Mapping bacterial effector arsenals: in vivo and in silico approaches to defining the protein features dictating effector secretion by bacteria

T4SEpp: A pipeline integrating protein language models to predict bacterial type IV secreted effectors

Many pathogenic bacteria use type IV secretion systems (T4SSs) to deliver effectors (T4SEs) into the cytoplasm of eukaryotic cells, causing diseases. The identification of effectors is a crucial step in understanding the mechanisms of bacterial pathogenicity, but this remains a major challenge. In this study, we used the full-length embedding features generated by six pre-trained protein language models to train classifiers predicting T4SEs and compared their performance. We integrated three modules into a model called T4SEpp. The first module searched for full-length homologs of known T4SEs, signal sequences, and effector domains; the second module fine-tuned a machine learning model using data for a signal sequence feature; and the third module used the three best-performing pre-trained protein language models. T4SEpp outperformed other state-of-the-art (SOTA) software tools, achieving ∼0.98 accuracy at a high specificity of ∼0.99, based on the assessment of an independent validation dataset. T4SEpp predicted 13 T4SEs from Helicobacter pylori, including the well-known CagA and 12 other potential ones, among which eleven could potentially interact with human proteins. This suggests that these potential T4SEs may be associated with the pathogenicity of H. pylori. Overall, T4SEpp provides a better solution to assist in the identification of bacterial T4SEs and facilitates studies of bacterial pathogenicity. T4SEpp is freely accessible at https://bis.zju.edu.cn/T4SEpp.

Fungtion: A Server for Predicting and Visualizing Fungal Effector Proteins

Fungal pathogens pose significant threats to plant health by secreting effectors that manipulate plant-host defences. However, identifying effector proteins remains challenging, in part because they lack common sequence motifs. Here, we introduce Fungtion (Fungal effector prediction), a toolkit leveraging a hybrid framework to accurately predict and visualize fungal effectors. By combining global patterns learned from pretrained protein language models with refined information from known effectors, Fungtion achieves state-of-the-art prediction performance. Additionally, the interactive visualizations we have developed enable researchers to explore both sequence- and high-level relationships between the predicted and known effectors, facilitating effector function discovery, annotation, and hypothesis formulation regarding plant-pathogen interactions. We anticipate Fungtion to be a valuable resource for biologists seeking deeper insights into fungal effector functions and for computational biologists aiming to develop future methodologies for fungal effector prediction: https://step3.erc.monash.edu/Fungtion/.

Use of Bastion for the Identification of Secreted Substrates

Bacteria use secretion systems to translocate numerous proteins into and across cell membranes, but have evolved more specialized secretion systems that can disrupt the normal cellular processes of host cells and compete bacteria or protect the bacteria from host defenses. Among them, Gram-negative bacteria utilize a variety of different proteins secreted by Type 1 to Type 6 secretion systems to transfer substrates into target cells or the surrounding environment, which play key roles in disease and survival. Therefore, these secreted proteins have attracted the attention of a wealth of researchers. The first step to characterizing new substrates of secretion systems is typically identifying candidates bioinformatically, and the Bastion series of substrate predictors provide biologists machine learning tools that can accurately predict these substrates. This chapter will explain how to use the Bastion series for identifying and analyzing secreted substrates in Gram-negative bacteria.

Features and algorithms: facilitating investigation of secreted effectors in Gram-negative bacteria

Gram-negative bacteria deliver effector proteins through type III, IV, or VI secretion systems (T3SSs, T4SSs, and T6SSs) into host cells, causing infections and diseases. In general, effector proteins for each of these distinct secretion systems lack homology and are difficult to identify. Sequence analysis has disclosed many common features, helping us to understand the evolution, function, and secretion mechanisms of the effectors. In combination with various algorithms, the known common features have facilitated accurate prediction of new effectors. Ensemblers or integrated pipelines achieve a better prediction of performance, which combines multiple computational models or modules with multidimensional features. Natural language processing (NLP) models also show the merits, which could enable discovery of novel features and, in turn, facilitate more precise effector prediction, extending our knowledge about each secretion mechanism.

PncsHub: a platform for annotating and analyzing non-classically secreted proteins in Gram-positive bacteria

From industry to food to health, bacteria play an important role in all facets of life. Some of the most important bacteria have been purposely engineered to produce commercial quantities of antibiotics and therapeutics, and non-classical secretion systems are at the forefront of these technologies. Unlike the classical Sec or Tat pathways, non-classically secreted proteins share few common characteristics and use much more diverse secretion pathways for protein transport. Systematically categorizing and investigating the non-classically secreted proteins will enable a deeper understanding of their associated secretion mechanisms and provide a landscape of the Gram-positive secretion pathway distribution. We therefore developed PncsHub (https://pncshub.erc.monash.edu/), the first universal platform for comprehensively annotating and analyzing Gram-positive bacterial non-classically secreted proteins. PncsHub catalogs 4,914 non-classically secreted proteins, which are delicately categorized into 8 subtypes (including the 'unknown' subtype) and annotated with data compiled from up to 26 resources and visualisation tools. It incorporates state-of-the-art predictors to identify new and homologous non-classically secreted proteins and includes three analytical modules to visualise the relationships between known and putative non-classically secreted proteins. As such, PncsHub aims to provide integrated services for investigating, predicting and identifying non-classically secreted proteins to promote hypothesis-driven laboratory-based experiments.

T4SEfinder: a bioinformatics tool for genome-scale prediction of bacterial type IV secreted effectors using pre-trained protein language model

Bacterial type IV secretion systems (T4SSs) are versatile and membrane-spanning apparatuses, which mediate both genetic exchange and delivery of effector proteins to target eukaryotic cells. The secreted effectors (T4SEs) can affect gene expression and signal transduction of the host cells. As such, they often function as virulence factors and play an important role in bacterial pathogenesis. Nowadays, T4SE prediction tools have utilized various machine learning algorithms, but the accuracy and speed of these tools remain to be improved. In this study, we apply a sequence embedding strategy from a pre-trained language model of protein sequences (TAPE) to the classification task of T4SEs. The training dataset is mainly derived from our updated type IV secretion system database SecReT4 with newly experimentally verified T4SEs. An online web server termed T4SEfinder is developed using TAPE and a multi-layer perceptron (MLP) for T4SE prediction after a comprehensive performance comparison with several candidate models, which achieves a slightly higher level of accuracy than the existing prediction tools. It only takes about 3 minutes to make a classification for 5000 protein sequences by T4SEfinder so that the computational speed is qualified for whole genome-scale T4SEs detection in pathogenic bacteria. T4SEfinder might contribute to meet the increasing demands of re-annotating secretion systems and effector proteins in sequenced bacterial genomes. T4SEfinder is freely accessible at https://tool2-mml.sjtu.edu.cn/T4SEfinder_TAPE/.

Integrated mass spectrometry-based multi-omics for elucidating mechanisms of bacterial virulence

Despite being considered the simplest form of life, bacteria remain enigmatic, particularly in light of pathogenesis and evolving antimicrobial resistance. After three decades of genomics, we remain some way from understanding these organisms, and a substantial proportion of genes remain functionally unknown. Methodological advances, principally mass spectrometry (MS), are paving the way for parallel analysis of the proteome, metabolome and lipidome. Each provides a global, complementary assay, in addition to genomics, and the ability to better comprehend how pathogens respond to changes in their internal (e.g. mutation) and external environments consistent with infection-like conditions. Such responses include accessing necessary nutrients for survival in a hostile environment where co-colonizing bacteria and normal flora are acclimated to the prevailing conditions. Multi-omics can be harnessed across temporal and spatial (sub-cellular) dimensions to understand adaptation at the molecular level. Gene deletion libraries, in conjunction with large-scale approaches and evolving bioinformatics integration, will greatly facilitate next-generation vaccines and antimicrobial interventions by highlighting novel targets and pathogen-specific pathways. MS is also central in phenotypic characterization of surface biomolecules such as lipid A, as well as aiding in the determination of protein interactions and complexes. There is increasing evidence that bacteria are capable of widespread post-translational modification, including phosphorylation, glycosylation and acetylation; with each contributing to virulence. This review focuses on the bacterial genotype to phenotype transition and surveys the recent literature showing how the genome can be validated at the proteome, metabolome and lipidome levels to provide an integrated view of organism response to host conditions.

Structural biology of the invasion arsenal of Gram-negative bacterial pathogens

In the last several years, there has been a tremendous progress in the understanding of host-pathogen interactions and the mechanisms by which bacterial pathogens modulate behavior of the host cell. Pathogens use secretion systems to inject a set of proteins, called effectors, into the cytosol of the host cell. These effectors are secreted in a highly regulated, temporal manner and interact with host proteins to modify a multitude of cellular processes. The number of effectors varies between pathogens from ~ 30 to as many as ~ 350. The functional redundancy of effectors encoded by each pathogen makes it difficult to determine the cellular effects or function of individual effectors, since their individual knockouts frequently produce no easily detectable phenotypes. Structural biology of effector proteins and their interactions with host proteins, in conjunction with cell biology approaches, has provided invaluable information about the cellular function of effectors and underlying molecular mechanisms of their modes of action. Many bacterial effectors are functionally equivalent to host proteins while being structurally divergent from them. Other effector proteins display new, previously unobserved functionalities. Here, we summarize the contribution of the structural characterization of effectors and effector-host protein complexes to our understanding of host subversion mechanisms used by the most commonly investigated Gram-negative bacterial pathogens. We describe in some detail the enzymatic activities discovered among effector proteins and how they affect various cellular processes.

BastionHub: a universal platform for integrating and analyzing substrates secreted by Gram-negative bacteria

Gram-negative bacteria utilize secretion systems to export substrates into their surrounding environment or directly into neighboring cells. These substrates are proteins that function to promote bacterial survival: by facilitating nutrient collection, disabling competitor species or, for pathogens, to disable host defenses. Following a rapid development of computational techniques, a growing number of substrates have been discovered and subsequently validated by wet lab experiments. To date, several online databases have been developed to catalogue these substrates but they have limited user options for in-depth analysis, and typically focus on a single type of secreted substrate. We therefore developed a universal platform, BastionHub, that incorporates extensive functional modules to facilitate substrate analysis and integrates the five major Gram-negative secreted substrate types (i.e. from types I-IV and VI secretion systems). To our knowledge, BastionHub is not only the most comprehensive online database available, it is also the first to incorporate substrates secreted by type I or type II secretion systems. By providing the most up-to-date details of secreted substrates and state-of-the-art prediction and visualized relationship analysis tools, BastionHub will be an important platform that can assist biologists in uncovering novel substrates and formulating new hypotheses. BastionHub is freely available at http://bastionhub.erc.monash.edu/.

Proteomic Identification of Coxiella burnetii Effector Proteins Targeted to the Host Cell Mitochondria During Infection

Modulation of the host cell is integral to the survival and replication of microbial pathogens. Several intracellular bacterial pathogens deliver bacterial proteins, termed "effector proteins" into the host cell during infection by sophisticated protein translocation systems, which manipulate cellular processes and functions. The functional contribution of individual effectors is poorly characterized, particularly in intracellular bacterial pathogens with large effector protein repertoires. Technical caveats have limited the capacity to study these proteins during a native infection, with many effector proteins having only been demonstrated to be translocated during over-expression of tagged versions. Here, we developed a novel strategy to examine effector proteins in the context of infection. We coupled a broad, unbiased proteomics-based screen with organelle purification to study the host-pathogen interactions occurring between the host cell mitochondrion and the Gram-negative, Q fever pathogen Coxiella burnetii. We identify four novel mitochondrially-targeted C. burnetii effector proteins, renamed Mitochondrial Coxiella effector protein (Mce) B to E. Examination of the subcellular localization of ectopically expressed proteins confirmed their mitochondrial localization, demonstrating the robustness of our approach. Subsequent biochemical analysis and affinity enrichment proteomics of one of these effector proteins, MceC, revealed the protein localizes to the inner membrane and can interact with components of the mitochondrial quality control machinery. Our study adapts high-sensitivity proteomics to study intracellular host-pathogen interactions, providing a robust strategy to examine the subcellular localization of effector proteins during native infection. This approach could be applied to a range of pathogens and host cell compartments to provide a rich map of effector dynamics throughout infection.