetobacter strains represented in SET-R. Solid branches are supported by all, and hatched branches by two out of three trees. A high-resolution image of this tree is provided in S2 Fig. (B) The maximum likelihood tree for all 3052 taxa in Set-F. Colored clades represent the same clades as in A). A high-resolution image of this tree is provided in S3 Fig. (C) The evolutionary backbone of the Acinetobacter genus with exemplary strains as clade representatives. The color scheme resembles that of Fig 2A. The pictograms next to the leaf labels indicate the sampling source of the particular strain. Red pictograms signal a strain that was isolated from an infected patient. The clipart used in this figure has been dedicated to the public domain (CCO 1.0 Universal) or was self-generated. https://doi.org/10.1371/journal.pgen.1010020.g002 To ease the integration of the phylogenetic information into the following sections, we used one species each to name the individual clades in the Acinetobacter phylogeny (Fig 2A).

Lifestyle and host switches during Acinetobacter evolution The two earliest branching clades, named after A. qingfengensis (QI) and A. brisouii (BR), respectively, solely comprise environmental species. A. apis, which was isolated from bees [57], appears as an exception. While Acinetobacter species are sporadically observed in the bee gut, they are not considered part of the gut microbiome [58]. Instead, they likely represent environmental bacteria that were taken up by the bee with the food [59]. Thus, the capability to colonize animals evolved later and most likely in the ancestral species prior to the split of the A. lwoffii (LW) clade (Fig 2B). Usually, members of the LW clade are non-pathogenic (Fig 2C). Repeated cases of human infection have only been reported for individual strains that mainly group with A. lwoffii and A. radioresistens, which manifested in vascular catheter-induced bloodstream infections with a low mortality rate [60]. Thus, human infection is likely an exception rather than the rule for this clade. In the species that diverged after A. baumannii last shared a common ancestor with A. baylyi (AB clade) and with A. haemolyticus (HA clade), we find increasingly often human pathogens. This suggests a progressive adaptation to humans as a host [61]. The monophyletic ACB complex (ACB clade) subsumes the A. pittii clade (PI), the A. nosocomialis clade (NO), and the A. baumannii clade (B). Its members are all potentially pathogenic, although A. calcoaceticus has been very rarely been seen in the context of human infection. It can be speculated that the few reported cases were due to a miss-classified strain from a different species (see subsection “Consistency-based phylogeny of the genus Acinetobacter” above). Thus, A. calcoaceticus substantially reduced its pathogenic potential if not lost it completely [27].

The evolutionary emergence of the ACB clade coincides with changes in quorum sensing and biofilm formation Quorum sensing and biofilm formation are key determinants of A. baumannii virulence [62–64]. Both functions are represented by ESGC ACB -0162 (14 genes) and 0410 (8 genes). ESGC ACB -0162 represents the regulatory module of this process. It harbors the Lux-type quorum sensing system (QS Lux ), which regulates motility and biofilm formation in A. baumannii, and additionally, a biosynthetic gene cluster containing a non-ribosomal peptide synthetase here referred to as NRPS cluster. Both genes of QS Lux , abaI and abaR, are separated by a short gene that was tentatively named abaM (Fig 7A; see also Figs E and F in S1 Text). This three-gene architecture is conserved in the ACB clade, and it is common in Burkholderia spp. [65], where the intervening short gene acts as a negative regulator of the QS Lux system [66]. Recently, initial evidence emerged that AbaM in the A. baumannii strain AB5075 indeed regulates quorum sensing and biofilm formation [67]. In the light of our results, we propose that AbaM is an understudied modulator of quorum sensing in the entire ACB clade. PPT PowerPoint slide

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TIFF original image Download: Fig 7. Three examples for ESGC ACB in the genome of Ab ATCC 19606. Bar plots indicate relative abundance within the selected taxonomic groups (see legend). The ESGC ACB are embedded into two flanking protein-coding genes on each side that are not part of the cluster. Cluster boundaries are indicated by a pink background. Genes indicated in yellow have been either added to or excluded from the automatically generated clusters based on microsynteny analyses across Set-R. (A) ESGC ACB − 0162 unites AbaR and AbaI (quorum sensing) with a non-ribosomal peptide synthetase cluster upstream. (B) ESGC ACB − 0410 encompasses the cluster necessary for the Csu pilus formation. Note the deviating abundance pattern for CsuC and CsuD, which is due to the presence of prpC and prpD, two paralogous genes from the photoregulated pilus ABCD (prpABCD), in the respective orthologous groups. Microsynteny analyses confirmed that the entire Csu cluster forms one evolutionary unit (S1 Data id:0410). (C) ESGC ACB − 0624 harbors a catalase and a cytochrome b561. Although the MFS transporter shares a similar abundance pattern across the taxa of Set-R, this transporter is not evolutionarily stably linked to the other two genes (S1 Data id:0624). https://doi.org/10.1371/journal.pgen.1010020.g007 The NRPS cluster (Fig 7A) produces a three-amino acid lipopeptide, Ac-505 [64], which likely plays a central role in regulating bacterial motility and biofilm formation [68]. Disrupting its biogenesis alters the expression of numerous factors involved in biofilm formation and surface adherence [69], in particular the chaperon-usher pili (CUP) and the archaic chaperon-usher pili (CSU). Consequently, host cell adhesion and virulence of A. baumannii are substantially reduced. Here, we provide first-time evidence that the evolutionary fate of the NRPS cluster is intimately intertwined with that of the QS Lux cluster. We found that the rare strain-specific loss of the QS Lux -cluster determines the loss of the NRPS cluster, which implies that they not only form an evolutionary but also a functional unit. Interestingly, strains lacking ESGC ACB -0162 are not randomly distributed. Most prominently, the cluster is missing in almost all (48/55) A. baumannii strains representing the international clone (IC) 8 (Fig 5). The formation of a higher-order module comprising the QS genes and an NRPS biosynthetic gene cluster is a repeated scheme during bacterial evolution. For example, the methane-oxidizing bacterium Methylobacter tundripaludum harbors an NRPS biosynthetic gene cluster that was integrated between the abaI and abaR orthologs. And the production of the corresponding extracellular factor is under control of the QS cluster [70]. NRPS-dependent molecules have been implicated to mediate interspecific communications across kingdoms both in symbiotic and pathogenic communities. In P. aeruginosa, the interplay of N-acyl-L-homoserine lactone-dependent quorum-sensing signaling and an NRPS-dependent biosynthesis of bacterial cyclodipeptides (CDPs), which act as auxin signal mimics, modulates the communication to its host plant Arabidopsis thaliana [71]. It can be speculated that ESGC ACB -0162 may similarly coordinate the communication between the bacteria and their human host. ESGC ACB -0410 harbors the Csu cluster responsible for biofilm formation on abiotic surfaces via archaic chaperon-usher pili [72,73] (Fig 7B) together with a transcriptional regulator of the TetR/AcrR family (TFTRs) (S5 Fig id:0410). TFTRs represent one-component systems that regulate a broad variety of cellular processes in bacteria, among them many that are related to virulence such as efflux pump expression and biofilm formation [74,75]. Notably, they are often encoded alongside their target operons. To the best of our knowledge, regulation of the Csu cluster via an adjacent TFTR has never been reported. Thus, next to the two-component systems BfmRS [76] and GacSA [77], a third hitherto undescribed one-component system, seems to be involved in regulating the formation of Csu pili. With few exceptions, ESGC ACB -0162 and ESGC ACB -0410 share similar abundance patterns (cf. Fig 5). This is in line with the finding that the regulation of the Csu cluster is under the direct control of Ac-505 [28,69]. Thus, Ac-505 likely acts as a modulator between biofilm formation on abiotic and biotic surfaces. However, contrary to the QS Lux —NRPS supercluster, the Csu cluster was lost multiple times independently in the ACB clade, e.g. in the CA and the L clades (Fig 5). Given its terminal position in the regulator-effector cascade, this indicates a lineage-specific fine-tuning of biofilm formation. Interestingly, within A. baumannii, we find that all 55 IC8 strains in our dataset lack both the QS Lux —NRPS supercluster and the Csu cluster, which indicates substantial changes in the way how IC8 strains regulate biofilm formation.

KatA–An ACB clade specific catalase The genome of A. baumannii ATCC 19606 harbors five putative catalases: katA, katE, katE-like, katG, and katX [25,78]. Note, that both Sun et al. [78] and Juttukonda et al. [25,78] refer to a catalase labeled katE. Despite the same names, the studies refer to different genes (locus tags A1S_1386/A1S_3382 and DJ41_RS22765/DJ41_RS10660 in A. baumannii ATCC 17978 and ATCC 19606, respectively). We, therefore, re-named katE of Juttukonda et al. to katE-like. KatA is the only catalase that is exclusively found in the ACB clade. The corresponding gene resides in a cluster next to a putative MFS transporter and a cytochrome b561 (ESGC ACB -0624, Fig 7C). The KatA cluster is highly conserved in all species of the ACB clade except A. calcoaeceticus, where it has been lost (cf. Figs 5 and 7C). Upon host infection, both neutrophils and macrophages recruit radical oxygen species (ROS) for bacterial clearance [79, 80], and thus ROS defense mechanisms are an essential contributor to bacterial virulence. However, an initial investigation in Ab ATCC 17978 found no obvious link between KatA and ROS protection [25]. Still, the abundance pattern of ESGC ACB -0624 indicate that this cluster may contribute to virulence in pathogenic members of the ACB clade. More comprehensive studies are needed to elucidate if and how ESGC ACB -0624 is involved in the infection process.

Metabolic adaptation–Micronutrient acquisition is refined in the ACB-clade Essential metals, such as iron and zinc, are actively sequestered by the host to starve invading pathogens [81]. This likely results in a strong selective pressure for the pathogenic ACB clade to optimize scavenging systems such that the reduced bioavailability of these metal ions in the host can be counterbalanced. Acquisition systems for iron, whose limited availability at the host-pathogen interface is considered one of the key obstacles for invading and persisting within the human host, are a showcase example. The iron transporter system feoABC represents the evolutionary core of iron uptake. It is complemented, in many but not all taxa [33] both inside and outside of the ACB clade, by the baumannoferrin cluster (Fig 8). Two further clusters extend the spectrum of iron uptake systems exclusively in the ACB clade. ESGC ACB -0498 represents the 2,3-dihydroxybenzoic acid synthesis cluster (entAB), which synthesizes a siderophore precursor [82]. ESGC ACB -0368 and 0369 together resemble the acinetobactin biosynthesis clusters bauA-F, basA-I and barAB. A third cluster, ESGC ACB -0485, that very likely represents an ABC-type Fe3+-hydroxamate transport system seems to extend the diversity of iron uptake systems in the ACB clade even further. It encodes a substrate-binding protein, an iron complex ABC transporter (permease), an ATP-binding protein, and an N-Acetyltransferase protein (GNAT family). The AraC-family-like transcriptional regulator, which is located downstream on the opposite strand in ESGC ACB -0485, likely controls the expression of this cluster. In line with this operon-like organization, these genes are jointly downregulated under mucin-rich conditions [83]. The complex and seemingly redundant infrastructure for iron uptake in the ACB clade seems at odds with a recent study in A. baumannii ATCC 17978, which stated that acinetobactin is the only system that is necessary for A. baumannii to grow on host iron sources [84]. Here we show that this conclusion does not generalize to the entire ACB clade. The pathogens A. nosocomialis and A. seifertii, for example, lost the acinetobactin cluster (cf. Fig 5). It is conceivable that the diversity of iron acquisition systems is an adaptation to diverse niches each requiring different strategies of iron scavenging. PPT PowerPoint slide

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TIFF original image Download: Fig 8. Abundances of micronutrient acquisition genes/clusters of Ab ATCC 19606 within and outside the ACB clade. Absolute abundances were based on cluster-evaluation using microsynteny. Gene clusters are annotated with the ESGC ACB identifiers in Fig 5. Genes that are part of a cluster but missed by the initial ESGC compilation share the same color but are marked as x. Gene clusters at the top right, e.g., feoABC, represent micronutrient acquisition clusters belonging to the genus’ core genome. Gene clusters at the top left, e.g., entAB, are confined to the ACB clade where they are ubiquitously present. https://doi.org/10.1371/journal.pgen.1010020.g008 Given the essentiality of zinc (Zn), it is not surprising to see that also Zn2+ uptake was refined on the lineage towards the ACB clade. The Zn uptake system Znu, including the distal znuD gene, which facilitates resistance to human calprotectin-mediated Zn2+ sequestration [25], is evolutionarily old and part of the genus-wide core genome (Fig 8). The histidine utilization (Hut) system (hutCDUHTIG, ESGC ACB -0215) is prevalent in the ACB clade, though not exclusively. This system ensures the bio-availability of Zn2+ via the histidine catabolism both under high availability and starvation of Zn2+. However, it requires histidine to be abundant. Interestingly, the most recent acquisition in Zn metabolism is the putative metallochaperone, ZigA. The corresponding gene resides directly adjacent to ESGC ACB -0215, and thus is likely an evolutionary more recent extension of this cluster. zigA was found active only under Zn starvation, where it increases the bioavailability of Zn also under histidine depletion [85] and counteracts nutritional starvation. Manganese (Mn2+) is required only in small amounts and is mostly used for coping with reactive oxygen species (ROS), as Mn2+, other than Fe2+, does not promote the Fenton reaction that converts H 2 O 2 to highly damaging hydroxyl radicals [86, 87]. Therefore, Mn uptake systems should be prevalent in bacteria frequently exposed to ROS stress, particularly in the pathogenic Acinetobacter strains. Thus far, only one Mn acquisition system, mumRTLUHC [88], has been identified in Acinetobacter spp. This system is represented by ESGC ACB -0611 and plays an essential role in protecting A. baumannii against calprotectin-mediated Mn depletion by the host and contributes to bacterial fitness in a murine pneumonia model [88]. ESGC ACB -0611 is found throughout the genus though less frequently in clades that are more distantly related to the ACB clade (cf. Figs 5 and 8). Within the HA-clade, several species including A. tjernbergiae, A. junii, A. beijnerickii, and A. haemolyticus lack both the putative manganese transporter gene mumT and the gene encoding a putative hydrolase mumU (S5 Fig id:0611). Within IC 3, several strains lack the entire cluster. These taxa either found alternatives to Mn2+-dependent processes for coping with oxidative stress, are more vulnerable to ROS, or they scavenge Mn2+ via a mechanism that is still hidden in functionally uncharacterized gene clusters. In summary, we see a clear signal that the ACB clade is enriched for genes and gene clusters that functionally complement the genus-wide available and evolutionarily old metal uptake systems. In line with the reinforcement hypothesis, these more recently acquired clusters seem particularly important for metal scavenging during infection, i.e. when the metals are actively sequestered by the host [84,85].

Carbohydrate metabolism—Evolution towards nutritional flexibility The ability of individual Acinetobacter strains to utilize a broad spectrum of carbon sources is important for their adaptation to different environments, including the human host [45,89–92]. However, it is largely unknown when the corresponding metabolic pathways were acquired during Acinetobacter evolution, how widespread they are, and if and to what extent they are connected to the pathogenicity of the ACB clade. More than 20 of the shortlisted ESGCs ACB represent pathways that shuttle metabolites into the carbohydrate metabolism of the bacterium (Fig 5 in red font, and see Fig 9 for a selection), many of which are prevalent in the human body. The corresponding gene clusters mostly channel these metabolites into catabolic processes (see below). However, the genes involved in the glucose/gluconate metabolism seem to fuel anabolic processes. PPT PowerPoint slide

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TIFF original image Download: Fig 9. Members of the ACB clade have extended their basal carbohydrate metabolism. The pathway map shows a model integrating four metabolic pathways represented by the ESGC ACB -0112 (Entner-Doudoroff pathway; yellow), 0497 (biosynthesis of Pyrroloquinoline quinone; magenta), 0568 (glucarate/galactarate catabolism; blue/violet) and 0016 (carnitine catabolism; green) into the bacterial carbohydrate metabolism (grey boxes). Abbreviations: KDPG—2-keto-3-deoxy-6-phosphogluconate; G3P –glycerinealdehyde-3-phosphate. The corresponding gene clusters are shown below the pathway map with one protein-coding flanking gene on either side that is not part of the cluster. The layout follows Fig 7. ESGC ACB -0112: GapD-like–glyceraldehyde-3-phosphate dehydrogenase [1.2.1.12]; GntK—gluconokinase [EC:2.7.1.12]; Edd—phosphogluconate dehydratase [EC:4.2.1.12]); Eda—2-dehydro-3-deoxyphosphogluconate aldolase / (4S)-4-hydroxy-2-oxoglutarate aldolase [EC:4.1.2.14 4.1.3.42]; GntP—gluconate permease [E2.7.1.12]. Note, GntP shares high sequence similarity with a gluconate transporter (H+ symporter) in Escherichia coli (98% coverage, 45% identity). ESGC ACB -0497: pqqA-F. No abundance profile is shown for pqqA, since its length excluded it from orthology prediction (indicated by an ‘!’. See main text for details). ESGC ACB -0568: GarD—galactarate dehydratase [EC:4.2.1.42]; gudD—glucarate dehydratase [EC:4.2.1.40]; kdgD—5-dehydro-4-deoxyglucarate dehydratase [EC:4.2.1.41]; aldH—2,5-dioxopentanoate dehydrogenase [EC:1.2.1.26]; gudP—MFS transporter, D-glucarate/D-galactarate permease; FadR—Fatty acid metabolism regulator protein. ESGC ACB -0016: ttuc—D-malate dehydrogenase [EC:1.1.1.83]; Bet-Aci01347—glycine/betaine transporter Aci01347; ach?–putative acylcarnitine hydrolase [EC:3.1.1.8]; CntAB–carnitine monooxygenase reductase subunit A and B [EC:1.14.13.239]; MSA-DH–malic semialdehyde dehydrogenase [EC:1.2.1.?]. Further colored pathways represent the clusters ESGC ACB -0627 (2-aminoethylphosphonate metabolism, orange), ESGC ACB 0453 (fructose transport/metabolism, red), ESGC ACB -0064 (trehalose biosynthesis, dark grey), and ESGC ACB -0078 tricarballylate metabolism, brown). Their corresponding genomic regions are available in S5 Fig. https://doi.org/10.1371/journal.pgen.1010020.g009

Glucose/gluconate metabolism Glucose and gluconate serve as carbon and energy sources for few species in the genus Acinetobacter, e.g., A. soli, A. apis, and A. baylyi. For A. baylyi ADP1 it was shown that the glucose catabolism involves the Entner-Doudoroff pathway [93]. Members of the ACB clade have lost the ability to use glucose and gluconate as a carbon source [94] (see also S6 Fig and S7 Table). It is, thus, surprising that we find the genetic infrastructure to feed both molecules into the bacterial metabolism almost exclusively in the ACB clade. ESGC ACB -0112 comprises the gluconate permease (GntP) that shuttles gluconate from the periplasm into the bacterial cell (Fig 9, yellow pathway). The cluster further encodes the kinase GntK, which phosphorylates gluconate into 6-phosphogluconate, and the enzymes Edd and Eda of the Entner-Doudoroff pathway, which link to the pentose phosphate pathway that produces pyruvate. Members of the ACB clade also possess two variants of a glucose dehydrogenase (gdh), which catalyze the reaction from D-glucose to D-gluconate in the periplasm [95]. The membrane-bound variant (gdhA) forms together with an outer membrane porin a cluster of two genes, ESGC ACB -0287 (cf. S1 Data id:0287), which is ubiquitous across Acinetobacter spp. We note that the porin is orthologous to OprB in P. aeruginosa, where it facilitates the diffusion of various sugars—including glucose—into the periplasm. The second, soluble Gdh (gdhB) is confined to and nearly ubiquitous in the ACB clade (S7 Fig and S6 Table: id:HOG3408). The prosthetic group for both Gdh, pyrroloquinoline quinone (PQQ), is a small, redox-active molecule that serves as a cofactor for several bacterial dehydrogenases. ESGC ACB -0497 comprises six genes that together represent the PQQ biosynthesis pathway: pqqABCDE and an additional membrane-bound dipeptidase referred to as pqqF in Klebsiella pneumoniae [96] (cf. Fig 9, pink). All genes reside contiguously on the same strand suggesting an operonic structure. The complete cluster is present in almost all genomes of the ACB clade, although we had to manually confirm the presence of pqqA, because its length (40 amino acids) is below the length cutoff of the ortholog assignment tool. Only 31 out of 2436 strains in the ACB clade have lost the ability to synthesize PQQ, among them the model strain A. baumannii ATCC 17978. Cluster abundance outside the ACB clade is low (<20%), but it is present in all strains of species with demonstrated ability to assimilate glucose and gluconate (i.e. A. soli, A. baylyi, and A. apis, cf. [94]; S1 Data id:0497). The holoenzymes GdhA and/or GdhB, in theory, could establish a gapless route for glucose via this gluconate ‘shunt’ into the cell for further degradation via the Entner-Doudoroff pathway, even in the absence of a dedicated Glucose transporter. Why then do none of the tested strains in the ACB clade grow with glucose as sole carbon and energy source? We hypothesize that they utilize this route for anabolic processes exclusively, e.g. for the production of polysaccharides as it was demonstrated for P. aeruginosa [97].

Carbohydrate catabolism The ACB clade has substantially increased its repertoire of catabolic pathways for alternative carbon sources compared to taxa outside this clade [94]. For a small number of mostly hand-picked A. baumannii strains, previous studies have experimentally confirmed the ability to grow on tricarballylate and putrescine, malonate, butanediol and acetoin, phenylacetate, muconate, glucarate, galactarate (mucate), and 4-hydroxyproline as sole carbon sources [45,89]. Our analyses identified the corresponding gene clusters among the ESGC ACB . Hence, the ability to use these resources is prevalent in the ACB clade, whereas non-ACB species have to rely largely on different carbon sources. We will highlight two examples that likely represent an adaptation to humans as a host. D-glucarate (saccharate) is a major organic acid in human serum [98]. ESGC ACB -0568 comprises all necessary genes for glucarate and galactarate (mucic acid) degradation (Fig 9). In Salmonella enterica serovar Typhimurium deletion of the D-glucarate/D-galactarate permease ortholog attenuated virulence [99]. Further, galactarate digestion was shown to increase the colonization fitness of intestinal pathogens in antibiotic-treated mice and to promote bacterial survival during stress [100]. ESGC ACB -0568 is almost exclusively confined to the ACB clade. This may indicate that this cluster contributes to colonization and virulence in pathogenic Acinetobacter species. It is therefore interesting that within A. baumannii the cluster is almost absent in IC2 strains (0.08% prevalence in Set-F; cf. Fig 5). Carnitine is essential for the oxidative catabolism of fatty acids in humans [101]. ESGC ACB -0016 comprises six genes necessary for catabolizing carnitine [102]. A LysR-type transcriptional regulator likely controls the activity of this cluster. The remaining five genes represent a putative tartrate dehydrogenase (ttuC), a BCCT-family carnitine transporter (Aci01347), a generically annotated alpha/beta hydrolase which possibly catalyses the conversion of D-acylcarnitine into L-carnitine (see Fig 9, green), and the genes encoding the two subunits of the carnitine monooxygenase CntA and CntB. The latter two genes are separated by a gene that is tentatively annotated as an NAD-dependent succinate-semialdehyde dehydrogenase. However, two lines of evidence indicate that the precise function of this gene as well as that of the putative tartrate hydrogenase might both differ. In the literature, the putative succinate-semialdehyde dehydrogenase is speculated to act as malic semialdehyde dehydrogenase [102], an enzyme that converts malate semialdehyde into malate. Further, the putative tartrate dehydrogenase belongs to the KEGG orthologous group KO7246 which is annotated as a D-malate dehydrogenase. The product of the latter enzyme, pyruvate, can be further processed into oxaloacetate, which serves as a substrate for the tricarboxylic acid (TCA) cycle, or into acetyl CoA (cf. Fig 9). Assuming that the putative malic semialdehyde dehydrogenase produces D-malate, then this cluster should allow the members of the ACB clade to utilize D-malate as a carbon source if an appropriate transporter is present. We tested this hypothesis and confirmed that Ab ATCC 19606 grows on D-malate (S6 Fig), which corroborates initial growth experiments [103]. We note that the unusual production of the D-malate enantiomer rather than L-malate would have a further interesting implication. It potentially allows the bacterium to accumulate D-malate in conditions when carnitine is abundant, without interfering with the stoichiometry of the remaining substrates of the TCA cycle. Thus far, two A. baumannii strains have been shown to use carnitine as sole carbon source [104]. We evaluated exemplarily that the absence of the cluster indeed correlates with Acinetobacter inability to grow on carnitine and is not functionally complemented by an alternative degradation pathway. Both, A. baylyi ADP1 and the A. calcoaceticus type strain (DSM 30006), which both lack the ESGC ACB -0016, did not grow on carnitine after 24h (S6 Fig). The clusters abundance profile reveals that the ability to metabolize carnitine occurs also outside the ACB clade (31 strains in Set-R; cf. Fig 5). However, 25 of these strains were isolated from infected patients, 2 from hospital sewage water, and only 3 strains were sampled from the environment (cf. S8 Table). The isolation origin of the remaining strain is unknown. The presence of the carnitine cluster, therefore, correlates surprisingly well with the pathogenic potential of a strain and it will be interesting to test a causal dependence. In support of causality, we find that the carnitine cluster is absent in A. calcoaceticus, the only species of the ACB clade that is nonpathogenic or at least has substantially reduced virulence (see S1 Text: Section ESGC ACB -0016).

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