BacFITBase: a database to assess the relevance of bacterial genes during host infection

Towards interoperability in infection control: a standard data model for microbiology

The COVID-19 pandemic has made it clear: sharing and exchanging data among research institutions is crucial in order to efficiently respond to global health threats. This can be facilitated by defining health data models based on interoperability standards. In Germany, a national effort is in progress to create common data models using international healthcare IT standards. In this context, collaborative work on a data set module for microbiology is of particular importance as the WHO has declared antimicrobial resistance one of the top global public health threats that humanity is facing. In this article, we describe how we developed a common model for microbiology data in an interdisciplinary collaborative effort and how we make use of the standard HL7 FHIR and terminologies such as SNOMED CT or LOINC to ensure syntactic and semantic interoperability. The use of international healthcare standards qualifies our data model to be adopted beyond the environment where it was first developed and used at an international level.

HPIPred: Host-pathogen interactome prediction with phenotypic scoring

Protein-protein interactions (PPIs) are involved in most cellular processes. Unfortunately, current knowledge of host-pathogen interactomes is still very limited. Experimental methods used to detect PPIs have several limitations, including increasing complexity and economic cost in large-scale screenings. Hence, computational methods are commonly used to support experimental data, although they generally suffer from high false-positive rates. To address this issue, we have created HPIPred, a host-pathogen PPI prediction tool based on numerical encoding of physicochemical properties. Unlike other available methods, HPIPred integrates phenotypic data to prioritize biologically meaningful results. We used HPIPred to screen the entire Homo sapiens and Pseudomonas aeruginosa PAO1 proteomes to generate a host-pathogen interactome with 763 interactions displaying a highly connected network topology. Our predictive model can be used to prioritize protein-protein interactions as potential targets for antibacterial drug development. Available at: https://github.com/SysBioUAB/hpi_predictor.

Bile salts regulate zinc uptake and capsule synthesis in a mastitis-associated extraintestinal pathogenic Escherichia coli strain

Extraintestinal pathogenic Escherichia coli (ExPEC) are major causes of urinary and bloodstream infections. ExPEC reservoirs are not completely understood. Some mastitis-associated E. coli (MAEC) strains carry genes associated with ExPEC virulence, including metal scavenging, immune avoidance, and host attachment functions. In this study, we investigated the role of the high-affinity zinc uptake (znuABC) system in the MAEC strain M12. Elimination of znuABC moderately decreased fitness during mouse mammary gland infections. The ΔznuABC mutant strain exhibited an unexpected growth delay in the presence of bile salts, which was alleviated by the addition of excess zinc. We isolated ΔznuABC mutant suppressor mutants with improved growth of in bile salts, several of which no longer produced the K96 capsule made by strain M12. Addition of bile salts also reduced capsule production by strain M12 and ExPEC strain CP9, suggesting that capsule synthesis may be detrimental when bile salts are present. To better understand the role of the capsule, we compared the virulence of mastitis strain M12 with its unencapsulated ΔkpsCS mutant in two models of ExPEC disease. The wild type strain successfully colonized mouse bladders and kidneys and was highly virulent in intraperitoneal infections. Conversely, the ΔkpsCS mutant was unable to colonize kidneys and was unable to cause sepsis. These results demonstrate that some MAEC may be capable of causing human ExPEC illness. Virulence of strain M12 in these infections is dependent on its capsule. However, capsule may interfere with zinc homeostasis in the presence of bile salts while in the digestive tract.

Time-Resolved Transcriptional Profiling of Epithelial Cells Infected by Intracellular Acinetobacter baumannii

The rise in the number of antibiotic-resistant bacteria has become a serious threat to health, making it important to identify, characterize and optimize new molecules to help us to overcome the infections they cause. It is well known that Acinetobacter baumannii has a significant capacity to evade the actions of antibacterial drugs, leading to its emergence as one of the bacteria responsible for hospital and community-acquired infections. Nonetheless, how this pathogen infects and survives inside the host cell is unclear. In this study, we analyze the time-resolved transcriptional profile changes observed in human epithelial HeLa cells after infection by A. baumannii, demonstrating how it survives in host cells and starts to replicate 4 h post infection. These findings were achieved by sequencing RNA to obtain a set of Differentially Expressed Genes (DEGs) to understand how bacteria alter the host cells’ environment for their own benefit. We also determine common features observed in this set of genes and identify the protein–protein networks that reveal highly-interacted proteins. The combination of these findings paves the way for the discovery of new antimicrobial candidates for the treatment of multidrug-resistant bacteria.

Pangenome Analytics Reveal Two-Component Systems as Conserved Targets in ESKAPEE Pathogens

The ESKAPEE pathogens are the leading cause of health care-associated infections worldwide. Two-component systems (TCSs) can be used as effective targets against pathogenic bacteria since they are ubiquitous and manage various vital functions such as antibiotic resistance, virulence, biofilms, quorum sensing, and pH balance, among others.

The two-component system (TCS) helps bacteria sense and respond to environmental stimuli through histidine kinases and response regulators. TCSs are the largest family of multistep signal transduction processes, and they are involved in many important cellular processes such as antibiotic resistance, pathogenicity, quorum sensing, osmotic stress, and biofilms. Here, we perform the first comprehensive study to highlight the role of TCSs as potential drug targets against ESKAPEE (Enterococcus faecium, Staphylococcus aureus, Klebsiella pneumoniae, Acinetobacter baumannii, Pseudomonas aeruginosa, Enterobacter spp., and Escherichia coli) pathogens through annotation, mapping, pangenomic status, gene orientation, and sequence variation analysis. The distribution of the TCSs is group specific with regard to Gram-positive and Gram-negative bacteria, except for KdpDE. The TCSs among ESKAPEE pathogens form closed pangenomes, except for Pseudomonas aeruginosa. Furthermore, their conserved nature due to closed pangenomes might make them good drug targets. Fitness score analysis suggests that any mutation in some TCSs such as BaeSR, ArcBA, EvgSA, and AtoSC, etc., might be lethal to the cell. Taken together, the results of this pangenomic assessment of TCSs reveal a range of strategies deployed by the ESKAPEE pathogens to manifest pathogenicity and antibiotic resistance. This study further suggests that the conserved features of TCSs might make them an attractive group of potential targets with which to address antibiotic resistance.

IMPORTANCE The ESKAPEE pathogens are the leading cause of health care-associated infections worldwide. Two-component systems (TCSs) can be used as effective targets against pathogenic bacteria since they are ubiquitous and manage various vital functions such as antibiotic resistance, virulence, biofilms, quorum sensing, and pH balance, among others. This study provides a comprehensive overview of the pangenomic status of the TCSs among ESKAPEE pathogens. The annotation and pangenomic analysis of TCSs show that they are significantly distributed and conserved among the pathogens, as most of them form closed pangenomes. Furthermore, our analysis also reveals that the removal of the TCSs significantly affects the fitness of the cell. Hence, they may be used as promising drug targets against bacteria.

DualSeqDB: the host-pathogen dual RNA sequencing database for infection processes

Despite antibiotic resistance being a matter of growing concern worldwide, the bacterial mechanisms of pathogenesis remain underexplored, restraining our ability to develop new antimicrobials. The rise of high-throughput sequencing technology has made available a massive amount of transcriptomic data that could help elucidate the mechanisms underlying bacterial infection. Here, we introduce the DualSeqDB database, a resource that helps the identification of gene transcriptional changes in both pathogenic bacteria and their natural hosts upon infection. DualSeqDB comprises nearly 300 000 entries from eight different studies, with information on bacterial and host differential gene expression under in vivo and in vitro conditions. Expression data values were calculated entirely from raw data and analyzed through a standardized pipeline to ensure consistency between different studies. It includes information on seven different strains of pathogenic bacteria and a variety of cell types and tissues in Homo sapiens, Mus musculus and Macaca fascicularis at different time points. We envisage that DualSeqDB can help the research community in the systematic characterization of genes involved in host infection and help the development and tailoring of new molecules against infectious diseases. DualSeqDB is freely available at http://www.tartaglialab.com/dualseq.

Bacteria use structural imperfect mimicry to hijack the host interactome

Bacteria use protein-protein interactions to infect their hosts and hijack fundamental pathways, which ensures their survival and proliferation. Hence, the infectious capacity of the pathogen is closely related to its ability to interact with host proteins. Here, we show that hubs in the host-pathogen interactome are isolated in the pathogen network by adapting the geometry of the interacting interfaces. An imperfect mimicry of the eukaryotic interfaces allows pathogen proteins to actively bind to the host's target while preventing deleterious effects on the pathogen interactome. Understanding how bacteria recognize eukaryotic proteins may pave the way for the rational design of new antibiotic molecules.