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

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.

Bakta: rapid and standardized annotation of bacterial genomes via alignment-free sequence identification.

Command-line annotation software tools have continuously gained popularity compared to centralized online services due to the worldwide increase of sequenced bacterial genomes. However, results of existing command-line software pipelines heavily depend on taxon-specific databases or sufficiently well annotated reference genomes. Here, we introduce Bakta, a new command-line software tool for the robust, taxon-independent, thorough and, nonetheless, fast annotation of bacterial genomes. Bakta conducts a comprehensive annotation workflow including the detection of small proteins taking into account replicon metadata. The annotation of coding sequences is accelerated via an alignment-free sequence identification approach that in addition facilitates the precise assignment of public database cross-references. Annotation results are exported in GFF3 and International Nucleotide Sequence Database Collaboration (INSDC)-compliant flat files, as well as comprehensive JSON files, facilitating automated downstream analysis. We compared Bakta to other rapid contemporary command-line annotation software tools in both targeted and taxonomically broad benchmarks including isolates and metagenomic-assembled genomes. We demonstrated that Bakta outperforms other tools in terms of functional annotations, the assignment of functional categories and database cross-references, whilst providing comparable wall-clock runtimes. Bakta is implemented in Python 3 and runs on MacOS and Linux systems. It is freely available under a GPLv3 license at https://github.com/oschwengers/bakta. An accompanying web version is available at https://bakta.computational.bio.

EDGAR3.0: comparative genomics and phylogenomics on a scalable infrastructure.

The EDGAR platform, a web server providing databases of precomputed orthology data for thousands of microbial genomes, is one of the most established tools in the field of comparative genomics and phylogenomics. Based on precomputed gene alignments, EDGAR allows quick identification of the differential gene content, i.e. the pan genome, the core genome, or singleton genes. Furthermore, EDGAR features a wide range of analyses and visualizations like Venn diagrams, synteny plots, phylogenetic trees, as well as Amino Acid Identity (AAI) and Average Nucleotide Identity (ANI) matrices. During the last few years, the average number of genomes analyzed in an EDGAR project increased by two orders of magnitude. To handle this massive increase, a completely new technical backend infrastructure for the EDGAR platform was designed and launched as EDGAR3.0. For the calculation of new EDGAR3.0 projects, we are now using a scalable Kubernetes cluster running in a cloud environment. A new storage infrastructure was developed using a file-based high-performance storage backend which ensures timely data handling and efficient access. The new data backend guarantees a memory efficient calculation of orthologs, and parallelization has led to drastically reduced processing times. Based on the advanced technical infrastructure new analysis features could be implemented including POCP and FastANI genomes similarity indices, UpSet intersecting set visualization, and circular genome plots. Also the public database section of EDGAR was largely updated and now offers access to 24,317 genomes in 749 free-to-use projects. In summary, EDGAR 3.0 provides a new, scalable infrastructure for comprehensive microbial comparative gene content analysis. The web server is accessible at http://edgar3.computational.bio.