Bacteria use structural imperfect mimicry to hijack the host interactome

Diverse genetic conflicts mediated by molecular mimicry and computational approaches to detect them

In genetic conflicts between intergenomic and selfish elements, driver and killer elements achieve biased survival, replication, or transmission over sensitive and targeted elements through a wide range of molecular mechanisms, including mimicry. Driving mechanisms manifest at all organismal levels, from the biased propagation of individual genes, as demonstrated by transposable elements, to the biased transmission of genomes, as illustrated by viruses, to the biased transmission of cell lineages, as in cancer. Targeted genomes are vulnerable to molecular mimicry through the conserved motifs they use for their own signaling and regulation. Mimicking these motifs enables an intergenomic or selfish element to control core target processes, and can occur at the sequence, structure, or functional level. Molecular mimicry was first appreciated as an important phenomenon more than twenty years ago. Modern genomics technologies, databases, and machine learning approaches offer tremendous potential to study the distribution of molecular mimicry across genetic conflicts in nature. Here, we explore the theoretical expectations for molecular mimicry between conflicting genomes, the trends in molecular mimicry mechanisms across known genetic conflicts, and outline how new examples can be gleaned from population genomic datasets. We discuss how mimics involving short sequence-based motifs or gene duplications can evolve convergently from new mutations. Whereas, processes that involve divergent domains or fully-folded structures occur among genomes by horizontal gene transfer. These trends are largely based on a small number of organisms and should be reevaluated in a general, phylogenetically independent framework. Currently, publicly available databases can be mined for genotypes driving non-Mendelian inheritance patterns, epistatic interactions, and convergent protein structures. A subset of these conflicting elements may be molecular mimics. We propose approaches for detecting genetic conflict and molecular mimicry from these datasets.

SNAREs: a double-edged sword for intravacuolar bacterial pathogens within host cells

In the tug-of-war between host and pathogen, both evolve to combat each other's defence arsenals. Intracellular phagosomal bacteria have developed strategies to modify the vacuolar niche to suit their requirements best. Conversely, the host tries to target the pathogen-containing vacuoles towards the degradative pathways. The host cells use a robust system through intracellular trafficking to maintain homeostasis inside the cellular milieu. In parallel, intracellular bacterial pathogens have coevolved with the host to harbour strategies to manipulate cellular pathways, organelles, and cargoes, facilitating the conversion of the phagosome into a modified pathogen-containing vacuole (PCV). Key molecular regulators of intracellular traffic, such as changes in the organelle (phospholipid) composition, recruitment of small GTPases and associated effectors, soluble N-ethylmaleimide-sensitive factor-activating protein receptors (SNAREs), etc., are hijacked to evade lysosomal degradation. Legionella, Salmonella, Coxiella, Chlamydia, Mycobacterium, and Brucella are examples of pathogens which diverge from the endocytic pathway by using effector-mediated mechanisms to overcome the challenges and establish their intracellular niches. These pathogens extensively utilise and modulate the end processes of secretory pathways, particularly SNAREs, in repurposing the PCV into specialised compartments resembling the host organelles within the secretory network; at the same time, they avoid being degraded by the host's cellular mechanisms. Here, we discuss the recent research advances on the host-pathogen interaction/crosstalk that involves host SNAREs, conserved cellular processes, and the ongoing host-pathogen defence mechanisms in the molecular arms race against each other. The current knowledge of SNAREs, and intravacuolar bacterial pathogen interactions, enables us to understand host cellular innate immune pathways, maintenance of homeostasis, and potential therapeutic strategies to combat ever-growing antimicrobial resistance.

Structural assembly of the bacterial essential interactome

The study of protein interactions in living organisms is fundamental for understanding biological processes and central metabolic pathways. Yet, our knowledge of the bacterial interactome remains limited. Here, we combined gene deletion mutant analysis with deep-learning protein folding using AlphaFold2 to predict the core bacterial essential interactome. We predicted and modeled 1402 interactions between essential proteins in bacteria and generated 146 high-accuracy models. Our analysis reveals previously unknown details about the assembly mechanisms of these complexes, highlighting the importance of specific structural features in their stability and function. Our work provides a framework for predicting the essential interactomes of bacteria and highlight the potential of deep-learning algorithms in advancing our understanding of the complex biology of living organisms. Also, the results presented here offer a promising approach to identify novel antibiotic targets.

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.

Fungal Effectoromics: A World in Constant Evolution

Effectors are small, secreted molecules that mediate the establishment of interactions in nature. While some concepts of effector biology have stood the test of time, this area of study is ever-evolving as new effectors and associated characteristics are being revealed. In the present review, the different characteristics that underly effector classifications are discussed, contrasting past and present knowledge regarding these molecules to foster a more comprehensive understanding of effectors for the reader. Research gaps in effector identification and perspectives for effector application in plant disease management are also presented, with a focus on fungal effectors in the plant-microbe interaction and interactions beyond the plant host. In summary, the review provides an amenable yet thorough introduction to fungal effector biology, presenting noteworthy examples of effectors and effector studies that have shaped our present understanding of the field.

Analysis of Host–Bacteria Protein Interactions Reveals Conserved Domains and Motifs That Mediate Fundamental Infection Pathways

Adhesion and colonization of host cells by pathogenic bacteria depend on protein–protein interactions (PPIs). These interactions are interesting from the pharmacological point of view since new molecules that inhibit host-pathogen PPIs would act as new antimicrobials. Most of these interactions are discovered using high-throughput methods that may display a high false positive rate. The absence of curation of these databases can make the available data unreliable. To address this issue, a comprehensive filtering process was developed to obtain a reliable list of domains and motifs that participate in PPIs between bacteria and human cells. From a structural point of view, our analysis revealed that human proteins involved in the interactions are rich in alpha helix and disordered regions and poorer in beta structure. Disordered regions in human proteins harbor short sequence motifs that are specifically recognized by certain domains in pathogenic proteins. The most relevant domain–domain interactions were validated by AlphaFold, showing that a proper analysis of host-pathogen PPI databases can reveal structural conserved patterns. Domain–motif interactions, on the contrary, were more difficult to validate, since unstructured regions were involved, where AlphaFold could not make a good prediction. Moreover, these interactions are also likely accommodated by post-translational modifications, especially phosphorylation, which can potentially occur in 25–50% of host proteins. Hence, while common structural patterns are involved in host–pathogen PPIs and can be retrieved from available databases, more information is required to properly infer the full interactome. By resolving these issues, and in combination with new prediction tools like Alphafold, new classes of antimicrobials could be discovered from a more detailed understanding of these interactions.

α-Helices in the Type III Secretion Effectors: A Prevalent Feature with Versatile Roles

Type III Secretion Systems (T3SSs) are multicomponent nanomachines located at the cell envelope of Gram-negative bacteria. Their main function is to transport bacterial proteins either extracellularly or directly into the eukaryotic host cell cytoplasm. Type III Secretion effectors (T3SEs), latest to be secreted T3S substrates, are destined to act at the eukaryotic host cell cytoplasm and occasionally at the nucleus, hijacking cellular processes through mimicking eukaryotic proteins. A broad range of functions is attributed to T3SEs, ranging from the manipulation of the host cell's metabolism for the benefit of the bacterium to bypassing the host's defense mechanisms. To perform this broad range of manipulations, T3SEs have evolved numerous novel folds that are compatible with some basic requirements: they should be able to easily unfold, pass through the narrow T3SS channel, and refold to an active form when on the other side. In this review, the various folds of T3SEs are presented with the emphasis placed on the functional and structural importance of α-helices and helical domains.

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.