Using host-pathogen protein interactions to identify and characterize Francisella tularensis virulence factors

Modeling SARS-CoV-2 spike/ACE2 protein-protein interactions for predicting the binding affinity of new spike variants for ACE2, and novel ACE2 structurally related human protein targets, for COVID-19 handling in the 3PM context

Aims

The rapid spread of new SARS-CoV-2 variants has highlighted the crucial role played in the infection by mutations occurring at the SARS-CoV-2 spike receptor binding domain (RBD) in the interactions with the human ACE2 receptor. In this context, it urgently needs to develop new rapid tools for quickly predicting the affinity of ACE2 for the SARS-CoV-2 spike RBD protein variants to be used with the ongoing SARS-CoV-2 genomic sequencing activities in the clinics, aiming to gain clues about the transmissibility and virulence of new variants, to prevent new outbreaks and to quickly estimate the severity of the disease in the context of the 3PM.

Methods

In our study, we used a computational pipeline for calculating the interaction energies at the SARS-CoV-2 spike RBD/ACE2 protein–protein interface for a selected group of characterized infectious variants of concern/interest (VoC/VoI). By using our pipeline, we built 3D comparative models of the SARS-CoV-2 spike RBD/ACE2 protein complexes for the VoC B.1.1.7-United Kingdom (carrying the mutations of concern/interest N501Y, S494P, E484K at the RBD), P.1-Japan/Brazil (RBD mutations: K417T, E484K, N501Y), B.1.351-South Africa (RBD mutations: K417N, E484K, N501Y), B.1.427/B.1.429-California (RBD mutations: L452R), the B.1.141 (RBD mutations: N439K), and the recent B.1.617.1-India (RBD mutations: L452R; E484Q) and the B.1.620 (RBD mutations: S477N; E484K). Then, we used the obtained 3D comparative models of the SARS-CoV-2 spike RBD/ACE2 protein complexes for predicting the interaction energies at the protein–protein interface.

Results

Along SARS-CoV-2 mutation database screening and mutation localization analysis, it was ascertained that the most dangerous mutations at VoC/VoI spike proteins are located mainly at three regions of the SARS-CoV-2 spike “boat-shaped” receptor binding motif, on the RBD domain. Notably, the P.1 Japan/Brazil variant present three mutations, K417T, E484K, N501Y, located along the entire receptor binding motif, which apparently determines the highest interaction energy at the SARS-CoV-2 spike RBD/ACE2 protein–protein interface, among those calculated. Conversely, it was also observed that the replacement of a single acidic/hydrophilic residue with a basic residue (E484K or N439K) at the “stern” or “bow” regions, of the boat-shaped receptor binding motif on the RBD, appears to determine an interaction energy with ACE2 receptor higher than that observed with single mutations occurring at the “hull” region or with other multiple mutants. In addition, our pipeline allowed searching for ACE2 structurally related proteins, i.e., THOP1 and NLN, which deserve to be investigated for their possible involvement in interactions with the SARS-CoV-2 spike protein, in those tissues showing a low expression of ACE2, or as a novel receptor for future spike variants. A freely available web-tool for the in silico calculation of the interaction energy at the SARS-CoV-2 spike RBD/ACE2 protein–protein interface, starting from the sequences of the investigated spike and/or ACE2 variants, was made available for the scientific community at: https://www.mitoairm.it/covid19affinities.

Conclusion

In the context of the PPPM/3PM, the employment of the described pipeline through the provided webservice, together with the ongoing SARS-CoV-2 genomic sequencing, would help to predict the transmissibility of new variants sequenced from future patients, depending on SARS-CoV-2 genomic sequencing activities and on the specific amino acid replacement and/or on its location on the SARS-CoV-2 spike RBD, to put in play all the possible counteractions for preventing the most deleterious scenarios of new outbreaks, taking into consideration that a greater transmissibility has not to be necessarily related to a more severe manifestation of the disease.

Supplementary Information

The online version contains supplementary material available at 10.1007/s13167-021-00267-w.

Cellular and molecular level host-pathogen interactions in Francisella tularensis: A microbial gene network study

Due to the high infectivity and fatal effect on human population, Francisella tularensis (F. tularensis) is classified as a potential biological warfare agent. The interaction between host and pathogen behind the successful establishment of F. tularensis infection within the human host is largely unknown. In our present work, we have studied the molecular level interactions between the host cellular components and F. tularensis genes to understand the interplay between the host and pathogen. Interestingly, we have identified the pathways associated with the pathogen offensive strategies that help in invasion of host defensive systems. The F. tularensis genes purL, katG, proS, rpoB and fusA have displayed high number of interactions with the host genes and thus play a crucial role in vital pathogen pathways. The pathways identified were involved in adaptation to different stress conditions within the host and might be crucial for designing new therapeutic interventions against tularemia.

A systematic bioinformatics approach for large-scale identification and characterization of host-pathogen shared sequences

Background

Biology has entered the era of big data with the advent of high-throughput omics technologies. Biological databases provide public access to petabytes of data and information facilitating knowledge discovery. Over the years, sequence data of pathogens has seen a large increase in the number of records, given the relatively small genome size and their important role as infectious and symbiotic agents. Humans are host to numerous pathogenic diseases, such as that by viruses, many of which are responsible for high mortality and morbidity. The interaction between pathogens and humans over the evolutionary history has resulted in sharing of sequences, with important biological and evolutionary implications.

Results

This study describes a large-scale, systematic bioinformatics approach for identification and characterization of shared sequences between the host and pathogen. An application of the approach is demonstrated through identification and characterization of the Flaviviridae-human share-ome. A total of 2430 nonamers represented the Flaviviridae-human share-ome with 100% identity. Although the share-ome represented a small fraction of the repertoire of Flaviviridae (~ 0.12%) and human (~ 0.013%) non-redundant nonamers, the 2430 shared nonamers mapped to 16,946 Flaviviridae and 7506 human non-redundant protein sequences. The shared nonamer sequences mapped to 125 species of Flaviviridae, including several with unclassified genus. The majority (~ 68%) of the shared sequences mapped to Hepacivirus C species; West Nile, dengue and Zika viruses of the Flavivirus genus accounted for ~ 11%, ~ 7%, and ~ 3%, respectively, of the Flaviviridae protein sequences (16,946) mapped by the share-ome. Further characterization of the share-ome provided important structural-functional insights to Flaviviridae-human interactions.

Conclusion

Mapping of the host-pathogen share-ome has important implications for the design of vaccines and drugs, diagnostics, disease surveillance and the discovery of unknown, potential host-pathogen interactions. The generic workflow presented herein is potentially applicable to a variety of pathogens, such as of viral, bacterial or parasitic origin.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12864-021-07657-4.

Prediction of human-Streptococcus pneumoniae protein-protein interactions using logistic regression

Streptococcus pneumoniae is a major cause of mortality in children under five years old. In recent years, the emergence of antibiotic-resistant strains of S. pneumoniae increases the threat level of this pathogen. For that reason, the exploration of S. pneumoniae protein virulence factors should be considered in developing new drugs or vaccines, for instance by the analysis of host-pathogen protein-protein interactions (HP-PPIs). In this research, prediction of protein-protein interactions was performed with a logistic regression model with the number of protein domain occurrences as features. By utilizing HP-PPIs of three different pathogens as training data, the model achieved 57-77 % precision, 64-75 % recall, and 96-98 % specificity. Prediction of human-S. pneumoniae protein-protein interactions using the model yielded 5823 interactions involving thirty S. pneumoniae proteins and 324 human proteins. Pathway enrichment analysis showed that most of the pathways involved in the predicted interactions are immune system pathways. Network topology analysis revealed β-galactosidase (BgaA) as the most central among the S. pneumoniae proteins in the predicted HP-PPI networks, with a degree centrality of 1.0 and a betweenness centrality of 0.451853. Further experimental studies are required to validate the predicted interactions and examine their roles in S. pneumoniae infection.

A Molecular Interaction Map of Klebsiella pneumoniae and Its Human Host Reveals Potential Mechanisms of Host Cell Subversion

Klebsiella pneumoniae is a leading cause of pneumonia and septicemia across the world. The rapid emergence of multidrug-resistant K. pneumoniae strains necessitates the discovery of effective drugs against this notorious pathogen. However, there is a dearth of knowledge on the mechanisms by which this deadly pathogen subverts host cellular machinery. To fill this knowledge gap, our study attempts to identify the potential mechanisms of host cell subversion by building a K. pneumoniae-human interactome based on rigorous computational methodology. The putative host targets inferred from the predicted interactome were found to be functionally enriched in the host's immune surveillance system and allied functions like apoptosis, hypoxia, etc. A multifunctionality-based scoring system revealed P53 as the most multifunctional protein among host targets accompanied by HIF1A and STAT1. Moreover, mining of host protein-protein interaction (PPI) network revealed that host targets interact among themselves to form a network (TTPPI), where P53 and CDC5L occupy a central position. The TTPPI is composed of several inter complex interactions which indicate that K. pneumoniae might disrupt functional coordination between these protein complexes through targeting of P53 and CDC5L. Furthermore, we identified four pivotal K. pneumoniae-targeted transcription factors (TTFs) that are part of TTPPI and are involved in generating host's transcriptional response to K. pneumoniae-mediated sepsis. In a nutshell, our study identifies some of the pivotal molecular targets of K. pneumoniae which primarily correlate to the physiological response of host during K. pneumoniae-mediated sepsis.

Cross-Species Proteomic Comparison of Outer Membrane Vesicles and Membranes of Francisella tularensis subsp. tularensis versus subsp. holarctica

Release of outer membrane vesicles (OMV) is an important phenomenon in Gram-negative bacteria playing multiple roles in their lifestyle, including in relation to virulence and host-pathogen interaction. Francisella tularensis, unlike other bacteria, releases unusually shaped, tubular OMV. We present a proteomic comparison of OMV and membrane fractions from two F. tularensis strains: moderately virulent subsp. holarctica strain FSC200 and highly virulent subsp. tularensis strain SchuS4. Proteomic comparison studies routinely evaluate samples from the same proteome, but sometimes we must compare samples from closely related organisms. This raises quantification issues. We propose a novel approach to cross-species proteomic comparison based on an intersection protein database from the individual single-species databases. This is less prone to quantification errors arising from differences in the sequences. Consecutively comparing subproteomes of OMV and membranes of the two strains allows distinguishing differences in relative protein amounts caused by global expression changes from those caused by preferential protein packing to OMV or membranes. Among the proteins most differently packed into OMV between the two strains, we detected proteins involved in biosynthesis and metabolism of bacterial envelope components like O-antigen, lipid A, phospholipids, and fatty acids, as well as some major structural outer membrane proteins. The data are available via ProteomeXchange with identifier PXD022406.

Early infection-induced natural antibody response

There remains to this day a great gap in understanding as to the role of B cells and their products-antibodies and cytokines-in mediating the protective response to Francisella tularensis, a Gram-negative coccobacillus belonging to the group of facultative intracellular bacterial pathogens. We previously have demonstrated that Francisella interacts directly with peritoneal B-1a cells. Here, we demonstrate that, as early as 12 h postinfection, germ-free mice infected with Francisella tularensis produce infection-induced antibody clones reacting with Francisella tularensis proteins having orthologs or analogs in eukaryotic cells. Production of some individual clones was limited in time and was influenced by virulence of the Francisella strain used. The phylogenetically stabilized defense mechanism can utilize these early infection-induced antibodies both to recognize components of the invading pathogens and to eliminate molecular residues of infection-damaged self cells.

Francisella tularensis subsp. holarctica Releases Differentially Loaded Outer Membrane Vesicles Under Various Stress Conditions

Francisella tularensis is a Gram-negative, facultative intracellular bacterium, causing a severe disease called tularemia. It secretes unusually shaped nanotubular outer membrane vesicles (OMV) loaded with a number of virulence factors and immunoreactive proteins. In the present study, the vesicles were purified from a clinical isolate of subsp. holarctica strain FSC200. We here provide a comprehensive proteomic characterization of OMV using a novel approach in which a comparison of OMV and membrane fraction is performed in order to find proteins selectively enriched in OMV vs. membrane. Only these proteins were further considered to be really involved in the OMV function and/or their exceptional structure. OMV were also isolated from bacteria cultured under various cultivation conditions simulating the diverse environments of F. tularensis life cycle. These included conditions mimicking the milieu inside the mammalian host during inflammation: oxidative stress, low pH, and high temperature (42°C); and in contrast, low temperature (25°C). We observed several-fold increase in vesiculation rate and significant protein cargo changes for high temperature and low pH. Further proteomic characterization of stress-derived OMV gave us an insight how the bacterium responds to the hostile environment of a mammalian host through the release of differentially loaded OMV. Among the proteins preferentially and selectively packed into OMV during stressful cultivations, the previously described virulence factors connected to the unique intracellular trafficking of Francisella were detected. Considerable changes were also observed in a number of proteins involved in the biosynthesis and metabolism of the bacterial envelope components like O-antigen, lipid A, phospholipids, and fatty acids. Data are available via ProteomeXchange with identifier PXD013074.

The D-alanyl-d-alanine carboxypeptidase enzyme is essential for virulence in the Schu S4 strain of Francisella tularensis and a dacD mutant is able to provide protection against a pneumonic challenge

Low molecular mass penicillin binding proteins (LMM PBP) are bacterial enzymes involved in the final steps of peptidoglycan biosynthesis. In Escherichia coli, most LMM PBP exhibit dd-carboxypeptidase activity, are not essential for growth in routine laboratory media, and contributions to virulent phenotypes remain largely unknown. The Francisella tularensis Schu S4 genome harbors the dacD gene (FTT_1029), which encodes a LMM PBP with homology to PBP6b of E. coli. Disruption of this locus in the fully virulent Schu S4 strain resulted in a mutant that could not grow in Chamberlain's Defined Medium and exhibited severe morphological defects. Further characterization studies demonstrated that the growth defects of the dacD mutant were pH-dependent, and could be partially restored by growth at neutral pH or fully restored by genetic complementation. Infection of murine macrophage-like cells showed that the Schu S4 dacD mutant is capable of intracellular replication. However, this mutant was attenuated in BALB/c mice following intranasal challenge (LD₅₀ = 603 CFU) as compared to mice challenged with the parent (LD₅₀ = 1 CFU) or complemented strain (LD₅₀ = 1 CFU). Additionally, mice that survived infection with the dacD mutant showed significant protection against subsequent challenge with the parent strain. Collectively, these results indicate that the DacD protein of F. tularensis is essential for growth in low pH environments and virulence in vivo. These results also suggest that a PBP mutant could serve as the basis of a novel, live attenuated vaccine strain.

Inter-Species/Host-Parasite Protein Interaction Predictions Reviewed

BACKGROUND

Host-parasite protein interactions (HPPI) are those interactions occurring between a parasite and its host. Host-parasite protein interaction enhances the understanding of how parasite can infect its host. The interaction plays an important role in initiating infections, although it is not all host-parasite interactions that result in infection. Identifying the protein-protein interactions (PPIs) that allow a parasite to infect its host has a lot do in discovering possible drug targets. Such PPIs, when altered, would prevent the host from being infected by the parasite and in some cases, result in the parasite inability to complete specific stages of its life cycle and invariably lead to the death of such parasite. It therefore becomes important to understand the workings of host-parasite interactions which are the major causes of most infectious diseases.

OBJECTIVE

Many studies have been conducted in literature to predict HPPI, mostly using computational methods with few experimental methods. Computational method has proved to be faster and more efficient in manipulating and analyzing real life data. This study looks at various computational methods used in literature for host-parasite/inter-species protein-protein interaction predictions with the hope of getting a better insight into computational methods used and identify whether machine learning approaches have been extensively used for the same purpose.

METHODS

The various methods involved in host-parasite protein interactions were reviewed with their individual strengths. Tabulations of studies that carried out host-parasite/inter-species protein interaction predictions were performed, analyzing their predictive methods, filters used, potential protein-protein interactions discovered in those studies and various validation measurements used as the case may be. The commonly used measurement indexes for such studies were highlighted displaying the various formulas. Finally, future prospects of studies specific to human-plasmodium falciparum PPI predictions were proposed.

RESULT

We discovered that quite a few studies reviewed implemented machine learning approach for HPPI predictions when compared with methods such as sequence homology search and protein structure and domain-motif. The key challenge well noted in HPPI predictions is getting relevant information.

CONCLUSION

This review presents useful knowledge and future directions on the subject matter.

Francisella tularensis D-Ala D-Ala Carboxypeptidase DacD Is Involved in Intracellular Replication and It Is Necessary for Bacterial Cell Wall Integrity

D-alanyl-D-alanine carboxypeptidase, product of dacD gene in Francisella, belongs to penicillin binding proteins (PBPs) and is involved in remodeling of newly synthetized peptidoglycan. In E. coli, PBPs are synthetized in various growth phases and they are able to substitute each other to a certain extent. The DacD protein was found to be accumulated in fraction enriched in membrane proteins from severely attenuated dsbA deletion mutant strain. It has been presumed that the DsbA is not a virulence factor by itself but that its substrates, whose correct folding and topology are dependent on the DsbA oxidoreductase and/or isomerase activities, are the primary virulence factors. Here we demonstrate that Francisella DacD is required for intracellular replication and virulence in mice. The dacD insertion mutant strain showed higher sensitivity to acidic pH, high temperature and high osmolarity when compared to the wild-type. Eventually, transmission electron microscopy revealed differences in mutant bacteria in both the size and defects in outer membrane underlying its SDS and serum sensitivity. Taken together these results suggest DacD plays an important role in Francisella pathogenicity.

Network Analyses in Plant Pathogens

Even in the age of big data in Biology, studying the connections between the biological processes and the molecular mechanisms behind them is a challenging task. Systems biology arose as a transversal discipline between biology, chemistry, computer science, mathematics, and physics to facilitate the elucidation of such connections. A scenario, where the application of systems biology constitutes a very powerful tool, is the study of interactions between hosts and pathogens using network approaches. Interactions between pathogenic bacteria and their hosts, both in agricultural and human health contexts are of great interest to researchers worldwide. Large amounts of data have been generated in the last few years within this area of research. However, studies have been relatively limited to simple interactions. This has left great amounts of data that remain to be utilized. Here, we review the main techniques in network analysis and their complementary experimental assays used to investigate bacterial-plant interactions. Other host-pathogen interactions are presented in those cases where few or no examples of plant pathogens exist. Furthermore, we present key results that have been obtained with these techniques and how these can help in the design of new strategies to control bacterial pathogens. The review comprises metabolic simulation, protein-protein interactions, regulatory control of gene expression, host-pathogen modeling, and genome evolution in bacteria. The aim of this review is to offer scientists working on plant-pathogen interactions basic concepts around network biology, as well as an array of techniques that will be useful for a better and more complete interpretation of their data.

Mechanisms of action of Coxiella burnetii effectors inferred from host-pathogen protein interactions

Coxiella burnetii is an obligate Gram-negative intracellular pathogen and the etiological agent of Q fever. Successful infection requires a functional Type IV secretion system, which translocates more than 100 effector proteins into the host cytosol to establish the infection, restructure the intracellular host environment, and create a parasitophorous vacuole where the replicating bacteria reside. We used yeast two-hybrid (Y2H) screening of 33 selected C. burnetii effectors against whole genome human and murine proteome libraries to generate a map of potential host-pathogen protein-protein interactions (PPIs). We detected 273 unique interactions between 20 pathogen and 247 human proteins, and 157 between 17 pathogen and 137 murine proteins. We used orthology to combine the data and create a single host-pathogen interaction network containing 415 unique interactions between 25 C. burnetii and 363 human proteins. We further performed complementary pairwise Y2H testing of 43 out of 91 C. burnetii-human interactions involving five pathogen proteins. We used the combined data to 1) perform enrichment analyses of target host cellular processes and pathways, 2) examine effectors with known infection phenotypes, and 3) infer potential mechanisms of action for four effectors with uncharacterized functions. The host-pathogen interaction profiles supported known Coxiella phenotypes, such as adapting cell morphology through cytoskeletal re-arrangements, protein processing and trafficking, organelle generation, cholesterol processing, innate immune modulation, and interactions with the ubiquitin and proteasome pathways. The generated dataset of PPIs-the largest collection of unbiased Coxiella host-pathogen interactions to date-represents a rich source of information with respect to secreted pathogen effector proteins and their interactions with human host proteins.

Elucidation of host-pathogen protein-protein interactions to uncover mechanisms of host cell rewiring

Infectious diseases are the result of molecular cross-talks between hosts and their pathogens. These cross-talks are in part mediated by host-pathogen protein-protein interactions (HP-PPI). HP-PPI play crucial roles in infections, as they may tilt the balance either in favor of the pathogens' spread or their clearance. The identification of host proteins targeted by viral or bacterial pathogenic proteins necessary for the infection can provide insights into their underlying molecular mechanisms of pathogenicity, and potentially even single out pharmacological intervention targets. Here, we review the available methods to study HP-PPI, with a focus on recent mass spectrometry based methods to decipher bacterial-human infectious diseases and examine their relevance in uncovering host cell rewiring by pathogens.

Mining kidney toxicogenomic data by using gene co-expression modules

Background

Acute kidney injury (AKI) caused by drug and toxicant ingestion is a serious clinical condition associated with high mortality rates. We currently lack detailed knowledge of the underlying molecular mechanisms and biological networks associated with AKI. In this study, we carried out gene co-expression analyses using DrugMatrix—a large toxicogenomics database with gene expression data from rats exposed to diverse chemicals—and identified gene modules associated with kidney injury to probe the molecular-level details of this disease.

Results

We generated a comprehensive set of gene co-expression modules by using the Iterative Signature Algorithm and found distinct clusters of modules that shared genes and were associated with similar chemical exposure conditions. We identified two module clusters that showed specificity for kidney injury in that they 1) were activated by chemical exposures causing kidney injury, 2) were not activated by other chemical exposures, and 3) contained known AKI-relevant genes such as Havcr1, Clu, and Tff3. We used the genes in these AKI-relevant module clusters to develop a signature of 30 genes that could assess the potential of a chemical to cause kidney injury well before injury actually occurs. We integrated AKI-relevant module cluster genes with protein-protein interaction networks and identified the involvement of immunoproteasomes in AKI. To identify biological networks and processes linked to Havcr1, we determined genes within the modules that frequently co-express with Havcr1, including Cd44, Plk2, Mdm2, Hnmt, Macrod1, and Gtpbp4. We verified this procedure by showing that randomized data did not identify Havcr1 co-expression genes and that excluding up to 10 % of the data caused only minimal degradation of the gene set. Finally, by using an external dataset from a rat kidney ischemic study, we showed that the frequently co-expressed genes of Havcr1 behaved similarly in a model of non-chemically induced kidney injury.

Conclusions

Our study demonstrated that co-expression modules and co-expressed genes contain rich information for generating novel biomarker hypotheses and constructing mechanism-based molecular networks associated with kidney injury.

Electronic supplementary material

The online version of this article (doi:10.1186/s12864-016-3143-y) contains supplementary material, which is available to authorized users.