Reconstruction of the temporal signaling network in Salmonella-infected human cells

phuEGO: A Network-Based Method to Reconstruct Active Signaling Pathways From Phosphoproteomics Datasets

Signaling networks are critical for virtually all cell functions. Our current knowledge of cell signaling has been summarized in signaling pathway databases, which, while useful, are highly biased toward well-studied processes, and do not capture context specific network wiring or pathway cross-talk. Mass spectrometry-based phosphoproteomics data can provide a more unbiased view of active cell signaling processes in a given context, however, it suffers from low signal-to-noise ratio and poor reproducibility across experiments. While progress in methods to extract active signaling signatures from such data has been made, there are still limitations with respect to balancing bias and interpretability. Here we present phuEGO, which combines up-to-three-layer network propagation with ego network decomposition to provide small networks comprising active functional signaling modules. PhuEGO boosts the signal-to-noise ratio from global phosphoproteomics datasets, enriches the resulting networks for functional phosphosites and allows the improved comparison and integration across datasets. We applied phuEGO to five phosphoproteomics data sets from cell lines collected upon infection with SARS CoV2. PhuEGO was better able to identify common active functions across datasets and to point to a subnetwork enriched for known COVID-19 targets. Overall, phuEGO provides a flexible tool to the community for the improved functional interpretation of global phosphoproteomics 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.

Resistance-Nodulation-Cell Division (RND) Transporter AcrD Confers Resistance to Egg White in Salmonella enterica Serovar Enteritidis

The excellent survival ability of Salmonella enterica serovar Enteritidis (S. Enteritidis) in egg white leads to outbreaks of salmonellosis frequently associated with eggs and egg products. Our previous proteomic study showed that the expression of multidrug efflux RND transporter AcrD in S. Enteritidis was significantly up-regulated (4.06-fold) in response to an egg white environment. In this study, the potential role of AcrD in the resistance of S. Enteritidis to egg white was explored by gene deletion, survival ability test, morphological observation, Caco-2 cell adhesion and invasion. It was found that deletion of acrD had no apparent effect on the growth of S. Enteritidis in Luria-Bertani (LB) broth but resulted in a significant (p < 0.05) decrease in resistance of S. Enteritidis to egg white and a small number of cell lysis. Compared to the wild type, a 2-log population reduction was noticed in the ΔacrD mutant with different initial concentrations after incubation with egg white for 3 days. Furthermore, no significant difference (p > 0.05) in the adhesion and invasion was found between the wild type and ΔacrD mutant in LB broth and egg white, but the invasion ability of the ΔacrD mutant in egg white was significantly (p < 0.05) lower than that in LB broth. This indicates that acrD is involved in virulence in Salmonella. Taken together, these results reveal the importance of AcrD on the resistance of S. Enteritidis to egg white.

Proximity Interactome Map of the Vac14-Fig4 Complex Using BioID

Conversion between phosphatidylinositol-3-phosphate and phosphatidylinositol-3,5-bisphosphate on endosomal membranes is critical for the maturation of early endosomes to late endosomes/lysosomes and is regulated by the PIKfyve-Vac14-Fig4 complex. Despite the importance of this complex for endosomal homeostasis and vesicular trafficking, there is little known about how its activity is regulated or how it interacts with other cellular proteins. Here, we screened for the cellular interactome of Vac14 and Fig4 using proximity-dependent biotin labeling (BioID). After independently screening the interactomes of Vac14 and Fig4, we identified 89 high-confidence protein hits shared by both proteins. Network analysis of these hits revealed pathways with known involvement of the PIKfyve-Vac14-Fig4 complex, including vesicular organization and PI3K/Akt signaling, as well as novel pathways including cell cycle and mitochondrial regulation. We also identified subunits of coatomer complex I (COPI), a Golgi-associated complex with an emerging role in endosomal dynamics. Using proximity ligation assays, we validated the interaction between Vac14 and COPI subunit COPB1 and between Vac14 and Arf1, a GTPase required for COPI assembly. In summary, this study used BioID to comprehensively map the Vac14-Fig4 interactome, revealing potential roles for these proteins in diverse cellular processes and pathways, including preliminary evidence of an interaction between Vac14 and COPI. Data are available via ProteomeXchange with the identifier PXD027917.

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.

Automating parameter selection to avoid implausible biological pathway models

A common way to integrate and analyze large amounts of biological "omic" data is through pathway reconstruction: using condition-specific omic data to create a subnetwork of a generic background network that represents some process or cellular state. A challenge in pathway reconstruction is that adjusting pathway reconstruction algorithms' parameters produces pathways with drastically different topological properties and biological interpretations. Due to the exploratory nature of pathway reconstruction, there is no ground truth for direct evaluation, so parameter tuning methods typically used in statistics and machine learning are inapplicable. We developed the pathway parameter advising algorithm to tune pathway reconstruction algorithms to minimize biologically implausible predictions. We leverage background knowledge in pathway databases to select pathways whose high-level structure resembles that of manually curated biological pathways. At the core of this method is a graphlet decomposition metric, which measures topological similarity to curated biological pathways. In order to evaluate pathway parameter advising, we compare its performance in avoiding implausible networks and reconstructing pathways from the NetPath database with other parameter selection methods across four pathway reconstruction algorithms. We also demonstrate how pathway parameter advising can guide reconstruction of an influenza host factor network. Pathway parameter advising is method agnostic; it is applicable to any pathway reconstruction algorithm with tunable parameters.

Inferring signaling pathways with probabilistic programming

Motivation

Cells regulate themselves via dizzyingly complex biochemical processes called signaling pathways. These are usually depicted as a network, where nodes represent proteins and edges indicate their influence on each other. In order to understand diseases and therapies at the cellular level, it is crucial to have an accurate understanding of the signaling pathways at work. Since signaling pathways can be modified by disease, the ability to infer signaling pathways from condition- or patient-specific data is highly valuable. A variety of techniques exist for inferring signaling pathways. We build on past works that formulate signaling pathway inference as a Dynamic Bayesian Network structure estimation problem on phosphoproteomic time course data. We take a Bayesian approach, using Markov Chain Monte Carlo to estimate a posterior distribution over possible Dynamic Bayesian Network structures. Our primary contributions are (i) a novel proposal distribution that efficiently samples sparse graphs and (ii) the relaxation of common restrictive modeling assumptions.

Results

We implement our method, named Sparse Signaling Pathway Sampling, in Julia using the Gen probabilistic programming language. Probabilistic programming is a powerful methodology for building statistical models. The resulting code is modular, extensible and legible. The Gen language, in particular, allows us to customize our inference procedure for biological graphs and ensure efficient sampling. We evaluate our algorithm on simulated data and the HPN-DREAM pathway reconstruction challenge, comparing our performance against a variety of baseline methods. Our results demonstrate the vast potential for probabilistic programming, and Gen specifically, for biological network inference.

Availability and implementation

Find the full codebase at https://github.com/gitter-lab/ssps.

Supplementary information

Supplementary data are available at Bioinformatics online.

Targeted interplay between bacterial pathogens and host autophagy

Due to the critical role played by autophagy in pathogen clearance, pathogens have developed diverse strategies to subvert it. Despite previous key findings of bacteria-autophagy interplay, asystems-level insight into selective targeting by the host and autophagy modulation by the pathogens is lacking. We predicted potential interactions between human autophagy proteins and effector proteins from 56 pathogenic bacterial species by identifying bacterial proteins predicted to have recognition motifs for selective autophagy receptors SQSTM1/p62, CALCOCO2/NDP52 and MAP1LC3/LC3. Using structure-based interaction prediction, we identified bacterial proteins capable to modify core autophagy components. Our analysis revealed that autophagy receptors in general potentially target mostly genus-specific proteins, and not those present in multiple genera. The complementarity between the predicted SQSTM1/p62 and CALCOCO2/NDP52 targets, which has been shown for Salmonella, Listeria and Shigella, could be observed across other pathogens. This complementarity potentially leaves the host more susceptible to chronic infections upon the mutation of autophagy receptors. Proteins derived from enterotoxigenic and non-toxigenic Bacillus outer membrane vesicles indicated that autophagy targets pathogenic proteins rather than non-pathogenic ones. We also observed apathogen-specific pattern as to which autophagy phase could be modulated by specific genera. We found intriguing examples of bacterial proteins that could modulate autophagy, and in turn being targeted by autophagy as ahost defense mechanism. We confirmed experimentally an interplay between a Salmonella protease, YhjJ and autophagy. Our comparative meta-analysis points out key commonalities and differences in how pathogens could affect autophagy and how autophagy potentially recognizes these pathogenic effectors. Abbreviations: ATG5: autophagy related 5; CALCOCO2/NDP52: calcium binding and coiled-coil domain 2; GST: glutathione S-transferase; LIR: MAP1LC3/LC3-interacting region; MAP1LC3/LC3: microtubule associated protein 1 light chain 3 alpha; OMV: outer membrane vesicles; SQSTM1/p62: sequestosome 1; SCV: Salmonella containing vesicle; TECPR1: tectonin beta-propeller repeat containing 1; YhjJ: hypothetical zinc-protease.

Elucidating synergistic dependencies in lung adenocarcinoma by proteome-wide signaling-network analysis

To understand drug combination effect, it is necessary to decipher the interactions between drug targets-many of which are signaling molecules. Previously, such signaling pathway models are largely based on the compilation of literature data from heterogeneous cellular contexts. Indeed, de novo reconstruction of signaling interactions from large-scale molecular profiling is still lagging, compared to similar efforts in transcriptional and protein-protein interaction networks. To address this challenge, we introduce a novel algorithm for the systematic inference of protein kinase pathways, and applied it to published mass spectrometry-based phosphotyrosine profile data from 250 lung adenocarcinoma (LUAD) samples. The resulting network includes 43 TKs and 415 inferred, LUAD-specific substrates, which were validated at >60% accuracy by SILAC assays, including "novel' substrates of the EGFR and c-MET TKs, which play a critical oncogenic role in lung cancer. This systematic, data-driven model supported drug response prediction on an individual sample basis, including accurate prediction and validation of synergistic EGFR and c-MET inhibitor activity in cells lacking mutations in either gene, thus contributing to current precision oncology efforts.

Synthesizing Signaling Pathways from Temporal Phosphoproteomic Data

We present a method for automatically discovering signaling pathways from time-resolved phosphoproteomic data. The Temporal Pathway Synthesizer (TPS) algorithm uses constraint-solving techniques first developed in the context of formal verification to explore paths in an interaction network. It systematically eliminates all candidate structures for a signaling pathway where a protein is activated or inactivated before its upstream regulators. The algorithm can model more than one hundred thousand dynamic phosphosites and can discover pathway members that are not differentially phosphorylated. By analyzing temporal data, TPS defines signaling cascades without needing to experimentally perturb individual proteins. It recovers known pathways and proposes pathway connections when applied to the human epidermal growth factor and yeast osmotic stress responses. Independent kinase mutant studies validate predicted substrates in the TPS osmotic stress pathway.

Downregulations of AKT/mTOR Signaling Pathway for Salmonella-Mediated Suppression of Matrix Metalloproteinases-9 Expression in Mouse Tumor Models

The roles of Matrix MetalloProteinases (MMPs), such as MMP-9, in tumor metastasis are well studied, and this in turns stimulates the development of MMP inhibitors as antitumor agents. Previously, Salmonella accumulation was observed in the metastatic nodules of the lungs after systemic administration. Salmonella significantly enhanced the survival of the pulmonary metastatic tumor-bearing mice. Based on our previous observation, we hypothesized that Salmonella could affect metastasis-related protein expression. The treatment of Salmonella clearly reduced the expression of MMP-9. Meanwhile, the MMP-9 related signaling pathways, including Phosph-Protein Kinase B (P-AKT) and Phosph-mammalian Targets Of Rapamycin (P-mTOR) were decreased after a Salmonella treatment. The Salmonella inhibited tumor cell migration by wound-healing and Transwell assay. The anti-metastatic effects of Salmonella were evaluated in mice bearing experimental metastasis tumor models. Consequently, Salmonella inhibited the expression of MMP-9 by reducing the AKT/mTOR pathway and metastatic nodules in vivo.

Innate Immune Recognition: Implications for the Interaction of Francisella tularensis with the Host Immune System

The intracellular bacterial pathogen Francisella tularensis causes serious infectious disease in humans and animals. Moreover, F. tularensis, a highly infectious pathogen, poses a major concern for the public as a bacterium classified under Category A of bioterrorism agents. Unfortunately, research has so far failed to develop effective vaccines, due in part to the fact that the pathogenesis of intracellular bacteria is not fully understood and in part to gaps in our understanding of innate immune recognition processes leading to the induction of adaptive immune response. Recent evidence supports the concept that immune response to external stimuli in the form of bacteria is guided by the primary interaction of the bacterium with the host cell. Based on data from different Francisella models, we present here the basic paradigms of the emerging innate immune recognition concept. According to this concept, the type of cell and its receptor(s) that initially interact with the target constitute the first signaling window; the signals produced in the course of primary interaction of the target with a reacting cell act in a paracrine manner; and the innate immune recognition process as a whole consists in a series of signaling windows modulating adaptive immune response. Finally, the host, in the strict sense, is the interacting cell.

Salmonella overcomes tumor immune tolerance by inhibition of tumor indoleamine 2, 3-dioxygenase 1 expression

Over the past decades, Salmonella has been proven capable of inhibiting tumor growth. It can specifically target tumors and due to its facultative anaerobic property, can be more penetrative than other drug therapies. However, the molecular mechanism by which Salmonella inhibits tumor growth is still incompletely known. The antitumor therapeutic effect mediated by Salmonella is associated with an inflammatory immune response at the tumor site and a T cell-dependent immune response. Many tumors have been proven to have a high expression of indoleamine 2, 3-dioxygenase 1 (IDO), which is a rate-limiting enzyme that catalyzes tryptophan to kynurenine, thus causing immune tolerance within the tumor microenvironment. With decreased expression of IDO, increased immune response can be observed, which might be helpful when developing cancer immunotherapy. The expression of IDO was decreased after tumor cells were infected with Salmonella. In addition, Western blot analysis showed that the expression levels of phospho-protein kinase B (P-AKT), phospho-mammalian targets of rapamycin (P-mTOR), and phospho-p70 ribosomal s6 kinase (P-p70s6K) in tumor cells were decreased after Salmonella infection. In conclusion, our results indicate that Salmonella inhibits IDO expression and plays a crucial role in anti-tumor therapy, which might be a promising strategy combined with other cancer treatments.