PLoS GeneticsGenomicsPeer-reviewed journal article
Evolutionarily stable gene clusters shed light on the common grounds of pathogenicity in the Acinetobacter calcoaceticus-baumanniicomplex
Bardya DjahanschiriiD, Gisela Di VenanzioiD, Jesús S. DisteliD, Jennifer Breisch, Marius Alfred DieckmanniD, Alexander GoesmanniD, Beate AverhoffiD, Stephan GöttigiD, Gottfried WilharmiD, Mario F. FeldmaniD, Ingo EbersbergeriD
Abstract
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 isolated from their functional contexts. Here, we explored more than 3,000 Acinetobacter genomes in a phylogenomic framework integrating orthology-based phylogenetic profiling and microsynteny conservation analyses. This allowed to 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, or are found preferentially in, the pathogenic ACB clade. They unveil, at an unprecedented resolution, the genetic makeup that coincides with the manifestation of the pathogenic phenotype in the last common ancestor of 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. Specifically, we could show 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. They disclose promising routes for future therapeutic strategies. Author SummaryThe spread of multi- and pan-drug resistant bacterial pathogens is a world-wide threat to human health. Understanding the genetics of host colonization and infection is essential for 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 comprising the A. baumannii-calcoaceticus complex from other and mostly apathogenic Acinetobacter species. We found a rich spectrum of pathways and regulatory modules that reveals 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 its allies have turned the table. They feed on kynurenine, and thus most likely also dysregulate 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.
- organism: human
- assay: whole-genome sequencing
- research focus: gene regulatory network inference
- approach: statistical model
- Citations
- 27
- FWCI
- 2.04
- Field percentile
- top 13%
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